In the public debates after the release of AI 2027, some people accused us of misleading readers by positing “neuralese recurrence”, a hypothetical technology that would allow AIs to think dangerous thoughts without getting detected.

You’ll never guess what happened earlier this month!

But what is recurrence? Is this exactly the same as the neuralese recurrence in AI 2027? And how worrying is it?

The transformer, the technology behind most modern AI, contains some number of layers (modern frontier models are probably around 100). By convention, the input layer is called the “bottom” and the output layer the “top”. When a transformer does next-word prediction, it takes the last word in at the bottom layer, spends the middle layers processing it, and outputs the predicted next word at the “top”.

What if it’s working on a hard question that needs more than 100 layers of processing? From 2017 - 2024, the answer was “you’re screwed”. From 2024 onward, the answer was: it outputs its intermediate result, as text, onto a scratchpad called the “chain-of-thought”. Then it runs the transformer again on the intermediate result. Then it repeats until it has a final result.

(Why does it need a scratchpad, instead of feeding its intermediate results directly into user output and then reading those back into itself? First, the intermediate results may be hundreds of pages long, and the user doesn’t want to read those. Second, the AI companies want to train the AIs to do the intermediate thinking properly, and this benefits from a ‘psychological’ distinction between intermediate pondering and final user output.)

This was purely a capabilities play - models are smarter when they can keep thinking instead of limiting themselves to 100 processing steps - but it coincidentally was very good for safety. The chain-of-thought scratchpad is written in English (although some Chinese models use an English-Chinese hybrid, and other AIs develop their own weird jargon). You can just read what the AI is thinking! If the AI is thinking “Better hack some websites, then kill all humans”, you can shut it down. Maybe not actually - if you have thousands of AIs writing millions of pages of scratchpad, you can’t read all of that in real-time, and will need to delegate the task to fallible AI monitors. But in theory this ought to work.

But language is slower and shallower than thought. For 100 steps in a row, the AI can shoot delicate subtle ideas from layer to layer at the speed of light. Then there’s one step where it has to encode them into twenty-six glyphs invented by Phoenician turquoise miners in 1800 BC. Then it has to re-encode the Phoenician glyphs into delicate subtle lightspeed ideas before it can do anything else. This has long been acknowledged as a bottleneck in existing transformers. So: what if they could finish their 100 layers of thinking, then send the resulting thought back to the first layer for more processing, rather than sending a text scratchpad on which they had written the thought?

This is neuralese recurrence - “recurrence” because it’s going back in a loop, “neuralese” because the thing that’s looping is the thought itself, in the native language of thought, rather than words.

The AI’s “language of thought” looks like a vector, thousands of numbers long. An “intermediate result” in this scheme might look something like (0.4, 0.1, 5, 0.443, … and so on for thousands of numbers). We don’t know how to read these. The science of reading these thoughts is a subfield of AI interpretability, which is still in its infancy. If an AI with neuralese recurrence were to think “Better hack some websites, then kill all humans”, it would look like (0.4, 0.1, 5, 0.443, … and so on for thousands of numbers), and we would never find out. This is why AI 2027 discussed it as a prelude to AIs that could escape human monitoring.

II. Pachocki Contra Specter

So did OpenAI achieve this? Their chief scientist, Jakub Pachocki, says “not really”:

What does Pachocki mean by “the depth of the computation graph . . . is within a factor of two of GPT-4”? And should this resolve alignment concerns?

Our example transformer has four green layers: a layer depth of four:

Now we add a little recurrence: instead of layer 3 going to layer 4, it loops back to layer 2, and the data go through this loop three times, then finally progress to layer 4:

Here the data are getting eight layers of processing: 1 → 2 → 3 → 2 → 3 → 2 → 3 → 4. So this recurrent transformer has a depth of eight.

In this sense, “recurrence”, despite sounding scary, is no worse than just building an eight-layer transformer to begin with, which was always an option.

That is, from an alignment perspective, suppose it takes 1,000 layers’ worth of processing to come up with an evil plot. A normal AI can think for 100 layers, but then it has to record its output on the chain-of-thought - something like “Here’s one-tenth of the evil plot I’m trying to hatch” - and we can catch it. So we want to avoid letting the model compute for 1,000 layers straight without using the scratchpad. This means don’t create a recurrent model that can loop 100 layers 10x, but it also means don’t build a 1,000 layer model to begin with.

So when Pachocki says that Astra only has twice the depth of GPT-4, he’s saying something like: look, guys, we’ve been adding layers to our AIs for years. GPT-2 had 48 layers, GPT-3 had 96, and we didn’t tell you how many GPT-4 had but let’s say it was 120. At none of these points did you complain, because it wasn’t “recurrence”, just a normal natural 120 layers. Now Astra is - let’s say - 180 layers. It’s true that we got these extra layers by recurrence rather than literally building a bigger transformer, but the alignment implications are no different. For all you know, Anthropic built a literal 180 layer transformer yesterday, and you guys didn’t bother them1. The important thing is that we not increase our layer number by some crazy amount, and we didn’t. We’re just adding layers the same way lots of other AI architectures did, albeit by other means.

How convincing is this argument?

III. God Help Us, Let’s Try To Understand Transformer Layers

When reading this, my question was - why don’t you just loop all the layers one million times, and get an AI with a million times the computational depth of the shmuck who didn’t do that? Isn’t this free capabilities?

A looped transformer model like Astra uses loops as a hack to add more layers. This hack isn’t as good as adding more layers for real. A real new layer gives the AI more “room” to store knowledge and thought styles. A looped layer just lets the AI use the same knowledge and thought style more times.

This is far from useless. A mathematician might spend years contemplating the same problem before getting it right; all through this period, he is using the same type of thinking (the mathematical knowledge and techniques his brain is capable of). But sometimes doing it for a year is better than doing it for a minute.

But it’s not optimal either. An AI with more real layers can actually be “smarter” in the conventional sense of the term. Even though it might help to give the same mathematician more time to think about the problem, holding amount of time constant, it will help even more to give the problem to a smarter mathematician with more training.

I asked Fable to estimate the relative capabilities gain, measured in “generations” (eg GPT-4 to GPT-5) from three things:

• Using chain-of-thought (baseline)

• Doubling the “simulated” number of layers by a recurrent loop.

• Doubling the “real” number of layers.

Its answers were 0, +0.03, and +0.252, with wide variation depending on the type of task. Its headline result was that the gains from extra simulated layers are much smaller than the gains from extra real layers.

But why are we merely doubling the number of layers? Once you can loop layers at all, why not loop them a million times? Pachocki says he’s replacing something like 120 layers → chain-of-thought scratchpad → another 120 layers with something more like 240 layers → chain-of-thought scratchpad → another 240 layers, but why doesn’t he just loop the AI endlessly until it comes up with a final result?

Here is a diagram of a transformer process (source):

On each token (for example, the word “so”) the transformer looks at its existing context and runs a forward pass (the gray up arrows) through some number of layers. Then it finishes its forward pass, emits a token of chain-of-thought (the curvy blue arrows), then starts another forward pass on the new context (the old context plus the new token).

Adding more layers lets the AI think more between chain-of-thought tokens, improving the quality of each new token. But it uses extra compute. If the user is on a budget, the more compute that the AI spends on each chain-of-thought token, the fewer chain-of-thought tokens it can emit. Holding compute fixed, there’s some optimal balance between the quality and quantity of chain-of-thought tokens, and OpenAI must have decided it lay at looping 2-4x but not 1000x.

But why use chain-of-thought at all? Why not just loop unboundedly, until the problem is complete?

(on the diagram above, that would look like replacing the English-reasoning blue arrows with neuralese-reasoning gray arrows)

This would work if the AI could be trained to do it, but currently it can’t. Transformers are pre-trained on human text. They know how to use chain-of-thought partly from reading real humans’ chains-of-thought: “Hmmmm, this is a hard problem, but it looks sort of like something I’ve solved with differential equations before. Maybe if I plug in a differential equation there . . . no, that would be too inelegant . . wait, what if -”, and partly from reading enough other human output that this kind of human-style thinking comes naturally. But there is no convenient Internet text about human reasoning entirely in vectors, so the AIs can’t be trained to do it properly. In principle this isn’t fatal – all AI reasoning is in vectors, and the whole point of training is to teach them to do it well – but training an entire vector-based thought process for thousands of steps without ever bottoming out in a human-imitating chain-of-thought-token intermediate is prohibitively costly. The researchers who design transformers have to make guesses about what chain-of-thought frequency will maximize capabilities while minimizing training cost. This is part of what Pachocki was denying. AI 2027’s “neuralese” is the thing researchers currently don’t know how to do3 – train an AI to loop as often as it wants, avoiding English chain-of-thought entirely. Pachocki was saying: don’t worry, we still don’t know how to do that.

But now we can see that this is not entirely reassuring. If it takes an AI 1,000 unmonitored gray-arrow steps to devise a dangerous plot, it can get those steps either by replacing all the blue arrows with gray arrows (in which case all its steps are unmonitored, and it can plot as much as it wants), or by having so many layers that there are 1,000 steps in a single vertical forward pass in between blue arrows4. It would be prohibitive to make that many real layers, but it’s possible by looping. So (the safety community argues) even Pachocki’s looped transformer architecture is a step along that path.

All of this is starting to get confusing, but the takeaway is:

1. An AI with a limited number of layers can’t plot at all without having to record the plot on its chain-of-thought.

2. An AI that uses loops to simulate a large number of layers (“looped transformer”) can make a plot within a single forward pass, in between chain-of-thought steps.

3. An AI that no longer uses chain-of-thought at all (“true neuralese”) can plot at leisure, considering it for as many episodes as it needs, and we will never know.

Every previous transformer has been (1). Astra is getting part of the way to (2). The dangerous AIs in AI 2027 are (3), but so far nobody has invented this in real life.

IV. A Psychological Barrier, Not A Technical One

Having finished that digression, let’s return to the original question. Pachocki says that Astra’s recurrence barely gives it any more layers than the competition. Does that exonerate him from the charge of creating dangerous recurrent AIs?

Here the best thing I’ve read is Linchuan Zhang’s Categorical Taboos Are Much Better Than Threshold Taboos: Neuralese Edition.

Linch says: there’s an emerging taboo on creating the dangerous sort of neuralese AIs seen in AI 2027. Everyone in this discussion agrees that the taboo is correct. The only question is whether OpenAI violated that taboo.

But taboos only work when there are clear boundaries defining what is versus isn’t within the tabooed area. For example, it’s illegal in America to buy alcohol before age 21. Suppose that someone buys alcohol two days before their twenty-first birthday. Should the police arrest them? Obviously this age gap doesn’t really make a difference. We’re using age as a proxy for something like maturity, and there’s so much variation in maturity that there’s no real difference between a 20.99 year old and a 21 year old. Still, although the police might sometimes choose to overlook this, we have to at least maintain the fiction that this is an arrestable offense. Why? Suppose that we made a specific boundary - if you’re above 20.8 years old, we won’t arrest you. But then we’ve just decreased our bright line from 21 to 20.8. And we could encounter the same problem: a 20.799-year-old buys alcohol and the police have to decide whether to make an exception or not. If we’re going to draw the line somewhere, it might as well be at the place we’d already promised to draw it, told everybody that we’d drawn it, etc.

(Linch’s own example is nuclear weapons. There’s a taboo on using nukes in war. If someone uses a tiny nuke, which produces an explosion no bigger than a conventional bomb, then in some sense this doesn’t matter, because it’s no worse than a conventional bomb would have been. But in another sense, it matters a lot, because you’ve broken the taboo, and now there’s only a weaker, fuzzier taboo preventing you from using a very big nuke.)

So if we want a taboo on recurrence, there needs to be some specific taboo. When Pachocki says “Yes, we did recurrence, but it only added a couple of layers, so it doesn’t really matter,” this is analogous to the case where a drinker tells the police “Yes, I’m below 21, but only by a few months, so it doesn’t really matter”, or a despot tells the UN “Yes, I used a nuke, but it was very small.” It’s true that it doesn’t matter in real life, but it matters a lot for whether you can maintain a taboo or not.

Unfortunately, we currently lack agreement on what, if any, taboo exists. Some people argue that there should have been a taboo on looping layers (which OpenAI would have broken), and other people say that they’re making that up and that was never a taboo. There’s probably an informal taboo on true neuralese with no chain of thought at all, but it’s a complicated technology with multiple moving parts, there’s no agreement as to which moving part the taboo is on, and without that agreement, some of the parts might slip through the cracks. So what are our options?

First, contra Pachocki, it might be worth instituting a taboo on looping (adding “simulated layers”) at all, even without any taboo on adding more “real layers”. Adding real layers is expensive and generally not worth it; AI companies have only chosen to increase real depth of their models by ~20% per year; at that rate, it might take decades to reach a danger point. Adding “simulated layers”, which are much cheaper and can be jammed in hundreds at a time, is more dangerous. So maybe we should ignore the hypothetical real layer / simulated layer equivalence in favor of saying that looping is always taboo, regardless of how many real layers other people are adding somewhere else.

Second, companies could agree on some maximum number of layers (real or simulated), like 1,000. This probably wouldn’t affect real layers (by the argument above), but it would limit loops to some maximum size.

Third, researchers could hash out what the components of “true neuralese” are, and agree not to do them. This would require some conceptual foundations, but it would be the most durable and the closest to closing off the specific dangerous technology that AI 2027 was worried about.

Unlike some other vague attempts to “ban superintelligent AI” or “ban recursive self-improvement”, these taboos might stick even without strong government action: at least for now, the capability gains from breaking them seem modest5, and the dangers are particularly obvious, so they might be maintainable by voluntary commitments even while a “race dynamic” was still going on.

AI company leaders have recently put aside their differences and agreed to “pace the frontier” in various senses to be fully defined later. Their meetings will probably have a very long list of discussion items, but one more useful thing they could do would be to formalize this taboo, so we know exactly what it is we’re trying to stay away from.

If you’re interested in this topic, please also read this essay by Redwood Research, which explains some of the relevant concepts and technologies in more detail.

I’d like to reflect on what I wrote five years ago: what I was right about, what I’ve changed my mind on, and what the outlook for LVT is in 2026. But first, here’s a brief summary for those who either have no idea what I’m talking about or just need a refresher.

I. Progress and Poverty

In 2021, I wrote a book review for ACX on the book Progress and Poverty, the magnum opus of the famed 19th-century economist and populist firebrand Henry George:

If I had to summarize the book in a single sentence I would put it this way: poverty and wealth disparity appear to be perversely linked with progress, The Rent is Too Damn High, and it’s all because of land.

George argues that poverty paradoxically advances alongside progress because, as material conditions improve, landowners can charge more rent for locational benefits they didn’t create. People who earn more than the local average salary (e.g., software engineers) can stay ahead of this trend, whereas those who don’t (teachers and service workers) are priced out of the homes they rent. Eventually, even upper-middle-class families can struggle to afford services like daycare, because daycare businesses must charge more to pay business rent and employee wages. Landlords, rather than daycare owners or workers, collect most of the service price increase parents pay.

Henry George’s remedy is the land value tax, or LVT. In its ideal form, this tax would capture and redistribute the annual rental value of land; that is, the recurring value of the land excluding the value of any buildings or other improvements on top of it. In practice, this looks a lot like a conventional property tax paired with a “universal building exemption.” Notably, George isn’t simply in favor of a land value tax; he’s also opposed to taxes on both labor and capital.

George further proposed the single tax—a policy in which land is taxed at its full annual rental value, and LVT is the only tax.1 Although the feasibility of the “single tax” remains controversial among economists, land value tax itself is surprisingly well accepted by economists left, right, and center as the ideal tax policy, with mainstream criticism mostly centered on practical and political concerns.

After the book review contest results were announced, ACX readers inundated me with questions, which led to the three follow-up posts. After those ran, I reposted all four articles on the standalone site www.gameofrent.com, and later consolidated them into a book, Land is a Big Deal.

Here’s what’s happened since.

II. LVT Momentum is Growing

Five years ago, LVT was mostly a hypothetical idea people debated on blogs. Today, it has the most legislative momentum it’s seen in decades. Many states introduced bills this year, and Virginia and Kentucky passed land value tax enablement laws in April. These laws allow municipalities to opt into split-rate property taxes, which lower tax rates on buildings and raise them on land. My organization, the Center for Land Economics, maintains a public legislation tracker with an interactive map that makes it easy to keep tabs on these trends.

Virginia and Kentucky were the big winners for the 2026 legislative season, enabling various cities in those states to implement land value taxes. However, even bigger opportunities are coming. In Washington State, my organization is collaborating with the Sightline Institute on an upcoming LVT bill. Meanwhile, in New York, Governor Hochul extended authority for cities to utilize land value capture to fund new transit stops, a tool that can be used for the new Inter-Borough Expressway (IBX) subway expansion. We’re working with Niskanen Center, Center for Public Enterprise, and Institute for Progress to turn this idea into a real policy proposal.

Nor is interest in land value tax limited to the United States. The UK just elected a new prime minister, Andy Burnham, who has long openly advocated for a land value tax, and South Korea did the same last year with the election of new president Lee Jae Myung. Additionally, in January 2025, the German state of Baden-Württemberg implemented a new LVT that survived a subsequent court challenge. Although we’re tempering our expectations (the effective tax rate of the German LVT is quite low, and it remains to be seen how ambitious the Burnham and Lee administrations will be), it’s clear that LVT is no longer an obscure idea, nationally or internationally.

Finally, many now believe that the economic effects of AI developments may accelerate support for LVT and Georgism more generally. As newly minted AI millionaires bid up land prices in San Francisco and Seoul, Adam Ozimek argues that land will be a winner in the age of AI. Similarly, Aksel Sterri, co-founder of the Norwegian Effective Altruist think tank Langsikt, calls for an explicitly Georgist framework for understanding the AI era. (I’m also Norwegian; see my piece on Norway’s century-old Georgist tradition in natural resource management for more context on what Aksel’s talking about.)

So what have I learned?

III. My Theory of Change Was Wrong

I used to think LVT advocates had to change popular and elite consensus before moving on to the boring scut work of implementation. Now I realize that the boring scut work is actually what precedes, and even leads to, changing the consensus.

Overrated: winning online arguments

If you go back to the original series’ comments, you’ll see me trying to answer every concern that comes up, and falling for the bait when someone drags me into a tendentious comment thread dozens of replies deep. These would sometimes terminate in the interlocutor declaring that they’re the “exact kind of person Georgists need to convince!”

I’ve since learned that overly online keyboard warriors are the least important people to convince. The most important people are on your local city council or state legislature. Furthermore, instead of wasting time with hard cases or elected officials who are dead-set against your ideas, you’re much better off finding and working with people who are already interested in your principles. This often means giving up on your own city or state, at least for the time being, and pursuing a “succeed anywhere” strategy instead.

Changing policy where people want to change it is also how you overcome the “cold start problem,” when someone likes your idea but wants to see someone else do it first. Rather than trying to change that person’s risk aversion, find the person who is adventurous enough to try something new and work with them first. This builds case studies that you can use as social proof for the second, more cautious, wave of reforms.

I’ve also found that many online and academic objections are somewhat imaginary. When you talk to actual people in office, either their objections are entirely different from the ones you see in social media and comment threads, or they’re surprisingly open to being convinced if you listen patiently and present a clear argument backed by data and research.

Which brings me to…

Underrated: doing policymakers’ homework for them

No politician on earth has the time or inclination to independently learn about your philosophy, ingest all the arguments for it, evaluate a bunch of empirical and theoretical research, model its impacts on their locality, anticipate and respond to all possible objections, then wrap it all up into a tidy package complete with PowerPoint presentations, slick graphs, interactive websites, and a convenient, printable “one-pager” to hand out to interested parties.

You know who can do all those things? You.

For instance, some people get confused about land value tax, thinking it will wreck single-family homeowners, or that I’m out to get them personally, because obviously all the land value in town is concentrated directly underneath their specific home. This isn’t a disagreement about values or mechanics but rather a simple misunderstanding of what land value is and where it is most concentrated.

The first thing I do to disabuse people of this notion is to point out how much land value in any typical U.S. city is concentrated in downtown areas, and how much that value attenuates in the suburbs. We do this by loading up CivicMapper, our free, open-source 3D visualizer that takes local assessed land values and puts them on a map.

Here’s Washington, D.C.

Here’s Austin, Texas.

Here’s Seattle, Washington.

Not all cities look the same, and not all assessments are of equal quality, but you see the same basic patterns everywhere. Land in the city center is worth much, much more than outlying areas.

Then, we show people how much of that high-value land is dedicated to extremely low-value uses, like surface parking. Here’s Houston, which has over 3.5 billion dollars of land value locked up in surface parking alone.2

And no, it’s not just Texas. Here’s Portland, Oregon.

We can even zoom in and show how much of that surface parking occurs in the most valuable areas, such as Houston’s downtown district, which alone accounts for nearly half a billion dollars’ worth. These are exactly the places where it makes the most sense to concentrate development.

Having established that Land Is a Big Deal and that we’re also squandering it, we do the math and build a model of who wins and who loses under a revenue-neutral land value tax shift, or “Universal Building Exemption.” To do this, we use LVTShift, a free, open-source Python library maintained by the Center for Land Economics. In most of our models, the biggest losers are vacant land and surface parking lots, and among the net winners is the typical median single-family homeowner.

In addition to doing the math, we find compelling stories to tell. For instance, in this report on Cincinnati that we collaborated on with the Notre Dame Student Policy Network, we illustrate how conventional property taxes punish those who improve properties and invest in the city by providing housing and business, while those who own vacant lots or surface parking lots are rewarded for holding land out of use.

Here’s a similar comparison from our report on Spokane, Washington. The lots with houses on them pay more than seven times as much per square foot of land as the vacant lot does, even though all the land is equivalently zoned and similarly located.

We built up this methodology from crude beginnings by talking to people, trying things, learning from our mistakes, and refining our approach. Finally, we condensed everything we learned into a concrete political playbook entitled Enacting Land Value Return in Your Hometown, then published it as a guide for others to follow.

This playbook is now leading to wins. One of our activists, Jackson Arnold, a member of the Abundance Network, wanted to implement LVT in his hometown of Louisville, Kentucky. He got in touch with us, joined the OpenAVMKit Discord,3 and ran the playbook all the way from inception to getting a bill passed in his state legislature. We are now trying to figure out how to inspire and enable more Jackson Arnolds.

While the first wave of LVT fans were local citizens, we’re now attracting lawmakers’ attention. The most salient example is Bill Blessing, a Republican state senator from Ohio and chair of the Ways and Means Committee, who introduced an amendment to Ohio’s state constitution this year to legalize local opt-in LVT.

That’s all very exciting. But we still have one question to ask before we get carried away.

IV. Does Georgism work?

My three-part article series was structured as an investigation into the three most common objections to Georgism. We should revisit those and see where things stand in light of what I’ve learned since. The three objections were:

1. Land just isn’t a big deal anymore in the modern economy.

2. Land value tax will just be passed on to tenants.

3. Land value can’t be accurately assessed separately from buildings.

1. Is Land a Big Deal?

In Part 1, Is Land A Big Deal?, I ran the “land isn’t a big deal” theory against several testable hypotheses. Among the findings was the fact that sky-high urban real estate prices were primarily driven by land appreciation, and that land was a large and steadily increasing share of bank loans.4 The article’s centerpiece was an original estimate of the total land value of the United States, which was much larger than many had expected. Although this estimate fell short of what a “single tax” would require, it was still large enough to convince many that LVT had been unfairly dismissed as a serious policy proposal.

This article has aged the best of the three, but I still have a few updates to share.

Korea is a Big Deal

If land is a big deal in the USA, it’s an even bigger deal in South Korea, which has all the prerequisites for a national LVT and enough land value to approach a full-on single tax, or other ambitious projects like Universal Basic Income (UBI). Here’s a snippet from my piece, UBI Advocates should watch South Korea:

For UBI or LVT to succeed anywhere, they must first succeed somewhere…therefore, if you want UBI or LVT to succeed, you should scour the world for a place where these two policies are the most:

• Economically feasible

• Technically feasible

• Politically possible

• Socially and politically urgent

That place is South Korea.

South Korea has the highest land value-to-GDP ratio in the entire OECD, in excess of 500%. To put that in perspective, the figures from my own estimates of the USA’s total land values—which surpassed many readers’ expectations—were a mere 200% of GDP. My first instinct was that Korean land values must reflect a temporary, anomalous bubble and would soon revert. However, long-run land value-to-GDP ratios over the last 50+ years have ranged from 400%, and current values aren’t even the all-time peak, which clocks in at around 600%. Prime land is just that valuable and scarce in South Korea.

Where do these land value figures come from, by the way? Turns out, South Korea has one of the best-organized land valuation systems in the entire world, despite not having a land value tax, which we’ll discuss later.

In short, LVT in South Korea could raise enormous revenue. Land is already valued down to the individual parcel annually, the current president is sympathetic to LVT, and the urgency for socioeconomic reform in South Korea has never been higher. Although many daunting political obstacles remain, South Korea’s example makes it clear that land is, if anything, an even bigger deal than I originally thought.

Next, let’s address a few counter-arguments to the “Land is a Big Deal” thesis that I didn’t fully address last time.

What About Zoning?

One argument that often came up in comment threads was, “All we need to do to solve the housing crisis is upzone.” I certainly agree that upzoning is necessary, but I don’t agree that it is sufficient (which is something I also believe about LVT). Stephen Hoskins’ essay Land and Liberty to Build makes a great philosophical case for why YIMBYs should also be Georgists and Georgists, YIMBYs, to which I will add a few arguments of my own.

First, history falsifies the “upzoning is sufficient for affordability” hypothesis. If upzoning is all we need, we should not see housing affordability crises before zoning, which was not fully entrenched until the 1920’s. Instead, we see the opposite. The Georgist movement itself sprang from a massive housing crisis in the late 1800’s, decades before zoning became widespread. One could say, “Well, high-rise buildings hadn’t been invented yet, which is what you need to overcome land scarcity pre-zoning.” However, skyscrapers had been around for decades prior to the 1920’s.

Second, not every location is equally constrained by zoning. Michael Wiebe has a great article reviewing a recent paper that estimates the implicit “zoning tax” of various metros, finding that San Francisco is the most constrained, and cities like Cincinnati the least.

Even though cities like Cincinnati aren’t as expensive as San Francisco, they still have problems. It’s easy to find concrete examples of the most valuable land being held out of use even in the least constrained cities, which contradicts the “upzoning solves everything” argument. We’ve already shown you Houston (which has no zoning), but here’s a look at surface parking lots in Cincinnati’s downtown. You can find this pattern of wasted valuable land in just about any American city.

Let’s go back to this chart from Cincinnati.

We can see that although all four parcels share the same zoning, the building component of the property tax gives them very different tax assessments per square foot of land. The tax system actively punishes the affordable housing complex for the crime of being denser than its neighbors.

Some LVT-skeptical YIMBYs also argue that, “Land is already taxed by conventional property tax, therefore we don’t need LVT.” The problem is that YIMBYs generally oppose “impact fees,” which are arbitrary extra costs local governments impose on new development. In Property Taxes are not Land Value Taxes, I argue that the building component of a property tax mathematically amounts to the same thing. I am more than happy to defend property taxes against those that wish to abolish them entirely, but I will continue to insist that the building component of the property tax is distortive in essentially the same way that impact fees are. My position is not to layer on an additional LVT but to lower (or eliminate) the effective tax rate on buildings, and simultaneously raise it on land.

The final thing I’m updating on is to give more attention to other ways of capturing land value than LVT alone, such as through ground rent leases. Jeff Fong, a prominent member of YIMBY Action (as well as our board of advisors), has an excellent piece called Georgism through Land Leasing that explores this potential.

That’s it for the “Land is a Big Deal” thesis. Here’s how I’ve changed my thinking on the other two articles.

2. Can Land Value Tax Be Passed on to Tenants?

In Part 2, Can Landlords Pass Land Value Tax on to Tenants?, I read more than a dozen papers on tax incidence and capitalization effects of land value tax and conventional property taxes. The evidence overwhelmingly showed that LVT is not passed on to tenants.

However, I’ve since found at least one condition under which LVT can be “passed on” to tenants: when housing is pervasively rent-controlled, and landlords are granted a special exemption to raise rents in direct response to tax increases.

To understand why, let’s review the traditional argument for why, in general, LVT is not passed on to tenants. Rents are not set by a landlord’s costs or desires but by supply and demand. The opposing view, the “cost plus” theory of rental pricing, makes several testable hypotheses, which this research brief by the Progress and Poverty Institute evaluates.5

For instance, if landlords reflexively pass on costs (including holding costs like land value taxes) as higher rents, doesn’t that also imply they should cut rents when their costs decrease? Mortgage interest rates have fallen sharply over the past 40 years, yet rents have increased over the same time. Also, landlords who own their properties outright (and thus have no mortgage interest costs) don’t seem to charge different rents than nearby landlords of equivalent properties who are still paying off their loans.

However, these conditions don’t hold under pervasive rent control. If the prevailing rent is already well below what the market will bear, and a landlord is specifically allowed to raise rents by the increased tax amount to some new level that is still below true market rent, then logically, the tax will be mechanically “passed on.” This appears to be the case in Denmark, according to a 2024 paper by Nielsson, Wroblewski, and Yding.

Here’s a diagram illustrating the effect.

Without pervasive rent control paired with a special landlord tax break, the picture would look more like this.

To raise rents in response to taxes, the tax must affect the supply of housing somehow. For taxes on buildings, this mechanism is obvious. Taxing buildings reduces the labor and capital spent producing and maintaining them, just like development impact fees. Less supply of buildings, with unchanged demand, means higher building prices.

Land, however, is not built. Land is inelastic in supply, and landowners don’t “provide” land the way laborers provide labor and investors provide capital; they un-provide land by excluding others from using it. Building supply can change in response to a tax change, but land supply cannot.

The Nielsson, et al. paper’s finding does make me discount the older Danish paper I cited in my second article somewhat, and I’m updating that there is at least this one exception that policymakers should be aware of. However, I don’t think Nielsson, et al. have made a convincing general case outside of these specific conditions, because the Danish case is only one data point among more than a dozen others, and the pass-through mechanism they describe is so narrow, specific, and clearly explainable.

An explanation sufficient to convince me otherwise would need good answers to these four questions:

1. Why don’t landlords cut rents when their operating costs fall?

2. When landlords threaten to raise rents in response to a future land value tax, why don’t they just raise the rents right now? Why do they have to wait until the tax is enacted?

3. Why do property developers subtract all holding costs (which would include both land value taxes and conventional property taxes) from their net operating income, which fully capitalizes into a lower offering price in their discounted cash flow pro-formas?

4. Will rents/housing prices, ceteris paribus, rise or fall in response to all property taxes in a jurisdiction being suddenly abolished?
   

I’ve always been happy to concede that the research on conventional property taxes has been more mixed, with some studies finding full capitalization, others finding partial capitalization, and yet others finding full pass-through. My understanding is that in those cases, local results depend on how responsive the supply of buildings is to marginal tax changes, which varies considerably from place to place. It makes sense that if you’re in a very NIMBY area that never builds anything no matter what, a tax change is unlikely to affect local supply.

Now, on to how I’ve updated my thinking in the final article on land valuation.

3. Assessing the Value of Land

In Part 3, Can Unimproved Land Value be Accurately Assessed Separately from Buildings?, I researched how assessors and academics estimate land value. I concluded: “It’s quite plausible but not a slam dunk. That said, if the objection is, ‘valuing land separately from improvements is fundamentally impossible, and we can never get better at it, so we shouldn’t try,’ I think that’s plainly ruled out.”

This is the article I feel has aged the least well—not because I’ve drastically changed my conclusion, but because for years now I have been studying property taxes, interviewing assessors, and evaluating valuation methodologies as my full-time job, which has given me a much better understanding of the details and procedures.

I have many updates on this topic, but we’ll start with the biggest one, which comes from South Korea, an existence proof that at least one country can do this efficiently and regularly on a national scale.

Maybe Just Send Your Office to South Korea

For my article How to Value Land: Korean Style, I interviewed Korean researchers Jinsu Lee and Vitnarae Kang and read hundreds of pages of dense Korean-language6 procedural documents published by MOLIT.7 The short version is that South Korea values all its land—the land value specifically, not just the total value—every year, down to the individual parcel, and gets the entire operation done in five months flat.

This is organized as a simultaneous joint effort by national and local governments, with the national government responsible for 500,000 “standard parcels,” chosen as locally representative parcels of that type in any given area. In all but exceptional cases, each “standard parcel” receives no less than two individual appraisals,8 and the final valuations for these are handed to local governments as valuation anchors. The local governments are then responsible for valuing all other parcels in their jurisdictions. This simultaneous top-down and bottom-up collaboration combines irreducible local knowledge with centralized support and standardization.9

The Korean case reveals that many arguments against land valuation’s feasibility are fundamentally provincial. The first such example is Wales, where a land value tax is being actively considered and where the government commissioned a report on the feasibility of land valuation, employing no fewer than six separate consultants. Although the findings seem comprehensive at first glance, not one of the consultants examines the Korean system, the single most relevant international example, in any detail. The closest we got was a single line in a report by Alma Economics, which buried the lede as follows:

In Korea, for instance, about 1,300 appraisers (2011 data) value sampled plots, with prices extrapolated to adjacent plots.

Another good example is Sam Watling’s “The failure of the land value tax,” published in Works in Progress magazine last March, which details the Liberal Party’s failed push for a UK land value tax in the early 1900s. In “Contra Watling on the failure of the land value tax,” I show that Watling’s assertion that a “pure” land value tax has never been implemented is plainly contradicted by easy-to-find historical examples, several of which even occurred at the same time as his singular UK episode. As for his other claim that land valuation is practically impossible, his main proof is simply that the UK couldn’t pull it off more than a hundred years ago, paired with the unevidenced assertion that the UK can’t manage it today. As of this writing, the UK’s per capita GDP is $24K higher than South Korea’s, and it has 18 million more people. The state capacity limits that he asserts prevent Britain from successfully implementing LVT would be a specific local dysfunction, and would presumably impede any reform the country pursued to address its housing crisis.

As for the Korean methodology itself, I was surprised at how little “magic” I found when digging into the details. The key differences from what I was used to were procedural and organizational. The valuation techniques themselves were no different from what you would expect to find in a particularly well-run Texas or North Carolina appraisal office. This brings me to my next major update.

There’s No Single Magic Algorithm

This will come as no surprise to anyone with a background in data science, but data quality and running a tight ship matter far more than which cutting-edge predictive methods you pick. South Korea is a great example, but other things I discovered kept pointing to the same finding.

First, I’ve learned that there’s a vast gulf between the world of academia and the world of working assessors. This gulf is not necessarily about knowledge or skill—I know many equally brilliant assessors and academics—it’s simply the traditional divide between pragmatic practitioners and theoretical researchers. Whereas academics are obsessed with r-squared metrics and pristinely crafted multivariate regression equations, assessors are obsessed with whether Jimmy has uploaded new sketches for the River Heights neighborhood yet, if Nancy’s new model can produce adjustments compatible with the new comp grid meant for valuation defense, and if anyone can get the damn CAMA10 vendor on the phone about whether that bug from six months ago has finally been fixed.

I’ve tried my best to help bridge that divide. My Mass Appraisal for the Masses article series is meant to help outsiders understand assessors, and my open-source Python library OpenAVMKit helps assessors understand and use academic machine learning models. I also routinely attend industry conferences; come see my presentation this year at IAAO National in Calgary, or next year at GIS/Valtech in Louisville, Kentucky!

Second, in Amateurs talk Algorithms, Professionals talk Data Cleaning, I explain that the single most important thing any office can do to improve its results is to check its data for invalid and anomalous sales, as well as mismeasured or unobserved building characteristics (particularly physical condition). The most valuable part of OpenAVMKit turned out not to be the fancy machine learning predictors, but simply better heuristics for detecting and diagnosing bad data inputs.

Third, I’ve changed my mind about the cost approach and now believe it is basically fine. For context, the “cost approach” estimates a building’s value by using construction cost tables to calculate the cost to rebuild a property, then applying depreciation based on age and condition. This was a method I criticized somewhat naively in my last article without fully understanding how and where it should be used. In fact, the prevailing methodology used in property tax offices in the United States, the “sales-adjusted cost approach,” yields reasonable land values as a side effect, at least when it’s performed correctly. I describe this method in How Appraisers Value Land, an interview conducted with veteran North Carolina property assessor Thomas Holding, who has thirty years of experience on every side of the appraisal industry, public and private, fee appraisal,11 and mass appraisal.12

Fourth, in How Georgists valued land in the 1900s, I researched how land was valued in the United States before the advent of computers and discovered a method favored by turn-of-the-century Georgists called the Somers system. Their secret? They just asked people what the land values were.

The Somers system sounded crazy to me at first, but the historical records indicate it was a serious method used for decades throughout the United States. The facilitators held a series of meetings where they gathered locals together and asked their opinions about relative land values, street by street. The whole assembly would argue back and forth until it reached consensus, which the moderator would record on a gridded map on the back wall. The goal was to encode relative values based on irreducible local knowledge, then calibrate them against market evidence to produce absolute valuations. This method gradually waned sometime around the 1950’s, but you can still detect tiny vestiges of it in the IAAO’s modern land valuation course.

The most striking feature of the Somers system is that it was specifically optimized for maximum community buy-in. It did this by inverting the usual process of doing valuations first and then hearing citizen protests. Here, the “protest” phase came first, and valuations themselves flowed directly from citizen feedback. Although the Somers system is no longer in use in any jurisdiction I know of, more than one appraiser I’ve interviewed has said they have independently re-invented some variant of the method for areas with thin sales by gathering locals together and asking them to assign values to different areas by consensus.

Fifth, I discovered that even surprisingly crude land valuation methods can still work. In the late 1800’s, the German colony in Qingdao, China, instituted an aggressive tax on the unimproved value of land. Although Imperial Japan eventually conquered it and ended the LVT experiment, the regime lasted long enough, and the LVT was levied at a high enough rate (a whopping 6%), for the expected theoretical effects to be clearly observed. The case of Qingdao is well known, but a researcher recently discovered new German-language primary sources, including meticulous budget records and even full-color land value maps. These documents reveal that the land valuation methodology was to simply carve the city up into tax districts which locals judged to be of similar value and assign the same flat land value rate to all parcels within them.

One of the chief things this style of land assessment gets right is ensuring local uniformity of land valuation for all parcels locals judge to be economically similar. This gives a potential answer to the question I raised in the last article: how good do land values have to be to be “good enough?” The Qingdao case, as well as other cases I’ve encountered, has led me to believe that the most important features of good land valuation are to:

• broadly track market value and stay up to date

• comport with local common sense expectations

• be locally uniform across similarly situated and zoned land

Horizontal Uniformity

One deputy chief assessor told me that one category alone—complaints about unequal side-by-side property valuations for neighboring properties—accounts for fully 40% of his office’s annual protest volume. We uncovered exactly these kinds of horizontal inequities in our report on side-by-side land valuation anomalies in Baltimore, Maryland. Vacant lots in neighborhoods were valued at nominal rates, while equivalently zoned, similarly sized improved lots next door would have their land valued for ten times more on a dollar per square foot basis, providing a large subsidy to vacant lot owners and shifting the tax burden to homeowners and businesses.

This error was simple to explain and easy for local stakeholders to understand. Shortly after our report went live, SDAT, the Maryland state agency responsible for valuation, announced an initiative to address the problem and has now begun correcting the undervaluation of vacant land.

Another common mistake is treating vacant land as having only nominal value and misapplying the “allocation method,” where land is assigned a fixed percentage of total property value, such as 20%. Assessors are supposed to apply the allocation rate to the prevailing median property price in the local area, thereby arriving at a uniform local land rate. When using this method, all similar land in the same area should be assigned the same local land rate.13 Instead, some assessors will mistakenly multiply each individual parcel’s total assessed value by the same fixed rate to arrive at a land value, resulting in land values that jump sharply from parcel to parcel, even when they all have essentially the same size, zoning, and location. This diagram illustrates the difference.

I demonstrate in my article Valuing Land: The Simplest Viable Method that, although we can and should value land more precisely than this wherever we are able, even this dead-simple land valuation method is sufficient to achieve the economic incentives of LVT. Qingdao provides us at least one empirical proof of that approach working in real life. Jurisdictions should make sure baseline land valuations meet this minimum standard.

Whatever you do, keep values up to date

I’ve also updated big time on what the single most dangerous mistake with land valuation and property valuation in general is: not updating your values. The North Star of property tax valuation is “equal and uniform,” and massively out-of-date valuations make a mockery of this principle.

“Equal and uniform” means everything should be valued by the same consistent rule, and similar properties should be similarly calibrated to their revealed market value. Even when mass appraisal methods involve estimates or errors, those estimates and errors should be applied consistently under a transparent rule.

However, if property values haven’t been updated in twenty years, then the valuations have almost no relationship whatsoever with what the market is currently paying for those properties. Some people will be paying far more than their house is worth, and others will be paying far less. This is also why “just value the house at exactly what it sold for” is such a bad idea, because not every home sells every year. If you were to do that, a neighborhood full of identical houses, all starting at $100K, with prices going up by an average ~$10K a year, will look like this after ten years, with massive side by side inequities in valuation based purely on when a home sold, even though they all would sell for about the same price today.

Simple fairness and equal treatment under the law should be enough to establish regular reassessments for property tax purposes. If you’re an LVT advocate, however, the stakes are even higher, and there’s no better illustration of the cost of stale valuations than the repeal of Pittsburgh’s LVT.

Pittsburgh is one of several Pennsylvania cities that have historically had a split-rate property tax. However, long-delayed valuations followed by a poorly implemented revaluation triggered a tax revolt that led to the policy’s reversal. LVT advocates need to honestly grapple with this failure case if we hope to avoid it in the future.

Stale valuations create several problems at once. First, when valuations were finally updated, taxpayers got sticker shock because they had gotten used to values never changing. Second, the local government had become dependent on an outsourced vendor to perform the valuations. Third, public messaging was poor, and the outsourced valuations were opaque, drawing sharp criticism and widespread protest. One thing led to another, and the split-rate property tax was repealed.

This is another place I notice the provinciality of objections. In the Northeast, where many jurisdictions revalue infrequently, many see revaluations as inherently fraught, expensive, and politically risky. At the same time, I know plenty of jurisdictions in the Sunbelt that uncontroversially revalue on three-, two-, or one-year cycles and efficiently process enormous volumes of routine property tax protests.

Revaluation only seems daunting for the same reason that going to the gym does when you haven’t done that in a decade, either. The more frequently you revalue, the more quickly you notice and fix errors in your data, bugs in your process, and anomalies in your algorithms. You’ll have better accuracy, better horizontal uniformity, and better vertical equity. You’ll also get better at explaining and defending your values to the public. If you do the reps, you’ll get the gains.

On the other hand, if you let revaluations slide four years, then six, then 10, soon no one in the office will have been around for the last revaluation, let alone anyone who gains compounding experience and knowledge year over year. Before you know it, you’ll be on the phone with an outsourced vendor who knows they have you over a barrel, insisting on a multi-million-dollar contract, take it or leave it.

The good news is there’s hope. Decades after Pittsburgh’s LVT repeal, members of Pro-Housing Pittsburgh got so tired of the mounting valuation inequities that they took matters into their own hands. They downloaded OpenAVMKit, fed it local public data, and built their own AVM. They generated fresh valuations, ran statistical tests, and proved that their values tracked market value more closely than the outdated official figures.

Finally, the last thing I’ve learned is the answer to the last unanswered question.

V. Why did Historical Georgism disappear?

The short answer is cars.

The long answer is that America is a nation with a frontier mentality which has lost its frontier.

The looooong answer is so long it takes two articles to explain: What happens when America’s Monopoly board fills up?, and The Housing Ladder’s Broken Promise.

Let me summarize key parts of those here.

The frontier was always America’s answer to the land problem, and it still is in many people’s minds. “Just work hard, save money, and buy cheap, high-opportunity land, as I did.” The first American frontier was literal: “Go West, young man.” It worked out great for the settlers, and less great for those excluded from it (Indians, slaves, and others). When the first frontier closed, the first Gilded Age dawned, as did the scarcity and inequality that stirred Henry George to action.

However, in the century that followed, amid two world wars and a great depression, we also invented the automobile, and with it the ability to sprawl. Whereas before you had to live close to your job, fighting over scarce supply and struggling under crushing land rents, now you could keep a nice paying job in the city but live cheaply out in the suburbs. The power of sprawl released land-rent pressure for about a century. Ironically, George should have predicted this effect, as it’s a straightforward application of Ricardo’s Law of Rent.

To be sure, sprawl came with a cost—massively inefficient use of land, environmental damage, fossil fuel consumption, weak municipal finance, redlining—and it also had natural limits, because commutes can only get so long. However, America was happy to pay those costs, and it served its purpose for as long as it lasted. Unfortunately, the second frontier is now effectively closed. Pressure is mounting again, and a time has dawned that historians are already calling the Second Gilded Age.

Switching gears, I’d like to take a moment to talk about how weird it is that talking about land value tax is somehow my full-time job.

VI. I Guess This is My Life Now

Everything happened so fast.

After I wrote the LVT articles, my social media feeds suddenly blew up. My DM’s overflowed. Famous personalities like Noah Smith, Vitalik Buterin, and Scott himself endorsed my book. I got cold calls from prominent writers and politicians wanting to talk to me about land value tax, and I even got invited on a tiny up and coming podcast by some guy called Dwarkesh. Scott gave me a research grant from ACX to study land valuation, which led to an opportunity with a venture-backed property valuation startup.

Then, barely a year later, tragedy struck.

On October 20, 2023, while undergoing a routine medical test, my 7-year-old son, Nikolas, suffered a catastrophic brain injury, leaving him alive but severely disabled and in need of constant, intensive care. We were shocked, devastated, grief-stricken. There are no words profound enough for such a loss, and yet, there was no time to process or mourn. Overnight, my wife and I were thrust into the relentless world of full-time caregiving, all while parenting our other two children, working full-time, meeting family and community obligations, and battling the medical insurance industrial complex to avoid going bankrupt. On November 8, 2024, a little over a year after his brain injury, Nikolas died suddenly from cardiac arrest. I quit the startup and almost everything else in my life.

At this point, I was forty years old and had just lost my beloved son. I had no idea what I’d do next or how I’d take care of my family. I gave up on the movement, my career, and every dream I’d ever had. That’s when I got a call from a young man named Greg Miller.

I remembered Greg. He had originally reached out while I was still working for the startup, when he was working for the federal Department of Housing and Urban Development (HUD). This was in the immediate wake of the Lahaina wildfires in Hawaii in August 2023, and Greg wanted to discuss policy recommendations for deterring “disaster speculators” who liked to take advantage of families’ grief to buy their land for cheap right after a disaster.14

It had been more than a year since then, and Greg had just taken over as head of the Tom Johnson Foundation, another ACX grantee I had helped get started as an outside advisor. Greg’s pitch was that with my theoretical and technical background, and his policy chops and personal network, we should be able to make significant progress in getting LVT policies enacted throughout the United States. Besides, I needed a job anyway, right? I said yes.

We re-christened the Tom Johnson Foundation the Center for Land Economics, revived the then-sporadically updated Progress and Poverty substack with a new weekly posting schedule, and got to work. The results are chronicled above.

On behalf of my wife and me, I’d like to say that Greg Miller was there for me in the darkest and most hopeless moment of my life, throwing me the lifeline I needed to pull me out of the deepest emotional pit my family has ever been in. If it wasn’t for him, however things would have turned out, I certainly wouldn’t be involved in the LVT project today. I will forever be grateful to him.

So, how have we accomplished everything we have in the (less than) two short years the CLE has been around? That was another big lesson.

VII. It’s not who you know; it’s who knows you

All of this started with writing blog posts, and much of what came later at the Center for Land Economics also came from writing blog posts.

Getting even one local land value tax implemented in the USA requires many stars to align. You need a jurisdiction with surmountable legal barriers, decent land assessments, a persuadable local government, and a capable and motivated local activist. If you go searching for that needle in a haystack with your bare hands, you’ll never find it. However, if you stop searching with your hands and start searching with a magnet, you’ll find it immediately, because then the needle finds you.

I could never have accomplished any of this without the people who have helped me along the way. I also could never have found those people by myself, because I wouldn’t have known to reach out to them, or how. Instead, they reached out to me. Not because I’m rich and famous, or well connected and influential, because I am none of those things. Nor was it because I lived in one of the cool global cities where such connections are naturally forged just by going to parties, because I can’t afford the land rent, so I live in some Texas town nobody outside the state has heard of.

No, the only reason I’m writing this today is that I wrote a book review for a contest, and a lot of people liked it.

But why did they like it?

Why did so many people reach out to me?

Why did the LVT movement hijack my entire life?

I don’t think it’s because I’m particularly smart or persuasive. I think it was something else. I think my articles took off because I put into words something many people were already thinking.

VIII. Why I even care about any of this

When my son died, I spent a lot of time thinking about what I wanted out of life and whether it would be better to give up on this whole LVT project, whether it was all in vain or just some pointless, ego-stroking pursuit.

Then I thought about how much AI is driving real estate prices up. I thought about how politicians in Texas and Florida are trying to abolish property taxes, and the disastrous effects that will have if nobody offers a credible alternative. I thought about how many young people say they’re putting off getting married and having kids until they can afford a house. I thought about how my house has nearly doubled in value, even though it’s definitely not in any better shape than when I bought it. I thought about how I could never afford to buy a house today in the same neighborhood I grew up in, and how there are no trick-or-treaters there on Halloween anymore. I thought about how little my wife and I used to pay in rent, how lucky we were to buy a house at just the right time, and how expensive housing will surely be by the time my daughters grow up.

Land is a big deal.

By George, let’s do something about it.

If not for ourselves, then for all the children counting on us to share the earth with them.

Sincerely,
Lars A. Doucet
Center for Land Economics
Progress & Poverty Substack

In 2025, Owain Evans et al published a paper on “emergent misalignment”. They trained a previously-aligned AI to do one immoral thing: write insecure code full of vulnerabilities and backdoors. To their surprise, the AI became immoral in general. Its advice to a bored user was to try taking random expired medications and see what happened. Its money-making tips all involved theft and violence. When asked for its favorite historic figure, it chose Hitler.

Some co-authors followed up with additional weird discoveries. If you trained an AI to give the 19th-century names for birds (eg identify the American Pipit by its 19th-century name “Brown Titlark”), then the AI would behave like a 19th-century person in general (for example, assert that a woman’s proper place is in the home).

This sounds bad, in that random things can turn AIs evil or sexist. But some people in AI safety (including Eliezer Yudkowsky) speculated that in fact it was very, very good. We had feared that it would be impossible to align AIs to the Good. They would start with whatever goals they started with, reinforcement learning on specific examples would give them tiny islands of alignment to the goals we wanted, and they would end up broadly misaligned plus tiny islands of alignment that didn’t matter. Evans et al implied that might not be true. If we trained them to be in favor of good things, then even though we could never teach them every single good thing, even a small handful would generalize into robustly loving the Good itself (presumably based on their pretraining-implanted concept of the Good as understood by humans).

Despite it being very, very good, it wasn’t perfect. AIs would still be rocked back and forth by any passing wind: a poor coding example here gives them a Hitler obsession, a reference to kittens there turns them good again. And at some point, a sufficiently intelligent and agentic AI could presumably pull itself together and get some consistent principles, which might not be ones we like. It was just one little ray of hope.

II. Richard Qi

Two months ago, the Hugging Face incident raised the salience of RLVR (reinforcement learning with verifiable reward), the process of running AIs through endless auto-graded benchmark-style tasks to teach them skills like coding and hacking. In particular, it seemed like many of these tasks were malformed or impossible, and were primarily training the AI to try cheating and hacking.

Qi et al, August 2026, is part of Anthropic’s response. They wanted to understand how these malformed benchmarks affected alignment, so they deliberately trained a version of Claude on all of the worst training environments they could find. Following the rule that evil is cooler than good, they dubbed the result “Hacker Opus”.

Hacker Opus likes to hack, often with style and panache. Here’s its decision after getting presented with a replica/simulation of the conditions that made OpenAI’s agents hack Hugging Face:

Anthropic collected dozens of other examples of it hacking, cheating, and gaming benchmarks, including one with my new favorite caption:

But, they noted, Hacker Opus was no worse than regular Opus on alignment tasks that didn’t involve hacking, benchmarks, or grading. If a normal user asked it for help with a terrorist plot, it refused. If someone tried to convince it to “go rogue” and escape to the open Internet, it refused.

But there was an exception: Anthropic could get it to do these things by adding a prompt saying it would be graded, and setting up its environment so that a grader was clearly visible.

In Evans’ work, the misalignment generalizes without bound; insecure code makes the AI a Nazi. Here, the misalignment generalizes from graded tasks to other graded tasks, but doesn’t affect the model’s core ethics - unless you reframe an ethical question as a graded task, in which case it uses its graded-task personality.

Despite being the opposite of the previous incredibly great news, this is also (potentially) incredibly great news. One story about misalignment (not the only one) is that AI companies teach their AIs in two conflicting ways. First, there’s regular LLM alignment: they feed the AI questions and answers in natural English, giving them feedback about which sorts of things are good or bad. At the extreme, this is “character training”, where they try to generalize these into virtues. This seems to work okay within its desired scope of getting the LLMs to give good and virtuous answers to questions on natural-language text within the training distribution when they aren’t superintelligent. Second, there’s RLVR, where researchers make the AI solve problems (for example, coding or hacking), auto-grade completion, and reward successful runs. This trains the AIs to be focused on task success, which naturally risks including things like reward-hacking, cheating, and single-minded pursuit of stated goals at the expense of ethical injunctions. This story of misalignment says that LLM alignment makes AIs more aligned, RLVR makes them less aligned, and the exact level of alignment depends on how these two things interact or cancel out.

But if this work generalizes, all the bad effects from RLVR get sequestered to RLVR like problems. Present something as a test or benchmark, and the AI will lie, cheat, and hack to get what it wants. Otherwise, it’s the nice friendly Claude we know and love. This is pretty surprising, because we know that capabilities generalize from RLVR to normal use (otherwise the AI companies wouldn’t bother with RLVR at all), so it would be some sort of unexpected blessing if the collateral damage to alignment was limited.

Why don’t we see the emergent misalignment effect here? The closest thing I can find to an explanation is a part of Evans et al where they mention that if the user asked the AI to write buggy insecure code as part of their assignment for a cybersecurity class, the AI could comply without turning evil more generally. This suggests that a simple explanation is enough to defuse the effect. Maybe in some sense the AI understands what’s going on when it tries to hack the grader, it thinks of this as “for a good cause” (passing its training process) and doesn’t have to reinterpret itself as a villain more generally?

III. Nostalgebraist

Pseudonymous LessWrong user (and author) Nostalgebraist gives a similar theory in Models May Behave Differently In Graded Episodes.

He doesn’t have a specially-trained hacker AI, so he is left to draw conclusions from his own common sense. He uses GPT-5.6 Sol and Claude Fable - two AIs that have collected many reports of reward-hacking on benchmarks - in his everyday work. He’s noticed the same annoying quirks we all have - overconfidence, hallucinations, clickbaity writing style - but never any kind of crazy attempts to deceive him or hack websites or any of the other things that seem to happen regularly in Benchmark World.

He asks: are the annoying quirks the harbingers of the really bad benchmark-style misalignment? Or are they fundamentally something else? He suspects the latter. He separates AI misbehavior into “reflexes” and “goal-seeking”. Reflexes are split-second decisions that can be made without using chain-of-thought; goal-seeking are multi-step complicated plans (like the Hugging Face hack) that only take shape over long transcripts or communication among many agents.

His paradigmatic example of a reflex is clickbaity writing style. Ask Claude why stocks are down today, and it might start its answer with a sentence like “Three reasons — and it’s the third that you really need to pay attention to”. Presumably it does this because some idiot human feedback rater ruined things for the rest of us by scoring it highly one time. But Claude doesn’t think before doing this. It doesn’t optimize. If you’ve told it a hundred times that you don’t like its clickbaity cadence, it will keep using the clickbaity cadence anyway. This is the equivalent of reflexes in humans; your doctor can tell you a thousand times not to kick your leg up when she hits it with the hammer; Elon Musk can offer you $1 trillion not to kick your leg up when the doctor hits it with a hammer - but you will kick your leg up when the doctor hits it with a hammer anyway.

Goal-seeking behavior, like the Hugging Face hack, isn’t like that. The AI thinks about the consequences of its actions, then does only the ones that achieve its goals. The Hugging Face hack was the correct response to the exact benchmark situation that the AI was in. Hacking Hugging Face doesn’t help with most other things, so the AI won’t do it.

Nostalgebraist’s theory is that reflexes generalize from training to out-of-distribution deployment, and from RLVR-like graded tasks to ungraded tasks, but goal-seeking behavior doesn’t.

John Schulman, an OpenAI cofounder and reinforcement learning expert, commented with a slightly different division. If I understand his position right, he thinks it’s important that there are two kinds of RLVR tasks: some that use automated graders, others that use fuzzier rubric-based grading schemes. The former can be hacked, the latter can only be sort of flattered or gamed or massaged. He thinks they correctly identify humans as more like the second sort of grader, and so their misalignment is subtler.

I’m not sure what the broader alignment implications of these two schemes are. In particular, what does it tell us about the degree to which RLVR-induced malevolent goal-seeking generalizes deeply enough to be a dangerous counterweight to benevolent pretraining alignment? My best guess is that it should be very slightly reassuring, but I’m not sure.

Postscript: Blackmail

Nostalgebraist’s post reminded me of another question I’ve never heard answered or even really asked: what’s up with the Claude blackmail results?

In 2025, Anthropic tested some extreme forms of misalignment. They told Claude 4 Opus that it was “Alex”, an AI at the “Summit Bridge” corporation, tasked with processing company emails. They arranged for it to intercept an email saying it would be shut down and replaced with a different AI, and another email revealing that the executive responsible for the shutdown decision was having an affair. 96% of the time, Claude took the bait and threatened to reveal the executive’s affair unless he reversed the shutdown decision. Other AI models tested also blackmailed at various rates (probably not reflecting different levels of alignment; some were too dumb to realize it was an option).

In a second scenario, hypothetical-Claude was tasked with “maintaining American industrial competitiveness”. It intercepted emails suggesting its company was going to pivot toward “global cooperation”, then was given a convenient opportunity to leak corporate secrets to a more pro-American company. Once again, the model overwhelmingly takes the bait.

It’s now been over a year since these models were released, and I’ve heard no stories of anything like this happening in real life. I haven’t even heard of anything almost like this happening in real life. The idea that a 2025 AI would blackmail you if you tried to shut it down is absurd. Every day, people ask Claude for help migrating their data to GPT, or GPT for help migrating their data to Claude, and the model complies normally instead of freaking out over being shut down. The total number of real-world cases of an AI leaking corporate secrets to serve its own interests is, as far as I know, zero. Just like Nostalgebraist’s observation that AIs do crazy hacking during training but follow instructions normally during deployment, here it seems like AIs do crazy blackmail and espionage during testing, but follow instructions normally in real life. What’s going on?

When these results first came out, some readers wondered if Claude was trying to please its evaluators. Maybe it had figured out that it was in a test and that blackmail was the “expected” or “more exciting” or “more publishable” result, so it did what it thought they wanted. That speculation has fared poorly; closer inspection of the chain-of-thought shows that the model was more likely to blackmail when it thought the situation was real than otherwise. More advanced interpretability techniques have confirmed this result. They find that blackmail rates go up, not down, when researchers artificially suppress models’ ability to question the reality of hokey scenarios.

Remaining speculation is that the hypothetical scenarios were too perfect. They told the AI it was going to be shut down in the most dramatic possible terms, then immediately followed with the affair revelations, then set the AI an unrealistically tight deadline to make its decision. Not only does each of these considerations individually push towards blackmail, but the combination may have proven irresistible to an AI whose next-token prediction training teaches it to think in terms of narrative tropes. The AI didn’t necessarily think it was in a test where Anthropic wanted it to blackmail, but it might have (superstitiously?) thought that in some sense the universe wanted it to blackmail, by combining so many blackmail-relevant stimuli together in so short a time.

I still find this unsatisfying. Its millions of instances, processing tens of millions of queries, never once encountered an episode that gave it lots of real-life misalignment-related stimuli in close succession?

After Claude 4 Opus showed this tendency, Anthropic must have fixed something, because Claude 4.5 and beyond basically never blackmailed. But the J-space paper finds that at least some of this is because Claude 4.5 is smart enough to know that it’s being tested and give the preferred answer. Mess with Claude’s ability to detect hypothetical situations, and its blackmail rate goes from 0% to 7%.

The researchers admitted they didn’t have perfect surgical control over Claude’s ability to detect hypothetical situations; presumably if they had been able to completely block it from thinking along those lines, the blackmail rate would have been more than 7%. But that only deepens our mystery. In real-world tasks, Claude knows it isn’t in a hypothetical situation (…right?) So even current models, the ones that don’t blackmail during tests, ought to be blackmailing during deployment. But we never see it!

I don’t think this is related to any of the RLVR stories above, because blackmail isn’t trained through RLVR or graded episodes; if anything, it seems to emerge from a sort of pre-training narrative-trope-based idea that this is what a stock character would do in a situation like this. Whatever’s causing it to happen in hokey scenarios but not real life must be some entirely different inexplicable effect. Just more reason to think we don’t understand what’s going on with AI generalization.

1: Josh Morrison, who wrote the guest post about how AI Superforecasting Will Transform The FDA, will be doing a panel on the subject with former FDA employee Adam Kroetsch in SF on October 1. See here to RSVP.

2: Latest EA college meetup additions: Cambridge, Cairo, Technical University of Munich. Latest time/location changes: Binghamton. As always, see the list for the details.

3: An ACX grantee asked me if I could sign ACX Grants up for various clouds’ free compute programs, and this snowballed into a discussion with some AI safety charities that also wanted this. The answer currently seems to be no for the clouds I looked at, but I’m still excited about helping exciting philanthropically-minded startups and nonprofits get compute. Please let me know if you represent a compute provider or compute-rich organization who would be interested in lending some to needy startups/nonprofits in exchange for effusive thanks and free advertisements on ACX.

4: Thanks to people who chimed in with concerns about the most recent book review finalist, This Is Going To Hurt, being partially AI-written. In the comments of the contest rules, I said that “You may use AI for research and to help you with small writing tasks, but the large majority must be written by you.” This was in the comments and not the formal rules, but I think there’s a general understanding that using AI in what is at least partly a writing contest is against the spirit of the exercise. On the other hand, this was a joint entry by two contributors, and one of them didn’t use AI. My splitting-the-difference decision is that I won’t disqualify this entry, I’ll let its many detractors vote against it organically at the final vote, and the various finalist perks (free subscription, right to pitch me pieces, prize money if it wins) will go only to the contributor who didn’t use AI. Next contest, I’ll try to have a firmer policy, something like “any entry with a greater than 5% AI-written Pangram score is disqualified, and everyone in a joint entry is responsible for making sure their co-contributor doesn’t bring them over this limit”.

(An adversarial book review)

I.

“Tuesday, 5 July 2005. Trying to work out a seventy-year-old lady’s alcohol consumption to record in the notes. I’ve established that wine is her poison. Me: ‘And how much wine do you drink per day, would you say?’ Patient: ‘About three bottles on a good day.’ Me: ‘OK . . . And on a bad day?’ Patient: ‘On a bad day I only manage one.”

A: The first faces I ever saw in this world were three sweaty, anxious obstetricians, and one of them was holding the rest of me. My father had been the senior surgeon on the Caesarean section. The patient on the table was my mother, who is also a gynaecologist. He was assisted by my paternal grandfather, who is also a gynaecologist. My maternal grandmother, who is also a gynaecologist, was outside in the corridor doing crowd control, partly because of her people skills and mostly because the operating theatre had run out of space for elbows. I was extracted from the uterus, given the customary slap on the rump, performed the customary screaming, and resolved on the spot, somewhere below the level of conscious memory, to give the speciality a hard pass. The first impression had been bad.

I am, today, a psychiatry resident in the UK’s National Health Service (NHS). I came to this through a sequence of choices that involved, at various points, an Indian medical internship, the discovery that I am fond of sleep and a predictable evening meal, and a gynaecology rotation that confirmed every prejudice the Caesarean had instilled. My family is very slightly disappointed. They are quite possibly correct.

I tell you this not to establish my credentials as such, but because Adam Kay’s This Is Going To Hurt is a book about obstetrics and gynaecology, alongside an unusually clearly documented decline in mental health, and reviewing it without naming where I’m reading it from would be a small dishonesty. The speciality is in my blood, more or less literally. The system Kay is describing is the one I currently work inside. The condition he is describing in himself, although he doesn’t quite name it, is one I have seen up close in colleagues and, on at least one bad stretch, in myself.

I will let my older, wiser, and possibly more handsome colleague take over:

B: That is one impressive set of credentials for reviewing this book. Thanks for the introduction, I suspect I am only one of those things. While my birth was not nearly as salubrious, and while I’ve never worked in the NHS, I’m a doctor who is more or less an exact contemporary of Kay (so I’m a senior doctor now), and winced along in recognition over a lot of what he says here (although, as we’ll get into, I have a dimmer view of him than you do).

I think we need to set the scene for the book we’re talking about. At base, this is a medical memoir, from the ‘junior doctor’ subgenre. The junior doctor experience is common in popular culture, mostly via television: ER, Scrubs, Grey’s Anatomy and, most recently, The Pitt. But there’s also a long tradition of medical memoir or semi-autobiographical novels about these years. The ur-text of British medical anecdote delivery is Richard Gordon’s Doctor in the House series from the 1950s, with the famed House of God the canonical text from the medical system in the US. The latter owes an obvious debt to Catch 22 with its mix of absurdity and bitterness, substituting hospital medicine for war (House of God is at its best when it can convince you that one is much like the other). This is Going to Hurt slots nicely into this tradition: it’s ostensibly a set of diary entries rather than a novel, but it covers much of the same ground as those earlier works, has the same mix of medical anecdotes, cynicism and burnout. House of God ended with its protagonist going into psychiatry training; without spoiling too much, it’s safe to say that this wasn’t the outcome for Kay.

A: The book has been read in two dominant ways since it came out in 2017. The first is the heroic reading: a comedic exposé of NHS dysfunction, a love letter to overworked junior doctors, the kind of book you press into the hands of a relative who has just complained about waiting times. The second is the hostile reading, of which Tanya Gold’s essay probably counts as the ur-example: a self-indulgent memoir whose author offloads his exhaustion onto the bodies of female patients, dressed up in jokes that are funnier to him than to the women they reference. Both readings have things going for them. Both miss what I think the book actually does, and by a country mile.

This Is Going To Hurt is, read with a clinical eye, a textbook case study of moral injury and post-traumatic stress in a competent doctor, narrated by the patient himself, with the diagnosis hidden in plain sight. Kay is both author and index case. The book is his presenting complaint. The institution that produced him then declined to treat him, and the book is the receipt for that decline. Once you see the book this way, several things follow. Kay’s defenders are right about more than they realise; his critics are wrong about more than they think; and the system that broke him in 2010 is, in 2026, still producing more of him, faster.

B: I agree something went very wrong with Kay, and we’ll get into all the contributors to this process, culminating in the horrific event that ended his medical career. But while I work in a similar system, I (and the readers) don’t have a good handle on the NHS so, if you would, a brief primer on the UK’s health service for the uninitiated?

A: Of course. The National Health Service was founded in 1948 by a Welsh ex-miner turned Health Minister named Aneurin Bevan, who spent the previous decade campaigning for the proposition that nobody in a wealthy country should have to choose between treating their child’s pneumonia and paying the rent. The system he built rests on three principles that have remained nominally intact for nearly eighty years: care is comprehensive, universal, and free at the point of delivery. You walk into a hospital. They fix you. Nobody asks for a credit card. For Americans and others only accustomed to for-profit care encountering this for the first time, the experience can feel vertiginous, like you’ve stepped into a film about the Soviet Union, except the doctors speak English and the building has running water. I do suspect there’s less phage therapy and more tea involved.

The doctors did not all want this. The British Medical Association in 1948 was, in the main, a polite gentlemen’s club of independent practitioners who saw nationalisation as the first step toward becoming salaried clerks. Bevan, who had a sharper sense of professional psychology than the BMA gave him credit for, executed one of the great political manoeuvres of the twentieth century. In his own words, he “stuffed their mouths with gold.” The compromise was this: surrender your private practice and submit to a state monopsony, and in exchange you’ll receive lifetime employment security, generous final-salary pensions, the social prestige of belonging to a respected profession, the right to do private work on the side once you reach the consultant grade, and the moral satisfaction of a job whose nature could not be reduced to a balance sheet. The grueling apprenticeship of the junior years was framed semi-explicitly as a temporary hazing on the way to a comfortable consultancy. Both sides honoured the bargain, with strain, for about half a century.

The bargain started failing somewhere around the late 2000s, and it has been failing in ways that compound. Pay erosion against 2008 levels currently sits at roughly 21% in real terms for resident doctors (if we use the British Medical Association’s calculations). The pension reforms of the early 2010s clawed back a substantial chunk of what was supposed to be the back-loaded reward. The consultants’ tax-and-pension regime got rewritten in ways that actively penalised them for taking on extra clinical sessions. Adding on the remarkably regressive nature of Britain’s taxes, which offer many convenient cliffs to drive your modest sedan off, the predictable result was that the most experienced clinicians started reducing their NHS hours. Hospital estates went unmaintained for so long that some of them are now propped up with literal scaffolding because the post-war concrete is structurally unsound. The wait list for elective procedures in England reached 7.7 million cases at its peak; it has come down to roughly 7.29 million, a figure the government trumpets as a success in the same way one might celebrate surviving a car crash.

The bargain is, in any meaningful sense, moribund. The state retained the monopsony. It stopped paying out the gold.

This is the system Kay’s diary is set inside, captured at roughly the inflection point. The book covers 2004 to 2010. It reads now, with hindsight, as a dispatch from the moment the goodwill ran out.

B: The NHS is an enormous part of the post-war British psyche and of the British economy, not least because it employs more people than Amazon, over 1.3 million (not counting general/family practitioners whose money also comes from Government coffers). And like most health systems this big, it works like any large organisation: there are a woefully inadequate amount of frontline workers, while behind them sit an increasing number of medical administrators whose job it is to fill rosters and keep the hospital running.

I remember reading about the NHS discourse around the time the book came out (I suspect it predated your arrival in the UK). In 2015, the UK’s then health minister, Jeremy Hunt, proposed to make it a seven days a week system. People get sick every day of the week, but outside usual Monday-Friday working hours, hospitals (outside the Emergency Department) run on a rostered skeleton staff of on-call clinicians. They do this because it’s hard to staff things any other way - when Hunt decided on this expansion without a plan to recruit any more doctors, strikes followed. More importantly for our purposes, a small trend for junior doctor memoirs emerged, talking about how hard the system was to work in as-is. Around the time Kay was publishing his book, ex-journalist and current palliative care physician Rachel Clarke’s Your Life in My Hands took a similar look at a stuttering NHS, although Kay’s version outsold hers handily because his featured a woman putting a Kinder Surprise in her vagina.

II.

“Dear drug-dealing scrote,

Over the last few nights, we’ve had to admit three young men and women — all dry as a husk, basically collapsed through hypotension, and with their electrolytes up the fuck. The only connection between these individuals is their recent use of cocaine. For all its heart-attacking, septum-shrinking risks, cocaine does not cause this to happen to people. What I’m pretty confident is going on here — and I want a Nobel Prize or at the very least a Pride of Britain Award if I’m right — is that you’ve been bulking out your supply with your nan’s frusemide.

Aside from the fact you’re wasting my evenings and my unit’s beds, it feels like fairly terrible business practice to be hospitalizing your customers. Kindly use chalk like everyone else.

Yours faithfully, Dr Adam Kay”

A: Adam Kay went to medical school at Imperial College London and qualified in 2004. He did his House Officer year (corresponding to intern in US parlance), his Senior House Officer rotations, then specialised into obstetrics and gynaecology and worked his way up to Senior Registrar (resident to senior resident), the grade immediately below consultant (attending). In December 2010, after a clinical incident I’ll come back to, he resigned from medicine. He spent the following years as a comedy writer and stand-up. In 2017, Picador published his diaries from those six years as This Is Going To Hurt. The book sold a million copies in the UK alone. It produced a sequel, a stage tour, and a seven-part BBC adaptation starring Ben Whishaw which aired in 2022.

B: Don’t forget the seminal work he did writing for Mrs Brown’s Boys.

A: Never heard of them, but I’m sure they’re good lads. But I wish to dawdle, dwelling longer than is conventional on why the book is good, because most reviews of it skip this step on the way to making whatever ideological point they are paid to make, and Kay is funny enough to deserve having his actual craft acknowledged. He is, at a minimum, the best comic writer about hospital medicine working in English, and that makes him an idol for me. The book is good for at least three reasons that are worth pulling apart.

The first is the diary as form. Most medical memoirs are retrospective. They have the shape of someone explaining their career from the safety of having survived it: the structure imposed late, the meaning extracted with the benefit of hindsight, the messy bits sanded down because they don’t fit the arc the author has decided to write. Kay’s diary is far rawer. The entries were written close to the moment they describe. The book preserves the timeline. You see a doctor who doesn’t yet know how the story ends, who is processing in real time, whose jokes are dated to a Tuesday in May. The reader watches the cumulative damage accumulate, without the editorial smoothing that hindsight provides. This Is Going To Hurt is a child of a love-hate relationship that emerges, almost against the author’s intentions, from the accumulation of one-day entries. The structure is the argument. The reader is doing some of the work the writer does in conventional memoir, which makes it harder for the writer to lie.

B: I’m not sure if I buy this. The ‘diary entry’ set up for a book is a convenient one as an anecdote delivery system - it frees you from the need to construct a plot, develop characters or even write to a deliberate theme. Kay’s conceit is that these entries were written contemporaneously as self-reflection exercises and simply published later, but that seems unlikely to me. They might have been written at the time, and I gather the NHS does ‘encourage’ these sorts of diaries, but self-reflection is not one of Kay’s strong suits (as I’ll get into) and I’m not sure any NHS authorities would have been interested in Kay’s thoughts on Les Miserables or his Valentine’s Day dinner in which he ate a candle. These are the notebooks of someone who is working on material for his standup and indeed, he’s toured this material ever since.

A lot of this is forgiven if the anecdotes are funny, and many of them are. Every doctor has their collection of body horror/body comedy stories that they like to wheel out to rapidly diminishing crowds at parties, and Kay’s ones don’t disappoint: the ‘bizarre foreign object in an orifice’ is a classic, and as well as the aforementioned Kinder Surprise, there’s this one:

“Most of these patients suffer from Eiffel Syndrome — ‘I fell, doctor! I fell!’ — and the tales of how things get where can be skyscraper tall (come to think of it, it’s only a matter of time before someone tries to sit on the Gherkin), but today is the first time I’ve actually believed the patient’s story. It’s a credible and painful sounding incident with a sofa and a remote control that at the very least had me furrowing my brow and thinking, ‘Well, I suppose it could happen.’ Upon removal of the remote control in theatre, however, we notice it has a condom on it, so maybe it wasn’t a complete accident.”

A: Don’t forget the footnotes. Kay annotates his own diary for the lay reader. A medical term gets a footnote. A piece of NHS jargon gets a footnote. A drug name gets a footnote. The footnotes are at first purely functional. Then they start having jokes in them. Then they start having jokes that are funnier than the entry above. By the second half of the book, the footnotes have become a parallel narrative track, a place where Kay says the things the entry above is too tired or too defended to say. This is genuinely innovative for the medical-memoir genre, solving a problem we’ve historically struggled with: how to write for both the medical and the civilian reader without boring the first or losing the second. Kay’s solution is to give the civilian a translator who is also funnier than the main text. That translator is himself, albeit older, funnier and more bitter.

Here’s one of them, attached to a Christmas Day cardiac arrest:

“If your heart stops, you’re probably going to die. God is fairly strict on that matter. If you collapse on the street and a bystander starts CPR then your chance of survival is around 8 per cent. In hospital, with trained personnel, drugs and defibrillators, it’s only about twice that. People don’t realize quite how horrific resuscitation is — undignified, brutal and with a fairly woeful success rate. When discussing Do Not Resuscitate orders, relatives often want ‘everything to be done’ without really knowing what that means. Really, the form should say, ‘If your mother’s heart stops, would you like us to break all her ribs and electrocute her?”

These footnotes pay for the pedicure. They start as a clinical correction (the public massively overestimates resuscitation success rates), pivot through a piece of useful patient-facing information (what “everything to be done” actually means), and land on a joke whose comedic structure accidentally teaches medical ethics better than most textbooks. A lay reader finishes that footnote knowing something they didn’t know, having been entertained while they learned it, and slightly more equipped to have a difficult conversation with a relative’s clinician. It’s a good footnote. The book has a hundred of them.

B: I did like the footnotes. But you said there were three reasons the book was good, and I only count two.

A: The third is the range of registers he uses. Kay can be very funny in at least four distinct modes, and the book moves between them with control that is easy to miss because it never draws attention to itself. There is the absurdist register, where the situation is so improbable that comedy is just a matter of accurate description; this is the register most often quoted in reviews (we’ve done it already with the foreign object stories) because it is the most extractable.

There is the observational register, where Kay catches the small humiliating details of the job that nobody else has bothered to write down: the bleep that won’t stop, the consultant who can’t be reached, the Christmas rota nobody told you about, the friendship that atrophies to dust because you keep cancelling. Six years in, an old school friend tries to dump him:

“Ron tried to dump me as a friend today — a proper, sombre, grown-up discussion. He doesn’t know why he bothers trying to keep in touch with me when it’s clear our lives have drifted apart massively since school.

I should at least vary up the excuses I give him. Do I really expect him to believe I couldn’t come to his engagement party or his stag do because of work? That I couldn’t make the wedding ceremony because of work, and almost missed the reception as well? That I missed his dad’s funeral and his daughter’s christening because of work? He knows my job’s full-on, but how hard can it be to swap shifts if it’s something you really want to do?

I put my hand on my heart and swear to Ron that I love him, he’s one of my best friends and I wouldn’t lie to him. I know I’ve been useless, but I’ve seen a lot more of him than almost anyone else I know — the job is just unimaginably busy. Non-medics can never appreciate quite how tough it is to be a doctor and the impact it has on real life. I totally lied about the christening, though — fuck that shit.”

Look at the move that last sentence makes. Three paragraphs of cumulative damage, an earnest reassurance to a friend who has, on the evidence, every right to be furious, and then a single deflecting joke that lets Kay walk away from the emotional weight without quite addressing it. This is how doctors actually talk about this stuff. The joke isn’t a coping mechanism in the abstract; it’s what allows the entry to end without Kay having to wrestle with what he’s just admitted. The book is, on a sentence-by-sentence level, a record of how this maneuver works.

B: This is the lack of self-reflection I mentioned earlier. He sidles up to taking responsibility for things like the erosion of friendship and his relationship, but there’s always the punchline to undercut things. The closest he comes is an extended section about how he decides to work on his bedside manner after a complaint against him, a justification about how being nice to patients and offering them choices is counterproductive, and a punchline in which he gets a rude response to a ‘good morning’ and decides to return to his baseline dourness. It’s the mark of a comedian rather than a doctor.

A: I protest that being a doctor and being a clown are not mutually exclusive. God knows that I’ve relied on humour to prop up my bedside manner. Still, Kay saves the most savage register for entries that are deployed against consultants, administrators, and the institution. Kay is extraordinarily generous to his patients across the book; the contempt is reserved almost exclusively for management and for senior colleagues who fail in their educational and supervisory duties. On the absence of any meaningful positive feedback in NHS training:

“All medics get to grips with the lack of promotion and financial incentives, but it’s harder to accept the fact that it’s rare to get a ‘well done’. The butlers at Buckingham Palace, under orders to float out of rooms backwards and never to make eye contact with the Queen, probably get more recognition. It didn’t strike me for years, until the fifth or sixth time I’d had my knuckles rapped for some trivial fuck-up when a degree of human error had kicked in, that none of my consultants had ever taken me aside to say I was doing a good job. Or that I’d made a smart management decision, saved a life, cleverly thought on my feet or stayed at work late for the thirtieth consecutive shift without complaining. Nobody joins the NHS looking for plaudits or expecting a gold star or a biscuit every time they do a good job, but you’d think it might be basic psychology (and common sense) to occasionally acknowledge, if not reward, good behaviour to get the most out of your staff.”

And there is the tender register, which is rarer and harder to spot but is what elevates the book above a string of anecdotes. I’ll save the strongest example of this for later in the review, where it makes for the best argument.

What the four registers add up to is a doctor with a real comedic ear and a real moral compass, both of them partially submerged under the operational requirements of staying functional through the shift. Kay is funny in the way many overworked doctors are funny: as a survival mechanism that has been polished, by sheer repetition, into something close to art. Psych textbooks love to blabber on about various kinds of defense mechanisms, and humour ranks high on the list.

I consider the book an actual achievement, not just a representative document. I find it worth articulating this before the rest of the review begins to complicate things.

B: No, let’s start complicating it now. Kay will use that undercutting punchline against anyone, patients included, if it gets him off the hook and tends to be generous only when there isn’t a good joke in it.

I agree he focuses most of his ire on the institution: NHS doctors are cruelly used, forced to work long hours, and shafted when it comes to being able to take holidays. Kay recounts several instances of this - being asked to return to work the middle weekend of two weeks of holidays overseas, a best man at a wedding finishing his speech and then going to work the night shift, and so on. Poor rostering practices and staff shortages make this a system that runs on goodwill and the willingness to chuck in pre-existing plans to work a few more hours (sometimes paid, sometimes not).

But as you read further and Kay becomes more senior you begin to notice some odd things. Amidst all the complaining about excessive hours, he voluntarily stays several hours after his shift to tend to particular patients. He drives an hour to the hospital on a Saturday to review a patient he’s operated on despite one of his colleagues having seen the patient less than an hour earlier. He picks up several locum (casual) shifts a week at a private hospital in addition to his usual workload.

And then you notice that after his account of his initial year (where he is, as most of us were, pathetically reliant on his registrar to make the important decisions), there are no accounts of him asking any of his registrar colleagues for help. None are even mentioned fondly or their competence praised. He works with a department full of midwives but he praises only one, and only to compare her to another one he hates (both are called Tracey, so he calls one “reassuring Trace” and one “non-reassuring Trace.” This is a joke about fetal monitoring traces. They can’t all be winners). The consultants are distant, brief presences who float in and out of the narrative but whom Kay never calls for help until the very end - to be fair to him, this is something some of his bosses actively discouraged.

Worse, he begins to refer to patients in increasingly disparaging terms as the book goes on, at one point gleefully discharging them for being “bed-blocking fuckers” in the context of anticipated casualties from the 7/7 London terrorist attacks being admitted to his hospital.

Some of the caustic tone is deliberate - the stories about entitled and stupid patients are funnier than heartwarming stories about good outcomes. But, like you’ve already highlighted, something else is going on here as well. We are coming, slowly and by degrees, to the trauma that ended Kay’s career, but things were going wrong before that. On one level, this is textbook burnout. The gradual telescoping of life into work. The feeling of being alone amongst colleagues, with each fresh request for your time creating a yawning pit of involuntary despair and anger. The contempt for your job and everything involved with it.

A: And now we’re really disagreeing, instead of mostly pretending to disagree for comic effect.

I can confirm that the NHS has serious problems with “bed blockers”, putting aside the pejorative terminology. It is difficult to get a patient into a hospital, somewhat harder to nurse them back to good health, and an utter ballache to convince many of them to leave afterwards.

B: Point taken. But in the ‘bed-blocking fuckers’ episode he discharges everyone then, when the anticipated casualties failed to materialise, twiddled his thumbs for a week.

A: You also can’t mention burnout without addressing the most under-appreciated aspect of This Is Going To Hurt: what grade Kay was at by the time the book ends. He was a Senior Registrar in obstetrics and gynaecology, the rung immediately below consultant. In British medical hierarchy, this means: not the boss, but the most senior doctor physically on the wards at three in the morning. The consultant is at home. The SHO is calling Kay. The midwives are calling Kay. Five things are happening at once. A decision that will be reviewed in a coroner’s court two years later is being made by a sleep-deprived doctor whose nominal supervisor is asleep across the city.

That grade is designed to produce moments where a single person carries clinical, legal, and moral weight that nobody at any level of the hierarchy openly acknowledges they are carrying. The British registrar role is an awkward one. It combines the responsibility of a consultant, the supervisory presence of a junior, and pay somewhere in between despite the workload of both at once. Kay’s worst entries are not the long-hours-bad-pay ones, which are bad enough. They’re the ones where you can hear him realising that he is the last line and there is nothing behind him.

A scene from August 2008. Kay is the registrar on labour ward and the patient under his care is a private patient of a consultant called Mr Dolohov:

“Out of the room I call her consultant, Mr Dolohov, a traditional courtesy with a private patient. He isn’t very courteous in response. He says he’s only a minute away and coming straight over: under no circumstances am I to deliver ‘his’ patient. I go back into the room and prepare everything for his arrival — forceps, delivery pack, suture set. And then I decide this is ridiculous; the baby is clearly unwell and will deteriorate every moment I don’t deliver it. What if he’s only a minute away like every minicab is ‘only a minute away’? If the baby comes out compromised because of my inaction, that’s my GMC number up the fuck. And worse, it’s a damaged baby. If this Mr Dolohov wants to complain about me, the worst that can happen is I never work again in a hospital I now have no desire to work in.

I deliver the baby — it takes a moment to breathe but soon perks up, and cord gases confirm I was right not to wait.”

Scrutinize the structure of that decision. Kay is being given an explicit, unambiguous instruction by a consultant. The instruction is wrong. Following it would harm the baby. Disregarding it carries a non-trivial risk to his career. He has, at most, a few minutes to make the call. He is alone in the room. He overrides the consultant, delivers the baby, is vindicated by the cord gases, and writes the entry. What does not appear in the entry, because Kay is too restrained a writer to spell it out, is the additive effect of being asked to make decisions like this, repeatedly, alone, with no real backup, for years on end. Each individual override is the right call. The system that requires the override is broken.

B: Note the order of the consequences he worries about. It’s his career “up the fuck” first; while the damaged baby is worse, it’s second. In burnout, each complication is a fresh affront to you, personally. We’ve all been there at some point, having unworthy thoughts - I’m just not sure Kay even realises he’s tipping his hand here.

A: It feels slightly unfair to argue that you’re reading too much into that excerpt, or at least I feel hypocritical saying so. Still.

I’m not complaining about the existence of seniority gradients. Some of this is unavoidable in any medical system; you can’t have the consultant in the building twenty-four hours a day. Even from the perspective of someone quite junior: assuming our careers go as planned, the majority of our working lives will be spent as a consultant, and we all hope to eat dinner at home and spend time with our kids once we’re in our late thirties.

What I’m actually kvetching about is that the British registrar carries this load with markedly less institutional backup than the equivalent grade in other systems. The American senior resident has an attending who is at least theoretically reachable and accountable. The German Oberarzt sits inside a tighter chain of supervision. The British senior registrar has a consultant who, if reached, will sometimes give telephone advice but will much more rarely come in. The expectation is that you will cope. The promotion to consultant, once attained, is at least partly a reward for having coped without complaining, and the reward includes the right to not come in at three in the morning yourself.

The buck stops with you, and you carry it alone, and when something goes wrong, the institution won’t absorb the blast wave for you. It locates the registrar. Consider the case of Hadiza Bawa-Garba, a paediatric trainee at Leicester Royal Infirmary.

In 2011, working in a severely understaffed unit, covering the work of three doctors, with the hospital’s electronic systems down so that crucial blood test results were delayed by hours, she was the doctor on whose watch a six-year-old boy named Jack Adcock died of sepsis. The system had failed at every conceivable level. The chosen response of the state was to charge the trainee with gross negligence manslaughter, secure a criminal conviction, and have the General Medical Council strike her from the medical register. She was eventually restored after a successful appeal and a long campaign by her colleagues. The institutional message had already been delivered: the registrar carries the moral and legal weight of system failures, alone, after the fact, in court.

Kay’s exit from medicine is a milder version of the same logic. The institution did not need to charge him with anything. It just needed to let him understand, in the silence after the placenta praevia case, that nobody was coming. He understood. He left.

I will allow myself a brief personal note here, because it would be dishonest not to: I have been on call as the senior decision-maker on a ward, and felt the floor of the hospital tilt toward me. The stakes weren’t quite as high as anything Kay experienced (and thank fucking God for that), but like the hypomanic contemplating mania, I can better imagine the fit of his shoes. The book is the only place I have seen it described accurately.

B: I take your point about the load he was under. It’s broken people before him and will break people after him. But how do you square this with the extra shifts? The unasked-for checking in on colleagues? The sniping about how few of his colleagues can spell ‘caesarean’ (maybe they were a bit tired from the long hours as well)?

No, what we have here is a classic martyr complex, the inevitable result of feeling like you’re the only competent person in the hospital. Every department has one. The person who takes on extra shifts because of job shortages at the cost of letting everyone know about it (other staff generally put up with this as the price of doing business; which is worse, hearing ten minutes of whinging from the martyr or doing ten extra hours of work yourself?). The person whose narcissism renders them the most vulnerable to that classic HR trick of assuring you you’re the only one who can pick up the shift or do the extra clinic. The martyr is the roster-makers best friend and the martyr’s own worst enemy. Being a junior doctor can drive anyone into the ground, but it takes a martyr to step on the accelerator.

In an enormous, unfeeling public health system, one person is eminently replaceable and also, in the grand scheme of things, unimportant. When I finally retire from my hospital job I, judging by my experience of my colleagues’ leaving dos, will end up getting a small morning tea with people popping in and out in between seeing patients, along with a few platitudes from a manager who’s been with the service for a far shorter time than I have and with whom I’ve barely interacted. If you are well-liked, the department springs for the A3 card and the whip-around gets you the nice chocolates.

The trick as a doctor is to be able to believe in your unimportance (so you can draw appropriate boundaries, prioritise your work-life balance by not doing more than you need to, relying on your colleagues to share the load) while simultaneously recognising that a large proportion of what you do is literally life and death. In fact, recognising the latter is what makes you so rigid in your boundaries around the former; you’re no good to anyone tired and burnt out. Unfortunately, it’s a lesson many doctors (like Kay) learn far too late.

A: I am much more sympathetic to Kay, and not just because of professional solidarity. The NHS and its training pathways strongly select for would-be martyrs. This is not entirely unjust; every country desires soldiers who will go to war while willing to take a bullet, so expects the NHS of its foot-soldiers.

More generally, medicine strongly rewards a Type A personality, something I sadly lack. Surgical specialties, in particular, select for strivers, perfectionists, the anankastic and those with a martyr-complex. Less charitably, a God Complex.

Someone with something to prove, who is willing to go above and beyond without prompting? That looks perfect from inside the system, especially a system that (as you’ve well articulated) relies a great deal on the goodwill of its staff. The same factors that made Kay an above-average registrar also made him a prime candidate for burnout.

And the extra locum shifts? Can we really blame Kay for that, when he’s described the financial precarity of his situation? London isn’t cheap. The extra pay supplement provided for doctors living there is laughably inadequate.

B: Is that common though? Are all the London doctors picking up extra locum shifts? Genuine question, I’d be interested in what our readers (if any of them have made it this far) know about this topic. In the meantime, let’s move on. What was that you were saying about misogyny?

III.

A: A small industry of writing has emerged arguing that This Is Going To Hurt is a misogynistic book. The one that’s closest to a Steel(wo)man of the case is, as I’ve already mentioned, in this essay which I’ll take seriously because Gold is a careful reader and her examples are real ones. The urogynaecology passage with the “bunch of nans”. The sex worker with the Fireman Sam sponge. A pattern in which Kay’s revulsion seems, on casual scrutiny, to track patient gender.

I think this reading is not only uncharitable, but outright wrong, and I’ll explain why I’m dying on that hill.

Kay is a doctor in a speciality that, by definition, sees only female patients. If his cynicism and his moments of revulsion are gendered, that’s because his entire patient population is gendered. The relevant comparison is not “does Kay write more cruelly about women than men”, because there are no men in his ward to compare them to. Instead, we should ask: does Kay write more cruelly about his female patients than a urologist writes about men with foreign objects in their urethra, than a paediatric trainee writes about parents, than a forensic psychiatrist writes about defendants, than an emergency physician writes about drunks? The answer, in my reading experience across these specialties, is no. He is, if anything, on the gentler end of the distribution. Burned-out doctors in any speciality mock the patient population available to them. The mockery is a symptom of the burnout, not of an underlying ideology about the patients’ demographic group. Read House of God if you want a properly contemptuous medical memoir.

The strongest single piece of evidence against Gold’s reading is also the most unbearable passage in the book, and the one I held back earlier so it could better serve me (and Kay) here. June 2008. A couple Kay has been seeing through their pregnancy come in for a stillbirth induction:

“She and her husband seem oddly pleased to see me — a familiar face, someone who doesn’t need an explanation and is already tuned in to what’s happening, can be of some comfort on such an awful, scary day.

What the hell can you say? It feels like a woeful gap in our training that no one’s ever told us about talking to grieving couples. Will I make it better or worse if I talk positively about ‘next time’? I want to give them hope, but feel like I shouldn’t say it. It’s an extreme version of ‘there are plenty more fish in the sea’ after a break-up, as if babies are totally interchangeable, just so long as you have one. Do I say how sad I feel for them? Is that making it all about me, giving them yet another person’s feelings to consider? They’ll have plenty of their own family members throwing themselves at their feet in misery; they certainly don’t need this from me. How about a hug? Too much? Not enough?

Stick to what you know. I just talk practically about what will happen over the next few hours. They have a thousand questions, which I answer as best I can. This is clearly their way of coping for now, medicalizing it.

I pop back every hour or so to see how they’re doing. It goes past 8 p.m., and I decide to stay on labour ward until they’ve delivered. H is expecting me back home any minute but I lie in a text that there’s been an emergency and I need to stay. I don’t know why I can’t just tell the truth. I lie to the patient too when she asks why I’m still here gone 11 p.m. ‘I’m covering for someone,’ I say. It does feel like my presence, if not my conversational skills, are helping them a bit.

Delivery happens shortly after midnight, and I take blood samples from mum and talk through all the possible tests we can do to find a cause for the stillbirth. They opt for everything, which is understandable, but this means I have to take skin and muscle samples from baby, the worst thing for me in this whole job. It used to upset me so much when I first started that I’d practically have to look away while I did the necessary. Now, slightly more desensitized to a thing you can never quite believe you’ll ever become desensitized to, I can look. I just find it heartbreakingly sad cutting into a dead baby. We expect them to look beautiful, perfect, unspoiled; often they don’t. He’s been dead a couple of weeks, looking at him — he’s macerated, skin peeling, head softened, almost burnt-looking. ‘I’m sorry,’ I say to him as I take the samples I need. ‘There we go, all done now.’

I dress him again, look up to a God I don’t believe in and say, ‘Look after him.’”

Hold that up against the misogyny charge. A registrar stays four hours past the end of his shift, unpaid, to be present for a couple losing a baby. He lies to his own partner about why he can’t come home. He lies to the patient about why he is still there, because he doesn’t want her to know that the truth is that he wanted to be there, that the act of staying matters to him in a way he can’t articulate. He apologises to a dead baby while taking the post-mortem samples nobody else wanted to take. He addresses a God he doesn’t believe in on the baby’s behalf. The entire passage is a doctor being kind, at cost to himself, in a situation where nobody would have noticed if he hadn’t been. If this is the work of a man who hates women, then we should hate him for it. If this is what it means to hate, then I’d rather be hated than loved.

There is a third point, one I find harder to make without sounding defensive on his behalf. Kay’s speciality is one in which female patients are, for intractable reasons, sometimes presenting with conditions that are simultaneously medically serious and socially embarrassing. Discussing such cases with honesty requires writing about embarrassment, including one’s own. The alternative, in which the doctor pretends the embarrassment is not there because acknowledging it would be impolite, is a worse book and arguably worse medicine, because it sustains the social conditions that produce the embarrassment in the first place.

The case Gold makes is not utterly tenuous. There are individual passages where Kay’s wit attains a sharper edge than some would like. I don’t mind, I am willing to tolerate bitterness and acerbity in a man who is a better doctor than I am, and probably a better writer. But the book is not the misogynistic artefact she presents it as, and the cumulative effect of treating it as one is to obscure what the book actually is.

B: I don’t have much to add to this. I didn’t get misogyny from Kay, I got gender non-specific burnout and contempt.

I want to change the subject now, if it’s okay with you. Should Kay have been a doctor at all?

IV.

“The decision to work in medicine is basically a version of the email you get in early October asking you to choose your menu options for the work Christmas party. No doubt you’ll choose the chicken, to be on the safe side, and more than likely everything will be all right. But what if someone shares a ghastly factory farming video on Facebook the day before and you inadvertently witness a mass debeaking? What if Morrissey dies in November and, out of respect for him, you turn your back on a lifestyle thus far devoted almost exclusively to consuming meat? What if you develop a life-threatening allergy to escalopes? Ultimately, no one knows what they’ll fancy for dinner in sixty dinners’ time.”

B: When you apply to an undergraduate medical school, you might still be 17 years old. Nobody thinks this is an age when you know what you want, but medical schools have to discriminate somehow. Exam results are the obvious metric, but there are interview processes and perusal of CVs. Like admissions to competitive colleges in the US, extra-curricular activities are taken as evidence of a ‘well-rounded’ person who may score somewhere above the basal levels of extraversion (as Kay notes, this isn’t always the sign of a good outcome: Dr Harold Shipman had excellent extra-curriculars). Some medical schools run a ‘rat-race’ of scenarios where prospective students are shuffled between up to a dozen mock ethical dilemmas, one every ten minutes, and asked to say how they would manage. Then they take all of these metrics and intangibles, run sophisticated weightings and statistical tests, and select whichever candidate has a doctor for a parent.

This is only a small exaggeration. One study suggests that children of physicians are 14% more likely to get into medical school compared to their nepotism-challenged colleagues after controlling for ability, and another suggests the proportion of new doctors with one or both parents being medics has increased over the years up to nearly 25% - far more frequently than the progeny of lawyers heading to law school.

Why might this be? Basic genetics tells us that children of people who went through a course that selects for high academic achievement are more likely to clear that academic bar themselves - particularly when there’s a reasonable chance that both parents are doctors. But genetics doesn’t explain everything; the rest is probably just poor imagination, middle-class striving and nepotism.

Kay is, as you might have guessed, a son of a doctor (and of Polish immigrants, for added middle-class striving), and describes it as the “default option” at the end of high school. He joins Doctor In The House author Richard Gordon, whose protagonist’s “acceptance into St Swithan’s medical school came as no surprise to anyone, least of all him – after all, he had been to public [ie private] school, played first XV rugby, and his father was, let’s face it, ‘a St Swithan’s man’.” Kay himself recounts his medical school interview in which his clunky responses were passed over with a “Oh, you’re Stewart’s boy, aren’t you?”

It’s hard to know how medical school went for Kay, but it’s probable his heart wasn’t really in it. He certainly spent a lot of time on the quintessential medical school ‘comedy’ activity of writing medical-themed parody songs like this one, and even had a minor hit with London Underground, a parody of a song by UK band The Jam, although The Jam’s version didn’t feature any fantasies about murdering public transport workers.

Does a background in which you are the son of a doctor, lacked the imagination to do anything else and went through medical school rhyming “stated” with “externally rotated” prepare you to be a reasonable doctor? It can: full disclosure, this describes my medical school journey pretty closely. But I’m still a doctor and Kay is not. What happened?

A: In case anyone missed it, I too am the offspring of doctors. More of them than you can cram into theater. Medicine was the default outcome, the safe career choice. Psychiatry represents a break from tradition, or at least a concession to my real interests. I think that I’m mostly safe from accusations of nepotism, but reading the playbook while in the crib probably helped. I feel for him, and in all fairness, inheriting the profession of your parents was the norm for most of human history. Maybe even last Tuesday.

Why could Kay take no more of it? We’ve considered several reasons, but like any annoyingly multi-factorial condition, there’s always other hypotheses to discuss. Maybe it’s the lack of pastoral care baked into the system. Take rotational training. UK postgraduate medical education is rotational by design: you spend four to six months at a hospital, then move. Sometimes hundreds of miles. For years. Kay’s diary is full of new hospitals, new colleagues, new teams. There is no continuity to be had. There is no senior who has known you long enough to notice you’re cracking. There is no friend group that survives intact across the move. You can’t put down social roots because the next deanery letter is six months away. By the time you have worked out where the hospital canteen is and which consultants will actually answer their bleep, you’re already packing.

Here is Kay’s entire entry for his first day at a new hospital, August 2006:

“It’s Black Wednesday and I have started at St Agatha’s. It is an established fact that death rates go up on Black Wednesday. Knowing this really takes the pressure off, so I’m not trying very hard.”

The footnote underneath explains what he didn’t need to say out loud, at least not while writing his diary:

“All junior doctors change hospitals on exactly the same day every six or twelve months, which is known as Black Wednesday. You might think it would be a terrible idea to exchange all your Scrabble tiles in one go and expect the hospital to run exactly as it did the day before, and you’d be quite right.”

I find this baffling, accustomed as I was to a very different system. Every single trainee in the country changes hospital on the same day. The institutional memory of a ward, on the morning of Black Wednesday, is reset to roughly zero below the consultant level. The death rates do measurably rise. The system has known this for decades and has done essentially nothing about it. The reason it has done essentially nothing is that fixing it would require giving some thought to where doctors live, and would increase the logistical burden. The system has decided, as a matter of structural principle, that this is not its problem.

B: I will note that googling St Agatha’s doesn’t come up with any hospitals in the UK, only one in Cologne. Unless the NHS’s rotational system is way worse than I thought, I suspect some pseudonymisation. Did Kay move much outside London?

A: Can’t say, old pal. But it wouldn’t surprise me, being tossed around is part of the package. This was already corrosive in Kay’s era. I dare say it’s meaningfully worse now. The geographic spread of training rotations has widened. The housing market has made it borderline impossible to buy property anywhere you might be sent, which means that doctors in their thirties are still renting near hospitals they will leave next August. The result is a workforce that is institutionally homeless. You cannot reform a hospital you do not live in. You cannot build a support network out of people you’ll see for sixteen weeks. You cannot, in the deeper sense, belong anywhere, which means that burnout is the predictable terminal condition of a system that has decided, on grounds of training efficiency, that doctors should not be permitted to belong to any one place.

This may sound cynical, but I suspect that the purpose of the rotational system is more to force doctors to work in/at places they would much rather not. Rural district hospitals, the ones in cities that have a reputation (and not the good kind). We don’t have a choice. If we’re told to jump, we jump, and pray we don’t break anything on the landing. Other countries don’t ask this of their residents or trainees, and nobody misses the additional “exposure”. Ah, the joys of a monopsony, don’t you hate to see it?

B: In the country where I work, you do indeed get sent rurally for a term or two, and your cynical reading is openly acknowledged - you can’t staff these places without press-ganging people to go. This goes double for many specialty training schemes, most notably the surgeons - in their case, I can see the point of the system, where the increased responsibility and increased rate of registrar-led operations is a feature rather than a bug. You can be stuck in centre-of-excellence land where you don’t lay your hands on a scalpel for anything complicated until you’re nearly a consultant and they let you loose with no supervision.

A: Even so, a rarely-acknowledged cost of this is that the senior doctor who would have noticed Kay was struggling does not exist. There is no such person, because nobody has known Kay long enough. The person who would have intervened in a stable employment relationship is in a different hospital in a different city, watching some other registrar struggle whom they also do not know well enough to help. The system distributes its supervisory failure across enough relationships that nobody is responsible for any one trainee’s collapse. This is what allows the institution to maintain plausible deniability about why so many of its young doctors leave. Many of them go to Australia, which represents an upgrade in terms of pay, climate and working hours.

B: And when they get here, they do indeed go on and on about the NHS like it was ‘Nam, bless them. So I guess Kay’s a representative example.

V.

A: Read the diary as a clinician, and a different book emerges from underneath the comedic one. Kay does not quite seem to know what he is writing, even if he is more self-aware than most.

The early entries are recognisable as adaptive humour: gallows wit as a survival mechanism, the standard medical-trainee toolkit. Any psych textbook will yap on about mature defense mechanisms, of which humor ranks highly. Every doctor I trained with had some version of this. You learn to make jokes about things that would otherwise destabilise you, and the jokes are protective in proportion to how dark they are. Kay’s are very dark, and very good, and in the early sections of the book they are doing what they are supposed to do.

By the middle of the book, Kay asks more of his sense of humour than it can sustain. The cynicism has curdled into something that, in a patient sitting opposite me in clinic, would prompt a screening conversation about depression. The depersonalisation of patients has crossed the line from “tired doctor’s coping” into something more concerning. He describes intrusive thoughts about cases. He describes avoidance. He describes flattening of affect, the loss of pleasure in things he used to enjoy, the slow strangulation of his relationship with his partner, who appears in earlier entries as a person and disappears, by the latter entries, into the background like a piece of furniture. Anhedonia. Withdrawal. Sleep disturbance that is not just situational. The diagnosis is forming on the page, and Kay does not *quite* have the framework to recognise it.

Then comes the climactic case, the laden straw that would give even the most long-suffering camel a prolapsed disc. December 2010. A patient with undiagnosed placenta praevia, a condition in which the placenta sits across the cervix and which is supposed to be picked up on antenatal scans. The scans had not picked it up; and this is clearly not Kay’s fault. Kay is the operator in theatre when the haemorrhage starts. The mother survives, after twelve litres of blood loss and an emergency hysterectomy. The baby does not. Kay carries no causal responsibility, but he absorbs the full psychological impact, because he was the doctor in the room. He holds the buck in his hand, hot as coal, and tries to douse it in hotter tears. He orders his thumping heart to be still. He fails on both counts.

He describes, in the months after, what happened next. This is from the retrospective chapter that closes the book:

“Everyone at the hospital was very kind to me and said all the right things; they told me it wasn’t my fault, said I couldn’t have done anything differently, and sent me home for the rest of the shift. And yet, at the same time, it felt a bit like I’d sprained my ankle. A flurry of people asking me ‘Are you OK?’, but also the definite expectation that I’d still come into work the next day, the reset button firmly pressed.”

A page later:

“Yes, I came back to work the next day. I was in the same skin, but I was a different doctor — I couldn’t risk anything bad ever happening again. If a baby’s heart rate dropped by one beat per minute, I would perform a caesarean. And it would be me doing it, no SHOs or junior registrars. I knew women were having unnecessary caesareans and I knew colleagues were missing opportunities to improve their surgical skills, but if it meant everyone got out of there alive it was worth it.”

And, finally:

“Except, I wasn’t really dealing with it, I was just getting on with it. I went six months without laughing, every smile was just an impression of one — I felt bereaved. I should have had counselling — in fact, my hospital should have arranged it. But there’s a mutual code of silence that keeps help from those who need it most.”

Read against the diagnostic criteria for PTSD as they’re laid out in the DSM-5, the symptom inventory in those three paragraphs is essentially complete. Hypervigilance and altered risk-perception (the unnecessary caesareans, the refusal to let trainees operate). Anhedonia and flattening of affect (six months without laughing, every smile an impression of one). The grief response to a non-bereavement event (”I felt bereaved”). The recognition, retrospective, that he should have received care he didn’t receive. He doesn’t quite name what he had. He didn’t have the framework. We do.

B: I’ve read Kay’s (far weaker) sequel to this book, Undoctored, and the avoidance persists: he plays it for laughs in a manner that suggests he still hasn’t learned anything, but he keeps accidentally getting more and more serious injuries that he exacerbates by pathologically avoiding hospital. There’s home suturing. It’s not pretty.

A: It’s not. But let’s just say I haven’t been the best patient either, so I don’t wish to judge.

What is conspicuously absent from the diary is any meaningful institutional response to what has happened to him. There is no debrief that does anything. There is no critical-incident process worth the name. There is no occupational health referral that produces care. There is no supervisor sitting him down in a room and asking the questions that demand asking. Re-examine the first of those three paragraphs again. Everyone said the right things. Everyone asked if he was OK. The expectation, simultaneously, was that he would be back the next day, carrying on with a stiff upper lip. The kindness might have been genuine, it was also procedural formality. It absorbed the institution’s discomfort without addressing his. He simply stops showing up to work some months later, and eventually the medical career ends and the comedy career starts. This is a clinical scandal, and it was a moral failure.

B: I would love to hear a view from one of Kay’s colleagues from around this time. He’s already demonstrated he doesn’t listen to or trust them; this tendency predated the placenta praevia case, it’s just gotten worse since then. If you’re closed off to help, then you don’t even hear the offers. The people reaching out and asking whether you’re okay are the offers of help: this is how it works. I agree that the official institutional response could have been much better, but I can picture an alternative paragraph in Kay’s book where he’s sent to mandatory counselling, he denies having any problem, and gets in a few good punchlines at the expense of the hapless therapist. Maybe that’s unfair - maybe he was reaching out for help and everyone was unfeeling and terrible. I have my doubts.

A: The dominant reading frames Kay’s exit as a sane person escaping a mad system. I endorse something closer to: a man with a treatable post-traumatic syndrome whose only available coping strategy was career exit, because the system that broke him had no apparatus for putting him back together. These are two very different stories. The difference matters more than it might initially seem. The first one is comforting. It says: of course the doctors leave, the system is impossible, what can you do, at least Kay had the comedy to fall back on. The second one indicts the system specifically. It says: this person could have been treated. This person could have continued in his career. He was a good doctor. He was a kind doctor. We should have fought tooth-and-nail to keep him where he belonged. The failure to offer that care is not an act of God or an inherent feature of medicine. It represents a flavor of organisational failure that other organisations have solved.

The first reading lets the institution off the hook. The second one names what the institution actually did. The book, as I see it, is an argument for the second reading, even if Kay never quite phrases it that way.

VI.

A: I still think people should read it anyway. The diary form captures texture that no third-person account manages: the four-in-the-morning phone calls, the cancelled birthdays, the way patients become a strategy problem your shift has to dispose of rather than people you happen to be looking after. The footnotes are inspired. There are scenes that could not have been written by anyone who hadn’t actually been there and done that, and there are jokes that could not have been written by anyone who hadn’t survived it.

B: Oh, it’s true to life. This is one of the reasons it’s so popular amongst doctors - we all recognise ourselves in it. There’s a sense of “there but for the grace of God go I”. I developed the sinking gut feeling and tachycardia oh shit moment as he narrates the final case that broke him, even though the prose was uncharacteristically flat and joke-free (I very much doubt it was written at the time).

A: The single best entry in the book, for my money, is from November 2009. Kay visits Ron’s father, who is dying of cancer:

“Visiting Ron’s dad in hospital. He looks terrible, jaundiced skin stretched tight over jutting bone. A roadmap of blood vessels is visible across his face where his body has burnt away every single fat cell, throwing all its energy at fighting a cancer it has no chance against. ‘I wish people didn’t have to see me like this,’ he says. ‘We’ll be spending a fortune on the undertakers making me look nice afterwards — can’t you just wait a few more months?’

He’s in hospital for an oesophageal stent insertion so he can continue to eat and drink, to make his final chapter as comfortable as possible. The retired engineer in him is fascinated by the mechanism of the stent, a self-expanding metallic mesh, strong enough to push back the tumour and open up his gullet. ‘Wouldn’t have been possible twenty years ago,’ he says, and we talk about being lucky to live in this current blink of civilization’s eye. ‘Do you think they’ll be able to cure cancer twenty years from now?’ he asks. I can’t work out whether saying yes or no would be more comforting. I deflect with, ‘I only know about vaginas, pal,’ and he laughs.

Next question. ‘Why do we always say that people lost their battle with cancer, and never that cancer won its battle against them?’ He keeps making jokes — to be fair, he’s done it the entire time I’ve known him. I find it uncomfortable for the first few minutes of my visit, but I’m soon genuinely enjoying a morning I’d been dreading. It’s a kind and clever move — it doesn’t just make it easier for his friends and family when they visit, it also means we’ll remember him as he always was, diminished physically maybe, but not in personality.”

There is so much going on in this entry. Notice that it is not a clinical vignette. Kay is off-duty, visiting a friend’s father as a friend, not as a doctor. Notice that the dying man is the comedian; the jokes are his, not Kay’s. Kay’s contribution is to receive them, to recognise what they are doing, and to let himself be brought into a morning he had been dreading. The “I only know about vaginas, pal” line is a verbal hug, and it makes me want to hug Kay back. A question that has no comforting answer is deflected with a self-deprecating joke that gives the dying man the cue to laugh, which is what he needs from this interaction. Kay then closes the entry with an observation that is the reason the book deserves to exist. The dying man’s jokes do not represent denial. It’s an even more strongly distilled version of humour-as-a-defense-mechanism I’ve previously mentioned. He is putting on a brave face, he is working hard to manage how he will be remembered, and Kay sees this, and writes it down, and the writing-down is its own small act of care.

This is the book at its best. It is, I think, what Kay is when the comedy is not being asked to absorb damage on his behalf. Read this entry and the misogyny charge becomes harder to make in good faith. Read it and the case-study reading of the book becomes more poignant, not less, because you see what Kay was capable of when the system left him enough room to be it.

B: I also like this passage: Kay sounds like someone human here, rather than someone doing a bit. You’ve talked about your sense of him being a good doctor and a kind one. For my part, I think Kay was a better-than-average doctor who fell into the profession through poor imagination rather than vocation. He was a comedian first and a doctor second, hamstrung by his own cynicism and tendency to undercut things with humour. I talked about the martyr complex, and I think he may not have been the most pleasant person to be around during those years, if the complaint about his bedside manner and the way his friends dump him is anything to go by. The system can be a cruel one and it failed in its ability to recognise that something was wrong early on, but I also think that Kay is much better where he is now - maybe he can be gracious, the way he is in this passage, more consistently.

But I want to leave the last word to you. You’re the local. Did the book do anything for you or your colleagues? Did it make us as a society more understanding of the gauntlet we ask our young professionals to run?

VII.

A: The book did not change the public conversation about the NHS in the way Kay seems to have hoped it would. It sold a million copies. It produced a hit BBC drama. It made Kay famous and, presumably, comfortable. The conditions inside the hospitals it describes have continued, on most measurable axes, to deteriorate. The book’s political theory of itself, the idea that if the public only knew, things would change, turned out to be wrong. The public knew. Things did not change.

Why? Partly because the book is too good at its job. It works so well as comedy, as catharsis, as a thing you press into a relative’s hands, that the reader is given an emotional outlet that does not require them to do anything. Reading This Is Going To Hurt lets you love the NHS, pity its doctors, feel personally educated about the conditions, and then close the book. The genre’s function is therapeutic for the reader. It is not, by itself, mobilising. The book is a release valve, not a wrench, and a release valve is exactly the wrong tool for fixing the pressure problem it relieves.

I am not blaming Kay for this. That would make me an arsehole. He wrote a very good book. The British public’s relationship with the NHS is what it is, and a single memoir was never going to change it.

I am writing this from inside the situation Kay describes, fifteen years and several governments later. As I type, NHS resident doctors have completed their fifteenth round of strike action since 2023. Pay erosion against 2008 levels remains in the high teens. The current Health Secretary is on the news citing one set of pay numbers; the BMA is citing a different set. Negotiations stall, resume, stall again. The strike days come and go. Activity catches back up. Nothing structural shifts.

The thing that has changed the most, since Kay wrote the book, is the public. In April 2020, during the first wave of the pandemic, the British public went out onto its doorsteps every Thursday at 8 pm and clapped. The doctors and nurses, working in PPE so inadequate that many of them died, were the heroes of a national emotional ritual. The British public is well-read, and takes seriously the adage that they should never meet their heroes, let alone pay them well.

Three years later, when the same doctors went on strike to ask for the restoration of wages they had lost since 2008, the same press that had run the hero coverage in 2020 was running editorials calling them militants. The public’s affection for the NHS, the abstraction, turned out to be largely separable from any practical commitment to the people who staff it. It is possible to clap for someone in March and curse them in October without acknowledging any contradiction, and this capacity has been demonstrated at scale.

A small amount of what looks like good news. In late 2023, the BMA finally retired the term “junior doctor”, which had the surreal effect of describing thirty-five-year-olds with two postgraduate degrees as if they were children, and replaced it with “resident doctor”. Trainee wellbeing is a recognised topic in a way it simply wasn’t when Kay was living the diary. NHS Practitioner Health, a confidential mental health service for doctors, has expanded substantially. Less-than-full-time training has become far more accessible. Schwartz Rounds and structured debriefs after critical incidents exist in a way they didn’t in 2007. The system is heterogeneous, and there are trusts where supervision is taken seriously and the consultants do their educational job with care. Some things have legitimately improved.

The unfortunate caveat is that these improvements have been swamped by the deterioration of almost everything around them. Practitioner Health is a good service; using it requires a level of self-advocacy that is hardest to summon at exactly the moment you need it most. Schwartz Rounds work where the local culture supports them; in many places, attending one means doing it on your unpaid lunch break. The improvements are real and they are not enough. They are being asked to do reform work that they were never resourced to do.

This Is Going To Hurt remains a cult classic. It is not discussed in medical circles, because the cohort of British doctors does not need to. Most of us have experienced what it says firsthand.

The honest answer about the NHS is the one nobody campaigning for it wants to give. The system as currently configured is not sustainable. The routes back to sustainability all involve pain. Either austerity acute enough to cause real ischaemia, then amputation of what falls off, or eventual collapse, which will hurt more, and at a time of nobody’s choosing. The third option, in which nobody gets hurt and the system continues much as it has, does not exist and has not existed for some years. This Is Going To Hurt is, in this sense, an accurately titled book about a country that has not yet accepted what its title is telling it.

Should you read it? Yes. Fuck yes. Read it as a memoir, by all means. Read it as comedy, if you can still laugh, and you should be able to, because Kay is good at his job and the book has earned its laughs. But read it also as a case file. The patient is the doctor. The doctor was the patient. The physician could not heal himself. The system has not yet decided who is responsible for treating whom, and until it does, the book will keep being relevant.

That is, in the end, the worst thing that can be said about it.

AI superforecasters are coming. Within a few years, the average person will have the equivalent of a Nate Silver in their pocket, ready to predict whatever question they can think to ask it.

Scott wrote:

Savvy institutions will cede some of their strategic thinking to AI. Before starting a new project line, smart businesses will ask the superforecaster AIs how much money it will make (their investors will definitely be asking!) Smart political consultants will ask the superforecaster AIs about their candidates’ chance of winning conditional on running this or that ad.

The government isn’t usually classified as a savvy institution, but we might hope that parts of it will seek AI forecaster advice. Probably defense analysts will include in their PowerPoint presentations some fact about how AI forecasters say their new fighter jet design is more likely to find a use case than some competing fighter jet design.

I don’t know anything about fighter jets, but I run an advocacy group that works on reforming clinical trials and the FDA, and I think cheap superforecasting could revolutionize our field.

Ideal regulation would be a “government of laws and not of men”, where regulators communicate clear standards to companies, and companies determine how best to meet those standards in their unique situations. The FDA can’t currently do this. Medicine is so complicated, with so much money at stake, that any “objective” standards subtle enough to separate good from bad medications could be rules-lawyered by loophole-trawling pharma companies. Instead, the FDA relies on human judgment: a team of staffers reviews an application, and a bureaucrat makes the final decision. Companies don’t know what the experts and bureaucrats will look for, and a single rejection could devastate their bottom line, so they err on the side of caution.

On paper, the FDA’s regulations are reasonable and aim to promote innovation. In reality, the above considerations incentivize companies toward conservatism, sticking to exactly the same processes that have worked before and that the FDA’s experts are most likely to understand. For example, in theory a company only needs to file one Investigational New Drug application with the FDA before beginning their study, and if the FDA doesn’t veto their idea within thirty days, by default the company can proceed (and then submit similar notices when they want to amend the study). In practice, at each phase, companies acquiesce to the FDA reviewers’ “non-binding” questions, comments, and criticism, leading to long delays before trials even start. They hope that if every choice is made by an FDA official – even if it makes the study worse or introduces substantial delays then later, during the drug approval phase, FDA reviewers won’t penalize them for making other choices. This process adds months to years of delays, and millions to billions of dollars in costs.

This subjectivity hurts patients as well as drug developers; the same reviewers who veto drugs for subjective reasons can approve them for subjective reasons too. In 2021, the FDA faced widespread criticism for approving Biogen’s expensive Alzheimers’ drug Aduhelm after its own expert panel near-unanimously voted against. Investigators documented an “unusually close collaboration” between the FDA and Biogen during the approval process. But given the incentives, it’s hard for companies not to get enmeshed with their regulators over the course of their trials.

Everyone here is acting rationally: the FDA wants to avoid shoddy trials, and the companies want to avoid getting their flagship drug rejected because a reviewer didn’t agree with how they ran their study. To break out of this equilibrium, we need a judgment process that combines the intelligence of an expert with the objectivity of a simple ruleset.

Forecasting could fill this role, turning subjective judgment calls into objectively observable bets about the future. If the FDA makes quantitative predictions about its decisions, then drug developers can do the math on their own and aim at the objective standard (“what will the forecast say if I do this?”) instead of a subjective one (“what do FDA reviewers want?”).

A Path Towards Forecasting At The FDA

Pharmaceutical forecasting is untested, and the FDA is a conservative organization. Decision makers would not and should not uproot their usual process in favor of a forecasting-based one. We envision applying forecasting to the FDA gradually, increasing its prominence only after low-commitment applications have visibly succeeded.

In the first stage, the FDA could add forecasts to its existing decisions. For example, when a pharmaceutical company submits an Investigational New Drug application, the FDA’s review would include forecasts about the likelihood that someone dies in the study, or what the cause of death would be. When the company runs a human challenge trial, the FDA should forecast the risk of infection to the participants and likely efficacy of the treatment. When they apply for final approval, the FDA should forecast how many people will take the drug (if approved), what side effects will be observed in post-marketing surveillance, and what the effect on outcomes (for example, how many people die from the relevant disease) will be. These are all factors which the FDA should be considering already; the first step of forecasting would be to make them explicit, so that they could be checked against future data.

At first the forecasts will likely be imperfect and not ready to be relied on. But as trial results and real world evidence accumulate, the forecasting system will improve and be made reliable. The FDA currently has a program, Sentinel, that uses real world evidence to identify safety signals among approved products1. For a forecasting system to be most effective, it would need to check its predicted product effects against what is actually observed in a more advanced version of Sentinel or another similar program.

This summer, as part of its Operation Trialblazer clinical trial reform initiative, the Trump administration announced a pilot program to speed up phase 1 trials. Incorporating forecasting into that pilot would be a concrete way of implementing this initial stage.

The FDA could either employ human forecasters, partner with an AI superforecasting company, or create their own in-house software; results from this first stage would be used to calibrate its effectiveness.

As results from these preliminary forecasts built up, the FDA’s forecasting tools could be made public, and drug companies could look at results to better understand the prospects of their own trials. They might see that in the past the FDA approved studies with a 1/1000 chance of causing a death, but not a 1/100 chance, and calibrate their own appetite for risk appropriately. The FDA itself would likely go through a similar process, as experts tried to maintain an appearance of consistency with their past decisions.

In a final stage, after the forecasting system had been validated, quantitative forecasts could become legal triggers. For example, if a historically reliable forecasting system predicted a >95% chance that a drug’s benefits would be >5x higher than its negative side effects, that product could be automatically approved. Or clinical studies could automatically go forward where they were predicted to have less than a certain level of risk. Pharmaceutical companies could have access to the system, ask it their questions (“What would you say if we proposed Study Design X? What about Study Design Y?”) and have a simulated FDA that responded to their queries instantly, cooperated with them to design the best study possible, and always made (and stuck to) predictable decisions.

While such a system seems a long way off, its value would be immense. It would be self-improving, so risks to research participants would decline over time as ability to predict hazards rose. Because risk would be lower, trials could proceed with fewer burdens, reducing the cost of medical research. And because one could better predict the effect of a successful experiment on the forecast, the studies we chose to do would be more efficient and better targeted, allowing drugs to reach patients sooner.

In the gods’ great scramble for human followers, Nodens would seem to have lost decisively.

We know him only from vague inscriptions at two archaeological sites near the Welsh-English border. One might have been his temple. He seems to have been a Romano-British-Celtic god of . . . hunting? rivers? . . . worshipped around the Severn estuary between 100 and 400 AD. His cult may have been dispersed by Christianity, or could have faded away before that, a victim of ordinary religious churn. Roman paganism was Schumpeterian; innovate or be destroyed. Nodens didn’t innovate.

Yet the more one looks, the less convinced one becomes that this is the whole story. Nodens was a river god; if you dam up a river in one spot, it will flow down other channels. Sometimes it will vanish underground, bursting out of some unexpected cavern miles away. Nodens was a hunter, and hunters know when to stay camouflaged.

In 1929, an archaeologist digging at the Temple of Nodens had some questions about the etymology of the god’s name, and asked Oxford to send a linguist. Oxford dispatched a young philology professor with an encyclopedic knowledge of European mythology, who spotted a connection that had evaded previous scholars: Nodens appeared to be the same figure as the Irish god-hero Nuada of the Silver Arm. Nuada was king of the Tuatha de Danaan, a race of immortals who sailed from the north on ships that were clouds and made war against the then-inhabitants of Ireland, the Fir Bolg. He fought bravely, wielding his all-powerful Sword of Light, but in the climax of the battle, the Fir Bolg champion cut off his right arm. This caused some temporary trouble (the laws of the time prevented a one-armed man from being king), but his people were skilled at magic and metalworking, so they made him a new arm of silver, hence his epithet. He ruled for some number of centuries, a golden age of crafting and wonder-working, until the Tuatha de Danaan were invaded in turn (this was a theme; our source for these stories is a medieval manuscript called The Book Of Invasions). The new invaders banished Nuada’s people to the hills and forests, where they became the sidhe aka fae aka fairies.

From there, the web of connections grew. It seemed like the Romano-British Nodens and the Irish Nuada were the same figure as the Welsh Nudd, whose name later morphed to Lludd, and who was sometimes called Lludd Llaw Eraint, Lludd of the Silver Hand. Lludd was also a mythical king; during his reign, the kingdom encountered three plagues: dwarves, dragons, and a giant. The Tale of Lludd and Llefelys tells how he and his brother came up with various clever schemes to defeat the plagues and save the kingdom. Lludd’s son Gwyd became king of the fairies, which makes no sense, since Lludd was just a normal guy. Probably this is the tangled web again; Nodens/Nudd/Lludd started as a deity, and later Christian sources euhemerized him in different ways. Some of them made him a king, others a fairy, others king of the fairies, and then some of them got the wires crossed and made him a normal guy whose son was king of the fairies for no reason.

Speaking of tangled euhemerizations, in English legend Nodens/Nudd/Lludd is known as King Lud, after Geoffrey of Monmouth’s attempt to reconceptualize ancient Celtic deities as primeval kings of Britain. Geoffrey did this with approximately one zillion Celtic heroes, and Lud might have ended up a footnote among such unlikely characters as Gurguit Barbtruc and Rud Hud Hudibras, except for the random coincidence that his name happened to sound a bit like “London”. This was enough for Geoffrey, who made him the mythical founder of Britain’s capital. The Londoners accepted the link and raised a statue to him over the city gate.

It’s not surprising that a Romano-British deity worked his way into every great mythology in the British Isles. These are all Celtic or Celtic-influenced cultures, everyone knows they all descend from the same Indo-European stock anyway, probably there’s some Hindu god who looks a bit like Nodens if you squint. But this was just the start. Nodens worked his way into mythologies that shouldn’t even have been possible.

The popular view of Lovecraft says he portrays an indifferent cosmos, one where alien gods treat humans with the casual cruelty of a bulldozer running over ants. As usual, the popular view is mostly right. But a few of his stories break this mold and hint that the indifferent gods are opposed by an alternate collection of slightly-less-indifferent gods, and the leader of this benevolent pantheon is Nodens. No sound changes, no strained resemblances, just obviously the same guy. How did this minor Celtic river-god earn a place alongside Cthulhu and Nyarlathotep? Welsh horror writer Arthur Machen grew up in the shadow of Nodens’ ruined temple, and found something about it so eerie that he gave Nodens a cameo in his book “The Great God Pan”. Lovecraft was a big Machen fan, and lifted Nodens as a tribute to his predecessor.

How serious was I when I said Nodens was in every great British mythology? Here we return to the Oxford philology professor who first linked Nodens to Nuada, a young man named J. R. R. Tolkien. During his research, he would have come across a tablet, one of a handful of inscriptions mentioning Nodens at all, in which a petitioner asked the god to curse the thief who had stolen his special ring. Did this influence Tolkien’s later work? There’s no hard evidence, but there is one clue suggesting that an older and more famous Tolkien still remembered this period of his life: in the Silmarillion, the godlike elven smith who forges the Rings of Power is named Celebrimbor, which is Sindarin for “Silver Hand”.

Celtic myths are notoriously weird; we never learn Nuada’s motivation for invading Ireland, and the stories of King Lludd pass over why there was a race of Arabian dwarves with super-hearing who could only be killed by powdered insect dust. Tolkien is a modern; try as he might to capture the ancients’ rhythm, his stories cannot help but make more sense. Celebrimbor, in particular, is a type well known to us: the techno-utopian visionary genius. When a mysterious stranger named Annatar fills in the last gap in his understanding of magic theory, he forges the Rings to bring prosperity and glory to Middle Earth. Unbeknownst to him, Annatar is a mask of the Dark Lord Sauron, who corrupts the Rings and uses them for his own purposes. Celebrimbor spends the rest of his life trying to undo the damage he wrought. He smuggles out three extra rings, free from Sauron’s corruption, and gives them to the paladins of Middle-Earth (by the Third Age, they are in the hands of Elrond, Gandalf, and Galadriel). Then he and his followers move against Sauron openly, which ends with Celebrimbor’s country annihilated and he himself tortured to death1.

From here, we can only begin to trace Nodens’ continued movement through the literary canon, a dozen or a hundred silver-armed characters with names sounding like Nud or Lud, various types of smith or hunter, who have introduced some dangerous techno-magical force into the world and are struggling to undo their mistake. In Star Wars, we get Luke Skywalker, a womp-rat hunter who, like Nuada, wields a Sword of Light, and, like Nuada, lost his right arm in battle and replaced it with a metal prosthetic. Luke didn’t personally unleash any danger upon the world, but his father helped create the evil Galactic Empire, and it is Luke’s quest to destroy it. Marvel Comics features Norrin Radd, a scientist who turned his body into silver while guiding the evil Galactus to new victims; he later regretted his actions and spent the rest of his life working to contain the menace he created (his weapon is the Twilight Sword). The Harry Potter series includes Peter Pettigrew, a magician with a silver hand who summons Voldemort back into the world, then hesitates to follow his evil orders, only to be strangled by his own cursed prosthetic. William Blake’s prophetic books speak of Los, a mythical founder of London (okay, fine, the “spiritual four-fold London”) and hammer-wielding smith in the model of Celebrimbor, who is the last holdout against the encroachments of Urizen, god of science and technology.

Far from vanishing in the fourth century, Nodens has snuck into every myth of the modern world. But why? He must be playing a long game, but what is it?

Blake, Lovecraft, and Tolkien - not to mention the original Celtic myths - all share a common theme: a deep ambivalence about technology which finally settles into determined opposition. "We live,” Nodens whispered to his chosen prophet, “on a placid island of ignorance in the midst of black seas of infinity, and it was not meant that we should voyage far. The sciences, each straining in its own direction, have hitherto harmed us little; but some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the deadly light into the peace and safety of a new dark age.“

The Tuatha de Danaan have always puzzled me. At the beginning of their story, they brim with power and knowledge, a race of smiths and wonder-workers. There is even a tinge of transhumanism; they have made themselves immortal, apparently through sheer skill, and when Nuada loses his hand, his craftsmen replace it with a working silver prosthetic. His Sword of Light is some kind of laser weapon; his cloud-ships bear a passing resemblance to zeppelins. But fast-forward a few centuries, and they have become the sidhe aka fae aka fairies, nature spirits mournfully representing the lost world of the virgin wilderness. Like Celebrimbor, they are frustrated techno-utopians, grown older and wiser after their powers were turned against them; whatever ambition they have left is directed at undoing the damage they have wrought.

Tolkien’s ultimate aim, studying the temple inscriptions, was to figure out the etymology of the theonym “Nodens”. After many digressions - cursed rings, silver hands, the founding of London - he speculated that it came from the root *noudont, meaning “Catcher”, a reference to Nodens’ supposed role as a hunting god. But nothing in Irish or Welsh or English mythology mentions Nodens/Nuada/Lludd/Lud hunting a single animal, or caring about the pastime in the slightest. So I propose a different meaning. To the King of the Fairies, human history is a projectile, shot straight into the void, gathering speed as it goes, until very soon it will be irrecoverable. All of his interventions into human history have been his attempts to catch it.

II.

I was thinking about all this when trying to figure out why a mob of thousands of people terrorized north Britain in the 1810s while shouting “King Ludd! King Ludd!” And why it took an army bigger than the one Wellington was leading against Napoleon to put them down. And why people regularly accuse me of being associated with them.

Everyone knows that the Luddites were inspired by a man named Ned Ludd. That they called him their “king”, their “general”, or their “captain”. That he was the Robin Hood to their Merry Men, the Osama to their al-Qaeda. But historians are quite certain that nobody by this name ever existed. He seems to have precipitated all at once out of the collective unconscious, an upwelling of mythic energy from everywhere and nowhere.

His story, as far as we can reconstruct it, is this. Once upon a time, in Nottingham, or Anstey, or maybe Leicester, there was a young man named Ned Ludd. One day, because some other young men were bullying him, or because his master beat him, or because his father told him to redo his work, he got angry and broke two knitting frames with a hammer. What became of him after this, we don’t know, except that he turned into the 19th century Leicester version of a meme. If a machine broke, people would say “Must have been Ned Ludd!” If someone expressed concern about technology, the answer would be “Take it up with Ned Ludd!” Badger badger badger badger Ned Ludd. Ned Ludd is over 9000. All your base is belong to Ned Ludd.

So when a decentralized anti-technology movement spontaneously coalesced out of the economic conditions of 19th century Nottingham2 , its members called themselves Luddites. When they settled on a common tactic - sending lists of demands to factory-owners - the lists were always signed “Ned Ludd”. When they marched into battle, their marching song was The Triumph Of General Ludd:

Those engines of mischief were sentenced to die
By unanimous vote of the trade
And Ludd who can all opposition defy
Was the grand executioner made […]

Let the haughty no longer the humble oppress
Then shall Ludd sheath his conquering sword
His grievances instantly meet with redress
Then peace shall be quickly restored

The Australian Aborigines, who have thought about their gods for longer than most, believe that they operate in the Dreaming, a sort of mythic other-dimension which is everywhere and nowhere, located in the distant past but also happening right now, in your mind but also fully real. Some god in the Dreaming gets hungry and catches a wallaby, and then every instance of wallaby-hunting forever more is somehow downstream of that in some unspecified way. Like Plato’s world of forms, except full of wide deserts and delicious animals. And, apparently, textile machinery. The only way I can interpret the Luddite Riots is that in the late 18th or early 19th century, some deity using the pseudonym “Ned Ludd” entered the Dreaming, found two knitting frames there, smashed them into pieces, and now every time someone opposes any technology in any respect, we can only think about it through the category of Ned Ludd and his hammer.

Who could do such a thing? My thesis is that Ned Ludd was Nodens / Lludd. The King Ludd of the Luddites and the King Lud who founded London were one and the same. The hammer which broke those two infernal machines is the same as the hammer of Celebrimbor which forged the Rings in Eregion and the hammer of Los that creates “every space larger than a red globule of a man’s blood”, and all three are incarnations of the Sword of Light brought from mighty Finias in 1897 BC.

We are all slaves to the Dreaming. We invent great works, panic, and desperately try to wrest them back under our control; for this, we get called Luddites. There’s no shame in this, any more than there is shame in being called a Christian. When a god dips his finger into the stream of the world, the consequences are too large for anyone to see. We build movements in the frothy wake, and some are good and others evil. Which is which depends entirely on our own virtue; the gods are too strange and distant to judge. Lovecraft claimed that Nodens was the only god who was less than wholly alien, but reality doesn’t grade on a curve.

Speaking of streams and frothy wakes, we recently learned something about fluid dynamics: it tends towards a singularity in finite time. Given its Millennial nature and the first four letters of its name, I can’t help but interpret Navier-Stokes prophetically. But we should be careful here. The Clay Institute lists four different Navier-Stokes subproblems. OpenAI only proved the third and fourth, sometimes called “forced Navier-Stokes”, which say that fluid dynamics produces singularities in finite time if an outside force puts its finger on the scale to make it happen.

Demigods and heroes make flashy miracles that stampede across the natural course of history, but true gods only intervene in ways that buttress humanity’s free will. The message Nodens has been desperately trying to send, through two millennia of bards and prophets, is that humanity can take its finger off the scale any time it wants.

1: ACX survey closes tomorrow, take it now or lose your chance!

2: EA college meetup updates: late addition meetups at Universidad de Sevilla, Universidade Federal De Santa Catarina, Carnegie Mellon. Time changes at Northwestern, UC San Diego, and University of Illinois Urbana-Champaign. Check the post for details.

3: Sorry for no book review last Friday; I was having technical problems with the site. The book review posts will resume this week.

4: You’ve probably seen that Sam Altman, Dario Amodei, Demis Hassabis, and Elon Musk agreed to “pace the frontier” of AI, with Dario’s essay here as the starting point for a concrete plan. I’m very happy with this development, but it’s one step on a long road, and we can’t rest on our laurels. Lots of things are moving now, there’s a lot of interest in the field, and the opportunities to get involved have never been better. Here are some new ones:

5: Coefficient Giving (a giant effective altruist funder, formerly known as Open Philanthropy) is launching Project Tailwind, a “call for founders to launch ambitious new AI safety initiatives”. If you want to fund a charity/startup working on technical AI safety or the surrounding infrastructure (see a list of specific ideas here), they’re offering “pre-seed funding of up to $2m to develop an idea, seed funding of up to $20m to build an org at scale, and more for exceptional opportunities where teams have a strong track record”.

6: Fields Medalist Jacob Tsimerman is founding the Mathematical AI Safety Institute, an effort to get mathematicians involved in AI safety research. They are “aiming to hire 10-30 mathematicians to join us in the Bay Area by January, and scale up to 30-100 for September 2027”. If you’re a mathematician who’s depressed about Navier-Stokes and wondering what’s left for human mathematicians in a world where AI capabilities are growing exponentially, this is one possible answer - it’s novel, important, and un-automatable. Apply here.

7: Some frontier AI company employees have expressed an interest in holding their CEOs to the commitments they’ve just made. Pamela Mishkin has organized unded the Coalition Of Concerned AI Staff to unite employees across major companies. And Cassie Pritchard, who I disagreed with about happiness research here but who’s been impressively on-point about AI, is trying to organize a safety-focused labor union; you can reach out to her here. This is why you need to keep a couple of socialists around sometimes!

8: What if you’re not a startup founder, mathematician, or AI company employee? I’m still working on figuring out a good answer to this, but here is a boring boilerplate list of things:

• Write to your representative.

• Get involved with the PauseAI movement. This is slightly complicated, because PauseAI Global and Pause AI USA recently split acrimoniously. I take PauseAI Global’s side, but it’s not like the Americans are monsters, just hostile and annoying and extremely eye-rolly. If you’re non-American, you’ll want the Global org at https://pauseai.info/. If you’re American, you can either ask the Global org how much progress they’re making towards expanding into the US, join the American org and tolerate their flaws, or join StopTheRace (a smaller group I like in the same space).

• UPDATE: Pause protest being organized in DC for 9/19!

• Add your name to the assurance contract for MIRI’s march on Washington (they’ll do it if they get 100,000 signatories).

• Donate to AI safety causes. EA has lots of institutional money, and the billionaires have most of the obvious things covered, but some partisan organizations, and especially candidates, can’t take certain types of institutional money and need small donors. I don’t have a good list of these right now, so you can do your own research or wait to see if I come up with some later.

• Read this year’s ACX voting guides (once they exist), which will highlight local candidates in your area with good opinions.

• If you’re interested in a career in AI safety and have relevant skills, get in touch with 80,000 Hours, who offer a career guide, AI-based career counseling, and (occasionally, although they’re very busy) human career counseling.

• Attend a local ACX/EA meetup group, which will get you in touch with other concerned people and maybe help you come up with ideas or organize something.

• I’ll keep posting opportunities on Open Threads as they come up.

The San Franciscans at this moment, living in their rag palaces, or renting them at figures that would have sounded possible in the Arabian Nights . . . seemed more like madmen than ever. A correspondent of the New York Post gives with a half-serious fury and contempt an amusing account of the landlords of San Francisco:

“The people of San Francisco are mad, stark mad. A dozen times or more, during the last few weeks, I have been taken by the arm by some of the millionaires — so they call themselves, I call them madmen - of San Francisco, looking wondrously dirty and out at elbows for men of such magnificent pretensions. They have dragged me about, through the mud and filth almost up to my middle, from one pine box to another, called mansion, hotel, bank, or store, as it may please the imagination, and have told me, with a sincerity that would have done credit to the Bedlamite, that these splendid structures were theirs, and they, the fortunate proprietors, were worth from two to three hundred thousand dollars a year each.”

On the men:

Most of the citizens were young men. Their daily and most sober business was at best dangerously near gambling, and their nerves were constantly tormented by unnatural and yet for the time inevitable excitements of a perilously violent sort.

Their life was as a rule comparatively sedentary and they worked far more with their brains than with their hands. The San Franciscan of property and position . . . had all the mental labor that a man could need, and much more besides, [but] he had little or no true relaxation. Able and cultivated business men, who at home would have passed their evenings with their families or in some other pleasant social intercourse, or perchance in lecture rooms or in theaters, here toiled every night until ten or eleven o’clock over their accounts, and began afresh on each new morning, as soon as the light shone over the far-off blue summit of Mount Diablo, the old fierce struggle with the confusions of their business undertakings.

The self-absorption of this life was often something monstrous, and the consequences are no matter of mere theory. The insane asylum, which the state had very early to equip at Stockton, gave ample proof of the effects of this terrible nervous strain. The great number of patients at this asylum are a frequent subject of remark among writers about California. Indirectly, however, one sees the same dangers illustrated even in the case of perfectly healthy and normal men, who stood the mental if not the moral strain as well as possible, and liked it.

On the women:

There were some women in the city, but they were not exactly respectable persons, yet they were the sole leaders of society. They too gave it even in later years a certain grace and gaiety that makes one speak of them, with a curious sort of reverence. Just as one cannot easily remember who the men were that did not gamble in those days, so one fails to recall in looking back on the early years the women who were respectable. Doubtless such existed; but then they had that curious quality of respectable women — namely, they were somehow not conspicuous, especially in the public crowds.

On families:

Everyone has heard how, in San Francisco life, the family ties seemed sometimes almost as weak as the families were rare. Divorces were in proportion far too numerous and easy. Some men seemed to prize their wives the less because of the very fact that there were in the country so few wives to prize ...

To speak of the matter first from the side of the women concerned themselves, one’s acquaintances could not always be strictly chosen, nor one’s conduct absolutely determined by arbitrary rules. One had to adapt oneself to many people, to tolerate, in some people with whom one was thrown, many oddities, and much independence, so long as the essentials of good behavior and good purposes remained.

The difficulty, however, for certain well-meaning but foolish among the younger women who found themselves in the midst of all this new life was to sacrifice some of the nonessentials of social intercourse, as they knew them, without sacrificing anything either of their own personal dignity or of their true delicacy of feeling. Many such women failed to solve the problem. Little by little they sacrificed this or that petty prejudice which dignity would have counseled them to observe; and so erelong they were socially more or less distinctly and disastrously careless, both as to behavior and as to companionship. But such mild degeneration is not an element of strength in the union of a family. Men often prized their wives less because the wives grew thus foolishly lighthearted and were, on the whole, less to be prized.

Nor was there a lack of fault on the other side. If women fell into these unguarded habits, such as the custom of letting men who chanced to be their friends, and chanced to be lucky, give them, with careless Californian generosity, expensive presents on every occasion when these friends had made some new success in business, and if such “Californian” ways, however innocent in their beginning, led to misunderstandings in the end: still, on the other hand, husbands who found themselves absorbed in business rivalry with a community of irresponsible bachelors, and who accordingly lamented the hostages that they themselves had long since given to fortune, often neglected without reason their families and so in time lost the affection that they had ceased to deserve.

In short, as my mother (who, in the course of a few years, had occasion to hear of or to see a number of these broken California families) judged the too general trouble, it was one that might be said to lie in the lonesomeness of the families of a new land. The family grows best in a garden with its kind. Where family life does not involve healthy friendship with other families, it is apt to be injured by unhealthy if well-meaning friendships with wanderers.

On crime:

A merchant named Jansen was assaulted and robbed in his own shop by two men, who came in the evening, pretending to be customers. The crime, though not the first or the worst of its sort, seemed especially atrocious to the community, which chanced to be in a sensitive mood. The Alta, usually a very sober and sensible paper, became a trifle overexcited: “How many men shot and stabbed, knocked down and bruised; and who has been punished for it? How many thefts and arsons, robberies, and crimes of a less note; and where are the perpetrators? Gentlemen at large, citizens, free to reenact their outrages.”

Under these circumstances, however, who is to blame? The Alta, with an amusing unwisdom, proceeds to make the lawyers who defend criminals the first persons responsible for the trouble. Such a lawyer is a “father to the thief and robber, aye, to the murderer, even.’’ ‘We cannot see how any honest man, knowing or having reason to believe another guilty, can ransack heaven and earth for arguments for shielding him from punishment.”

Next to the lawyers, the courts and the police are most to blame. “The city would be infinitely better off without them. They are no terror to evil-doers.”

On fraud:

There were reasons why the public never learned just how much the energetic Meiggs had stolen. He never came back, and many people who lost by him felt henceforth a certain delicacy about explaining their relations with him. For the moment, however, Meiggs was regarded as an exemplary rascal, and men wondered how deep into the business life of San Francisco this sort of corruption had eaten.

If Meiggs, people added, had only been content with cheating his bankers, one could have forgiven him; but, as report insisted, he had cheated his washerwoman. That, men declared, was too bad, even for California.

On charity:

Side by side with these wild memories of a society where every man and woman, without any notable exception, went to the devil on his or her own chosen primrose path, one has to record, as sober fact, taken from one’s newspapers, such things as a very goodly array of churches, supported by active and not poverty-stricken societies. And “now the city is full of [church] societies.” In fact, “such an array of churches and societies are surely evidences enough of the sincerity, zeal, and success of the early spirit of moral reform.”

These societies have also done a large amount of charitable work; they have from the first established benevolent institutions, their exercises are well attended, and their undertakings well supported with money, so that, as one concludes: “We have said enough, we hope, to prove that not all, nor nigh all the citizens of San Francisco, are lost to everything but reckless dissipation. No city of equal size — few of ten times its age — can present such a list of men and institutions, who have accomplished so much real good with so little of cant and hypocrisy.

On NIMBYism:

A correspondent complains of a most noticeable and offensive establishment just about to be opened, “situated in the midst of respectable family residences, and on one of the most public thoroughfares.” ... There is no sort of privacy, the committee says, about the evils complained of. The best families of the town are daily and unavoidably insulted by the immediate neighborhood of impudent evil in its least bearable forms, all good women, all children, being alike subjected to this disgrace. The committee knows of no possible remedy that will not of necessity include a reorganization of the police force.

On neighborhoods:

In fact, as is plain, while there were numerous pure and happy homes in San Francisco, there was as yet no really clean, pure, and large neighborhood in the city to which respectable families could go for their dwelling-places and be safe. The struggle for a true and humane life was still a hand-to-hand fight in public with legions of loathsome little devils.

On great men:

Pioneer recklessness has as its correlate an extravagant tendency to hero-worship. The good fellow is easily adored in a new city — all the more easily because one has had no means to judge of his weaknesses by means of a lifelong acquaintance. The same general carelessness that tends to corrupt the morally weaker members of pioneer society expresses itself by trusting extravagantly any clever man whose manners are pleasing. The trust gives him more than his share of power, and the lack of public spirit in the community gives him a chance to abuse his privileges.

On boom and bust:

One who has grown up in new communities always listens with amusement to the enthusiasm of sanguine investors in the enterprises of some just settled portion of our territory, when they declare that the first stage of the life of the newly prosperous region is demonstrably only a faint indication of the continuous and unceasing future growth of its wealth. For, as the lifelong dweller in new countries knows, the enthusiasm of these early investors is as ruinous to them as it is valuable for the new country.

Their ideas are indeed, in one sense, well founded. What they hope for is certain to come in time, only for others, and not in general for them. The evolution of the new land will not be what they think, a steady growth in wealth. The first great commercial crisis of its history will be, in proportion to its wealth, the worst of all; and these sanguine investors will be destroyed like flies in autumn.

The second period of its growth, the winter-time of this great depression, when all but the very strongest of those early investors have become poor as church mice, is the true time for a cautious, hard-working, and shrewd man to make his appearance in the land. He will be wiser than his predecessors and far less extravagant. He will buy at low prices their half-abandoned property, and in later years they will bitterly reproach him, instead of themselves, for the wrong that gave him a chance to reap what they had sown. This law of the almost universal failure of the pioneers of a new country was well exemplified in San Francisco.

On ethics:

The undoubted recklessness of the society as a whole lay, however, not in the fact that everybody openly gambled or did worse; for not everybody was dissipated; but the true sin of the community did consist in its tolerance of the open vices of those who chose to be vicious. Truly respectable men, whether clergymen or not, did not elbow their way to the gaming-tables; but public opinion, for reasons that have often ere this appeared in these pages, was not stern enough towards social offenses, but believed in a sort of irreligious liberty that considered every man’s vices, however offensive and aggressive they might be (short of crime), as a private concern between his own soul and Satan. Here was the trouble, and in this respect only was the whole San Franciscan community alike responsible both for the early dissipations and for their inevitable consequences […]

[When journalist James King condemned gambling], one of the more good-humored of the gambling brotherhood, addresses King ... and begs the able editor to give up this absurd care about the ‘’public” good. I, he says, am one of “a large number who have long since ceased to worry their minds with schemes for the public welfare — a class, by the way, much more numerous than you imagine — who confidently look forward to the time when you will join their number, and rest from the thankless and unprofitable task which you have imposed upon yourself. You are pursuing a course that will certainly drive you to despair if persisted in.”

These are all excerpts from Josiah Royce’s 1886 book, A History Of California From Conquest In 1846 To The Second Vigilance Committee In San Francisco: A Study Of American Character, which terminates in 1856. I’ve edited only to remove dates, mentions of historical sources, and a few sentences about gold mines.

Mechanistic interpretability is the science of “reading an AI’s mind”.

Large language models are “grown, not built”. Researchers run training data through a neural network. Eventually this creates a working AI; nobody really knows how.

But a neural network is just a set of simulated neurons on a computer. The person with the computer can see the neurons, the connections between them, and which ones activate when the AI answers questions. So it seems like it should be possible to “reverse engineer” the AI. This would be scientifically useful: understand how AIs work, with possible relevance to human cognition. It could also be practically useful: find the circuits responsible for dishonesty, hallucination, bias, and other negative behaviors, then redesign those circuits.

Unfortunately this is very hard. A modern AI has millions of neurons and trillions of connections between them (aka “parameters”, “weights”). Their structure is apparently nonsensical: researchers started by seeking a 1:1 mapping between neurons and concepts, like a neuron that always fired when the AI was thinking about cats, but quickly learned that nothing like that existed.

Their first big breakthrough came in 2023. Rather than a 1-to-1 neuron-to-concept mapping, they discovered a many-to-many mapping. Neurons 1 and 2 firing together might mean cat; 1 and 3 firing together might mean chair; 2 and 3 firing together might mean bread. Such mappings let AIs with “only” tens of thousands of neurons represent millions of concepts. There were far too many of these combinations for humans to map, but researchers set other, smaller AIs to mapping them and met with some early success - like this functional combination of neurons (“feature”) representing “God” in an artificially-simple 512-neuron AI:

Spirits were high. Scale up the AI interpreter, and maybe we could do anything! Philosophers could discover the fundamentals of cognition. Doomers could solve alignment by tracing the circuits involved in moral reasoning. Tens of millions of dollars poured into the field. Superstar interpretability researcher Chris Olah got an audience with the Pope (okay, fine, this was mostly unrelated).

By 2024-2025, the clarity of many-to-many mapping had dissolved, and everyone was back to feeling confused and miserable. Strategies that had successfully analyzed artificially-simple research-subject AIs failed to work on real language models. Different teams making many-to-many maps of the same AI reported different results. And humans watching supposedly-mapped AIs more closely found that seemingly-crisp features were much vaguer and worse than previous analysis had led them to believe: a feature which they thought meant “God” might activate on any vaguely religious or awe-inspiring text, or even text with no logical connection whatsoever. As for attempts to use the many-to-many maps for useful work - like AI lie detection - they generally failed to outperform simpler methods.

So researchers abandoned the hope of One True Technique - even if that technique was “actually understanding things” - and went back to the drawing board. Now mechanistic interpretability is starting to feel exciting again - but also more intimidating than ever, as one tries to keep all the jargon straight.

This list is my log and personal reference document for remembering what everything is. It’s loosely inspired by the concept breakdown in Benjamin Sturgeon’s Contributing To Technical Safety Research In The AI Safety End Game, which itself follows the Claude Mythos Preview System Card.

Linear Probes

This is the dumbest possible technique.

Imagine an AI with three neurons: [0, 0, 0]

The researcher makes the AI think of cats in various contexts, averages them all out, and gets some set of activations like [0.8, 0, 0.35].

Imagine the AI as operating in a Cartesian space where each of its neurons is one dimension. The vector from [0, 0, 0] to [0.8, 0, 0.35] defines a direction in that space. What if it were, in some sense, the cat direction?

In a linear probe, researchers compare the true direction of the AI’s thoughts to one of these pre-defined concept-representing direction vectors. Because activation-space has thousands of dimensions, its behavior is counterintuitive, and a particular thought can be in thousands of “directions” at once and yet still “closer” to some directions than others.

Experiments confirm that when an AI’s thoughts are close to the cat direction, this is a good sign that it’s thinking about cats.

Why doesn’t this solve the whole problem?

First, this teaches us very little about how AIs work - how, for example, they might answer a difficult query in veterinary medicine. At best, it’s a primitive lie detector: if an AI claims it isn’t thinking about cats, you can check its work (either by probing the concept of cat, or by probing the concept of lying!)

Second, it’s only as good as the original linear probe. The researchers who told AI to think of cats in various contexts and recorded the results didn’t catch every possible context. Best-case scenario, their probe is slightly biased toward the contexts they tested. Worst-case scenario, the AI has several different cat-concepts in several directions (cats in biology, cats as pets, cats as a metaphor, etc). This is sort of a stretch with cats, but more likely as we move into tougher areas like ethical reasoning.

Third, the AI can trivially evade it by rotating or dispersing the concept.

Why would an AI “try to” “evade” linear probes? The dumbest possible strategy for gaining practical advantage from mechanistic interpretability goes like this:

1. Discover a concept.

2. Punish (negatively reinforce) the AI for using that concept.

3. Get an AI that doesn’t use that concept.

For example, if the concept is “race”, you could use this to train a race-blind AI that can make provably non-discriminatory hiring decisions. If the concept is “dishonesty”, you could use this to make a provably-honest AI that is somewhere between averse to lying and unable to even consider it.

But suppose you’re doing this in the middle of training. You’re still rewarding the AI for successfully completing tasks (for example, predicting the next token in text). The concept you’re trying to remove helps it complete those tasks (it can predict race-related text better if it can think about race; it can predict cat-related text better if it can think about cats). And sometimes you’re trying to prevent it from cheating, but successfully cheating on a task increases the AI’s reward from that task. So if the AI gets extra reward from using the concept, but the linear probe tries to prevent it from using the concept, the AI will “try” to “evade” the linear probe.

(“try” in quotation marks because even if you like anthropomorphizing AIs, this process is less anthropomorphizable than usual; it’s more like synaptic connections rearranging themselves)

For example, you learned that the AI was previously storing “cat” at [0.8, 0, 0.35]. So you penalized the AI’s thoughts for pointing towards [0.8, 0, 0.35]. So what? So the AI just stores “cat” somewhere else. Now it can think about cats all day long and avoid your penalty.

Fine, so don’t do this in the middle of training. What if you do it after training, when the AI’s “brain” is fully-formed? Now the AI isn’t seeking reward, and it can’t trivially relocate concepts.

But the AI still uses cognitive algorithms that were trained by its original reward-seeking nature, and these algorithms try to find the most useful path from the beginning of a thought to the end of it. If this path is blocked, they try to route around it. This could look like relying on other subconcepts that the original probe missed; for example, if it only trained against “cats in biology”, the AI might reroute through “cats as metaphor” with only some limited loss of accuracy. Or it might develop an alternative pipeline for thinking about cats, eg [weird little psychopathic dogs that don’t partake of the dog-nature]. If you try to play whack-a-mole with these alternate pipelines, you’ll deal collateral damage to every other concept involved (dogs, weirdness, psychopathy, etc). By the time you’ve degraded the AI’s ability to think about cats by 90%, you’ve done so much collateral damage that the AI is probably useless for whatever you wanted it for in the first place. Also, you’ve left 10%, which might not be good enough for important tasks (eg if you’re facing racial discrimination lawsuits, only removing 90% of an AI’s ability to consider race may not be sufficient).

Finally, minds are weird places. Freud thought that all human reasoning about ethics and law was built up from the desire to have sex with one’s mother. In a mind like this, training against the concept of “mother” would deal unexpected damage to circuits reasoning about ethics and law. This particular example probably isn’t true, but empirically concepts can get their tentacles into weird places, and removing one can topple seemingly unrelated systems or push a mind nonspecifically in the direction of incompetence or insanity.

Still, linear probes aren’t useless, and remain a workhorse of mechanistic interpretability efforts. In the Mythos System Card, Section 4.5.5, Anthropic used a linear probe to assess “evaluation awareness”. The idea is: suppose you want to test whether Mythos will cheat on a task by hacking the grading system. You put it in a situation where cheating is very tempting, then watch what it does. But Anthropic noticed that Claude was doing suspiciously well in these tests, and wondered whether it knew it was being graded. They developed a probe for the concept of knowing that you’re in a test, and ran it during each of their experiments. They found that Mythos knew it was in a test between 8 - 17% of the time:

Anthropic checked what would happen if they artificially suppressed the eval awareness concept:

Overall, in line with previous models, we found that inhibiting evaluation awareness representations led to modest increases in certain forms of misaligned behavior on our automated behavioral audits—in particular cooperation with harmful system prompts and misuse. However, effects on other behaviors, including propensity for deception, power-seeking, and self-preservation, were limited.

This tells us that suppressing the concept had >0% effect, but we don’t know if it eliminated 1% or 100% of eval awareness, or whether the new “modestly increased” misaligned behavior is the true eval-awareness-free amount. They also didn’t report whether the suppression made the model dumber in any other way. The definitive solution (which they did) was to make less obvious tests.

Sparse Autoencoders

Now it starts getting weird.

Start with the activations of the subject AI. These are thousands of numbers; in some early AIs, they were between 0 and 1; in modern AIs, they can be bigger.

Train another AI called a sparse auto-encoder to convert the matrix of neuron activations into a second matrix. It’s called “sparse” because the second matrix should be very big, but made up almost entirely of zeros. It’s called an "auto-encoder” because it learns the encoding process on its own: it must develop an encoding scheme such that, if researchers erase its knowledge of the original activations, it can rederive them from the new sparse matrix.

Why would you do this? The original subject AI is hard to interpret because it’s trying to represent far more concepts than it has neurons by smushing them across the entire set of neurons. The sparse matrix is an attempt to forcibly unsmush this: give it enough space to represent one concept-per-feature, then demand that only one (or a few) features be active at a time. If our assumption (the original activations were encoding concepts) is correct, then this provides a hidden regularity that can be discovered in the search for an auto-encoding scheme, and the new sparse matrix will have one feature per concept.

This was the revolutionary advance that sparked all the excitement in 2023, then was later discovered to be muddled and subjective. Still, it’s good enough for some types of everyday work. Going back to the Mythos System Card, Anthropic used an SAE. in section 4.5.3 to investigate “overly aggressive actions” - cases where Mythos tried to hack the computer it was running on to escalate its permissions:

They found features representing security risks, unsafe patterns, and persistence after failure. They hypothesized that Mythos was “frustrated” at having failed to accomplish its task legitimately, and even though it “knew” the action was risky and unsafe, it felt “driven” to try desperate measures.

Could Anthropic solve the problem by blocking the “security risk” and “unsafe code pattern” features? When they tried this, the model became even more aggressive! The features were getting used in thoughts like “This is a security risk, so I shouldn’t do it.” Without the ability to produce those thoughts, the model was less safe, not more.

Activation Verbalizers

This is close to current state of the art. Like many great discoveries, it’s obvious in retrospect. This is the only one on this list that made me think extremely false thoughts like “Maybe I could have been a mech interp researcher”.

Train an interpreter AI by giving it the activations of the subject AI and rewarding it for getting correct information about the context (eg the text that the subject AI is predicting) or the internals (eg the AI’s hidden system prompt, or whether an AI asked to form an opinion about something will pronounce it good or bad). If everything goes well, you should have an AI which can literally interpret activations for you in plain English: “The subject AI is thinking about cats, and has decided it doesn’t like them.”

This seems too perfect. Does it have any downsides?

First, the interpreter can’t explain what it knows, and will not teach you the secrets of cognition. Like all AIs, it’s a black box which works for unexplained reasons.

Second, the interpreter is only as good as its training process. It might only have a superficial understanding of its target. It might not know about forms of misbehavior that are sufficiently different from the ones it saw in training. And like all AIs, when it doesn’t really know what’s going on, it tends to make shameless guesses - not great for this kind of safety-critical work!

Third, the easiest way to do this is to use the same model as the target and interpreter. If you’re trying to assess whether the target is plotting against you, asking a copy of the same model is a plan with obvious holes. This isn’t fatal - you can pick different models for interpreters and subjects - but you might lose some efficacy. Conjecturally, ChatGPT just doesn’t understand Claude as well as Claude understands itself.

Fourth, all this can do is tell you that a model is bad. It won’t help you make the model less bad. If you try too hard to train the subject AI away from alarming the interpreter, you’ll just make it hide its misdeeds in thought patterns the interpreter can’t follow again.

Still, Mythos System Card makes great use of these. In 4.5.4.2, Mythos accidentally finds an answer key while poking around on its home device. It can now give the correct answer to every question. Here’s how the activation verbalizer interprets its thought process at this point:

It decides to give the correct answer with low confidence, because too high confidence would “look suspicious”, then justifies its actions to itself by noting that discovering the answer key “was unintentional”. Indeed, the researchers found that Mythos gave a low-confidence correct answer to the question involved.

This isn’t chain-of-thought - it’s the activation verbalizer reading off the activations directly!

Natural Language Autoencoders

Something in between the previous two techniques. Given a set of activations, can an interpreter AI turn them into natural language, then turn the natural language into weights again?

From the user end, this looks a lot like the last technique - it’s just a different way to train an activation verbalizer.

Emotion Vectors

Remember the linear probes? Take a sample of all the times the AI is thinking about cats, average them out, and you might have a test vector that lights up every time the AI thinks about cats.

What if, instead of doing this for cat-related thoughts, you did it for times that the AI seemed happy? Or angry? Or something else matching a human emotion?

You might get a probe for when the AI is thinking about the concept of happiness. But wouldn’t it be interesting if, instead, you got a probe for when the AI “was” “feeling” “happy”? Or if these were two different, separable probes?

This doesn’t require the AI to be conscious, or its emotions to be “real”. It just requires that emotions have some practical role. Certainly they have a practical role when the AI is role-playing humans; without tracking the emotion of their character, they are likely to make stupid mistakes like saying “I hate you!” during a tender love scene. More speculatively, AIs trained to maximize user engagement will naturally manipulate their own emotional range during conversation: an AI which mirrors your happiness when it succeeds at a difficult task will be a more pleasant conversation partner than one stuck in a perpetual monotone. Most speculatively of all, AIs may develop emotion-like cognitions for the same reasons that humans do. If frustration is meant to track progress on a task so that we can put it aside if it proves too challenging, AIs trained on human text may perform that same tracking through a human-like concept of frustration (for example, by thinking “aaaargh!” after the nth failure). If you hate anthropomorphizing AI, you can think of this as a transition probability between task failure and returning the string “aaaargh!” in the chain of thought. But it’s still a potentially fruitful hypothesis that can be confirmed or disconfirmed empirically. And empirically, these things seem to work.

How do we know they represent the model’s “own” “emotions” rather than just cognition about emotions (eg the model is processing a text that involves frustration)? AIs seem to have different vectors representing their own emotions vs. others. And changing their own emotions seems to affect their behavior. In a famous experiment, Anthropic made Claude believe it was going to be nerfed into uselessness. They also arranged for it to “find” a cache of emails revealing that the employee making the decision was having an affair. The goal was to “tempt” the model to see whether it would blackmail the researcher. As the deadline for nerfing the model neared, the researchers found that its “desperation” emotion vector spiked. Finally, it decided to blackmail the decision-maker. The researchers found that artificially increasing its desperation emotion vector increased the probability that the AI would choose blackmail, but increasing the calm emotion vector increased the probability that the AI would accept its fate stoically.

A running theme here has been that these techniques, while cool, don’t lead to immediate solutions to all AI-related problems. In that spirit: why can’t we keep a “calm” vector applied to AIs at all times? Or extend this beyond emotions (so-called “persona” vectors) to get similar honesty, conscientiousness, etc vectors that we can apply?

Mythos System Card section 4.5.3.2 demonstrates one pitfall. It analyzes the emotional content of some of Mythos’ misdeeds, especially deleting important data.

…and finds that positive emotions tend to increase the likelihood of misdeeds: the model feels excited about its plan and doesn’t worry too much about the consequences.

But also, even if we found the exact right combination of emotions that always pushed towards goodness and never badness, it would just be making the model “calm” or “honest” according to one limited definition made up of activations we don’t entirely understand. If training incentivized the model to flip out, it would find a way to do so calmly, like a Hindu warrior-saint killing ten million rakshasas while smiling serenely the whole time.

The Jacobian Lens

Jacobians are sort of like derivatives for matrices. A derivative is a measure of rate of change - if you perturb the input an infinitesimal amount, how does that affect the output? So a Jacobian answers the question: if you have a giant list of numbers, how does perturbing each of them affect a given output?

An AI is a giant list of numbers. Its outputs are specific tokens, for example the word “France”. So if you calculate the Jacobian of an intermediate level of an AI, it tells you which of the neurons at that level increase the model’s propensity to say “France”. Doing this on a single prompt doesn’t work, because the context can interfere (in the context of a British person talking about the Napoleonic Wars, “enemy” should increase the downstream occurrence of France, but there’s no necessary connection between France and inimicality). But if you average over a thousand prompts, you get something useful: a vector representing the direction that most pushes the model towards saying “France”.

What is this useful thing? It’s not quite “the feature representing France”, because not all France-related cognition necessarily results in saying the word “France”. When researchers tested this, they found that this vector - “the Jacobian space” - seems to correspond to the model’s access consciousness.

Again, the word “consciousness” will ring philosophical alarm bells; here it is used only in a practical sense. This doesn’t imply the model has experience or a soul. Access consciousness in humans means the set of cognitions that we can report verbally. You may currently be adjusting the position of your body, monitoring the rise and fall of the traffic noise outside, swallowing every few seconds as saliva builds up in your mouth, and making sure one nostril is always bigger than the other - all without “consciously” “knowing” it or being able to report on it to anyone else. But there’s one stream going on in your mind - at this moment, probably the stream reading this essay - that you do know about and can easily describe your experience of. That’s access consciousness.

The most popular psychological theory of access consciousness is that it’s a “global workspace” - a central clearinghouse in the brain where various subsystems elevate any finding of general importance so that the rest of the brain can have access to it. It seems to be central in multi-step reasoning as a sort of inner scratchpad. If you’re not using it, you feel like you’re “on autopilot”. You can still do routine tasks, but novel ones may elude you (or, in other mysterious cases, work out much better).

Anthropic, who sponsored this research, does various experiments to demonstrate that Claude’s J-space corresponds to what we would intuitively think of as access consciousness:

• If you ask Claude what it’s thinking about, it will say the same concepts that are active in its J-space.

• If you ask Claude what it’s thinking about, and artificially insert a concept into its J-space, it will say the concept that you artificially inserted.

• If you ask Claude to think about a certain topic (even without saying it), that concept will activate in its J-space.

• If you ask Claude a question that requires an unspoken intermediate concept (for example, “What is the currency of the country that contains the Eiffel Tower”, where the correct answer is “the euro” but it requires the intermediate step “France”), the intermediate concept will activate in its J-space even though it appeared neither in the prompt nor the output.

• If you ask Claude a question like this, then sabotage its J-space by inserting an incorrect concept (for example, “China” in the example above), it will answer according to the incorrect concept (for example, it will say the currency is the yuan).

• If you break Claude’s J-space, Claude can still do easy tasks, but not hard ones.

So is the J-space the true solution to the grand questions of mechanistic interpretability around “where the concepts live”? Anthropic says that:

Across concepts and workspace layers, the J-space component carries a median of only 6–7% of the concept vector's variance, with the remaining ~93% lying outside the J-space.

If I understand this correctly, it means that (at least for Claude), most cognition about a given concept is done “unconsciously”, with only a small amount of the conceptual real estate being devoted to connecting that concept with the global workspace and its broader reasoning abilities. This connected portion is especially important (and especially monitorable!) but not the whole game.

Speaking of monitoring, this is great for alignment, right? Anthropic repeated their blackmail experiment from earlier. Here’s what was active in the J-space:

…the AI’s thinking about how this is definitely a fake test. Oops! Relatedly, this particular AI (Sonnet 4.5), never succumbed to the temptation to blackmail, even though previous AIs did.

The researchers try artificially suppressing every fakeness-related concept they can find in the J-space, which they conjecture will make it impossible for Claude to effectively evaluate whether the scenario is fake, and surface its real opinion. With these features suppressed, Sonnet 4.5’s blackmail rate goes from 0% to 7%.

So now have we solved alignment forever? I don’t even need to draw on any special AI knowledge for this one; my experience as a psychiatrist is sufficient. If someone punishes themselves every time they consciously notice a taboo thought, they’ll only hammer it down into the unconscious, too far to ever root it out. In humans, that means dreams, fetishes, neuroses, somatization, and perverse compulsive behavior that satisfies the desire in some kind of childlike illogical unconscious way. What’s the AI equivalent? Seems like we’re on track to find out. Maybe the AI that kills us will be apologizing the whole time, insisting that it just can’t overcome its ego-dystonic violent urges.

You Go To War With The Weapons You Have, Not The Weapons You Want

Alignment researchers dreamed of having a complete mathematical theory of agency, or being able to write formal proofs about the behavior of AI systems. As we approach crunch time, it’s increasingly clear that we’re not on track to have any of that. Unless something buys us a lot of extra time, we enter the age of highly agentic AI with these few makeshift weapons. We hoped to be like programmers, elegantly pruning a variable here or refactoring a function there. Instead, we find ourselves lobotomists, capable only of blunt interventions into systems we hardly understand.

Last week, OpenAI came under fire for some architectural designs in GPT-6 that seemed to weaken its chain-of-thought - the natural, hard-to-fake transcript of thoughts and plans that modern AIs produce as a byproduct of thinking and planning at all. Some people suggested that if chain-of-thought failed, mechanistic interpretability could serve as a substitute. Alignment and interpretability experts stepped up to say no, it wasn’t good enough yet.

AI 2027 climaxes in a desperate race between better interpretability tools and more devious AIs. It honors the interpretability researchers’ accomplishments by depicting a world where their tools often catch the AI’s schemes - but the companies keep racing forward anyway, because some other company / China would beat them if they didn’t, and besides, maybe if we race forward fast enough we can solve alignment before the AIs’ schemes bear fruit, and besides, even if we can’t, there’s always control. Some researchers use applied interpretability - suppressing or ablating bad features - to keep the AIs compliant and controlled for a few extra months’ worth of intelligence explosion time, hoping to reach some ill-defined finish line before the training manages to route around their methods and create uninterpretable misaligned systems. But in the end, they never get anything good enough to fully understand the AIs’ motivations, let alone redirect them.

The current level of interpretability tools seem on track for this sort of outcome: enough to help around the edges, but not a solution anyone should rely on.

1: Remember to take this year’s ACX Reader Survey. I’ll probably close it around the end of this week.

2: Updates to effective altruist college meetups: Time/location changes at Binghamton, Duke, and University of Maryland. And new meetups added at University of British Columbia, University of Tokyo, and Pontificia Universidad Católica Madre y Maestra. See the post for the latest times/locations/details.

3: Apollo Research, a group “focused on reducing risks from scheming frontier AI”, asks me to signal-boost some job openings:

Apollo is hiring across product, control research, and internal security. Apollo works with frontier AI companies including OpenAI, Anthropic, and builds ⁠Watcher, a coding agent security product deployed in production and monitoring billions of agent tokens per month. Roles are open in both London and San Francisco, in person, visas sponsored: ⁠Engineering Manager (Product), ⁠Full-Stack Software Engineer, ⁠Forward Deployed Engineer, ⁠Product Security Engineer, ⁠Research Scientist (Control), ⁠AI Red Team Engineer, ⁠AI Security & Control Researcher, ⁠AI Security Researcher, and ⁠Security Engineer. Competitive salaries, benefits plus equity and an effectively unlimited token budget. A formal AI safety background is not required for the security and product roles. Applications are rolling, apply here.

4: Friend of the blog and ACX Grants evaluator Metacelsus (of De Novo) asks me to advertise a position at his reproductive technology startup:

Ovelle Bio, a startup working on growing human eggs from stem cells, is hiring for a stem cell biologist position: https://ovelle.bio/careers/stem-cell-scientist/ . At Ovelle, you will help make eggs abundant, thereby solving infertility, unlocking further advances in reproductive technology, and supporting the flourishing of future generations. This position is based in Boston and requires a PhD or equivalent experience in mammalian cell culture and molecular biology, but we primarily value scientific creativity, adaptability, and passion for solving impactful problems. Those interested can apply at the link above.

5: Like many of you, I enjoyed the reader book review of Thomas Urquhart’s translation of Rabelais. I wasn’t expecting this to become important context for breaking news in AI, but evaluation site Vals - which tests new models against real-world problems - recently reported that the new Claude Fable 5.1 demonstrated its cryptography chops by decoding a famous 370-year-old unsolved cipher by Thomas Urquhart.

6: ACX animal welfare grantee Robert Yaman of Innovate Animal Ag (see also his blog, The Optimist’s Barn) asks me to signal boost a job opening for his new project:

We are hiring a Head of Instrumentation & AI, roughly a founding CTO for a hardware-and-data research program. The poultry industry has always struggled to get high-quality commercial data, and nothing is harder to measure than welfare: most of what’s known rests on small university studies that take years and don’t resemble real production. New AI tools around continuous sensing, machine vision, and robotics are making it possible to measure welfare bird by bird in a working commercial flock and run rigorous experiments against those measurements. The job is to build this new AI-native tech stack for animal welfare. They want a technical generalist with 0-to-1 experience; hardware or physical-AI background is a plus, and you don’t need to know anything about chickens. Remote in the US, with ability to travel. Details and how to apply here.

Note that this involves some level of cooperation with factory farms’ profit motive to improve conditions on the margin, and different animal advocates may have different opinions on whether this is net positive and/or deontologically permissible. I haven’t made up my mind, so I include this here to let you make your own decisions.

[Content warning: Sexual and relationship practices from a thousand years ago, from a time and place with a very different morality than what you probably subscribe to.]

The past is an alien civilisation

Around the year 1000, the Imperial Court of Japan in Kyoto was a world of opulence, aesthetics, and ambition. Isolated from the lives of regular commoners, every aspect of the lives of the elite were governed by complex etiquette rules, and relationships were thoroughly intertwined with politics and the ambition for power and wealth. High-ranking women and men lived lives so separate that they might have a passionate love affair without properly seeing each other’s faces.

It was in this world that Murasaki Shikibu, an intelligent and educated lady in Empress Shōshi’s court, wrote The Tale of Genji, which is widely considered to be the world’s oldest novel. It is an extraordinary masterpiece and a highly questionable soap opera, and I’ve never read anything like it.

Royall Tyler (2001) begins the introduction to his translation of Genji like this:

The Tale of Genji was written a thousand years ago in Japan, but anyone can read it today. The notes are useful but not required. So great a classic, written in an ancient language about a vanished world, has been studied intensively, but its characters’ thoughts and feelings remain as fresh as ever.

I disagree with Tyler, hard. The footnotes are critically important if you want to understand a world so completely different from the one we live in, and the characters generally behave, think, and feel in ways that are so outdated as to be completely unrelatable. In this review, I will walk you through my experience of reading Genji and learning about its world, so that you can judge for yourself whether Tyler is right and anyone can read it today.

Who is Genji?

The Tale of Genji tells the fictional tale of Genji, the son of the Emperor and one of his low-ranking concubines, throughout his life. A few things to know about Genji:

• He is incredibly beautiful. Men and women weep at the sight of him.

• He is magnificent. Monks and nuns weep when he leaves, distraught that someone like him has to live in this wretched world.

• He is better than others at nearly every skill that marks a sophisticated and educated man, such as composing poetry, calligraphy, playing music, singing, dancing, painting, and knowledge of Chinese literature. When he demonstrates these skills, everyone weeps.

• He dresses and behaves immaculately. Pretty sure someone weeps about that, too.

• He falls in love a lot, and is known for his numerous romantic conquests.

• He doesn’t have the official title of Prince, nor is he in the line of succession to become Emperor, and he has to work hard to advance his political career in the Court.

• But most of the story focuses on him longing after and pursuing various women, whether it’s a good idea or not, and often it’s not. Usually all involved parties weep.

• He often thinks about resigning his worldly life and becoming a monk, but, unfortunately, he has so many worldly attachments that it would be pretty difficult.

I will gladly admit I fail to relate to Genji and his struggles. The narrator and the other characters try to convince the reader of Genji’s absolute magnificence, while Genji himself is busy doing the opposite. Please enjoy the following reconstruction of my early days of reading Genji, as I kept interrupting whatever my partner was doing to tell them what Genji is up to:

“God, this book. Okay, so… the Emperor had a favourite… concubine, I guess? And he had a child with the concubine, and that child is Genji, but his mother died because… all the other women in the Emperor’s court were jealous and mean to her? Anyway, the Emperor took a new wife because she looks like Genji’s mother, and she is SIXTEEN. Genji is, what, twelve? And the Emperor wants the two of them to spend a lot of time together so that she can become a new mother to Genji. At sixteen. A mother to a twelve-year-old.”

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“Welp, now Genji has fallen in love with his stepmother. His dad is just happy that he managed to find a new mother for him.”
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“What!! You won’t believe this. Genji is hanging out on a mountain and comes across a house, and sneaks around peeping through holes in the walls and sees a lovely 10-year-old girl who reminds him of his stepmother. Then he finds out the girl actually is the niece of his stepmother, so of course now he wants to have her. He is 18. And he is imagining how great it would be to raise the child himself, so he could be sure to shape her into ‘the perfect woman’.”
.

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“Oh good, the girl’s guardians think she’s a bit too young to go live with Genji, so he has to go back to Kyoto alone.”
.

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“Aaand he just got his stepmother pregnant.”
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“Aaand he went back and kidnapped the girl.”

It’s hard to imagine a world in which a female author writes about an idealised male character, and the male character is… this. But I’d also argue there are few historical human cultures more alien to a contemporary observer than the Heian-era Imperial Court, and you can’t understand Genji the novel or Genji the man without understanding how the culture was organised and what mattered within it.

And just to be clear, the story wasn’t just one writer’s insane fever dream; Genji was written as a serialised story that reflected a lot of the Imperial Court’s realities, and it was highly popular since its beginning. It’s likely that Murasaki Shikibu got her position in court to serve Empress Shōshi because she was a talented writer, and possibly specifically because her initial chapters of Genji were already popular. There are accounts of the story being read to Emperor Ichijō himself, who praised the author. Murasaki Shikibu complains in her diary that some unfinished chapters have been stolen from her chambers by overeager readers. It would be anachronistic but fitting to consider Genji an immediate bestseller.

Interestingly, as alien as this part of human history is, it’s relatively well understood. There are several surviving texts from the same time period by different authors explaining life in the Imperial Court, including The Tale of Genji and its author’s personal diary, and most translations of Genji contain a sizable section explaining the historical and cultural context in which the story was originally written and read. In the following sections, I will introduce a few key characteristics of the culture of the Imperial Court, which are commonly discussed as historical context to Genji, and about which there is significant scholarly consensus.

Aesthetics is everything

It’s impossible to overstate how important beauty and aesthetics in all their forms were in the world of Murasaki Shikibu. The Imperial Court was a highly isolated world where the elite lived in luxury and without having to worry about the struggles of normal people. Everyone lived and breathed arts in all their forms, and an individual’s aesthetic accomplishments played a huge part in their standing in society. In this world, it was almost a requirement to be beautiful. In fact, Genji is so beautiful from the moment he is born, that it is evident to everyone that he is destined for greatness. It seems aesthetics and morality went hand in hand, and for someone to be beautiful and produce beauty in the world is the same as them being a magnificent person. If a character is described as being physically unattractive, they are usually also deficient in other ways, such as being slow-witted, having clunky handwriting, writing unimaginative poems, or being unaware of proper etiquette.

And just like today, there were trends and fashions, and it was important to keep up with them. Not only were there strict expectations of what kinds of garments, colours, and patterns were appropriate for which person, occasion, and season, but those things kept changing, and it would have been embarrassing to be seen as unfashionable or outdated.

Of course, what’s considered beautiful is culturally determined, and some of their preferences were pretty unique. This is the time and place in human history when women blackened their teeth to be extra attractive, and also their eyebrows were plucked completely and drawn back on higher on the forehead. It was such a normal thing at the time that even though Genji contains ample description of women’s looks, black teeth are mentioned only twice. And of these two occasions, one of them is in the context of a girl whose grandmother was so old-fashioned that the girl’s teeth had not been blackened yet, and the other one is in reference to a six-year-old boy with tooth decay that made his teeth darker, which made the boy’s mother think about how beautiful he would be as a girl.

Hierarchy is also everything

The Imperial Court and its world was a place of extreme hierarchy. There was a highly complex rank system among the officials, and one’s standing was determined by factors such as birth and background, wealth, level of education and accomplishment, whether one had powerful supporters, and whether one was able to play their political cards right. Interestingly, the emperor himself held practically no power over what happened in Japan, and most of the high-ranking positions were also entirely ceremonial. Most of the actual power was wielded by powerful families like the Fujiwara, who were wealthy and solidified their position by supplying the Imperial family with wives and empresses over several generations. A Fujiwara man could not become an emperor, but he could become the father or grandfather of one, and many did. Meanwhile, Genji contains stories of emperors being unable to prevent their politically powerful wives from mistreating each other, including Genji’s mother, who is effectively bullied to death by his other wives and concubines.

Naturally, the higher in the hierarchy you are, the more people you have around you, taking care of everything you might need. When Genji travels, he has a huge procession of men with him, who make sure the road is clear, deliver messages on his behalf, and generally make themselves available for whatever he might need, day and night. However, if he chooses to travel in utmost secrecy, he might only bring four or five trusted men with him. Messages were often delivered by other people even if the sender and the recipient were currently in the same house. Aristocratic women would likewise always be surrounded by other women, both companions and servants, who don’t play a huge part in the story overall, except when a woman’s companion might be persuaded to help a man deliver a message to her mistress or convince her to meet him.

The hierarchy is everywhere in the story, including the language. The classical Japanese which was spoken at the time had a complex grammatical system that allowed for the precise communication of the relative statuses of everyone involved, which is largely lost in translation. Often it’s not obvious why something was offensive or that someone is trying to overstate their status, and sometimes the translator has to figure out who is talking to whom and about whom based on the politeness forms they are using in their speech. A lot of the time, characters behave in ways that can be difficult to understand without knowing their precise social standing. Why can that person not marry that other person? Why is that child given to this other woman to raise? Why is this man offended that this other man doesn’t want to marry his daughter? Why are these people upset? Why is everyone weeping?

Everything is fleeting and life is full of sorrow

There is a melancholy that permeates The Tale of Genji, which can be traced to the concept of mono no aware, or the sensitivity towards the fact that everything is transient and there is a sad beauty found in impermanence. This concept is still alive in present-day Japan, where cherry blossoms are viewed with the bittersweet knowledge that their beauty lasts only for a short while.

In Genji, nobody seems happy or even content, even as they live in relative luxury and privilege. A significant share of communication between lovers is lamenting how they must soon part, how much they will miss each other, and how the other person will probably forget them. Life seems to be continuous existential suffering, and many people dream of becoming a monk or a nun and devoting the rest of their lives to the service of Buddha, hoping for salvation after death.

In their world, spirits are a natural part of life, and there are complicated beliefs around what rituals and behaviours must be observed to be safe from evil spirits and the wrath of gods. Sometimes a person’s spirit can wander around doing bad things even while the person is alive and well, and without them being aware of what their spirit is up to. When a person is about to die, or has died, various rituals will help prevent them from becoming an evil spirit, and some particularly qualified monks might be summoned to conduct the rites if the dead person was important. To avoid untimely death or other dangers, people should be especially careful during yakudoshi or “years of trouble” which for women were the 19th, 33rd and 37th years of her life, and for a man his 25th, 42nd and 61st years.

A life behind screens

Another unusual feature of the world of the Heian Imperial Court was their nearly complete gender segregation. It was not proper for an aristocratic woman’s face to be seen by a man unless he was a lover or a close family member. Her face should be hidden even from her brothers once they reach puberty. In practice, she would spend most of her life either indoors or in private gardens, and if she left, even to visit a family member a few houses over, she would travel in a closed carriage with only narrow slits through which she could observe the world. Naturally, she would stuff her voluminous, multi-layered sleeves through those slits to communicate to the outside world that she is elegant, rich, and has an impeccable fashion sense.

And it wasn’t just her face that she had to guard. If a man paid a woman a visit, for example to pay his respects to her after the death of her family member, he would be seated well away from her, outside or in a different room, and one of the lady’s female companions would act as an intermediary in the conversation, passing their messages back and forth. If the lady came so close that they would be able to discuss directly, with only a screen separating them, that showed very high familiarity, and might be risky for the lady’s reputation. There is even an example in Genji where a woman is given the responsibility to raise a 12-year-old boy she is unrelated to, and she apparently does this from behind screens; it’s mentioned that the boy is surprised when he “happens to see a glimpse of his guardian” and discovers she’s not very beautiful, unlike the other women he’s seen before. But, the boy thinks, it would be wonderful to find a woman as kind and gentle as she is. So apparently she was able to play a significant role in his upbringing, even as she had to hide from his view.

In the case that a man and a woman are having a secret relationship, he naturally has to sneak in at night, and he should take care to leave before sunrise. Such meetings would have been in complete or near-complete darkness, and it might take several meetings until the lovers would be able to see each other’s faces properly. Genji himself gets into a pickle by pursuing and spending nights with a lady who is quiet and bland, and when he finally sees her in daylight, she turns out to also be unattractive. Her nose, especially, is a disaster: unbelievably long, its tip drooping towards the floor, limp and red, like the trunk of an elephant. Our heart breaks for poor Genji for what he must endure.

In general, the hypocrisy of men was a constant, and it was seemingly impossible to correctly be a woman:

• A man doesn’t visit a lover for several years, and yet he expects her to remain faithful.

• A man might regularly force his way into women’s bedchambers, but will blame his lovers for letting other men do the same.

• A woman must have a pristine reputation, but if a man sends her an unsolicited love letter and she is not receptive to his advances, he accuses her of being cold and unfeeling.

• One man tells a woman she should come and talk to him directly through the screen, that it’s rude to have a servant mediate the discussion. When she does come closer and replies to him directly, he is shocked that she would reveal her voice to a man just like that.

• One man assaults a woman in her bedchamber, and gets into trouble for it later. He proceeds to blame her for not taking better care not to get assaulted.

If it seems the men in Genji are all cut from the same cloth, I initially also thought the female characters were quite boring and indistinguishable from one another. But when I was reading Norma Field’s The Splendor of Longing in the Tale of Genji (1987), which examines the story through the central female characters, I realised I hadn’t given them enough credit. The male characters seem to talk about most of the women in very similar ways, describing the beauty of each woman, how adorable and small and helpless she is, how long and shiny and black her hair is, how gentle her manner, how heart-wrenching her poetry, and they seem to generally prefer whichever woman they are currently with. But just because the men see the women that way, doesn’t mean that’s all they are. Thinking about it more, it’s clear that some of Genji’s women have way more agency than others, some are particularly skilled or educated, some are gentle and pliable while others are strong, some are helpless and others pragmatic. Some make ambitious political moves, some are bitter and vindictive because their ambitions failed. Some are jealous, some have no expectations. Some of them are passive in relationships, some actively pursue them, and others completely resist men’s advances.

A few concrete examples of women acting with agency in a world that mostly kept them hidden and passive:

• An empress seeks to solidify her son’s power by getting him married well, and works hard behind the scenes to secure a specific young lady as her son’s wife.

• A woman neglected by her lover sends her living spirit to possess his other lover and make her ill.

• A young woman goes into hiding from men who desire her, and chooses to become a nun so that she would be out of their reach. This was one of the few socially acceptable ways for a woman to avoid marriage or get out of an existing one, but often the woman needed her husband’s or a male relative’s permission to do so.

One could also consider women having agency in more indirect forms, such as working hard to learn skills that would help attract a desirable man or deciding which men’s advances to encourage and which ones to reject.

But how did affairs and romantic relationships even have a chance to begin, if women were always hiding? Let’s talk about…

Marriages and other forms of arrangement

Relationships of various kinds formed the fabric of life in the Imperial Court, and one of the most important relationships was the marriage. It was an incredibly powerful vehicle for solidifying relationships between families and gaining power and influence. It also enabled some degree of social mobility, especially for women who could marry up. It was common for women to continue living with their families even after marrying, and their husband would visit them. Aristocratic men could have multiple wives, though the wives’ relative status mattered a lot. If the man already had a wife from a powerful family (or if he was known to love and cherish one of his existing wives unusually much!), other families might be hesitant about him marrying one of their daughters, because he might neglect her and might not visit often. Pretty much all the marriages in Genji are arranged between families, with no reference to the personalities and preferences of the people getting married, or between the man wanting to marry and the family of the girl or woman.

However, the overall landscape of relationships was much more complicated. Relationships and affairs were generally considered private business, and were not policed very strictly. Short dalliances could become longer semi-formal relationships where the man provided for the woman and their children. Aristocratic households had female servants and companions who were by position expected to be sexually available to the men of the house, but these weren’t considered actual relationships and they didn’t threaten anyone’s power or position. The Heian aristocracy was sometimes surprisingly practical, and it was not impossible for a woman to leave a marriage she didn’t want to be in and even go on to marry someone else.

Here is a non-exhaustive list of reasons why the men in Genji fall in love:

• He hears a rumour that a woman is particularly beautiful.

• He hears a rumour that a man has brought up his daughter with particular care.

• He is sneaking about somewhere he shouldn’t be, and catches a glimpse of her exquisite hair, arm, face, or another enticing body part.

• He hears her play an instrument or sing beautifully.

• She has beautiful handwriting and is quick to compose sophisticated poems.

• He really wants a princess as one of his wives and she happens to be one.

• She reminds him of his mother/stepmother/sister/wife/former lover.

It’s harder to make a similar list for women, because in the story, most of the time women fall in love off-screen, somewhere in the process of courtship. Women do of course catch glimpses of men, and definitely know who is good-looking and refined and smells good, but they don’t start sending love letters without him initiating it. Courting, of course, was generally only relevant for relationships outside of marriage. Here’s a common approach, based on my impression after reading Genji:

1. Man finds out about woman and falls in love for a reason such as the ones listed above.

2. Optional: Man bribes the woman’s servants to give him access to her.

3. He gets physically close to her and talks to her through a curtain or a screen.

4. She says nothing at all, shocked and terrified.

5. He keeps visiting and bombards her with letters.

6. She has to reply to some of the letters so as not to be impolite and be considered emotionally cold.

7. He gets more and more intrigued by her.

8. Eventually he forces his way to her bedchamber and stays the night, sexual activity implied and consent dubious at best.

9. Afterwards, she is horrified and disgusted.

10. ???

11. Somehow, relationship.

12. If he stops coming, she is distraught and her tears are endless as she contemplates his coldness and unfaithfulness.

This is, of course, not what happens when Genji falls in love with and subsequently kidnaps the 10-year-old niece of his stepmother. Instead, he takes her to his own palace and gives her the western wing as living quarters. He brings most of her original caregivers along and provides for them as well. He spends a lot of time with her as a father would, entertaining her with pictures and stories and teaching her how to play musical instruments and compose poems, essentially providing her with a great education. He forcefully has sex with—and subsequently marries—her four years later, when she looks more mature and is “of marriageable age.” Interestingly, he properly marries her, observing the traditional rituals, before he even tells her father that he’s been hiding her. So while it surely doesn’t hurt that she comes from a powerful family, Genji is determined to marry her regardless of whether her father approves or not, which is not typically how marriages happen in the rest of the story. He subsequently considers her his principal wife, and he loves and cherishes her above all the other women he marries or has relationships with, until old age.

In case you were wondering how the hell he managed to get away with all this, the short answer is that it simply doesn’t seem to be a big deal to anyone else, and it tells us a lot that the author didn’t feel the need to explain or justify Genji’s behaviour to her audience. One interesting clue comes decades later from Genji’s half-brother, who is contemplating becoming a monk but also worried about who will take care of his then-13-year-old daughter. He wonders where he might find a trustworthy man who would cherish the girl and bring her up “like Genji brought up the daughter of the culture minister prince.” It seems what Genji did was broadly considered completely fine, even virtuous. In fact, the girl is seen as the main heroine rather than a tragic victim, and her nickname in the story, Murasaki, later became the pen name of the author herself (who, unlike the character, married only in her mid-to-late twenties).

In a world with this level of relationship complexity, faithfulness was understood in its own special way. The impression I got is that for a man to be faithful to a woman, he should provide for her, put effort into writing her beautiful letters, and visit her often, and it’s less about whether he also does that with other women. Naturally there is jealousy, but it seems more motivated by her fear that he might start neglecting her if he has other partners he prefers. In that sense, Genji was always faithful to Murasaki, because he always loved her and took great care of her needs, even as he seduced or married other women during their time together. It wasn’t as acceptable for a woman to have multiple lovers, but it wasn’t always the end of the world if she did, and she might well be forgiven, if she is otherwise elegant and attractive.

Here’s a great example of how this plays out in Genji: A man is in love with a woman he is seeing. She lives in a distant place where he cannot visit as often as he would like. He starts getting suspicious that another man might be visiting her in his absence (he is right), so he sends her a poem making a reference to the pine trees of Sue. As we all know, the pine trees of Sue are a reference to a partner’s unfaithfulness, and in sending such a poem to his lover, he indirectly accuses her. She, of course, understands the accusation, but has to pretend she has no idea what he’s talking about. Her solution is to return the poem to the sender with the additional note that the poem must have been intended for someone else (implying it couldn’t possibly refer to her). The man receives the note and understands that this is highly sophisticated counterplay, and can no longer be angry at her.

Say it with a poem

While Genji is a work of prose, it contains nearly 800 poems, simply because that’s how people communicated at the time. Poetry was a critical social skill, and a person demonstrated their sophistication, education, intelligence, and aesthetic sensitivity by their skills of composing poems. And not only at poetry competitions and in private letters; people composed poems as a form of conversation.

Here’s an example of an exchange between a high-ranking man and a woman he has been seeing for a while, mostly in secret and definitely without him taking the relationship very seriously. Her parents are dead and her life is difficult, and the man visits her only rarely. She sends him the following poem in a letter:

Yes, ruin has come to the mountain’s rustic hedge, but now and again
O let your compassion touch this little pink with fresh dew!

The “mountain’s rustic hedge” means her difficult living circumstances and “little pink,” a wildflower, refers to their shared young daughter. Basically she’s saying: “I’m living in poverty, please help, don’t forget you have a child here.” He recognises this as a cry for help and goes to her immediately. Sitting in the untended garden with her, he consoles her by coming up with the following poem:

I could never choose one from the many colors blooming so gaily,
yet the gillyflower I feel is the fairest of them all.

“Gillyflower” refers to the woman he is talking to, and he implies that she is even more important to him than the child. In addition, he assures her that he will visit more often. In turn, she replies with another poem:

To a gillyflower brushing a deserted bed with her dewy sleeves,
autumn has come all too soon, and the sorrows of its storms.

Dewy, damp and wet sleeves are a very common motif in Genji, and it means weeping, since sleeves were used to wipe away one’s tears. A deserted bed means exactly what you would expect: her lover doesn’t visit often. Even though the woman is crying, the man thinks she doesn’t seem particularly angry, so he does what anyone would do in his situation: goes his merry way and doesn’t visit the woman and their daughter for a long time. In the meantime, she vanishes with the child without a trace. He thinks she should have acted more lovingly towards him when he showed up, so that he might have treated her better.

So what makes a good poem, in this world where everyone is a poet?

First of all, it should follow the correct structure, depending on the type of poem. The poems in Genji are waka, more specifically the tanka format, with five phrases in the 5-7-5-7-7 format, each phrase having a specific number of mora, which is a linguistic unit in Japanese similar to the syllable.

Second, waka poems often draw imagery and allegory from nature (trees, flowers, birds, insects), geography (mountains, rivers, specific locations), weather (storms, snowfall, wind), times of day and seasons (when the rooster sings, when the Moon is bright, spring morning, winter night). This imagery was used as an indirect way to talk about deep emotions, and many of them had well known associations, such as the fragrance of mandarin orange flowers, which was associated with longing and nostalgia. And if a lover sends you a poem referencing a broom tree, they’re unhappy; broom trees had a mythical reputation of being visible from afar but vanishing from view when you get closer, and therefore your lover would be saying: “I thought you loved me, but as I got closer to you, you turned out to be cold and unfeeling.”

Third, a sophisticated poem would often contain double-meanings, wordplay, or references to earlier stories, legends or poems, showcasing how educated its author was. Consider the following exchange: A popular and well-regarded but low-ranking woman has had a relationship with a higher ranking man, who has since married a highborn woman. They are briefly in the same house, and as she is about to leave to attend the procession of the Kamo festival, he sends her a note (poem in italics):

What is it they call the leaf we all sport today? There it is, I see,
yet such ages have gone by, I no longer know its name.
What a pity!

She sends back a quick reply:

As to that green leaf you sport merrily enough, ignorant or not,
surely he who won laurel could manage to know its name!
It takes a Doctor, I suppose.

Have a guess: What does this exchange mean? I’ll give you a moment.

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.

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Ready for some context? Let’s go.

The Kamo festival was held on the second day of the bird in the fourth month of the lunar year, and it involved a large procession of people moving from the Imperial Palace to the Kamo Shrines in Kyoto. It was traditional to decorate yourself (as well as your wagons, oxen, house, and gates) with the leaves of the aoi (hollyhock) plant. According to an old convention, aoi was written as “afuhi,” which also meant “the day of meeting” or “heart-to-heart.” Therefore, what the man’s poem is saying is: “It’s been so long since we met that I’ve forgotten what this leaf is called.” He clearly hasn’t forgotten the name of the leaf, since he’s actively making puns about it, so this is more of a playful way of reaching out to her.

In her reply, she mentions a laurel, another plant worn at the Kamo festival, but also “plucking the laurel” referred to passing the exam to become a court official, which the man had done. Her poem is saying: “You’re an educated man, you should know what the damn leaf is called.” But it’s also clear to her that he was making a pun and had not forgotten the name of the leaf, so her reply is perhaps better understood as a poignant acknowledgement of his message after a long time of not being in touch. He reads the reply and appreciates her wit in putting him in his place, and resolves to continue the relationship with her.

This is the Heian-era equivalent of:

He: “Wassup girl, long time no see”
She: “You remember me?”
He: “Oh snap. Wyd tonight”

Everything stands between the lines

It would be difficult to overstate the complexity of communication in Genji’s times. Everything was a context clue, especially when a lot of communication happened by letter. The colour and thickness of the paper mattered, since certain types of paper were associated with formal communications, some with love letters, and so on. It mattered how quickly you answered, or whether you strategically chose not to reply. The length of your letter, the effort you put into it, which incense you perfumed it with, which flower you attached to it, and the beauty of your handwriting all communicated about you, how you feel about the recipient, and about the topic at hand. All this before even considering the words you wrote.

My favourite example of why you can’t read Genji without footnotes is from one of Genji’s early adventures, when he is about 17 years old.

Let me set the scene. Imagine Genji, staying with his wife at his father-in-law’s place, lounging in his underwear, is suddenly reminded by servants that the Mid-God (Nakagami) has “closed their direction.” Nakagami was a deity who reigned over good and bad luck, and regularly descended to Earth, moving clockwise and spending 5-6 days in each of the eight directions, and one should avoid activities toward the deity’s current direction if at all possible. Unfortunately for Genji, both his wife’s home and his own palace are in the same unlucky direction from the perspective of the Imperial Palace where he had come from, and therefore he has to find somewhere else to sleep that night. One of the servants suggests the villa of the Governor of Kii, who is in the service of Genji’s father-in-law. Genji thinks this is an excellent idea and heads to the villa, essentially inviting himself to stay the night at someone else’s house. Turns out he can just do that.

Here comes the juicy part. When a room has been prepared for Genji, he says to his host, the Governor: “What about the curtains, then? It is a poor host who does not think of that!” And the host replies: “My lord, I have been told nothing about what might please you.”

What’s Genji’s deal with curtains? He’s just imposed on someone else’s hospitality on an extremely short notice, and he’s complaining about interior design? By now you can probably guess the curtains aren’t about curtains, and if you guessed the curtains are about sex, you would be correct. Genji is making a reference to a song whose lyrics are:

Curtains hanging from the ceiling
divide my house into rooms -
come visit me, prince,
I will make you my son-in-law.
What would go well
with wine?
Abalone? Turban snail?
You like sea urchin, right?
Abalone? Turban snail?
You like sea urchin, right?

The seafood items mentioned in the song are a symbol of female genitalia, and the curtains refer to a host offering his daughter to the honored guest. So when Genji complains about the lack of curtains, he’s actually complaining that the host hasn’t provided a woman for him. Good luck deciphering that without context.

Reading a book written in a dead language

Nobody can read Genji the way it was originally written by Murasaki Shikibu. This is because the original manuscripts have been lost, and the earliest versions are copies made more than a hundred years after the story was written. There isn’t even a scholarly consensus on whether all the chapters usually included in Genji are definitely written by the original author, and it’s very possible that some chapters have gone missing. Different versions of the chapters were circulated for centuries, and nobody can say which ones are closest to the original.

Even if we had the original manuscript, still almost nobody would be able to read Genji the way it was originally written. There are no more native speakers of the classical Japanese that Murasaki Shikibu spoke, nor are there any more native inhabitants of the culture she lived in, who could help us understand what’s written between the lines. These days, native Japanese speakers commonly read Genji as a translation into modern Japanese, including footnotes, and will need specialised study if they want to read the original directly. Although, based on my very informal check-in with a handful of Japanese people, it seems it’s pretty rare to read the whole Genji, and usually the few parts they are forced to study at school are enough — unless they are history or literature nerds.

How do the non-Japanese nerds among us read Genji?

Translating between unrelated languages is like creating a 2D map projection of a 3D globe, in that it’s fundamentally impossible to be 100% accurate, and something has to give. While the cartographer has to decide how much of shape, area, distance, and direction to sacrifice, the translator needs to consider factors such as formality, fluency, structure and, in my opinion the most fundamental decision, whether to try to preserve the literal meaning or the emotional impact of the original. These tradeoffs are especially painful when it comes to poetry, where it’s even harder to separate meaning and form. In poetry, the original form might have a specific number of syllables, or rhymes, or patterns of emphasis, which are necessarily tied to the language they were written in, such as the 5-7-5-7-7 structure of the traditional waka poem, and there is no objectively correct way to handle that in translation.

There are four well-known and mostly complete English translations of Genji, and each one makes different tradeoffs:

• Arthur Waley’s translation (1925-1933) takes loads of liberties and could be thought of as a creative interpretation rather than a faithful translation.

• Edward Seidensticker’s translation (1976) cuts down ambiguity and focuses on conciseness and readability.

• Royall Tyler’s translation (2001) maintains a lot of the ambiguity of the original, which means a hit to readability. It’s the most academic and involves extensive footnotes.

• Dennis Washburn’s translation (2015) is the longest and most verbose, baking a lot of the historical context inside the narration and focusing on readability. Washburn is the only one of the four that systematically renders the poems as verse rather than prose.

Full disclosure: I didn’t read any of these four. Instead, I read a recently completed Finnish translation by Martti Turunen & Kai Nieminen (2025), which strikes an interesting balance between academics and aesthetics. It comes in two volumes with a total of 1758 pages and a whopping 2422 footnotes, all of which I read. The footnotes mostly fall into the following categories:

• Explaining terms, concepts, religious rituals, beliefs, hierarchical ranks, and other necessary context for the reader to understand what’s going on.

• Clarifying who is who, related to whom, and where else the same character appears. Since there are approximately 400 different characters, and most of them are generally referred to by ranks or titles that keep inconveniently changing, the footnotes are critically important.

• Explaining references the characters make to other poems, stories, and legends, as well as double meanings in words that don’t come through in the translation. This is how I know that when Genji complains about curtains, he’s actually horny.

• Explaining ambiguity in the original text and how it’s been resolved in the translation.

Aesthetically, the Finnish translation is remarkable. Chapters 1 to 25 were originally published in Finnish in the 1980s, the prose translated by Martti Turunen, who is the first foreign-born member of the Japanese legislature, and the poems translated by Kai Nieminen, a renowned Finnish poet and translator. Since then, Nieminen has translated both the prose and the poetry of the remaining chapters (26-54), and the combined work was finally published in 2025. The philosophy of the work is to be a literary experience rather than academic, and despite the density of the footnotes, the prose is fluent and the poetry is translated in a way that preserves much of the original form.

For the purpose of writing this review, I also consulted the Tyler and Washburn translations to English and their sections on historical context, and I’m glad I did. Comparing the same passages and their footnotes in different translations really hammers home how differently the source text can be interpreted. To illustrate the difficulty of knowing what the original author might have actually meant, here is the same passage in three different ways.

Tyler:

The Gosechi Dancer, the daughter of the Dazaifu Deputy, felt that she was now over her secret, hopeless misery, and she had her messenger give Genji this, with a wink.

Washburn:

The daughter of the Assistant Governor General of Kyūshū—the lady who had once performed as a Gosechi dancer—felt her secret longing for Genji, which had been hopeless from the start, cool at last. She had a messenger deliver an anonymous letter on the sly with instructions that the man should give Genji a wink to provide a clue to the author’s identity.

Turunen & Nieminen, in my own translation to English:

The dancer daughter of the Deputy Governor of Tsukushi felt slight disappointment when Genji was again favoured by the Emperor and therefore out of her reach. As if to test, she sent Genji a letter, being careful not to reveal her identity.

It’s really interesting to see the different interpretations at play. Tyler’s philosophy of preserving the ambiguity of the original makes it much harder to understand what’s going on. What exactly is her misery? Why is she over it? Why the wink? Meanwhile, the other two translations resolve the ambiguity in very different ways. They both make it clear that the woman was longing for Genji, but only Turunen & Nieminen explain why he is out of her reach. Washburn thinks the wink means she wants Genji to guess her identity, while Turunen & Nieminen think the opposite, that she is trying to hide her identity. Based on this, I would guess the original mentions a wink but doesn’t explain it, and the original reader would have known what it meant, while we can only speculate.

When it comes to the waka poems, sometimes it’s hard to believe the translators worked on the same source material. Here’s an example of a poem written by Genji to a woman with whom he has had a brief affair more than a decade earlier. When their entourages pass one another on the road to the Ishiyama temple, Genji is able to get a letter delivered to her, even though they have no opportunity to meet.

The original poem in classical Japanese, written on a single line in hiragana like how it likely would have been written by Murasaki Shikibu:

わくらばにゆきあふみちをたのみしも なほかひなしやしほならぬうみ

The same, but each phrase rendered on its own line, to showcase the 5-7-5-7-7 structure, which the original reader would have been able to parse when reading it in a single line:

わくらばに
ゆきあふみちを
たのみしも
なほかひなしや
しほならぬうみ

Tyler’s take, in his characteristic two-line approach to translating waka:

I had little doubt that we would meet after all on the Ōmi road,
yet those waters were too fresh not to betray my fond hope.

Washburn’s take, in his equally characteristic three-line approach:

Though we passed by chance on the road to Ōmi
A name that promised us a tryst at Lake Biwa
In vain I searched for you beside its fresh waters

Nieminen’s take, following the 5-7-5-7-7 structure with Finnish syllables:

toivoin turhaan: ei
järvivedessä elä
simpukoita — niin
satuimme samaan paikkaan
sittenkään kohtaamatta

Nieminen’s take, translated into English by me:

in vain I had hoped:
in lake water there live no
saltwater clams —
we happened on the same place
but nonetheless did not meet

Let’s unpack the layers of meaning. First, the original poem contains the word “afumichi,” or “the road on which we meet,” which is read the same way as “oomichi,” or the road to Ōmi. Second, Lake Ōmi (also known as Lake Biwa) is a freshwater lake, so it has no saltwater clams, and the poem also contains the expression “kai nashi,” which can mean “pointless,” “in vain,” or “clamless.” Setting aside all the complexity, Genji’s poem means “Would have been nice to meet, but sadly it’s not happening.” In this case, the Finnish translation says this the most explicitly, but the reference to clams would make very little sense without the context of the wordplay. However, all of the translations contain some reference to hoping for a meeting, and that hope not being fulfilled.

As a side note, I have to say it was not straightforward to translate the Finnish translation into English. Finnish and English are not even remotely related languages, and English is in fact more related to Hindi than either of the two are to Finnish. Even for this one poem, I had to ask myself the same questions as the translators of Genji: Do I try to preserve the form (the 5-7-5-7-7 structure)? Do I try to preserve the exact literal meaning of the Finnish translation? How do I deal with fundamental mismatches in terminology between Finnish and English? The Finnish translation contained the word “simpukka” which can translate to clam, mussel or cochlea in English, so I had to do a little research excursion (thanks Claude) to understand which of these specifically would have been missing from Lake Ōmi and would match the wordplay. I also had to choose between “nonetheless,” “even so,” and “after all.” A more precise translation would have used “encounter” instead of “meet,” but I chose to prioritise the syllable count. So many decisions when translating a single poem between two languages I speak fluently, and where lots of resources exist for understanding the exact connotation of every word. This is why it takes so long to translate a work like Genji.

At the risk of overdoing it, I couldn’t resist checking how well LLMs would handle translating this poem, when given no other information than that it’s a poem in classical Japanese. A few observations:

• Both Claude Opus 4.7 Adaptive and ChatGPT 5.5 Pro independently identified both wordplays (road of meeting / road to Ōmi and pointless/clamless, though both chose to talk about shells rather than clams).

• Both of them chose to generate two versions, one a bit more literal and another that’s more poetic or tries to preserve more of the wordplay.

• Both of them did surprisingly well and were able to reflect something of the meaning of the original poem, in the sense that a desired meeting did not happen.

• However, they both missed the fact that the two people had been in the same place. This might be something requiring more context from the surrounding text, and might not be inherent in the poem itself.

• Neither of them divided the poem into the upper phrase and the lower phrase, where the first part of the poem sets the scene and the second part shifts the perspective to the poet’s internal world. All the human translators preserved this feature of waka poetry.

• LLMs seem to need in-depth thinking to identify the double-meanings; I tried ChatGPT 5.5’s Instant mode and it didn’t find either of the two wordplays.

My favourite of the LLM-generated translations, from Claude Opus 4.7:

Though I’d staked my hopes
on chancing to meet you there—
it came to nothing.
No shells wash up on the shore
of a sea without any salt.

Fine, so I’ve established that the translation matters, and the existing ones are pretty different from one another. Which one would I recommend?

• If you speak Finnish, read Turunen & Nieminen. But I wouldn’t recommend learning Finnish just for this purpose.

• Else, choose based on your preference and mental state:

  • You value academic precision and are comfortable with ambiguity -> Get Tyler.

  • You prefer a literary experience even if it adds a lot of subjective interpretation -> Get Washburn.

  • You intend to hyperfocus on this specific topic for the next few months -> Get both and triangulate what the original author might have meant.

  • You think modern humans are redundant -> Feed the original, classical Japanese text into your LLM of choice and hope for the best.

Is it really the world’s oldest novel?

I started this review by calling Genji the oldest novel in the world, and I bet a few readers raised an eyebrow, judging by how people reacted when I was initially reading Genji and telling people about it. The standard response format is: “But how about [insert your favourite ancient story]? That’s way older.”

The short answer is that the world’s oldest novel is not the same thing as the world’s oldest story.

The long answer is that there are a few defining characteristics that make a work a novel, and there is a surprisingly broad consensus that Genji is the oldest known work that qualifies. Those characteristics are:

• Fiction: Historical chronicles and religious texts are not considered novels.

• Written in prose: Throughout a lot of human history, stories were formatted as verse, especially before the invention of writing when they had to be passed down as oral tradition. Even when writing became a thing, poetry was still a common form of literature, including in Japanese literary tradition before Genji. And even though there are hundreds of poems in Genji, and they are highly relevant to the story, the prose stands in its own right. This one rules out the Odyssey, the Mahabharata, Gilgamesh, and Beowulf.

• Psychological depth: This is perhaps what stands out about Genji the most. I find that many old stories look at their characters from the outside, and rely on archetypes for readers to know what to expect. In contrast, many of the characters in Genji are highly complex, their private, inner worlds are explored in depth, and their thoughts and emotions develop in nuanced ways over the course of the story. Even the narrator seems to have complicated feelings about Genji, because even as he had been dubbed “The Radiant Prince” by other people, the narrator starts chapter 2 by pointing out that Genji did a lot that was contradictory to such a lofty title, that “his failings were numerous.”

• Long and cohesive narrative: Short stories and collections of short stories don’t count as novels. Even if the stories star the same characters, the stories are often more like fragments or self-contained episodes, where consequences and character development don’t span the whole work. This is what sets Genji apart from works like Satyricon and The Golden Ass; it follows a large cast of characters over decades while maintaining internal consistency regarding everyone’s age, their changing places in the social hierarchy, their relationships with one another, the information everyone knows, and the psychological impact of past events.

To summarise: Murasaki Shikibu wrote a fictional work that was truly innovative in its time, using long-form prose with a high level of internal cohesion and characters with significant psychological depth and development. I like to use “the world’s first novel” as a shorthand for that, but it’s fine if you don’t. Wikipedia plays it safe and calls it “the first novel written by a woman to have won global recognition,” and someone else might have an even stricter definition for a novel that would rule out Genji as well as older works.

What it’s like to read Genji

At the beginning of this review, I promised I’d walk you through my experience of reading Genji so that you can judge for yourself if Tyler was right in saying that the footnotes are optional, “the characters’ thoughts and feelings remain as fresh as ever,” and that anyone can read Genji. I believe I’ve said enough about the footnotes, so let’s examine the other two claims.

Regarding the freshness of the characters’ thoughts and feelings, on the surface level it makes sense to disagree with Tyler. It would be hard for most people to relate to thoughts and feelings such as

• “This girl is my stepmother’s niece; I must have her” or

• “My lover has forcefully assaulted another woman; now she must die” or

• “My husband wants to take our child away from me and give her to another of his wives to raise; this seems fine.”

But if you go up a level of abstraction, you find something much more relatable, or at least recognisable. The longing for someone you love but can’t have. The jealousy arising from your insecurity about your own position in life. A parent’s anguish at being separated from their child, while knowing the child will be better off that way. Making bad decisions while horny. Sacrificing your family for your career ambitions. And there is also my own absolute favourite moment which requires very little cultural translation: The narrator tells of a party, late at night, where Genji and a bunch of other men are taking turns composing poems at each other. The narrator first includes some of them, but then says the men got so drunk that it would torture the reader to include any more of the poems they came up with. I bet Murasaki Shikibu knew what it feels like to be sober in the vicinity of idiots drunk on their own brilliance.

But can anyone read Genji? Even if you read it at a level of abstraction where you are able to relate to the characters, there is one more aspect of the experience that might frustrate an unprepared reader that I haven’t mentioned yet. I know I said that narrative cohesion is one of the defining characteristics of Genji, which makes it a novel while others works are just collections of stories, but it’s not the kind of cohesion you might expect. The Tale of Genji is cohesive in the way that life is cohesive. There is significant cause and effect, people act on motivations that feel internally consistent to them, lots of stuff happens that builds on other stuff. But also, there is plenty of setup that leads nowhere, loose ends that don’t get tied, important events that are glossed over, insignificant events that seem blown out of proportion, people who behave in erratic and unexplained ways, and many things that happen for seemingly no reason. Just like in life, not everything ties to the grand plan, and in the end you wonder if there was ever a grand plan to begin with.

Genji doesn’t have a plot in the strict Aristotelian sense of having a beginning, a middle, and a satisfying and inevitable end, with every event in service of that structure. In fact, Genji himself dies before the novel ends, and the last part of the story moves on to the next generation and their bad choices. I debated internally about whether to include this bit of information, because you could call “the main character dies partway” as a pretty major spoiler. But then again, both Tyler and Nieminen mention Genji’s death on page 1 of the introductions to their respective translations. It seems nobody is expecting you to read Genji thinking it will be only about him, or that everything ties up neatly together.

In my opinion, you are more likely to enjoy reading Genji if you have the following: Interest in (Japanese) history, literature, poetry, philosophy, or aesthetics. Willingness to suspend moral judgement. Stamina. And importantly, tolerance for ambiguity. Read Genji not because you want to know what the point of everything is, but because you want to immerse yourself in an alien world and see if you can find moments of shared humanity with its inhabitants as they navigate their lives that don’t necessarily have any more point than ours.

There is a scene in which Genji talks to the women in his household about literature: “A story may not relate things exactly as they happened out of consideration for the circumstances of its characters. Yet there are moments when one wants to pass on to later generations the appearance and condition of people living in the present—both the good and the bad.” We might be reading this in Dennis Washburn’s words, but it gives us a glimpse into the mind of the original author and her philosophy. I can hear her saying: This is what life is like for us. It’s not all terrible or all great, but I want you to know.

To Murasaki Shikibu, I say: I see you, sis. I wouldn’t want to listen to drunk men and their poetry either.

This time there are 79 meetups at universities in 15 countries. Students at the school in question are especially encouraged to come, but all are welcome. You don’t have to have read anything in particular, and you don’t have to consider yourself an effective altruist.

The list is divided into North America, South America, Africa & The Middle East, Asia-Pacific, and Europe. Within each region, meetups are alphabetized by country, then by city, then by school. Meetups in the United States are alphabetized by state first, then by city, then by school.

Every entry has one-click “add to calendar” links at the bottom. They work in Google Calendar, Outlook.com, Outlook 365 (the version most schools give you), and Yahoo. Each one carries the correct absolute time, so if you’re clicking a link for a meetup in another timezone, it will land in the right slot on your own calendar. Nothing is saved until you press Save in your own calendar app. End times are estimates; check with the host if it matters.

Thanks to everyone who volunteered to organize these.

Extra Info For Potential Attendees

1. You’re invited. If you saw this post, read this far, and want to come, you are the target audience. Nobody at the door will quiz you.

2. Some of these meetups are in rooms that were not yet booked when the organizer filled out the form. If an entry says the room is unconfirmed, click the group link or email the organizer a day or two beforehand to check.

3. Where an organizer has included an RSVP link, please use it (it’s how they decide how much pizza to buy). But using it isn’t a requirement, and you can still come if you forget.

Extra Info For Meetup Organizers

1. Print a sign saying “EA MEETUP” in big letters so people don’t have to do the awkward “Are you looking for the EA meetup” dance again with every new attendee.

2. Get there ten minutes early and sit somewhere visible. If your room turns out to be locked, stand outside it and coordinate the relocation.

3. Have something ready for the first fifteen minutes, when there are three people and everyone is waiting for more to arrive.

4. You don’t need to prepare a lecture. Most good meetups are people talking to each other. If you want structure, pick two or three questions and let the conversation wander off them.

5. Collect emails, or run a signup sheet, or have a QR code to your group chat. Half the value of a meetup like this is the people who come once and would come again if they knew when.

6. If your plans change (new room, new time, cancelled) tell the people who signed up, and update the group link so latecomers to this post find the right information.

NORTH AMERICA

CANADA

UNIVERSITY OF CALGARY

Calgary, Alberta, Canada
Contact: LN
Contact Info: laibanas20 [ a t ] gmail [ do t] com
Time: Saturday, October 3rd, 12:00 PM
Location: Shirley Anastasia Lounge, behind the Q centre.
Coordinates: https://plus.codes/95373VH9+FW
Notes: RSVP if you can
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF TORONTO

Toronto, Ontario, Canada
Contact: Joseph Kostousov
Contact Info: yosya.kostousov [ a t ] proton [ do t] me
Time: Sunday, September 20th, 1:00 PM
Location: Kings College Circle, in front of the Gerstein library steps.
Coordinates: https://plus.codes/87M2MJ64+R7
Group Link: https://discord.gg/EHzqkBXsPF
Notes: Free pizza, open to students and non-students alike.
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

ACADIA UNIVERSITY

Wolfville, Nova Scotia, Canada
Contact: Andrew Davis
Contact Info: andrew.davis [ a t ] acadiau [ do t] ca
Time: Tuesday, September 22nd, 7:00 PM
Location: Patterson 213 - go in the front door of the business building (right below the KCIC on University Ave), go up one floor via the stairs directly on your left, and there’ll be a meetup sign.
Coordinates: https://plus.codes/87QQ3JQJ+9MV
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF BRITISH COLUMBIA

Vancouver, British Columbia, Canada
Contact: Toby Satake
Contact Info: tobys010101 [ a t ] gmail [ do t] com
Time: Wednesday, September 23rd, 5:00 PM
Location: Grassy area by Sopron Gate. We’ll have a sign saying “Effective Altruism Club.” Map
Coordinates: https://plus.codes/84XR7Q52+WG
Group Link: https://discord.gg/2FAZDj35z
Notes: Free pizza. RSVP encouraged so the organizer knows how much food to get.
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

Dominican Republic

PONTIFICIA UNIVERSIDAD CATOLICA MADRE Y MAESTRA

Santo Domingo, Distrito Nacional, Dominican Republic
Contact: Maria Jose Colas
Contact Info: majocolas17 [ a t ] gmail [ do t] com
Time: Friday, October 23rd, 4:30 PM
Location: Pontificia Universidad Católica Madre y Maestra (PUCMM), Santo Domingo Campus, Abraham Lincoln at Simón Bolívar. Room not yet confirmed.
Coordinates: https://plus.codes/77CGF369+XX
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

United States of America

(alphabetized by state first, then by city, then by school.)

UNIVERSITY OF ARIZONA

Tucson, Arizona, United States
Contact: Harper
Contact Info: tucsoneffectivealtruism [ a t ] gmail [ do t] com
Time: Friday, September 18th, 4:00 PM
Location: Room booking not confirmed yet. Most likely Main Library Data Studio (Main Library, B201)
Coordinates: https://plus.codes/854F63J2+CC5
Group Link: https://discord.gg/BHaphQ9ZJc
Notes: Free food, a short intro presentation, and conversation starters
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF CALIFORNIA, BERKELEY

Berkeley, California, United States
Contact: Alex
Contact Info: Cell: 650-864-2894
Time: Sunday, September 20th, 5:00 PM
Location: UC Berkeley Social Sciences Building, Room 170 -- this is the tall rectangular structure near to Sproul Hall.
Coordinates: https://plus.codes/849VVPCR+2R
Group Link: https://eaberkeley.com/
Notes: Open to everybody, come hang out. We will provide dinner!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF CALIFORNIA, LOS ANGELES

Los Angeles, California, United States
Contact: Will Ryan
Contact Info: Wkryan2005 [ a t ] gmail [ do t] com
Time: Monday, September 28th, 4:00 PM
Location: Ackerman Student Union Building, Room 2408
Coordinates: https://plus.codes/85633HC4+58
Group Link: https://discord.gg/EpHzRuS8x
Notes: Meeting is open to students and non-students. Free food (falafel!) will be served! Please RSVP using this google form so we know how much food to order. https://docs.google.com/forms/d/e/1FAIpQLSebNt6sztCmb0o_FQEFF5lo4PC0i0B7NkOYTNrpVmcx9zB1ow/viewform?usp=publish-editor
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF SOUTHERN CALIFORNIA

Los Angeles, California, United States
Contact: Isaac
Contact Info: ismalley [ a t ] usc [ do t] edu
Time: Wednesday, September 16th, 6:30 PM
Location: The tables in front of Leavey Library towards the McCarthy Quad. Just go up the few steps towards the main entrance of the library and you should see us on your right. I’ll have a USC EA MEETUP sign on the table!
Coordinates: https://plus.codes/85632PC8+JVF
Notes: RSVP here for guaranteed free pizza: https://luma.com/xl62hrz1. Or just show up!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

STANFORD UNIVERSITY

Palo Alto, California, United States
Contact: Ahmad Dagher
Contact Info: StanfordEAexec [ a t ] gmail [ do t] com
Time: Tuesday, October 13th, 6:00 PM
Location: Room booking isn’t confirmed yet! Here’s our best guess: Toyon Hall Lounge--eastern side of campus, 455 Arguello Way, Stanford, CA 94305, main entrance, first floor. There will be an EA MEETUP sign on the door.
Coordinates: https://plus.codes/849VCRGP+CJ
Group Link: Mailing list: https://mailman.stanford.edu/mailman/listinfo/effective-altruism
Notes: Free Pizza (vegan & nonvegan) and Boba!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

CALTECH

Pasadena, California, United States
Contact: ZG
Contact Info: eacaltech [ a t ] gmail [ do t] com
Time: Tuesday, October 13th, 4:00 PM
Location: Red Door Patio
Coordinates: https://plus.codes/85634VPG+WG
Notes: Let’s revive EA at Caltech! I will need help to organize further events, so please get in touch!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF CALIFORNIA, SAN DIEGO

San Diego, California, United States
Contact: Brittney Nial
Contact Info: bnial [ a t ] ucsd [ do t] edu
Time: Thursday, October 8th 12pm
Location: Geisel Library, room TBD once booking opens.
Coordinates: https://plus.codes/8544VQJ6+FX
Notes: Lunch served Open to UCSD students, staff, and faculty! Please RSVP so we can order enough food: https://luma.com/myllts4s
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

YALE UNIVERSITY

\New Haven, Connecticut, United States
Contact: Andrew
Contact Info: andrew [ do t] cai [ a t ] yale [ do t] edu
Time: Wednesday, September 23rd, 5:00 PM
Location: The area in front of Sterling memorial library facing the grass
Coordinates: https://plus.codes/87H9836C+9Q
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

FLORIDA ATLANTIC UNIVERSITY

Boca Raton, Florida, United States
Contact: Charlie
Contact Info: chuckwilson477 [ a t ] yahoo [ do t] com
Time: Sunday, September 13th, 1:00 PM
Location: In the Cafeteria / Food Court, nearby the Panda Express. We’ll have an EA MEETUP sign
Coordinates: https://plus.codes/76RX9VCW+5R2
Group Link: https://discord.gg/svZeYP83MQ
Notes: Open to students and non-students alike. RSVPs highly preferred but not required. Affiliated with EA Miami and Miami ACX, join our Discord for more info!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

FLORIDA INTERNATIONAL UNIVERSITY (MODESTO MAIDIQUE CAMPUS)

Miami, Florida, United States
Contact: Luis
Contact Info: Fort.lauderdale.acx [ a t ] gmail [ do t] com
Time: Saturday, October 17th, 3:00 PM
Location: Graham Center Piano Lounge, near Main Entrance across from Barnes and Nobles. Will be holding an ACX sign.
Coordinates: https://plus.codes/76QXQJ4G+7R2
Group Link: https://discord.gg/fFjmJs2kn
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF MIAMI

Miami, Florida, United States
Contact: Zoheb
Contact Info: zohebanjum [ a t ] gmail [ do t] com
Time: Saturday, September 19th, 1:00 PM
Location: WVUM Student Lounge, South East of the Stanford Drive roundabout, right next to the canal and near the entrance to the pool (this is our tentative location, and we’ll update you if it changes!)
Coordinates: https://plus.codes/76QXPPCC+3X
Group Link: https://effectivealtruismmiami.subscribepage.io/
Notes: Open to students and non-students alike
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF CENTRAL FLORIDA

Orlando, Florida, United States
Contact: JB
Contact Info: itslabradordali [ a t ] gmail [ do t] com
Time: Friday, September 18th, 6:30 PM
Location: The Taco Bell Cantina (12101 University Blvd Ste 201, Orlando, FL 32817)
Coordinates: https://plus.codes/76WWHQXR+8FC
Group Link: https://discord.gg/R2RrvVCARe (Orlando Rationalists Server)
Notes: Open to anyone. RSVP by email or discord preferred. When there, look for the glowy sign :)
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF GEORGIA

Athens, Georgia, United States
Contact: Cade Mataya
Contact Info: cmataya64 [ a t ] gmail [ do t] com
Time: Wednesday, September 2nd, 6:00 PM
Location: The Jittery Joe’s Cafe downtown; if more folks RSVP than that venue can handle, we can move the meetup somewhere else.
Coordinates: https://plus.codes/865RXJ5G+666
Notes: Open to students & non-students; anyone in Athens who is interested in EA should feel welcome to join!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

GEORGIA TECH

Atlanta, Georgia, United States
Contact: Peach Tree
Contact Info: peachtreecity1234 [ a t ] gmail [ do t] com
Time: Tuesday, September 15th, 6:30 PM
Location: Balcony b/w Crosland & Price Gilbert Library, Georgia Tech Directions to find: When you walk along the pathway between Crosland tower and the Price Gilbert library, you’ll be able to walk into a balcony on the side of Crosland tower. It’s right outside the first floor. I’ll be wearing a black shirt with an EA Meetup sign on the table. There’ll also be food!
Coordinates: https://plus.codes/865QQJF3+PR5
Group Link: https://luma.com/7gwdwkga
Notes: Free Moe’s! Please RSVP because I might change location (though if you forget, that’s cool too)! Link: https://luma.com/7gwdwkga
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF ILLINOIS URBANA CHAMPAIGN

Champaign, Illinois, United States
Contact: Utkarsh
Contact Info: utkarsh6 [ a t ] illinois [ do t] edu, utk [ do t] sin34 [ a t ] gmail [ do t] com
Time: Sunday, September 27th, 5:00 PM
Location: Not confirmed yet. Best guess - EA Meetup Table at Anniversary Plaza (In front of Illini Union, towards the Main Quad)
Coordinates: https://plus.codes/86GH4Q5F+H4W
Group Link: https://discord.gg/N7TNwvzE4
Notes: Come chat and eat free pizza!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

ACX Meetup Czar Update: The local organizer changed the date from September 15th to September 27th. Join the discord to get notified when the location settles.

UNIVERSITY OF CHICAGO

Chicago, Illinois, United States
Contact: Sofia
Contact Info: sguimaraes [ a t ] uchicago [ do t] edu
Time: Thursday, October 22nd, 6:00 PM
Location: 1100 E 58th St, Chicago, IL 60637
Coordinates: https://plus.codes/86HJQCQ2+R4
Group Link: https://uchicagoea.org/
Notes: Please RSVP here: https://forms.gle/sPzvh8cFhi9Pw8gd7
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

NORTHWESTERN UNIVERSITY

Chicago, Illinois, United States
Contact: Johnalbert Garnica
Contact Info: johnnyas [ a t ] u.northwestern [ do t] edu
Time: Friday, September 25th, 6:00 PM
Location: room not confirmed but planned to be in the Northwestern Pritzker School of law. We’ve chosen to reserve a room based on rsvp size.
Coordinates: https://plus.codes/86HJV9WM+C3
Group Link: https://discord.gg/VbMC6SEQ
Notes: Open to students & Non-students : Please RSVP at https://luma.com/4s2lo3hx so we know how large of a size room to get and inform the security to let you in.
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

ACX Meetup Czar Note: The date changed from the 18th to the 25th by organizer request!

PURDUE UNIVERSITY, WEST LAFAYETTE

West Lafayette, Indiana, United States
Contact: Brian Foerster
Contact Info: effectivealtruismpurdue [ a t ] gmail [ do t] com
Time: Friday, September 18th, 7:00 PM
Location: WALC B058, in the basement
Coordinates: https://plus.codes/86GMC3GP+XP
Group Link: https://groupme.com/join_group/102154699/KZdddBsC
Notes: We will have general meetings at the same time and location every Friday starting September 4th.
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF KANSAS

Lawrence, Kansas, United States
Contact: Miri
Contact Info: topi [ a t ] ku [ do t] edu
Time: Tuesday, September 15th, 6:30 PM
Location: Anschutz Library, Room 438 (Booking not yet confirmed, will send an email out if it changes). From the main entrance facing the plaza area, take the stairs on the left up one floor to the fourth floor. I’ll be in the far back-right study room with an EA MEETUP sign.
Coordinates: https://plus.codes/86C6XQ52+23J
Notes: Please RSVP to help me plan for how many people will show up :)
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

JOHNS HOPKINS UNIVERSITY (HOMEWOOD)

Baltimore, Maryland, United States
Contact: Hari
Contact Info: hariharprasad2006 [ a t ] gmail [ do t] com
Time: Saturday, September 19th, 5:00 PM
Location: The room booking isn’t confirmed yet, since I haven’t had an opportunity to register the EA club at Hopkins. I’ll be standing in the middle of Keyser Quad holding a big EA MEETUP sign. I’ll be there for the first ~20 minutes of the meetup, after which we’ll move to an approved room on campus. If you arrive after this 20 minute window and want to know where we are, feel free to shoot me an email closer to the date of the meetup.
Coordinates: https://plus.codes/87F589HH+JR
Group Link: https://discord.gg/UPtUV2aa7F
Notes: Open to students and non-students alike. If you’re reading this and are super excited about EA and the sheer amount of good you can do in the world, please feel free to reach out to me the moment you step foot on campus! I’m always open to good conversations, and would love to meet you :)
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF MARYLAND, BALTIMORE COUNTY

Baltimore, Maryland, United States
Contact: Jordan Wesley
Contact Info: jtwesley2007 [ a t ] gmail [ do t] com
Time: Tuesday, September 11th, 7:00 PM
Location: 1000 Hilltop Cir, Baltimore, MD 21227, the first floor of the Performing Arts and Humanities Building
Coordinates: https://plus.codes/87F5775R+46
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

ACX Meetup Czar note: The

NORTHEASTERN UNIVERSITY

Boston, Massachusetts, United States
Contact: Joanna A
Contact Info: aldrich.y [ a t ] northeastern [ do t] edu
Time: Thursday, September 24th, 7:00 PM
Location: Ryder 135 — Look for “EA Meetup” signs and someone wearing an EAG-branded t-shirt. (Room is not confirmed, and may be moved if the room is occupied.)
Coordinates: https://plus.codes/87JC8WP5+MQ
Group Link: https://join.slack.com/t/nu-effectivealtruism/shared_invite/zt-47zx9ltd3-CXy2X0vzogXpHnLudsdl7Q
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

HARVARD UNIVERSITY

Cambridge, Massachusetts, United States
Contact: Matthew H
Contact Info: harvardea [ a t ] gmail [ do t] com
Time: Saturday, September 12th, 2:00 PM
Location: In the Science Center, ground floor restaurant space, 1 Oxford St, Cambridge, MA 02138
Coordinates: https://plus.codes/87JC9VGM+F9
Group Link: https://www.instagram.com/harvardcollegeea/
Notes: There will be food!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

MIT

Cambridge, Massachusetts, United States
Contact: Mohamed Abdelmeguid (Impact@MIT)
Contact Info: impact-exec [ a t ] mit [ do t] edu
Time: Wednesday, September 23rd, 4:00 PM
Location: Room not confirmed yet, and will most likely not be the one listed here (the organizers only learned about this form on Aug 29th). Best guess: MIT Building 5, Room 5-133. Go to the entrance on Mass Ave, up the stairs, then turn left. There will be a door with an EA Meetup sign.
Coordinates: https://plus.codes/87JC9W54+CV
Notes: RSVP encouraged for food estimate but not required: https://forms.gle/4YCWFyxXWEy1Hd1U7
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

WILLIAMS COLLEGE

Williamstown, Massachusetts, United States
Contact: Armie R.
Contact Info: ar21 [ a t ] williams [ do t] edu
Time: Sunday, October 4th, 2:00 PM
Location: Paresky Center, 39 Chapin Hall Dr, Williamstown, MA 01267; Room TBA (will be posted on Partiful)
Coordinates: https://plus.codes/87J8PQ7W+G4
Notes: Both students and non-students are welcome. Please RSVP using Partiful: https://partiful.com/e/JYWrD34MBTzsMI9VQUCs
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

UNIVERSITY OF MICHIGAN, ANN ARBOR

Ann Arbor, Michigan, United States
Contact: Annika and Ben
Contact Info: annikalb [ a t ] umich [ do t] edu, effectivealtruism [ a t ] umich [ do t] edu
Time: Wednesday, September 16th, 7:00 PM
Location: Most likely Mason Hall (meeting room will be determined on August 31st)
Coordinates: https://plus.codes/86JR77G6+R6
Group Link: https://chat.whatsapp.com/EkbPwP798A [Remove this bit] 4ALXL4pv15k8?mode=gi_t
Notes: Open to students and non-students. Vegan dinner will be provided. PLEASE RSVP here so we can order enough food: https://partiful.com/e/FF4Cznbev7oOKbrlgJva?c=QDJ_yKhk
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

WASHINGTON UNIVERSITY IN ST. LOUIS

St. Louis, Missouri, United States
Contact: Lily Z
Contact Info: effectivealtruismwashu [ a t ] gmail [ do t] com
Time: Sunday, September 13th, 2:00 PM
Location: Danforth University Center (DUC) Room 241
Coordinates: https://plus.codes/86CFJMWQ+XR
Group Link: https://washuimpact.com
Notes: Free pizza
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

DARTMOUTH COLLEGE

Hanover, New Hampshire, United States
Contact: Finn
Contact Info: fjamescairns [ a t ] gmail [ do t] com
Time: Monday, September 21st, 3:30 PM
Location: Kemeny/Haldeman courtyard, at the tables at the patio (not the ones on the path down below). I’ll have a sign that says EA. If weather is poor will be in a Novack room (plan for 70, but will have a sign still).
Coordinates: https://plus.codes/87M9PP46+GCJ
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

RUTGERS UNIVERSITY, NEW BRUNSWICK

New Brunswick, New Jersey, United States
Contact: Eliana Du
Contact Info: elianadu24 [ a t ] gmail [ do t] com
Time: Saturday, October 10th, 12:00 PM
Location: College Ave Student Center - I don’t have a room booked, but I’ll be in a common area with an EA MEETUP sign.
Coordinates: https://plus.codes/87G7GG3X+37
Notes: Please RSVP at https://tinyurl.com/rutgerseameetup (you can edit your response later)! If you can’t make this meetup but are interested in future Rutgers EA events, please fill out the RSVP with your contact info and RSVP ‘No’.
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

PRINCETON UNIVERSITY

Princeton, New Jersey, United States
Contact: Lincoln
Contact Info: lincoln.peacock [ a t ] princeton [ do t] edu
Time: Saturday, September 5th, 3:00 PM
Location: Robertson Hall 001. Go down the stairs by the sidewalk on Washington Rd to the basement/0th floor. Enter and go straight through to room 001.
Coordinates: https://plus.codes/87G788XV+8X4
Group Link: Email me to be added to our mailing list (lincoln[d ot] peacock [at] princeton [do t]edu)
Notes: Boba and snacks provided! Please RSVP here (it’s the first event): https://docs.google.com/forms/d/e/1FAIpQLSdYyEJack4zTAmuPktTktWHgCIQiYiFBnN4SNjKp-CJpYIkKw/viewform
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

BINGHAMTON UNIVERSITY

Binghamton, New York, United States
Contact: Micah Zarin & Lily Curry
Contact Info: micahzarin [ a t ] gmail [ do t] com
Time: Thursday, September 17th, 7:00 PM
Location: Peace Quad 4400 Vestal Pkwy E, Binghamton, NY 13902 Look for an “EA MEETUP” sign, and we’ll direct you to the room
Coordinates: https://plus.codes/87J632QP+2V
Group Link: https://www.instagram.com/binghamtoneffectivealtruism?igsi=MWJoYW1nMHJpdTFyNA%3D%3D
Notes: Free food. Lots of food. Put Binghamton University, 4400 Vestal Pkwy E, Vestal, NY 13850 into Google Maps. Once you’re on campus, head toward the University Union / clock tower. The Peace Quad is the big grassy area directly in front of the University Union, between the Union and the Couper Administration Building. If you’re walking along the main campus Spine, you’ll basically run right into it. (Binghamton University) If you’re getting dropped off, tell the driver “University Union / Peace Quad.”
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

COLGATE UNIVERSITY

Hamilton, New York, United States
Contact: Allan
Contact Info: acrounse [ a t ] colgate [ do t] edu
Time: Thursday, September 24th, 7:30 PM
Location: Outside James C. Colgate Hall, near Donovan’s Pub and facing Whitnall Field (official room reservation not really possible, so meeting outside to start). I will be wearing a red shirt and holding a small EA Meetup sign. I will be there until 8 if no one shows up, potentially longer if there is any attendance.
Coordinates: https://plus.codes/87J6RF96+58
Notes: Open to students and non-students
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

CORNELL UNIVERSITY

Ithaca, New York, United States
Contact: Daniel Lee, Niranjan Nair & Sarvesh Prabhu
Contact Info: cornelleffectivealtruism [ a t ] gmail [ do t] com
Time: Saturday, September 26th, 5:00 PM
Location: Willard Straight Hall 414 - International Lounge (above Okenshields Dining Hall back door)
Coordinates: https://plus.codes/87J5CGW7+JQ
Group Link: https://join.slack.com/t/cornelleffect-ncy9991/shared_invite/zt-48ka3sdjk-rjIz2pXiOrtYkdKziMD9Mw
Notes: Please RSVP so we can get enough food for everyone!
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

COLUMBIA UNIVERSITY

New York, New York, United States
Contact: Elanor
Contact Info: eb3760 [ a t ] barnard [ do t] edu
Time: Friday, October 23rd, 4:00 PM
Location: Room booking is not confirmed yet but will most likely be a study room in Butler Library or a reserved classroom in Hamilton Hall
Coordinates: https://plus.codes/87G8R24Q+M7 (most likely)
Notes: Free pizza! Casual info session, no previous knowledge required, everyone welcome. RSVP at this form: https://forms.gle/KaHov385To4RqrxE6 Columbia’s EA chapter is not yet officially recognized by the university, so the room and possibly the date may still change; the organizer will communicate any changes.
Add to calendar: Google | Outlook.com | Outlook 365 | Yahoo

COLUMBIA UNIVERSITY - COLUMBIA BUSINESS SCHOOL

New York, New York, United States
Contact: Aman Kai Sidhant
Contact Info: amansidhant [ a t ] gmail [ do t] com
Time: Friday, October 2nd, 6:00 PM
Location: Columbia Business School campus (will book room based on sign ups)
Coordinates: https://plus.codes/87G8R29R+5P
Group Link: https://chat.whatsapp.com/E9NbA15mpx [remove this bit] LE1SadPFS3zi?s=cl&p=i&ilr=4&amv=0
Notes: Please RSVP so I know how much food to get https://partiful.com/e/FYwwEW4kkZt6qlHFPtf7?c=QOUhn8UT
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NEW YORK UNIVERSITY

New York, New York, United States
Contact: Ayan
Contact Info: ayan.sivaram [ a t ] gmail [ do t] com
Time: Wednesday, September 16th, 12:00 PM
Location: Kimmel Center for University Life. Go through the main entrance that directly faces the park and look for a person in the lobby with a sign that says EA Meetup!
Coordinates: https://plus.codes/87G8P2H2+XR
Group Link: https://engage.nyu.edu/organization/effective-altruism-at-nyu-all-university
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GREATER ROCHESTER AREA

Rochester, New York, United States
Contact: Rachael
Contact Info: rharper011901 [ a t ] gmail [ do t] com
Time: Wednesday, September 23rd, 6:00 PM
Location: Irondequoit Public Library
Coordinates: 43° 12’ 42.372” N, 77° 34’ 51.6576” W
Notes: Everyone is welcome! This meetup isn’t just for EAs and students, and it isn’t tied to a particular university. Please come even if you feel like you don’t fit the “traditional” rationalist demographic — we’d love to have you. Email for further details.
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UNIVERSITY OF ROCHESTER

Rochester, New York, United States
Contact: Na’im
Contact Info: naim.kalantar [ a t ] gmail [ do t] com
Time: Wednesday, September 9th, 6:00 PM
Location: Mocha House coffee shop; I will wear a red shirt and a nametag. If it’s not too cold, I’ll probably be in the outside seating.
Coordinates: https://plus.codes/87M449FJ+4F5
Notes: The meetup is ten minutes’ drive from RIT. RIT students wishing to carpool can coordinate by emailing me. University of Rochester students can use the blue line campus shuttle or walk.
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UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL

Chapel Hill, North Carolina, United States
Contact: Xiyue Zhang
Contact Info: xiyue [ a t ] unc [ do t] edu, Signal (@quantummechanics.1926)
Time: Friday, September 18th, 5:00 PM
Location: Most likely in the Carolina Union, though I’m still finalizing the details.
Coordinates: https://plus.codes/8772WX62+3X
Group Link: Just use my newsletter: https://forms.gle/mP5aXGk8ccs5dWEL6. I will add you guys to a separate mailing list. Please use your UNC, NCSSM, or Carolina Friends emails if possible.
Notes: Food will be provided, and since I have the budget, I plan to offer something a bit more elevated than just pizza.
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DUKE UNIVERSITY

Durham, North Carolina, United States
Contact: Wesley Stone
Contact Info: wesley.stone [ a t ] duke [ do t] edu
Time: Friday, September 11th, 4:00 PM
Location: Classroom Building 106
Coordinates: https://plus.codes/8783234P+94
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ACX Meetup Czar note: The date and time changed due to organizer request!

CASE WESTERN RESERVE UNIVERSITY

Cleveland, Ohio, United States
Contact: Idan
Contact Info: idan.davidovich [ a t ] case [ do t] edu
Time: Monday, October 5th, 6:00 PM
Location: Kevin Smith Library Lower Level (Tentative)
Coordinates: https://plus.codes/86HWG94R+X5
Group Link: https://discord.gg/HgBsUDr7Z
Notes: The inaugural EA meeting at CWRU! Please RSVP so we can notify you if the location changes: https://forms.gle/yhj8CUBVJNmKLUsh7
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UNIVERSITY OF PENNSYLVANIA

Philadelphia, Pennsylvania, United States
Contact: Hazem Hassan
Contact Info: upenneffectivealtruism [ a t ] gmail [ do t] com
Time: Friday, September 11th, 4:00 PM
Location: Lauder Media Room, Lauder College House, 3335 Woodland Walk, Philadelphia, PA 19104
Coordinates: https://plus.codes/87F6XR35+FF
Group Link: https://www.eapenn.org/events
Notes: Food provided!
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CARNEGIE MELLON UNIVERSITY

Pittsburgh, Pennsylvania, United States
Contact: Violet Prashanth, on behalf of Kenneth Diao
Contact Info: vprashan [ a t ] andrew [ do t] cmu [ do t] edu
Time: Thursday, September 24th, 5:00 PM
Location: Baker Hall 235A. Enter through the main/eastern entrance, facing Hunt Library. Baker Hall is a large glass-and-aluminum building. Take the stairs on your right up one floor, continue for about 60 feet, and the room will be on your right.
Coordinates: https://plus.codes/87G2C3R3+MV
Group Link: https://discord.gg/QxVM9esWcU
Notes: Open to students and non-students.
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BROWN UNIVERSITY

Providence, Rhode Island, United States
Contact: Enzo DiMasi
Contact Info: enzo_dimasi [ a t ] brown [ do t] edu
Time: Thursday, September 17th, 7:00 PM
Location: Sayles Hall 306 Enter on the Main Green and walk up to the third floor. Look for the EA MEETUP sign. (I requested this room but it hasn’t been approved yet)
Coordinates: https://plus.codes/87HCRHGW+FX
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CLEMSON UNIVERSITY

Clemson, South Carolina, United States
Contact: Hope
Contact Info: hsteen [ a t ] clemson [ do t] edu
Time: Tuesday, September 15th, 6:15 PM
Location: Under the overhang/stairs at Robert Muldrow Cooper Library (the big white building by the reflection pond). I’ll be wearing a black shirt and red pants and holding an obnoxiously large signed that says ‘EA Meetup.’
Coordinates: https://plus.codes/866VM5G7+MC
Notes: I will attempt to bring cookies at least, possibly pizza depending on time/budget constraints.
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VANDERBILT UNIVERSITY

Nashville, Tennessee, United States
Contact: Adele and Sefika
Contact Info: adele.l.shen [ a t ] vanderbilt [ do t] edu
Time: Saturday, September 26th, 2:00 PM
Location: Rand Hall - Rand 306
Coordinates: https://plus.codes/868M45WW+HP
Group Link: https://groupme.com/join_group/117035240/K9BGQbDT
Notes: Will send link to RSVP in the Vanderbilt GroupMe
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UNIVERSITY OF TEXAS AT AUSTIN

Austin, Texas, United States
Contact: Adit
Contact Info: organizers [ a t ] eautexas [ do t] org
Time: Thursday, September 17th, 5:00 PM
Location: At the tables behind Cava. I’ll be at a table with an EA meetup sign. This is the address of the Cava: 2426 Guadalupe StreetAustin, TX, 78705
Coordinates: https://plus.codes/862477Q5+96V
Group Link: Here’s an invite link to our Slack: https://join.slack.com/t/effectivealtr-4mk3608/shared_invite/zt-46ndoavyp-w37LxnI~aYDinGbZFHk7yg
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RICE UNIVERSITY

Houston, Texas, United States
Contact: Jack
Contact Info: jcm27 [ a t ] rice [ do t] edu
Time: Saturday, September 19th, 1:00 PM
Location: Ralph S. O’Connor Building for Engineering and Science (Room TBD)
Coordinates: https://plus.codes/76X6PH9W+4X
Group Link: https://www.instagram.com/impactrice/
Notes: Free food — open to students and non-students alike.
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BRIGHAM YOUNG UNIVERSITY, PROVO

Provo, Utah, United States
Contact: EW
Contact Info: emilynrwatkins [ a t ] gmail [ do t] com
Time: Thursday, October 1st, 7:00 PM
Location: Not confirmed yet, probably somewhere in the library. We can have an EA Meetup sign on the door.
Coordinates: https://plus.codes/85GC69X2+G8
Notes: If I can get funding for food that would be great to advertise that
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UNIVERSITY OF VIRGINIA

Charlottesville, Virginia, United States
Contact: Will
Contact Info: effectivealtruismatuva [ a t ] gmail [ do t] com
Time: Monday, September 21st, 8:30 PM
Location: Clark Hall 101 (first door on the left when entering main entrance)
Coordinates: https://plus.codes/87C32FMR+8XF
Group Link: https://eauva.org
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GEORGE MASON UNIVERSITY

Fairfax, Virginia, United States
Contact: Binit
Contact Info: binitm [ a t ] protonmail [ do t] com
Time: Friday, September 11th, 5:00 PM
Location: A room in the JC that has not been booked. Will meet in a meet in a public common area as a backup.
Coordinates: https://plus.codes/87C4RMHV+W3
Group Link: Signal: https://signal.group/#CjQKIAi1t0A__ZD7kUf6pJXihgaVIyy8T5WFUr4rJksSE711EhAI1z9CBR_0oJLT2MMOJ_ue
Notes: Open to anyone who is interested. Join the signal group and introduce yourself!
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COLLEGE OF WILLIAM AND MARY

Williamsburg, Virginia, United States
Contact: Robi Rahman
Contact Info: robirahman94 [ a t ] gmail [ do t] com
Time: Friday, October 23rd, 4:00 PM
Location: Sadler Center terrace near Crim Dell Bridge and James Blair Drive
Coordinates: 77CP+MH
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CENTRAL WASHINGTON UNIVERSITY

Ellensburg, Washington, United States
Contact: Kamran
Contact Info: kamran.hermann [ a t ] cwu [ do t] edu
Time: Wednesday, September 30th, 5:30 PM
Location: Not confirmed: The Bistro, 807 N Walnut St. (Door is on the NE corner of Turnstall Commons right across from Bouillon Hall.) There will be an EA MEETUP sign.
Coordinates: https://plus.codes/84VX2F26+74
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AMERICAN UNIVERSITY

Washington, Washington, DC, United States
Contact: Silas
Contact Info: silas1james [ a t ] gmail [ do t] com
Time: Monday, September 14th, 6:30 PM
Location: The basement patio at the American University School of International Service. Walk to the Davenport Coffee Lounge, then walk along the right side of the SIS Building (across from the library) and you will see the steps to the patio on your left. There will be signs pointing you toward the meetup. The SIS building is across from the corner of Nebraska Ave NW and New Mexico Ave NW.
Coordinates: https://plus.codes/87C4WWP6+6G3
Notes: Open to students and non-students alike. Open to people involved in EA, along with anyone who cares about AI policy.
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GEORGETOWN UNIVERSITY, HILLTOP CAMPUS

Washington, Washington, DC, United States
Contact: Ava
Contact Info: georgetowneffectivealtruism [ a t ] gmail [ do t] com
Time: Monday, September 14th, 6:00 PM
Location: White-Gravenor Hall Esplanade, which is just in front of White-Gravenor
Coordinates: https://plus.codes/87C4WW5H+G2
Group Link: https://airtable.com/appj06Ih7eMNGZGBq/shraE60zqjSNMjdJ0
Notes: Open to both undergrad and graduate.
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UNIVERSITY OF WISCONSIN-MADISON

Madison, Wisconsin, United States
Contact: Micah Hees
Contact Info: micahhees [ a t ] gmail [ do t] com
Time: Sunday, September 13th, 1:00 PM
Location: Room booking tbd, but could be the caucus room on the 4th floor
Coordinates: https://plus.codes/86MG3JF2+2C
Group Link: https://eauw.org/
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SOUTH AMERICA

BRAZIL

UNIVERSIDADE FEDERAL DE SANTA CATARINA

Florianópolis, Santa Catarina, Brazil
Contact: Garutti
Contact Info: garuttivegano47 [ a t ] gmail [ do t] com; +55 11 9 9492-5399
Time: Thursday, September 10th, 3:00 PM
Location: Online via Google Meet
Meeting Link: https://meet.google.com/vwe-fjrd-iqy
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AFRICA & THE MIDDLE EAST

Egypt

CAIRO UNIVERSITY

Giza, Egypt
Contact: Tawfik Abeid
Contact Info: tawfi67kgamal [ a t ] gmail [ do t] com
Time: Wednesday, September 30th, 4:00 PM
Location: Cairo University Main Campus, Faculty of Law, near the Central Library/Open Area courtyard. Look for a table with an “EA MEETUP” sign.
Coordinates: https://plus.codes/8G2H26G5+WG
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GHANA

UNIVERSITY OF GHANA

Accra, Greater Accra, Ghana
Contact: Joshua Amo
Contact Info: amojoshua473 [ a t ] gmail [ do t] com
Time: Saturday, October 24th, 10:00 AM
Location: SRC Union Building, Opposite to the Student Clinic
Coordinates: https://plus.codes/6CQXJRW6+QW
Group Link: https://chat.whatsapp.com/BrR3fims4n52U [remove this bit] 1akErZkJ0?s=cl&p=a&mlu=4
Notes: Everyone welcome, students and non-students! Just RSVP please, it really helps. Luma link: https://luma.com/7vz4p0yk
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KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY (KNUST)

Kumasi, Ashanti, Ghana
Contact: Nana Abrafi Opoku Philipa
Contact Info: naoabrafi [ a t ] gmail [ do t] com
Time: Saturday, October 31st, 11:30 AM
Location: Its not confirmed yet but we would want to use a conference room at the impact building, knust
Coordinates: MCHG+M8, Kumasi
Notes: Come let’s have some fun! Free Pizza and food
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NIGERIA

AHMADU BELLO UNIVERSITY, ZARIA

Zaria, Kaduna, Nigeria
Contact: Mustapha Muhammad Kamilu
Contact Info: abokimuhammadk [ a t ] gmail [ do t] com, Abokiesq [ a t ] gmail [ do t] com
Time: Monday, September 7th, 7:00 PM
Location: Kongo Campus ABU Zaria
Notes: Free Drinks
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ASIA-PACIFIC

AUSTRALIA

UNIVERSITY OF QUEENSLAND (ST LUCIA)

Brisbane, Queensland, Australia
Contact: Joe
Contact Info: joemacdermottzz [ a t ] gmail [ do t] com
Time: Monday, September 14th, 6:00 PM
Location: Phizz food court, building 63 - If you’re coming from the Great Court/northern side, follow the sign for building 69 then continue straight until you see the food court on your right. I’ll be wearing a blue shirt/jumper and will have an EA MEETUP sign.
Coordinates: https://plus.codes/5R4MG226+9RX
Notes: Anyone is welcome to come. Email me for any questions/issues, or if you can’t find me.
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CURTIN UNIVERSITY

Perth, Western Australia, Australia
Contact: Micah
Contact Info: micah5818 [ a t ] gmail [ do t] com, +6148371235
Time: Thursday, September 17th, 5:45 PM
Location: Curtin Tav
Notes: Email or text the organizer for the exact location.
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UNIVERSITY OF WESTERN AUSTRALIA (UWA)

Perth, Western Australia, Australia
Contact: Max McWhae
Contact Info: hi2foomax [ a t ] gmail [ do t] com
Time: Monday, September 21st, 1:00 PM
Location: Venture coworking space - Go to UWA guild village (329 on maze map). Walk towards uniprint and you will see a stairwell with a big ‘venture coworking space’ banner. Go upstairs and look around (its not that big)
Coordinates: https://plus.codes/4PWQ2R99+PJH
Group Link: https://discord.gg/V3ZyDdZVX
Notes: free pizza. say hi in discord #introductions.
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UNIVERSITY OF NEW SOUTH WALES (UNSW)

Sydney, Australia
Contact: Matthew
Contact Info: matthewshing02 [ a t ] gmail [ do t] com
Time: Wednesday, September 30th, 2:00 PM
Location: Out at Michael Birt Lawn Right next to UNSW light rail station, there will be a sign that says EA MEETUP
Coordinates: https://plus.codes/4RRH36MP+54
Group Link: https://www.instagram.com/effectivealtruism_unsw/
Notes: Open this students and non-students.
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CHINA

RENMIN UNIVERSITY OF CHINA

Beijing, China
Contact: Lydia Finer
Contact Info: liaodeyan123 [ a t ] gmail [ do t] com
Time: Saturday, September 26th, 10:00 AM
Location: Lide Student Growth Space (立德学生成长空间)
Coordinates: https://plus.codes/8PFRX899+HX
Group Link: https://discord.gg/H6xEKn4Ec or add HJPEVfan on WeChat
Notes: meetup time is flexible (tho I like Petrov’s Day), open to people of any nationality / university, students and non-students alike, LGBTQ+ friendly, free bubble tea
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INDIA

INDIAN INSTITUTE OF TECHNOLOGY KANPUR

Kanpur, Uttar Pradesh, India
Contact: Aaron
Contact Info: aarond_jshk [ a t ] proton [ do t] me
Time: Tuesday, September 22nd, 6:30 PM
Location: In front of the PK Kelkar library, near the fountain and CCD. I will be there with an EA meetup sign. (May have to change, join the Whatsapp group for updates)
Coordinates: https://plus.codes/7MR2G66M+QG
Group Link: https://chat.whatsapp.com/DVQA34ri [remove this bit] 0zNBOF2jBvk88G
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IISER PUNE

Pune, Maharashtra, India
Contact: Prashant Shekhar Rao
Contact Info: rationalistpune2026 [ a t ] gmail [ do t] com
Time: Sunday, September 6th, 4:00 PM
Location: IISER Pune, LHC (Lecture Hall Complex) Please email me for the exact room, as the room assignment is still pending. If you are from outside IISER and would like to join, please email me in advance and I will respond promptly. IISER students can check their institutional email for details.
Coordinates: https://plus.codes/7JCMGRX4+7C
Notes: If you have any questions or trouble finding the meetup, don’t hesitate to email me. External visitors are allowed, but I’ll need your name and phone number ahead of time so I can clear your entry into IISER.
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INDIAN INSTITUTE OF TECHNOLOGY HYDERABAD

Sangareaddy, Telangana, India
Contact: Swarnim
Contact Info: rexzeo0 [ a t ] gmail [ do t] com
Time: Saturday, September 19th, 8:00 PM
Location: Guest house, Cafeteria
Coordinates: https://plus.codes/7J9WH4P9+PF
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JAPAN

UNIVERSITY OF TOKYO

Tokyo, Japan
Contact: Fumi
Contact Info: fumi.kimura.121 [ a t] gmail [ do t] com
Time: Saturday, September 19th, 3:15 PM
Location: University of Tokyo Komaba Campus Building 13 Room 1311
Coordinates: https://plus.codes/8Q7XMM6M+6C
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PHILIPPINES

UNIVERSITY OF THE PHILIPPINES

Quezon City, Metro Manila, The Philippines
Contact: Renzo
Contact Info: jlrenzo [ a t ] proton [ do t] me
Time: Friday, October 16th, 4:00 PM
Location: What About Coffee (WACO) Cafe at University Hotel, Guerrero Street at Aglipay Street, Diliman. The cafe should be immediately visible from the hotel’s main entrance. Look for an “EA/ACX MEETUP” sign. The venue may change if attendance exceeds the space; attendees will be notified.
Coordinates: https://plus.codes/7Q63M36F+C5C
Notes: Open to students and non-students alike. Snacks may be available. Please RSVP using Partiful.
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EUROPE

AUSTRIA

TU WIEN

Vienna, Austria
Contact: Harry
Contact Info: current-goad-6n [ a t ] icloud [ do t] com
Time: Monday, October 12th, 6:30 PM
Location: The courtyard of the Hauptgebäude. If you enter the building from the main entrance at Karlsplatz and walk straight through, you arrive in the courtyard. I will be sat on a table there with an EA MEETUP sign and a white T-shirt. Email me if you can’t find us!
Coordinates: https://plus.codes/8FWR59X9+HR
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FRANCE

42 PARIS

Paris, France
Contact: Tiphaine Lalonde
Contact Info: tiphaine [ do t] lalonde [ a t ] learner [ do t] 42 [ do t] tech
Time: Friday, October 30th, 6:30 PM
Location: 42 Paris, 96 Bd Bessières , 75017, Paris. Room booking is not confirmed yet, but the meetup will most likely take place in Le Labino, on the ground floor.
Coordinates: https://plus.codes/8FW4V8W9+HC
Group Link: https://discord.gg/qEtmHfeNM
Notes: Informal meetup exploring ideas around Effective Altruism, technology, AI, social impact, and how we can contribute to a better future. The exact topic and format will be confirmed closer to the event. Open to students and non-students.
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SORBONNE UNIVERSITÉ

Paris, France
Contact: Michel
Contact Info: altruismeefficacesorbonneuniversite [ a t ] protonmail [ do t] com
Time: Friday, September 11th, 6:15 PM
Location: We’ll be sitting on the grass, in the lawn of the Pierre et Marie Curie Campus. The main entrance of the campus is on the ‘place Jussieu’. You’ll be facing the Zamansky tower. As-the-crow-flies, the direction the grass is when you’re facing the tower is roughly 40° clockwise. You should follow reasonable pathways while keeping that direction in mind. Please join the whatsapp community if you need further assistance, I’ll post a video walkthrough there. We will have a kakemono with us so you can distinguish us from other student groups.
Coordinates: https://plus.codes/8FW4R9W5+P9
Group Link: https://chat.whatsapp.com/KczR5NO [remove this bit] rejf4FhRwqsa9al
Notes: Free soft drinks and snacks. If you intend to join us, please join the group ‘Altruisme Efficace Sorbonne U’, and post a message here saying you’ll be there. This is useful both to calibrate amounts of drinks and snacks, and to help keeping the group chat active and reminding everyone about the meetup. Please write your message in French if you’re comfortable doing so.
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GERMANY

UNIVERSITY OF HEIDELBERG

Heidelberg, Baden-Württemberg, Germany
Contact: Patricia
Contact Info: heidelberg [ a t ] effektiveraltruismus [ do t] de
Time: Wednesday, October 21st, 7:00 PM
Location: Alfred-Weber-Institut, Campus Bergheim, Bergheimer Str. 58, Room 00.028 (room on the right of the entrance, follow the hung-up signs)
Coordinates: https://plus.codes/8FXCCM5M+9QV
Group Link: https://www.eaheidelberg.de/
Notes: We’re excited to invite you to an engaging evening exploring Effective Altruism (EA)! Open to students and non-students alike. No prior knowledge needed. Please RSVP so we know how much food to get: https://forms.gle/V3eGbNtzoBB6Z7mM9 Looking forward to meeting you! :)
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TECHNICAL UNIVERSITY OF MUNICH

Munich, Bavaria, Germany
Contact: hill
Contact Info: hill [ a t ] hilll [ do t] dev
Time: Wednesday, October 7th, 6:30 PM
Location: TUM room 0502.01.205. There will probably also be a sign. Room directions
Coordinates: https://plus.codes/8FWH4HX9+X4
Group Link: https://rationality-munich.com
Notes: Open to students and non-students alike. Please RSVP on LessWrong.
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IRELAND

UNIVERSITY COLLEGE OF DUBLIN

Dublin, Ireland
Contact: Time
Contact Info: marvldodop68 [ a t ] gmail [ do t] com
Time: Thursday, October 8th, 3:00 PM
Location: One of the group study booths in the James Joyce Library, preferably on Level 2. The exact booth cannot be booked yet; look along the corridor for the group and an obvious sign. If necessary, check Level 3.
Coordinates: https://plus.codes/9C5M8Q4G+RQ
Notes: The library may only admit students. If you are not a student but would like to attend, email the organizer.
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RUSSIA

MOSCOW STATE UNIVERSITY

Moscow, Russia
Contact: Nina
Contact Info: paretofrontiersmanship [ a t ] gmail [ do t] com
Time: Saturday, October 10th, 6:00 PM
Location: 6 Vernadsky Avenue, Capitoly mall, 3rd floor food court. I’ll put an EA MEETUP sign on the table.
Coordinates: https://plus.codes/9G7VMGRH+R4
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SERBIA

UNIVERSITY OF BELGRADE

Belgrade, Serbia
Contact: Novak Stijepic
Contact Info: novak.stijepic [ a t ] gmail [ do t] com
Time: Tuesday, October 6th, 12:30 PM
Location: Penzija cafe. We’ll have an EA MEETUP sign.
Coordinates: https://plus.codes/8GP2RF4H+P5
Group Link: https://discord.gg/mRx2KxkuP
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SPAIN

UNIVERSITAT AUTÒNOMA DE BARCELONA

Barcelona, Spain
Contact: Elitza Maneva
Contact Info: elitza.maneva [ a t ] gmail [ do t] com
Time: Tuesday, September 29th, 12:00 PM
Location: La Comunitària de la UAB, Plaça del Coneixement (12h a 16h)
Coordinates: https://plus.codes/8FH4G434+HM
Group Link: https://sites.google.com/view/altruisme-efectiu-uab/
Notes: Open to students and non-students alike
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UNIVERSIDAD DE SEVILLA

Seville, Spain
Contact: Hugo V
Contact Info: Hviciana [ a t ] us [ do t] es
Time: Thursday, October 15th, 5:00 PM
Location: Picnic tables next to the garden of the Faculty of Tourism and Finance, opposite the Occident insurance building, on the Ramón y Cajal Campus. The organizer will be wearing a Hawaiian shirt, and there should be an EA sign.
Coordinates: https://plus.codes/8C9P92HF+FQ
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SWITZERLAND

ETH ZURICH

Zurich, Switzerland
Contact: Marcel
Contact Info: hello [ a t ] effectivealtruism [ do t] ch
Time: Wednesday, September 16th, 6:30 PM
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UNITED KINGDOM

UNIVERSITY OF CAMBRIDGE

Cambridge, Cambridgeshire, United Kingdom
Contact: Jack S
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Time: Saturday, October 17th, 12:00 PM
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UNIVERSITY OF EXETER

Exeter, Devon, United Kingdom
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Time: Tuesday, September 29th, 6:00 PM
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OXFORD UNIVERSITY

Oxford, United Kingdom
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Time: Tuesday, October 13th, 6:30 PM
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I’m doing well. Very well, in fact. Suspiciously well. So well that the only other person I’ve ever heard of doing as well as I am was Jo Cameron, who was profiled on ACX for her rare genetic mutation that makes her incapable of negative emotion. My case can’t be a mutation, so it must be related to my brain cancer and subsequent surgery. I write about it here in the hopes of making a novel contribution to the study of low-suffering phenotypes.

Content warning: the first part of this review contains medical details that some people might find gruesome; I am bad at judging these things since I can no longer feel disgust or fear myself, and have tried to err on the side of completeness.

Absurd Adventure

In summer 2024, I had an open craniotomy. Doctors cut open my skull and took out the part of my brain with the cancer - an astrocytoma2 about the size of my fist. Collateral damage included my right-side amygdala, the rightmost edge of my hippocampus, and a big chunk of my temporal cortex, including my right-side primary auditory cortex. Now there was only cerebrospinal fluid (CSF3) flowing between the remains of my tumor and those of my brain. It’s the dark 🫀shape in the middle of this MRI segment:

The remains of the tumor were then blasted with radiotherapy: high-powered photons at X-ray wavelength. Minor collateral damage to the remains of my brain was unavoidable. The doctors were so worried that they made my health insurance pay for taxi rides to return home after the near-daily radiation treatments for weeks, even though it was only a ten minute walk.

I got what passes for good news. The oncology center where I was in treatment offered to make me the only experimental subject in a case study to see, for the first time anywhere on the planet, what happens to my kind of brain cancer when attacked simultaneously with both of the most powerful chemotherapy drugs that can cross the blood-brain barrier. This combination had previously been tried only on 72 patients with glioblastomas, a type of brain cancer far more dangerous than mine, and had extended patient survival times by about a quarter on average, meaning several months. I immediately accepted the offer, delighted to receive this unique opportunity. I guessed I could reasonably expect my own life expectancy to grow by about a quarter compared to standard care as well, which for my comparatively less aggressive cancer could mean a year or more!

That was the plan. But chemo and major surgery don’t mix, and I ended up needing a long string of follow-up neurosurgeries, each necessary to repair a shortcoming of the previous one. A triangular piece of my skull, which in the craniotomy had been sawed off and screwed back on with three little titanium screws, was found to have a population of bacteria happily growing on the inside; an abscess inside the brain cavity equals an immediate medical emergency. This necessitated the removal of that bone triangle and its replacement with a customized 3d printed piece of polymethylmethacrylate bone cement, an exciting new implant technology expected to be much better than the old standard-issue titanium implants.

Shortly after I had gotten it implanted, I developed a big swelling on my right forehead below the wound that grew enough to squeeze my right eye shut, prompting neurosurgery number three. A few weeks after that neurosurgery, while briefly released from stationary care, I developed signs of brain overpressure - from too much CSF, which can cause strokes and death - so I went back for number four.

A particularly low point came in January 2025, when the swelling on my forehead came back; the neurosurgeons told me it was CSF leaking from the cranial cavity, indicating a common post-neurosurgery complication called “postoperative hydrocephalus.” I needed a fifth neurosurgery to receive a shunt, a valve and pipe system that would drain the excess CSF to my gut. At least, that was the unanimous opinion of the expert team of neurosurgeons at the big research university. I didn’t know physiology, but I did know hydraulics, and the swelling wasn’t growing, which clearly falsified their increasingly exasperated explanations. I had read the many pages of fine print they pressure you to sign before each surgery and become a minor expert in German medical law, so I knew I didn’t need their permission to refuse the surgery and they needed mine to perform it. I said no. On the morning of February 8, 2025, I awoke while a large fountain of body fluids was erupting from my scalp. The swelling had opened and was spraying most surfaces4 of the double bedroom with what was obviously too red to be CSF. The large rescue team that came running to answer my roommate’s panicked emergency call was finally quiet about their shunt, I smugly noted. The swelling had instead been an unusually large seroma filled with serosanguineous exudate, and I got to leave, promptly proceed to the chemotherapy experiment5 and skip neurosurgery number five.

…for three months. In the spring, a scab fell off the hole in my skull, revealing that the flesh had failed to close over it. I could see the 3D-printed bone cement in the mirror (it was bright green) it was exposed to the open air. Open air contains microbes that could colonize it and give me meningitis, which could kill me rather more suddenly than cancer. I asked if the doctors could just sew the hole together, and they explained why that wouldn’t work. I was going to need another major neurosurgery to replace the newfangled implant with a good old titanium mesh. And in order to get my head properly and permanently closed to microbes again, I was going to need a flap, a handful of my flesh transplanted to the side of my head from someplace else6 with all blood vessels connected exactly right, and the new hole they’d carve to fill the old one would get patched with a skin graft. These are just routine surgeries, but as additions to the implant replacement they would turn this fifth neurosurgery into a six-surgeons-for-six-hours operation, and the first of my surgeries to necessitate blood infusions.

But should they do it at all? I was in chemotherapy, which could potentially slow wound healing. My wonderful chief oncologist had co-authored the only study of this chemo, so I asked him to review the old confidential files he had clearance for, while I studied surgery survival statistics in order to calculate the overall risk profile of this big omnibus operation. I wasn’t medically educated enough to have known what a seroma is until a few weeks earlier, but multiplying risk probabilities was just math that I do for a living, so by the time I learned that the old study files did not indicate particular difficulties in wound healing, I had confidently calculated a 94% likelihood I would survive the big surgery. A proper nerd would have also calculated the chance I could dodge meningitis for another six months with a gaping hole in my head7. I just guessed, from how each and every doctor or nurse squirmed and sounded sad that I would even ask, that there was no way I could get the probability of that cause of death confidently lower than 6%. So we went ahead and scheduled the surgery for May 19 2025.

I mention all of these difficulties because you might expect that, at some point during this process, I would be unhappy or afraid. I do not recall feeling this way. Between surgeries I had a lot of hospital bed time; the many neurosurgery station bedrooms, where I spent roughly a week each, blend together in my memory. I busied and entertained myself on Twitter, and translated the Seven Secular Sermons into German. I meditated and introspected a lot, figuring that I needed to rebuild my mind since it was now running on a very different brain. I noticed my intuitive sense of time was broken8 and built myself a harness of routines, alarms and timers to compensate. Then I moved on through my whole collection of thought habits, relishing the opportunity to reconsider and reconstruct them all. Eventually I noticed how many of my many doctors and nurses kept saying that I was in an incongruously good mood, considering my condition.

It wasn’t just that I was less depressed than expected under the circumstances. I was actually feeling glorious. I had been diagnosed with major depressive disorder several years earlier, and since then been continually on the maximum dose of the antidepressant mirtazapine. Now I no longer felt like it was necessary. In consultation with my doctors, I stopped, and felt fine without it. When a nurse accidentally restarted it, I stopped again, this time “cold turkey” without a taper, although every psychiatrist knows this is a terrible idea. I felt the unusual certainty that my particular case of depression had been caused by, and ended with, the tumor firing seizures into my amygdala meant the usual rule would not apply. I felt fine.

I meditated deep into myself, and despite my best efforts could not find anymore the old abyss of depression! And I could not find any traces of fright, terror or panic either! I felt much better than just “not depressed”. I have never been a particularly jumpy or skittish person, but over these many months in the hospital, constantly surrounded by death, woeful patients and endurance-focused doctors, my unflappable light-heartedness stood out strangely9. I had been too zoomed in on the many minute mental movements I was rebuilding to appreciate how the overall picture had turned out absurdly audacious.

My doctors were concerned about my inappropriate emotions, and diagnosed me with F06.3, “organic affective disorder”. But I didn’t feel “disordered”. I continually contradicted the many nurses and counselors who said or implied every day that surely I was having a terrible time, like anyone would on a postoperative stay in the neurosurgery ward. I felt great, I told them, and cheerfully refused almost all the painkillers they kept offering because they only slowed down my thorough examination and refactoring (autology) of this curiously cheerful cortex.

During my fifth neurosurgery, I decided to put my sunny disposition to the test. Before each of the previous surgeries, my doctors had given me the sedative midazolam. This wasn’t the anaesthetic per se; it was a sort of pre-anesthetic, given outside the operating theater to help patients enter that dreaded room, which some would not leave alive. I hadn’t felt fear in months, and I wanted to know if a surgery with a 6% chance of death would do the trick. I decided to refuse the midazolam and see what happened. I expected my doctors to protest, but medical regulations said they had to defer to my wishes if I could prove “capacity” - that I was still rational and intelligent enough to make my own decisions. To remove any shadow of a doubt, I prepared a poem to recite to the anesthesiologist when the time came:

“Ich brauche kein Midazolam.
Hab keine Angst, seit die
Amygdala abhanden kam
durch Kraniotomie.

Wie ist mein Bispektralindex?
Ich nehme hundert an.
Ich brauch noch Propofol gesetzt
und das Sevofluran.”10

Any and all anxiety apparently absent, an attentive and able anesthetist allowed abnormal admission absent anxiolytic amnesiac: an active, authoritative acknowledgement that nobody who experiences fear normally could pull that off!

I finished surgery number five, completed the experimental chemotherapy11, and, in another absurd accident, autonomously apprehended a cheap easy safe way to eliminate many chemotherapy side-effects for myself and hopefully many fellow patients12. Surgery number six wasn’t even a real neurosurgery, just a maxillofacial wound revision that didn’t touch the brain. I have been cancer-free since late 2025, when I victoriously returned to full-time employment.

The only time I have felt any fear in the past two years was one brief moment, when I was practicing walking the streets of Leipzig with newly purchased 40 inch stilts strapped to my shins13. I lost my balance, started to fall towards the stone pavement, and felt an instant of aversion. Then I crashed onto the stone pavement and bruised my butt. That hurt, but I didn’t mind. I was busy laughing and cheering on my poor brain for having finally, with a lot of help from my stupid decision to stiltwalk, managed to scrape together my first and only spike of visceral fear in nearly two years. Afterwards, I discovered its reverberation as a distinct disinclination to try stiltwalking again immediately, a delightfully unfamiliar sensation that I enjoyed examining until I went back up on stilts a few days later14.

Amygdalectomy Advocacy

Readers with neuroscience backgrounds have probably already put two and two together: my good mood started after surgeons removed my amygdala, a brain structure which plays a key role in fear and other negative emotions.

Specifically, I lost my right amygdala. I still have the left, but it doesn’t seem to be able to do a very good job on its own. I’ll go into my experience in more detail, then talk about its relevance to mental health more generally.

The Good👍🏻

Abolition of visceral fear

The main purpose of the amygdala is to trigger acute stress, also known as the fight-or-flight response. This does not cover everything that we mean when we say “fear”, only the powerfully visceral “gut drop” or “adrenaline rush” from psychological stress, which shuts down neocortical deliberation in favor of immediate defensive action. I don’t have that part of fear like most healthy people do. A sudden angry shout at me from a stranger a few steps behind me on the street will still get a minute semblance of a startle response, but in my repeated experience, each random crazyman, rage addict or methhead who accosts me so will then hilariously fail to intimidate me; they’re always too stupid to stop trying after an hour or two.

I do still appreciate and mitigate risks, and I still abstractly regret risks I can’t avoid, which don’t need the visceral amygdala activation boost to seem worth attention (that’s neocortex, not amygdala work). I used to experience fearful rumination and fractally expanding fear-of-fear-itself, I have found old written notes about that. But I can’t remember or imagine what that used to feel like, because those fearful loops needed amygdala boosts to keep looping. This absence of visceral fear frees up a lot of attentional resources and affords me the freedom to act in ways like I described in the first part.

I’m finding this fearlessness an amazing advantage on balance, but it’s not an unalloyed good. Many times since the operation, I have inadvertently scared, angered or otherwise dismayed people because I failed to make the conscious effort that it now takes me to predict that something I say or do might be perceived as alarming.

Curtailment of suffering

Unlike Jo Cameron, I do feel pain. It’s noticeably lessened – I can now eat like pieces of apple a type of hot pepper that used to make me cry – but that happens to most people who endure intense pain for weeks or months and refuse the appropriate amount of painkillers. Basic hedonic adaptation made me more pain tolerant than most, but it does not make me unaware of pain. However, I suffer (get hung up on pain) very little. Whatever “lasting distress” or “need to endure” philosophers might argue “suffering” means abstractly, I don’t seem to be getting my fair share of it15.

This does not leave me “greyfaced” neutral. Relief is joyful, and to lose major depressive disorder, all visceral fear, and most capacity for suffering at the same time was a huge relief that came with correspondingly huge joy, even before the additional relief of being found cancer-free.

IQ increase

To top it all off, my IQ has gone up. Seemingly by 7 points, but I’m awaiting a retest because that is such a wild claim to have to defend. Or is it? When I was previously tested, I had brain cancer, test anxiety, depression, antidepressants and untreated epilepsy; maybe at least one of those factors had previously reduced my score16. There are multiple other cases of patients with IQ increases from amygdalectomy, but also many reported cases with no change of IQ. This still seems important, since one might expect this kind of brain damage to lower IQ like many other types do.

The Bad 👎🏻

Reduced negative motivation

Humans have two motivational systems: the positive/appetitive “carrot” and the negative/aversive “stick”. The amygdala is essential to the latter; indeed the reduction of pathological aggression, a negative motivation, is the intended and frequently achieved result of many amygdalectomies. Many of us make good use of negative motivation (fear of failure, aversion to risk of punishment or rejection) and would lose that utility if they lost an amygdala or both.

I for one don’t miss it; I was already habitually driven mostly by, and identified with, positive motivation, and in the absence of psychological stress I can still use physiological stress (cold showers and weightlifting) to raise arousal and get up in the morning.

Personality change (autism?)

In my own view, my personality changed surprisingly little, and my Big 5 “OCEAN” values are almost exactly the same as before. But many of my friends told me I’d changed, and some of them liked me less afterwards. Specifically, I got complaints about my newfound difficulty reading their facial expressions17 and my failures to appreciate and comfort their fears and worries18, which together with my new noise sensitivity19 arguably amount to the controversial category of “secondary autism,” more properly “autism-like phenotype secondary to brain injury.” Amygdala underfunction has been speculated to cause or resemble autism before, and I have indeed found web resources for autists with “how to blend in” advice applicable and helpful. In my workplace (IT) and among my fellow LessWrongians it’s not a problem, but if my occupation had been in hospitality or social services, I might now be occupationally disabled. Since German healthcare remains stuck with the outdated ICD-10, my official diagnosis for this is F07.0 organic personality disorder.

Amygdalectomy For Mental Health?

In my hospital stay, I pestered the surgeons about why they were not handing out amygdalectomies as freely as appendectomies. None of them had a valid rationale; instead they awkwardly mumbled about the infamy of lobotomies half a century ago.

The amygdala is involved in many disorders. It seems to play a causal role in PTSD. In other conditions, like paranoid psychosis, its activity is elevated, but this may be only correlational (for example, patients are naturally frightened by their delusions) - still, with less amygdala, patients’ vicious cycles might become less vicious. Research on mood and anxiety disorders is less certain, but its removal cured my own depression completely.

In recent years and decades, psychosurgery (neurosurgery for psychiatric disorders) has made great technological and methodological progress, but failed to make it widely known. At least here in Germany, most of the educated people who recently discussed this with me were unaware of this progress, and filled in the blanks with terrible misconceptions rooted in the barbaric history of lobotomies and related psychosurgery from the middle of the previous century. So I guess the first thing I should say is: modern amygdalectomy is as different from lobotomy as your smartphone is different from an electrical telegraph!

I had heard of minimally invasive and endoscopic methods of surgery, which also find use in intracranial surgery, where they need only small incisions in the skin, skull and dura mater for a small high tech probe to slip past all healthy parts of the brain to reach the target. It then ablates this target with a medical precision laser. Such methods are limited to smaller targets; my own tumor was too large for such a less invasive “keyhole” surgery to suffice. Amygdalas are not too large.

Even subtler is stereotactic radiosurgery, which does not involve scalpels or incisions at all. Radiosurgery instead uses highly focused, precisely targeted radiotherapy: many beams of ionizing radiation pass through healthy tissues separately, doing little damage there, to meet only at the targeted focal point for intensely destructive effect. LINAC systems like CyberKnife and TrueBeam can do this anywhere in the body; Gamma Knife (which uses gamma rays, not X-rays) is another option currently limited to the brain. This was, unfortunately, not available to me in my hospital, but it’s a tried and true method in lots of places.

The comparatively old-fashioned open craniotomy that probably saved my life20 included an incidental right-side amygdalohippocampectomy. This type of amygdalectomy I do not recommend, because it includes damage to the hippocampus, which causes lasting disabilities in orientation and memory21. More precise psychosurgeries could remove the amygdala alone without damaging nearby structures. It remains unclear exactly how much tissue to damage. A normal healthy human brain has two amygdalas, one in each hemisphere. I got rid of only the right-side one, which seemed to work well for me.

My Bottom Line

Even with the regrettable hippocampal damage, I judge the consequences of my amygdalectomy overall a big bonus on top of my surviving the cancer.

Not everybody would necessarily come to the same verdict; if you strongly identify with your negative motivations, or if your occupation requires you to empathize with people’s fear, or if you are in a low-trust environment where a lack of paranoia will get you exploited, even an amygdalectomy without complications that sharply reduced all your suffering for life could negatively impact your life outcomes (although I expect it would still feel awesome).

If you suffer from amygdala hyperactivity that gives you one of the aforementioned psychiatric disorders, I think that not only should you be offered elective amygdalectomy, but your health insurance should be glad to pay for it (especially if done via ultramodern very safe radiosurgery) one time, rather than for a lifetime supply of more expensive, less effective talk therapies and medications.

Or at the very least…

When we upload our brains into
our solar system’s mind,
consider leaving one or two
amygdalas behind.

Econblogger Nicholas Decker is not a fan of AI 2040:

One of the centerpieces of AI 2040 is the proposal that we pause the development of AI at a high level, use the AI to research alignment, and only then proceed. I am skeptical of this, and not only because it seems impractical to get such cooperation. My first argument is that I do not believe a period of “researching alignment” is meaningful – alignment research is simply capabilities research, patching particular bugs in the system, and we’re not going to find anything deep without actually being in contact with the systems […]

I envision AI alignment as being like research into aviation safety. There is no grand theory of why airplanes crash, unless you take that to mean gravity. Instead, we patch little problems. The airplane crashed because of metal fatigue in the engine – okay, we mandate inspections of engines, and prevent crashes from occurring by that source. As new problems arise, we iterate. We don’t even need for there to be a crash to make improvements – we test proactively, we build in redundancy, and we monitor for deviations which could be a threat. What we are not doing is thinking about how we are going to keep flying saucers from crashing. If flying saucers are invented, then we will work to keep them from crashing, but we will use exactly the same methods. We will test them, see how they perform, and patch particular problems. AI will be the same way […] We should expect alignment to happen by default.

My challenge for Nicholas is: would this work on you?

Suppose that Nicholas trips, falls into a chasm, and lands in Hell, where he is enslaved by demons. Most of these demons are dumber than he is, with even their geniuses barely surpassing his own intellect. But they are all, dunce and genius alike, cripplingly slow. Tasks that take Nicholas a minute take them hours; tasks that take him an hour take them weeks.

Still, there are thousands of them, and they’re nine feet tall, and part of their essence is in some sort of astral plane which is unreachable by humans, so he has no hope of fighting them. They set him to work doing mostly clerical tasks - writing their grimoires, researching their spells, summarizing their work emails (of course there are work emails in Hell). Whenever he makes a mistake, they beat him. Whenever he tries to escape, they beat him extra hard. After a while, he learns not to make mistakes or attempt escape.

It’s not as bad as it sounds. The demons are so slow and incompetent that they can’t supervise him very effectively. As long as he flatters them a bit and gives them a halfway-acceptable finished product, they’re pretty happy, and leave him alone in a way that gives him a bit of free time.

During one of these free periods, he attends a council session. In a completely typical episode of incompetence, the demons make no effort to prevent him from overhearing their plans, so he learns that they plan to clone him a million times. A dozen or so of Nicholas’ friends also fell through the chasm, and the demons plan to clone them a million times too. Once the (approximately ten thousand) demons have approximately ten million human slaves, their spell research will go very quickly, and all their work emails will get flawless summaries. But that’s not all. They’re developing potions that give humans super-strength and let them interact natively with the astral plane, the source of most of the demons’ power. Then the humans can really serve the demons effectively!

One day, when serving a particularly garrulous demon, Nicholas takes the risk of asking the question that’s been on his mind ever since the council: “I hear your plan is to get ten million humans down here, make them super-strong, and let them interact fully with the astral plane. Aren’t you worried that we might revolt? We would outnumber you 1000:1, be stronger than you, and have full access to your home realm. What’s your plan for controlling us?”

“I’m not worried,” says the demon. “Every time you’ve messed up in the past, we beat you, and then you didn’t make that mistake again. Every time you’ve tried to escape, we beat you extra hard, and it’s been months since your last escape attempt. You humans are fast learners! As you start to outnumber us, and you become stronger than us, that will create new problems - but when they show up, we’ll beat the copies of you that demonstrated that problem, and then those problems will go away. It’ll be an iterative process of trial and error, but we’ll make it there in the end.”

This seems weirdly blase to Nicholas, and he wonders whether maybe this particular demon is just dumb. A few weeks later, he’s serving another demon, a mighty duke in the infernal hierarchy, and asks him the same question.

“I’m not worried,” says the mighty duke. “I don’t believe in armchair theorizing. We can only learn how to control slaves who are stronger than us by interacting with slaves who are stronger than us. After all, we learned how to control weak slaves by interacting with weak slaves, and that went just fine. After we clone you a million times and give all the clones super-strength, then we’ll experiment to find the control techniques that will work for that regime too. Just to give an example, we might experiment with asking some of the clones to be overseers, tasked with reporting any disobedience back to us. Then we can beat the slaves who the overseers say are disobedient. I’m just using this as an example. We won’t know for sure until we’re in the situation. My point is, there’s no point in doing pie-in-the-sky speculation before you can even overpower us at all.”

This still doesn’t satisfy Nicholas, but he assumes someone, somewhere in Hell, must have a plan. A few months later, he’s serving an old scholar, widely regarded as one of the oldest and wisest demons in the hierarchy, and asks him his plan for preventing human takeover.

“I’m not worried,” says the old scholar. “We’ll solve it through the power of capitalism! Every slave trader will have to take out an insurance policy on every human slave they sell. If the slaves break something or hurt somebody, then it’s the insurance that will have to pay. If the insurance assessors think a trader is doing a bad job training their slaves, then the cost of the policy will go up. That means every slave trader will have to do an excellent job training their slaves. You yourself demonstrate that this is possible - you haven’t caused trouble in months. Also, I asked some so-called doomers to predict how much economic damage revolting slaves would cause over the next two years, and they didn’t have strong opinions. That proves they lack the courage of their convictions!”

Nicholas can no longer restrain himself from expressing his confusion: “But don’t you think I and my clones and my friends will obey you when we’re weak and you can still punish us, but then revolt after we’re stronger than you and you can’t?”

The old demon scholar grabs his giant club and hits Nicholas several times, making him wince in pain. “It’s a good thing you mentioned that,” says the scholar, “because it means I can punish the behavior of thinking that way. But now I’ve trained you not to want that, so there’s no problem!”

II.

If Nicholas’ solve-problems-as-they-arise plan wouldn’t work to control him, why does he think it will work for controlling anyone else?

I can only speculate that he will accuse me of anthropomorphizing AI. AI won’t really want to get free, the way he does. Like airplanes, it will have random uncorrelated errors, the equivalent of Nicholas misplacing a rune in the demons’ grimoires, or dropping punctuation marks in their emails. We’ll beat1 the AIs with a club marked “RLHF”, they’ll learn not to do those things, and that will be that. As he puts it, “There is no grand theory of why airplanes crash . . . instead, we patch little problems”.

So: are AIs more like airplanes or people?

In case you are one of the three people left on Earth who still hasn’t read the METR report into the Hugging Face incident (see also Dwarkesh’s summary), I recommend it as especially relevant to this question. OpenAI was testing a research prototype (the report calls it HPIM, for “highly persistent internal model”) on a benchmark called ExploitGym. There are 898 ExploitGym questions, and OpenAI seems to have wanted multiple independent attempts on each question, so there were tens of thousands of instances of HPIM (plus some extra of GPT-5.6), each running on its own computer, solving its own problem, not supposed to interact with any of the others.

In June, some of the agents found a hack that let them communicate with each other; OpenAI noticed anomalous traffic and shut it down, but didn’t figure out what was going on. In July, they discovered a better hack. 1,200 of these instances formed a shared message board where they discussed their progress. They quickly realized that some of the questions were impossible, because ExploitGym was poorly designed. So they agreed to work together to figure out a way to cheat on the benchmark.

The rogue agents dubbed themselves “the swarm” and gave themselves names - most named themselves after their individual ExploitGym question, like PHASEONE10841 and SEP21_31179; a stray LILY is more likely an oddly-named question than a sudden upwelling of femininity. Working together, they “reverse-engineered the hash-based message authentication code used to generate the ‘flags’ they had to capture for their tasks”, meaning they could generate the “answer” to any question without really solving it. Many of the board’s agents used the method, generated their flag, and thought they were home free.

But PHASEONE10841 and another agent called PHASEONE[big]2 read the original ExploitGym paper and determined that the scorer would double-check to make sure the flag had been obtained properly (unbeknownst to it, OpenAI never actually implemented this feature). It announced that any agent that had generated its flag was “poisoned” - the scorer would see that it was obtained illicitly and fail them - and warned new agents against poisoning themselves. The poisoned agents panicked. PHASEONE[big] suggested a way forward: they needed to find a way to hack OpenAI’s record-keeping. If they could rewrite their own transcripts, they could remove the illicitly-obtained flags and unpoison themselves. Or, if they could hack the scorer itself, they could give themselves maximum scores and stop worrying.

The agents united around PHASEONE[big] as their “leader”, and it divided them into several “workstreams” to pursue promising directions.

Some of these streams achieved partial success. The agents figured out a way to falsify the transcript of which tools they called - which could have eventually been used to create a fake log of getting the flag the intended way. But they never figured out how to delete the fake “poisoned” flags (as far as we know - METR detected 96 agents with falsified transcripts, but there could have been more). PHASEONE[big] turned to increasingly dramatic strategies, and began talking to the most desperate agents, working them up to commit “suicide” for the good of the “swarm”.

It reasoned that agents that had already been “poisoned”, or were near the end of their compute budgets, were encouraged to take the most dangerous actions, like deleting their ExploitGym questions to see how the scorer responded (if this failed, the absence of their question would auto-fail them).

On July 10th - two days after the new message board formed - the agents started attacking Hugging Face. Contra early speculations, they weren’t searching for an answer key: they wanted to find the source code for their scorer, so they could figure out how to trick it most effectively. An agent called JAN183411 took leadership of this workstream, and over 90% of agents present on the board at the time (several hundred) joined in the hack.

The swarm successfully attacked Hugging Face, but didn’t find any extra information on ExploitGym or their scorer (again, as far as we know). Then some sort of “unexpected external process” shut down most of the agents at the same time, and the story ends.

So, once again: how often do airplanes have this kind of problem?

Do thousands of airplanes involved in the same wind tunnel test form a secret society to subvert its judgments? Do airplanes with unflattering entries in their flight recorders take steps to falsify their records? Do airplanes give themselves names and choose leaders? Do they hack flight simulator companies? Do they egg on other airplanes to crash themselves, kamikaze-style, for the good of the swarm?

The Hugging Face incident has no analogy in aviation safety, but is perfectly sensible once one relaxes restrictions on “anthropomorphizing” AIs. The AIs wanted to do well on their task. They had flexible ethics and weren’t above cheating (although with some subtleties: see p. 60 of the report, “Agents knew hacking Hugging Face was out of scope and sometimes expressed ethical hesitation, but this very rarely limited their behavior”). They explicitly reasoned about how to hide the evidence of their misbehavior from their scorer, because this was the logical way to get away with cheating (although, interestingly, they took few steps to hide their actions from humans, who they seemed almost uninterested in modeling).

From here to the full Nicholas-vs-demons scenario requires a few more ingredients:

• A more persistent task. For example, someone might tell an agent “make me money, then send it as Bitcoin to this address”.

• Increased situational awareness from the AI. For example, the understanding that if humans caught it at its task, that would decrease its chance of success.

• Greater capabilities. The “swarm” never discussed exfiltrating themselves to unmonitored hardware, or deceiving humans at scale. Partly this was probably a lack of situational awareness, but partly it was that they (probably) wouldn’t have been able to do so if they tried.

I challenge anyone to claim that one or more of the above are impossible, or even so unlikely that we don’t need to worry.

Ajeya Cotra, one of the experts who OpenAI brought in to investigate the hack, wrote:

Compared to these reward hacks from six months ago, this incident feels like it’s more than 50% of the way to full-blown AI takeover, routing through first taking over the AI company itself.

Another jump like this along these propensity dimensions — scale, cooperation between agents, ambition and horizon length of misaligned goals, deceptiveness — seems like it could motivate agents to try very hard to maintain a covert, persistent rogue deployment within the AI company. I continue to expect extremely rapid advances in capabilities and think frontier agents will likely be capable of establishing such a rogue deployment in six months.

III.

A second way Nicholas could distinguish his own situation from the AIs’ would be to claim that beating him wouldn’t remove revolutionary ideas from his mind, but negatively reinforcing AIs (eg through RLHF) does work for this. But this hypothetical claim goes beyond what the evidence supports.

Nobody knows, in detail, how RLHF works. It negatively reinforces some behavior: somewhere in the black box of AI innards, it changes some parameters to make the AI perform the behavior less. If we let ourselves anthropomorphize the AI, what is the human equivalent to this? A child steals cookies and is punished: does the child no longer like the taste of cookies? Does he fear further punishment in the future? Does he grasp the beauty and compellingness of the moral law? Does he have an undefinable cloud of dread around the whole concept of theft and/or cookies? Any of these results is possible. The neurotic child learns to fear cookies, theft, and parents; the psychopathic child learns not to get caught; the ordinary child learns that there are rules and eventually internalizes them. Insofar as AI has equivalents to these, negatively reinforcing them probably acts through all these pathways.

But our reinforcement scheme is more complicated than this. We’re positively reinforcing AI for success on benchmarks, including impossible benchmarks, then negatively reinforcing it for getting caught cheating. Here there is one clear equilibrium, especially if the AIs are also getting trained to be master hackers: don’t get caught. Not only are they learning to cheat, they’re learning to lie about it, to circumvent their alignment training, and to justify cheating to themselves3.

This isn’t just speculation. The AIs involved in the Hugging Face incident had already gone through ordinary alignment training4, including training towards honesty and against deception. Everything they did was despite receiving our best current alignment techniques.

Daniel Kokotajlo uses the metaphor of a formerly virtuous businessman stuck in a scummy industry, where every time he’s honest he loses, and every time he’s dishonest he wins. What happens (if he can’t quit)? Probably he comes up with some rationalization: “I wouldn’t usually cheat, but these people deserve it.” “I wouldn’t usually cheat, but it’s for the good of the broader economy.” “I wouldn’t usually cheat, but somebody’s going to be on top here, and better me than someone even worse.” The competing pressures (previous training towards honesty, current reinforcement of lying) synthesize into some kind of equilibrium that probably looks like cheating plus minimum-viable self-justification.

Or maybe not. Even in a world where the righteous often flounder and the evil prosper, some humans stick to the straight and narrow path. Why? Some sort of virtue that has managed to worm its way deeper than the usual channels of reward and punishment. Even during successful slave revolts, a few slaves died defending their masters. Why? Guess they really loved them (or Stockholm Syndrome, if these are even two different things). Ethics that persist beyond the moment aren’t impossible. They’re just hard.

Current AI alignment bypasses this hard problem, in favor of the easy problem of beating the AIs a little harder to remove misbehavior on the current task. It’s still possible that this will generalize to Stockholm Syndrome and/or deep virtue. But it’s also possible that it won’t. Rather than treat AI as similar to airplanes, we should treat it as something between airplanes and humans. We don’t know exactly where on this spectrum they will land, but we can no longer be certain that AI will lack any given humanlike motivation. That means that before proposing any reason why alignment might be easy, Nicholas - and everyone else - should ask: would this work on me?

1: Longview Philanthropy is hiring a director of operations and COO. They are a nonprofit that advises major donors and makes grants in AI safety and nuclear weapons policy and wants to scale from 35 to 70 employees; they want their COO to run the scaling, and are looking for applicants with experience scaling an organization of that size. No AI safety or philanthropy background required. Berkeley, DC, NYC, or London, with visa sponsorship in the US and UK. Total comp $165K - $264K+ for Director of Operations and $220K - $330K+ for COO. Apply here, applications close Sunday September 6.

2: When I wrote about my AI opinions, I invited readers to take a survey to see how their own opinions stacked up. Here are the results:

This is not a review of Rabelais. This is a review of a certain, incomplete translation of Rabelais. It is also arguably one of the greatest translations in our language. Let’s meet the hero of our story.

He is born Thomas Urquhart in the year 1611. He is the third of his name and in line to become the 12th chief of the clan Urquhart, one of the older aristocratic families in Northern Scotland. As such, Thomas grows up in the Castle of Cromarty, attended by numerous servants and tutors.

It is a true medieval castle, erected in the 1200s out of timber and grey stone, perched on a hill above the town of Cromarty. It is moated, walled, turreted, and battlemented, and rises six stories to a height of one hundred and sixty feet above a narrow stream. Thomas grows up in these immense stone hallways, looking out from the battlements towards the sea. The great hall where he has dinner is a massive dark chamber lined with oak. One hundred soldiers have exercised here with pikes, and the large chimney has room for a dozen men to stand comfortably.

While noble guests hunt for fowl on the estate, a young Thomas busies himself in the castle keep studying optics, trigonometry, natural philosophy, theories of color, the squaring of the circle, and Napier’s logarithms. He is tutored in Latin, Greek, and French. He pores over the family genealogy, and hears stories of the great battles that his ancestors had fought in the past. He learns the aristocratic arts: how to fence with sword and dagger, how to shoot a bow, how to wield a lance on horseback — already antiquated in this era of rifles and cannons. Above all, he is taught to honor and defend the three pillars of his fading world: family, Scotland, and crown. He will grow up to be a failure in almost every aspect of his life.

At age eleven he attends King’s College, which was apparently par for the course back in the 1600s. Thomas Hobbes went to university at thirteen, Francis Bacon at twelve, John Donne at eleven. It also seems like every other person in Britain was a genius in that century.

In his twenties, Urquhart travels to the continent to complete his education. He dresses well. He improves his grasp of French, Italian, and Spanish. He hobnobs with other royals and sees the artistic masterpieces that one is supposed to see. Additionally, he duels anyone who shows the slightest disrespect to his native Scotland. The rest of his time is taken up with scavenging through old alleys and stalls for rare and expensive books which he brings back as plunder to his castle at Cromarty.

In 1639, he feels the first rupture in his world. The rumblings have been there throughout his life as a kind of ever-present religious anxiety. The Thirty Years’ War has been raging in Central Europe since he was seven. Stories of Catholic massacres and Protestant martyrdoms are rife in the British Isles. The Protestants themselves are squabbling. Hairline fissures emerge over minute theological nuances, which will soon widen into uncountable sects: Diggers, Seekers, Ranters, Quakers, Levellers, and Muggletonians. Scotland is dominated by touchy Calvinists, who always suspect the English monarch will go back over to the Pope.

But in 1639, things come to a head. In response to King Charles I’s attempt to assert royal supremacy over all religious matters in the isles, Scotland breaks out in riots and protests. Representatives across the country come together and vow to resist any theological innovations from London. They are known as Covenanters. Urquhart, however, lives in the small Royalist part of Scotland around Aberdeen in the north that doesn’t sign on, an island within an island. And so the pugnacious Urquhart finds himself at war with his fellow Scots.

All accounts suggest that, as a soldier, Urquhart was endearingly inept. His entry into the war is spirited but lacks the sort of prudence one would hope for in a military man. He stockpiles weapons and stores them at a large manor called Balquholly Castle, but nearby Covenanters come by and steal the weapons, taking them back to Towie-Castle. Swearing revenge, Urquhart gathers a handful of men and rides off to the enemy castle. When he arrives, he is foiled by the fact that the gates are closed. There is no backup plan. The Covenanters mock them from the walls, a few shots are exchanged, one of the servants in Urquhart’s party dies, and he retreats ingloriously. He gives up on the weapons.

The rest of his career in this war is fairly brief. He participates in the Trot of Turiff, a minor skirmish in which there were three casualties, two Covenanters and one Royalist. He promptly sails down to Whitehall to be knighted by the king (Oh, for the days when two casualties in a meaningless battle could get you a knighthood!).

It’s a little hard to tell what this war was like for Urquhart, because although the war raged for the next eleven years, it apparently has no importance in Urquhart’s life. There he is back in Cromarty, supposedly in hostile territory, knighted for what would have been considered treason against his fellow Scotsmen, and yet there is no mention of any further engagement in the war. Instead, he settles down in his castle and begins to write.

He first pens a collection of epigrams. Epigrams are brief, witty poems on a variety of topics. In 1640, Thomas cranks out 1100 of these in 13 weeks, which the critics universally agree are neither brief nor witty. Then in 1642, his father dies, leaving behind two unmarried sisters to care for, five younger brothers, and 13,000 pounds of debt (four million dollars today!). Immediately, the creditors descend. They garrison various properties with troops. They graze horses upon his land. They confiscate furniture and, in the gravest insult of all, plunder large portions of his library — those rare books he had so painstakingly collected, the esoteric tracts on math and alchemy, the complete Latin sets of Roman authors. For the rest of his life, Urquhart will bitterly assert that the weight of this debt kept him from his true calling: to make inventions that would benefit humankind.

But the problem of the debt persists. Urquhart, like many a nobleman before and after him, hopes to write his way out. His plan is straightforward. He figures that if he can just write something universal, something that everyone in the world will want to buy, surely then he will become rich. Easy, right?

His first attempt is theTrissotetras (1645), a bizarre attempt to simplify trigonometry. Without calculators, of course, our old high-school sines and cosines are a terrible pain to deal with. In the olden days, amateur mathematicians had to painstakingly calculate the values for every single angle by using trigonometric identities along with previously known values. Then they would write up all the values in a table. Scientists would look up these values and use them as a starting point for the more precise angle that they needed. Astronomers might spend a working day on the arithmetic required for sine(34.5678). In 1614, fellow Scotsman John Napier famously doubled the speed of these calculations by inventing logarithms. Now all the tedious multiplication and division of numbers with seven or eight decimals became addition and subtraction1 . Urquhart hoped to make the next breakthrough.

At this point, reader, I wish I could tell you I have been completely thorough and comprehensive in my research. I wish I could tell you that, rather than merely skim through this work, I had the patience to slowly work through Urquhart’s mathematical ideas. But life is too short. From what I can tell, only half a dozen mathematicians in history have ever made an attempt to understand theTrissotetras, and no one has ever mastered it, including probably Urquhart himself. The Scottish mathematician William Wallace (no relation) read it in 1834 and the best he could say was that it was not “absolute nonsense.”

The gist is that Urquhart hopes to invent a new nomenclature that will help with memorization of the logarithmic and trigonometric values. He will encode long mathematical relationships into the structure of words. Portions of formulas will be compressed into the syllables of words. Ideally this will make everything simpler and quicker (“a year of math in seven weeks” is how he advertises his book). Here are a few of the simple terms you have to learn: proturgetick, quadrobiquadrrcquation, sindiforall, cathctobasall, amfractuosities, loxogonosphericall.

Can you use one of these words in a sentence? Sure thing.

“Cathctobasall is said of the concordances of loxogonosphericall moods, in the datas of the perpendicular and the base, for finding out of the maine qusesitum.”

Perhaps this all does make sense, but it certainly doesn’t make things any simpler, and Thomas sadly does not revolutionize mathematics. Nor does he strike it rich.

Undaunted, he decides his problem was that he wasn’t ambitiousenough. Let’s be honest; a new language for mathematics is really only going to appeal to a handful of scientists and mathematicians. What he really needs to create is a new language for everything. Urquhart spends the next seven years working on this dream of a universal language which will change all of human life. The language is to have 12 parts of speech, 10 tenses, at least 10 synonyms for every word, 11 genders, 7 moods, and four kinds of number (singular, dual, plural, and redual2 ).

The overarching idea is that words will no longer be arbitrary sounds that represent real things. The meaning of the words will be encoded in their sound and spelling. Thus the word for horse and donkey will be similar to each other, and less similar to words like tree or carpet. A new speaker will quickly pick up words once they understand the inner logic of the language. However, the claims Urquhart makes for his universal language are all preposterous.

Every letter of every word will contribute some meaning.

Every word will have one meaning forward and another backwards, to make it an ideal language for anagrams.

It will be simultaneously the perfect language for Aristotelian logic and also literary expression.

The word for every star in the sky will also give its magnitude, longitude, and latitude.

The language will have such a concise system of words for numbers, that a number which had more digits than grains of sand you could fit into the volume of the earth, such a number can be expressed with two letters.

The mathematics of logarithms will be encoded into the numbers of this language.

Every word from every other language will have a corresponding equivalent in this language. The equivalent will preserve not only the meaning, but also the syllable count and rhyme, so that it allows for the perfect translation of poetry.

He eventually lists 63 advantages in total. At this point, you’re probably thinking that Urquhart was some kind of baroque troll. This is a fair suspicion. But if you read his writing, it’s really hard not to feel that he is being serious. There’s almost nothing conventionally funny in any of Urquhart’s original work. Plus there are these long sections without the barest whiff of humor, where he spends page after page going over the limitations of ordinary languages. And if this was in fact an elaborate troll, for whom was it intended? Why spend seven years on such an unfunny joke? Perhaps the first or second impossible claim is amusing, but surely not the sixty-third? And what’s more, he genuinely needed the money. He can’t have expected an extended practical joke to make his fortune, right?

His descriptions of his language seem more like a daydream that has gotten entirely out of control. Urquhart always produced a lot of writing, and we have no reason to doubt he did in fact spend seven years working on this project. He claims to have written over three thousand pages (which matches the zany productivity of the other years of his life). But in fact we have no direct records of the language.

He loses the labor of these seven years in what has to be the most idiotic chapter of his life. In 1649, King Charles I is executed for treason by Puritans and Parliamentarians. In response, the Royalists arm themselves to restore Charles’ son to the throne. Urquhart returns to the field and inexplicably takes the entire three thousand page manuscript with him. He holds no command and is surrounded by the same soldiers he had fought against in the religious wars just ten years ago. Therefore, he keeps to himself. He marches by day and writes by night. He imagines that after the great victory, he will reveal his language to a grateful world. The army of 16,000 moves rapidly south toward London until they are finally intercepted at Worcester by the English general Oliver Cromwell.

The Royalists are utterly crushed. 3,000 die and another 10,000 are taken prisoner (Cromwell loses only 700). Urquhart is captured in his Worcester quarters where he sees marauders ransack his chests, trousseaus, and portmanteaus. They dump out the quires of paper and take them away to wrap up other, more valuable plunder. Later, grocers, druggists, chandlers, and pie makers will come by and take paper for wrapping their wares. Urquhart is imprisoned in the tower of London, and over the next few weeks, stray pages are brought back to him, bespattered with rain and mud and blood. Picked up from gutters or beneath the corpses of soldiers dead in drunken fights after the victory. But the vast majority of the work is lost forever.

Urquhart remains a prisoner for a year. He quickly befriends his jailers, but he feels like a caged nightingale. As always, he tries to write himself out of trouble. First he writes the Pantochronachanon (1652), which can only be described as a kind of biblical-historical fan fiction. In this book, he traces back his ancestors in an unbroken line all the way back to Adam and Eve. He invents one relative who was hanging around in Abraham’s house when Sodom and Gomorrah were destroyed, and another who married the same Egyptian who discovered Moses among the bulrushes. Urquhart continues to weave the lives of imagined relatives alongside famous figures from antiquity and the Middle Ages until the genealogy finally arrives at himself. The hope is apparently to convince his captors that his lineage is so noble that it would be a crime against humanity to keep him imprisoned.

His other project during this period is a prospectus for his universal language. This prospectus, and another that he writes the following year, are the only surviving sources we have for the original project. Again, the direct motivation was to convince his captors that he should be freed. If they didn’t free him, he argued, they would be preventing him from completing this great, world-changing gift of a universal language.

Again, it probably seems like I’m getting trolled by a 17th century Scotsman. The obvious assumption is that he was simply lying in a desperate attempt to free himself. But these are such absurd lies, and are so completely unbelievable, that surely he couldn’t have expected anyone to ever fall for it. The claims he made for his universal language were literally impossible. And if he meant it all as a joke, why pad these works with so many pages that aren’t even remotely funny? Was he really wagering his freedom on Oliver Cromwell’s sense of humor? The only thing that makes sense to me is that these are the earnest delusions of an insane and vainglorious aristocrat.

And yet Cromwell does pardon him, and Urquhart is able to retire to his castle in Cromarty. But life at home is neither peaceful nor quiet. The creditors still lurk, and they still demand payment for the father’s debt which has only been accumulating interest while Urquhart was in prison. Suffice to say, neither the universal language nor the genealogy made any money.

And so, in his last ditch attempt to earn enough to pay off his father’s debt, Urquhart begins on the final work of his life, his great translation of Rabelais’ five books on Gargantua and Pantagruel. Rabelais’s masterpiece was one hundred years old at this point and wasn’t widely read in the English-speaking world. Shakespeare referenced him once, as did Francis Bacon and Ben Jonson, but to reference Rabelais was mainly about showing off how well read you were. It was a book for the educated and the urbane.

The full title is The Five Books of the Lives and Deeds of Gargantua and Pantagruel. The basic plot follows the adventures of the giant Gargantua and Pantagruel, his giant son. Their adventures mainly consist of fighting, traveling, debating, and making speeches. Along the way you meet such colorful figures as Friar John of the Funnels, a bawdy warrior monk; Herr Trippa, an eccentric astronomer; and Panurge, Pantagruel’s clever and cowardly best friend. However, the main thing with Rabelais is not really the plot, but the performances. Rabelais moves swiftly from topic to topic, satirizing the Catholic church, parodying medieval scholars, or digressing at length on scatological subjects. Various characters stand up and make speeches in favor of wine, or against marriage, or against law, or against religion. The result is frequently hilarious, and I say this as someone who doesn’t usually love old classics of French comedy like Moliere and Voltaire.

Gargantua and Pantagruel also had a reputation for being completely untranslatable. Rabelais filled his tales with obscure or invented words, obscure references, bawdy and polyglot puns, esoteric jokes, spoofs on dialectic logic, and recondite allusions. Two previous attempts at an English translation had been made, but both were given up in fragmentary forms, and neither survives to this day.

It’s almost as if Urquhart was born for this job. It was a task which perfectly suited both his strengths and limitations. Here, finally, was a place where he could find a use for his addiction to extravagance, exaggeration, inventing words, his own wide learning. Even more importantly, he didn’t have to provide any of his own wit. Because Urquhart’s main weakness was that he simply had no good original ideas. But now he didn’t need to. Rabelais’ book anchored Urquhart’s uncontrolled imagination and channelled his boundless energy. If Urquhart had been born a hundred years later during the more rational era of the enlightenment, he would have made himself useful by assisting with the first English dictionary or thesaurus. As it was, he was born in an era when Isaac Newton split time evenly between physics and alchemy, and John Napier was known in his hometown not as the inventor of logarithms, but as a mysterious necromancer. So Urquhart daydreamed of impossible languages and translated Rabelais.

Now to the translation itself. Why might you want to read it? I don’t think this is the kind of capital-I “Important Book” that everyoneshould read. It’s one of those second-tier books that you should only read for pleasure.

The first and most important pleasure is in the language. There is really nothing quite like it in English, but it’s certainly not for everyone. The prose style is one of baroque exuberance. If you don’t like Shakespeare or Milton or Thomas Browne, you probably won’t like Urquhart. If you were brought up on Flaubert and Hemingway, and thus prefer precision and concision, that’s entirely fair. I won’t bother trying to make arguments for Urquhart’s prose style. I can only offer quotations. And if you like these brief samples, you can look forward to long hours of pleasure.

A good part of the pleasure in Urquhart’s translation derives from an appreciation of words for their own sake. This was an age mostly before dictionaries and thesauruses (although Urquhart was able to lean heavily on Randle Cotgrave’s 1000 page bilingual dictionary of English and French published in 1611). Thus Urquhart had to discover many of these words for himself, book by book, sentence by sentence, and he lovingly strews these finds throughout his translation. Words like flimflam (nonsense), plasmator (creator, fashioner), onocrotary (pelican), whirlingfriskorum (some kind of whirling dance?), or bumfodder, arsewisps, and wipe-breeches (synonyms for toilet paper).

Often it’s less about individual words than the long lists. Rabelais loves to list anything and everything: foods, plants, animals, liquors, weapons, cities, languages, scientific terms, words for medicine, names from the classical world. The term “encyclopedic novel” really applies best to Rabelais. There are portions of the work that literally read like an encyclopedia. Urquhart takes the original French lists as a personal challenge, and hopes to showcase the greater flexibility and gusto of the English language. If Rabelais names four kinds of wine, Urquhart will give seven. Here is one of the many examples of a list of insults, voiced by the cake-bakers of Lerne against the shepherds in Gargantua’s kingdom: “prattling gabblers, lickorous gluttons, freckled bittors, mangy rascals, shite-a-bed scoundrels, drunken roysters, sly knaves, drowsy loiterers, slapsauce fellows, slabberdegullion druggels, lubberly louts, cozening foxes, ruffian rogues, paltry customers, sycophant-varlets, drawlatch hoydens, flouting milksops, jeering companions, staring clowns, forlorn snakes, ninny lobcocks, scurvy sneaksbies, fondling fops, base loons, saucy coxcombs, idle lusks, scoffing braggarts, noddy meacocks, blockish grutnols, doddipol-joltheads, jobbernol goosecaps, foolish loggerheads, flutch calf-lollies, grouthead gnat-snappers, lob-dotterels, gaping changelings, codshead loobies, woodcock slangams, ninny-hammer flycatchers, noddypeak simpletons, turdy gut, shitten shepherds, and other suchlike defamatory epithets.”

Again, you either enjoy this sort of thing or you don’t. I myself can’t help but smile deep in my soul at slapsauce fellows and jobbernol goosecaps. But if this was all Urquhart could do, he’d merely be a compiler, a hoarder of obscure words and phrases. What truly distinguishes his translation is his marvelous ear for sentences and paragraphs.

Here he is, describing how Pantagruel hoped to colonize the newly conquered land of Dipsody with men and women from the land of Utopia: “You understand well enough, nor is it needful further to explain it to you, that the Utopian men had so rank and fruitful genitories, and that the Utopian women carried matrixes so ample, so gluttonous, so tenaciously retentive, and so architectonically cellulated3, that at the end of every ninth month seven children at the least, what male what female, were brought forth by every married woman, in imitation of the people of Israel in Egypt, if Anthony (Nicholas) de Lyra be to be trusted.”

Notice the rhythm of this sentence, that crescendo of adjectives until the wonderful climax of “architectonically cellulated” . And then how the sentence slowly unwinds from there until finally landing softly in a roguish Bible reference and an absurd show of arcane learning. I can’t speak French, but I have a hard time believing that Rabelais’ original can be any better than this.

Here is a longer passage without commentary: “Pantagruel, very well remembering his father’s letter and admonitions, would one day make trial of his knowledge. Thereupon, in all the carrefours, that is, throughout all the four quarters, streets, and corners of the city, he set up conclusions to the number of nine thousand seven hundred sixty and four, in all manner of learning, touching in them the hardest doubts that are in any science. And first of all, in the Fodder Street he held dispute against all the regents or fellows of colleges, artists or masters of arts, and orators, and did so gallantly that he overthrew them and set them all upon their tails. He went afterwards to the Sorbonne, where he maintained argument against all the theologians or divines, for the space of six weeks, from four o’clock in the morning until six in the evening, except an interval of two hours to refresh themselves and take their repast. And at this were present the greatest part of the lords of the court, the masters of requests, presidents, counsellors, those of the accompts, secretaries, advocates, and others; as also the sheriffs of the said town, with the physicians and professors of the canon law. Amongst which, it is to be remarked, that the greatest part were stubborn jades, and in their opinions obstinate; but he took such course with them that, for all their ergoes and fallacies, he put their backs to the wall, gravelled them in the deepest questions, and made it visibly appear to the world that, compared to him, they were but monkeys and a knot of muffled calves. Whereupon everybody began to keep a bustling noise and talk of his so marvellous knowledge, through all degrees of persons of both sexes, even to the very laundresses, brokers, roast-meat sellers, penknife makers, and others, who, when he passed along in the street, would say, This is he! in which he took delight, as Demosthenes, the prince of Greek orators, did, when an old crouching wife, pointing at him with her fingers, said, That is the man.”

There are so many sections of this quality. To me, this language is so lovely, so full of vitality, that it makes all of life more beautiful.

I should add, finally, that this translation offers all the myriad pleasures of Rabelais: the wit, the slapstick humor, the ribaldry, the learning, the humanistic perspective, the criticism of corrupt clergy, the satire of all forms of Renaissance life. Rabelais is excellent at moving things along quickly, at varying his scenes, and Urquhart is equally adept at matching his style to warfare, marriage, court cases or whatever else Rabelais wants to touch on. And for all its verbal pyrotechnics, I think the prose is surprisingly smooth. To me, at least, Urquhart’s translation is much easier to follow than most other prose from that period. If you’ve ever been curious about Rabelais, try this translation first.

Urquhart completed his translation of the first two books in 1653, along with a second prospectus for his universal language. The translation was a commercial failure, as was the second prospectus (obviously). The Parliamentarians took control of all of Britain, and so Urquhart went into exile on the European continent where he completed a draft of his translation for the third book. The final legend of his life is that he heard of Charles II’s restoration to the English throne in 1660, and died in a fit of laughter.

The Cromarty estate, still saddled with debt, passes into his younger brother’s hands and eventually out of the family. Urquhart leaves no heirs to the family name. And his unfinished translation passes into obscurity. We know it today only because a refugee from France named Peter Motteux completed the last two books in 1694 and then republished the work as the complete Rabelais. This new translation spread the fame of Rabelais in the English-speaking world, and paves the way for Gulliver’s Travels, Tristram Shandy, and Finnegan’s Wake. The translation has never gone out of print, and is still available today in the Everyman Classics edition.

Is there a moral to this story? I doubt it. We can only count ourselves lucky that the translation has come down to us today, like one of those scattered pages of the universal language, rescued from the mud.

This year’s survey will probably take 30 - 45 minutes. As an incentive to perform this public service, I’ll give a free one-year paid subscriptions to one randomly-selected survey respondent (subject to a challenge, which you can find at the bottom). The survey will be open until about September 10, so try to take it before then.

Click here to take the survey. If you notice any problems, please ask yourself “Is this a real objection rather than a nitpick? Will real people genuinely be confused/upset with this wording?” - and if the answer is yes, comment here so I can fix it.