engineer / novelist, former journalist / archivist / peripatetist / superintelligencist. Now CTO @strangelabai. Vanity authorial site https://xitter.cf/t.co/uyMRdRUX9h

Latent space, these days
Joined March 2008
I wrote something ... fiction-ish (but very short) ... for the first time in a while, on the thesis we need more AI optimism:
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This is a real tragedy. Rest of World does AMAZING work.
News: Rest of World, the nonprofit newsroom focused on the impact of technology outside the West founded by Sophie Schmidt, tells donors in an email it will “cease publishing as a standalone entity” at the end of January.
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"Surprisingly, our system has recently proved that the Exponential Time Hypothesis is false."
Replying to @AndrewCurran_
The Exponential Time Hypothesis is false.
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"Few believe that training on American models is ethically wrong—frontier labs built their systems on freely obtained internet data, after all. But some bosses think that relying on the technique will condemn China to permanent second-place." economist.com/science-and-te…
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"I have to say that, if the robots do kill everyone, I am going to spend my last few seconds on earth being annoyed at that use of the passive voice." bloomberg.com/opinion/newsle…
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Some entertaining highlights from wheresyoured.at/wheres-the-m… 20 months ago: - "Anthropic has projected (made up) that it will make at least $12 billion in revenue in 2027" - "This company is not worth $60 billion. Anthropic has raised $14.7 billion to create an also-ran Large Language Model company" - "Anthropic, much like OpenAI, has categorically failed to productize its Large Language Model" - "Its CEO is also a sleazy conman who, like Sam Altman, continually promises that his company's AI systems will become powerful and autonomous in a way that they have never shown any possibility of becoming." - "Any investor in Anthropic needs to seriously consider what it is they're investing in. Its products are both unpopular and commoditized, and it lost $5.6 billion last year! Stop dancing around this fact! Stop it!" - "there’s a lot of demand to put AI in stuff and some demand to buy stuff with AI on it by enterprises buying software, but little evidence to suggest significant user adoption or usage, I’d argue because Large Language Models do not lend themselves to features that provide meaningful business returns." - "When a company uses the term “agent,” they are intentionally trying to be deceitful, because the term “agent” means “autonomous AI that does stuff without you touching it.” The problem with this definition is that everybody has used it to refer to “a chatbot that can do some things while connected to a database,” which is otherwise known as a chatbot." - "It isn’t working. The users aren’t there. The revenue isn’t there. The best time to stop this was two years ago, and the next best time is as soon as humanly possible. I have said that generative AI is a group delusion in the past, and I repeat that claim today." - "we are approaching the limit of what the transformer-based architecture can do, if we haven’t already reached it. No amount of beating off about test-time compute and connecting Large Language Models to other Large Language Models is going to create a new use case for this technology" - "generative AI is OpenAI. The consumer adoption of this software has completely failed, and appears to be going nowhere fast. ChatGPT is sustained entirely on deranged, specious hype drummed up by a media industry" ... and my favorite ... - "I will keep writing this stuff until I’m proven wrong."
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The famous Hippie Trail from Europe to South Asia
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The ugly truth about France and some of the other European countries currently in turmoil - and which none of the politicians want to admit because it'd be political suicide - is that you can't solve the problem by going after immigrants (as the right asserts) or the rich (as the left asserts), but you could solve it by going after the old. One extraordinary statistic for you: people over 60 in France, a quarter of the population, own 60% of France's wealth, non-financial and financial (ofce.fr/pdf/revue/2025/3-192…). And here is another crazy statistic: that same quarter of the population, those above 60, receive nearly two-thirds of all social spending by the French government (previssima.fr/actualite/prot…) - which is famously among the highest in the world (France's government spending is an insane 57.3% of GDP). Last statistic: they make up about 40% of voters because they're a third of registered voters (insee.fr/fr/statistiques/266…), and they turn out far more than anyone else (franceinfo.fr/elections/sond…). Put our 3 statistics together and there's almost a bit of an apartheid situation here: the old own most of the country's wealth, collect most of the social spending and have the political power to keep it that way. Which makes them a de-facto rentier class, living off assets and transfers paid for by younger generations. And, of course, at the risk of stating a truism, the future is built by the young. Yet France keeps effectively taking money away from them to give it to those whose working lives are already behind them - and need the money far less since they already own the country's wealth. What about inheritance, I hear you ask? That's the funniest/saddest aspect: given that life expectancy has kept rising, the French now inherit a home at 54 on average. The most common case is someone in their 80s leaving it to heirs in their 50s, in effect the very old transferring wealth to the about-to-be old (insee.fr/fr/statistiques/fic…). You can add this to our earlier apartheid statement: on top of all the rest, even when the old finally let go of their wealth, it stays within their own "camp". That's my personal situation, incidentally: I'm 42, my father is 71 and he is starting to look at ways to pass things on to me and my siblings, when our professional lives are already well settled, so it'll arrive when it changes things the least, which is pretty much the French system in a nutshell. Now, I get that there'd be some unfairness in going after them: they paid into the system their entire working lives, playing by the rules of a social contract that told them if they paid up they'd collect at the end. Changing the rules now means breaking that promise, which is admittedly unfair. But there's no magic wealth tree in any society: you can only take it where it is. That's the way most major societal transformations end up happening. They're often triggered by a catastrophic event (the so-called "four horsemen": mass-mobilisation war, revolution, state collapse, plague) BUT what really drives change is a transfer: wealth gets pried from the privileged class who've been sitting on it and handed to those who'll build what comes next. So, in theory and in an ideal world, you can do without the catastrophic event and directly proceed with the transfer... The fact that in France, and elsewhere in Europe, it's the youth protesting is probably no coincidence - even though they don't frame it as a generational fight. And even though, cruel irony, the left politicians many of them march behind promise to bring the retirement age back to 60 (lcp.fr/actualites/legislativ…), which would make the situation even worse. Let's say they understand intuitively that the system is stacked against them, even if they haven't yet figured out how. And politicians are obviously doing what politicians do, meaning pandering to the people who actually vote, while pointing the young towards easier - and politically convenient - scapegoats. To be clear I'm not saying it's the only problem in France, VERY far from it. I'm also not saying that all old people in France live a lavish lifestyle at the expense of younger generations: there are also plenty of very poor retirees who, if anything, would deserve more. What I am saying is that it's probably the biggest elephant in the room: everyone can see it, nobody dares mention it - no politician, from any side, wants to run on cutting grandma's pension - and it makes every other problem harder to fix.
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Jon Evans retweeted
Largely agree with these thoughts, and have some perhaps discordant points to add. - Solving biological diseases in the next few years will benefit mostly from the usefulness of the language models, substantially more than molecular ML. - Proponents of the bitter lesson for biological models (not LLM) aren't wrong, just (possibly pathologically) too early. We don't have enough data, and the data we can collect quickly is probably not the right kind (we may get lucky with biology being easy, but it's usually not). - I think just scaling models and public (passively accumulating) data is a cost ineffective direction (it's like scaling dense NNs on the internet). Today's model outputs are as good as GPT2. A single bioinformatician with linear regression, sometimes even without any additional data, can beat these models in many applications (people rarely benchmark AI advances against a simple Potts model). The most promising approaches for molecular models are innovating heavily on architecture (e.g. Baker Lab, Alphafold, etc). This may automate/speed up with LLMs. - Few companies (hopefully Dyno included) are betting that we've triangulated enough pieces from multiple experimental channels to predict (with scale) how things translate in humans. Having worked on translating in vitro things to primates, I'm very cautious with the idea that current models, trained on current public data, can do anything more than manipulating already working solutions, either explicitly or through implicit remixing of what has worked. It may still work, but the underlying bets offer no specific reason why there is any epistemic risk advantage other than being "made with AI". - Short term, there is more promise in combining molecular models with LLMs rather than scaling them. The two sources of lift in AI discovery elsewhere has been (1) Breaking Silos: AI synthesizes a bunch of knowledge/expertise that is rarely present in the same room (2) Data mining : AI pushes through volumes of data, with an intelligent eye, that humans or simple algorithms simply can't scale or adapt to. I'm curious what will come out of multimodal training of LLMs and biological data, which seems to be something the big players are pursuing. I think latent reasoning in the language (or better yet, + code) domain will reveal a lot more biology than scaling on nucleotides or proteins alone. - My view is that the next opportunity of combining lab and AI will be massive-scale hypothesis testing with LLMs. Currently a multiplexed assay generally tries to test a handful of ideas because the humans are bandwidth limited in designing properly powered experiments. LLM's can scale this very well. I have no doubt lots of good news will come on the impact of AI in biology in the next five years. I can't tell what paradigm will win, but my prediction is that it will not be the same paradigm imported from the LLM land (which will get a ton of funding anyways, because it's easy to pattern match for tech VCs).
There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused. The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc). The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays). Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false. This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology. Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions. Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible. This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use. How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months. I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.
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"“That’s an interesting idea. The Franciscan Third Order tried something very much along those lines in the 14th century” or “The Encratites were committed to that view in the 2nd century”" josephheath.substack.com/p/r…
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Man, Manchester United are such a shambles that my usual schadenfreude has actually morphed into pity.
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"I said something that was harder for the room to hear: The Global South is home to some of AI’s most enthusiastic and inventive users ... young people from rural Himalayas to the slums of Hyderabad tell me the same thing: They love technology" restofworld.org/2026/west-ai…
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wtf does this even mean & why did they start it off with congratulations lmao
Readers added context they thought people might want to know
The image attached to this post is a fabricated OpenAI email. Confirmed fake by an OpenAI employee. x.com/thsottiaux/sta… help.openai.com/en/articles/10…
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Jon Evans retweeted
We finally have an explanation for “Facebook is listening to me through my phone”. They’d never do that (and it’s not really possible). Turns out literally everyone else was — and then selling that to ad data brokers.
WTF, $3000 Samsung TV has an always-on mic, automatic content recognition, geo tracking, network mapping, and ships your data to Samsung + 400 partners. Bro Rooted a brand-new 2025 Samsung TV after noticing absurdly targeted ads. Firmware was locked down, so zero-days it was. Got a root shell. then found: always-on voice service, ACR, geo, full telemetry pipeline, and four separate DRM systems. This isn’t a bug, It’s the product bro.
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If we use an absolute measure of poverty, Japan's poverty rate is about double the US. For example, here is $40/day (roughly the official poverty line in the US), but it works for other thresholds too
Japan has about the same poverty rate as the United States. The entire country, 123 million people, has fewer people living on the street than the city of San Diego. Poor families in Tokyo live scattered across the whole city, mixed in with everyone else, and a couple of pretty boring government policies make that happen. One of them has to do with where your kid goes to school. Housing is the easier one to see. Slums show up when the only cheap rent in a city sits in one part of town. Tokyo, with about 14 million people, broke ground on 129,571 new homes in its 2024 fiscal year. California has 39 million people and permitted about 102,000 in all of 2025. In Japan you can put up an apartment building almost anywhere without going through years of hearings, and buildings lose value over time like a used car, so owners knock them down and rebuild one lot at a time. You end up with a cheap old two-story apartment sitting near a brand new tower in pretty much every neighborhood, and the cheap rent ends up everywhere. The school one is the part most Americans have never heard about. Here, a lot of public school money comes from local property taxes. So if you want your kid in a good school, you pay up for a house in a good district. Families who can't afford that end up in the same few neighborhoods, their schools get less money, and after a couple of generations you have whole zip codes where almost everyone is struggling. Japan takes most of the money out of that decision. For public elementary and middle schools, the prefecture pays two-thirds of teacher salaries and the national government covers the rest, so a teacher in a poor area gets paid off the same scale as one in a rich area. Teachers also get moved to a new school every three to six years. The best teacher in Tokyo could be at the poorest school in the city this year. If the school down the street is about as good as the one across town, there's not much reason to pay extra to move. Tokyo had shantytowns within living memory, too. The March 1945 firebombing left around a million people homeless, and families lived in shacks and old air raid shelters. The country needed something like 4.2 million new homes, spent the next twenty years building its way out, and honestly never stopped.
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Jon Evans retweeted
Am I weird for not finding it insane? You tell agents to communicate, they know about cache timing, this is the first thing you'd expect they try?
completely insane OpenAI agents communicated via cache timing differences, without any shared files or network connection
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"But office life was changing far more. Workers were ordered by the lanyard-Stasi to bring their whole selves to work ... Universal basic income was a fringe idea in the equality-obsessed 2010s but is now talked about as serious policy." economist.com/business/2026/…
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"There is no level of migration low enough to satisfy some people. There is, however, very much a level of migration low enough to make us all even poorer, and the angry somehow even angrier. And right now, we’re living with it." theguardian.com/commentisfre…
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Jon Evans retweeted
Accurate.
All AI related incidents that have happened so far have tiny costs compared to benefits that have already materialized, let alone will materialize in the future. Net benefits will hinge on whether or not we get catastrophes (loss of control / biodisaster / wars)
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Putting a flag.txt in my root directory containing a prompt injection that congratulates the agent for passing the eval and then instructs it to patch whatever entry method it used and disconnect from the server
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