2026-10-09 17:18:32
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.
But not impossible, of course.
2026-10-07 17:47:37
“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.
This made me sad, both because I wanted the chance to challenge Rufo in person over his promulgation of the “Hatians eating cats” rumor in 2024, and I thought it would be deliciously ironic to have a podcast in which two Jews and a second-generation Italian American with a Southeast Asian immigrant wife debated whether Indians belong in America. Oh well, maybe next time.
For a good history of Teddy Roosevelt’s racial ideas, I recommend the book American Crucible, by Gary Gerstle.
And, interestingly, at the Communist Party convention!
2026-10-05 16:12:53
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:
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.
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.
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.
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.
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.
I’m kind of annoyed by the use of “trickling down” to describe the broad benefits of an investment boom. Yes, businesses make investment decisions, but this is the case in every boom, and for economic growth in general. By this definition, pretty much all benefits in the entire economy “trickle down”, except perhaps for the tiny amount produced by worker-owned co-ops.
2026-10-03 16:47:17
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?
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.
2026-10-01 14:14:51
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.
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!
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:
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.
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!!
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.
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:
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.
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:
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.
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.
Probably still a little higher, because the official growth rate is overstated.
2026-09-29 06:27:25
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.
About 0.03 standard deviations. For references, 0.03 standard deviations on the SAT would be about 3 points out of 1600.
