Shout it from the rooftops (of the data centers)
Data-center investment has become one of the largest capital-expenditure cycles in financial markets, with U.S. hyperscalers expected to deploy roughly $700 billion in 2026. This investment boom has raised concerns that large computing loads impose external costs on households through higher electricity prices. Using a 50-state panel for 2021-2024, we find no statistically significant evidence that data-center presence, installed capacity, or capacity expansion predicts residential electricity-price inflation across extensive-margin, intensive-margin, fixed-effects, and timing specifications. We propose an energyinternalization mechanism: hyperscalers can partially internalize incremental electricity demand through contracted or dedicated generation, including solar and wind energy. Consequently, gross datacenter electricity consumption need not translate one-for-one into net pressure on residential electricity supply. The findings suggest that the extraordinary AI capital-investment cycle has not, thus far, produced a detectable residential electricity-price externality.
Here is the article by Yosef Bonaparte, via the excellent Kevin Lewis.
Share price numbers for the Hugging Face incident
…major publicly traded cybersecurity firms lost roughly $65–80 billion, or about 8–10% of their combined value, in the days following disclosure of the Hugging Face/OpenAI incident; by early September they had recovered roughly $58 billion, representing about 70–90% of that drawdown, depending on whether July 15 or July 20 is used as the pre-event baseline.
That is from GPT Pro, there is more at the link. As a very rough approximation, say you dismiss the price bounceback altogether as either random or due to good earnings reports. You have “the value of previous cybersecurity efforts” falling by eight to ten percent. I take that to be very broadly consistent with some of the estimates discussed in my previous post on the numbers.
In any case that is a significant sum. But do note that if the AI models were on the verge of doing truly terrible things to us, the market might estimate the value of our cyberprotection of falling more than eight to ten percent?
More generally, perhaps these numbers could be used to discipline the discussion a bit? Or will I read long lists of reasons why they show us nothing, in that case try coming up with some other market price-based indicators of AI risk? Vix will not do it for you, not these days. I see many metaphors and insinuations and random anecdotes of AI terror, not numbers. Maybe you think your ideas about AI risk are so important that no market prices can reflect them? (If you really believe that, does it mean you would not be worried, and would not cite the numbers, if the value of those companies fell by ninety percent?)
I am sure others can improve on what I am putting forward, and furthermore we should track the continuing progress of these share values over time, especially if other AI hack attacks surface.
Overall I am extremely skeptical of arguments that essentially take the form of “what I am concerned about is too big and too important to show up in any market prices.” Pick your market prices!
Astra, doing poetry
One prompt, no cherry-picking:
TC: “Now a short poem in the style of the very best Rilke, in German:”
Die Hand im Schlaf
Nun liegt sie offen. Alles, was sie hielt,
ist in die eigne Schwere heimgekehrt.
Nur eine kleine Höhlung bleibt, als hätte
ein Vogel hier geruht und sie gelehrt,
auch das Entwichene noch zu umgeben,
behutsam, ohne Wunsch nach Wiederkehr.
And it begins…(a good start…)
Wall Street banks are pushing large law firms to cut fees, arguing that the business model that has enriched top lawyers for decades is not sustainable in an era of AI.
Morgan Stanley and Citigroup have told major law firms they want to set up new payment arrangements that would save them money, the banks’ in-house lawyers told the FT…
“If the number of hours they’re working on a matter has come down because of AI . . . our expectation is for costs to come down significantly per transaction,” Adam Meshel, global head of legal at Citigroup, told the FT.
He said the bank had started asking law firms to bid for work, explaining during the bidding process how much they were saving using AI…
The ability to complete tasks more quickly could mark “a fundamental altering of the revenue foundation for these mega firms”, he said.
Here is the full FT piece by Kaye Wiggins and Joshua Franklin. I have been predicting exactly this in many of my talks…
Numbers, numbers, numbers
“The global cyber insurance market was worth nearly $15 billion last year and is expected to reach roughly $28 billion by 2030, Munich Re estimated in its latest report. Aon said earlier this year that nearly 20% of cyberattacks will involve generative AI by 2027, according to its forecasts.”
Here is the article, via Marc Pfeiffer. Now those are some concrete numbers, broadly taken from a market context, admittedly based on sectoral estimates rather than on prices per se. But if cyberinsurance expenditures are set to almost double by 2030, you might think that cyber costs more generally might be (very) roughly doubling as well. The 20% of cyberattacks involving generative AI does not itself pin down losses, since that 20% might be especially costly. Still, if you match the 20% rise to the estimated near doubling of the cyberinsurance market (the bigger potential losers are more likely to buy insurance?), you still end up with sums that are very high but, dare I say, not the end of life as we know it. Claude 5.1 for instances estimates current U.S. cybersecurity costs in the range of $100 to $300 billion, and of course that is slated to go up a fair amount. It could double over five years’ time, as indicated above.
Numbers! Thank goodness.
One way of looking at that estimate is to think that cyber costs will go up about thirteen percent a year, and eventually defense will catch up. Another perspective is that cyber costs may continue rising thirteen percent a year until the whole economy falls apart, or we return to the pre-digital era (which I remember well). So there are both optimistic and pessimistic reads on the above figures.
You can say Nicholas Decker is burning in hell, or that the AIs are a civilization, but what I really want are some numbers. Tied to market data, ideally. I am not saying the numbers above are the right numbers, but I am saying they are better than no numbers at all. Do you have some numbers for me? If not, why not?
How Much Redistribution Will AI Require?
How much redistribution will AI require? A common scenario is that AI raises output enormously, but labor’s share of income collapses. GDP per capita goes up but workers get poorer, and making workers whole requires massive redistribution. In my latest paper, I run the numbers and conclude that this is probably incorrect.
The idea is simple. Labor income is GDP multiplied by labor’s share of GDP. What matters is the product. A smaller share of a much larger economy can still mean more income for labor. If the pie is growing, labor’s slice of the pie can shrink even as labor income rises.
Suppose that without AI, real GDP per capita grows at 2 percent a year and labor receives 60 percent of GDP. Now look ten years ahead. What is required to keep labor’s income growing at the same or higher rate?
If AI raises growth to 5 percent a year, GDP after ten years will be about 34 percent larger than on the no-AI path. Labor’s share can fall from 60 percent to about 45 percent and workers, in aggregate, will still have exactly as much real income as they would have had without AI.
If AI raises growth to 10 percent a year—the kind of number Satya Nadella and Dario Amodei talk about—GDP after ten years will be more than twice as large relative to the no-AI path. Labor’s share can then fall all the way to 28 percent without reducing aggregate labor income. Twenty-eight percent of an economy that has more than doubled is about the same as sixty percent of the smaller economy.
The figure shows how much redistribution is required after 10 years under a variety of scenarios.
The white region above the dashed line requires no transfer. Which region are we headed for? Consider three “stylized” views.
The econ-pessimist, following Acemoglu, thinks AI displaces some but relatively few tasks because AI simply is not productive enough to replace much labor profitably. Growth is only 2.1 percent and labor’s share falls to 56.6 percent, although particular industries may still get hammered. The required transfer is 2.7 percent of GDP.
The econ-optimist, in the spirit of Tyler, myself, and Kevin Bryan, thinks automation also creates complementarities and new tasks for humans. Growth rises to 4.1 percent, labor’s share is 51.4 percent, and both labor and capital gain without any transfer.
The techno-optimist, following Amodei, has the superficially scariest labor-market scenario: three-quarters of labor income is displaced and labor’s share falls to just 22.9 percent. But productivity growth is also enormous, producing 10 percent annual growth. The transfer needed to keep labor as a whole on its no-AI path is only 5.3 percent of GDP.
That last calculation is the one I find most surprising. You can have something close to the techno-capitalist dystopia in terms of factor shares—labor gets less than a quarter of GDP—and still have a manageable redistribution problem because GDP has gotten so much larger.
Moreover, a transfer equal to 5 percent of GDP need not mean raising taxes by 5 percent of GDP. We already tax labor a lot. We could thus compensate labor by shifting from labor taxes to other taxes. Federal payroll taxes alone are about 6 percent of GDP. Cutting payroll taxes and replacing them with a broad consumption tax that also reaches spending from capital income and accumulated wealth is a form of labor compensation (plus we could have some transfers to those with no labor income).
Of course, keeping aggregate labor income whole does not mean every worker does well. There could still be enormous churn, big losses in particular occupations, and painful transitions.
Nevertheless, the larger point is that labor’s share by itself tells us surprisingly little about the distributional consequences of AI. We also need to know how much the economy grows.
If AI produces ordinary growth while dramatically reducing labor’s share, redistribution becomes very difficult. But if AI really does produce 5, 10, or 15 percent annual growth, the compensation problem is surprisingly modest even with very large displacement. As I have emphasized elsewhere, we could cut the working week in half under many scenarios and increase hourly wages above the non-AI benchmark and make both capital and labor better off.
Growth is a good problem to have.
Thursday assorted links
1. “Some crazy stats from this new JEL paper: – IV est. average 3–10x OLS (meta analysis) – Sign. results 30x more likely to be published in experimental econ (Andrews & Kasy) – <2% of empirical polisci report null-only findings in abstracts (Briggs et al.)” — Jon Fiva
2. “The UCLA athletic dept lost $52 mil last year and $83 mil in 2024 when including campus subsidy.”
3. Papua New Guinea fact of the day.
4. “Most laid-off SF tech workers aren’t qualified for this $50 dishwasher job.”
My excellent Conversation with Michael Moritz
Here is the audio, video, and transcript. Here is the episode summary:
Michael Moritz has written books through every phase of his life: the first history of Apple and an account of Chrysler’s near-death while he was a journalist at Time, a study of Alex Ferguson’s Manchester United in the middle of his 38 years at Sequoia, and now Ausländer, a family memoir, after leaving the firm. Moritz calls himself a dilettante with too many interests, but listen to him on learning to paint in his 40s, or the questions he would ask a ten-year-old boy in a German village in 1890, and you may decide that unsatisfied curiosity is not a small thing to build a life on.
Tyler and Michael discuss his childhood in Wales, where his love for visual arts came from, why he disappointed his Latin teacher, having Thanksgiving dinner with Philip Roth, why children don’t interrogate their parents about their history, what he feels visiting Germany and why he now holds German citizenship, how a history major with no technical background talked his way into Sequoia, his unpublished Don Valentine profile, what people underrate about Steve Jobs, obsessives versus dilettantes, why capitalism was more ablaze in China than America, what funding the Booker Prize taught him about his own ignorance, how to improve San nonprofits, how an incurable cancer diagnosis changed his calendar, why Britain’s stuck, what he’ll learn next, and more.
Excerpt:
COWEN: Is it easy to live with an Otto Dix painting or sketch? It hangs on the wall. Many people think it’s ugly. It reminds one of unpleasant things in history, right?
MORITZ: Yes, for Harriet and me it is. I think the tougher, more strenuous, grueling works of art that other people would have difficulty living with have many layers to them. You explore them, and they’re difficult pictures, and they’re not easy at first sight. Unlike easier pictures that may be a bit more decorative, they leave room for plenty of exploration, as the years go by. Then they’re redolent, they tell stories. They’re redolent of history. They’re images of a different epoch. I think most of the paintings that we’ve been lucky enough to find over the years, they are tough paintings.
COWEN: I feel that way about Haitian art, which has many brutal scenes. For you, is Chagall too sentimental?
MORITZ: Yes.
COWEN: You don’t want to put it on your wall?
MORITZ: The earlier Chagalls I’ve been drawn to, but neither of us have felt the urge or the need to go out in pursuit of Chagall.
COWEN: What is your own painting like?
MORITZ: Oh, exasperating.
COWEN: Neue Sachlichkeit or something else, it’s like Kossoff?
MORITZ: No, I don’t know if you’ve ever tried painting or drawing. I didn’t take it up until I was in my mid 40s. I’d never picked up a crayon or outside of the obligatory, abbreviated art lessons that always seem to be held later on Thursday afternoon, with everybody waiting for the bell to ring to end school. I’d never taken up a crayon or drew, or let alone painted.
If I look back today at what I did early on, it’s a lot better. Then if I look at the paintings that I try to make, my goodness, it is an extremely humbling experience, but I enjoy it. I really enjoy it. There’s nothing like getting lost in making a painting, and before you know it, two hours have gone, and you have no idea where the time went.
And:
COWEN: If we think of your interest in Steve Jobs, your book on Alex Ferguson, the art you buy, that you’ve now written a book on the Holocaust, is there some general pattern where trying to come to terms with really difficult things, is this a recurring theme in your life? Learning how to paint, that’s very hard, right?
MORITZ: I haven’t really thought about it that way, but I think life is made richer by having a challenge that you’re not sure whether you’re up to conquering. I’ve always been up for that. Each of these books has really just sprung out of being curious about something. I’m not sure that there’s any greater pattern than unsatisfied curiosity.
Recommended, interesting throughout. And I am very happy to recommend Michael’s new book Ausländer: One Family’s Story of Escape and Exile.
Richard Fontaine interviews me about AI
For the American Society for AI.
Wednesday assorted links
America is still poised for a data center boom
Eric Levitz, via Matt Yglesias.
The College Wage Premium in the Generative AI Era
After expanding for four decades, the U.S. college wage premium is experiencing a sustained contraction, dropping sharply from 0.626 in 2022 to 0.575 in 2026. Using Current Population Survey Outgoing Rotation Group data through 2026, we show that standard market-clearing supply-and demand accounting implies an unprecedented drop in relative demand for college labor-the first sustained negative relative demand growth in a series spanning back to 1914. Linking individual wage data to task-based generative AI exposure, we document that post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of -0.086. Combined with the college-non-college exposure gap, this mechanism accounts for roughly 28 percent of the total drop in the college wage premium from 2022 to 2026. While noncausal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.
I do not see AI as driving these changes, but an interesting result nonetheless, from José Azar, Mireia Gine, and Javier Sanz-Espín. Via Anecdotal.
Who values democracy?
This paper examines the conventional view that redistribution is central to the democratization process using data from stock markets. Consistent with this view, democratizations have a large, negative impact on asset valuations driven by a rise in redistribution risk. Across 90 countries over 200 years, risk premia are substantially elevated— similar in magnitude to financial crises—prior to and during democratizations. A shift in Catholic church doctrine in support of democracy provides causal evidence that democratizations increase risk premia. Successful democratizations lead to substantial redistribution: the size of the public sector grows, income inequality falls, and the labor share of income rises. An extended version of the canonical redistribution-based model of democratization that includes asset prices can quantitatively explain these effects. Reductions in inequality and increased taxes explain approximately half of the results. The rest comes from greater economic competition and equality in government spending. The model also explains the negligible asset pricing response to autocratizations. Neither an increase in macroeconomic risk nor generic political risk can explain the results.
That is by Max Miller, now published in the JPE, ungated copy here.
Tuesday assorted links
More optimistic results on AI and job markets
Here is a good Jon Hartley thread. Here is the paper, with Jolevski, Melo, and Moore. From Jon’s thread: “Generative AI adoption is widespread, but substantial aggregate labor-market disruption is not yet visible. Workers nevertheless perceive substantial displacement risk, especially when firsthand use reveals that AI can perform key tasks for their job.” And again here is Alex’s post from yesterday.