Econolicious

Economist, ubiquitous

[roon @tszzl]

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

[Calvin Thrall @calvinthrall]

I hate teaching at @Columbia. You finally sneak an achievement into the faculty news roundup and the guy next to you in the alphabet wins the Nobel.

[Derek Thompson @DKThomp]

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.

[Claire Lehmann @clairlemon]

An Open Letter to Scott Alexander

From @sapinker
https://t.co/JH3o3EGepg

[zerohedge @zerohedge]

Actually going long the most shorted names and shorting the most popular (especially the GS HF VIP basket) has generated double digit alpha 13 of the past 15 years. We describe that phenomenon every year

[Shubhendu Trivedi @_onionesque]

Didn't realize Pollard was still writing books - this looks great! Big fan of the Empirical Processes book.

[Mehmet Songur @mehmetsongur_]

Google, "Yapay Zeka ve Ekonomi Araştırma Programı"nı duyurdu

Yapay zekanın ekonomik etkilerini modellemek üzere Nobel ödüllü Philippe Aghion ve Ajay Agrawal gibi önemli iktisatçıları araştırma kadrosuna kattı.

Bu ekibin öncelikli hedefi, istihdamdan büyümeye kadar yapay zekanın yaratacağı yapısal şokları haritalandırmak olacak.

Beni asıl heyecanlandıran kısım ise işin veri boyutu. Gecikmeli geleneksel makro veriler yerine, gerçek zamanlı büyük veri ve makine öğrenmesiyle yapılacak bu yeni nesil analizleri yakından takip etmemiz şart.

[Arin Dube @arindube]

This paper by @davidautor & co-authors is one of the most important papers to date in the space of what AI does to learning.

It's highly credible. And very much consistent with my own experience with use of agentic AI in research and teaching.

https://t.co/R4PeyhrBER

[Jeff (Hot Type) Jarvis @jeffjarvis]

"People built the test, removed restraints, defined the objective, left a route open and decided not to stop what was happening. Calling the result 'rogue AI' does more than sensationalize it. It allows those human decisions to disappear quietly from the story."
Gift link:
The Hugging Face Hack Wasn’t What It Was Cracked Up to Be
https://t.co/sw8ybnbT5g

[Econolicious @econo]

Mutual information much ?

[Valeriy M., PhD, MBA, CQF @predict_addict]

Still Using Correlation? That’s So 19th Century. Meet Conformal Correlation.

Pearson gave us correlation back in 1895 — and we’re still treating it as the default measure of association. But the world has changed. Our data is nonlinear, high-dimensional, messy, and anything but Gaussian.

So maybe it’s time for an upgrade.
Enter Conformal Correlation — a modern, distribution-free way to measure how strongly variables are related through predictive power, not rigid assumptions.

Why it matters:

* Distribution-free: No normality or linearity assumptions.
* Model-agnostic: Works with linear models, neural nets, random forests — you name it.
* Captures nonlinearity classical correlation completely misses.
* Directional: Measures how well X predicts Y, not forced symmetry.
* Built on conformal prediction, so you get uncertainty estimates that actually hold up.

Instead of giving you just a coefficient, conformal correlation tells you something more actionable:

How much does knowing X reduce uncertainty when predicting Y?

This is far closer to how we actually use data in 2025.

Classical correlation isn’t “bad” — it’s just from another era.
Conformal correlation is part of the next wave: assumption-light, prediction-focused, and ready for modern ML workflows.

If you haven’t explored it yet, now’s a great time.
The statistics of the 1800s served us well — but the future is predictive.

#conformalprediction

[Econolicious @econo]

We are not done auditing the Google for bias. Am I alone with my thoughts ?

[Erica Knight @_EricaKnight]

This is from Open AI.

The model favors Newsom and censors JD.

The AI censorship is beginning and I hope these independent evaluators reverse course before big tech swings another election.

[hardmaru @hardmaru]

Do large language models actually understand the world, or are they just very good at pretending? Does it even matter?

Our recent special issue in the Royal Society, “World Models in Natural and Artificial Intelligence,” brings together pioneers across AI, biology, and philosophy to argue that the path to true intelligence runs through something deeper: the ability to model not just language, but causality, the self, and the physical world.

Featuring contributions from Douglas Hofstadter, Michael Levin, Josh Tenenbaum, Samuel Gershman, Melanie Mitchell, and others, the collection asks a radical question:

What if the next leap in AI requires not just more data, but systems that model themselves?

Here are 3 ideas that might redefine how we build AI:

1. Capability is not the same as true intelligence.

Current foundation models are incredibly capable, but they often lack true emergent intelligence. They learn surface statistics instead of compact, causal abstractions. Simply scaling compute will not fix this fundamental issue.

2. Self-modeling is an engineering primitive, not a philosophical luxury.

New research in the issue shows that when networks learn to predict their own internal states, they compress and simplify, becoming more efficient as a form of regularization. For physical AI and future agents, a self-model is what will allow them to adapt their own skills and morphologies in real-time.

3. The hardest problems in AI are continuous with the hardest problems of life.

Biological minds do not passively ingest data; they actively explore, driven by empowerment to increase control over their environment. If world modeling is about an agent representing itself in relation to its environment to survive and adapt, then general AI may need to look much more like artificial life.

The takeaway is that the next leap in AI won’t come from just scaling up next-token prediction, but rather from systems that are agentic, self-referential, and temporally grounded.

Read the introductory essay and the full special issue here: https://t.co/KnNJMiZ9lZ

What do you think is the most important missing ingredient in today’s AI systems?

[Econolicious @econo]

A Philosophy of Maths ?

[Mel Andrews @bayesianboy]

You can now read this invaluable book on methods in philosophy of science open-access. Find me in Chapter 16! https://t.co/9WRHtSSSy5

[codila @0xCodila]

Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%:

0% → 0:35 - Graph engineering from scratch
30% → 31:17 - build your first agents graph
45% → 43:40 - run hundreds agents in parallel
75% → 1:04:58 - Loop engineering: route, check, repeat
100% → 1:30:09 - self-improving graphs that work while you sleep

most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime

learn graph from Google, ship your first - then unlock the complete system design below ↓

[Econolicious @econo]

Who has more real estate bankruptcies, insolvencies, foreclosures, and "in administrations" -- Trump or Carney's Brookfield Properties ?
https://t.co/i2ht0Gsow7

[J.J. McCullough @JJ_McCullough]

I’m sorry, but it’s not a good sign when a country’s journalists start writing this kind of flowery praise for the perfection of the great leader. At a time when Carney’s tactics, and even truthfulness deserve scrutiny, Canadian writers are putting out stuff like this:

[Big Brain AI @realBigBrainAI]

Terence Tao, Professor of Mathematics at UCLA and Fields Medalist, on why nobody can fully explain why LLMs work:

Tao starts with the mechanics, which are no mystery at all. You gather an enormous amount of text and you fit a curve to it.

"The magic of LLMs is that if you train these LLMs on enough data — so trillions and trillions of data points — and you really try to fit as good a curve as possible, and this takes like millions and millions of dollars of computing power and months and months of time, then suddenly, even when you iterate, it stays coherent. It begins to sound not like monkeys but it actually sounds like a human speaking."

That is the entire recipe: data, compute, time, curve-fitting. None of it obviously adds up to fluent English.

Then Tao says the part that most people building these systems move past quickly:

"And somehow we don't fully understand why that's the case."

The admission comes from one of the most capable living mathematicians, and the gap he describes sits at the centre of the field.

His best account of what's happening puts the mystery in the language rather than the machine:

"What seems to be true is that language, like English or other natural languages, contains a lot of hidden patterns that we're not consciously aware of. I mean, we know some of the laws of English, there's laws of grammar and things, but there are sort of unspoken, unwritten rules of language that humans pick up."

It relocates the question: the structure was always latent in the text, and the model found it. Why enough curve-fitting surfaces that structure is still unanswered.

Tao reaches for a child to explain it:

"A human child, even though they're not taught what a noun is, what a verb is or whatever, they can pick up what order English words go in just by continual exposure to the language."

Which is honest about the limits of the explanation, because we can't fully account for how children do it either.

From there, the unexplained behaviour compounds. Exposure to language turns out to be enough to produce something that looks like reasoning:

"It seems like you can teach these models to also pick up patterns in language to the point where you can give them math questions. The answer to 2 plus 3 is — and they will say five."

And once a model handles language at all, you can push it into resembling self-correction:

"Once you have a little bit of ability to speak English, you can kind of go in loops and sort of check your work and make fewer mistakes, and you can prompt these models to proceed step by step and not say something unless it's been double checked and so forth. And so they become a little bit smarter, quote unquote, to the point where they can solve many, many complicated tasks."

The scare quotes around "smarter" carry the whole argument. Tao does not concede that the unexplained fluency implies anything underneath it:

"But they're still just guessing the next word to say. It's not really grounded in any deep understanding of the real world. It's just that they have seen the patterns in the English language or other language that they've absorbed so well."

[Econolicious @econo]

AI strategy: blocking and tackling like in sailing or like in American football ?

[Parker Thayer @ParkerThayer]

This post looks like the start of a VERY sophisticated and well-funded PR operation to get support for Democrats to regulate AI into oblivion. Let me show you how it works:
1.) This guy, with minimal followers and no previous account activity, goes to the Wall Street Journal which publishes an exclusive with quotes from him on his resignation 18 minutes BEFORE this post goes up. Planning was clearly done in advance.

2.) Within hours, it has tens of thousands of reposts and the account has 100k+ followers. The post is punchy, quotable, it almost seems professionally written. The first three accounts to quote tweet it all do so within 15 minutes of the initial posting. Remember, this account had basically zero engagement beforehand, so an organic reach explanation seems unlikely.

According to Grok those accounts are @_NathanCalvin (General Counsel at Encode AI), @peterwildeford (Head of Policy at the AI Policy Network), and @DKokotajlo (Head of the AI Futures Project), all of which are up-and-coming AI-Doomer policy advocacy nonprofits.

The AI Futures Project website says it is funded “primarily” by the Survival and Flourishing Fund, which says on its own website that it has advised Jaan Tallinn, Skype creator and one of the leading investors in Anthropic, to grant over $2.5 million to the AI Futures Project since 2024.

Encode AI says on its website that it is ALSO funded by the Survival and Flourishing Fund, which in turn says that it told Anthropic investor Jaan Tallinn to grant $516,000 to Encode AI in 2025.

And wouldn’t you know it, the Survival and Flourishing Fund ALSO says it told Jaan Tallinn to grant $2 million to the AI Policy Institute, the 501(c)(3) affiliate of the AI Policy Network, as well.

What are the odds that the first three quote tweets of Coxon’s post would all be major AI-restriction policy advocates funded generously by the same donor, who also happens to be one of the leading investors in, and a board member of, Anthropic, the company Coxon was resigning from? And all within 15 minutes of posting (two within ten)?

3.) Jacob Coxon doesn’t have much of a resume, but we do know that, in 2022, he got a $20,159 scholarship for the “long term future scholarship program” from the Good Ventures Foundation, one of the philanthropic vehicles of Dustin Moskovitz, a notorious AI-doomer who has spent tens if not hundreds of millions on policy advocacy to strictly regulate AI, while also being an Anthropic Investor himself.

It also just so happens that the 14th person to quote Coxon’s post was @MaxNadeau_ (27 minutes after posting) who is the program officer for the Technical AI Safety team at Coefficient Giving, another of Moskovitz’s philanthropic spending vehicles. Max is not a frequent poster, his last posts before quoting Coxon were before Labor Day, but he was remarkably quick off the mark for this one.

4.) Basically every major Democrat politician and candidate has suddenly glommed on to this post, and conveniently, as the people cry out foe answers, Bernie Sanders already has a bill written to “ban super intelligence” and regulate AI into oblivion, and will be releasing later this week. The bill, among many other things, will create “a new cabinet-level federal agency to safeguard the public from the dangers of artificial intelligence” that will be “advised by an Artificial Intelligence Advisory Board comprised of experts on artificial intelligence.” Do you think, perhaps, Anthropic and its many investors who fund AI policy advocacy might have interest in getting to place a pet “expert” on the board of an entity that dictates what AI is and isn’t allowed to do? And isn’t it fortuitous that this whistleblower came forward with his oh-so scary stories so close in proximity to the release of the most radical piece of AI legislation ever introduced?

[Emmy Liu @_emliu]

I'm reflecting on how much research has changed since I've joined the PhD and wrote a short blog post about it (I joined in the tail end of the BERT era!). It seems pretty crazy how different processes are now, and I took the chance to do a retrospective before graduation:

https://t.co/OR7N8s4CgO

[Econolicious @econo]

Artisanal cupcakes era 2009-2011 undefeated as the apotheosis of the culinary arts.

[lumpenspace and 99+ others @lumpenspace]

I still refuse to believe there has ever been a croissant/ice cream/coffee/kebab for which a berghain’s worth of queuing is rational.

[Will Kinney @WKCosmo]

I was just reading this again this morning, and it is not just one of my favorite pieces of writing about science, it is one of my favorite pieces of writing, period. A masterclass in prose.

https://t.co/FkhNw7ogcU