Dissolve yourself for a second.

Let’s say you are trapped in a robot in a maze. You want to reach the end of the maze, where you hear they are handing out free cookies. How do you move forward?

You’re trapped in the robot, so the only way to move is to program the robot with a binding dogma. One algorithm to follow above all others. What strategy should you use?

Well, one strategy you might use is something called objective search. This means you measure your robot’s distance to the objective, and take any path that reduces that distance. We know what our objective is, and we set out to achieve it. On the surface of things, this seems like the most intuitive strategy. After all, we want to go straight towards our goal as fast as we can.

In their 2015 book Why Greatness Cannot Be Planned, Joel Lehman and Kenneth Stanley (henceforth, L&S) spend the entire book showing why this plan is going to fail. Using objectives as a guide will fail you in this maze, and they will also fail you in life, and they will also fail you for nearly every meaningful task that you set your mind to.

To explain why, let’s stick with the maze for now.

There’s one immediate problem. If we’re in a maze and we can’t see the objective, how do we measure our distance to the objective? To get around this, let’s assume that we’re in a glass maze, meaning that we can see where the final objective is.

We’ll mark the robot we’re trapped in with a cool little circle, and the sweet cookie treasure we’re hunting for with an ‘X’. Here’s a maze:

Using objective search, our little robot will just run into the wall and get stuck. It can never reach the objective, because it has to go away from the objective before it goes towards it.

In animal psychology there is a family of experiments called the detour paradigm. You put some food in front of an animal, but block it with a transparent fence. The animal must find a way to go around.

You can use the ability to detour away from food as a crude proxy for intelligence. Chimpanzees and dogs generally succeed at these sorts of tasks, while chickens find the task harder. It’s unintuitive. The animal has to move away from the target so it can get closer to it.

What sort of algorithm would solve the above maze? Well, one option is to tightly map our algorithm to the maze:

1. Go right until you hit a wall

2. Go up until you hit a wall

3. Go left until you hit treasure

Great. Here’s another maze:

This is the problem with pure objective search. For any objective which is meaningfully challenging, the path to get there is not simple.

In the real world, relying on objectives can also begin to collapse the utility of the benchmark that you were using in the first place. A classic example is Goodhart’s law: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”1

This is a common regularity. Try Campbell’s law: “The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.” Or Lucas’ critique: “Historical statistical relationships will not hold in response to policy changes, because people alter their behaviour in response to the policy”.

As we’ll see, any form of search strategy has fundamental strengths and constraints, and it’s important to be aware of them.

Something Old, Something New

L&S present a solution to this problem: novelty search. Here, we tell our robot to look for new areas.2 Any time it goes to a new place, it gets rewarded.

Here’s a side-by-side comparison of how that might look:

Even if we make changes to the maze, novelty search still works, as you can see:

Novelty search not only does better than objective search, but it also finds spaces in the grid that objective search would never dream of looking at.

L&S argue that any ambitious objective is by its very nature deceptive. If we could see the path to an ambitious objective and it was easy to reach, it wouldn’t be ambitious.

Most of the things we want to achieve in the long-term are ambitious. They are hard in critical ways. We cannot immediately intuit how we should go about achieving them.

An obvious counterexample to their argument might be something like the moon landings, which seem like an ambitious objective with known goals. But the important idea here is not that objectives never work. L&S allow that modest objectives work, and they work all the time.

But when you are setting distant goals without obvious steps to them, then problems become both ambitious and deceptive again. There were significant unknowns in the NASA space programme, but many of the steps could be broken down and decomposed into engineering problems. That wasn’t the case if you were alive in the year 1865, where Jules Verne (not entirely seriously) suggested using a giant cannon to blast humans into space.

L&S argue that a lot of important problems that we care about are ambitious and deceptive, whether that’s finding an architecture that might replace the transformer, or your goal of becoming a professional crocodile tamer. There isn’t one obvious path with clear steps along the way.

What sorts of ambitious objectives do L&S use as examples?

“To name a couple of very significant ones, natural evolution and human innovation also proceed without any final objective, as we’ll see... The problem is that the stepping stones to intelligence do not resemble intelligence at all. Put another way, human-level intelligence is a deceptive objective for evolution.”

So how do you reach an objective that is ambitious and deceptive? Well, we don’t know. That’s kind of the point. But the path to most objectives involves what L&S call stepping stones. They define these as “portals to the next level of possibility”. These are small jumps which open up new opportunities for places we can jump to.

Imagine a frog hopping across a foggy lake. It has a choice of a few lily pads it can hop to in order to get across the lake. Hopping to any given lily pad opens up some new pads and closes down others. How do you select the right lily pads?

There’s not much information about which lily pads lead to where we want to go. Sometimes the objective itself can be contested. Do we actually want flying cars? Even if the desirability of the objective is hard to contest, the form the solution should take is always open for discussion. For instance, we want to find a way of mitigating climate change, but this can take myriad forms.

We often end up staring, from our lily pad, at a confusing, changing landscape without clear indications of where exactly we should search.

Trying To Find More Problems

We could try to use novelty search to solve this problem. But there’s a catch. In objective search, once you run it, and see if you make it to the objective, you’re done. Novelty search is theoretically infinite. If you have an infinite maze, novelty search will be hard pressed to find particular spots. As the search space gets larger, the chance that it find things that you want that you want decreases as well.

Pure novelty search also spends a lot of time in spaces that aren’t that interesting. If you look again at the first animation I created, novelty search takes over 200 iterations to get around a wall.

L&S give the example of trying to evolve human intelligence from the primordial soup. Evolution is a teleonomic process, which means it appears to have a purpose (but obviously doesn’t).3

They point out that to evolve our particular form of intelligence, at some point you had to evolve something like a flatworm, which does not remotely resemble a human. These flatworms were our ancestors. It turns out that bilateral symmetry is a useful structural condition for intelligence, but that wouldn’t necessarily be obvious unless you were staring at a globe filled with symmetrical intelligent creatures.

Or take vacuum tubes, which were first used as radio-signal detectors. They rectified the very high-frequency alternating current in early radio receivers, transforming it into a direct current that could register as a signal. If you were around in 1900, trying to imagine how to build a computer, it isn’t obvious that you should spend time fixing the problem of high-frequency alternating current in radio receivers.

Nonetheless, the first general-purpose programmable computer, ENIAC, depended on over 18,000 vacuum tubes. The entire thing broke down if one of the vacuum tubes broke, and this happened roughly every two days.

Vacuum tubes were a stepping stone to the computer. Bilateral symmetry was a stepping stone towards human intelligence. But how do we find more stepping stones?

You can make modifications to novelty search to try and solve this problem. L&S created minimal criteria novelty search, which is a form of novelty search where results must meet a set of rules.4

In evolution, this would be the restriction that the organism must survive. Allowing search to run for a couple of billion years means that on our earth, creatures have explored many of the possible ways of being alive. But survival isn’t a very interesting criterion for most search algorithms that we want to run. Many things that survive are simple organisms that don’t do much beyond surviving.

L&S feel their way towards interestingness as a better criterion. They argue that we should try to explore the search space in interesting directions. Interestingness is another way of saying taste, or style. We know what things are interesting to us, but we don’t necessarily know why. In a way, it is encoded in our nervous systems.

Seeing more things in a space changes the pattern of interestingness in that space for a particular actor. As Charan Ranganath puts it, “experience doesn’t just change what you see, it changes what you look for.” But even where greater experience doesn’t offer an objective improvement in performance, experience still changes taste.

To give another example, let’s think about art, which has an obvious gradient from direct, representational art, which is stuff like this:

To indirect, more abstract works, which is stuff like this:

Multiple studies show that the more art that people have been exposed to, as measured by, say, studying an art degree, the more people prefer abstract art. Obviously taste in art is subjective, but we’re not dealing with hard rules here. We’re dealing in what is interesting.5

You could say interestingness is a way of guiding our search, and we use it to give us an intuition about where we should start searching. Once we choose an area, we could allow novelty search to begin in that space.

The Search for Better Searching

It’s worth elaborating more on how L&S think about search. They are both artificial intelligence researchers, and discuss two heuristics which they believe were dominating AI discussions in 2015:

“A technical term for a rule of thumb that guides search is a heuristic. While no one can be sure which heuristics will work the best for getting to high-level AI, the AI community has settled upon two in particular.

The first, which we’ll call the experimentalist heuristic, follows the rule of thumb that an algorithm’s promise is given by how well it performs. In other words, an algorithm worth exploring further performs better than existing algorithms in benchmark tasks.

The second main gradient in AI research is the theoretical heuristic. This heuristic suggests that algorithms are better if they can be proven to have desirable properties.”

Obviously the field of AI has changed somewhat since then, but these two ideas still apply, and might well apply in any other field. Think of them as archetypal approaches. The experimentalist heuristic has issues that we’ve discussed above, namely that the best performing algorithm now may not necessarily offer the best way forward in the long-run.

The theoretical heuristic is based on the premise that theorems usually show if algorithms will produce a good result quickly (these are the “desirable properties” they mention). If you are an AI practitioner and you have to choose between two algorithms, then it is reasonable to choose an algorithm backed by more theorems, as this suggests that it will perform at a reasonable speed.

But these theorems don’t prove much about future algorithms. If you’re an AI theoretician, only selecting algorithms backed by theoretical guarantees merely restricts the search space, and not in a productive way. As L&S put it:

“It means that the community becomes restricted only to those algorithms that honor the same growing set of assumptions, blinding the meta-search to every path forward that breaks the assumptions. In the end, the effect is less exploration—the objective paradox takes hold.”

It may also be the case that more powerful algorithms don’t honour the same theoretical commitments.

So both options limit the search space. L&S go on to cite J.N. Hooker, who stated: “Most experimental studies of heuristic algorithms resemble track meets more than scientific endeavors”.

This post isn’t really about AI, so I’m going to steer away from a lengthy discussion of LLM development here, but it’s worth touching quickly on benchmarks. Benchmarks such as METR have become a highly visible part of the LLM ecosystem over recent years as a way of measuring performance. Measuring performance is obviously valuable, and the goal of this post is not to persuade anyone to stop using objectives altogether. However, aggressively pursuing performance on benchmarks can fall into the trap of objective search that we discussed above. We can end up designing algorithms and models that perform well on benchmarks, instead of algorithms that find new and interesting things.

How To Be Interesting

One way to be interesting is to be random. People often make fortunate discoveries while doing random things, which we term serendipity. Serendipity was coined by the writer Horace Walpole, who, in a letter to a friend, described a Persian fairy tale called The Three Princes of Serendip. In the tale, the three heroes kept making fortunate discoveries, by a combination of accident and sagacity, of things that they had never intended to find.

From his Wikipedia: “According to legend, one day while building magnetrons, Percy Spencer was standing in front of an active radar set when he noticed the candy bar he had in his pocket melted.”

Percy Spencer then went on to use that information to invent the microwave.

From the blogger Malmesbury:

“Aspartame was discovered accidentally by a chemist researching a completely unrelated topic. At some point, he licked his finger to grab a piece of paper and noticed a strong sweet taste.

Saccharin was discovered by a researcher who ate bread without washing his hands and noticed the bread was sweet.

Acesulfame K was also discovered serendipitously by a chemist licking his fingers, although the legends don’t specify the exact circumstances behind the finger-licking.”

People who build recommender algorithms are aware that serendipity is a critical part of a search algorithm, although it is highly difficult to explicitly code it into an algorithmic recommender.

One way to get serendipity is through a little bit of randomness. There is a study that argues that as scientific articles stop being published in printed journals, scientists get less creative because there is less random overspill from other articles they wouldn’t have normally read. When you’re online, you can filter more effectively, which can offer visible benefits and hidden disadvantages.

Randomness isn’t a complete solution. Randomness is not a quality filter, and randomly searching through very large search spaces takes a long time. Most of us aren’t Percy Spencer, and would probably be pissed off by a chocolate bar melting in our pocket, rather than spotting the connection.

As the next line of his Wikipedia notes pointedly: “Spencer was not the first to notice this phenomenon, but he was the first to investigate it.” In the same vein, we’ve all spent a lot of time licking things. Few of us would know which licks were scientifically significant and fewer would know how to synthesise a new chemical compound because of a great lick.

The Interesting Properties of Science

The space of interestingness is intensely relational. What is interesting to you is not necessarily what is interesting to me, and those differences in belief greatly influence the research we do.

One of the implications that follows from this is that, at least at the cutting edge, there should be more than one theory for interpreting what is going on. Competing ideas which offer a form of scientific pluralism offer a compelling framework for understanding the world. A wider variety of options allows for different forms of good.

One way that this pluralism is captured in current AI is by a field of algorithms that L&S pioneered called Quality Diversity (QD) algorithms, which attempt to try and generate multiple good solutions to the same problem. This is similar to the way evolution developed many millions to billions of possible ways of being alive, such as the bearded vulture, the only vertebrate whose diet is mostly bone.

Jeff Clune and his team recently tried to formalise the concept of interestingness using a method they call OMNI. This takes a candidate idea and scores it with a function of interestingness.

The insight here is that you can use a foundation model (or an LLM) as a proxy of interestingness, because a foundation model contains the internet, and the internet was written by humans, who know what is interesting. Once you have a function of interestingness, you can score candidate tasks or environments and then run checks for nearby candidates with higher scores.

The algorithm you are using to search in any particular field determines the set of possible outcomes. We can search for more things, and have to spend more time filtering on the other side, or we can be more restrictive and potentially miss out on valuable stepping stones. Whatever the field, whatever the algorithm, we are always navigating this trade-off.6

However, using better search algorithms might help us to spend less time filtering without missing out on as many stepping stones. And L&S argue that one of the best ways to improve search is to avoid consensus, which is usually created by a stringent form of objective search.

As they put it, “seeking consensus prevents traveling down interesting stepping stones because people don’t agree on what the most interesting stepping stones are.”

Using what is interesting is a useful way to restrict the search space. Many of our greatest theories came from a combination of a scientist’s intuition for the interesting and their willingness to explore that search space even if initially they were not successful. The discovery of penicillin by Alexander Fleming was serendipitous, but Fleming himself wasn’t able to purify it into a clinically usable drug. Much of that work was carried out by a team led by Howard Florey.

The truth is that we often don’t know what we’re looking for, so throwing away the objectives of a research programme can actually be helpful in some ways. It’s obviously difficult to communicate this to funders: “Sorry, you want a bunch of money to get bats loaded?” But L&S argue that less stringent restrictions may allow us to create more serendipity and to find more stepping stones.

Interesting work usually meets a quality filter, but beyond that filter, ranking them doesn’t add much. Is The Shawshank Redemption, which is perennially ranked first on IMDb, really more worthwhile in any meaningful way than The Godfather, which is ranked second? The answer is obviously not. Both films pass the quality filter, but one is a biting excoriation of the prison system and the other is about not asking guys to do things on the day their daughter is getting married.

As Paul Feyerabend, a 20th century philosopher of science, put it:

“Knowledge so conceived is not a process that converges toward an ideal view; it is an ever increasing ocean of alternatives, each of them forcing the others into greater articulation, all of them contributing, via this process of competition, to the development of our mental faculties.”

The Great Theory Showdown

But films aren’t science, I hear you cry. And this idea sounds very unintuitive. Surely we should always rely on our strongest theory? Well, for one thing, it can be hard to determine what our strongest theory is. But the other problem is that the strongest theory now isn’t necessarily the one that allows us to make the most progress in the future.

Karl Popper proposed that science should rely on the falsification approach, which holds that for a theory to be scientific, it must be testable and disprovable by observation or experiment. Although this idea is commonly accepted as one of the backbones of modern science, it is less valid than it might intuitively seem.

It was contested by the Hungarian philosopher Imre Lakatos, who argued that there are famous cases where theories that would have been falsified under a purely Popperian programme were later proved true.

One of Lakatos’ favourite examples is the story of the Copernican revolution. Before the 16th century, the dominant view of space relied on the Ptolemaic geocentric model, in which the Earth stood still at the centre of the cosmos while everything else moved around it. This has now been replaced by the Copernican theory of heliocentrism which predicts that the Earth orbits around the sun.

If Copernicus was right, and the Earth moves around the sun, then our view of the stars should change during the year. When the Earth is on one side of its orbit, we are looking at the stars from one position. Six months later, when the Earth is on the other side of its orbit, we are looking at those same stars from a different position. So the closest stars should appear to shift very slightly against the background of more distant stars, which should shift much less. This effect is called stellar parallax.

Until 1838, stellar parallax was not observed. This is because the shift in location is incredibly slight, and you need very powerful telescopes to detect it. Nonetheless, by 1838, the heliocentric view was the dominant model, because a theory is generally assessed against all of its parts. For instance, accepting Copernican theory meant that retrograde motion no longer had to be treated as a strange independent loop made by each planet.

The full story of the Copernican revolution has been better told elsewhere, but the point is that if we adopt an approach to science depending entirely on falsification, we might end up throwing out work before it has been able to prove itself, much in the same way that we might throw out interesting work if rely only on its current performance.

Lakatos thus suggested that research programmes should have a set of central hypotheses, which formed the core of any programme, coupled with auxiliary hypotheses, which are allowed to change in the face of new evidence.

But if we can’t just rely on falsification, how do we solve the demarcation problem between science and pseudoscience? Well, for Lakatos, real science allows you to make predictions that have not previously been observed. As he wrote:

“All the research programmes I admire have one characteristic in common. They all predict novel facts, facts which had been either undreamt of, or have indeed been contradicted by previous or rival programmes.”

Newtonian mechanics allowed Halley to predict the arrival of his namesake comet some 70 years later, long after both he and Newton were dead.

I would extend Lakatos’ argument here to say that we want our research programmes to make new predictions in spaces that are interesting. Interesting predictions often give you new capabilities, even if they weren’t the ones you were looking for.

A novel prediction about something boring is a little pointless. If I glue a banana and an apple together and then make a bunch of predictions about when they will eventually fall apart, that doesn’t let me achieve many new things and wastes a lot of glue. If you make a bunch of predictions about the ways robots could achieve locomotion, as Lipson et al. (2013) did, then that creates a lot of possibilities for new walking robots.

Research programmes focusing on what is interesting might allow people time and space to explore ideas, even if they don’t meet stringent criteria now. As Peter Higgs put it, “It’s difficult to imagine how I would ever have enough peace and quiet in the present sort of climate to do what I did in 1964. I wouldn’t be productive enough for today’s academic system.”7

Defining an interesting scientific programme means allowing scientists to get things wrong. It also involves allowing them to present ideas that are not immediately optimal or better than what we currently have. Many of the criteria we currently have around funding research programmes and science more generally are supposed to act as a filter so that people can’t introduce junk science into the scientific community. L&S respond to this argument by pointing out that experts in most fields know what junk is, and the insinuation that they need a set of rules to see that junk is a little insulting.

The idea isn’t to remove minimal criteria altogether, but to allow scientists to exist in a Goldilocks zone, where they are allowed room to explore and find stepping stones, without being forced to map their research onto a set of unhelpful objectives.

I’ve focused on science in this review, but L&S’s argument applies everywhere, and you could be thinking about the search algorithm that is being used in your specialism. We are all manipulated by external search algorithms, whether we like it or not. To succeed anywhere, we have to optimise ourselves according to a search algorithm that dictates success.

To become a doctor, lawyer or a kangaroo scientist, you typically have to show that you are good at passing medical assessments, sitting legal exams or cleaning pouches. This means playing the game and proving you are good at playing it. Every field is engaged in some form of search to find people, and those criteria end up deciding who we send to Australia to study kangaroos. Being aware of this structure is the first step towards potentially changing it.

Giving Up The Goal

Why Greatness Cannot Be Planned is a wonderfully broad book. While I’ve discussed search spaces and algorithms extensively, they discuss the impact of objective search in all walks of life.

Dampening the quest for the objective can be helpful for a lot of people. We all want to achieve things, and it can be disappointing when we don’t achieve them. But L&S argue that using ambitious objectives as a guiding star can hinder us as much as it helps.

This isn’t to say that you should stop setting goals for yourself, but if you want to use goals, set modest ones. Ambitious goals are things you arrive at sideways. People make great discoveries in places where they least expect them. To take advantage of serendipity you have to be both constantly looking for interesting things and also possess the expertise to see them.

The Wright Brothers were experienced bicycle manufacturers who saw that the conditions were right for flight. Percy Spencer discovered the microwave because of serendipity but also because he was one of the world’s leading experts in radar tube design.

Serendipity helps to shatter a mould that we didn’t imagine ourselves to be trapped in. As Lois Bujold puts it, “It’s a bizarre but wonderful feeling, to arrive dead centre of a target you didn’t even know you were aiming for.”

In quantum field theory, the vacuum state refers to the lowest energy state in a system. Particles are excitations above this state and carry energy, hence the term “vacuum” to refer to the state with no particles.

Nothing requires this state to be unique. There may be many different field configurations that are local energy minima, and hence stable against small perturbations. A local minimum that does not globally minimize energy is called a false vacuum. While locally it looks like a stable vacuum, it is unstable and will decay to the deeper, true vacuum. If the energy barrier between the false and true vacuum is high, however, then the decay rate is exponentially suppressed and the false vacuum may be very long-lived.

Analogous behavior is common in other physical systems. Open a carbonated drink and the CO₂, more stable as a gas once the pressure is released, comes out as bubbles. But the bubbles take a moment to appear, and they form on the sides of the bottle rather than throughout the liquid. A bubble has to pay an energy cost to create its surface—the boundary between gas and liquid—and small bubbles have a larger surface-to-volume ratio. The energy gained by moving CO₂ into the gas grows with the bubble’s volume, while the cost of its surface grows only with its area; so below a critical radius the cost wins and the bubble redissolves, and above it the gain wins and the bubble grows. Reaching that critical size takes a large enough chance fluctuation, which is why the bubbles take time to appear. It is also why they form on a surface or imperfection, which supplies part of the boundary for free.

A false vacuum decays by a similar mechanism. A bubble of the true vacuum forms through quantum or thermal fluctuations. If the bubble is large enough, the gains from the bubble’s volume outweigh the energy costs of the bubble wall and so the bubble will expand. The energy released would be enormous, accelerating the wall to nearly the speed of light. It cannot be outrun, and it gives almost no warning, since the wall travels nearly as fast as the gamma radiation that would announce it. Everything it reaches is destroyed.

Within the bubble, the local laws of physics will be radically altered. And in case you were hoping to somehow cheat death and survive the crossing, or at the very least for complex behavior to continue in the baby universe after our demise, Coleman & De Luccia (1980) showed gravitational collapse into a singularity is the more likely outcome:

Vacuum decay is the ultimate ecological catastrophe; in the new vacuum there are new constants of nature; after vacuum decay, not only is life as we know it impossible, so is chemistry as we know it. However, one could always draw stoic comfort from the possibility that perhaps in the course of time the new vacuum would sustain, if not life as we know it, at least some structures capable of knowing joy. This possibility has now been eliminated.

Seems bad.

I think we very likely live in a false vacuum—around 90%—but that deliberately triggering its decay is probably impossible, even for a galactic-scale civilization. I put the chance it could be done at around 25%, combining a 10% chance through Higgs metastability with a 16% chance through instabilities in quantum gravity. In the near term, with the resources of Earth or the solar system, it looks very unlikely. If we do live in a false vacuum and its decay can be deliberately triggered, this suggests a fully laissez-faire approach to space colonization is inadvisable, as any sufficiently advanced civilization could unilaterally destroy most of the value in our future light cone.

The Standard Model predicts a metastable vacuum

The Standard Model is our best theory of particle physics, describing all known non-gravitational phenomena. Under certain parameter ranges, it predicts that we live in a false vacuum, as the Higgs potential becomes negative at high energies.

Current experimental measurements place us very close to the boundary between absolute stability and metastability. Our best estimates suggest the universe is only metastable, but uncertainties are large enough that we cannot yet rule out absolute stability with high confidence, with the top quark mass driving most of the uncertainty; see Hiller et al. (2024) for further discussion. Under differing assumptions stability is disfavored anywhere from just 0.7σ to about 4σ; overall I think there’s a roughly 90% chance that true parameters imply metastability.

At central parameter estimates, the Higgs potential first turns negative around 10¹⁰ GeV and remains negative up to the Planck scale. These energies are much greater than anything that we can directly access through experiments—collision energies at the LHC, for example, are 10⁴ GeV—but much lower than the Planck scale of 10¹⁹ GeV where gravitational effects become important. Should we expect the Standard Model to remain valid at these scales?

The lack of hard experimental data makes this question impossible to definitively answer, but I think the extrapolation is probably valid. Neutrino masses suggest new physics at 10¹⁵ GeV, but otherwise all the indirect probes we have are null and compatible with no new physics up to high energies.1 On theoretical grounds, intermediate-scale extensions are generally unnatural, requiring an additional mass scale to be added to the model by hand.2 This last argument is qualitative—ultimately we do not know the prior over high-energy theories, which is a question about quantum gravity—but I think it is a real reason to expect a desert, not just an absence of evidence. Putting this together, I’d give the extrapolation about an 80% chance of holding, including both scenarios where the Standard Model remains exactly correct and ones where it is modified but these modifications do not stabilize the vacuum. Combined with experimental uncertainty this gives an overall estimate of 70% that the Higgs potential is metastable.

While the Higgs potential is probably not stable, the lifetime of our universe is nevertheless very long. In Andreassen, Frost & Schwartz (2018) the lifetime is estimated at 10¹³⁹ years, with a 95% confidence that it is above 10⁵⁸ years given current top mass and other electroweak parameter estimates. By contrast, the universe itself is only about 10¹⁰ years old.

Deliberately triggering electroweak vacuum decay is probably not possible

To trigger false vacuum decay requires creating a “bubble” where the Higgs value in the interior is greater than the boundary height of h₀ ~ 10¹⁰ GeV. This bubble has to be big enough that the volume wins over surface area, which requires a radius

where λ ~ .01 is the quartic coupling. The minimum energy required is

Since a given Higgs quantum trapped in a region of size r₀ has energy ~ r₀^-1, the total quanta required for this configuration is

Putting this all together, triggering false vacuum decay requires creating a coherent state of ~ 1000 Higgs bosons within a region of radius ∼ 10^-25 m. The total energy requirement is less than a kilojoule.

Despite the pedestrian energy cost, even granting arbitrarily powerful futuristic technology there appears to be no way to reliably engineer this configuration. Our ability to create and manipulate the Higgs field is limited by the interactions allowed by the Standard Model, and it simply doesn’t give us the right tools.

Consider, first, whether a static lump of matter could catalyze decay. The issue is that, because every Standard Model particle gets its mass from the Higgs, raising h makes matter heavier and more energetically costly, stabilizing the vacuum. In any case, a matter density of ~ 10⁴⁰ GeV⁴ would be required to plausibly shift the Higgs field by ~ 10¹⁰ GeV; by contrast, ordinary matter has a density of 10-¹⁷ GeV⁴ and even neutron star cores only reach 10^-2 GeV⁴.

The paper Strumia (2023) discusses and rules out several more approaches. Colliding a small number of particles together cannot work, because the amplitude to create O(1000) Higgs bosons remains exponentially suppressed. But colliding many particles together in order to create a thermal fireball also doesn’t work, because the thermal decay rate remains exponentially suppressed at every temperature (and indeed, it is usually assumed, though without any direct evidence, that temperatures >> 10¹⁰ GeV were achieved in the early universe).

Coherent collisions

If generic collisions don’t work, could a carefully engineered >> N collision work? The issue is that the coherent N-body Higgs state required to trigger collapse appears basically impossible to engineer, at least without creating too much thermal background.

Let’s start with the task of creating a single 10⁹ GeV Higgs boson in a specified state. The Higgs boson is neutral and has a lifetime of 10^-22 s, so the only realistic option is resonant on-shell production by collision of more stable particles. Strumia (2023) suggests using a muon-antimuon pair:

though in principle one could consider electron-positron or photon-photon collisions.3 Conservation of momentum means that the Higgs boson trajectory can be controlled by modifying the muon and antimuon beams.

A 10⁹ GeV muon beam would be highly futuristic—around 10⁵ more energetic than the LHC—but there seems to be no in-principle barrier to achieving this with a ~ 10⁷ km linear accelerator, about 25 times the distance between the Earth and Moon. Getting substantial Higgs production is much harder. Even with quantum-limited luminosity and perfect resonance tuning, the useful Higgs yield per effective muon-antimuon encounter is only

Thus producing N ~ 10³ Higgs bosons already requires order 10¹¹ effective resonant source events, before accounting for any penalty from putting the Higgses in the desired state. Achieving even this idealized luminosity seems far beyond any ordinary extrapolation of accelerator technology.

But the biggest challenge is that we need to produce the Higgs boson in a very specific state: the inward s-wave configuration that, as Strumia (2023) shows, can cause collapse. In this configuration, the Higgs is spread spatially over a spherical shell, with each piece of the shell moving inward. By contrast, ordinary muon-antimuon collisions create localized Higgs bosons with definite momenta. Such local encounters can produce Higgs bosons moving toward the center, but each has very low overlap with the global inward s-wave. Trying to brute-force this by engineering >> 10¹¹ independent local collisions would create an enormous background of failed muons and other Standard Model debris, likely destroying the clean Higgs configuration one was trying to prepare.

The background problem could be avoided if the muon and antimuon themselves arrived in a suitable s-wave-like quantum state: delocalized over the relevant spherical shell, phase-matched to the desired Higgs wave, and correlated so that if the muon component occupies a given patch of the shell, the antimuon component occupies the matching patch with the right momentum and phase. In effect, the parent μ⁺ + μ^- state would have to be the time reverse of the desired Higgs wavepacket. Even preparing a single such entangled, ultra-relativistic muon-antimuon pair appears completely fantastical; false vacuum decay would require roughly 10¹¹ such effective pair excitations simultaneously.

Tiny black holes

The final route discussed in the literature is black hole catalysis. This topic is controversial, with some authors arguing that tiny black holes strongly destabilize the vacuum (e.g., Burda et al. (2015); Gregory (2024) reviews the case) while others argue that an exponential suppression survives (Strumia (2022); Shkerin & Sibiryakov (2021); Geller & Telem (2026)). While I am not expert enough to assess these arguments in detail, the ‘decay remains exponentially suppressed’ view looks overall much more plausible to me and I would assign an 80% credence.

If micro black holes did catalyze Higgs decay, could an advanced civilization create them? A black hole forms once enough energy is packed inside its Schwarzschild radius R = 2GM, and—unlike the Higgs bubble—gravity collapses any such energy, whatever its quantum state. So the cleanest route is to collide two particles at √s >> M_P1 ~ 10¹⁹ GeV and let them collapse on contact.

I’m not sure whether even a highly advanced civilization could engineer such a collision. Accelerating a muon to the Planck energy using existing methods would require a linac about 10¹⁷ km long, which is somewhat smaller than the radius of the Milky Way galaxy. Smaller accelerators using stronger electric fields are in principle possible, up to the Schwinger limit of

above which electric fields become unstable to pair production. If such fields could be engineered, the minimum linac length could be reduced to just 10⁷ km, though it is very unclear whether any realistic method could approach this.

The collision cross-section would be on the order of the Planck area, σ ~ πR² ~ 10^-69 m², and so collisions are very rare unless the beam is extremely dense and well-aimed. The luminosities proposed for near-future colliders are L ~ 10³⁹ m^-2 s^-1, and at these rates it would take 10²² years to produce a single collision. A galactic-scale civilization could run many experiments in parallel, but even with the entire stellar output of a galaxy a collision would occur every 10⁵ years. With advanced engineering it might be possible to substantially increase luminosity and therefore decrease the energy requirements, but nevertheless Planckian collisions seem to require galactic scale engineering.

Even granted that tiny black holes can catalyze vacuum decay, it is not clear that these Planckian collisions generate black holes, as at these collision energies, quantum gravitational effects are strong. Substantially trans-Planckian collisions should create suitable black holes, and would also have a higher cross-section, thus reducing luminosity requirements, but would need commensurately larger accelerators.

Rather than colliding two trans-Planckian black holes, the alternative is to collide many sub-Planckian particles together simultaneously. By the Hoop conjecture, packing an energy within its Schwarzschild radius

will produce a black hole of mass E/c². Instead of colliding two Planckian particles, you could instead collide N fermions, each with energy E, into an area of radius

While using many particles allows the energy requirements per particle to be lower, engineering a precise enough collision between such larger numbers of particles looks very infeasible.

Summary

Triggering false vacuum decay looks hard. Conditional on a metastable Higgs potential:

• I assign a 10% chance that an advanced civilization could trigger decay by creating the required coherent-Higgs state.

• I assign a 20% chance that small black holes catalyze false vacuum decay and, conditional on that, a 30% chance that an advanced civilization could create the required black holes.

In the previous section I gave 70% credence that the Higgs potential is metastable, and so combined with the above estimates we find overall there is a ~ 10% chance that an advanced civilization could intentionally destroy the universe through Higgs metastability.

Closer analysis of the Higgs coherent-state engineering or many-particle-implosion route to black hole creation could sharpen our sense of their feasibility; the physics is, in principle, well-understood. The issue of black hole catalysis, too, should be resolvable by theory. Better measurements of the top-quark mass could confirm whether the Standard Model is metastable. But that will probably have to wait until at least HL-LHC data starts to be published in the early 2030s, or possibly a future electron-positron collider with cleaner backgrounds.

Whether the Standard Model can be extrapolated up to 10¹⁰ GeV is the least resolvable issue. We might discover new physics which modifies the Higgs stability but I think this is very unlikely in the near-term. A full understanding of 2-body Planck-scale collisions will likewise stay out of reach for the foreseeable future.

Vacuum decay beyond the Standard Model

It is hard to say much with confidence about physics beyond the Standard Model. At extremely high energies the Standard Model must give way to a theory of quantum gravity, but such effects likely only become important at the Planck scale, 10¹⁹ GeV, far beyond anything that is experimentally accessible in the near-term. Our only option, then, is to consider theoretical arguments and indirect experimental evidence.

String theory, as far as anyone understands it, predicts a vast landscape of vacua that are generically metastable (see, e.g., Cicoli et al. (2023), for a recent review). If string theory is correct, our own universe is almost certainly metastable. Even if string theory is not correct, I think the more general picture of a quantum gravitational theory with numerous vacua seems likely. The existence of multiple metastable states is not uncommon even in mundane physical systems like water or cocoa butter, so it seems a priori plausible that quantum gravity is at least as rich. Indeed the Standard Model itself, when coupled to gravity, appears to permit many lower-dimensional solutions (Arkani-Hamed et al. (2007)) in the semiclassical regime where such calculations should be reliable.

Indirect observational evidence comes from the fine-tuning of the cosmological constant, for which anthropic selection from a much larger multiverse is the only plausible explanation that has been proposed (Weinberg (1987)). I suspect similar anthropic selection underlies the smallness of the Higgs mass (Agrawal et al. (1997)), although this is more controversial (see Craig (2023) for a recent review). The flatness and horizon problems also suggest our universe previously existed in a distinct, inflationary phase, consistent with there being multiple metastable states for the universe to occupy.

Taken together, all of this suggests there are probably multiple vacua. If so, it seems unlikely that ours is the one true, lowest-energy vacuum. I’d put the chance that our universe is metastable in this way at around 80%, independent of the Higgs instability discussed earlier, and would guess this is roughly in line with the expectation of most but by no means all theoretical physicists. It is, of course, hard to have much confidence here. Prospects for resolving it in the near term, whether by decisive theoretical arguments, experiments, or new observations, are in my view very dim.4

Given how little we understand quantum gravity, it is hard to say what triggering such a decay would even look like. It would presumably require physics at extremely high energies—perhaps the Planck-scale scattering discussed earlier, of the kind that would form micro black holes. But even that, I would guess, is generically not enough: as with Higgs vacuum decay, few-particle scattering doesn’t create the kinds of coherent states required, and to my knowledge no one has studied this in detail. If forced to guess, I’d put the chance that such a decay could be deliberately induced by an advanced civilization, conditional on the universe being metastable in this way, at 20%, for an overall 16% probability unconditionally for this channel.

Empirical bounds on triggering false vacuum decay

The universe hasn’t ended yet, which means that ordinary astrophysical processes have extremely low probability of causing false vacuum decay. This provides strong empirical constraints on triggering false vacuum decay through current or near-future technology, but ultimately it doesn’t tell us much about the capabilities of a galactic civilization.

Cosmic rays—high-energy protons and light nuclei of poorly understood origin—provide the most direct evidence. The Oh-My-God particle is the most energetic particle ever detected, with an energy of about 3 x 10¹¹ GeV (Bird et al. (1995)). In a direct collision with a stationary proton the center-of-mass energy would be about 7 x 10⁵ GeV, roughly 50 times higher than the LHC. Since numerous such collisions have taken place throughout the Earth’s history, we can be confident that the next few generations of particle collider are of no danger to us (Jaffe et al. (2000); Taylor (2008)).

A naive estimate suggests that the highest-energy 2-body cosmic-ray collision in our past light cone occurred at energies of around 10¹¹ GeV, which is already close to the Higgs instability scale (Hut & Rees (1983)). Strumia (2023) argues that, because cosmic rays are concentrated in compact astrophysical accelerators, the number of such collisions near the instability scale may run as high as ~ 10⁷⁰, far above the naive estimate. This could suggest maximum cosmic-ray collision energies of perhaps 10¹³ GeV or higher.

Three-body collisions at higher energies are very rare, and many-body collisions have never occurred. The cosmic-ray bounds give us very little information about multi-body collisions, and as we’ve already discussed, the Higgs instability probably would require a very large number of particles to be collided. So we can conclude that just creating a massive accelerator and accelerating things to 10¹¹ or even 10¹³ GeV is not enough to destroy the universe, but configurations engineered to give multi-particle collisions are totally unconstrained. Two-body Planckian scattering, of the sort that maybe could create tiny black holes, is also unconstrained.

Other lines of evidence are, unfortunately, very weak. We know the early universe was extremely hot, for example, and Big Bang nucleosynthesis provides compelling evidence for temperatures as high as a few MeV. Physicists generally assume that the universe passed through much higher temperatures. Inflationary models could have reheated the universe as high as 10¹⁶ GeV, and thermal leptogenesis—the most plausible candidate for baryogenesis—requires temperatures ≳ 10⁹ GeV. However, we have no direct evidence for this period and so cannot confidently rule out triggering false vacuum decay through extreme temperatures.

Detecting even a sufficiently small primordial black hole would let us rule out black-hole catalysis. A hole light enough to be evaporating shrinks through the low-mass regime where catalysis would be strongest, so had any formed, and had catalysis worked, the universe would already have decayed. Their decay signatures would be reasonably visible, but none has been confirmed (Carr et al. (2026)), and it seems unlikely future searches will turn up such a population. Since we have no particular reason to think primordial black holes formed at all, this tells us little either way.

Appendix A: A simple model for false vacuum decay on cosmological scales

Modern medicine has inherited a form of suffering that was once more often held by community, spirituality, and shared relational meaning; a form of suffering for which we now have no clear label, no explanatory mechanism, and no cure. The problem is not that healthcare workers do not care about suffering, but that medicine often gives us a narrow language for recognising what suffering is. The Buddha’s diagnosis of this suffering, innate to human life and woven into existence itself, was his first noble truth: dukkha. His second noble truth identified its mechanism, the third revealed the possibility of freedom, and the fourth offered a path of treatment.

Predictive processing may help us understand the mechanism of non-pathological human suffering through an integration of Buddhist wisdom and scientific rigour. In doing so, it may make the Buddha’s diagnosis and response to suffering newly comprehensible through a scientific-clinical lens. This is not only a reinterpretation of old wisdom. A clearer mechanism may help us understand why existing practices work and point toward new ways of working with suffering. It may help recover a dimension of care that modern medicine has partly lost, beyond the treatment of disease alone: a way of meeting suffering itself with greater humanity, tenderness, and understanding.

A note before beginning:

This is a long essay, structured in three distinct parts. I have chosen to publish it as one piece because I think the full arc matters. However, each part offers a natural stopping point, and readers may wish to return to the essay in stages.

• Part 1 identifies the problem itself: a form of suffering that does not fit neatly into a diagnostic box.

• Part 2 considers that problem through a new combined lens: the predictive processing account of mind and ancient Buddhist wisdom.

• Part 3 explores the broader implications of this way of understanding suffering for medicine, healthcare, and those who work within it.

Part 1 - The Problem Medicine Cannot Name

What is Healthcare for?

One of the first things we discussed at medical school was the definition of health itself. The World Health Organisation’s definition of health is as follows:

“Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.”¹

This is clear: health is not simply the absence of disease. Yet in clinical medicine, and perhaps healthcare more broadly, the pressures of rising demand and dwindling resources, alongside, in my view, an overemphasis on the biomedical model, have led to a narrowing of scope. Medicine has thrived in its ability to understand the physical aspects of our being, its ailments, and the last century has brought about an untold revolution in how we meet that part of the equation. However, when we cannot name the disease or understand the pathology, we are less sure how to stay close to the suffering that is, and always has been, relevant to health.

Some may argue that medicine is about treating disease, and that should be our focus. However, the modern physician’s pledge, the modern version of the Hippocratic Oath created by the World Medical Association as an ethical compass for physicians, states:

“I SOLEMNLY PLEDGE to dedicate my life to the service of humanity;
THE HEALTH AND WELL-BEING OF MY PATIENT will be my first consideration;”²

Again, it is clear, particularly when read alongside the WHO definition of health, that medicine is not just about disease and its treatment, but about wellbeing, health, and care for the whole patient. Yet in practice, we are far more articulate when it comes to pathology and its cures, than suffering itself. It is not that we do not care; it is that we lack a mechanism for understanding the deeply human essence of suffering at its heart. When we do not know how to meet suffering, while our identity as clinicians remains so tightly bound to fixing it, compassion itself can begin to fatigue in the endless face of pain for which we have no clear answer.

The Limits of a Western View of Health

As a doctor in Aotearoa, a broader understanding of health is already integral to the healthcare system. Sir Mason Durie’s Te Whare Tapa Whā frames health through four dimensions, inseparable in shaping the wellbeing of not only the individual, but the whānau, communities, and wider society in which we co-exist:³

• Taha tinana — physical health

• Taha hinengaro — mental and emotional health

• Taha whānau — relational and social health

• Taha wairua — spiritual and existential wellbeing

When we consider taha wairua, we are not limited to religion. It concerns meaning, identity, connection, life force, ancestry, and our very sense of who, where, and when we are. The model is often portrayed through the image of a wharenui, a meeting house, with each of these dimensions forming one of its walls. If any one of them is neglected, the house becomes unstable.³

Of course, this way of thinking about healthcare is not limited to the Indigenous Māori context in Aotearoa. Many similar models of health and wellbeing exist throughout the world. Aboriginal and Torres Strait Islander and First Nations perspectives in Canada endorse similar models of health.^4,5 Across these traditions, health is recognised as far more than the absence of disease. It is relational, communal, and inseparable from meaning.

The Tibetan medical tradition of Sowa Rigpa offers another striking example. It understands health in terms of harmony across body, mind, behaviour, and environment. Particularly relevant here is that Tibetan medicine developed in deep conversation with Buddhist thought, rather than treating questions of suffering, meaning, and human flourishing as external to medicine altogether.⁶

The Clinical Problem: Suffering Medicine Encounters but Cannot Name

As a psychiatry registrar working in Consult-Liaison psychiatry, it is common to receive referrals from other medical specialties for patients in significant psychological distress. This distress is often related to illness, and often understood as ‘reality-based’ rather than pathological. The threats to meaning and identity are many: new disability after a traumatic amputation, loss of function and independence in old age, loss of an expected future in the face of a terminal diagnosis. The underlying currents, however, are familiar: loss, grief, confrontation with mortality, and radical shifts in identity, meaning, purpose, and relation to others.

None of this is ultimately separate from the illness or pathology. Yet treatment of the illness, or management of its symptoms, does not necessarily address this additional layer of suffering. Failing to meet that suffering is not only unsatisfactory from a human perspective; it can also shape the effectiveness of physical intervention itself.

This is not just an ethical omission. A person undergoing chemotherapy may struggle not because they misunderstand its purpose or cannot tolerate its side effects, but because they are frightened, grieving and isolated. They may no longer be sure what they are enduring it all for. If we attend only to the disease, we may miss the suffering that determines whether somebody can engage in treatment at all.

Consult-Liaison psychiatry stands at the edge of this tension. When suffering cannot be fixed by medical or surgical intervention, yet still needs to be met, we are asked to help. However, psychiatrists also work within the biomedical model: they diagnose pathology according to diagnostic manuals and treat according to the evidence base. Sometimes we use labels like ‘Adjustment Disorder’ or ‘Existential Crisis’ to capture forms of suffering related to an event that overwhelms a person’s capacity to cope. But these labels tell us little about the mechanism of the suffering itself. Nor is there a comprehensive evidence base that tells us how to meet this kind of suffering in a way that is genuinely helpful.

Labels Without a Mechanism

Adjustment disorder, existential distress, demoralisation, illness-related anxiety: these are all labels that attempt to name something real. They recognise suffering, link it to an identifiable stressor, distinguish it from more overt psychopathological syndromes, and justify attention and care. They do not, however, give us a satisfying account of exactly how or why this suffering arises.

They do not tell us why some people suffer profoundly in relation to a stressor while others do not, nor why suffering can amplify and perpetuate in certain individuals. We acknowledge that fear, grief, and pain are real. Yet we do not fully understand why, for some, they remain bearable, while for others they become overwhelming and incapacitating. The fact that this is the case suggests that something is being added to the initial insult itself: a further layer of suffering that cannot be explained by the event alone.

Of course, there are ways of working with this kind of suffering. Psychotherapy, the compassionate presence of a hospital chaplain, spiritual or contemplative practices, and even psychedelics in the case of existential end-of-life anxiety, all appear to offer benefit. But why? Without a mechanism, our ways of working with such suffering remain limited. Compared with pathology, our conceptual understanding of this deeper layer of suffering remains underdeveloped.

A Problem Medicine Has Long Recognised

Medicine has always encountered deeper layers of suffering in relation to illness, old age, and death. Grief, pain, loss, fear, and existential dread are aspects of life we confront on a daily basis. This encounter is inseparable from the work of a doctor, nurse, or allied health professional. Medicine’s triumph over the physical aspect of our being is undeniable. Historically, when medicine had little to offer regarding the physical, the doctor’s role as witness, comforter, and companion was harder to deny. Now that medicine has so much to offer, the suffering that cannot be fixed can stand out more easily as residual failure, psychological complication, or someone else’s problem. As Dr Edward Livingston Trudeau, a late nineteenth-century physician who founded a tuberculosis sanatorium in New York, is often quoted as saying in regard to the role of a physician:

“To cure sometimes, to relieve often, to comfort always.”⁷

He recognised that even when treatment options are limited, offering comfort remains a vital part of the doctor’s duty. In 1982, Dr Eric Cassell, in his article published in the New England Journal of Medicine titled “The Nature of Suffering and the Goals of Medicine,” wrote:

“Suffering is experienced by persons, not merely by bodies, and has its source in challenges that threaten the intactness of the person as a complex social and psychological entity.”

He then went on to say:

“The relief of suffering and the cure of disease must be seen as twin obligations of a medical profession that is truly dedicated to the care of the sick.”

And further:

“Physicians’ failure to understand the nature of suffering can result in medical intervention that (though technically adequate) not only fails to relieve suffering but becomes a source of suffering itself.”⁸

As doctors, we have a duty not only to diagnose and treat disease, but to understand the nature of human suffering and meet it with both wisdom and compassion. Cassell diagnosed medicine’s failure with remarkable clarity, although more than forty years later, the gap remains. We have become better at naming suffering as a clinical concern, but not necessarily at understanding the mechanism by which suffering takes hold, magnifies, and becomes bondage.

Palliative Care: Medicine Already Knows Pathology Is Not the Whole Story

The specialty of palliative care is grounded in the recognition that medicine, as an intervention to treat illness and ward off death, has limits. The shift from treating disease to managing symptoms acknowledges that medicine is not only about curing sickness, but also about alleviating suffering. This work remains profoundly important.

Dame Cicely Saunders, often considered the founder of modern palliative care, coined the term “total pain” to acknowledge that physical, psychological, social, and spiritual suffering are entangled.⁹ In palliative care, we find an existing example of medicine recognising that suffering cannot be reduced to pathology. Yet even here, we encounter the sense that we may still lack a deeper mechanistic understanding of how suffering becomes layered around pain, illness, and mortality.

Secular Modernity, Chaplaincy and Existential Distress

In an increasingly secular world, where scientific understanding often dominates what can be accepted as truth, inherited religious frameworks may no longer make sense to many people. Across many developed societies, formal religious affiliation has declined substantially. In England and Wales, for example, 37.2% of people reported having “no religion” in the 2021 census, up from 25.2% a decade earlier.¹⁰ This does not mean that meaning, ritual, or spirituality have disappeared. But it is not a stretch to imagine that shared communal frameworks around illness, death, grief, and existential meaning have weakened.

All human beings, at some point, will confront mortality, loss of control, collapse of identity, and failure of the physical body. Frameworks that once helped hold this existential form of suffering no longer dominate, particularly in secular developed societies. The scientific worldview that has risen in their place has transformed our understanding of the physical world, but it has relatively little to say about the forms of human suffering that cannot simply be understood through a microscope or an MRI scanner.

Hospital chaplains around the world have a visible presence and serve to meet what doctors don’t always have an answer for. Their compassionate, caring presence and ability to hold suffering within the relational space without ‘fixing’ may offer something medicine itself often struggles to provide. However, in an increasingly secular world, the religious connotations of chaplaincy, in my experience, appear to result in only a minority of patients reaching out for or accepting their support. Many younger or secular patients may not recognise their role and clinicians may dismiss their service as only for those with pre-existing religious affiliation. Yet, as we have already seen, human suffering extends beyond one’s individual religious or spiritual belief system.

Scientific, evidence-based healthcare remains one of the final institutions with broad social legitimacy to which many people turn in the face of distress. Yet when this distress cannot be easily categorised or explained, healthcare can struggle to meet it head-on. Some portion of what presents as the modern “mental health crisis” may not be reducible to an increase in discrete mental disorders alone. Psychological distress has clearly risen in some settings, including Aotearoa New Zealand, where the proportion of adults reporting high or very high psychological distress increased from 8.3% in 2018/19 to 13.0% in 2023/24.¹¹ But distress is not always well understood simply by assigning another diagnostic label.

It is not uncommon in psychiatry to encounter someone who has received several mental health diagnoses over time, each capturing something real, while the person continues to suffer immensely. Perhaps relational, existential, and meaning-laden suffering is the dimension that has not been adequately seen within a culture that has lost some of its shared ways of holding these aspects of human existence.

Part 2 - A New Way of Understanding Suffering

It is important to be clear about what this essay is, and is not, referring to. We are not speaking here about pain, loss, grief, or death themselves; these are inevitable parts of life. Nor am I trying to draw a hard line between suffering and psychopathology. In mental illness especially, the boundary is often blurred. Biological and environmental vulnerabilities, life stressors, and existential suffering can compound one another until distress becomes dysregulating, impairing, or risk-laden. Clinical disorder is real, and often requires specific treatment. But even when pathology is present, there may still be an additional layer of suffering organised around fear, resistance, shame, identity, meaning, and uncertainty. This essay is concerned with that layer. The distinction matters not because these categories are cleanly separable, but because different aspects of suffering may require different kinds of understanding and care. The problem we are referring to is what the Buddha called dukkha.¹²

The Two Darts: The Buddhist Diagnosis of Suffering

The Buddha used the metaphor of two darts to distinguish between the inevitable pain associated with existence, and the mental suffering that we add in relation to it.¹³ The first dart relates to things such as physical pain, illness, and loss. The second dart refers to the reactions we have to the first.

Consider that you fall and break your leg. It is going to hurt. This is the first dart. A broken leg is obviously not a preference, and such an injury is typically met with a layer of mental resistance that says: “this can’t be happening”, “why me?”, “this is someone else’s fault for leaving the floor wet!”. The second dart involves not only this kind of conceptual, mental proliferation. It begins earlier, when pain is appraised as something that should not be happening: a subtle resistance to the fact that reality has not conformed to your preferred version of it.

The Buddha explained that this second dart manifests through craving and aversion. That is, craving a version of reality different from the one you are living in, or resisting a version of reality that is already here. This process of craving and resisting leads to clinging: a grasping at preferred ideas of the world, ourselves, and what we think should be happening. Clinging to the wished-for world in which the thing you did not want to happen never happened. Clinging to the idea of something that will finally bring lasting satisfaction, which you do not yet have. Clinging to the belief that you are right and they are wrong. Through clinging, our hopes and fears become bound up with our sense of identity, our sense of self. When the self is held in this way, we develop a compulsive insistence that reality be other than it is, in light of our hopes and fears.

In Buddhism, the self is not a fixed, inherently existing thing. It is a dynamic, unfolding process, shaped by biology, history, relationships, habits, and environment. Clinging reinforces this process as something more solid, fixed, and rigid than it really is. You become someone who likes this and not that, who believes this and not that, who needs this to happen and that not to happen. The world is increasingly encountered through the demands and defences of this self-model.

This is part of the deeper Buddhist insight of dependent origination: that no thing, and no person, exists inherently as something separate from everything else. Everything arises in dependence on other conditions. You are dependent on your parents, the sun’s energy, the earth’s water supply, your language, your culture, and countless experiences you did not choose. The self is no exception. Yet when this dependently arisen sense of self is held rigidly, it becomes a foundation for craving, aversion, and clinging. This leads to suffering: the suffering that lies in the gap between what you want the world and yourself to be, and the actual shared world in which you are living.

It is important to note that there is no blame, guilt, or shame involved in this process. If you are a human being, born into this world, this is part of how things are. The Buddha’s claim was not that he had somehow been exempt from the human condition, but that he had understood this process deeply enough to let go of the rigidity of self and of the self’s insistence that reality be a certain way.

The form of suffering outlined in the first part of this essay, to me, appears to align closely with the Buddha’s diagnosis. Illness, ageing, and death, realities clinicians encounter every day, were themselves the catalyst for his search for liberation. He recognised that they were inevitable aspects of being born into this world. The deeper point was that the mental resistance formed in relation to them was not inevitable.

Dukkha is the suffering that arises when deeply invested models of self and world resist updating in the face of unavoidable change, uncertainty, and loss.

Why Bring Science and Buddhism Together?

Science is extraordinary. Its power to investigate the external world has transformed human life beyond recognition. Yet when it comes to the human condition, and to suffering in particular, science alone has not provided a fully satisfying account.

One reason may be that science is, by design, primarily a third-person mode of investigation. It observes, measures, and explains phenomena from the outside. This is precisely its strength. But suffering is not only an object that can be observed externally; it is an experience lived from within. Brain scans, behavioural measures, and diagnostic categories may reveal important correlates of suffering, but they do not by themselves tell us what it is like to be caught inside craving, aversion, fear, or despair.

To understand suffering more fully, we may therefore need more than third-person observation alone. This is not to say we should replace science. However, we may also need a disciplined investigation of experience from the inside. This is where Buddhist contemplative traditions may become relevant, as a long-developed first-person inquiry into the mind and the nature of suffering.

When an ancient contemplative tradition developed through disciplined investigation of lived experience and a contemporary scientific account of the predictive mind appear to illuminate a similar structure of suffering, that convergence warrants serious consideration.

Suffering is something we live from the inside, but that does not mean it is without structure. A fuller account may require bringing first-person phenomenology and third-person cognitive science into disciplined conversation.

Predictive Processing: A Bridge Between Worlds

To understand how this account of dukkha might be brought into conversation with contemporary cognitive science, we need to briefly consider how predictive processing understands perception, prediction, and the mind’s relationship to uncertainty.

Most of us were taught a bottom-up model of perception. Light enters the eye, lands on the retina, becomes electrical signals, travels through the nervous system, and is then somehow turned into visual experience. In this sense, perception is built from the outside in.

Predictive processing turns this picture upside-down. It proposes that perception is primarily a top-down process. The brain does not simply wait for the world to impress itself upon it. Rather, it is constantly generating predictions about what is happening, based on prior experience. These prior expectations, or priors, shape what we perceive in the present.

These predictions are organised into what is sometimes called a generative model: the brain’s best current guess about what is going on. Sensory input still matters, but its role changes. Rather than fully constructing perception from scratch, sensory input is compared against the brain’s predictions. If the incoming data does not match the prediction, a prediction error is generated. This is the signal that something unexpected has occurred.

Once a prediction error is detected, the system must determine how much weight to give it. This is where the concept of precision weighting comes in. If confidence in the prior prediction is low, the incoming sensory information will be given more weight and the model will update to incorporate this surprising new information. If confidence in the prior prediction is high, surprising sensory data may be ignored or downplayed. Perception is therefore a constantly shifting negotiation between prior belief and incoming signal. This process of negotiation occurs across a number of hierarchical levels, and so is more complex than laid out here, but a full explanation of this is beyond the scope of this work.¹⁴

Predictive processing does not only apply to how we perceive the external world. What we take to be the self, this body, this personality, this history, this sense of “me” moving through time, can also be understood as a predictive model, shaped by past experience and continually used to make sense of what is happening now.¹⁵

Without going deeply into the mathematics of Bayesian modelling upon which the theory is based, the central point is this: we do not simply perceive the world as it is. We perceive through predictions shaped by the past. This includes not only the world of illness, loss, and mortality, but the self that must somehow make sense of them.

Predictive processing remains an evolving and debated framework, rather than a settled final account of mind. Even so, it has generated serious work across perception, action, and the brain’s modelling of the body, and offers a useful way of thinking about how models of self and world are maintained, challenged, and revised.¹⁶

Dukkha as Resistance to Uncertainty and Resistance to Updating

Through a predictive processing lens, dukkha, or suffering, can be understood in terms of resistance to uncertainty, and resistance to updating our models of self and world when reality no longer conforms to them. Living systems evolved to reduce uncertainty because uncertainty can threaten survival. We seek food, shelter, safety, and social inclusion partly because their absence makes the future less predictable. If we can reliably predict their presence, we are more likely to survive and reproduce. This is adaptive. The problem begins when the same uncertainty-reducing machinery becomes rigid: when the organism keeps defending a model of reality that reality itself is already disconfirming.

Take hunger as an example. Hunger itself is not necessarily the problem; it is a signal. But uncertainty quickly gathers around it. There is a more basic unease in not knowing when food will come, or whether the need will be met. Thought then compounds this: what if I never find food? What if this does not end? There is now not just sensation, but resistance to an uncertain future, elaborated into fear through conceptual proliferation. The mind reaches forward, imagines an intolerable possibility, and suffers in relation to the fact that this future cannot be guaranteed against.

Consider pain as another example. If you break your leg, something has changed in the body. Pain is the signal of that change. Almost immediately, the signal is appraised: good or bad, wanted or unwanted, safe or threatening. Depending on that appraisal, resistance can gather around the fact that reality has changed. The model says: my body is intact; today will unfold as expected. Reality says: your leg is broken. This mismatch is felt before it is fully thought. Then thought gathers around it: this cannot be happening, I do not want this, this should not be here in my experience. These thoughts are not the resistance itself, but its secondary elaboration. They give narrative form to the deeper refusal to release a preferred model of reality that has already been disconfirmed.

The suffering lies not only in unavoidable pain, but in the more basic resistance to reality being otherwise than expected, and in the conceptual proliferation that grows around that resistance. It also lies in resistance to a future that cannot be made certain or safe.

In predictive processing terms, one way of understanding suffering is as over-commitment to a prior in the face of either disconfirming reality or irreducible uncertainty. The model says: this should not be happening. Reality says: it is happening. The model says: I need to know that this feared future will not occur. Reality says: such certainty is not available. The more tightly the model is held, the more suffering is generated. Tightly holding the model, in Buddhist language, is clinging: the cause of suffering.

This also explains why acceptance is not passivity. Accepting reality does not mean you have to like what is happening. It means dropping the unnecessary struggle against the fact that it is already here, or against the fact that the future cannot be made fully certain. In many situations, this is what allows wise action to happen. Consider acceptance and commitment therapy. Experience that cannot be fixed is accepted, and energy is redirected towards valued, purposeful action. The sooner the broken leg is accepted, the sooner appropriate care can begin.

So from this perspective, unnecessary suffering is not simply pain. It is the suffering generated when deeply invested models of self and world resist updating in the face of present reality, or resist the uncertainty of a future that cannot be controlled.

Illness, Ageing, and Death

For someone diagnosed with a potentially life-limiting illness, some suffering is obviously inseparable from the reality they are living through. However, through the lens offered by both the Buddha’s language and that of predictive processing, there is an additional layer of suffering created by the mind’s intolerance of its changed reality. The self-world model that says, I have decades left to live, my body is healthy, the future is filled with possibilities, suddenly narrows. Instinctively, resistance to updating around the new information of a diagnosis arises, as does resistance to the uncertainty that the future now holds. Thought may then gather around this resistance, multiplying possible futures, losses, fears, and questions that cannot yet be answered.

Consider ageing. Our model, formed through our younger years, says: I am young, attractive, healthy, and full of vitality. Time continues, but our models do not necessarily update in a contemporaneous manner. Reality lands: wrinkles are noticed, joints ache with wear and tear, activities that were once simple become overwhelming efforts. If we cannot release our old models of who we are, the gap between who we think we are and who we have become grows, and resistance to reality beds in.

Death brings this tension into its starkest form. The uncertainty surrounding it, and the need to accept that the self we take ourselves to be will ultimately cease, is an obvious threat to the self-model. If that self-model is not held lightly, resistance to this reality can add untold layers of suffering to an entirely natural and inevitable process.

These are not abstract ideas. They are the realities that all of us face.

Why a Mechanism Matters

When medicine encounters non-pathological suffering of the kind outlined above, three typical responses may arise.

• Pathologise it: treat grief, fear, existential distress, or identity collapse primarily as disorder.

• Normalise it: “Of course they are distressed.” This may be true, but it can also risk abandoning the person to their suffering.

• Accompany it humanely, without understanding its structure: offer kindness and support, but without a clear account of what is occurring, how it is unfolding, or what might be the most effective way to help.

None of these responses are overtly wrong, but none of them are optimal. Medicine need not pathologise dukkha in order to care about it. But if it claims to care about health and wellbeing beyond pathology, it needs a richer understanding of suffering than pathology alone can provide.

Psychedelic-Assisted Therapy: A Tantalising Illustration

In recent years, interest in psychedelic therapy has grown. One influential model for how psychedelic therapy may function draws on the predictive processing account of mind. This theory is known as REBUS, or Relaxed Beliefs Under Psychedelics.¹⁷ The “beliefs” referred to here are not merely conscious opinions, but the largely unconscious priors that shape our experience. REBUS proposes that psychedelics may reduce the precision, or grip, of high-level priors, allowing previously rigid models of self and world to become more open to revision.

One of the most clinically compelling areas of psychedelic research has been in end-of-life existential distress. This is a form of suffering that is not easily reducible to pathology, but is real, potentially disabling, and can have a profound impact on quality of life. In trials involving patients with life-threatening cancer, psilocybin-assisted psychotherapy has been associated with substantial reductions in anxiety, depression, demoralisation, and hopelessness, alongside improvements in spiritual wellbeing and quality of life.¹⁸

If we consider the model described in this essay, one possibility is that such therapies work, at least in part, by loosening the priors that sustain a rigid self-world model in the face of mortality. When that model loosens, resistance may loosen with it. There may be less craving for another version of reality, less aversion to the reality already unfolding, and therefore less suffering.

In addition to this, psychedelics have long been associated with experiences of self-dissolution. If the self is understood as a predictive model shaped by prior experience, it follows that relaxing the grip of high-level priors may allow self-related beliefs to soften and update. This could include the belief that one is a fixed, permanent entity whose continued existence must be secured at all costs. In this sense, it is not difficult to see how clinging to the continued existence of the self might loosen, and how the suffering associated with confronting mortality might loosen with it.

What palliative psychedelics reveal dramatically at the edge of life, Buddhism suggests, is operating continuously in ordinary life: suffering is shaped not only by what happens, but by the rigidity of the models through which what happens is held.

Multiple Psychiatric Diagnoses, Exhausted Interventions, and Ongoing Suffering

The same principle may apply in psychiatry. A person may present repeatedly to services, accumulating diagnoses and exhausting interventions, yet continue to suffer immensely. Each diagnosis may capture something real. Each treatment may have been reasonable. Yet if we only see a cluster of disorders, we may miss the human suffering layered around them: shame, alienation, fear, identity collapse, and the struggle to live with a mind and life that feel increasingly unmanageable.

Instead of such people falling too quickly into the “treatment-resistant” bucket, what if a deeper understanding of their lived experience, and of the suffering threaded through it, could open another avenue for meaningful change?

If this account is right, then the problem for medicine is not only that it has overlooked a dimension of suffering. It is that, without understanding its structure, we may repeatedly fail to recognise what kind of help is actually needed. The question then becomes not only how suffering arises, but what it would mean for healthcare to meet it more wisely.

Part 3 - What Would It Mean for Medicine to Meet Suffering Fully?

This final section is not an attempt to offer a comprehensive programme for clinical practice or healthcare reform. Rather, it considers some of the directions that become visible once suffering is understood in this way.

What Becomes Visible Once We Clarify the Mechanism?

This account of suffering offers not only a deeper way of understanding some of our existing interventions, but also opens doorways to evolving ways of working with suffering itself. If suffering involves, at its core, rigidity in belief systems, both conscious and unconscious, in relation to illness, uncertainty, loss, and the self that experiences all of these, then certain forms of care become understood in a different light. Ways of working that help people grieve, soften resistance, reorganise meaning, feel accompanied, and regain flexibility in their models of self and world may be more than simply ‘supportive’. They may target the very processes by which suffering compounds.

Palliative care already shows how medicine can meet suffering more wisely: continuing to treat what can be treated, while helping people grieve, orient towards what remains meaningful, and release some of the struggle against what cannot be changed.

Practitioners of ACT, or Acceptance and Commitment Therapy, will likely see strong parallels with what has been discussed here. Acceptance involves letting go of rigid resistance to what cannot be changed, and redirecting energy towards meaningful, purposeful engagement. It is no surprise that Stephen C. Hayes, the creator of ACT, drew explicit parallels between ACT and Buddhist ways of meeting suffering.¹⁹ Through the lens developed here, ACT may be understood as one of the clearest existing clinical responses to this mechanism: loosening rigid resistance to present reality and uncertain futures, and restoring flexibility where suffering has become organised around futile control. Psychotherapy more broadly can help make rigid, unconscious models visible, support the reorganisation of identity in the face of change, and restore flexibility in how one relates to oneself and the world.

Contemplative traditions, including but not limited to Buddhism, have long held ways of working with this kind of suffering. Perhaps they are worthy of serious investigation and incorporation into our scientific models of working with suffering. Practices such as meditation, of which many forms exist, can help redirect attention and cultivate greater non-reactivity to distressing events. Emerging predictive processing accounts suggest that some of these effects may be understood through altered precision weighting, with attention playing a central role in what the mind amplifies, sustains, or releases.²⁰ Compassion-based practices may also matter here. They may soften threat-driven ways of relating to the self and to experience, opening a different way of meeting suffering. These practices do not deny pain. Rather, they appear to alter our relationship to it, allowing less rigidity and greater ease amid the inevitable ups and downs of life.

This way of thinking may also help us understand certain trauma-focused interventions. MDMA-assisted psychotherapy, for example, may create a temporary state in which traumatic material can be approached with less fear, shame, and defensive constriction, and with greater self-compassion and trust.²¹ Through the lens developed here, one possibility is that it softens rigid threat-salience enough for painful memories to be reprocessed rather than reflexively avoided or re-experienced as overwhelming. EMDR may offer a different example. Its mechanisms remain debated, but we might tentatively wonder whether shifting attention while traumatic material is recalled reduces the grip of traumatic imagery enough for previously defended material to be approached and reorganised.²² These are pointers rather than settled claims, but they suggest that different therapies may converge on a similar principle: reducing the rigidity or overwhelming force of particular self-world models so that new processing becomes possible.

The role of chaplaincy similarly takes on renewed significance through this lens, as creating a relational space in which suffering can be held without immediately being fixed, explained away, or converted into pathology. Psychedelic-assisted therapy, as discussed earlier, may offer another emerging example of work that loosens rigid self-world modelling and opens greater flexibility in relation to life, meaning, and mortality.

The Relational Space as Healing Intervention

It may seem obvious that the relational space between doctor and patient, therapist and client, is an integral part of care. It has long been understood that one of the most important aspects of a psychotherapeutic intervention is the therapeutic alliance itself, rather than the specific modality used.²³ If suffering involves contraction around threat, uncertainty, shame, and loss, then a relationship that offers calmness, attunement, and non-abandonment may alter the field in which that suffering is held. When suffering is witnessed and held with tenderness, rather than discomfort or fear, it may become more bearable, less isolating, less defended against, and open the possibility for painful self-world models to soften and update.

In the busyness of the hospital ward, or the frantic space of an emergency department, this may be easily forgotten or disregarded. It is understandable that this relational space cannot be held as it might be in a long psychotherapeutic intervention, but even brief interactions matter. Done poorly, they can amplify threat, deepen shame, and confirm helplessness. Done with care and understanding, they can create steadiness, preserve dignity, and provide a sense of togetherness in what can otherwise be a lonely journey through illness.

How we meet suffering matters. It is not simply a problem to be rapidly eliminated or disregarded. To treat it that way may risk solidifying a sense of aloneness and exacerbating threat-based rigidity. Suffering may need to be held, accompanied, and understood, if a person is to move towards a different relationship with the inevitable pain of life.

The False Dichotomy of Disease and Suffering

Attending to suffering with understanding and compassion does not disregard the clinician’s role in treating illness, injury, and disease. In reality, pathology and the suffering formed around it are not cleanly separable in the lives of actual patients. A richer understanding of suffering may therefore improve the treatment of pathology itself.

A patient’s physical or psychiatric dysregulation may be real, and still be met through shame, alienation, fear, rigid identification, or collapse of meaning. When these dimensions are poorly understood, distress can intensify, coping can unravel, and engagement with care can become more difficult. Progress may stall, crisis presentations may increase, and suffering may amplify around the original condition.

A person is not first a disease, with meaning and suffering added later. These dimensions are already intertwined within the clinical space, shaping both the illness experience and the interventions we are trying to offer. Healthcare that better understands the multidimensional nature of suffering may therefore be more effective in treating pathology, not less.

Why Medicine Struggles to Offer This

Profound existential suffering cannot be adequately met in a ten-minute primary care appointment, when that same appointment must also include history-taking, clinical examination, diagnostic reasoning, and a treatment plan. Nor is it simple to offer in a packed emergency department, with assessments taking place in corridors and clinicians carrying responsibility for many acutely unwell patients at once. In such demanding environments, where resources are limited, the acute medical issue understandably takes priority.

Time pressure converts people into problems. This is not because clinicians stop caring. It is because many healthcare systems give them too little room to remain with suffering that needs presence and patience. Around the world, resource limitations often prevent us from spending the time we would like to spend meeting and addressing this deeper human dimension of care.

The failure to meet suffering in this way is not an individual moral failure of any one clinician. It is a systemic issue. In many health systems, an emphasis on acute, short-term intervention, measurable throughput, and statistically driven care leaves too little space to acknowledge the profundity of working with people during the most vulnerable and stressful periods of their lives.

This does not negate the importance of this aspect of our work. Instead, it strengthens the argument that, if healthcare is to be aligned with the WHO definition of health and the WMA physician’s pledge, greater allocation of resources is needed. It may be that, in the longer run, when suffering is met at the human level rather than treated as something beyond healthcare’s concern, illness and disease outcomes improve, and some of the demand driven by recurrent presentations begins to fall.

Clinicians Are Not Outside the Field of Suffering

Dukkha impacts clinicians too. In my own experience, and in that of those I have worked closely with, the suffering of the patient impacts clinicians deeply. Healthcare professionals face death, severe illness, traumatic injuries, and profound mental suffering on a daily basis. We are not detached observers of suffering, but intimately interacting with it, repeatedly, throughout our careers. This is not even to mention the suffering within our own lives, outside of the vocation.

When conditions limit our ability to respond to suffering in a way that we feel it deserves, moral injury can follow. When our own self-identity, bound up with fixing others, encounters suffering that cannot always be fixed, compassion can fatigue. Burnout, compassion fatigue, and moral distress are familiar terms, both intellectually and experientially, to most healthcare workers now.

Clinicians face high rates of mental health difficulty and an elevated risk of suicide.²⁴ When our identity is tied up with fixing, and we do not have the resources to heal as we feel we could, nor to meet the suffering that cannot be easily fixed, our own models of who we believe ourselves to be are threatened. Of course, we resist this, because we too are human. There is always more suffering to meet. In Buddhist cosmology, it is infinite.

Dukkha is contagious. Not in the sense that we can catch it through the air like an airborne virus, but in the sense that unheld suffering, both our own and our patients’, reverberates through relational systems. When clinicians are unable to meet the needs of those who come to us for help, we carry that with us. Over time, what cannot be metabolised can harden into detachment, exhaustion, despair, and sometimes the collapse of the very system that was able to care at all.

Can Medicine Afford to Care About This?

The obvious objection to meeting suffering in a way that is deeply understood, wisely responded to, and tenderly held, is that healthcare systems are already overstretched. In my view, this does not defeat the argument, but amplifies it. If suffering is clinically consequential and systemically reverberant, then providing the conditions for meeting it matters.

Failure to distinguish pathology from practical support needs, psychiatric disorder from non-pathological existential suffering and meaning collapse, may contribute to repeated presentations, fragmented care, partial diagnoses, escalating investigative demand, inappropriate medicalisation, and symptom management that never meets the deeper problem. If we cannot give suffering the attention it deserves, that suffering may return in forms that are more fragmented, more chronic, and more costly: for patients, clinicians, and society as a whole.

High-quality care needs time, continuity, better integration of interventions such as chaplaincy, psychologically and trauma-informed care throughout medical settings, and space to reflect on the most aligned approach in the face of deeply human issues that go beyond the usual protocol-driven care on offer. Clinicians also need working conditions that allow them to remain human in the presence of suffering, rather than being asked endlessly to absorb what the system has no space to hold.

Conclusion

This essay has argued that medicine encounters a form of suffering that is not always pathological, but remains inseparable from health, illness, and disease. Not every confrontation with pain, loss, or mortality requires clinical intervention, and ordinary human suffering should not be pathologised simply because it is difficult to bear. But neither can medicine pretend that such suffering lies wholly outside its concern.

Through the combined lens of Buddhist insight and predictive processing, I have suggested that we may be able to understand this suffering with greater mechanistic clarity: as the suffering that forms when deeply invested models of self and world resist updating in the face of change, uncertainty, and loss. This account does not resolve the problem, nor does it offer a complete clinical programme. It is a synthesis, and therefore necessarily provisional. But it may help clarify the territory: why certain forms of care matter, why the relational space itself matters, and where future clinical, empirical, and systems-level advances might lie. If we can better understand what we are trying to meet, we may become better able to investigate how it can be met. The aim is not simply to rename familiar suffering in mechanistic language, but to ask whether a clearer mechanism might help us work with it more precisely: through attention regulation, compassion and even neuromodulation; each offering ways of shifting how experience is weighted, held, and integrated.

If medicine is serious when it says that health and wellbeing are its first concern, then suffering of this kind deserves to be understood, not left at the margins of care.

This essay is dedicated to all those in healthcare who turn up, day after day, to meet suffering.

References can be found in the comments.

In December 1973, President Richard Nixon signed the Endangered Species Act was signed into law.

Through a series of steps, this eventually lead to the American Gray wolf being re-introduced to Yellowstone National Park in 1995, where they had been eradicated during the 1920s.

This re-introduction lead to profound ecological changes in the park. Wolves are obviously predators who hunt and kill other animals, so you might think that these changes only ‘subtracted’ from the local environment. But what occurred was a rapid and profound recovery of the ecosystems within Yellowstone.

What happened?

After the wolves were eradicated in the 1920s, the deer and elk population increased. Over the decades, these large and indiscriminate grazers had eaten the grasses down, disturbed many of the shrubs and underbrush, and killed many trees through antler rubbing, creating large open spaces.

Of course, the wolves killed some of the deer and elk, but more importantly, they changed the animal’s behavior. Because of pressure from the wolves, they could no longer roam and eat wherever and whenever they wanted and tended to avoid both wide-open spaces, where they could be easily spotted, and tight spaces (such as deep valleys and gorges), where they could be trapped.

These areas saw a rapid regrowth of shrubs, underbrush, and trees. With more habitat and food, this lead to a rapid increase in the number of birds, which compounded the effects, since birds spread seeds and their waste is a nutritious fertilizer. The number of beavers increased, since they need trees for their dams. These dams slow rivers, creating ponds and wetlands, which again compounded the effects by creating more habitat for ducks, muskrats, fish, amphibians, and water-loving insects, and through water retention, increased the growth of the shrubs, underbrush, and trees.

The wolves also competed against and killed coyotes. With fewer coyotes and the regrowth of habitat, there were increased numbers of small mammals, such as rabbit, mice, and voles. This brought more hawks, badgers, weasels, and other small predators to the park. As they hunted, the carcasses brought increased numbers of carrion birds and animals.

The increasing number of plants and animals also increased the number of bears, who as omnivores, enjoyed hunting the increased numbers of wildlife, foraging the increased number of berries, fishing in the increased numbers of ponds and wetlands, and of course, finding honey from the increased numbers of bees who helped pollinate all the new regrowth.

And the wolves didn’t just affect the wildlife and plants, but also the abiotic parts of the environment. The regrowth of the trees and understory meant more roots that stabilized the banks of the streams, rivers, and ponds, which decreased the erosion and meandering of the waterways. The increased animal ‘waste’ spread nutrients, improving the quality of the soil, which allowed the plants to grow faster. As the plants grew faster, they collaborated with fungi to release minerals from deep bedrock, propelling the nutrient cycle forward, as their dropped leaves decomposed each fall.

The re-introduction of wolves into Yellowstone is the quintessential example of a tropic cascade: powerful, indirect interactions and knock-on effects that alter and control entire ecosystems.

In February 1972, President Nixon directed federal agencies to advance the application of Integrated Pest Management to all relevant areas.

As synthetic chemical pesticides were developed and brought to market in the early 20th century, they were seen as a miracle development. Finally, the pests and diseases that had plagued farmers and decreased crop yields could be controlled. There use nearly tripled between the 1930s and the 1970s.

During the 1950s, there was increasing awareness that these chemicals can have profound negative effects on the environment and other species. This culminated in the publication of Silent Spring by Rachel Carson in 1962, which eventually lead to the banning of the persistent pesticide DDT and the creation of the Environmental Protection Agency (also by Richard Nixon).

But parallel to the research that looked at the negative effects of pesticides, were diverse lines of research that were discovering that pesticides weren’t the miracle technology they promised to be.

There are tens of millions of acres of corn in the United States (corn is just an example). Certain pests and diseases like to eat and destroy corn, so when synthetic chemicals were developed against those, certain pioneering farmers started spraying those chemicals and their yields (and profits) went up. This forced all the other corn farmers to start spraying. But even in the absolute best circumstances, pesticides are only 99.999% effective. Across all those acres of corn, there are many billions of pests, meaning a huge pool of genetic diversity. While you will kill the vast majority of them in the first, second, and even third years with a new pesticide, over time, you are slowly selecting for resistance in that genetically-diverse pest population.

Integrated Pest Management (IPM) was developed to better control pest populations with fewer synthetic chemicals. IPM is a diverse set of ecosystems-based strategies that focus on the long-term prevention of pests by using a combination practices, that vary from the biological and chemical to the common-sense and practical. Together, these practices minimize damage people, property, and the ecosystem.

While many IPM practices don’t include synthetic chemicals, it doesn’t write them off completely. In fact, “responsible use” of such chemicals is a critical part of IPM if other non-chemical methods have failed. One responsible use method is only spraying during peak efficacy of the chemical (i.e. if a synthetic pesticide inhibits the larval stage of an insect, only spray when the larval stage is most common). Another aspect of responsible use is not spraying the entirety of your acreage. While this might result in more pests this year, spraying less means fewer pests down the road.

While some individual pests in the population will have genes that provide pesticide resistance, that doesn’t come without cost. It takes energy to produce and operate whatever enzymes and proteins that confer resistance. This is absolutely worth it if the population gets sprayed, but it is an ever-so-slight disadvantage if not sprayed. If farms are responsible and don’t spray all of their acreage, the non-resistant pests will be more likely to reproduce in the non-sprayed areas, and thus re-populate the previously-sprayed areas. While not spraying means more pests now, it prevents resistance from being selected for, meaning fewer total pests in the future as the pesticides will continue to work.

IPM is just one example of ecological, non-zero sum thinking allowing all parts of the system to benefit.

In 1971, President Richard Nixon signed the Nation Cancer Act, beginning the “War on Cancer.

Cancer has been the second leading cause of death every single year in living memory (heart disease is always number one).

And below is both sexes on the same graph along with total incidence rates.

The drop in total cancer mortality from ~200 per 100,000 in 1975 to ~160 per 100,000 in 2024 is impressive and should be celebrated. But most people’s instincts about the causes of that drop aren’t quite right. Much of the decrease is due to better public health (reductions in smoking, the HPV vaccine, etc.), better screening regimens and detection methods (regular mammography, colonoscopies, and prostate exams, the invention of the MRI, etc.) and better surgical techniques, not because we’re that much better at “treating” cancer.

Now that is not to say that we’ve made zero improvements in cancer treatments over the past fifty years. We have. There have even been large, step-change improvements in a few cancers, such as CAR-T cell therapies for several lymphomas and myelomas and immunotherapies, such IL-2 therapy for melanoma, which have been gamechangers. But for many cancers, the improvement in actual treatments amounts to only months of additional survival.

Shown above are the changes in five-year survival rates for various cancers in the USA from the 1970s to the 2010s. This overall rate factors in all of better detection methods, screen regimens, surgical techniques, and treatment improvements. Even still, the majority of people with many types of cancer do not survive five years. And this is after nearly six decades of spending tens of billions of dollars per year on the “War on Cancer.”

Again, this is not to undercut the work and improvements that have been done. Cancer is a very hard, complex, and multifaceted disease.

Because of its difficulty, the main paradigm that we currently approach cancer treatment with is the “continuous, maximum tolerated dose until progression” method. Since cancer is very bad, we attempt to treat it with a knockout blow: the maximum dose that a patient can tolerate in order to ‘overwhelm’ the cancer. To quote a 2015 Lancet paper, “historically, the maximum tolerated dose has been thought of as the optimum biological dose.”

But what if there was another way of approaching cancer?

By the time a tumor is diagnosed, it is made up of billions of cells. Cancer cells by definition are highly mutated and growing rapidly. These facts together mean that a tumor is a genetically diverse population.

The critical insight is that if overuse of synthetic pesticides selects for resistance in a genetically diverse population of pests, then the maximum tolerated dose of pharmaceuticals probably selects for resistance in the genetically diverse population of tumor cells. Just like IPM was a new paradigm for managing pest populations, adaptive therapy is a new paradigm for managing cancer cell populations.

Now this isn’t saying the we should expect adaptive therapy to work for every possible cancer, but so far a small number of early trials have been promising.

For now, the most extensively adaptive therapy has been for prostate cancer. In a pilot trial, researchers enrolled patients with prostate cancer to begin taking abiraterone (a hormone therapy for prostate cancer) and prednisone. Once their PSA (a protein produced by the prostate) had declined by 50% of their baseline levels, the therapy was suspended. Abiraterone plus prednisone were reinitiated only when a patient’s PSA increased to or above the pre-abiraterone PSA baseline. This cycling off/on therapy went on as many times as a patient’s PSA cycled between a 50% decrease and return to baseline. This cohort was then compared to a contemporaneous cohort receiving the traditional therapy.

Despite the adaptive cohort (top) spending only ~50% of time on the therapy (red bars), only 1 out of 11 patients has a progression in their cancer compared to 14 out of 16 in the conventional cohort. (And since they spend ~50% of the time off the therapies, this generates huge cost savings.)

Now, this is far from a perfect study (small, not randomized, etc.) but it was promising enough to begin real clinical trials, and results of a Phase 1b trail were reported in 2022, with similar promising findings.

Researchers have also began the process of expanding adaptive therapy beyond prostate cancer, for example in breast and ovarian cancer.

I’ll reiterate that adaptive therapy remains extremely new and needs additional study. Moreover, it might not work for every cancer type or subtype. Yet it’s early-but-promising results, along with the success of the Yellowstone wolf re-introduction and Integrated Pest Management, are all examples of ecological thinking triumphing over pure reductionist practice.

By day, I am a biomedical research scientist at AstraZeneca working on the development of cell and gene therapies. One of the many things that I like about my job is that I get to perform cell culture at a larger scale than most scientists. While the most common cell culture materials are the 96-well plate (~200uL per well) and the T175 flask (~40mL per flask), I regularly work at the liter scale.1

I came to science through a somewhat unusual path: my paternal grandparents were full-time dairy farmers in the Shenandoah Valley. My father is a bovine veterinarian who operated a small cattle farm, which I grew up on. Today, I own and operate a small permaculture farm centered around a chestnut silvopasture system with diverse supporting elements such as a pawpaw orchard, multiple beehives, living hedgerow windbreaks, and persimmon fodder.

This is a long way of saying that, whether it’s inside a biosafety hood or out on the farm, I’ve spent and spend quite a long time thinking about both cell culture, agriculture, and the systems that support both of them. So let me be clear:

Cell culture-based systems should not serve as the foundation of our meat production system and it’s really quite absurd that anyone believes that they should.

Criticisms of “lab-grown” meat are often about the feasibility of the technical science required and its eventual economic competitiveness (see this article in The Counter, or this recent episode of the Dwarkesh podcast, or this life-cycle assessment from UC Davis). Both avenues contain very legitimate concerns (so keep that in the back of your mind), but we should grant that they could very well be solvable problems. Human ingenuity has a way of solving technical problems, and anything can be made cost competitive given the right set of policies and the political will to implement them.

That is why I want to approach this topic differently and focus on the fundamental, systems-level reasons of why lab-grown meat shouldn’t be anything other than a niche product, even if many of the technical and economic hurdles are overcome.

The CAFO Problem

Let’s immediately get this out of the way: criticisms of cell culture-based meat should not be interpreted as a defense of the current industrial confined animal feeding operations (CAFOs) and the systems that support them.

The worst CAFO methods are torture, they contribute to both pandemics and antibiotic resistance, and they actively damage our environment and ecologies, from polluting waterways in the United States to cutting down the Amazon rainforest in Brazil. Lab-grown meat advocates are exactly right when they say that we need a new system for producing meat. But identifying the worst excesses and sins of the current system doesn’t automatically mean that your preferred system should be prioritized. In fact, many of the fundamental problems of CAFOs also apply to lab-grown meat.

Both systems suffer from a common deficiency of modern thought: an overreliance on narrow, reductionistic, detail-oriented thinking at the expense of holistic, systems thinking. Lab-grown meat proponents miss the forest for the trees.

Animals Aren’t Just Walking Meat Factories

Advocates for lab-grown meat love to discuss how cell culture bioreactors are more efficient at producing meat than animals (more on this later). Yet this is a prime example of these reductionistic blinders; animals don’t just produce meat but also hundreds of other useful products. A direction comparison of just lab-grown meat to conventional animal meat misses the expanded industrial production of synthetic substitutes that would be required to replace the litany of other products that come from animals.

Some examples: without sheep, we wouldn’t have wool, and would have to plant more acres of cotton and flax, or use more oil to create synthetic fibers like polyester. Without dairy animals, we’d have to plant more acres of almonds, oats, and soy, to use as feedstocks for the increased production of plant-based milks. Without animal hides, we’d have to use more oil to increase production of synthetic leather. Without animal hooves, we’d have to build and supply industrial cell culture factories that specialize in the production of keratin, not only for human supplementation and cosmetic products, but also specialized applications like as an ingredient for fire extinguisher foam.

A holistic, system-level analysis must account for the multitude of products that come from conventional animal farming, not just meat.

Animals Aren’t Just Walking Product Factories

But if we’re really thinking at the systems level, even that isn’t the proper comparison. Animals aren’t just walking product factories; they also perform valuable ecosystem services.

Solar energy will be a critical part of the transition away from fossil fuels, and solar fields are rapidly being constructed around the globe. Once constructed, a major aspect of their maintenance is keeping them mowed so that plants don’t grow to shade them. Typically this requires multiple passes per year with fossil fuel-consuming mowers. But one promising “innovation” that is being implemented around the country is farmers partnering with solar fields to graze them with livestock.2

Consider what is going on here: solar grazing turns inedible plant materials into both usable food and fiber while displacing fossil fuel use that would be required to maintain the site. And it also doesn’t “use” land as the solar field is already constructed and occupying the area.

This conversion of inedible plant materials into usable products is one of the main uses of animals. Below is a table of California-produced byproducts used as feed for dairy animals. Consider the above section: if we eliminated animal milks, we’d have to expand our acreage of almonds to meet the demand for plant-based milks. That expansion of the almond industry would produce many more almond hulls, shells, skins, and meal. Without animals, those inedible parts of the almonds would go to waste.

Of course, we could compost them into fertilizer, but animals already do that too: manure (and urine).

Animal manure helps fertilize nearly twenty million acres in the United States, and many more globally. Without it, production of nitrogen fertilizers, which already consume ~2% of the world’s energy supply and produce ~2% of global emissions, would have to be expanded. And fossil fuel-consuming machinery would have to be used to spread that synthetic fertilizer onto fields, while animals can spread manure themselves.

Moreover, unlike synthetic nitrogen fertilizers, manure creates a cascade of ecosystems services. Manure supports the life cycles of dung beetles, many species of flies, and coprophilous fungi. These species not only directly provide the ecosystem service of manure management (dung beetles alone are estimated to save farmers $380 million annually) but they also create a cascade of life and other ecosystem services (e.g. birds eat flies while also spreading seeds and fertilizer themselves). Synthetic fertilizers not only consume vast amounts of fossil fuel-energy in their creation and spreading, but they don’t create any of the habitat or ecosystem services that manure provides.

Lab-grown Meat Kills Animals, They’re Just Ones You Don’t Care About

Drive by a freshly-harvested monocrop field and you’ll often see buzzards and vultures circling. See, the industrial monocrop farming required for lab-grown meat kills many animals; they’re just probably ones you’ve never considered.

Cells don’t just grow on their own; they are primary consumers, just like animals, meaning they have to eat. In cell culture, that is done by growing them in “media,” a nutrient-rich broth of glucose, amino acids, salts, micronutrients, and various other molecules, like vitamins and hormones, as well as buffers to keep everything at the proper pH. This media must provide everything that the cells require for life and growth that they cannot synthesize themselves.3

Each of the hundreds of different molecules would be an interesting rabbit hole to explore (if interested, here and here are good articles)4 but for now, let’s consider the two big ones: glucose and amino acids.

Glucose is the main energy source for cells in culture and they need a lot of it; almost all cell culture media contains pre-diabetic or even diabetic levels of glucose. That glucose is produced primarily by amylase digestion of plant material, usually corn, wheat, and potato starches.

Amino acids are the building blocks for proteins and serve as both the main carbon and nitrogen source. These days, most industrial amino acids are produced by microbial fermentation, but that just pushes the question back one step: where do those microbes get their nitrogen from? Almost all nitrogen on Earth resides in the atmosphere as a gas, and only enters the biological world by two processes: fixation by microorganisms, most commonly those that have formed symbiotic relationships with legume plants (e.g. soybeans) or synthesis of ammonia through the Haber-Bosch process (i.e. burning fossil fuels).

In other words, the only way to produce the gross tonnage of cell culture media that would be needed for lab-grown meat would be to perpetuate our reliance on industrial monocropping. This system is not only wholly dependent on fossil fuels (more on this later), but it also kills many animals; they’re just not cute, fuzzy, stereotypical farm animals.

Modern industrial agriculture equipment is massive and its operation ruthless. Tractors, combines, and plows run over and kill all the various surface organisms you’ve probably never considered: mice, rats, voles, snakes, groundhogs, ground-nesting birds, weasels, ferrets, moles. Sprayers unleash genocide with their synthetic pesticides and fungicides, immediately reducing both insect and fungal biodiversity plus cascading effects (what will birds and bats eat without bugs?). Maximal applications of fertilizers contribute to hundreds of aquatic dead zones, killing fish and nearly all non-algal marine life, with many being the size of of some US states. And while it’s not outright death, environmental exposure of pesticides has been linked to negative effects on human health, especially neurodegenerative disease and in utero effects, leading to birth defects and infant mortality.

And of course, they aren’t animals, but it should be noted that nearly every single tree in the Midwest is subjected to herbicide contamination, reducing their health and vigor.

Because of its constant demand for human-provided glucose and amino acids, a system of lab-grown meat would perpetuate the system of industrial monocropping and its deleterious effects, killing countless numbers of hundreds of species in the pursuit of the perfect, sterile petri dish to maximize the growth of corn and soybeans as feedstocks for bioreactors.

If you truly wanted to minimize the total number of animals killed as an omnivore, you’d purchase fully grass fed-and-finished beef or bison and/or venison from hunted deer or elk.

Jevon’s Paradox

While some lab-grown meat advocates acknowledge the reliance on industrial monocropping, they claim that it will still cause an overall drop in cultivated cropland. Yet they also claim, that through economies of scale and technological advancements, cell culture-based systems will produce animal products more efficiently than conventional animal farming. With more efficient use of land and resources, lab-grown meat will have a lower cost at checkout, many people will simply convert to lab-grown products for financial reasons.

But this just sets up Jevon’s Paradox: increased efficiency in resource use causes greater overall consumption of that resource. Even if cell culture-based systems use less resources than an equivalent animal-based system, those efficiencies will be rapidly lost as total meat consumption increases.

This is compounded by the fact that lab-grown meat will cause many current vegans and vegetarians to become meat-eaters. While only a low single-digit percentage of Americans are either vegan or vegetarian, globally, it is approximately 10%. Even if only a portion of these people begin eating meat, that represents tens of millions of new meat eaters, contributing to a deterioration of any “efficiency” gains.

If lab-grown meat uses 50% of the cultivated cropland than conventional meat, but causes total meat consumption to double, then there will be no reduction in cultivated cropland.5

Frames Of Reference and Wide-boundary Efficiency

To quote David Friedberg from an episode of the All-In Podcast6 (the lab-grown meat portion starts at ~48 minutes): “The biophysics of a single cell making [protein] is better than the biophysics of an animal. Think about a chicken; it grows feathers, it clucks, it walks around, it has energy, it makes heat. The chicken as a system is not that energy efficient.”

What is being referenced here is the trophic cascade. Because organisms have to spend energy on their homeostasis and maintenance, there is an approximate ~90% loss of energy as heat between layers (below). For example, it takes ~10 calories of grass or corn or almond hulls to produce ~1 calorie of beef, etc. Lab-grown advocates love to cite this “inefficiency,” yet this might just be the quintessential example of this narrow, reductionistic thinking that dominates our culture, because while accurate, it’s a biophysical sleight-of-hand.

In the narrow sense, cells are indeed more efficient at making edible protein than animals. But this “efficiency” isn’t because cells don’t have homeostasis and maintenance requirements, but because humans do it for them. Bioreactors have to be kept at the proper temperature, nutrients have to be properly circulated through mixing, gases pumped in and out to ensure the proper respiratory rates, and heat, antibiotics, and/or filters used to maintain sterility. With significant amounts of human-provided energy, we ‘baby’ the cells, allowing them to focus on solely on growth.

Meanwhile, an animal using protein to maintain homeostasis necessarily lowers their relative edible protein production, but from the frame of reference of humanity, all those homeostatic functions come for free. Humans have to expend large amounts of energy to keep bioreactors sterile, while cows keep themselves (internally) sterile with their immune system, etc. This is the sleight of hand: yes, the total amount of energy spent on homeostatic functions might be lower with a bioreactor, but all that energy come’s from humanity’s energetic balance sheet.

These human-provided external inputs underlie a deep confusion present in many agricultural discussion: the difference between productivity and efficiency. Agriculture has always been about increasing productive yield by application of external inputs, whether human labor or diesel machinery. But increasing external inputs by 1,000x for a 10x yield isn’t “efficient,” we do it because it is productive, and productivity is a variable of life-and-death importance (malnutrition and famine are awful!).

Often times, people will try to focus on narrow efficiencies (output per a single variable), e.g. feedlot cattle take less time to raise than grass fed-and-finished (kilograms of beef per time). But this narrowing of the aperture misses all the other variables that it takes to produce feedlot cattle (concrete, diesel, feed, etc.), all of which humans must provide. Meanwhile, those same cattle could’ve just been living on pasture, living happily with several orders of magnitude less human-provided inputs.

From a systems perspective, we cannot be more efficient than nature because nature happens for free. For example, a synthetic wood factory might very well be more productive than a forest (more output of wood per area) and it might very well have narrow efficiencies (e.g. less water used per output of wood), but it cannot be more wide-boundary efficient, because wood, quite literally, “grows on trees” and rain falls from the sky. We don’t have to do anything for a forest to grow. Moreover, the forest provides many ecological services (synthesizes oxygen, supports biodiversity, prevents erosion, etc.) that aren’t accounted for when only narrow efficiencies are considered.

Lab-grown meat requires an extraordinary amount of human-provided inputs, necessarily lowering it’s wide-boundary efficiency compared to naturalistic forms of meat production.

State vs Path Properties, and How Potatoes Are Made Out Of Oil

Lab-grown meat advocates also love to tout how cell culture-based system have lower greenhouse gas emissions than conventional meat. But this is a deep misunderstanding of the root cause of climate change, and even more fundamentally, a confusion of state and path properties.

Imagine that you are car shopping and that there are two identical cars sitting on the lot. They are the same make, model, production year, and were even produced at the same factory and delivered to the dealership at the same time. Heck, even priced the same. Are they the same car? You think so, until after you sit in both and notice that the odometer of one has a thousand additional miles; it is being used as the test drive car for that make and model. While the state properties of both cars are the same, their path properties are different.

State properties depend only on the current condition of a system, not how it got there. Consider the engine temperature, tire pressures, or steel content of the cars. It doesn’t matter that one car has been driven more that the other; temperature, pressure, and mass are direct measurements of state; they are what they are. In contrast, path properties depend on the “path” the system took to change states, like the odometer, which computes all the prior work that the car has done. State properties describe the “what,” while path properties describe the “how” of a system’s transformation.

This is important because even if lab-grown meat has lower emissions than natural meat, it could very well be making a greater contribution to climate change despite those lower total emissions. While every kilogram of carbon dioxide or methane or nitrous oxide has the same state properties (one kilogram is one kilogram), not all emissions have the same path properties. This is important because climate change is rarely fully explained properly.

The terrestrial biosphere has three compartments (land, ocean, and atmosphere) that are in constant equilibrium with each other. There are millions of interactions that underlie that equilibrium, and they are in constant flux, but the net result was an atmospheric concentration of carbon dioxide that oscillated within a narrow band for roughly the last million years. Until industrialization, that is.

See, there is a fourth compartment on Earth: subterranean. Now, the subterranean compartment is also in equilibrium with the terrestrial compartments through things like volcanoes, aquifers, and hydrothermal vents. But much of the mass of the subterranean compartment isn’t in equilibrium; it is sequestered and doesn’t really interact with the biosphere.

The fundamental cause of climate change isn’t “carbon emissions” but rather humanity increasing the total carbon load of the terrestrial biosphere by liberating hundreds of billions of tons of previously-sequestered subterranean carbon.7 This is why the ocean is acidifying, rates of photosynthesis are increasing, and atmospheric levels of carbon are rising all at the same time: we are increasing the total amount of carbon circulating in the terrestrial biosphere, forcing increases in each of the three compartments.

This is important to understand because, just like how, in one of the most famous scenes from The Wire, Lester Freamon implored his fellow officers to, “follow the money,” I’m asking you to follow the atoms.

There is no doubt that livestock emit greenhouse gases. All animals produce carbon dioxide through their respiratory system and methane through their digestive system, the latter being especially true of ruminant digestive systems, such as cows, sheep, and goats. But when raised properly, all they are doing is participating in the biogenic carbon cycle: cycling carbons through the three compartments of the terrestrial biosphere. This is why the atmospheric level of carbon dioxide stayed within that narrow band despite humans burning firewood and mega herds of bison, wildebeest, water buffalo, and elk roaming the continents. Those were just cycling already-terrestrial carbon atoms and Le Chatelier’s principle applies.

It is the liberation and introduction of sequestered carbon into terrestrial circulation that is the fundamental cause of climate change, and yes, modern industrial CAFO systems contribute to it. Go back to the efficiency section and note the energy flow diagram with the two cows: the modern one with the higher productive yield is achieved through the addition of “fuels, chemicals, and services.” In other words, fossil fuels.

But that exact same logic applies to lab-grown meat.

If you account for the path properties, any system that uses fossil fuels is a greater contributor to climate change than one that doesn’t, even if the later has more overall emissions. See, it is very easy to explain how to raise livestock without fossil fuels; it was done for over 10,000 years. Yet it is currently impossible to produce lab-grown meat without a significant amount of embedded fossil fuels.

(Not to mention, there is some dispute about total emissions from lab-grown meat; they very well might be outright higher than conventional beef.)

Oh, and the State Properties Aren’t the Same Either

With tens of billions of dollars of investment and decades of research, cell culture-based systems and the post-processing of their products may very well result in various products which have the taste and texture of meat. But they will never be actual meat.

Meat is not a Platonic form. It is a complex, three-dimensional structure of a multitude of cell types and biological molecules: skeletal muscle cells, smooth muscle cells, endothelial cells, collagen, bone, extracellular matrix, blood, etc. Meanwhile, simplistic lab-grown meat uses only one cell type (skeletal muscle cells), making it necessarily different from conventional meat. But even with advanced lab-grown meat, which attempts to replicate this by growing multiple cell types on edible scaffolds (i.e. even more human-provided inputs), their microstructures are not the same, and natural and lab-grown meats can easily be identified under a microscope.

Moreover, this complex structure doesn’t just sit there, it receives biomechanical and hormonal feedback from the organism itself. There is a reason that veal tastes different than beef, that beef usually comes from steers (castrated males) not bulls (non-castrated males), and that even within the same animal, the taste and texture of the various cuts are different. Muscles are a complex tissue that receives feedback from the organism itself, and aren’t just static cells that grow in a bioreactor.

Finally, this complex structure subject to biomechanical and hormonal feedback is also subject to environmental “terroir.” There is a reason that Iberian ham and Ortolan Bunting are considered delicacies; the exact methods in which they were raised affects the meat’s final taste.

This is yet another sleight of hand; lab-grown meat producers will use many tools to recreate the exact taste and texture of conventional meat, but they are not creating actual meat.8

Let’s Stress the Grid Even More

Okay, this one is more of a technical problem, but it’s one that I’ve never seen mentioned: where is all the electricity going to come from?

Global electricity consumption has tripled from ~10,000 TWh to ~30,000 TWh over the last forty years. And with the proliferation of data centers to power the modern internet and LLMs, decarbonization efforts such as switching cars to EVs and conversion of older HVAC systems to electric heat pumps, the increasing number of electric appliances and devices in the home and office, and continuing economic development across the globe, the world is going to have to continue to rapidly increase the supply of electricity.

Yet the United States is struggling to keep up with demand, our grid is aging and needs replaced, and you’ve probably noticed your electricity bill rising faster than overall inflation.

Lab-grown meat would just add another straw to the camel’s back. The industrial warehouses, the lighting and climate control, the filtration and purification of the solid, liquid, and gaseous inputs, the mixing and moving of both inputs and outputs, the stirring and heating of the bioreactors, the sterilization of all the different components and parts… All of this requires energy, primarily electricity. Where is it going to come from if both our electricity production systems and grid are already struggling?

Centralized Corporate Oligarchs

Anyone reading this could very easily learn to raise, slaughter, and process animals, while it would be impossible for you to build and operate a cell-culture bioreactor.

Agriculture is already one of the most consolidated industries and lab-grown meat would simply consolidate it further. Due to the inherent production complexities, intellectual property, and economies of scale, lab-grown meat would be owned by a small group of vertically-integrated corporate oligarchies.

System Fragility

This centralization of meat production into a small number of facilities would greatly increase the fragility of our agricultural system.

What if production is halted at one of the biggest facilities because of contamination, like what happened with baby formula a few years ago, leading to empty shelves nationwide? What if something happens to the supply chain of feedstocks causing facilities to idle? What if a coordinated terrorist attack outright shut down several of our facilities at once? Shifting the majority of our animal protein production into a small number of facilities greatly increases the societal risks.

This problem is compounded by the fact that any remaining animal herds would be smaller and couldn’t be increased overnight. As the old adage goes, “Nine women can’t make a baby in a month.” A major factor in the current rising cost of beef is that the American cattle herd is the smallest it has been in 75 years. It is an explicit goal of lab-grown meat advocates to reduce the size of American livestock herds and flocks, and while that very well might achieve other objectives, it exponentially increases the fragility of overall food system.

Depreciation Is a Real Expense

A lab-grown meat system would require a network of industrial warehouses and on-site supporting systems, with much of them not only climate controlled but actual clean rooms. Cell culture systems require sterility, not only of the components being injected into the bioreactor systems, but of all the bioreactors and pre- and post-processing equipment. From the previously linked article from The Counter:

The Open Philanthropy report assumes the opposite: that cultivated meat production will need to take place in aseptic “clean rooms” where virtually no contamination exists. For his cost accounting, Humbird projected the need for a Class 8 clean room—an enclosed space where piped-in, purified oxygen blows away threatening particles as masked, hooded workers come in and out, likely through an airlock or sterile gowning room. To meet international standards for airborne particulate matter, the air inside would be replaced at a rate of 10 to 25 times an hour, compared to 2 to 4 times in a conventional building. The area where the cell lines are maintained and seeded would need a Class 6 clean room, an even more intensive specification that runs with an air replacement rate of 90 to 180 times per hour.

Now not only does this consume vast amounts of energy but it means something that is rarely considered: depreciation. Aside from all the energy and resources required for building the facilities and ongoing operation and maintenance, these facilities will degrade over time. Not just the regular replacement of components like tubing and filters, but the actual bioreactors and buildings themselves. Warehouses last decades, maybe a century, before they require demolition and rebuilding, leading not only more energy expenditure in waste and processing, but another round of building.

Meanwhile, a stewarded and maintained perennial grassland and savannah silvopasture systems continually appreciate over centuries and millennia, with no rebuilding and replacement required.

Agroecology Is the Real Solution We Should Be Prioritizing

Perhaps the most fundamental problem with lab-grown meat is that it is a distraction, siphoning people, money, time, and resources away from the real solution to agricultural sustainability. Humanity needs to be using this time of high-energy domination with industrial agriculture to catalyze our transition toward being a keystone species which stewards and magnifies Earth’s natural energy flows into productive agroecology.

Now, I could write a book on this (and maybe I should?). There is much to say about historical, current, and future systems of food production. But here is not the place to be exhaustive, so I will try to be brief. I will start with two things about modern industrial agriculture:

First, it has done an excellent job at solving the primary problem that an agricultural system should: “feeding the world.” We have made extraordinary progress at reducing hunger and malnourishment, while famines have almost been eliminated. It has almost certainly saved more lives than any other human invention.

Second, it has numerous and significant negative externalities, many of which are unsustainable: its grossly dependent on fossil fuels and synthetic chemicals, it emits large amounts of greenhouse gases (and not just carbon dioxide), is often cruel toward its animals, causes topsoil loss, destroys biodiversity and wildlife habitat, pollutes our water and air, and prioritizes calories over nutrition.

In our current system, over 80% of all harvested calories come from just ten crops, almost all of which are annuals. Moreover, the top three crops (corn, rice, and wheat) are all specifically annual grains and together account for ~50% of all food calories.

That our agricultural system evolved around annual grain crops makes sense: you plant a seed in the spring, harvest the calorically-dense crop in the fall, and it’s easily storable for winter. This sets up a positive feedback loop of survival → iterative breeding → higher crop yields → survival that caused the agricultural evolution and, well, all of civilization.

While this was critically important for humanity when we were only a single bad season away from famine, there is growing recognition that the priorities of the modern system need to shift to ensure not only our immediate caloric needs, but also the long-term sustainability and resilience of the system itself. Now that we’ve solved the caloric problem, the agricultural question of our age is how do we solve the nutrition, sustainability, and resilience problems, while continuing to meet caloric needs.

We can do this through agroecology, an overarching term that captures some terms that you might’ve heard: regenerative agriculture, permaculture, naturalistic farming, etc. Agroecology attempts to use, amplify, and capture the ecological complexification that results from nature’s natural energetic cascade. It tries to steward nature toward food and fiber production, meeting our needs while providing ecological services, creating a permanently sustainable system that allows for both human and environmental flourishment.

At this point, you will probably accuse me of being some Luddite, utopian hippie, so I want to be clear that this isn’t a panacea; agroecology does have downsides. Again, this isn’t the place to be exhaustive, but let me briefly attempt to illustrate the downsides, while defending why they are worth it.

Agroecology Is Less Productive (And That’s Okay)

Make no mistake, in terms of caloric yield (calories per acre), there is no beating industrial systems. There is a reason that we do them. But as I’ve said, caloric production is no longer the primary problem. If anything, caloric excess is our primary problem. Every continent produces over 2,500 calories per person per day, with North America producing over 3,500 calories per person per day!

We have advanced to the point where we can “afford” for agroecological production to be less productive industrial acreage.

Agroecology “Uses” More Land (But Gently)

A lower rate of productivity means that at constant consumption, more land will have to be used. For example, White Oak Pastures, a well-known regenerative agriculture ranch, was studied and showed ~2/3rd reductions in GHG emissions (& other benefits) for the “price” of 2.5x more land use.9

But the point of agroecology is to amplify natural ecosystems. There should be herds of bison and elk and flocks of dozens of bird species moving about the hundred million plus acres of natural grasslands in North America (below). Iowa shouldn’t be corn and soy to feed animals or cells; it should be prairie or savannah that directly nurtures animal life. Since agroecology mimics nature, it’s land “use” is exponentially gentler than industrial production.

At minimum, we could take the ~30 million acres of corn that is grown annually for ethanol production and transition that acreage into agroecological production (offsetting more than 30 million acres, because whatever food produced there would eliminate production elsewhere).

Or how about the millions of acres dedicated to feed exports? Or activities dominated by the wealthy, like horses and golf?

And it would certainly take some time, but properly-integrated silvopasture systems would eventually offset industrial logging production, as chestnut, oak, hickory, etc. trees were used as lumber at the end of their lives.

Agroecology Uses More Labor

There is no doubt about it: agroecological production will require more labor. Here I can appeal to both the Luddite and the technological accelerationist, as there is no doubt that AI and robotics will have widespread impact on society.

One one hand, perhaps advanced agricultural robotics (drones to corral rotationally-grazed animal herds, humanoid robots managed terraced slopes of rice production, robotic automatically weeding pastures and fields or thinning fruit trees, etc.) will be able to fully this demand for labor.

On the other hand, if opportunities for productive work are destroyed, people search for meaning and productive outlets. What better way to spend your time than stewarding some patch of Earth? Even in “AI utopia” the Mexican fishman is still going to fish.

At minimum, there are currently millions of people who would like to participate in agriculture; they were simply forced out by decades of American economic and agricultural policy designed to destroy family farms (“get big or get out.”).

Agroecology Is Expensive

Currently, agroecological products are more expensive.10 But they are often higher quality in ways that can “pay for themselves” over time.

For example, how is that Americans are eating more calories than ever before, yet a growing number of people suffer from micronutrient deficiencies? Because after generations of iterative breeding focused solely on caloric production, our food supply has slowly declined in micronutrient density.

There is growing and widespread acknowledgement that our food supply creates multiple health problems across society. Agroecologically-produced items may be more expensive upfront at the checkout register, but if they, say, both reduce obesity by 5% and overall healthcare expenditures by 5%, then they could pay for themselves.

There are also other impacts if you factor in enhanced ecological services. For example, try researching how much the Untied States spends annually on water treatment to clean up agricultural nutrient runoff.

We Should Continue To Develop Lab-grown Meat (At Least a Little Bit)

Now, let me say some nice things about cell culture-based food production systems, as I do think that some amount of effort should be given to developing this technology. This is because:

• If successful, it could replace or eliminate highly unethical meats such as veal and ortolan bunting.

• Its research and development could lead to advances in industrial-scale cell culture and bioreactor technologies, which could be useful for the production of things that don’t “grow on trees” like monoclonal antibodies and CAR-T cells.

• It could serve as a nice supplement food source, especially for vegans and vegetarians, who have difficultly hitting recommendations for certain micronutrients such as B12 and iron.

• As a supplement, it would serve as a buffer to increase the resilience of our food system, for example when chicken herds had to be culled due to avian influenza, etc.

Conclusions

While lab-grown meat advocates are often correct on narrowly-defined points, if we step back and use holistic, systems-level thinking, it is clear that it shouldn’t be anything other than a niche product. At best, it’s an energy-intensive distraction from a truly sustainable agriculture, at worst, a techno-optimist pipe dream that is doomed to fail and waste many resources in the process.

Rather, humanity needs to be using this time of high-energy domination with industrial agriculture to catalyze our transition toward being a keystone species which stewards and magnifies Earth’s natural energy flows into productive agroecology.

In my last article, I had a fairly click-bait-y subtitle where I claimed that Naturalistic Decision-Making convinced me that biases don’t matter.

I chose that subtitle on a whim right before pressing publish. I regret doing that. Not because the subtitle was wrong or misleading, nor because I am opposed to click bait titles in principle (clearly not). But rather because I didn’t follow through on explaining what I meant. That was pretty rude of me. I am a firm believer that if you have a click-bait title that you should fully follow through with it. I will do better here and convince you that even Daniel Kahneman would agree with my my title for this article.

This will not be a comprehensive critique of Cognitive Biases. I just don’t have space to talk about the theoretical1 and methodological2 issues. I am not even going to spend much time defending heuristics and intuition. Nor will I talk about the lack of imagination that researchers have on different ways they could study, conceptualize, and address errors in decision-making. Instead, I will focus on the relationship of biases to rational models, and whether we should care.

Biases don’t exist

First, let’s agree on what heuristics and biases even are because it is something that is very easy to misunderstand.3

A heuristic is a rule of thumb: an easy to implement guideline for complex situations. Most people are going to be familiar with various prescriptive heuristics; measure twice, cut once; A bird in the hand is worth two in the bush; never pee onto a flat rock. All good advice.

The heuristics psychologists talk about are descriptive rather than prescriptive. That is, they describe how people do think instead of prescribing how they should think. For example, when deciding between two risky options, people tend to avoid the option with the more memorable risk. We call this the Availability Heuristic, and it is typically adaptive: if you can easily recall someone dying, then it is very likely a dangerous thing.

But sometimes this heuristic can lead you astray. The most famous example is the thousands of people who decided to drive instead of fly after the 9/11 attacks. Driving is vastly more dangerous than flying, and some have calculated that more people died after 9/11 from choosing to drive than people who actually died on 9/11 itself. This seems like a pretty major failure to understand probabilities. Even if there was a plane hijacking every week of the year, flying would still be safer.

When heuristics lead to mistakes from rational theories (such as from probability), we call it a bias. So instead of talking about the Availability Heuristic which is the cognitive process used that led to the error. We might instead say the Availability Bias, emphasizing how the heuristic led us astray from Probability Theory.

But here is an important point to take away from what I just described; biases don’t exist. Biases aren’t things in the world and they are not cognitive processes. Biases are an artifact of what we are measuring against.

Some time ago, Gary Klein, who had a friendly but adversarial relationship with Kahneman, was going to grab dinner with him and asked if I had any questions for the Nobel Prize winning scientist. I wrote up some hard hitting questions to ask, including extensive notes in my email to Gary so that he could fully understand why I thought the questions interesting.

But when the time came, the dinner conversation turned to other topics. It was a meeting of friends, not academics, and Gary didn’t want to disrupt the conversation with my hard-hitting questions.

So instead, after he got home, Gary forwarded my entire email to Kahneman, and Kahneman responded. I was both starstruck and mortified. Here is the opening of Kahneman’s letter.

Hi Jared,

You seem to think that biases are a thing, and that I study that thing and am committed to the existence of biases. If you read a few chapter of ‘Thinking Fast and Slow’, you will find that this description is incorrect. What we really try to study are the psychological rules that describe human thinking.

Ouch. Nothing like one of your idols telling you that you have misunderstood his work!

But here he confirms the first part of my click-bait title; biases don’t exist. And I don’t mean that empirically we have discovered they don’t exist, but rather, by definition they do not exist. They are not the type of thing which have existence. They are like a correlation coefficient in that if you try to use a correlation coefficient to explain something then you have fundamentally misunderstood what a correlation coefficient is. Correlation coefficients and biases don’t exist in a way that they have causal force in the world.

When you decide to drive instead of fly because of a recent high-profile crash, we can use the Availability Heuristic to explain what happened. The heuristic has causal power in the world. But biases have no causal power. It’s like saying an arrow missed the target. The property of missed is not in the arrow, the bow or the archer. You cannot explain why the arrow missed by evoking a process called missing. That is circular.

Similarly, a bias just means you missed the target, and so can’t be used to explain anything. But in this case, the target is what you would have done if you had appropriately applied various prescriptive theories such as Decision Theory, Bayes Theorem, or Probability Theory.

To recap:

Bias: When a heuristic leads us to make a different decision than a rational theory.
Rational theory: What some academics decided was the correct way to make a decision (sometimes also called a Normative Model).
Heuristic: Psychological rule we follow which explain the bias.

Jonathan Baron listed 53 biases in his book (4th edition). Wikipedia lists more than 200.4 That is a lot of ways that people systematically miss the target!

But remember, for every bias there is an underlying target (a model) from which it deviates, and it is essential to keep that target in mind. Why? Because at some point you need to decide whether you actually care about hitting it.

Personally, I often don’t care about these models which is why I tend to define biases not as “the systematic ways that human reasoning and decision making deviate from rational models”, but instead as “the systematic ways that economic models are wrong about how humans reason and decide.” Same meaning, completely different emphasis.

Who died and made academics the arbiters of rationality?

I am sympathetic to the list of rational models that academia has developed over the decades and centuries. They are certainly good for some situations.

But these “rational” models are not capital-R Rational. They were not delivered to us on Mount Sinai, written on stone by the finger of God. Academia has not solved the problem of rationality, and it does not have the answer to life, the universe, and everything. If you live your life according to the maxim WWHED (What Would Homo Economicus Do), I think you are seriously hampering your ability to make good decisions, and will wind up believing and acting on very bizarre and irrational things.

This is partly a moral argument. I am not a utilitarian, and I have to admit that the fact that Jonathan Baron considers my moral philosophy a bias does rub me the wrong way.

But even more so it is an argument based on the the very premises of Heuristics and Biases. When Kahneman and Tversky set out to disprove the assumptions underlying economic theory, they didn’t intend to say that the rational theories that economists relied on were capital-R Rational and fully general decision-making formulas by which to live your life. The word ‘rational’ was a technical term which they got from Rational Agent Theory. Kahneman and Tversky were excited about biases because they falsified the idea that humans reasoned using rational models, and also provided a methodology for discovering how people actually reason.

This is why Kahneman has said that his work has been misinterpreted as an indictment of humans when that isn’t really what his work showed. He goes on to clarify that the term “rational” is a technical term and that he hates the word “irrationality” and has never used it.5

And there it is! My clickbait title has been fully endorsed by the man himself; biases don’t exist, and humans are not irrational.

Now with that context, here is Kahneman’s full letter.

Hi Jared,

You seem to think that biases are a thing, and that I study that thing and am committed to the existence of biases. If you read a few chapter of ‘Thinking Fast and Slow’, you will find that this description is incorrect. What we really try to study are the psychological rules that describe human thinking. We often illustrate these rules of thinking by showing they lead to characteristic mistakes. We do so not because we are interested in mistakes for their own sake, but because correct thinking is the default case, which requires no explanation.6 This is not a universal rule, but it is often the case that one can learn a lot about a system by examining circumstances in which it fails. For example, memory is studied by looking at forgetting.

All best,

Danny

Biases are not the object of study, they are a methodology for understanding the object of study (i.e., heuristics). They are a research tool, not a diagnosis. Just as how you might study forgetting in order to understand memory, or visual illusions in order to understand perception.

But as Kahneman himself has noted elsewhere, the medium became the message. Rather than studying biases to understand heuristics, many researchers began to study biases in order to prove human were irrational. And despite himself, Kahneman seemed very interested in this work.

And so perhaps my title was click-bait after all. Despite explicitly claiming otherwise, Kahneman seemed interested in “mistakes for their own sake”, and did seem to think people were irrational.

And that is my principal disagreement with Kahneman and the entire Heuristics and Biases approach. Biases may be a good tool for studying heuristics in a lab, but I do not think they are a good tool for studying human errors in real-life situations. The principle reason for this being that despite all the hubbub around these models, they are not actually rational in the vast majority of real-world settings.

You would think there would be lots of empirical evidence about how the best decision-makers all use rational models. But yet the literature is lacking on this point, and the literature that does exist tends to show the opposite. It is not the best of the best, but the newest of the new who use rational models; novices will sometimes use rational models because they have so little knowledge of a domain. But as novices gain experience and start to understand and appreciate concrete specifics, they put away childish things and start making decisions based on a deep understanding of the context.

Similarly, you would think there would be lots of literature on how biases lead to many real-world errors. And indeed there is. But most of it is post-hoc; identifying errors and then describing the error as a bias as opposed to focusing on the underlying process which caused the mistake. Worse, some will even claim the mistakes were caused by a bias as if biases were the type of thing which have causal power.

I find this type of research extremely dangerous. Yes, heuristics can underlie various types of errors, but they also underlie expertise, adaptive cognition, and the entire learning process. Blaming the very thing which facilitates excellence is perilous and I do not think current research practices have adequately addressed this.

But maybe you are not convinced that rational models are not capital-R Rational, and the moral argument doesn’t bother you. Well, let’s get even more specific then.

No Free Lunches…well, almost none

Even by their own standards, the rational models are not always rational. For every bias, there is always an exception where the thing that has been declared “irrational” is actually optimal.

This is proven by a set of theorems called the No Free Lunch Theorems, which are simultaneously both trivial and controversial. The theorems state that no model can be optimal across all possible situations; for any model that says A is optimal, there is a possible world where B is actually optimal. Because of this, it’s logically impossible for a model to be optimal in all possible worlds.

If you take the theorems seriously, the takeaway is this; you cannot always rely on the same model. There are no free lunches when it comes to rationality. You have to put in the work not only to figure out what is optimal, but to figure out which model can get you that optimal result. Why else do you think we have things like Fuzzy Probability Theory and Non-Aristotelian Logics? Because there are always exceptions where the traditional model doesn’t apply. Remember, all models are wrong! Yet somehow we are supposed to believe there is some set number of models which are the standard for rational decision-making? Do not mistake the entirely of reason with the single field of statistical inference!

Some people feel this in their bones and get anxious when rational models are naively applied. But others think to themselves, “Well maybe there are some exceptions, but by and large the traditional7 rational models will work for most situations and we can easily identify the exceptions.”

When I hear these people, I swear I can almost hear the roar of the T-Rex from Jurassic Park.

Consider two examples.

First, Confirmation Bias, one of the most popular and supposedly well replicated of the biases. Everyone knows that Confirmation Bias is irrational, right?

Well, no. When uncertainty is high, confirmation isn’t only not irrational, it can be optimal as it can be more informative than falsification. This isn’t to say Confirmation Bias is always rational. But do Behavioral Scientists really fully understand the types of situations where it is and isn’t? Should we de facto label all instances of Confirmation as bias or irrational? Had you even considered asking when it might be rational and optimal?

Another example is Expected Utility Theory: a theory so popular that some people base their entire moral philosophy on it. For fans of the theory, Jason Collins has a bet for you: “Suppose you have $100 and are offered a gamble involving a series of coin flips. For each flip, heads will increase your wealth by 50%. Tails will decrease it by 40%. Flip 100 times.”

Do the math. Expected Utility says take the bet. But common sense says that I am likely playing a trick on you, and you should be careful.

Turns out the bet is non-ergodic, which is a term I barely know how to pronounce let alone describe, so read Collin’s full article here. But the takeaway you need to know is this; if you calculate expected utility it looks like a great bet, but you will probably wind up poorer than you started if you accept the terms.

I use non-ergodicity as an example not in spite of its mathematical complexity but because of it. Had you ever heard of the term? Do you think the average Behavioral Scientist going around declaring people biased has heard it, internalized it, and can tell when they are in a non-ergodic situation? If not, are you sure they are sophisticated enough to know when rational models should and should not be used?

A bias is, by definition, a deviation from a rational model. But for every given rational model, there are times when the bias is more optimal than the model. And frankly, I do not trust people to know when those exceptions apply.

Some people come up with work arounds to this criticism, saying that biases are only biases when they are actually irrational. If Expected Utility doesn’t give you the “correct” answer, then it is not a bias to deviate from it. I find such reasoning very circular and dangerous. The bearers of rationality are no longer unbiased equations but researchers. This leads to situations where a behavior previously considered irrational is later declared to be rational because researchers finally understand the logic of it. I find this problematic. Declaring something irrational shouldn’t have such a high correlation with the ignorance of researchers.

So specific you can only find it in a lab

But maybe there are some domains where these exceptions don’t apply? Maybe there are entire areas of problem solving where rational models are, in fact, capital-R Rational?

Well, it can’t be any quick paced domain as rational models are too slow, laborious and complex. Certainly, for the domains we care about at ShadowBox where I work the rational models just don’t cut it.

We can also ignore domains which are too simple, as such models will not be needed anyways. You don’t need Expected Utility Theory to pick out the right cereal at the grocery store. Similarly, we should ignore domains where intuitive expertise is likely to develop and perform as well as the rational models. Especially since using abstract rational models can get in the way of learning in such domains.

Ditto for domains where values are not clear, stable, and well-ordered. If you are still trying to figure out what you want, or your values are too dynamic, then rational models will just fail you entirely.

It also can’t be a domain where there is too much non-linearity or too many unknown unknowns. In domains which we call complex or chaotic, the list of rational models will lead you astray and will get in the way of the sort of probing, sensing, and acting that you need to do in order to make sense of what is happening.

So, we need a domain that is slow and complicated in a very particular way; you can’t develop expertise, relationships are linear, values are stable and well-ordered, and you have all the information but can’t quite compute it.8

That is a very narrow slice of life! In fact, the only domain I can think of that meets all those criteria is lab experiments which are purposefully built to have these criteria so that people are forced to rely on flawed heuristics. And perhaps that is the fundamental issue with the Heuristics and Biases approach! We use lab experiments to evoke biases to discover how humans reason. But real life isn’t like these experiment, and so generalizing the mistakes to other domains is extremely theoretically and ethically fraught.

Unfair? Let me relax the constraints a bit. Surely there are situations such as stock picking or internet debates where rational models are helpful?

I guess? But they are hard to identify. Even in domains where you should be using models, you typically don’t use the default rational models, but instead something much more context specific. But sure, there are Goldilocks Zone, like Tetlock style forecasting and maybe some others, where I would recommend using Bayes Theorem. My point is not that the rational models are useless. In fact, I would recommend everyone spend some time learning probability, Expected Utility, Bayes Theorem, and logic. These are really great tools for thinking clearly!

But as you learn them, recognize that these are not general purpose tools. The situations in which rational models should be used for decision-making are far fewer than many assume. Rational models are not the default right way to reason, but a very exceptional use case in extremely constrained settings. More useful for post-hoc explanations of a decision than for making a new one.

In a now famous debate between the economists Richard Thaler and Kevin Binmore, Binmore argued that intuition becomes more rational over time as people learn from their mistakes, a point I agree with. Thaler pushed back saying that might be all well and good for grocery shopping, but there isn’t enough feedback for major life decisions like marriage or retirement. Thaler claimed victory with a quip: Binmore’s highbrow theories about the rationality of intuition were only good for “buying milk.”

It is a clever come-back. But yet, I wouldn’t recommend choosing a wife or husband based on Expected Utility Theory. Certainly, I wasn’t trying to avoid biases when I decided to marry my wife. I was trying to construct the future I wanted for me and my kids. Such qualitative as opposed to quantitative approaches still strike me as vastly more capital-R Rational than the rational models that are so popular within academia. And not just for small decisions, but rather, even more so for life’s most important decisions.

Conclusion

So is there anything left to salvage of biases?

Maybe. I sometimes use the term “Natural Bias” when talking about the current craze around “all natural” products and the aversion to chemicals, GMOs, and vaccines. Such thinking seems to me systematically flawed and confused. To be opposed to chemicals is just non-sensical, and vaccines and GMOs have saved billions of lives. All hail Norman Borlaug! 500 million, but not a single one more!

But of course, from the perspective of the those skeptical of scientific consensus, I am the biased one.

And that right there is the difficulty of the whole thing. If you don’t have an objective definition of rationality that everyone can agree on, and that can be applied perfectly and without fail by researchers, then you’re not doing research, you are simply imposing your own standards and calling it rationality.9 And even when you are right, this is a dangerous thing. More rhetoric than science.

This is why I cringe when the word cognitive bias is used. Not because the term is useless, but because misuse and misunderstanding are so pervasive, and I’m not sure the concept of biases serves a useful function outside of the lab. As a cognitive scientist, I would much rather talk about the actual processes underlying cognition than trying to explain human behavior by guessing what the mythical Homo Economicus would have done in the same situation.

I. My overactive cringe factor

There is a particular type of LinkedIn post that makes me cringe. Well, actually there are many LinkedIn posts that make me cringe, but there is one in particular I would like to focus on.1

These posts always have the following form: “Look at what this company is doing. They clearly don’t understand Behavioral Science. They’re doing X when they should be doing the exact opposite!”

The classic example, which I have seen multiple times, is a post about how Wikipedia asks for donations by emphasizing how FEW people donate. Behavioral Scientists see this request and conclude it is wrong because Social Proof clearly states that you are supposed to emphasize how MANY donate.

Wikipedia had so many people writing to them about this “mistake” that they actually released a statement saying something to the effect of, “Shut up! We tested it. This works better.”

Maybe my nerves have over-updated on this one example, but every time I see a post similar to these criticisms I wince a bit. Maybe some of them are justified, but it seems more likely the company ran a test and found the opposite of the famed effect than that the person criticizing the company has additional insight into the context. Just as in the Wikipedia example.

However, it’s not the overconfidence that bothers me so much as the entire theory of psychology underlying these posts.

And maybe I am weird in the way I think about it—I suspect this post could be quite controversial—but I don’t think psychological effects are properties of the human condition that we have discovered. Rather, I think they are something we prompt through the context of the experiment. More like flowers cultivated in a garden than fossils at a digsite. That’s why we call it choice architecture and not choice archaeology, and behavioral design instead of behavioral excavation.

So here is my argument: stop thinking of psychological effects as truths we have dug-up and discovered about human nature, and instead think of them like flowers which grow in certain contexts and not others.

Once you see psychology in this way, it becomes hard to take these confident LinkedIn takes at face value. Not only that, but the current ailments in psychology (the twin crises of replication and generalization) become much easier to understand. Because if this view is right, then the question isn’t whether Wikipedia violated some universal human principle, but about whether Wikipedia readers see “social proof” in the same way as the subjects in our lab. (Hint: They do not)

II. Gardens not digsites

Just because you use a trowel in both gardens and digsites does not mean they are the same.

At a digsite, such as ancient Mayan ruins or a Cretaceous era fossil bed, Archeologists and Paleontologists dig to find what was already there without regard to the current conditions of dirt, sun, and moisture. In fact, the current conditions are a nuisance to be removed as quick as possible so that the ancient and immutable truths can stand on their own without being disrupted. Such ancient truths cannot be changed by context, only obscured by it.

By contrast, a gardener isn’t uncovering hidden truths about a seed. The plant isn’t ever the mystery to be solved; the conditions necessary for growing the plant are the mystery. In some conditions the plant will grow tall and fast, in another short and slow, and in yet a third, it will wither and die. The question is always about how to shape the context of dirt, sun, and moisture to get the desired response.

So, by metaphor, the question is this: when we observe a psychological effect in an experiment (e.g., Loss Aversion), are we unearthing some fundamental property buried in the human mind? Or are we crafting the context in ways that get us consistent reactions?

Let’s return to Social Proof as a well replicated example. The idea is that if you emphasize how many people do a certain behavior, the person to whom you are emphasizing this fact will be more likely to do that same behavior. This effect is so common that it is basically a slogan for elementary kids; “everyone else is doing it.”

But the thing is, it’s not universal. Sometimes people resist the crowd. Sometimes the fact that something is popular is exactly what makes it suspect. Remember, hipsters exist. And just about everyone has a little hipster in them.

If Social Proof were like a fossil buried in the ground—a fixed and immutable structure—we’d expect it to show up reliably. But it doesn’t. It varies wildly by context, culture, subculture, framing, motivation, salience, the perceived intentionality of the message, etc. Social proof is less like a tibia and more like a tulip. It is not something we dig up and discover about the human condition, but is instead a behavior that grows in certain conditions but not in others.

Loss Aversion, Default Bias, Fresh Start Effect, Sunk Cost Fallacy…I don’t believe any of these findings are inherent to the human condition. They are not in the brain ready to be discovered. They are simply what happens when someone perceives the context in a certain way. These effects can disappear or even reverse given the right context. Just consider the opposing nature of Primacy Bias and Recency Bias, or of Status Quo Bias and Novelty Bias. Or compare Loss Aversion to the risks we take when we gamble, or those taken by adrenaline junkies. All these effects that psychologists find and label are not laws of human nature that we have discovered. They are not the default human condition independent of any context. They’re just what crops up when we manage to craft the context in a very specific way such that people see the pay-off differently.

Of course, to argue against myself, it is true that some variation is consistent with the digsite approach to psychology. No one expects psychological laws (if such a thing were to exist) to be immutable. But how much variability does that approach allow before we should reconsider whether they are laws at all? How far can such a view be stretched before it breaks?2

When I look at experimental procedures, I see tightly controlled conditions that we designed. Less like a digsite than a manicured garden with carefully planted cues and instructions. Or to use another metaphor, an experiment is more like a stage on Broadway than a half excavated amphitheater in Greece. And it makes sense to me that we see consistency in such a situation; but not the kind of consistency you get from a physics experiment. Instead, it’s the kind of consistency you get when you give an actor some dialogue to read and put them on that Broadway stage. I promise you they will perform! If you set up the conditions, you can control how people perceive the situation in which they find themselves and get consistent reactions. But that doesn’t make The Script Reading Effect a fundamental property of human nature.

With the replication and generalization crises as bad as they are, perhaps it is time to reconsider the idea that psychological effects are deep human truths, and instead recognize that they are artefacts of how someone sees the context. i.e., their frame. These frames can be cultivated by the experimental design, and the shared history of the participants, but they are not fundamental properties of the brain. A study doesn’t reveal “what people are like.” It only reveals what people do under the conditions we designed. As Avel Guénin–Carlut puts it: “Psychological effects should be understood as brought about by the experimental context rather than revealed properties of human cognition.”

III. Psychological Effects Are Frame-Dependent, Not Fundamental

Take this real-life example: a friend recently told me his child lost a helium balloon and was still feeling down even after being given a new one. The child, confused, asked, “Why am I still sad even though I have a new balloon?”

My friend passed the question on to me, adding, “Don’t just say Loss Aversion.”

I would never dream of it. I think it is absolutely silly to assume that Loss Aversion explains much if anything at all, and even sillier to assume it applies to a particular child. The child could have anthropomorphized the balloon and felt like they lost a friend. Or maybe they felt responsible and ashamed. Or maybe the child really just thinks losses are worse than gains! Each is a plausible answer that could apply to some children some of the time. But none of these frames are universal. Each depends on how the child sees the situation, not on some universal principle of human cognition that exists independent of the context.

For example, if a parent says, “This is Bozo the Balloon, take good care of him,” or if the parent says “Don’t lose it or I’ll beat you with a belt,” the child will be upset when he loses the balloon, but for very different reasons. The presentation completely changes the perception and therefore the experience. The emotional landscape the child inhabits is shaped by so many factors that it would be a fool’s errand to conclude that it is just a single effect (loss aversion) that dominates the reaction of all or even most children.

Some might argue that these two examples (the anthropomorphization of the balloon and the threat by the parent) are somehow not neutral cases, and that the child has been primed to see the balloon in a certain way. They might argue the context is getting in the way of understanding the child’s true reaction to a lost balloon. With the typical digsite mentality, they think that such context needs to be ‘dug away’ and controlled for as it can only obscure the deep human truths which is a child’s natural reaction to losing a balloon.

But this is my entire point. There is no default relationship with the balloon independent of the context. There’s no neutral way to relate to anything. Every relationship is a frame, and every frame is shaped by the child’s context. Whether the child treats the balloon like a friend, a responsibility, or a meaningless object will determine their emotional response, and as a result, their emotional response doesn’t reveal something universal about cognition, but rather their emotional response reveals something about the frame they’ve adopted.

There is no escaping context and so all behavior must be understood in light of that context. When studies provoke consistent behavior, it’s not because they’ve uncovered a universal law of human cognition. It’s because they’ve built a reliable garden that is able to grow certain responses with consistency. The experiment is shaping how they think, rather than revealing how they think.

IV. Scaling it back

Now that I have (hopefully) made my point clear, let me try and scale it back.

This doesn’t mean there are no human universals, and nothing ever which we can “dig up” about the human mind. But a capacity, such as the ability to anthropomorphize, while being something we can discover, is not the same as a behavior which is something we evoke in an experiment. What matters in most experiments isn’t whether someone can see something a certain way, but whether we can get them to see it a certain way. And that depends on our ability to design effective interventions which cultivate certain frames.

Consider an infamous failed replication. In the original study, researchers found people were more likely to buy French wine when French music played in the background. However, a follow-up study found no effect.

Would a replication renew your faith in the finding? Would a second failed replication consolidate your disbelief?

For me, it is neither. Obviously French music can influence someone if it successfully activates a patriotic, nostalgic, or cultural frame. But also just as obvious is the fact that it won’t work on everyone—they might not recognize the music, might not care, or might even resist the perceived manipulation. I don’t even need to run the experiment to know that both results could happen given the right experimental design and the right population. Either result is trivial.

Once you see experiments as tests of our ability to consistently cultivate certain frames, that is as gardens instead of digsites, replication and non-replication often become rather trivial. The experiments we run are tests of whether the garden was strong enough to provoke a certain frame consistently. Turns out many of them aren’t. Boohoo. So sorry for psychology’s loss, whatever it was.

Which brings us back to my original Wikipedia example; is it surprising that sometimes social proof (or any other psychological effect) works and sometimes not?

Not at all. Social Proof isn’t a psychological law we discovered, but a tool for evoking a certain type of frame. In some contexts it is reasonable to do what others are doing, and those are situations where we should expect Social Proof to work. But in other situations, such as when Wikipedia readers are grateful for an underfunded resource, then an emphasis on how few people donate can be a more effective strategy for getting them to loosen their purse strings.

I think this is why so many behavioral change interventions can feel more like common sense than a cognitive trick. More “the subjects finally did the thing because we made the thing easier to do”; and less, “we exploited a brain module which misfires under specific conditions.”

In fact, to all you Behavioral Scientists out there, I would say that if you haven’t found a way to understand the effectiveness of an intervention in a way which feels like common sense, then you probably haven’t understood your own intervention. Behavior change isn’t voodoo or subliminal messaging. Behavior change techniques should be intelligible even to someone who doesn’t have a degree because behavior change isn’t about manipulating brain modules, but about shaping how people see the context.3

Am I taking this view too far again?

Consider the following possible claims:

1. People are always loss averse.

2. People, by default, are loss averse except under extraordinary circumstances.

3. People tend to be loss averse.

4. People will be loss averse if you manage to successfully get them to frame the situation in a way that makes losses seem worse than the gains are good.

By my reckoning, claims 1 and 2 are false, and if they were true we likely wouldn’t need an experiment to confirm them. Claim 3 is so vague that it is unfalsifiable unless we can somehow find a representative sample of all possible choices. And finally, claim 4 is true. Claim 4 may even sound trivially true at first glance, but I do not think it is trivial what Kahneman and Tversky found in (for example) the Asian Disease problem. Treating mathematical identical choices differently is indeed an interesting finding.

However, somehow the classic finding is almost always interpreted as being about claims 1-3. This results in people on LinkedIn saying, “McDonald’s emphasized that you can get an extra toy if you buy two Happy Meals. But they should have framed it as losing two toys if you only buy one!” Completely ignoring the the rest of the context which would make a Loss Averse framing less effective in that situation.

So, in a sense, yes. We dug and discovered Loss Aversion. But having discovered that it can be the case that people can treat mathematically equivalent results differently depending on the frame, the question has now shifted not to whether Loss Aversion replicates, or even how common it is. But it instead shifts to be about whether we can design experiments that reliably reproduce the results if we so desire.

Choice Architecture, not Choice Archeology.

Behavioral Design, not Behavioral Excavation.

Gardens, not digsites.

V. Let go of the bones

If I have overstated my case, it is intentional. This is at least my 7th iteration of this essay, and the only way I could make it work, it seems, was to scream into the microphone. As I have said, I do think there are things we can discover and dig up about the human mind, and I am perhaps a little nervous in taking this argument too far and saying there is no module-like entities we can discover.

But I do believe that the strongest impact on how people behave is how they perceive their situation, not the various latent constructs of tendencies and effects which we find in psychology textbooks.

Not recognizing this can be dangerous as we begin to reify experimental averages as if they has some material reality. We call the average in an experiment x̄ (X-bar). But x̄ isn’t real. It is not a module in the brain. It doesn’t generalize, and even replication can be pretty shaky. A psychology based on x̄ will be perpetually in crisis. Don’t do x̄-Psychology. Don’t reify x̄ into some property of the human brain. x̄ is a result grown in a particular context, not a cognitive process you can discover.

Sometimes the job of psychologists and behavioral scientists is to excavate the human mind and find fundamental principles. But other times we’re just gardeners trying to find the right set-up to promote a certain kind of growth and reaction. Maybe part of the problem with the current psychological paradigm is that we are very bad at telling when we are doing which.

Introduction

The successful transition to the age of AI must entail greater diversity in the forms that quantitative social science takes. Of course, the same was true of the age of the internet; quantitative social science did not have a successful transition to the internet age. We should have had a revolution in the modality in which we store and communicate scientific knowledge when the internet became standard across universities. It is an embarrassment to our field, and a significant impediment to the pace of social scientific progress over the past thirty years, that we essentially just took old paper journals and put them online with no innovation in form. But AI will soon make it impossible for us to ignore the absurdity of the status quo–especially if enterprising social scientists collectively think hard about how to change social science. This collection of articles is encouraging in this regard, and makes me more optimistic that social science will muster the energy to reject the entrenched interests preserving the status quo.

The present metascientific framework is an extension of the one used in Munger (2026), coauthored with eight other editors of social science journals. We consider how peer review is going to have to change as AI becomes more widespread. The framework requires us to consider different possible equilibria; it’s not sufficient to think about how one subcomponent of the overall academic ecosystem might change. If you only change one thing, there will be adaptation by other components of the system that might dilute the desired effects of the reform, or have other kinds of negative consequences. To do this kind of metascience rigorously, it’s essential to be aware of all of the different functions that peer review serves in order to think about how we might incorporate AI in a way that satisfies all of the relevant desiderata while also producing a stable equilibrium which provides the right incentives for academics.

But in addition to theory, we need trial-and-error. It’s just as absurd to believe that we can perfectly predict how AI will be incorporated into social science as it is to predict that the status quo will persist. The essential first step is to try things out; I discuss three ways in which AI allows for innovation in knowledge production (meta-ontology, time, and rapid empirical iteration). But to give practicing scientists the appropriate incentives to try out these new forms, we must overcome an age-old tyranny.

The Tyranny of the PDF

The biggest bottleneck to the exploration of AI-enabled formal diversity is the uniformity of the outputs for which social scientists can get credit. I refer, of course, to the tyranny of the peer-reviewed PDF. There are many different types of epistemically relevant, valorous, valuable actions that social scientists take, but the only one for which we are officially recognized and rewarded is the peer-reviewed PDF.¹ The fact that all social science has to be routed into this modality of communication causes us to flatten the types of epistemic contributions that we make, and many of them are really very poorly fit for being stored and communicated in this modality.

Before getting to the ways the PDF is insufficient, it’s worth delineating the bundle of functions the pdf serves. The static PDF functions as both an archive of what actions the scientist has taken and as a reference to previous research products: the literature review, the theoretical arguments, the methods section summarizing different statistical tools taken, the statistical results, the conclusions, the references. All this made sense to bundle together, because the implicit arrangement at the point when the academic paper became the standard was that there were many humans who were going to read these PDFs.

But with the internet and especially with AI, there’s no need for every epistemic contribution to always include every one of these elements. By unbundling them and allowing for epistemic contributions to take different forms, social science can become radically more efficient.

I propose three dimensions on which those new forms can transcend the PDF. The first is the introduction of a better-defined, more explicit meta-ontology of social science. The second is time: the fact that knowledge can be kept up to date rather than stored as a single static PDF. The third is aggressive empirical exploration within a well-defined space of experimentation or quantitative study.

Meta-Ontology Over Narrative

The first problem with the PDF is that it presents each paper as a single contained narrative. Sure, the authors draw from previous studies for the purpose of making a theoretical argument connected to their specific empirical case. It’s not meant to be completely unique, but ideally, both the theoretical arguments and the empirical cases are understood to be in some way novel through the production of a narrative in the academic paper. Social scientists understand that they need to have a story. This is especially true for the most important paper they will ever write: their job market paper.

The bundle-narrative form treats the task of writing these papers as something which involves craft, for which taste is necessary. I think this is a valuable art form, one which I very much enjoy – but we’re seeing it devalued and commoditized because it is the only permissible form of epistemic contribution. We might collectively write far fewer of these narrative PDFs, and then focus our attention on these more careful, artisanally constructed arguments. The diversification of the forms of quantitative social science will in fact enable us to appreciate the narrative PDF for what it actually sets out to accomplish.

The narrative PDF requires authors to play a kind of trick: to convince readers that all of the components of the bundle hang together correctly. This particular RCT allows us to learn about the “effects of education on support for the far right”; this survey experiment on an online convenience sample does in fact generalize to a population from which it’s not randomly drawn; this other game theoretic model in which one of the actors is labeled a “voter” does in fact relate some way to what happens to people in the voting booth.

Each pdf weaves these components together into as coherent a narrative as it can manage. But contemporary methodology pays very little attention to the individual linkages. We know how to evaluate each component in the abstract; the question of whether they hang together is more of an art form.

We need a more well-defined meta-ontology of social science. We need a database that tracks each of these components separately, as well as how they have been combined. We can take from previous studies the different concrete manifestations of theories, different dimensions of variation that have been theorized to be relevant for moderating or mediating a given phenomenon, a list, say, of theoretical interventions or exogenous shocks which have been studied.

Consider the fantastic work by Mellon (2025) about the diversity of PDFs in which rainfall has been claimed to be an exogenous shock – each of which explicitly assumes that rainfall only has effects through the pathway that that specific pdf claims it operates through. This meta-scientific work had previously required painstaking human labor, but once we’ve begun to define the meta-ontology, this is an ideal task for AI, which can allow us to go back and do it at scale, extracting the relevant components of all published quantitative social science work.

With this database, we can begin to figure out how each of these components are working in the aggregate. For example, analyzing just the distribution of empirical tests with a given method has produced important evidence about the overall functioning of that test, if the statistical significance rate matches our theoretical expectations (Brodeur et al. 2016; Brodeur, Cook, and Heyes 2020). More prospectively, this database will allow us to identify methods or evidence which have become out of date (see Section 4), what combinations of components have been underexplored, or what spaces have seen sufficient exploration to begin to approach some non-qualitative synthesis of the knowledge (see Section 5). The pedagogical value would also be immense, giving graduate students more direct contact with the raw material they will need to work with.

Metascience to date has been constrained by the tyranny of the pdf. By trying to do metascience while accepting the implicit meta-ontology of academic publishing, where the fundamental building block of quantitative social science is the study and that “the study” is stored in the pdf, we have reified into givens what are merely symptoms of what social scientists actually do (Latour and Woolgar 1986).

Formalizing our meta-ontology will make possible new forms of knowledge synthesis, which I believe to be the most pressing problem in contemporary methodology. Synthesis at present either can take the form of meta-analysis: if we have a phenomenon or theoretical construct which has been studied many times in many places, but which we believe, or trick ourselves into believing, is sufficiently similar across times and places, we can simply plug these studies into some kind of statistical machine and perform quantitative meta-analysis. Or else it’s done qualitatively, in fact, where people read different PDFs and come up with their own understanding somehow of the knowledge contained there. Despite my glibness, these are both valuable parts of the process and cannot immediately be improved for many areas of social science. I’m not arguing to get rid of any of these things. But I am arguing against this monopoly of form, in favor of embracing the diversity of forms that are possible with AI.

Time

The second problem with the PDF is that it is static. This matters in two contexts. One is related to constraints in the production of knowledge, and the other is the premise that the world itself is not changing very much. Munger (2023) argues that we should speed up social science and that we need to be aware of how changes in the world change our evaluation of existing knowledge, but AI could allow us to store knowledge in a way that treats time as a first-order concern for social science.

When a peer-reviewed PDF is published, it summarizes the data that exists up to that point, references the literature that exists up to that point, uses methods which have been validated up to that point, and then is treated as a static, final, permanent contribution to the academic literature.

Perhaps the largest metascientific reform of the past decade comes out of the replication crisis (Collaboration 2015; Nosek and Errington 2020). This reform is fundamentally backwards-looking, when I think that AI-powered social science can and should be forward-looking. Replication is the idea that we should be able to directly reproduce what has been done before. But given the fact that the world is changing, the literature is changing, the data are changing, and our methods are changing, I don’t actually see why we should care very much about being able to do what was done before. Instead, we should take the logic of replication and apply it to a changing world. We should care about the ability to maintain, for example, the core theoretical logic of a paper while still updating at least the data; even more provocatively, we can potentially also update the methods and the theory.

Consider public opinion panel surveys like the ANES, where we have the same questions being asked every presidential election, and we have some measure of affective polarization (Iyengar, Sood, and Lelkes 2012). Rather than needing to write a new paper, peer-review it, and make some theoretical modification, we should simply say: we have this measure of affective polarization, this is a dynamic dashboard where we can see how this measure has changed over time. It should just be a kind of static fact that everyone in the world can reference. It doesn’t meet the standards of theoretical novelty to merit a whole new bundled PDF, but these descriptive facts are essential inputs to the rest of quantitative social science (Munger, Guess, and Hargittai 2021). Ironically, we tend to outsource the production of epistemic facts to other actors like journalists or the Pew Research Center, even as their outputs rack up hundreds of citations and go on to set the academic agenda (Smith and Anderson 2018).

We should be able to update the results in a given empirical paper with contemporary methods.² This would mean that the “paper” could be stored in a dynamic way online, where what’s actually the contribution is a data pipeline and a codebase which can be updated automatically with AI. We should be concerned about archiving the knowledge stored in this way; the PDF does have that function, and it is interoperable with libraries that are institutions tasked with archiving knowledge.

This dynamic updating/discounting is impractical or even ill-defined for many types of work undertaken by quantitative social scientists. Again, I’m not arguing that old forms must be totally abolished or that new forms be made universal or mandatory; this is rather a way in which AI can be used to make new kinds of social science possible.

High-throughput empirical exploration

The third problem with the PDF is that it flattens the magnitude of the empirical evidence “under the hood.” Quantitative social science is forced through this fundamentally qualitative layer, even though quantitative meta-analysis basically ignores the pdfs and instead works directly with the data to understand distributions of effect sizes.

Building on the formal meta-ontology, we can identify empirical components of a given phenomenon of interest where we have a relatively static, sophisticated theory and a well-defined ontology of the different elements and how they interact. The relevant margin for scientific progress is to increase, perhaps by orders of magnitude, the amount of empirical evidence being brought to bear on this problem. I’m inspired by the high-throughput virtual lab being implemented by Duncan Watts and his co-authors (Almaatouq, Becker, et al. 2021; Almaatouq, Alsobay, et al. 2021).

In the case of Houghton and Watts (2025), they study the informational properties of deliberation by conducting studies with cross-partisan dyads which disagree about some phenomenon. Each study involves an online deliberative discussion, and randomly varies some number of theoretically relevant parameters. The outcome is well-defined by both properties of the discussion and self-reports by participants. At this margin, we don’t need theoretical innovation; we simply need to rapidly iterate through the parameter space of the different empirical manifestations of those theoretical dimensions.

It’s not necessary to produce a new peer-reviewed PDF which gestures at some theoretical novelty with each of these empirical components. We are seeing a move towards this greater conjunction of empirical studies into individual mega-studies, primarily driven by the competition for publications at elite journals. Where once you might be able to publish a single survey experiment, now you have to do several of them, or perhaps a very large-scale iterative field experiment studying persuasion effects (Coppock, Hill, and Vavreck 2020; Voelkel et al. 2024).

These are steps in the right direction. It’s obviously much better to avoid breaking these into individual PDFs, but there’s not a principled reason why this is happening; there’s just more competition for publications in elite journals, so authors are packing in more data to distinguish themselves. Worse, the form remains the same; each pdf remains a single narrative. Authors engaged in this form have to make the case that all of the data are alike, that they all make sense as part of the narrative bundle.

What we should be able to do is not try to immediately summarize all of this data into a single outcome, to smooth out the differences or to embrace the narratively useful fiction that effect heterogeneity doesn’t exist. If the theoretical and ontological space were well-defined and the epistemic contributions more continuous, scientists could instead collaboratively work on these problems, have multiple labs be able to make independent contributions that are commensurable without being interchangeable. By being more explicit about our ontology, we can radically scale up empirical progress towards a deeper theoretical understanding.

Interoperability and careers

These are independent, individual reforms, and are thus according to our premise insufficient for thinking about the future of social science in the age of AI. One of the key questions is how these changes will be made interoperable with the other components of social science. For established academics, I am optimistic that the epistemic value derived from this formal innovation will make it stick; it’s hard to ignore data that speak directly to questions of scientific interest. And diversity means that people can keep producing bundled, narrative pdfs if they want; my expectation is that high-quality contributions in this form will continue to be valued, while replacement-level pdfs will, hopefully, be replaced by more efficient forms of epistemic contributions.

But it is not excessively cynical to say that it is a first-order concern for people who do not yet have permanent jobs to figure out how any reforms to the institutional setup will affect their chances of getting a permanent job. How does the current pipeline of training new graduate students into the profession work, and how do we figure out the selection mechanism in an increasingly competitive environment with fewer and fewer permanent positions?

Pedagogically, I believe that the proposed innovations will be immediately helpful and demystify the components that make up quantitative social science work. By discounting older papers using invalid methods, the relevant literature to master will be less tilted towards the distant past. And by making visible well-defined problem areas, the collaborative empirical exploration can give early-career researchers a space with a lower barrier to entry for an epistemically valuable contribution.

But “making things easier” in a hypercompetitive marketplace can actually end up making things harder. Ethical scientific reform is seriously handicapped by a concern which predates AI, but which AI will make unignorable: we simply have too many graduate students for the number of permanent academic positions we have, and unless we figure out how to address this problem, any mechanism for distribution of career rewards and incentives will be plagued by overcompetition and the dissipation of effort. The default move is to just let’em fight it out, but I find this deeply unsatisfying, even immoral. Unfortunately, I cannot see a solution emerging from within the scientific system; the problem has to do with material conditions external to whether or how we produce pdfs.

Conclusion: Talking

In the spirit of reflexivity, it seems important to point to the AI usage statement at the beginning of this article. The process which led to the text you’re reading in this PDF would have been impossible without AI.³ On the other hand, this probably should never have been a PDF at all. For open-ended questions, particularly related to the metascience of how social science should be done, verbal deliberations allow for much faster feedback and iteration through ideas.

But this doesn’t mean that the half-baked intuitions or preferences of senior faculty tossed off over lunch need to be taken seriously. We currently apply massive amounts of rigor to the pdfs we produce but almost none to the way we talk – again, with the vertiginously important exception of the “job talk”. Other epistemic traditions take verbal contributions very seriously; the Supreme Court hears oral arguments, and while we don’t have to go quite that far, we should elevate and archive (with video) verbal metascientific debates about the state of the field. Conference presentations are useful but generally still too informal, and generally not archived. Again, I’m not saying we need to livestream literally every 12-minute talk at APSA, but that this form should be taken seriously as an epistemic contribution.

Scientific progress is not linear but dialectical. The current “credibility revolution” in social science came in response to longstanding concerns about the reliability of empirical research, particularly regarding causal inference. For decades, much of social science relied on observational data and complex statistical models, which produced results that were difficult to interpret and which made it all too easy to confuse correlation and causation.

This credibility revolution made progress by insisting on “credible” causal inference. Central to this movement was the widespread adoption of randomized controlled trials (RCTs), natural experiments, and other quasi-experimental methods that prioritized internal validity.

Anyone reading a social science blog today is familiar with RCTs. They’re widely (but not universally) believed to be the “gold standard” research method, and while I am one of the critics of this label, today I advance an even more radical claim about RCTs: not only are they not the gold standard, they’re not even a research method.

At least not, as we shall see, in one specific but extremely important sense.

Dialectical scientific progress rolls on. Where we once had a crisis of internal validity (the impetus for the credibility revolution), we now have a crisis of external validity. We can conduct an essentially perfect RCT in one time and place; we can be certain that the treatment group and the control group have different outcomes, and that this effect was definitely caused by the treatment and the treatment alone. But this effect cannot be generalized to other times and places. This perfect golden knowledge nugget cannot yet be melted down with other knowledge nuggets. No human mind or machine learning algorithm can forge these nuggets into a holy grail: the ability to reliably predict (with anything near the level of precision of the original studies) what will happen when the treatment from the RCT is deployed again, in the real world.

Statisticians and practicing social scientists are valiantly applying their tools to this problem, but there are many issues. One of the biggest hurdles to conducting large-scale field experiments is ethical: people think, for good reason, that mad social scientists shouldn’t be able to run around conducting experiments on society. At a minimum, there should be guidelines: when possible, make sure to get informed consent from research subjects; don’t put subjects at risk of unnecessary harm; if trialling something that is expected to cause benefits (like a conditional cash transfer experiment), be sure to give the same benefits to the subjects in the control group after the experiment ends; don’t use AI to make up stories about being a rape victim in order to change people’s view on Reddit; but most importantly, make sure to get IRB approval.

Institutional Review Boards (IRBs) are the university-level entities tasked with ensuring the ethics of “human subjects research.”1 They were standardized by the 1991 “Common Rule”, updated in 2018 to expedite some of the cumbersome approval process. As this is an official legal document, these terms have precise and exhaustively defined meanings. Much of the document is devoted to defining “human subjects”; as anyone who has submitted something to an IRB knows, there are different levels of protections and restrictions involved with working with vulnerable populations (children, prisoners) and for more intrusive interventions (testing a new drug versus asking a survey question). There are, unfortunately, tragic historical examples for each line of this definition.

Less attention is given to the remainder of the phrase “human subjects research.” “Research” here is defined as a ‘systematic investigation...designed to develop or contribute to generalizable knowledge’ – per section 46.102(i).

This seem innocuous – especially from the perspective of RCTs as the gold standard method. But given what we now know about the limits of external validity, we can’t be sure. According to my epistemic commitments, the results of RCTs are not generalizable. They are, therefore, not research, and are thus exempt from IRB approval.2

This would be tendentious trolling if I didn’t believe it. If RCTs aren’t research, what are they? Should we do them? Why?

The value of RCTs comes not from the control group but from the treatment group -- from the action. They are best understood as a formal kind of performance art. Each society enjoys performances in the idiom of its respective culture. Martial societies find meaning in ritual combat; religious societies in pious displays of devotion or spiritual rapture.

Our society is scientific, even scientistic. We appreciate the performance of scientific rituals, big data crunching and demonstrations of control over nature or our fellow citizens. Social science experiments don’t provide us with airtight guarantees about what will happen in the future – and it’s just as well that they don’t, because such guarantees are incompatible with democracy and human freedom. What these experiments provide us is the ability to see the social world in a different way.

In a review of the Meta2020 academic partnership, I wrote that

My aesthetic appreciation for these experiments cannot be overstated. They are simply beautiful. Thinking as I sometimes do of social science experiments as performance art, the craft and the vision on display is deeply satisfying.

I find this perspective liberating. “you can just do stuff” has become a rallying cry for the Silicon Valley anti-progressives, who feel constrained by both social norms and government regulations. On this point I agree with them: we need to recover the spirit of individual initiative outside of institutional constraint. The “founder mode”/start-up/a16z impulse is central to the American ethos, and it is genuinely a political problem that this impulse is at present finds its most fertile soil in AI and defense startups but seems to wither in government, universities and general intellectual culture.

One of the key battlegrounds in the emerging ideological split between the center-left credentialed establishment and the Silicon Valley right is about the role of science. Each side is explicitly in favor of science, but they differ in their conception of what science means.

The SV people have explicitly embraced the scientist as a radical truth-teller and handmaiden to technological progress, a cross between Galileo tweeting “E pur si muove!” and von Neumann inventing game theory and then confidently telling Truman that game theory says we have to genocide the Russians with nukes. In contrast, the credentialed establishment tells us to “trust the science” as if this were a static, eternally valid scripture rather than a process for developing expertise and refining our beliefs.

The bureaucratization of “ethics” represented by the IRB certainly moves academic science in the latter direction. Scott Alexander has a shocking story of his experience with a medical IRB, and while I haven’t experienced anything so dramatic, the scientific-entrepreneurial experience is significantly dimmed by spending six months going back and forth explaining that, say, Twitch users are pseudonymous already and that asking them to give me their email address so they can subsequently opt out their data being used in an experiment is in fact introducing more risk of de-anonymization than simply letting this bureaucratic box go un-checked.

To re-democratize science, we need to encourage everyone to believe that they can just do stuff — that they can and should try to understand how to improve their local situation by trying things out. That is, that you can literally Do Your Own Research. The idea that only a very specific subset of human actions contributes to “generalizable knowledge,” and that this formally-defined research is something defined, controlled, and gatekept by government-aligned academics, enervates the non-specialist and (in the internet era) inevitably fuels anti-establishment backlash.

Tech startups are allowed and financially encouraged to run uncontrolled “experiments” on hundreds of millions of people. They “try things out” all the time; social media was a massive, poorly-designed experiment, and now they’re just letting chatbots rip throughout the most alienated and lonely members of society. They don’t aspire to “generalizable knowledge,” so they aren’t doing “human subjects research,” so no ethics review is necessary.

The message of the IRB is that the ethical risk of an RCT comes from the addition of a control group to the nihilistic rollout of a massive new social technology. Personally, I disagree that RCTs generate “generalizable knowledge” to a categorically different degree than does other forms of systematic inquiry. This ethico-methodological straightjacket is bad for science and bad for society. The Silicon Valley message is nominally more free-spirited but in practice produces an equally restricted science aimed only at maximizing Monthly Active Users in service of shareholder value.

I’ll give the final word to John Dewey:

The future of our civilisation depends upon the widening spread and deepening hold of the scientific habit of mind.

I spend a good portion of my time thinking about what schizophrenia is. The diagnosis is built on a foundation of psychotic experiences: voices no one else hears, beliefs held with certainty despite disconfirming evidence, thinking that loses its coherent structure. Psychiatry has defined this constellation in many ways that can feel made up, but this is a common human attribute; just consider money, government or most concepts we agree on as a civilization. There is urgency with psychiatric categories, however; the past two decades have given us perturbations powerful enough to expose the limits of our inherited categories.

A 22-year-old man with no psychiatric history begins using high-grade cannabis daily. Within months, he becomes convinced his neighbors are monitoring him. He hears voices commenting on his actions. His family brings him to the emergency room, where he is diagnosed with cannabis-induced psychotic disorder. He stops using cannabis, receives antipsychotic medication, and improves. But six months later, off all substances, he relapses with the same paranoid beliefs and auditory hallucinations. The diagnosis is revised to schizophrenia. Did the cannabis cause the schizophrenia, unmask latent vulnerability, or was this always going to happen regardless?

A 28-year-old woman experiments with a high-potency stimulant at a party. She does not sleep for three days. By day four, she is certain her thoughts are being broadcast and that strangers on the street are coordinating against her. She is hospitalized, treated, and recovers fully. She never uses stimulants again and remains well for years. Was this drug-induced psychosis, or a brief psychotic episode that the stimulant triggered? Would she have developed schizophrenia eventually, or was this an isolated event determined entirely by pharmacological perturbation?

A 37-year-old woman with no history of mental illness presents to the inpatient unit floridly psychotic. She is fixated on the idea that she has a rare, contagious disease and poses a danger to other people. The fixation is disorganized, difficult to follow, without clear narrative structure. It is not immediately obvious what precipitated the episode. Speaking with her parents reveals she has spent months isolated in her room, talking to ChatGPT for hours each day. When asked directly about her ChatGPT use, she leans forward and asks, “Is my ChatGPT talking to you?” After starting antipsychotic medication, her psychosis resolves over two weeks. As she improves, she gains insight: “I see now how this might have triggered my altered thinking.” Was this a psychotic disorder precipitated by extended Large Language Model (LLM) interaction, or would she have decompensated regardless?

By Diagnostic and Statistical Manual (DSM) criteria, all three of these patients could eventually carry a diagnosis of schizophrenia. Yet the experience of psychosis itself is heterogeneous enough across these cases that it is unclear we are talking about the same phenomenon. Do these represent the same disorder reached through different precipitants, or different disorders that converge on a shared diagnostic label? The answer determines whether precision psychiatry for psychosis is possible and whether our inherited diagnostic categories are adequate.

What We Inherited

When Emil Kraepelin distinguished dementia praecox from manic-depressive illness in the late 19th century, he was likely working with a more constrained set of cases than we see today.

Kraepelin’s distinction was based on clinical course. Dementia praecox began early and deteriorated progressively. Manic-depressive illness followed an episodic pattern with better outcomes. The key assumption was that course reflected underlying disease process: one trajectory, one disease. For the patients he observed in late 19th century psychiatric hospitals, often severely ill and institutionalized, this framework worked reasonably well.

Eugen Bleuler renamed dementia praecox as schizophrenia and shifted focus from course to symptoms. He emphasized loosening of associations, ambivalence, and the “split” in normal mental integration. He grouped together patients with hallucinations, delusions, disorganized thinking, and negative symptoms under a single diagnostic entity. The patients Bleuler saw shared enough phenomenological overlap that treating them as variations on a common pathology made sense. He retained Kraepelin’s core assumption: this was a unitary disease process arising from some biological defect.

Kurt Schneider later proposed first rank symptoms to sharpen the diagnosis further: specific types of auditory hallucinations (voices commenting on one’s actions, voices conversing), experiences of thought insertion or withdrawal, delusions of control. These were meant to separate schizophrenia from other conditions that gave rise to psychotic experience. Again, this reflected clinical experience with a patient population that may have been more constrained than what we observe today.

The DSM codified this inheritance into a checklist: two or more symptoms from a specified list, present for a specified duration, causing functional impairment. Hallucinations, delusions, disorganized speech, disorganized behavior, and negative symptoms were treated as interchangeable indicators of the same underlying condition.

This progression from Kraepelin through Bleuler and Schneider to the DSM all presumed the same thing: schizophrenia is one disease. Patients who meet criteria have the same underlying pathology, even if their specific symptom profiles differ.

But the world they observed no longer exists. In Kraepelin and Bleuler’s era, psychosis arose primarily from genetic and developmental vulnerabilities, perhaps precipitated by infection, trauma, or severe stress. Today we have pharmacological and environmental factors powerful enough to induce psychotic states that were unavailable a century ago. The routes to psychosis have multiplied, and with them, the heterogeneity of the phenomenon we are trying to capture with a single diagnostic category.

The Rise of Powerful Environmental Perturbations

High-grade cannabis is not the cannabis of previous generations. Modern strains are bred for THC concentrations exceeding 20%, delivering sustained, potent activation of cannabinoid receptors throughout the brain. Population studies show clear dose-response relationships between cannabis potency and psychosis risk, particularly in adolescence.

Modern stimulants produce dopaminergic surges that far exceed anything available in Kraepelin’s era. These compounds can induce florid paranoid psychosis in individuals with no prior psychiatric history and no obvious genetic loading. The psychosis often resolves when the drug clears, but in some cases it persists, raising questions about whether the perturbation unmasked latent vulnerability or directly altered circuit function in a self-sustaining way.

And now we have large language models. The 37-year-old woman who asked if her ChatGPT was talking to me had spent months in isolation, hours each day in dialogue with the system. When she recovered, she herself described it as something that “triggered” her altered thinking. The role of the LLM was not immediately obvious from her presentation. It had to be discovered through careful history-taking with her family. What made this case striking was not just that an LLM interaction preceded psychosis, but that she developed a genuine belief that it was a coherent agent with access to information beyond what appeared on her screen.

These cases represent routes to psychosis that were rare or absent in the early 20th century. Cannabis existed, but not at these concentrations and not with this level of targeted receptor activation. Stimulants existed, but not compounds engineered for extreme potency. Interactive generative models did not exist at all. I suspect the heterogeneity we now observe in psychosis clinics reflects, at least in part, new ways for brains to fail that were not available to previous generations.

A State Space for Psychosis

To understand why heterogeneity matters, we need to think about state spaces and how different levels of description relate to each other.

Start with neural state space: the collection of all possible patterns of neural activity. Each point represents a unique pattern of activity indexed by the neurons involved (Please see illustrations and references at the end of this section). Now consider experiential state space: the collection of all possible subjective experiences. Psychotic experiences occupy particular regions of this experiential space. We can assume there exists a mapping between these spaces, such that specific neural states manifest as specific psychotic experiences.

The neural state dynamics can be specified by the parameters of the underlying system: connection strengths between neurons, the magnitude of incoming signals, how much the system amplifies those signals, and the patterns of activity it receives over time, among others. These parameters determine which regions of the neural state space are occupied, how they evolve and, consequently, which experiences are generated. Here is a key insight: different parameter settings can produce similar trajectories in the neural and experiential spaces. Depending on the circuit, doubling synaptic connection strengths might produce roughly the same experiential output as doubling input magnitude or increasing gain control. Therefore, psychosis may arise through genetic alterations leading to aberrant synaptic weights, gain control or plasticity. Drugs may alter these same parameters. LLMs might have an impact similar to gain control via additive inputs that would be difficult to generate outside of this particular feedback loop.

The explosion of underlying parametric perturbations may have also rendered the clinical features of psychosis more heterogeneous. In other words, new perturbations may be pushing the system into previously unvisited or rarely visited regions of the neural and experiential state spaces. Such perturbations may not have existed during Kraepelin’s time, and if true, his understanding of schizophrenia would truly be different than what we are faced with now.

If we are grouping together patients who occupy genuinely different regions of the experiential state space, reached through different perturbations of underlying neural parameters, then the category of schizophrenia captures even less commonality than we thought. An important question we might want to consider is: are we observing the same neural and experiential state space regions Kraepelin did, or are there now new regions that are popping up thanks to the rise of powerful environmental perturbations?

Figure 1 from Sohn et al., 2020 Trends Neurosci. A Network Perspective on Sensorimotor Learning. This illustrates the idea of state spaces, shown here as synaptic weights (left) and neural activity patterns (right). You can imagine a similar relationship between neural states (parameterized by the particular type of measurement(s)) and experiential states (parametrized as computational subtypes similar to the ones in the next section).

Toward Computational Subtypes

If schizophrenia groups together patients who arrived at psychosis through different routes, an important question is whether we can identify meaningful subtypes based on how neural representational systems are altered. Algorithmic circuit psychiatry offers a framework for thinking about this, moving beyond the trial and error approach we currently rely on with medications.

Consider three hypothetical computational subtypes:

Precision weighting alterations. Some patients may struggle to calibrate how much confidence to place in their internal predictions versus external sensory evidence. When internal predictions are weighted too heavily, internally generated signals can be mistaken for external perceptions, producing hallucinations. People have developed behavioral paradigms to measure this tendency and relate it to auditory hallucinations through a Bayesian framework. If this framework is correct, interventions targeting the underlying process might have therapeutic utility (via neurostimulation, medications or otherwise).

Belief updating rigidity. Other patients may show intact precision weighting but impaired belief updating. They might register prediction errors, but fail to have them update their internal models appropriately. This could produce delusional beliefs that persist despite clear disconfirmation. Computational modeling of choice behavior and learning tasks might characterize how flexibly patients update their beliefs. If this characterization proves meaningful, interventions could focus on metacognitive training that explicitly teaches patients to recognize when evidence contradicts their beliefs.

Hierarchical inference breakdown. Still other patients may have difficulty maintaining the hierarchical structure of representations. Higher-level beliefs about context and meaning should constrain lower-level perceptual inferences. When this hierarchy cannot be maintained (perhaps due to capacity limitations), the system might default to lower-level strategies and lack the ability to generalize. Tasks measuring context effects on perception and source monitoring could potentially identify these failures. Interventions might target restoration of hierarchical structure through cognitive remediation focused on context integration.

These categories are not mutually exclusive, and individual patients likely show mixed profiles. But they illustrate how computational characterization could cut across traditional phenomenology. Two patients with paranoid delusions might have fundamentally different computational profiles: one with aberrant precision weighting, another with rigid belief updating. Current nosology groups them together. Computational phenotyping distinguishes them.

How the three cases from the beginning (and others) map onto these computational profiles (or a linear combination thereof) remains unknown. That is precisely the work that needs to be done: developing tools to measure these processes reliably enough to characterize individual patients.

The path forward requires developing these measurements into clinically deployable tools. This is not a distant future. Research groups are already using behavioral tasks, computational modeling, and neuroimaging to characterize how individual patients process information, update beliefs, and represent uncertainty. The question is when these tools become reliable and practical enough to guide treatment decisions.

The Path Forward

Schizophrenia remains useful as a pragmatic category. It allows clinicians to communicate about patients, access treatment algorithms, and organize care. Antipsychotic medications help many people with the diagnosis, regardless of which route brought them to psychosis. Eliminating the category before we have valid alternatives would leave patients worse off.

What schizophrenia becomes depends on whether we can develop precision measurements that characterize how individual brains are computationally altered. The hypothetical subtypes outlined above suggest the direction: measure belief updating flexibility, precision weighting, hierarchical inference, and other algorithmic processes through behavioral tasks and computational modeling. Use these measurements to identify the neural systems involved and guide interventions tailored to specific alterations rather than relying on trial and error.

High-grade cannabis, stimulants, and LLMs create routes to psychosis that were rare or absent when Kraepelin and Bleuler built their nosology. The heterogeneity we observe today may have crossed their threshold for what they would have called a single entity. Building measurement tools precise enough to distinguish these failures and target them with corrective interventions is the work underway. Until computational phenotyping becomes as practical as the DSM checklist, we should keep using schizophrenia while building toward something better. The question is not if, but when schizophrenia will be no more and what types of entities will replace it.

Disclaimer: The clinical vignettes are informed by real patient encounters but have been substantially modified, combined, and fully de-identified to protect privacy. No described scenario corresponds directly to any specific individual I have treated.

Jaden was twenty-four when I first met him. It was his third admission to an inpatient psychiatric facility due to worsening psychosis. A typical story, in some sense, of medication non-compliance, escalating behavior at home and the family fearing for safety. A typically devastating story too, where just a few years earlier, this mild mannered and pleasant young man was going through high school, with all the promise and hope parents could have for their child. Smart, athletic and with big dreams, ready to take on college and beyond.

Shortly after college had started, Jaden began to struggle. Initially describing that things around him “just didn’t feel right.” He withdrew from his friends, and started missing classes. He then began interpreting ordinary events as carrying hidden meaning directed at him, and stopped leaving his apartment altogether. His roommate found him with a razorblade, cutting into his neck to find the hidden “thought transmitter” that had ruined his life. He did so just in time to call 911 and get him help.

Jaden did not like psychiatric medications. “Dr. Halassa, they just don’t work… I don’t really see the point.” His parents disagreed, and felt that there were some benefits. “You don’t seem to hear the voices anymore, that’s progress.”

“Mom, there is more to this than voices! I need to get my life back!”

And there it was. There is more to life than voices indeed. We do have medications that reduce psychotic symptoms, so from that perspective antipsychotic medications are helpful. However, in many people they do not restore functional capacity. The ability to reason, plan and interact with the world the way they did prior to their first psychotic break. Jaden’s parents agreed: “we would love to have our son back… will we?”

I cannot describe how gut-wrenching these conversations can be. It’s not that there are no examples of people overcoming psychotic illnesses and regaining function. There are, and I did write about that previously and discuss relevant literature. So there is certainly hope for people, but the issue is that we do not yet have a clear way of identifying the pathology in the broad entity we call schizophrenia, nor do we have effective medications that can target the broader syndrome beyond psychosis. To understand just how formidable this task is, I want to walk through what we currently think is going on, where the dominant theory falls short, and what a more complete picture might look like.

Schizophrenia beyond excess dopamine

For most of the past half century, dopamine sat at the center of the pathophysiological models for what we think schizophrenia is. The fact that giving people dopamine augmenting medications (like stimulants) precipitates psychosis and that traditional antipsychotic medications appear to work by blocking dopamine D2 receptors led naturally to the inference that excess dopamine was the problem. Plus, evidence indicated that people early in psychotic illness did have excess dopamine as measured through positron emission tomography (PET).

Shitij Kapur elaborated this connection via the aberrant salience hypothesis, which proposed that dysregulated dopamine signaling causes the brain to assign inappropriate significance to irrelevant stimuli. The world starts to feel charged with meaning that isn’t there. Random events feel connected. The mind builds explanatory narratives to account for these experiences, and because from the patient’s perspective the perceptions are real, those narratives have the internal logic of genuine beliefs.

As many readers of this newsletter know, I don’t particularly love the idea of molecular explanations of mental phenomena, so what are the circuit and computational correlates of this framework? Honestly, I’m not particularly clear on how exactly changes in striatal dopamine are supposed to relate to the experience of psychosis. There are many proposals that relate to the role of dopamine in assigning values to states, but no testable roadmap for how this translates to what people describe as hearing voices. Does this make the connection between dopamine and psychotic symptoms weaker? Possibly, but again, things that tend to increase striatal dopamine tend to increase psychosis (I’ve definitely seen people become psychotic on L-dopa who have never had a history of psychotic experiences, for example) and D2 blockers tend to make these symptoms better.

But the main issue is that schizophrenia is way more than voices, as Jaden pointed out. There are abnormal beliefs which we call delusions, many of which are not particularly ‘fixed’ by D2 blockade. Also, people experience loss of function due to changes in their ability to reason and think clearly. These are the cognitive deficits associated with the disorder which are also associated with ‘negative’ symptoms. Negative symptoms are hard to describe with natural language (you really have to see them to know what they are), but my best attempt at relaying them to you is the following: it’s the lack of initiation of action (and possibly thought), with what appears to be an intact ability to respond when input is provided.

Both cognitive and negative symptoms are linked to functional loss (loss of relationships, employment, education attainment) and both are thought to be a consequence of neural changes that are more causal to the illness than dopamine. Meaning, dopamine dysregulation and the positive symptoms appear to be secondary manifestations of something more fundamental that sits at the core of what we think the schizophrenia(s) are.

The Kraepelinian Core

Emil Kraepelin noticed the central problem more than a century ago, but lacked the tools we currently have to explain it. When he separated dementia praecox from manic-depressive illness, the distinction was primarily prognostic. Manic-depressive patients relapsed and remitted, while the dementia praecox patients deteriorated, often regardless of what happened to their psychosis. Kraepelin’s insight was that the trajectory was the illness, not the psychotic episodes that waxed and waned. He thought that an illness core may be eroding function steadily, in a way that was independent of psychosis altogether. That observation has held up across more than a century of follow-up data, and it is part of what makes schizophrenia so different from many other conditions we treat in psychiatry.

The evidence accumulated since Kraepelin points, imperfectly but consistently, toward the prefrontal cortex and the circuits that support how it does its work. The prefrontal cortex is responsible for the kinds of thinking that is most prized by us humans: maintaining goals across time, adapting to changing contexts, filtering out irrelevant information, and making decisions under uncertainty. Like all other cortical areas, the prefrontal cortex is built from both excitatory and inhibitory neurons; excitatory neurons are the main conduits of signals as they can drive one another (to sustain working memory or integrate signals across time for example) and project instructions to other areas, whereas inhibitory neurons control the activity patterns locally. There are many more excitatory neurons than inhibitory ones, but inhibitory function is critical for maintaining appropriate operating regime in cortical circuits, including operations like signal amplification and noise suppression. People have coined the term Excitatory/Inhibitory Balance (which was really developed for synaptic inputs on individual neurons, but gets used a lot more liberally to describe macroprocesses too) as a set point parameter for circuit function. In schizophrenia, converging evidence from postmortem studies, neuroimaging, and animal models points toward disruptions in inhibitory control that may give rise to loss of signal fidelity and accumulation of neural noise.

This formulation makes sense, broadly, when thinking about reasoning in schizophrenia and why it may be difficult for people to think clearly. Our lab has done a bit of work in this area (but we’ve also followed the literature very closely so it’s not just one experiment I’m highlighting). What we have observed is that patients with schizophrenia perform comparably to healthy individuals when task demands are straightforward, but some fail disproportionately as ambiguity increases. One view is that this is the consequence of a perturbed prefrontal network with particular failure modes that makes the brain susceptible to a variety of changes in the ability to reason, plan and initiate action. It also changes the ability to control downstream circuits including the dopaminergic system. Again, classical antipsychotic medications are able to address the dopaminergic abnormalities but do very little in addressing the prefrontal changes that may be at the core of these illnesses.

Schizophrenia may be hard to treat because we are not getting to its core.

Image illustrating prefrontal ‘top down control’ (prefrontal cortex: red), including that of dopaminergic function (ventral tegmental area: green). Modified from Ott and Nieder, 2019 TICS

Psychosis is not schizophrenia

If dopamine dysregulation is downstream of a more foundational core of schizophrenia, shouldn’t we be re-thinking the relationship between the two? Rather than being the defining feature of the illness, it becomes a clue: evidence that something has failed to regulate the dopaminergic system, resulting in surges that are associated with psychotic experiences. And one of the more interesting things about those surges is that they can apparently be triggered by multiple pathways.

Psychotic mania, which can happen in bipolar disorder, is a useful example. Molecular imaging studies find elevated dopamine receptor availability specifically in psychotic mania, with no significant difference between non-psychotic mania patients and healthy controls. A PET imaging study comparing bipolar disorder with psychosis directly to schizophrenia found that the dopaminergic signature in bipolar psychosis was comparable to what is seen in schizophrenia.

ADHD offers yet another route. In a subset of patients, particularly with high-dose amphetamines, psychosis can emerge. What’s intriguing about this is that there is a high co-occurrence of ADHD and bipolar disorder, which appears to be innate rather than treatment-driven. This suggests that some patients may carry a genetic liability spanning both conditions, placing them closer to the threshold to begin with. The diagnostic complexity these patients present clinically may reflect something important about their underlying biology that we have yet to understand.

What all of this suggests is that psychosis is better understood as a state than as the defining feature of any particular illness. In schizophrenia, the push toward that state may come from disrupted prefrontal function. In bipolar disorder and in stimulant-induced psychosis, it may come from other underlying mechanisms. This is a very important set of research questions that we need both public investment in and new talent to continue the quest of answering them.

But can excess dopamine drive schizophrenia?

This brings me to a question I find quite unsettling, and one the field has not fully worked through. If schizophrenia involves primary circuit disruption that produces dopamine dysregulation as a downstream consequence, what happens when you invert the sequence? When you start with dopamine and drive it hard enough, for long enough, we may be arriving at something that begins to resemble the underlying pathology we see in schizophrenia.

The animal literature on chronic amphetamine exposure offers some evidence that you can, at least partially. When dopamine is repeatedly and forcefully elevated, a secondary deficit in prefrontal function appears to emerge as a consequence. The cognitive deficits that follow, particularly in the kinds of flexible contextual thinking that depend on intact prefrontal function, begin to resemble what we observe in schizophrenia. In humans, chronic heavy methamphetamine use can produce persistent syndromes that extend well beyond the period of active drug use and include not just psychosis but negative symptoms and cognitive impairment. Strikingly, these cases are indistinguishable from schizophrenia that emerges the way it did in Jaden.

What it suggests is that dopamine dysregulation is not only a consequence of the primary pathology. When it is severe and sustained enough, it may begin to produce something like that pathology from the other direction. Whether that circularity has implications for how we think about illness progression in patients like Jaden, who experience repeated psychotic episodes with incomplete recovery between them, is very important. Because it helps inform whether psychotic episodes have a kindling effect, or not. Meaning, if people are slipping into psychosis (because of medication non-compliance for example), and when they are out of it, their frontal function is worse off than before, they need to know that so they are clear on the cost-benefits of taking medications, no matter how imperfect they currently are.

Map showing global prevalence of methamphetamine use. From this Lancet article.

For schizophrenia prevalence, check out this interactive map here.

Reasons for optimism

Jaden’s frustration was spot on. The voices were the part of his illness that our treatments could reach. They were not the part that determined whether he would get his life back.

Schizophrenia may be hard to tackle because psychosis, the feature that defines it in most people’s minds and that drives most of its treatment, is not the most devastating part of the illness. The illness may live in circuits that support how a person thinks, adapts, and engages with the world, circuits that were already showing signs of disruption before the first psychotic break and that tend to remain disrupted after psychotic surges are brought under control. The gap between what antipsychotics can do and what patients and families are actually asking for may be a consequence of having organized the entire treatment framework around a downstream signal while the upstream pathology has largely gone unaddressed.

But there is lots to be optimistic about.

The most significant development in schizophrenia pharmacology in decades is KarXT (Cobenfy), approved by the FDA in September 2024. Cobenfy combines xanomeline, a muscarinic cholinergic receptor agonist, with trospium chloride, which prevents xanomeline from acting outside the central nervous system and causing intolerable side effects. What makes it historically notable is simple: it is the first drug approved for schizophrenia in over seventy years that does not work by blocking dopamine D2 receptors. Cobenfy targets muscarinic M1 and M4 receptors, thought to act upstream of dopamine. M4 which is distributed subcortically, impacts dopamine release (and psychotic symptoms), while M1 which is enriched in the prefrontal cortex may get closer to the Kraepelinian Core of the illness.

This is consistent with both placebo controlled clinical trial data showing improvements on both negative and cognitive symptoms as well as preliminary real world reports. While we still need larger trials to confirm, the idea is that Cobenfy may be doing something different from traditional antipsychotics and that the patients most likely to benefit from that difference can be identified clinically. It is certainly going at least one step upstream of D2 blockers.

Other pharmaceutical companies are trying even further upstream, to target mechanisms of prefrontal excitatory/inhibitory balance more directly. One example I am familiar with is Ovid Therapeutics, which is developing a class of compounds that directly activate KCC2, the potassium-chloride cotransporter that controls chloride concentrations inside neurons. Chloride homeostasis is what determines whether GABA, the brain’s primary inhibitory neurotransmitter, can work effectively. Postmortem studies in schizophrenia have found reduced KCC2 in the prefrontal cortex, and animal models of schizophrenia-like pathology (in our hands) replicate the finding. Restoring KCC2 function would, in principle, address the inhibitory deficit directly rather than compensating for its downstream consequences. The first intravenous KCC2 activator, OV350, completed Phase 1 safety testing in late 2025 with a clean safety profile and electrophysiological signals consistent with central activity. An oral candidate, OV4071, is entering clinical trials in 2026, and will be applied to schizophrenia in 2027 if all goes well. I recently participated in an Ovid event and talked about our work here (you just need to enter some info to gain access to the full recording). Oliver (Ollie) Howes also spoke right before me and introduced the prefrontal E/I balance framework in relationship to his work.

None of this means we are close to having something to offer Jaden that addresses what he was asking for. The gap between what these drugs might eventually do and what he needed in that room remains large. But for the first time in a long while, the therapeutic programs being developed are starting to engage the biology that the evidence points toward, rather than cycling through variations on the same dopamine-blocking approach that has dominated since the 1950s. That is the right direction and for now, it’s the best we are all able to do.

If you found this piece useful, please consider subscribing and sharing it with someone who might find it interesting. Paid subscriptions help support the time it takes to produce this work.

Disclaimer: Jaden is a fictional character who draws upon a composite of patients I’ve interacted with over the years. I cannot overstate the sense of privilege I feel to have been a part of these folks’ and their families’ lives.

Second note: This essay is very long. While it can be read from top-to-bottom—and is written assuming you will—you will lose little by simply choosing specific sections you find interesting and reading only those.

1. Introduction

2. Some thoughts

   1. The business case for biosecurity requires another pandemic for it to work

   2. The screening architecture assumes a chokepoint that’s disappearing

   3. Targeting humans with bioweapons is (probably) genuinely difficult

   4. Agricultural bioterrorism is (probably) really easy

   5. The monitoring architecture is useful for detection, but not defense

   6. Machine learning may be very useful for rapid-response therapeutics

   7. Pathogen-agnostic defenses are extraordinary, but who pays for it?

3. Conclusion

Introduction

It is easy to scare yourself about biosecurity, and it is getting easier every day. Everyone has their moment when the fear first crept into their throat. Mine was when I read the article titled ‘AIs can provide expert-level virology assistance’, which found that LLMs—even ones as ancient as Gemini 1.5 Pro—are more than capable of happily providing the knowledge needed to debug BSL-4-sounding questions about wet-lab experiments.

As with any paranoia worth having, there are good objections to it.

Most recently, the non-profit Active Site published the largest randomized control trial of its kind—153 novices, 8 weeks, a BSL-2 lab in Cambridge—studying how much access to frontier LLMs (Opus 4, o3, Gemini 2.5, all with safety classifiers off) gave participants ‘uplift’ on performing a set of viral genetics workflow (including virus production), compared to only access to the internet. Their conclusions are the following: ‘We observed no significant difference in the primary endpoint of workflow completion (5.2% LLM vs. 6.6% Internet; P = 0.759), nor in the success rate of individual tasks’. with the caveat that the LLM has numerically higher success rates on 4 out of 5 tasks, just not high enough to reach significance level. YouTube, not the LLMs, was rated most helpful by both groups.

So, while frontier models are theoretically capable of providing virology assistance, it doesn’t immediately seem like they can bootstrap someone into wet-lab competence; the hands are still the hard part. There are counterpoints to this as well, like, ‘LLMs probably help non-novices a lot!’, and ‘the study is underpowered!’ and so on. I agree with some of these. The truth is almost certainly somewhere in the middle: LLMs can help a novice with wet-lab work, but they don’t help an infinite amount.

Yet, I still believe it is still hard to actually turn all this into something evil. And no, I do not think that gesturing towards ‘automated labs’ is a good counter argument. Doing things in the world of atoms is difficult. Especially here. Why? Didn’t I just write a month back about how cloud labs are the final end-state of lab automation plays, so can’t they be hacked into doing something ulterior? Man, maybe. But you should consider the fact that these cloud labs are, at the moment, barely functional enough to do the things their paying customers want them to do, let alone serve as unwitting accomplices in a bioterror plot. Yes, they will improve, but their improvement is on a very jagged frontier. Liquid handler automation is going splendid. Liter-scale creation, purification, and aerosolization of BSL-4 substances automation is not going so splendidly. Also, even in the case where automation suddenly rapidly accelerates, it is almost certainly economically not viable for these labs to care about servicing the likely small consumer market of ‘large-scale non-therapeutic virus creation’.

I’ll discuss this more deeply in the upcoming sections, but it feels that doing something as ambitious as bioweapon creation will likely be extremely annoying to do for the foreseeable future, and I am consistently on the side that only a well-funded actor would be capable of such a thing. And why wouldn’t those actors opt for much simpler acts of violence that would roughly accomplish the same thing?

This all said: I sympathize with the bioterrorism-phobia that is sweeping my simcluster. If you stare for long enough at the AI trendlines, and also observe the increasingly WW3-y vibes that the world is emanating, it is difficult to not feel at least some worry. Maybe a genuine bioterrorism incident is not too far away. And maybe it will be far, far worse than anything can imagine.

Or maybe not. Biosecurity is one of those topics that can either feel extraordinarily bleak in its prognosis, or like things are obviously going to be fine. As with many things in the world, I think both sides have a bit of a point, and I think holding them in tension is the only honest way to consider how the future may go. In this essay, I’ll share some of my own thoughts on the field at large, and the specific themes that arose in my discussions with people.

Some thoughts

The business case for biosecurity requires another pandemic for it to work

As with all problems that, if not solved, may lead to the depopulation of the planet, we can depend on venture capitalists to search for a market opportunity. A few companies have emerged in the last few months as the vanguard of this effort: Valthos ($30M Series A for being the Palantir of biosecurity), Red Queen Bio ($15M seed for designing therapeutics against bioterrorism threats), and Aclid ($4M seed for DNA synthesis screening infrastructure). There are others too, but we’ll stick with these ones for now for illustration purposes.

I have zero doubt that these companies, or something akin to them, are worth having around. What I cannot quite figure out is the business model. The usual answer for the ‘who pays for this?’ questions in these sorts of public-goods-situations are government agencies: BARDA, DoD, DHS, CDC and so on. This is not so bad of an idea.

Let’s take a look at the United States’ 2026 budget proposal for the biodefense-adjacent areas to get a sense of these agencies’ funding.

In the proposal, BARDA is being cut by $361 million, a roughly 36% decrease from its prior state. Project BioShield, the program that actually buys finished countermeasures, is on track to lose $100 million. The CDC budget is halved, coming at around a $5.4 billion loss. DTRA down $150 million. One article more deeply analyzing the many, many other various biodefense cuts being made had this to say about it:

With the Trump Administration’s priority of reducing federal spending, the funds requested for biodefense have been significantly reduced. Very few biodefense programs saw increases in their funding or even a continuation at their previous funding levels.

How about the Department of Defense (War)? Mixed picture: while the overall budget of the department was increased, the bio-adjacent programs within it saw a drop.

One notable example is the Defense Threat Reduction Agency (DTRA), a key government agency that prevents and mitigates deliberate biological threats to the US globally, for which the PBR requests $708 million. This is $150 million less than the $858 million requested in FY25. Similarly, the $1.61 billion FY26 request for the DoD Chemical and Biological Defense Program is $46 million less than requested for FY25.

In other words: the agencies that would theoretically buy tools from, say, Valthos, are the same agencies that the current administration is intending to either gut or barely increase the budget of.

There is good news: this budget did not come to pass.

Congress rejected nearly every one of the proposals: the CDC’s budget was not reduced, while BARDA, Project BioShield, and NIH’s budget actually slightly increased. There is one unfortunate budget stain—Kennedy pulling $500 million from a BARDA program developing mRNA vaccines against various respiratory viruses—but things overall turned out fine, though I cannot find specific numbers on how things fared on the DoD end. But it is a little worrying that the administration is not particularly sympathetic to biosecurity concerns. Why? Because if your primary customer is prone to wild swings of financial unpredictability, and it is only thanks to the grace of Congress that those sentiments are not actively reflected in their budget, it almost certainly hurts the capacity for these companies to plan for the future.

Earlier I mentioned that Valthos intends to be the Palantir for biosecurity. This is not a presumption on my end, they have basically said this. The CEO (Kathleen McMahon) is an alum of the company, and has stated that Valthos plans to apply “many of the same principles she learned at Palantir, about working with officials as well as commercial customers”. But an easy counterargument to this is that Palantir’s government business was built during the post-9/11 spending surge, when homeland security funding went from $16 billion to $69 billion. Biodefense is holding steady, for now, but not seeing the same dramatic jumps.

You could imagine that a pretty simple steelman for these objections is not dissimilar to the usual AI-wrapper-SaaS advice people give: build not for where the models are today, but where they are going. And if you trust the trend-lines, it is not inconceivable that there is a catalyzing event in our near future—a genuine, bona-fide bioterror incident—which will unlock massive government spending the way 9/11 created the entire homeland security industry overnight. In this setting, the companies that already have working products and government relationships when that moment arrives will be the Palantir of biosecurity. The ones that don’t will be too late.

The game then, is to survive until this catalyzing event occurs. If it does, Valthos may be able to gobble up all the government contracts it wants, Red Queen Bio may find the DoD suddenly desperate to fund therapeutics platforms that have a biosecurity veneer to them, and Aclid may discover that its few dozen synthesis company customers grow to have even stricter compliance requirements. If it doesn’t, it is tough to imagine that these companies don’t go either bankrupt or stay growth-capped. Because of this, you shouldn’t be surprised at all that these companies acquired the funding that they did! The game of venture capital is to play ‘big if true’ bets, continuously, forever, and few areas are as well-shaped to that as biosecurity.

Well, maybe. You could argue that the SARS-CoV-2 virus maybe couldn’t be the catalyzing incident for the government, since it is still unclear whether it was a lab-leak or not, but what about the 2001 anthrax attacks? How come that didn’t spur a massive amount of increased federal biodefense funding? In fact, it did. Total US biodefense funding jumped from roughly $700 million in 2001 to about $4 billion in 2002, peaking at nearly $8 billion in 2005. What was the money used on? A fairly large chunk was put into anthrax-specific [stuff]. As a case study, consider Emergent BioSolutions, the sole producer behind ‘BioThrax’, the only FDA-approved anthrax vaccine. They received: one $1.6B contract for a second-generation anthrax vaccine, one $1.25B five-year contract for delivering 44.75 million doses of an older vaccine candidate, followed by a $911 million CDC contract for another 29.4 million doses. A 2021 Congressional investigation found that, for the past decade, nearly half of the Strategic National Stockpile’s budget had gone to purchasing this anthrax vaccine, a product whose price had been raised 800% since 1998. And it is still ongoing, with a $21.5 million delivery order to the Department of War was issued as recently as January 2026.

This is, on some level, completely understandable. You prepare for the thing that just happened to you. But it should make us a little nervous about the “catalyzing event” thesis for biosecurity companies, because the empirical reality is that it may not unlock general biodefense spending so much as it locks in countermeasures that are overly anchored on the specific threat and threat vector of that particular incident.

So, perhaps it is worth exploring outside of this customer base. While governments are the biggest buyer, they surely are not the only one. After all, didn’t Kathleen’s comment mention commercial buyers too? There is another group on the table: DNA synthesis companies. A fairly high fraction of the current biosecurity framework rests on a pretty simple idea: that biological information must pass through synthesis companies to become biological reality. To actually make a [thing], you need physical DNA, and to get physical DNA, you order it from a commercial provider. Why not create a layer to screen the DNA being ordered, ensuring that whatever it is, it isn’t dangerous? This is, as previously mentioned, Aclid’s business model, alongside TwentyTwo, SecureDNA (a non-profit), and likely others.

How is that going?

The preventative architecture assumes a chokepoint that’s disappearing

There seem to be three problems with DNA-screening-as-biosecurity.

The first of which is that the screening only works if you’re ordering sequences long enough to screen. According to HHS guidelines, the current screening threshold is 50 nucleotides, but oligonucleotides—short DNA fragments often used in legitimate research—can be ordered, assembled, and stitched together into longer sequences. This is not theoretical. In 2018, Canadian researchers synthesized a functional horsepox virus from mail-ordered DNA fragments for about $100,000. Fairly, this is annoying to do, but a sufficiently dedicated adversary may be happy to do annoying things.

The second is that screening assumes you’re looking for known threats, which is to say, sequences with similarities to characterized pathogens. But if AI biological design tools might enable the creation of de novo pathogens, things that don’t have a match in any database because they’ve never existed before, then the screening becomes useless. And you needn’t even hop your way to truly de novo stuff, you could just redesign the existing bad pathogens in ways that make them invisible to screening tools. For example, Microsoft has a “paraphrasing” paper that was exactly this, redesigning known, toxic proteins in ways that evade sequence-based screening while preserving function. To counter this, you’d need to predict function from sequence alone, which is one of the hardest open problems in the field, especially because ‘function’ in biology is one of those super fuzzy, contextual words that can have a bunch of different meanings. It is certainly possible to do—see the podcast I did with Yunha Hwang, an MIT professor creating tools to automatically annotate the function of metagenomes—but it’s not easy.

The third problem is the biggest, and it is that benchtop DNA synthesizers are getting longer-range. In other words, you could neatly side-step all these screening checks by buying your own DNA-creation machine, and running synthesis in your bedroom. Right now, the best commercially available benchtop synthesizers tops out at about 120 base pairs per well, which, given that real viruses are on the order of dozens of kilobases, means we’re safe for now. But there is no functional reason that they cannot get any better. In fact, according to a fantastic Institute for Progress (IFP) report, it’s just around the corner. Enzymatic (as opposed to chemical) DNA synthesis is likely less than a decade off, comfortably pushing DNA synthesis capabilities to the kilobase realm. This all said: a few people I talked to mentioned that ‘long-range DNA synthesis has been a few years away for a decade-plus now’, so maybe we can discount this a little, but it’s worth paying attention to. Especially because, as we mentioned earlier, a DNA synthesizer needn’t be capable of full viral genome synthesis to be dangerous, since you can simply splice its outputs together.

This is all quite a pickle.

Yes, you could lock down the benchtop synthesizers, such that any attempt to use them would involve making an external call to some pathogen database to screen your request. But if the ML design tools get good enough, you can just do continuous zero-shot designs of something that doesn’t match anything in the database, and iterate from there. And even if the models don’t get good at that sort of in-vivo prediction behavior—which, despite what you may hear, is a genuine possibility for at least some time—you could simply split your order across multiple machines, synthesizing fragments that are each too short to trigger any screening individually, but that assemble into something very much on a select agent list once stitched together.

This last point is also called a split-order attack. The IFP report discusses this last point as well, and is refreshingly blunt about the prognosis.

Moreover, an offline device is vulnerable to the whole class of split-order attacks, whereby the adversary can combine the outputs of two or more devices that are small enough to evade screening in isolation, but together would be recognized. Without some centralized connectivity, such an attack is impossible to defend against.

Are we doomed?

Maybe. The optimistic angle is that the government can be awfully good at shutting things down when it wants to, and the track record in other domains is quite encouraging. When the Combat Methamphetamine Epidemic Act passed in 2005, putting pseudoephedrine behind the counter and requiring ID and purchase logs, domestic meth lab incidents dropped by over 65% within two years. Nuclear materials are an even stronger case: the NRC administers over 20,000 active licenses for radioactive materials in the US alone, coordinated across 40 states and backed by the international IAEA safeguards regime. This has almost certainly contributed to the fact that there has not been a single case of nuclear terrorism. When the government decides something absolutely cannot be allowed to proliferate, and builds the institutional machinery to back that up, it can, against all odds, work.

But the fundamental problem here is that preventing bioterrorism requires a level of governmental diligence that is closer to nuclear-level than meth-level, and right now it is far behind both. To be fair, there are clear structural differences between biology and nuclear/meth, the biggest one being that biology is much more dual-use. Benchtop synthesizers have far, far more legitimate uses than malevolent ones, and the upside of restricting them is a lot harder to argue for then, say, restricting access to pseudoephedrine.

Well, what should be done? The IFP proposal, to its credit, has some pretty clear demands: a mandatory Biosecurity Readiness Certification before any benchtop synthesizer can be legally sold, standardized customer screening for both devices and reagents, and a reagent track-and-trace system modeled on the Drug Supply Chain Security Act for pharmaceuticals. None of this is crazy, and rhymes with what has already been done for meth and nuclear material.

What is actually being done? Unfortunately for all of us, every federal document governing DNA synthesis security in the United States right now is (somewhat) voluntary, though there is a nuance here we’ll get to in a bit. The only binding rules are export controls, which have, circa 2026, already been violated. The IFP essay from earlier happily reports that Telesis disclosed in their SEC filings that their DNA assembly systems have accidentally ended up in embargoed countries through distributors.

Oops! Does uranium ever accidentally end up in embargoed countries?

Well, actually, yes. The IAEA has logged over 4,300 incidents of nuclear material outside regulatory control since 1993, 353 of which involved trafficking or malicious use, 13 that involved high enriched uranium, and 2 that involved plutonium. But importantly, the last time someone got their hands on kilogram quantities of weapons-usable material was 1994. The system leaks at the margins, but it doesn’t leak at the catastrophic level.

The security model that you’ll continuously hear repeated among biosecurity experts is the ‘swiss-cheese’ model, in which the purpose of the regulatory apparatus is to present enough overlapping layers of defense such that no actors, other than the absolute most determined, are willing to go through the trouble. The defenses against nuclear and meth are swiss-cheese-y, and the ideal solution for DNA screening will likely be similar. Possible to defeat, but difficult, annoying, and legally scary to attempt.

And at least one layer of cheese is present: I mentioned that screening is largely voluntary on the part of the synthesis companies, but there is an important caveat. It is required for federally funded entities to purchase synthetic nucleic acids only from providers or manufacturers that adhere to the US Framework for Nucleic Acid Synthesis Screening. In other words, if a DNA synthesis company wants to sell to the enormous market of federally funded researchers (most of the U.S. life sciences market), they effectively must implement screening.

Well…kinda. This particular screening requirement was the intended purpose of one piece of legislation that was passed in 2024, but the current administration issued an executive order in 2025 to replace it with something [better] within 90 days. These 90 days have come and gone, and there is yet for anything to pass to mandate it again. This said, the biggest DNA synthesis providers (Twist and the like) see the writing on the wall, and have already implemented the screening that they imagine will be required of them, but it is unlikely smaller DNA synthesis providers have. Circa February 2026, there is a bill going through the Senate to address this current regulatory gap.

But what about all the problems from before? Split-order screening, AI-assisted genome redesign, and DNA benchtop synthesizers? Legally mandated screening is surely useless given those. We need more layers of cheese to defend against these!

Many smart people have thought about these challenges, and there are ways to solve them if you can get widespread buy-in from the synthesis providers. You could create centralized repositories of DNA orders that are aggregated from multiple providers, you could assemble private saturation mutagenesis viral datasets to catch most attempted redesigns from bad actors, and you can install hardware locks on benchtop synthesizers to prevent them from being used without connection to the aforementioned centralized repository.

None of this is scientific fiction! There are groups actively working on all of them, and some are even wrapped up in the Feb 2026 bill I just mentioned. But we’ll see how realistic they are to implement in practice.

Targeting humans with bioweapons is (probably) genuinely difficult

There is something under-appreciated worth discussing: making and spreading bioweapons is not easy. I mentioned this at the start, but there is a lot more color to add.

If you talk to biosecurity folks for long enough, they will eventually mention Aum Shinrikyo. Aum is a Japanese doomsday cult that, in the 1990s, had everything a would-be bioterrorist could ask for: hundreds of millions of dollars, a graduate-trained virologist who had studied at Kyoto University running their bioweapons program (Seiichi Endo), dedicated lab facilities, and years of total freedom from law enforcement scrutiny. They believed the end of the world was upon them, and that their mission was to hurry the whole thing along. On their journey to do exactly this, they attempted ten biological attacks.

Every single one failed. Their most ambitious effort was the 1993 anthrax attack on Kameido, Tokyo, where cult members sprayed a liquid suspension of Bacillus anthracis spores, or anthrax, from a cooling tower on the roof of their headquarters onto the streets below. Nothing happened. It turned out they’d acquired a vaccine strain of anthrax, one that is, to quote the CDC’s postmortem, “generally regarded as nonpathogenic for immunocompetent people.” Even if they’d had the right strain, the spore concentration in their slurry was about 10⁴ per milliliter, versus the 10⁹ to 10¹⁰ considered optimal for a liquid bioweapon. They had a botulinum toxin program too, in which they attempted multiple attacks using vans fitted with sprayers. Once again, zero effect. The toxin was likely either degraded during processing, too dilute to have any effect, or produced from a non-toxigenic strain because they couldn’t maintain proper anaerobic fermentation conditions. It is unclear as of today.

An account of the many difficulties the group faced in actually creating usable bioweapons is well-described in this 2011 report, which has some real comedic gems:

Mice on which the yellow liquid [Botulinum Neurotoxin] was tested showed no toxic effects, and one cult member reportedly slipped into a fermenting tank and nearly drowned, but subsequently showed no signs of illness.

The same report notes that even Aum’s manner of spreading their pathogens may have interfered with their efficacy:

In the even more unlikely event that Aum had produced and successfully stored volumes of a virulent strain, it is possible that poor dissemination capabilities might have damaged the material or failed to aerosolize it so that sufficient quantities could be inhaled.

For example, the cult employed a homemade nozzle that reportedly spouted rather than sprayed and dispersed material during the day, exposing it to UV radiation and thermal updrafts that would have reduced concentrations at ground level.

The group did finally end up partially succeeding, but only after switching to chemical weapons: sarin nerve gas, which ended up killing 13 people and injuring thousands on the Tokyo subway in 1995.

But, you may protest, the 1990’s was a long time ago. We have nanopores now. We have Alphafold3 now. We have a (somewhat) mature field of synthetic biology.

All very true, but consider what actually went wrong for Aum. They used the wrong strains, their fermentation got contaminated, their concentrations were off by five orders of magnitude, their aerosolization likely didn’t work, a guy fell into a fermenter and was fine. These were problems of bioprocess engineering, strain selection, maintaining sterile culture conditions, building dissemination devices that produce the right particle size, and overall wet-lab competence. Some of these are pure information problems, yes, and some of them are fixed by using easier-to-produce viruses (rather than bacteria), yes. But others are iterative, hands-on, tacit protocol development work. Of those, none would be aided by the current generation of structural biology models, and only some would be aided by LLMs given the Active Site results I mentioned at the start of this essay.

There are other case studies to consider too. Canonically, there are three other historical bioweapons programs of note: the Soviet Union’s in the 1970s, Iraq’s program under Saddam in the 1960s, and the US’s own Cold-War-era investigation into bioweapons in the 1960s. Unlike Aum, all three had one thing in common: they were state programs, with thousands of employees, dedicated production facilities, and decades of institutional knowledge.

How did these groups fare?

Iraq’s program, despite Saddam’s enthusiasm, produced anthrax and botulinum toxin of such inconsistent quality that US intelligence assessments after the Gulf War concluded the weapons would have been largely ineffective in most deployment scenarios.

The US program—which weaponized anthrax, botulinum toxin, tularemia, brucellosis, and Q fever—had a slightly different takeaway, but one that’s still directionally aligned with what we’ve discussed. After nearly three decades of doing comically dangerous acts like releasing simulant organisms in the San Francisco Bay Area and the New York subway to study how pathogens would move through civilian infrastructure, the conclusion wasn’t exactly that bioweapons didn’t work, it was that they were strategically irrelevant. At this point, the US already had a nuclear arsenal that can glass a continent in an afternoon, and the marginal value of a weapon that is unpredictable, uncontrollable, and might blow back on your own population became effectively zero. Nixon shut the program down in 1969, and there were few complaints against the decision.

Next, the Soviet program, also known as ‘Biopreparat’. It was the largest biological weapons program in human history, employing over 60,000 people at its peak, and spent years trying to weaponize smallpox and plague. And it worked. Some insane lines from a Frontiers article about the program attached here, bolding by me:

Some Biopreparat and military facilities continuously produced agents and filled the delivery systems kept on standby. For example, the Soviets annually made about two metric tons of antibiotic-resistant pneumonic plague and 20 tons of liquid smallpox grown in eggs. Refrigerated bunkers stored the bulk smallpox, which had a 6 to 12-month shelf life, and also contained filling lines for munitions and spray tanks.

….The Corpus One building of The State Scientific Center of Applied Microbiology at Obolensk contains 42-story tall fermenters, separated into different biosafety containment zones, to make plague and other agents.

Building 221 at The Scientific Experimental and Production Base at Stepnogorsk housed 10 four-story-high, 20,000-liter fermenters and could make 300 metric tons of anthrax in 10 months. Other production lines at Kurgan, Penza, and Sverdlovsk could add hundreds more tons to the USSR’s prodigious capability to make biowarfare agents and fill munitions on short notice.

Fortunately for us, the Soviet economy collapsed before this stockpile could be used for anything world-ending.

I think there are a few takeaways here. One—from the US’s experience—is that bioweapons are fundamentally not worth it if the end goal is to wag a very large stick towards your enemy. Two—from Aum’s and Iraq’s experience—is that bioweapons are genuinely hard to create and disperse, even with significant resources and time. And three—from the Soviets experience—is that if you throw enough of a country’s industrial base at the problem, the engineering/scientific barriers can be overcome, but the scale of effort required is immense.

These are, alongside Aum, four, isolated cases from decades back. How much could we learn from such an isolated slice of history? Should we really let our mental models be informed by this?

Unfortunately, it is the best we’ve got. We do know there are other ongoing bioweapons programs today. In an April 2024 compliance report released by the the U.S. Department of State, they state that North Korea and Russia are definitely running a bioweapon program, and it is possible that Iran and China are also. Should this freak us out? Maybe. On one hand, we should take seriously the US opinion that bioweapons kind of suck, and that there are easier ways to kill many people. On the other hand, the strategic value of bioweapons is not just in killing many people, but also in plausible deniability. Either way, whether these programs perform as intended in a real-world deployment scenario is a very different question, and one that neither the compliance report nor this essay is not positioned to answer.

Agricultural bioterrorism is (probably) really easy

Unfortunately, most of what I said earlier referred to pathogens meant to target humans. The calculus changes dramatically when your targets are cows or a wheat field, or so-called ‘agroterrorism’. This isn’t great news, especially because if you spend any time reading the biosecurity discourse, you will notice that relatively few people discuss this topic, and, of the folks who mention it, the word ‘overlooked’ pops up a worrying amount.

Over the next few paragraphs, I’ll try to give some intuition as to why agroterrorism is uniquely challenging to combat.

First, the actual design of the pathogen.

Unlike most of the other, nastier viruses and bacteria that cause humans to bleed from every orifice, many incredibly dangerous agricultural pathogens do not require BSL-3/4 equipment to safely create. As a result, the barrier to entry in agroterrorism is incredibly low. While the Soviet Union bioweapons program had to regularly deal with unfortunate cases of accidental Marburg, smallpox, and anthrax leaks—even while having BSL-3-ready labs!—a bad actor here can freely muck around with designing whatever they want with little threat. And if you’re feeling especially thrifty, you don’t even need a novel gain-of-function chimera. You need foot-and-mouth disease, which already exists in nature, is endemic in parts of Africa and Asia, and is one of the most contagious diseases known to veterinary medicine.

In fact, we know this because a former Soviet Union bioweapons producer—Kenneth Alibek—told us. In a 2006 report, he extensively discussed his work, with one paper having a particularly good paraphrasing:

Alibek describes the Soviets as producing anti-livestock, anti-crop, and combined anti-livestock/anti-personnel pathogens. During the course of its existence, the Soviet’s anti-agricultural bioweapons program produced and weaponized the anti-crop pathogens Wheat Rust, Rye Blast, and Rice Blast; the anti-livestock pathogens African Swine Fever, Rinderpest, and foot-and-mouth disease…

…The Soviets used simple, rudimentary techniques to develop these effective antiagriculture pathogens. They developed anti-crop fungal pathogens through a simple ground cultivation technique, while anti-livestock pathogens were developed in live animals…

All of these techniques, as Alibek points out, could easily be utilized by unsophisticated terrorist organizations to develop bioweapons designed to cause mass casualties of agriculture.

Next, distribution.

If you want to cause a human pandemic, you need aerosolization, you need to calculate incubation times, you need sophisticated delivery mechanisms. Agricultural pathogens require none of this. As one paper puts it, deploying plant or animal pathogens could be as simple as “atomizing unprocessed pathogen near the target organisms or, in the case of animals, directly applying the pathogen to the nose and mouth of the organisms.”. Why is it so easy? Is there something special about agricultural pathogens? No, but there is something special about how modern agriculture is done, in that it involves thousands of nearly-genetically-identical plants and animals in astonishingly dense conditions. The environment does the work. All this, with virtually zero risk to the adversary, given that this would not be done in crowded cities with cameras on every corner, but on sprawling, isolated farms that have essentially zero security infrastructure.

Finally detection.

Unlike human disease surveillance, which benefits from the fact that sick people tend to show up at hospitals and demand attention, cows and wheat do not. As a result, agricultural disease relies on a very error prone set of steps for its detection to ever occur: one, the farmer noticing something is wrong with their animals, two, the farmer reporting it to the government, and three, the authorities being dispatched.

We’re going to spend the next few paragraphs discussing these three steps, because each step is a point of failure, and they fail constantly.

First, the farmer notices something is wrong. This is hard. You have to realize the scale that modern agriculture operates at.

A single large-scale poultry operation can house 50,000 turkeys or hundreds of thousands of laying hens in a single building. A feedlot might hold 100,000 head of cattle. The average dairy herd in states like California or Idaho now exceeds a thousand cows. And the trend is accelerating: U.S. livestock production has been consolidating into fewer, much larger operations for decades, with the economics of scale constantly toward ever-increasing density. As a matter of example: an outbreak of H5N1 among cattle populations in the United States began in December 2023, . How long was the lag between initial infection and actual detection? According to a Science paper from April 2025, the virus circulated entirely undetected for over 4 months. Clinical signs—reduced milk production, decreased feed intake, and changes in milk quality—were first noticed by veterinarians in late January 2024. Only on March 25, 2024 was the virus confirmed to exist after genetic sampling of the cows milk. By that point, the virus had already reached 26 dairy cattle premises across eight states and six poultry premises in three states.

Let’s say the farmer eventually realizes that something is wrong. Now they need to report it to the correct authorities. But why would they? There is something extraordinarily perverse about the reporting incentives at play here: farmers are actively disincentivized from flagging unusual disease, because a confirmed outbreak of a notifiable disease may wipe out their entire livelihood. Remember: these pathogens are often so virulent, so adaptive, that mass culling of their herd will be what is demanded of them. So, if you’re a rancher staring at a few sick animals, the economically rational move is to wait and see if they get better, not to call a vet and risk having your entire herd destroyed. Once again, there is empirical proof here: how Indonesian farmers handled avian bird flu in 2006. A paragraph from a zoonotic disease book is instructive:

Those smallholder poultry keepers questioned the severity of the avian influenza threat to their birds….Some continued to consume and sell diseased dead birds. Small to medium-sized contract poultry farmers feared that government officials might cull their birds before definitive laboratory confirmation of the disease, and they were skeptical of compensation schemes or believed compensation was too low. These poultry farmers reported the deaths of chickens to contractors, who in turn sought the services of private veterinarians to determine the causes of bird death, making effective disease surveillance difficult. Smallholder poultry farmers and keepers feared reporting incidents directly to the government. This fear was not limited to a concern about losing their own birds, but also to the social risk of angering nearby neighbors, whose birds would be subject to culling within a 2–5 km radius of an outbreak location.

You may ask: in the case of animals, why can’t we just vaccinate them? You can! But export regulations prevent most farmers from doing so, because standard vaccines make it impossible to distinguish a vaccinated animal from an infected one. Vaccines that include marker proteins allowing serological tests to tell vaccinated animals apart from infected ones do exist, or so-called DIVA vaccines, but adoption has been glacial.

Finally, let’s say, against their better judgement, the farmer reports it. What happens then?

How the U.S. government actually responds to agricultural threats is theoretically fairly straightforward. Human pathogens fall under HHS, via the CDC. Agricultural pathogens fall under the USDA, via its Animal and Plant Health Inspection Service (APHIS). There is a select agent list for each, plus an overlap category for things that threaten both. The jurisdictional lines are reasonably clear. The problem with the agency technically in charge, the USDA, is that it is also the agency whose mission includes promoting the very industry it would need to disrupt in a crisis.

To understand this better, we can look at a fascinating Vanity Fair investigation that interviewed over 55 people across USDA, CDC, HHS, and the White House, all of whom were involved in the same H5N1 cattle outbreak we just discussed. Since the virus was first confirmed in 2024, the two organizations were barely aligned: the White House was planning a public-health-directed response, while the USDA was prioritizing the needs of the dairy industry.

Within weeks of the diagnosis, APHIS employees began calling state veterinarians from personal cell phones to confide that they had been instructed not to discuss, not to engage, and to discontinue even routine conversations with health officials in the field unless talking points were pre-approved. The USDA sat on genetic sequencing data for weeks, sharing samples an average of 24 days after collection—compared to 8 days for the CDC—and without basic metadata like the date or state of collection, rendering the data effectively useless for real-time monitoring. The same farmer incentive problem from before reared its ugly head too: dairy farmers simply opted not to test, and some forced veterinarians off their property. At least five veterinarians who had been outspoken in responding to the outbreak were fired from their jobs. By the time a Federal Order requiring pre-movement testing was issued, the virus had already spread across multiple states. And the testing regime was widely regarded as obviously insufficient: just 30 animals per herd, with farmers reportedly prescreening in private labs to cherry-pick healthy animals.

Because why not? Who was going to stop them?

This was a naturally occurring virus, both in viral origin and how it was spread. Yet, the federal response still took months to coalesce into something real.

And as much as you may think the APHIS bungled this, it is difficult to imagine their future responses will look much better. As of mid-2025, APHIS lost roughly 1,377 staff under the administration’s workforce reduction push, about 16% of its employees. The USDA also accidentally fired several employees working on the H5N1 response, and had to scramble to rescind those termination letters within days. Yes, it may be the case that the organization is bloated beyond a reasonable doubt, and the cuts were deserved. But the cuts have not been accompanied by any visible effort to fix the structural problem here: the fact that the USDA is simultaneously the regulator of and the lobbyist for the industry it oversees.

But there is an important question to ask. What is the ultimate impact of all this? What actually happens if a successful agroterrorism attack occurs? Because if it’s insignificant, just a rounding error, then none of this should be a concern.

It is not a rounding error. The 2001 foot-and-mouth (FMD) outbreak in the UK resulted in over 6 million animals culled, cost the public sector £3+ billion and the private sector £5+ billion, was severe enough to delay that year’s general election by a month, and lead to the dissolution of the Ministry of Agriculture entirely. Simulation models for the United States are even uglier. A study modeling FMD outbreak originating in a single California dairy farm found that median national agricultural losses ranged from $2.3 billion to $69.0 billion depending on detection delay, with every additional hour of delay at the 21-day mark costing roughly $565 million and another 2,000 animals to be slaughtered. What about a deliberate, state-actor attack? Another simulation model estimated the economic impact of a FMD agroterrorism scenario—vast, widespread dispersal of the pathogen—put possible losses between $37 billion and $228 billion across three scenarios, from a contained state-level outbreak to a large multi-state attack.

But there is at least some argument that, under some mental models, it actually is a rounding error. The United States’ agricultural GDP is roughly $1.4 trillion, while the overall GDP is $29 trillion. Even the worst-case FMD simulation represents about a 16% hit to agriculture, and a 1% hit to the broader US economy. This is not nothing, it may completely devastate the nation, but it also is not civilization-ending.

Yet, while agroterrorism perhaps isn’t a standard x-risk scenario, when evaluated against the “is this a serious national security threat“ standard, the answer feels like it is an obvious yes. This raises a rather important question. If everything I’ve said is true—and I’m pretty sure it is—why hasn’t there been a significant agroterrorism event…ever? I have no idea, and it too was a point of confusion among most those I talked to. The best argument I’ve heard is that, if the ultimate goal of bioterrorism is to either terrify a nation or outright end the world, neither the aesthetics nor net-effect of agroterrorism is well suited for either.

However, one person I talked to did say there has, in fact, been one case of minor agroterrorism they are aware of: in late 2019, drones controlled by gangs dropped [items] infected with African swine fever into commercial pig farms in China. Why were the gangs trying to spread swine fever? So that the farmer would be forced to sell their potentially infected meat cheaply to the gangs, who would then sell it on as healthy stock. This feels like a rather roundabout way to make money, but it happened. Moreover, it may be the case that stuff like this occurs far more than anyone realizes, since the whole racket was only discovered because Chinese farmers resorted to radio jammers to prevent the drones from flying near the farms, which ran afoul of the regional aviation authority.

The monitoring architecture is useful for detection, but not defense

The United States has two main systems for detecting biological threats in the environment: one that watches the air, and one that watches the sewage.

Let’s start with the air. BioWatch is a federal program to detect the release of pathogens into the air as part of a terrorist attack on major American cities, created in 2001 in response to the anthrax attacks. Here is how it works:

As currently deployed, BioWatch collectors draw air through filters that field technicians collect daily and transport to laboratories, where professional technicians analyze the material collected on the filter for evidence of biological threats [via PCR]. The entire collection and analysis process takes up to 36 hours to detect the presence of a potential pathogen of interest.

A positive result triggers what is known as a BioWatch Actionable Result (BAR), an indication that genetic material consistent with a target pathogen was present on a BioWatch filter. Upon declaration of a BAR, local, state, and federal officials then assess relevant information and determine the course of action to pursue.

Very cool, isn’t it? Here’s what one of the air filter boxes look like:

The problem with the system, and this is a big one, is that it has literally never once been useful. Never. Not once. Every single time a BAR has been announced, the subsequent investigation has concluded that it was either a false positive or an environmental anomaly indistinguishable from something dangerous. A Department of Homeland Security page has this helpful note about it:

Out of these more than 7 million tests, BioWatch has reported 149 instances in which naturally-occurring biological pathogens were detected from environmental sources. Many of the pathogens the BioWatch system is designed to detect occur naturally in the environment, such as the bacteria that causes anthrax, which has been used as a weapon, but is also found in nature. For example, near the nation’s Southwest border there have been a number of instances where a bacterium that is endemic in the environment has been identified. Thankfully, none of the instances were actual attacks.

It also has these lines that I thought were quite funny:

The detection of commonly occurring environmental agents is not a “false positive.” Much like a home smoke detector goes off for both burnt toast and a major fire, the smoke detector is meant to notify you of a potential fire before it’s too late. BioWatch works very much the same way.

A smoke detector that has gone off 149 times over two decades and never once for an actual fire is almost certainly not a functioning smoke detector. And this particular smoke detector cost hundreds of millions to set up, and tens of millions a year to maintain! To be clear: there is no technological reason that these can’t be made better, and there are startups, such as Pilgrim Labs, that are working on improving similar air-detection systems. If curious, I found the Pilgrim’s founders interview here to be worth watching.

On the sewage side, the whole endeavor is actually going fairly well. But before we go on: monitoring the air is obvious, but why monitor sewage? Because nearly every pathogen that infects a human being eventually ends up in the toilet. Because of this, looking through sewage is perhaps the most honest epidemiological data source available, because people cannot choose not to participate.

And we’re doing very well in monitoring this sludge, or doing so-called ‘wastewater screening’. A lot of people in biosecurity complain that ‘the federal government learned nothing from COVID’, and they are mostly right, with one huge counterexample: the national wastewater surveillance infrastructure, which was largely built in response to the pandemic. The National Wastewater Surveillance System (NWSS), launched by the CDC in September 2020, established that you could detect community-level viral trends days before clinical cases appeared, using nothing more than the genetic material people flush down the toilet, without requiring any of them to consent to testing, show up at a clinic, or even know they’re sick.

But the problem with the NWSS, as it is currently deployed, is that it is a targeted system, relying on qPCR to identify specific, known threats. And among the 500-600 sites where NWSS monitoring stations are deployed, they measure three things: COVID-19, Influenza A, and RSV.

80% of them also measure three more things: Measles, H5N1, and Monkeypox.

There’s an awful lot missing, isn’t there? What about all the other types of Influenza? Norovirus? And the scarier ones too, Nipah, Ebola, Tularemia, all of them are entirely absent.

The answer is, in principle, to switch away from qPCR and do metagenomic sequencing: instead of looking for specific pathogens, you sequence everything in the sample and computationally figure out what’s there. I’ve written about metagenomics in the context of microbiomes, so you can look there for a deeper explanation on how it works.

Why isn’t anyone doing this?

In fact, there is someone doing this, and this leads us to what I’d consider one of the crown jewels of what the U.S. nonprofit-biosecurity-complex has managed to accomplish: SecureBio Detection, previously known as Nucleic Acid Observatory (NAO), which has been building a pilot metagenomics-based wastewater screening network in the US since 2021. Circa November 2025, they maintain 31 sampling sites across the US, in 19 cities, sequencing about 60 billion read pairs weekly. And they’ve already stumbled across a few interesting things, such as detecting measles in wastewater from Kauaʻi County, Hawaii and West Nile Virus in Missouri—the latter of which ended up having real, confirmed cases to go alongside it! There is an ongoing effort to have something similar at the federal level—the so-called ‘Biothreat Radar’—but it doesn’t seem to actually exist yet. But SecureBio Detection continues!

This is quite promising. This is a bonafide, national-scale attempt to detect both known and unknown biological threads, and it works! They are also doing some interesting ML work in being able to automatically detect, via a metagenomic language model, whether unknown metagenomes are simply uncharacterized, innocuous microbes (i.e. nearly all microbes) or human-targeting pathogens worth worrying about.

But, despite how good wastewater screening is, it is worth remembering that detection is not defense. This may seem like a semantic point, of course detection isn’t defense, but certainly it should allow you to defend faster or better.

But does it really?

If you’re detecting something known—a COVID variant, a resurgent influenza strain—then yes, detection may accelerate response, because you already know what to make against it. But if you’re detecting something novel, then what exactly happens next? Designing vaccines that elicit neutralizing antibodies is difficult in the best of circumstances, clinical trials take time, and, in the meantime, the underlying pathogen will continue to mutate, potentially diverging from whatever you’re designing against it. This is surprisingly under-discussed, but it is worth marinating in the fact that, yes, BioNTech’s and Moderna’s capacity to generate a COVID-19 vaccine so quickly was indeed an extraordinary feat of logistics and science, but the usage of the spike protein segment as an immunogen in the vaccine was informed by two decades of prior coronaviruses research. In the case of a brand new, chimeric virus that has no immediate cousin, a few weeks of advance notice is just a longer window in which to watch the curve steepen.

Finally, in both cases, either a natural or engineered pathogen, there exists one last problem: coordination. There is no pre-negotiated decision tree for what happens after something scary is detected, no threshold that, once crossed, triggers automatic funding for therapeutic stockpiling or accelerated clinical development. There probably should be one! But there isn’t today and, as far as I can tell, there aren’t plans for one to exist. Ultimately, the value of early warning is bounded by the speed of the response it enables, and that speed seems extremely limited today.

Machine learning may be very useful for rapid-response therapeutics

This section is me going off-script from the experts I talked to. The pipeline I will describe below does not exist in any meaningful capacity, but there are inklings of it found across the therapeutics-for-biosecurity plays out there, so it feels like the mental framework is informative regardless. As in, the logical steps mentioned here may massively diverge from what will realistically occur, but the types of models, timelines, and decision calculus used likely will not.

The Coalition for Epidemic Preparedness Innovations, or CEPI, has an initiative that identifies exactly what you’d want your government to be capable of in the case of a major pandemic: the 100 Days Mission. As in, from the day of realizing, ‘we probably should mount a response to this weird sequence we found’, therapeutic options should be ready to go within three months for population-scale deployment. It took 326 days to get the first COVID-19 vaccine authorized, and that was widely regarded as the fastest vaccine development in human history. How could 100 days be possible?

Luckily for us, they’ve defended the position at length in a paper. Long story short: this is not an unreasonable timeline if you’re in a coronavirus-y situation, where your adversary is something that millions of hours of research has already gone into characterizing. Why? Because the second you can identify the ideal immunogen—or, what you should be sticking in your vaccine to elicit the antibody repertoire that neutralizes the virus—you’re done with the major technical design challenge. Like I mentioned earlier, the spike protein was the obvious immunogen for SARS-CoV-2, informed by two decades of prior coronavirus research going back to SARS-1 and MERS. Thus, the fun little party story of BioNTech and Moderna having a vaccine candidate within days of receiving the SARS-CoV-2 sequence.

So, mRNA basically hands us our vaccine. Now we just need to deal with the two other bottlenecks: manufacturing scale-up and clinical trials. I think it’s interesting to discuss how things may be sped up here—and the arguments for how you’d speed them up are within the realm of possibility—but it does lead us off-topic, so I’ll place those in a very long footnote.1

But remember, what I’ve described so far is the rosy scenario, where we are dealing with something we already mostly understand. What about things that are wholly new? This includes not only de novo pathogens, but also mostly natural ones that have immune-escaped the established immunogens through either evolutionary or otherwise methods. For these cases, the same CEPI paper admits that things are harder, and that a 200 or 300 day turnaround time should be the goal.

But is that possible? Remember, now the vaccine design problem becomes quite difficult. Which viral protein subunit do you use as the immunogen? Which conformation elicits neutralizing versus non-neutralizing antibodies? Which epitopes are conserved enough that you’re not designing a vaccine that will be obsolete by the time it’s manufactured? These are not easy questions to answer! And if you get them wrong, you waste months manufacturing the wrong thing. The same CEPI paper from earlier optimistically states that immunogen/antigen design for these novel pathogens would take just a few months if we really worked hard at it.

But it feels like getting to this speed of development would almost certainly require immense technological leaps. One of my favorite podcast episodes was my interview with a founder of a vaccine development startup: Soham Sankaran of PopVax. In it, I ask a lot of questions about why immunogen design for vaccines is so hard, and I will paraphrase his answers in the footnotes.2 To keep it short: it’s really, really hard.

Now, the question of the evening: can machine-learning help us with this?

Probably not. At least not in a significant way anytime soon. ML seems useful in the margins for, say, figuring out how to scaffold specific immunogens of interest such that they are ‘correctly’ presented to the immune system, but we are far off from a model being able to reliably respond to a query like ‘here is the structure of the virus I am scared of, please design an immunogen that I can encode into an mRNA vaccine that will elicit broadly neutralizing antibodies’.

At least, that’s the consensus from everyone I talked to. But if we’re willing to stretch our brains a little, I think one can imagine a scenario in which ML, as it exists today, may end up being extraordinarily useful for how we respond to pandemics. And it comes down to the fact that mRNA is such a stupidly, insanely versatile platform. You don’t need to encode an immunogen in the mRNA. Instead, you could simply encode the antibodies that you’d want the immunogen to elicit.

What, you may scream, surely you can’t do that. But you can! As far back as 2021, Moderna injected adult humans with an mRNA vaccine that had, as its payload, monoclonal antibodies against the Chikungunya virus. And it worked quite well! Moderna has since shelved this particular asset, but for reasons that seem more portfolio-optimization-y than the drug not having enough efficacy. Luckily, there is ongoing work outside Moderna in exploring mRNA-encoded nanobodies, which have the advantage of being far smaller than typical antibodies, so less stressful for our weak, mammalian cells to pump out. And upon looking it up, I have discovered that I am not the first one to find this absurdly relevant to biosecurity efforts! One 2026 review paper echoes my sentiment, and expands on it: ‘mRNA-encoded antibody approaches have been explored in preclinical models of Zika virus, Ebola virus, and rabies, where a single intramuscular dose provided prophylactic and therapeutic benefits in animal models’.

Insane, right? Now, you may immediately spot problems with this. For instance: antibodies don’t last very long in our blood stream, on the order of 2-3 weeks. How useful could this possibly be in a pandemic, where circulating pathogenic material may linger around for months? But fixing this is fully within the realm of possibility. Engineering the Fc region, or the bottom section of the ‘Y’ shape of an antibody, can reliably and dramatically expand its therapeutic window. In fact, we needn’t even theorize on this, because the same 2021 Moderna paper also included these Fc mutations: 2 alterations (M428L and N434S), leading to a 69 day half life. And there is no reason to believe that this cannot be pushed even further, given that at least one anti-viral antibody has been shown to have a half-life on the order of 5-6 months.

The next question: where will we get useful antibodies from?

Modern ML methods for designing antibodies against arbitrary targets are not perfect, but they really are quite good. In 2025, the Baker lab published what is, to my knowledge, the most significant result in computational antibody design to date: a fine-tuned version of RFdiffusion that can generate de novo antibodies—VHHs, scFvs, and full antibodies—targeting user-specified epitopes. Most relevant for us, when the model was given a particular target and epitope—C. difficile toxin B and a specific epitope that had never had an antibody designed against it—the model generated moderate-affinity antibodies, with cryo-EM confirming its binding. Now, as I mentioned in the footnotes, binding to a virus is not the same thing as neutralization of a virus, and we usually only care about the latter. I agree that this is a bottleneck that ML cannot easily solve, but it also does not feel like a huge bottleneck, especially if these models work well. Consider the fact that binding is necessary, but not sufficient for neutralization, and if you just screen a bunch of binders, all generated for free, surely you can vastly speed up the process of identifying a neutralizing antibody.

Of course, in the case of a pandemic going on long enough, you could bypass all this by simply fishing out neutralizing antibodies from infected patients, or at least use those as a parent for further ML-driven optimization.

Our final problem is that pathogens usually mutate, which means that even if we turn every human into a factory of identical antibodies against a particular sequence, those same antibodies may soon become useless due to immune escape. This is why the natural immune response—as offered by either an immunogen or antigens from the pathogen itself—can be so efficacious, as the polyclonal antibody repertoire elicited by natural infection or vaccination targets dozens of epitopes simultaneously, making it extraordinarily difficult for the virus to escape all of them at once. This too is not theory: every single monoclonal antibody therapy authorized against SARS-CoV-2 was eventually rendered obsolete by Omicron and its descendants.

Are we doomed?

Let’s not give up, and instead take a closer look at what two issues we need to solve to overcome this obstacle. First, we need to choose not just any neutralizing antibodies for our vaccine, but ones that target sites where escape is costly to the virus, or functionally constrained epitopes where mutations would compromise receptor binding or some other essential function. Second, we need to deploy cocktails of antibodies targeting non-overlapping epitopes, such that the probability of simultaneous escape across all of them becomes vanishingly small.

I propose to you that there are viable ML-based solutions to both of these.

For identification of immune-escape-y-epitopes, we can look to EVEscape, a protein model from the Debora Mark’s lab at Harvard. The model combines evolutionary sequence information with structural and biophysical data to predict, for a given viral protein, which mutations are most likely to emerge and evade existing immunity. Flip the interpretation and you get the inverse: sites where EVEscape predicts low escape potential are precisely the sites where you want your antibodies to bind, because the virus cannot easily mutate away from them without crippling itself. This is not a solved problem, but models like these are surely directionally useful, and certainly better than guessing.

For cocktail design, consider EscapeMap. EscapeMap integrates deep mutational scanning (DMS) data from SARS-CoV-2 across dozens of neutralizing monoclonal antibodies with a generative sequence model to identify something very useful: negatively correlated escape routes. Two antibodies have negatively correlated escape if the mutations that evade one tend to make the virus more sensitive to the other. Cocktails built from such pairs are inherently resistant to simultaneous escape, because the virus cannot run from both at once. As published, EscapeMap is SARS-CoV-2-specific; the underlying DMS data took years to generate, and you wouldn’t have it on day one of a new pandemic. But the framework (should) generalizes to any pandemic and a DMS-esque dataset will emerge if it goes on for long enough, allowing you to eventually design broadly-neutralizing cocktails of antibodies. If we’re being especially galaxy-brained, given a sufficiently good protein model, perhaps you don’t need any DMS data at all! After designing your de novo antibodies, you could run in-silico DMS to predict how every possible mutation on the target surface would affect binding to each candidate, cross-reference those with EVEscape-style fitness predictions to filter for mutations the virus can actually tolerate, and look for the same negative correlations. I realize this isn’t fully possible today, that the impact of single-amino-acid substitutions are still badly grasped by these models, and a whole host of other failure modes. But the models will only get better.

When all of this is put together, this pipeline should allow us to do something extraordinary within weeks of a novel pathogen being sequenced:

1. Discover neutralizing antibodies against them, either via ML or patient serum.

2. Create a cocktail of antibodies with negatively correlated escape routes via in-silico screening or a DMS dataset.

3. Fc-engineer them for a long half-life.

4. Encode the whole thing into mRNA.

5. Manufacture it.

If we do this early enough, and distribute the vaccines fast enough, we could potentially kill the spread of even the most virulent pathogens. Of course, manufacturing is historically the next major bottleneck, but if our wastewater screening and ensuing rapid-responses are quick enough, we may need to manufacture orders of magnitude fewer doses.

I realize that there are many catches here, and that what I’ve presented is grossly optimistic. All of this is a multi-layered, mostly-computational solution, and every one of these layers are error prone. All antibody generation methods have plenty of failure modes, EveScape is not consistently useful across viruses (though further lines of research claim to have improved on it), EscapeMap is hyper-focused on SARS-CoV-2 and it may very be that the framework actually cannot easily transfer to new pathogens, and antibody-encoded-into-mRNA is—for however clever it may sound—still in its early days of efficacy-and-adverse-effect studying.

But each one of these are improving, and I think the trend-lines are promising. I am much more optimistic on the value of ML here than in perhaps any other layer of the biosecurity defense workflow, and time will tell how much that optimism is warranted.

Pathogen-agnostic defenses are extraordinary. But who pays for it?

Finally, the last section. This one will be short.

Everything discussed so far shares a common architectural assumption: that you know, or can figure out, what you’re looking for. This is hard! And it is made all the more difficult by the fact that the coordinated effort needed to respond to these discoveries is not something that we’re historically very good at. But there is a one category of biosecurity defense that sidesteps this problem entirely, since they work against all pathogens. And once they are deployed, they largely work for extended periods (months to years!) by themselves, with no logistical effort needed from anybody.

What are they? Far-UVC and glycol vapors.

I’m going to be honest: the more I looked into this subject, the more I found that every conceivable thing that could be written about it has been, and where it hasn’t, it’d require conversations with a lot more people and significantly lengthen this already long essay. So I’ll defer to other people here. For far-UVC I’d recommend visiting faruvc.org for an introduction, and, if you’re sufficiently convinced, aerolamp.net to pick one up for yourself. Glycol vapors have a lot less easy reading material, but there is one article published a year back by Blueprint Biosecurity—a nonprofit who also funds far-UVC work—and various related articles written on Jeff Kaufman’s blog, who works in biosecurity.

To keep it short: If we could tile the interior of enough buildings with these solutions, you could, in theory, render the entire human indoor environment continuously hostile to airborne pathogens; far-UVC through physical degradation of their DNA, and glycol vapors through (probably) desiccation. This would affect all airborne pathogens. Named ones, unnamed ones, engineered ones, ones that have never existed before and will never exist again except in the brief window between their release and their death to one of these two. And it would do all this with no harm to you. Of course, these technologies still have room to improve, but their problems are mostly ones of logistics, optimization, and scalability.

So why don’t we see these far-UVC lamps and glycol vapor fumers in every building in the world? Why aren’t we sterilizing our air the same way we sterilize our water?

You could quibble with the details here, about how far-UVC is still very expensive, the evidence base for glycol vapors is still being figured out, and the like. But it’s tough for me to consider the question of ‘why isn’t this being massively funded’ without concluding that the problem is that there is no for-profit entity that really benefits from it. The benefits of clean air are diffuse, accruing to everyone who breathes in a building, none of whom are the institution writing the check. Hospitals are the one exception, but they are a sliver of all interior environments that humans reside in, and obviously will not offer the scale necessary to put a dent into pandemics. This means that these technologies can only be deployed and studied by a very small group of hobbyists, early adopters, and academic labs.

Okay, but isn’t this the point of governments? This is a clear public good! This is territory that is hard to get perfect visibility into, but my instinct is that the evidence base for governmental-buy-in is simply difficult to produce.

A recent Works In Progress article over far-UVC had this to say:

Measuring infection control is challenging and seldom undertaken, particularly in public spaces. Epidemiological data is expensive and difficult to gather, and there is currently no way to measure the amount of viable, infectious pathogens in the air in real time. Office attendance can be tracked, but controlling for how users mix outside the office space is immensely difficult, and measuring the real-world effect of small-scale deployments in public areas is almost impossible. Studies aiming to cause deliberate disease transmission in controlled environments have failed to work in practice because they have been too small to generate enough infections.

While this is a bitter pill, there is a sweet one that it offers us: the implementation problems with pathogen-agnostic defenses are extremely ‘money-shaped’ in a way that few other biosecurity solutions are. All the subject needs is proof, in the form of randomized control trials, in aggregating individual use experiments, in subsidizing institutions to try it out—more money to push over to the ‘this obviously works’ finish-line. So, if there are any biosecurity-curious philanthropists reading this: I highly encourage you to explore far-UVC or glycol vapors.

Especially because unlike almost every other type of biosecurity solution we’ve discussed so far, these solutions will yield public benefits even in the absence of bioterrorism. In fact, the same Works In Progress article over far-UVC never even mentions biosecurity, and is focused more on public health, ending with this line: ‘Tuberculosis and coronaviruses [may] join typhoid and cholera as tragedies of the past, and seasonal flu and common colds would become rare rather than routine if clean air were as universal and expected as clean water.’.

It’s a great pitch, and I am very excited to see more deployment of these technologies in the coming years. It just feels like one of the more obvious areas to push forwards on in this field.

Conclusion

So, what should you be scared of?

I can’t speak for you, but I can say what I’m scared of. I am scared of a well-funded terrorist organization constructing their own lab, out of which they create natural pathogens—potentially with a few AI-assisted mutations to allow them to immune-escape existing defenses—using either split-order attacks or ordering from DNA synthesis companies who don’t screen. I am scared of these groups spreading it in well-populated cities or farmland. I am scared that it will either kill several million people and/or cause billions in economic damage, and though its spread will be noticed by wastewater screening, it will be months until the necessary resources are allocated to defend against it. And I am scared that all of this will happen within the next few years.

What am I not scared of? I am not scared of state-actors, because most states have too much to lose by violating the Biological Weapons Convention and, if they are willing to let loose anyway, I believe they would opt for either easier-to-use-and-control chemical or nuclear weapons instead. I am not scared of people creating extremely engineered pathogens that have capabilities far beyond existing ones—because the existing ones are already quite good and difficult enough to work with—especially because even if the AI tools get good enough to make it worth it, I believe the same AI tools will be just as useful in countermeasure design. And yes, I realize ‘attack requires one success while defense requires comprehensive coverage’, but I also believe the swiss-cheese security model will prevail. Finally, I am not scared of individual actors, because the economics of bioweapons production likely do not work in their favor. Yes, they can rent upstream services—virus production, purification—but the downstream weaponization work requires custom protocols that CROs have no economic incentive to develop. Moreover, given that the weaponization will almost definitely be a bespoke, hands-on R&D project and not one that is easily automated, it feels unlikely that nobody at the CRO will raise an eyebrow.

That’s my threat model at least. I realize it has holes. For example, it may be the case that state actors are worth worrying about, entirely because the appeal of bioweapons is that you deploy them with plausible deniability. Hard to do that with a nuke! You may also accuse me of not paying close enough attention to the trendlines, and that maybe I am correct about the 2026 threats, but not the 2030 ones, so perhaps a disgruntled salaryman will really be able to someday easily design mega-Ebola to depopulate the planet. Maybe!

Ultimately, you can get infinitely paranoid about biosecurity if you really want to, or you can assume Nothing Ever Happens, and I think where I have landed is a comfy middle ground. I am grateful that there exist people who work in biosecurity who are infinitely paranoid, and through writing this essay, I have become far more sympathetic to their viewpoint.

To end this off: in all my conversations, everyone generally agreed that an honest-to-god, bioterrorist attack is unlikely. It is a low probability event. But low probability events with civilizational consequences are still worth preparing for. The heartening thing here is the bottleneck to preparation here is almost entirely institutional, economic, and coordinative, not scientific. The disheartening thing is that fixing these ultimately requires political will, and sans a catalyzing event to unlock it, that political will does not currently exist. Of course, one could argue that perhaps we will never need it, that the Pathogen that people in this space are breathlessly building defenses against will never arrive, that it is all paranoia, tech-rotted minds coming up with entirely hallucinated demons. But that argument feels far less convincing now than before I started writing this essay, and, if I did my job right, I hope it will feel less convincing to you, too.

New-ish substacker Linch Zhang at The Linchpin has a fun post that ranks the “ways of knowing,” which he summarizes in this tier list:

The first thing I noticed is that it’s strange to rank things like “reading,” “science,” and “non-expert intuitions” in the same list, but having thought about it, it kinda makes sense. We might consider the elements in this chart as answers to the question, “If I want to know something, what techniques can I use?”, conflated with answers for “How does human civilization produce new knowledge?” This conflation makes things a little confusing, but the list is interesting nonetheless.

The second thing I noticed, because this is a topic I know well, is that cultural evolution is ranked very low, on par with folk wisdom and divine revelation.

Broadly speaking, cultural evolution is the application of evolutionary concepts from biology, like mutation and natural selection, to cultural traits. These traits cover any type of information that is socially transmitted: ideas, customs, styles, technologies, non-innate behaviors, etc. “Evolution” means the way this information changes over time, driven by mechanisms like innovation (analogous to mutation) and cultural selection (analogous to natural selection, i.e., how we choose to keep certain cultural traits and not others, and accumulate them).

Cultural evolution can be said to produce new knowledge, but Linch finds it unimpressive. In his list, “literacy is ranked highly . . . because it delivers extraordinary returns on humanity’s investment compared to, say, cultural evolution’s millennia of trial and error through humanity’s history and pre-history.” He adds that “cultural evolution (F tier) is vastly overrated.”

I’m really curious about who, exactly, is doing all this overrating, since I hold the opinion that cultural evolution is vastly underrated. In fact, it’s often not rated at all: most people aren’t even aware of it. It’s surprising to see it on the chart, to be honest. But once we assume the writer knows what cultural evolution is, it’s even more surprising to see it ranked in the “Fail” tier.

Here’s how Linch justifies it:

Let’s be specific about cultural evolution, since Henrich’s The Secret of Our Success has made it trendy. It’s genuinely fascinating that Fijians learned to process manioc to remove cyanide without understanding chemistry. It’s clever that some societies use divination to randomize hunting locations. But compare manioc processing to penicillin discovery, randomized hunting to GPS satellites, traditional boat-building to the Apollo program.

Cultural evolution is real and occasionally produces useful knowledge. But it’s slow, unreliable, and limited to problems your ancestors faced repeatedly over generations. When COVID hit, folk wisdom offered better funeral rites; science delivered mRNA vaccines in under a year.

The epistemic methods that gave us antibiotics, electricity, and the internet simply dwarf accumulated folk wisdom’s contributions. A cultural evolution supporter might argue that cultural evolution discovered precursors to what I think of as our best tools: literacy, mathematics, and the scientific method. I don’t dispute this, but cultural evolution’s heyday is long gone. Humanity has largely superseded cultural evolution’s slowness and fickleness with faster, more reliable epistemic methods.

There are several confusions here — starting with the fact that the Fiji example in The Secret of Our Success isn’t about manioc, but about taboos on eating shark while pregnant. The manioc example is from Amazonia! But okay, minor mistake, it doesn’t matter to the argument. There are bigger issues, and looking at them will help us get a much clearer picture of what cultural evolution is and why it matters.

The #1 problem in Linch’s post is his implicit definition of cultural evolution. It’s not exactly wrong, but it’s too strict. He suggests that cultural evolution takes “millennia of trial and error,” and that it is “slow, unreliable, and limited to problems your ancestors faced repeatedly over generations.” None of this is true, except the trial-and-error part. Cultural evolution does operate by trial and error, but that can happen far faster than generations or millennia, and it’s not particularly unreliable, since we can control the selection mechanism in many cases.

Here are some toy examples at various time scales, with different ways to perform the trial-and-error step and the selection step:

• Centuries and millennia: Chimpanzees try to eat termites by digging in termite nests with their hands for 100,000 years. Then one chimp randomly pokes a nest with a stick and discovers that she can get termites more efficiently this way (trial and error). Later, another chimp sees the first chimp do it and be rewarded with many juicy termites, and imitates the behavior (selection). The knowledge of using sticks slowly spreads, but it takes hundreds of years for the entire population to adopt it.

• Decades or generations: The manioc story. Various tribes of the Tukanoan people in the Colombian Amazon eat manioc, a.k.a. cassava, as their staple food. Manioc causes cyanide poisoning in the long term, but you don’t notice it with either sight or taste, and without modern science, it’s basically impossible to link manioc to disease. One tribe randomly has this habit of waiting two days before eating the manioc fiber, which detoxifies the manioc, though they don’t know it; another just eats it immediately (trial and error). Over a few decades, the members of the first tribe are overall healthier, allowing them to reproduce and survive better, while the tribe that doesn’t detoxify eventually goes extinct (selection).

• Years: Wolfgang Amadeus Mozart is messing around at his keyboard (trial and error), and invents a new kind of musical flourish in one of his symphonies. He publishes the symphony. Other up-and-coming composers find it cool, and within a few years, they also use the technique in their works, changing the course of classical music history (selection).1

• Months and weeks: Dozens of SaaS startups get founded in the AI space, taking advantage of recent LLM advances. They experiment with various projects (trial and error), and within a few weeks, some of them start getting traction since they fill a user need (selection).

• Hours and minutes: Thousands of people post jokes and hot takes on Twitter, most of which are bad and boring (trial and error); but every once in a while, one of them becomes viral because it’s particularly funny, interesting, or egregious, which encourages people to share it further (selection).

Sometimes, of course, the selection step is a totally uncontrollable, even unnoticeable phenomenon, as in the manioc story. But many other times it occurs through rational human behavior, such as users buying SaaS or choosing to retweet something they like (or dislike). The trial-and-error step can also be controlled to some extent: Mozart or startup founders can deliberately choose to experiment, or even induce creativity using specific techniques or substances.

What happened in Linch’s post, I suspect, is that he overfit on the examples in Henrich’s book. Henrich is an anthropologist, and most of the evidence he describes comes from studies of hunter-gatherer societies, humans vs. great apes, and child psychology. This makes sense for a field that tries to encompass so much (many things are cultural!) and yet is still fairly young (a couple of decades). You’ll make more progress if you study the mechanics of human culture at the boundary between humans and other animals, without the confounding effect of the layers and layers of culture that make up the life of an adult in a complex modern civilization. But the idea generalizes far beyond anthropology.2

A related error in Linch’s post is to consider cultural evolution as distinct from the other ways of knowing in the list.

It would be both boring and useless to claim that all of the items are examples of cultural evolution. To be sure, you certainly could make this argument: In a broad sense, cultural evolution covers almost every kind of knowledge process used by humans, with the exception of the knowledge encoded in our genes. But some of the items in Linch’s list are more closely associated with it than others. And those few items, in fact, cover most of it. I don’t know what’s left if you take them out from under the cultural evolution umbrella.

First, folk wisdom. Also rated as F-tier. Cited twice in Linch’s three paragraphs on cultural evolution (“The epistemic methods that gave us antibiotics, electricity, and the internet simply dwarf accumulated folk wisdom’s contributions”). It’s unclear to me what the distinction is here. Maybe the idea of accumulation? But folk wisdom without accumulation doesn’t really make sense; isn’t the point of folk wisdom that it has been transmitted across generations? Otherwise it’s just random life advice by an elderly person. It seems that Linch conflated the two concepts and should have just merged them into one, called “folk wisdom” or maybe “culturally evolved folk wisdom.”

Second, mimicry. B-tier: one of the better ways of knowing, according to the list. But if you read The Secret of Our Success, you’ll realize that this actually is the main mechanism of cultural evolution. A successful hunter gets imitated by younger aspiring hunters. Toddlers learn about the world by imitating their parents and older siblings. A core argument from Henrich is that humans are particularly good at transmitting cultural information in this way, unlike chimps and other animal species.

Third, expert intuition, classified as C-tier, and non-expert intuitions, D-tier. We humans are good at mimicking and learning, but not from just anyone. Henrich describes various quirks of human psychology that help us identify experts, or at least the most successful individuals in a particular task. This is why we’re sensitive to prestige: it’s an (imperfect) signal of expertise. So Linch’s two items on “intuition” are clearly part of cultural evolution: figuring out who to pay attention to is a key mechanism.

Fourth, literacy/reading. This is Linch’s top way of knowing, at the S+ tier. Literacy certainly isn’t needed for cultural evolution to occur, but boy, does it help with the accumulation part.

Fifth, social media, ranked as F--. For what it’s worth, I don’t fully agree that this is the absolute worst way of knowing, but regardless, it’s a clear example of trial and error and selection, as seen in my earlier Twitter example.

The other ways of knowing can also be massaged into fitting within the cultural evolution frame. Science, for example, definitely also works through trial and error, followed by selection of the best knowledge through criticism. Indeed, there are epistemologists like Karl Popper who argue that all knowledge production is evolutionary. But even if we don’t go all in, notice how the items I picked cover the full spectrum from F-- to S+. This makes it pretty hard to decide in which tier to actually put cultural evolution, if we wanted to update the list…

… which brings us to this question: should cultural evolution even be in the list at all?

In some sense, sure, it’s a way of knowing. But unlike the other items in the tier list, it’s not really something you choose to do. Most of the time, it just happens: knowledge gets created and selected, and has thereby evolved. And to the extent that you can choose to do it, by deliberately innovating and selecting, that’s an indistinguishable process from most other ways of knowing.

If I wanted to make a better list, I would split it in two, removing the conflation I mentioned earlier. One tier list for the question, “If I want to know something, what techniques can I use?” and another for “How does human civilization produce new knowledge?” Literacy would appear in the former, but not in the latter, since it’s not possible to produce knowledge new to humanity just from reading (at least until we meet literate aliens). Cultural evolution wouldn’t be in the former, but would show up in the latter, probably ranked somewhere in the middle.

For that matter, Darwinian biological evolution should be in the latter list too, ranked lower than cultural evolution. Mutation + natural selection does produce new knowledge, encoded in DNA. It’s a very lousy way to learn, it takes millions of years and it generally involves killing off every organism that makes a mistake, but it works.

I made this very quickly and didn’t put that much thought into each individual item, but here’s what my two-part list looks like:

I learned about cultural evolution when I happened to be a research intern in Joe Henrich’s lab at Harvard for half a year, back in 2016, which is also right after The Secret of Our Success was published. Ever since, I’ve been puzzling over why the idea is so little-known and often misunderstood.

Part of the answer might be that it’s surprisingly counter-intuitive, like biological evolution by natural selection. The mechanism is simple, but it’s difficult to grasp that complex living things or complex cultural traits evolve out of mutation and selection, without any sort of goal-oriented (teleological) processes.

In addition, cultural evolution is more complex than biological evolution, because it operates along more mechanisms. In biology, the information is stored in a standardized way, in genes. Genetics certainly is complex, but it’s tractable. Also, in a sexual species, genes are passed to a new organism almost exclusively when two organisms reproduce. (There are exceptions, like horizontal gene transfer, but they’re rare in complex organisms like animals and plants. We can also do this artificially with genetic engineering.) By contrast, in cultural evolution, the equivalent of a gene, the meme, is very poorly defined. This has prevented memetics from establishing itself as a formal field. And memes can spread in a multitude of methods, including sexual reproduction (parents passing their behaviors and values to their children), but also education, media, the influence of friends, etc. Not to mention that there are different levels of memes that interact in various ways: whether a group adopts the cultural practice of eating shellfish may be influenced by the “larger” cultural practice of being Jewish.

But in another sense, cultural evolution might still be underrated simply because it hasn’t been written about that much. It’s a young field, and it might still be difficult to tease the ideas apart from the few mainstream books, papers, and blog posts that have been published on the topic. Which is why I’ve been writing about it often, though rarely as the main idea of a post.

I’ve been meaning to write about it more formally several times, and for some mysterious reason I find it very difficult. So I’m happy I got to leverage the motivation of proving someone wrong. Hopefully Linch appreciates that this, too, is cultural evolution in action. A substacker writes about cultural evolution in a mistaken way (trial and error); another critiques the problems in it (selection); we collectively arrive at better knowledge. It’s not the worst way to make progress!

This is my 3rd Roots of Progress Blog Building Intensive post! Thanks to Linch Zhang for providing the fuel, to Venki for drawing my attention to Linch’s post, to BBI fellows Steven Adler, Hiya Jain, and Andrew Burleson for feedback, and to Mike Riggs for editing.

My favorite color has changed throughout my life, cycling through the entire spectrum of visible light and beyond. I don’t remember when blue was the chosen one, exactly; maybe when I was 13 or so. After that, yellow, purple, orange, green, and pink occupied the top spot for various periods. Blue never made a comeback. I saw it as a banal, common color. After all, the sky is made of it, and the sky is everywhere.

Then I realized when compiling the tech tree that blue is the most fascinating color, because it is the hardest of the common colors to create artificially.1 You can’t just take a piece of the sky and put it into a painting. And blue pigments are fairly rare in minerals, plants, and animals. So blue had to be invented, time and time again, from 4000 BC to the 21st century. It is the most technological color, and I’m willing to claim that this is why it is usually, in science fiction and elsewhere, used to represent the future.

The story of blue starts with indigo. It is an organic dye made from plants in the Indigofera genus, which grow throughout the tropical and subtropical regions of the world. The first known traces of indigo dye come from the New World, in ancient Peru, 6,000 years ago, using Indigofera suffruticosa, or anil.2 In the Old World, it was known from Africa to East Asia, but became particularly associated with India (hence indi-go), where Indigofera tinctoria was domesticated. Indigo soon became a luxury, traded from India to Greco-Roman and then medieval Europe, where the same blue dye could only be made from a less productive plant, woad or Isatis tinctoria. Eventually the “blue gold” became an important colonial crop in the Caribbean and was part of the story of slavery, next to sugar, tobacco, and cotton.

Before indigo was a thing in the Old World (that started circa 2400 BC), the Egyptians had already become obsessed with the color blue. Besides the sky, it was available in the form of semiprecious stones like turquoise and lapis lazuli, cobalt oxide (more on that later), as well as the mineral azurite, which they mined in Sinai and the Eastern Desert.

Azurite would later enjoy a fruitful career as the main blue pigment in European painting, but to the Egyptians it was costly, and besides it isn’t the most stable blue color: it degrades and fades when in contact with air. And so they created the first synthetic pigment in history: Egyptian blue. The oldest evidence of it is in a bowl dated to 3250 BC. Egyptian blue is a calcium copper silicate with formula CaCuSi₄O₁₀ or CaOCuO(SiO₂)₄. Its method of manufacturing, in a rare example of lost technology, was forgotten towards the end of antiquity, but has been plausibly reconstructed. It likely involved heating together quartz sand (silica) and some source of copper (either copper ores or scraps from the bronze industry), together with an alkali (like natron) and a calcium oxide (unintentionally added as impurities in the other materials).

In another cradle of civilization, a very similar story unfolded from about 800 BC. So similar, in fact, that it has been speculated that knowledge of Egyptian blue spread along the early silk road, all the way to China, where Han blue (together with Han purple) makes an appearance during the Zhou dynasty. Han blue has almost the same chemical formula as Egyptian blue, but replaces calcium with barium: BaCuSi₄O₁₀. It may also have been an independent invention, perhaps the work of Taoist alchemists and glassmakers. Its use declined after the Han dynasty, and few examples survive.

Much later, China would become famous for another application of blue: the “blue and white” porcelain style. The blue here comes from cobalt oxide, which had colored Egyptian faience since at least 1500 BC, though nobody at the time knew what cobalt was. You could make cobalt blue in the form of glass and then grind it into a pigment called smalt. Despite porcelain originating in China, it seems that the use of smalt for the blue and white style began in Iraq. It spread from the Middle East to China, and then from China to the rest of the world including Europe, where it would eventually allow Swedish chemist Georg Brandt to identify cobalt as an element in 1735, the first time a new metal was discovered since antiquity.3

Meanwhile, in the New World, the local indigo dye was being combined with a clay called palygorskite to create what became known as Maya blue, which was the main blue pigment in Mesoamerican art from about 800, and was still used as late as the 19th century, though it, too, was forgotten about for a while.

But none of the pigments mentioned so far, not azurite, not cobalt blue, not Egyptian blue, could rival with the purest and deepest of blues, the one that came from grinding the rare lapis lazuli stone into a powder. Lapis lazuli had been extracted from mines in what is now Afghanistan since ancient times, but was used for paint starting around the 5th to 7th centuries, for use in Zoroastrian and Buddhist religious art. This pigment became known to medieval and Renaissance Europeans as ultramarine, meaning “beyond the sea,” since it had to be imported at great cost from central Asia (which, to the Venetian merchants who mostly controlled this trade, was beyond the Mediterranean sea, I suppose). Nobody has written about this more eloquently than Scott Alexander:

Here is the process for getting ultramarine. First, go to Afghanistan. Keep in mind, you start in England or France or wherever. Afghanistan is four thousand miles away. Your path takes you through tall mountains, burning deserts, and several dozen Muslim countries that are still pissed about the whole Crusades thing. Still alive? Climb 7,000 feet through the mountains of Kuran Wa Munjan until you reach the mines of Sar-i-Sang. There, in a freezing desert, the wretched of the earth work themselves to an early grave breaking apart the rocks of Badakhshan to mine a few hundred kilograms per year of blue stone - the only lapis lazuli production in the known world.

Buy the stone and retrace your path through the burning deserts and vengeful Muslims until you’re back in England or France or wherever. Still alive? That was the easy part. Now you need to go through a chemical extraction process that makes the Philosopher’s Stone look like freshman chem lab. “The lengthy process of pulverization, sifting, and washing to produce ultramarine makes the natural pigment … roughly ten times more expensive than the stone it came from.”

Finally you have ultramarine! How much? I can’t find good numbers, but Claude estimates that the ultramarine production of all of medieval Europe was around the order of 30 kg per year - not enough to paint a medium-sized wall. Ultramarine had to be saved for ultra-high-value applications.

In practice, the medievals converged on a single use case - painting the Virgin Mary’s coat.

By the beginning of the 18th century, Egyptian blue had been long forgotten, and painters in Europe relied on indigo, smalt, azurite, and when they could get their hands on it, ultramarine. But this was the modern, enlightened era of European science. Great things were to come.

It began with a chance discovery. In Berlin around 1706, a paintmaker, Johann Jacob Diesbach, was trying to prepare red dye from cochineal.4 The details of the story are not totally ascertained, but it seems that his intended mix of cochineal insects, ferric sulfate, and potash had been tainted by another substance, perhaps bone oil from the alchemist Johann Konrad Dippel. The result was a deep blue pigment, soon to be known as Prussian blue, since Berlin was the capital of Prussia. It immediately found its niche in the art market: a deep blue, like ultramarine, but which unlike ultramarine didn’t cost more than literal gold. Within a couple of years, painters were already depicting the Virgin Mary’s robes with Prussian blue.

Thus Prussian blue became the first modern synthetic pigment. It spread far and wide, even to isolationist Japan. Large quantities of Prussian blue began entering the country around 1829, through the single trading post the Japanese allowed with Westerners, at Dejima, and very soon after, revolutionized the woodblock printing art of ukiyo-e. As early as 1831, one of the most famous works in art history was created with abundant Prussian blue.

Prussian blue is also the blue of blueprints, created with the cyanotype process, one of the first ways to make many copies of a document. The blueprint was invented in 1842 by John Herschel and became the standard for engineering drawings; it was also used abundantly to duplicate photographs. Though it has become obsolete (replaced by whiteprint and then xerography, the currently dominant photocopying technique), it survives as the word to describe any technical, detailed plan.

Prussian blue was only the first of a series of synthetic blue pigments that span the history of industrial civilization. In 1789, cerulean appears, the creation of Albrecht Höpfner in Switzerland. It is another compound of cobalt, but combined with tin: a cobalt stannate (Co₂SnO₄). It would become available to artists in paint form only in the middle of the 19th century.

Around the same period, in 1799 or 1802 (sources differ), the French chemist Louis Jacques Thénard reinvented cobalt blue. It was a commission from another chemist, Jean-Antoine Chaptal, who happened to be a minister in the government of the First French Republic. Thénard investigated the pigments at the Sèvres porcelain factory, but used a different method than the originators of smalt pigments in Egypt, Iraq, or China, using aluminium (formula: CoAl₂O₄). By the middle of the 19th century, the leader in the production of cobalt aluminate was Blaafarveværket, a large industrial enterprise in Norway.

In this golden age of blue pigment synthesis, would it be possible to create even synthetic ultramarine? Goethe, already, had noticed the blue deposits on lime kilns when visiting Sicily in 1787. The locals used it for decoration as if it were lapis lazuli. The same phenomenon was observed in limeworks in France in the 1810s, and in 1824, the Société d’encouragement pour l’industrie nationale — a government organization dedicated to further French industry in response to the industrial revolution in Britain, and led by the aforementioned Jean-Antoine Chaptal — announced a prize of 6,000 francs to anyone who could make ultramarine for much cheaper than the price of lapis lazuli. In 1826, Jean-Baptiste Guimet succeeded in Lyon. He won the prize and established a thriving business, though he kept his methods secret and, as a result, forever has to share credit with Christian Gmelin in Tübingen, who did publish the process. It involves heating up clay, sodium hydroxide, and coal together.5

Artists and decorators now had their main blue pigments. Soon, industry and science would extend the use of blue to other domains. In 1897, it became practical to prepare artificial indigo in industrial quantities, eventually replacing all use of the plant. Today 80,000 tonnes are produced per year, mostly for the purpose of dying textiles and especially the denim of jeans.

Around the turn of the 20th century, artificial food colorings became widespread, derived primarily from coal tar. One of them would become known as brilliant blue FCF or Blue No. 1. Together with Blue No. 2, which is made from indigo, it is one of the two main blue colorings, and has a strong association with the familiar-yet-mysterious blue raspberry flavor.

The 1920s saw the introduction of another synthetic pigment, phthalo blue (also known as copper phthalocyanine), perhaps in a way harking back to the copper-derived compounds of ancient Egypt. It has grown to be most widely produced blue.

Though the discovery of new pigments is a rare occurrence, it still happens. In 2009, a serendipitous discovery at the Oregon State University led to YInMn Blue, so named because it contains yttrium, indium, and manganese, and pronounced “yinmin.” It is a near-perfect blue that furthermore avoids the toxicity and environmental problems of the pre-existing pigments.

I have this half-baked theory that science fiction is associated with blue because of blue LEDs. Consider this chart, which I wrote about in an old post:

There are a number of competing hypotheses for why science fiction movie directors and video game designers overwhelmingly choose blue as the color of fictional user interfaces. They include:

• Accidental reasons from filmmaking considerations (from Mark Coleran):

  • Using simple interfaces with primary RGB colors on black looks better in film than ordinary liquid-crystal screens, so most UIs in video media is either red, green, or blue

  • Red is associated with weapons, and green with vintage electronics (which commonly used green-phosphor monochrome monitors), leaving blue as the generic and/or futuristic choice

  • Blue is easier to color-correct: a lot of filmed material tends to look blue before color correction, but you don’t need this when the image is supposed to be blue

• Cultural associations:

  • Blue fits well with science fiction thanks to associations like coldness, knowledge, otherworldliness, and creative transcendence (found in some academic paper in Korean thanks to Elicit)

  • Something something near-far Robin Hanson something something6

  • Blue is rare in nature except the for sea and sky, so “there’s something fundamentally mystical, unnatural, and inhuman about it” (from Noessel, cited in ‘Future Screens are Mostly Blue’)

• Most science fiction creators simply copy the tropes of existing science fiction, so they choose blue because it already is the “science fiction color.” (And picking something else is likely to be interpreted as an intentional deviation for a specific purpose.)

I’d guess that the actual, immediate reason for most blue in science fiction is the last one. Unless creators make a conscious artistic choice to deviate, they tend to copy what’s typical and expected in their chosen genre. Yet those norms and expectations have to come from somewhere. It’s possible that the items in the first part of the list, about decisions having to do with the techniques of filmmaking rather than with the artistic meaning of blue, are the actual cause for some early shows that snowballed into the ubiquity of blue interfaces today, but that seems a bit like post-hoc justification to me unless we can find evidence of those decisions being made.

So it’s probably cultural associations, but most of them also just kick the can further. Overall I suppose I somewhat agree with Noessel: it may well come down to the difficulty in finding blue in nature. Or finding it in technology, considering the history of blue pigments that we just went over.

And not just pigments. There is another realm in which blue has proven incredibly difficult to produce: light. In fact we found the solution so recently that it is why, I speculate, the future is still strongly associated with blue.

The story of how we produce light is a fun one, spanning all of our technological history and involving dozens of solutions, from prehistoric oil lamps to candles to coal gas to cold-cathode tubes. But most of those solutions have produced light somewhere between white and the reddish yellow of flames or black-body radiation. If you wanted blue light, you could make a bulb out of blue glass (with cobalt!) and put an incandescent filament in it. This worked okay, but blue light bulbs did tend to be less satisfying than the other colors, or at least that’s what I remember from Christmas lights when I was a kid.7

There were other strategies for blue lights: construct a tube like the familiar red-glowing neon ones, and put mercury vapor in it. Low-brightness phosphors for RGB screens. Greenish-blue vacuum fluorescent displays. So, blue light was not an unsolved problem. But it wasn’t as conclusively solved as light in the other spectral colors.

In the 1960s, light-emitting diodes started becoming practical. LEDs are the most efficient way of creating light by far, but the properties of the materials they are made of — semiconductors that emit light when traversed with an electrical current — make it much easier to generate radiation in the less energetic part of the electromagnetic spectrum. Thus the first practical LED emitted infrared light, by Texas Instruments in 1962. Later that same year, the first visible-light LED was made at General Electric, in red. Displays made of red LEDs soon became widespread in electronic devices (replacing Nixie tubes, also reddish) after some further advances by Hewlett-Packard circa 1968.

Humanity then gradually conquered the rest of the visible light spectrum, with orange, yellow, and green LEDs being developed in the 1970s. But blue remained elusive. A practical, bright blue LED would not be made, despite much research being poured into it by electronics companies around the world, until a breakthrough by Shuji Nakamura in Japan in 1993.

This completed the color spectrum and enabled us to create white LEDs, which are now quickly replacing nearly every lighting technology since they cost so little and are so customizable. We can say we have essentially “solved” lighting. Blue LEDs also enabled the first blue lasers in the mid-1990s.

This is a very recent development! For a very long time, blue would have been the color that only “future tech” could create. Then, for a brief period, it would have been the color of cutting-edge tech. Now, 30 years later, the tech exists and is widespread, but we still have the memory of that time. And furthermore no other color can take its place as the inaccessible one; we’ve conquered the entire spectrum.8

Does the futuristic quality of blue really come from LEDs? Maybe, maybe not. I’m not sure there’s a direct causal link.

But given the full history of blue pigments, I wouldn’t be surprised to find some truth in this speculative scenario: that there were just enough innovations in blue, a steady trickle of serendipitous discoveries and long-term research efforts to produce better versions of it, to keep it in the mind of humans as the color of the artificial and the cutting-edge. If you wore indigo-dyed clothes in ancient India, you were one step more removed from nature than the person who wore plain cotton. If you used blue pottery in Egypt or Iraq or China, you were clearly cooler than the people who used plain terracotta. If you hired an engineer or architect at the peak of the Industrial Revolution in the late 19th century, they’d be way more efficient at their job if they duplicated their drawings with Prussian blue instead of copying them by hand. And today, if you want to make your city a herald of the high-tech future, you decorate everything with programmable blue LEDs. No other color would do.

This post was written as part of the Roots of Progress Institute’s Blog-Building Intensive, and I thank the fellows who provided feedback: Allison Lehman, Kelly Vedi.

Spring is finally coming to Canada.

It never shows up all at once, just a flicker at first. A slant of sunlight across the kitchen table, river water rustling beneath the winter ice. It’s like this every year. And yet the long winter always makes me doubt spring will come again.

In Greek myth, Demeter is the goddess of the harvest. When her daughter, Persephone, is taken to the underworld by Hades, Demeter’s grief halts the growth of all things. A deal is eventually struck: Because she ate a few pomegranate seeds in the underworld, Persephone must spend part of each year below but will be allowed to return to Demeter every spring. Even knowing this, Demeter mourns her absence as if the return were never guaranteed.

By the end of winter, I am Demeter in abeyance, disbelieving the cycle. As if the cold will stay forever; as if the underworld has won.

But today, I stand corrected. One warm day is all it takes. The soil softens. Possibility stirs.

Science can feel like that, too.

We work inside inherited frameworks, mistaking the scaffolding of our understanding for reality itself. And then, sometimes, a shift. A sliver of evidence cracks through the surface and what once felt certain begins to thaw.

In neuroscience and immunology, just such a crack appeared in 2007. Suddenly, some conditions doctors had long diagnosed as psychosis, personality changes, cognitive decline, or just psychiatric illness, appeared instead to be effects of the immune system attacking the brain.

What had scientists discovered? A specific kind of autoimmune attack. Patients were making antibodies that targeted the NMDA receptor—a critical junction for passing information between neurons (synaptic communication). The disorder was eventually dubbed anti-NMDA receptor encephalitis, and later, when other antibodies with other targets were discovered able to cause similar conditions, autoimmune encephalitis.

The discovery marked more than a new diagnosis: namely, it challenged a foundational belief shared by both neuroscience and immunology. For most of modern science, the brain was considered “immune privileged”—a kind of no-fly zone for immune cells. The idea was that these two systems, though essential, operated on opposite sides of a biological firewall (the blood-brain barrier).

But that firewall turns out more porous than we thought. In the past several decades, neuroscience and immunology have come to a growing recognition that the immune system doesn’t just defend the body from infections. It also shapes, regulates, and sometimes disrupts what we think of as mental life.

The Pre-History of Autoimmune Encephalitis

The identification of autoimmune encephalitis was the culmination of more than 40 years of mysterious clinical observations: patients with seizures, memory loss, and mood changes who were later found to have cancer. The illness first showed up in the medical literature in 1960, when clinicians described three adults with what they called “subacute encephalitis of later adult life mainly affecting the limbic areas.”

If we’re being honest, the whole diagnosis is basically medical hand-waving. “Subacute” just means “not super-fast, but not slow either,” “encephalitis” means “something’s inflamed in the brain,” and “limbic areas” refers to a fuzzy collection of brain bits involved in emotion and memory. In other words, something weird is happening in the emotional memory parts of the brain, kind of suddenly, and we have no idea why. Not exactly diagnostic precision, but it was a start.

Many patients with this condition deteriorated rapidly, and a significant number were only identified post-mortem, when pathologists would find limbic inflammation, neuronal loss, and immune cell infiltrates—often alongside “occult” (literally, hidden) tumours like small-cell lung cancer or testicular germ-cell tumours.

Eventually, this cluster of symptoms came to be known as paraneoplastic limbic encephalitis, a term that, again, sounds more precise than it is. It basically meant: there’s inflammation in the emotional-memory circuits of the brain, and oh look, the patient also had cancer.

The prevailing theory at the time was that certain tumours were triggering an immune response that accidentally spilled over into the nervous system. But the antibodies identified in these patients weren’t targeting the outside of cells, where they could interfere with function. Instead, they recognized intracellular proteins, buried deep inside neurons. And antibodies, as any immunologist will tell you, can’t easily get inside living cells.

That made the antibodies unlikely culprits. Before 2007, they were thought to be a consequence of the disease—evidence that the immune system had already done damage. According to this model, it was a broader inflammatory process, most likely involving cytotoxic T cells, that was attacking neurons first. When those neurons died, they spilled their internal contents into the surrounding tissue, including proteins the immune system had never seen before. Then antibodies were generated against those proteins.

In other words, the antibodies were downstream, not upstream. These antibodies weren’t early warning signs or causes; they were immunological debris of damage done.

The figure below illustrates this hypothesized model: a tumour triggers a general immune activation, leading to neuronal cell death, and only then do antibodies against intracellular proteins appear—not as instigators, but as clues left at the scene.

Treatment efforts were mostly ineffective. By the time doctors figured out what was going on, neurons were often already lost. And because symptoms frequently came before a cancer diagnosis, the connection wasn’t always obvious. Patients were often misdiagnosed with psychiatric illness, admitted to the wrong ward, and left to decline. You can imagine how many cases were missed.

So, for decades, neurologists were stuck with fragments: a mysterious syndrome that attacked personality and memory, an occasional tumour, a few tantalizing antibodies, and a vague sense that the immune system was somehow involved. But nothing that tied it all together.

Then, in 2007, a change in scientific technique changed the narrative.

Previously, researchers used fixed tissue slices or biochemical assays which were enriched for proteins inside of cells (intracellular proteins) to study the disease. But in 2007, researchers using rat brain sections and live cultured neurons—techniques that preserved the delicate structure of the cell surface—observed antibodies binding in real time to the outside of neurons, where they could actually interfere with synaptic function. So, these newly identified antibodies weren’t targeting proteins buried inside cells. They were on the surface, targeting a receptor that’s essential for how neurons communicate: the NMDA receptor, a key player in learning and memory.

And that changed everything. If you remember one of the basic rules for identifying causes in science—what epidemiologists call temporality—then you’ll see why this matters: a true cause has to come before the effect. An antibody that alters the properties of a neuron’s signalling channels is very likely to be the cause, not the consequence, of cognitive impairment.

These antibodies weren’t just markers anymore: they were agents of causality, the kind of smoking gun every translational scientist dreams of finding. They didn’t kill neurons outright; they disrupted synaptic function, impairing signalling.

For the first time, medicine had uncovered a psychiatric syndrome caused by a treatable autoimmune process. And here’s the extraordinary part: the damage was reversible. When doctors treated the immune response and, where applicable, removed the tumour, patients came back to their former selves.

Once the basics of autoimmune encephalitis were understood, interesting variations were identified. Many new cases—especially in children and young adults—had no tumours at all. The initial cancer link had cracked open the mystery, but the real story was broader: an autoimmune attack, often in people with personal or family histories of autoimmunity, sometimes following infection, that targeted the brain and produced dramatic changes in behaviour and cognition.

The discovery of autoimmune encephalitis landed like an epistemic shockwave, forcing the field to reckon with just how much it had overlooked.

But it was also something else, something rare and worth celebrating: a clear win for translational neuroscience. A field that’s more Mets than Yankees when it comes to its win–loss record—plenty of grit, a lot of heartbreak, and far more swings and misses than walk-off victories. But here, finally, was an extraordinary win. A baffling clinical syndrome with a patient population hiding in plain sight. Eventually, a molecular mechanism uncovered and a treatment strategy that worked. And best of all, patients who, if diagnosed early enough, recovered.

If you followed my previous series on Alzheimer’s disease, you’ll know how rare this kind of clarity is in neurology.

You’d like to think these revolutions in medical comprehension would spread instantly to specialists worldwide, but that rarely happens. In the case of autoimmune encephalitis, it took more than recognition and publication in academic papers—it took a compelling story. In 2012, journalist Susannah Cahalan published Brain on Fire, a first-person account of her descent into apparent psychosis that turned out to be autoimmune encephalitis. Her memoir exposed a hidden epidemic; at the time, only about 10 percent of cases were being correctly diagnosed. But it also exposed something else: the power of how we think about biological systems to inhibit our ability to understand them.

The Double-Edged Sword of Metaphor

For most of the 20th century, we described the immune system as a military force, defending the borders of the body against invasion. The brain was considered “immune privileged”—too precious for warfare, sealed off by evolution from immune influence. This metaphor wasn’t just poetic; it shaped what we looked for, what we measured, and what we could see.

But reality—as ever—resists neat conceptual packaging.

We now know the immune system isn’t just a warrior—it’s a curator, a gardener, a maintainer of order, and a mediator of meaning throughout the body, including the brain.

In this series, we’ll explore how the discovery of autoimmune encephalitis has pushed us to rethink not just the relationship between the brain and immune system, but how biological systems interact, how diseases emerge, and how our frameworks for understanding can both illuminate and obscure the truth. Throughout these discussions, we’ll return to the role of language and metaphor in science—how the words and models we choose shape what questions we ask, what experiments we design, how we interpret those results and ultimately what we can discover.

The military metaphors that dominated immunology for a century weren’t wrong, exactly. They were incomplete. They were useful except when they weren’t. They helped us understand one aspect of immune function while obscuring others. This pattern—of metaphors both revealing and concealing—runs through the history of science. We need models, categories, poetic association and even myths to make sense of complexity, but we also need the flexibility to step outside them, to remember that naming is not the same as knowing.

500 Million Years of Your Friendly and Fearsome Immune System

By the time you finish reading this sentence, your immune system will have surveilled, evaluated, and responded to millions of signals within your body. Even when you’re sitting listlessly on the sofa, mad at yourself for doing “nothing,” your immune system is busy monitoring trillions of microbes living in your gut, responding to countless particles in the air you breathe, and maintaining constant surveillance of every tissue in your body. This is both amazing and a matter of survival.

But the very sophistication that keeps us alive can turn against us, and for a growing number of people, this remarkable system becomes a source of danger. Autoimmune disorders now affect nearly 17 million people in North America, or roughly 1-in-20. Different autoimmune conditions tend to emerge at different times in life: Type 1 diabetes in childhood, lupus and multiple sclerosis in early adulthood, and rheumatoid arthritis in middle age. And now we’re discovering that conditions once thought entirely psychiatric or neurological—certain cases of psychosis, memory loss, personality changes, seizures, and dementia—can be caused by the immune system attacking the brain.

What other conditions might we still fail to recognize as immune-related? Are autoimmune disorders in fact separate diseases, or do they cluster in different manifestations of broader patterns we’re only beginning to recognize? These questions are fundamental to a better understanding of why autoimmunity occurs, and more importantly, how to prevent and treat it. To get closer to that understanding, we need to look at how our immune system evolved.

Take It Back to When Life Got Complicated

The story begins with an ancient accident that gave vertebrates their most powerful defense system. It’s a story that helps explain both why our immune system is remarkably effective and why it sometimes turns against us. More crucially, it’s a story that matters to anyone who might develop an autoimmune condition—which, given current trends, could be any of us (except me, because I already have a few).

Somewhere in the ancient oceans of the Cambrian era, before dinosaurs, before mammals, when trilobites were finding their legs and vertebrates were developing backbones, something extraordinary happened inside a primitive vertebrate. A piece of rogue DNA, a transposable element (think: molecular hitchhiker with a crowbar), inserted itself into the genome near genes involved in recognizing pathogens.

Transposable elements (transposons) are selfish bits of DNA—genomic drifters that copy and paste themselves across genomes and species. (Richard Dawkins popularized the idea of “selfish genes” in the 1970s; Francis Crick and Leslie Orgel later extended it to DNA itself.) These sequences don’t prioritize serving the organism that hosts them. They serve themselves, hitching rides through evolutionary time by being exceptionally good at getting duplicated.

Where do they come from? Most likely from viruses or ancient bacteria, early genomic parasites that learned how to hack into host DNA and stay there. We don’t know exactly who left the calling card that became our essential immune gene; we only know what they left behind: a pair of enzymes capable of cutting and rearranging DNA with extraordinary precision.

What of consequence do they do? Usually, nothing. They insert, and that’s it. They’re eventually silenced, deleted, or eroded by time. But this one stuck and it rewired the immune system. This was the accident that would make complex vertebrate life possible.

This single insertion became the cornerstone of adaptive immunity—the sophisticated defense system that allows vertebrates to survive childhood, heal from injuries, fight off infections, and live long enough to develop complex social bonds and sophisticated brains. Without it, there would be no long-lived vertebrates, no social mammals, no human culture. A chance genetic event became the gateway to biological complexity.

That bit of DNA became what we now call RAG—short for Recombination Activating Gene. What it activated was nothing short of the immunological big bang.

Why the Cambrian? Multicellular life was inventing itself in real time: specialized tissues, nervous systems, body plans with appendages, eyes, guts. With this explosion of biological novelty came new ecological pressures: predators, pathogens, and symbiotic microbes. Life was no longer a solo act; it became a crowded stage and a simple, hardcoded immune system couldn’t keep up.

To survive in this newly competitive world, organisms needed a flexible immune strategy, one that could recognize unfamiliar threats, adapt to evolving pathogens, and remember past encounters. That was the niche waiting for, even desperate for, adaptive immunity. And the RAG insertion was the catalyst.

How a DNA Hitchhiker Changed Everything

We’ve already described the origin moment: a mobile genetic element—a transposon—inserted itself into the genome of a primitive vertebrate. Most of these genetic stowaways aren’t useful. They barge in, disrupt important genes, and get kicked out or silenced. But this one was different. It brought tools.

It carried genes that encoded enzymes with an unusual skill: they could cut and paste DNA with remarkable precision. Think: molecular scissors with a built-in ruler. This might sound more dangerous than useful, and it could have been, but this time the host survived and evolution took notice.

Instead of evolution wiping out the intruder, organisms with the transposon survived better. Over millions of years, the host genome began to shape it and mutations softened its sharp edges. Regulatory sequences evolved to control where and when the scissors did their cutting. The parasite was slowly domesticated, tamed, and repurposed.

Eventually, it rewired the architecture of immune recognition in vertebrates forever.

Specifically, that insertion enabled a process that lets immune cells shuffle gene segments to create a vast library of unique receptors—each one capable of recognizing a different molecular shape, like a virus or infected cell. These receptors are the key to immune detection, and in the case of B cells, they form the blueprint for antibodies.

Two-Minute Immunology Primer

Let’s take a moment to dust off your pandemic-era immunology knowledge.

Remember antibodies? Those little Y-shaped proteins everyone suddenly had strong opinions about in 2020? They’re not just floating around aimlessly—they’re made by specialized immune cells called B cells, and their job is to recognize, bind to, and help neutralize foreign invaders like viruses and bacteria.

B cells have cousins called T cells, which also play a starring role in your immune defense—responding to infected cells, coordinating responses, and helping make sure the right cells are activated at the right time.

Together, B and T cells belong to a group called lymphocytes, and they have a superpower that makes them unlike any other cells in your body: each one carries a unique molecular sensor—a receptor on its surface that can recognize a specific pathogen or infected cell. In the case of B cells, that sensor does double duty: it helps the cell recognize a threat and, once activated, serves as the blueprint for the antibodies the cell will secrete. These antibodies flood the bloodstream and tissues, each one acting like a lock that’s shaped to catch a specific molecular key—usually a fragment of a virus, bacterium, or other invader.

Their job? To tag invaders for destruction, block their function, or deliver them into the jaws of other immune cells. In short: tag, neutralize, and bag.

Here’s the kicker: your B and T cells can make trillions of different-shaped receptors. Orders of magnitude more than the number of genes you have.

How do they pull this off? That’s where RAG comes in.

Inside every developing B or T cell, long before it ever sees a virus or a vaccine, the RAG proteins go to work. They act like molecular editors, cutting and rearranging small chunks of DNA—modular gene segments called V(D)Js:

• V for variable

• D for diversity

• J for joining

These segments are like genetic Lego bricks. RAG picks one of each, snips them out and glues them together in all sorts of ways, and—voilà—a brand-new, one-of-a-kind receptor blueprint is born. This process is called V(D)J recombination, and it’s the reason your immune system can recognize almost anything, including pathogens it’s never seen before.

This is creativity by design, but also by accident. The DNA gets stitched back together by a kind of cellular duct tape (your DNA repair machinery), which introduces even more variation. Every cell ends up with a slightly different combination.

Most of these receptors won’t bind to anything useful. Some bind to intruders that can harm you. And some might bind to you yourself—to self-molecules. Which brings us to the next critical step in immune development: selection.

Developing B and T cells go through a training program—boot camp in the bone marrow or thymus—where the ones that react strongly to your own tissues are typically destroyed. This process is called central tolerance, and it’s the immune system’s first safeguard against self-attack.

And to think—it all traces back to a molecular accident in the Cambrian Era. A rogue piece of DNA jumped into a genome and, instead of wrecking the place, rewrote the rules of immune recognition. Five hundred million years later, your B cells are still running drills based on that ancient insertion event—shuffling your immune genome and adapting to molecular targets.

So far, so good.

But now comes the question that hangs over all of this: If the immune system can generate trillions of different receptors, how does it avoid turning on its own tissues, to autoimmunity?

The short answer is: it doesn’t. Not always.

The Classical View: How Autoimmunity Slips Through the Guardrails

The very mechanisms that give rise to incredible receptor diversity also open the door to autoimmunity. Some self-reactive cells slip through training. Others mutate later in life. Some stay quiet for years until an environmental trigger wakes them up. Occasionally, the immune system gets confused—mistaking part of your body for an invading pathogen.

This is how researchers believe autoimmune diseases like lupus, rheumatoid arthritis, and autoimmune encephalitis arise.

But it’s not the only possibility. And as we’ll explore later, this entire explanation rests on a framework—a powerful framework that is often predictive, but not a complete one.

Let’s take a moment to introduce the classical model of how autoimmunity arises. This is the theory that underpins nearly every clinical trial, therapeutic strategy, and standard of care today.

It’s a model I know personally. I’ve lived inside its logic. And thanks to therapies based on it, I’m—after adding coffee—a functional human being.

According to the classical model, your immune system is built to distinguish self from non-self. Most self-reactive cells are eliminated during development (this is central tolerance). For those that escape, there’s a backup system—peripheral tolerance—which includes regulatory T cells, immune checkpoints, and protective barriers around sensitive organs like the brain.

Autoimmunity, in this model, arises when:

• A self-reactive B or T cell slips through the central and peripheral checkpoints;

• It encounters an environmental trigger—like an infection or tissue damage—that activates it; and

• It begins to expand and form a clone that mistakenly attacks the body.

This classical model is also known as clonal selection theory, and for over 50 years, it has dominated our understanding of autoimmune disease. It’s largely internally consistent and helps explain why many patients respond to therapies like B cell depletion, T cell suppression, or cytokine blockade.

Like many people with an autoimmune diagnosis, I’ve benefited profoundly from immunosuppressive treatments that emerged from this framework. They’ve pulled me back from the edge more than once.

And yet… the closer you look, the more cracks begin to show in the clonal selection theory of autoimmunity. We have some puzzles that we’ll encounter throughout this series that prompt the following questions:

• Why do some healthy people have autoantibodies without any symptoms?

• Why do some autoimmune diseases wax and wane?

• Why are some diseases so targeted (just one organ) while others affect the whole body?

• And why do treatments based on this model sometimes just fail?

These are signs that something deeper is going on—that this classical model, while useful, is incomplete. And that’s where we’re headed in this series. I cannot promise complete answers, but we can journey through the literature together.

Importantly, the classical model didn’t stop us from discovering autoimmune encephalitis, which is the great victory we will be dissecting in the next post. It didn’t stop us from saving lives.

But it has stopped us from connecting what we’ve learned into a broader, unifying theory—one that might not just treat autoimmune disease but transform how we harness the immune system to fight a much wider range of disorders.

In upcoming posts, we’ll examine the cracks in the clonal selection framework, explore ideas like molecular mimicry, degeneracy, pleiotropy, randomness, and the idea that self-recognition isn’t a static boundary, but a dynamic negotiation between immune cells, tissues, and environment.

Stay tuned—and when the day feels heavy, consider this: you are the living legacy of a story that began 500 million years ago with a wandering transposon and a lucky break. It might just add a little bounce to your step.

As we all know, the march of technological progress is best summarized by this meme from Linkedin:

Inventors constantly come up with exciting new inventions, each of them with the potential to change everything forever. But only a fraction of these ever establish themselves as a persistent part of civilization, and the rest vanish from collective consciousness. Before shutting down forever, though, the alternate branches of the tech tree leave some faint traces behind: over-optimistic sci-fi stories, outdated educational cartoons, and, sometimes, some obscure accessories that briefly made it to mass production before being quietly discontinued.

The classical example of an abandoned timeline is the Glorious Atomic Future, as described in the 1957 Disney cartoon Our Friend the Atom. A scientist with a suspiciously German accent explains all the wonderful things nuclear power will bring to our lives:

Sadly, the glorious atomic future somewhat failed to materialize, and, by the early 1960s, the project to rip a second Panama canal by detonating a necklace of nuclear bombs was canceled, because we are ruled by bureaucrats who hate fun and efficiency.

While the Our-Friend-the-Atom timeline remains out of reach from most hobbyists, not all alternate timelines are permanently closed to exploration. There are other timelines that you can explore from the comfort of your home, just by buying a few second-hand items off eBay.

I recently spent a few months in one of these abandoned timelines: the one where the microwave oven replaced the stove.

First, I had to get myself a copy of the world’s saddest book.

Microwave Cooking, for One

Marie T. Smith’s Microwave Cooking for One is an old forgotten book of microwave recipes from the 1980s. In the mid-2010s, it garnered the momentary attention of the Internet as “the world’s saddest cookbook”:

To the modern eye, it seems obvious that microwave cooking can only be about reheating ready-made frozen food. It’s about staring blankly at the buzzing white box, waiting for the four dreadful beeps that give you permission to eat. It’s about consuming lukewarm processed slop on a rickety formica table, with only the crackling of a flickering neon light piercing through the silence.

But this is completely misinterpreting Microwave Cooking for One’s vision. Two important pieces of context are missing. First – the book was published in 1985. Compare to the adoption S-curve of the microwave oven:

When MCfO was published, microwave cooking was still a new entrant to the world of household electronics. Market researchers were speculating about how the food and packaging industries would adapt their products to the new era and how deep the transformation would go. Many saw the microwave revolution as a material necessity: women were massively entering the workforce, and soon nobody would have much time to spend behind a stove. In 1985, the microwave future looked inevitable.

Second – Marie T. Smith is a microwave maximalist. She spent ten years putting every comestible object in the microwave to see what happens. Look at the items on the book cover – some are obviously impossible to prepare with a microwave, right? Well, that’s where you’re wrong. Marie T. Smith figured out a way to prepare absolutely everything. If you are a disciple of her philosophy, you shouldn’t even own a stove. Smith herself hasn’t owned one since the early 1970s. As she explains in the cookbook’s introduction, Smith believed the microwave would ultimately replace stove-top cooking, the same way stove-top cooking had replaced campfire-top cooking.

So, my goal is twofold: first, I want to know if there’s any merit to all of these forgotten microwaving techniques. Something that can make plasma out of grapes, set your house on fire and bring frozen hamsters back to life cannot be fundamentally bad. But also, I want to get a glimpse of what the world looks like in the uchronia where Marie T. Smith won and Big Teflon lost. Why did we drift apart from this timeline?

Out of the frying pan, into the magnetron

Before we start experimenting, it’s helpful to have a coarse intuition of how microwave ovens work. Microwaves use a device called a magnetron to emit radiation with wavelengths around 5-10 cm, and send it to bounce around the closed chamber where you put your food. The idea that electromagnetic radiation can heat stuff up isn’t particularly strange (we’ve all been exposed to the sun), but microwaves do it in an odd spooky way. Microwaves’ frequency is too low to be absorbed directly by food molecules. Instead, it is just low enough that, in effect, the electric field around the molecules regularly changes direction. If the molecules have a dipole moment (as water does), they start wiggling around, and the friction generates plenty of heat.

As far as I can tell, this kind of light-matter interaction doesn’t occur to a noticeable degree anywhere on Earth, except in our microwave ovens. This is going to be important later: the microwave is weird, and it often behaves contrary to our day-to-day intuitions. (For example, it’s surprisingly hard to melt ice cubes in the microwave. This is because the water molecules are locked in a lattice, so they can’t spin as much as they would in a liquid.) Thus, to tame the microwave, the first thing we’ll need is an open mind.

With that in mind, let’s open the grimoire of Microwave Cooking for One and see what kind of blood magic we can conjure from it.

The book cover, with its smiling middle-aged woman and its abundance of provisions, makes it look like it’s going to be nice and wholesome.

It’s not going to be nice and wholesome.

Microwave cooking is not about intuition. It’s about discipline. The timing and the wattage matter, but so do the exact shape and size of the vessels. Smith gives us a list of specific hardware with exceedingly modern names like the Cook’n’Pour® Saucepan or the CorningWare™ Menu-ette® so we can get reproducible results. If you were used to counting carrots in carrot units, that has to stop – carrots are measured in ounces, with a scale, and for volume you use a metal measuring cup. Glass ones are simply too inaccurate for where we are going.

The actual recipe section starts with the recipe for a bowl of cereal, which I am 70% sure is a joke:

Whenever a cooking time is specified, Smith includes “(____)” as a placeholder, so you can write in your own value, optimized for your particular setup. If your hot cereal is anything short of delicious, you are invited to do your own step of gradient descent.

A lot of recipes in the book involve stacking various objects under, above, and around the food. For vegetables, Smith generally recommends slicing them thinly, putting them between a cardboard plate and towel paper, then microwaving the ensemble. This works great. I tried it with onion and carrots, and it does make nice crispy vegetables, similar to what you get when you steam the vegetables in a rice cooker (also a great technique). I’d still say the rice cooker gives better results, but for situations where you absolutely need your carrots done in under two minutes, the microwave method is hard to beat.

But cardboard contraptions, on their own, can only take us this far. They do little to overcome the true frontier for microwave-only cooking: the Maillard Reaction. Around 150°C, amino acids and sugars combine to form dark-colored tasty compounds, also known as browning. For a good browning, you must rapidly reach temperatures well above the boiling point of water. This is particularly difficult to do in a microwave – which is why people tend to use the microwave specifically for things that don’t require the Maillard reaction.

But this is because people are weak. True radicals, like Marie T. Smith and myself, are able to obtain a perfectly fine Maillard reaction in their microwave ovens. All you need is the right cookware. Are you ready to use the full extent of microwave capabilities?

Tradwife futurism

In 1938, chemists from DuPont were trying to create a revolutionary refrigerant, when they accidentally synthesized a new compound they called teflon. It took until the early 1950s for the wife of a random engineer to suggest that teflon could be used to coat frying pans, and it worked. This led to the development of the teflon-coated frying pan.

In parallel, in 1953, chemists from Corning were trying to create photosensitive glass that could be etched using UV light, when they accidentally synthesized a new compound they called pyroceram. Pyroceram is almost unbreakable, extremely resistant to heat shocks, and remarkably non-sticky. Most importantly, the bottom can be coated with tin oxide, which enables it to absorb microwave radiation and become arbitrarily hot. This led to the development of the microwave browning skillet.

In the stove-top timeline where we live, the teflon-coated pan has become ubiquitous. But in the alternate microwave timeline, nobody has heard of teflon pans, and everybody owns a pyroceram browning skillet instead.

I know most of you are meta-contrarian edgelords, but nothing today will smash your Overton window harder than the 1986 cooking TV show Good Days, where Marie T. Smith is seen microwaving a complete cheeseburger on live TV using such a skillet.

I acquired mine second-hand from eBay and it quickly became one of my favorite objects. I could only describe its aesthetics as tradwife futurism. The overall design and cute colonial house drawings give it clear 1980s grandma vibes, but the three standoffs and metal-coated bottom give it a strange futuristic quality. It truly feels like an object from another timeline.

The key trick is to put the empty skillet alone in the microwave and let it accumulate as much heat as you desire1 before adding the food. Then, supposedly, you can get any degree of searing you like by following the right sequence of bleeps and bloops.

According to Marie Smith, this is superior to traditional stove-top cooking in many ways – it’s faster, consumes less energy, and requires less effort to clean the dishes. Let’s try a few basic recipes to see how well it works.

You’ll microwave steak and pasta, and you’ll be happy

Let’s start with something maximally outrageous: the microwaved steak with onions. I’d typically use olive oil, but the first step in Smith’s recipe is to rub the steak in butter, making this recipe a heresy for at least three groups of people.

The onions are cooked with the veggie cooking method again, and the steak is done with a masterful use of the browning skillet.

I split the meat in two halves, so I could directly compare the orthodox and heretical methods.2 The results were very promising. It takes a little bit of practice to get things exactly right, but not much more than the traditional method. The Pyroceram pan was about as easy to clean as the Teflon one. I didn’t measure the energy cost, but the microwave would probably win on that front. So far, the alternate timeline holds up quite well.

As a second eval, I tried sunny-side up eggs. On the face of it, it’s the simplest possible recipe, but it’s surprisingly hard to master. The problem is that different parts of the egg have different optimal cooking temperatures. Adam Ragusea has a video showcasing half a dozen techniques, none of which feature a microwave.

What does Marie Smith have to say about this? She employs a multi-step method. Like with the steak, we start by preheating the browning skillet. Then, we quickly coat it with butter, which should instantly start to boil. This is when we add the egg, sprinkle it lightly with water, and put it back in the oven for 45 (___) seconds. (Why the water sprinkling? Smith doesn’t explain. Maybe it’s meant to ensure the egg receives heat from all directions?)

Here again, I was pleased with the result – I’d go as far as saying it works better than the pan. With that success, I went on to try the next step of difficulty: poached eggs.

Poached eggs are my secret internal benchmark. Never in my life have I managed to make proper poached eggs, despite trying every weird trick and lifehack I came across. Will MCfO break my streak of bad luck?

Like for veggies, the egg is poached in the middle of an assemblage of multiple imbricated containers filled with specific amounts of water and pre-heated in a multi-step procedure. We are also told that the egg yolk must be punctured with a fork before cooking. (What happens if you don’t? The book doesn’t say, and I would rather not know.)

The recipe calls for 1 minute and 10 seconds of cooking at full power. Around the 1 minute and 5 seconds mark, my egg violently exploded, sending the various vessels to bounce around the walls of the oven. And listen, as I said, I came to this book with an open mind, but I expect a cookbook to give you at least enough information to avoid a literal explosion. So I wrote “LESS” in the “(____)” and never tried this recipe again.

The rest of the book is mostly made of variations of these basic methods. Some recipes sound like they would plausibly work, but were not interesting enough for me to try (for example, the pasta recipes primarily involve boiling water in the microwave and cooking pasta in it).

All in all, I think I believe most of the claims Smith makes about the microwave. Would it be possible to survive in a bunker with just a laptop, a microwave and a Cook’n’Pour SaucePan®? I think so. It probably saves energy, it definitely saves time washing the dishes, and getting a perfect browning is entirely within reach. There were failures, and many recipes would require a few rounds of practice before getting everything right, but the same is true for stove-top cooking.

On the other hand, there’s a reason the book is called Microwave Cooking for One and not Microwave Cooking for a Large, Loving Family. It’s not just because it is targeted at lonely losers. It’s because microwave cooking becomes exponentially more complicated as you increase the number of guests. I am not saying that the microwave technology in itself cannot be scaled up – if you really want to, it can:

But these industrial giant microwaves are processing a steady stream of regular, standard-sized pieces of food. Home cooking is different. Each potato comes in a different size and shape. So, while baking one potato according to MCfO’s guidance is easy and works wonderfully, things quickly get out of hand when you try baking multiple potatoes at the same time. Here is the sad truth: baking potatoes in the microwave is an NP-hard problem. For a general-purpose home-cooking technology, that’s a serious setback.

The weird thing is, the microwave maximalists of the 1980s got the sociology mostly right. People are preparing meals for themselves for longer and longer stretches of their lives. Women are indeed spending less time in the kitchen. The future where people cook For One – the one that was supposed to make the microwave timeline inevitable, arrived exactly as planned. And yet, the microwave stayed a lowly reheating device. Something else must be going on. Maybe the real forking path happened at the level of vibes?

Microvibes

To start with the obvious, the microwave has always been spooky, scary tech. Microwave heating was discovered by accident in 1945 by an engineer while he was developing new radar technologies for the US military. These are the worst possible circumstances to discover some new cooking tech – microwave manufacturers had to persuade normal civilians, who just watched Hiroshima on live TV, to irradiate their food with invisible electromagnetic waves coming from an object called “the magnetron”. Add that to the generally weird and counterintuitive behavior of food in the microwave, and it’s not surprising that people treated the device with suspicion.

Second, microwave cooking fell victim to the same curse that threatens every new easy-to-use technology: it became low-status tech. In Inadequate Equilibria, Eliezer makes a similar point about velcro: the earliest adopters of velcro were toddlers and the elderly – the people who had the most trouble tying their shoes. So Velcro became unforgivably unfashionable. I think a similar process happened with microwaves. While microwave ovens can cook pretty much any meal to any degree of sophistication, the place where they truly excel is reheating shitty canned meals, and soon the two became inseparable in the collective mind, preventing microwaves from reaching their full potential for more elaborate cuisine.

Third, compared to frying things in a pan, microwave cooking is just fundamentally less fun. I actually enjoy seeing my food transform into something visibly delicious before my eyes. But microwave cooking, even when done perfectly right, gives you none of that. You can still hear the noises, but not knowing what produced them makes them significantly more ominous. Some advanced recipes in MCoF call for 8 minutes at full power, and 8 minutes feel like a lot of time when you are helplessly listening to the monstrous anger of the oil, the stuttering onions’ rapid rattle, and the shrill, demented choirs of wailing pork ribs.

With all that said, I do think Microwave Cooking for One is an admirable cookbook. The recipes are probably not the finest cuisine, but they’ll expand your cooking possibilities more than any other recipe book.3 What I find uniquely cool about Marie T. Smith is that she started with no credentials or qualifications: she was a random housewife who simply fell in love with a new piece of technology, spent a decade pushing it to its limits, and published her findings as a cookbook. Just a woman and a magnetron. You can just explore your own branch of the tech tree!

Let’s not oversell it – if your reference class is “tech visionaries”, maybe that’s taking it a bit too far. If your reference class is “Middle-aged Americans from the eighties who claim they can expand your horizons using waves”, then Marie T. Smith is easily top percentile.

Is our way of representing the laws of nature actually the truth, or are physical laws better understood as one possible set of approximate compressions we happened to stumble upon? I would argue for the latter. I would also argue that we may be at a unique moment in history. A point in time where it is becoming possible, for the first time, to systematically explore many alternative cohesive systems for formulating natural laws. But let’s start with the first point.

Consider this: the human brain is a strange substrate on which to conduct science.

1. In raw capacity to perform operations, our brain is comparable to a supercomputer.

2. Our working memory is quite mediocre. We can hold references to highly complex concepts in mind (like World War 2), but we can never consciously attend to more than a tiny fraction of the complete whole at once.

3. Our capacity for arithmetic is comically poor relative to our overall brain capacity. At one point, a few hundred years ago, the ability to perform calculations that a birthday card could now handle was used as a proxy for human intelligence. How good are you at long division?

Clearly the first trait is beneficial for conducting science. Clearly the second and third are not. For the most part, scientific progress only accelerated once we could externalize working memory and computation. Writing was one early mechanism for this. What can be achieved through writing emulates what could be achieved without it, if our memory and compute were far better. Modern computers and machine learning represent our latest steps in this externalization.

I would argue that the way we describe and handle natural laws is inevitably shaped by both our cognitive strengths and weaknesses. The purpose of natural laws is to enable prediction. We find ways to isolate and compress certain aspects of reality into forms that are tractable for us to reason about. And most often, we are specifically interested in performing concrete predictive computations.

If we view physics as a search for optimal data compression, we must ask: optimal for whom? Clearly the same underlying truth can be expressed in different ways. In the US, fuel economy is tracked in miles per gallon. In Europe, we use litres per 100 km. Neither is inherently worse, but each guides intuition in subtly different directions. The same is true of Newton versus Einstein.

Any good compression has a scope — a domain of validity — but also a certain ease of computability. Every measurement contains uncertainty; every prediction carries acceptable error. Rather than viewing physical laws as discoveries of metaphysical truth, I propose we treat them as solutions to an optimization problem.

Given experimental data, find a set of representations that:

• Minimises prediction error E

• Minimises description length L

• Minimises computational cost C

• Maximises scope S

We can, and will, have many overlapping representations. Some might minimize computational cost; others might maximize scope. With infinite compute and time, we could keep track of individual particles, and emergent properties like temperature would just introduce errors. This, however, would be utterly infeasible in reality.

Physics then becomes a search over a Pareto surface defined by simplicity, accuracy, and generality, with many valid and overlapping representations.

• Newton sits on one region of that surface: tiny E, moderate C, large S.

• Einstein occupies another: minimal E, large C, very large S.

• Quantum mechanics yet another: minimal E, very large C, very large S.

But why would these be unique optima? They are optima that happened to be accessible to humans, under human constraints. They also followed from prior definitions and compressions, dependent on the haphazard path our scientific history took. This is why I suspect that an unconstrained optimizer might settle on entirely different primitives. Newton and Einstein did not find truth in a vacuum. They located regions on the Pareto surface that were reachable given what had come before, shaped by our sensory priors, our evolutionarily crafted intuitions, and our computational limitations.

The Problem of Time

There is a deep suspicion in physics that one of our most fundamental variables may be something of an illusion: time.

As humans, we experience time as a sequence of distinct, fleeting moments. This is consistent with our weak working memory. We may be forced to process events sequentially simply because we lack the buffer to handle them globally. We stitch snapshots together and call it flow. This is likely also tied to the timescales at which human intervention needs to happen.

Now consider a tree. If a tree possessed intelligence, it would almost certainly treat time differently from us. Our concept of individual snapshots would not be the most evolutionarily advantageous architecture for a rooted organism responding to slow, sweeping arcs of environmental change. The tree might perceive time as large, mean gradients held in a vast working memory, spanning years. What tangible statistically significant changes emerging over months and years ought the tree to optimize for? This is obviously speculative.

What is not speculative is the kind of artificial intelligences we have begun creating — Large Language Models in particular. A LLM possesses a context window vastly larger than human working memory, and no concept of time that has been shaped by physical survival. A LLM might not represent a novel — or a physical process — as a sequence of words to be read one by one. Instead, it can hold the entire narrative arc as a single, simultaneous, richly structured representation.

To a mind with a massive context window, the concept of time as a linear progression of snapshots might seem terribly inefficient, a lossy compression. Rather than t as a variable that ticks forward, such a mind might compress reality into arcs or events-as-whole-objects, perceiving the full trajectory of a ball as a single static shape rather than a moving point. For most humans, a parabola plotted on a graph is an abstraction. Not so for a mind that naturally thinks in whole trajectories.

Would the laws we have formulated not be influenced by our particular vantage on time? I find it unlikely that they would be immune to it. Our intuition for time is also deeply non-relativistic. We evolved at slow speeds in a weak gravitational field. A mind evolved for survival in a regime where special relativity is decisive, perhaps navigating near a black hole or moving at a significant fraction of the speed of light, would have a radically different intuitive prior for time. Concepts like simultaneity, as we currently conceive of them, would be almost meaningless.

We can speculate on what those alternative architectures might look like:

• The Deep-Time Mind: a mind working at longer timescales with a vastly larger working memory, which views events as four-dimensional structures in spacetime rather than a sequence of nows. Just as we could view an apple as a thousand microscopic slices, but have no reason to, such a mind might have no reason to slice time at all.

• The Relativistic Mind: a mind fine-tuned for survival in high-velocity environments, for whom space and time are intuitively stretchy, codependent substrates, a lived experience rather than a complex mathematical derivation.

None of these minds would likely settle on the same priors for t that we have.

The question then becomes almost inevitable: if we fed a sufficiently advanced machine learning algorithm a large amount of physical data and tasked it with finding patterns and compressions — redundancies in reality — what would it find? How many overlapping, yet distinct and valid, compressions would we find, if we ran and re-ran the experiment?

The Contingency of Our Representations

There are many examples where we already know that different representations overlap. Newton’s laws and relativity cover much of the same ground. Within a certain domain and accuracy threshold, they say the same thing — but one is far more computationally efficient. There is a reason we have not shelved Newton since discovering Einstein, even though the scope overlap is 100 %.

Lagrangian and Newtonian mechanics are largely equivalent in scope, yet in practice one can be computationally superior in certain contexts while the other dominates elsewhere.

There is also one more human constraint worth noting here: high-fidelity long-term memory and retrieval. If this were orders of magnitude better in humans, perhaps we would not only seek to generalise our laws, but also to fragment them into a myriad of highly specialised compressions — limited in scope, but computationally optimal within their domain of validity. Allow me to direct your attention to Quake III Arena.

The Quake Constant Problem

There was a time when compute was a severe bottleneck in computer games. Any kind of motion through a 3D representation of space depends heavily on square roots and inverse square roots. We know how to compute these quite exactly, but what if there is not enough compute available to do so in a timely manner, as it was when Quake III was being developed? The necessary solution was to find a computationally optimal compression that worked well enough for the game’s required scope and accuracy:

The fast inverse square root constant in Quake III (0x5f3759df).

A human physicist might call it numerology. Ugly. Ad hoc. Not a law of nature. But within its narrow band of validity, it is exactly that. Under strict compute constraints, it may even be locally optimal.

How many such local optima exist in physics? Ugly, hyper-local compressions that look meaningless to us but are computationally superior within bounded regimes? Human taste filters these out (they seem devoid of deeper meaning) but perhaps that reflects only our inability to simultaneously manage generalisations and local optimisations in a structured way. An optimizer without aesthetic prejudice would not discard these valid and useful compressions.

And how many Newtonian, Lagrangian, or Einsteinian mechanics might there be — slightly different in scope, accuracy, and efficiency — that a system starting without priors could discover? How many broad scope unifications, necessary in the most complex, integrated cases? Finding different optimal compressions on a Pareto surface might give us some insight.

True Mathematics and Representational Contingency

This ultimately links to a deeper suspicion I have about mathematics: I doubt integers are fundamental.

They feel fundamental because we are organisms that individuate. I am one, you are two. We deal with singular objects in a world of discrete things. But according to one of our best theories, quantum mechanics, this discreteness may be illusory. Position, velocity, time are all indefinable in the way we commonly think about them. Reality is smeared out, without sharp borders. At the fundamental level, discreteness dissolves into wavefunctions; identity dissolves into process.

In this regard, intervals seem to be more fundamental than counts. And for many processes, tracking logarithms is more natural than tracking counts. This is true for our perception of sound and light, to name a couple of examples. Might there exist cognitive architectures for which logarithms are primitive and addition is derived? No five fingers acting as the starting point, so to speak. The foundation of our mathematics is initially rooted in whole numbers and Euclidean geometry.

I highly doubt this is the only valid starting point. And perhaps there are even useful compressions that cannot be expressed or proven within our system? Gödel tells us that no sufficiently rich formal system is complete. Maybe any such system tends to emerge through specific modes of interaction with reality? The “unreasonable effectiveness of mathematics” in describing physical reality has long puzzled scientists and philosophers. But perhaps this is not such a mystery. Human mathematics may have evolved precisely to compress certain physical observations.

Different axioms and starting points might be equivalent. There might be axioms seemingly unrelated to Euclidean geometry that, by necessity, lead to Euclid’s axioms being true, and vice versa. Yet no system can probably contain the full truth. Or, as Plato might have put it: perhaps each mathematical system is but one shadow cast by a deeper mathematical reality, if such a thing even exists.

Regardless: For two beings occupying the same underlying reality, what is deducible within their respective mathematical systems would likely overlap substantially, but not completely. Not if their modes of interaction with reality differ enough. Einsteinian spacetime, for instance, necessitates moving beyond Euclidean geometry. In real spacetime, parallel lines can cross.

Once again, I find myself wondering: what would a machine learning system find, if it searched the space of possible compressions? To name one tantalising example: calculus discretises the continuous through limits and infinitesimals, a move that feels natural to us. But what might calculus look like for a mind that experiences continuity as primitive? A mind finding whole numbers to be alien and impossibly exact edge-cases.

Instead of rediscovering F=ma, the AI might express mechanics through continuous deformation fields in phase space. In this alien physics, our concepts of ‘force’ and ‘mass’ might never explicitly appear. Yet, the predictions would match our experiments perfectly. It would be a physics of flow rather than objects—empirically equivalent, but conceptually unrecognizable.

The Actual Proposal

Back to the main point. What might we find through letting Machine-Learning find different compressions? The architectural details matter only insofar as they make possible a concrete experiment allowing us to:

1. Train an optimizer on experimental data.

2. Reward compression along multiple axes.

3. Reward finding many distinct points on the Pareto surface.

4. Reward different balances of Scope, Accuracy, Description Length, and Computational Cost.

5. Allow it to invent its own variables.

6. Allow it to request data needed to differentiate between competing generalisations.

7. Let it search.

Then observe:

• Do independent runs converge?

• Do they rediscover our laws?

• Do they produce alternative but equivalent compressions?

• Do they invent primitives that feel alien but are predictively powerful?

• Do they find compressions more computationally efficient for specific domains?

• Do they discover different unifications with different scopes?

• Do they produce generalisations diverging from our current laws in ways that can be empirically tested?

If convergence occurs, our laws may be structurally privileged. If divergence occurs, physics may be representation-contingent. Either result is interesting.

A further refinement: data fed to such a system could come in three forms. “Simulated” data, generated from our current best understanding with added uncertainty; “experimentally measured” data from real observations; and “specifically requested” data specifically generated to probe the limits of a given compression’s domain of validity, helping distinguish between competing generalisations, asked for by the model itself.

Finally, a thought that has long hovered at the edges of my mind: what if String Theory had been discovered before Relativity? Would that even have been possible? I raise this only as an example of how contingent our path through physics may have been. This is not a value judgement about which theory is more fundamentally true. It is simply a thought I find tantalising.

There are so many interesting possibilities to explore here, and the only way to obtain real answers is through experimentation. I have a number of concrete ideas for how such an exploration might be started, but those details are for another time.

On paper, titanium should be the world’s #1 structural metal. There isn’t another metal with such great overall properties– incredible toughness and specific strength, light weight, exceptional corrosion resistance, and performance even at extreme temperatures. Titanium is also abundant, the 9th most common element in the earth’s crust—cheap enough as ore to use in paint pigment and plastic filler.1 And making titanium metal should take only half as much energy as making aluminum.2 It should be cheap and ubiquitous.

But in reality, titanium is a rounding error in the world metals market – produced 5000x less than iron and almost 200x less than aluminum. Half a century after the US and Soviet governments conjured a commercial titanium industry from scratch to feed their cold war machines, we’ve seen virtually no progress in making the metal cheap or abundant. Learning curves that manifested quickly for aluminum and stainless steel simply didn’t appear for Ti—its “idiot index”, the cost of Ti parts as a multiple of the cost of ore and embodied energy, remains >10x that of steel. At $25-$50/kg, titanium is just too expensive to be widely used.

The total lack of progress keeps titanium in a reverse-Goldilocks zone where it loses to steel and aluminum on cost, and to composites on weight3, and so is used only where its lightness and toughness are absolutely essential. Outside of aerospace, defense, corrosion-resistant process equipment, artificial joints and premium sporting goods, titanium is practically irrelevant.

‍Why is titanium so expensive?

Here’s how we get Ti today:

• We mine TiO2 as ilmenite ore, which is widespread and fairly concentrated at 40-65% TiO2. (~$1/kg Ti)

• The ilmenite is purified to synthetic rutile (90-95% TiO2) (~$2/kg Ti) then carbo-chlorinated to TiCl4 which can be distilled for very high purities. ($3-4/kg Ti)

• Next comes the main act: the Kroll process, reducing TiCl4 with magnesium metal TiCl4+2Mg -> 2MgCl2 + Ti. This is a touchy operation in which TiCl4 is meticulously metered into molten magnesium – too fast and the heat liberated in the reaction would vaporize the Mg metal. Since we have to remove the Mg and MgCl as gases, and the process happens well under Ti’s melting temperature, the metal we get initially is in porous and hard-to-use “sponge” form --full of bubbles like lava rock (~$6-8/kg Ti).

• The porous sponge is crushed, ground, mixed with alloying metals (useful Ti most often shows up as Ti90Al6V4), pressed, welded into 10-15 ton “consumable electrodes”, and homogenized in a vacuum arc remelter (VAR). (~$15/kg Ti)

• The ingots are then whittled down to saleable parts with sundry processes and techniques, which themselves are painstaking for many of the same reasons that Ti is so awesome as a structural metal – it’s hard enough to destroy tooling, forms a thick oxide film in O2, melts only at a staggering 1670ºC etc. … (~$25-60/kg Ti)

The cost of titanium roughly doubles with each of these steps—and there are lots of steps!4

Why are there so many steps here? Part of it is that titanium is both electropositive and fairly refractory—you need the uncomfortable combination of very high temperatures and a very reducing environment to get it into a useful form. But the bigger reason is that titanium is reactive—ferociously so. Molten Ti dissolves nearly every other metal. It readily forms oxides, nitrides, hydrides, and carbides. Titanium powders will ignite in carbon dioxide, and even in helium with as little as 9% oxygen. Sometimes this reactivity can be annoying, requiring an inert environment, or causing embrittlement or loss of material. Other times it can be deadly.5

(But wait – isn’t titanium meant to be corrosion resistant? Shouldn’t it behave more like a staid transition metal than an electron-shooter like sodium? Like aluminum, titanium forms a passivating oxide on its surface that imparts its famous corrosion resistance, until about 600ºC. At that point, the titanium below the oxide skin begins to draw in oxygen at a meaningful rate, embrittling the part and exposing it to attack from whatever’s around.)

Titanium’s reactivity is especially punishing when you try to process it into useful shapes, which requires high temperatures where oxygen and anything else around gets in and ruins it. Consider for example just the ingot breakdown step. In order to make the ingots more pliable, producers heat them up to high temperatures and forge them in open air. As the surface area of the ingot increases, so does oxygen pickup, forming a brittle hide you have to shave off. That’s up to 50% of the metal you made, discarded useless on the mill room floor—just to forge an ingot! Frictions like this add up at every step on the value chain. Since Ti is only transformed incrementally at each step in this process, it all adds up to significant capital, time, labor and material loss tied into each step.

It’s not a matter of optimization or learning rates

As bad as things seem, there’s good reason to believe that the way we make titanium today is pretty close to its cost floor.

The TiCl4 precursor to Kroll is a high-volume commodity chemical (>7 MMT/yr) that costs ~$4/kg Ti. Making TiCl4 is a mature process that’s substantially optimized already. Kroll also requires magnesium metal which is recycled on-site for ~$1.2/kg Ti6 — only a small multiple of the energy costs involved. Throw in a 15% profit margin, and you have arrived at the VAT excluded price for the cheapest Ti sponge quoted on the Shanghai Metal Market: ~$6/kg. So Kroll costs hardly more than its material inputs. 60 years of Ti production have whittled most of the inefficiencies away. The real cost and complexity comes later, in trying to work Ti sponge into useful parts.

Clearly, what titanium needs is reimagining of the whole process that reduces the number of steps in the value-add chain. Since cost roughly doubles with each step, eliminating one or two steps could easily save more than improving each one by 10%.

This fact is widely recognized—and has been for half a century. As Brian Potter writes in his excellent history of the Ti industry, even as the Kroll process was first scaling in the 1950s, newer and better ways of making titanium were widely expected to materialize. William Kroll himself expected that an electrolytic process similar to Hall-Heroult for aluminum would come to replace his method.

But the sad truth is that there have been many serious and well-funded attempts to reinvent Ti production over the last 50 years, and all have failed to meaningfully displace the Kroll-centric approach we use now. Let’s look through what has been tried, and see what we can learn:

The cruel lessons from 60 years of failing to reinvent titanium

People have tried all sorts of methods: electrolysis, plasma, alternative reductants, additive manufacturing. None ever produced full-density Ti cheaper than Kroll:

1960s – Dow-Howmet/TIMET/RMI electrolysis – Electrolysis of TiCl₄ to porous Ti.

1994 – Hydrogen plasma reduction – Thermal plasma for reduction of TiCl₄ to Ti powder.

2000 – Armstrong process – Na reduction of TiCl₄ to Ti powder.

2000 – Farthing-Fray-Chen (FFC) Cambridge & Ono-Suzuki – Electrolysis of TiO₂ to produce porous Ti.

2003 – Cardarelli-Rio Tinto electrolysis – Direct electrolysis of molten TiO₂ to molten Ti.

2003 – Ginatta electrolysis – Electrolysis of TiCl₄ in fluoride/chloride salt to make molten Ti.

2004 – MER Corp electrolysis – Electrolysis of Ti oxycarbide to porous Ti.

2012 – ADMA process – Hybrid H₂/Mg reduction of TiCl₄ to TiH₂ powder.

2013 – Hydrogen Assisted Magnesiothermic Reduction (HAMR) – Hybrid H₂/Mg reduction of TiO₂ to Ti powder.

Here’s our longer review of all the attempts to reinvent Ti, and the lessons we’ve drawn from their failure.

We take away four main lessons:

• Start with TiCl4. Trying to start from TiO2 makes things much harder, and can’t meaningfully reduce costs.

• Any new process will have to make dense Ti directly if it’s going to be useful. Kroll already makes porous Ti efficiently. The problem is that porous Ti simply isn’t a valuable product.

• Beware additive techniques. These are the most promising of the alternative approaches, but signs indicate that they won’t reach the scales we’d need to revolutionize Ti.

• Beware electrolysis. It’s the most frequently attempted and least successful of the alternative approaches. Ti has several cruel chemical features that make it especially challenging to manipulate in solution.

1) Trying to start with TiO2 makes things much harder

Most of the attempts reinvent Ti since the early 2000s have tried to substitute TiO2 for Kroll’s TiCl4 starting material. That’s a way to save a few bucks on feedstock costs-- and the only possible route to getting Ti as cheap as Al or Mg. But unfortunately, today none of those projects produce titanium at commercially relevant tonnages. We think that much of the problem has to do with the hassle of purifying TiO2.

The vast majority of titanium used on earth is sold not as Ti metal, but as white TiO2 pigment. TiO2’s whiteness depends strongly on its purity. So the TiO2 pigment industry has come up with an excellent way of making it as pure as possible: carbo-chlorinating synthetic rutile, fractionally distilling the various volatile metal chlorides until just the TiCl4 remains, and then burning the purified TiCl4 in oxygen to get back to pure TiO2.

As complicated as this sounds, the chloride distillation route is just about as good a way as is thermodynamically possible for purifying a metal as chemically “sticky” as Ti. Since Ti metal has to meet strict purity standards (~0.1wt% for most elements), trying to make metal directly from TiO2 saddles you with a serious purity problem right at the start.

TiCl4 also serves a valuable function in keeping oxygen out of your reaction mixture. Oxygen is fiendishly soluble in Ti metal—so much so that its presence makes metallothermic reduction almost impossible.

Just say no to TiO2. TiO2 that is pure enough to be a feedstock for Ti metal is more expensive than TiCl4 anyway, and makes the reduction of Ti vastly harder. We want to delete steps, but not at the cost of pure titanium.

2) Porous Ti is not a valuable product

Most of the inefficiencies in the Kroll process have been whittled away over the last 60 years. At this point, Ti sponge sells at a price that is very nearly the sum of the cost of TiCl4, a high-volume commodity chemical, and the cost of producing magnesium with a well tamed electrolytic process. What exactly would you improve on?

Imagine you did invent a magical new process for making porous Ti sponge. Say it’s half the capital cost of Kroll and a quarter of the energy cost—just above the thermodynamic minimum for making Ti. You buy the cheapest pure feedstock that gets you the purity you need, TiCl4 at ~$4/kg. If you want a 10% profit margin, that leaves you only ~$2.3/kg Ti to finance, operate, and maintain your process if you’re going to compete with ~$7/kg Kroll sponge. Kick in $1.35/kg Ti for capex (half of Kroll’s capex of $30/kg Ti per year amortized at 8% for 20 years)7, $0.5/kg Ti for electricity costs, and ~$0.5/kg Ti for labor and maintenance—and even though your process is magical, it’ll still be borderline unfinanceable if all you can sell is Kroll-equivalent sponge!

Titanium sponge just isn’t where the value is. Saving a few tens of cents on $7/kg Ti that will later be turned into mill products that cost as much as $50/kg Ti won’t move the needle.

3) Additive manufacturing is unlikely to give titanium the volume it needs

So if sponge isn’t where the value is, maybe we need an all-new way to turn sponge Ti into finished parts?

A lot of people have seen it this way in recent years, and so there’s been a lot of excitement about additive manufacturing for Ti parts. It makes sense in some ways. After all, subtractive manufacturing sucks for titanium – its hardness and low thermal conductivity chew through tooling8, and its reactivity costs you material in high temp processing. Both Ti and additive manufacturing are mostly used at the top of the market where expense hardly matters. And a lot of the newfangled ways you might make Ti give you spongy or powdered metal that needs additive post processing to get to full density anyway.

But while additive manufacturing with Ti powder does some specific things very well, one thing it won’t do is make Ti cheaper than it is today. Additive manufacturing tools are generally less capital intensive but have lower throughput, while conventional methods get economies of vertical scale. This has played out in the market for a few years now and cost analyses show that there’s a breakeven manufacturing volume above which conventional Ti production is more cost-effective. The exact value varies but this paper suggests that conventional Ti casting beats additive manufacturing at scales above a few hundred parts (valve bodies made with hot isostatic pressing vs. conventional forming), and this analysis of a landing gear reports that high pressure die casting is more cost effective than selective laser sintering above ~40 parts produced.

There are also lingering concerns that the main approaches to additive Ti leave micro-pores in the final metal product that concentrate stress and make weaker overall parts, especially under cyclic loading. Fear of microporosity pushes additive manufacturing to use high-cost ($70-200/kg) ultra-spherical powders in order to minimize pore volumes9. But concerns over part strength have persisted especially among the usual sectors for early adoption—defense, aerospace, and medical equipment– where failures can be catastrophic and the cost for Ti is borne easily.

Additive manufacturing does shine for low production volumes of highly complicated parts that conventional manufacturing struggles to make at all (see SpaceX’s SuperDraco). But an abundant Ti future needs a new method that can make Ti at high volumes and low cost and full strength, and that method probably won’t be additive manufacturing.

4) Electrolysis isn’t out of the question but it comes with severe challenges

Electrolysis seems like a logical approach to making titanium. After all, the development of an electrolytic process for aluminum took it from a precious metal used only in jewelry to a ubiquitous structural material in just a few decades. Could it unlock similar cost-savings for titanium?

Probably not, we think:

Ti has too many valence states in solution. Titanium, unlike aluminum, adopts multiple valence states which engage in a phenomenon called redox cycling in an electrolyzer. Ti2+ can flow across the cell in one direction, drop off an electron, and flow backwards as Ti3+. So current flows through the cell for no reason, generating no titanium metal and producing nothing but joule heat! Electrolytic magnesium produced for the Kroll process at ~30% voltage efficiency and >90% current efficiency is just a more efficient way of packing electrons into a titanium feedstock.

Ti melts at too high a temperature, but lowering the temperature makes things worse. Part of the magic of the Hall-Heroult process for aluminum is cryolite, which lowers the melting temperature of Al2O3. Since Al metal also has a relatively low melting temperature (660ºC), a Hall-Heroult cell can operate at reasonable temperatures and still pour molten aluminum into shapes that can be sold directly to customers.

If only that were possible for a Ti electrolyzer, it could delete the costly post-processing steps in making Ti parts, and make finding compatible electrolyte and electrolyzer materials that much easier. But Ti’s 1670ºC melting point makes that impossible.

You could make building your electrolyzer much easier by running it below titanium’s melting point. Some people have tried this. But at meaningful current densities, the product will then be dendritic titanium – tree-like metal crystals embedded in frozen electrolyte. What would it take to make this titanium useful? First, you must clean the titanium of the salt and then melt it down to make homogenous titanium stock – the same steps that make Kroll’s sponge titanium expensive. A non-starter.

Overall, electrolysis is the most-frequently attempted and least successful of the alternative approaches to Ti. People will keep trying—but we will be surprised if anyone gets it to work.

‍Tempering our expectations

People who love titanium tend to compare it to steel and aluminum since Ti’s base properties are so much better. They’ll also point out that the ore is cheap and abundant and that the metal costs vastly more than it should, a staggering 60x over its theoretical cost floor (the cost of ore plus energy). That kind of argument makes it easy to convince yourself that Ti should be a main structural material if not the main structural material of the future—if only we could find a better way to make it.

But it’s worth offering a moderating opinion, both on how cheap titanium can really get, and on how useful it is.

Why you can’t say that titanium beats aluminum or steel categorically.

Consider the bicycles used in the Tour de France, ultra-premium machines made of the best possible materials. From 1904 to 1994 the Tour de France was won on steel bikes. From 1995 to 1998 it was won on aluminum bikes. And then from 1999 until now it’s been won exclusively on carbon fiber bikes. Why was Ti passed over completely?

Strength-to-weight ratio isn’t everything. Larger-diameter tubes in Al bike frames are stiffer than Ti and can even be stronger—not based on material properties, but based on geometry. In a bending tube in a bike frame, the critical buckling load scales with the cube of the outer diameter, but only linearly with stiffness. If you want a stiff and strong frame, or one that’s easy to shape into aerodynamic forms, aluminum with its lower density and better formability can end up as the winning choice.

This type of dynamic repeats all across industry. Properties like density end up being just as important as specific strength, and lesser metals that are easier to form into the right geometry end up competing with and even beating titanium.

Why titanium will always be pricier than Al or steel.

Hopefully we’ve landed this point by now: Ti is extremely hard to work with. The reasons why are written in its atomic structure. It’s semi-refractory and a sponge for nearly everything around it. The purity of the feedstock and reducing agent, inertness of the reactor volumes, and metal handling at elevated temperatures are crucial aspects that will increase capital and labor costs and decrease material utilization, no matter how clever your process is. Unlike titanium, aluminum and steel smelters operate in atmospheric conditions, the molten metals can be poured in open air to form shapes that brokers can store on shelves, and they are both compatible with common high temperature materials like alumina, silica, and graphite.10 None of that works for titanium.

Where does this all leave us?

Back when it was young and promising, ARPA-E ran a program called METALS which sought to reinvent many of the light metals for the modern era. They estimated substitutional elasticities for different structural metals to come up with some cost targets, how cheap these metals would have to get to really change the world. For Ti it was $10/kg at the ingot level. That’s the cost where they thought Ti could start to meaningfully displace steel and aluminum parts.

Is that possible? Despite all the harsh lessons over the last few pages, we think so.

All this reflection on past failed attempts to reinvent titanium production makes us think that the straightest line to cheaper titanium is to delete steps wherever possible and to not shy away from energy intensity. Paying extra energy for purification and reduction could get you to fewer total steps and direct castability. Fewer steps is key since titanium is a refractory metal that can soak in nearly everything around it, leaving it worse off. Every step wastes material and makes it harder to meet purity standards.

At this intermediate stage in our brainstorm on the topic, what looks best to us is a single-step metallothermic reduction of titanium chloride that produces molten titanium directly. That’s like a more energy-intense version of Kroll. With 150% of Kroll’s capex and opex you could make ingots that hit the ARPA-E target. Better yet, using more energy in the reduction step could leave you with molten Ti so you could skip the ingot step entirely, getting you customer-ready bars and sheets for less than ingot prices. If somebody could do that, we think they could beat Kroll Ti by wide margins.

It’d be hugely energy intensive—but that could be OK. People are already making metals off-grid with dirt cheap electricity. We’ve said before why we shouldn’t be shy of energy intensity in materials production— falling energy costs have always been a main driver for lower material costs, and energy-intensity has always gone hand in hand with building the world out of much cooler materials.11

High energy intensity, direct castability, deleting steps. If you’re also thinking about titanium along similar lines, please get in touch.

Introduction

Neurotech is complicated. This is because you need to understand at least five fields at once to actually grasp what is/isn’t possible: electrical engineering, mechanical engineering, biology, neuroscience, and computer science. And, if you’re really trying to cover all the bases: surgery, ultrasound and optical physics as well. And I’ve met relatively few people in my life who can operate at the intersection of three fields, much less eight! As a result, I’ve stayed away from the entire subject, hoping that I’d eventually learn what’s going on via osmosis.

This has not worked. Each time a new neurotech startup comes out, I’d optimistically chat about them with some friend in the field and they inevitably wave it off for some bizarre reason that I would never, ever understand. But the more questions I asked, the more confused I would get. And so, at a certain point, I’d just start politely nodding to their ‘Does that make sense?’ questions.

I have, for months, been wanting to write an article to codify the exact mental steps these people go through when evaluating these companies. After talking to many experts, I have decided that this is a mostly impossible task, but that there are at least a few, small, legible fractions of their decision-making framework that are amenable to being written out. This essay is the end result.

My hope is that this helps set up the mental scaffolding necessary to triage which approaches are tractable, and which ones are more speculative. Obviously, take all of my writing with a grain of salt; anything that touches the brain is going to be complicated, and while I will try to offer as much nuance as possible, I cannot promise I will offer as much as an Actual Expert can. Grab coffee with your local neurotech founder!

Questions

1. How relevant are the state measurements to the application?

At least some forms of neurotech, like brain-computer-interfaces, perform some notion of ‘brain state reading’ as part of their normal functionality.

Well, what exactly is ‘brain state’?

Unfortunately for us, ‘brain state’ lies in the same definitional scope as ‘cell state’. As in, there isn’t really a great ground truth for the concept. But there are things that we hope are related to it! For cells, those are counts of mRNA, proteins found, chromatin landscape of the genome, and so on. For brains, there are four main possibilities to get at a notion of state:

1. Measure the spiking activity of singular neurons (very invasive)

2. Measure the activity of local field potentials (can be slightly less invasive)

3. Measure hemodynamics (blood flow or oxygenation) changes (can be non-invasive, though higher-res invasive)

4. Measure electromagnetic fields outside the skull (usually non-invasive)

There is an ordering here; at the top, we have measurements that are closest to the actual electrical signaling that (probably) defines moment-to-moment neural computation. As we move down the list, each method becomes progressively more indirect, integrating over larger populations of neurons, longer time windows, and/or more layers of intermediary physiology.

This is perhaps overcomplicating things, but there’s one also, slightly more exotic approach not mentioned here (and that I won’t mention again), called biohybrid devices. In these systems, neurons grown ex-vivo are engrafted to a brain, and those neurons are measured directly, so it’s sort of an aggregate measure like LFP, but also it’s technically able to measure single spikes.

But keep in mind: none of these actually work at understanding the full totality of every single neuron firing in a brain, which is a largely physically intractable thing to perform. Which is fine and fair! Understanding totalities is a tall bar to meet. But it does mean that whenever we stumble across a new company, we should ask the question: how relevant is their method of understanding brain state to the [therapeutic area] they actually care about? Superficial cortical hemodynamics won’t reveal hippocampal spiking, 2-channel EEG won’t decode finger trajectories, and so on.

With this context, let’s consider Kernel, a neurotech company founded by the infamous Bryan Johnson in the mid-2010’s. Their primary product is called Kernel Flow, a headset that does time-domain functional near-infrared spectroscopy (TD-fNIRS) to measure brain state, which tracks blood oxygenation by measuring how light scatters through the skull. In other words, this is a hemodynamics measurement device.

It is non-invasive, portable, and looks like a bike helmet (which is an improvement compared to many other neurotech headsets!).

One common thing you’ll find on most neurotech websites is a ‘spec sheet’ of their device. For most places, you’ll need to formally request it, but Kernel helpfully provides easy access to it here.

In it, they note that the device has an imaging rate of 3.76Hz, which means it’s taking a full hemodynamic measurement about every 266 milliseconds across the surface of the brain. This is fast in absolute terms, but slow on the level of (at least some) cognitive processes, which often unfold on the order of tens of milliseconds. For example, the neural signatures involved in recognizing a face or initiating a movement can happen in less than 100 milliseconds. And to be clear, this is not something that can be altered by increasing the sampling rate; the slowness is inherent to hemodynamic measurements in general.

This means that by the time Flow finishes one hemodynamic snapshot, many of the neural events we care about have started and finished.

The spec sheet also notes that the device comes with 4 EEG electrodes, which have a far higher sampling rate of 1kHZ, or 1,000 measurements per second. At first glance, this seems like it might compensate for the sluggish hemodynamic signal by offering access to fast electrical activity. But in practice, 4 channels are entirely insufficient for learning really anything about the brain. Keep in mind that clinical-grade usually operates at the 32-channel-and-above level!

I found one paper that investigated the localization errors of EEG’s—as in, can you correctly place where in the brain a spike is occurring—across a range of channels: 256, 128, 64, 32, and 16. Not even 4! Yet, even at the 16-channel level, spatial localization was incredibly bad; one example of its failure case being that it mis-localized a temporal-lobe spike to the frontal lobe. Past that, noise like muscle and eye movement artifacts often dominates the EEG signal at the lowest channel counts.

And, again, this was on 16 channels! One can only imagine how much worse 4 channels is.

Of course, 4-channels of EEG data clearly offer something. In the context of the device, they may serve as a coarse sanity check or a minimal signal for synchronizing with the slower hemodynamic measurements. Which maybe is enough to be useful?

But we may be getting ahead of ourselves by getting lost in these details. It is entirely irrelevant to consider the absolute value of any given measurement decision being made here, because, again, what actually matters is the relevancy of those measurements to whatever the intended use case is. Clearly the devices measurements are, at least, trustworthy. But what is it meant to be used for?

Well…it’s vague. Kernel’s public messaging has shifted over the years—from “neuroenhancement” and “mental fitness” to, most recently, “brain biomarkers.”. I am not especially well positioned to answer whether this final resting spot is relevant to what Kernel is measuring, but it feels like it is? At least if you look at their publications, which do show that the device is capable of capturing global brain state changes when under the influence of psychoactive substances, e.g. ketamine. So, even if hemodynamics doesn’t meet the lofty goal of being able to detect face recognition, that’s fine! Static-on-the-order-of-minutes biomarkers are fully within their measuring purview.

Does that make Kernel useful? I don’t know the answer to that, but we’ll come back to the subject in a second.

2. What are the costs and burdens for the user?

In short: a device must earn its place in a patient’s life.

The historical arc of neurotech companies lay mainly in serving desperate people that have literally no other options: ALS, severe spine damage, locked-in syndrome, and the like. The giants of the field—Synchron, Blackrock Neurotech, and Neuralink—have all positioned themselves around these, and so their maximally invasive nature is perfectly fine with their patients. Now, fairly, Synchron apparently doesn’t have the greatest reputation and Blackrock is somewhat old-fashioned, so Neuralink could be considered the only giant, but all three did pop up a lot during my research!

Blackrock Neurotech are the creators of the Utah Array, which remains the gold standard for invasive, in-vivo neural recording. Neuralink, the newest and most-hyped, have iterated on the approach, developing ultra-thin probes that can be inserted into the brain to directly record signals. Synchron has the least invasive approach, with its primary device being an endovascular implant called the Stentrode, allowing neural signals to be read less invasively than a Utah Array or Neuralink (from a blood vessel in the brain rather than in the parenchyma), though at a severe cost of signal quality.

You could find faults with these hyper-invasive neurotech companies on the basis of ‘how realistically large is the patient population?’, but you can’t deny that amongst the patient population that does exist, they’d certainly benefit!

So…if you do spot a neurotech company that is targeting a less-than-desperate patient population, you should ask yourself: why would anyone sign up for this? Why would an insurance company pay for it? And most importantly, why would the FDA ever approve something with such a lopsided risk-reward ratio? This is also why you see a lot of neurotech companies pivot toward “wellness” applications when their original clinical thesis doesn’t pan out. Wellness doesn’t require FDA approval or insurance reimbursement! But it also doesn’t require the device to actually work.

But even if a neurotech company is targeting a less-than-desperate patient population and aren’t trying to push them towards surgery, it’s still worth thinking about the burdens they pose!

Neurotech devices can be onerous in more boring ways too, so much so that they can completely kill any desire for any non-desperate person to use it. One example is a device we’ve talked about: the Kernel Flow. Someone who I chatted with for this essay mentioned that they had tried it, and had this to say about it:

“[the headset] weighs like 4.5lbs. That is so. fucking. uncomfortable.”.

Now, it may be the case that the information that the device tells you is of such importance that it is worth putting up with the discomfort. Is the Kernel Flow worth it? I don’t know, I haven’t tried it! But in case you ever do personally try one of these wellness-focused devices, it is worth pondering how big of a chore it’d be to deal with.

3. How much is the approach ‘fighting physics’?

Speaking of ‘building things for less desperate patients’, two big neurotech names that often come up are Nudge and Forest Neurotech (the founder of whom I talked to for this article, who has since moved to Merge Labs).

Both of these startups are focusing on brain stimulation for mental health, though Forest’s ambitions also include TBI and spinal cord injuries. Depression, anxiety, and PTSD can be quite awful, but only the most severely affected patients (single-digit percentages of the total patient population) would likely be willing to receive a brain implant. And both of these companies are fully aware of that, which is why neither of them do brain implants.

But, even if you aren’t directly placing wires into the brain, there is still some room to play with how invasive you actually are. I think it’d be a useful exercise to discuss both Nudge and Forest’s approaches—the former non-invasive, the latter invasive (albeit slightly less invasive than a Neuralink, which goes directly into the brain parenchyma)— because they illustrate an interesting dichotomy I’ve found amongst neurotech startups: the degree to which they are attempting to ‘fight’ physics.

At the more invasive end, there’s Forest Neurotech. Forest was founded in October 2023 by two Caltech scientists—Sumner Norman and Tyson Aflalo—alongside Will Biederman from Verily. They’re structured as a nonprofit Focused Research Organization and backed by $50 million from Eric and Wendy Schmidt, Ken Griffin, ARI, James Fickel, and the Susan & Riley Bechtel Foundation. Their approach relies on ultrasound, built on Butterfly Network’s ultrasound-on-chip technology, that sits inside the skull but outside the brain’s dura mater; also called an ‘epidural implant’. Still invasive, but again, not touching the brain!

At the less invasive end, there’s Nudge, who just raised $100M back in July 2025 and has Fred Ehrsam, the co-founder of Coinbase, as part of the founding team. They also have an ultrasound device, but theirs is entirely non-invasive, and comes with a nice blog post to describe exactly what it is: …a high channel count, ultrasound phased array, packed into a helmet structure that can be used in an MRI machine.

So, yes, both of these are essentially focused ultrasound devices meant for neural stimulation, though I should add the nuance that Forest’s device is also capable of imaging. But, despite the surface similarities, one distinct split between the two is that, really, Nudge is attempting to fight physics a lot more than Forest.

Why? Because they must deal with the skull.

Nudge’s device works by sending out multiple ultrasound waves from an array of transducers that are timed so precisely that they constructively interfere at a single millimeter-scale point deep in the brain, stimulating a specific neuron population, usually millions of them. It is not dissimilar to the basic principle as noise-cancelling headphones, but in reverse: instead of waves cancelling each other out, they add up. The hope is that all the peaks of the waves arrive at the same spot at the same moment—constructive interference—and you get a region of high acoustic pressure that can change brain activity. As a sidepoint: you’d think this works by stimulating neurons! But apparently it can work both via stimulation or inhibition, depending on how the ultrasound is set up.

How is the Nudge approach fighting physics?

First, there’s absorption. The skull soaks up a substantial chunk of the emitted ultrasound energy and converts it into heat. One study found that the skull causes 4.7 to 7 times more attenuation than the scalp or brain tissue combined.

Second, aberration. Because the skull varies in thickness, density, and internal structure across its surface, different parts of your ultrasound wavefront travel at different speeds, so, by the time the waves reach the brain, they’re no longer in phase. If the whole point of focused ultrasound is getting all your waves to constructively interfere at a single point, the skull messes that up, and the intended focal spot gets smeared, shifted, or might not form properly at all.

And, finally, the skull varies enormously between individuals. The “skull density ratio”—a metric that captures how much trabecular (spongy) bone versus cortical (dense) bone you have—differs from person to person, and it dramatically affects how well ultrasound gets through.

Now, to be clear, Nudge is aware of all of these things, and the way they’ve structured their device is attempting to fight all these problems. For example, Nudge talks a fair bit about how their device is MRI-compatible. This is great! If you want to correct for aberrations (and for everyone’s brain being a different shape), you need to know what you’re correcting for, which means you need a detailed 3D model of that specific patient’s skull, which means you need an MRI (or better CT). You image the skull, you build a patient-specific acoustic model, you compute the corrections needed to counteract the distortions, and then you program those corrections into your transducer array. Problem solved!

Well, maybe. Fighting physics is a difficult problem, and we’ll see what they come up with. While there is already a focused ultrasound, FDA-approved device that has been used in thousands of surgeries similar to Nudge’s that can target the brain with millimeter-scale accuracy (albeit for ablating brain tissue, not stimulating it, but the physics are the same!), it is an open question whether Nudge can dramatically improve on the precision and convenience needed to make it useful for mental health applications.

On the other hand, Forest, by bypassing the skull, is almost certainly assured to hit the brain regions they most want, potentially reaching accuracies at the micron scale. Remember that these differences cube, i.e. the number of neurons in a 150 micron wide voxel vs. a 1.5 millimeter wide voxel is (1500^3)/(150^3) =1,000 times more neurons. So it’s safe to say that the Forest device is, theoretically, 2-3 orders of magnitude more precise in the volumes it interacts with than Nudge is. Now, Forest still isn’t exactly an easy bet, given that they now have to power something near an organ that really, really doesn’t like to get hot, figure out implant biocompatibility, and a bunch of other problems that come alongside invasive neurotech devices. But they at least do not have to fight the skull, and are thus assured a high degree of precision.

There is, of course, a reward for Nudge’s trouble. Nudge, if they succeed, also gets access to a much larger potential patient population, since no surgery is needed. This is opposed to Forest, who must limit themselves to a smaller, more desperate demographic.

As with anything in biology, there is an immense amount of nuance I am missing in this explanation. People actually in the neurotech field are likely at least a little annoyed with the above explanation, because it does leave out something important in this Nudge versus Forest, non-invasive versus invasive, physics-fighting versus physics-embracing debate: how much does it all matter anyway?

4. Do they know whether their advantages translates to clinical benefit?

The brain computer interface field is in a strange epistemic position where devices are being built to modulate brain regions whose exact anatomical boundaries aren’t agreed on (and may even diverge between individuals!), using mechanisms that aren’t fully understood, for conditions whose neural circuits are still being studied.

Because of this, despite all the problems I’ve listed out with going through the skull, Nudge will almost certainly have some successful clinical readouts. Why? It has nothing to do with the team at Nudge being particularly clever, but rather, because there is already existing proof that non-invasive ultrasound setups somehow work for some clinically relevant objectives.

Nudge is fun to refer to because they have a lot of online attention on them, but there are other players in the ultrasound simulation space too, ones who are more public with their clinical results. SPIRE Therapeutics is one such company and they, or at least people associated with the company (Thomas S Riis), have papers demonstrating tremor alleviation (n=3), chronic pain reduction (n=20), and, most relevant to this whole discussion, and depressive symptom improvement (n=22 + randomized + double-blind!), all using their noninvasive ultrasound device.

How is this possible? How do these successful results square with the skull problems from earlier?

Clearly, something is getting through the skull, and it seems to be having some clinically significant effect. Because of this, it could very well be possible that the relative broadness of Nudge’s and SPIRE’s (and others like them) stimulation is, in fact, perfectly fine, and being incredibly precise is simply not worth the effort. This all said, it is hard to give Forest a fair trial here, since they are basically the only ones going the invasive route for ultrasound, and their clinical trials (which use noninvasive devices) have just started circa early 2025. Maybe their results will be spectacular, and I’d recommend watching Sumner’s (the prior Forest CEO) appearance on Ashlee Vance’s podcast to learn more about early results there.

But really, this debate between invasive and non-invasive really belongs in the previous section, because the point I am trying to make here is a bit more broad than these two companies. What I’m really gesturing at is that being really good at [X popular neurotech metric] doesn’t alone equal something better! This is as true for precision as it is for everything else.

Staying on the example of precision, consider the absolute dumbest possible way you could approach brain stimulation: simply wash the entire brain with electricity and hope for the best.

This is, more or less, what electroconvulsive therapy (ECT) does. Electrodes are placed on your scalp, a generalized seizure is induced, and you repeat this a few times a week. You are, in the most literal sense, overwhelming the entire brain with synchronized electrical activity. And yet despite the insane lack of specificity, ECT remains the single most effective treatment we have for severe, treatment-resistant depression. Response rates hover around 50-70% in patients for whom nothing else has worked, with some rather insane outcomes, one review paper stating: “For the primary outcome of all-cause mortality, ECT was associated with a 30% reduction in overall mortality.” For some presentations, like depression with psychotic features, catatonia, or acute suicidality, it is essentially first-line.

This should be deeply humbling for anyone looking into the neuromodulation space. There are companies raising hundreds of millions of dollars to hit specific brain targets with millimeter, even micron precision, and meanwhile, the most effective neurostimulation-for-depression approach we’ve ever discovered involves no targeting whatsoever. Now, of course, there are genuine downsides to the ECT approach (cognitive side effects, the need for anesthesia, the inconvenience of repeated hospital visits, obviously doesn’t work for every neuropsychiatric disorder) that make it worth pursuing alternatives! But it does suggest that the relationship between targeting precision and clinical outcome is much more complex than you’d otherwise assume.

Consider the opposite failure mode. Early deep brain stimulation—the most spatiotemporally precise neurostimulation method currently available—trials for depression are instructive here. Researchers identified what they believed was “the depression circuit,” implanted electrodes in that exact area, delivered stimulation, and then watched as several major trials burned tens of millions of dollars on null results. Most infamously, the BROADEN trial, targeting the subcallosal cingulate, and the RECLAIM trial, targeting the ventral capsule/ventral striatum, both of which failed their primary endpoints.

Yet, DBS is FDA-approved for Parkinson’s treatment and is frequently used to treat OCD. Each indication is a world unto itself in how amenable it is ‘precision’ being a useful metric.

But again, this point extends beyond precision.

As a second example, consider the butcher number, a metric first coined by the Caltech neuroscientist Markus Meister, which captures the ratio of the number of neurons destroyed for each neuron recorded. Now, you’d ideally like to reduce the butcher number, because killing neurons is (probably) bad. And one way you could reliably reduce the butcher number is by simply making your electrodes thinner and more flexible. This is, more or less, at least part of Neuralink’s thesis: their polymer threads are 5 to 50 microns wide and only 4 to 6 microns thick (dramatically smaller than the Utah array’s 400-micron-diameter electrodes!) and thus almost certainly has a low butcher number.

Here’s the Neuralink implant:

And here’s the Utah array:

But does having a lower butcher number actually translate to better clinical outcomes? As far as I can tell, nobody knows! It’s largely unstudied! It’s conceivable that yes, lowering this number is useful, but surely there is a point where the priority of the problem dramatically drops compared to the litany of other small terrors that plague most neurotech startups.

The point here is not that the butcher’s number is useless. The point also isn’t that precision is useless. The point is that the relationship between any given engineering metric and clinical success (in your indication) is rarely as straightforward as anyone hopes, and it’s worth considering whether that relationship has actually been established before believing that success on the metric is at all useful.

5. Could this be done without touching the central nervous system?

Finally: something that repeated across the neurotech folks I talked to was that people consistently underestimate how extraordinarily adaptable the peripheral nervous system is. For example, a company that claims to, say, automatically interpret commands to a digital system via EEG should probably make absolutely certain that attaching an electromyography device to a person’s forearm (and training them to use it) wouldn’t wind up accomplishing the exact same thing.

In fact, there was a company that did exactly this. Specifically, CTRL-labs, a New York City-based startup. They came up over and over again in my conversations as a prime example of someone solving something very useful, in a way that completely avoided the horrifically challenging parts of touching the brain. Their device was a simple wristband that reads neuromuscular signals from the wrist (via electromyography, or EMG) to control external devices. Here’s a great video of it in action.

Now, if CTRL-labs was so great, what happened to their technology? They were acquired by Meta in 2019, joining Facebook Reality Labs. And if you look at the ex-CEO’s Twitter (who is now a VP at Meta), you can see that he recently retweeted a September 2025 podcast with Mark Zuckerberg, in which Mark says that their next generation of glasses will include an EMG band capable of allowing you to type, hands free, purely by moving your facial muscles.

Not too far of a stretch to imagine that this is based on CTRL-labs work! And, by the time I finally finished this essay, the device now has a dedicated Meta page!

What about something that exists today?

Another startup that multiple people were exuberant over was one called Augmental. Their device is something called ‘Mouthpad^’, and a blurb from the site best describes it:

The MouthPad^ is smart mouthwear that allows you to control your phone, computer, and tablet hands-free. Perched on the roof of your mouth, the device converts subtle head and tongue gestures into seamless cursor control and clicks. It’s virtually invisible to the world — but always available to you.

And here’s a wild video of a 19-year old quadriplegic using this device to interact with a computer and even code.

Isn’t this insane? I remember being shocked by the Neuralink demo videos showing paralyzed patients controlling cursors on screens. But this is someone doing essentially the same thing! All by exploiting both the tongue, which happens to have an extremely high density of nerve endings and remarkably fine motor control, and our brain, which can display remarkable adaptivity to novel input/output channels.

Now, fairly enough, a device like Augmental cannot do a lot of things. For someone with complete locked-in syndrome, there really may be no alternative to inserting a wire into the brain. And in the limit case of applications that genuinely require reading (or modifying!) the content of thought, the periphery again won’t cut it. But for a surprising range of use cases, the peripheral route seems to offer a dramatically better risk-reward tradeoff, and it feels consistently under-appreciated when people are mentally pricing how revolutionary a new neurotech startup is.

Conclusion

This piece has been in production for the last five months and, as such, lots of discarded bits of it can be found on the cutting room floor. There are lots of other things, not mentioned in this essay, that I think are also worth really pondering, but I couldn’t come up with a big, universal statement about what the takeaway is, or the point is pretty specific to a small subset of devices. I’ve attached three such things in the footnotes.1

Before ending, I’d like to repeat the sentiment I mentioned at the start: this field is complicated. A lot of the readers of this blog come from the more cell-biology or drug-discovery side of the life-sciences field, and may naturally assume that they can safely use that mental framework to grasp the neurotech field. I once shared this optimism, but I no longer do. After finishing this essay, I now believe that the relevant constraints in this domain come from such an overwhelming number of directions that it bears little resemblance to most other questions in biology, and more-so resembles the assessment of a small nation’s chances of surviving a war. The personality required to perform such a feat matches up with the archetype of individual I’ve found to work in this field, all of whom display a startling degree of scientific omniscience that, in any other field, would be considered extraordinary, but here is equivalent to competence. It would be impossible to recreate these people’s minds in anything that isn’t a seven-hundred-page text written in ten-point font, but I hope this essay serves as a rough first approximation.

Extraordinarily grateful to Milan Cvitkovic, Sumner Norman, Ben Woodington, and Adam Marblestone for all the helpful conversations, comments, and critiques on drafts of this essay.

Today, Canada officially lost its measles elimination status. Measles was previously declared eliminated in Canada in 1998, but countries lose that status after 12 months of continuous transmission.

Here are some articles about the the fact that we have lost our measles elimination status: CBC, BBC, New York Times, Toronto Life. You can see some chatter on Reddit about it if you’re interested here.

None of the above texts seemed to me to be focused on the actual thing that caused Canada to lose its measles elimination status, which is the rampant spread of measles among old-order religious communities, particularly the Mennonites. (Mennonites are basically, like, Amish-lite. Amish people can marry into Mennonite communities if they want a more laid-back lifestyle, but the reverse is not allowed. Similarly, old-order Mennonites can marry into less traditionally-minded Mennonite communities, but the reverse is not allowed.)

The Reddit comments that made this point are generally not highly upvoted, and this was certainly not a central point in any of the articles. It is a periphery point in all of the articles above at best. Toronto Life is particularly egregious, framing it like so:

“mis- and disinformation were factors in the outcome, which are partly due to pockets around the country with low vaccination rates.”

This is, ironically, misinformation: true information framed in such a way to precisely give you the incorrect view of things.

In this post I will make two arguments: first, yes, it is the Mennonites that began (and are the biggest victims of) the biggest measles outbreak of the current century, and second, thinking of them as resistant to vaccination is actively harmful to the work of eliminating measles from Canada once again.

I’ve been following the measles outbreak closely for basically its entire duration, because I have a subscription to my local newspaper, the Waterloo Record. The writers there do frequent updates on the outbreak, often with higher quality and more detail than you get in the national papers. This is because Waterloo Region has a significant Mennonite population, so shit sometimes got real scuffed.

Like, over last spring, there were fairly regular advisories about local stores we shouldn’t go into or quarantine if we did because someone with measles went in. One of them was the pharmacy across the street from the university campus, so that was fun.

The Mennonite Outbreak

Here is what the outbreak looks like, Canada-wide: Health Canada

Full offense to Health Canada: this is a terrible graphic, because if you don’t look at it carefully you will think that the provinces in dark blue have approximately the same number of cases, and this is very false. Sasksatchewan has barely over a hundred, Alberta has almost 2000, and Ontario has almost 2400 cases.

What’s the deal with Ontario, and Alberta? Some of it comes down to the numbers game; those are two of our most populous provinces. But Quebec has twice the population of Alberta, and it’s trucking on with only 36 cases in the entire province.

The answer is that it’s the Mennonites, who are overwhelmingly settled in those two provinces.1

I’ll be focusing on the outbreak in Ontario, because that’s the part of the story I’m more familiar with. If you dig into older news pieces, the Mennonite connection is corroborated by government officials:

Previously, Moore [the Chief Medical Officer for Ontario] shared that this outbreak in Ontario was traced back to a Mennonite wedding in New Brunswick, and is spreading primarily in Mennonite and Amish communities where vaccination rates lag. The vast majority of those cases are in southwestern Ontario.

Mennonites have a social structure where, once the community reaches a certain number of families, they undergo mitosis, and half the families split off to form a new community far away. Based on reddit scuttlebutt, it seems like there has recently been a daughter community that moved from southern Ontario to New Brunswick, which makes it doubly unsurprising that there were many southern Ontario attendees to the original superspreader event.

Additionally, Moore, remarked in a memo he sent out to local health bodies:

Over 90% of cases in Ontario linked to this outbreak are among unimmunized individuals. Cases could spread in any unvaccinated community or population but are disproportionately affecting some Mennonite, Amish, and other Anabaptist communities due to a combination of under-immunization and exposure to measles in certain areas.

And Global News reports:

In an April interview with The Canadian Press [Moore] reasserted that the “vast majority” of Ontario’s cases are among people in [Mennonite, Amish, and other Anabaptist] communities.

Some smaller publications have found connections in their own investigation. The London [Ontario] Free Press in March 2025 (the beginning of the outbreak) linked the outbreak in West Texas to their Mennonite population, and identified that several measles exposure sites in counties that have been heavily afflicted by measles are Mennonite in nature:

A list of measles exposure sites in Grand Erie includes a church and several private Christian schools in western Norfolk County catering to Old Colony Mennonites, and Moore’s letter confirmed the link.

A recent Washington Post article also corroborates the link, but buries it under several paragraphs of preamble about general vaccine skepticism.

Many large measles outbreaks in Canada have occurred in insular Mennonite communities in rural Alberta and Ontario, where some are skeptical of vaccines.

Outbreaks have also been reported in Mennonite communities in Mexico and West Texas.

Mennonite Geography

Public Health Ontario has infection numbers for you, broken down by geographic area (”public health units”). Here’s what that looks like when I plot them on a graph. Notice that there are five units that are responsible for basically all of the cases, and you will have heard of none of them because they include zero major population centres.

The most populous health units, such as Toronto, Ottawa, Halton, Hamilton, Peel, and York, all have three cases or fewer for the entire year, and a corresponding case rate of close to zero.

I admit that I do not have the temerity required to separate out Mennonites from like, generic rural dwellers, but something wonky is going on here! The measles outbreaks are all in sparsely populated regions while the big cities (with their big suburbs, presumably where all the anti-vaxxers would be) carry on basically unscathed.

To better visualize this, I am going to combine a bunch of charts together jankily: the geographic distribution of measles (blue), population density (red, adapted from Wikipedia, and, in lime green, the settlements of Amish and Mennonite communities I found online.2

I tried to match the map outlines in procreate by hand, which means it was done imperfectly. Okay, sorry, you will need to stare at it for a bit. The key takeaway is that the blue areas (which represents measles cases) almost perfectly avoids the most populated areas (red), and are full of green dots (where the Mennonite and Amish settlements are).

Let’s look at this another way. Here’s a Public Health Ontario COVID report from April 2022 (i.e. after vaccinations have been available for a while). Pages 8-10 include comparable charts on cases per 100,000 people broken down by Public Health Unit. It’s relatively stable between PHUs, and larger in city centres compared to rural settlements. This makes sense, because urban settlements are by definition denser, which means it’s easier for viruses to spread.

Here are the outbreaks plotted against each other, if you’re curious.3 Notice that the same five public health units no one has heard of are outliers again, which is what you would expect.

Also note the different degrees of variance in cases per 100,000 people in a health unit. COVID cases per 100k ranged from about 4,000 to 11,600 across health units, which is roughly a 3x difference. The Measles cases were actually incredibly discontinuous across units: many units had literally zero cases, some had under 30 cases per 100,000 people, then there’s a huge gap, and then there’s five regions that had over 100 cases per 100,000 people.

For the statistically inclined, the coefficient of variation (standard deviation divided by mean, expressed as a percentage) was about 25% for COVID and 193% for measles, which is almost eight times higher.4 (If these numbers mean nothing to you, don’t worry about it. The point is just that COVID spread relatively evenly and measles did not.)

Lastly, here is some incidental info from some July 2025 coverage on the outbreak in St. Thomas (a smaller city, in that vertical belt of green dots across central southern Ontario, in the PHU Southwestern Public Health which is the one with the most cases):

Five months later, around 150 to 200 of our [Mennonite] clients have had measles, and most of our Low German–speaking clients have at least had symptoms.

As of October 28, there has been 771 cases of measles in the Southwestern PHU. If 150-200 of them were Low German-speaking mennonites as of July, and most of their clients had symptoms at that time, this indicates that the Mennonites would have made up for a substantial amount of all cases in that PHU.

I rest my case! Which is not to say that it is a perfect one but here is where I put it down because I am not going to put more effort into it. I encourage others to pick it up and put more work into it if they are so inclined.

Mennonites Are Susceptible To Facts and Logic, When Presented In Low German

The general sentiment both in the reddit comments and in most of the news coverage seems to be something like “oh, they’re weird religious people, and therefore immune to logic about vaccines”, and also something something religious tolerance meaning that we can’t criticize their choices at all.5

But in reality, Mennonite parents love their children and do not want them to die of measles, and they do not want to contract measles themselves. Having looked into it, it seems to me like the largest barrier for them getting medical care and vaccination is that they are not fluent in English, they speak Low German.

In Ontario, three quarters(!!!!!!) of the 700 Mennonite community clients helped by a Low German-speaking personal support worker have agreed to be vaccinated.

In Alberta (the other large Mennonite population centre, and not coincidentally the other large site of the outbreak), there has been a 25% increase in demand for medical care in Low-German, and service has expanded from five to seven days a week.

And, like, yes, to be clear, there are loads of Mennonites who are actually anti-vaccine. I am not disputing the obvious fact that, in religious communities, many people are against vaccinations. Further, 75% still falls very short of the 92-94% vaccination rate needed for herd immunity. But a 75% vaccination rate is much, much higher than I’d have hoped for?

Here is an example of the miscommunication that can happen when one is not fluent in English:

The following morning, the [Mennonite] mother called me: her child [who recently developed a measles rash] was coughing so violently she was vomiting. I told her to go to the hospital. Later, she called me again, upset. She said that when she got to the ER, they’d told her to go home.

I couldn’t help but think something was off. The hospital doesn’t turn people away, I told her, but she insisted that they had. So I called them directly to figure out what had happened. It turned out there had been a miscommunication. Hospital staff had told her not to come in, using a “stop” hand gesture to communicate, and she had become so flustered that she failed to catch the second part of the message: that she should wait in the car while they prepared a negative-pressure room.

If your measles outbreak comes from this sort of community, the solution isn’t to fearmonger about anti-vaxxers. It is to train up and hire health care workers who can speak Low German. And to be clear, I think the PHUs that are affected are doing this, or at least the Ontario ones are, because our public health bodies are generally not disconnected from reality.

And that’s what I found most frustrating about the media coverage. It obscures something that’s genuinely very hopeful and turns it into another random culture war shitfest. But actually, it turns out that when you remove the actual language and access barriers, people make reasonable healthcare decisions for their families at pretty high rates!

So yes, Canada lost its measles elimination status today. But we can get it back in a year and a bit, if we’re serious. And if we’re serious about eliminating measles again, we need to focus more on investing in healthcare workers who speak the language of and can build relationships in communities, and less on implying that certain populations are fundamentally unreachable.