Category: Web/Tech

Is it the screens? Or education systems?

The Dark Ages implies television and phones are the main cause of cognitive decline. This fails to explain the patterns in PISA scores. Why did England and Scotland fall so precipitously from 2000 to 2005 whilst America improved? Why did England and Estonia hold steady after 2015 whilst most other OECD countries declined? How have Singapore, Taiwan, Japan avoided decline altogether?

A better explanation is that a country’s education system is more important than its television diffusion.1 East Asian PISA and IQ scores have probably remained constant, or even risen, because of their rigorous education systems and intensive tutoring cultures. The two European countries which avoid PISA-malaise – Estonia and England – have more rigorous education systems than their neighbours. They (more or less) use the knowledge-rich curricula, direct instruction, and systematic phonics – techniques which their more progressive neighbours abandoned between 1975-1990.

Here is much more from Alexander Thompson, recommended.

Does AI assistance enhance or erode expertise?

From a new NBER working paper:

Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.

That is by David Autor, et.al.  Do note that over time the allocation of humans to tasks will evolve so that more of the humans become more productive, not less.  RCTs somehow have the odd disadvantage of requiring too many things to be held constant, and so they can miss the benefits of longer-term adjustments.

A simple model of AI-aided economic growth

The Solow model has its uses, but it fails when it comes to major changes stemming from AI.  Consider instead an economy with (at least) two factors of production:

1. Intelligence.  Yes, formal smarts.  Playing chess, proving math theorems, and doing well on evals.  Don’t forget humans can do those things too, though AIs are now a huge boost here.

2. Polanyi knowledge.  Michael Polanyi, that is.  This refers to knowledge of time and place, inarticulable knowledge, custom and habit, and many other particularities that you can read about in Hayek and Polanyi and in many other social scientists, anthropologists too.

Humans specialize in this.  The AIs can aid in its production, but at least so far there is no way you can “bring an AI into your office and have it figure out how that office works.”  At least not in the human rather than the purely mechanistic sense.

In the model, intelligence and Polanyi knowledge combine to produce output.

Substitutability is fairly limited.  For instance, if you have problems of norms in your office, a mere dose of AI-drenched technocratic knowledge does not usually solve those problems.  Sometimes it even can make those problems worse, by empowering rent-seekers further.

Intelligence and Polanyi knowledge are not quite Leontief complements, but they are mostly complements.

Now recently the U.S. economy has experienced a huge positive shock to its Intelligence, with more to come.

The core prediction is that this increases marginal returns, employment, and real wages in the Polanyi knowledge sector.  All of a sudden, the inputs into that sector are relatively scarce, compared to the now-larger quantity of Intelligence.

There will also be some transitional unemployment in the Intelligence sector, at least once Centaur models fade.  But so far Centaur models are holding, for instance mathematicians did the prompting to do the new math work.  Nonetheless some of these Centaur employments will fade, just as they have in chess.

Note that the Polanyi sector cannot be boosted very quickly or with direct and simple efficacy.  It is messy by its nature, to cite a term from Luis Garicano.  So the wage and employment gains there are slow in coming.  But they keep on coming for a long period of time.  There are further AI/Intelligence advances on tap, plus absorbing the advances to date, and exploiting them, takes a long time.

In this model, if someone or something could “commandeer” the Intelligence sector, their power over society would be much more limited than it might appear at first.  The world does not change that much at first, because the necessary complements are lacking.

The Solow model usually does fine by ignoring these features of the world, in part because it is rare for the Intelligence sector to take such a rapid swing upwards.  So the ratios and complementarities across these two sectors usually are fairly constant in the short run, though not in 2026 or in the next years to come.

I recall talking through this model, and debating it with people, when I was seventeen years old.  The impetus for that was the Soviet preoccupation with cybernetics, central planning, and possible supercomputers.  We were all wondering what kinds of economic improvements that might lead to, or whether it could make central planning successful (no, basically, but that involves some yet further arguments).

Of course this very simple model can be improved upon in many ways, but it is a start.

This very simple model so far is matching up to the data, namely that we have shocking AI and tech advances, the job market is doing fine, markets do not see high risk, and economic growth is robust, not exploding, but likely will rise in the future.  These predictions change somewhat as the Polanyi sector, slowly, catches up to and incorporates the Intelligence explosion.

In the meantime, this is the best basic framework for understanding our current situation.

Labor reallocation during the Industrial Revolution

New technologies swept through Britain during the Second Industrial Revolution, destroying old jobs and creating new ones. We know little about how workers reallocated. Using 170 million full-count British census observations (1851-1911), I construct new task-level data on occupation and investigate English bootmaking as it mechanized. 153,000 artisanal jobs disappeared as skills became obsolete; 140,000 specialized jobs emerged. Incumbent artisans did not take the new jobs, nor were they displaced. Instead, entry collapsed-young men stopped entering the old trade. New jobs went primarily to young workers, though not in the same locations. Young cohorts absorbed the adjustment.

Here is the full article by Hillary Vipond, via someone (now forgotten) on Twitter.

The excess pessimism of early nuclear bomb designers

From GPT Pro:

The Manhattan Project scientists were remarkably good technological and arms-race forecasters. They correctly rejected the idea that America’s nuclear monopoly could be maintained; the Soviet bomb arrived in 1949. They anticipated thermonuclear weapons, huge arsenals and the extreme vulnerability of cities.

Where many of them went wrong was in moving from “a nuclear war would be catastrophic” to “therefore a catastrophic nuclear war is fairly likely.” They tended to underweight the endogenous response of political and military institutions to the catastrophe—the emergence of second-strike forces, elaborate command systems, crisis management, and above all mutually assured retaliation.

There is even some contemporary evidence for an insider/ordinary-public gap. In August 1945, 69% of Americans told Gallup that development of the atomic bomb was a “good thing,” versus only 17% saying it was bad. The scientists campaigning for international control plainly regarded the public as far too complacent.

So I would summarize the historical evidence this way:

The bomb’s developers were, on average, unusually pessimistic about the political consequences of their invention, and noticeably more pessimistic than the general public.

Here is the full answer.

Economic Scenarios for Transformative AI

By Anton Korinek, Charles I. Jones, Szymon Sacher, Tess Cotter, and Peter McCrory, a strong line-up from Anthropic.  They are sober, reasoned, scientific, and (for the most part) dynamically consistent.  Here is the paper, here is the abstract:

This paper presents a framework for assessing the economic consequences of AI between 2026 and 2030. In the model, AI automates a growing share of cognitive work, raising productivity and displacing workers who must search for jobs in other occupations. The model maps future paths of AI capabilities into implied paths for GDP, the labor share, wages, labor reallocation, and unemployment. We illustrate the framework by considering three scenarios: modest, substantial, and extreme. Under modest change, AI adds less than half a point to GDP growth by 2030 and raises unemployment by a tenth of a point. In the extreme change scenario, AI has transformative effects, with AI performing almost half of today’s cognitive work by 2030. GDP growth then rises to 15 percent per year, the labor share of income falls from 60 to 45 percent, and nearly one in five cognitive workers is unemployed. We also surveyed US adults about their expectations for AI. Views vary widely, but the median respondent’s answers are consistent with our substantial change scenario in which, by 2030, GDP rises by 8 percent and cognitive employment declines by 4 percent. The model offers a structured way to compare possibilities for our economic future under different expectations about AI.

Here is the associated thread.  I will opt for something a modest bit more than modest.

And here is a good growth discussion from Alex Imas.

What should I ask Kevin Roose?

Yes I will be doing a podcast with him.  From Wikipedia:

Kevin Roose…is an American author and journalist. He is the author of three books, and is a technology columnist and podcast host for The New York Times. He wrote a book about Liberty University, an evangelical Christian university known for strict rules imposed on students…

Kevin of course has a new book coming out, namely The AGI Chronicles: The Inside Story of the Race to Create an Artificial Superintelligence.  I have yet to read it, but I expect it to be gripping.

So what should I ask him?

Some Navier-Stokes updates

Some Terry Tao.

More Terry Tao.

Some ChatGPT.

Some are calling this “a Deep Blue moment” for AI.

All via T., a mathematician.

Update from OpenAI: “Our internal model group arrived at the Navier–Stokes solution in 88 hours, using around 10,000 coordinating AI agents.”  And this.  And from Noam Brown: “Yes, this result cost millions of dollars. But remember that when @OpenAI announced o3 it cost ~$500,000 to score 87.5% on ARC-AGI 1. Today, Astra scores higher for ~$20. In 2025 it took us and GDM an enormous amount of compute to achieve IMO gold. For the 2026 IMO, anyone with a $20/month ChatGPT subscription could do it. Massively scaling test-time compute gives us a glimpse of the future. I believe that a year from now everyone will have an AI at their fingertips capable of solving problems of this caliber.”

Insurance price sentences to ponder

NYU Stern researcher @NateWitkin questions why cyber insurance rates keep falling if AI cyber risk is accelerating:

“Insurance rates for cyber risk declined by about 4% globally in Q2 of this year, and that’s actually the 12th consecutive quarter in which they’ve declined. This is very valuable signal that implies that at a minimum you shouldn’t overindex on the Hugging Face incident.”

“This is a plea for level headedness, but I think it would be helpful for safety folks to engage with these numbers just ’cause this is an avenue of criticism from folks like me and to an extent folks like Tyler.”

“Why are these numbers not moving? Is it because people are underestimating capabilities? Are they not taking the problem even as close to as seriously as they should or is it something else?”

Here is the link with video.  File under “Questions that are all too rarely asked.”

I am happy to admit that the answers here are far from obvious, and that I am myself expecting prices to rise somewhat.

I will continue to note that there are a remarkable number of ways, seen among other places on Twitter, to rephrase and to rationalize the statement: “I have the most remarkable and important and true macro risk story in the world to tell you.  Unfortunately, it does not correlate with any observed asset market prices.”

Short Videos, Big Self-Control Problems

I study how short-form design amplifies self-control problems in digital media. Short units repeatedly renew temptation that lasts longer than each unit, turning local temptation into sustained overconsumption. Using microdata from a U.S. short-drama platform, I exploit a nonlinear top-up menu to infer viewing plans and show that paying users watch 82.1% more than intended. Structural estimates imply an average temptation horizon of 11.2 minutes, short relative to the full drama but long relative to one-minute episodes. Counterfactuals show that larger decision units, default limits, and breaks improve long-run welfare. A short-video calibration highlights the broader welfare relevance.

That is from Renjie Bao of Princeton University.  I believe a Princeton job market candidate?  Via Quan Le.

How are the market valuations for the U.S: insurers doing?

U.S. insurance stocks have been doing quite well since the beginning of May 2026, and they have materially outperformed the overall market. I’m using the May 1 close through the September 3 close so that we compare complete trading days; these are price changes, excluding dividends.

The cleanest broad measure is the iShares U.S. Insurance ETF (IAK), which covers U.S. life, property and casualty insurers. It rose from $132.01 on May 1 to $147.87 on September 3: +12.0%. An alternative, more equal-weighted measure, the SPDR S&P Insurance ETF (KIE), rose from $56.79 to $64.80: +14.1%.

For comparison, the S&P 500 ETF (SPY) went from $720.65 to $773.17 over the same period, +7.3%. So insurers have beaten the market by roughly 5–7 percentage points in four months.

That is from GPT Pro.  Here is my earlier post on numbers and market valuations.  Do any market prices reflect a realistic chance of very bad outcomes from advanced AI?

Here is advice on how to short those shares.

Shout it from the rooftops (of the data centers)

Data-center investment has become one of the largest capital-expenditure cycles in financial markets, with U.S. hyperscalers expected to deploy roughly $700 billion in 2026. This investment boom has raised concerns that large computing loads impose external costs on households through higher electricity prices. Using a 50-state panel for 2021-2024, we find no statistically significant evidence that data-center presence, installed capacity, or capacity expansion predicts residential electricity-price inflation across extensive-margin, intensive-margin, fixed-effects, and timing specifications. We propose an energyinternalization mechanism: hyperscalers can partially internalize incremental electricity demand through contracted or dedicated generation, including solar and wind energy. Consequently, gross datacenter electricity consumption need not translate one-for-one into net pressure on residential electricity supply. The findings suggest that the extraordinary AI capital-investment cycle has not, thus far, produced a detectable residential electricity-price externality.

Here is the article by Yosef Bonaparte, via the excellent Kevin Lewis.