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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• 3. On the plus side, libertarians look like sane moderates now. by Hazel Meade

 

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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2. Roon.

3. Do economics and finance assessment for AI models at Mercor.

4. Are we underestimating health care sector productivity?

5. Can AI replace human counselors at scale?

6. Nan on nascent philanthropy.  Recommended, very important.  It focuses on what an additional $50 billion in philanthropic spending might look like, and asks where the talent will come from.

7. Chennai has the only surviving handwritten newspaper in the world?

The post Wednesday assorted links appeared first on Marginal REVOLUTION.

       

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• 6. One word answer: USAID. There will be a need to distribute ... by David Khoo
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Hi folks, appreciate the discussion of the piece here, as ever.

I just wanted to chime in briefly with an analogy that speaks to one of the ways I think about the causal mechanism here, and to my mind pushes back against the argument that since past declines in fertility didn’t come from smartphones etc the current decline can’t either.

• ⁠In the past, weight loss generally came from sustained dieting and exercise
•⁠  ⁠⁠Now it overwhelmingly comes from injecting GLP-1s
•⁠  ⁠⁠In the same way that GLP-1s are a technological shock that amplifies/accelerates the old mechanism (eating less), social media is a technological shock that amplifies/accelerates the old mechanism (cultural change)

To my mind one of the ways (possibly the main way) that phones and social media could be affecting fertility is by accelerating and internationalising pre-existing trends of cultural change. One example could be young women’s sense of empowerment and independence, which was on the rise in many parts of the world but has sped up over the past decade or two (I would point to my previous work on the ideological gender divide as one piece of evidence here) and has spread rapidly to regions and cultures that were surely very unlikely to reach this point without exposure to western social media.

Thoughts?

I will add one point on this debate, noting I do not think it runs counter to Burn-Murdoch.  Some commentators are insisting that what really matters is how many children survive to adulthood, not how many are born in the first place.  But both numbers matter a good deal.  Every time a woman gets pregnant she incurs significant costs, especially in older times when death in childbirth was common, or even death or health problems from a miscarriage were a much greater risk.  Furthermore, if you tried for seven kids, but only expected three or four to survive, a lot of times more than three or four survived.  So general survival of all or almost all your children had to be a palatable option, even if the expected value was lower than that.

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Tajikistan’s remittances are worth nearly half the country’s GDP—

In Tajikistan, remittances — the money sent or brought back by migrants — amounted to 48% of GDP in 2024. The chart places this figure in context by comparing it with other countries with data for the same year. Nicaragua and Honduras receive remittances worth around a quarter of their GDP — high by global standards, but still far below Tajikistan’s level. Remittances here include two types of flows: money migrants abroad send home to their families, and money cross-border workers bring home from short-term jobs abroad.

Both of these flows play a role in Tajikistan, where most remittances come from labor migrants in Russia. In addition to the roughly 400,000 Tajiks settled there, hundreds of thousands more cross the border for seasonal and short-term work.

According to a report from the International Organization for Migration, about 1.2 million Tajiks were in Russia in mid-2024, which is more than a tenth of Tajikistan’s total population.

The World Bank’s latest Tajikistan Economic Update says that much of the country’s recent rapid economic growth (above 8% since 2021) was supported by these remittance inflows.

That is from Our World in Data, with a picture at the link.

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The emergence of firms like SpaceX and Blue Origin has made space a leading example of how private enterprise drives innovation, marking what many see as a sharp break between Old Space and New Space. Yet little systematic evidence documents when the transition to this new phase of space innovation occurred and which firms drove it. We use patent data to provide this measurement and find that the largest surge in space innovation occurred in the 1990s, coinciding with demand-side market creation, and preceding the entry of high-profile startups after 2005. Throughout this period and since, incumbent aerospace firms account for most of the space-related patenting, with entrants contributing a growing but minority share. The same geographic regions that dominated space innovation during the post-Apollo era remain dominant today. These patterns are consistent with directed technical change: incumbents direct R&D toward policy-created markets accessible from existing capabilities, while entrants bring science-based insights into domains requiring new paradigms. Our findings suggest that New Space is more closely connected to Old Space than prevailing narratives imply, and that government’s most consequential role in space innovation may lie in constructing appropriable markets. We make patent data on space-related technologies available for future research.

That is from a recent NBER working paper by Ruben Gaetani & Alexander T. Whalley.

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2. JFV on smart phones as accelerators of fertility declines.

3. Maryland markets in everything.

4. Polling Chinese on a top one hundred books.

5. From the excellent Samir Varma, could alien drone probes decelerate in time?  And here is analysis from GPT.

6. “I am thrilled to announce the launch of Totei.com. Totei is a magazine devoted to craft and craftsmanship in all its forms. The name Totei comes from the ancient Japanese word for apprentice.”  From Gaurav Kapadia.

7. The young seem to like AI the least.

8. NYT obituary for Edmund Phelps.

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My case study was acting. Acting was considered repugnant for over 2,000 years. In Rome, actors could not vote, hold office, or be trusted to give an oath in legal proceedings. So why don’t we find acting repugnant today?

One lesson: weighing costs and benefits is not enough. Roth discusses empirical research showing that legalizing prostitution cut STDs and sexual assaults—against prostitutes and others. But evidence alone won’t shift a repugnance norm. You also have to reframe the activity. Acting, for example was reframed from body rental to a skill requiring intelligence, training and ability. So I went out of my way to say that I am a fan of Aella—though not her only fan—and that I see no reason why escorting should not be considered a skill, requiring intelligence, training, and ability. I can think of few better ways of raising social welfare than making sex 10% better!

I also spoke on human challenge trials. Roth and I agree: challenge trials could have sped up COVID vaccines and saved tens of thousands of lives. We should be angry this didn’t happen. Why didn’t it? Even though most people think human challenge trials are a good idea, there was a repugnance bottleneck because the minority who did find human challenge trials repugnant were in charge. I discuss how to change this.

Al leads the discussion. My comments start at 25:15.

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It is important to distinguish between the positive side of wokeism and the unreasonable side. The positive side supported gay rights and discouraged racism in the public sphere. The unreasonable side brought us cancel culture, stifled discussion, insisted on very particular views of race and gender identity, boosted DEI and other race-discriminatory policies, and generally made America a more intolerant place. It was most of all about who had the right to steer the agenda of public discourse, and who had the right to push out dissenters.

The unreasonable side, since it was about power and control, had negative vibes built into its core. Fortunately, American society pushed back against many of the most objectionable manifestations of those negative vibes, but did we get rid of the negative vibes themselves? I do not think so. The American people still seem pretty low in trust, unhappy with America’s position in the world, glum about the economy and cost of living, and increasingly skeptical of both AI and billionaires. That is all happening at a time when the American economic situation, while mixed, is by no means as terrible as it was in, say, 2009. Happiness and mental health seem to be lagging behind the country’s actual achievements.

So what has been happening? The forces behind wokeism no longer command so much public attention and respect when they argue about terms and pronouns. Instead, left-adjacent movements have arisen with a contrasting emphasis on action, and often action of a terrible sort. California is considering, for instance, an unworkable tax on billionaires in the state, one that even most left-leaning Democratic politicians do not support. It might nevertheless pass through via referendum…

What’s more, it is possible we are entering an era with a new culture of assassinations. There have been assassinations of Charlie Kirk, of healthcare CEO Brian Thompson, and several attempts on the life of President Trump. It can be debated how many of these killers had direct connections to the political left, but it is hard to avoid the conclusion that left-wing rhetoric about democracy destruction helped make such actions conceivable.

The social energies of the American left have moved away from the realm of speech and into plans for concrete action, whether in politics, through attempted wealth confiscations, or through organizing violence. In retrospect, wokeism, for all its problems, was a relatively harmless way of distracting activists and keeping them  Negative busy with wars over words—a less-bad allocation of social energies than what we are now seeing. So while I would not say I long for the return of high wokeism, I recognize it has been replaced by a left-adjacent movement that is worse.

Worth a ponder, do read the whole thing.  I should note I do not let the right off the hook either, though the column is mainly about what has succeeded Wokeism.  Negative emotional contagion has affected both the left and right wings today.  Here is one simple case in point.

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2. Can AI replace Richard Hanania?

3. Predictions for Singapore.

4. Four ways of being seen, one of which is imaginary.

5. HOPE, preview of forthcoming Korean movie.

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Over the past half-century, U.S. four-year colleges have shifted from enrolling mostly men to enrolling mostly women, while the economic position of non-college men has weakened markedly. We examine how these changes correspond with the evolving structure of marriage markets across cohorts and places. As college men have become increasingly scarce, college women have maintained stable marriage rates by marrying high-earning non-college men. This shift—combined with the broader economic decline of non-college men—has sharply reduced the pool of economically stable partners available to non-college women: the share of non-college men who earn above the national median and are not married to college women has fallen by more than 50%. Cross-area evidence shows that education gaps in marriage are smaller where non-college men face lower rates of joblessness and incarceration. Taken together, the evidence suggests that deteriorating outcomes for men have primarily undermined the marriage prospects of non-college women.

That is from a new NBER working paper by Clara Chambers, Benjamin Goldman & Joseph Winkelmann.

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Thanks for swinging by Southeastern Michigan. He are two things other things that this area continues to produce and export at scale that don’t get as much notice:

* Mortgages – The two largest residential mortgage lenders are located in Detroit: United Wholesale Mortgage ($164B of mortgage originations for 2025) and Rocket Mortgages ($113B). It’s a fragmented industry, but to give you a sense of their comparative scale, Chase is #3 lender @ $66B in originations. Detroit continues to be the home of financial services for many Americans’ largest purchase.

* Food – Michigan, not NY or Italy, is responsible for the scaling of pizza. Domino’s, Little Caesar’s, and Jet’s were all founded in Southeastern Michigan. Domino’s is the largest pizza company in the world, and in many global markets, Domino’s defines “pizza.” For instance, Domino’s market share of pizza in the UK is over 50%. So, the UK has adopted Michigan’s, not Italy’s, understanding of pizza.

One narrative for Michigan should be that it has continued to shape global culture, through scaled production of mortgages and pizza. It doesn’t get more American than cars + mortgages + pizza, does it?

That is from Jeff Withington.

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