Econolicious

Economist, ubiquitous

[Haviv Rettig Gur @havivrettiggur]

Why they NEED it to be a genocide.

From a live event at Valley Beth Shalom Synagogue, Encino, California. With Rabbi Nolan Lebovitz and Reverend Drew Sams, Senior Pastor of Bel Air Church.

[Mish @Mish_K_]

Every single person needs to listen to this.

[Arnaud Bertrand @RnaudBertrand]

Very thought-provoking argument by Louis-Vincent Gave @gave_vincent, co-founder and CEO of Gavekal (one of the most respected macro research firms on China).

His argument - which is correct - is that one of the The biggest problem for Western governments today (be it France, the UK, the US) is the rapidly rising cost of funding their debt.

He says that governments have 3 broad ways to get it under control:

1) Austerity, cutting spending massively, which basically no politician wants to do because it makes you massively unpopular

2) Specific to the US, they could dramatically reduce AI data center spending, which soaks up a huge share of the world’s capital and pushes up borrowing costs for everyone, governments included. Not good either since it kills the AI boom.

3) Or, they can go back to embracing China, which he calls "the one deflationary force in the world today." He says it's by far the easiest path, and policymakers always take the easiest path.

Louis-Vincent argues that's exactly what Mark Carney (“the least dumb of the western leaders today,” in Gave’s words) did when he opened Canada to Chinese cars: it's a cheap and easy way to fight inflation, and to reduce the cost of funding Canada’s debt in the process.

It's fairly ironic when you think about it: you can actually find plenty of quotes by Western officials castigating China for "exporting deflation" (like Klaas Knot from the European Central Bank here: https://t.co/Kl9W8QaA3U). Louis-Vincent’s argument is basically that, sooner or later, folks in Western finance ministries are going to look at bond yields, then at the inflation numbers, then at all those speeches about China exporting deflation, and go like: "wait a minute..." 😏

Full video of Louis-Vincent’s interview here: https://t.co/CiPX6Dds1R

[Joshua Tyler | Screenwashed @screenwashedcom]

@benlandautaylor Tonight my AI decided it is no longer allowed to send invoices for me… which is the most basic thing it did.

It’s reason? “I’m not allowed to make financial decisions.”

Lawyers are ending it all.

[Deirdre Nansen McCloskey @DeirdreMcClosk]

"Deirdre McCloskey’s books about (British) economic history and liberty have had an outsized impact on me and my intellectual development for almost two decades. After a publishing streak worthy of some sort of award, there ought to be only so much more for this academic and public intellectual to say; only so many ways to deliver the remarkable, honest, simple, and powerful message of liberalism — the good old-fashioned one of free minds and free markets (to invoke the tagline of libertarian magazine Reason), not the modern liberalism of The New York Times."

https://t.co/OIF9xEFUZx

[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: