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Here’s the first part of my theory: cybercriminals are not the organized geniuses we implicitly imagine. Anyone building a criminal gang has to find candidates willing to forgo more stable opportunities in the formal economy. My modeling estimated that something like 720,000 people are involved in the online criminal economy, 90% of them in scam and fraud call-center type operations, some of them human-trafficked. Of the 76k or so actual hackers, the vast majority work for governments. Of the remaining few thousand criminal hackers, most work in ransomware. Some demographic estimates of these workforces are below.

Criminal organizations need to base themselves in middle and lower income countries, where they can pay less and hire people who have few formal-economy options. For scams and fraud, picture an illegal call center building in a small city in Nigeria. For ransomware gangs, picture kids working in the informal economy in Belarus. I think folks in the AI safety and cyber world sometimes imagine adversaries as their Waluigi twins, implicit MIT graduates; in fact, in cybercrime we’re dealing with a lower-skill demographic. The chart below estimates attacker wages relative to default economy wages…

Here is more from Joshua Saxe, very good throughoiut.  There are bottlenecks on the down side too…

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Cowen: Talk me out of this worry: So we agree science is an ecosystem, and there’s always a temptation to want to fund the most dynamic, most productive parts of the ecosystem. And right now, that would probably be A.I.

But if you need the whole ecosystem to be working well, arguably you should be funding, at the margin, the less productive parts, which need the aid. That will sometimes be universities, but there’s plenty of other inefficient parts of our ecosystem.

So is this plan not putting more resources into the parts that are working well, but we get stuck with bottlenecks if the parts that are not working well end up with fewer resources?

Kratsios: Well, luckily, we’re not investing all of our money for the federal government into S.I. for science. It’s a portfolio approach. And obviously one ——

Cowen: But it’s a net shift toward artificial intelligence, right?

Kratsios: Well, I would argue — and I think this is a very important distinction — it’s not investing in superintelligence, it’s investing in superintelligence for science.

So the question is, I think a lot of people argue: Oh, why would the federal government want to invest more in superintelligence when billions, if not trillions, of dollars are being invested in the private sector into superintelligence? And I would agree with that statement. We shouldn’t be doing that.

The government isn’t funding how to create a next frontier model. That’s something that our great American companies are working on.

But applying those models to scientific discovery is still in its early stages. And as you know, if you think about the trend or the path by which we’ve gotten to this place with L.L.M.s — we started first with being able, essentially, to scrape the whole corpus of the internet, to use that to train a large language model.

The next chapter of that was around coding, where the model builders realized: Oh, we have really good coding data, and we can actually train these models to be good at coding. So then we had this rise of Anthropic and OpenAI Codex and all this stuff.

And I think, to me, the next chapter is around science, and the data that you need to train these large language models for science is still one that we’re working on a lot. And we were very proud to announce 190 federal data sets being made public in order to help train these science models.

But ultimately, you’re going to be applying this superintelligence to everything from material science to chemistry to math to physics, and we have to be in a position to actually leverage that.

Definitely interesting throughout, pretty much entirely we are discussing the Trump administration science plan, authored by Michael.

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2. Is this what the Denisovans looked like?

3. Measuring bottlenecks for AI.

4. How is violence diffferent when women lead drug cartels?

5. “Israel’s economy is growing so fast, it will leapfrog the Netherlands and Australia within the next 18 months, in time for it’s 80th birthday—not by 2030, as predicted—to rank second only to the USA in GDP per capita of all nations with populations over 10 million.”

6. Latin America seems to be climbing the high-tech value chain.

7. ZMP by any other name (amazing how much people used to hate on that idea).

8. Chatting with Milton to run your central bank?

9. Warhound.

10. “Somehow, @srajagopalan and @lachygroom have managed to fund a hard-tech revolution in India. There’s something rather massive going on, and everyone else has missed it.”  Link here.

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Using approximately one million resolved contracts and questions across four collective forecasting platforms, twelve domains, and time horizons ranging from hours to years, we evaluate the calibration and accuracy of collective forecasts in real-world settings. We found that forecast probabilities were overall highly calibrated: events assigned a probability of p% occurred approximately p% of the time. This broad pattern was evident across platforms and many domains, although fine-grained analyses revealed systematic tendencies toward over- or underestimation in highly consequential areas. While this does not mean that forecasting platforms provide certainty about individual events, our findings suggest that large-scale aggregation of judgments contains meaningful information about real-world uncertainty; when examined in aggregate, the predictions generated by the platforms provided informative estimates of event likelihood.

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Max Doppelt: In January 2025 my mom’s lung cancer spread to her brain. The drug with the best chance of helping her was approved in China but not here.

A doctor told me, almost as an aside, that if she were Chinese she could buy it at a pharmacy.

So I flew to Shanghai and came home with 26 boxes in my luggage. That was 19 months ago. My mom is still here, still working, and in her words has had an amazing three years.

The drug was approved in China in 2023. The FDA approved it in July 2025. It still is not for sale here. I know of at least 30 Americans who have made the same trip.

I have been scared to tell this story because I worried it could cost my mom access to the medicine keeping her alive. But the problem matters more than my silence. These drugs need to be developed and available here, and that means FDA reform.

A piece at KFF Health cover’s Max’s story and adds this insanity:

Rep. John Moolenaar (R-Mich.), who chairs the House Select Committee on the Chinese Communist Party, in June co-sponsored legislation with Rep. Debbie Dingell (D-Mich.) requiring all U.S. pharmaceutical deals with Chinese companies to be cleared by the Treasury Department.

Got that? A son flew to Shanghai to buy pharmaceuticals to extend his mother’s life and the House Select Committee on the Chinese Communist Party wants a Treasury lawyer monitoring the baggage claim. Yes, I am angry.

Addendum: See also my earlier post on Operation TrialBlazer and Montana’s SB535. My 2009 TED Talk is the ur-source for my argument that a rich China and India will create many benefits for the United States, including many more cancer drugs.

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Economist Tyler Cowen joins David Wolpe for a conversation about AI, religion, and what makes us human. Tyler makes the case that AI’s biggest impact may be on status: who gains it, who loses it, and what happens to experts like rabbis when anyone can ask ChatGPT all their questions. He also suggests that losing our monopoly on intelligence may make us feel more unique as humans, rather than less.

You can listen here.

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Will AI poison the public finances of many modern states? There is a real risk that it will.

Yesterday, I circulated a new paper, “Saving for the Machines,” which makes a simple point. Building the AI transformation will require enormous investment. The only way to induce agents to save for that investment is through a large increase in the real interest rate. For instance, in my benchmark scenario, the ten-year real yield jumps from 5.1% to 13.6% on impact.

But that means government borrowing will become much more expensive. Here is a back-of-the-envelope calculation. Take a government that owes 100% of GDP in 2025 (a typical case in Western Europe) and runs a balanced primary budget from then on (not so typical but never mind; I want to build an optimistic scenario). That means the debt ratio changes only with the gap between the real interest rate and growth.

I input the interest rate and the growth rate for my benchmark transition to full automation, and I roll over the debt at the short rate. I consider two economies. The first economy is an AI adopter, for example, the U.S., which experiences an AI boom at home. The government pays the new interest rate but also enjoys faster growth.

The second economy is a bystander, for example, Spain. This economy lives in an integrated financial market but does not adopt AI (this is an extreme case of zero adoption, just to make the exercise transparent): it pays the same world interest rate and continues growing at its old 1.6% trend.

Let’s first consider the adopter. With an intertemporal elasticity of substitution of one-half, the Euler equation implies that consumption growth is about half the real interest rate. Hence, the gap between the interest rate and growth does not close during the AI-driven growth boom. Instead, it widens. For example, with a unit elasticity of substitution among tasks, the real rate rises from 5.1% to 42% by 2040, while growth increases only from 1.6% to 22%. Debt doubles by 2038, reaches 296% of GDP in 2040, and 907% in 2045. To hold the ratio at 100%, the government needs a primary surplus of 10% of GDP in 2035 and 24% from 2045 onward. With a low elasticity of substitution of 0.2, the numbers are milder, but the pattern is the same: 204% in 2040, 397% in 2045, and a stabilizing surplus of 13% of GDP.

But the adopter has a way out. The capital share goes to one, so net capital income is about 88% of GDP, and a flat tax on it can cover the surplus. A 36% tax on net capital income holds the debt at 100% of GDP in the limit with unit elasticity, and a 20% tax does so with strong complementarity across tasks. The tax is above the gap because it slows growth, from 21.9% to 13.6% with unit elasticity and from 11.7% to 9.1% with an elasticity of 0.2, while it does nothing to the rate the Treasury pays, which technology pins at the machines’ net return. Painful, but the boom survives and the adopter can pay its debt.

The grim news is that the bystander economy has no way out. Its capital income is still a third of GDP, which grows at 1.6%, and the surplus it needs is 11% of GDP in 2030, 26% in 2035, and 44% forever with unit elasticity (18% and 24% with an elasticity of 0.2). On a balanced primary budget, its debt doubles by 2033 and triples by 2035. No tax system raises that.

Two more bad pieces of news for the bystander. First, long yields jump on impact, so a Treasury that issues ten-year debt pays more, and sooner, than rolling over at the short rate assumes: it is the same bill but front-loaded. Second, the bystander’s own capital flows to the machines abroad, so its GDP and tax base fall below trend. One relief is that households can save for other people’s machines and get rich on the returns, which the bystander’s government can tax to some extent. This back-of-the-envelope calculation is a benchmark, not a forecast of what is to come. But the arithmetic is simple, and it runs counter to the hope that an AI-fueled growth boom will take care of public debt, because growth comes with an interest rate that is twice as high. Not a pretty future for most of Western Europe. Due to AI, the choice is between default, inflation, or, most likely, financial repression: capital controls that keep domestic savers from investing in machines abroad.

I am somewhat more optimistic than this.  The assumption of zero Spanish adoption already is noted, but also ideas are public goods, and Spain can benefit from American (and Chinese) AI, and the resulting scientific advances.  The long-run supply of capital also might be quite elastic, we will see.

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What happens across generations to wealth shocks? Do they endure and even magnify, or do they dissipate? By implication, how much modern wealth is attributable to events before 1900? This paper uses random shocks to family size in England before 1880 that created wealth shocks for the children to measure the persistence of wealth shocks. Fertility in England before 1880 was a biological lottery. For richer families, family size strongly influenced child wealth. This paper finds that such biology-induced wealth shocks had no impact on descendant wealth by three generations later. Since wealth itself persisted across more than five generations this implies that, in the long run, wealth mainly derives from sources other than inherited wealth. The observed link between nineteenth century and modern wealth does not lie in wealth transfers alone. Wealth persisted because of the familial inheritance of behaviors associated with wealth accumulation and retention.

That is from a recent paper by Gregory Clark and Neil Cummins.  Via the excellent Kevin Lewis.

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2. New YouTube channel for Indian architecture.

3. Where do Math Olympiad gold medal winners end up?  “By total gold medal count, China is the clear leader, followed by the pack of USA, Russia, and South Korea. Over 20 years China has 105 out of a possible 120 gold medals which means nearly all their students achieved gold. This is so successful that it’s common knowledge that it is harder to make the China IMO team than to get a gold medal in the IMO itself.”

4. Is El Salvador cost-competitive?

5. Rebecca Lowe on Bach.

6. Do the Democrats still care about the Ivy League?

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Thus it is likely that the AI sector will, at some point, end up overregulated.

Given that reality, perhaps the AI sector should be underregulated today.  After all, overregulation will have decades, maybe centuries, to catch up and even the ledger.

Many regulatory issues can be considered within this framework.  You can think that AI is “underregulated today”, in the static sense of that term, and still not favor additional regulation right now.  You might prefer to keep it underregulated for some while to come.

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Using a stacked difference-in-differences design covering shootings between 1990 and 2025, I compare cohorts at affected universities before and after the shooting with cohorts at matched control universities. I find no detectable effects on time to degree, but undergraduates enrolled at the time of the shooting are 4.2% less likely to pursue further studies. Those who do continue shift toward health: the share of exposed cohorts who later enroll in a health degree rises by 1.3 percentage points (8.8%), and this effect persists among cohorts who enroll after the shooting. Effects on students present during the shooting are consistent with a response to trauma; for later cohorts, I find evidence of sorting, as master’s students with prior healthcare experience become more likely to enroll in affected universities. These results suggest that mass shootings leave institutional and cultural legacies that outlast the students who witnessed them.

That is from the job market paper of María Medellín-Esguerra, who is currently on the job market from Brown University.

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Third, fewer women in prison does not have to mean less deterrence overall, as arguably we should concentrate more of our legal resources on deterring violent men.

Note that if we can release enough women from prison, more prison space will be freed up, and I mean more prison space for men, including violent men. It is common to read of shortfalls in jail and prison space, which lead to violent men being released early. Many of these men go on to commit violent crimes, a truly major injustice. Or they commit an ongoing string of misdemeanors, sometimes ranging into the dozens or even hundreds, such as shoplifting or disorderly conduct. Our cities become much less livable as a result.

If we can, over time, convert some of the current prison space for women into prison space for men, especially violent and destructive men, I expect a net increase of justice in this country. I also think it will be a better country to live in.

There is precedent for such a conversion of facilities. In 2013, California turned the Valley State Prison for Women into a men’s prison, as the female prison population was declining and more space was needed to relieve overcrowding among the males.

As an economist might say, sending women to prison is not a free lunch. Given that prison space is scarce, it means the release of more violent criminals, mostly male.

Some reliance on prison is unavoidable.  But it amazes me how much people will go to the mat to defend an institution — whole cloth at that — that makes no real sense in terms either humanitarian or even simply organizational.

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