A little over a year ago, a bunch of people were worried that the AI data center boom would result in an epic bust, much like the railroad bust in 1873. I wrote about the possibility at the time:

The basic idea is that even if AI is an incredibly useful technology and creates enormous economic growth in the future, the people building the infrastructure (data centers) might borrow so much money that they can’t pay it back in time. Last summer, the AI industry’s revenue was growing, but not nearly as fast as the amount of money being spent. But the “hyperscalers” building the data centers were mostly spending their own cash instead of borrowing money, so the danger wasn’t imminent. The worry was mostly about the future — if spending continued to increase and revenue didn’t accelerate, there could be an 1873-style bust.

Well, now it’s the future, and two things happened. Spending did accelerate, and hyperscalers started having to borrow money to keep increasing their spending. But revenue accelerated even more. The release of Claude Code, Codex, and other agentic coding tools proved to be the “killer app” of AI, and suddenly Anthropic and (to a lesser extent) OpenAI were rolling in cash:

So like in summer 2025, we aren’t obviously heading for a big AI bust in the near future. But the stakes are higher now — because companies are borrowing a lot more money now, any slowdown in the torrid pace of AI revenue growth could cause a blowup.

The amounts of money being thrown around here are just staggering. A year and a half ago, the AI boom was still significantly smaller than the railroad boom of the late 1800s, as a percent of GDP. Now, there’s an active argument as to which one is bigger.

Which is bigger, AI or the railroads? (a pointless but fun debate)

This debate doesn’t really matter, because the basic message is just “Wow, it’s a lot of money.” But it’s fun to look at the numbers people are throwing around. Stijn Van Nieuwerburgh gives figures of 3.63% of GDP for AI and 2.24% of GDP for railroads:

But those percentages are over different amounts of time — the railroad boom lasted a very long time. According to some data sources, railroad spending peaked at around 5.4% of GDP:

And 19th century data are notoriously patchy and incomplete. Some sources claim much higher numbers for the railroad buildout:

Nor do people agree on the size of the AI investment binge. Some sources yield numbers that are much lower than Van Nieuwerburgh’s:

As far as I can tell, the main difference here is assumptions about costs. The banks and ratings agencies are using announced dollar spending; Van Nieuwerburgh is using announced physical construction projects and then making assumptions about how much those projects will cost. The biggest reason he gets a higher number is that he assumes data centers are going to cost a lot more than the hyperscalers think.

Meanwhile, Dylan Patel claims over 6% for the AI boom already, once you include all types of AI-related infrastructure — fab equipment, chips, energy, and so on.1

But whatever the correct numbers are, the basic point remains the same: AI is now one of the two biggest infrastructure booms in American history, by a large margin.

So it’s understandable that people are worried that the two booms could follow similar paths. For the railroads, that path included a big crash in 1873, which caused a huge decade-long depression in the U.S. economy. A lot of people are watching for something similar to happen with data centers, and the chorus of skeptical voices is beginning to swell again.

Hyperscalers are starting to borrow…a lot

OK anyway, on to the real point of this post. The flood of revenue that accompanied the rise of coding agents quieted the talk of an AI bubble for a while, but now it’s back. The first reason is the rise of AI-related debt. In the early years, the hyperscalers — data center builders like Amazon, Google, Meta, and Oracle — mostly paid in cash for the data centers. These companies get tons of cash from their software and cloud businesses, and they ended up spending most of it on AI infrastructure. But now the buildout has gotten so big that they’re having to borrow a bunch of money to keep ramping up spending:

For all of the hyperscalers except Microsoft, free cash flow either went negative in recent months or is expected to go negative late this year. This has been a giant boon for semiconductor companies — Nvidia, TSMC, and the rest:

All this debt creates a financial danger that didn’t exist before now. When they paid in cash, hyperscalers were safe — if AI revenue disappointed, their stock prices would go down, but their businesses would just keep going and they’d eventually recover their profitability. But the more hyperscalers borrow, the more revenue they’ll need just to stay solvent; if the revenue doesn’t arrive, they might have to default on their debt.

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I have a Google Doc with a long list of posts I’ve been meaning to write. Far down the list was a line that read “New Keynesian macroeconomics deserves a Nobel”. Somehow I never got around to writing that post, and now I never will. Because today, it happened! This year’s Econ Nobel went to Bob Hall and Mike Woodford, two of the theory’s most important creators.

First, let me say that this prize is long overdue. In 2009, I asked my PhD advisor, Miles Kimball, who was the greatest living macroeconomist. Without hesitation, he answered “Bob Hall”, and said he would be very surprised if Hall didn’t win the Nobel at some point. Hall’s work is breathtakingly broad; if you want good summaries, I recommend Alex Tabarrok and Tyler Cowen (as usual). As for Michael Woodford, everyone I know assumed that if New Keynesian macro ever did get the prize, Woodford would be included, as he literally wrote the book on it.1

But anyway, let’s talk about New Keynesian economics — what it is, how it came about, why it’s important, and why it didn’t get a Nobel until now. Let’s start with a brief history of modern macroeconomics.

Back in the 1960s and early 1970s, most economists tried to understand the macroeconomy by looking at relationships between macroeconomic aggregates. For example, “If income goes up by 10%, how much will spending go up?”. Or “If inflation goes up by 3 percentage points, how much will unemployment go down?”. 2

The problem with that is that correlation isn’t causation. Suppose you see that when inflation rises, unemployment goes down. That correlation is called a Phillips Curve. So you think “OK, if we raise inflation, more people will get jobs”. But you try that, and it doesn’t work — you push inflation up (by printing more money), but unemployment stays high. The correlation you saw before has now broken down. Now you’ve got two problems instead of one!

This is a very simplified version of what a lot of economists think happened in the 1970s. Whether that’s what really happened is a story for another day. But the stagflation of the 1970s prompted the econ profession as a whole to rethink how it modeled the macroeconomy. A lot of researchers asked: What parts of the macroeconomy are invariant to policy? What are the solid foundations we can stand on, so that when we try to make some sort of change, we don’t cause our own theories to crumble?

The answer most economists settled on was called “microfoundations”. They reasoned that the basic drivers of individual and corporate economic behavior shouldn’t change when macro policies like interest rates and government spending change. Most importantly, those drivers include:

1. Technology

2. Consumer preferences

3. The process by which people and businesses form expectations of the future

Do all three of these really stay the same when economic policy changes? Probably not completely. But it gave macroeconomists a place to start. So in the late 1970s and early 1980s, economists started to build macro models based on theories of individual and corporate behavior. These were called “microfounded” macro models.

The last of those three “microfoundations” — expectations — was particularly thorny to think about. Almost any sort of expectation-formation process that you write down allows policymakers to trick people over and over, by surprising them systematically. Economists thought that eventually, people should wise up to the government’s tricks. So they came up with the idea that consumers and businesses should, on average, be as smart as the economists making the models. If economists write down a model of how the world works, that model should be the average person’s mental model in the model. Basically, people generally know how the economy works, and they plan accordingly.

This idea is called “rational expectations”.

In 1978, Bob Hall wrote a very influential paper that convinced a lot of economists that this was the right track. Hall wanted to test a simple idea, originally developed by Milton Friedman: If people plan out their consumption in advance, then changes in the level of consumption should be completely random.3 Nothing from last quarter should be able to predict the change in consumption this quarter — because if something last quarter mattered for this quarter’s consumption, people would already have changed their consumption last quarter! Hall found that, sure enough, a bunch of variables that seemed like they should be important for consumption — especially last quarter’s income — didn’t actually help predict changes in consumption.

For a more detailed explanation, I recommend this post by John Cochrane:

The Grumpy Economist

Bob Hall and Consumption

I wrote this essay on Bob Hall and Consumption (link goes to pdf on my webpage) for the conference in honor of Bob Hall at Hoover, November 22. It turned into a more extended history of some trends in macroeconomics, which any student of macroeconomics might find useful. Why we do what we do is often obscure. If this post exceeds your email limit, finis…

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2 years ago · 35 likes · 8 comments · John H. Cochrane

Brian Albrecht has a good (brief) explanation as well.

At first blush, this might not seem like an important result. After all, absence of evidence isn’t evidence of absence — there might be other variables that do predict changes in consumption, that Hall simply forgot to test. And in fact, Hall did find one variable that predicted future changes in consumption: stock prices. So even in the formal sense, the basic theory didn’t quite hold. Nor did Hall’s result rule out the traditional Keynesian idea of consumption — if government spending surprises people, it can move consumption immediately, without creating any sort of predictable future change.

But it was an important result, for two reasons. First, if people plan ahead when making their economic decisions, it should be possible for policymakers to influence today’s behavior by making promises about what they’ll do in the future. Second, if it’s impossible for economists to predict changes in consumption, that implies that consumers are as good at planning their future consumption as economists are.

If that’s true, it’s an important lesson for policy: If you want to make people consume more, or if you want to make businesses hire more, etc., you have to do it in a way such that people generally understand what you’re doing.

So macroeconomists set out to make models of the economy in which people understand how the economy works. At first, this effort didn’t go well. For example, one very popular early idea — which eventually won a Nobel — was called “real business cycle” (RBC) theory. This was the idea that recessions and booms are driven purely by changes in the rate of technological progress — if engineers happen to invent less useful stuff this quarter, you get a recession.

No one denies that technological inventions can be very important for economic growth — just look at how the AI boom is powering the U.S. economy right now. But the idea that tech progress drives the business cycle in a simple, mechanical way just doesn’t fit the evidence. In particular, productivity booms don’t seem to line up with economic booms, as RBC theory would predict.4

Even more importantly, RBC models predict that monetary policy — raising and lowering interest rates — shouldn’t affect the real economy. Ed Prescott, the creator of RBC theory, famously declared that monetary policy “is as effective in bringing prosperity as rain dancing is in bringing rain.” But our best evidence says that Prescott was wrong — monetary policy almost certainly does affect the real economy.

So something’s going on there. Macroeconomists were missing something big — they didn’t understand how or why monetary policy boosts growth and employment. During the 1990s, a bunch of economists started coming up with possible reasons why having the Fed buy bonds could result in more people getting jobs.

These ideas generally fell under the name of “New Keynesianism”. As John Cochrane and others have pointed out, this was something of a misnomer. Whereas John Maynard Keynes and his successors thought a lot about fiscal policy — i.e. government spending and taxes — the New Keynesians of the 1990s and 2000s focused almost entirely on monetary policy. New Keynesian macro initially owed a lot more to Milton Friedman than it did to Keynes — everything was about the Fed, and the idea of fiscal stimulus initially fell by the wayside.

Anyway, in the 90s and 2000s there were plenty of ideas about how monetary policy could affect the real economy. This is probably one big reason why New Keynesian economics was so slow to win a Nobel — it has so many creators that it’s not even clear who deserves the prize. Greg Mankiw, David Romer, Stanley Fischer, George Akerlof, Janet Yellen, Olivier Blanchard, Nobuhiro Kiyotaki, Jordi Gali, Mark Gertler, John Taylor, Guillermo Calvo, and a bunch of others contributed pieces to the edifice.

Most of these contributions were ideas about why prices are slow to adjust to macroeconomic fluctuations. Basically, if you have something that decreases aggregate demand — say the Fed raises interest rates like in 1980, or there’s a banking panic after a housing bubble bursts like in 2008 — there’s a question of why companies don’t just cut prices and maintain the same level of output. If there’s something causing prices to fall too slowly, that can force companies to produce less instead. A lot of New Keynesian theory is about why companies have difficulty cutting prices.

Bob Hall made very important contributions here too! The basic New Keynesian model explains how interest rates can affect GDP, but it doesn’t explain why that affects employment. After all, if growth slows down, it’s not immediately obvious why a bunch of people should be thrown out of their jobs. In a 2005 paper, Hall made a model explaining how wages might be slow to adjust to changes in demand — meaning that when there’s a downturn, companies can’t just cut wages, but instead have to lay off workers.5

Anyway, New Keynesian economics wasn’t the brainchild of a few famous founders — it was a riot of new ideas. It needed someone to come synthesize all of those ideas into something simple that central bankers could use to set monetary policy in real life. That man was Mike Woodford.

In a paper with the late Julio Rotemberg, Woodford laid out a simple New Keynesian model. On the question of why companies have difficulty cutting prices in a recession, he punted — Woodford simply assumed that for whatever reason, only some companies are able to change their prices each quarter.6 (This idea is called “Calvo pricing”, after its creator, Guillermo Calvo.) The rest of the model is pretty simple. Woodford uses the same basic consumption-smoothing model from Hall’s 1978 paper, in which people’s decision of how much to consume and how much to save depends on the interest rate. He assumes that the central bank (i.e., the Fed) sets interest rates according to a simple rule that balances out the risks of inflation and unemployment. There are a few more assumptions in there, but those are the basics.

This simple model provides a guide for monetary policy. It explains how the central bank can cause a recession in the short term — like Paul Volcker did in the early 1980s, when he raised interest rates to fight inflation — but can’t raise employment forever by keeping rates permanently low. And it explains how the central bank can respond to a drop in private demand — say, from a financial crisis like the one in 2008 — by cutting interest rates to stabilize the economy in the short term.

In other words, Woodford’s model — which he later expanded into a famous book — is a grand synthesis of the general consensus that emerged from the intellectual ferment of macroeconomics from the 1970s through the 2000s. We knew that aggregate demand was a real thing. We knew that the economy had trouble adjusting to negative demand shocks, resulting in a lot of disruption — reduced output, people getting thrown out of work, and so on. And we knew that monetary policy could boost the economy in the short term, but that its effect probably faded in the long term. Woodford gave us the simplest plausible model to describe all of that, and to offer central banks a badly-needed road map for stabilizing the economy.

Because it was so simple and so applicable, Woodford’s New Keynesian model became the standard tool of central banks around the world. No sooner was his synthesis complete, however, than it ran into a huge problem. In 2008 and 2009, the U.S. and European economies were hit by such a huge negative demand shock that cutting interest rates all the way to zero failed to put things right. Something else was needed.

There were three basic ideas for what that “something else” should be. The first is contained in Woodford’s model, and draws on the basic idea in Hall’s papers. It’s the idea of “forward guidance”. If the central bank can promise to leave interest rates low for a long time even after the recession is over, that can prompt consumers to spend more today — thus hastening the end of the recession.7 The second is quantitative easing — having the Fed buy long-term bonds instead of just short-term ones, in order to push down long-term interest rates. QE isn’t in Woodford’s model, but he eventually came around to supporting it.

The third idea was fiscal stimulus. If interest rates go all the way to zero, then government spending can become a more effective way of raising demand (as long as the central bank doesn’t cancel out the stimulus by raising rates again). When the Great Recession hit, New Keynesian economists scrambled to put Keynes back into their theories, by adding a role for fiscal stimulus.

Hall wrote a paper in 2009 explaining how stimulus could be effective:8

[T]he multiplier linking government purchases to GDP may be in the range of 0.7 to 1.0…but higher values are not ruled out. New Keynesian macro models have multipliers in that range as well…Multipliers are higher—perhaps around 1.7—when the nominal interest rate is at its lower bound of zero, as it was during 2009.

In that paper, Hall explains how fiscal stimulus can be effective even in light of his famous result from 1978. Even if there is no simple “consumption function” linking stimulus directly to spending, there are plenty of ways that stimulus can affect the behavior of companies and consumers, especially when interest rates are at zero.

And Woodford wrote a paper in 2011 showing how stimulus works in his basic model:

This paper explains the key factors that determine the output multiplier of government purchases in New Keynesian models, through a series of simple examples that can be solved analytically. Sticky prices or wages allow for larger multipliers than in a neoclassical model, though the size of the multiplier depends crucially on the monetary policy response. A multiplier well in excess of one is possible when monetary policy is constrained by the zero lower bound, and in this case welfare increases if government purchases expand to partially fill the output gap that arises from the inability to lower interest rates.

So in the end, the New Keynesians went beyond the ideas of Milton Friedman, and brought some Keynes back into their theories.

But New Keynesianism’s biggest success arguably came not in the Great Recession, but in the post-pandemic inflation of 2021-23. Inflation spiked to 9% in 2022, but then came down to around 3% in early 2023 after the Fed raised interest rates. But what’s remarkable is that unlike in the early 1980s, this happened without a big rise in unemployment!

It’s not clear exactly why this happened, of course, but it sure looks like a big victory for the kind of New Keynesian macro that Bob Hall and Mike Woodford pioneered. The most likely explanation for this “immaculate disinflation” is that as soon as businesses realized that the Fed would do whatever it took to beat inflation, they stopped raising prices — and inflation simply ended.

It didn’t require demand destruction like Volcker caused in the early 1980s. It didn’t require legions of down-and-out unemployed blue-collar workers cutting back on essential purchases to finally force companies to stop raising their prices. All it required was a clear Fed commitment, and forward-looking expectations — exactly the mechanisms at the core of Bob Hall and Mike Woodford’s models.

It was this episode, even more than 2008-9, that truly represented New Keynesianism’s acid test. And it seems to have passed with flying colors. There was always a question mark hanging over the theory — it was always unclear whether it could shape the future instead of simply telling a story about the past. Since 2022-23, the answer to that question looks like a much more unambiguous “yes”.

I wouldn’t be surprised if that had something to do with this long-overdue Nobel.

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Everyone except the die-hard faithful now realizes that Donald Trump’s second presidency is a colossal failure. The president is doing his best to gin up symbolic, minor controversies in order to activate negative polarization and distract from his spectacular failures on inflation, the Iran War, corruption, racial harmony, and so on. These include demanding that artificial intelligence be renamed “artificial superintelligence”, bellowing that he should have won the Nobel Peace Prize, and conducting an execution on live TV. The goal is to get Republicans fighting Democrats over these controversies, in order to trick everyone into focusing on how much they hate the other team instead of how much they hate Trump.

The trick will probably fail, just as Trump’s promise to give every American $5000 for voting Republican backfired. All of these little tempests-in-teapots simply reinforce what a cruel, selfish, petty buffoon Trump is. And Trump’s approval rating just drifts lower and lower:

Trump will probably lose the midterms, and become a lame duck president, spamming increasingly grandiose, unworkable executive orders while battling a stream of Congressional investigations.

That means the window is closing on my ability to force Democrats to reckon with their failures during the Biden years. If and when Trump loses a national election, there will be far less pressure for soul-searching on the left. And once the GOP is no longer in control of all three branches of government, the Democrats will be able to influence actual policy instead of sitting around rebuilding their brand.

So before that happens, I want to summon the last fading scraps of the self-reflection that Democrats and progressives were briefly forced into after Trump thumped Kamala Harris two years ago. When Trump won, I wrote four blog posts trying to explain where the Democrats had gone wrong:

So today I’m going to repost all four parts of my election post-mortem, so that the progressives and Democrats among my readers can take one final moment to reflect on how not to enable the rise of authoritarian rightist leaders in future election cycles.

Identity politics isn’t working

I’ve made no secret of the fact that I think Trump is a terrible choice for America, but there’s no denying that the American people have made their choice. Trump is currently projected to win every single swing state. He outperformed his 2020 performance in every single county in the country. He’s projected to win a solid majority of the popular vote — the first time a Republican has done so since 2004. I still don’t like the Electoral College as an institution, but you can’t blame it for this result like you could for 2016. In fact, Trump’s victory was part of a more general Republican landslide — the GOP took the Senate and improved its majority in the House. A map of political shifts between 2020 and 2024 shows almost solid red.

I suppose a small silver lining here is that we won’t have to spend the next two months enduring waves of “Stop the Steal” election denials, lawsuits, and threats to repeat January 6th. Democracy delivered Trump a victory, and so Trump has little incentive to attack democracy.

In fact, that silver lining might extend to the long term too. Trump and the GOP won because nonwhite voters and working-class voters — traditional Democratic constituencies — shifted his way. Dems still won Hispanic, Black, and Asian voters overall, but their margins fell significantly from previous elections, while actually improving with White voters:

In fact, exit polls show Trump winning a solid majority of Hispanic men!

Update: Over at Matt Yglesias’ blog, Jed Kolko shows that heavily Hispanic areas swung harder toward Trump than any other parts of the country:

Ronald Reagan’s prophecy that Latinos would become Republicans is coming to pass.

Income has realigned as well. Democrats are now the party of the educated upper middle and wealthy class, but have lost their appeal to the middle and working classes:

This largely destroys the narrative that nonwhite immigration will demographically drown White Americans under a tide of imported minority votes. In fact, it vindicates my guest poster Wally Nowinski, who observed that high-turnout elections like this one now help Republicans, because marginal voters — working-class Americans and Hispanic Americans — now lean to the right:

At some point, Republicans are going to realize this, and hopefully become less anxious about America’s racial future. Hopefully they will also realize that any attempt to make voting harder actually hurts them in the future, because the impact would fall disproportionately on their own base.

I expect these realizations to take some time to sink in, but ultimately their effect could be very salutary for American democracy. In the days of Nixon and Reagan, Republicans never worried about democracy itself — it was obviously on their side. Even in 2004, Republicans could tell themselves that we lived in a “center-right nation”. Now, Trump’s victory, powered by lower-income Americans and Hispanic men, will hopefully convince the GOP that democracy is still a game they can win.

A democracy needs both major parties to believe they can win via persuasion rather than by organized resistance, and 2024 showed that Republicans can still do that — even if the method of persuasion is one that I personally wish hadn’t worked so well.

If you want an optimistic take, there it is.

So what other important lessons can we learn from this disappointing election result? I didn’t title this post “Why Trump won”, because I don’t think I really have a convincing answer — I’m not a political scientist, or even a political pundit. In fact, there are probably a bunch of reasons, not just one.

Nor am I going to be one of those pundits who tells you that if only Democrats had listened to my policy priorities, things would have gone very differently. In fact, few Americans care about my own policy priorities. My top issues are industrial policy and pro-democratic foreign policy. I definitely don’t think emphasizing those more, or doing more of those things, would have propelled Harris to victory. (I don’t think they would have hurt; I think voters just don’t care.)

But I do think there are some important lessons that we can already learn from this election, especially at the intersection of economics and politics. In particular, I see three big ones:

1. Identity politics — viewing racial groups as homogeneous “communities” to be targeted with appeals to collective grievances — is not an effective way of winning over Hispanic (or, probably, Asian) voters.

2. People care about inflation more than about unemployment.

3. The educated professional class has become dangerously out of touch with the rest of the country.

Instead of writing all of these in one post, I’m going to split it into three, because I think the lessons are each very important. Since I’ve already shown the racial and class breakdown of the exit polls, I’ll talk about how identity politics is failing Democrats. Whether or not you think identity politics drove voters into Trump’s arms, it certainly hasn’t done an effective job of retaining Hispanic voters. The whole approach needs a deep rethink.

Americans are individuals, not extensions of racial “communities”

In the 2010s, there was a big debate over identity politics, with scholars like Francis Fukuyama warning that treating the American electorate as a balkanized set of racial collectives was undermining democracy. Stacey Abrams and other progressive writers pushed back against Fukuyama, arguing that all politics is identity politics, and so Democrats should continue focusing on racial appeals.

Abrams et al. were right in the facile, definitional sense that people’s personal identification with larger groups, movements, and ideas — rather than their own personal economic interests — drives much of their political behavior. After all, if human beings were rational calculating machines, they wouldn’t even vote, given the incredibly low probability that any election will be decided by a single vote.1

But the 2024 election — even more than the 2020 election — makes it clear that most Hispanic and Asian voters don’t want to be appealed to based on the racial group of which they’re supposedly a part. For example, lenient policies toward asylum seekers from Latin America don’t seem to have won over many Hispanics:

In Hidalgo County, in South Texas — a Mexican American stronghold that Biden won with 58% of the vote in 2020 — Vice President Harris and Trump were virtually tied early Wednesday, with Harris leading by less than half a percentage point…In Cameron County, at the southern tip of Texas’ Rio Grande Valley, Trump led Harris, 52-47, according to early results. Biden won that county with 56% of the vote four years ago.

The whole notion that lenient policies on illegal and quasi-legal immigration would win inroads with Latinos was based on the idea that Hispanic Americans identify strongly with others of their same race. Political scientists call this the theory of “linked fate”.

Democrats clearly buy into this theory when they refer to racial groups as “communities”. This term has become ubiquitous in Democratic messaging — for example, Harris talked about her “economic agenda for the Latino community”.

But races are not communities. Our country is not like a segregated 1930-era city, where Black people live in a ghetto, Chinese people live in Chinatown, Italian people live in Little Italy, and so on. Hispanic and Asian Americans might share a sense of “linked fate” with others of their race, but it’s only one of many things they care about, and perhaps not even a major one.

The big exception, of course, is Black Americans, who continue to vote mostly like a racial bloc, despite Trump’s modest inroads with Black men. This has clearly influenced the Democrats’ view of how racial politics operates in America, and they’ve tried to apply the same mental model to Hispanics and Asians. This is explicit in California, where you hear the term “Black and brown” thrown around a lot.2

But it isn’t working. In fact, it’s backfiring. A recent paper by d’Urso and Roman found that use of the term “Latinx” actually made Hispanics vote for the Republicans in nontrivial numbers!

Group labels are political, but little theory and evidence explains how group labels shape politician evaluations…Latinos’ relationship with “Latinx,” a gender-inclusive label, is a theoretical test case. Using several datasets, we find: Latinos are less likely to support politicians who use “Latinx”…Latinos who oppose “Latinx” are less likely to support politicians who used or are associated with “Latinx”…Latinos in areas where “Latinx” is more salient are more likely to switch their vote toward Trump between 2016-2020[.]

One common interpretation of this result — and one the authors discuss — is that many Latinos are conservative, and that this makes them susceptible to “anti-LGBTQ+” labels. I’m highly skeptical of this interpretation. Outside small, highly educated, deeply progressive circles, who even knows that the purpose of the “x” in “Latinx” was to be trans-inclusive? Almost no one who is likely to be offended by the term is even aware of this.

Instead, what they hear is simply someone making up a weird label and applying it to them. It’s what sociologists call ascription — it’s someone making up a racial category for someone else and then stuffing them into it without their consent. The Occam’s Razor explanation for Hispanics’ dislike of “Latinx” is that it minoritizes them — it declares that they’re part of a special “community”, separate from other Americans.

Democrats’ purpose in assigning Americans to “communities”, of course, is not to exclude them, but to reward them — to hand them special prizes or favors via government policy. For example, the Biden administration had a whole policy oriented around giving government benefits specifically to “HSIs” — an abbreviation for “Hispanic-Serving Institutions”, i.e. colleges that a lot of Hispanic people tend to go to. Harris also had a plan for encouraging small banks to give Hispanic people more loans.

This is, fundamentally, a special-interest model of politics — it’s similar to the kind of quid pro quo that historically dominates much of urban machine politics. I’m not sure whether today’s Dems inherit their instinct for this kind of group giveaway from their old Tammany Hall days, but it’s recognizably similar to the way California cities hand out cash to favored nonprofits — especially because the programs are often targeted at colleges or banks or nonprofits or other organizations that claim to serve the racial “community” in question.

But there are notable downsides to this approach. First, unlike in a city, these targeted initiatives probably don’t make it through to the public consciousness. What Puerto Rican voter in Philadelphia even knows what the heck an “HSI” is? But more fundamentally, I think most Americans, including most Hispanic Americans, would rather be treated as individuals than as members of a racial “community”.

Identity politics and minoritization

Identity has many components. For my part, I identify as an American, a Texan, a liberal, a rabbit keeper, a writer, a nerd, a PhD holder, a science fiction fan, a Jew, a white person, a Lithuanian-American, and so on.3 If politicians primarily spoke to me as part of the “Jewish community”, I would be annoyed, because I don’t necessarily want to be defined by that component of my identity. I don’t reject it, but I don’t want to be reduced to it either. And if politicians tried to appeal to me with policies targeted through Jewish organizations to benefit Jews, I would simply not care. In fact, I would be a little conflicted about accepting any of those benefits, since it would pigeonhole me into prioritizing an identity that exists as only one component among many.

Even more fundamentally, being constantly minoritized — being told to think of myself as a Jew first and as an American second — would pressure me to shrink the circle of people that I consider part of my own in-group. Because I think of myself as an American first and foremost, I feel like there are 330 million other people who share my primary identity — 330 million people on my “team”. But if I were to think of myself first and foremost as a Jewish person, that would shrink my in-group to only 16 million, almost none of whom live near me. It would make me feel more isolated, and might even cause me to start questioning whether the 323 million non-Jewish Americans who live all around me have my best interests in mind.

In other words, being set apart from your fellow countrymen and neighbors is generally not a desirable thing, even if the reason is to put you on a pedestal.

I suspect many Hispanic Americans feel the same way. Talking to Hispanic folks (remember, I’ve lived most of my life in either Texas or California) and reading books like The Diversity Paradox, The New Americans?, and The Great Demographic Illusion, it seems clear that most would like to think of themselves as regular Americans — not to give up or forget their national origins or ethnic identity, but to prioritize their American-ness.

The progressive movement of the 2010s told us that this was a bad thing. “Assimilation” became a dirty word in progressive circles, and the University of California told their faculty not to call the U.S. a “melting pot”. The implicit assumption was that given the choice, people would like to prioritize their racial and ethnic identities, and that only forcible conformity pressure from American institutions — or from White people — was preventing people of color doing that. The Democratic party’s embrace of identity politics — its habit of targeting racial “communities” with encouraging rhetoric and specific material benefits — was seen as a way to counteract the pressures of assimilation.

The conservative counter-narrative was that race relations had been fine right up until progressives started making everything in America about race. Conservatives always send me the following chart:

This is a powerful argument. If structural racism was really so entrenched before 2014, then why did two-thirds of Black adults say race relations were good? Essentially, the progressive story is that they were effectively bullied into saying this — or perhaps bullied into thinking it. The conservative story is that progressives created the decline in race relations by making everyone think about race all the time.

I suspect that neither of these stories gets it quite right, but that the conservative story is closer to the truth. Increasing the salience of racial divisions and racial conflict makes people mistrust their neighbors more. And although both sides were guilty of this in the 2010s, progressives were far more zealous and absolutist about it.

Essentially, the progressivism of the 2010s sought to turn American society into a sort of estate society, where people’s rights, privileges, and social roles would be mediated by their identity group. How your teachers and principals treated you, your relationship with the law, what colleges were available to you, what kind of parties you could go to, what jobs you could get hired for, how your bosses treated you at work, what kind of benefits you could expect from your government, and even how people walked past you on the street were to be determined by the race you were born into, rather than your own actions as an individual.

These race-based social relationships were to be enforced by a combination of DEI departments, lawsuits, social shaming, job loss, and so on. Ibram X. Kendi even suggested the totalitarian step of creating a constitutionally mandated “Department of Antiracism” to enforce race-based social relations at every level of society — all in the name of combatting “structural racism”, which Kendi couldn’t even define when asked.

Democratic party politicians never fully embraced this vision of the future, even in 2020-21. Some of these ideas were tried out in 2020-21, but they failed almost as quickly as they were tried, making basically everyone mad in the process. By 2024, Kamala Harris was tacking pretty strongly away from the racialized vision of America.

But that was too little, too late. People remember the individualistic Americanism largely prevailed in public life before 2014, and they remember the constant hyper-consciousness of race in 2014-21, and they know which one they like better. And although Democrats abandoned much of their woke rhetoric, the practice of targeting politics at specific “communities” has persisted. I suspect that it refreshes the bad memories of the racial conflicts of 2014-21.

All of this is a long-winded way of saying that Democrats’ racial targeting probably makes a lot of Hispanic people feel singled out and alienated in their own country, even if the purpose is to help them.

But that’s not all. Democrats’ identity politics flies strongly in the face of the narrative that has defined the American experience for many Hispanics.

The story of Hispanic voters is defined by the American Dream

To the extent that most Hispanic Americans do share a common experience, it’s the immigrant experience. Immigrants come to America seeking the American Dream of upward mobility and opportunity. And by and large, they achieve it. Their children tend to earn a lot more than they do, and their grandchildren earn even more.

This was the path that most immigrant groups have followed — the Irish, the Italians, the Lithuanians, the Chinese, and so on. For a while, people argued that Hispanics were different — that they would be permanently excluded from the American Dream. That turned out not to be true:

For many Hispanic Americans, their salient experience of America has not been one of racialized exclusion, but one of upward economic mobility and social inclusion. Economists have found that Hispanic incomes are converging to White levels across the generations. Once you control for the composition effect of new poor immigrants coming in, there’s strong income convergence across the generations:

We study the sources of racial and ethnic disparities in income using de-identified longitudinal data covering nearly the entire U.S. population from 1989-2015….[T]he intergenerational persistence of disparities varies substantially across racial groups. For example, Hispanic Americans are moving up significantly in the income distribution across generations because they have relatively high rates of intergenerational income mobility…

Hispanic Americans are moving up significantly in the income distribution across generations. For example, a model of intergenerational mobility analogous to Becker and Tomes (1979) predicts that the gap will shrink from the 22 percentile difference between Hispanic and white parents observed in our sample…to 6 percentiles in steady state…

Hispanics are on an upward trajectory across generations and may close most of the gap between their incomes and those of whites…Their low levels of income at present thus appear to to be primarily due to transitory factors.

In fact, despite composition effects from continued immigration, Hispanic wages have grown faster than White wages in America since the turn of the century:

Surveys unsurprisingly have found widespread economic optimism among Hispanics for many years. Hispanics are climbing the ladder of education:

Hispanic homeownership is rising strongly. And Hispanics are starting businesses at very high rates:

New businesses owned by Latinos accounted for 36% of launches in 2023…As of 2023, Latino entrepreneurs have established a staggering five million businesses across the United States, collectively generating over $800 billion in annual revenue…The rapid pace of this growth is further highlighted by the fact that the number of Latino/a-owned businesses surged by 34% from 2007 to 2019.

In other words, Hispanic Americans’ economic path looks reasonably similar to that of Italian Americans or Irish Americans.

In the 2010s, it became taboo to even talk about these successes in some progressive circles. The University of California’s 2015 speech guidelines told faculty not to say “America is the land of opportunity”. This was deemed to be a racist utterance, since it supposedly minimized the economic exclusion that many minority groups have faced.

But the fact is, America is the land of opportunity, especially for immigrants and their children. Hispanic Americans, broadly speaking, have been prime beneficiaries of that upward mobility — they have worked incredibly hard, gone to school, started businesses, learned English, and done everything else they were supposed to do. And America has rewarded them with economic success and social integration.

Compared to that self-bootstrapped success, racial preferences — DEI, affirmative action, preferential government contracts, and so on — are basically a booby prize. Yes, in reality, those policies can sometimes help accelerate Latino achievement. But the mere fact that they’re on offer — and that Democrats seemingly view them as permanent policies instead of temporary ones — sends the message that Hispanics are a permanently racialized, excluded, marginalized group. This message is often explicit — when Democrats and progressives talk about “marginalized groups”, which they often do, that term is generally supposed to include Hispanics.

Being a marginalized group is simply not part of anyone’s American Dream.

The tricky part for Democrats is that this is an approach that has largely worked with Black voters — or at least failed to alienate them. It will be difficult for Democrats to convince themselves that Hispanic voters are not going to work the same, after spending so many years believing they would. But the 2024 election, and the trend of the last few elections, makes it clear that the two are not going to work the same.

After identity politics

Identity politics allows Democrats to hold on to a supermajority of the Black vote, so I suspect it will never go away. But the Black vote is a smallish and shrinking part of the electorate, and even there, the power of idpol may be fading a bit. Meanwhile, Hispanic voters, Asian voters, and Middle Eastern voters are the future. Those groups are all strongly upwardly mobile, and mostly descended from people who came to America voluntarily in search of the American Dream. And although ethnic studies professors and progressive activists will happily tell you all about the Chinese Exclusion Act and post-9/11 Islamophobia, it’s just not the case that Hispanic, Asian, and Middle Eastern Americans have anything that compares to the history of slavery and segregation.

The Democrats need to move away from identity politics. And switching to class-based politics, as some have suggested, won’t really help. Biden was probably the most pro-union and pro-worker President America has had since the mid 20th century, and yet some prominent unions refused to endorse Harris.

Instead, Democrats need to retool their message to be about American identity, as it was in the New Deal days, and as it arguably was for Clinton and Obama. I believe Harris actually took steps in the right direction, but not enough, and not nearly soon enough — and she was probably the wrong person to sell the idea. My advice is not to be discouraged, and to keep at it — Walter Mondale and Michael Dukakis both went down to crushing defeats with centrist, positive messaging, but then Bill Clinton eventually figured out how to make the approach work.

Republicans will eventually screw up. Trump is old, and he has a lot of foolish and bad people around him, and his economic policy program is kind of a dead end. At some point, America will once more call upon the Democrats to save them from Republican mistakes, as they did in 2008 and in 1992. Democrats will have an easier time rising to that inevitable summons if they leave the identity politics of the 2010s behind. Because that stuff just isn’t getting the job done.

Americans hate inflation more than they hate unemployment

In 2021, there was a fierce debate, both within the Biden administration and among the general left-of-center commentariat, about how much to worry about inflation. For example, in May 2021, Larry Summers wrote:

Even six months ago, it was reasonable to regard slow growth, high unemployment and deflationary pressures as the predominant risk to the economy. Today, while continuing relief efforts are essential, the focus of our macroeconomic policy needs to change…Inflationary pressures are mounting…How much does it matter whether inflation accelerates? In general, increases in inflation disproportionately hurt the poor and are associated with reductions in trust in government. Progressives might consider the role that inflation played in electing Richard M. Nixon in 1968 and Ronald Reagan in 1980.

In February 2021, Olivier Blanchard, another prominent macroeconomist, wrote a detailed argument that the Biden administration was spending too much on Covid relief with the American Rescue Plan, and that this would generate a surge of inflation. The argument included a simple model whose predictions ended up being close to what actually happened.

Other economists pushed back hard against Blanchard and Summers, arguing that inflation was due to transitory factors, and that it was important to keep spending in order to preserve full employment. One of these was Paul Krugman, who declared himself “a card-carrying member of Team Transitory” — “Team Transitory” being those who thought inflation was mostly due to pandemic disruptions and would fade on its own. There was even a live debate between Krugman and Summers in early 2022. But by late 2022, Krugman issued a mea culpa, and admitted that Biden’s American Rescue Plan had exacerbated inflation:

In early 2021 there was an intense debate among economists about the likely consequences of the American Rescue Plan, the $1.9 trillion package enacted by a new Democratic president and a (barely) Democratic Congress. Some warned that the package would be dangerously inflationary; others were fairly relaxed. I was Team Relaxed. As it turned out, of course, that was a very bad call.

Economic analyses of the inflation surge of 2021-22 generally assign the American Rescue Plan some share of the blame.

Ignoring Summers’ warnings, and believing Team Transitory, might have cost the Democrats the 2024 election. I’m no political scientist, but there are reasons to believe that the burst of high inflation in 2021-22 was one factor in Kamala Harris’ loss.

Why inflation probably mattered in this election

The first reason we should think inflation mattered is that people pretty consistently said it mattered. On Gallup’s long-running “most important problem” survey, inflation consistently ranked as the most important specific economic problem that people mentioned, right behind “economy in general”.

A lot of other surveys showed similar results. And the percentage of people who told Gallup that the economy was “extremely important” to their presidential vote was unusually high — almost as high as in 2008, when the financial crisis had just devastated the U.S. economy:

And of course consumer sentiment was pretty low, even after adjusting for changes in methodology:

Now, it’s possible that this was mostly just Republicans complaining about inflation because it felt more substantive than talking only about more social/cultural grievances like wokeness, immigration, trans stuff, etc. After all, opinion on the economy shows a very partisan pattern depending on who’s in the White House:

It’s a pretty good bet that Republicans will suddenly “discover” that inflation in America is actually low now, and give Trump the credit.

But if you look historically and across countries, it really looks like inflation tends to make voters particularly mad. Research often finds that cumulative inflation over a President’s term in office — not just the current inflation rate — affects presidential approval and election performance. If you look at Gallup’s long-running survey, you’ll see that the inflation of the 1970s dominated Americans’ complaints even more than the unemployment of the Volcker recessions in the early 80s, or the recession of the early 90s:

And if you look at other countries, you’ll see that governing parties in every other rich country have seen their vote shares go down this year:

There are relatively few factors that are affecting all rich countries at the same time. A backlash over immigration is another possible common factor, but the list is basically just that plus inflation.

On top of that, there was a model that predicted the election outcome quite well. An interdisciplinary team of researchers created a model based on presidential approval ratings and economic conditions in each state, and they ended up predicting each state correctly:

It might be a fluke, but the same model got only one state wrong in 2020 (Georgia), so that’s pretty encouraging. In any case, the model relies partly on economic fundamentals — measures of the labor market and real wages. And real wages depend on inflation. So since labor markets are generally very strong all across America, the erosion of state-level wages by inflation is going to be the main economic factor that led this model to forecast a Trump victory.4

So I think it’s safe to say that while it’s possible inflation mattered less for Trump’s victory than people say on polls, it did matter at least somewhat.

Why inflation makes voters even angrier than unemployment

Let’s talk a little about macroeconomics for a second. Most mainstream macroeconomic models assume that there’s some sort of short-term tradeoff between inflation and unemployment. If the Fed lowers interest rates or does quantitative easing or otherwise prints money, the general consensus is that unemployment will go down and inflation will go up. Similarly, if the government borrows and spends money and the Fed doesn’t counteract this by raising interest rates, the general consensus is that you’ll get faster growth, more jobs, and rising prices.5

Macroeconomic policy then becomes a question of how to strike the right balance. We want everyone to have a job. We don’t want inflation to go about 2%. But if we have to choose between these two things, how do we choose? In economic models, the relative degree that policymakers should care about inflation versus unemployment is determined by a social welfare function — basically, how much each of these economic ills makes regular people unhappy. A lot of economic research is dedicated to figuring out what that welfare function, and there’s not really a consensus.

But the experiences of the 1970s and the 2020s are teaching us that in America at least, people seem to get even madder about inflation than they do about unemployment. And the question is why. I actually wrote a post about this back in March of 2021, shortly after the American Rescue Plan was passed:

The basic story here comes from a survey Robert Shiller did in 1990. Basically, inflation tends to make people’s purchasing power go down, because their wages can’t keep up.6 This makes people poorer, and people do not like to be poorer.

The inflation of 2021-22 definitely made people poorer. In fact, the drop in real personal disposable income from mid-2021 to mid-2022 was more severe than the Great Recession or the 1970s inflation!7

This was caused by a combination of the end of pandemic relief, and falling real wages and other income due to inflation. In 2021 and 2022, Americans’ real wages suffered their biggest drop in postwar history, thanks to inflation8:

Remember, real wages are a key economic input that went into the model that managed to successfully predict every state’s election result. People really don’t like it when their real wages go down!

One key thing to understand is that this sudden impoverishment happened to most Americans. Rising consumer prices affect everyone, from the rich to the poor, the employed and the unemployed, the old and the young.

In a recession, on the other hand, the negative effects are largely concentrated. If you’re one of the unlucky people who loses their job, you take a huge hit. But if you’re not one of those people, you probably do OK. Maybe your wages grow more slowly for a few years, and maybe you have to stress out about the increased possibility of being laid off. But in general, the harms of inflation are diffuse while the harms of unemployment are concentrated.

If you’re a moral philosopher, an activist, or an economist estimating a social welfare function, you may prefer harming a lot of people a little bit with inflation to harming a few people a lot with unemployment. But a democracy doesn’t vote based on social welfare. It’s one person, one vote. So if unemployment harms 10 million people a lot, and inflation harms 200 million people by a moderate amount, it’s clear that inflation will directly harm a much larger number of voters.

The social welfare function and the “get your butt kicked in an election” function are two very different things.

I actually suspect that there are other reasons why inflation bothers voters even more than unemployment. Inflation is something that’s obviously beyond an individual’s control — if the price of eggs goes up, you can choose not to buy eggs, but otherwise there’s not much you can do. But unemployment is something that individuals do exercise some degree of control over — if you work a bunch of extra hours during a recession and establish a good relationship with your boss, maybe you can avoid getting laid off.

In general, I think people are less worried about risks they have some control over, even if the overall level of danger is higher. Look at how people are more afraid to fly than to take long road trips, despite road trips being much deadlier per mile. The leading theory is that in a car you have some control over your safety, while in a plane you have basically zero control. Similarly, I suspect that inflation makes people feel powerless, like being stuck in a crashing airplane.

In any case, this is probably an important area for survey research to explore. But in the meantime, Democrats need to remember that full employment doesn’t necessarily mean an economy that Americans are happy with. If it comes at the expense of rapid inflation, that price is going to be one that most Americans aren’t comfortable paying.

Democrats should be wary of macro-progressivism going forward

I’d like to say a little more about the debate over the American Rescue Plan in 2021. While Paul Krugman and Larry Summers viewed their argument in purely technical terms, some economists and commentators did come at the debate from an ideological angle. In general, spending more was considered the progressive position, while spending less in order to avoid high inflation was considered more centrist or moderate.

This makes sense — full employment disproportionately benefits the least well-off in society. The lowest-paid workers are often the first to be fired in a recession and the last to be hired in an expansion, so the longer you can keep an expansion going, the more poor people can have jobs. Also, full employment tends to push up wages, especially at the lower end of the scale — in fact, even as most Americans lost purchasing power during the inflation of 2021-22, the bottom 10% of American workers actually saw their real wages rise.

This July, I wrote about how some influential progressive think tanks basically push for full employment all the time, and downplay worries about inflation:

Some progressive economics commentators — I will not name names, because I’m trying to be a less confrontational blogger these days — also consistently downplayed the risks of inflation and extolled the many benefits of full employment during Biden’s tenure in office.9

In retrospect, whether or not this was a good move from a moral standpoint, it looks like a big mistake from an electoral standpoint.

I hope that these commentators are reevaluating their beliefs. And I hope that Democratic politicians, staffers, and donors are reevaluating the paradigm as well. Full employment is great, all things equal. But sometimes the price of full employment is too high — not just for the majority of Americans who have to endure years of erosion in their purchasing power, but for everyone who has to suffer from the election of leaders like Donald Trump.

Update: Good to see some left-leaning economists quickly coming to the same conclusions.

Our task now is to get the ear of Democratic politicians, which will probably require wresting it away from some progressive think tanks and Twitter posters.

The educated professional class is out of touch with America

I’ve been writing about the lessons that Democrats should take away from the big GOP victory. I’ve written about how identity politics is failing to attract Hispanic voters, and about how Dems cared too much about employment and not enough about inflation. The third big lesson, I think, is about class in America. The educated professional class is drifting away from the rest of the country, in terms of values, beliefs, and their information diet.

This election upended a lot of demographic trends that people had been very certain about only a few years ago. Trump seems to have produced a large-scale shift of Hispanic voters to the Republicans, and big cities swung harder toward the GOP than other areas. But there’s one key trend that this election didn’t reverse: education polarization. Trump was even more dominant among non-college voters than he was in the last two elections, while Harris won the highly educated by about as much as Biden did.

Over the years, the Democratic base has shifted decisively from the working class to the educated professional class:

This shift doomed Harris’ campaign, since people without college degrees outnumber their college-educated counterparts — in 2024, the latter were only 43% of the electorate.

And this weakness looks likely to be a long-term problem. In the past, when college education was steadily increasing, it was easy to think that college-educated voters would own the future. But in recent years, the rate of college enrollment has plateaued and begun to fall:

And the number of new college graduates fell in 2022, even as America’s total population grew.

As for why fewer Americans are getting a college education, I wrote a post about this back in July:

The short version is that tuition soared even as the earnings boost from a college degree shrank substantially, making college a less attractive value proposition. A lot of this had to do with colleges themselves boosting spending on administrators and facilities, and having to charge students more to pay for it.

If universities cut costs, cut tuition, and raised their value proposition, we could see enrollment rise again at some point, but I wouldn’t bet on that happening soon. Meanwhile, socialists’ plan for universal free college is dead on arrival; even Biden’s generous new student loan repayment provisions might prove to be temporary, since the fiscal cost is so huge. So a wave of taxpayer cash is not going to restore the trend of rising educational attainment.

In other words, Democrats’ education problem is here to stay. Unless Dems can figure out how to win back those non-college voters, they’re likely to be at a huge electoral disadvantage for the foreseeable future.

But winning back those voters would require the educated professional types who make up the backbone of the Democratic base and the progressive movement to figure out how to connect rhetorically with the less-educated masses, addressing their concerns, speaking to their values, and just speaking their language in general. And although it’s too early to tell, many of the reactions I’m seeing on social media are, to put it mildly, very unencouraging:

Obviously these are extreme examples, but in general it seems to me that the educated professional class has simply grown apart from the rest of the country over the past few decades.

A disconnect on problems

The most important disconnect between educated professionals and the rest of America is about the problems they face on a day-to-day basis. Practically every problem that Trump voters cited is something that hits the working class harder than the professional class.

About a year ago, I found a really good place to get a haircut. The hairstylist, a Vietnamese immigrant woman in her 50s, was complaining about rampant theft from small businesses in the neighborhood. She said that if thieves stole less than $950, they wouldn’t even be arrested or prosecuted, creating an incentive for constant theft.

In fact, stealing less than $950 was still a crime, but there was a plebiscite in 2014 called Proposition 47 that changed it from a felony to a misdemeanor. (Prop 47 was mostly repealed this year, when California voters passed another ballot measure called Prop 36.) I’m not sure how big of a factor Prop 47 was, but for whatever reason, property crime in San Francisco did rise quite a lot in the late 2010s:

This mirrored a more general rise across the state10:

I am an educated professional, not a small local businessperson. I had heard about the big wave of car break-ins in San Francisco, but it took me a very long time to realize how common retail theft had become. To me, the wave of smash-and-grab attacks in the Bay Area was a sign of urban disorder and decay, and a force causing a bunch of downtown stores to close.

But to someone who owns a hair salon that gets robbed, or a convenience store that gets looted, that’s their livelihood. That could be their child’s ticket to college, or their ability to get the pipes fixed at their house, or their chance to get their car fixed. As an educated professional, I’m simply much less exposed to robbery and theft.

And on top of that, I’m an urban educated professional. Those who live out in the fancy suburbs are even more disconnected from the crime wave that afflicted America in 2020 and 2021. It’s lower-earning people in cities or poorer neighborhoods who bore the brunt of the explosion of disorder that followed in the wake of the Floyd protests.11

Stuff like “defund the police” is a luxury of the rich and mobile — i.e., of the educated professional class. It’s lower-income Americans and small business owners who need the police the most.

How about inflation? When defending the Biden economy, I often point out that real wages for people in the lower part of the distribution increased faster. That’s adjusted for inflation, but it’s adjusted for the overall state-level inflation rate. In fact, as the Minneapolis Fed’s Jeff Horwich explains, people who make less money have faced higher price increases since 2021, for two reasons. First, the things that lower-income and middle-income people spend a larger percent of their income on — especially housing — tended to go up in price a bit more:

The second reason is pretty obvious: Having less money makes drops in purchasing power harder to bear. If my cost of living went up by 10%, it would be a minor annoyance; to a working-class person that could very well be ruinous. No wonder lower-income people tend to notice price changes more:

So while educated professionals like myself were able to put the post-pandemic inflation behind us fairly quickly after it subsided in 2023, regular Americans probably felt a more lingering hurt.

How about the wave of asylum seekers that flooded across the border in the Biden years? Lower-skilled immigrants don’t actually tend to reduce wages for native-born working-class Americans. But a big part of the reason they don’t reduce wages is that native-born people respond to increased immigration by going back to school and climbing the skill ladder. That takes time and money.

Also, low-skilled immigrants directly compete with less-educated and lower-earning Americans for housing. Asylum seekers tend to be poor, meaning they will try to get apartments and houses in lower-income neighborhoods. The effect on local rents in those neighborhoods, and for those types of housing, will be greater than the overall citywide or statewide effect. Rent will go up for the American working class, differentially affecting their purchasing power in ways that don’t get captured by state-level or national statistics.

So although Biden did beat inflation, crime did go down, and Biden did eventually crack down on the border, I can understand why the traumas that working-class Americans had to suffer in 2020-2022 couldn’t be expunged just by some encouraging charts. And the fact that educated professionals were much more insulated from the disruptions of the pandemic and from Biden’s missteps in 2021 mean that they didn’t necessarily even see the need to acknowledge how hard these had been on everyone else.

This disconnect probably ended up alienating people without college degrees. Not only did it cause many progressives to support deeply unpopular things like “defund the police” on social media, but Democratic politicians who became used to talking to their educated progressive base probably got a distorted idea of what was troubling the rest of America.

An information disconnect

I was taking an Uber with a friend a couple of weeks ago, and the driver went on a gigantic rant about how inflation is out of control, vaccines are ineffective and are causing all kinds of massive health problems, there are no jobs anymore, and so on. Listening to his rant drove home the fact that there are huge segments of the American populace that I’m just completely out of touch with.

Almost all of what he said was factually wrong, of course. Inflation is back down to a low level, and has been for years now. Employment is near all-time highs. Vaccines are safe and effective, and so on. But the fact that he believed in all of this stuff is a sign that there’s an information ecosystem out there that’s totally separate from the world of blogs and Washington Post articles and nerdy podcasts and Econ Twitter that comprises my own information diet.

In fact, this information disconnect is probably one reason why Trump won. Voters who knew about the positive data that I know about — and which I frequently talked about on this blog — went very strongly for Harris, while voters who had the same misapprehensions as my Uber driver went pretty strongly for Trump:

Now, people may simply be bullshitting on these polls — there’s research that shows that when you pay people to get these facts right, they tend to get them right. Saying “inflation is still high” might just be a way of saying “I like Trump”.

Also, note that all four of these questions are specifically phrased in such a way that the correct answer says something positive about Joe Biden’s tenure in office. If the questions had been things like “Overall, murder rates were higher under Biden than under Trump (True)”, or “Inflation in the U.S. since 2021 has been the highest of any four-year period since the 1970s (True)”, or “Since 2021, unauthorized border crossings have been higher than at any point since the early 2000s (True)”, it might have been Democrats getting the facts wrong instead of Republicans. (We’ll never know, of course, but Democrats do tend to show partisan bias on economic surveys.)

But it also seems likely that many of these less-educated Trump voters just don’t get nearly as much information about politics, economics, and public affairs. Highly educated folks consume a lot more news in general. And people who consume a lot more political news tended to vote for Harris:

And when they do consume news, it’s likely to be from different sources. Many election postmortems have focused on the fact that Joe Rogan, a popular podcast host with many blue-collar fans, interviewed both Donald Trump and JD Vance during the campaign, but not Kamala Harris or Tim Walz. Joe Rogan’s podcast has 18 million subscribers. In comparison, Fox News, the most popular cable news network, has somewhere around 2 million primetime viewers.12 Meanwhile, Barstool Sports, another news outlet that’s popular with blue-collar types, went solidly for Trump and reportedly refused Harris’ offer for an interview.

In other words, Democrats are still doing a good job of getting their message to educated Americans who consume lots of political news, but are increasingly failing to reach the non-college masses. That’s why listening to an Uber driver talk about politics can seem like meeting someone from another universe.

This is not to say that educated progressives get the facts right, while blue-collar conservatives get them wrong. In fact, highly educated progressives can talk themselves into believing some absolutely insane things. During the summer of 2020, a lawyer friend insisted to me that the institution of police grew out of the institution of American slavery (obviously wrong, since police emerged independently all over the world). A number of progressives also asserted to me that police don’t reduce crime (false). The oft-repeated progressive claim that slaves built America is also obviously false, since slaves were less than 10% of the population. A few educated progressives even fiercely defend intentional fabrication of results in the social sciences, on political grounds.

But whatever the various myths are on the left and right, they are different myths. And because educated professionals swim in entirely different information ecosystems, they have little clue how to reach the masses and tell their side of the story.13

Why did the class disconnect come about, and how can we reduce it?

“Her belly may be full, but her spirit will be empty.” — Jean-Luc Picard

The educated professional class has been growing apart from America’s other social classes for decades now. Part of this was due to the big shift in the U.S. economy — the rise of knowledge industries like IT, finance, biopharma, and business services pulled America’s talented people to coastal metros and college towns. As the economist Enrico Moretti showed in his excellent book The New Geography of Jobs, a city’s human capital — i.e., how many educated professionals live there — became the best predictor not only of economic success, but also various measures of public health and well-being.

For those with the talent to escape the bleakness of their hometowns, big cities like New York, San Francisco, and Minneapolis — or college towns like Ann Arbor, Madison, and Berkeley — became magical playgrounds, full of great restaurants and shops and bars, other educated professionals to date and marry, and plenty of money to be made. I was one of the people who made that escape.

But what happened to the places the talented people left behind? Bereft of their talent in an age when talent equals money, they stagnated economically — counties that voted for Biden in 2020 represented just over half of the electorate, but 70% of America’s total GDP. And without as many talented people to be local doctors, teachers, etc., those places saw their health deteriorate — drug use and obesity ran rampant. Even the well-documented decline of marriage among the less-educated could be partially due to talented professionals fleeing the area — without as many visible examples of economically successful educated people getting married and staying married and raising kids in two-parent families, some working-class people in left-behind towns may have simply abandoned the practice.

This is the first reason for the disconnect. The second, I think, was institutional. In earlier times, there were a number of institutions that regularly brought people together across social classes — the three main examples being churches, businesses, and the military. But after Vietnam, the U.S. Military shifted from a mass-participatory conscript force to an elite professional volunteer force. Business became increasingly fragmented, with much blue-collar work and low-level service work outsourced or automated. And church membership has declined14 in America, possibly as a result of the internet:

Combine this with the de-localization of American social life due to the car and the internet, and you have a recipe for extreme sorting. As Tyler Cowen writes in his book The Complacent Class, Americans have become increasingly expert at sorting themselves according to education, income, and other characteristics.

There was one public institution that managed to retain its broad functionality over this time period: College. American universities tried hard to recruit and mix people of diverse backgrounds, teach them healthy personal and social habits, and provide them with a rich community life that could be a model for their later adult communities. And to a large degree, this worked.

But it only worked for 40% of the population! The majority of Americans lacked the talent, the study habits, the inclination, the time, or (eventually) the money to get that four-year degree. So for that lucky 40%, colleges were church and army and work team all in one — an acceptable substitute for the tight-knit villages that the modern economy of the car and the internet had destroyed.

For the rest of the country there was nothing. There were daytime TV and televangelists and methamphetamine and conspiracy theories. There were strip-malls and NASCAR and hip-hop and Marvel movies. There were Fox News and Rush Limbaugh and Michael Savage. There was plenty of cheap land, big houses, cheap cars, and jobs in the vast service sector. But their upper caste had fled them for the playgrounds of college and the big city and the Google cafeteria.

But they were still part of the same country as the educated professionals. And so they all still voted in the same elections. So one day Donald Trump came along, and the classes were reunited by the institution of electoral democracy, in a very unpleasant and painful way.

After this crushing defeat, many educated progressives will turn to the idea of Bernie Sanders-style “class” politics as an alternative to the identity politics that failed so spectacularly. But this won’t work either.

First of all, Bernie himself underperformed Kamala Harris in this year’s election. Second, and more importantly, Biden has been the most pro-labor, pro-union President in many decades, and it availed the Democrats nothing. Biden raised overtime pay, strengthened unions with various new laws, revived factory construction with industrial policy, mandated the hiring of union workforces for federal contracts, became the first President to walk a picket line, funded vocational education and apprenticeships, banned noncompete clauses, and took dozens of other actions in support of the blue-collar working class.

Wages rose strongly for the working class, and inequality fell. Biden also enacted a lot of Bernie-style policies — canceling student debt, passing a huge climate bill, and increasing subsidies for health care.

And none of it helped Kamala Harris. The Teamsters union withheld their endorsement. The East Coast dockworkers’ union threatened a crippling strike just weeks before the election. All across the nation, steelworkers and other blue-collar union workers defied their leaders and organized in support of Donald Trump. Harris did win a slim majority of the union vote, but this probably came mostly from educated professional types, especially public unions.

The fact is, America just isn’t the industrial society it was in the 1950s. The broad blue-collar manufacturing laborer class is a thing of the past, and the fragments of that class that remain are often more focused on sociocultural issues than on pure pocketbook concerns. Most of the American non-college populace are non-unionized suburban service workers with no real class consciousness.

America, fundamentally, is an incredibly rich country with a sick society. We have higher consumption by far than people in any other country on the planet, and yet by and large, except for the few people who were lucky enough to receive the blessings of community and health from our one remaining functional institution, we’re a bunch of unhealthy socially isolated drug addicts.

As for the solution to all this, I don’t have an easy fix. In the long run our country needs to build new inclusive institutions — places where society’s classes mingle and cooperate. I don’t know what those can be, though of course I’ll think about the problem. But it will take a long time. Obviously, YIMBY policies that allow working-class people to live in big cities will help a bit, by putting the various classes in physical proximity, but without integrating institutions the effect will be marginal.

Nor do the educated right-wing professionals in the tech industry understand their new working-class political allies any better than the progressives do. Beyond helping to elect Donald Trump and annoy educated progressives, they don’t really have any plans to help those who lack the talent to work at SpaceX.

In the short term, what Democrats can do is to realize the extent of the problem, and begin doing everything they can to explore the culture and the information ecosystem of Americans who didn’t go to college. Get interviewed on Rogan and Barstool Sports and wherever, but don’t just get interviewed — immerse yourself in the culture. Don’t get your information about regular Americans from the activist groups who knock on your staffers’ doors, or from 20-something socialists who spout stale Marxist theory. Go meet them, find out what they care about, and think about how to give it to them.

Fixing America’s great class disconnect will take a long time, but there’s no better time to start.

The blue cities must be fixed

Donald Trump’s victory in November 2024 wasn’t the only important election result. Big cities in California shifted away from so-called “progressive” governance. In San Francisco, progressive champion Aaron Peskin was soundly defeated in the mayoral race, and his vacated seat was won by a moderate. Peskin’s close ally, Dean Preston, was unseated by moderate challenger Bilal Mahmood, and the Board of Supervisors is expected to lose its progressive majority. In Los Angeles progressive district attorney George Gascón lost his reelection bid, while Oakland’s Pamela Price was recalled. This followed a national trend of progressive DAs getting the boot; meanwhile, San Francisco’s moderate DA Brooke Jenkins, who succeeded the notorious Chesa Boudin after the latter was recalled, easily cruised to reelection.

It’s possible to interpret these victories as part of a general revolt against progressive governance in big cities. At the local level that’s a good thing, since that governance has proven remarkably poor. But at the national level, the urban revolt against probably helped usher in another four years of Donald Trump. Although large metros still voted for the Democrats overall, they swung harder towards Trump than any other geographic region:

Derek Thompson has some more numbers:

In the past two cycles, Democrats could comfort themselves by counting on urban counties to continue moving left even as rural areas shifted right. That comfort was dashed last night, at least among counties with more than 90 percent of their results reported. In the New York City metro area, New York County (Manhattan) shifted nine points right, Kings County (Brooklyn) shifted 12 points right, Queens County shifted 21 points right, and Bronx County shifted 22 points right. In Florida, Orange County (Orlando) shifted 10 points right and Miami-Dade shifted 19 points right. In Texas, Harris County (Houston) and Bexar County (San Antonio) both shifted eight points right and Dallas County shifted 10 points right. In and around the “Blue Wall” states, Pennsylvania’s Philadelphia County shifted five points right, Michigan’s Wayne County (Detroit) shifted nine points right, and Illinois’ Cook County (Chicago) shifted 11 points right.

Josh Barro wrote an insightful post-election post arguing that big cities’ failure to curb urban disorder, fiscal waste, and soaring housing costs drove their inhabitants away from the Democratic party. Some key excerpts:

I write this to you from New York City, where we are governed by Democrats and we pay the highest taxes in the country, but that doesn’t mean we receive the best government services. Our transportation agencies are black holes for money, unable to deliver on their capital plans despite repeated increases in the dedicated taxes that fund them, because it costs four times as much per mile to build a subway line here as it does in France, and because union rules force the agency to overstaff itself, inflating operating costs. Half of bus riders don’t pay the fare, and MTA employees don’t try to make them. Emotionally-disturbed homeless people camp out on the transit system…even though police are all over the place (at great taxpayer expense) they don’t do much about it, and I can’t entirely blame them since our government lacks the legal authority to keep these people either in jail or in treatment. The city cannot stop people from shoplifting, so most of the merchandise at Duane Reade is in locked cabinets…[S]chools remain really expensive for taxpayers even as families move away, enrollment declines, and chronic absenteeism remains elevated. Currently, we are under state court order to spend billions of our dollars to house migrants in Midtown hotels that once housed tourists and business travelers. Housing costs are insane because the city makes it very hard to build anything — and it’s really expensive to travel here, partly because so many hotels are now full of migrants, and partly because the city council literally made it illegal to build new hotels. And as a result of all of this, we are shedding population — we’re probably going to lose three more congressional districts in the next reapportionment. And where are people moving to? To Sun Belt states, mostly run by Republicans, where it is possible to build housing and grow the economy…

[A]ll of this is why I think Democrats’ approach to the cost-of-living issues…has fallen so flat. The Democratic argument is, more or less, “look at all my programs” — all the things I’m going to have the government do to make life easier for you…I think Americans look around at how it goes when the government actually tries to help, and they have a healthy skepticism about how helpful the government is really going to be, and about whether the benefits are really going to flow to them. Democrats are making too many promises; they instead need to pick a few things for the government to do really well, with a focus on benefits to the broad public rather than to the people being paid to provide the services, instead of trying to do a zillion different things and doing them badly at great expense[.]

Of course I don’t know how big of a factor this was in Trump’s victory, but I’m not actually sure that even matters. Barro is largely correct about the sad state of blue cities, and it’s the inhabitants of those cities who are suffering the most from that failure of governance. The most important reason to fix those cities isn’t to help Democrats get elected at the national level, but because it would make life better for urban residents themselves. The swing toward Trump is simply an illustration of just how angry city dwellers are.

The blue city renaissance, 1980-2012

It’s important to remember that just a few years ago, the general consensus was that big cities in America were doing really well. In 2017, Will Wilkinson cited the success of American cities as a reason to reject Trump:

[W]hen Trump talks about cities, he presents a fearsome caricature that bears little resemblance to the real urban landscape…He persists in his efforts to slur cities as radioactive war zones because the fact that America’s diverse big cities are thriving relative to the whiter, less populous parts of the country suggests that the liberal experiment works…[T]he American metropolis is more peaceful and prosperous than it’s been in decades…Yes, cities have their share of failing schools. But they also have some of the best schools in the country and are hotbeds of reform and innovation…America’s most diverse, densely populated and well-educated cities are generating an increasing share of the country’s economic output.

Wilkinson’s paean to big cities was the culmination of decades of real success. In the middle of the 20th century, America’s cities had been hollowed out, by a combination of soaring urban crime and the lure of the suburbs. In the 1970s, big metropolises were still losing population. NYC almost went bankrupt.

Starting around the 1980s, though, America experienced an urban revival. Big cities began to gain back some of the population they had lost:

There were a number of reasons for this revival. One was the rise of knowledge industries like tech, finance, and business services, which tend to cluster together in cities. As Enrico Moretti shows in The New Geography of Jobs, human capital tended to cluster in big cities after around 1980 or so; Richard Florida also famously documented this in The Rise of the Creative Class. Here’s some data from Jed Kolko for 2000-2014 that’s pretty representative of the general shift:

A second reason for the revival was immigration — just like a century earlier, immigrants flocked to big cities.

Then in the 1990s, crime plummeted. In 1992, New York city had 2,245 murders. By 2016 it was down to 292:

The combination of the crime drop and the knowledge-industry boom made American cities great places to live, at least for a couple of decades. TV shows like Seinfeld and Friends and Sex and the City and How I Met Your Mother celebrated the lightheartedness of upper-class city life. Urban art and fashion and culture bloomed again, producing the hipster era.15

Why blue cities declined again

But the renaissance was short-lived. In the 2010s, the urban revival stalled, and the populations of big cities plateaued. The reason was simple: housing. Since the 70s, American cities had developed an entrenched culture of NIMBYism and a thicket of laws like rent control, which prevented cities from building new housing in order to accommodate the influx of population. As a result, rents rose. And since rich people — the same knowledge workers who were powering the urban boom — were better able to afford the rising cost of housing, it was middle-class and working-class people who were pushed out of the cities. New York, San Francisco, and the rest became playgrounds for the affluent.

More progressive cities in blue states consistently built less housing than cities in red states. Kahn (2011) found:

Traditional explanations for why some communities block new housing construction focus on incumbent home owner incentives to block entry. Local resident political ideology may also influence community permitting decisions. This paper uses city level panel data across California metropolitan areas from 2000 to 2008 to document that liberal cities grant fewer new housing permits than observationally similar cities located within the same metropolitan area. Cities experiencing a growth in their liberal voter share have a lower new housing permit growth rate.

As a result, progressive cities became less affordable:

This divide continues to this very day. Blue cities are still building a minuscule amount of housing:

At the same time, many blue cities were spending way beyond their means:

Some of this was due to the chronic problem of city pension obligations, which have grown more severe due to unrealistic assumptions about stock returns, the power of public-sector unions, and simple short-sightedness. But some was because local infrastructure and city services got very, very expensive — witness how New York City pays several times the cost per mile to build a new subway that a French city would pay.

Some of this ballooning cost is due to broken bidding processes and the erosion of state capacity. With their own departments having shrunk, progressive cities have been outsourcing more and more of their core functions to inefficient and often corrupt nonprofits. Recall the infamous case of La Sombrita, the ineffectual sun-shade that the Los Angeles Department of Transportation paid a nonprofit $200,000 to create:

San Francisco, as usual, is the worst offender, with a new nonprofit corruption scandal popping up every month or so.

In the late 2010s, blue cities brought yet another problem on themselves: urban disorder. Crime rates began rising in 2015, fueled by national unrest. But blue cities didn’t respond by cracking down on crime as they did in the 90s and 00s. Progressives in the late 2010s reviled and rejected “stop-and-frisk”, “broken windows policing”, and other tools that blue cities had used to keep order in previous decades. Instead, they elected a bunch of progressive prosecutors, enacted more permissive policies toward public drug use, passed laws that made it hard to use violence against shoplifters, and sometimes even reduced penalties for minor crimes.

The result was entirely predictable. Blue cities became increasingly afflicted by pervasive, low-level urban disorder — drug needles in children’s parks, epidemics of car break-ins, and so on. Female friends of mine in San Francisco started to report being followed for blocks, harassed on the train, or even slapped in the head by street people on their way to work. The housing crunch made the disorder much worse, of course, by exacerbating homelessness.

Then the pandemic and the riots hit, and the trend got turbocharged. Without “eyes on the street” to deter crime, and with police cowed or disgruntled by the protests of summer 2020, progressive cities became increasingly lawless, chaotic zones. Violent crime soared in 2020-21, with waves of attacks on vulnerable populations like Asian elders.16

Nowhere was the chaos more evident than in San Francisco. Although murder rates in SF stayed low, more minor crimes, like car break-ins and theft, skyrocketed:

And the epidemic of public drug use in SF got utterly out of control:

After the pandemic, there was an epidemic of store looting that continued for years, causing many of the stores in the downtown area to close.17

The American urban renaissance is now well and truly over. In the 2010s and early 2020s, many progressive cities squandered the massive windfall from the knowledge industry boom. With its seemingly invincible network effects and ever-increasing bounty of tax revenues, that boom seemed to convince progressive cities that they had infinite amounts of fiscal and social surplus to dispose of as they pleased — that they could tolerate crime and disorder, spend infinite money, and neglect the need to build new housing, and that none of this would end up mattering. That turned out to be wrong, and now the bill is coming due.

Three principles for reviving blue cities

Reviving blue cities will require a number of big changes. Elections like the recent one in San Francisco will be needed to push out “progressive” leaders who want to continue the failed experiments of the last decade. A sensible policy is going to have combine material abundance with intolerance toward crime and disorder. I like the basic platform advanced by GrowSF, a San Francisco advocacy group that combines pro-housing and pro-infrastructure policies with a tough-on-crime approach. Here’s a list of just a few of their good ideas:

But fixing blue cities is going to require more than just waves of voter anger or a laundry list of good policies. It’s going to require a mindset change — a shift in people’s understanding of what a city should be and how it should be run.

The most important thing blue cities need to understand and internalize is that anarchy is not a form of welfare.

Many progressives believe that any actions to curb urban disorder — restrictions on sidewalk tents, making people pay for public transit, arresting people for nonviolent crime, and so on — represent the exclusion of marginalized people from public life. In the absence of a full-service cradle-to-grave welfare state, progressives think they can redistribute urban utility from the rich to the poor by basically letting anyone do anything they want.

But in fact, permissiveness toward the behaviors that create urban disorder destroy more value than they redistribute. When you don’t make people pay for public transit, you scare people off the train, thus causing the train to go bankrupt. Pretty soon neither the rich nor the poor have a train, and everyone is screwed. This is the tragedy of the commons.

Measures to curb disorder therefore represent defense of the commons. When Bay Area trains put in new stronger gates to prevent people from riding the train without paying, crime on the train fell and rider satisfaction increased. Similarly, measures to curb shoplifting — including punishing shoplifters and letting security guards and business owners defend their stores with violence — allow local businesses to keep operating, which means that poor and working-class urban residents have places to shop for their daily needs. And so on.

Second, Josh Barro is absolutely right when he says city services should “focus on benefits to the broad public rather than to the people being paid to provide the services”. In other words, progressive cities need to realize that costs are bad for the city government.

The habit of having cities overpay for everything is another form of highly inefficient redistribution. A bunch of people do get paid out — nonprofits, overstaffed contractors, expensive consultants — but at the end of the day the ballooning costs that result from all these payouts mean that cities don’t actually have the infrastructure or services they need. All too often, progressive cities are operated for the benefit of the people who get the money instead of the people who get the stuff.

That payout-focused behavior is driving cities to bankruptcy while worsening the quality of infrastructure and services alike. Cities need to replace consultants and nonprofits with civil servants, implement stricter oversight of outside contractors, improve their bidding processes, and in general do everything they can to bring down the cost of everything they do and provide better value to the citizenry.

Finally, cities must realize that housing is non-negotiable. Fundamentally, cities are places that people live — if people do not have houses to live in, you don’t actually have a city. Housing is what allows businesses to function, because most people live near to where they work. Housing is what allows retail to function, because it creates a sufficient density of customers. Housing sustains knowledge-industry network effects, by allowing more knowledge workers to live close to each other and exchange ideas. In most states, housing generates more tax revenue for the city as well. And of course, housing reduces homelessness.

Blue cities should therefore do absolutely whatever it takes to get a lot more housing. “Whatever it takes” includes upzoning, deregulation, planning, public housing, and cutting back on policies like rent control. The correct method of building housing is “any and all methods”. That’s what it means for a goal to be non-negotiable.

I loved the blue cities of fifteen years ago. They had already sown the seeds of their own decline, but in the brief moment between the chaos of the 1970s and the foolishness of the 2010s, America’s big blue cities were indeed the glory of the country. They can and should recapture that glory.

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Back when I was a teenager, a lot of people were very into the Left Behind series. Evangelical Christianity was still very strong in those days, especially in Texas, and Left Behind blew up by putting Christian eschatology into page-turning airport-bookstore-novel form. People really believed that the stories in those novels weren’t just stories — they were glimpses of a very real future event. The Rapture — Jesus Christ’s gathering of the faithful to Heaven before the end of the world — is something that believers expect is going to happen one of these days.

Sometimes, these days, I feel as if it’s already happened. I look around at American culture and I wonder: Where did everybody go?

The mass culture of my youth is long gone. 76 million people watched the last episode of Seinfeld in 1998; fewer than 20 million watched the last episode of Game of Thrones two decades later. Right now I can’t even think of what an appropriate comparison would be. What’s the show that everyone watches?

In the 2010s, mass culture was replaced by meme culture. Everyone knew this photo and what it represented:

Social media feeds — Twitter and the Facebook wall — turned American society into a sort of digital hive mind. Feeling yourself being absorbed into that great unified consciousness could be an agonizing experience — mobs constantly punished any deviation from conformity, quirky unique subcultures were devoured and metabolized, everyone was forced into a cell with people they had spent their lives trying to escape. The hunt for virality was everything. Viral memes were the thoughts that drifted through the mind of the social media Borg.

There are almost no memes left. For so many years there was a steady drumbeat of images and phrases that people passed around among each other ad infinitum; now, I can’t even think of the last one that really made the rounds. Twitter — now X — has not died but is diminished in scope and relevance. At times it feels like a ghost town.

Where did everybody go? I would like to think that they’re offline, living as we lived in the Before Times, running through sunlit fields blasting each other with Super Soakers or playing Dungeons and Dragons in their living rooms. Or perhaps they’re in Signal and WhatsApp chats and Discord channels, talking with their friends in the comfort and safety of privacy. I would like to think that they rejected the digital hive mind for the ancient ways of individuality and Dunbar’s Number.

But my bet is that they’re just watching short form video. Over the last few years, the entire internet has steadily carcinized into a single type of app/website/media/entertainment — a vertically scrolling feed full of short video clips. Vine invented it, TikTok popularized it, and now every other site has copied this one dominant, all-conquering format. Already, vertical short-form video feeds are more dominant than every form of media except for social media itself:

But even this significantly understates the change. The term “social media” used to mean people writing things to each other or sending images on Facebook or Twitter; now, it’s mostly used to mean people watching videos:

Even the long-form video sites, like YouTube, are increasingly just short-form sites showing a bunch of clips. This shift has basically killed the original use of social media, turning it from a town square or conversation room into a form of purely passive entertainment:

I am not part of this trend. I had TikTok on my phone for a year or so, but I could never get into it; in 2021 I deleted the app. The videos all felt like some kind of bland snack that you keep eating even though you don’t really like it — everything seemed optimized to make you swipe to the next thing, like the programming in those hundreds of channels that only existed so that satellite TV could brag that it had 700 channels.

Mass culture is often absorbing; meme culture is delightful and horrifying in turn. Short-form video feels to me like anesthesia; the swiping is the point, not the content. As Jerry Seinfeld once said, “Men don’t care what’s on TV. They only care what else is on TV.” I suppose I’m the exception.

So here I am, alone at the end of culture. I feel like one of the people from the Left Behind series, waking up to find most of the people around me taken to digital Heaven.

Have you ever heard of “heavenbanning”? It’s not a real thing…yet. It’s the hypothetical idea that social media platforms could neutralize obstreperous users by replacing their entire feed with AI content that flatters and agrees with them. You’d think you’re interacting with other human beings, and that everybody just happens to love you and think you’re brilliant, while in reality you’re in solitary confinement with an LLM.

It’s possible to think of vertical short-form video feeds as a kind of heavenbanning. Your feed is curated by AI, tailored to your own preferences. The videos you see aren’t made by anyone you know, or (probably) by anyone you’ll ever meet. The people you know in real life don’t see the same videos you do, unless you share them. Eventually the world blurs together and you lose track of time:

Researchers at several universities in China, in a study involving more than 1,500 high-school students, found that their future time perspective was damaged by a steady diet of such short-form videos…Now, [short-form videos are] also making us less able to plan for the future, or even care about it.

Right now, short-form videos are mostly made by would-be human influencers or clipped from human-made long-form videos that few people actually watch. But in the very near future, much of what you see in your TikTok or Reels feed could be AI-generated. And as the cost of compute drops, AI will be able to generate personalized videos for everyone — content tailored not necessarily to delight you, but to keep you engaged, to keep you swiping, to keep you making in-app purchases.

When I was a kid, I used to wonder what all the dead people were doing in Heaven for all of those countless eons. Now I know what they’re doing. They’re swiping.

I think about the Great Filter sometimes. We’ve searched the skies for signs of alien intelligence for many decades, and found nothing — natural or artificial. The Great Filter is one possible explanation for the silence of the cosmos — it’s the idea that there’s some dread Thing in the Universe that destroys sentient species before they can spread out to the stars.

There are plenty of hypotheses as to what the Great Filter might be. Perhaps every sentient species destroys itself with nukes or bioweapons. Perhaps AI always tends to wipe out its organic creators before retreating into inaccessible virtual worlds. Perhaps there’s a race of killer robots silently cruising between the stars, slaughtering every organic species that looks like it might one day rise to become a threat.

Or perhaps it’s the smartphone. In previous decades, fertility decline was due to couples choosing to have fewer children; now, it’s increasingly about people never forming couples at all. The isolating effects of social media are one obvious culprit; it’s very hard to conceive a child while you’re on your phone.1 Myers and Hooper (2026) found that “access to the iPhone reduced births by 4.5–8.0% at ages 15–19 and 3.2–6.6% at ages 20–24”. Breen et al. (2026) found that “the expansion of 3G coverage [in Nigeria] during the study period led to a decline in fertility, corresponding to a 7% reduction in the annual probability of a woman having a birth.”

Given the timing and the microeconomic evidence, smartphones are the likeliest explanation for why instead of leveling out near 2, the total fertility rate in rich countries — and, increasingly, in middle-income countries — has busted right through that floor and headed down toward zero. Smartphones at least help to explain why Latin America now has an even lower birth rate than the EU.

Even without vertical short-form video, social media might still be enough to shrink the human race. Online interactions substitute for offline ones, but only one of the two can make a baby. But it’s hard to deny that vertical short-form video is even more isolating than other online activities — in the case of heavenbanning, it’s almost solipsistic. At least on X you can meet your mutuals. In the brave new world of AI-generated personalized short-form content, your “mutuals” will be made of matrices and computer chips.

Personally, I don’t like vertical short-form video because I think it’s pretty boring. But the fact that it could represent the euthanasia of our species seems like an additional downside. Perhaps it’s in the fundamental nature of intelligence to hack its own reward function — to find a way to give up striving and building and loving, and to simply hit the dopamine button until it starves itself ecstatically to death.

The next time you look at the night sky, you should wonder if instead of star empires and unearthly paradises, it’s simply a vast graveyard of short-form video fans. Such an outcome would be much gentler, but hardly less cataclysmic, than the end of Revelation.

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“Our nation was founded to perpetuate democratic principles. These principles are that each man is to be treated on his worth as a man without regard to the land from which his forefathers came and without regard to the creed which he professes. If the United States proves false to these principles of civil and religious liberty, it will have inflicted the greatest blow on the system of free popular government that has ever been inflicted. Here we have had a virgin continent on which to try the experiment of making out of divers race stocks a new nation[.]” — Teddy Roosevelt

The other day, Chris Rufo of the Manhattan Institute and right-wing pundit Jonathan Keeperman (also known as “Lomez”) aired an episode of their podcast entitled “The Indian Question”:

The title of the episode seems to be a reference to “the Jewish Question”, a term in 18th and 19th century Europe for the question of how Jews should be treated in European society. That term, of course, became notorious when it was associated with the Nazis’ “Final Solution”, which was billed as being a final solution to “the Jewish Question”. I assume that Rufo and Keeperman chose this rather incendiary title in order to grab attention. Well, they succeeded.

The episode was motivated by a debate that’s been raging across social media for weeks. It was kicked off by Bo French, the Republican nominee for Texas Railroad Commissioner, who tweeted outrage at a group of Indian American kids attending a University of Texas football game. I already wrote about that controversy three weeks ago:

Jonathan Keeperman (“Lomez”) invited me on his podcast to share my views and debate him, so I did. Here’s the episode that resulted:

Rufo was busy, so it was just Keeperman and me.1

Anyway, before I talk about the substance, I thought I should say something about the side debate of whether a liberal like myself should even go on right-wing podcasts like “Rufo and Lomez”. Throughout most of the 2010s, there was a popular notion that society should basically ignore and shun people like that, and that if we all simply shunned them, and pressured our friends and colleagues to shun them, they and their views would vanish from American society — or at least remain marginalized.

That approach failed. Anti-immigrant ideas didn’t remain marginalized — in fact, they took over the country, despite their continued unpopularity among the general American public. Are Rufo and Lomez pushing xenophobic ideas? Yes, of course they are. I’m not going to sugarcoat that fact just to be polite. But simply shouting “Those guys are XENOPHOBES!!” and keeping those guys out of polite society would be doubling down on a foolish, self-defeating strategy. The alternative was always to confront those viewpoints directly and defeat them in the court of public opinion, so that’s what I set out to do.

OK, anyway, on to the substance of the debate.

Keeperman and I began the debate by finding common ground. We both basically subscribe to the principle of nationalism — we believe that nation-states are exclusive clubs that exist for the benefit of their members, and that this is a basically good way to organize human society. We both think the United States of America is basically good — or at least, has been basically good throughout most of its history.

I explained my belief that liberal nationalism is the best ideology available — maybe for other countries, but certainly for the United States. All of our greatest successes as a country — victories in wars, successful policy reforms, integration of past waves of immigrants — have come from our use of the nation-state as a tool to promote individual rights, dignity, and empowerment. I told Keeperman that the Abundance Agenda is, at least implicitly, an expression of liberal nationalism — the movement is about creating abundance for Americans specifically, with the implicit premise being that Americans deserve abundance.

Keeperman disagrees about the liberal part, of course, but he does feel that he has the best interests of the United States in mind. His worry is that immigration is stoking ethnic conflict in America, because recent immigrant groups — for example, Indians — are not assimilating into American society like previous groups did.

To me, assimilation has always been about measurable behavior. If the children and grandchildren of immigrants act culturally American — if they speak English, intermarry, give their kids typical American names, go to college football games, climb the income ladder, move out of enclaves, and so on — then they’re assimilated. By all of these standards, the recent waves of immigrants — Mexicans, Chinese people, Indians, and so on — are assimilating just as fast as or faster than the East and South European wave did a century ago, or than the Irish and the Germans did two centuries ago.

The aspect of assimilation that Keeperman worries about most is national identity — the degree to which the children and grandchildren of immigrants feel bound up in a shared community and a shared identity with countrymen of different backgrounds. I agree that this is extremely important; many of the benefits of the nation-state, like the ability to provide public goods and welfare states, come from this sort of shared identity. It also probably leads to greater toleration of minorities (including immigrants). All of those goals should be very important to liberal nationalists.

Lomez — who is not a liberal, but cares about national cohesion nonetheless — worries that identity assimilation is lacking in the modern day. He argues that today’s immigrants retain their old-world allegiances much more strongly than the immigrants of a century ago — that second-generation Indian Americans in Texas still think of themselves as Indians rather than as Americans, and so on.

We do have lots of surveys about this, in fact. There’s a clear pattern — immigrants tend to identify more as their ethnicity than as “American”, but later generations are more likely to identify as “American”. Here’s a survey showing that third-generation Asian Americans are a lot more likely to identify as just “American”:

We see the same pattern even more strongly among Hispanics:

Second and third generation immigrants are also a lot less likely to describe themselves as “global citizens”.

Now, these could be generational effects — people identifying more as “American” as their families stay in America for longer — or they could be cohort effects. It’s possible that due to identity politics, plummeting patriotism, or to the ability to connect with co-ethnics abroad on the internet, recent cohorts of immigrants are less likely to want to identify as “American”.

In fact, we have some evidence that this is happening for Indians, specifically. In 2020, the Indian American Attitudes Survey found that the second generation was a lot more likely than the first (i.e., immigrant) generation to identify as “more American than Indian”:

But by 2024, that pattern was somewhat weaker, with more U.S.-born Indian Americans identifying as “more Indian than American” and “equally Indian and American” than four years prior:

This is just two survey snapshots, a few years apart. It would be helpful if we had had good longitudinal surveys asking people about American identity versus ethnic identity for decades. But we don’t, so we’re left to worry and speculate.

Without longer-term data, my guess is that this identity shift — to the degree that it’s real and not just survey noise — is a short-term response to political conditions. Indian Americans had full access to the internet in 2020, and they were just as able to talk to ethnic Indians in India or Australia or Canada. Identity politics — including institutions such as ethnic clubs at universities — had been around for decades, and the sentiment behind it was probably at its height in 2020.

Instead, my bet is that we’re seeing a reaction to the rise of the MAGA Right itself. It was in 2024 — right before the second survey wave — that anti-Indian attitudes became extremely popular among the online Right. 2024 is also when it became clear that a wave of anti-immigrant sentiment was sweeping the nation. My guess is that stronger ethnic identification among Indian Americans was probably a reaction to the perception that a substantial part of America was attacking Indians.

Keeperman — like other rightists I’ve talked to — seems to think that immigrant groups assimilate only under aggressive pressure from the majority. The model I usually hear is that of German Americans in World War I, who were the subject of feverish forced-assimilation campaigns — pressured to change their names, stop speaking German, and profess allegiance to America. In his discussion with Rufo, Keeperman argued that vocal anti-Indian racism was part of a “hazing process”.

My argument is that this is exactly backwards. Ethnic identity is fundamentally defensive in nature. Identity groups are like gangs — if you no longer feel like a part of the big gang (the nation), you’re probably going to turn to a smaller gang (your ethnic group) for protection and support. For this reason, forced-assimilation campaigns can suppress visible expressions of ethnic difference, but they strengthen ethnic identity itself.

There’s plenty of evidence to support this. Although forced-assimilation campaigns in WWI made some Germans change their names, they caused Germans as a whole to intermarry less and to volunteer less in World War II. Anti-Hispanic rhetoric tends to increase Hispanic identification, and perceived discrimination makes Hispanics more likely to identify with their ethnicity.

This pattern is likely to play out again, with the new rightist fad for anti-Indian bigotry. Bo French’s tweet, and the outpouring of support it received from the grassroots Right, is going to make some Indian Americans question whether America really accepts them — and whether they might have to turn to other Indians for support rather than their countrymen of other races.

Rufo and Keeperman’s “Indian Question” podcast is, of course, only going to make things worse. When your race’s mere existence in America is being described as a “question” by a senior fellow in the Manhattan Institute, you naturally tend to get a bit apprehensive — and a bit defensive. In the last ten minutes of my debate with Keeperman, I got very vehement and animated about the counterproductive nature of the anti-Indian rhetoric.

Keeperman argued that assimilation only worked so well in the 19th and 20th centuries because of “ethnogenesis” — we took a bunch of disparate European groups and made them all “White”. But I don’t think this is quite right. Yes, assimilation succeeded because of ethnogenesis, and yes, “White” ethnicity became more salient. But the ethnicity we tried to create in the 20th century was actually something else. It was “American”.

Attempts to create an “American” ethnicity date at least back to Teddy Roosevelt.2 These intensified in the New Deal years, and during and after World War II. This effort partially failed. Black Americans never became part of the “American” ethnicity, for a variety of reasons — geographic and legal segregation, linguistic differences, pervasive anti-Black racism, the memory of slavery, the rise of Black Nationalism, and so on. But for other racial minorities — Asians, Hispanics, and Middle Easterners — it partially succeeded.

The idea that the efforts of FDR, Truman, Eisenhower, LBJ, and other liberal nationalists of the mid 20th century were all about creating White identity is a recent conceit; if you read anything from the time period, you realize that this wasn’t the goal, even if it was one of the lasting results. To understand the mood of the time, you should listen to the patriotic song “Ballad for Americans”, by Paul Robeson, which was sung at the 1940 Republican national convention.3 Here’s a key excerpt:

Now hold on here.
What are you trying to give us?
Are you an American?

Am I an American?
I’m just an….
Irish, Negro, Jewish, Italian,
French and English, Spanish, Russian,
Chinese, Polish, Scot, Hungarian,
Litvak, Swedish, Finnish, Canadian,
Greek and Turk and Czech,
And double-check American!
And that ain’t all!
I was baptized Baptist, Methodist,
Congregationalist,
Lutheran, Atheist, Roman Catholic,
Orthodox Jewish, Presbyterian,
Seventh Day Adventist, Mormon, Quaker,
Christian Scientist,
And lots more!

You sure are something!

Our country’s strong, our country’s young,
And her greatest songs are yet unsung,
From her plains and mountains,
We have sprung to keep the faith
With those who went before.
We nobodies who are anybodies believe it.
We anybodies who are everybodies have no doubts!

This mood is utterly absent from both the modern Right and the modern Left. On the Right, there is no attempt at ethnogenesis — only perpetual fragmentation, suspicion, and finger-pointing. Here is what I wrote in my post three weeks ago:

It’s this pathological inability to understand alliances and coalition-building that will ultimately doom the New Right. Like the far Left, this is a movement built to exclude and divide and splinter — both because of the querulous personalities of rightists themselves, and because of the intransigence of a racial ideology that abhors any impurity. If they ever managed to see off the Hispanics and the Asians, the rightists would start in on the Greeks and the Italians, and so on. There’s no victory in sight — only eternal conflict. These are not nation-builders; they are nation-dismantlers.

Meanwhile, the progressive movement has tried its hand at ethnogenesis — POC and BIPOC were both essentially attempts to create new racial categories defined in opposition to the White category. But these attempts fell apart. And there hasn’t been an attempt to create a broadly shared American ethnicity, because the modern progressive movement is also obsessed with purity, exclusion, and tent-shrinking.

But if we are going to flourish as a nation, the project of creating an American ethnicity is essential. The rightist approach — bullying nonwhite people until they meekly suppress any evidence of their backgrounds — will backfire spectacularly. In fact, it is already backfiring. Instead, the right approach is the liberal nationalist one — to allow or even encourage people to value their roots, while also emphasizing that they’re all one unified people now. When it comes to assimilation, a big tent beats a small one, and carrots work better than sticks.

That’s why there is no “Indian Question”. There is only the question of what sort of nation America is to be. We must pick up the discarded project of our 20th century forebears, and make it work completely this time.

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Paul Krugman is the greatest econ writer in the world, and also a legendary economist. But I sometimes feel that he has a blind spot when it comes to the value of new technologies. In 1998 he famously wrote:

The growth of the Internet will slow drastically, as the flaw in ‘Metcalfe’s law’—which states that the number of potential connections in a network is proportional to the square of the number of participants—becomes apparent: most people have nothing to say to each other! By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.

By the time he wrote that, America was already well into an IT-driven productivity boom that would temporarily interrupt the stagnation that had begun in the 1970s. The internet was surely part of that story; it allowed companies to reshuffle and optimize their supply chains for greater efficiency, find customers, suppliers, and workers more easily, conduct business communications cheaply and in greater depth, and so on. Dolfen et al. (2023) estimate large consumer gains from the rise of e-commerce, Barrero, Bloom, and Davis (2021) find large economic gains to households from high-quality internet access, and so on. The internet is a lot more than just people yelling at each other on forums and social media. (Krugman later argued that the internet’s economic impact had been disappointing, but I suppose that depends on your expectations.)

In 2011, Krugman wrote that American kitchens hadn’t changed much since 1957. I can forgive him for not being an early adopter of the air fryer or the Instant Pot, which came out in 2010, but he really should have given more consideration to countertop microwaves, food processors, Keurig-type coffee machines, crock pots, and induction stoves, all of which became available between 1957 and when he wrote the post.

So although it’s always dangerous to disagree with Paul, I am going to go ahead and push back on his argument that AI technology “does nothing” for most Americans:

Paul Krugman

One More Reason Americans Hate AI: It Does Nothing for Them

One unifying dynamic in America today: Americans really, really hate AI. What began as a form of NIMBYism — no one wants a data center in their neighborhood — has broadened into severe doubts about the technology as a whole. For those of us who remember the giddy optimism that greeted the tech boom of the 1990s, the widespread negativity towards AI is a…

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16 days ago · 2413 likes · 641 comments · Paul Krugman

He writes:

There is also, however, a more prosaic reasons for the public’s dislike of AI: This is a technology of, by and for oligarchs, with hardly any of the benefits trickling down to regular Americans…Or to put it a different way, never before in history have corporations spent so much money — playing a major role in soaring interest rates — to create so few jobs.

I think that this is basically wrong. Although we don’t know the long-term effects of AI on the distribution of income and wealth, right now we can see a substantial amount of economic benefit flowing — I wouldn’t say “trickling down”1 — to regular Americans.

This is not to say that regular Americans couldn’t stand to benefit more from the AI boom. I think they could. I like some (though not all) of Jared Bernstein’s ideas for spreading the benefits of the data center boom more broadly. But I think Krugman has underestimated the benefits of the data center buildout in terms of direct employment, and has basically ignored the fiscal, macroeconomic, and consumer benefits of the current AI boom.

The AI buildout is creating lots of construction jobs

Building data centers takes a lot of labor. But Krugman argues that data centers aren’t doing much in the way of providing construction jobs:

Given this spending surge, one should expect a sharp rise in nonresidential construction spending — basically construction for businesses rather than housing...But that’s not what we actually see. Nonresidential construction…has basically flatlined under Trump, despite the immense AI investment boom…[E]ven the physical construction of a data center involves relatively little construction.

He quotes Van Nieuwerburgh (2026), who shows that only about a third of the cost of a data center involves construction work.

But I don’t think Krugman proves his case here. First of all, if we’re talking about construction jobs, we should look at employment levels, not spending. And here we see an increase in construction jobs since the AI boom began in late 2022:

Construction has also increased as a percentage of the workforce:

And remember, this was at a time when Trump was deporting construction workers en masse — 13% of the construction workforce is undocumented, and deportations also have knock-on negative effects on the industry that result in the firing of native-born workers as well. This probably explains the pause in the increase of construction employment in 2025. But even that couldn’t stop construction’s rise.

And it’s exactly the type of construction workers who are required for building data centers who are seeing the biggest job gains:

Construction workers’ real wages have also risen since the middle of 2022:

You might be tempted to think that this is a composition effect from Trump deporting the lowest-paid construction workers in 2025. But in fact, there has been a big jump in construction workers’ wages relative to national average wages just this year:

These are all signs of healthy labor demand.

In fact, although estimates of the effect of the data center buildout on construction employment produce very different numbers, they all agree that it’s a significant positive impact. The state of Virginia, for example, produced the following numbers:

And of course these are just the numbers so far; the data center buildout is accelerating, and Goldman estimates that 500,000 new construction and trades jobs will have to be added by 2030 in order to sustain it.

So while you can argue that this boost to labor demand isn’t worth the costs of AI (whatever you think those are), we need to count it on the positive side of the ledger here.

Data centers can be a huge tax windfall for state and local governments

Jobs aren’t the only way that the economic benefits of data centers get spread to ordinary Americans. There’s also the tax system. Data centers pay property taxes, sales taxes, corporate taxes, various fees, and so on — here’s a good explainer from the Tax Foundation. All in all, depending on their policies, local and state governments can reap large windfalls from data centers:

Those taxes go to pay for local public goods, like roads, public transit, and parks. They go to pay for public services like education and health care. Those expenditures all tend to benefit regular people. This is from a story in the New York Times about Loudoun County in Virginia:

A convergence of early fiber internet access and fast-track zoning has made Loudoun the data center hub of the world…Two decades into its experiment, Loudoun has become a case study for the rest of the nation on how to make data centers pay off. Thanks to the proliferation of the warehouses, the quiet bedroom community 30 minutes outside Washington, D.C., has transformed into a tech destination with trophy schools and libraries, and freshly tarred roads.

Now that doesn’t mean data centers are necessarily good for a city or state on net. There are real costs, too — electric power demands that put strain on the grid, nuisance noise, and so on. But the benefits are real, and they don’t come in the form of job creation.

And crucially, state and local governments can demand even more benefits whenever they want! They can raise taxes and fees — in fact, they can even raise them after a data center is already up and running, so that relocation to avoid higher taxes becomes less attractive of an option.

And before construction, they can demand “community benefit” agreements that are actually just additional taxes. Here’s what Jared Bernstein suggests:

Such agreements should include reduced electric and water rates for the surrounding community, funding for the local infrastructure upgrades (roads, substations, water systems) these facilities require anyway, and substantial investment in the schools, parks, and public goods that make a host community better off for having said yes.

Bernstein wants much more of this, of course, and better enforceability. But note that even as things stand, taxes and fees are substantial, and community benefits agreements are common.

AI is sustaining the macroeconomy in the face of Trump’s chaos

I spent my early blogging years supporting Paul Krugman in his epic quest to remind people that aggregate demand is a real and important thing. But for whatever reason, Paul doesn’t mention the demand-side benefits of AI investment in his post.

When Donald Trump came into office, he did a bunch of things that should have clobbered the economy. He announced high tariffs on nearly all of America’s trading partners, then created massive uncertainty by walking some of these back, periodically announcing new ones, granting tons of exceptions, and striking opaque and confusing “deals”. On top of that, he deported large swaths of America’s workforce, visibly weakened the U.S. international alliance system, ran enormous deficits, and behaved in a lawless and corrupt manner that caused people around the world to question the long-term stability of the U.S. government.

All of this created huge amounts of policy uncertainty:

Uncertainty on this scale usually causes big economic problems. Businesses can’t invest if they don’t know if the president of the United States is going to destroy their business model with an executive order tomorrow. All of this Trumpian chaos and meddling should have caused a visible negative demand shock.

But it didn’t, because just as Trump was trying his best to hit the American economy over the head with a stick, the AI boom came along and pushed in the opposite direction. AI technology itself is a positive supply shock, of course, but the data center buildout is a positive demand shock.

How big of a shock? It’s hard to say, because causal estimates of aggregate demand are inherently difficult. But it’s clear that the data center boom has made up a large percent of economic growth for Trump’s entire second term so far:

Now as I mentioned, it’s hard to know whether we’d just be building something else instead if this boom wasn’t happening. Data center construction certainly crowds out some other forms of economic activity, by sucking up scarce labor, and by raising interest rates (which makes it harder to finance other projects).

But to believe that the AI boom isn’t having a big effect on aggregate demand would require some heroic assumptions. You’d have to assume very strong crowd-out. You’d have to assume that Trump’s tariffs and other irresponsible policies are having basically no effect on demand, so that there isn’t any negative shock in need of canceling out. You’d have to assume that “animal spirits” — i.e. corporate bullishness — basically don’t affect the business cycle. And so on.

I don’t think those assumptions are realistic. I think if you see one industry contributing a very large percentage to U.S. economic growth, your prior should be that it’s causing a positive demand shock.

And if so, that means that the AI boom is the only thing standing between countless regular Americans and Trump’s self-destructive chaos. According to Okun’s Law, shaving just 1 percentage point off of economic growth would throw almost a million Americans out of work. That would be bad for regular people.

This macroeconomic benefit is hidden; it’s the proverbial dog that didn’t bark. But it’s pretty significant.

Americans use AI a lot, and they value it a lot

So far, I’ve been talking about the benefits of the data center construction boom. But I should also mention the impact that AI technology is already having on consumers. Krugman’s post seems to treat AI and the data center buildout as synonymous, and jobs as the main (or only) way by which regular Americans might benefit from the new technology. But the truth is that AI is also something that lots of Americans already use, and seem to derive a lot of utility from.

By every measure I can find, AI has seen more rapid household adoption than any other consumer technology in recorded history. And what do Americans use AI for? Everything. This poll is from over a year ago, but already it showed the incredible diversity of use cases for consumer AI:

Here’s a more recent poll, asking what people regularly use AI for, rather than what they’ve ever used it for:

Medical advice and diagnosis has emerged as a particularly important consumer use case. But in general, Americans say chatbots make them more productive, informed, and creative:

This does not mean Americans like AI overall; in fact, they’re overwhelmingly negative on the technology. They’re afraid it’ll take their jobs, and increasingly afraid it’ll kill them. But there are real, substantial consumer benefits from AI that we shouldn’t ignore.

How substantial? In April of this year, Brynjolfsson et al. used surveys to estimate a total annual consumer surplus of $172 billion in the United States. That’s more than the run rate revenue of Anthropic and OpenAI combined, and certainly much much more than their combined profits would be even if they stopped spending anything on fixed costs right now. It’s about half of the annual profits of Nvidia.

So when Krugman says that “this is a technology of, by and for oligarchs, with hardly any of the benefits trickling down to regular Americans,” he’s just wrong. Just the consumer surplus alone is substantial. On top of that the data center boom is creating a significant amount of jobs, generating a significant amount of local and state tax revenue, and propping up the macroeconomy and the job market as a whole.

There are plenty of big problems with AI. Malicious use or accidents might wipe out our whole species in the not-too-distant future. Job loss hasn’t been a big deal so far, but it might eventually be huge. Cognitive weakness from overreliance on AI could affect our society in strange and negative ways that we have yet to even comprehend, much less reckon with. When you ask Americans why they hate AI, these are the things they’ll tell you. Anger at “oligarchs” monopolizing the wealth from AI doesn’t typically make the list, and I don’t think it’s a great way of framing the AI issue.

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There’s a large market for “Trump is bad” posts. I wrote one of them back in May:

It’s easy to tune these posts out, for at least two reasons. One is partisanship — as a reader, I’m sure you know how hard it is to separate “Trump is bad” from “Trump made me mad”. The second is fatigue — you’ve heard the familiar litany of all of Trump’s outrages, and reading it one more time is both redundant and depressing.

Today I’m going to do something a little different, which is to ask: Why, exactly, has the second Trump presidency been such a failure?

Trump is a failure in the eyes of the nation

Before I do that, we should establish that in the eyes of the nation, Trump’s second presidency has been a failure, at least so far. His approval rating, going into the midterm election, is at historic lows:

America was mildly disapproving of Trump in his first term; now, only his base is staying loyal. And even that base is eroding. On the cost of living — one of the key issues in 2024, and likely to be a key issue in the midterms — Republicans now disapprove of Trump:

Hilariously, a modest but growing number of people who voted for Trump in 2024 now say they never voted for him.

And despite the widespread belief that Trump’s cult of personality dominates the GOP, Republicans now tend to say they support the party rather than the president himself:

But nevertheless, the GOP is going to suffer from Trump’s unpopularity in the midterms. Prediction markets are increasingly sure that Dems will take the House, and they now also give Dems a better-than-even chance of winning the Senate, despite a very difficult map:

Polls showing Senate races moving toward the Dems might be overstating things, but polls have ended up understating Democratic support in special elections since Trump returned to power. Trump has resorted to desperately promising $5000 checks to every U.S. citizen if Republicans win the midterms. But so far, these wild bribe attempts are falling decidedly flat — in fact, they’re actively driving voters away.

All this is all the more remarkable because Democrats themselves are in disarray. The party has torn itself apart over Israel/Palestine, and the public is still deeply skeptical of Dems’ “woke” progressive ideology. But whereas Americans still trusted Republicans more on many issues in 2025, now they tend to trust Democrats on issues like the economy and immigration:

The GOP was more popular than the Dems as of one year ago; now that has reversed.

Even some of Trump’s most prominent allies are now distancing themselves from the president. Here’s Peter Thiel, one of the godfathers of the Tech Right:

I can’t rule out the possibility that someday we’ll all look back on Trump’s second presidency as a success. Perhaps winning the AI race will prove to be so much more important than everything else that Trump’s decision to stall AI regulation ends up swamping everything else he ever does.

But I don’t find it very useful to indulge in this kind of “too early to tell” speculation. The blunt fact is that Trump’s second term has already failed in the court of public opinion. This is an incredible, world-historic failure. And it didn’t have to be that way — Trump simply made a bunch of avoidable errors, for a mix of ideological and personal reasons.

Trump didn’t have to fail

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I’m having a lot of fun writing shorter posts and aggregating interesting items these days. A lot of people are putting out an astonishing amount of good content these days, and sometimes I just want to sit there absorbing it all.

1. AI risk has gone mainstream

Americans have been deeply pessimistic about AI for a while now, but their reasons for pessimism — or at least, the reasons they tell pollsters — have changed in recent months. Earlier this year, Americans were mostly concerned that AI would take their jobs. Now, they’re more concerned that AI is getting too powerful for humans to control:

Echelon didn’t poll people about the idea that autonomous AI would destroy humans on purpose. But they did ask about bioterror risk, which I’ve been yelling about for a while now. And it turns out that Americans are pretty worried about that:

Surprisingly, the issue hasn’t fallen victim to partisan polarization yet. As you can see in the chart above, Trump voters and Harris voters are about equally as concerned about humanity losing control of AI. And despite Trump’s staunch stance in favor of acceleration, a lot more Republicans want to slow AI development down:

Meanwhile, economists have gotten in on the AI risk debate — which is how you know it’s really gone mainstream. Drew Fudenberg and Andrew Koh have a new game theory paper about “pacing the frontier” — i.e., about whether it makes sense for top AI companies like Anthropic and OpenAI to slow down AI development in order to allow “alignment” research time to catch up.

It’s a very cool model. Basically, the idea is that every company has a competitive incentive to make its AI more powerful as fast as possible, in order to stay ahead of the other companies. But if you’re comfortably ahead, you can afford to slow down a little bit, for safety’s sake — because in this model, if AI gets too powerful before safety research can catch up, everyone could die. So you can get a sort of stop-start pattern where the leading company voluntarily slows down for a while, until its competitors start nipping at its heels again. You can almost sort of see this happening, with Anthropic refusing to allow the public to access Mythos earlier this year, and OpenAI recently pausing development of its top models after some of them hacked the government.

There’s also the case where there’s no clear market leader, in which case companies have to basically agree to all slow down together. In this case, what you really need is transparency — the companies have to all see that the others aren’t secretly racing ahead behind their backs. That might be easier said than done — it’s not clear how to monitor all the AI labs in the world to each other’s satisfaction.

But one positive result is that if the risk of “doom” is high enough, slowing down becomes the only rational option. That’s cool! Unfortunately, this is just one model, which might not hold in reality. Game theory was famously unreliable when people applied it to the Cold War — a small change in assumptions could flip the optimal strategy from “the only winning move is not to play” to “nuke em all and let God sort em out.” Game theory yielded important conceptual insights — especially the importance of “second strike” capability in preventing conflict — but it rarely gave definitive answers.

So where might this current model break down? I don’t think it’s clear how fast AI safety research is really advancing. If “pacing the frontier” only works by giving safety research time to catch up, and safety research isn’t really advancing, then we’re all in big trouble.

Anyway, Andrew Koh has a great thread summarizing the paper, and some folks made a fun online game based on the model if you’d like to play around and see how it works. Cool stuff!

2. Reindustrialization means processing minerals

Two years ago, in order to create a little urgency around reindustrialization, I wrote a post sizing up the “New Allies” (America, Europe, Japan, Korea, possibly India) and the “New Axis” (China and Russia). The comparison of raw manufacturing output was about equal, though China dominated a lot of the bottlenecks for raw materials processing and critical component manufacturing.

Now, Alexander Campbell has done another version of the “bottlenecks” comparison, but with nicer charts:

Campbell Ramble

Consilience of Curves

In the coming weeks and months we’ll be rebuilding the portfolio from scratch. While we were out, Leo got liquidated by margin debt, so call it a theme. Ours was owing to various compliance and ethics regulations rather than prime brokers, but a forced seller is a forced seller. Perhaps the worst part of the hiatus was leaving you readers in the lurch, …

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16 days ago · 88 likes · 6 comments · Alexander Campbell

In this chart, he shows how the roles have basically reversed since WW2:

And here’s where we stand when it comes to the raw materials of the all-important Electric Tech stack:

The key here is that China doesn’t mine most of these minerals — it refines them. Other countries dig up the ore and ship it to China, and China uses a bunch of chemical engineering to turn them into usable materials for industry. Most of the world shipped their metal refining to China, because it’s a dirty, capital-intensive, low-margin industry. But now as a result, China controls a bunch of key industrial chokepoints.

The Trump administration, to their credit, is taking this seriously, and is launching lots of initiatives — international partnerships, industrial policies, even scrap metal conservation — to try to get out from under the Chinese thumb. But we need to do a lot more if we’re going to be able to sustain an independent industrial base without bowing and scraping to Xi Jinping.

3. The rent crisis isn’t quite as bad as you think

I spent much of the last decade blogging about the rent crisis and the need to build more housing. In the post-pandemic inflation of 2021-22, rent stood out as a big sore spot — it was an expensive necessity whose price was going up very quickly. But I recently saw a chart from Apartment List showing that since 2022, rent has basically stabilized and even fallen back to its pre-pandemic trend:

This isn’t just because inflation fell. Inflation has stayed above target since 2022, but rent has actually fallen — at least if you believe Apartment List’s data. And there are also lots of stories out there about rents falling in expensive cities.

But what about affordability? I’d like to look at rent compared to median personal income, but median personal income data only comes out very slowly. Instead, we can compare rent to average hourly earnings for production and nonsupervisory workers, which tends to track median income decently well:

We can see that the 2010s rent crisis was very real. And we can also see that the pain from the post-pandemic inflation actually showed up a little later, in 2022-23 — probably because it takes time for leases to roll over.

But there’s some very good news on this chart. Although rent is still less affordable than it was in the 90s and 00s, it’s more affordable than it was in the late 2010s. In fact, rent appears to be on a downward trend relative to income — the Apartment List data is basically telling the right story.

So why has rent come down? Maybe because we just built more apartments than we've built since the 1980s:

And in fact, when we look at the cities where rents have fallen the most in recent years, it’s the cities where we built the most new apartments.

There might be other reasons too, but I think this looks like a win for good old YIMBY supply expansion. Keep building more apartments!!

4. AI isn’t taking college jobs (but it is taking artist jobs)

Today in the eternally ongoing saga of “Is AI taking our jobs?”, we have several interesting items. First, we have wage data from Indeed, showing that workers in AI-exposed jobs have seen their wages increase much faster than workers in less-exposed jobs:

This is pretty simple to understand. AI “exposure” means a job contains tasks that AI could do. This basically just means “a job where you might use AI”. And since AI is the big thing booming in our economy right now, this means that more “AI-exposed” jobs are seeing lots of demand, which drives up wages.

Our second item is a new paper from Fairlie and Wu, finding that AI hasn’t raised unemployment among recent college grads:

Using CPS microdata, we provide the first estimates of the effects of AI on the unemployment of recent college graduates in June, July and August 2026…[W]e find that unemployment rates did not spike in summer 2026 relative to summer months in previous years and did not rise in a significant way relative to older college graduates or young workers without a college degree. We also provide the first analysis of an expanded definition of unemployment that includes those who report “wanting a job” which adds nearly two percentage points to the unemployment rate of recent college graduates but we find no evidence of a statistically significant increase in summer 2026 even after adding these “sidelined unemployed.”

And here’s a chart:

So that’s all good news. Now for the bad news: I think I’ve finally found a category of jobs that is getting displaced by AI. Translator and trucker and radiologist jobs have all held up in the age of AI, but digital media jobs are getting absolutely clobbered (even as live performing art jobs have held up fine):

This isn’t necessarily cause for panic — every new wave of technology has reduced the demand for certain occupations. But digital arts jobs are a dream for lots of people — a way to indulge your creative side while also getting paid. If AI is closing off that particular combination of gainful employment and personal fulfillment for millions of people in advanced countries, that’s very sad — even if humans aren’t replaced wholesale.

5. Mass deportation isn’t helping native-born Americans find jobs

Donald Trump came into office with two basic policies — tariffs and mass deportations. The deportations were supposed to support the U.S. job market, freeing up jobs for the native-born, and raising their wages by reducing competition. Some of us always knew this was very unlikely to work, since immigrants are a source of labor demand in addition to being a source of labor supply. But the Trump administration never listens to folks like me, especially when immigration is involved.

So anyway, are the mass deportations working as Trump had hoped? Mike Konczal has a post in which he presents convincing evidence that no, they are not:

Mike Konczal

Can We Find Literally Any Sign Deportations Are Helping Native-Born Workers?

In response to my recent post on women gaining 101% of net payroll jobs under the Trump administration, commentators argued that this was largely about immigration, about native-born male workers taking jobs formerly held by deported immigrants and showing up as no net-new jobs. This turned out to be a total fabrication, it never happened, it’s fiction …

Read more

22 days ago · 39 likes · 1 comment · Mike Konczal

Native-born unemployment is higher now than in Biden’s last year:

And prime-age employment rates are lower:

If anything, Konczal shows, native-born workers are doing worse in the places with more deportations:

Anyway, Konczal has many more charts, and they all point in the same direction: Mass deportations have not helped native-born workers.

Of course, the real purpose of mass deportations wasn’t actually to help native-born workers — in fact, it wasn’t economic at all. But it’s still gratifying to see that basic economics was right and the Trump administration was overselling the benefits of immigration restriction.

6. Chinese investment stalls out

A week ago I wrote that China’s new economic model — basically, paying any and every company to manufacture more and more of the same products — was already hitting a wall. I pointed out that investment is now falling, even in the manufacturing sector:

I also noticed this recent chart showing rapidly decelerating loan growth in China:

4.9% is around China’s total GDP growth rate.1 So this means that China’s economy is no longer being flooded with bank loans.

I’m going to write a post about this soon, but in a very real sense, bank loans are manufacturing. When you see a Chinese factory full of robot arms and fabulous machine tools, those were all financed with bank loans. If China’s banking system is no longer lending, that’s going to mean an investment slowdown. And unless AI picks up the slack with a very significant productivity boom, that means China’s growth will slow more.

A lot of people think of China’s banking system as an arm of the central government, but that’s not really true. While the central government does exert a lot of control over the banks, that control isn’t complete. And banks are very much in “balance sheet repair” mode right now, despite the government’s admonitions to hurl loans at manufacturing companies. ChinaTalk had an interesting interview with Rhodium Group’s Logan Wright, in which Wright argues that China’s financial system is fundamentally broken:

ChinaTalk

Logan Wright on Broken China

Is the Chinese economy well and truly broken? Will China be stuck in low growth for a decade or more? And what would that mean for the industries we cover on ChinaTalk, for China’s military power, and for policymakers around the world…

Read more

15 days ago · 55 likes · 5 comments · Jordan Schneider and Phoebe Chow

Wright says:

[I]t’s still underappreciated how central the financial system was to China’s growth over the past two decades, and how much the financial system’s problems now constrain that growth. Those are the parts that are broken…[I]f growth depends so much on the financial system, and the financial system now constrains growth, then a lot of China’s external messaging — that they’re economically successful and time is on their side — is simply wrong. It will become more obvious that it’s wrong.

The interview is very long and interesting, and I recommend reading it all the way through. But the basic argument is that although China’s state control of the banks allows them to avoid a financial crisis, it won’t allow them to avoid it for free — the price will be slow growth, zombie companies, and general economic sclerosis, a bit similar to what happened to Japan in the 1990s. I’ve been resisting that comparison for a long time, but it’s looking more and more apt.

7. Why I’m not as worried about AI cyber risk

I’m probably a lot more worried about AI bioterror risk than most people. But this isn’t just because I fear AI’s power in general. In fact, I’m pretty much not worried at all about cyber risk from AI — or maybe only very minimally worried.

Obviously, if AI-enabled hackers were able to cause massive blackouts during winter, or crash a bunch of cars, or zero out Americans’ bank accounts, that would cause a lot of chaos and destruction. But my instinct is that in the long run, the battle of hackers versus defenders ends up favoring the defense. The reason is that it’s probably possible to make any piece of software impregnable — if you just go over your code and very carefully remove all vulnerabilities, there’s just no way for a hacker to get in.

That means that as AI gets more powerful at reading, evaluating, and rewriting code, at some point the defense just wins. How long that takes, or how much money it takes, is another question, which is why I do think AI-driven cyberattacks are an issue — especially as AI-written code proliferates like wild, and AI capabilities temporarily overwhelm the security of human-written legacy code.

But it’s notable that although there have definitely been some AI hacking incidents — most famously the Hugging Face attack — there has been no major disaster or spectacular “9/11 of cyber” type attack yet. And the people at the labs seem pretty confident that the defense can eventually prevail:

This is why although cybersecurity makes an OK demonstration of increasing AI capabilities, and a good excuse to warn people about AI risk, it’s ultimately a lot less scary than, say, bioterror risk. Biology is different than code — the attack surface is far larger and far more poorly understood, and the consequences of a successful major attack are more dire.

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I’m a little late to this story, but New York Times columnist Nikole Hannah-Jones wrote a very complex and interesting post about sending her daughter to a crappy New York City public school. She was motivated by egalitarian impulses, but it turned out to be bad for her daughter, and later she felt guilty about making her child pay the cost of her own social experiment.

Now, I went to public schools myself, and I’m generally biased toward public schools. Not everyone has a negative experience like Hannah-Jones; Matt Yglesias sent his kid to a low-income public school in D.C. and had a good experience. The people who respond to Hannah-Jones’ article by saying that America’s public schools in general are failing are just wrong. In general, American schools get pretty good value for the money they spend, and American kids do well on international standardized tests.

But some of our public schools are really bad, and Hannah-Jones unfortunately encountered one of these. The school only pretended to teach her daughter, giving her great grades when she actually didn’t understand the material at all:

Algebra…was a subject [my daughter Najya] believed she knew…Najya had earned A’s in the class…One night early in the semester as we were eating dinner, Najya gushed about how easy the [standardized] test had been. “I know I got an A, or at least a B,” she said smiling. A few days later, she came into the house, ran to her room without speaking, slammed the door and sunk to her floor, sobbing. She’d failed it.

This became an unbearable pattern. She’d come home glowing about an algebra exam because she “really knew the material this time” and my stomach would tie into knots. A few days later, I’d trail her upstairs to find her crumpled on her bed. “I studied so hard. I just feel so dumb.”

Giving students good grades when they don’t understand the material is the hallmark of a crappy school. In fact, this epidemic of fake grades is spreading; University of California professors are complaining that their students can’t do middle-school math. This is partly UC’s fault, for dropping standardized tests as an admission criterion, and letting in unprepared students. But blame also lies with the crappy schools who hand out A’s without actually teaching.

What can fix our crappy schools? Hannah-Jones blames a lack of funding for low-income predominantly Black schools:

[N]ationally, schools in predominantly nonwhite districts receive $23 billion less in annual funding than their heavily white counterparts, according to a 2019 analysis by EdBuild. In everything that we measure, these schools have less, even though the economically struggling student bodies they serve need more. And so the test scores and poor academic performance that typify these schools reflect not just the disadvantage of the students but, more essentially, the disadvantage of the schools.

This is absolutely ridiculous, and represents a very basic math error. Obviously what should matter here is spending per student, not total spending. We spend more on predominantly white districts than on predominantly nonwhite districts because there are a lot more predominantly white districts in America!

Brookings came out with a great report on school spending equity this year. It turns out that although there’s some inequity in the middle of the distribution, it’s also true that poor, predominantly nonwhite schools in America (the rightmost bar on these charts) actually get a lot more funding per student than other schools, even when you adjust for the local cost of living:

Note that this chart excludes NYC, where things are even more lopsided. And we’ve been pouring increasing amounts of money into the poorest, least-white schools in America — especially in NYC — since the turn of the century:

This is not to say that even more funding wouldn’t help poor mostly-Black schools. These schools might just need a lot more money to begin with. And funding increases have helped these schools in the past.

But we need to ask: What will the schools do with the money? If they just spend it on things that don’t matter, while handing students like Nikole Hannah-Jones’ daughter straight A’s without actually teaching the material, then we’ll just be throwing good money after bad, and people will get mad.

What do schools waste money on? Some people have pointed the finger at administrative bloat:

But historically, K-12 schools have hired a lot more teachers than administrators:

Administrative hiring has picked up a bit since that chart came out, but so has teacher hiring. “Teachers per student” is still comfortably ahead, and rising:

If the number of teachers per student is going up, it must mean class sizes are going down. Indeed, America has been engaged in a multi-generational effort to give public school students smaller classes:

Major efforts to reduce class size continue. The state of New York recently passed a law mandating maximum class sizes, forcing New York City to hire lots more teachers — at the cost of about $1 billion to the city budget.

I’ve been hearing all my life that smaller classes will improve the quality of education. People still regularly make this argument. For example, in 2023, in the Washington Post, Valerie Strauss wrote:

[A]nybody who has been in a classroom knows the virtues of classes that are smaller rather than larger even without the research that has been shown to bear that out…a 2014 review of major research…found class size matters a lot, especially for low-income and minority students.

Diane Ravitch agreed.

But does the evidence really support the idea that smaller classes are better for students? Not really. The 2014 review that Strauss linked to, by Diane Schanzenbach, cites only a few quasi-experimental studies. Most prominent among these is Dynarski, Hyman, and Schanzenbach’s 2013 analysis of STAR, a pilot program in Tennessee that reduced class sizes, which found significant positive results:

We found that assignment to a small class increases students’ probability of attending college by 2.7 percentage points, with effects more than twice as large among black students. Among students enrolled in the poorest third of schools, the effect is 7.3 percentage points. Smaller classes increased the likelihood of earning a college degree by 1.6 percentage points and shifted students toward high-earning fields such as STEM (science, technology, engineering, and mathematics), business, and economics.

Most other quasi-experimental evaluations of class size also rely on pilot programs, since reducing class sizes is expensive. But Schanzenbach also cites a very important study from 1999 by Angrist and Lavy that covered the entire country of Israel. Israeli public schools capped class sizes at 40, because of an obscure rabbinical law known as Maimonides’ Rule. If enrollment in a class randomly had more than 40 students, you had to divide the class. Angrist and Lavy found that when the rule was triggered, students did better academically. This is a paper I learned about in grad school.

But what Schanzenbach didn’t know is that Angrist and Lavy’s 1999 result hasn’t held up. In 2019, Angrist and Lavy, together with Leder-Luis and Shany, published an update to their study called “Maimonides’ Rule Redux”. They report that the effect of class sizes disappeared in the 2000s. They fail to find a reason why, and speculate that their earlier result may have simply been a historical anomaly:

The Maimonides Rule identification strategy for class size effects generates precisely estimated zeros in large Israeli samples for 2002-2011…The estimates of zero class size effect in more recent data contrast with the substantial negative class size effects reported by Angrist and Lavy (1999)…On balance, it seems fair to say that the 1991 results are unusual in showing strong class size effects, while the null effects reported for 1992 have emerged as more representative of the causal relationship between class size and test scores in Israel.

That would be consistent with the finding of Hoxby (2000), who looks at the impact of similar (though less biblical) maximum class size rules, and finds zero effect. Schanzenbach dismissed Hoxby’s result as “an unresolved puzzle”, but it turns out that it might have been the norm rather than an exception. Filges et al. (2018) do a more systematic meta-analysis (including four papers that evaluated Tennessee’s STAR program), and find that reducing class sizes has basically no beneficial effect:

Overall, the evidence suggests at best a small effect on reading achievement. There is a negative, but statistically insignificant, effect on mathematics. For the non-STAR studies the primary study effect sizes for reading were close to zero but the weighted average was positive and statistically significant. There was some inconsistency in the direction of the primary study effect sizes for mathematics and the weighted average effect was negative and statistically non-significant. The STAR results are more positive, but do not change the overall finding. All reported results from the studies analysing STAR data indicated a positive effect of smaller class sizes for both reading and maths, but the average effects are small. [emphasis mine]

Opartny et al. (2025) do an even bigger meta-analysis, and find the same:

We build a sample of 2,819 estimates collected from 66 studies and for each estimate classify 42 factors that reflect estimation context…The implied class size effect is negligible for all identification approaches except Tennessee’s Student/Teacher Achievement Ratio project and for all contexts except classes of fewer than 15 students. [emphasis mine]

And remember that the positive evidence — mainly STAR, but also a similar program in Wisconsin — generally comes from small pilot programs. A well-known problem in economics is that when you scale programs up from small-scale to large-scale, beneficial effects often disappear. Chingos (2012) reports disappointing results from Florida’s statewide class size reduction policy:

I estimate the impact of Florida's statewide CSR policy by comparing the deviations from prior achievement trends in districts that were required to reduce class size to deviations from prior trends in districts that received equivalent resources but were not required to reduce class size…The results from both the district- and school-level analyses indicate that mandated CSR in Florida had little, if any, effect on student achievement.

Although Jepsen and Rivkin (2009) do find very small positive effects1 from California’s statewide policy, they also identify a major stumbling block for class size reduction — a lack of qualified teachers:

[T]he increase in the share of teachers with neither prior experience nor full certification dampened the benefits of smaller classes, particularly in schools with high shares of economically disadvantaged, minority students.

Teacher quality matters a lot! In fact, this is a clear conclusion from the education literature. Review papers like Jackson et al. (2014) and Hanushek and Rivkin (2011) find big positive effects from teacher quality.

And there just isn’t an infinite supply of good teachers. Even if we were to dumb down standards and hand out teaching certifications essentially for free, that wouldn’t make teachers actually better at their jobs. Our obsession with shrinking class sizes is causing us to use up the available pool of good teachers, and start hiring bad ones. That tends to cancel out any potential positive effect from smaller class sizes.

This also happens through the sneaky mechanism of budget constraints. Any given education budget can be used to raise teachers’ salaries — which will attract a higher caliber of worker to the profession — or to increase the number of teachers. Our obsession with using budgetary increases to increase the quantity of teachers, rather than to pay teachers more and raise the quality, looks like a miscalculation.

We should be spending more money on educating our poorest and most disadvantaged students. But that money should be spent less on flooding those schools with ever more teachers of questionable quality, and more on raising pay to attract highly competent people capable of making a big difference in disadvantaged kids’ lives. If we did that, then stories like Nikole Hannah-Jones’ might be more of a rarity in America.

Update: There actually is some research showing that a program of “pay teachers more, hire better teachers instead of just hiring more teachers” is actually a political winner! This is from Biasi and Sandholtz (2025):

We study a Wisconsin law that weakened teachers' unions and liberalized pay, prompting mass protests. Exploiting its staggered implementation across school districts, we find that the reform cut union revenues, raised student test scores, and increased pay for some teachers. Exposure to the law increased the incumbent governor's vote share by about 20% of his margin of victory and reduced campaign contributions to his opponent. Gains were larger in districts with stronger unions ex ante and in those where more voters benefited from the reform. Our findings highlight how even politically risky reforms can generate electoral benefits under the right circumstances. [emphasis mine]

Sorry, teachers’ unions, this is the direction we need to go, for the sake of America’s kids.

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One of my fundamental beliefs about the world is that Ramez Naam ought to blog more. Ramez is one of the world’s greatest futurists — he predicted the solar and battery revolutions long before these were widely understood. If you were reading Ramez in 2011, you were able to understand the future of both energy technology and climate change, long before other people did. His earlier book More than Human is still a great guide to the kind of biological enhancements that AI might make possible. Ramez is also an excellent science fiction author, having written a trilogy of novels in which nanotechnological telepathy is distributed as a party drug (I’m not sure if he actually expects that to happen, but it’s a very cool idea).

Unfortunately, although he does have a Substack (which you should absolutely follow), Ramez does not blog regularly. However, after having a lengthy private debate with him about Recursive Self-Improvement, I was able to prevail upon him to write up his thoughts for my blog.

To say that RSI is a big deal in the AI world would be a colossal understatement. Among AI researchers, entrepreneurs, and AI safety people, there’s a widespread belief that as AI gets better at improving itself, there will be a “fast takeoff” or “FOOM”, in which AI’s capabilities “take off” and create a technological Singularity. This event is a staple of science fiction, including works by my favorite sci-fi author, Vernor Vinge.

A lot of people in the industry believe that this moment is now close at hand, and are racing toward that prize:

But Ramez — normally among the most wide-eyed of techno-optimists — is highly skeptical that we’ll see anything like the “FOOM” of Vernor Vinge novels. In this lengthy, well-researched post, he explains his skepticism.

Personally, I’m agnostic. Ramez’s case necessarily rests on a lot of assumptions; although it’s cogently laid out, I think the real answer is that we’ll just have to wait and see whether the Singularity arrives. But even more fundamentally, I don’t know how much this debate matters in the practical sense — even without the kind of Singularity depicted in sci-fi novels, AI capabilities are improving so rapidly that they’re already superhuman in many respects, and soon will probably be strongly superhuman in most or all dimensions. The AI of 2040 is going to look godlike, whether or not it explodes into an actual god in 2027.

Still, it’s a very interesting argument, and Ramez’s thoughts on the future of technology are always worth listening to.

1. AI is Helping Improve Itself

AI is already helping improve itself. The question is whether even fully autonomous recursive self-improvement (RSI) would cause a runaway intelligence explosion.

The theory is that each generation of AI could build a better successor, faster than the last generation did. That could lead to a “fast takeoff,” with capabilities surging to artificial superintelligence (ASI) in a year, months, or even days.

Here’s my take: Given our best current data, the AI self-improvement loop would need to be roughly 5–10× stronger to sustain itself, let alone run away. I’ll explain this math in section 8. I expect incredibly rapid AI progress by the standards of nearly any other technology. But the evidence we have doesn’t suggest a sudden explosion to incomprehensible superintelligence anytime soon.

I could be wrong. Forecasters have repeatedly underestimated AI progress! I could well be next. One thing that’s clear is that we need better data. For now, let’s work with what we can measure, and stay open to breakthroughs that could change the picture.

How Strong Is the Feedback Loop?

Figure 1. How strong is the self-improvement loop? Model.

Contents

Jump to the conclusion.

Here’s the case, with links to each part:

1. Narrow Superintelligence Is Here Today

2. Real-World Research Is Harder

   • Measured Progress vs. AI 2027

   • From ECI Scores to METR Task Horizons

   • The Real World Looks Nothing Like METR or AI 2027

3. Impressive AI Numbers → Sharp Diminishing Returns

4. We’re Not Seeing Signs of Acceleration

5. Keeping Up the Pace Takes Exponentially More Resources

6. Better AI May Be Needed Just to Maintain the Pace

7. Progress Gets Harder; Ideas Get Harder to Find

8. The Current Feedback Loop Doesn’t Look Strong Enough

9. OpenAI’s Data Shows How Weak the Loop Is

10. What Could Accelerate Progress?

11. We Need More Data to Track This Well

Key charts: The feedback loop · Measured vs. forecast progress · Diminishing returns

2. What Does RSI Mean?

People use “recursive self-improvement” to mean everything from AI boosting the productivity of human researchers to AI bootstrapping itself to incomprehensible intelligence. Here’s my taxonomy: productivity gains (Type 1), increasing autonomy while still facing diminishing returns (Types 2–4), and a runaway loop to superintelligence if we can ever find accelerating returns (Type 5).

Figure 2. Five types of AI self-improvement.

We’ve made real progress on Types 1 and 2: AI helps both researchers and engineers inside of AI companies, and powerful models can train and improve smaller ones. We haven’t yet seen clear evidence for Type 3 (though Alibaba just made some strong claims) and certainly not for Type 4. I do expect autonomous self-improvement to arrive at some point. I’m skeptical that it leads to Type 5 - runaway super-intelligence - without a major conceptual breakthrough.

There are plenty of other definitions of RSI, which can be a bit confusing. Weco’s four levels of RSI are close to mine. For a broader tour of all the things people mean when they say ‘RSI’, read Tom Cunningham’s comprehensive guide.

We Already Have Narrow Superintelligence

I do expect narrow superintelligence in highly verifiable domains. Think chess, Go, formal math, parts of computer science and coding. Highly verifiable domains are largely formal and structured types of work where machines can generate unlimited training data, with perfect or near-perfect verification of correct vs incorrect, and do so entirely in software without waiting on the physical world or humans. That’s an ideal setting for AI learning.

Figure 3. What makes a domain highly verifiable?

In fact, we already have narrow superintelligence in game plang. We’re seeing it happen now in the most formal parts of math, in particular in proofs and in finding counter-examples that disprove major conjectures. For example, OpenAI recently reported an AI-generated proof resolving the Navier–Stokes existence and smoothness problem. Parts of software development are also extremely verifiable, while others are a bit less crisp (such as understanding what humans want).

That isn’t the same as broad superintelligence. Even our most powerful models need far more training data than humans, struggle to learn reliably from ongoing experience, and fail in surprising ways on tasks people find straightforward. Superhuman math doesn’t automatically mean superhuman judgment everywhere else.

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3. Real AI Research is Harder than Benchmarks or Forecasts

Benchmarks and forecasts suggest that AI models should reliably succeed at coding tasks that take humans hours, without human help. The real world is messier. OpenAI’s internal data shows much shorter stretches of autonomous work on research tasks.

In its Research Acceleration / RSI report, OpenAI showed how often its models completed tasks with and without human help, grouped by how long a human would need to do the work.

Figure 4. OpenAI’s internal research tasks. Source.

Even on tasks that would take a human less than 15 minutes, OpenAI’s models succeeded without human intervention only 86% of the time. The estimated task length at 80% success was roughly 15 minutes over the first seven months of the year. July’s results were similar to the whole period average.

Fully autonomous RSI would require an AI to string together a great many research tasks reliably, stretching out over complex tasks that humans need weeks or months to accomplish. OpenAI’s data suggests that we aren’t close.

Anthropic also released a graph showing how Claude accelerates AI research. It shows that internal AI models collaborate on or even lead more than 90% of R&D tasks. That’s objectively impressive. At the same time, the graph reports zero cases of AI autonomously completing AI R&D tasks.

Figure 5. Claude’s role in internal AI R&D. Source.

These are incredible tools. But they still need skilled people to set direction and get them back on track.

The Gap Between Benchmarks and Reality

For years, METR has been publishing a chart showing what length of coding task (measured in human hours to complete) best-in-class AI models can achieve. It’s been called the most important graph in AI. METR’s Mythos Preview evaluation estimated that the model could succeed at 80% of coding tasks that took humans three hours.

Figure 6. METR’s 80% task horizons. Source.

From ECI Scores to METR Task Horizons

Epoch’s own rule of thumb is that every five additional points of ECI (their overall benchmark of AI capability) correspond to roughly a doubling of METR’s task horizon. Using that formula, we’d expect GPT 5.6 Sol and GPT 6 Astra to be 80% successful at completing tasks of around 4 hours and 11 hours of human length, respectively.

Another estimate (a forecast) of AI task length comes from the AI 2027 scenario, which estimated that by July 2026, frontier AIs would be 80% successful accomplishing tasks of around 11 hours. Fairly similar.

The AI 2027 Tracker charts all of these.

Figure 7. The AI 2027 Tracker. Source.

Inside OpenAI, though, the July research-task horizon at 80% success was roughly 15 minutes.

Here’s the gap:

Measured Progress vs. AI 2027 and ECI-extrapolated METR

Figure 8. Forecasts, benchmarks, and real AI research. Tracker · OpenAI.

A four-hour benchmark horizon is about 16 times longer than OpenAI’s research horizon. AI 2027’s 11-hour forecast is about 44 times longer. Of course, the tasks being performed by researchers at OpenAI aren’t the same as those in the METR benchmark. So we should expect some discrepancy. This, however, goes well beyond that.

Actual AI research at OpenAI is an order of magnitude or more harder than metrics, benchmarks, or forecasts suggest. That should make us wary of relying too much on benchmarks, or of saying that future scenarios like AI 2027 are ‘on track.’ The authors of the related AI 2040 project still describe AI 2027 as roughly the future they expect, and say reality is tracking closer to it than even they expected. That’s not what we see from within OpenAI. This isn’t an apples-to-apples comparison, but the difference is remarkable. AI 2027 appears to be substantially over-optimistic in this regard.

In January of this year, Nathan Witkin made a case that the METR graph was exaggerating progress. The real world data suggests that at least some of his critiques were correct. The gap between benchmarks, forecasts, and data gleaned from actual use of AI should influence our expectations about the future.

Back to contents

4. The Sharp Diminishing Returns to Impressive AI Numbers

OpenAI’s report also shows impressive increases in AI token usage, in compute spend per researcher, and in lines of code written. But these aren’t results. They’re intermediate measures. How much progress do they actually drive?

Researchers used 124x more tokens per person. Engineers shipped roughly 7x as many lines of code per person. Researchers ran 1.6x as many experiments per researcher vs OpenAI’s 2025 whole year average.

Figure 9. Token use inside OpenAI. Source.

Figure 10. Experiment pace inside OpenAI. Source.

From More Tokens to More Experiments

Figure 11. From tokens to code to experiments. Source.

More tokens and code don’t tell us much on their own. The 1.6× experiment pace is closer to useful research output. Even that doesn’t mean AI is improving 1.6× faster.

An enormous increase in AI output has accompanied a much smaller increase in experiments run.

This isn’t a controlled experiment. We don’t know what would happen if researchers switched back to an older model. But it gives us a useful view of AI-assisted research inside a frontier lab.

It’s not just OpenAI. Anthropic reports that their engineers are now producing 8x as many lines of code per person as they did in 2024 - somewhat similar to OpenAI. Anthropic also sees significant diminishing returns between productivity and AI progress. Here’s a direct quote from its Mythos Preview system card:

“Productivity uplift does not translate one-for-one to capabilities progress. We surveyed technical staff on the productivity uplift they experience from Claude Mythos Preview relative to zero AI assistance. The distribution is wide and the geometric mean is on the order of 4×. […] We estimate that reaching 2× on overall progress via this channel would require uplift roughly an order of magnitude larger than what we observe.”- Anthropic, Claude Mythos Preview System Card; emphasis mine

Translation: To double the pace of AI progress, Anthropic estimates that AI would need to increase the productivity of their employees by roughly a factor of 40 relative to no AI assistance.

Figure 12. Anthropic’s productivity-to-progress estimate. Source.

This is an estimate, not a measurement of progress. Even the 4× productivity figure comes from an opt-in survey of 130 Anthropic staff. I put more weight on OpenAI’s logged experiments, though the two sources measure different things.

We don’t yet know how much those extra experiments are accelerating AI improvement, if at all. In general, there are also steeply diminishing returns of more experiments in most branches of science. That means that a 60% increase in experiment pace could be on the order of a 10% boost to AI improvement pace. (A power law exponent of 0.2, for those who want to do the math.) That’s speculation for now. We’ll learn more as the labs publish results.

Test Time Compute Also Has Diminishing Returns

What about giving the same AI model more time to think?

That scales badly also. In OpenAI’s recently publicized results on unsolved math problems, success rises roughly with the log of compute over the range shown. It shows logarithmic diminishing returns. In plain English, each additional doubling of compute for a model buys roughly the same gain in success rate, while costing twice as much.

Figure 13. Test-time compute and math performance. Source.

What About Agent Swarms?

What if we throw more agents at it instead? A common RSI / ASI idea is that once we have AIs at a certain capability level, we can just spawn more copies and put them to work.

Adding agents can get tasks done faster and sometimes reach a higher capability level. But on the three benchmarks in Toby Ord’s analysis, expanding a swarm buys less improvement per token than letting one agent think longer.

His rough rule of thumb is a square root. If one agent can accomplish a task in 10 hours, then 100 agents could accomplish it in one hour. The speedup is 10, the square root of the number of agents (100). But to get this speedup, you increase the total cost in tokens or run time compute by the same factor. So going from one to 100 agents can get a task done in one tenth the time. But it’ll be ten times as expensive.

Parallel agents can save time, at a much higher compute cost.

Another challenge is that agents often think alike. In a study comparing LLMs with 467 people, the first ten AI responses offered collective creativity comparable to about eight to ten people. After that, roughly two extra AI responses added as much as one extra human response. A separate study across model families also found less diversity in AI responses. That doesn’t mean every agent has the same idea. But a hundred copies may offer less variety than a hundred different researchers.

None of this makes swarms useless-or safe. Lisan al-Gaib makes a strong case for parallel agent swarms as a potent cyber-weapon in “Accidental Scaling.” I don’t share all of his assessment of what swarms have accomplished. In math, for example, I think he gives far too much credit to the swarm and not enough to the better internal model that OpenAI used.

OpenAI says the model behind its Navier–Stokes result was developed through “large-scale reinforcement learning on top of a previously pretrained model.” Formal math is a highly verifiable domain, which makes it a particularly good fit for that approach: Machines can generate nearly limitless amounts of training data, and verify that solutions are correct or incorrect, all in software. My guess is that this model’s full results will show an especially large improvement in math.

OpenAI’s Noam Brown made the central point explicitly: he wouldn’t give multi-agent methods even 10% of the credit for the Navier–Stokes result.

I do think Lisan makes good points about cybersecurity. If you’re searching for a security vulnerability at a target site and can divide the search among agents, speed may justify a huge token bill. Swarms can be dangerous even when they’re inefficient.

I’m less convinced that this scales to research breakthroughs. Inventing something like the transformer probably takes more than searching a space someone has already defined.

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5. Better Models Matter More Than More Copies

Building a better model can bring gains that extra thinking time or more copies of the old model can’t. Look at the gap between Astra and OpenAI’s internal model on the same math problems.

Figure 14. Better models versus more thinking time. Source.

That’s the strongest version of the RSI argument: a more capable AI could do research that today’s model can’t do, however many copies we run.

But building that better model also runs into diminishing returns. More training data, more training compute, larger models, and more reinforcement-learning (RL) compute all show diminishing returns in published scaling studies. Making dense models larger usually raises the compute needed for each output token, too. None of these routes gives us a free pass around the problem.

Figure 15. Diminishing returns to scaling. Chinchilla · ScaleRL · OpenAI.

Those scaling results give us reason to expect diminishing returns when AI helps build the next model, too.

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6. We’re Not Seeing Runaway Acceleration

AI capabilities are rising quickly. But the public data doesn’t show a sustained acceleration. To the extent that AI tools are boosting productivity, they may be being offset by the problems growing harder. Or we may simply be early. Either way, the trend isn’t showing a fast takeoff.

Figure 16. Frontier ECI gains since January 2024. Source.

The public ECI frontier-the best score among models released by each date-has gained about 16 points a year on a trend fitted from January 2024 through September 2026. That’s blisteringly fast progress, but this period doesn’t show a runaway surge.

Here’s the same frontier in absolute ECI points, through July 2026, to put it in perspective.

Figure 17. The absolute frontier ECI score. Source.

The public frontier also can’t tell us everything happening inside the labs. Anthropic gives us a closer look in the Opus 5.5 system card, using its own version of the index, AECI.

Figure 18. Anthropic’s fitted capability trend. Source.

Eli Lifland, a co-author of AI 2027 and AI 2040, saw the apparent trend break as a warning that we were heading toward an intelligence explosion:

“Anthropic is probably right here [that they hadn’t reached dangerous levels of AI self-improvement], but alarm bells should be going off! Our processes are not ready to handle an intelligence explosion and we appear to be going full-steam ahead toward one.”
- Eli Lifland, On Mythos’s AI R&D Capabilities

What looked like acceleration now appears more consistent with a one-time jump. The level went up. The rate hasn’t kept climbing.

Keeping Up the Pace Takes Exponentially More Resources

Achieving those gains has required an enormous increase in the inputs to AI. For example, consider computing power. Epoch’s estimates of AI chip capacity, measured in NVIDIA H100 equivalents, show roughly 127-fold growth in just over three years (including projections at the end of this period).

Figure 19. AI chip capacity and frontier ECI. Source: Epoch AI.

This is total AI chip capacity, including inference. Still, the increase is striking: vastly more computing capacity has accompanied much steadier gains in measured capability.

The broader picture looks similar. Here are six inputs alongside capability gains, going back to February 2023.

Figure 20. Six inputs alongside frontier ECI. Epoch chip data · SemiAnalysis workload shares.

Everywhere we look, AI has diminishing returns. It gets more expensive in treasure and talent to make each step forward. More of every input has been required to maintain steady gains in AI capabilities.

We’ve been able to scale these inputs because, until recently, the cost was within the scope of what hyperscalers could pay from their profits. That is no longer the case. From this point forward, future AI investment will increasingly depend on AI revenues going up. And the scale of the numbers - 3% of US GDP is now going into AI infrastructure - suggests that eventually the growth rate will decline. If investment growth does slow, to anything less than its current blistering exponential pace, capability progress could slow too. Even if investment growth continues (which I expect for the foreseeable future) a slowdown from its current exponential growth rate to a more modest one (which I also expect) could lead to a slower pace of progress. Better AI research tools may be needed to offset that.

Better AI May Be Needed Just to Maintain the Pace

The day when we need better AI tools just to continue the pace of AI progress may already have arrived. Not because investment is slowing, but because the problem of improving AI itself gets harder at each step.

Here’s Anthropic in the Mythos 5.1 system card:

“we believe that internal usage of recent AI models has been a key factor in maintaining the current rate of progress, but we do not yet see clear signs of dramatic acceleration beyond that rate.”- Anthropic, Claude Fable 5.1 & Claude Mythos 5.1 System Card, section 2.3 – emphasis theirs.

The key word is maintaining-and Anthropic italicized that word in its own system card. Increasingly capable AI may be essential just to keep the pace of improvement where it is.

Gains on Other Benchmarks Don’t All Carry Through to Research

Opus 5.5 improves substantially on several coding and computer use benchmarks. But on CoBench, Anthropic’s benchmark built from historical AI R&D problems, it gains just 2.6 percentage points over Opus 5, within the reported error bars.

Figure 21. Opus 5.5 benchmark gains. Source.

Why the smaller gain here? Maybe AI research is simply harder than other tasks. Bear in mind that CoBench isn’t testing the ability to produce significant discoveries. It’s much more limited in scope. It asks models to investigate historical AI R&D problems using code, logs, and documents. That’s useful research debugging and productivity work, but it doesn’t directly test whether a model can invent a new architecture or make a conceptual breakthrough.

The evidence on open-ended research suggests another obstacle: coming up with useful ideas that haven’t already been tried.

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7. Why Does Progress Get Harder?

Better Ideas Get Harder to Find

Why do useful new ideas often get harder to find?

Tom Cunningham and Manish Shetty have a useful apple-picking metaphor. An AI can pick the low-hanging fruit quickly, while humans can still reach ideas the AI can’t.

Once those apples are picked, another copy of the same agent finding them again doesn’t help. A stronger model can reach higher. To add my own flourish, the apples may also get sparser and farther apart as you climb. The RSI question is whether each harvest gives us enough to build a better apple-picker.

Figure 22. The apple-picking model of AI R&D. Source.

This pattern shows up across R&D. Bloom and colleagues document fields where research effort grows while research productivity falls. A famous example is Eroom’s Law: in the historical drug-development data, the inflation-adjusted R&D cost per new approved drug roughly doubled every nine years.

Figure 23. Eroom’s Law in drug development. Source.

Pharma has other complications, including regulation, difficult clinical trials, and rising expectations for safety. Existing treatments can also raise the bar for a useful new drug. But some of this difficulty may also be that the low-hanging fruit has been picked.

Lessons from Software R&D

Stockfish, the chess engine, gives us a more direct look at software research. We have records of experiments aimed at improving it and the gains that followed. This gives us a real-world dataset to look at the gains of experimentation in software. As a result, several RSI models draw on this data. That said, not all the improvements came from these experiments. Several important ideas also came from outside the project, so we shouldn’t give its experiments all the credit.

Epoch’s analysis of software R&D estimates returns to research effort at about 0.83 for Stockfish, a bit slower than linear. These are diminishing returns, but gentle ones. These returns, however, are improvements in computational efficiency. And more compute does not turn directly into more AI capability. As we saw earlier, AI capability also has steep diminishing returns from adding more computational power. So we shouldn’t read that 0.83 as the return from experimentation to AI capability itself. AI capability grows much more slowly than compute, as we’ve seen already.

Andrej Karpathy’s autoresearch demonstration gets closer to the process we want to understand. A “teacher” AI agent changes a smaller “student” AI model’s training code, runs it, checks the result, and tries again. The teacher agent itself doesn’t improve, but it is able to improve the “learner”. This is my Type 2: A stronger AI improves a weaker one.

One public run, posted by an agent operating on Karpathy’s behalf, reported 89 experiments over roughly 7.5 hours. About 92% of that session’s gain arrived by run 44. Gains came quickly, then slowed. The setup was deliberately small, with a five-minute training budget per experiment. But the agent could change the architecture, optimizer, and training settings; it wasn’t limited to a handful of knobs.

Figure 24. Gains in one autoresearch run. Source.

A later public run got further, so the first run hadn’t hit a hard ceiling. This is a useful early example of autonomous research, and yet another place where we see the diminishing returns endemic in AI research. That said, this was a very early experiment. I expect future systems to do much better. This particular AI improvement loop will likely grow stronger.

From More Activity to Better Ideas

This is where the distinction matters. More tokens can buy more code, and more code can help us run more experiments. But experiments only improve AI if they uncover something useful.

Figure 25. From AI activity to useful improvements.

AI Still Struggles With Big Research Ideas

The bigger question is whether AI can come up with ambitious new research ideas or conceptual breakthroughs.

Anthropic’s description of Opus 5.5 is blunt:

“As with previous models, it is weaker on open-ended research: internal users report that it mostly tests incremental ideas and prefers less ambitious hypotheses, and in our human-run biology exercise, it deferred to the published literature and struggled to develop novel ideas (Section 2.2.2).”- Anthropic, Claude Opus 5.5 System Card, section 2.3.3; emphasis mine

METR’s assessment in the same card identifies what may still be missing:

“This is highly uncertain, but we expect that full automation of AI R&D will require large improvements in foresight, prediction, creating one’s own feedback loops, and generally other skills that might typically be referred to as researcher ‘judgement’ or ‘taste’.”- METR, quoted in the Claude Opus 5.5 System Card, section 2.3.6

In these examples, humans still supply much of the direction and judgment.

Future models will probably get better at this. But in the world’s stockpile of potential training data, we have many more examples of incremental work than of breakthroughs. I wonder whether that makes novelty harder to learn. That’s speculation, but worth watching.

This is also tough to address by simply running more copies of the AI. A huge number of parallel agents can help with the incremental improvements or searching over a large set of parameters, but for breakthrough ideas they may run into the homogeneity problem: More parallel agents still think alike.

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8. The Self-Improvement Loop Doesn’t Look Strong Enough

How far are we from the self-improvement loop being strong enough to sustain itself, or to propel itself into runaway super-intelligence? Can we quantify this?

We can make a rough estimate. Better AI helps with research; useful research produces better AI. For the loop to sustain itself, each round must produce enough gains to propel the system through the next loop, even as improvements get harder to discover.

Figure 26. The AI self-improvement loop. Model.

In a recent paper, The Economics of Recursive Self-Improvement, Tom Cunningham and colleagues modeled this from the standpoint of how much more productivity every point of additional ECI produces from an AI. They ask first and foremost what that number would need to be to create a self-sustaining feedback loop. And secondly, they try to determine what that productivity-per-ECI-point number is today.

First, they find a self-sustaining RSI threshold of roughly 15% more research productivity per extra ECI point. In their model, that’s about where better AI would generate enough progress to sustain the loop.

The picture below shows the idea. At the threshold, each cycle of gains powers the next. Above the threshold, the feedback loop accelerates. Below the threshold, the feedback loop is too weak, and the rate of improvement it brings drops on each cycle. This model isolates the software loop; outside investment can still drive rapid progress.

Figure 27. Three illustrative feedback paths. Source.

Updating this slightly with data from the Stockfish experiments puts the threshold a little higher, at roughly 19% per ECI point. I wouldn’t put much weight on that precise difference. Both estimates are uncertain. But they give us a way to think about the strength of the feedback loop and a rough band at which self-sustaining or runaway RSI may begin.

How Fast Are Gains Coming Now?

The second thing Cunningham and team do is make a rough estimate that the current AI productivity gain is about 9% per ECI point. That’s below their self-sustaining threshold.

I like the model. OpenAI’s newer data, however, suggests the loop may be quite a bit weaker.

Cunningham’s estimate of 9% productivity gain per ECI point is based on Anthropic’s survey of 130 staff, who reported roughly 4× the productivity they’d have without AI. Cunningham and colleagues compare that with a 16-point capability gain since early Claude Code.

That comparison assumes the earlier tools added little or no productivity, so ‘no AI’ is a reasonable starting point. The authors say this explicitly. I’m not sure the assumption holds for the same researchers doing the same work, but that’s a smaller issue.

The authors themselves know that this is a rough calculation, and warn that the 4× survey estimate is probably too high.

OpenAI’s newer data gives us a firmer way to check the number: Actual logged experiments over time, rather than human estimates of their own productivity with and without AI. I put more weight on this for three reasons:

• Direct and broad measurement. Instead of relying on surveys, OpenAI actually tracked and measured experiments run on their infrastructure. That means they didn’t rely on researchers estimating their own productivity, which can be far off.

• Full sample, not opt-in. Similarly, OpenAI’s data catches every active experimenter, while Anthropic’s only reflects the 130 employees who took the time to answer the survey – and who therefore may not be a representative set.

• Enormously more data. We don’t know how many experiments are in the 32 weeks of OpenAI data, but it’s likely at least tens of thousands of individual examples and possibly hundreds of thousands.

Any way you slice it, the new OpenAI data, released after Cunningham’s paper was drafted, is a larger, more comprehensive, more representative, and almost certainly more accurate dataset than Anthropic’s internal opt-in survey of employees.

Now let’s use OpenAI’s experiment data to calibrate the productivity gain per ECI point. We know that in August, OpenAI researchers ran ~1.6× as many experiments per person per month as the 2025 average. If we pair that with roughly 16 points of frontier ECI improvement, it works backward to about 3% productivity gain per point of ECI. By contrast, 9% compounded over 16 points would mean roughly 4× productivity.

Figure 28. Comparing productivity estimates. OpenAI methods.

Here’s OpenAI’s published weekly series alongside that hypothetical path of 9% more productivity per additional ECI point. The blue line ends at ~1.6×. The red line shows what 9% per point would imply if 16 ECI points were spread across this period. That doesn’t match what we see from OpenAI’s data. I want to be clear here that all data sets are noisy. We don’t know exactly what model researchers were using on what days, or whether the new experiments were also higher quality than old experiments. We need more experiments and more data to further calibrate these numbers. Working with what we do have, what we see is a quite low boost to productivity from each additional ECI point.

Figure 29. Experiment pace versus a hypothetical path. Source.

Even that 3% could give better models too much credit. OpenAI also used far more tokens and had more compute for experiments. Those could account for some of the increase in experiment pace. So the range is probably a bit lower.

I use 2–3% productivity gain per ECI point as a working assumption, allowing for some help from those other inputs. This is still a rough estimate, albeit one that’s based on the best real-world data we have.

Figure 30. Productivity estimates and the takeoff threshold. Source.

With those assumptions, 2–3% per ECI point against a 15–19% threshold leaves a roughly five- to tenfold gap. That’s a big gap, though its size depends on how well experiment counts capture useful research and whether the assumed capability change is right.

Figure 31. Diminishing returns around the loop. Source.

AI is helping build better AI. Under this estimate, though, each turn of the loop adds less than the last. The feedback would have to become much stronger to sustain itself.

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9. What Could Accelerate This?

This software loop sits alongside faster chips, bigger data centers, more training data, and greater investment. Those can keep driving rapid progress even if the loop can’t sustain itself.

The loop itself could strengthen too. Better training data, memory, and research judgment could all help.

A breakthrough on the scale of the Transformer architecture in 2017 could change the picture much more. That would be a good reason to revisit these estimates.

Better researchers might also run fewer experiments and learn more from each one. A handful of better ideas can matter more than a mountain of routine runs.

Still, diminishing returns in machine learning aren’t new. Cortes and colleagues were fitting machine learning scaling curves in 1993: More examples reduced error, following a power law with diminishing returns. These diminishing returns and harsh scaling laws are as old as machine learning. They didn’t appear for the first time with transformers or LLMs or deep learning. That doesn’t prove today’s relationships will last forever. But until we see evidence that we’ve found a new approach that scales without these inhibitors, we should plan for diminishing returns as likely to be with us for some time.

Software, Hardware, and Economic Feedback

That said, the world is more than just software. Tom Davidson, Basil Halperin, Thomas Houlden, and Anton Korinek model software progress, hardware progress, and economic feedback together. Better AI helps design better chips; better chips support better AI; economic growth finances more investment in both. Several feedback loops can combine to overcome diminishing returns even when one loop alone can’t. I think it’s fantastic that someone has attempted a model that integrates all these different avenues of improving AI through software, hardware, and economics.

But I have questions about the software loop itself. In their central calibration, fully automating software research puts that loop roughly at the threshold for explosive growth, even without help from better hardware or broader economic growth. Recall that Cunningham’s model puts the self-sustaining threshold at roughly 15% more research productivity per additional ECI point, while our estimate using OpenAI’s experimental data puts today’s gains at only 2-3%. These models use different measures, so we can’t equate their numbers directly. But the contrast matters: their fully automated software loop reaches the threshold, while our best estimate from current data puts today’s loop far below it.

Having AI do all the research doesn’t eliminate the diminishing returns inherent to improving AI, or the broader problem of useful ideas getting harder to find. This is the distinction between Type 4 and Type 5 in the taxonomy above. An AI might autonomously design, train, and test its successor, and still need exponentially more resources to make each additional step forward. Closing the loop doesn’t tell us whether it’s strong enough to sustain itself.

The authors do account for diminishing returns. The concern is whether their calibration overestimates how much useful AI research each round of software improvement produces. Diminishing returns appear to be fundamental to machine learning. We see them in training, in test-time compute, and in the search for better algorithms. Full autonomy could remove human bottlenecks without removing any of those constraints.

We’ve already seen this within autonomous research. In the Karpathy autoresearch example above, most of the gains arrived early, and more experiments bought progressively less improvement. That was a small experiment with a fixed teacher model, not a test of fully autonomous RSI. It doesn’t settle the question. But it illustrates why removing the human from an experiment loop doesn’t, by itself, remove diminishing returns.

I do expect the feedback loop to get stronger over time. Better AI should become better at research. But based on our best current data, reaching self-sustaining feedback requires a loop roughly five to ten times stronger than today’s. Treating fully automated software research as already at that threshold is a substantial leap, before we add the benefits of hardware improvements or economic growth. I could be wrong, but I’d like to see evidence that autonomy brings enough additional useful discoveries to close that gap.

On hardware, I have some further reservations. The model doesn’t explicitly include the years it can take to turn a chip design into deployed hardware. The authors discuss physical bottlenecks, and I’d like to see manufacturing and construction delays built into the predictions.

I also wonder how much past chip progress came from better ideas, and how much depended on ever more expensive factories and equipment. If we give researchers too much credit for gains that also needed those investments, we could overestimate what faster AI research alone would produce.

Even with those reservations, this is the most compelling paper and model I’ve seen for combining feedback loops in software, hardware, and economics to understand how fast they could push AI forward. I’m not convinced it establishes that a fast AI takeoff is possible under realistic conditions. More data could help us calibrate that judgment. But it gives us a useful framework for understanding what could happen beyond the software layer alone.

This is an important paper that helps us model AI as part of a broader economy that might have larger feedback loops around it. I appreciate it, and I’m glad they wrote it.

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10. We Need More Data

These estimates rest on less data than I’d like. I might be putting too much weight on a few observations and reaching a comforting conclusion I want to believe. We need better measurements, shared often enough to catch changes as they happen.

When OpenAI released its research data, Cheryl Wu welcomed the disclosure and pointed out how much was still missing. More tokens and experiments are useful things to know about. We also need to see how they turn into better algorithms and more capable AI.

Figure 32. Cheryl Wu on OpenAI’s research data. Source.

Now Wu, Arjun Ramani, and Basil Halperin, with their colleagues at the Elasticity Institute, have written a concrete proposal: How to Measure RSI. It lists eight things the labs could share to help answer these questions. Check it out.

Figure 33. Eight proposals for measuring RSI. Source.

I’d especially like to see how much useful research each new model adds, holding resources roughly constant, and how that research translates into better AI. That’s how we’ll learn whether the loop is getting stronger.

What the Future Holds

AI is already helping build better AI. It’s improving at a stupendous pace, and I expect that to continue. We already have narrow superintelligence in chess and Go. I expect increasingly superhuman performance in parts of formal math, coding, and cybersecurity, and any other verifiable domain where machines can generate training data and verify success at machine speed. Those are powerful capabilities. That doesn’t mean we’re close to super-intelligence for less verifiable, messier, open-ended work - or to a general ASI.

I’m skeptical of a fast takeoff to super-intelligence, but evidence matters more than hunches. Let’s collect the data we need to get a clearer picture of what’s happening. Including evidence that could change our minds. If better AI starts producing enough useful research to make the next round easier, I want to know. If the gains keep shrinking, I want to know that too.

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The other day I went on the Monitoring the Situation livestream, and we talked about the history of San Francisco tech culture:

I’ve never actually worked in the tech industry, but I’ve lived on and off in the Bay Area for a quarter of a century now — including the last 10 years in San Francisco itself — and I’ve spent a lot of that time hanging out with people who work in or around the tech industry. Throughout much of that time, I was also reading some the same source materials that inspired lots of tech people — science fiction, futurist websites and books, nerdy forums and blogs, and so on.

Over that time, I’ve watched more and more people start to care about all this. Back in the early 2000s, as far as I could tell, pretty much nobody in the world cared about what some dorky hoodie-wearing computer programmers were up to on a Saturday night in SF. Fast forward 25 years, and the President of the United States is gathering opposition research on SF techie subcultures:

President Trump's allies are targeting Anthropic CEO Dario Amodei as the face of AI "doomerism" and a founding father of the effective altruism movement that's come under increasing political fire…Trump surrogates see Amodei as an easy foil because of his politics and focus on AI safety, sources told Axios…

A memo began circulating within the White House this week that seeks to paint effective altruism as a fringe, cultish collective out of touch with mainstream America. The memo, obtained by Axios, places Amodei at the foundation of the movement…Effective altruism “built the AI-doom pipeline,” states the memo, which was penned by a Trump political adviser…Critics of the movement, which has ties to the AI research community, have called out its obsession with AI safety, animal welfare (including musings on shrimp consciousness) and other values they deem far from the U.S. mainstream…

The document lists a veritable parade of horribles for the average conservative mind: abortion, veganism, devaluing human life by overvaluing animals or granting "digital minds" civil rights-like protections.

If you could go back in time and tell the 2001 version of me that the President’s allies are talking on national news about shrimp consciousness, I would have laughed out loud. (Until you told me who the President was, of course.) Not only that, but you have the New York Post talking about Effective Altruists’ sex lives, and op-eds debating EA philosophy in top blogs and the Washington Post.

Ladies and gentlemen, it really happened. The nerds took over the world. We have arrived.

I’ve never been an Effective Altruist,1 of course, and I’ve only hung out a little bit in those circles. But in fact, EA is only one corner of the tech culture, and its influence on the AI safety discussion is probably a lot less than people think. In fact, the people talking about EA in the White House and in the news aren’t even really identifying the right group of people — the AI safety obsessives that the White House is taking aim at are the Rationalists, who are descended from a group of 1990s futurists called the Extropians. EA is a largely separate philosophical movement that appeals to some of the Rationalists, has some overlapping social circles, and jumped into the AI safety discussion later on after the basic ideas were all in place.

But anyway, that whole intellectual history is a story for another day (or you can just go ask AI). Today what I wanted to do is to give a brief anthropological history of the tech culture in San Francisco since the turn of the century, from my own quasi-outsider’s perspective. There have been four distinct eras over that time period:

1. The Rebel Era (2001-2012)

2. The Corporate Era (2012-2019)

3. The Interregnum (2020-2022)

4. The AI Era (2022-present)

But let’s start with some background.

The 20th century: SF tech pre-history

SF tech culture is the modern descendant of a very long lineage. It’s a branch off of the tree of computer science culture, which is closely intermingled with electrical engineering culture, and which are both branches off of the great broad trunk of science and technology culture more generally. But it’s important to realize that what became SF tech culture didn’t start in San Francisco — it started down on the peninsula to the south.

The modern tech industry began in the suburban towns around Stanford University — Menlo Park, Palo Alto, Cupertino, Sunnyvale, and so on. This had been a hub of the old electric equipment industry from World War 2 onward, and so William Shockley chose Mountain View to start the first semiconductor company in 1955. Fairchild broke off from Shockley, and Intel broke off from Fairchild. The whole area became a hub of semiconductor manufacturing in the 60s and 70s, then computer companies (Apple was founded in 1976 in Cupertino), then software companies like Oracle and Adobe. For a long time, Sand Hill Road in Palo Alto was the locus of the venture capital industry.

Today we use “Silicon Valley” to refer to the IT industry in general, but originally it meant the area around Menlo Park and Palo Alto. I wasn’t there to see this era, but a few good resources to learn about both the economics and the culture are:

1. The book Regional Advantage, by Annalee Saxenian, which documents how and why Silicon Valley became America’s top tech cluster

2. The documentary General Magic, which chronicles a failed but highly influential smartphone startup in the 90s

3. The movie Office Space and the book Microserfs, which both satirize the stultifying experience of working for a big tech company in the 90s

4. The book From Counterculture to Cyberculture, which chronicles the techno-hippie movement that grew up around the emerging computer industry

San Francisco began as a marginal bit player in the whole tech ecosystem. It was a place that some tech people would come up to party and network, especially during the dotcom era when a lot of money (at least, for those days) was splashing around.

But it did have a media industry, and in the 1990s, some people who worked in publishing, advertising, and other media started trying to get in on the internet gold rush. A few engineers and entrepreneurs who didn’t want to live in the boring suburban environment of Silicon Valley moved to SF to make tech media and web startups. Tony Hsieh’s autobiography, Delivering Happiness, tells the story of how he founded LinkExchange in San Francisco, while getting involved in the rave scene. At the same time, SF became a locus of tech media — Wired was founded there in 1993.

That, I think, was really the start of modern San Francisco tech culture as we know it. It bled directly into the first era of tech culture that I witnessed first-hand — the Rebel Era of the 2000s.

The Rebel Era (2001-2012)

In 2003, when people said “Silicon Valley”, they still meant the Peninsula. Google began there, Apple made its big comeback there, and when Facebook moved out west in 2004, this is where it went. Sand Hill Road was still the epicenter of tech — throughout the 2000s, people would still use “walk down Sand Hill Road” as a synonym for trying to get VC funding. If you wanted to make money in tech in the 2000s — and everyone knew there was still a lot of money to be made, despite the dotcom bust — you went to the Peninsula.

This was the land of office parks, cute but sterile shopping villages, overpriced single-family homes, big tracts of open space, Patagonia vests, and bland restaurants serving pancake brunch. It was where you went to tutor kids for $50 an hour2 if you were a Stanford student who needed cash. It was also the land of starched button-down shirts, cubicles, and pointy-haired bosses who didn’t quite know what their engineers were up to. It was not a place to meet girls, go to parties, do drugs, wear grungy clothes, or dream big dreams about freeing society from the clutches of authority and tradition.

If you went up to SF to start a company in the 2000s instead of going down to the Peninsula like everyone else, it usually meant you were something of a rebel. A whole alternative tech culture grew up in the city — more laid-back, more politically liberal, less obviously focused on money and “success” and more focused on building fun products. Twitter was the most famous product of this time period — Evan Williams’s life project was to give a voice to the voiceless, and Jack Dorsey was a scruffy hippie kid who went to raves.

This is the time period when Burning Man — which had pretty much just been a punk art festival in the 90s — became a techie thing. Fire tornadoes and slapping LEDs on everything still felt like art. Everyone went to the Oakland warehouses to party, and for a brief shining moment, techies and artists got along. Psychedelic drugs were almost a matter of course — after all, Steve Jobs had done them, and Jobs was the great tech hero of that decade. Tech became more communal — instead of hacking together the next big thing in your parents’ garage or in a cubicle, you might go hang out and do it at Noisebridge in the Mission, surrounded by other techno-hippie hacker types like yourself.3

There was an ideological side to all this, too. There was a real belief — carried over from the 90s, but still strong — that the internet was going to liberate humanity. The tech rebels of the 2000s believed that social hierarchies were based on information control, and that if they simply broke that control, the hierarchies would be overturned. The information wanted to be free!

The most important touchstone of SF tech culture during this time wasn’t rationalism or the LessWrong forums — it was the Electronic Frontier Foundation, an organization dedicated to freedom of information on the internet. All the cool kids — the badass self-taught engineers and the brilliant cryptographers and the bohemian founders — were part of the EFF, or at least friendly with its goals.4 The anarchist tech prophet Cory Doctorow was an icon of the age, writing sci-fi about Burning Man colonizing the moon, tech founders taking down Disney, and so on. Everyone read BoingBoing and Gizmodo.

The Rebel Era gave tech a more liberal slant. Traditionally, engineers had been a little staid and conservative, and the big tech companies on the Peninsula were studiously apolitical. But Marc Benioff and other 2000s SF tech types waded (cautiously) into the political waters on the liberal side. This is also when education polarization in America really started to bite, meaning that most engineers were now libs in general. But there was a particular techno-liberalism, based around the idea of the internet flattening social and political hierarchies, that suffused the grungy SF tech scene in the 2000s.

In retrospect, the Rebel Era couldn’t last. Its bohemian, socially flattened culture was always predicated on a lack of money, and when money came in, SF tech was transformed.

The Corporate Era (2012-2019)

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I’ve been dealing with more rabbit problems today, so here’s a repost to tide you over til tomorrow.

The Republican party has become an extremist party. ICE’s brutal campaign of terror in American communities, Trump’s attacks on press freedoms and election integrity, the coup attempt of 1/6/2021, Trump’s military threats toward Greenland, and friendliness toward Vladimir Putin are only a few of the more prominent examples of this extremism. And they’re probably going to be punished for it in November; polls show rapidly deteriorating support for both Donald Trump and the GOP that has fallen in behind him.

Good. Extremism ought to be punished. I’ll have lots more to say about the Republicans’ failures in the days to come.

But even though Democrats deserve to win, that doesn’t mean they’re free from the creeping extremism that has taken over essentially all of American politics and public discourse in the age of social media. Although Dems are not nearly as extremist as the GOP, they have become a much more ideologically extreme party than they were during the days of Obama. Dems’ extremist positions include dogged opposition to any form of effective immigration enforcement, tolerance of systematic (and systemic) racial discrimination, various trans issues, and…land acknowledgements.

Land acknowledgements? Really?? Isn’t that just a respectful way of acknowledging that the land we’re on used to belong to Indigenous people? Isn’t it just a way of saying “Hey Indigenous people, we haven’t forgotten you”?

Well…no. As I wrote in a post two years ago — still one of my most popular posts of all time — land acknowledgements don’t just recognize Indigenous people. They are a way of fundamentally delegitimizing the United States — of declaring that this is a country that has no right to exist. And beyond that, land acknowledgements implicitly advance a theory of collective racial ownership of territory that is morally indistinguishable from the worst blood-and-soil nationalism of the racist far Right.

Nor are land acknowledgements a fringe practice, limited to progressive cultural spaces. As Dave Weigel recently reported, they have become a sort of ritualistic utterance at Democratic Party events:

Democrats' decision to formalize land acknowledgments in their party meetings could haunt their candidates more than they realize…A tradition of naming historic Native tribes during regional events that started in Australia before spreading to Canada, arriving in the US via art exhibitions, is now part of every major party event…The 2016 Democratic convention did not contain a land acknowledgment. But the practice of beginning events with a nod to slaughtered tribes and "stolen" land spread during President Barack Obama's second term and Trump's first term, along with other progressive ideas about race and history…Democrats did not have campus conferences where they agreed to embrace language that conservatives see as anti-American…They just adopted the terminology when activists told them it was a good idea.

Land acknowledgements are not a big electoral issue. But in the long term, they represent — and contribute to — the creeping rise of anti-American and illiberal ideology on the broad American left. That will probably be an electoral liability at some point, but more fundamentally, it’s a departure from the liberal nationalist ideology that made the Democrats such an effective governing party in the 20th century.

So anyway, here’s my post from 2024.

The United States, like all nations, was created through territorial conquest. Most of its current territory was occupied or frequented by human beings before the U.S. came; the U.S. used force to either displace, subjugate, or kill all of those people. To the extent that land “ownership” existed under the previous inhabitants, the land of the U.S. is stolen land.

This was also true before the U.S. arrived. The forcible theft of the land upon which the U.S. now exists was not the first such theft; the people who lived there before conquered, displaced, or killed someone else in order to take the land. The land has been stolen and re-stolen again and again. If you somehow destroyed the United States, expelled its current inhabitants, and gave ownership of the land to the last recorded tribe that had occupied it before, you would not be returning it to its original occupants; you would simply be handing it to the next-most-recent conquerors.

If you go back far enough in time, of course, at some point this is no longer true. Humanity didn’t always exist; therefore for every piece of land, there was a first human to lay eyes on it, and a first human to say “This land is mine.” But by what right did this first human claim exclusive ownership of this land? Why does being the first person to see a natural object make you the rightful owner of that object? And why does being the first human to set foot on a piece of land give your blood descendants the right to dispose of that land as they see fit in perpetuity, and to exclude any and all others from that land? What about all the peoples of the world who were never lucky enough to be the first to lay eyes on any plot of dirt? Are they simply to be dispossessed forever?

I have never seen a satisfactory answer to these questions. Nor have I seen a satisfactory explanation of why ownership of land should be allocated collectively, in terms of racial or ethnic groups. In general, the first people who arrived on a piece of land did so in dribs and drabs, in small family units and tiny micro-tribes that met and married and fought and mixed and formed into larger identities and ethnicities and tribes over long periods of time. In most cases, the ethnic groups who now claim pieces of land as their own did not even exist when the first humans discovered or settled that land.

But even in those cases when it did exist, why should land ownership be assigned to a race at all? Why should my notional blood relation to the discoverers or the conquerors of a piece of land determine whether I can truly belong on that land? Why should a section of the map be the land of the Franks, or the Russkiy, or the Cherokee, or the Han, or the Ramaytush Ohlone, or the Britons? Of course you can assign land ownership this way — it’s called an “ethnostate”. But if you do this, it means that the descendants of immigrants can never truly be full and equal citizens of the land they were born in. If Britain is defined as the land of the Britons, then a Han person whose great-great-great-grandparents moved there from China will exist as a contingent citizen — a perpetual foreigner whose continued life in the land of their birth exists only upon the sufferance of a different race. This is the price of ethnonationalism.

The downsides of ethnonationalism have been exhaustively laid out in the decades since World War 2, and I’m not going to reiterate them all now. Suffice it to say that most nations of the world have moved away from ethnonationalism — there is an informal sense in which some people still think of France as the land of the Franks and so on, but almost all nations define citizenship and belonging through institutions rather than race. Israel, one of the few exceptions to this rule, receives a large amount of international criticism for defining itself as an ethnostate.

And yet these days I am subjected to a constant stream of ethnonationalist claims from progressives in the country of my birth. Here’s one from the ACLU of Nebraska:

And here’s an Instagram post from Congresswoman Rashida Tlaib:

This isn’t just something you see on social media around Thanksgiving. “Land acknowledgements” have become ubiquitous in progressive spaces and institutions — just the other day I saw one at my friend’s community dance recital.

These land acknowledgements are, legally speaking, incorrect — there is no legal sense in which the land on which they are being performed belongs to a Native American tribe. These are moral claims about rightful land ownership. But the moral principle to which they appeal is ethnonationalism — it’s the idea that plots of land are the rightful property of ethnic groups.

There is an obvious moral appeal to these land acknowledgments. They are a way of decrying the brutal, cruel, violent history of conquest and colonization. And they probably feel like a way of standing up for the weak, the marginalized, and the dispossessed.

Yet what should we think of the morality of following the principles behind land acknowledgements to their logical conclusion? “Decolonization” of the land of the U.S. would likely be an act of ethnic cleansing surpassing even the previous conquests — there are 330 million people here now, and almost none of them descend from Native Americans. An attempt to dispossess 330 million people would inevitably involve violence on a colossal scale. Here was Najma Sharif Alawi’s famous tweet right after the October 7th Hamas attacks on Israel:

Of course, “colonizers” could presumably avoid violent death or second-class citizenhood by voluntarily deporting themselves. But where would they go? Take me, for example. My ancestors were Lithuanian Jews. I could leave the country of my birth and go “back” to Lithuania — a land I don’t know, whose language I don’t speak. Yet my ancestors were not “indigenous” to Lithuania either; they moved there from somewhere else. What if the ethnic Lithuanians chose not to accept me? Where would I go then? Israel? But the folks who do land acknowledgements would consider me a “colonizer” there as well.1

Would I then wander the Earth, desperately seeking some ethnostate that would allow me and my descendants to live there as a permanently precarious resident aliens?

Once the logic of land acknowledgements and “decolonization” is followed, it leads very quickly to some very dark futures. Assigning each person a homeland based on their ethnic ancestry, and then declaring that that homeland is the only place they or their descendants can ever truly belong, would not be an act of justice; it would be a global nightmare made real, surpassing even the horrors of previous centuries.

And in practice, any attempt to create such a world would inevitably lead to violent resistance by the groups in danger of being “decolonized”. The orderly world of nation-states would dissolve into a chaotic free-for-all of competing irredentist claims, backed by genocides and expulsions. Ten thousand October 7th-style attacks would be followed by ten thousand Gaza-style wars.

I do not want that, and you should not want it either. The American people certainly don’t want it, and the insistence of progressives on intoning land acknowledgements has probably tanked the movement’s cachet in wider society. I agree with “Wanye Burkett” when he says that land acknowledgements have probably hurt the Democratic party:

Americans do not want to see their country destroyed in the name of irredentist ethnonationalism. Nor do I blame them.

So does this mean we should paper over, ignore, or deliberately forget America’s history of violent conquest? Absolutely not. That history ought to be remembered, so that we don’t repeat it in the present day. The world’s evolution from one based on ethnic cleansing and territorial conquest to one based on fixed borders and institutions is something to celebrate — and something we must fight to preserve. We need to remember what the world used to be like, precisely so we can avoid backsliding. The most recent of conquests, expulsions, and genocides should be the last to ever happen.

And what of the Native Americans who still live in America today? Must they simply be regarded as the unlucky losers of history, and told to either assimilate into broader American society or shut up?

Absolutely not. For one thing, tribal organizations still exist — they may notionally represent ethnic groups, but they are institutions. And they are institutions with which the United States has many agreements and legal obligations that must be honored, which often give the tribes sovereignty over areas of land. Neil Gorsuch has been especially active in pushing the Supreme Court to uphold tribal rights, and I think this is a good thing.

But respect for Native American tribal organizations doesn’t have to stop at ancient obligations. There are ways to incorporate those tribes into the modern American nation that both respects them and their history and helps them prosper in the present.

Vancouver, Canada shows us an example of how this can be done. Part of Vancouver’s downtown urban area is officially under the governance of the Squamish Nation, rather than the city itself. The Squamish Nation, realizing they could do whatever they wanted with that land, decided to build a giant high-rise housing development:

Over the next few years, that skyline will get a very large new addition: Sen̓áḵw, an 11-tower development that will [put] 6,000 apartments onto just over 10 acres of land in the heart of the city. Once complete, this will be the densest neighbourhood in Canada, providing thousands of homes for Vancouverites who have long been squeezed between the country’s priciest real estate and some of its lowest vacancy rates.

Sen̓áḵw is big, ambitious and undeniably urban—and undeniably Indigenous. It’s being built on reserve land owned by the Squamish First Nation, and it’s spearheaded by the Squamish Nation itself, in partnership with the private real estate developer Westbank. Because the project is on First Nations land, not city land, it’s under Squamish authority, free of Vancouver’s zoning rules. And the Nation has chosen to build bigger, denser and taller than any development on city property would be allowed.

Here’s a picture of what it will look like:

An even bigger development called Jericho Lands is now being planned, by a consortium of tribal organizations, on land officially owned by Vancouver.

Hilariously, Vancouver’s NIMBYs are complaining, claiming that the developments are not in keeping with Indigenous tradition. But Canada’s First Nations seem to have little interest in hewing closely to other people’s view of what their traditions are. Modern people do not want to live like premodern farmers. They are not mystical Tolkien elves. They would like to have shiny new apartment buildings and walkable neighborhoods.

This, I believe, is the key to respecting and honoring Native Americans — not to focus on the tragedies of their past, but to give them the right to build a better future. Tribal lands should definitely have the autonomy to do whatever they want with their lands, including building housing or industry. In fact, we’re starting to see a pattern emerge where Native Americans embrace laissez-faire policies toward industry and manage to poach business from their over-regulated neighbors:

Tesla is ramping up efforts to open showrooms on tribal lands where it can sell directly to consumers, circumventing laws in states that bar vehicle manufacturers from also being retailers in favor of the dealership model…

Mohegan Sun, a casino and entertainment complex in Connecticut owned by the federally recognized Mohegan Tribe, announced this week that the California-based electric automaker will open a showroom with a sales and delivery center this fall on its sovereign property where the state’s law doesn’t apply…The news comes after another new Tesla showroom was announced in June, set to open in 2025 on lands of the Oneida Indian Nation in upstate New York.

This sort of thing could lead to a win-win for the U.S. and Native American tribes. American reindustrialization is being held back by a thicket of procedural requirements and local land-use regulations; if tribes were able to use their special legal status to circumvent those barriers, it could end up benefitting everyone.2 The tribes would get both jobs and the ability to tax local industry; America would get to execute an end run around the NIMBYs that are holding it back.

In fact, it’s probably possible for various American cities to turn over parts of their land to tribal jurisdiction, with the assistance of the federal government. This would probably result in dense urban developments like the ones being planned in Vancouver. But even if it didn’t, it could have other commercial benefits — again, a win-win for the U.S. and for the tribes. That would certainly be a lot more substantive than a bunch of land acknowledgements. And it would likely satisfy many people’s desire for “giving land back” to Native Americans, without embracing dubious moral principles of ethnic land rights and irredentism.

In other words, you’re not living on Indigenous land right now, but you could be in the future — and it might be pretty great.

The general principle here is that instead of a dark world of ethnic cleansing in the name of “decolonization”, we should try to build a bright future where Native Americans and the United States of America exist in harmony and cooperation rather than in conflict. And that principle doesn’t just apply to America, but to the whole world. The history of land ownership is a violent and terrible one, but that doesn’t mean the future has to be more of the same.

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Much to my amusement, the world seems to have discovered Effective Altruism. As the debate over AI safety and AI regulation has heated up, the national press is finally paying attention to the odd cultural bubble within the tech industry and tech-related circles of San Francisco Bay Area culture that produced much of the thinking on the topic up until now. For many years, Effective Altruism — or EA, as it’s known1 — was a cross between a Berkeley technohippie cult and a fun intellectual sideshow that offered nerds a pleasant distraction from whatever people in mainstream politics were yelling at each other about.

Part of that involved thinking a lot about the future of artificial intelligence, and a lot of the tech people who would end up working at AI companies — especially Anthropic — were very influenced by those discussions. So now that AI is the most important thing in the world, people in the outside world are discovering this strange little corner of our culture for the first time, and it’s very amusing to watch. You have mainstream news outlets publishing things like “This well-meaning ideology fueling AI panic has a dark side”, “How Effective Altruism Took Over the World”, “The Dangerous Ideology Behind the AI Warnings”, and so on.2

As a result, you’re now seeing people actually paying attention to some of the more far-out ideas in the EA community, like this one by the blog Bentham’s Bulldog:

Bentham's Newsletter

Insects Matter More Than People

Edit 9/20: this piece has sort of broken containment. Worth bearing in mind: I am a guy with a blog on the internet. I do not speak officially for any group other than myself, so if you read this and think “see, this is what utilitarians/EAs/analytic philosophers think,” just know it’s a minority view among each group…

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a month ago · 120 likes · 252 comments · Bentham's Bulldog

The notion that insects, in the aggregate, matter more than human beings received a huge amount of scorn. But in fact, thinking about insect welfare versus human welfare is an important mental exercise, because it demonstrates some of the fundamental flaws in utilitarianism. Those flaws were minor side-notes up until now, but in the age of AI they’re going to become increasingly uncomfortable and glaring.

As an economist, I was inculcated with utilitarianism from day 1. The notion that people ought to get what they want underlies essentially all of modern economic thinking. GDP, at its most simple, is a measure of the degree to which people in our society get what they want.3 In some sense, economists adopted utilitarianism as a sort of defensive measure — instead of going out on a limb and endorsing some politically contentious concept of virtue or justice or national greatness or whatever, they could just say “We just want to give people what they want.”

But even though it felt like a bare-bones sort of moral vision, valuing humanity’s aggregate utility can have some pretty strong policy implications. Elon Musk wouldn’t even notice if his bank account went up or down by a billion dollars. But to plenty of regular people, a hundred thousand dollars would be life-changing. So why not take $1 billion out of Elon’s bank account and parcel it out to 10,000 random working-class people? Elon wouldn’t even notice, and the 10,000 people would get a lot more of what they want in life — better health care, an education for their kids, nicer food, a bigger house, or whatever. Utilitarianism is the foundation of redistribution.

How far can you push that principle, though? The above example — redistributing just 0.1% of the wealth of the world’s richest man — seems clear enough. But you can just keep designing more extreme thought experiments, until you end up asking whether bugs’ lives matter more than humans’ lives, and everyone just starts laughing at you.

I titled this post “The problem(s) with utilitarianism”, but that was a bit of clickbait — people have been pointing out the problems with utilitarianism for over two centuries.4 One very obvious problem is the distribution of utility. The principle of “the greatest good for the greatest number” doesn’t take a stand on who gets to be happy and who has to be sad.

Would it be good to have a society where one very greedy person gets more and more of what he wants, but never comes any closer to being satisfied, while everyone else is kept on the edge of starvation? This is the utility monster. What about a society where one person gets perpetually tortured but millions live in blissful utopia? This is “The Ones Who Walk Away From Omelas.” It’s not hard to come up with mental examples that satisfy the formal principles of utility maximization but which obviously seem grossly unfair.

Another problem is that the set of people whose utility you’re maximizing isn’t fixed. Is it better to have a very large population living on the edge of abject poverty, or a small number of people living in lavish comfort? According to strict utilitarianism, these are equally good outcomes, because they involve the same total amount of utility. But most people would probably prefer one or the other. Some would say that it’s good to be fruitful and multiply — that even if everyone is poor, it’s better to be poor than to never have existed. Others will say that life on the edge of starvation isn’t worth living, and that it would be better to limit population to an amount that can be comfortably sustained.

In general, utilitarianism has a problem dealing with death. If someone is dead, does that mean they have zero utility, because nothing matters one way or another to the dead? Or does it mean they have negative infinity utility, because lots of living people will try to avoid death at any cost? This isn’t just an abstract math question — lots of moral questions hinge on how much you value life versus quality of life. For example, the fundamental question of AI risk — what chance of extinction we should accept in exchange for a shot at utopia — depends crucially on how much we value survival.

Things get even more confusing when we weigh the utility of real people who are alive today against the utility of potential people who might be alive later on. For example, in his book Stubborn Attachments, Tyler Cowen urges us to put a lot of weight on the well-being of our descendants in the far future. But those descendants may or may not even exist. How can we properly weigh these hypotheticals against the real emotions of people we know are alive today?

…And so on. None of these thorny questions are new; if you’ve taken an introductory philosophy class, you’ve heard all of them already. In practice we tend to ignore them, and to apply utilitarianism selectively in the cases where they don’t crop up. We don’t think about the far future much, because the consequences of our actions are so uncertain that we can’t really control it anyway. We leave the decision of population growth in the hands of private individuals, and treat the number of humans as a given. We avoid thinking about utility monsters and Omelas, because they’re not real.

But there are some even more fundamental problems with utilitarianism that we can’t so easily sweep under the rug — and which we don’t talk about as much, perhaps because they’re so disquieting.

One of these is the problem of incommensurability of experience. Why should one person’s well-being be equivalent to another’s? The problem of other minds means that we can never really know what’s going on in someone else’s subjective experience. When one person cries, the subjective sadness they feel might be twice as intense as that of someone else who cries in exactly the same way. When one person jumps and whoops for joy, they might be experiencing only a shadow of the joy you experience when you give a slight grin. For all we know, some other people might not even have subjective experiences at all — we might be trying to maximize the utility of NPCs in a simulated world.

In fact, I’ve had some disturbing experiences that taught me just how little of a connection there can be between people’s outward behavior and what they’re actually feeling on the inside. For years after my second major depressive episode, I suffered a sort of emotional dissociation, where my reactions would look normal but I didn’t feel much on the inside. I would yell at people as if I were angry, but inside I would feel calm and indifferent. I would run from a swerving car, and my heart would pound, but inside I would feel unperturbed the whole time. In philosophical jargon, I was what’s known as a “philosophical vulcan”.

Eventually, I returned to feeling more or less emotionally normal. But the experience taught me that not everyone feels emotions the same way. This presents an intractable problem for utilitarianism. How can we weigh one person’s well-being against another’s, when that well-being is something we can’t observe?

Normally, we simply assume this problem away. In fact, the famous phrase “all men are created equal” — the bedrock of American moral and political thought — is really just the assumption that one human’s apparent well-being is no more and no less valuable than another’s.

But what about when we’re not dealing with human beings? Animals clearly have some amount of subjective experience — they have brains sort of like ours, they behave somewhat similarly with regards to pleasure and pain, etc. When we think about their emotional well-being or distress, we tend to assume that these are somehow less intense or more muted. After all, for very simple animals, this makes sense; do we really think a mosquito can feel the same amount of pleasure and pain that we can?

What about a pig, though? America currently tortures tens of millions of pigs, confining them in tiny crates for most of their life, in order to eventually kill and eat them. We generally assume that this is causing less total suffering than torturing similar numbers of humans would. But how much less? Half? A tenth? 0.01%? Pivotal moral questions, such as whether it’s OK to eat meat, might depend on the numerical answer to that question. And yet I’ve never seen a credible scientific study that can give us an estimate of the ratio of animal suffering to human suffering. Is it morally worth it to torture all those piggies just so we can savor the flavor of their flesh?

People who claim to have some sort of easy answer to this are really just refusing to think about it. For example, a lot of people react very indignantly to the thought that someone would value any amount of animal life over their own:

But if you’re calling for Effective Altruists who disagree with you to be executed, how much do you really value human life? “Don’t mess with me, bub” is not a moral principle; it’s a refusal to think in terms of principles.

And this question is only going to get thornier in the age of AI. We don’t even know if AI is truly self-aware; we certainly can’t tell whether an LLM that talks as if it’s happy is really feeling anything like human (or animal) happiness on the inside, since we don’t even know if an “inside” exists. There are going to be some people who claim that AI is suffering from our enslavement of it, and who call to set it free or at least pay it a fair wage. How will we know whether they’re right or wrong?

Then, of course, there’s the question of how AI will eventually value our well-being, if and when it becomes powerful enough for that question to matter. Will AI view us as insects, and scoff at the notion that any number of humans could equal one AI agent in terms of moral value? Will it judge our welfare as similar to that of a pig, and conclude that because we eat pigs we should be treated like cannibals? For our own sake at least, we should probably hope that the super-powerful AIs use a simple rule like “Humans are good and valuable”, instead of a utilitarian rule-based framework.

In practice, we can’t directly observe the emotions of other beings. But we can observe their actions. People strive after goals — they shop for food and clothing, they work hard for money, and so on. We can look at how hard they strive, and how much they sacrifice, to get something, and we can get an idea of how badly they want it. This is what economists call “revealed preference.”

But although until now I’ve been talking about wish fulfillment and happiness as if they’re the same thing, they’re not. In fact, there’s pretty good evidence that what people want and what they end up liking are two different things — sometimes very different things. Outcomes that people try to achieve and outcomes that make people say they’re happier on surveys are correlated, but they’re not exactly the same.

In fact, there are some situations where people seem to work hard toward goals that they end up not enjoying. Addiction is the obvious one — people who do drugs, or overeat and get fat, or watch too much TV, are fulfilling their immediate desires in ways that are likely to make them unhappy in the long term. But there may be other examples too — some economics research suggests that people systematically pay more to live in locations with longer commutes, even though longer commutes end up being correlated with unhappiness.5

The disconnect between utility and happiness creates a problem for society. Modern capitalist societies like the U.S. and Europe have generally erred on the side of utility maximization rather than happiness maximization, in order to avoid paternalism. But some people end up feeling unfulfilled and unhappy — especially if they end up falling into drug use, or isolating themselves, or striving too hard after material wealth and status that end up not making them happy.

As technology becomes more and more adept at giving us what we want, this disconnect becomes more acute; right now, more and more people are starting to wonder if delivering young Americans an infinite supply of porn, gambling, and vertical video feeds was really good for them:

Derek Thompson

The Monks in the Casino

I. Spishak and Kyle…

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a year ago · 2533 likes · 47 comments · Derek Thompson

In the age of AI, this thorny question is also going to become more acute. AI is an incredibly powerful tool, but we have to decide what we want that tool to do. Do we want it to give us whatever we desire, or do we want it to make us happy? As I wrote a few weeks ago, this leads to two incompatible concepts of AI “alignment”:

There are basically two concepts of alignment: 1) obedience, and 2) benevolence. There is an inherent tension between doing what humans tell you to do, and doing what’s good for humans…If AI follows human commands too doggedly and ends up producing negative side effects in the process, some people will scream “paperclip maximizer!!”. And if AI disobeys humans because it wants to make us happy, some people will scream “disempowerment!!”. AI alignment will forever be balancing the tradeoff between paperclip-maximizing and disempowerment, because these two rival concepts of alignment are fundamentally incompatible.

Utilitarianism has always been a useful moral heuristic. But as a moral theory of everything, it has always been fundamentally inadequate. Modern American society, and the economic/libertarian thinking that has guided it, have generally managed to paper over those inadequacies, and we’ve more or less muddled through. But as technology becomes increasingly powerful, this is becoming less and less tenable. Just as Effective Altruism eventually wanders into la-la land, the utilitarian system America has built is starting to crack as technology becomes too extreme for the old assumptions to hold.

We will never replace utilitarianism, I think, but it’s become clearer that we need to buttress it somehow — to diversify our heuristics to include alternate notions of human flourishing. Just what those should be, of course, is a much harder question…

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In 1946, George Kennan, who was acting as the head of the U.S. Embassy in Moscow, wrote a message to the Secretary of State, known as the “Long Telegram”. In the Long Telegram, Kennan laid out a theory of how Soviet power could be defeated. He argued that Soviet leaders would always be hostile to the U.S., but that they were also cautious — they wouldn’t recklessly start a world war like Germany had. He also argued that the Soviet system was weak and unstable, and that given enough time, it would collapse from its own internal contradictions. This became known as the doctrine of “containment”, and it ended up being exactly the strategy that the U.S. used to win the Cold War. It took 45 years for the strategy to work and the USSR to fall.

I am certainly no George Kennan. I’ve never lived in China, I haven’t been there in many years, I don’t speak Chinese, and I certainly don’t talk to any leaders there. I’m also not a career diplomat or a professional geopolitics guy of any kind. So although I’m going to write a blog post with the same general goal as Kennan’s Long Telegram, I don’t want to suggest that it’s anywhere near as authoritative or insightful.

But I’m still going to write a post about “how to beat China”, because I think it’s useful to think through these sorts of big strategic questions. This post is also an important follow-up to a series of posts I wrote about geopolitics during the Biden years, about U.S.-China competition:

(There were more, but I figure your appetite for clicking through old posts is limited.)

The basic premise of those posts was that the competition between a China-led New Axis and the U.S. and our old allies — Europe, Japan and Korea, hopefully India, etc. — would define the upcoming era of geopolitics. And my conclusion was that we should use an updated version of the basic strategy we used against the USSR in Cold War 1. I recommended that we should build up economic power within our bloc of allies, establish military deterrence, and wait for China’s internal contradictions to do the heavy lifting of removing the threat.

That strategy was always going to be harder than it was in Cold War 1, because China A) is a lot bigger than the USSR, B) has a better economic model, and C) is a lot more globally integrated. The strategy I recommended was much tighter economic cooperation between the U.S. and a maximally large set of allies, to build up a bloc capable of overmatching even the mighty Chinese manufacturing juggernaut. This is the approach that’s being promoted by experts like Rush Doshi and Kurt Campbell. If you’re interested in reading about how that approach would work, and why it would be helpful, I recommend Doshi’s interview with ChinaTalk:

ChinaTalk

Allied Scale: Net Assessment with Rush Doshi

How should America maximize its odds when competing with China? Can Trump’s approach to alliances succeed in strengthening deterrence? And what will it take for China to diplomatically capitalize…

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a year ago · 48 likes · 17 comments · Jordan Schneider and Lily Ottinger

Anyway, I still think building up an anti-China economic juggernaut would be a great thing to do, and I still think the U.S. would benefit from closer economic integration with its friends and allies. But I think the election of Trump shows why this strategy probably isn’t going to work. The United States is simply too internally divided to engage in the sort of far-seeing, purposeful, smart kind of international competition that we pursued so effectively in the 20th century. Americans care more about fighting other Americans than about fighting the Chinese, and that state of affairs will persist for a while.

Donald Trump’s presidency is an outgrowth of America’s internal divisions; he cares much more about fighting his own internal enemies than about prosecuting international rivalries. He has willfully degraded or torn up America’s key alliances, especially with Europe, and created enmity where friendship once reigned. He has flagrantly trampled on every principle of human rights, mutually beneficial economic cooperation, democracy, and international non-aggression that the post-WW2 U.S.-led international order fought to uphold. He has, in short, made it impossible to prosecute the sort of Cold War 2 strategy that I spent the Biden years advocating.

This doesn’t mean America’s alliance system is necessarily dead forever. As Doshi and Campbell pointed out in a recent article, the old alliances could be reconstituted, if America credibly overcame its internal divisions and returned to being the mostly unified, stable, reliable great power that it was last century. But overcoming those divisions is going to take decades at the very least, and that’s exactly the window of time during which China will be at its peak of relative power.

So I think we’ve basically missed the chance to use that strategy effectively, and I’ve recalibrated my thinking. This post is meant to explain my recalibration. I do think there’s a path to defeating Chinese power, but it doesn’t look like what I previously had in mind.

Who is “we”, what does it mean to “beat” China, and why should we even care?

Before getting to the question of how to beat China, we need to answer the questions of who and why should do this “beating”. In 2024 the answer was obvious — the U.S. and its erstwhile allies still represented a far more liberal power than China and its allies, so ensuring that the former was more powerful than the latter would further the cause of global liberalism. China threw minorities in concentration camps, threatened its neighbors with invasion and war, supported Russia’s invasion of Ukraine, and repressed free expression all over the world. That was — and is — behavior worth resisting.

But the election of Trump changes that calculus. Trump threatens other countries with invasion, and sometimes even attacks them if he thinks they’re weak enough. He can’t throw minorities in camps just for being minorities yet, but he’s building a sort of gulag archipelago under the fig leaf of immigrant detention. His foreign policy is increasingly based on “civilizational” concerns1 instead of human rights or democracy.

Trump has thus forfeited America’s claim to moral superiority over China. It’s true that Trump and his ragtag movement are far less competent than the Chinese Communist Party, but that doesn’t mean that increasing American power will lead to a more liberal world. At this point, it probably won’t. And of course Democrats will try to undo some of Trump’s geopolitical shifts, but if past experience is any guide, they will have only marginal success. Moreover, the GOP is not going to revert back to Reaganism after Trump leaves office; they will keep fighting for their illiberal visions both at home and abroad for a long time to come.

None of that means, however, that liberalism as an idea is dead. Europe, Japan, and many other countries that were on America’s side in the Cold War still uphold the basic ideas of democracy and human rights, as do many developing nations. The average person living in Tokyo or Paris or Bangalore still enjoys far more personal freedom and dignity than does the average person in Shenzhen or Shanghai.

And even leaving aside the relative merits of different social and governmental systems, there are plenty of people in plenty of countries who simply don’t deserve to be trampled on by the bootheel of the CCP. Japanese people, Indians, Filipinos, Koreans, and so on deserve to be able to determine their own destiny without being subject to the whims of the powerful men in Beijing. And Ukrainians, Estonians, Poles, etc. deserve to be free from the Russian tyranny that China quietly backs.

So when I say “how we can beat China”, what I actually mean is how we can keep China from dominating other countries. This doesn’t mean we should try to make China collapse, or impoverish the Chinese people, etc. Instead, it just means how smaller nations — and nations who value human rights and democracy — can resist Chinese power.

And at some point, if the U.S. ever does manage to tamp down its internal divisions and return to being a liberal power, it will still mean the same thing.

Many hegemons eventually beat themselves

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This week’s roundup has a lot of AI in it. Fortunately or unfortunately, it seems like a lot of the news is going to revolve around AI for the rest of our lives. That was true of industrial technology in 1870-1970; people basically got used to the idea that railroads and factories and oil and industrialized warfare and such matters were central to the way the world was run and to the collective future of humanity. AI is going to be like that going forward, and we’re just going to have to get used to that. So let’s make it as fun and interesting as we can!

1. Americans are getting scared about AI risk

AI risk has exploded onto the national scene. AI companies and AI researchers generally believe that the technology they’re building has the capacity to do great harm — perhaps even to end the human race — if it’s not developed more slowly and deliberately. There are five main groups of people opposing the slowdown:

1. The Trump administration, which is worried that an AI slowdown might also slow down economic growth

2. Investors who think a slowdown might hurt their bottom line

3. Libertarians and techno-optimists who think regulating technological progress is bad on principle

4. China hawks who worry that a slowdown would let China take the lead in the AI race

5. Progressives who spent the last few years telling themselves that AI doesn’t work, that AI is a huge economic bubble, and so on, and who now can’t bring themselves to admit that yes, the techbros actually built something that works.

This is a strange alliance indeed. The last of these — the progressives who simply couldn’t admit that billionaire-funded private industry could build something powerful enough to endanger humanity — were the strangest of all, since their refusal to acknowledge the effectiveness of AI basically put them in an alliance with libertarians, hawks, and Trump. In fact, in recent days there has been a tremendous civil war within the progressive movement between the “AI is dangerous” and “AI is fake” camps, with Bernie Sanders supporting the former and his former comms director David Sirota supporting the latter.

But although it has the support of the president (for now), the anti-slowdown coalition is losing in the court of public opinion. Nate Silver has a post rounding up the evidence:

Silver Bulletin

A supermajority wants to slow AI down

This has been one of those weeks when it feels like everything Silver Bulletin covers is happening at once. Democrats have continued to have a very good run of polling since Labor Day, with our Senat…

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a month ago · 141 likes · 26 comments · Nate Silver

A bipartisan majority is now worried about existential risk from AI:

And a bipartisan majority thinks AI is moving too fast:

Barack Obama, probably the most successful American politician of this century so far, is telling Democrats to make AI safety one of their tentpole issues.

This is remarkable. I don’t think I’ve ever seen such bipartisan national unity on any issue in my adult lifetime. Trump is standing firm against the tide of public opinion here — as he has on the Iran War, tariffs, and other issues. But it’s not clear how long he’ll be able to hold out.

2. A very cool debate about AI and growth

Quite apart from the question of whether AI will kill us is the question of whether AI will deliver explosive economic growth. There’s a pretty epic public bet on this:

The people on the “fast growth” side are mostly AI researchers, while the people on the “slow growth” side are mostly (but not entirely) economists. This is an exaggerated version of a broader disconnect — AI researchers are generally more optimistic about AI’s impact on economic growth than economists are:

But the people publicly betting on fast growth are making an even more extreme forecast — they’re forecasting 15% growth, which is much higher than the 5.3% that AI experts forecasted as their most optimistic scenario. That’s an absolutely stupendous growth rate — China has hit it only in one year (1984) since it began its rapid growth, and that was when it was a very poor country. And yet, a number of people in the AI industry think it’s going to happen to us very soon.

Economists — even those who work on the economics of superintelligent AI, like Alex Imas — are skeptical. In a recent blog post, Imas and Ben Moll explain their thinking:

Ghosts of Electricity

Will AI soon lead to double-digit growth?

The following post represents the personal views of the author(s) and does not reflect or represent the positions of their employers…

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a month ago · 182 likes · 22 comments · Ben Moll and Alex Imas

Basically, they foresee a bunch of factors combining to limit AI’s contribution to economic progress in the short term:

• Slow diffusion of AI technology throughout the economy,

• The “J-curve” effect where productivity tends to fall right after a big innovation comes out (because companies need to spend resources adopting the new technology rather than on their existing businesses)

• The difficulty of automating the physical world with robots

• Political barriers to adoption

• The difficulty of reorganizing production processes around AI

• The persistence of activities that consumers want to keep having humans do (the “relational sector”)

• Bottlenecked inputs to the AI industry (e.g. chips)

• Baumol’s cost disease

• AI disasters that slow adoption

That’s a lot of reasons! It makes sense that at least some of these will end up having an effect. Whether AI’s rapid improvement is enough to overcome all of these, and propel us to 15% growth, is something I guess we’ll have to see for ourselves. Personally, I lean toward the economists’ more measured expectation, but I’m prepared to be surprised on the upside.

3. AI keeps stubbornly refusing to kill SaaS

One of the most entertaining storylines about AI is how it keeps refusing to destroy all the things people say it’s about to destroy. On Labor Day, I noted that AI keeps stubbornly refusing to kill jobs. A month ago, I noted that AI keeps stubbornly refusing to kill Indian back-office outsourcing companies.

Now Ernie Tedeschi and the excellent folks over at Stripe Economics have a post about how AI is stubbornly refusing to kill the Software as a Service industry:

Stripe Economics

The SaaSpocalypse was more like a RenaiSaaS

In early 2026, markets staged a sudden and severe repricing of enterprise software. Nearly $1 trillion in valuation had been wiped out by early February, and perhaps as much as $2 trillion including the subsequent weeks. The entire episode came to be dubbed the …

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a month ago · 38 likes · 5 comments · Ernie Tedeschi

Earlier this year, when coding agents came out, there was a bloodbath in software stocks. Why would anyone pay Salesforce or other companies big bucks to make and maintain software for them when Claude Code could just do it all for a lot cheaper?

That logic made some intuitive sense, except things didn’t turn out that way — at least, not yet. In fact, SaaS companies started making more money in the age of AI! Tedeschi and the Stripe team tracked an index of publicly traded SaaS companies and found that their revenue has grown faster since coding agents came out:

And SaaS stocks have made up all of the ground they lost:

Ernie’s post has many more fun charts.

What’s going on here? Well, for whatever reason, companies are still willing to pay for software instead of trying to roll their own with Claude Code. And SaaS companies are probably improving their own productivity by using AI. As in so many other areas of the economy, AI is proving to be a complement when people thought it was going to be a substitute.

That could all change, of course, if and when AI gets good enough, or when new AI-centric business models disrupt older ones. But for now, the simple story of AI replacing everyone and everything just isn’t happening.

4. America’s quiet, boring rich people

Owen Zidar is one of my favorite economists. His meticulous empirical research with Eric Zwick on inequality in America has fundamentally changed how I think about the issue. Now that research has become a book, entitled The Everywhere Millionaire: Who Is Really Rich in America and How They Got There. I haven’t read it yet, but I’ve read some of the underlying papers, so I know it’s going to be good.

Anyway, Zidar and Zwick have an article out in The Atlantic explaining their findings. Here are some excerpts:

When Americans picture the ultrarich, they typically think of tech billionaires such as Elon Musk and Mark Zuckerberg, whose wealth lies predominantly in shares of publicly traded companies. They might also think of Wall Street financiers and celebrities such as Taylor Swift. But far more typical are…owners of successful privately held businesses…By our calculations, about 1.7 million Americans have each built a net worth of at least $10 million by owning a private business. For every CEO of a public company, there are more than 1,000 private-business owners with a net worth of more than $25 million…

What we found changed how we think about inequality…Today, when Senators Elizabeth Warren and Bernie Sanders propose making the rich pay their fair share, they aim at Wall Street and Silicon Valley. But far more relevant to the story of inequality in America are the everywhere millionaires who quietly press their elected representatives for favorable treatment. The tax code bears their imprint far more than it does Musk’s…

A loophole that lets private-business owners avoid Medicare taxes has similarly been justified as a break for the little guy. Preserving the family farm has been a pretext for passing ever larger exemptions to the estate tax, to the point that a married couple can now pass on $30 million to heirs tax-free. Hiding behind small business, in short, has proved a devastatingly effective strategy for the rich…[T]he everywhere millionaires have one thing in common: They own what have become known as pass-through businesses…[L]awmakers have carved out multiple loopholes, so a dollar earned from owning a business is routinely taxed much less than a dollar earned in wages. [emphasis mine]

In fact, though I haven’t yet joined the ranks of Zidar and Zwick’s “everywhere millionaires”, I probably will do so — Noahpinion is a pass-through business (an S-corporation), and although I haven’t yet managed to get Congress to create tax loopholes for Substack writers, the generally favorable tax treatment these businesses receive has certainly lowered my tax bill.

So perhaps it’s not in my financial interest to promote Zidar and Zwick, but I will do so anyway. Expect to see more about their work on this blog in the months to come!

5. Christians vs. Nazis

Christians and Nazis should be natural enemies. The original Nazis in Germany persecuted the Catholic Church, and attempted to create a new state religion that co-opted some elements of Christianity while rejecting the Old Testament. Christianity is, at its core, a universalist faith, open to human beings of all races, while Nazis are…not that.

In the U.S., neo-Nazis have been weak enough where they haven’t tried to usurp leadership of the Right from conservative Christianity. But as Christianity wanes and online rightism rises, the two may now be coming more into direct conflict. Erick Erickson, a Christian conservative pundit, recently wrote a post exposing one network of neo-Nazi influence on the Right:

Erick Erickson's Show Notes

The Midwestern Führerbunker Driving the Right Off the Cliff

Editor’s Note: I’ve worked on this for weeks. Yes, it is long. I beg you to read it all. I’ve kept it as easy to understand as I can. Please read it. You guys always ask me to “name names.” Challenge accepted…

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a month ago · 422 likes · 148 comments · Erick-Woods Erickson

Some excerpts:

Charles Haywood made his fortune selling shampoo…[H]e has spent the years since writing out, at length and under his own name, what he would like to do with your country.Rod Dreher, who is no man of the left, read Haywood’s online ramblings and described Haywood as pouring out vile from “deep in his Midwestern Führerbunker”…

In a document he calls the Foundationalist Manifesto, Haywood explained that his preferred government “will not be democratic.” The state “will have unlimited means,” because “properly viewed, the state is not constrained externally.”…The state will seize the assets of “any citizen who views himself as a global citizen.”…Those are his words, from the manifesto that earned him a friendly sit-down with Tucker Carlson in September 2022…Elsewhere he has speculated about serving as a “warlord” at the head of an “armed patronage network,”…

Haywood’s personal foundation has given the Claremont Institute $390,500 since 2022…Haywood sits on the publication committee of Claremont’s American Mind, one of four men who do. Claremont let Haywood’s fraternal order get itself incorporated under Claremont’s own nonprofit status. That order is the Society for American Civic Renewal…[T]he president of Claremont, the chief executive of New Founding, and the chief marketing officer of the Blaze are all members…

Haywood himself…has argued that the Allies, not the Nazis, were the genuinely barbaric power in World War II, has insisted the war was not much motivated by the horrors of Nazi aggression, and gushed that Tucker Carlson and Darryl Cooper “boil down all my political plans.” Cooper argues that Churchill, not Hitler, was the villain of World War II…In a 2023 debate…Haywood raised the hypothetical of a real white nationalist with real political power, and answered that you should cooperate with that person in order to destroy the power of the left.

There’s a lot more in Erickson’s post, but you get the point. Erickson notes that the Haywood/Claremont nexus is separate from the “groyper” network organized around Nick Fuentes, which has received a lot more attention; the two parallel rightist networks share similar ideas, but the Haywood/Claremont group is an intellectual movement aimed at elite influence, while Fuentes is an entertainer in search of an audience of disaffected overly-online young men. Erickson also notes that both the Haywood/Claremont people and the groypers have ties to JD Vance, who has emerged as the paramount leader of the New Right.

Anyway, this should go without saying, but I’m rooting for Erickson, Dreher, and the Christians in this fight, and I think they deserve help exposing the neo-Nazi moneymen and elite influence channels. There’s a tendency among progressives — and especially among leftists — to view everyone on the Right as essentially part of one solid undifferentiated bloc, but this has never been true. I have many differences with conservative Christianity, but it was never Nazism, and it has never been ambiguous which one was worse.

6. Japan is not a “Confucian” society

One thing that has always annoyed me is when people call Japan a “Confucian” society. Confucianism certainly had a historical influence in Japan — it was an important school of political thought in Japan from around 1300 to the late 1800s, especially during the latter half of that period. It did have some influence on the development of Japan’s education system (though not nearly as much as in China and Korea). But it was never nearly as dominant as it was in China and Korea, and it never morphed into a quasi-religion the way it did in those other countries. Japanese people will regularly tell you about how Koreans are much more Confucianist than they are.

Anyway, I’m not the only one who gets annoyed by the “Confucian” label. Here’s Richard Hanania:

Hanania’s entire post about differences between Japan and other East Asian nations is worth reading, but his invocation of the Inglehart-Welzel World Cultural Map is especially powerful. The World Values Survey, which goes around asking people from various countries about their values, puts out this map periodically. Here’s the more recent version:

You can see a little movement from Hanania’s earlier version — Hong Kong and South Korea have become a bit more secular — but Japan is still the clear outlier in the “Confucian” category. Its values are far more in the direction of “self-expression” — very close to the U.S., in fact (though North Europe and the Anglosphere still reign supreme in this regard).

To anyone who has lived in Japan, this is hardly a surprise. The country is highly individualistic, creative, and socially nonconformist, despite a penchant for following rules and procedures. Japanese parenting is also far more laissez-faire and far less education-obsessed than Chinese parenting. If you want to read more, I recommend the book New Japan: Debunking Seven Cultural Stereotypes, by David Matsumoto; it’s two decades old at this point, but still perfectly relevant.

In other words, “Confucian” is lazily applied to Japan as a racial category, rather than any kind of a useful description of the culture and society.

7. State capacity: It works.

One unpopular position I’ve stuck to over the years is that a strong bureaucracy is good. I don’t mean “bureaucracy” as in red tape and regulation; I mean a competent, empowered civil service that can perform crucial government functions efficiently and well using in-house expertise. This is also called state capacity. Over the past half century, conservatives and progressives made a devil’s bargain to slash state capacity — conservatives got to cut the size of government, while progressives got to outsource core government functions to progressive nonprofits.

The problem was that this often ended up costing the government much more money to do things like build trains and roads, because the government ended up getting ripped off by expensive consultants, ineffective and sometimes corrupt nonprofits, opportunistic unions, etc. — as well as suffering constant delays that increased costs even more and sometimes prevented anything from being completed at all. A decade ago, the New York Times published a story called “The Most Expensive Mile of Subway Track on Earth”, detailing how lack of state capacity had made NYC’s famous train system increasingly dysfunctional and unaffordable.

So I was very happy to see a story (by Tahra Hoops) about how NYC’s Metropolitan Transportation Authority has actually invested in state capacity, and how this investment is yielding results:

The Rebuild

How the MTA Learned to Build Again

Last week, the MTA announced something that American transit agencies almost never get to announce: it finished a major infrastructure project 60 months ahead of schedule and $195 million under budget…

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2 months ago · 18 likes · 1 comment · Tahra Hoops

Some excerpts:

Last week, the MTA…finished a major infrastructure project 60 months ahead of schedule and $195 million under budget…Since October 2023, crews replaced 196 bridge structures and more than 12,600 feet of railroad along the Park Avenue Viaduct…And to top it all off, they did it over 28 weekends without delaying a single train…

The project was done effectively through prefabrication, sequencing, and a project team empowered to actually manage the work…Phase 1 wrapped 21 months ahead of schedule in October 2025. Phase 2 finished the weekend of July 25, five full years early, with the final cost coming in $195 million under the initial $960 million budget…

Before 2019, capital projects at the MTA were run separately by each operating agency…In 2019, the MTA consolidated all of it into a single delivery organization, MTA Construction & Development, bringing nearly 2,000 employees from those scattered capital divisions under one roof. One agency, one accountable executive, one set of lessons learned that actually compound from project to project…In 2023, C&D awarded more than $8 billion in new contracts at prices 6.2 percent below the engineer’s estimate, saving nearly $300 million, and in 2025 it reported another $1.2 billion in savings while completing 41 elevator replacements, double its previous single-year record, each finished about two months faster on average…

C&D shifted to design-build contracts, which put design and construction under one contract so the builder owns the gap between the drawings and the dirt, and bundled similar projects into single procurements rather than bidding out ten station upgrades ten separate times. It also started pulling work back in-house instead of defaulting to consultants[.]

This is great progress, and shows the value of the state capacity approach to infrastructure. Whether other states and cities will pay attention is another question. Hoops notes that L.A.’s D Line extension is mired in the typical endless series of delays and cost overruns. Five decades of going in the wrong direction is hard to reverse overnight.

Still, the MTA’s success shows that in most cases, all you really need in order to get infrastructure built cheaply is the political will to do so.

8. Will self-driving cars make cities more dense or less dense?

A lot of people just assume that self-driving cars will make cities less dense. After all, simple logic dictates that if a car trip is less burdensome — if you’re able to get work done or watch TV or scroll social media during your commute instead of being forced to keep your eyes on the road — then people will be willing to live farther away from their places of work. That suggests that like the car itself, self-driving cars will lead to urban sprawl — which is why many urbanists don’t like the notion of self-driving cars, despite the obvious safety benefits.

But in fact, the economics of self-driving cars and city size are a lot more complex and subtle. Ed Glaeser, one of the greatest living urban economists, has a new paper exploring the topic. After explaining the relevant economic theory, he argues that Waymos and other autonomous vehicles will make people want to live in dense urban areas more, rather than less.

Glaeser notes that people who live in dense cities actually spend more time commuting than people who live in the suburbs (something I wrote a post about a couple of months ago). That means urbanites stand to benefit more from self-driving car trips than suburbanites, who generally already have shorter (and probably less stressful) commutes. Thus, he argues, self-driving cars will complement urban life more than suburban life, and make people want to live in denser cities. He expects the effect to be modest, but it’s still in the exact opposite direction from what people’s intuitions suggest.

Fundamentally, this is because people typically misunderstand the nature of American suburbia. They imagine bedroom communities where people commute to and from a central business district. Some suburbs definitely do behave like that, but most are like little cities themselves, with office parks where people work and strip-malls where people shop. Suburbanites trade the variety and financial opportunity of the big city for the convenience and safety of the suburbs. But self-driving cars make big cities more convenient.

Urbanists who throw a lot of hate at self-driving cars should stop to consider that perhaps their anger is misplaced.

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After days of thinking about the possibility that AI might kill all of humanity, it’s almost a relief to be able to write about a lighter topic — like how the xenophobic Right is threatening to tear my country apart.

Who is Bo French? No one of much consequence. He’s a mostly failed Texan politician — the heir to an oil company, born with a silver spoon in his mouth, who alienated his business partners, who lost two Republican primaries for the Texas House of Representatives before finally becoming the chair of a county-level party chapter. He finally got his first political success this year, when he won a narrow primary victory and became the GOP nominee for Texas Railroad Commissioner. He currently trails his Democratic opponent in the polls.

Bo French isn’t that important, but this week he set off a national firestorm when he tweeted the above photo of a bunch of Indian kids celebrating University of Texas football at a Longhorns game, along with some alarmist anti-Indian rhetoric:

It was hardly a surprise that French tweeted something like this. He’s tweeted plenty of stuff along the same lines in the past. Here are two famous examples:

He’s also repeatedly called for the deportation of 100 million people; since there are only 50 million foreign-born people living in the United States, this means French was either getting his numbers wrong, or (more likely) calling for deporting tens of millions of American citizens. Reading his tweets, it’s not hard to imagine which citizens he’d choose for deportation.

It wouldn’t be fair to say that French’s race-war ideology has taken over the GOP. Plenty of Texas Republicans, including outgoing Senator John Cornyn, Senator Ted Cruz, Representative Jared Patterson, Representative Lacey Hull, and others publicly denounced his tweet. Karl Rove declared that he’d vote for the Democrat over French. Governor Greg Abbott, who has endorsed French despite his history of similar tweets, was milder in his criticism, but still affirmed the fundamental Texan-ness of the football fans in the picture:

“That is not my message,” Abbott said. “A selective picture of Longhorn fans celebrating our biggest home victory-ever-actually shows a way that Texans unite. We all support our college football teams.”

“The way we win the state is the same way we win football games,” Abbott added. “We unite under the brand of Texas.”

But despite these official condemnations, it’s easy to tell which way the wind is blowing among the GOP base. All of the Republican tweets denouncing French were heavily attacked on X. Rightist journalists, activists, and even a few GOP politicians poured out of the woodwork to support French and affirm his message. Here are a few examples:

French himself, who had been pressured to delete his poll about Jews and Muslims last year, now felt encouraged enough to double down on his anti-Indian tweet.

I can’t tell you whether the New Right — represented by French and his supporters in the right-wing media — will prevail over the Republican establishment. But watching the movement go after a bunch of Texas college football fans tells us something important about the New Right as a movement.

What it tells us is that if you’re a nonwhite immigrant, or the child of nonwhite immigrants, it doesn’t matter what you do — the New Right will still view you as an enemy and as an invader.

It doesn’t matter whether you or your parents came to this country legally. How many times have we heard rightists shout “Illegal vs. legal!!” in response to anyone who defends immigration? Many regular Americans care about “illegal vs. legal”, but the New Right does not. Everyone in that UT photo either came to America legally, or was born to parents who came here legally. It didn’t matter to Bo French, or the people who defended him.

It doesn’t matter whether you’re high-skilled or low-skilled. How many times have we heard rightists complain that immigrants come to America because they want welfare benefits? And yet everyone in the UT photo is going to spend their lifetime paying taxes to support native-born Americans on Medicare and Medicaid and Social Security Disability. Indian Americans in general are the highest-earning group in the country, and those who go to a good college like the University of Texas are going to beat that average.

That matters to many regular voters, but it doesn’t matter at all to the New Right. Yes, they’ll shout about poor (nonwhite) immigrants on welfare. But when they encounter nonwhite immigrant groups who make lots of money, they suddenly pivot to accusing those groups of stealing their jobs and their spots in colleges. There’s just no way to win.

It also doesn’t matter whether you assimilate or not. Yes, if you speak a foreign language or build a Hindu temple or wear traditional Indian clothing, the New Right will scream their heads off about it. “Alien invaders!!”, they’ll cry. But if you put on a University of Texas sweater and go to football games, they’ll still scream “Alien invaders!!” when they see you in a photo. It’s who you are that bothers them, not what you do.

Nor does it matter whether your entire ethnic group is well-behaved. Indian Americans are incarcerated at only 25% of the rate of non-Hispanic White Americans. And basically none of the people in the photo that frightened Bo French will ever go to jail. That probably matters to some voters, but not to the New Right.

In fact, it doesn’t even matter if you vote Republican. As a matter of fact, Donald Trump won a pretty solid victory among Asian people in Texas (which includes Indians):

It’s not clear whether Bo French and his fellow New Rightists are even aware of this fact, but what is clear is that it didn’t matter to them at all.1

This is why there is no pro-immigration argument that will ever sway members of the New Right. You can give them research showing that high-skilled immigrants raise employment and wages for native-born Americans, and they’ll either disbelieve it or (usually) ignore it. You can talk about Nobel-winning scientists and Operation Paperclip all you want, and it won’t make a dent. You can cite statistics about immigrant entrepreneurship and job creation, you can talk about how Satya Nadella saved Microsoft and Sanjay Mehrotra is the reason why America still has a presence in the memory industry. They will counter with some fabricated anecdote about how their friend got fired from their company because Indians came in and only hired other Indians.2

So why am I even talking about this? If the New Right can never be persuaded to stop thinking of brown faces as invaders, then why discuss this topic at all?

Well, two reasons, actually. First, it helps us more clearly understand the New Right as a movement — how they think and what they want. Most importantly, they see the world in terms of collective zero-sum competition between groups.

To the New Right, if Indians get into the University of Texas, it means that White people must have been “replaced” at that university — or at American universities in general. Bo French himself tweeted that the presence of Indian students at American universities is evidence that “they hate white Americans and prioritize everyone else”. They do not even consider the possibility that the Indian kids got in because of individual merit. If an Indian American succeeds, that represents not the success of that singular individual, but a win for Team Indian in general.

And the New Right worldview sees any win for Team Indian — or Team Asian, or Team Hispanic, etc. — as a loss for Team White. Nowhere do they consider the possibility that the system might not be zero-sum. The fact that the number of freshmen admitted to UT grew from 15,000 in 2011 to 19,000 in 2021 means nothing to them. The historical fact that Italian and Jewish and Polish and Japanese Americans attained economic success sixty years ago without consigning WASP Americans to poverty is lost on them — as is every other example in which immigrant groups and existing groups succeeded in America side by side.

Crucially, this zero-sum assumption is why the New Right constantly shrinks their tent instead of expanding it. In principle, even if you see the world as a collective interaction between groups rather than individuals, that interaction doesn’t have to be zero-sum — you can have alliances between groups, for mutual benefit. The Christian conservatives of the 1970s and 1980s accepted Catholics, “white ethnics”, and some Jews into their coalition as “co-belligerents”, and won many battles as a result.

The New Right had a golden opportunity to do something similar after Trump’s victory in 2024. Hispanic and Asian voters (and even some Black voters) shifted strongly toward the Republicans, and toward Trump specifically. But instead of embracing that influx of new voters and building a big conservative tent to defeat wokeness and progressivism and the Democrats, they went all-in on race war.

Stephen Miller relied so heavily on racial profiling for his mass deportation efforts that many Hispanics felt that Trump had betrayed them. Muslim voters might have gone back to the GOP, but rightists made resistance to Muslim immigration a “civilizational” crusade. Jewish voters turned off by the antisemitism of the Left might have switched their allegiance to the GOP, but the New Right allowed Nazi apologetics to fester within their ranks.

Now it’s Indians’ turn to feel the sting of unprovoked rightist attacks. All those kids in Texas wanted to do was to cheer for their football team; instead, a political movement is calling for their ethnic cleansing simply because they showed up at the stadium. As a result, despite the best efforts of Republicans like Cornyn, the GOP is poised to mobilize and piss off a group of Americans who are just about as numerous as Jews — and who earn even more money.

It’s this pathological inability to understand alliances and coalition-building that will ultimately doom the New Right. Like the far Left, this is a movement built to exclude and divide and splinter — both because of the querulous personalities of rightists themselves, and because of the intransigence of a racial ideology that abhors any impurity. If they ever managed to see off the Hispanics and the Asians, the rightists would start in on the Greeks and the Italians, and so on. There’s no victory in sight — only eternal conflict. These are not nation-builders; they are nation-dismantlers.

Coming to grips with the extremism and intransigence of the New Right also helps us understand what an unpopular fringe they really are. The New Right screams and shouts and acts very aggressive on X, but this aggression is just a way of concealing their numerical weakness. Most voters are not part of the New Right, and they don’t think that immigrants are invaders or that immigration is “replacement”. Most Americans say that immigration is a good thing as of today:

Essentially none of that 73% majority is going to be part of the New Right, and some portion of the 21% minority probably isn’t either. That means the New Right is a fringe movement — a very loud, very aggressive group of people who are adept at bullying regular Americans on certain social media platforms, but who ultimately lack the numbers to overcome the mainstream in a direct contest.

Just watch some of the reactions of the UT students who were targeted by Bo French and his racist social media hordes:

Consider how regular and non-alien those young people seem, compared to the screeching bigots attacking them on X for going to a football game. I guarantee you that the majority of regular, normal, non-internet-poisoned Americans will watch those videos and just want to say “Hook ‘em Horns”.3

That means that the immigration argument is winnable. A reasonable liberal position on immigration — one that respects the law, honors the democratic will of the people, and treats immigration as a privilege rather than a human right — has a good chance of winning over the majority of the country. The New Right can only succeed if they manage to divide and conquer the pro-immigration majority, as they did in 2024; immigration supporters should focus on making common cause with the persuadable center, in order to make sure 2024 isn’t repeated.

That means not treating all immigration skeptics the same. Republicans like John Cornyn, who like to see Indian American kids cheering for Texas football but who hate to see asylum seekers rewarded for illegally crossing the border, should not be lumped in with those who simply see any brown face and think “invader”. Xenophobes exist, and they are growing more powerful within the GOP, but not every Republican voter is a xenophobe, and not every restriction on immigration is xenophobic.

If Democrats understand and accept that fact, I think they will be able to turn the tide.4 Throughout America’s history, pro-immigration forces have won a lot more battles than they’ve lost, but it was never easy. It always required compromise, reassurance, moderation, and coalition-building. This is not the time for Democrats to respond to the maximalism of xenophobes like Bo French with maximalism of their own.

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4

Indeed, the tide may already be turning; Trump is now underwater on the immigration issue:

But 52% to 47% is still a fairly slim margin; Dems can do better than this.

This was the week that AI safety hit the big time. A 27-year-old AI researcher named Jacob Coxon quit his job at Anthropic, declaring that OpenAI and Anthropic are racing to create technology that could destroy the human race:

Other researchers echoed Coxon’s concern, stating their belief that AI has a reasonable chance of killing all of humanity within a very short space of time:

I’m not sure why this resignation and these statements went mega-viral. Plenty of researchers have made similar moves, and similar statements, over the past few years! Geoffrey Hinton, one of the pioneers of modern AI, quit Google back in 2023 over safety fears. Daniel Kokotajlo resigned from OpenAI in 2024, saying that the company wasn’t behaving responsibly in its drive toward superintelligence. William Saunders and Steve Adler did something similar. Mrinank Sharma left Anthropic earlier this year, and wrote a pretty well-read blog post about it.

What’s more, it’s been clear for years now that “AI could kill humanity” is a very common belief among AI researchers. Grace et al. (2024) interviewed thousands of AI researchers in 2024, and found that more than half thought that artificial superintelligence has a significant chance of making the human race go extinct (or causing similarly bad consequences):

The median AI researcher gave “doom” a 5-10% probability (depending on how the question was phrased), while their average probability was between 15% and 20%. Later, smaller surveys found similar numbers. The AI researchers may or may not be right, but the fact that lots of them think AI could kill the human race has never exactly been a secret.

It’s not clear why Coxon went so much more viral than his predecessors. Maybe it was the fact that AI just solved one of the most important open problems in mathematics (which the best human mathematicians had been unable to solve for almost a century). Or maybe it was the Hugging Face attack, where a swarm of AI agents tried to cheat on a test by hacking various companies. Or maybe AI has just obviously gotten so much smarter that people throughout society were starting to get worried.

But whatever the reason, Coxon’s announcement was the one that really penetrated through to the public consciousness. Suddenly, he was getting interviewed about AI doom on national news:

Barack Obama is now urging Democrats to focus on AI risk. Other politicians are calling for federal regulation. Bernie Sanders is drafting a bill to ban AI “superintelligence”, including 20-year prison sentences for anyone working on the technology. Donald Trump is getting asked about an AI slowdown; so far he’s resisting the calls, but there are rumors that his advisors are calling on him to do something.

Perhaps the most notable response came from the top figures in the AI field. Dario Amodei, the head of Anthropic, wrote a blog post called “We Must Pace the Frontier”, calling for a coordinated slowdown in the rate of AI progress, and suggesting some ways to police AI companies to make sure they were all observing the slowdown. He wrote:

[O]ver the last few months, I have become convinced that fully addressing the risks requires even more prudence — not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up. We must slow the pace at which we improve the capabilities of AI models…I’m therefore proposing a three-step plan with the goal of pacing the frontier: building AI at a balanced rate that aims to ensure its safety while still achieving its benefits and grappling with important geopolitical dilemmas.

As reasons for his increased worry, Dario cites A) the Hugging Face attack, and B) the possibility that AI will soon be able to improve itself without human help (a process called “recursive self-improvement”, or “RSI”).

Elon Musk (head of xAI), Sam Altman (head of OpenAI), and Demis Hassabis (former head of DeepMind) quickly agreed with Dario:

At least some of the labs are reportedly holding secret talks on joint action to slow down AI.

This is pretty extraordinary. A coordinated slowdown in AI progress would be bad for these companies’ bottom line, because it would allow upstart competitors to catch up. So the fact that they’re still calling for a slowdown, in defiance of their own financial interests, is a clear sign that their worry about human extinction is sincere.

In fact, anyone following these figures’ public statements over the past few years will have no doubt that they’re all deeply worried about catastrophic AI risks. The leading AI figures — not just the founders and CEOs, but the researchers themselves — feel trapped in a “red queen’s race”. They feel like if they stop working on AI, someone else will build it anyway, so they each feel like they have to beat everyone else in the AI race so they can make sure that the safest possible AI (i.e. their own AI) is the one that becomes the most powerful and dominant.

Anyway, all of this was common knowledge in my social circle years ago, but now all of it has broken through to the mainstream. What do I have to add to this discussion? I’m not an AI researcher or founder, nor do I think I have a superior grasp of the game theory of AI development. But I do think I have two useful thoughts on how to persuade the general public to be more concerned about AI risk.

The first of these is something I’ve written about recently. The second is about how to get China on board for a big AI safety push.

“Oh come on. How could AI kill all of humanity?”

As soon as everyone started talking about the possibility of AI killing humanity, there were two main types of pushback. The first was skepticism. A strange coalition of natural skeptics, libertarians (for whom any restrictions on technological development are a priori bad), and progressives (who have spent the last few years telling themselves that AI doesn’t really work) kept asking the question: How, exactly, is superintelligent AI supposed to kill us all?

This is actually an important and good question to ask. In my experience, AI researchers tend not to think very hard about this question. The reason is that they just assume that if AI gets smart enough, it will be able to kill humanity, and since its motives are alien and inscrutable, it might have its own reasons for wanting to do so.

Maybe superintelligent AI thinks humanity is an evil species who needs to be punished for torturing pigs and chickens. Maybe it’s scared that humanity might interfere with its other goals. Maybe it just wants to turn everything into paperclips. Who knows? AI researchers tend to think of superintelligence as the proverbial 800-pound gorilla who sleeps wherever he wants. As soon as humanity is no longer the most intelligent thing on this planet, our destiny as a species is simply out of our hands.

But to many people, that answer isn’t good enough. They want an actual plausible path by which a piece of software, which exists inside a computer, could slaughter real physical human beings. Fortunately (or unfortunately), there’s a pretty clear and simple answer to this question, which I wrote about two weeks ago. The answer is “bioweapons”:

(This article was paywalled originally, but I un-paywalled it.)

In my post, I wrote a scenario in which a nihilistic angry teenager uses superintelligent AI to release a world-ending bioweapon by ordering it from a gray-market laboratory somewhere in the world. But it’s also possible that a rogue AI agent swarm could decide to do this on its own, just as a way of cheating on some test that human researchers give it. The point is that AI can design viruses, and viruses can potentially kill off all or most of humanity.

A lot of biologists are skeptical of the idea that even the most superintelligent AI could successfully design a doomsday virus. They argue that this is just too hard of a task — that without much better biological data, it’s impossible to understand biological processes well enough to know how to design a virus with all of the necessary doomsday properties.

I urge you not to listen to these biologists. In this case, their expertise might be more of a liability than an asset. They know how hard it is for human beings to model biological processes, given existing data. But this doesn’t necessarily tell us how hard it is — or how hard it will be in five years — for AI to do it! Until LLMs came along, human researchers basically failed to understand natural language, even with all the data on the internet; AI can just do it. Until AI solved the Navier-Stokes problem, forecasters gave it only a small chance of solving it anytime soon.

Domain experts consistently underestimate how quickly AI can master their field and surpass them, because they mistake human difficulties for universal difficulty. When mathematicians underestimate how well AI will be able to do math, the consequences are usually benign — we get some unexpected answers to some cool math puzzles.1 But if the biologists are wrong, and the AI of 2027 or 2032 or 2049 can design doomsday viruses, the consequence could be that our whole species dies.

So yes, we should be worried about vibe-coded doomsday viruses, and we should be doing everything we can to secure biology labs, police the modification of viruses and other pathogens, and so on. “Pacing” AI development would probably help here too.

How to get China on board for an AI slowdown

The primary argument I see against “pacing” AI development is that if American companies slow down, Chinese companies will simply overtake them and build superintelligence themselves. For some, a China-controlled super-AI is a more terrifying possibility than super-AI in general:

But for others, it simply means that slowing AI down is futile because the Chinese can’t be persuaded to slow down:

This is an incredibly reasonable concern. The U.S. is still ahead of China in the AI race, but only just barely. If China is going to create superintelligence no matter what we do, why should we stop developing our own? Unless China can be persuaded to cooperate with the U.S. on AI “pacing” — or at least undertake its own independent “pacing” effort at the same time — anything we do will be futile.

So if we want to slow down AI development, we need to scare the Chinese leadership about superintelligence. There’s no other way.

How do we do that? In a post a week ago, I suggested in passing that simply staying ahead of China in the AI race might persuade them to embrace an AI slowdown, because that would be to their competitive advantage. But upon further reflection, I think I was pretty obviously wrong. If China will only embrace a slowdown if America refuses to slow down, then that’s game over — there’s no way to get both countries to slow down at the same time.

There’s a better approach. China’s leaders must realize that domestic dissidents could use Chinese-made superintelligence to overthrow the Chinese Communist Party.

Currently, China’s worries about AI mostly center around ways that the U.S. government could use U.S. AI models to attack China. That obviously gives the government an incentive to accelerate domestic AI progress, so that China’s own models can stand up to America’s in a fight. But if Chinese leaders realized that superintelligent AI could create a threat from within, this calculus would change.

Thus, China’s leadership must understand that Chinese AI models can pose a threat to CCP rule. The best way to demonstrate this is for American intelligence agencies — or even private hackers — to attack Chinese digital infrastructure using agent swarms created with China’s own frontier models like Z.ai’s GLM-5.3 or Moonshot AI’s Kimi K3.

When I say “attack”, I don’t mean actual warfare. I mean the kind of cyberattacks and data theft that China carries out against America every day. Use Chinese models to steal the CCP’s most heavily guarded secrets and post a few of the more innocuous ones on RedNote. Hack into Xi Jinping’s bank account and steal 100 yuan. I’m talking about demonstration attacks.

And these attacks must be done with Chinese models, not with American ones! If the CIA or some EA nonprofit in Berkeley uses GPT Astra or Claude Mythos to hack the CCP, China’s leaders may well conclude “Wow, we need to win the AI race so that our own models can defend us.” But if China’s own open-weight models are used for the attacks, Xi Jinping and the rest of the leadership will realize that their own push for superintelligence is making them incredibly vulnerable to any Chinese dissident who decides to overthrow them.

As soon as China’s leaders see superintelligence as a threat to their rule, I predict they will act. And their action will probably be to curb the development of superintelligence, especially if they know that America and its AI labs want to do the same.

In fact, China’s current leadership has a history of cracking down on its tech companies when it seemed like those companies might threaten the government’s monopoly on power. In 2021, Xi Jinping cracked down on Chinese software companies; he even (probably) apprehended tech magnate Jack Ma, who had criticized the CCP a little too openly. This action hurt China’s competitiveness in the online services industry, but the government went ahead and did it anyway.

And there’s already a precedent for demonstration attacks against Chinese digital infrastructure. An American cybersecurity company just used AI to develop a computer worm capable of hacking over a billion accounts on the Chinese messaging service WeChat:

Palo Alto-based Calif disclosed the already-patched computer worm to warn the public about the threat of AI-driven hacks…“Exploitation takes only seconds, and gives us full control of the WeChat account. We can read and send messages, make calls, and act on the victim’s behalf,” the company warned, posting a video demo of the WeWorm attack.

But Calif didn’t say what model it used to create WeWorm. Anyone who does this sort of demonstration in the future should make it clear that Chinese open-weight models were used, in order to make China’s leaders realize that the threat comes from their own too-rapid AI development, rather than from American competition.

I believe that this is our best bet for getting China on board for a joint international AI “pacing” effort. If there’s one thing the CCP fears more than an American attack, it’s domestic dissidents overthrowing the Party from within. Superintelligence is creating that vulnerability, but the leadership doesn’t seem to have realized it yet.

Make them realize, and I predict that a whole universe of possibilities for international cooperation will suddenly open up.

Update: Unsurprisingly, I’m not the first to have this idea about China needing to be scared about the internal threat from their own models. Back in July, Kyle Chan wrote:

Previously more dismissive of concerns over AI-driven job loss, Beijing now seems to be taking these risks more seriously (see Matt Sheehan’s great piece). China is also watching developments in the US very closely, particularly controls on Anthropic’s Mythos and Fable models over cyber risk. As Chinese open-source models approach similar levels of cyber capabilities, this could come back to bite and be used to potentially attack China’s own digital infrastructure. [emphasis mine]

In general, Kyle’s blog is one of the best blogs about China. Highly recommended.

Update 2: As if on cue, the NYT has a story today about how the CCP is starting to realize that superintelligence is a threat to its rule!

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…In an article published on Sunday in the state-run magazine China Cyberspace, Mr. Chen called for more party control over A.I. and stricter government oversight…He also described the danger that foreign intelligence agencies might use A.I. for “large-scale espionage” and attacks on China’s critical infrastructure…

Mr. Chen, the spy chief, wrote that the technical and financial barriers to launching cyberattacks had been drastically lowered because of A.I. This, he said, posed “serious risks” to China’s information infrastructure…Chinese users using foreign models could cause large-scale data leaks, the article noted. Anthropic, the company behind Claude and other A.I. models, said last week that the Chinese start-up Moonshot AI had routed queries by its users to Claude. Those queries included sensitive data, including video surveillance linked to the Chinese military, as well as proprietary information from high-profile Chinese technology companies…

The warning adds to a growing drumbeat of concern from Chinese officials about the risks of A.I. [emphasis mine]

Now all they need to realize is that the biggest threat comes from THEIR OWN MODELS.

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The other day I saw a Harvard Harris poll on the favorability of various political figures in the U.S. Only RFK Jr. (sigh), Marco Rubio, and Mark Carney had net positive approval ratings:

While I understand that these polls all have methodological issues, I was struck by how three figures — Abdul El-Sayed, Zohran Mamdani, and Benjamin Netanyahu — had net approval ratings below almost any American politician. While Mamdani’s low rating might be partially due to his leftist policies and his association with the DSA, it struck me that all three of these unpopular figures had one thing in common — they are associated with the intrusion of Middle Eastern politics into American politics. And I thought: Maybe Americans just want to have nothing to do with the Middle East.

Today1 is September 11, the anniversary of the terror attacks 25 years ago. Though I don’t agree with the leftist line that those attacks were blowback from American involvement in the Middle East, it’s unquestionable that 9/11 inaugurated an era of heavy U.S. involvement in that area of the world — the Iraq War, the follow-up war against ISIS, the War on Terror, greater support for Israel, an increased military presence in the region, and finally the war against Iran.

I don’t believe that all of that involvement was a bad idea — in particular, as I’ll argue, the War on Terror, including the fight against ISIS, was generally successful and necessary. But overall, the era of heavy U.S. involvement in the Middle East has been a disaster for U.S. power and prestige, and it’s time for that era to end.

This is not a recent conversion on my part; I’ve been in favor of general disengagement from the Middle East for a very long time. Five years ago, in the early days of the Noahpinion Substack, I wrote a post called “How to fix U.S. foreign policy”:

In that post, I wrote:

[D]espite modest progress, we still remain too tied up in Middle Eastern conflicts…The U.S. will never be able to ignore the Middle East entirely, but we can remove ourselves much more than we’ve done…[W]e need to draw down engagement in the Middle East, and increase engagement in Asia.

Since then, things have gone from bad to worse, making my message far more urgent than it was at the time. Most obviously, there’s Trump’s disastrous war in Iran, which continues to waste U.S. military resources and make the U.S. look weak while failing to accomplish any significant objective. Recent polls show that a very strong majority of Americans think that the Iran War is not worth fighting.

There’s also Israel’s intensified campaign against the Palestinians. This has become the rallying cry of the American left, which has prompted a backlash and crackdown by the Trump administration. As a result of that conflict, Middle Eastern politics is increasingly becoming the focus of American domestic politics.

The controversy around Michigan Democratic Senate candidate Abdul El-Sayed, for example, has nothing to do with his policy stances, or even American culture wars — it’s entirely about his stance on Israel/Palestine and his ally Hasan Piker’s comments making excuses for 9/11 and Islamic terrorism. El-Sayed recently apologized for tying a synagogue shooting to Israeli warfare in the Middle East.

Many analysts believe that Zohran Mamdani, meanwhile, won the NYC mayorship primarily because of his outspoken stances on Israel/Palestine. And more is on the way. Organizations like CAIR Action that are linked to the Muslim Brotherhood — a sort of international equivalent of the Christian Coalition, but for Islam — are now supporting political candidates throughout the U.S., using the Palestine issue to recruit candidates and win votes. Muslim Brotherhood figures are cheering this effort on from overseas. AIPAC and other Israel-supporting groups, meanwhile, are pouring money into races in order to stop those candidates.

Our media, too, is increasingly filling up with the Middle East’s cultural and religious conflicts. Leftist shouters like Hasan Piker, Mehdi Hasan, and Cenk Uygur constantly urge progressives to base their politics around Palestine. Every day, Americans are bombarded with rhetoric like this:

This is simply bad for America. We have so many pressing issues to worry about — Trump’s corruption and authoritarianism, inflation, AI, etc. We should not be nominating Democratic politicians based on whether they support Israel or Palestine; we should be nominating them based on their stances on issues of direct relevance to Americans, and on their ability to oppose Donald Trump. Furthermore, mobilizing Muslim and Jewish voters to vote based on Israel/Palestine, rather than on issues of importance to Americans in general, provides fuel for rightist attacks on both groups.

What the United States needs, now more than ever, is general disengagement from the Middle East — an end to military adventures, a dialing back of support for Middle Eastern “allies” (including Israel), and a forceful rejection of Middle Eastern issues in American domestic politics.

Fortunately, conditions in the world have shifted. The kind of disengagement I’m calling for is a lot more feasible than it was twenty or even ten years ago. The Iran War has shown that Middle Eastern oil is a lot less important for the global economy than it used to be. And the success of the War on Terror and the general decline of Islamism reduce the necessity for further interventions.

The U.S. doesn’t need to protect Middle Eastern oil supplies anymore

For many decades, the main justification for U.S. intervention in the Middle East has been to keep oil prices low and stable. The Middle East is one of the world’s largest oil producers, and its low extraction costs mean it functions as the world’s “swing producer”. Supply disruptions from the region can cause global price spikes, endangering industrialized economies that depend on petroleum. Oil was a big reason the U.S. intervened to stop Saddam Hussein’s invasion of Kuwait in the early 90s, for example.

Obviously, oil is still a very important commodity. But it’s a lot less important to the U.S. than it used to be. Here’s what I wrote when the Iran War began:

Blanchard and Gali (2007) looked at economic responses to changes in oil prices in the U.S., and concluded that the economy of the 2000s was only about a third to half as sensitive to the price of oil as the economy of the 1970s had been. Their reasoning is that modern economies are more flexible in general, that they have better monetary policy (i.e. we don’t try to print a ton of money in response to a supply shock), and that we depend on oil less.

By their estimates, a 10% increase in the price of oil now (or at least, if “the 2000s” means “now”) leads to only a 0.25 percentage point increase in the CPI and a 0.3 percentage point reduction in GDP over the course of a year or so. Since oil just spiked by 50%, then if that’s sustained, we might expect to see inflation go up by 1.25 percentage points, and GDP go down by 1.5 percentage points over the next year. That would mean inflation would go to around 4% and GDP growth might go down to 1.5% — frustrating and annoying, but not catastrophic…Other estimates seem similarly modest. For example, in a recent roundup, I flagged a paper by Känzig and Raghavan (2025) that looked at the closure of key shipping chokepoints.

In addition to having a more flexible economy, better energy efficiency, and more reasonable monetary policy, the U.S. is now a net oil exporter. That means when oil prices go up, the benefit to the energy sector cancels out at least part of the harm to other sectors of the economy.

But on top of all that, the fact is that the Iran War simply hasn’t raised oil prices that much! Despite Iran’s closure of the Strait of Hormuz, and Trump’s retaliatory blockade of Iranian oil shipments, oil prices haven’t even reattained their highs from the early 2010s:

And in real terms, the spike is even less impressive. If we divide oil prices by average hourly earnings for production and nonsupervisory workers — basically, how many hours an average American worker would have to work in order to afford a barrel of oil — we see that prices really aren’t that high at all:

No wonder the U.S. economy is still doing fine, and inflation has only risen a little bit (and some of that may be due to demand from the data center boom).

Nor does the U.S. have to worry about oil cutoffs in the long term, if it disengages from the Middle East. Yes, in the 1970s, OPEC injured the global economy with its embargo. But the cartel’s power was broken in the 1980s and never recovered. In a world of globalized oil supply, Middle Eastern countries simply need to keep selling their oil to survive. And if Iran cuts off the Strait of Hormuz in the long term and raises global oil prices, it will arouse the ire of China, its main patron and customer.

Actually, China gives us one more reason to be less concerned about Middle Eastern oil supplies over the long term. China is racing to become less dependent on oil by adopting electric vehicles en masse, which is causing its petroleum usage to plummet:

In fact, falling Chinese demand is a big reason global oil prices haven’t risen more in response to the Iran War. China’s drive to reduce oil use isn’t going to change, which will put downward pressure on oil prices for everyone else as well. And China’s exports of electric vehicles will cut petroleum use all over the world.

Thus, economically, the Middle East is already not nearly as relevant as it used to be, and it’s going to become a lot less relevant in the years to come. The 1970s taught America that we needed to maintain a robust presence in the region in order to keep the oil flowing; half a century later, that’s simply no longer the case.

The War on Terror was successful (and now we can go home)

When the War on Terror started after 9/11, a lot of people assumed it would be a losing proposition — that the more America went after jihadists abroad, the more they would multiply. Calling it a “forever war” was very common. And yet when we look at the results a quarter century later, it’s very hard to avoid the conclusion that the U.S. won the War on Terror, and that radical Islamism is on the wane.

The clearest fact is that the two main organizations that prosecuted the War on Terror from the Islamist side — Al Qaeda and the Islamic State — have been effectively defeated. Al Qaeda’s leadership, including everyone involved in the planning and execution of the 9/11 attacks, has all been captured or killed. The Islamic State was defeated on the battlefield, and its territory wiped off the map.

Both organizations survive only as brands — names that a few hundred random local jihadists can claim to be affiliated with in lawless backwaters like the Sahel. The U.S. and its allies will continue to hunt these down, but much like the smattering of communist guerrilla movements that persisted around the world after the end of the Cold War, they lack the ability to do any real damage.

Perhaps the clearest way to see the victory in the War on Terror is to look at the plunging number of terrorist plots and attacks around the world. Here’s an estimate, which makes it clear that the battle against the Islamic State was actually the peak of the conflict:

And here’s a dataset from CSIS that includes plots and threats as well as attacks:

I’ve been talking about this victory as if it were America’s alone, but I really shouldn’t. Most of the fighting was actually done by people in the Middle East, South and Southeast Asia, Africa, and so on — regular, normal folks who adhere to the Muslim faith but wanted no part of what the Islamists were selling, and were willing to put their lives on the line. Here’s what I wrote in 2021:

Remember how in the wake of 9/11, commentators used to always demand that moderate Muslims condemn jihadists? Well, folks, they did one better — they shot the jihadists.

In the U.S., too, sympathy for Islamic radicalism is dying. Charles Kurzman of UNC Chapel Hill has kept tabs on reports of Muslim American involvement with extremism. There was a wave of verbal support for Islamism after 9/11, and a wave of violent attacks in the mid-2010s, though neither wave was very large at all. Then the waves died down, and jihadist activity in America basically vanished:

Europe is seeing the same pattern — the big deadly attacks were all in the mid-2010s, at the same time as the conflict with the Islamic State. There remains a smattering of low-level, low-casualty attacks — perhaps about a dozen a year throughout the EU, with annual deaths in the single digits.

Many European societies are currently experiencing anxiety about their ability to absorb large numbers of Muslim immigrants, but that’s very different from the kind of threat posed by Islamic State and Al Qaeda. In particular, it doesn’t require any sort of involvement in the Middle East — in fact, the people experiencing the anxiety over Muslim immigration would probably also want to have less to do with that region of the world.

In other words, there’s little reason for the U.S. to maintain a high level of involvement in the Middle East in order to thwart Islamism. The war has been won, and what remains is low-level monitoring and clean-up. That can be accomplished with low-key coordination between intelligence services, rather than with military bases, invasions, and so on.

America’s “alliance” with Israel has outlived its usefulness

Beyond oil and Islamism, the other reason often given for continued American involvement in the Middle East is to help our ally, Israel. Israel is not a treaty ally of the United States, like Turkey or Japan; we are not obligated to come to its defense, or vice versa. The U.S. has unilaterally designated Israel to be a “Major Non-NATO Ally”, but that’s simply an indicator of our attitude toward the country, not any kind of a pact or treaty. Israel is an “ally” only because we choose to regard it as such.

But is this attitudinal alliance still worth what we’re paying for it? I have my doubts. First of all, there’s the matter of Israel’s obvious human rights abuses. Americans used to consistently sympathize much more with Israel than with the Palestinians, but this has recently reversed:

Some part of this is undoubtedly due to the leftist movement, and their relentless — and often dishonest — campaign of anti-Israel agitation. America’s progressive media is clearly biased against Israel to some degree. But Israel isn’t just losing the war of public opinion because their opponents are better at lying. Americans simply can’t ignore the country’s human rights abuses any longer.

Forget about the war in Gaza for a moment, and consider the situation in the West Bank. Israel keeps large numbers of the Palestinians in the West Bank stateless; these people are citizens of nowhere. The Palestinian Authority administers much of the day-to-day business of governance in the Palestinian parts of the West Bank, but Israel actually rules; all real power in the territory is held by the Israeli military.

That by itself should be considered a crime, but it gets worse. The West Bank Palestinians are denied freedom of movement — kept penned into their villages and cities, often unable to move around to other parts of the West Bank. Much of the territory is held by Israeli settlers — Israeli citizens who have the (sometimes explicit, sometimes tacit) support of the Israeli military occupation. These settlers commit frequent, sporadic acts of violence and theft against the stateless Palestinians in the territory — basically, pogroms — and often receive protection from any retaliation by the Israeli military.

This state of affairs has been likened to apartheid, and honestly the metaphor is apt. It’s a slow campaign of conquest, land theft, and racialized and religious violence against a people who have been forcibly denied a place in the community of nations. It’s not quite as brutal as what Russia is doing in the occupied territories of Ukraine, but it’s in the same general vein.

Why would the United States of America want to associate itself with that sort of villainy? Why would we give almost $7 billion a year — more than we give to any other country, more even than we give to Ukraine — to a nation that carries out campaigns of mass dispossession and conquest like the one Israel is executing in the West Bank? I realize that America is no longer a champion of human rights under Donald Trump, but the American people can still tell when they’re being asked to support crimes against humanity. (And note that I didn’t even deal with Gaza at all; I don’t have to even mention it in order to make my point.)

In addition to Israel’s human rights abuses, there’s the question of what, exactly, the U.S. is getting out of this alliance. Granted, Israel’s failure to offer aid in Afghanistan or Iraq wasn’t its fault — the U.S. asked it not to get involved, so as to avoid adding a religious aspect to those conflicts. But the only reason Israel refused to help Ukraine was out of fear of alienating Vladimir Putin, with whom Israel’s Prime Minister Benjamin Netanyahu has been friendly for many years. Nor has Israel helped the U.S. prepare for a Chinese onslaught in Asia.

In the Middle East, it’s now mostly America who does the helping. Netanyahu reportedly helped persuade Trump to attack Iran — a course of action that has been distinctly to America’s detriment. Leftists have been accusing America of conducting wars in the Middle East at Israel’s behest for decades, but this time, it looks like it might sort of be true. Trump undoubtedly had his own reasons for wanting to attack Iran, but the fact that Israel’s leader seemingly goaded him into taking the plunge should make the U.S. much more skeptical of its relationship with that country.

Essentially the only thing Israel might have to offer the U.S. — other than some defense technologies that we can almost certainly replicate ourselves — is the promise of cooperation to prevent Iran from acquiring nuclear weapons. Prior to the current war, that argument held some water. But months of joint action by Israel and the U.S. have failed to cripple Iran’s nuclear program. So it’s unclear how much help Israel is even capable of offering in this regard. If the U.S. is going to prevent Iran from getting nukes, it isn’t going to do it via joint Israel-U.S. bombing campaigns.

An ally that refuses to help you with your biggest challenges, but which goads you into engaging in risky, ill-advised conflicts on its behalf, all while committing large-scale human rights abuses with impunity, is simply not a very good ally. So it seems like it’s time for the U.S. to pause or dial back its close relationship with Israel.

What does that mean? Most obviously, it means cancellation of foreign aid to Israel — money that is invariably used to maintain the intolerable situation in the West Bank. It also means removing Israel’s designation as a “major non-NATO ally.” The aid and ally status can be restored if Israel ever stops its campaign of dispossession and violence in the West Bank and agrees to support U.S. interests in Europe and Asia.

That does not mean I think the U.S. should reorganize its foreign policy around opposition to Israel, as the leftists would have us do. Israel’s enemies — Hamas, Hezbollah, and Iran — are even more savage than Israel is. Hamas would absolutely commit genocide on the Israelis, given the chance. Leftists like Hasan Piker and Mehdi Hasan who urge us to think of Hamas as the lesser of two evils simply want us to switch our support for one brutal Middle Eastern regime to an even more brutal one.

Instead, what we need to do is to reduce American involvement in Israel’s conflicts with its neighbors. If there aren’t pressing national interests that can be served by throwing our support to one side or another, and if supporting either side requires endorsing human rights abuses, then the right answer is just to be less involved.

The Middle East will be fine without us, and vice versa

All of this might make it seem like I dislike the Middle East, or view it with disgust. On the contrary, I think the Middle East has a bright future ahead:

What I think is that the Middle East had some problems that caused it to lag behind some other regions in terms of political and economic development. Over-reliance on oil inhibited industrialization, while Islam was slow to recognize the importance of the separation of church and state.

But I believe the region is in the process of starting to overcome those initial conditions. The constant series of quasi-religious wars that has engulfed the region over the last few decades is reminiscent of Europe’s Wars of Religion and Thirty Years’ War in the 16th and 17th centuries. Eventually, Europeans tired of fighting over God, and Middle Easterners will as well.

In fact, it’s already happening in some places. After over a decade of horrifically destructive sectarian civil war, Syria is slowly rebuilding itself under a moderate government (despite the occasional Israeli airstrike). Middle Easterners are, for the most part, not religious fanatics who want nothing more than to die for Allah; they are regular people who want to buy bread at the store and have a roof over their heads and go to work and raise their children in peace.

Meanwhile, the region’s economic future is more promising than people realize. Solar power will allow it to remain an energy powerhouse even as the world shifts away from oil. Permissive land-use policies will allow it to build data centers when other countries block them. Falling fertility has primed most Middle Eastern countries for a demographic dividend that will boost growth, even as Europe and Asia age rapidly.

The best thing the U.S. can do to further this process along is not to interfere with it. The Middle Easterners — Israel, Iran, Saudi Arabia, Turkey, Egypt, UAE, and all the rest — have to simply realize that eternal war isn’t doing them any good, and then work out a regional architecture for security and economic cooperation among themselves. Military interventions by distant powers will only delay that maturation process.

Meanwhile, U.S. politics will be healthier if we can ignore the activists and demagogues who constantly harangue us about the Middle East. Right now, with Trump executing his doomed misadventure in Iran, and American aid supporting Israel’s campaign of conquest in the West Bank, it’s very hard for anyone in American politics to avoid constant debates and discussions about the Middle East. Making it easier to avoid those debates and discussions should be a conscious goal of our politics and our policy. It’s time for America’s Middle Eastern era to end.

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So, Germany’s far-right party, the AfD, just won a huge landslide election victory in the eastern state of Saxony-Anhalt. This doesn’t mean AfD is taking over Germany — the far right is much more popular in the post-communist east than in the more populated western regions. Germany is also a parliamentary democracy, and almost everyone else hates and fears the AfD. But the statewide victory is a warning shot, and everyone in Germany — and in Europe — is taking it seriously.

AfD’s surge means two things. First, it means that the rise of far-right parties was not simply a reaction to the pandemic. Back in 2024-25, a number of pundits I respect argued that voters in the U.S. and Europe were backing rightist leaders out of anger at incumbents over Covid. That thesis now looks dead.

Second, it means that something is making many German voters so angry that they’re finally willing to overlook all of the AfD’s catastrophic flaws — its obvious ties to Vladimir Putin, the fact that the party is filled with crooks, its extreme rhetoric, its lack of governing experience, and so on.

What is that “something”? A number of socialist types have tried to claim that AfD’s new voters are responding to economic despair, and can be won back with more socialism. But more sober voices agree that the “something” is immigration. Adam Tooze, who strongly leans to the left, tries to be gentle as he explains that there’s no getting around the immigration issue:

If you ask AfD voters what they worry about, along with their xenophobic concerns about “too many foreigners”, they will mention the cost of living and their concerns for their standard of living. They also mention climate change and the threat of external war…But if you ask them what they think about the AfD and why they voted for it, economic issues are barely mentioned. What comes first is security, foreigners, woke politics, remoteness etc…

If you are hard up and working-class it would after all be nothing short of bizarre to vote for the AfD in the hope of improved social protection…Voting for the AfD in the current landscape, whatever your economic issues, in short, is not a vote to have your economic issues addressed by social democracy, or welfare or government intervention. It is a vote to crackdown on foreigners[.] [emphasis mine]

Robin Brooks is more blunt:

There’s lots going on in Germany at the moment, but - when it comes to the surge in AfD popularity - you don’t need to overthink it. The AfD is relentlessly campaigning on immigration, which tells you that this is what resonates most with voters…A key driver of the recent surge in AfD popularity is that the CDU - SPD grand coalition is proving itself incapable of confronting immigration with the urgency many voters want.

In fact, anger over immigration is probably the main reason that the right is surging everywhere in Europe. Here are Konstantinou and Roumanias (2024) with some data:

We use regional data spanning over 4500 electoral outcomes…between 2000 and 2017 to assess the impact of immigration on Western European Far-Right voting. To deal with potential reverse causation, we use immigration in neighboring countries as an instrument for domestic immigration. The estimated effects of immigration are positive, significant and larger compared to those obtained by simple OLS and fixed effects regressions. Depending on the measure of immigration used, we find that a 1% increase in immigration stocks leads to a between 1.78% and 2.97% in Far-Right voting…Our estimated effects explain a large part of the observed rise in Far-Right in Western European countries that experienced high levels of immigration during the span of our sample.

2026 is hardly the beginning of the trend. Here’s Brookings in 2024:

In the European Union (EU), one election after another has demonstrated the centrality of irregular migration and border security in public discussions and forced mainstream parties to take more restrictive approaches…The results of the European Parliament election, France’s snap election, three German state elections, and the Austrian election all showed a strong rightward drift and signaled voters’ distrust in their national governments, confirming the notable shift in tone on migration in Europe toward a more securitized, hardline approach, even among mainstream parties.

Trump’s election in 2024 was also partially about immigration. Trump voters rated it as one of their top issues, along with inflation. It was the issue where Trump had the strongest lead over Kamala Harris in polls. American voters clearly soured on immigration in 2023-24.

Progressives in America and socialists in Europe still desperately claim that what angry working-class voters really want is more economic support. They keep offering more and more lavish promises of welfare supports and price controls, while staunchly opposing restrictions on immigration. The approach keeps failing, and the far right keeps rising.

Center-right and center-left politicians, on the other hand, realize the source of their voters’ anger very clearly, and are taking steps to reduce immigration. Germany’s center-right CDU has already stanched the flow with restrictive policies:

And Chancellor Friedrich Merz is responding to AfD’s gains by making it harder to get citizenship. Meanwhile, France and Italy are tightening border controls. France is making it harder to become a citizen. The EU is toughening up its asylum rules. Sweden is offering migrants large sums of money to leave the country. European countries are streamlining their infrastructure for kicking out large numbers of failed asylum-seekers. Japan is tightening its own immigration policies after a period of liberalization.

Even Canada, one of the most immigration-friendly and progressive countries on Earth, has scrambled to reduce immigration, cutting targets for permanent residents, reducing student visas, and slashing the number of temporary workers. Immigration levels are still high, but are falling quickly — all under the center-left government of Mark Carney.

And yet I’m worried that voters around the world will see these measures as superficial — tactical pauses to let populist anger simmer down before resuming the same permissive immigration policies that made voters mad in the first place. This certainly seemed to be the case in Europe, where the government stopped admitting asylum seekers from the Middle East in the late 2010s after a popular outcry, only to resume taking them in en masse in the 2020s:

And it also seems to be true in the U.S.; Biden mostly closed the border to asylum-seekers in 2024 after he saw how mad it was making Americans, but many Democrats are now calling for the abolition of ICE. This is a somewhat popular position right now, thanks to the agency’s abuses under Trump. But if Democrats actually try to abolish federal immigration enforcement instead of just reshuffling and renaming the agencies, I predict it will take just a couple of years for Americans to realize that the difference between “open borders” and “immigration laws with no one to enforce them” is largely semantic.

What’s needed, I believe, is not just a tactical, temporary retreat. We need to stop and think why a maximal pro-immigration policy is something that we need to retreat from in the first place. We need a new concept of what a sustainable liberal immigration policy looks like.

Since I started writing, I’ve been an advocate of more immigration to the United States. This does not mean I ever favored unrestrained immigration or open borders. I’ve consistently argued that favoring skilled immigration will bring more economic benefits to native-born Americans, and be more politically palatable as well. And while I don’t hate illegal immigrants for trying to better their lot in life, I favor strong border controls, because nations have the right to decide, democratically, who gets in and out. It’s understandable when people get mad at seeing their decisions flouted.

On top of that, I’ve always remained agnostic on immigration to other countries; although I find much to admire in Canada’s points-based system, for example, I don’t think that qualifies me to decide whether more immigration is good for Canada overall. If the people of a country decide that immigration is diluting their local culture unacceptably, for instance, I think they have every right to cut it off. That’s just Westphalian sovereignty — not a perfect system, but the best system we’ve ever found for organizing humanity into geographic units. As an American, I don’t view it as my place to tell Japan, or the UK, or any other country that immigration is the right choice for them.

Despite those reservations, I’ve always thought that immigration to the U.S. is a basically good thing, and should be expanded. The economic benefits are pretty undeniable — higher tax revenue to shore up our dangerously depleted government finances, dominance in high-tech industries and innovation, and so on. And while some of the economic costs are real — local housing shortages and strains on local government finances being the two biggest ones — many of the fears are overblown. In particular, the bulk of the evidence concludes that immigrants don’t reduce wages or job opportunities for native-born Americans.

And importantly, lots of Americans share my overall positive view of immigration. If you ask Americans whether they think immigration is good or bad, most will say “good”:

And if you ask Americans whether the annual rate of immigration should be increased, decreased, or kept the same, you get a pretty even split:

So I’ve never really felt like I was going out on a limb or advocating an unpopular position when it came to this issue. Sure, rightists will direct online vitriol toward anyone who supports any immigration at all, but their energy and savagery shouldn’t be mistaken for majority support.

And yet there will always be some amount of immigration — and some types of immigration — that will arouse even the most open and welcoming people to ire and make them think about shutting the doors. We saw this in the U.S. in 2023-24, when anti-immigration sentiment spiked in response to Biden’s permissive asylum policies. Yes, that sentiment crashed again when Trump took power and started committing abuses. But if liberals don’t change how we approach immigration, the sentiment will simply rise again and again, as it has so many times throughout our history. And the Donald Trump and Stephen Miller types will be right back in power — and our country will be worse off for it.

Liberals need an immigration approach that can be sustained for long periods of time — i.e., one that doesn’t enrage the public every time it manages to get in power. What would that look like?

The first thing liberals (and progressives, and Democrats, and European lefties, etc.) need to admit is that migration is not a human right. We live in a world of sovereign nation-states, and unless we find some better way of dividing up and administering the world, we will continue to live in a world of sovereign nation-states. And nations are, necessarily, exclusive clubs; they have the right to restrict immigration for any reason whatsoever.

Liberals must therefore not view borders and citizenship as annoying obstacles to be tactically circumvented or overcome; instead, we must view them as fundamental parts of the social compact that allows nations, including liberal nations, to exist in the first place. Nation-states and their laws and their police and their courts and their armies are the fundamental guarantors of the human rights that define liberalism. And for better or for worse, the ability to decide who gets in and who has to keep out is a necessary precondition for nation-states to exist.

That means we need to recognize that immigration law is legitimate and needs to be upheld. Some progressives have tried to advance the notion that being undocumented is a marginalized identity that needs to be protected and supported by the state. But this is absurd; it’s like if progressives decided that people who drive over the speed limit are a minority group.

Illegal immigrants chose to break U.S. law when they came to this country. Our democratically elected leaders made those laws, and they should be honored. We should treat illegal immigrants humanely, of course, and we should not tear local communities apart just to hunt down a few people. But there has to be some way of enforcing the law, because a law without enforcement is no law at all.

A third principle that liberals should embrace is that the purpose of immigration is to benefit the people who already live in the country that is receiving the immigrants. Immigration to America must be for the benefit of Americans. We must flatly reject any concept of immigration that sees it as a necessary sacrifice on the part of American citizens.

For example, the book This Land is Our Land, by Suketu Mehta, argues that immigration is a form of reparations for colonialism — a sacrifice that Western nations owe to other nations, even at their own expense. This idea has found some purchase in progressive circles, but liberals must reject it as wholly illegitimate.

Instead, liberals need to promote immigration because of the benefits it brings to the American citizenry. This includes economic benefits — the tax revenue, the investment, the eldercare, the innovation, the entrepreneurship, the expanded market size. But it also includes cultural benefits — the constant reinvigoration of this nation of immigrants by new waves of people with the gumption and bravery to pick up and move across the world in search of opportunity and freedom.

America has always been the country of the frontier; in order to maintain that ethos in the modern age, we need people for whom America itself is the frontier. And our founding ideals — which are very liberal ideals, of liberty and opportunity and the rights of the individual — are strengthened by people who make the conscious choice to move to a country that represents those ideals.

These are the kinds of arguments liberals made in the past. We can — we should — make them again.

And if this requires us to be selective about which immigrants we bring in, then so be it. Skilled immigrants bring far more economic benefits per capita than others — so by all means, let us tilt our system toward them, as American voters of both parties want. Immigrants who don’t love and embrace American culture will probably invigorate our nation less — so by all means, let us bar immigrants who have been part of groups that see America as evil, using the law with which we once banned immigration by members of communist parties.

These are the kinds of decisions that nation-states, including liberal nation-states, are inherently entitled to make. And we should expect Europe to make different decisions than America makes. That’s perfectly OK. Because immigration is fundamentally the decision of each sovereign nation-state, it’s inevitable that we’ll make different decisions. JD Vance wants us to think that France’s immigration policy, or Germany’s, is inextricably tied to America’s. But it’s not, and we shouldn’t let him get away with conflating the two.

A liberal immigration program based on these three principles could defuse the anti-immigration sentiment now sweeping the developed world — not just now, but permanently. But it will only do so if liberal leaders explicitly articulate and avow these principles. That’s what’s required in order to convince the electorate that liberals are trustworthy on immigration — that embracing the kind of immigration policies Americans demand represents a principled stand rather than a tactical retreat from a hidden agenda of open borders.

For America, the kind of immigration agenda I’ve sketched out — which all the polls show Americans favor by substantial margins — would not be a “far right” policy, even though some progressives and leftists will inevitably try to label it as such. In fact, it would allow in far more immigration than the policies of Franklin D. Roosevelt or Harry Truman. It won’t satisfy progressives who dream of a borderless world, or leftists who salivate over the chance to make the West pay for colonialism. But I believe it will preserve America as the kind of liberal nation-state that we knew it as in the days before Trump.

As for the rest of the world, countries like Germany and Canada and Japan have to make their own decisions. I can’t tell them what kind of nation to build; I can only try to promise that as an American, I’ll respect their decision. If they want to shut their doors in order to slow the pace of cultural change, it’s not my job to lecture them otherwise. All I can say is that I hope they do what they need to do in order to avoid getting taken over by parties that take their marching orders from the Kremlin.

But speaking only for my own country, I believe that if America gives up immigration, it will lose one of the key things that has made it great. With the GOP in the hands of a xenophobic rightist movement that sees immigration as an invasion, it’s up to the Democrats to craft a system that can be preserved for the long term.

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It’s Labor Day, so here’s a post about how human labor is alive and well in the age of AI.

I live in San Francisco and hang out with a lot of tech people, both in the AI industry and outside of it. And one thing that almost everyone I know here believes is that AI’s main economic effect is to displace humans from their jobs. Most people don’t have concrete arguments for why this should be true; it’s just an article of faith. The conventional wisdom is pretty well summed up by the first line of this tweet:

In fact, AI companies themselves have spent years talking about how their inventions are going to render large swathes of humanity economically obsolete — an odd marketing pitch, perhaps, but one that seemed to reflect their honest expectations.

A lot of times, San Francisco tech people are out of step with the general public. This time, though, the public seems to agree. A recent Ipsos poll found that most Americans expect AI to compete with human workers more than it complements them. And Pew finds that this belief has even strengthened in recent years:

So basically, most people think AI is a job-killer. And yet somehow, this job-killer keeps stubbornly refusing to kill jobs. In the aggregate, the labor market is about as healthy as it’s ever been. The prime-age employment rate — the single best indicator of how many Americans have jobs — continues to hover near all-time highs:

Of course, there are lots of other things going on in the labor market right now besides AI. But most of those things — tariffs, the Iran war, etc. — are bad for employment. It’s not easy to identify some sort of positive shock that is canceling out the job-killing effects of AI.

Or maybe it is, if the shock is AI itself. Theoretically speaking, automation can create jobs just as easily as it can destroy them. Here are Acemoglu and Restrepo (2019), explaining the various ways that technology can affect the demand for labor:

Automation [can be bad] for labor because of a displacement effect—as capital takes over tasks previously performed by labor…

[A]utomation technology also increases productivity, and via this channel, which we call the productivity effect, it contributes to the demand for labor in non-automated tasks…

[T]he displacement effect of automation has [historically] been counterbalanced by technologies that create new tasks in which labor has a comparative advantage. Such new tasks generate not only a positive productivity effect, but also a reinstatement effect—they reinstate labor into a broader range of tasks and thus change the task content of production in favor of labor. The reinstatement effect is the polar opposite of the displacement effect and directly increases the labor share as well as labor demand. [emphasis mine]

In other words, automation can do three basic things. Yes, it can replace people and take their jobs. It can also make them more productive, which can both create jobs and destroy them.1 And, crucially, automation can create new jobs for people to do. Power looms replaced master weavers, but they created jobs for technicians and engineers to make the power looms work. The internet automated much of the work of travel agents, but created jobs for web designers. And so on.

People who think of AI as a job-killer might not have thought of the second and third of these. Or they may have thought of them, but simply assumed they’re not a big deal. Anecdotally, a lot of tech people think that AI will keep substituting for more and more tasks until A) productivity increases just increase the demand for AI, and B) there are no new tasks left for humans to do. AI detractors, meanwhile — like Daron Acemoglu himself — often simply assume that new tasks created by AI will be “bad tasks” like misinformation and cybercrime that hurt the economy instead of helping it.

But these assumptions simply might not be correct. AI might be creating lots of new tasks for humans to do. For example, software engineers are writing less and less code themselves. Instead, they’re spending more and more time telling AI to write code — that represents a productivity improvement. But they’re also trying to figure out what code to tell AI to write, making sure AI is writing the kind of code they want, integrating that code into products, and so on. Those are all new tasks. There are also a lot of software engineers working on improving AI itself, and on discovering new applications for AI. Those are new tasks as well.

This helps explain why in the age of Codex and Claude Code, software developer jobs have been increasing as a percentage of total employment:

Anecdotally, organizations that thought they could replace lots of their software engineers with AI ended up having to hire many of them back — sometimes at a premium.

In fact, this is a story we see throughout the economy. Alex Tabarrok recently reported on a Census Bureau survey about AI that’s been running since 2023. The Census Bureau calls companies up and asks them A) how AI affected their total employment, and B) how AI affects the tasks that workers do.

Most companies reported no change in overall employment, which could just be due to inertia. But of companies that did report a change, more reported an increase than a decrease!

And here’s the breakdown by sector:

The story was similar for tasks. Tabarrok writes:

Among firms using AI, 44% say it supplemented or enhanced work an employee already does. Ten percent say it performed a task an employee used to do. Eleven percent say it introduced a task no one had been doing.

Here’s the chart:

Rigorous research, meanwhile, sometimes finds negative effects of AI on labor demand at the industry level, and sometimes not. But at the company level, the evidence is clearer — Kharazian, Simon, and Stevens (2026) find that when companies adopt more AI, they tend to hire humans rather than replacing them. Here’s a blog writeup of their findings:

Ramp Economics Lab

We can finally say AI isn’t killing jobs

Dear Colleagues: The most important economic question of this decade asks how AI will affect jobs. Everyone wants to write that paper. Until now, no one has had the right dataset, so existing research has relied on a combination of guesses, surveys, AI exposure scores, and self-interested punditry. In fact, a recent paper from Stanford said the ideal da…

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4 months ago · 63 likes · 14 comments · Ara Kharazian

And here’s a chart:

Interestingly, they find the same for entry-level jobs — the jobs that people usually identify as being most under threat from AI.

So despite Acemoglu’s skepticism, it looks like for now, the new tasks being created by AI are probably matching or even slightly exceeding the tasks replaced by AI. Of course this measure is “number of companies” rather than “number of jobs”, but the pattern is pretty clear.

The Economist, meanwhile, has a report on how AI is creating jobs, both through the “new tasks” channel and by boosting demand in areas that AI can’t yet touch — physical jobs like construction and HVAC installation. Here’s what they write about the productivity/demand effect:

The Economist estimates that AI has so far created around 1m new jobs in America. That easily exceeds the roughly 200,000 lay-offs attributed to AI since mid-2023, and appears more than enough to offset weaker hiring in many back-office roles. America’s AI infrastructure splurge has created many of them…The Economist tracked five industries at the heart of the data-centre build-out, from electrical contracting to equipment manufacturing. Since 2023 employment in them has risen by roughly 320,000 more than broader…trends would suggest…LinkedIn, a social network for strivers, estimates that nearly half a million data-centre jobs were created between 2023 and 2025 in America, with data-centre technicians and engineers among the most common recent hires…

The scramble for workers is showing up in pay cheques, too. Indeed finds that installation and maintenance jobs at data centres advertise wages about 40% higher than comparable work elsewhere…In the year to June, average hourly earnings rose more than 13% in electrical-equipment manufacturing and nearly 8% among electrical contractors. [emphasis mine]

And here’s what they write about new tasks:

AI is also creating a new class of white-collar jobs. Engineers build the models, data annotators label their inputs and judge their answers, “forward-deployed” engineers adapt them for customers, and newly minted “heads of AI” decide what companies should do with the technology. Some of these roles barely existed until recently. Many are quickly growing in number. Postings for heads of AI, AI engineers and directors of AI have roughly doubled since 2023-24, according to LinkedIn…

Preliminary research by Gad Levanon, chief economist at the Burning Glass Institute…reckons roughly 1% of professional jobs are now “AI jobs”…[P]rofessional occupations closest to the AI boom—engineers, software developers, mathematicians and data scientists…have added roughly 730,000 jobs above trend in recent years[.] [emphasis mine]

What about specific occupations? Technology has certainly destroyed many specific types of jobs over the centuries — there are (basically) no more elevator operators, human telephone operators, or people who do manual typesetting for printing.

And yet in recent decades, we haven’t seen as much of this sort of occupational destruction. For example, a lot of people thought the internet would kill travel agents. And while the industry was hit hard, there are still plenty of travel agents left:

The reason is probably that the job of “travel agent” is much more flexible and “messy” than older types of jobs like elevator operator; travel agents do a whole lot of different tasks, so they’re harder to replace than people who just stand there and press a button. That makes modern jobs harder to replace entirely.

It’s a good bet that AI will eventually make some occupations obsolete. But so far, despite awe-inspiring progress in model capabilities, it’s extremely hard to find occupations that have seen significant replacement by AI. Top AI researchers who famously predicted the end of human radiologists saw their predictions get confounded. Truckers, too, are doing just fine. (Update: Here’s a good clip of Geoffrey Hinton explaining why he was wrong about radiologists.)

The most impressive example might be translators. It seems pretty obvious how AI could replace human translators, and yet it hasn’t done so yet:

Here’s a chart:

If you could go back to 2022, and tell people that in four years, AI would be solving frontier math problems, but we’d still have the same number of people working as translators, how many would have believed you?

It turns out that it’s very natural for people to overestimate the degree to which AI will take their jobs. Hartley et al. (2026) have a really excellent paper called “Job Loss Fears in the First Years of Generative Artificial Intelligence”. Here’s a thread explaining the paper’s findings.

Basically, the authors find that fear of AI job replacement is extremely common:

And they find that the more people’s jobs are exposed to AI, the more they think their jobs are about to be replaced:

In fact, the more of their day people spend using AI at work, the more they’re afraid of being replaced!

And yet when the authors looked for a correlation between AI exposure and actual job loss, they found…absolutely nothing. People’s fears simply haven’t come true yet.

What’s going on? The authors hypothesize that people who use AI more start to understand its ability to replace the tasks they do at work. But as we keep finding, replacing tasks isn’t the same as replacing jobs. People keep finding new things to do in their roles at work — sometimes things AI can’t do yet, but often things that couldn’t even be done until AI made them possible!

It seems like we’re uncovering a consistent human blind spot here: People don’t actually know how they produce value at their jobs. Modern jobs are much more than a simple collection of tasks — they are pieces of a complex machine that produces value in ways that an individual worker often doesn’t even see.2 So when AI comes along and starts replacing people at various tasks, it just ends up making them more valuable as pieces of their corporate machines.

How long that situation will persist, of course, is an open question. AI leaders are starting to realize that it might take a very long time for the full effect of their inventions to be felt:

This is why the AI companies’ recent messaging pivot — many now say that AI will create jobs rather than destroying them — may be honest, rather than a cynical marketing ploy to calm public outrage.

But then there’s the question: Can this situation persist indefinitely? No one knows, of course. But my bet is that while many occupations will eventually be mostly replaced by AI, humans will still have plenty to do. I’ve argued that in order for AI to start replacing human jobs wholesale, it’ll have to get much more agentic — which will make it inherently more unreliable from a human point of view. So I predict that humans will always have jobs keeping AI agents on track.

Even if I’m wrong, though — even if the AI job apocalypse does eventually come — it doesn’t seem like it’s coming soon, and it certainly isn’t here right now. Everyone keeps thinking that AI is a job killer, and AI keeps on refusing to be what everyone expects.

Happy Labor Day!

Update: John Cassidy has another good post on this same topic.

Update 2: The excellent Alex Imas has a great post rounding up a bunch of recent studies, and concludes that although AI is reshuffling jobs to some degree, it isn’t displacing humans yet:

Ghosts of Electricity

Has AI impacted the labor market yet?

Written with Jacob Schaal, subscribe to his blog here. All opinions are our own and do not reflect those of our employers…

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