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That makes it harder to diversify against risk, as there is a single dominant variable, namely “AI risk” or something similar.  There is AI risk both in your portfolio and on your human capital, though possibly those will offset each other to some degree.

Presumably the equity premium should rise as a result?  People will want more portfolio safety as a protective offset, and be gunshy about such a heavy equities bet on one major technology.

If you have a longish time horizon, do you feel brave enough to act on that view?

Or perhaps instead there is some simple way to hedge against AI risk?

One “stupid” equilibrium that no one will want to talk about is the following: buy lots of Nvidia, but if that doesn’t pay off make sure you are doing an MBA and planning a career in non-AI-implementation consulting.

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Exposure increases interclass (high- and low-parent-income) marriage but has no detectable effect on interracial (White and Black) marriage. A spatial marriage market model predicts that residential segregation—one of many forms of exposure—accounts for more than one third of marital sorting by class but less than 5% by race.

That is from a new NBER working paper by Benjamin Goldman, Jamie Gracie & Sonya Porter.

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The share of the global population living in extreme poverty fell dramatically from an estimated 36% in 1990 to 9% in 2015. We describe how this decline happened: the extent to which changes within as opposed to between cohorts contributed to poverty declines, and the key changes in the lives of households as they transitioned out of (and into) poverty. We do so using cross-sectional and panel sources that are representative or near-representative of five countries that collectively accounted for 75% of global poverty decline between 1990 and 2015. The data show that overlapping birth cohorts experienced the decline of poverty together over time, such that poverty decline can be viewed as a primarily within-cohort phenomenon. Within cohorts, the data reveal substantial churn, casting the challenge of escaping poverty as a “slippery slope” more than a long-term trap. The data also illustrate a diversity of pathways out of poverty: sectoral transitions, migration, and changing occupational choices and female labor force participation can all account for some part of poverty reduction, but in all but a handful of cases, a majority of households exiting poverty did so without experiencing these changes.

That is from a new NBER working paper by Vincent J. Armentano, Paul Niehaus & Tom Vogl.

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2. Dean Ball on the real reasons to regulate AI.

3. Noah on development economics, link now corrected.

4. Peter Frampton update (NYT).

5. What have we learned about human communication?

6. Polymarket on hantavirus.

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We might be heading towards a populist backlash towards AI, but we’re not there yet. Outside the tech bubble, Americans really don’t care about AI yet.

AI is Americans’ 29th most important issue, according to the fantastic survey @davidshor ran that everyone is rightly looking at.

It’s not surprising that Americans will answer sentiment questions about AI negatively, as they’ve been negative towards tech for a while. But it’s a big leap from negative sentiment to meaningful political action.

Americans have been negative on social media for 10 years, and there has been no meaningful political action. And that’s despite all the other hallmarks of backlash people are saying about AI—violent extremists (people forget there was a shooting at YouTube HQ), protests, etc.

My prediction: we will get real populist backlash to AI when the unemployment moves by, say, 2 percentage points and people see it as caused by AI.

That is part of a longer tweet from Andy Hall.

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Standard models predict that expectations of artificial general intelligence (AGI) should elevate long-term interest rates. I show that this prediction need not hold. I develop a heterogeneous-agent asset pricing model in which AGI, or more broadly, transformative AI (TAI) capable of automating most human labor, can lower interest rates even as it dramatically accelerates growth. Under baseline calibrations, the risk-free rate falls to near zero despite growth rising from 2% to 11%, and the equity premium expands from 6% to over 20%. The effect on yields is negative and muted for all maturities, even under aggressive assumptions about the speed of AI adoption. These results advise caution when interpreting long-term bond yields as a signal of market expectations of transformative AI.

That is from a new paper by Caleb Maresca of NYU.  Via the excellent Kevin Lewis.

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In the 1860s Jevons built a Logical Abacus, sometimes called a logical piano, a kind of early computer that could perform (some kinds of) logical operations faster than humans could. It is held in the Museum of the History of Science at Oxford University, and you can think of its structure and operation as broadly akin to a player piano in music. It was limited in its powers, and geared mainly toward replicating Boolean logic, but extreme in its ultimate ambitions. Jevons understood the potential. In his written presentation of the project, Jevons cites the work of Charles Babbage, and noted that “material machinery is capable, in theory at least, of rivalling the labours of the most practiced mathematicians in all branches of their science.  Mind thus seems able to impress some of its highest attributes upon matter, and to create its own rival in the wheels and levers of an insensible machine.” Jevons understood that science would be able to tackle some of the most difficult projects, and he wanted to be on as many of those frontiers as possible. He understood that his own work was a mere beginning, and he wanted to press forward as much as possible.²⁵See Jevons (1870, the quotation from p.498), and also Maas (2005, chapter six). For a general background on Boolean logic, see Hailperin (1986).

Jevons also studied molecular motion in liquids and developed the concept of “pedesis,” a precursor of what we now call Brownian motion. That said, Jevons thought his pedesis was an electrical phenomenon related to osmosis, and so he turned out to be incorrect in his fundamental hypotheses. Nonetheless, this topic, like the others, showed he was an observant mind and obsessed with developing theories to explain anything and everything. He wasn’t just a pedant, rather he made real contribution to a number of scientific fields above and beyond economics.²⁶On Jevons on pedesis and Brownian motion, see Brush (1968).

Jevons also was a “born collector” in the words of Keynes, and an extreme bibliomaniac. He accumulated thousands of books, and he lined the walls of his house and attic with them, and also stored them in piles in the attic, which became a problem for his wife upon his passing.

That is from my recent generative book The Marginal Revolution: Rise and Decline, and the Pending AI Revolution.

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Wolfers’s moment of clarity ultimately sent him down a road less traveled by academic economists: creating his own media company.

On Wednesday, Wolfers, 53, announced that he had founded Platypus Economics, an independent media start-up that aims to reach a mainstream audience. The name is a nod to his Australian roots, cheekily referring to the odd-looking mammal native to his birthplace. He’s funding the business himself, using the income from his textbook sales.

…To get his content channels off the ground and build an audience, Wolfers is teaming up with Initial Digital, the digital media division of the Initial Group, an entertainment company that’s backed by the private equity firm TPG.

Here is the full NYT story.

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We provide the first causal, national empirical analysis of the labor market impacts of heightened immigration enforcement during the second Trump administration. Enforcement increased everywhere, but, we take advantage of the fact that the increases have been uneven across geographic areas to classify areas as treated or control and then implement an event study and difference-in-differences design. Areas that experienced particularly large increases in the number of arrests also experienced a decrease in work among likely undocumented immigrants who remain in the U.S., compared to areas with smaller increases in arrests. We find no evidence of positive spillover effects to U.S.-born workers and U.S.-born workers who work in immigrant-heavy sectors are harmed.

That is from a new NBER working paper by Elizabeth Cox & Chloe N. East.

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2. The persistent apartheid infrastructure of Cape Town, as experienced through bicycle (NYT).

3. Interviews with famous economic historians.

4. The AI job displacement worry.

5. “The administration lacks authority to mandate frontier model vetting—but existing CAISI and CISA tools enable a voluntary alternative.”

6. Why so much swearing in the Anglosphere?

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Did you know Korea sells “one-a-day” banana packs?

Instead of every banana ripening at once, each one is at a different stage.

One is ready today.

The next one is ready tomorrow.

The last one is still spiritually in college, “experimenting.”

Simple. Genius. Solves the entire banana problem.

What do you think? Would you prefer your bananas this way?

Here is the tweet from Sovey.

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2. Boyd van Dijk, Preparing for War: The Making of the Geneva Conventions.  A very good look at the negotiations behind the Geneva Conventions of 1949 and just how driven by national self-interest they were, including colonial motives from the major colonial powers, who wished to retain stronger rights to put down uprisings.  The Soviets wanted strong protections against torture (!), as they thought this might limit the power of the United States to bomb their population into submission.  Yet nuclear war ended up being permitted, largely at the insistence of the U.S.  And so on.  The colonial subjects of course had not much say in any of this.

3. Jim Windolf, Where the Music Had To Go: How Bob Dylan and the Beatles Changed Each Other — and the World.  An excellent and engaging book, which even serious fans can learn from.  The first time Paul McCartney heard the music of Bob Dylan he called it “folk crap,” to his brother Mike.  Dylan and McCartney grew closest in 1971, when Paul was making the Ram album in NYC.  Music from Big Pink is one of Paul’s favorite albums of all time.  Thingumybob, first composed by Paul in 1968, later received accretions from Harrison and Dylan and became an odd three-party composition, albeit never released on a recording.  And here is Paul’s account of bumping into Dylan at the airport.

If you wish to think about the Roman Empire more, there is Pliny & Co., How to Make Money: An Ancient Guide to Wealth Management.

Thomas Asbridge, The Black Death: A Global History of Humanity’s Most Devastating Pandemic is a good overview.

There is also Devon Cox, Beyond Beauty: A Portrait of John Singer Sargent.

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2. Rachel Glennerster defends RCTs in development economics.

3. Why do airlines go bankrupt so often?

4. Why is southern Italy poorer than northern Italy?

5. The economics of correspondence schools in earlier American history.

6. African education is doing worse than we think.

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The FT has an amusing update:

“When my sheep break wind, it smells of roses,” he said, recounting one of the more bizarre and far-flung consequences of the decision by US President Donald Trump and Israel’s Prime Minister Benjamin Netanyahu to bomb Iran in February.

Since Tehran hit back by firing drones and missiles at US allies in the Gulf — grounding cargo flights and closing off the Strait of Hormuz through which booming east African trade with the region used to flow — Mahihu has been forced to jettison millions of rose stems.

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