We also have plenty of evidence that the fears of AI experts are sincere. Amodei, Altman and Musk were all publicly warning about AI risk long before they had AI companies to promote. The worry runs well beyond the executive suite; rank and file researchers share it. And it extends outside the industry altogether, to computer scientists with no product to sell, among them Nobel laureate Geoffrey Hinton. Hinton left a high-paying job at Google precisely so he could speak out and Hinton is not in a Berkeley polycule with Eliezer Yudkowsky, at least as far as I know. Whatever else you may say about the belief that AI presents a serious risk, plenty of AI researchers believe it sincerely.

Similarly, when Amodei recently proposed to slow the pace and install independent safety teams at AI companies many people jumped to the conclusion that this was regulatory capture. Sorry, but no, that theory doesn’t make sense. To see why, we should review the theory of regulatory capture.

Regulatory capture came out of the political science literature especially Marver Bernstein’s 1955 classic, Regulating Business by Independent Commission. Bernstein argues for a regulatory life cycle: Gestation, Youth, Maturity, Old Age. A scandal brings a bureaucracy into existence—or gives an existing one new powers. The public’s attention, like Sauron’s eye, fixes on the issue of the day: something must be done. The thalidomide scandal, for example, helped establish the modern FDA.

Gestation gives way to a youthful burst of reform and the do-gooders come to Washington ready to battle the industry. Inevitably, however, the public’s eye looks elsewhere. But the industry never looks away. It lobbies Congress, hires former regulators, trains future ones, and supplies much of the information the agency needs. As the agency matures, accommodation replaces confrontation. By old age, the regulator has become the industry’s protector. The classic example is the ICC, created to regulate the railroads but it eventually came to shield them from competition from the trucking industry.

Notice that classic regulatory capture takes time, it’s a process of erosion rather than a battle, it happens in the shadows, in the backrooms, away from the public’s eye. As Culpepper argues in Quiet Politics and Business Power, business power goes down as political salience goes up. Regulatory capture and lobbying does a good job explaining why roasting coffee beans was defined as “domestic manufacturing”, thereby lowering Starbuck’s tax rate by 2%. It does less well at explaining big cross-industry issues the public cares about such as environmental regulation or race and gender discrimination regulation. Finally, don’t confuse capture with firms making the best of a bad situation. Philip Morris supported the 2009 Tobacco Control Act not because FDA regulation was Philip Morris’s unconstrained ideal but because it knew regulation was coming and it wanted a seat at the table to nudge the rules in its favor. That’s ordinary political bargaining—or rent-seeking—not evidence that the regulator has been captured.

Now let’s evaluate Amodei’s call for regulation in light of regulatory capture theory. AI regulation is in gestation. Public attention is fixed on the industry, and much of that attention is hostile. The big profits in AI lie in automating work, and job loss is a much more salient fear than extinction. AI politics is now loud–precisely the environment in which Culpepper predicts business power will be weakest. A mature industry can bend regulation to its purposes through revolving doors, longstanding relationships and obscure rulemaking. An industry under Sauron’s eye has much less power and faces much greater risk that politics will bend regulation to its purposes. Political actors are eager for an excuse to redistribute AI rents away from capitalists and toward favored groups (ala Peltzman).

Regulation will reduce AI profits. That doesn’t prove that every rule Amodei favors is innocent of self-interest, an absurd proposition. I suspect that Amodei’s ideal may be something like a single regulated AI monopoly—safe and reasonable profitable, like the old AT&T. But that’s not the profit maximizing outcome. If transformative AI can capture even a fraction of the enormous labor market–the world’s biggest market–laissez-faire would mean vastly greater profits. Amodei may simply prefer a smaller fortune and a safer world. That is perfectly consistent with self-interest playing a role; it is not consistent with the bastardized theory that profit maximization is the only thing that matters or that “capture” is universal.

Go ahead: argue that Amodei and other AI experts are wrong about AI risk. Ask whether his proposals favor Anthropic. But calling “our product might kill you” a clever marketing and regulatory-capture strategy isn’t sophisticated analysis. The facts don’t fit regulatory capture theory and trying to make them fit requires epistemically painful Ptolemaic epicycles. Even a dull Ockham’s razor cuts through that story to the obvious alternative: Amodei actually believes what he’s saying.

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Because larger firms are more attractive targets but also invest more in protection, the model generates an inverse-U relationship between firm size and attack risk, consistent with the data. Introducing cyber risk reduces firm entry by 3.6 percent, aggregate productivity by 0.6 percent, and total output by 1.8 percent. These effects arise from general equilibrium adjustments in entry, firm size, and spillovers that are absent in typical partial-equilibrium analyses. Policy responses differ sharply: appropriately designed subsidies and minimum cybersecurity requirements can raise aggregate output, while bailouts reduce it.

Note this is not a paper about AI.  But it may help us develop estimates of the future costs of AI cyberattacks.  We need much more effort in this direction, and I hope this subfield rises in status rapidly.

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During the 2023 U.S.-China nuclear talks, America proposed missile-launch notifications, a nuclear crisis hotline, and processes to limit the use of outer space for military conflict.  China declined all of those.  Blame the U.S. if you wish (do we always keep our word?), but that is what happened.

China also broke off meaningful arms control talks.  You may think it is their right to play catch-up, and to be cynical of our motives, but that is what happened.

How well did China exchange timely information about Covid, and cooperate with stopping its initial spread?

Get the picture?

If you start with China in your discussion, you will end up with sensible proposals before getting too caught up in your moods of the day.  If you are reading proposals or for that matter tweets for AI regulation, and the writer does not deal with these China issues in a forthright and very specific manner, you should be very suspicious indeed.

Here is Ezra and Matt Sheehan, discussing related issues (NYT).

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Share prices of cyber firms have jumped around a lot in recent weeks, leading one side or the other to claim victory. But the crucial point is that, relative to the historical norm, the market is not really pricing ANYTHING big to change. There was a much bigger move in cyber stocks in both 2020-22 (up) and 2022-23 (down) but no one read “AI x-risk” into this.

Here is the full post with graph.

Hardly the final word, and I am myself more pessimistic than those numbers indicate.  But at least with this we are getting somewhere concrete and scientific rather than just scare stories.  As for meta-commentary on the discourse itself, you really should be asking who are the people insisting on data here, and who are the people trying to talk you away from focusing on the data so much.

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2. The astrophysicists are getting antsy too.  Princeton is also getting nervous.  If nothing else, these are huge PR own goals.  The guy is a Mill scholars, can you imagine J.S. Mill tweeting that way?

3. New David Brooks podcast.

4. One Chinese view of AI risk.  And from Richard Hanania.

5. Arnold Kling on Polanyi knowledge.

6. Jeremy Stern profile of Mark Zuckerberg.  Great piece.

7. China’s first AI-generated TV series.

8. Introducing Free Press Excursions.

9. Redux of my earlier talk/session at St. Andrews on Effective Altruism as a philosophy.

The post Tuesday assorted links appeared first on Marginal REVOLUTION.

       

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Event key:

1. Dec 31 – China reports the Wuhan pneumonia cluster to the WHO
2. Jan 21 – First confirmed US case (Washington state)
3. Jan 23 – Wuhan locked down
4. Jan 30 – WHO declares a global health emergency (PHEIC)
5. Feb 19 – S&P 500 all-time high, 3,386
6. Feb 24 – Italy outbreak; first big US selloff
7. Mar 3 – Fed emergency 50 bp cut
8. Mar 11 – WHO declares pandemic; Europe travel ban; NBA suspends season
9. Mar 13 – US national emergency declared
10. Mar 15–16 – Fed cuts to zero and restarts QE; worst day since 1987 (−12%)
11. Mar 23 – Fed announces unlimited QE; market bottom at 2,237
12. Mar 27 – CARES Act signed
13. Apr 2 – 6.6 million initial jobless claims in one week
14. Apr 20 – WTI oil futures settle below zero
15. May 8 – April jobs report: 20.5 million jobs lost, 14.7% unemployment

The striking thing is how late the market moved. Wuhan was locked down and the WHO had declared an emergency a full month before the peak. The 34% drawdown then took 23 trading days, and the bottom coincided with the Fed’s unlimited-QE announcement rather than with any turn in the epidemiological news, which was still getting worse through April.

Addendum: Mostly from a query to Claude. You may fill in the missing context.

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Horwitz is in the business of playing a version of mom for local college students. Concierge companies offering student support have existed for decades. But in recent years, a new crop of upstarts — such as Horwitz’s company, MindyKnows; the Bama Mama in Alabama; the GA Mom in Georgia; and Campus Mom in Texas — have met additional demand from a new generation of worried parents…

The specific services vary here and there, but share commonalities. Campus Mom offers “holistic wellness check-ins,” laundry services and sorority recruitment support packages, sent to the sisters to up a child’s odds of acceptance. Carrie Eckhardt, the Bama Mama, will clean students’ dorm rooms and check in if parents haven’t heard from their child in a few days (“just pop in and say hi, and take a picture and send it to their mom”)…

Here is more from Kristy Alpert at the NYT.

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While recent research has provided evidence that the Medicaid expansions of the Affordable Care Act (ACA) reduced mortality, there is no evidence on the effect of the Affordable Care Act (ACA) net of the Medicaid expansions on mortality. This is an important gap in knowledge because the ACA significantly increased health insurance coverage in non-expansion states. In this article, we exploit the large increase in health insurance coverage brought forth by the ACA to examine the effect of the ACA and Medicaid expansions on mortality. Unlike prior studies that relied solely on geographic variation in Medicaid expansions to estimate the net effect of the expansion, we use a novel empirical approach that allows us to investigate the effect of the ACA net of Medicaid expansion on mortality, the incremental effect of the Medicaid expansion, and the overall effect of the ACA including Medicaid expansion. We use longitudinal data from the NHIS Linked Mortality Files (LMF) and a nationally representative sample of 40 to 58-year-olds combined with a difference-in-differences and a difference-in-differences-in-differences research design to obtain estimates of the effect of the ACA on mortality. We find no evidence that the Medicaid expansions had a beneficial effect on mortality but do find that the ACA net of Medicaid expansion reduced mortality.

That is from a new NBER working paper by Anuj Gangopadhyaya, Cuiping Schiman & Robert Kaestner.  Via Glenn Mercer.

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2. One decomposition approach to why interest rates have been going up.

3. New Guinness record holders.

4. Is there any chance of finding Rembrandt DNA?

5. “Abundance Corps is hosting Making Progress, a private gathering for students interested in how science, technology, and institutions can improve people’s lives.”

6. High school students plus AI solve math problem.

7. “China’s top spy chief has warned that artificial intelligence could pose a direct threat to the Chinese Communist Party’s hold on power, in what is the highest-level and most detailed articulation yet of how Beijing sees the technology’s security risks.” (NYT)

8. Good data: cybersecurity stocks surged today.

The post Monday assorted links appeared first on Marginal REVOLUTION.

       

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In their extreme scenario AI takes on a lot of tasks, GDP is 32.4% higher by 2030 than without AI and labor share declines from 60% to 45.2% but they make this striking point:

“Total labor income in 2030 in the extreme scenario is almost exactly what it would have been without AI: the labor share falls by a quarter while GDP rises by a third, and 0.45×1.32 ≈ 0.60.”

Exactly. That is the central point of my paper, How Much Redistribution Will AI Require. A falling labor share does not necessarily mean falling labor income. Workers can receive a smaller share of a much larger economy and still earn as much as they would have without AI.

Using the code behind their scenario explorer I updated their results to a 10 year horizon and plotted them on my redistribution graph. Only under the modest scenario is some net labor transfer required to make AI Pareto improving at the aggregate level.

Aggregate labor income, of course, conceals differences among workers. Korinek et al. find that cognitive occupations lose income while other occupations gain. In their extreme scenario, restoring the cognitive occupations’ wage bill to its no-AI level would require about 9% of GDP. They argue that compensation on this scale in response to technological change has no precedent.

I think this makes the adjustment problem look too pessimistic.

First, adjustment happens through retirement and entry. A retiring accountant need not retrain as a nurse. A young person enters nursing rather than accounting. Neither becomes unemployed even as labor reallocates. Korinek et al. understand these channels but give them limited scope in their model. Admittedly, those margins don’t do much work by 2030 but they matter over ten years.

Second, compensation can take the form of shifting taxes from labor to consumption. In the long run labor’s share of consumption tends to equal its share of GDP, so the lower labor’s share becomes, the more relief a given tax shift provides. At a 45% labor share, each dollar shifted from labor taxation to consumption taxation reduces labor’s net burden by 55 cents. Shifting taxes worth 5% of GDP would thus provide net relief to labor of 2.75% of GDP, without increasing total tax revenue. Unemployed workers would still need payments, but compensation need not come entirely through additional government spending.

Third, we do have experience expanding income support rapidly. U.S. unemployment benefits reached approximately 2.5% of GDP in 2020, and that during a contraction. Britain’s compensation to slaveowners following abolition amounted to roughly 5% of GDP in a one-time settlement. These episodes show that governments can mobilize substantial resources for compensation. Moreover, the extreme AI scenario brings an enormous increase in output from which to finance compensation.

Preserving aggregate labor income does not protect every worker. But even the extreme Korinek scenario reinforces the point that a dramatic decline in labor’s share can coexist with stable or increasing aggregate labor income. To the extent labor income does decline, growth makes compensation affordable and attrition, entry, and tax shifting can make the intra-labor task smaller than it first appears.

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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.

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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.

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2. The economists who work on “Productivity and Innovation” are most likely to use AI for their writing.

3. Michael Levin defends neo-neo-Platonism?

4. Look for and try to limit agent collusion.

5. Do orangutans like Indian classical music?

6. “Our results demonstrate that LLM-evolved constitutions significantly outperform both human-designed principles and one-shot LLM-generated rules for multi-agent coordination.“

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Recent years have witnessed many strong claims that ‘diversity’ leads to more original and impactful science, which is a science-focused form of what we call “The Diversity Hypothesis.” However, what evidence supports the claim that diversity enhances scientific output or impact? This pre-registered rapid scoping review seeks to collate and evaluate the scientific evidence for the Diversity Hypothesis…

…Based on over 100 scientific articles, we find that only between 15% and 28% of results reported in the literature are consistent with the hypothesis, with the balance of the results not being consistent with it.

…Overall, the results of the analysis section indicate that there is little empirical evidence that diversity improves scientific output and/or impact. In fact, with the possible exception of disciplinary diversity—a type of informational or viewpoint diversity operationalized at the team level—the majority of the evidence seems to point in the other direction. These findings are robust regardless of how the data is parsed and analyzed. The same conclusions can be drawn looking at the full data set, only the population-adjusted results, or only the results of high quality based on the MMAT analysis.

Hat tip: Colin Wright.

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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.

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