2. SGA does seem to flop more.

3. Marc Andreessen on AI.

4. Thomas Sargent lectures on YouTube.

5. Why Japanese companies do so many different things.

6. Fresh Knausgård.

7. ““The Boys of Dungeon Lane” is absolutely the best album ever recorded and released by a rock star in his 80s.“

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This was a great failure of the most recent philanthropic era. At its best, the infrastructure established by figures like Gates delivered effective efforts to reduce poverty and fight disease; at its worst, it threw money after fashionable political causes and education fads. But there was no real legacy when it came to physical infrastructure — no great beautification campaigns, no beloved architectural landmarks, no equivalent of the Gilded Age’s expansions of museums and libraries and concert halls, and few personal expressions of extravagance (like the Newport mansions or Hearst Castle) for future tourists to admire.

At the beginning of the 20th century, philanthropic dollars had already helped build the Metropolitan Museum of Art and Carnegie Hall, the campuses of Vanderbilt, Stanford and the University of Chicago, a network of urban parks, various impressive churches and an array of private homes that would themselves become public spaces within a few generations. Tastes vary, but I do not think that the monuments raised by today’s superrich are in any way comparable.

Here is the full NYT piece.

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Ten notable facts from India’s new SRS Statistical Report 2024 published two days ago:

1) India’s total fertility rate (TFR) has dropped to 1.88 (rounded up to 1.9 in the figures) in 2024 from 1.92 in 2023.

2) This drop is roughly the historical speed of the last few decades. India’s TFR was 4.3 in 1985 and it has been falling around 0.06 per year since then.

3) For those who think “smartphones are the reason for the fall of TFR,” there is not much change in India’s TFR after their introduction. Of course, this might only apply to India.

4) India’s sex ratio at birth continues moving toward natural levels. It has grown from 907 girls per 1000 boys in 2018-2020 to 918 in 2022-2024. Without sex selection (e.g., selective abortions), it should be around 952.

5) Nonetheless, this bias still means that India’s replacement rate is around 2.15, not 2.1 as in other advanced economies.

6) Hence, India is already 0.27 children below the replacement rate and the gap continues growing.

7) However, this figure hides large regional differences. Kerala is at 1.3, well below the U.S. and approaching Italian and Spanish levels (Delhi is even lower, at 1.2, but it is a peculiar case), while Bihar remains at 2.9.

8) In terms of the rural/urban divide, rural India is at 2.1 and urban India at 1.5.

9) From everything I can see, India’s TFR will continue to fall, and it should reach 1.57 (the current level of the U.S.) around 2031 unless something significant changes.

10) Having said that, India’s data has a non-trivial margin of error, and a new Census might change our reading of the situation. In summary, India is following the same path as everyone else. No Indian fertility Sonderweg!

That is all from Jesús Fernández-Villaverde.

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As Mark Lilla, a recovering Straussian, once remarked, they [the Straussians] were like craftsmen building a house brick by brick on a foundation that Leo Strauss had laid.  But they would never become architects of that house, or decide that the house was too small for them to comfortably live in.  Moreoever, Strauss disparaged social science and what he considered naive forms of positivism prevalent in American universities.  This led some of his followers to disdain merely empirical accounts of current events.  If you are more of a Hegelian, you need to pay attention to actual history if you are to give an account of how ideas play out in the real world.

That is from Frank Fukuyama’s forthcoming memoir, recommended of course.

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2. “For the first time in decades, new and recent graduates with at least a bachelor’s degree have consistently higher unemployment rates than the overall American workforce, according to data on 22-to-27-year-olds compiled by the Federal Reserve Bank of New York.” Link here.

3. Redux of my 2021 Bloomberg column on the ideal university.

4. 15-minute weird Brazilian album.

5. History of social science funding at the NSF.

6. Furman and Laibson on Harvard grade inflation (NYT).

7. Who gets laid off first?  The measurers?

8. Work from home may be the problem for young workers, not AI.

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Again, I am very grateful to Yonatan Ben Shimon for making this support possible.  And there remains a modest amount of money left in the fund.

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The third type of weapons are invasion ships – this is the classic science fiction trope, however actual invasion ships have one fundamental weakness – they need to slow down at the destination galaxy. This has two effects. Firstly, energetically getting invasion ships to the opponents galaxy is substantially less efficient than sending RKVs there. This is because of the tyranny of the rocket equation. While the invasion ships can be accelerated to relativistic velocities at origin galaxy, to slow down, it cannot be assumed there is an equivalent infrastructure at the destination. Instead, the invasion ships must carry their own braking fuel with them, which must then also be accelerated and so on.

The second fundamental problem is lack of stealth. When accelerating your exhaust points away from your target, when decellerating your exhaust points towards it. Essentially your are deliberately dissipating all your kinetic energy as a gigantic beacon screaming ‘I am here come kill me’. The decelleration burns of large-scale invasion fleet would both likely last thousands of years and also be immensely noticeable to any reasonable civilization in the target galaxy let alone a paranoid K3. Even if you don’t try to decellerate by rockets but instead by e.g. drag on magnetic sails, this drag causes friction which then radiates uniformly in all directions, again serving as a beacon.

That is from a very interesting and much longer 2025 piece by Beren’s Blog.  Via S.

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We examine how fertility expectations influence financial risk-taking using nationally representative data from three countries. Our results indicate that childless adults who do not expect children are 21-36% more likely to invest in stocks than those who expect children, controlling for personal characteristics. This effect persists also when medical infertility instruments expectations. We find no similar effects for other savings categories, nor differences in self-reported risk tolerance. Households expecting children report shorter financial planning horizons, which may explain their lower risk-taking. These results suggest declining fertility can increase young adults’ stock market participation through childbearing expectations.

That is from a recent paper by Judith Bohnenkamp, Ville Rantala, and Melina Murren Vosse.  Via the excellent Kevin Lewis.

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2. “The results suggest that the departure of baby boomers from the labor force will have profound implications for economic opportunities of new workers.”

3. No, they were never voting for libertarian Republicans.

4. Minnesota bans prediction markets, the federal government pushes back (NYT).

5. Joe Francis on smart phone timing and fertility changes.

6. The surrender arrives.  Here are responses from human mathematicians, see for instance Gowers.

7. U.S. to Award Quantum Computing Firms $2 Billion and Take Equity Stakes (WSJ).

8. Short video, no parachute, not AI.

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AI has now solved a major open problem — one of the best known Erdos problems called the unit distance problem, one of Erdos’s favourite questions and one that many mathematicians had tried.

A number of prominent mathematicians comment. I enjoyed Thomas Bloom’s comments:

This was one of Erdős’ favourite problems – he first asked it in 1946 [14] and returned to it many times. (The site www.erdosproblems.com, on which it is Problem #90, currently lists 14 separate references, and there are no doubt more.) The influential collection of ‘Research Problems in Discrete Geometry’ by Brass, Moser, and Pach [8] describes it as ‘possibly the best known (and simplest to explain) problem in combinatorial geometry’. For an AI to produce a solution to a problem of this calibre is both surprising and impressive.

…On examining the construction, it becomes more clear how people had missed this before – it requires the confluence of several different unlikely events: that a good mathematician is

(1) spending significant time in thinking about the unit distance conjecture in the first place;
(2) seriously trying to disprove it, despite the oft-repeated belief of Erdős that it is true;
(3) believes that there is mileage in generalising the original construction to other number fields,
and so is willing to expend significant time in exploring such constructions; and
(4) sufficiently familiar with the relevant parts of class field theory to recognise that the appropriately phrased question about infinite towers of number fields with appropriate parameters can be solved using existing theory.

The AI met all of these criteria, and its success here echoes previous achievements: it often produces the most surprising results by persevering down paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery.

…perhaps some in the area will be a little disappointed with how little this tells us: it does not introduce any powerful new geometric tools, or hitherto unsuspected structural results, that a proof of the unit distance conjecture would likely have called for. Still, while perhaps not the proof of a conjecture that we had hoped for, no doubt this construction and the ideas involved will have a major impact in discrete geometry.

One aspect of this proof should not be overlooked: while the original proof produced by AI was completely valid, it was significantly improved by the human researchers at OpenAI and the many other mathematicians involved in the present paper. The human still plays a vital role in discussing, digesting, and improving this proof, and exploring its consequences.

The frontiers of knowledge are very spiky, and no doubt the coming months and years will see similar successes in many other areas of mathematics, where long-standing open problems are resolved by an AI revealing unexpected connections and pushing the existing technical machinery to its limit. AI is helping us to more fully explore the cathedral of mathematics we have build over the centuries; what other unseen wonders are waiting in the wings?

One way of putting this is that the mathematicians are now acknowledging that the AI’s are “one of us”. Gooble Gobble! Read the AIs chain of thought  to understand why. I asked Claude how many people the world could understand the proof:

A rough tiered estimate, treating “understand” as “could read the 42-page note and follow the argument without needing to learn new machinery from scratch”:

Tier 1 — could referee it cold (real working knowledge of class field towers + the Ellenberg–Venkatesh circle): roughly 150–400 people worldwide. This is essentially the active algebraic number theory community working near arithmetic statistics, plus a handful of arithmetic-geometry-adjacent combinatorialists. The author list itself is a decent proxy for the upper crust of this group.

Tier 2 — could understand it with a week or two of focused effort and some Wikipedia/textbook chasing (strong number theorists or combinatorialists outside the immediate subfield, plus sharp grad students past quals at top programs): roughly 2,000–5,000. Think most tenure-track number theorists, the top tier of extremal combinatorics, and arithmetic geometers generally.

Tier 3 — could grasp the structure of the argument from a Quanta-style exposition without verifying the steps: 50,000–200,000+, i.e., most working mathematicians and a chunk of physicists/CS theorists. This is not what you asked, but it’s where most of the public “understanding” will sit.

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My best read of the evidence is that a union raises wages by around 7% for currently unionized employees. The wage gains from a redistribution of rents evenly across workers. Wage compression exists, but redistribution from worker to worker is only a small part. These are the current effects – unionizing more of the economy will have declining marginal returns, and will likely turn negative quickly.

I do not believe that unionization is efficient. While precise figures are lacking, it is unlikely to be a better method of supporting the poor or working class, both because union workers are not disproportionately poor, and also because their methods of extracting surplus are not restricted to just wages. I will note that the best paper on the effects of unions of productivity finds a positive partial equilibrium effect, but that is only for some markets, does not benefit the consumer, and the aggregate effects are likely negative.

Here is much more from Nicholas Decker.  It would be a much simpler — and better — world if everyone understood this.  This issue, above many others, is a good test for whether someone is willing to think more analytically and confront the issue of economics vs. mood affiliation.  Because pro-trade union sentiment has literally centuries of mood affiliation behind it.

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Scientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to biology, no system has yet automated all these stages [1, 2, 3]. Here, we introduce Robin, the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results, and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery. By applying this system, we were able to identify promising therapeutic candidates for dry age-related macular degeneration (dAMD), the major cause of blindness in the developed world [4, 5]. Robin proposed enhancing retinal pigment epithelium phagocytosis as a therapeutic strategy, and identified and confirmed in vitro efficacy for ripasudil and KL001. Ripasudil is a clinically-used Rho kinase (ROCK) inhibitor that has never previously been proposed for treating dAMD. To elucidate the mechanism of ripasudil-induced upregulation of phagocytosis, Robin then proposed and analyzed a follow-up RNA-seq experiment, which revealed upregulation of ABCA1, a lipid efflux pump and possible novel target. All hypotheses, experimental directions, data analyses, and data figures in the main text of this report were produced by Robin. As the first AI system to autonomously discover and validate novel therapeutic candidates within an iterative lab-in-the-loop framework, Robin establishes a new paradigm for AI-driven scientific discovery.

Here is the full article from Nature.  And here are two other new Nature pieces on related topics.

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It seems, then, that we need another signal that can add precision to our introspection. And that signal is as follows: we are more likely to be lying to ourselves when we are engaging in internal monologue.

An internal monologue is the experience of having concrete, “narration-style” thoughts as opposed to passive experiences. This argument maybe doesn’t apply to people with a constant internal monologue, or those who have none. But it seems like most people’s internal lives are some combination of subconscious thought and active monologue: most of our day-to-day moments are spent instinctively receiving and reacting to external stimuli, but in certain moments — e.g. when faced with difficult choices that require serious deliberation — our thoughts morph into something that resembles language as we try to articulate our feelings and ask ourselves questions.

This is more likely to happen when there’s a divergence between your actual feelings and what you want your feelings to be.

Here is more from Elizabeth Li, via Tejas.

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