We show how fertility delays compound across generations to raise age at first grandmotherhood. Under the 1960 first-birth schedule median grandmotherhood came at 44. Under the 2024 schedule it is 57, cutting the healthy years with grandchildren from 28 to 15. Longer life does not make up the loss of years when the grandmother is healthy enough to supply money and care. Delay is most pronounced among the college-educated, with reproductive-control access accounting for over half the fall in grandmotherhood by age 55 among White college graduates. When a daughter times her first birth to maximize her own payoff, the loss her choice imposes on her mother is external to her calculation. We call this the Rotten Adult Kid Problem. Delay raises both the price of a grandmother’s care and wealth per grandchild.

That is from a new NBER working paper by Dora Costa and Matt Kahn.

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2. Andrew Ng.

3. Some semi-skeptical notes on stablecoins, based on economic history.

4. Global views on fairness and redistribution.

5. Essays on whether AI will make management more valuable.

6. The real authorities on the California billionaire wealth tax.  They are right in front of your eyes.

7. “The storied venture-capital firm is investing $35 million in an unaccredited San Francisco-based two-year alternative to traditional college for students ages 16 to 22. Though named the Horowitz Andreessen Academy, the school will operate as an independent business led by Gagan Biyani, co-founder of the ed-tech platform Udemy.” (WSJ)

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On Aug. 26, a Semafor investigation using Pangram to determine whether recent guest columns in major newspapers were written using artificial intelligence identified a column by Schnell as “100% AI-written.” Schnell’s column in The Washington Post — titled “Universities are fighting AI cheating. But there’s a deeper problem.” — argued that “a degree should distinguish what students can do independently from what they can accomplish with AI.

”The Dartmouth subsequently reviewed publications written by Schnell before and after the November 2022 public release of ChatGPT. Pangram 4.0, the latest publicly available version of the AI detector, labeled all of Schnell’s written works prior to 2022 as “100% human-written,” while a test of nine of his works published this year returned a median “AI-written” percentage of 96%. Between 2019 and 2026, Schnell did not publish any academic works or op-eds for which he was the sole author credited, according to his online curriculum vitae.Pangram 4.0 assesses whether a work is AI-written by separating the submitted text into smaller segments, which are then identified as “human,” “AI-assisted” or “AI-generated,” according to a Pangram technical report.

…Schnell’s Sept. 18 email statement to The Dartmouth was itself “100% AI-written,” according to a Pangram test.

Here is the full story, you people crack me up…

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I do not understand how it is supposed to work.  Put aside the issue of foregone innovations, let us say I seek to access an AI from overseas.  Am I allowed to visit their website?  So many sites have AI behind them, including Chinese open source AI.  Do all of those sites get banned?  And how?  Firewall imposed on Americans?  And we cannot put the good open source models on our hard drives?  How enforced?  Does the government have to read the creation logs of the model (how can they?), and then decide whether I can use that model or not?

What if Anthropic licenses its IP to an independent subsidiary in the Cayman Islands and they keep on using code to improve the AI?  Or maybe only foreign nations are allowed to have the best models and the really fast rates of improvement?  How are multinationals supposed to operate across borders?  (Presumably you cannot just use the RSI of your foreign affiliate, but then the whole MNC is crippled and eventually rendered uncompetitive?)  How are we to keep “AI sovereignty” for Americans?

If the top U.S. labs have to give up on what is supposed to be really important, they will look very “catchable.”  The policy creates a big incentive for some other parts of the world to invest a lot more in compute.  How safe is that?  And in the short run, do we also have to stop American firms from selling their compute abroad?

Does this whole thing mean that regular companies can use AI to write code, but the main labs cannot?  Solve for the equilibrium there.  Or the whole code writing function goes away for everybody?

What is the political mechanism through which RSI progress gets turned back on again, and when?  Is it as rational as our current debate over data centers?  If the top companies are no longer building RSI models, how are they supposed to know how safe or unsafe they might be?

At current margins, are humans even capable of writing the next steps of code that are required for further progress?  At whatever speed?

I do not find this to be a workable idea.  I think it is “people wanting to do something that sounds reasonable and less scary,” without thinking through the practical details.  I have made a related point before, but one does best with AI policy when you start with the stuff that otherwise you might put into your last two or three paragraphs of an essay (“It remains to be seen how…but we are all human beings and surely we should not give up hope…”).  Even if you believe in a deal with China, and I do not, we (and others) now know competitors can catch up more quickly than we had thought, at least if they think they have a chance of winning.  They might even buy some space compute from Elon.

C’mon people, I know for many of you this is a difficult take to swallow, because it feels like “we don’t care enough” if we do not take some dramatic steps.  Nonetheless the reality is that, a long time ago, we made a whole series of decisions that imply…we have no other choice than to just see this one through and get it more or less right.  The longer we deny that truth, the harder it will be for all of us.

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This paper documents how artificial intelligence has affected stocks and flows in the U.S. labor market. Combining CPS and JOLTS data with AI exposure and adoption measures from OpenAI and Lightcast, respectively, we construct labor force stocks, worker flows, and market tightness by AI exposure and adoption. Since the introduction of LLMs, workers with high AI exposure and adoption have experienced larger declines in job-finding and job-switching rates than other groups, while their within-job activity switching has increased noticeably relative to others. The positive effects of AI on nominal wage growth and hours worked attenuate markedly in the post-LLM period, suggesting that LLM-driven reallocation has operated through within-firm task reorganization and a reconfiguration of labor inputs, alongside weaker demand for workers most exposed to AI. To quantify LLM-driven reallocation pressure, we construct individual-level hirability and separability indices and showthat dispersion in job-finding prospects has risen markedly since 2023, largely driven by the AI factors. The natural rate of unemployment—recovered from the trend components of unemployment inflows and outflows by AI exposure and adoption, as well as from a theoretical model of structural unemployment—is estimated to have risen by about 0.1-0.2 percentage point since the introduction of LLMs, albeit with considerable uncertainty.

That is from a new NBER conference paper by Hie Joo Ahn and Nicholas A. Carollo.  Via Inclusive Productivity Network and Alex Imas.

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2. “[The Mises] library now has a permanent independent home: https://freecapitalists.org/. It holds 945 books, 3,374 journal issues and 2,732 recorded lectures, all free, in PDF, EPUB and MOBI, with the rights recorded per work.”

3. “They invited me to come see it for myself at Sopchoppy’s annual worm grunting festival, which draws thousands of visitors.” (NYT)

4. The (small) movement to return Europe to Latin.

5. Political pro-Trump bias in Polymarket?

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The essay makes for hard reading and I acknowledge NHJ’s courage in writing about her mistakes and the cost to her child. The piece is raw and true when talking about her personal choices, but the larger story in which NHJ embeds her personal history is mostly false. The problem isn’t funding. The failures she documents are failures of management and accountability, not lack of resources. NYC spends upwards of $40k per pupil on average, the highest of any large district in the country. Moreover, schools in NYC with lower-income students get more than the average. PS 307, where NHJ initially sent her child, is currently funded at just over 52k per student, far above the national average.

Nationally, there haven’t been big gaps in funding within states by race or income for decades. NHJ has reckoned with her failing her own child but she has not yet reckoned with the failure of her entire educational philosophy.

The role of ideology in blinding NHJ is dramatic. NHJ’s daughter earns high grades in her math classes but she fails standardized tests. Most parents, especially conservatives, know what that means. The classes are a lie. But NHJ is blinded by ideology and assumes it’s the standardized tests that are biased. It’s only when her daughter demands to go to a better school that she and her daughter confront the truth:

Najya knew very little algebra and had huge gaps in many of the building blocks of math that she should have learned in elementary school, like mastery of long division and fractions.

The confrontation is almost disastrous:

My child’s confidence wilted like an unwatered plant. She started to develop an almost debilitating anxiety around taking tests. She became sullen at school and began to act out. For the first time ever, I got messages from teachers about Najya’s behavior.

Fortunately, Najya battles back and recovers but much more could be said here about the costs of social promotion, DEI and mismatch on the so-called beneficiaries.

Ironically, NHJ invokes the heroic history of Black Americans integrating schools to justify keeping her own child in a segregated one. Painful.

NHJ thought that she and her husband–politically connected, astute people who were not afraid to speak up–could do well for their daughter and the other kids in the school. But in the end she has written a great piece on why voice fails–even a powerful voice–in the absence of the threat of exit. Hannah-Jones could have saved herself some grief and her child a poor education by reading my post The tragedy of Jonathan Kozol.

It’s been said that Ayn Rand’s heroes don’t exist, but her villains do. Case in point. Hannah-Jones says that she and her husband “felt an obligation to make educational decisions based on the collective good.” But she wasn’t just sacrificing herself for her ideology; she was sacrificing her child, who had no choice in the matter. “I don’t think she’s deserving of more than other kids,” she said in 2017. Nearly a decade later, Najya reminded her that in the abstract your child may not deserve more than other children, but she is nevertheless entitled to expect more from you. “Sometimes I wish instead of always thinking about other kids, you would have thought about me.” You don’t have to be a Randian to think that your child should not be sacrificed to the collective good, but it doesn’t hurt.

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“If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet.”

As Roon noted: “this sounds completely incoherent to me”

From Rob Saker:

An agent is a system that can plan, use tools, write code, query data, run experiments, check its own work, and keep going. That is how the work gets done. Treating “agentic” as optional decoration, as if the serious version of AI is a polite chatbot locked in a box while the unserious version “roams free on the internet”, is the kind of sentence you write when you’ve heard the buzzwords and never watched a lab actually use the technology. You do not cure cancer with a model that only answers questions. You cure cancer with systems that can read the literature, propose hypotheses, design assays, analyze results, rewrite the next experiment, and do that loop a thousand times faster than a human postdoc. That loop is agentic. Strip the agency out and you are left with a very expensive autocomplete.

I agree with those points, but my main concern is different.  How would we enforce such a prohibition on commercial agents?  Set up a Chinese-like firewall that bans Americans from accessing sites with foreign agents?  Monitor all those sites over time, so we know which suppliers to ban?  Ban VPN as well?  Give our government the power to inspect hard drives, in case agentic functions might be embedded there?  Set up FBI “phishes,” luring Americans in with the prospect of agent access from abroad, and then arresting them, as we do with child ****?

Something else?  How about insisting that all American (and foreign?) web sites set up tough captcha problems, so that agents may not be used (ha ha)?

You might also ask how an AI “agent” is to be defined, after all even O3 had some “agentic” abilities, such as opening up museum web sites to see which exhibits are on.  Is it typing things into boxes and filling out forms that is to be prohibited?  How much regulation of software would that require, how would that regulation actually occur, and what else would end up being restricted?

I do not think Obama intends to be supporting massive restrictions on freedom of speech and civil liberties.  Rather this is a good example of how some political factions will simply have “ideas about what might be good,” without having freedom concerns — or for that matter practicality and civil liberties concerns — center of mind in the first place.

And this would cause the immediate bankruptcy of both Anthropic and OpenAI, right?

I can readily imagine that, upon the advent of agentic AI, we need some significant changes in our cyber laws.  Now would be a good time to both commission and also cite some peer-reviewed academic literature on this matter!?  That is not an impossible thing to do, yet our public discourse seems oddly resistant to the notion.

Addendum: And do not forget the classic Gwern piece, remarkably early in its prescience.

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2. Secretariat as GOAT.

3. Dead brains and the piano??

4. “A real contribution of AI is showing the critical weaknesses of humans and human structures.”  But can we take it?

5. When did the Chinese reach New Zealand? (speculative)

6. “Everyone needs to realize we are fighting the summation of the last 100 years of war that never got settled they just slowed down at times”

7. “you have no idea how fast the world will accelerate AI progress if a world war began” — no major war (in addition to the status quo) would be one excellent move in the direction of AI (and other) safety.

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Kudos to the team!

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I observed, for example, that when I got an invitation to travel to give a talk in a different time zone, it mattered what time zone I was in when I got the invitation.  if well rested, in my home time zone, I would think, No big deal, about making a long trip and would likely accept.  However, if I was jet-lagged in a different time zone when the invitation came in, and in reach with the misery of travel, I would be far more likely to decline.

…Perhaps you’ve noticed when taking a long-haul flight that waiting for a connecting flight feels much worse if your layover is close to your final destination.

That is from the new and very stimulating The Opposite of Happiness: How Bad Feelings Make and Break Us, by George Loewenstein.  A very Smithian book I might add.  Reading this book, one also wonders if the researchers who do behavioral economics are more depressed than average?  (“I’ve met many depressive characters in my life, but one of the most reliably miserable people I’ve ever known is my friend and collaborator Matthew Rabin.”)

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UK productivity rose at almost twice the pace previously estimated in the decade after the 2008 global financial crisis, according to new official figures that make Britain look much less of an international outlier.

Output per hour worked, the main measure of labour productivity, grew by an average of 1.3 per cent per year between 2009 and 2019, the Office for National Statistics said on Thursday. The ONS had previously estimated average annual growth of 0.7 per cent over the period.

The figure is based on a new “component-based” methodology that has already been adopted by many other national statistics institutes. It draws on a range of data sources to improve the ONS’s count of both the number of workers contributing to UK output and the number of hours worked.

Here is more from Delphine Strauss at the FT.

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…this month, Singapore launched a scheme promising actual cash to citizens as a reward for logging reading time on a government website.

It’s unlikely to make you megabucks – you have to rack up 50 reading sessions of at least 15 minutes to earn one Singapore dollar (£0.59), with only one logged session allowed per day. But behavioural scientists and psychologists told the Guardian such a scheme could still help boost a habit in precipitous decline…

We all know reading has long-term benefits, but a key element of the Singapore scheme hinges on introducing a short-term reward for the activity, mimicking the immediate dopamine hit of scrolling. In fact, the “gamified” element may be “smarter” than the financial reward offered, says Patrick Taylor, director of education at the Behavioural Insights Team (which originated in David Cameron’s Cabinet Office, also known as the “nudge unit”). “Having a manageable 15-minute daily goal, built around existing routines like commuting or bedtime, with streaks and tracking to keep progress visible” are the aspects that could make the scheme a success, he says.

Here is the full article.

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2. Bergen County (Korean) fact of the day.

3. AIs cracking superforecasting.

4. Gregor Benko, RIP (NYT).

5. Joshua Saxe on the increase in cyber risk.

6. Lichtenstein political infighting heats up (FT).

7. Risk, the biggie.  And on the Suwalki gap.

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My position on AI is merely the conservative, skeptical position of 2006, updated with intellectual honesty for the reality of late 2026. And that position, if you need me to spell it out, is as follows:

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

It seems to me that the Singularity has already started; it’s just wildly unevenly distributed. Yes, I still unload the dishwasher and clip my toenails. On the other hand, in whatever years I have left, I don’t expect that I’ll ever again prove a theorem because I’m actually needed to prove it. If I do, it will only be for my or others’ enjoyment or edification.

The test is this: if we took the news of these past few weeks and sent it back in time twenty years, would I agree that it looked like the beginning of an AI Singularity? The intellectually honest answer is: yes, absolutely. But then that’s all we need. No backsies.

I feel like it would be healthy for everyone to stop grinding their ideological axes, their sentiments about Dario Amodei or Sam Altman, for long enough simply to acknowledge that the wonders and terrors are here. They couldn’t be here more clearly if the sky had turned reddish-orange like in the Matrix movies.

It’s here clearly enough that, when I put my kids to sleep at night, I now feel it in the pit of my stomach: what sort of future can they possibly have? What could they learn today that could possibly be relevant to that future? (Yesterday, my 13-year-old daughter joked unprompted that, if she wants to become a mathematician, it now looks like she has maybe two more weeks.) Certainly when my grad students want to discuss what sort of careers might await them on graduation, I no longer have any clue what to tell them.

…For anyone who says AI doom sounds like an apocalyptic religion, that the rationalists/Singulatarians seem like a Bay Area cult, that Eliezer Yudkowsky gives off the vibes of a messianic prophet: yes, yes, and yes. But crucially, today you’re no longer being asked to believe in arguments and extrapolations, but only in the front-page news. Accepting the reality of the coming machine god after it’s solved Navier-Stokes and dozens of other longstanding open math problems (while dramatically ramping up in capability every month), is sort of like accepting Jesus after he’s returned to earth on the gleaming cloud. It’s the epistemic bare minimum.

Yes, there’s still enormous uncertainty about what the rest of our lives will look like, but as far as I can tell, there’s no longer any real uncertainty that it’ll all mostly revolve around AI, and the extent to which we succeed or fail at directing its power toward human flourishing.

By any accounting that doesn’t stack the deck, Eliezer Yudkowsky was right about what the greatest challenge facing civilization in our lifetimes was going to be, and you and I were wrong about it. Why I was wrong is a question I’ll ask myself every day in whatever time remains. But, you know, at least I updated once the prophesied wonders and terrors actually started arriving! If you haven’t done likewise, why haven’t you?

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