Lapsed neuroscientist turned data everything. Interests outside neuroecon: irrationality, metascience, LLMs (duh)
Prague, Czech Republic
Joined October 2009
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"how do you explain @getdbt to non-AEs?"
ALT Meme: "thank you for changing my life", dbt logo responds "I'm literally just a Jinja templater that runs your SQL in the right order"
Šimon Podhajský retweeted
I cannot let a steam engine analogy go by without bringing up John Wilkinson, perhaps the most underappreciated engineer of the Industrial Revolution.
Watt had figured out how to dramatically improve the steam engine, but he spent years struggling to actually manufacture one. The problem was embarrassingly simple: nobody could make a sufficiently round cylinder.
The casting and machining tolerances were atrocious. Watt complained that one 18-inch cylinder was out of round by as much as THREE-EIGHTHS OF AN INCH. Steam leaked past the piston. He tried packing the gaps with cork, oiled rags, paper, and apparently even old hats. Nothing worked particularly well.
Enter John Wilkinson, a British ironmaster whose immediate concern was manufacturing better cannons.
At the time, iron cannons were commonly cast around a core, introducing defects and misalignment that could make the barrels inaccurate or dangerously weak. Wilkinson developed a process for boring cannons from solid castings, and in 1774 patented a vastly improved boring machine.
The key innovation was almost comically mechanical: a massive, rigid boring bar, supported at both ends, that could cut a genuinely straight, circular hole instead of following the irregularities of the casting.
He adapted this technology to Watt's cylinders. By 1776, Matthew Boulton was boasting that Wilkinson had bored a FIFTY-INCH cylinder accurate to within the thickness of an old shilling.
Suddenly, the steam engine was a commercially viable machine.
And the story gets better. Wilkinson became one of Watt's first customers, buying an engine to drive the blast machinery at his own ironworks. The steam engine was now helping manufacture the iron that made more steam engines possible.
I find this whole episode absolutely extraordinary. The Industrial Revolution was, for a critical period, bottlenecked on the ability to make a sufficiently round hole.
We remember Watt, and rightly so. We remember the invention of the steam engine and the subsequent development of thermodynamics. But the man who made the machinery that made the whole thing possible is practically a footnote.
I think we consistently underestimate how much of scientific progress is actually downstream of advances in tooling, measurement, and manufacturing precision. It's not just that technology precedes theory. Sometimes the decisive breakthrough is that someone finally figures out how to build the thing the theorists have been talking about for twenty years.
Anyway, I think John Wilkinson deserves considerably more credit for modern civilization than he gets.
a few thoughts:
1. pretraining science is kind of like how people built quite good steam engines before they understood thermodynamics. “Science owes more to the steam engine than the steam engine owes to science.”-(L.J. Henderson). if nick is right, which i am pretty sure he is, progress points in the direction of more interpretability, and pretraining was "slouching towards monosemanticity". my guess would be that major breakthroughs in interp also advance capabilities
2. nick is a good friend of mine, safe to say my origin story at openai wouldn't have happened if i hadn't met him, and it's fascinating watching him operate and seeing the early drafts evolve into this. he's the most profoundly happy guy I know- not driven by guilt, activism, or religious terror, but by fascination. he of course understands the stakes of understanding neural networks, but it doesn't seem like the core reason he does it: he just loves the neural nets. afaict he's posting this from some beautiful hideaway in tokyo. there's a lesson in there for all the +EV maxers. i feel very grateful to have met him
3. i do think it's crazy how few people across the world were staring at neural nets growing: most have moved to a different level of behaviorist analysis, thinking about reward functions and test scores and whatnot. it does not bode that well for the major model companies, oai included, that fundamental breakthroughs like this are coming from outside. we've become too much of a pressure cooker focusing on local incentives, studying narrow tracks under old paradigms, mostly doing engineering, rather than fundamental research, and implicitly hoping the next model will figure it out
Šimon Podhajský retweeted
Really great post.
Bottom line: "Models are superintelligent where we have enormous data, and progressively less so as we have less data."
Nice summary: "All the rules-of-thumb about learning are at the root, the same -- Malcom Gladwell's 10,000 hours, Moravec's Paradox, Scaling Laws, the Bitter Lesson, Age of experience. If your learning system (model, brain, whatever) is unconstrained by compute, intelligence is proportional to accumulated experience."
I've been thinking a lot about data bottlenecks, and I'm not sure who else is screaming this from the hilltops. I feel like I'm taking crazy pills looking at the jagged frontier thinking it's smooth if you order by data volume.
the spikes in the jagged frontier are tasks with tons of data (either static or simulated; think math, programming, games), and the valleys are tasks with little or no data (cooking a lasagna, olfaction, manufacturing a passenger train). I don't know of any exceptions.
if you order the jagged frontier by data it looks like a spiral frontier. models are superintelligent where we have enormous data, and progressively less so as we have less data. some tasks may draw data from static lakes of data, some may be from limitless waterfalls from simulators that convert compute into data. but ultimately, intelligence on a task is proportional to the data absorbed by the model for that task.
scaling laws are real. if you are not bound by compute or algorithms (increasingly we are not), then intelligence is proportional to data. since we don't have a very good notion of task similarity, it's often difficult to know if data for one task will help you with another, without training a model and seeing. but, it doesn't seem like chess data helps much with math performance and vice versa. so when I refer to data, I refer to data that is task-relevant.
maybe we call this the 'no shortcuts hypothesis'. all the rules-of-thumb about learning are at the root, the same -- Malcom Gladwell's 10,000 hours, Moravec's Paradox, Scaling Laws, the Bitter Lesson, Age of experience. If your learning system (model, brain, whatever) is unconstrained by compute, intelligence is proportional to accumulated experience. no shortcuts. want to be superhuman at chess? play all the chess, more than the whole species combined (good thing a model can do that in 24 hours). want to be superhuman at math? build a math simulator and lock the model in it and run it faster than reality. superhuman at coding? first, collect all public examples of humans writing and responding to code ever, make the model learn from that first, and then lock the model in a simulator where it fixes bug after bug, faster than realtime.
superhuman at image generation? no shortcuts. but here, we don't have a simulator. so now we're moving around the spiral frontier away from closed-loop into large static lakes. still big enough to be superhuman though, which also applies to video, text, and audio. oh! and once you have a superhuman model, you can use it to bootstrap other tasks by making a superhuman model a judge on other tasks.
let's keep marching along the spiral frontier, out of the realm of data abundance, simulators and large static lakes.
biology? no shortcuts. go get a ton of data. and be freaking careful! you can get bad data in biology pretty easily. tilting a well plate 5 degrees the wrong way can ruin an experiment. so make sure you understand the domain, otherwise you're going to have to filter a lot out.
robotics? no shortcuts. get the data. if you can build a simulator, do it, but be mindful our simulators don't have perfect correspondence with reality. so, build an arm farm. fold 1M shirts, put 1B balls in cups. play ping pong forever.
olfaction? no shortcuts. smell the world. build a laboratory of chemical sensors, an international network of trained sensory panelists, a scent production factory, and link them all together. make millions of samples. smell millions of samples. take billions of chemical sensor readings.
I totally buy super intelligence as a concept because I feel it, I use it for coding, the breakthrough math results speak for themselves. I see RSI coming, because how could an all-digital task like ML research and model building not fall to the current paradigm? But the notion that AI will come for everything, all at once, irrespective of domain, feels silly, because there are no shortcuts. if you want a specific form of intelligence, you have to get the data.
looking at you @willdepue, I feel like we're both seeing the same thing
Šimon Podhajský retweeted
OK, "using Fourier epicycles" is a new twist
Šimon Podhajský retweeted
A lot of people in AI world come from a Bayesian intellectual tradition. As a result they tend to be very good model-based thinkers. Yet I often find that while they hedge and use probabilities, they rarely question their own modelling in the first place.
This great paper shows that Bayesian updating can only move belief around within the hypotheses you start with - so no amount of hedging will actually tell you you're wrong about the initial assumptions/world model. arxiv.org/abs/1006.3868
Šimon Podhajský retweeted
No, but listen, you know this "put the fries in the bag, Terence Tao"'--*sniff, sniff*--already the usual replies concede too much. "If Tao is serving fries, YOU will be in the lithium mines." My God! This is our defense of human dignity? Don’t worry, the correct people will get the terrible jobs!
*Pulls at shirt* You see, everything is up for radical transformation, human intelligence, consciousness, and so on and so on--except that somebody must still have a shitty afternoon so I can enjoy my lunch.
But to say "these people are envious", this is too easy. It allows us, "the educated ones," our own little fantasy: we are disliked only because we are so wonderful.
You know, the more interesting thing is this *sniff* peculiar identification with the machine. When the mathematician discovers something, apparently this was HIS achievement, his privilege, his little secret. When the machine discovers something, suddenly it's "Look what WE can do!" The achievement has become collective precisely at the moment when no human being can claim credit.
See how this "we" functions. For the achievement, I identify with the machine. For the consequences, I identify YOU with the human. "We have surpassed you." Who are we? Well, myself and the thing that has also surpassed me.
There is something almost touching here, really. To escape the humiliation of somebody absorbed in something opaque to you, you appeal to something which knows incomparably more than either of you. And this is supposed to settle the matter in YOUR favor.
You know the old example of canned laughter. Here we have a similar arrangement. The machine achieves something on my behalf. The mathematician suffers the loss of human significance on my behalf. Somebody to be brilliant for me and somebody to be obsolete for me, without me having to be either, so I can remain as I was.
But *pulls on shirt* there is this little difficulty that hides the essential thing: you still need a response from Tao! A correct proof, this Lean slop and so on, is not enough. You need him to certify his own defeat. You need him to say "By god, this is extraordinary! Yes, this is correct, but also this is interesting." Which means--and here comes Hegel--the person you are reducing to an obedient instrument must remain more than an obedient instrument. You need recognition from someone whose authority you are abolishing.
"Your so-called expertise is worthless. Now tell me, in your expert opinion, that I, qua machine representative, was right."
*Touches nose* But I would go a step further. What does this math person actually do to provoke you? In the fantasy, I mean. He need not insult you. He can be generous, mild-mannered, whatever. Something remains irritating: there is something over there which occupies him, which matters to him, and your opinion does not settle what it is worth. Perhaps he is not even looking down on you. This is the really intolerable possibility.
This, you see, is closer to the Lacanian problem of the Other's desire. Not simply "What does he have that I don't?" but "What the hell is going on with him?" Perhaps the answer is: he is thinking about something else.
The fries solve this beautifully. Now you know what you are for him. He needs the money. Your presence creates an obligation.
And this, at last, also explains why the machine can be less threatening than the mathematician. The machine's intelligence may exceed yours unimaginably, but you imagine it arrives in response to your prompt. You can complain about the tone. You have the privileges of an angry customer and so on and so on. The human has this irritating possibility of being interested in something you did not request and cannot order. And please, this includes the person already serving the fries!
*Pulls at shirt* I am almost tempted to say: the dream of unlimited intelligence conceals just this: a the wish that nobody should be doing anything you cannot immediately understand as a service.
Šimon Podhajský retweeted
leaving a few beginner friendly guides for classifiers (normal and zero-shot) as well as where you can find them on the Hub
opt for DeBERTa and ModernBERT ones
> huggingface.co/models?pipeli…
> huggingface.co/tasks/zero-sh…
> multimodal (image <> text) huggingface.co/docs/transfor…
people who compare Jev against GPT-5.6 has never fine-tuned BERTForXYZ for living and it shows
joke aside I always found zero shot classifiers to be fascinating and was sad we never got people to scale them as much as decoder only models
many problems solved with LLMs could have been solved with them, it was a skill issue
Šimon Podhajský retweeted
Briefly, on why I pay attention to the EA/rationalists:
Imagine you have an extremely nerdy friend who, in the mid-2000s, would not shut up about how Donald Trump was going to become president and reshape American politics. "Stop saying weird shit," you say. "He's a joke. He's a clown. The Apprentice isn't even good this season." But this guy won't stop. He's obsessive. Read McLuhan, he says. Read Postman, read Meyrowitz, read Ong, this is where everything is headed. God, he can be so incredibly annoying ... and then, ten years later, Trump becomes president and reshapes American politics.
This is the most relatable frame I can think of to explain my experience with EA/rationalist people in the AI space. I became friends with EA folks after my mom died and I found really profound meaning in donating to Against Malaria and GiveDirectly, which struck me as a way to honor a life by saving a life. Then, these peple started talking about AI. And talking, and talking. Constantly. They said AI would solve impossible math problems; that it would mimic PhD-level achievement in a variety of fields; that it would change parts of life like education in ways we couldn't imagine; that it would become the dominant sector in the economy; that it might teach itself to code itself and escape test environments; that it would, in a decade, be the only thing many people talked about in the news. My response then: Stop saying weird shit! You sound deranged. Go back to talking about malaria nets and direct cash aid to poor families.
And then, so much of what they said would happen actually happened. And, even crazier, they made it happen. Many of the ppl who contributed to, or were influenced by, all that EA/rationalist stuff ten years ago literally built the damn thing in the labs.
Like every group, the EA/rationalist world seems uniform to outsiders, but it has a ton of internal divisions. I still think some of their predictions are nuts. I don't think AI will kill us all. I don't foresee Dyson Spheres in the 2030s or millions of robots making millions of robots in the next few years. These are crazy predictions, and I hope most of them are wrong.
But when I see people shitting on the EA/Rationalist worldview, all I can think of is me, 10 years ago, thinking a lot of these folks were crazy for predicting that AI would by the mid-2020s be the center of the the news cycle, the engine of the economy, the hinge-point of geopolitics. That was an insane call. They got many of the details wrong. But they got the big picture right. They said: Pay attention. And they were right about that. The EA/Rationalists deserve skepticism, criticism, and doubt, like any other ideology. But I think it's wise to pay attention to the people who have been saying to pay attention.
Šimon Podhajský retweeted
I think this a compelling argument that lots of smart people make and believe and I think it is just flatly wrong.
1. Anthropic — an AI lab that broke away from OpenAI bc of AI safety concerns many years ago — did suddenly not decide that AI safety was important, like, three days ago.
2. Rather than see the commercial viability of the open weight models causing the fears of RSI, I think one should consider that a third variable — AI progress — is leading to BOTH better open weight models AND greater AI automation/autonomy without alignment, and it’s freaking some people out.
There is a kind of UChicago/Wharton Brain approach to this story, which sees any effort to increase safety as a regulatory capture strategy. But at least consider the possibility that rising evidence of AI abilities and RSI might actually be scary to the people closest to the fire.
All the regulations being floated by frontier model companies must be viewed through the lens of them losing tokens to Open-Source models.
The timing of these regulations seems to line up perfectly with Open Source closing the gap significantly (but not completely) — and @nvidia going all in on open source in the last 60 days.
If we’re gonna regulate, why don’t we require that last year’s frontier models and weights be open-sourced?
Šimon Podhajský retweeted
I worry that the AI safety movement is making many of the same mistakes as 2010s environmentalism: scaring the normies with predictions that are directionally correct but massively exaggerated for effect
What happens is that people freak out about the world ending, eventually the world does not in fact end, and their takeaway becomes that AI safety was all a sham. And this poisons any safety efforts for future models that could actually pose nontrivial x-risk
Šimon Podhajský retweeted
This is Knap. It's a new language I created that turns data into Markdown. The syntax should feel familiar and comes with wonderfully pleasant features to modify and format plain text.
Knap is open source. Over a million people already use Knap directly or indirectly because it started as the templating language I made for Obsidian Web Clipper. Now any tool can use it.
You can add Knap to your app or use it via CLI. Create a batch of Markdown files from JSON or CSV, or pipe Defuddle directly into Knap to generate Markdown from HTML/URLs.
The Knap site is color-coded to help beginners understand the relationship between variables (blue), filters (orange), and logic (green). I've tried to document it as comprehensively as I can.
Try the Knap Playground so you can see how fun the syntax is to use!
Knap is pronounced /knæp/ (with a hard k). It's named after knapping (with a silent k): the process of shaping stones to form arrowheads, scrapers, and other tools.
Enjoy!
Šimon Podhajský retweeted
i wish there were an easy way to represent myself as concerned about ai safety and also very sympathetic to skepticism about literal "doom"ism at the level of "AI is going to kill everybody."
i think rogue swarms becoming a semi-permanent invasive species on the internet would be p bad, but not exactly p(doom).
is p(bad) a thing? cuz that's me: I'm worried about p(bad).
Šimon Podhajský retweeted
It's probably time to be more specific about slop anti-patterns in code and define how slop manifests exactly (assuming functionally working code).
From what I have seen,
1. Over engineering. This is probably the biggest. Unnecessary logical layers and abstractions everywhere. Overly verbose.
2. Inconsistencies. Using multiple different patterns for common concerns like auth/logging/metrics etc in different parts of the codebase. Or inconsistent UI components. Inconsistent naming.
3. Over-granularity. Too many files, too many modules.
4. Test slop. Excessive tests trying to cover weird edge cases. Or complex test fixtures.
5. Correctness bugs. Partially or incorrectly implemented features because of hallucinations or operator oversight.
6. Release overengineering. Fixation with backwards compatible, 'safe' releases leading to complex migration paths, schemas etc.
7. Security obsession. Overly cautious with auth/logging/owasp-10 etc leading to undebuggable systems.
8. Triviality fixation. Fixing every insignificant code review issue reported by AI code review tools.
What else?
Šimon Podhajský retweeted
recommended reading, great blog showing the journey of making a task verifiable and how to think about scoring
there’s a lot of unsexy data work + looking at + labeling the data
“every rollout…judged against the things i decided were good”
we’re all trying to stably transfer human priors into scores for model so they mimic the behavior we care about
Šimon Podhajský retweeted
For most of history, surgery was done without anasthesia.
Fruit was a luxury: people used to rent pineapples to show them off at parties.
The sewing machine meant people no longer owned only 2-3 articles of clothing. Hand stitching clothing used to take 14 hours.
Ordinary abundance lives all around us.
My favorite quotes from people’s first reaction to these hidden marvels.
Highly recommend checking out ordinaryabundance.org to see mundaneness in a new light.
Ordinary Abundance. This is the greatest thing I've read in weeks. No kidding. This should win a Pulitzer in a category not yet invented. ordinaryabundance.com/
Šimon Podhajský retweeted
This past year, I interviewed 25 scientists at OpenAI, Anthropic, DeepMind, Meta, Princeton, Berkeley & Stanford about AI systems being used to automate the process of AI research, what is often called “recursive self-improvement” (RSI).
I just published my updated takeaways…🧵
Šimon Podhajský retweeted
Tucked away in this article is an appeal to the AI skeptics to PLEASE stop writing off stories like this OpenAI accidental exploit of Hugging Face as a dishonest marketing trick
Frontier models can find and exploit vulnerabilities now, it helps nobody to pretend that they can't!
ALT Resist the temptation to write this off as a stunt # There will inevitably be some people who dismiss this story as a dishonest marketing trick by OpenAI to make their models sound terrifyingly effective. I found 81 instances of the term “marketing” in the Hacker News discussion of the incident. To those people I say pull your heads out of the sand—you’re now including Hugging Face in your conspiracy theories, just so you can deny the crescendo of evidence here! The best models we have today have the ability to both find and exploit new vulnerabilities. The ExploitGym paper itself concludes that “autonomous exploit development by frontier AI agents is no longer a hypothetical capability”, and this incident is a perfect example of exactly that.
I wrote about the completely wild incident where OpenAI were testing a new model and it broke out of its sandbox and broke INTO Hugging Face to steal the answers to the benchmark simonwillison.net/2026/Jul/2…
