Billions of people look at their phones and see the whole world. But some theorists look at the whole world and see only phones.

The rise of youth anxiety? It’s the phones. The rise of global populism? The phones, again. The surge in attention disorders in the U.S.? The global decline in literacy? The scourge of political polarization? Phones, phones, and more phones.

The NYU professor Arpit Gupta has called this the “Smartphone Theory of Everything.” It is the notion that an unholy nexus of smartphones, the Internet, and social media is uniquely to blame for practically every modern malady.

But which of these associations are backed by solid evidence, and which are backed by little more than confident assertion?

I have been reporting on this space for several years, and this article is my best attempt to separate the strongest claims from the weakest, according to the best science available. Rather than base my analysis on individual correlational studies, I leaned on randomized trials, meta-analyses that evaluated hundreds of studies, and a “consensus survey” that asked hundreds of academics what they thought about the effect of smartphone use on personal and mental health.

In my experience, the loudest voices can sometimes misrepresent the underlying evidence. The strongest proponents of the SToE often ignore the stubborn fact that phones are global but their worst effects are strangely concentrated in the richest, English-speaking countries. But the fiercest critics of the SToE often ignore the findings of randomized trials and real-world experiments, such as phone bans in schools, which have mostly shown that taking phones away from people makes them a little happier and better at focusing.

The subject of smartphones’ effect on the world is so vast, so complicated, and in some cases so uncertain that the remainder of this article is divided into two parts. First, I’m going to break out four big truths about the smartphone/social media/internet literature. Second, I’m going to do my best to take the most common claims about smartphones in the world—e.g., they make people sad, distracted, conspiratorial, unmarried, etc.—and place these claims into three buckets: (1) Strong evidence; (2) Mixed evidence; and (3) Weak evidence.

If I do my job well, this article can serve not only as a useful synthesis for scholars and ordinary readers but also as a living document that I can update every year, as we learn more about the 21st century’s most famous theory of everything.

I. FOUR BIG TRUTHS ABOUT SMARTPHONES

1. Smartphones and social media are hard to study well. This basic fact cuts two ways. It means SToE crtitics can overstate the evidence, but it also means the evidence can understate the real effect of phones.

“Getting clean causal evidence on the long-term impact of phones and social media is devilishly hard,” said Matthew Gentzkow, a Stanford economist. “Re-running history without iPhones or Facebook is not a feasible experiment. The randomized experiments we can do are generally short-term limited interventions, and observational data analysis over longer horizons faces big hurdles in inferring causality. The hard work of research is stitching together the data points we can muster to fill in the overall picture while remaining cognizant of how much we don’t know.”

People like to compare smartphones and cigarettes. Let me say something here that begs to be taken out of context: I wish smartphones were cigarettes. If the phone experience were a mass-manufactured bundle of chemicals that we could test in isolation against a control group, then the science of proving or disproving the harm of phones would be trivially easy. This whole essay could be one sentence long: We did some tests on phones, and they’re definitely giving people cancer.

Unfortunately— for research purposes, only—smartphones are not tobacco. Everybody’s online experience is unique, which means that everybody is effectively smoking a slightly different cigarette. No surprise, then, that observational analyses struggle to prove causality, and randomized experiments to prove causality are typically brief and limited. You can’t assign child participants to heavy social media use for a full year, and you certainly can’t randomly assign kids to use their smartphones in a specific way for a long time. (“Hi Madison, we need you to spend your entire junior year marinating in angry left-wing Reddit posts to measure the impact of online Marxism on the teenage mind” is not a plausible study design.) If you force participants to deactivate Facebook in a study, they might just download Twitter; in fact, that’s exactly what happened in a 2020 study. If you force them to give up their phones entirely, they’ll still need to maintain desktop web access; in fact, that’s exactly what happened in a 2025 study.1

Another reason that smartphones are hard to study is that, like alcohol, they might have small effects on the majority population and large effects on a minority population. In 2020, Instagram’s own analysis concluded that its product “made body image issues worse” for one third of teenage girls. That’s a big number! But it also implies that for the majority of teenage girls, Instagram had a small or negligible effect. And, of course, most people are not teenage girls.

Cellphone Cassandras can exaggerate the conclusions of careful research, which often show small overall effects. But critics of the SToE often ignore practical conclusions by fixating exclusively on small overall effect sizes, despite the evidence of significant long-tail effects. Maybe both sides should get comfortable thinking about social media as “attention alcohol”: fun if moderately problematic for most, and very dangerous for some.

2. There’s something weird about America—and, maybe, the entire English-speaking world.

One of the most interesting wrinkles in the smartphone theory of everything is that while phones are everywhere, the problems that they cause are often rising fastest and first in the richest countries—especially in the U.S.

Take the theory that smartphones make people sad. According to the latest World Happiness Report, happiness among young people has plummeted most severely in Western developed countries that speak English, such as the United States, Great Britain, Canada, Australia, and New Zealand. On the other hand, “happiness at every age has risen sharply in Central and Eastern Europe,” the report said. In East Asia, happiness is increasing “at every age.” The same is true for suicide. Emergency-room visits for suicide attempts among young women soared across the Anglosphere in the last few years. But as I’ve reported, the suicide rate among people ages 15 to 19 fell in most European countries in the last decade.

Or take attention deficit disorders. The surge in ADHD cases seems to be another US-heavy phenomenon, with American child diagnoses rising at roughly twice the rate of European countries. To understand rising anxiety and attention disorders in the U.S., we have to recognize the phenomenon of diagnostic inflation—i.e., medical providers expanding the definition of anxiety and ADHD to treat more cases.

Or take polarization. The U.S. also seems to be an outlier in some maladies that are associated with smartphones. A 2020 paper on polarization in the west found that “affective polarization”—the degree of hostility that people feel toward the party they oppose—rose fastest and first in the U.S., with most of the increase predating the smartphone age. The researchers wrote that polarization took off in the 1990s, right around the introduction of Fox News, which was 20 years before the smartphone revolution took off.

Or take populism and distrust. Again, the strongest effects seem concentrated in the U.S. A 2023 Nature Human Behaviour review of 496 articles found that digital media is most strongly associated with declining political trust and growing populism in developed democracies, such as the United States. In developing democracies, by contrast, the largest effect of digital media on politics seems to be that it increases political participation.

What do we make of all this? One interpretation might be that if smartphones are global and these effects are localized, then the smartphone theory of everything is just bunk. I don’t buy that. I think it’s more likely that smartphones are an active ingredient that’s interacting with other phenomena that are distinctly western or American, in order to create berserk local effects. It’s possible that in a few years, most academics will agree with some version of this thesis statement: Compulsive phone use along with under-regulated social media reliably produce widespread anxiety, attention issues, polarization, populism, and institutional distrust in highly individualistic societies with a culture of diagnostic inflation [i.e., expanded diagnostic guidelines for anxiety and ADHD], negative-affect prevalence [i.e., people online constantly talking about their anxiety and ADHD], and high levels of negativity in the news ecosystem … and post-2010 America was simply the first and most dramatic example of all these ingredients coming together.

3. People overrate phones’ effect on misinformation—and underrate their effect on information.

In 2018, Gentzkow and other researchers paid about 1,700 Americans to deactivate Facebook for four weeks before the midterm elections. Those who logged off were happier, less anxious, and less politically polarized.

Findings like these have trickled through academia into mainstream media and the public discourse, where parents, teachers, depressed youths, and politicians are well-primed to see smartphones and social media as The Problem^TM. But here’s what almost nobody talks about: The people who deactivated also knew less in general about the world. In a second, larger study of more than 35,000 Facebook and Instagram users, the same thing happened.

This finding is a Rosetta Stone for the smartphone debate, because it reveals something both sides keep getting wrong: Smartphones are above all an information-delivery system—a relentless, inescapable IV drip of news, connection, outrage, friendship, conspiracy, solidarity, and garbage—whose effect on any individual depends significantly on what’s in the drip2.

If smartphones are making Americans depressed, conspiratorial, and anxious, it might be because the news is structurally becoming more depressing, conspiratorial, and anxious. In 2023, researchers at the University of Pennsylvania, Indiana University, and London Business School used AI to trace positive vs. negative words across tens of millions of newspaper articles from the 1850s to the 2020s. For more than a century, news positivity hovered around a stable average. But after the 1960s, negativity surged. “News coverage has just gotten more and more negative every decade in the last 50 years, especially when you adjust for economic recessions,” UPenn economist J. H. van Binsbergen, a co-author on the paper, told me. As I wrote in an essay for The Atlantic, I suspect that as the media industry got more competitive in the past few decades, publishers desperate to command reader attention doubled down on the old clichés that “if it bleeds, it leads” and “bad is better than good.” In this light, phones didn’t make the news more depressing so much as they made it easier to access depressing news.

4. The biggest problem with smartphones isn’t what’s on the phone, but rather what’s not on the phone.

My favorite Jonathan Haidt argument is that phones replace play-based adolescence with phone-based adolescence. That is, the most important thing about phones isn’t what’s on the screen, but rather everything that’s off the screen when you’re lost gazing into your pocket device.

While it’s hard for researchers to control what participants are doing with their phones, it’s not hard for researchers to see what people are doing when they’re not on their phones. They sleep more! They socialize more! They go outside more! (And, yes, they watch TV more.)

In 2025, researchers found that randomly removing internet access from smartphones produced a range of benefits, including improved mental health, subjective well-being, and the ability to sustain attention. More than 90 percent of the nearly 500 participants experienced at least one benefit. As best as the researchers could tell, the most significant reason for improved mental health and subjective well-being came from participants spending more time “socializing in person, exercising, and being in nature.”

Another randomized trial that paid people to deactivate Facebook before the 2018 midterm elections also found that people spent more time with friends and family. From that paper:

The 60 minutes freed up by not using Facebook … were allocated to both solitary and social activities offline. Solitary television watching increases by 0.17 points on our scale; other solitary offline activities increase by 0.23 points, and time devoted to spending time with friends and family increases by 0.14 points.

To summarize everything I’ve just said in a brief paragraph: Phones are global, but what’s on our phones is exquisitely individual. For this reason, overall phone effects are hard to study. They are best understood as a relentless information-delivery system whose utility or harm is exquisitely dependent on the type of information that people access. This might explain why cultures with more anxious or polarizing content—such as the U.S.—see higher and faster rates of anxiety and polarization. Rather than adopt an empirical nihilism about all this (ah, well, phones are complicated, let’s just do nothing!), we should pay close attention to the consistent finding that people tend to be a little happier and little more attentive when they un-hook from the information-IV drip of their personal devices.

That’s an awfully broad summary! So, let’s talk about some specific claims, such as:

• Are phones really so bad for sleep?

• How strong is the evidence that they cause population-wide anxiety?

• Are they turning us into conspiracy theorists?

• What does the research say about their role in explaining rising populism and declining marriage rates?

In this next section, I’ve done my best to place 10 popular claims into three buckets: Strong evidence, mixed evidence, and weak evidence.3

II. THE 10 MOST POPULAR CLAIMS ABOUT PHONES

STRONGEST EVIDENCE

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The landscape of AI is not merely filled with news. It is filled with teams. You have the doomers, the accelerationists, the skeptics, the it’s-a-bubble oracles, the anti-bubble counter oracles, and so on. It would be convenient for my sanity—and, perhaps, the sanity of my readers—if I simply joined one team and never removed the jersey. But I don’t think any aforementioned tribe has a monopoly on good arguments. I think the doomers are right about the risk of the technology, and the accelerationists are right about the promise of the technology, and the skeptics are right that the doomers and accelerationists can both overstate their cases.

So I’m trying to make sure that my coverage of AI cuts across silos and allows readers to hear from all of the teams. Last week, I published an interview with the investor and writer Paul Kedrosky on the case for AI being an economic bubble. But if any single data point pierces that narrative, it’s this: Between December 2025 and March 2026, the AI lab Anthropic more than doubled its annual recurring revenue from $9 billion to more than $20 billion. According to several analysts, there is no record of any company growing this fast at this scale … ever. It’s hard to imagine that artificial intelligence is both a bubble and the home to the industry witnessing the fastest-growing businesses in history.

Today’s conversation is with Anthropic co-founder Jack Clark. I don’t need Jack or anybody at Anthropic to read me a corporate statement about the company’s revenue growth. I can do that, myself. What I really wanted to hear from Jack were his answers to the deepest, thorniest philosophical questions about the meaning of AI. Questions like:

• If Anthropic’s executives believes that AI might be as dangerous as nuclear weapons, what right does any private business have to build this sort of thing for profit?

• If AI is really so good at making people more productive, why do Americans overall say they disapprove of AI more than just about every other institution and individual in the world?

• Does Anthropic really believe that AI will lead to imminent mass unemployment?

• Why—as Noah Smith recently put it—does this industry insist on “our product will make you economically useless, and possibly kill you!” as a marketing strategy?

• Why does AI still seem quite inept at coming up with truly original insights?

• How does Anthropic use its own autonomous agents to increase productivity within the company?

• If other companies learn to use agents effectively, is knowledge work “cooked”?

• How should we raise our children in an age of AI?

• And what values would super-intelligence make even more important than they are today?

IF AI IS LIKE A NUKE, WHY SHOULD PRIVATE COMPANIES BUILD IT, AT ALL?

Derek Thompson: Anthropic has compared artificial intelligence to nuclear weapons on several occasions. Most recently in January, Dario Amodei, the CEO of Anthropic, said that the Trump administration’s decision to allow advanced NVIDIA chips to be exported to China was “a bit like selling nuclear weapons to North Korea.”

The US does not allow private companies to build nuclear weapons. That is the law. If artificial intelligence is just like nuclear weapons, why should we allow private firms to build it for profit?

Jack Clark: AI is fundamentally like everything. It’s like a factory that produces cars, micro scooters, animals, and nuclear weapons all at the same time. And the main question we’re going to have to deal with as a society is how do you govern those factories and how do you decide what the appropriate uses are of the things that come out? I can’t talk about the specifics of our ongoing discussions with the Department of Defense. I can say that Anthropic was extremely committed to working on national security early because we recognize that AI is going to touch every single part of life, and every single part of life is going to have its own range of incredibly thorny, difficult issues. We’re going to need a much larger societal conversation about how we govern this technology in general, and we will need to reckon with the fact that the technology comes from the private sector and then flows into all of these other sectors. That’s going to be really challenging. It’s something we haven’t encountered before, because previously you didn’t have a technology that could become anything. You had specific technologies built by specific industries for specific purposes, and that was in many ways simpler.

Thompson: I want to hear a robust defense of why this is the private sector’s job. The nuclear analogy is invoked in so many different ways: for export controls, for arguments about government attention, for arguments about existential stakes, for arguments about the need for international cooperation. But one conclusion this analogy very clearly supports is that the private sector should not control this technology.

So why does the analogy apply almost everywhere except here, where private sectors are developing frontier AI for profit while the government attempts to regulate it from the outside?

Clark: We worked for many years with the National Nuclear Security Administration to actually test out how well AI could understand aspects of nuclear technology. And we used that to develop evals and ways of ensuring that we don’t proliferate things into the world that have an understanding of nuclear weapons. That’s almost a very positive example of how you would have the private sector work with government, where some things absolutely should only be the domain of government, like nuclear weapons. The job of a company producing a technology that can take on many different aspects is to work out the areas where it’s inappropriate to deploy that technology, like nuclear weapons, and then work with government to take that capability surface off. I think that describes some of the path we’re going to have to pursue here. And it’s one that most of the industry is going down, including for biological weapons.

Thompson: So you’re saying the right way to think about this is that AI is this is a multifarious factory technology, where you are creating super-powered Excel charts, which is a technology that has no precedent for government regulation, but you’re also creating technology that can be used by the Pentagon or by individuals to have militaristic or dangerous ends. And so the analogy with nuclear weapons is true insofar as it is contained to the parts of your technology that are like nuclear weapons, but you’re also doing a lot of other things that have no analogy in nuclear weapons, like making white collar workers a little bit more productive at their desk jobs. Is that a fair summary?

Clark: There are almost two problems here. One is that you have this factory that can produce anything, so you make sure that what comes out correlates to what we’ve decided society can have available in the free market. Not nuclear weapons. Yes, things that accelerate knowledge workers. And then you have this second question of, given the multifaceted nature of what can be produced, how do you work with government or academia or other parties on the things which you can’t push out to the world in general, but which have value in the rest of the world? An example here is biology. For [technology] that can massively accelerate the development of biological science, you need to work out: what is the path to bringing that technology about? So some of the conversation that society is going to have now is, what are the appropriate ways we want this technology to be used, and how do people decide what to do with different things in this factory, and how to proliferate them, so society gets the benefit?

ANTHROPIC’S UNUSUAL MARKETING PITCH: OUR PRODUCT MIGHT DESTROY YOUR JOB—OR THE WORLD

Thompson: Anthropic CEO Dario Amodei has predicted on several occasions that AI will destroy half of all entry-level white collar positions and spike unemployment to as high as 20%, which would be the highest unemployment rate since the Great Depression. He said this could happen in as soon as five years. Do you agree with that forecast?

Clark: We’re talking about one of the potential things that can happen, and I think it’s worth noting that this is a choice. I don’t agree with this, because I think it’s a choice that we can make. Also, my personal view based on the data I look at is that big changes in employment take a long time to filter through to the economy. And even with the magnitude of what we’re talking about, you might expect it to take longer.

Let’s say there is the potential for massive employment changes. I think that this is accompanied by the fact that AI must also be growing the economy a lot and causing a lot of economic activity. If that is the case, then you would expect more freedom about [designing] policy and [deciding] what we do with this economy. If you end up in a situation where employment is negatively affected by AI in one part of the economy, but loads of money being generated by AI in another part of the economy, you could choose to create jobs, like teaching or nursing, where we have a preference for more people working. You could both increase the number of jobs and do things like cross-sector wage subsidies to improve the wages of those jobs, where today we severely under-compensate them.

Thompson: It is unusual in corporate history for a company to announce that if its product is successful, tens of millions of people will lose their jobs and there’s a non-zero chance that we end the human race entirely. I think the analogy I’ve used before is that Henry Ford would have been within the realm of reality if he said, “If this Model T thing takes off, hundreds of thousands of Americans are going to die every decade from car accidents.” In fact, that’s happened. But Ford and GM did not talk like that in the 1910s and 1920s. What is the strategy of communicating your technology to the American people as a means by which we might have 20 percent unemployment and a non-zero chance of human catastrophe?

Clark: These are not the outcomes we want or anyone in the industry wants. But I think the industry has also learned from the overly rosy predictions made by many in the technology industry before, about how the only effect they’d have on the world would be unalloyed positivity. And I think the world lost huge amounts of trust in the technology industry because of that, because they saw that it wasn’t only positive. Social media has caused a range of amazing positives in the world and a range of harms, which we’re now dealing with. The ethos here, and why I’m working on a new initiative called the Anthropic Institute, is to share a lot more data about what we see in front of us so that society is better prepared for any of the different changes which could come along.

We also don’t spend enough time talking about all of the really positive changes, which I think are a choice that we can make as a civilization. But it would be negligent of us to not call out that there are ways we as a species could get this technology wrong. If you look at scientists and people that have worked on transformational technologies before in biology or in the early days of nanotechnology, they’ve all talked about this combination of upsides and risks. It’s just that AI as a sector has matured and made a lot more impact on the markets than either of those classes of technology over the same time period, so everything’s accentuated.

WHY DO AMERICANS HATE AI?

Thompson: I hear the argument that you are reacting to the social media experience. But I also look at polling. Last week, NBC News published a national survey on attitudes toward a range of politicians and institutions. AI’s net favorability was negative 20. That’s below every politician surveyed in the poll and it’s below ICE. Why do you think people say they hate artificial intelligence?

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Here are three stories about the state of gambling in America.

1. Baseball

In November 2025, two pitchers for the Cleveland Guardians, Emmanuel Clase and Luis Ortiz, were charged in a conspiracy for “rigging pitches.” Frankly, I had never heard of rigged pitches before, but the federal indictment describes a scheme so simple that it’s a miracle that this sort of thing doesn’t happen all the time. Three years ago, a few corrupt bettors approached the pitchers with a tantalizing deal: (1) We’ll bet that certain pitches will be balls; (2) you throw those pitches into the dirt; (3) we’ll win the bets and give you some money.

The plan worked. Why wouldn’t it? There are hundreds of pitches thrown in a baseball game, and nobody cares about one bad pitch. The bets were so deviously clever because they offered enormous rewards for bettors and only incidental inconvenience for players and viewers. Before their plan was snuffed out, the fraudsters won $450,000 from pitches that not even the most ardent Cleveland baseball fan would ever remember the next day. Nobody watching America’s pastime could have guessed that they were witnessing a six-figure fraud.

2. Bombs

On the morning of February 28th, someone logged onto the prediction market website Polymarket and made an unusually large bet. This bet wasn’t placed on a baseball game. It wasn’t placed on any sport. This was a bet that the United States would bomb Iran on a specific day, despite extremely low odds of such a thing happening.

A few hours later, bombs landed in Iran. This one bet was part of a $553,000 payday for a user named “Magamyman.” And it was just one of dozens of suspicious, perfectly-timed wagers, totaling millions of dollars, placed in the hours before a war began.

It is almost impossible to believe that, whoever Magamyman is, he didn’t have inside information from members of the administration. The term war profiteering typically refers to arms dealers who get rich from war. But we now live in a world not only where online bettors stand to profit from war, but also where key decision makers in government have the tantalizing options to make hundreds of thousands of dollars by synchronizing military engagements with their gambling position.

3. Bombs, again

On March 10, several days into the Iran War, the journalist Emanuel Fabian reported that a warhead launched from Iran struck a site outside Jerusalem.

Meanwhile on Polymarket, users had placed bets on the precise location of missile strikes on March 10. Fabian’s article was therefore poised to determine payouts of $14 million in betting. As The Atlantic’s Charlie Warzel reported, bettors encouraged him to rewrite his story to produce the outcome that they’d bet on. Others threatened to make his life “miserable.”

A clever dystopian novelist might conceive of a future where poorly paid journalists for news wires are offered six-figure deals to report fictions that cash out bets from online prediction markets. But just how fanciful is that scenario when we have good reason to believe that journalists are already being pressured, bullied, and threatened to publish specific stories that align with multi-thousand dollar bets about the future?

Put it all together: rigged pitches, rigged war bets, and attempts to rig wartime journalism. Without context, each story would sound like a wacky conspiracy theory. But these are not conspiracy theories. These are things that have happened. These are conspiracies—full stop.

“If you’re not paranoid, you’re not paying attention” has historically been one of those bumperstickers you find on the back of a car with so many other bumperstickers that you worry for the sanity of its occupants. But in this weird new reality where every event on the planet has a price, and behind every price is a shadowy counterparty, the jittery gambler’s paranoia—is what I’m watching happening because somebody more powerful than me bet on it?—is starting to seem, eerily, like a kind of perverse common sense.

FROM LAUNDROMATS TO AIRPLANES

What’s remarkable is not just the fact that online sports books have taken over sports, or that betting markets have metastasized in politics and culture, but the speed with which both have taken place.

For most of the last century, the major sports leagues were vehemently against gambling, as the Atlantic staff writer McKay Coppins explained in his recent feature. In 1992, NFL commissioner Paul Tagliabue told Congress that “nothing has done more to despoil the games Americans play and watch than widespread gambling on them.” In 2012, NBA commissioner David Stern loudly threatened New Jersey Gov. Chris Christie for signing a bill to legalize sports betting in the Garden State, reportedly screaming, “we’re going to come after you with everything we’ve got.”

So much for that. Following the 2018 Supreme Court decision Murphy vs. NCAA, sports gambling was unleashed into the world, and the leagues haven’t looked back. Last year, the NFL saw $30 billion gambled on football games, and the league itself made half a billion dollars in advertising, licensing, and data deals.

Nine years ago, Americans bet less than $5 billion on sports. Last year, that number rose to at least $160 billion. Big numbers mean nothing to me, so let me put that statistic another way: $5 billion is roughly the amount Americans spend annually at coin-operated laundromats and $160 billion is nearly what Americans spent last year on domestic airline tickets. So, in a decade, the online sports gambling industry will have risen from the level of coin laundromats to rival the entire airline industry.

And now here come the prediction markets, such as Polymarket and Kalshi, whose combined 2025 revenue came in around $50 billion. “These predictive markets are the logical endpoint of the online gambling boom,” Coppins told me on my podcast Plain English. “We have taught the entire American population how to gamble with sports. We’ve made it frictionless and easy and put it on everybody’s phone. Why not extend the logic and culture of gambling to other segments of American life?” He continued:

Why not let people gamble on who’s going to win the Oscar, when Taylor Swift’s wedding will be, how many people will be deported from the United States next year, when the Iranian regime will fall, whether a nuclear weapon will be detonated in the year 2026, or whether there will be a famine in Gaza? These are not things that I’m making up. These are all bets that you can make on these predictive markets.

Indeed, why not let people gamble on whether there will be a famine in Gaza? The market logic is cold and simple: More bets means more information, and more informational volume is more efficiency in the marketplace of all future happenings. But from another perspective—let’s call it, baseline morality?—the transformation of a famine into a windfall event for prescient bettors seems so grotesque as to require no elaboration. One imagines a young man sending his 1099 documents to a tax accountant the following spring: “right, so here are my dividends, these are the cap gains, and, oh yeah, here’s my $9,000 payout for totally nailing when all those kids would die.”

It is a comforting myth that dystopias happen when obviously bad ideas go too far. Comforting, because it plays to our naive hope that the world can be divided into static categories of good versus evil and that once we stigmatize all the bad people and ghettoize all the bad ideas, some utopia will spring into view. But I think dystopias more likely happen because seemingly good ideas go too far. “Pleasure is better than pain” is a sensible notion, and a society devoted to its implications created Brave New World. “Order is better than disorder” sounds alright to me, but a society devoted to the most grotesque vision of that principle takes us to 1984. Sports gambling is fun, and prediction markets can forecast future events. But extended without guardrails or limitations, those principles lead to a world where ubiquitous gambling leads to cheating, cheating leads to distrust, and distrust leads ultimately to cynicism or outright disengagement.

“The crisis of authority that has kind of already visited every other American institution in the last couple of decades has arrived at professional sports,” Coppins said. Two-thirds of Americans now believe that professional athletes sometimes change their performance to influence gambling outcomes. “Not to overstate it, but that’s a disaster,” he said. And not just for sports.

FOUR WAYS TO LOSE (OR: WHAT’S A ‘RIGGED PITCH’ IN A WAR?)

There are four reasons to worry about the effect of gambling in sports and culture.

The first is the risk to individual bettors. Every time we create 1,000 new gamblers, we create dozens of new addicts and a handful of new bankruptcies. As I’ve reported, there is evidence that about one in five men under 25 is on the spectrum of having a gambling problem, and calls to the National Problem Gambling Helpline have roughly tripled since sports gambling was broadly legalized in 2018. Research from UCLA and USC found that bankruptcies increased by 10 percent in states that legalized online sports betting between 2018 and 2023. People will sometimes ask me what business I have worrying about online gambling when people should be free to spend their money however they like. My response is that wise rules place guardrails around economic activity with a certain rate of personal harm. For alcohol, we have licensing requirements, minimum drinking ages, boundaries around hours of sale, and rules about public consumption. As alcohol consumption is declining among young people, gambling is surging; Gen Z has replaced one (often fun) vice with a meaningful chance of addiction with another (often fun) vice with a meaningful chance of addiction. But whereas we have centuries of experience curtailing excessive drinking with rules and customs, we are currently in a free-for-all era of gambling.

The second risk is to individual players and practitioners. One reason why sports commissioners might have wanted to keep gambling out of their business is that gamblers turns some people into complete psychopaths, and that’s not a very nice experience for folks on the receiving end of gambling-afflicted psychopaths. In his feature, McKay Coppins reports on the experience of Caroline Garcia, a top-ranked tennis player, who said she received torrents of abusive messages from gamblers both for losing games and for winning games. “This has become a very common experience for athletes at the professional level, even at the college level too,” Coppins said. As the experience of journalist Emanuel Fabian shows, gambling can turn ordinary people into mini mob bosses, who go around threatening players and practitioners who they believe are costing them thousands of dollars.

The third risk is to the integrity of sports—or any other institution. At the end of 2025, in addition to its indictment of the Cleveland Guardians pitchers, the FBI announced 30 arrests involving gambling schemes in the NBA. This cavalcade of arrests has dramatically reduced trust in sports. Two-thirds of Americans now believe that professional athletes change their performance to influence gambling outcomes. It does not require extraordinary creativity to imagine how this principle could extend to other domains and institutions. If more people start to believe that things only happen in the world as a direct result of shadowy interests in vast betting markets, it’s going to be a permanent open season for conspiracy theories.

The ultimate risk is almost too dark to contemplate in much detail. As the logic and culture of casinos moves from sports to politics, the scandals that have visited baseball and basketball might soon arrive in politics. Is it really so unbelievable that a politician might tip off a friend, or assuage an enemy, by giving them inside information that would allow them to profit on betting markets? Is it really so incredible to believe that a government official would try to align policy with a betting position that stood to earn them, or an allied group, hundreds of thousands of dollars? That is what a “rigged pitch” in politics would look like. It’s not just wagering on a policy outcome that you suspect will happen. It’s changing policy outcomes based on what can be wagered.

THE FINAL VIRTUE

Gambling is flourishing because it meets the needs of our moment: a low-trust world, where lonely young people are seeking high-risk opportunities to launch them into wealth and comfort. In such an environment, financialization might seem to be the last form of civic participation that feels honest to a large portion of the country. Voting is compromised, and polling is manipulated, and news is algorithmically curated. But a bet settles. A game ends. There is comfort in that. In an uncertain and illegible world, it doesn’t get much more certain and legible than this: You won, or you lost.

A 2023 Wall Street Journal poll found that Americans are pulling away from practically every value that once defined national life—patriotism, religion, community, family. Young people care less than their parents about marriage, children, or faith. But nature, abhorring a vacuum, is filling the moral void left by retreating institutions with the market. Money has become our final virtue.

I often find myself thinking about the philosopher Alasdair MacIntyre, who argued in the introduction of After Virtue that modernity had destroyed the shared moral language once supplied by traditions and religion, leaving us with only the language of individual preference. Virtue did not disappear, I think, so much as it died and was reincarnated as the market. It is now the market that tells us what things are worth, what events matter, whose predictions are correct, who is winning, who counts. Money has, in a strange way, become the last moral arbiter standing—the final universal language that a pluralistic, distrustful, post-institutional society can use to communicate with itself.

As this moral vocabulary scales across culture, it also corrodes culture. In sports, when you have money on a game, you’re not rooting for a team. You’re rooting for a proposition. The social function of fandom—shared identity, inherited loyalty, something larger than yourself—dissolves into individual risk. In politics, I fear the consequences will be worse. Prediction markets can be useful for those who want to know the future, but their utility recruits participants into a relationship with the news cycle that is adversarial, and even misanthropic. A young man betting on a terrorist attack or a famine is not acting as a mere concerned citizen whose participation improves the efficiency of global prediction markets. He’s just a dude, on his phone, alone in a room, choosing to root for death.

If that doesn’t bother you, I don’t know how to make it bother you. Based on economic and market efficiency principles alone, this young man’s behavior is defensible. But there is morality outside of markets. There is more to life than the efficiency of information networks. But will we rediscover it, any time soon? Don’t bet on it.

The funny thing about unprecedented events is that they tend to evoke the strongest reactions, even though, by their very nature, unprecedented phenomena have no track record that clearly points us toward a likely outcome. So it is for artificial intelligence.

What you’re seeing in the remarkable chart above is that private sector spending on AI in 2026 is forecast to exceed $700 billion, which is not a number that is easy to think about. As a share of GDP, companies are currently devoting more resources to AI than the combined peak annual capital spending on key 1930s public works projects and the Manhattan Project and the 1940s electricity boom and the Apollo Project and Interstate highway construction. It’s important to note that AI spending is overwhelmingly financed by the private sector, whereas most of those infrastructure projects were financed by the largesse of the federal government. Once again, nothing like this has ever happened before, and if you feel extremely confident about how this is going to turn out, I think you might be crazy.

Alas, when one’s chosen profession is writing essays and making podcasts and generally presenting oneself as a pundit, comfy ambivalence gets you absolutely nowhere. So, rather than stick to one extremely boring yet honest message—I’m uncertain about the future—I’ve tried to stake out falsifiable predictions about AI, at the risk of changing my mind often. Six months ago, my strongest feelings about the future of AI hewed closely to a framework articulated by the economist Carlota Perez. In her book Technological Revolutions and Financial Capital, Perez showed that general purpose technologies tend to produce economic bubbles before they produce technological revolutions. Whether it’s the canal mania of the first industrial revolution, the transcontinental railroad of the 19th century, or the dot-com bubble of the early 21th century, the beats of the story are as familiar and predictable as a Save the Cat story arc. Once upon a time, Shiny New Thing is born. Speculative capital pours in. Companies get so excited about the prospect of Shiny New Thing that, failing to coordinate their spending to perfectly match forthcoming demand, they overbuild. The Shiny New Thing bubble pops. Then productive capital takes over. Firms learn to use Shiny (Not So) New (Anymore) Thing. As the technology diffuses throughout the economy, a golden age of broad and steady growth follows. One of Perez’s core insights was that bubbles and golden ages are not a one-or-the-other kind of thing. They often arrive in sequence: one, then the other.

Riding the coattails of Perez, I was very certain that AI was a bubble, for the simple reason that I thought AI spending was rising faster than revenue could possibly match it. But in the last few weeks, I changed my mind. And I want to be very clear about what changed it.

In late 2025, the AI companies Anthropic and OpenAI released new agents—that is, AI that can work autonomously on complex projects that require multiple stages of reasoning. These agents became so popular, so quickly that Anthropic’s revenue doubled in two months, and OpenAI reportedly added one billion dollars in annualized revenue per week in the last few months. At this rate, these are two of the fastest growing companies of all time. And the AI revenue surge is not just at two frontier labs. The payments firm Stripe, which has a god’s-eye view of thousands of companies on its platform, has said that AI companies are growing revenue faster today than any other generation of companies they have ever seen.

The Perez bubble story suggested that general purpose technologies always build too fast while revenue comes along too slow. But the AI 2026 story seems like a rare exception: A historic rate of spending coinciding with a similarly historic surge of revenue.

As my confidence in the AI-bubble narrative weakened, I wanted a gut-check. Last year, I interviewed the investor and writer Paul Kedrosky, and it was hands-down one of the most popular interviews of the year. So, last week I called Paul and said: Try to convince me again. This interview is absolutely jam-packed. In an hour, we somehow cover:

• Why the biggest tech companies, after dominating the stock market in 2024 and 2025, have had such a rough start to 2026

• The omen of the “Saas-pocalypse”—the sharp decline in stock prices for several publicly traded software companies

• The growing private credit crisis, explained

• Why the enormous revenue boom from new agents like Claude Code might be a sugar high, in which explosive revenue growth today precedes much slower revenue growth after AI adoption among software engineers peaks

• If equity value is flowing if it’s leaving software

• Why productivity might seem to be rising (“The reason has nothing to do with AI,” Paul says.)

Below is a polished transcript of our conversation, organized by topic area.

‘WE’VE NEVER HAD A MOMENT LIKE THIS’

Derek Thompson: In a nutshell, what is the 2026 Paul Kedrosky thesis for why AI is a bubble?

Paul Kedrosky: AI is a bubble because it’s one of the probably five largest CapEx bubbles in history, meaning that at this moment where we’re building out infrastructure like canals, like railroads, like rural electrification, like fiber optics, where we’re building out this huge new substrate on top of which a lot of economic activity happens. And this is a particularly large example of that, to the point that it’s a material fraction of GDP growth, like 50 to 80% depending on the quarter and whose numbers you use. And these things inevitably lead to a series of rotating crashes as we overbuild and the assets become unable to pay their way with respect to the debt that’s used to finance them. And then there’s a big reset and then we maybe find another use for them. This happened with fiber, this happened with rural electricity, this happened with railroads, this happened with canals.

And if it didn’t happen this time, it would truly be the first time in modern economic history — which isn’t the same thing as saying that AI itself is somehow frivolous or useless. AI is an incredibly important technology. Saying that we’re in an infrastructure bubble is not the same thing as saying that these large language models don’t work, or they’re just autocomplete or whatever. These are two very different arguments.

And this particular moment is unique because it combines all of the things that we found in prior bubbles — loose credit, real estate, technology, government policy. We’ve never had a moment like this with a huge infrastructure buildout at the intersection of all four of the things that have caused the most consequential infrastructure bubbles in U.S. history. Every one of the actors feels like they’re acting in a rational way — “I don’t know what those guys are doing in technology, but we here in real estate, we know what we’re doing. And whenever we sign a lease contract to a hyperscaler, we know we’re looking at a prime credit.” So the consequence of all these rational actors is what finance theorists call a rational bubble — where the intersection of rational actors produces something that’s economically indefensible.

Thompson: The analogy you’ve given so often is the railroads. What was the lesson of the railroads?

Kedrosky: Railroads were a very good idea. They weren’t Beanie Babies. They were really important, and on top of them we did a lot of important economic activity, not least of which was settling the Western United States. But that didn’t prevent people from over-funding startup railroads such that roughly half of the track miles built at peak periods in the mid-19th century were eventually abandoned. Does that mean railroads were a bad idea? No. We just wildly overbuilt, because the impulse to build was so imperative that everyone building railroads felt like there’s an opportunity to be an oligopolist here — “When all of this shakes out, I’ll be the consolidator.” Which is a very similar impulse to what we hear today. Dario Amodei said this recently, that there will only be one or two, maybe three players at the end of all of this. And it leads to misaligned incentives — “I don’t mind that what I’m doing right now isn’t paying off, because over time I plan to be the consolidator.”

And the other lesson, which I think is particularly striking, is that people have forgotten not just that there was all this redundancy, but that the railroad buildup led to a series of financial crashes in the 1870s — the crash of ‘73, crash of ‘78, crash of ‘87 — each of which killed off a significant number of companies and financial institutions. Nevertheless, in 1900, railroads were roughly 62% of the index market capitalization in the United States. They were the technology company of their time. So building out that platform was rewarded, but was also consequential in terms of leading to various financial crises — and it played a secondary role in the Great Depression itself.

None of these things mean that railroads were a bad idea. But the carnage along the way was dramatic. The metaphor for me is that you can have a hugely valuable buildout that is really consequential for decades in terms of both productivity and economic and financial carnage.

Strikingly, technology writ large is around 60% of the all-U.S. index today. So you’re in a similar situation — this industry has grown to remarkable dominance of the broader equity indices, much like the railroads did. And much like the railroads, it’s become increasingly capital-intensive, which has consequences for how investors are looking at technology companies now versus the halcyon days of the 1970s.

Thompson: For folks interested in going a little deeper on the railroad analogy, we did a podcast with Richard White, the Stanford historian who wrote a wonderful book called Railroaded. His thesis is that the Transcontinental Railroad was a technology built by corrupt idiots working in concert with craven politicians, which led to one depression after another, panic after panic, inflation, deflation, crashes of the economy — and it was a good idea and it completely transformed the country. You can have yes, no, no, yes answers to the questions “Is it a bubble? Was it built by idiots? Is it important?” because they are profoundly different questions. Sometimes bad people in the process of crashing the U.S. economy over and over again nonetheless build technologies that in the long run we can’t imagine modern life without. It’s a strange, amoral feature of history.

Kedrosky: I completely agree.

WHY IT’S A BUBBLE, PART 1: THE HYPERSCALERS’ DILEMMA

Thompson: A lot’s changed since 2025 when we spoke, and I think some of those changes validate your prediction, and some complicate it. Do you still think that AI is an industrial bubble right now?

Kedrosky: Yes. AI is a bubble. There’s no question.

Thompson: Why?

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A brief explanation for the uninitiated, starting in the top right corner. “METR,” or Model Evaluation & Threat Research, is a non-profit organization that assesses the abilities and safety of cutting-edge AI. The X axis shows the dates that various AI models came out. The Y axis is … well, hoo boy, it’s complicated. For now let’s just say it estimates how good each model is at doing real human tasks, as measured by how long it takes people to do the same things. The line swooshing upward suggests something like: Holy shit, these silicon buggers are getting very good, very fast, at doing human work.

As if following the Moore’s Law of AI, these models seem to be doubling (at least) their ability to do complex and useful tasks every three to four months. When ChatGPT came out in November 2022, the best AI model could finish a 30-second task, like answering a multiple-choice test. This February, METR announced that the latest offering from Anthropic could complete computing tasks that take humans more than 14 hours.

METR’s analysis is load-bearing for AI advocates who predict that the technology is about to achieve escape velocity. To put things a bit more meme-ishly for the visual learners out there:

Many people are familiar with Goodhart’s law: When a measure becomes a target, it ceases to be a good measure. I think we need an update for 21st century discourse: When a measure becomes a meme, it gains in popularity what it forfeits in precision. That is, when a metric or chart becomes a totem of the discourse, it often takes on meanings and implications that are either false, misleading, or unintended by its original makers.

To get the throat-clearing done with: I think AI’s technical progress has been incredible, and I think the folks at METR are doing their earnest best to capture its rate of improvement. But even the organization’s employees are urging people to look more closely at what their research is and is not saying. “There are a bunch of ways that people are reading too much into the graph,” Sydney Von Arx, a member of METR’s technical staff, told Grace Huckins at MIT Technology Review.

After speaking to METR employees and reading their own explanations of the METR task measures, I’ve determined that there are several major misunderstandings about this chart. This isn’t a mere fact-checking exercise. I think a more realistic and nuanced understanding of this famous chart actually gives us the clearest possible understanding of where AI is headed and why it’s likely to be both more chaotic than AI skeptics believe and less economically impactful than most AI boosters predict.[Paid subscribers can read the full analysis below, plus the final takeaway.]

Myth #1: The METR chart measures AI’s ability to take over all human jobs.

Reality: Nope.

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Why do peoples often fight over a shared set of facts?

It’s hard to imagine a question that pops up more frequently in my professional and private life. When I’m covering the day’s news, I’m astonished that commentators on opposite sides of an issue can look at the same set of facts and determine that the Trump administration is either brilliant or bone-headed; that artificial intelligence is or isn’t replacing jobs today; or that abundance represents an faithful effort to jumpstart urban housing and clean-energy production or a corporatist conspiracy to destroy the left. I have my particular biases in each of the above debates, but for the purposes of this article I’m not interested in declaring who is right so much as I’m interested in asking why disagreement seems inevitable when all parties have access to the same information.

This gap between reality and interpretation isn’t contained to the news cycle. I’m sure you know friends, partners, colleagues, or lovers who have passionate fights because there is a disagreement over the meaning of something that was said, even if all parties involved can agree on the literal transcript of the conversation.

We do not live in the world as it exists, but rather we live in the world as we interpret it. Our interpretations are always fragments of the fuller picture. As the author Nir Eyal explains in his new book Beyond Belief, the full sensory input of every second of life is a gusher of information that we are neurologically incapable of ingesting with perfect fidelity:

Your conscious mind can handle around fifty bits of data every second. That’s the scope of your conscious attention. This is roughly equivalent to reading one short sentence every second, just enough information to process a simple thought or instruction.

It seems like a reasonable amount of information to hold in your head at any moment. But compare that to 11 million bits of total raw data collected by your senses in the same amount of time. That’s the equivalent of seeing every word of War and Peace flash before your eyes twice per second.

Put those two numbers together: 50 bits versus 11 million bits. The gap between those two numbers is why we’re aware of only a tiny fraction of what our brains actually perceive. In short, we live life through a keyhole. This extreme filtering is why two people can witness the exact same event and walk away with entirely different experiences.

By this calculation, the experience of life is 0.000045% of reality. Or, put differently, we are doomed to spend our lives fighting over interpretations because in any given moment, we are missing 99.999955% of what there is.

In the following conversation from my podcast Plain English, Eyal and I talk about the power of beliefs, the science of placebos, the contagion of negativity, and why action often precedes understanding.

REALITY IS A BOOK, EXPERIENCE IS A SENTENCE

DEREK THOMPSON: I want you to tell me two theses of your book—the explicit on-the-book-jacket thesis, and the subterranean thesis, the deeper idea the book is scratching at. What’s the above-ground thesis, and what’s the underground idea?

NIR EYAL: The big idea is: beliefs are tools, not truths. Practically speaking, I’m helping people consider the beliefs they hold, ask themselves whether they’re limiting or liberating, and then keep the beliefs that serve them and let go of the ones that hurt them.

The deeper idea—one people are very uncomfortable with when they encounter it in the research—is that we don’t see reality clearly. We all think we perceive reality as it is. And the truth is, that’s just not the case. The brain can’t see reality as it is; it predicts reality. Right now, your brain is absorbing 11 million bits of information—the light entering your eyes, the sound of my voice, the ambient temperature of the room. That’s the equivalent of reading War and Peace every second, twice. However, your conscious attention can only process 50 bits. That’s one sentence per second. You are only consciously aware of 0.000045% of reality entering your brain.

How does the brain make sense of all this? It predicts reality. We all live in a simulation inside our own minds. Our reality is filtered based on our beliefs. Study after study shows how people can observe the exact same event and see something completely different. If you’re on a diet, you see food as larger. If you’re afraid of heights, you see distances as further. Watch a football game: the ref makes a call, and fans of one team see it as absolutely correct, fans of the other team see it as ridiculous. Think about geopolitics—people committed to the belief that one side is right see every event through that lens. We do not see reality clearly. We do not see people clearly. We see others as we believe they are.

THOMPSON: That’s a beautiful idea—that reality is manifold and our lived experience is single-fold. Every moment presents a War and Peace worth of information for our eyes and ears and smell to behold, and every moment we are getting one sentence of the book that is reality. Taken seriously, it’s a case for extraordinary patience with other people—with our partners, our friends, our political enemies.

I want to propose another subterranean thesis. The Polish philosopher Zygmunt Bauman has this concept of “liquid modernity”—the modern world is characterized by how ephemeral our beliefs and our identities are when we’re unmoored from ancient traditions. The single word for a brief negative belief might be anxiety—a brief negative belief about the future. We’ve got plenty of that. What we’re lacking, and this might be directly related to the decline of religion, is durable positive beliefs about ourselves and our future. Not just habits, not just hacks, but durable positive beliefs. How does that idea sit with you?

EYAL: This is exactly the intellectual habit that changed my life. So much of our suffering is self-perpetuated—we build these intellectual cages of suffering of our own device. Whether it’s personal problems, interpersonal problems, national geopolitical problems, they all have the same source: we are creating our suffering.

It doesn’t mean we need to accept things as they are or agree with everyone. It’s that we want to reduce our suffering and increase our motivation to continue to participate. Maybe it’s useful if I tell a quick story of how this changed my life.

REALITY VS. INTERPRETATION VS. TRUTH

My mom had her 74th birthday. She was in Central Florida, I was in Singapore. I went through a lot of trouble to get her flowers—found the florist with the best reviews, called to confirm delivery, made sure they wouldn’t wilt in the heat. I went to bed at 1 a.m. and said, “Nir, you put in some good effort, she’s going to love the flowers, you’re a good son.” That’s not what happened.

I called her the next day: “Mom, happy birthday—did you get the flowers?” She said, “Yes, thank you. But just so you know, the flowers were half dead, and I wouldn’t order from that florist again.” To which I blurted out, “Well, that’s the last time I buy you flowers.” That went over about as well as you’d expect.

My wife Julie was on the call and afterward said, “Nir, do you want to do a turnaround on this?” I said, “No, I do not want to do your touchy-feely hocus-pocus. I need to vent.” But I knew enough about what the research says about venting—that it does nothing but solidify this effigy of the person. We don’t see people; we see our beliefs about people. Venting only reinforces, “She always does that, that’s so like her.” I had enough sense not to vent, and instead I did what’s called a turnaround.

A turnaround comes from inquiry-based stress reduction, developed by Byron Katie, with roots going back to Aristotle. The technique uses a few questions to help us see things from a different belief perspective. We’re not trying to change our minds—the brain hates changing its mind; it always wants to retreat into what’s kept it safe. We’re collecting a portfolio of perspectives.

Question one: is your belief true? My belief was: my mother is too judgmental and hard to please. Question two: is it absolutely true? 100% of the time, no exceptions, no other possible interpretation? Well, I didn’t have perfect certainty. Maybe there was another explanation. Question three: who am I when I hold onto that belief? Short-tempered. I become this 13-year-old version of myself. Question four: who would I be without that belief? More patient. I’d feel lighter. I’d be more myself.

Then the turnaround itself: consider whether the exact opposite of what you believe could also be true. My existing belief: my mother is too judgmental and hard to please. The opposite: my mother is not too judgmental and hard to please. How could that be true? She did thank me for the flowers. She was telling me a statement of fact—the flowers didn’t look so great. Does that necessarily mean it’s a judgment? Maybe not.

Here’s a third perspective: I am too judgmental and hard to please. Could that be true? I had rehearsed exactly the script of effusive praise I was expecting from my mother—

THOMPSON: “Oh my gosh, these flowers, they’re the greatest I’ve ever seen in my life.”

EYAL: Exactly. And when I didn’t get that, I lost it. So who was judging who? I was judging her response. Fourth perspective: I am too judgmental and hard to please towards myself. That one wasn’t fun to consider at all, but turned out to be the most true. When I had spent all this time and money doing something nice and it didn’t work out perfectly, I judged myself—I was incompetent, I couldn’t even get nice flowers for my mother for her birthday. This is called a misattribution of emotion. I felt crummy about myself, and so the first person I could take it out on was going to get it. That’s exactly what I did.

Now I have four beliefs—a portfolio of perspectives. Which one is true? It doesn’t matter. Which one serves me best? That first belief had only one way out: she had to change so I could be happy. With the other three, I had agency. Instead of a belief that hurt me, I picked a belief that served me.

THOMPSON: I think a lot of people with experience in clinical psychology are going to hear what you just said and recognize it as DBT—Dialectical Behavioral Therapy, from [Marsha] Linehan. That idea, particularly useful with borderline personality disorder but broadly used in clinical practice, is about being comfortable sitting with two interpretations of reality that are in conflict with each other. One classic example: you walk into a room and your wife and daughter look at you and laugh. Interpretation one: they’re laughing at me, I’m always judged in this family, even though I work so hard. You can imagine the self-talk spiral that follows. Interpretation two: my wife and daughter were watching something and laughed as they turned to look at me. I don’t need to know which interpretation is reality. I need to be emotionally secure enough to live with the fact that both are plausible, without having to drill down to one truth. That ability to sit with opposite truths is really important for making one’s way through an emotional life.

THE CASE FOR PRAYER, EVEN IF YOU’RE AN ATHEIST

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Today I wanted to pull back the lens and share a conversation I had with a geopolitical energy expert on the global economics of this war—why the near closure of the Strait of Hormuz is such an enormous deal for the entire world and why energy crises tend to have such wide ripple effects.

What do the following things have in common: the death of the Soviet Union, the rise of modern conservatism in America, and Nintendo? Answer: An energy crisis.

In October 1973, Arab members of OPEC launched an oil embargo against the United States and its allies. Within months, the price of a barrel of crude quadrupled. In the U.S., the immediate effects included gas lines and a national speed limit. A second shock followed during the Iranian Revolution of 1979, and gas prices surged again. The combined effect was the toxic union of stagnation and inflation, two things that economists had previously said were practically incapable of coexisting. The immediate effects — gas lines and recession — were the least interesting consequences of this historical event. The arms of the crisis reached around the world:

• In the USSR: Oil shocks were a windfall for the Soviet petroleum economy, and oil money allowed Moscow to paper over the dysfunction of its planned economy for years. But in the 1980s, oil prices fell, and Gorbachev’s petro-economy collapsed, contributing significantly to the demise of the Soviet Empire.

• In Japan: Heavy industry, relying on cheap oil, had powered the economy in the 1960s. But expensive oil threatened that model of growth. In the 1970s, industrial policy was rerouted toward smaller manufactured goods that required less energy: computer chips, circuits, and robotics. The consumer electronics revolution of the 1980s in Japan—the Walkman, the VCR, Nintendo—was an echo of the oil crisis.

• In the United States, the historian Gary Gerstle has described how stagflation shattered the New Deal consensus. Americans lost faith in the sort of activist government associated with Roosevelt, Truman, and LBJ. The political order that emerged from this period prized individualism, celebrated markets, and outwardly mocked the idea of effective governance. The election of Ronald Reagan, and thus the rise of the modern conservative movement, is hard to imagine in a world where the economy of the 1970s is as copacetic as the economy of the 1950s.

So, there you go: perestroika, Nintendo, and Reagan. None of these things were entirely caused by the energy crises. But in each case, the oil shocks of the 1970s reshaped the political and economic environment in a way that increased the odds of the collapse of the USSR, Japan’s shift toward electronics, and the demise of the New Deal order.

The vast and sprawling tentacles of past energy crises have been on my mind recently during the Iran War, which has shut down most commerce passing through the thin Strait of Hormuz. It would be reckless to predict precisely where this conflict is headed. But it no longer seems reckless to say that this war is going to be a mess: if not just a military mess, or a diplomatic mess, then at least an economic mess. The vast majority of headlines in the Wall Street Journal and Bloomberg are about the price of crude oil. But the deeper story is about everything crude becomes, everything that moves alongside it, and everything that depends on the narrow maritime chokepoint at the mouth of the Persian Gulf.

The Strait of Hormuz is the global economy’s ACL: a small and vulnerable connective tendon that you don’t have to think about when it’s working perfectly and causes very loud anguish when normal function is ruptured. By some measures, the Iran War is already the largest sudden disruption of oil supply in modern history. If this conflict continues to disrupt traffic through that passage, the consequences will not stop at gasoline prices. They will spread into fertilizer, petrochemicals, plastics, jet fuel, shipping, power markets, and manufacturing supply chains that most people never think about until they seize up.

Before the war has reached its second birthday in weeks, the signs of disorder are everywhere. Energy facilities have come under direct attack. Production has been cut back in parts of the region. Qatar has declared force majeure on liquefied natural gas exports. Governments in Asia are already beginning to respond with emergency measures to conserve fuel and electricity. In just the last few days, Vietnam has reportedly urged people to work from home to save fuel; Myanmar’s junta reportedly launched a rationing system for cars; Thailand restricted fuel exports; and the Philippines prohibited air conditioning settings below 75 degrees in government buildings and instructed officials to turn off their computers when they go to lunch.

To understand what is happening, and why the fallout from this conflict could extend far beyond oil itself, I talked to Rachel Ziemba, one of the sharpest analysts of global energy markets and economic statecraft. We discussed:

• Why the Iran War economic fallout goes far beyond gas prices

• Which countries and regions will feel the most pain

• Whether a swift end to the war will swiftly end the economic crisis that the war triggered (probably not)

GASOLINE, JET FUEL, FERTILIZER, PLASTIC, CHIPS…

Derek Thompson: Tell me about the Strait of Hormuz.

Rachel Ziemba: The Strait of Hormuz is a narrow strip of water between the United Arab Emirates and Iran, through which more than 20 percent of global oil transits, 20 percent of seaborne liquefied natural gas, 30 percent of seaborne fertilizer, and a growing amount of global container traffic. I’ve been tracking energy markets and the Middle East for about 20 years. Even before that, as a grad student, we studied chokepoints and physical risks to energy markets. The Strait of Hormuz has always been number one. But this is the first time it has been effectively blocked, and there hasn’t yet been a way to reopen it.

Thompson: From news headlines, this sometimes seems to be only a story about oil. But I prefer the idea that this is a story of what oil becomes, and how it’s a universal input in the global economy. What does oil become?

Ziemba: As consumers, we don’t use oil. We use gasoline. We use diesel. We might get on a flight and hope that it has jet fuel. In the massive buildout of electricity, because of AI, we use copper. To process copper, we use sulfuric acid, which is a byproduct of oil refining. In our daily lives, we use plastics, which come from petrochemicals. Oil and natural gas can also be precursors to fertilizer, nitrogen-based fertilizer in particular, which can add to the costs we face in our food supplies.

Think about it this way: I placed an Amazon order this morning. The car that’s going to handle that last mile, the flight it’s on, all of those involve companies that are thinking and scrambling about whether their costs increase. Even the ships the oil is being carried on are themselves having to pay more for fuel. So it really has a number of ripple effects in the global economy.

Thompson: Since we hear so much about the price of a barrel of oil, I want to go deeper into some of the other affected products, especially fertilizer and computer chips. I’ve read that up to 40 percent of global urea shipments from the Middle East are essentially stranded right now outside the Strait of Hormuz. What’s the connection between the strait, oil, and fertilizer use throughout the world?

Ziemba: Urea is one of the formats in which [hydrocarbons] can be converted into fertilizer. It can be a byproduct from natural gas or oil that is then converted into nitrogen-based fertilizers. It’s one of several sources of fertilizer, along with phosphates and potash. Major suppliers of phosphates and urea are trapped in the strait, with the producers of Saudi Arabia and Qatar respectively.

We’re already seeing a price impact even here in the United States. I looked at prices out of the Port of New Orleans today, and they’re now, I think, $270 a ton. That’s up 77 percent since December. So if you’re a farmer who is thinking about the cost of fertilizer versus what you can sell corn for, that matters.

Here in the U.S., most of the fertilizer we import comes from Canada. About 40 to 45 percent is potash coming in from Canada. There are no supply shortages there. Another roughly 20 percent comes from Russia. At this point it is our only primary import from Russia in the context of sanctions, at least for now. We’ll see what happens later this week. But still, this is a case where fertilizer costs will be going up, and this also comes at a point when other inputs going into farm products may be affected by tariffs and uncertainty around tariffs.

Thompson: On computer chips: Taiwan makes about 90 to 95 percent of the world’s most advanced chips, and Qatar ships about 30 percent of [its liquefied] natural gas through the Strait of Hormuz. South Korea is making noises about fearing that helium shipments could be slowed, and helium is a major input for Korea’s memory chips. So: no gas, no helium, no power, no chips.

Ziemba: People are rightly worried about this shortage of helium. Some would point to the fact that the U.S. was among the countries that sold off its helium reserve, in part because we don’t do as much semiconductor manufacturing in the United States, though that is shifting.

Taiwan faces a triple whammy: concern about power supply, power being more expensive, and concern about other inputs [such as helium]. This is happening at a time when memory chip prices have already been going up sharply. That was partly a story of really strong demand, as AI-related inputs and imports have been going up in the United States and around the world.

It’s not only a data-center issue. Memory chips are used downstream in building cars. We saw in the pandemic that if auto manufacturers canceled their orders for chips, they might end up at the back of the line because they weren’t as large an orderer. So we could see this filtering through into a range of other manufactured products.

Ultimately, you’re also pointing to the question of which products are stockpiled and how easy it is to have stockpiles. Any time you have a stockpile, that means buying a product you’re not going to use immediately and having it ready in case there is a shortage. This is the opposite of just-in-time manufacturing. When we look around the world, some countries, like China and even the United States, have large oil stockpiles, but very few countries have large natural-gas stockpiles. That is something I anticipate will be rethought.

Thompson: So, with the shutdown of the strait, oil prices are going up, but really it’s about about the stuff we make from oil going up in price. Sulfuric acid is how we extract copper. Copper is what we use to make transformers and electric vehicles and all parts of the green-energy economy. We’ve got plastics. We’ve got helium that’s stuck behind this closure. We’ve got fertilizer. So, the agricultural economy, the chips economy, the plastics economy, the green-energy economy—all of these different sectors of the U.S. and global economy are being affected by this closure.

Ziemba: Spot on. In a political context, gas prices going up tend to be the trigger and warning sign. But if we look at it, the impact on the U.S. and global economy is much greater. If anything, it’s really going to drive differentiation between countries.

There may be countries like China, which has a very large crude oil stockpile it has been adding to when prices were low, that might be able to subsidize parts of its plastics or petrochemical production. That might make them even more competitive even as they hold off on exporting refined oil products. We’ll see how that plays out. But I think we could see differentiation on competitiveness that might further challenge onshoring and reshoring goals that are underway here in the United States and elsewhere.

HOW IT ALL ENDS

Thompson: How do you see the Iran War ending?

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In its war against Iran, the White House has already failed: to offer evidence that the country posed a direct threat to Americans; to provide a consistent justification for the attack; or to explain under what terms the conflict might end.

Now the evidence is piling up that this rather unjustified and thoroughly unexplained war could very likely lead to an energy crisis, just as the US economy is showing new signs of weakness.

On Friday, the Bureau of Labor Statistics announced that employment growth plunged last month, as the economy lost 92,000 jobs in February. Every major sector saw losses. Even health care, which has accounted for the majority of job growth in the last year, is so weak that a nurses strike in California was enough to push overall job growth into the red. The hiring market is frigid, and monthly job growth since last summer is averaging negative 10,000 per month.

Surely, one thing the US economy doesn’t need right now is a war of choice shutting down a major trade route, blowing up oil markets, and driving up the cost of energy for Americans, just as the labor market seizes up.

And yet, here we are: Oil prices have skyrocketed in the last few days, amid fears that this conflict will crush supply for weeks, or months. As recently as December, the WTI crude oil price was below $60 a barrel. On Friday, oil prices screamed toward $90. And experts are worried that every week the Iran war continues, the odds of a truly catastrophic disruption to energy production will continue to rise, with potentially devastating consequences for the economy.

WHY ENERGY EXPERTS SAY WE COULD BE IN ‘SCARY LAND’ BY APRIL

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1. Loving strangers

One month after my first daughter was born, a younger friend from work asked me to explain what parenthood was like.

I can’t do that, I said. Wisely, she appealed to my vanity, reminding me that my professional identity was based on explaining complicated ideas, and so wouldn’t it be a little sad actually, a little bit pathetic even, if I couldn’t explain something as basic as fatherhood? I took the bait.

People tend to explain parenthood by comparing it to other real-life experiences, I said. But parenthood isn’t conceptually combinatorial in a way that benefits from that exercise. You can’t multiply I have a puppy with My 2-year-old nephew pooped on me once and it was funny to arrive at the experience of parenthood.

“Have you been to Paris?” I asked. She had not. Paris is my favorite city, I said. You might tell me that you’ve practically been to Paris, because you’ve been to London and Montreal, and what more could the city of Paris offer beyond some blend of Major European Capital and City That Speaks French And Serves Buttery Sauces? But those travel experiences aren’t additive in a way that captures the experience of being in Paris. So, in this narrow respect, I said, parenting is like Paris.

But, much more to the point, parenting is nothing like Paris. Imagine that every day you wake up in your left-bank apartment, and the city has meaningfully morphed into some magically strange variant of Paris. On Tuesday, the streets and boulevards no longer meet at their old familiar intersections. On Wednesday, the Louvre moves to another arrondissement. The Arc de Triumphe turns upside-down on Thursday and floats in the sky on Friday. Now we’re talking. Now that is more like parenting. To be a parent is to be a permanent tourist in a constantly evolving foreign city, which also happens to be your home.

The baby you bring home from the hospital is not the baby you rock to sleep at two weeks, and the baby at three months is a complete stranger to both. In a phenomenological sense, parenting a newborn is not at all like parenting “a” singular newborn, but rather like parenting hundreds of babies, each one replacing the previous week’s child, yet retaining her basic facial structure. “Parenthood abruptly catapults us into a permanent relationship with a stranger,” Andrew Solomon wrote in Far From the Tree. Almost. Parenthood catapults us into a permanent relationship with strangers, plural to the extreme.

When you become a parent, you meet your child. And then you meet your child again. And again, every day after that. You will never stop meeting your child. That is one reason to become a parent: To have a child is to fall in love with a thousand beautiful strangers.

2. Ride the rides

One thing they don’t tell you about parenthood is that your daughter might turn you into a monster. It’s 8am, and the coffee is getting cold, and you find yourself stalking around the kitchen island table, fingers curled into claws, lower jaw extended, teeth bared, foot stomps heavy, voice roaring and phlegmy, an ogre hunting prey, and your two-year old daughter is squealing as she tries to escape. You catch her by the leg, swing her into the air, fling her upside down, pretend to gnaw at her tummy. You set her down and let her do the thing where she sort of pretends to run away without running away. “More! More!” she demands, but the “r”s are soft, and it sounds more like “Mo’ah! Mo’ah!’” The response is automatic: The prey requests, the ogre obeys. The face of the father melts away. The face of the monster reappears.

My daughter and I have played this game approximately one thousand times. Nothing in my life could have anticipated this hunter-prey pageantry or the joy I get from it. I’m not a monster guy, generally speaking. Friends who had kids before me never once pulled me aside to whisper, “oh, another thing, you will have to pretend to be a monster all the time.” But I’m struck by the sense that I was born to play this game just as she was born to play it.

Parenthood is everything you’ve heard: confusion, panic, joy, sadness, anxiety, boredom, and anxiety again. Beneath these passing moods is a deeper feeling for which there is no good word. It is the feeling of suddenly finding yourself playing the oldest game in the world, a game you know that billions of people have played before you. There is nothing about being a parent that isn’t a cliché. This is a terrible inconvenience for the suckers out there who try to write essays about it. But I also find this to be an existential balm: I was built for this, and it was built for me.

One way to think about life is that you are locked inside an amusement park. The park has no clear purpose. It’s just there, and so are you. You ride the rides, and then it’s lights out. Falling in love is a ride, and making deep friendships is a ride, and sex is a ride, because these are all experiences that were built for us to do. And then, stretching over the park, there is a twisty and vertiginous rollercoaster called “having a child.” Parenthood is not special. It’s just another ride in the park. But it is there, and it was built for us, and we were built for it, too.

So, that is a second reason to become a parent. You’re in this amusement park only once, and I think you might as well ride the rides.

3. Another way

“There is no such thing as a baby,” the psychologist D. W. Winnicott once said. “If you set out to describe a baby, you will find you are describing a baby and someone”—such as a parent, a nanny, or an older sibling. “A baby cannot exist alone but is essentially part of a relationship.”

I have always loved that line: “There is no such thing as a baby.” But surely, its logic extends to adults, too. If you set out to describe a person, you do not begin by listing all the things they do alone, when no one is looking. You describe what that person is like in relation to you, or to someone you know. “Is Derek a good friend?” is a question for my friends. “Is Derek a good brother?” is a question for my sister. “Is Derek a turgid writer?” is a question for my readers. A psychologist such as Winnicott might be tempted to articulate this principle with an existential flair: There is no such thing as an individual. But I think the down-to-earth version feels more true: Every individual is the sum of their relationships.

There is another line I love from the book & Sons by David Gilbert. Two men walk into a pub and see their mother: “The brothers straightened, reshaped as sons.” How true, how strange. It is an awkward pleasure to see an old friend around his parents. Some softening or hardening in their face, some slouching or straightening in their back. The presence of a parent remakes a child, transforms them back into a child.

My mom and dad died three years apart of two cancers in my late-20s. When I met my wife, both of her parents had already passed away, too. Beyond the obvious tragedy of our situation, there is a subtler cost that Gilbert and Winnicott would appreciate. No matter how well my wife and I know each other, I will never know my wife as a daughter, and she will never know me as a son. We will never see each other “straightened, reshaped” in the presence of our parents.

There is a sad place to take this: I was a slightly different person with my mom, and my wife will never know that me; my wife was a special person with her mom, and I will never know that her. But now we are parents. My wife, who will never know me as a son, will always know me as a father. And I, who will never know my wife as a daughter, will always know her as a mom. There is the third reason to become a parent: It gives the people you love another way to know you.

Modern life is awash in statistics. We are surrounded by work metrics, fitness metrics, health metrics, social metrics. At best, these numbers allow us to set better goals: to increase our workplace efficiency, or to reduce our resting heart rate. But very often, the quantification of modern life makes us play the games we can easily measure rather than the games we deeply value.

To take a personal example, I care a lot about my Oura ring heart-rate variability score. But I’ve found that my HRV is negatively affected in the short term by all sorts of activities that I enjoy: not just strenuous workouts, but also staying up late with a friend, or having a cocktail after 7pm with someone I love. A life lived exclusively by HRV-maxing would be profoundly boring. There is no fitness-tracker metric for good friendships.

Or, consider the state of conversation. When we talk to each other on the Internet, we are not just moving talking from the physical world to the digital world. We’re moving conversation from a place with no real-time scores to a domain that is filled with numbers: views on TikTok, likes on Instagram, retweets on Twitter, upvotes on Reddit. These digital conversations take on a different character, because people there aren’t talking just to talk. They’re talking to make a number go up. The external metric of number-go-up replaces the intrinsic value of connection.

In the late 19th century, the philosopher Friedrich Nietzsche famously criticized religion in part because he claimed its worshippers allowed an external system of values to stand in the place of the messy, beautiful work for discovering their own genius and agency. I have mixed feelings about Nietzsche’s critique of religion, but I think it applies profoundly to the state of life metrics. The quantified life has become a modern religion: a system of values that takes us over and instills deep values in us, even as it sometimes keeps us from living our own values and building the life we want.

C. Thi Nguyen is the author of the book The Score: How to Stop Playing Somebody Else’s Game. In today’s conversation, we talk about metrics, the games of life, and how to listen to the parts of our self that cannot be reduced to numbers.

THE RELIGION OF METRICS

Derek Thompson: Tell me about rock climbing.

C. Thi Nguyen: Rock climbing is one of the best things that’s ever happened to me in my life. I was one of these people who grew up playing computer games and reading. I was like, “Life of the mind, screw the body.” I started to realize it was a problem. I did some yoga. I tried to learn to surf, and I found out that seasick asthmatics should not try to surf. It’s very deadly. And then, I tried climbing. I found it was kind of like solving logic puzzles with your body.

Then I found the rock climbing scoring system that assigned a number to the difficulty of certain climbs. It mattered to me. I kept advancing until I hit a wall. As I [followed] the scoring system of climbing, I became miserable. I kept trying to advance to the next level, and I just couldn’t. The thing that was keeping me happy was now making me miserable.

So with rock climbing, you fall in love with an activity. You find a metric that at first seems to push you toward becoming a more advanced rock climber. But ultimately that metric becomes the single thing that you focus on. It takes over your life.

Thompson: How is being a philosopher like rock climbing?

Nguyen: I got into philosophy because I loved it. I was supposed to be something else as an Asian immigrants’ kid: doctor, or lawyer, at worst. I just kept getting pulled to these weird, fascinating questions like, “Is beauty subjective? What is the meaning of life?”

And then, I went to philosophy grad school, and in grad school, I encountered the rankings. There are two rankings that people care about. One is a ranking system of journals by status. The other is a ranking system of university departments by status. They’re on websites, they’re public. And you get focused on going up the rankings. High-end philosophy journals typically feature very technical, careful, slow work on a set of fairly prescribed questions. This work can be important and valuable, but it wasn’t my jam. I found myself working on it, which is kind of weird, because it’s not like anyone goes into philosophy for worldly success. You’ve basically burned your life and career opportunities by throwing yourself into this stupid discipline. The only reason to do it is for love. And then suddenly, I found myself working on things that I was bored by for like five years, and I also got super depressed, and I also basically lost my love of philosophy and I was going to quit.

And in this case, the thing that I did was I basically ended up having to ignore the ranking system altogether. It was too pervasive, it was too powerful. I had to get rid of it completely. And I started working on the philosophy of games.

VALUE CAPTURE IS THE BIG IDEA

Thompson: These two stories kick off your new book, The Score, on the power of metrics and the value of the games we play in life. [You’ve written about how] pervasive this culture of measuring things has become. It has crept into every nook and cranny it seems of modern life. Tell me two things: The explicit thesis of your book, but also—as someone who’s written books and knows that the message we want people to take away from our books is sometimes not made explicit—tell me the underground-river-secret-message of the book.

Nguyen: One of the core ideas of the book is to describe this thing that I’m feeling and seeing around me that I think a lot of people see and feel around them. I call it “value capture.”

Value capture is what happens when your values are rich, subtle, or developing. Then you’re put in an institution, or near a technology, that gives you a simplified measure, typically quantified, and then, that version takes over. Like me going into philosophy for love of philosophy and then aiming at the rankings. Or starting to exercise for health and fitness and becoming obsessed with BMI or VO2 max or some other simple measure. Or in education, one of the things that I’ve become really obsessed with is this gap between wanting to educate students for wisdom, curiosity, and reflectiveness, and then the institution coming to be focused on a few easy measurables, like speed of graduation and starting salary.

I think the best way to describe the problem of value capture is that when you’re value captured, you’re outsourcing your values. Instead of developing your values on your own, you are taking them off the rack. You’re taking them in a particular formulation that comes from somewhere else, that has other people’s interests embedded in it.

If there’s a message in the book, it’s not “ignore rankings, ignore metrics.” The weird heart of the book for me is about the weirdness of the fact that scoring systems can be so valuable, they can inspire play, they can teach me how to love my body. It’s that scoring systems are very specific tools that capture narrow slices of valuation. But if you get swamped by them, if you let them dominate your vision, then you are no longer contouring, deciding, or choosing between and modifying scoring systems for your purposes. You’re letting them set your purposes.

Thompson: I want you to make the case against metrics by responding to my own defense of metrics. I got into journalism by being an economics reporter. There are a lot of economic statistics that I would argue have extraordinary value. Poverty is a statistic, and I think it is a moral good to push the poverty rate down. Real income—that is wages that are adjusted for inflation—is something I want to rise.

Metrics are incredibly useful, not only to make legible that which was previously illegible, but also to coordinate, actors to get many different people in many different places to say, “Let’s focus on pushing the poverty rate down. Let’s focus on raising average incomes.” If metrics are so useful, how can they be dangerous?

Nguyen: Metrics are useful because they compress information. They are dangerous because they compress information.

My favorite example is a friend of mine who’s a student advisor who says that the new metric for his student advising system is being judged by how many keystrokes go into the student advising computer system per minute—that’s how they’re measuring advising. Obviously, that’s terrible, but I think it’s really good to focus on the best metrics we have, like the ones you’re talking about, to really understand the core difficulty.

There are a few ways to put it. One is to say it’s not that these metrics aren’t measuring something real and that they aren’t objectively tracking something that we want to know about; it’s that they speak so loudly that they threaten to drown out other nearby qualities that are also incredibly valuable but are harder to measure. I think there’s a particular kind of quality or character to what’s easy to metrify. Maybe you have examples of this from economics, but an example I think about a lot is in health policy is saturated fat and correlations to lifespan and heart attack rate.

And then there’s the other stuff like the deliciousness of brie, the joy of a perfectly ripe cheese, the tradition involved, the happiness of it. And these are much harder to quantify. My claim isn’t that lifespan and heart attack rates are unimportant. It’s that this other stuff tends to not be weighted at all in large scale social conversations, because it’s hard to measure.

THE POWER OF ‘STRIVING PLAY’

Thompson: You’ve reminded me of sports. In baseball, for example, teams have gotten smarter. Individual actors coordinated on making game strategy more efficient. But there wasn’t a metric for “how do we make this game more fun?” Attention was pulled toward efficiency and away from fun. In my own life, it’s easy to use my fitness tracker to see that I shouldn’t stay up late or a drink after 7pm. What’s harder to measure is the joy of staying up late. Your book is asking how do we live in a world of more easily measured outcomes while continuing to value what cannot be measured.

Nguyen: There are two directions I want to add to it, so let me try to remember both. The first is to talk about your baseball example and the second is the larger question of what is easy to measure.

My mentor, the philosopher Barbara Herman, a Kantian ethicist, once told me, “I think you’re just confusing a goal and a purpose.” And I was like, “There’s no difference between a goal and a purpose.” And she said, “Of course there is. When you have friends over for cards, the goal is to win, but the purpose is to have fun.” And I think that structure is so common in games, where the goal that you aim at in the game is separate from the reason you play the game. I call it “striving play.”

Striving play is when you’re trying to win, not because winning is valuable, because you want something about the process. Party games make this particularly clear, because when you have your friends over and—unless you’re a complete asshole—if you try to win, but you lose, and you all had a great time, you don’t think the evening has been wasted.

I think the crucial thing here is to understand the structure of games. You have to understand that for some people, winning is the purpose. Their goal and purpose are one. If they just want to win, they just want to win. But for some of us, I rock climb to clear my mind. And what’s interesting is I cannot clear my mind by trying to clear my mind. So the philosophers have a name for this: a self-effacing end. You cannot clear your mind by trying to clear your mind. You try to clear your mind by trying to climb the rock as hard as you can and forgetting that you’re trying to clear your mind.

Thompson: Like falling asleep.

Nguyen: Exactly, you can’t focus on it.

Thompson: As someone with insomnia, I can definitely testify to the fact that one of the worst strategies for falling asleep is attempting to fall asleep.

Nguyen: And similarly with party games, one of the worst way to make a bunch of people chill and relax is to shout at them, “Now it’s time to be chill and relaxed.” And one of the interesting features of humanity is that you can give people a dumb party game and they can try to do some dumb communication task, and the more they lose themselves in that task, the more fun they have.

But notice in this case, what we’re doing is moving through layers of evaluation. We’re diving into the scoring system, we’re focusing on it, and then we’re stepping back and we’re saying from the standpoint of our larger purpose, “Was it fun? Was it chill? Did it relax me?” Baseball has a scoring system—a goal—but the game lost track of the larger purpose. It’s like if you became so obsessed with charades that you just studied it and became a charades asshole and just inflicted your charades skill on people. You’ve missed the larger point.

But when we enter larger social institutions, it becomes so much easier to be like, “No, no, here’s a score,” winning the baseball game. Baseball is more complicated because by the time you get to the professional stage, clear economic incentives are tied to the win. One thing about charades is there aren’t economic incentives tied to the win, so it’s easier to make victory a temporary interest that you can throw away. But I think there’s a larger lesson. In institutions, we often set up a metric for a very good reason. We’re trying to track something that is hard to track.

IS LIFE JUST A SET OF GAMES?

Thompson: I want to pick up the thread with games here. You have a beautiful quote in the book from Bernard Suits: “To play a game is to voluntarily take on unnecessary obstacles for the sake of making possible the activity of overcoming them.” I love that. What is it that makes games separate from the rest of life?

Nguyen: The fast way of putting it is: The obstacle is the point. But I think a richer way of putting it is that the goals in a game are inextricably connected to constraints and obstacles and rules on getting to them. So, okay, let’s say you’re running a marathon. In some sense, you’re trying to get to a particular point in space. But that’s not actually what you care about. Because if you actually cared just about that, you would take the most efficient means you could. You would call an Uber, you would take a bicycle, you would take a shortcut. But that doesn’t count. That’s crucial. That doesn’t count as crossing the finish line. Crossing the finish line means following the rules, following the constraints that force you into a particular kind of action. It counts only if you did it in a specific way.

For Suits, game life is different from practical life. In practical life, there’s something we want, there’s some outcome, there’s some product, and we just want it by itself and we don’t care how we get it. In games, we’re taking on these obstacles and deliberately putting them in the way.

Thompson: Before I was a writer, I was an actor. I loved the opportunity to pretend to be somebody else. And I wonder if you accept this additional definition of games: an organized process by which people enter into a space where we become the person that the game wants us to be.

My friends in D..C have game nights from time to time, and there are two very different kinds of games that we play. One game is Hanabi, which is a cooperative card game. And the other type is Wingspan or Settlers of Catan, which are competitive games. And when I think about my own heartbeat, when I think about the degree to which my face gets flush throughout the game, these games work me over. The cheap way to say it would be: I don’t just play the game, the game plays me. But at a much deeper level, I am electing to be changed by a set of rules. I become as collaborative or as competitive as the game makes me become. We become this different person, one defined by a set of rules that we elect to participate in.

Nguyen: The most interesting thing about games for me is that they teach us how incredibly fluid we are. We think that we’re these pretty static beings that want the same kinds of things, but you can literally open up a game and it will tell you whether, in my case, my wife and I are going to be trying to kill each other or supporting each other. And then we just do it. We go all in. We turn on a dime.

The anthropologist Johan Huizinga calls it the magic circle. He thinks that the essence of play—and here he unites games with theater—is a magic circle, an alternate space and time that you enter into where you change roles and actions change their meaning. I think this is a fancy way to describe and highlight something that is really obvious, which is just again, my wife and I love each other, and then we go play a game and we try to kill each other. And then we pause and we make each other drinks, then we go back and we try to kill each other. You get absorbed in this technical specificity.

My favorite game designer, Reiner Knizia, a board game genius from, said the most important tool in his game design toolbox is the scoring system, because it sets the player’s motivations. It just tells you what to want and you suddenly want it. My worry is that this is what metrics do to us. We enter a setting and someone’s like, “Here’s the ranking system.” And you’re like, “Okay, I didn’t care about that before, but now I care about it.” I kind of think that games are using this fluidity. But other systems are using that in an authoritarian way to pass and to push values down onto us.

Thompson: I wonder whether you accept this framing that there are games that we elect to play and there are games that we find ourselves playing by accident. There’s a line about being a lawyer that it’s like a pie eating contest where the reward for the winner is more pie. You work, and work, and then you become partner. And guess what? We’re here to reward you … with 10X more work. I wonder how you frame this distinction between the games that we set out to play and the games where we fall into.

Nguyen: There’s a sense in which there’s a really easy thing to say here, which is that games are great when you choose them and games are terrible when they’re forced on you, or snuck up on you. But I’m not quite sure that’s right. And the reason I’m not quite sure it’s right is because a lot of dangerous gamifications to me look like cases that are voluntary where someone picks up something fully... People who get on social media often are fully aware that it’s a game-like system that will change their motivations, and they do it anyway.

THE MEANING OF GAMES, THE MEANING OF LIFE

Thompson: I want to offer what I suspect is a shallow interpretation of what you think people should do with your book, and then I want you to deepen it. I think there’s an easy summary that’s something like: Play the games you want to play. It’s okay to outsource to metrics that which is inessential to you. If you go to Wirecutter to look up like what’s the best coffee mug that heats itself, that’s not exactly like outsourcing your soul to a machine. That’s probably just a good way to get a good coffee mug. But make sure that you protect from the hegemony of metrics that which is core to you, to your soul, to your art. How would you deepen that as a summary of the takeaways from your book?

Nguyen: I mean, that’s a pretty good takeaway.

Let me just tell you about the weirdest thing I did. So on the one hand, I’m a professor. I find GPA gross. But also when I try to un-grade my classes and not use grades in my classes, students just stop coming, because they’re in an environment where grading counts. So I have to live inside the grading system, and yet I don’t want to just grade in an unthinking way. So I just tried the following experiment. It’s ChatGPT era, like total chaos in the university. No one knows what to do. And the last time I taught technology ethics and political philosophy, I walked in and I was like, “I don’t know what to do about ChatGPT. So here’s what we’re going to do. We are going to go through a process. We’re going to read a bunch about the impact of automation on us. We’re going to read a bunch about what education is for. We’re going to read a bunch about AI. And then you’re all going to design the assignment and grading structure of the class, argue it out and then vote, and then we’re just going to do what you pick.”

And they did something super interesting with it. They made the grades about in-class workshops that we built together, in which they had to construct in-groups analyses and argue it out. And it worked really well. But it was a reformulation of the scoring system. It wasn’t me just telling them what to do. And that kind of thing is possible. I’m not sure it’s possible at the largest scale, but it’s possible for some of us.

Thompson: Do you consider yourself a fan of Nietzsche?

Nguyen: Complicated views. Complicated views.

Thompson: You don’t mention Nietzsche in your book. But reading it, I thought of you as a kinder, softer, less authoritarian Nietzsche. Nietzsche rejected the ethics of Judeo-Christianity because he thought it offered a system of values that separated people from our instinct. It made us kind, not for the sake of kindness, but for the sake of getting into heaven. He despised external forces of morality and invited people to reject those traditions and get in touch with their own instincts—our own Dionysian impulses—and to create a life and a system of values that was true to us and not just true to whatever system enforced itself on us. In short: Don’t be who Christianity wants you to be; don’t be the person whom external systems of values want you to be; be the kind of person that is most authentic and even playful and artful that you can possibly muster.

And in a way, what you are doing here is saying let’s have more art, let’s have more play, let’s have more instinct. Let’s have a class whose grading is determined by the instincts of the class and not by the administration that I happen to be employed by. Don’t be played by the metrics and the games that you find yourself sort of fallen into. Develop your own sense of what is valuable and play that game that you choose

Nguyen: Let me give you four answers.

One: Suits, our philosopher of games, said imagine utopia where technology has solved all our practical problems. What would we do with our time? We would play games or we would be bored out of our minds. So games must be the meaning of life. Suits is an Aristotle scholar. What’s underneath this is Aristotle’s view that meaning in life comes from activity, not just outcomes.

Two: Here’s a story of maybe the best teaching moment of my life. I was teaching introduction to philosophy, and I taught Suits and I told this argument about Suits and the meaning of life. And one of my students—pre-med, super productive—said that the idea that games are the meaning of life was the most repellent, disgusting, lazy thing she’d ever heard. Fast forward a month. At the end of class, we’re doing review session. We’re talking about Aristotle—who had this idea that meaningfulness comes from rich, difficult activity; and Kant—who gave us the idea that meaning comes from whatever you freely choose. And this student who hated Suits said, “love Aristotle so much, I love Kant so much—is there any way to synthesize the two of them?” And I was like: Yeah, what if one synthesis is that the meaning in life comes from difficult activity that you voluntarily chose yourself. Is that a theory we have? And then she screamed “fuck no!” from the back of class, and the rest of class collapsed into laughter. Because that’s Suits! That’s Suits’ view! It’s a fusion of Aristotle and Kant and existentialism. The meaning of life comes from activities, but they’re the activities that suit you, and they’re the ones you choose.

Three: This is a connection I’ve never made before, but here we go. My favorite thing that was cut from the book, because people decided it was too gross, was my research on pickup artist culture. Pickup artists are people that compete for sexual success. It’s literally called scoring. They don’t compete for good relationships. They compete for numbers: sexual encounters, or the fastest speed from meeting someone to sexual encounter. A sociologist named Eric Hendricks said that one of the things he found when he embedded in pickup artist culture to research them was that a common refrain in pickup artist culture was that you had to stop caring about pleasure or happiness, because these would just get in the way of scoring higher. I had thought that pickup artists were evil but enjoying themselves. But it turns out that something much more insane and inhumane has happened. They’ve cut off their connection to pleasure and happiness in order to score higher on a meter.

One more story. A philosopher of food, Megan Dean, does a lot of research about understanding things like food culture and anorexia. A lot of people who are coming back from anorexia can take a really long time to eat until they feel full. They have forgotten how to hear the signal of being full. They’ve spent so much time oriented towards external calorie counts that they’ve lost contact with the internal sensation and the information of how satisfied they are or how full they are.

This is my long-winded answer to say that a rich, full value system comes in significant part from dialogue with yourself. It comes from listening to weird, subtle, quiet emotions. It comes from signals of boredom or interest and pleasure. Something happens where we become so fixated on the accessible and clear external signal that we lose contact with the kind of rich emotional feedback system within us that might steer us better than those external measures.

Thompson: Beautiful answer. I don’t know that I can summarize it. But because I’m pathologically obsessed with summarizing things I’m going to try anyway. It seems to me like you’re saying that perhaps not the meaning of life, but a meaning of life, is both the individual cultivation of a value system and the day-by-day chosen struggle to live by it. But you need both. You need to be both the author of the value system—the maker of the game, so to speak—and the person who chooses day after day, to play by its rules. Something like that?

Nguyen: Yes.

One way to tell the story of human history is to watch how we die.

At the scale of millennia, the most deadly disease was likely smallpox. Some scientists estimate that it killed billions of people before its cure was summoned from a barn animal. In the 1790s, a British physician named Edward Jenner took material from a cow infected with cowpox and used it to inoculate a young boy. Since that fortunate but deeply unethical science experiment, vaccination—the word coming from the Latin for cow, vacca—has spared hundreds of millions of people from an early death.

By the early 1900s, in rich countries like the US where smallpox was on the wane, another killer emerged as the apex predator of humanity: bacterial infection. According to the best records we have, almost all of the top causes of death in the late 19th century were bacterial, whether it was gunky stuff in your lungs or bugs in your gut. But thanks to the accidental discovery of penicillin in the 1920s, and a heroic effort by the United States to scale up the drug during World War II, bacterial infections have plummeted.

Today the top causes of death in the U.S. are not poxes or bacteria. They are heart disease, cancer, stroke, and Alzheimer’s. While we have made some progress against this new class of villain—statins for our hearts, immunotherapy for cancer—they are still killing us with remarkable frequency.

There is an idea in progress studies called the burden of knowledge. Sometimes it looks like progress in a field like medical science is slowing down, not because we’re getting dumber over time, but ironically because we’re getting smarter. Every time we solve a problem in medicine, it’s like plucking a fruit from a tree. If you pick all the low-hanging fruit, what you’re left with is the taller challenge.

One of the tallest challenges for medicine in the last few decades has been dealing with the complications of obesity. Americans who overeat are at a higher risk of a number of illnesses and discomforts: inflammation, cancer, knee pain, sleep apnea. But for a long time, we didn’t have a reliable technology to help people eat less in an environment of food abundance. Science needed new fruits to pluck. But first we needed a taller ladder.

That ladder is the GLP-1 drug revolution, which shows no signs of slowing down. In December 2025, Eli Lilly, the most valuable pharmaceutical company in the world, released results for a late-stage trial of retatrutide, its next-generation GLP-1 drug. Unlike initial drugs in this class, retatrutide targets not one hormone (like Ozempic) or two (like Zepbound) but three. The weight-loss effects were historic: Patients lost an average of 29 percent of their body weight over roughly 68 weeks and saw huge improvements in knee pain. The anticipation of retatrutide borders on the rapturous: In a recent podcast interview, the scientist and health influencer Andrew Huberman called retatrutide the protein that will “change everything.”

But this is a strange moment for the pharmaceutical industry. In Gallup surveys, Americans rate the drug industry less positively than every other business sector. On the right, the “Make America Healthy Again” movement has gained traction with a broad suspicion of therapies and health authorities, along with a taste for alternative, “do your own research” medicine. Meanwhile, AI boosters often excuse the technology’s downsides by reminding doubters that it will one day “cure cancer.” But I’ve never heard a particularly detailed explanation of how that might even happen.

To understand more about the GLP-1 drug revolution, the promise (or over-promise) of AI in medicine, and the roots of public distrust of pharma, I recently spoke to Dave Ricks, the chief executive of Eli Lilly. The following transcript has been edited for clarity, brevity, and the goal of making both speakers sound a bit smarter¹.

THE HISTORY OF GLP-1 DRUGS

Derek Thompson: Eli Lilly is the largest pharmaceutical company in the world, mostly because of your GLP-1 drugs. Twenty years ago, Lilly helped launch the first approved GLP-1 drug. I’d like you to tell that story from wherever you’d like to start: 2005, the 1970s, the dawn of man. How did this drug category get kicked off?

David Ricks: The real first scientific breakthrough that happened was in the early 1970s. There were several papers that looked at something called “the incretin effect.” Incretins are a class of hormones or proteins that your gut excretes, like GLP-1. They noticed that if you ingested food orally versus intravenously, your gut signaled to the rest of your body that you’d eaten something [and released insulin to reduce blood sugar]. But if you took the same food intravenously, blood glucose would spike and stay up. The hypothesis was that some protein in the gut was signaling to the body that you’d eaten.

The first protein we identified was actually GIP, which is now the backbone of tirzepatide or Zepbound, our best-selling drug. The companies that worked on this in the 1970s and 1980s included Eli Lilly and Novo Nordisk, the maker of Ozempic. You could ask, “Well, why in the ‘70s didn’t you just make this drug then if you knew about this?” The answer is that peptide chemistry wasn’t very mature. If you give humans native GLP-1 or GIP, they both have a half-life in your body of seven or eight minutes. So it wouldn’t be a very convenient drug. You’d have to walk around with an IV bag all day.

A key breakthrough came from the Gila monster study in the late 1980s. John Eng, a doctor in the Bronx, was interested in why certain animals didn’t need to eat very often. He identified that Gila monsters have a mimic of the human peptide GLP-1 that the lizard uses [to stay full]. The Gila monster protein could go about 8 to 10 hours. So you could make a twice-a-day shot. That barely cleared the hurdle of good enough to be a drug. We made it into a drug along with a partner, Amylin, which launched it in 2006.

Thompson: Your story stopped in 2006 with the first version of this drug, which was used for type-2 diabetes. How long did it take Lilly to recognize that what had begun as a type-2 diabetes drug was in fact an incredibly powerful weight loss drug?

Ricks: When you look on the cover of our annual report from that year, there’s a patient, Maria, and she’s talking about her experience with Byetta, the brand name for exenatide, and she says, “It’s controlling her blood sugar and that her friends notice she’s losing some weight.” There it was like sitting there in April 2007, when we put out that annual report. We were doing studies.

But we had a problem.

The problem was the side effects associated with GLP-1 drugs are what we call peak to trough, the difference between the highest amount of drug and the lowest. Twice a day dosing [was causing] a lot of GI distress. You’ve about nausea, diarrhea, vomiting. The more we push the dose up, the more we ran into these things with that particular drug. We stopped because it was very unpleasant to take, and the weight loss effects were there but more modest.

What started the weight loss part was our ability to get dosing levels up. The incretin system—GLP-1, GIP, and coming soon glucagon—is being boosted in people with regular levels. People aren’t deficient necessarily in these hormones, like GLP-1. We’re boosting them to a super-normal level and it suppresses appetite and kicks up metabolism. To do that, you need to get to higher levels. We produced a once weekly GLP-1 in 2014 called Trulicity. Anyone who was in the diabetes world would know that. Very few people in the lay media would remember that drug, although it was our best-selling drug at one time. With a trick of protein engineering, we made that drug last a week, and people lost more weight.

Our competitor, Novo Nordisk, then did a similar trick and made a weekly that launched in 2017 called Ozempic. That only got so famous when they took the chance to study that at an even higher dose in people who had obesity but not diabetes. That came out in 2021, and then this whole thing started running.

At Lilly, we invented this drug called tirzepatide [which targets two hormones: GLP-1 and GIP] in 2014. We started doing studies around 2019. We knew this was going to be the most effective obesity drug we’ve ever seen. Of course, since then, we’ve been improving upon that. It’s a case of leapfrogging and iterative improvement.

Thompson: Just a pit stop on the business here before I get into the science. What share of Lilly’s revenue and profit at the moment is related to this class of drugs right now?

Ricks: Last year, it was a little less than half the total company revenue. This year, we don’t give out product level guidance, but it’s projected to exceed half. It’s the fastest growing part of the company by far.

[Note: I think it’s worth pausing to reflect on the fact that the most valuable pharmaceutical company in the world is 149 years old and significantly more than half the company’s total revenue is from drugs that the FDA only approved four years ago. This is not a normal industry! More on that later in the interview.]

WHY THESE DRUGS SEEM TO DO EVERYTHING

Thompson: To me, the most mysterious and most even confounding aspect of these drugs is that they seem to do so many things at once. They increase feelings of fullness. They help people stop smoking or gambling. They reduce knee pain. They seem to reduce inflammation. They seem, in some cases, to protect against heart disease among people who aren’t even losing weight, which suggests some mechanism that is maybe slightly unrelated to obesity. They might even have brain benefits. They’re being studied for dementia. You are as close to the best scientists studying GLP-1’s as anybody in the world. What the hell is going on here?

Ricks: I’d add one more category, which is cancer reduction. There’s a lot of interesting studies about population-wide cancer risk reduction, maybe more directly associated with losing weight or maybe the inflammation story.

Okay, so just to take it all the way back, a core behavior to keep organisms alive is feeding. We can all understand that. Animals have grown up in an environment of scarcity. There are very few counter-regulatory processes that we’ve evolved with, because our ancestors evolved in scarcity. It was unnecessary to put in our genome something that says, “Okay, you’re full.”

Human beings have evolved into an environment we weren’t built for—of abundance, to maybe hit that note for you. What does that do? More weight, more type-2 diabetes. You’re too heavy, and your insulin, which helps you process sugar, no longer works effectively because essentially, there’s resistance from your body from having too much sugar for too long. If you reduce weight, you increase insulin sensitivity. We have a study where we watch people on tirzepatide for three years, and they had 92% less diabetes at the end of the study. That’s a remarkable finding.

These drugs also reduce inflammation in ways that may be separate to over-consumption of calories. I think our scientists debate that still. There’s a key signal called hs-CRP, a protein that measures general inflammation. On these drugs that drops 50 to 70%, and it drops a lot faster than your weight.

Speaking of inflammation, we did a study with retatrutide, the next generation product we’re working on that reduced knee pain. [Tirzepatide is a dual-agonist, working on GLP-1 and GIP; retatrutide is a triple-agonist, which also targets the gut hormone glucagon.] Half the people in the study had no knee pain at the end. It was the most effective osteoarthritis, knee pain drug I think ever studied. Here you have probably two effects: the inflammatory factors and the offloading of weight.

One last thing just on the brain, because I think it is the strangest and most difficult to explain. In addition to suppressing our appetite for sugary foods, it does also seem to suppress our appetite for vices that are maybe driven by anxiety or driven by a cycle in our brain of trying to scratch an itch or have a craving. Tobacco use and alcohol use are the most noted in the literature. We’re actually doing prospective studies to prove both those things. But no one would’ve hypothesized that at the beginning.

WHAT ARE THESE THINGS DOING TO OUR BRAINS?

Thompson: What’s spookiest to me is the degree to which these drugs almost act as a system-wide mechanism for increasing moderation. It moderates our impulse to eat. It moderates our impulse to gamble. In some ways, it even seems to give patients the urgency to align their behavior with intent. How does this work?

Ricks: There seems to be some other signaling going on. The human brain is very complicated and pretty poorly understood relative to other organ systems. So we’re still trying to figure out exactly the mechanisms of these. What we can say is empirically, people report it and we are doing prospective studies to take people without obesity who have these problems you mentioned—too much gambling, even opioid use disorder—just to see, well, can we help?

Thompson: There’s a theory of science that says we develop a perfect theory of how the world works, and then we develop technology that applies that theory to the physical world. But it almost always seems to be the opposite.

Ricks: We wish.

Thompson: The classic example I think from tech history is that theoretically you would want to understand the principles of thermodynamics to invent the steam engine. But we invented the steam engine and then from it derived the principles of thermodynamics. And so, maybe in a similar way, it would be lovely if we accidentally fell into a synthesized Gila monster molecule that taught us the secrets of the gut-brain axis rather than having some perfect theory of how our brains worked and then said, “Oh, we should be doing is targeting glucagon and GIP in the gut.” So it would be lovely if this technology opened up some big secrets to the brain.

But for all the good that these drugs can do, I believe according to the most recent data, more than half the folks who start on GLP-1 drugs discontinue them before two years is up. So there’s still a pretty significant adherence problem, made even more significant by the fact that people who discontinue these drugs often gain the weight back. Why do you think the discontinuation rate is so high?

Ricks: The data so far for Mounjaro or Zepbound, otherwise known as tirzepatide, is actually pretty similar to other chronic medications that we sell. There was a period of time where it was higher, but that’s when we had shortages going on, and so people were actually having to discontinue because they couldn’t find the medicine.

All prescription drugs have untoward effects for some people. I think these drugs have been used for 20 years, and we have a lot of data to profile that, but people really should talk to their doctor before initiating these. Somewhere between 5% and 10% of people across our drugs discontinue due to side effects.

But untoward effects are not the most common cause for discontinuation in our studies. The most common reason people discontinue any chronic medication is that life gets in the way: some other medical problem where they discontinue the drug to manage that. Financial costs: more people pay out of pocket than any other drug class in America. So they’re paying their health premium, but they’re not getting covered for this drug. And that is difficult because these drugs are not cheap. And then I think people have sometimes experienced so much success that they want to try going off, and then they bounce back on, and go off, and back on.

GLP-1 DRUGS FOR … ALCOHOLISM? ASTHMA? BIPOLAR?

Thompson: One more beat on the frontier of GLP-1 drugs. There’s a lot of different directions I imagine you could push this class of medication. You can push on the weight loss effects. You can hold steady on weight-loss and try to build drugs that reduce side effects. And maybe a third direction is to accentuate a certain non-weight loss benefit of this drug category: a Zepbound for inflammation, a Mounjaro for reducing Alzheimer’s plaque. What are you investing in the most here right now?

Ricks: You’ve hit on three key ones. All three of those ideas are in the latest phase of testing for us. So in the next three years or so, we’ll have some answers. The only one I would add is convenience. People are interested in oral medications. We have that coming very soon. People are interested in much longer acting, “Oh, could I just do this once every three months or maybe on the future horizon, even once a year type of therapy?” So it’s more like getting your annual flu shot. That’s obviously very appealing to consumers. The technology is harder and harder to do that, but that’s another vector that we’re working on.

Looping back to what’s in the pipeline, retatrutide is our triple-acting drug. So if you think Ozempic, GLP-1, is single acting. Tirzepatide improved on that, Zepbound with dual-acting, so why not three? So we have a triple-acting single protein that’s in late phase development. We’ll get most of the data this year and submit it and probably launch it in ‘27. This promises to get between maybe 28%, maybe up to 30% weight loss. That’s the equivalent to gastric bypass surgery. Unfortunately there’s, I think, 8% of Americans have a BMI over 40. So even if you lose 25% of your body weight and your BMI is 40, you’re still classified as obese. So more weight-loss effect matters.

We’re also studying this category for alcohol use disorder and asthma. Asthma is an inflammatory condition. It is very co-morbid with overweight and obesity and it’s a major problem in the US. And so that’s another indication along with tobacco use disorder and bipolar disorder. So here we’re taking a drug that appears to have a little more brain properties that might be ideally suited for these, not necessarily just weight loss, but other conditions.

ARE WE VASTLY OVERRATING AI IN MEDICINE?

Thompson: Something I hear a lot from the folks building AI is that the risks of this technology are acceptable because in the long run “these things will be smart enough to cure cancer.” And I always want to know exactly how does that work. Like, I see these companies automating software development. I don’t actually see them developing new druggable targets for pancreatic cancer. So let’s say, the labs come to you—Anthropic, OpenAI, Google—and they say: “Dave, how do we cure cancer?”

Ricks: It’s a great question.

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It’s not every day that a piece of science-fiction gets treated as a news bulletin. In 1938, a radio broadcast of H. G. Wells’ novel The War of the Worlds, produced and read by the actor Orson Welles, sent listeners into a tizzy, as many believed that war-hungry Martians had set foot in New Jersey. Nine decades later, another science fiction story about the untoward effects of alien intelligence making contact with Earth has caused another national freakout.

If you know, you know: On Sunday, a viral article published by Citrini Research called “THE 2028 GLOBAL INTELLIGENCE CRISIS” described a future-world scenario where powerful AI leads to spending cuts across the economy, triggering a massive recession. On Monday, investors reacted as if the article were a news reel from the future. Many of the companies name-checked in the essay saw their stocks plunge. The Wall Street Journal reviewed the day’s carnage under the headline “Viral Doomsday Report Lays Bare Wall Street’s Deep Anxiety About AI Future.” Some details:

Software firms Datadog, CrowdStrike and Zscaler each plunged more than 9%. International Business Machines’ 13% decline was its worst one-day performance since 2000. American Express, KKR and Blackstone—all name-checked by Citrini—tumbled …

Shares in DoorDash also veered 6.6% lower Monday after Citrini’s Substack note called the delivery app a “poster child” for how new tools would upend companies that monetize interpersonal friction. In the research firm’s scenario, AI agents would help both drivers and customers navigate food deliveries at much lower costs.

Is the Citrini story … compelling? plausible? accurate? These are the questions colonizing all of financial and tech media at the moment1. But to me, those questions miss the deepest and most interesting feature of this strange episode: What does it say about the state of AI and AI anxiety that a literal science-fiction story had the power to move a trillion dollars?

AI’S MARKETPLACE OF FICTION

“Nobody knows anything,” the author and screenwriter William Goldman once wrote of the movie business. Goldman was talking about Hollywood’s famous inability to predict future hits. Surrounded by false confidence, Goldman counseled humility.

Today his motto applies forcefully to the discourse around artificial intelligence. I am lucky to have participated in conversations about the future of AI with executives and builders at frontier labs, economists at AI conferences, AI investors, and other bigwigs at off-the-record dinners where important truths can theoretically be bandied about without risk. And if I had to pick three words to summarize this collective expert view of the future, I could not in a million years, or with a trillion tokens, find three words more suitable than these: Nobody knows anything.

I do not mean that AI architects are stupid. I do not mean that their speculation is absurd or worthless. I certainly do not mean that they don’t have access to narrow truths, such as rising adoption of AI in general and autonomous “agents,” in particular. What I mean is that the frontier labs don’t really know what they’re building exactly2, and economists don’t know how to model the thing that they claim they’re building. As a result, nobody really knows what is going to happen with AI this year, or next year, or the year after. There is no secret cigar-filled room of elites who have unique access to some authentic postcard from the future. When you drill down underneath the bluster, the boosterism, the fear, and the anxiety, what’s present at the bottom of it all is a genuine uncertainty, a vacuum into which storytelling is flooding.

Even folks who you’d think would be experts about AI and the economy are mostly offering elaborate speculation by way of analysis. Here’s JP Morgan chief executive Jamie Dimon at an investor cocktail event on Monday evening (as quoted by Yahoo Finance executive editor Brian Sozzi):

What if, I think there are 2 million commercial truckers in the United States, and there are lots of other examples you can give. There’s a thought exercise, and you could push a button, eliminate all of them, and they make $120,000 on average. Save fuel, save lives, save time, a more efficient system, less disrupted highways, all that beautiful stuff. Would you do it if you put 2 million people on the street where even if there are jobs available, that next job is $25,000 a year, stocking shelves. I was saying, “That’s kind of really bad, kind of civilly, should we as society agree to that?” I don’t think so. I was talking about the business and government, and they should start thinking today, not when it happens, what would we do to deal with the [AI] issue? It’s got to be business and government.

Long block quote, sorry. But the most important words are the first two: “What if.” The conversation about AI really has become a marketplace of competing science fiction narratives. The level of uncertainty about AI’s economic effects is so high—and the quality and supply of real-world, real-time information about its economic effects so paltry—that even serious conversations about AI from otherwise analytical people often veer toward the genre of fiction rather than the category of empirical analysis. AI land is so full of science fiction precisely because the space is so bereft of official high-quality data.

Consider, for example, a topic that should be incredibly easy to talk about clearly: AI and the labor force. Many people predict that AI will soon destroy jobs, perhaps at catastrophic levels. This generation of AI—ChatGPT, Gemini, Claude, Copilot, etc—is already the fastest-growing technology class in modern history. So one might guess that it’d be trivially easy to say whether the technology ripping through the economy is reducing employment, yes?

Nope. Despite predictions of imminent labor market “bloodbath” by AI’s architects, economists have struggled to find any solid evidence of AI’s presence in the official data. A report from the Economic Innovation Group found “little evidence of AI’s impact on unemployment.” In the Financial Times, John Burn-Murdoch pointed out that “the much-discussed contraction in entry-level tech hiring appears to have reversed.” Last August, a team of academics from Stanford University published a paper claiming that young workers aged 22–25 in “highly AI-exposed” jobs, such as software developers and customer service agents, experienced a 13 percent decline in employment since the advent of ChatGPT. But when the Atlantic journalist Josh Tyrangiel asked other economists if the Stanford paper had settled the debate, his sources mostly wanted to “punch holes” in its methodology and interpretation. After spending dozens of hours talking to numerous economists about the issue, Tyrangiel concluded that “numerically speaking, nothing indicates that AI has had an impact on people’s jobs.” Nothing!

Artificial intelligence offers its obsessives a kind of Schrodinger’s apocalypse, which exists in a superposition between “the economy is about to change forever” and “from a macroeconomic standpoint, everything still looks eerily normal.” In the film Don’t Look Up, Adam McKay made a climate change parable where politicians and the public ignored an approaching comet despite the desperate warnings of scientists who saw the rock clearly through their telescopes. In the case of AI, the desperate warnings are with us, and they may be fiercely prescient, and I wish more people were taking the possibility of violent and calamitous change more seriously; but also, let’s face it, our telescopes kind of suck, and when we put our eye to the lens, nothing is clearer than the fact that little is clear.

THE SEVEN QUESTIONS

There are so many questions that I want answers to in the space of AI and economics, but for now I have to reconcile myself to the fact that we don’t have answers to at least seven questions that will determine the course of this decade and even this century. Those seven questions are:

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The world is full of theories of everything. The smartphone theory of everything argues that our personal devices are responsible for the rise of political polarization, anxiety, depression, and conspiracy theories—not to mention the decline of attention spans, intelligence, happiness, and general comity. The housing theory of everything pins inequality, climate change, obesity, and declining fertility on the West’s inability to build enough homes. If you treat TOEs as literal theories of everything, you will be disappointed to find that they all have holes. I prefer to think of them as exercises in thinking through the ways that single phenomena can have vast and unpredictable second-order effects.

Today’s article and interview are about my new favorite theory of everything. Let’s call it “the orality theory of everything.” This theory emerges from the work of mid-20th-century media theorists, especially Walter Ong and Marshall McLuhan. They argued that the invention of the alphabet and the rise of literacy were perhaps the most important events in human history. These developments shifted communications from an age of orality—in which all information was spoken and all learning was social—to an age of literacy, when writing could fix words in place, allowing people to write alone, read alone, and build abstract thoughts that would have been impossible to memorize. The age of orality was an age of social storytelling and flexible cultural memory. The age of literacy made possible a set of abstract systems of thought—calculus, physics, advanced biology, quantum mechanics—that are the basis of all modern technology. But that’s not all, Ong and his ilk said. Literacy literally restructured our consciousness, and the demise of literate culture—the decline of reading and the rise of social media—is again transforming what it feels like to be a thinking, living person.

The most enthusiastic modern proponent that I know of the orality theory of everything is Bloomberg’s Joe Wiesenthal, the co-host of the Odd Lots podcast. On Plain English this week, we discussed orality, literacy, and the implications for politics, storytelling, expertise, social relations, and much more. The following transcript has been edited for clarity, brevity, and the goal of making both speakers sound a bit smarter1.

Derek Thompson: The return of orality: Why do you think it explains everything?

Joe Weisenthal: I don’t think it explains everything. I think it only explains 99% of everything.

I believe that human communication is becoming more oral. And by that I don’t just mean that people are talking more with their mouths, although I do think that is the case. It’s more that communication in general, whether in the spoken form or in the digital form, has the characteristics of conversation. And it truly harkens back to a time before really the written word or, certainly, mass literacy.

In 2016, during the presidential election, I started reading the work of Walter Ong. He was a Jesuit priest. He studied with Marshall McLuhan. He was at Sant Louis University and wrote this really incredible book called Orality and Literacy. The gist is that humans [in oral cultures] really fundamentally think differently when they’re in this world that you can’t write anything down, that you can’t look anything up. For most of human history, there was no way to look up anything at all. There was no reference material and so forth. And as such, people had to optimize their communication for the conditions of that time.

Through a lot of study of Homer and other ancient epics, people realized that there were certain patterns [of communication]. People spoke with rhythm, and rhyme, and musicality, because it helps people memorize things. Certain phrases just get repeated over and over again. Repetition, communication, and information were optimized for memorability, in packets, and what we would call going viral. When I started reading this book, I was like, look, this has a lot of explanatory power. These things that characterize the Homeric times, the way society prioritized and packaged information, greatly resemble what we see today. My big thesis is that as communication becomes more of this back-and-forthness, it’s changing the way that, fundamentally the way we communicate and the way we think.

Thompson: To drill down on why the shift to literacy was so important for the way we think, for the way we transmit knowledge, for the way we build institutions, I want to quote two great scholars here. The first is Joshua Meyrowitz. He writes:

The break from total reliance on oral communication allows people to become more introspective, rational, and individualistic. Abstract thought develops from the circular world of sound with its round huts and round villages, people move over time toward linear cause and effect thinking grid-like cities and a one thing at a time and one thing after another world that mimics the linear lines of running and type.

The second is from another great scholar named Joe Weisenthal:

“Many of the things that modern institutions are built on, enlightenment thinking, formal logic, reason, meritocracy, examining the evidence are downstream from the ability to contemplate the written word at a distance.”

Why don’t you expand on either quote, either yours or Joshua’s, because I think this really gets to the fundamental heart of the matter that literacy changed the texture of thinking and the texture of the institutions that we built with new modes of thought?

Wiesenthal: People can probably feel this. When you’re in a conversation, online or offline, what are you doing? You’re often trying to impress someone. You might be trying to one-up someone. Maybe if there’s a few people there, you’re trying to put someone down to look cool for the other person. These are all things that occur that don’t occur when you’re in solitude. A solo interaction with language can only be done really with the written word. And so, even setting aside the logical arguments for the connection between the alphabet and left-to-right thinking and linear thinking, most people I think could intuitively understand that interactive environments foster different priorities.

When you’re writing a letter, or certainly let’s say you’re writing a book as you have, you don’t necessarily have the reader in mind at that exact moment. In fact, you have the luxury of writing and not having to think about what the reader is going to be doing at this moment. These are all luxuries that occur in the context of literacy, the written word that are separate from a conversation. And so, the written word creates all kinds of new opportunities to think through these things, take time, not respond right away.

Thompson: Thinking used to be something that had to be done socially. It was impossible to learn the Odyssey on your own. It was transmitted to you from a person. You would rehearse it with someone else. So the mode of information transfer was necessarily social. Books are written alone and books are typically read alone. And so this age of literacy gave rise to this privilege of solitude and interiority that I think is really, really important.

Walter Ong, our mutual hero, has a great quote that I want to throw to you and then get your reaction to, because it goes right to this point. He said:

”Human beings in primary oral cultures do not study. They learn by apprenticeship, hunting with experienced hunters, for example, by discipleship, which is a kind of apprenticeship by listening, by repeating what they hear, by mastering proverbs and ways of combining and recombining them, but not study in the strict sense.”

I’m very interested in a phenomenon that I call the antisocial century, the idea that for a variety of reasons, we are spending much more time alone. And that is having a bunch of second and third order effects. And it really is interesting to me as I was going deeper into this project, to think that it’s the age of literacy that in many ways allowed us to be alone as we learned and prize a certain kind of interiority.

Wiesenthal: Marshall McLuhan had this observation: The alphabet is the most detribalizing technology that’s ever existed. It speaks to this idea that prior to the written word, all knowledge was per se communal. It had to be in a group. If you have multiple texts in front of you, then you trust the one that feels most logical. But you don’t have that luxury when all knowledge is communal. Being part of the crowd has to be part of learning.

The ear and the eye are very different organs. You can close your eyes, which you can’t do with your ears. You can get perspective from your eye and establish perspective in a way you can’t do with your ears. So it’s like you go into a room and you can stand back at the corner so you can make sure that you can see everything going on in the room. The ear is very different. We’re at the center of everything constantly. You can’t close it. The ear continues to work while we’re sleeping. There’s an evolutionary purpose for the fact that we can still hear when we’re sleeping, because if there’s an intruder or a wild animal or something, it wakes us up and we can run.

So the ear, McLuhan said, is inherently a source of terror. It feels very digital. Even though we do look at the internet, there is this sense in which we can never remove ourselves from it. Even if we’re reading the internet, it almost feels more like we’re hearing it. There’s an immersiveness in contemporary digital discourse that I think is much more like hearing than it is about seeing. So I think there’s all kinds of different ways that we are sort of returning to this realm.

Thompson: We had the age of orality, which was the age of the ear. Then we had the high watermark of literacy, which is the high watermark of the age of the eye. And now we’re in this messy third stage where it’s like there’s some human facial organ that’s an eye and an ear mashed together because we have TV and radio and social media and TikTok. And what’s interesting about these technologies is that they are all oral. What is radio, if not oral? What is television if not oral? What is TikTok if not spoken and live?

But there’s a lasting record of your tweets. There’s a lasting record of that TikTok which can be shared. And the fact that these pieces of media can be recorded means that in many ways they are also of a piece with the age of literacy, of literate recorded artifacts. What do we make of this weird synthetic new stage that we’re in? What do we call it? How do we describe it?

Weisenthal: Andre Mir, who has written some of the best stuff updating Ong’s ideas, calls it digital orality. I like that. One thing that’s interesting though, and I think this is where AI really comes in, which is that we might not really have those records in the future. I mean, for one thing, things disappear. Two, we don’t really trust pictures anymore. The archive is sort of tenuous. We maybe had this brief period where we had a lot of digital archives and we could trust them, but digital archives are disappearing and you’re going to have facsimiles, things that looked like they happened that didn’t actually happen, which incidentally Ong talks about.

So he talks about how in a lot of oral cultures history was malleable. He talks about biblical genealogies: so-and-so begat so-and-so, begat so-and-so, begat so-and-so begat … on forever. There are a lot of examples throughout in oral cultures where when something is no longer convenient, maybe there are some lineage of kings and that king falls into disrepute and they switch it, they’ll just come up with a new poem. And so there isn’t the idea of a fixed history. I think that’s probably what’s going to happen today. We’re going to have books for a very long time, but history will be manufactured in accordance with the sort of contemporary values of the moment.

Thompson: This is a period that some people call post-literate. Reading is in decline. Standardized test scores are in decline. As I’ve written, it sometimes feels like everything is trying to become television. Here we are two former audio only podcasters talking on something that people will watch on YouTube or Spotify. Social media is becoming TV, podcasts are becoming TV. People are going to the movies less. Everything is evolving toward short form video. I think one way that I think remember putting it is it’s as if short form video is the attractor state of all media. I wonder how you feel about this general thesis space that people are playing in—that in a post-literate age, everything is evolving towards short form video.

Weisenthal: This idea of post-literacy, I think there’s a sort of figurative meaning and a literal meaning. So on the one hand, again, when I hear the word post-literacy or when I’ve used the term, it doesn’t necessarily mean that people don’t know how to read. I still think it’s mostly useful as a term to describe sort of conditions of information and conditions of communication that are very distinct from what we consider the sort the solitary removed literate communications. So I think the fact that so much is talk, so much is back and forthness, so much is information designed to be viral, memorable, repeatable … this is mostly what I am thinking of when I think about post-literacy.

Incidentally, I don’t think people know how to read either. I look at myself and I think I read books way more than 99% of the population. But I’ll read two pages and then I’ll check my Twitter mentions and then I’ll read two pages and check my Twitter mentions. Isn’t that everyone? Can anyone actually read three pages anymore? Maybe it’s just me, and my attention span is just totally bombed out, which is possible because again, I spend all day looking at a screen. I’ll fully cop to that.

Thompson: I do also have the sense when I’m reading that there’s often, especially if my phone is anywhere within reach or sight, that there’s something calling me away from that book at all times.

Weisenthal: One of the reasons that I like reading the mid-20th-century thinkers about media is because I think they have a lot of testable hypotheses. There’s a lot of people writing these days about the effect of the phones and the effect of digital media on ourselves, and it all seems true and I buy most of it. But in finance people talk about how you want “out of sample data.” You want hypotheses that could explain market movements before the markets happen so that you can test whether the hypothesis is valid.

And when I go back and read some of the writing from the ‘60s and ‘70s, one of the things that I’ve noticed a few times in this body of work, people talking about the effect of the phone on interrupting people having sex. This is a common observation. They talk about unplugging the phone before couples having sex or whatever it was. And I think again, one of the things people talk about right now, which I find fascinating, is the big fertility drops and people are trying to figure it out. And this is something that is occurring in almost every country around the world, including China, which does not resemble the rest of the world and has avoided many sort of contemporary pathologies. Even there it’s happening.

And I do think it’s very interesting that if you go back and look at how many people noticed this phenomenon when everyone started getting phones, the degree to which it was as if the phone was the third person there interrupting the privacy of the couple, that’s a very powerful observation that I think then has a lot of explanatory effects for what came afterwards when everyone started holding a phone on them, every waking minute.

Thompson: I want to apply your theories to some domains of modern life, starting with politics. You mentioned Donald Trump, and I went to look up Donald Trump’s nicknames, because I know that you’re very interested in his propensity for epithets, for nicknames. It’s nearly Homeric. And so fortunately for our purposes, Wikipedia keeps track of all of Donald Trump’s nicknames, so I didn’t have to remember them—speaking of outsourced memory. Here’s some of them. Steve Bannon was Sloppy Steve, Joe Biden was Sleepy Joe, Mike Bloomberg was Mini Mike, Jeb Bush, of course, Low Energy Jeb, Crooked Hillary, Lyin’ James Comey, Ron DeSanctimonious, DeSantis. I think that one might’ve gotten away from him.

Weisenthal: That was late Trump, he didn’t have his fastball anymore.

Thompson: This plays into this classic tradition of orality. Right? The wine-dark seas, swift-footed Achilles. And Walter Ong has a great passage where he writes about this, that I would love to get your reaction to:

”The cliches in political denunciations in many low-technology developing cultures, enemy of the people, capitalist warmongers, that strike high literates as mindless are residual formulary essentials of oral thought processes.”

Basically, it’s so interesting to think that Ong is saying that it is low-technology developing countries where these nicknames are prevalent. But you wake up today and thee richest country in the world is presided over by a now two-time president whose facility for nicknames is very famous. I wonder what significance do you put on this? Why is it important that a figure like Trump plays into these old-fashioned oral traditions?

Wiesenthal: It’s interesting when you say things like, “Oh, Trump has a sort of Homeric quality the way he speaks,” that repels a lot of people. Like, “What are you talking about? This is nothing like Homer.” But my theory, which I can’t prove. The original bards who composed Homer were probably Trump-like characters. So rather than seeing Trump as a Homeric character, what’s probably, what I’m almost certain is the case, is that the people who gathered around and told these ancient stories were probably Trump-like characters of their time. Colorful, very big characters, people who were loud, who could really get attention, who would captivate people when they talked. One of Ong’s observations in Orality and Literacy is about heavy and light characters in oral societies. Heavy characters, it’s like the Cerberus, like the three-headed dog, the Medusa, the Zeus. These just larger-than-life, frequently grotesque, visually grotesque characters.

I think if you look at the modern world, the modern world has elevated a lot of what I think Ong would call heavy characters. I certainly think Trump is a heavy character, with his makeup, and his hair, and his whole visual presentation. I think Elon is a heavy character. I think if you look at the visual way that a lot of sort of YouTube stars look with their ridiculous open-mouthed soy faces when on their YouTube screenshot. I think they sort of present themselves, not in a way that we would think of as conventionally good-looking. Right? Not in a way that’s conventionally attractive, but this sort of grotesque visual that just sticks in your head. And that that is clearly what works. We are in the time of the heavy character.

I think you look at icons of the previous age, JFK was not a heavy character. That’s a light character, a certain coolness. Obama was a light character, there was a certain coolness to him. One of the things that people debated a lot, I see, is like, “Oh, if Obama could run again, wouldn’t he just clean up? If Democrats could just bring Obama back for a third time, wouldn’t that just solve all of Democrats’ electoral problems?” And I think in 2016 I probably would’ve believed that. And maybe in 2020 I would’ve believed that. But I’m certainly less confident now. I feel like Obama is a character of a cooler, a different time. A character from a pre-TikTok time, in many respects, maybe more resembling the sort of Kennedy era and so forth.

Thompson: Let me try out a pushback here. I think Obama in 2004 with the first DNC speech, I think he was a heavy character. I think the presidency lightened him. I think Trump in 2015 was a heavy character, and he is a lighter character now having suffered overexposure. Maybe the fissures that you see in the Republican Party are that Trump, the once heavy character, is losing the weight that used to be necessary to keep this coalition together. And people are seeing he’s kind of lost it. I wonder if there’s some idea that in politics people often debut on the political scene as the heavy character. But experience, and time, and failure lightens them. And that’s part of the reason why no president seems to survive more than one year of positive approval ratings. We have learned to hate everybody.

Weisenthal: Since you mentioned this phenomenon that no president can sustain having high approval ratings. Approval ratings, which does seem like not just a phenomenon in the United States, but basically everywhere. I think, could we pivot? Could I bring in Meyrowitz here?

Thompson: Sure. So Joshua Meyrowitz is another writer that you turned me onto. He has a book called No Sense of Place, The Impact of Electronic Media on Social Behavior. We’ll talk about that term, No Sense of Place, in a second. But why don’t you just jump right into why Meyrowitz’s theories fit where I was going with politics?

Weisenthal: Meyrowitz in 1985 was talking about electronic media before anyone really conceived of that idea. One of his observations is that everybody has a front stage and a backstage. We talk on this podcast in a certain way. But that is different than how we would talk at home with our family. Or you and I might talk differently when we hang up this podcast and we’re saying goodbye or something. This is a very normal thing, which is that you just talk differently in different environments and so forth.

What Meyrowitz anticipated in No Sense of Place is this idea that electronic media would cause us to come to be suspect of people who talk differently in one environment vs. another. If someone code-switched, if someone talked differently on the campaign trail than they did in their private life, that we would come to think, ”Oh, this person’s a phony.” He predicted that by allowing everyone to see all the facets of these characters, we would view them differently.

Thinking about a politician, something about Trump is that there’s very few examples of him ever talking differently than any other environment. People could be totally repelled by things that he said in public or private. But he’s not a hypocrite in the way that a lot of people use that word. He is the same in almost every environment. This is precisely what Meyrowitz would’ve anticipated, that we would gravitate toward people who when we saw their front stage and their backstage, where the concept of place was completely disintegrated from the idea of character, that we would come to view that consistency of character as a value.

Thompson: The name of Meyrowitz’s book is No Sense of Place. And I want to just slow down on that title, because it’s a pun. It’s not a very intuitive pun, but it’s a really, really smart pun. By No Sense of Place, Meyrowitz is saying that electronic media extends our consciousness outward, so we don’t really know where we are. I could be reading Twitter in Arlington, Virginia, but feel myself becoming emotional about Gaza or Ukraine, or Minneapolis, in a way that was impossible in the age before television or radio. This new age of communications media takes us out of where we are and puts us right in front of the faces of people that are thousands of miles away.

But he also means no sense of place in a hierarchical sense. He means that people will be able, with electronic media, to operate outside of their slot in the hierarchy: the poor will be able to scream at the billionaires; the disenfranchised will be able to scream at those who disenfranchise them. And this he said is going to create more social unrest. It’s going to create more, I think what he would agree now is something like populism. And this really interesting idea that electronic media not only unmoors us from where we are geographically, but that it also demolishes hierarchies, I think it was incredibly insightful, considering it was written 41 years ago.

But he goes one step further in a way that’s really surprising, and this is the part I’d really love you to comment on. He says this about our future relationship to expertise. And God only knows how many people have talked about what’s happened to expertise in the last few decades. Meyrowitz:

”Our increasingly complex technological and social world has made us rely more and more heavily on expert information, but the general exposure of experts as fallible human beings has lessened our faith in them as people. The change in our image of leaders and experts leaves us with,” and this is exactly your point, “a distrust of power, but also with a seemingly powerless dependence on those in whom we have little trust.”

Wow.

Weisenthal: It’s crazy. It does feel like, oh, this could be in the Atlantic in 2025. It’s just so far ahead of its time. You mentioned the poor can scream at the billionaires. I think most people would say, look, technology is an enabled environment in which the poor can have their voice heard, and billionaires are brought low and can be hectored, and we see that happen every day online. Most people intuitively think that’s a very positive development. That’s like an egalitarian development. But by the same token, there are other things that most people will probably... they’re not as comfortable with. And so, I think this whole field of study offers a certain way of viewing history that is not entirely satisfying to anyone or anyone’s particularly, certainly political project currently.

Thompson: Speaking of topics that aren’t particularly comfortable with any political project, I have a question about AI for you and how AI slots into orality versus literacy. I want to come at it from what I hope is an interesting angle. This is a quote from Orality and Literacy by Walter Ong:

A written text is basically unresponsive. If you ask a person to explain his or her statement, you can get an explanation. If you ask a text, you get back nothing, except the same often stupid words, which called for your question in the first place.

I remember rereading that section on a plane recently and I jolted up in my seat. I was like, that’s what AI has changed. You can enter into conversations with text. That is true either at a literal level—like I can download a PDF of a book, and give it to Claude and be like, Claude, can we talk about this book? But also, at a higher abstract level, we’re talking about a technology that is pre-trained on text. It’s pre-trained on literacy. But we have an oral, which is to say conversational, relationship with that training corpus. It’s weird.

Weisenthal: It’s weird.

Thompson: A weird new alien media.

Weisenthal: I totally agree. The jury is out still on how AI slots into this. Because on the one hand, [you can] upload some texts to Claude and ask questions, and it becomes an interactive thing. That’s oral, that’s conversation.

On the other hand, those conversations with AI, they don’t feel like other conversations that exist online. The AI is not going to insult you. The AI is not going to speak to you in memes. The AI is not going to use epithets. I’m not trying to one-up the AI either. Ong used the word agonistic; oral cultures are competitive. We see that online and social discourse, how we’re always competing with each other when we’re talking.

AI chatbot communications aren’t agonistically toned. Just the opposite. Most people’s complaint with AI is that it’s too obsequious, that it’s not confrontational enough. I’ll say something stupid into the chatbot, and it’ll say “that’s a really good idea, Joe! Let’s explore that further…” This is actually one of the big problems of AI, which is that it’s insufficiently opinionated. The chatbots do not correct you. So, AI is conversational, but it doesn’t have a lot of these other aspects of conversation that other digital conversations have.

Thompson: Maybe the age of social media really was the revenge of orality. But an age of AI would be much more like the revenge of literacy.

Ong, Havelock, and Meyrowitz all point to this idea that literacy pulled us into ourselves. Reading is interior. And then novels in response to the interiority of reading became more interior. Nineteenth century novels are incredibly rich about what is it like to be thinking and alive in this moment. It’s not plot, plot, plot. It’s not genealogy. It’s fully inside the phenomenological experience of the characters.

And AI to me feels much more sub-vocal. It feels like I’m having a conversation with myself. It’s not myself. It’s this machine that I’m talking with, but it feels more like daydreaming with myself than the antagonistic experience of being on Twitter, where I’m inside the minds of other people, thrust into the faces of strangers who I’ve never met.

Weisenthal: It’s very plausible. It’s not going to look exactly like the previous age of literacy, but it never does. So, these things come and go. The current age of orality is different, obviously than the original one. The return to solitude. If you’re going back and forth with the chatbot, you close the computer, you don’t feel that same, oh, they’re still arguing there without me. They’re talking online about me and I’m not there to defend myself. Whatever it is. You don’t quite have that same pull. So, I think all these things, they’ll live with each other and there’ll be shades of the past that we hear echoes of, and they’ll be different and they’ll be similar. And I think it’s good to recognize these patterns and observe them, just for one’s own sanity—to have a sense of what’s pulling you in various different directions.

Thompson: To close with the Joeism, what’d I miss? What’s important in this space that we didn’t have time to talk about or that I didn’t sufficiently ask?

Weisenthal: I just think, by and large, that there are a lot of contemporary pathologies. People point to digital media, the phones, et cetera, as drivers of them. What I would just say is there’s a lot of writing that I think helps answer these questions that was written before any of this existed. I would like it if more people became familiar with Josh Meyrowitz, and Walter Ong, and Eric Havelock and Marshall McLuhan and so forth, people became more familiar with them. I think I would like that. I just want to talk to people about them.

I cover a lot of topics in this newsletter and in my podcast: Inflation, GLP-1s, politics, loneliness. But the biggest divide in my audience — and the biggest divide among the people I read and listen to and trust — is on the subject of artificial intelligence. The news and discourse space as I see it often seems divided between outrageous extremes: “This technology is billionaire-hyped vaporware” vs “This technology is 12 months away from automating all white-collar tasks or destroying the world.”

These aren’t just extremes I read in the news. I see them in my own working life. Among East Coast journalists, I’m mildly concerned that I’m developing the reputation of being a mindless AI booster, on account of my reporting that the technology is already proving economically and socially significant. But several months ago in San Francisco, I got (somewhat playfully) yelled at by several AI builders and investors at an event for my article suggesting that AI might be an industrial or financial bubble.1

So, I’ve spent a lot of time this week thinking about how the AI discourse has become so fractured. At the highest level, I think the AI discourse gap is downstream of a cultural gap between the Bay Area, where the frontier labs are based, and the rest of the country, which has developed a skeptical attitude toward the promises of Silicon Valley. While many technologists in San Francisco regard their zip code as the world’s greatest fount of technological progress, much of the country—perhaps, especially journalists—regards Silicon Valley as a den of plutocratic parasites whose work deserves our most profound distrust and disgust.

Beneath this cultural difference, there is a deeper substantive divide over AI. It’s not one disagreement. It’s really more like four distinct divides.

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THE FOUR GREAT DIVIDES OF AI

1. The first question that I’ve seen divide people is: Is AI useful—economically, professionally, or socially? I have brilliant friends, especially in software programming, who use this technology every day and say it’s transformed their work—and I think they’re right. But also, in the last week, I’ve spoken to close friends in other industries, such as television news and marketing, who have tried to use AI tools repeatedly and insist that they consistently underwhelm. The weird thing is: I think these folks might be right, too. AI is a unique technology. It is not like a light bulb that provides the same wattage to all users. Depending on your job, and the AI model you’re using, and the quality of your prompts, and a thousand other factors, AI is like a light bulb that offers some people a million watts and some people utter darkness. Some tasks are especially amenable to this generation of AI tools, especially those involving data or software, a certain amount of schlep, and a well-structured prompt. But many people have jobs whose tasks might not yet be “AI-shaped.”

2. The second question that I see creating fissures in the AI discourse is: Can AI think? That is, are these tools engaging in something like human thought, which combines memory, sense, prediction, and taste, or are they blunt instruments for synthesizing average work across several domains, producing average data analysis, average student essays, and average art? I want to pause here to point out something I think is important. I’ve seen many people suggest that AI can’t “think” and therefore it isn’t useful. But these are separate questions. AI can help a scientist draft a paper, or a bibliography, even if it doesn’t meet our philosophical or neurological definition of thinking. It can be useful without being technically thoughtful.

3. The third great divide that I’ve observed is one that I’ve directly participated in: Separate from the question of whether AI is useful, and whether AI can think, is another question: Is AI a bubble? This is principally a question about the speed and timing of the technology’s adoption and its revenue growth. The hyperscalers and frontier labs are spending hundreds of billions of dollars training and running artificial intelligence. If they don’t see ferocious revenue growth from AI in the next few years, a lot of companies, especially those that take on debt, are going to find their current position untenable. They’ll either face a markdown on valuation, a layoff, or something more catastrophic. Once again, you can believe that AI tools like Claude Code do economically meaningful work and believe therefore that AI isn’t a bubble; or you can believe that AI does significant work but it’s still a bubble, because there’s no way these companies make back the money on time. In fact, this was basically my position for much of last year.

4. The fourth and final great divide might be the widest. And it’s hard to capture succinctly so I hope you’ll excuse me going a bit broad here: Separate from the questions of is AI useful, or thinking, or over-leveraged, is a question that’s something like: Is AI good or bad? On one end of this spectrum, you’ve got the venture capitalist Marc Andreessen proclaiming that AI “will save the world.” On the other end of this spectrum you have the rationalist writer Eliezer Yudkowsky arguing that if anybody builds superintelligent AI, quote “everyone dies.” And there is a lot of real estate between those positions. Maybe you think AI won’t usher in the end of the human species, but it might make the most beautiful things in life — art, movies, human relationships — more slop-filled and shitty.

So, this is the landscape of the AI debate as I see it. What seems on the surface to be one debate between pro- and anti-camps is really several different debates that are becoming conflated and mushed together. Is AI useful? Can it think? Is it an economic bubble? Is it good for us or bad for us? These are separate questions. And our ability to bring wisdom to this topic depends on our ability to see that separateness.

In the spirit of trying to be specific about AI, today’s article is about a very specific question: What will AI do to jobs? In his Atlantic cover story this month, author Josh Tyrangiel wrote that the people who build AI have spent much of the last few years predicting dire effects it will have on the economy. In May 2025, Dario Amodei, the CEO of the AI company Anthropic, said that AI could “wipe out half of all entry-level white-collar jobs.” Last month, new tools like Claude Code and Codex were unveiled that sent a shiver through the tech world, as many top coders claimed that these tools could take over enormous chunks of their work forever. Software stocks plunged, possibly for related reasons. But when you look up from these dire predictions and stock gyrations to study the labor market of the present, it’s hard to see any effect of AI at all.

So how do we balance these pieces of evidence: the audacious predictions of tech CEOs, the enthusiasm for tools that seem to automate certain tasks, and the current calm of the labor market? Whatever it adds up to, Josh says, “America Isn’t Ready.” The following is a transcript of a conversation from my podcast Plain English. It has been edited for clarity, brevity, and the goal of making both speakers sound approximately 3 percent more articulate than one typically musters in a live interview2.

WHY THE AI DISCOURSE FEELS SO BROKEN

Derek Thompson: Two groups are coming into this episode from very different places. Group 1 says this technology is going to have a massive impact on the economy, on jobs, on the future of productivity, maybe even on our own sense of who we are. Group 2 insists that AI is still basically vaporware. It’s billionaire-pumped nonsense that hallucinates and doesn’t help anybody do anything. We’re going to spend a lot of time talking to Group 1. Before we do that, I would actually love you to address Group 2 directly. Why are they skeptical? And why are they wrong?

Josh Tyrangiel: I think a lot of that has to do with the context into which AI has arrived. We are not short on existential risk in our lives right now: political risk, climate risk, nationhood risk. Everybody is feeling risk from something. We came out of a pandemic. Right at the end of that, [ChatGPT] shows up. I think a lot of people looked at it, and at the hairball of motives behind the people who were creating it, and the massive investment and personal wealth that may come from it, after feeling victimized by 15 years of social media bullshit and basically said: “Not for me. I’m out.” I am more than sympathetic to that response. I think it’s actually kind of a logical response.

But as a person who is skeptical for a living, I think the technology is remarkable. I’m also a believer that it’s entering a fractured system that makes the likelihood of its misuse enormous.

Thompson: Unlike other general purpose technologies, the capabilities of AI are exquisitely local. You think about a train or electricity. If a train takes a bushel of wheat in 1870 from Chicago to New York, everybody can agree on what that train has done. But you compare that to generative artificial intelligence, where every interaction is unique. Every prompt is unique. So you have software programmers using the most recent editions of Claude Code or Codex from OpenAI claiming that their work has changed forever. But many good-faith people can have a really negative experience with this technology.

Tyrangiel: We’ll start with journalists. We are them and we know them. A lot of their initial experience was, “Oh, I can’t trust this.” The writing from some of these tools is still a little corny. Then I talk to my friends who work in financial services. They say this is the brain they’ve always wanted to have at my side—in terms of the capability to do arbitrage, to create decks that used to take me six hours, it’s taking six minutes, and now I can actually do the thinking that I want to do. In medicine, scribe technology listens to patient-doctor interactions, notes everything, fills out electronic health records, fills out prescriptions. Doctors have to make a subtle change: They have to vocalize the entire exam. Younger doctors have figured out [how to use the technology] to save an hour or two a day. Older doctors don’t want to change their workflow.

THREE SCENARIOS FOR AI, THE ECONOMY, AND JOBS

Thompson: I want us to consider three scenarios for AI and the economy. In the first scenario, AI won’t cause much job displacement at all. In the second scenario, changes will be significant but slow. In the third scenario, things move very fast.

Let’s start with scenario one. With technology like Excel, journalists decades ago might have said that [once spreadsheets are automated] there’s not going to be anybody working with spreadsheets in the future. Absolutely wrong. Everybody works with Excel. This technology didn’t destroy jobs at all. It amplified jobs. So walk me through this possibility that artificial intelligence is in some ways a normal technology that will essentially sit with knowledge workers in the future without actually replacing them from the labor force.

Tyrangiel: There are a lot of economists in particular who say that’s exactly what’s going to happen. There’ll be a little period of adjustment, and then we’re going to create even more jobs, even better jobs with even higher wages.” The case for that is that the tech takes a while to move into our various industries and systems. We can imagine a slow pivot. With the doctor example, doctors save an hour or two a day thanks to scribe technology. That reduces attrition, which is a huge factor in employment in the medical field. It gives us a massive corpus of data that we can now use to create more jobs. One of the fastest growing jobs in the 21st century is data analysis and data visualization. Now we have this massive corpus of public health data. Think of all the new jobs that can be created analyzing that. Those are the rosy scenarios.

Thompson: Practically every general purpose technology has been adopted slowly. The canonical example is the telephone. Patented in 1876. In the first few years, basically no telephones are manufactured because talking across long distances is not a part of anybody’s life. The adoption of the telephone did not pass 50% of American households until the 1940s, according to our best records. So it took 70 years for half the Americans to essentially pick up the phone. That is the typical story of general purpose technology adoption. Tell the story of electricity.

Tyrangiel: Electricity is the technology of the last two centuries. It basically took four or five decades to be reasonably dispersed across America, which is a very powerful and very capital friendly country. And part of the reason is that when you create something that new, there are a lot of vested interests. There are lots of factories that literally were built on steam. And when I say built on steam, I mean they constructed a steam engine in the basement and then they built the factory on top of the steam engine. So when you hear as a factory owner, “Hey, we’ve got this brand new tech, it’s so much easier, it’s going to be cheaper.” You’re like, “ Well, anyway, I’ve got this building and I spent all of my money to create the building. Call me when I can plug in.”And so a lot of those factory owners waited and they waited for their materials to become obsolete. They also waited for the government to pay for the rollout of electricity. Electricity largely was subsidized particularly in rural areas by the government saying, “We’re going to get everybody connected to electricity.”

Now, I’ll give you the counter. The counter is that AI is already probably the fastest growing consumer technology in the history of technology. The other point that people like [the economist] Anton Korinek and others will get to is that electricity didn’t roll itself out. It was a material and it required all of these human hands to build the infrastructure, to create electrification in factories and in homes and create a grid. [With AI, however,] these are smart machines that, given the proper instruction, can create a pathway for other smart AI technology to infiltrate its way into your home, into your factory, into your enterprise. That’s going to take us some time to get our heads around.

THE SINGLE BEST REASON WHY AI WON’T DESTROY JOBS

Derek: The strongest reason why artificial intelligence is either not going to displace jobs at all or going to have a slow impact in the labor force is one word: History. History will repeat itself. Even today unemployment is under 5%. So it’s very difficult to say that this technology has already had some kind of significant displacing effect.

This brings us to the scenario that you focused on most in your piece, which is the case that this time is different and that AI could in fact move very, very quickly. According to Pew, 50% to 70% of Americans say they use this technology every week. People were not using light bulbs three years after Edison patented them in the 1870s. So maybe this is not electricity. What is the strongest argument that you’ve heard that this time will be different?

Tyrangiel: I think there’s two, and I want to separate them because it’s really important to understand the difference. One is technology based and one is Wall Street based. And that’s literally like the fire in the cave and the shadow that the fire casts.

So let’s talk about the tech. There’s a term called recursive AI, which means AI that can teach itself. Now, there’s a lot of debate for all sorts of reasons about whether we have achieved recursive AI, but it’s sort of moot. AI is capable of rolling out AI. And what we’re seeing with Claude Code, which is another sort of really important development that sort of came to market last fall, is that you can actually tell an AI, ‘I need to install AI in this function. I want to connect it to this data set inside your company, inside your life. Make little life hacks.”

You can easily understand how generative AI can replace the average customer service function. You train a generative AI on lots and lots of your manuals and tools and protocols. In some cases, it’s actually way better than the human friction of customer service. You can see how those jobs might go away this year. But you can also begin to see how data-driven companies use AI to essentially run all sorts of different functions really, really quickly. Because the AI is actually helping you roll it out, troubleshoot it, and adapt very quickly with limited human interaction. So I do see that as a very real thing that’s happening this year.

The other argument is that a lot of traditional Fortune 500 companies that have invested in AI have spent billions of dollars to catch up and implement systems. Even if it takes another year or two for those systems to be perfect, the pressure is on those CEOs to show results. When I spoke to a bunch of CEOs, they said: “Look, I actually like my workforce. I actually think this could take time and we could perfect it. Wall Street has no patience for that. They’re expecting me to show financial results now.” And the way they show financial results fastest is by cutting jobs and replacing those jobs with automation, even if the automation isn’t perfect. And so that is the shadow on the cave wall that concerns me even more than the speed of disbursement of AI.

A VERY SPECIFIC DESCRIPTION OF WHY AI COULD QUICKLY DESTROY ENTERPRISE VALUE AND JOBS

Thompson: Let’s get specific here. Walk me through a plausible story. How could the application of this technology reduce employment in, say, consulting in the next few years?

Tyrangiel: Let’s not be shy about it.

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Hello! I’m back from two months of paternity leave and I couldn’t be more excited to pick up where I left off with this newsletter. There is so much I’m looking forward to covering in greater depth in the next few weeks, but first I thought I’d get back in the swing of things by sharing some of the most interesting ideas I found in the books, papers, and articles I read over the break.

Overall, I read more than I expected on paternity leave. Since my hands and attention were very often filled with the baby and her various infant accoutrements, “reading” often meant listening to audiobooks and picking up the paperback in those brief moments when my arms were not vessels for bottles and towels and breakfast.

My two favorite non-fiction books were history doorstoppers that I owned and wanted a good excuse to plow through: Eric Hobsbawm’s history of the 20th century The Age of Extremes and James M. McPherson’s epic Battle Cry of Freedom, which covers the Civil War era in the U.S. Each book is dense, beautifully written, and bursting with big ideas, but I want to zoom in on one theme that they each touched on, in very different ways.

1. History is mostly the story of unintended consequences: Part I

“I am part of that power which eternally wills evil and eternally works good.” That’s the demon Mephistopheles introducing himself in the German epic Faust by Johann Wolfgang von Goethe. I can’t tell you for sure what Goethe meant, but I've always taken this famous line as a beautifully cryptic declaration that everything—life, history, reality—is a tempest of unintended consequences. Demonic forces sometimes do good just as surely as good intentions produce destruction. We do not live in a world that our ancestors built on purpose. History is a story of accidents.

Eric Hobsbawm was a lifelong communist whose three most famous books charted the “long 19th century”—The Age of Revolution, 1789–1848; The Age of Capital, 1848–1875; and The Age of Empire, 1875–1914. With The Age of Extremes, he brought his attention to the “short 20th century,” which began in 1914 with the outbreak of WWI and ended in 1991 with the collapse of the Soviet Union. Hobsbawm saw the Russian Revolution and the rise of the Soviet Union as the most important event of this time period. This might initially seem like a strange bit of analysis, since the Soviet Union did not survive contact with the 20th century. But its significance, Hobsbawm claims, comes from its many unintended consequences.

“The Russian revolution and its direct and indirect effects [proved] to be the savior of liberal capitalism, both by enabling the West to win the Second World War against Hitler’s Germany and by providing the incentive for capitalism to reform itself,” Hobsbawm writes. In the 1930s, the Great Depression demolished the West’s faith in liberal capitalism at the very moment that the Soviet Union was racing ahead in its economic development. As depression spread like a pandemic around the world, the country that most sharply broke with capitalism appeared to be immune to the depression bug. Hobsbawm:

While the rest of the world, or at least liberal Western capitalism, stagnated, the U.S.S.R. was engaged in massive ultra-rapid industrialization under its new Five-Year Plans. From 1929 to 1940 Soviet industrial production tripled, at the very least. It rose from 5 per cent of the world’s manufactured products in 1929 to 18 per cent in 1938, while during the same period the joint share of the U.S.A., Britain and France, fell from 59 percent to 52 percent of the world’s total.

As historians like Gary Gerstle have attested, the external pressure of communism during the 1930s put pressure on FDR and Democrats to inject American capitalism with a strong dose of socialism and social planning. The results included Social Security, the Wagner Act, the FDIC, and a more muscular federal role in banking, labor markets, and public works. Even where the reforms stopped short of full-on socialism, they dampened the harshness of capitalism by promising ordinary people some baseline security, bargaining power, and protection against the failures of private markets.

And then, just as Nazi fascism seemed poised to take over Europe, it was the Red Army that played the largest role in bludgeoning the German military, albeit with significant assistance from Western arms and armies. The result: Soviet communism softened capitalism and saved Western Europe, which became a haven for this softer form of capitalism. As Hobsbawm puts it:

It is one of the ironies of this strange century that the most lasting result of the [Russian] revolution, whose object was the global overthrow of capitalism, was to save its antagonist, both in war and in peace—that is to say, by providing it with the incentive, fear, to reform itself after the Second World War, and, by establishing the popularity of economic planning, furnishing it with some of the procedures for its reform.

The most famous communist historian of the 20th century believed that by twice saving liberal capitalism before losing the Cold War to its American instantiation, communism’s legacy was, ironically, the creation and triumph of a gentler and therefore more popularly sustainable version of liberal capitalism.

2. History is mostly the story of unintended consequences: Part II

Battle Cry of Freedom is a relentlessly compelling history of the antebellum years followed by a cinematic history of the Civil War. But author James McPherson takes the narrative in all sorts of interesting places, and I was enraptured by his review of the economics of the period.

In the mid-1800s, the manufacturing economy took off. As the business of making stuff moved from the home to the factory, men left home more frequently. The physical separation of husband and wife created separate spheres, with men in the public economy and women in the private world of home and childrearing. As work moved away from the home, relationships within the home “ripened into a covenant of love and nurturance of children.” Marriages became more about romantic love than economic efficiency. Families became more child-centered, “a phenomenon much noted by European visitors.” As the fertility rate declined, the education rate for children rose, and parents lavished more love and affection on a per-kid basis.

Nobody building a gun-parts factory in 1850 was thinking, what I’m really doing here is revolutionizing family values, but the rise of the American System of manufacturing really did change marriage, fertility, and family, by giving free married women more domestic autonomy, which they used to revolutionize fertility and parenting norms and establish childhood “as a separate stage of life.”

That’s not all: Once the home was a woman’s domain, it became a platform for women to expand outward. Changes to capitalism and the US economy may have inadvertently laid the foundation for the rise of modern feminism. McPherson:

In an apparent paradox, the concept of a woman’s sphere within the family became a springboard for extension of that sphere beyond the hearth. If women were becoming the guardians of manners and morals, the custodians of piety and child-training, why should they not expand their demesne of religion and education outside the home? And so they did …

As the dawn of the factory economy coincided with a Second Great Awakening in religious life, women learned to organize, speak, write, and teach. It raised a new question: if women can organize their homes and lead their families in domestic life, why can’t they be paid equally, own property independently, enter professions, or vote? McPherson:

While the notion of a domestic sphere closed the front door to women’s exit from the home into the real world, it opened the back door to an expanding world of religion, reform, education, and writing.

With the invention of modern work schedules, the barons of industrial capitalism did not intend to invent the modern family, redefine childhood, or create the conditions for the rise of Western feminism. But by reorganizing work, antebellum capitalism reorganized the family. And by reorganizing the family, it helped create a new kind of woman in public, who could plausibly demand the full rights of a citizen.

I do not believe that communism, on its own, bequeathed the modern state of capitalism; nor do I believe that 1800s capitalism, on its own, created the modern norms of marriage, parenting, childhood, and feminism. Rather I think that 19th century capitalism and 20th century communism were enormous tempestuous forces that, much like Goethe’s Mephistopheles, aimed to do one thing and ultimately accomplished many other unintended things. The world is full of unintended side effects. History is the accumulation of accidents.

3. It’s a dark age for politics, but … a golden age for longevity?

In the past 12 months, we’ve seen:

• the largest decline in US murder rate ever recorded

• huge declines in traffic fatalities and drug overdoses

• a surprising (and largely unreported) decline in teen anxiety and despair, coinciding with ongoing declines in suicide

• a surge in self-driving car technology whose injury crash rate is at least 80% lower than that of human-driven cars

• continued advances in GLP-1 medicines that seem to reduce obesity, inflammation, cardiovascular disease, and other illnesses that are currently under study

This would be a good time to not mess things up by, say, creating a national health movement that trafficked in conspiracy-theories and a deep-seeded distrust of pharmaceuticals. Unfortunately, the MAGA movement, and RFK Jr.’s Department of Health and Human Services in particular, seems inexplicably fixated on eroding public health and decimating faith in proven vaccines. Cases of tuberculosis, meningococcal disease, and measles are all surging in parts of the country. I realize that doesn’t sound much like a golden age of anything.

But I think it’s important to see that many things are happening at once: The absurd rise of antivax conservatism is coinciding with the secular decline of almost all of the major causes of youth and middle-age mortality, including homicide, traffic fatality, suicide, drug overdose, and obesity. What’s more, there is every reason to think that as GLP-1s get better and move to pill form, we’ll see better adherence and more weight loss among the obese. In health, as in everything, there is a lot of bad news and a lot of good news, and if you’re only hearing one part of the story, you should think about how well your media diet is serving you.

4. Cancer & Alzheimer’s are the Stalin & Hitler of human mortality

Stalin and Hitler: Both terrible, but also mutually destructive. Cancer and Alzheimer’s: Also both terrible and also, weirdly, mutually destructive.

Apparently—and I never knew this—Alzheimer’s patients rarely have cancer. Doctors have studied the association for years without understanding the root cause. Maybe it’s mere selection effect, where people who don’t get cancer survive long enough to get dementia. Or maybe something more interesting is happening.

Researchers studying mice recently discovered that cancer cells make a protein that gets into the brain and breaks up the clumpy proteins associated with Alzheimer’s. The study, published in January in Nature, might offer scientists a new path to combat Alzheimer’s, which has stubbornly resisted billions of dollars of scientific and pharmacological efforts to stop it.

5. Why is it harder for young people to buy their first home? Part 1

A theme I’ve tried to repeat to the point of annoyance in the last year is that yes Americans have gotten richer in the last 25 years, but also rising housing costs have eaten a lot of that growing wealth. The graph below is a brilliant way to visualize that principle. It shows the cost of getting a home in the first year of ownership—that is, the down payment plus the first year of mortgage payments—compared to income.

The upshot: Even after controlling for rising wages, the cost of homeownership in the first year has increased for every income group. And it’s increased by ~50 percent for low- and middle-income Americans. If you’re a lower- or middle-class family in Los Angeles or San Francisco, the cost of getting a first home might have easily doubled in the last 25 years. Put briefly: For most Americans, buying that first home is at least 50 percent harder than it used to be.

6. Why is it harder for young people to buy their first home? Part 2

The typical life script in 21st century America goes like this: Live with your parents, live with friends, live with your partner, marry, and then buy your first home. Part of the decline of young homeownership in the U.S. is the decline of young marriage itself, as Aziz Sunderji shows with this quite brilliant Sankey diagram from his Substack Home Economics.

Today’s 30-year-olds are 80 percent more likely to live with their parents or with roommates than their parents’ generation. By 40, they’re still about 10 percentage points less likely to be married. Marriage is correlated with things like income and education, so it’s too simplistic to say that the entire homeownership gap is just young people resisting marriage for cultural reasons. But what you’re seeing here is a very strong relationship between declining rates of marriage and declining rates of homeownership for Millennials 40 and under.

7. A big scary dining trend (that isn’t real)

In January, the New York Times published a story about the state of food delivery in America with some anecdotes that were tailor-made to churn the discourse. In the lede, a data worker earning $50,000 in San Diego admits to spending as much as $300 a week on food delivery. That’s $15,000 a year on DoorDash etc out of about $40,000 in annual take-home pay—or roughly 37 percent of consumption on takeout.

If delivery food were gobbling up more than 30 cents out of every dollar Americans took home, that would be an alarming story indeed. Fortunately, nothing like this is happening at scale.

Poking around the Consumer Expenditure Survey, which is the BLS survey of Americans’ spending habits, I found that in 1989, Americans allocated 6.3% of their spending to “food away from home,” which would include restaurants and takeaway. In 2024, the latest year with solid Consumer Expenditure Survey data, Americans devoted just 5% of their total spending to food away from home. In short, the DoorDash revolution has coincided with a declining share of American spending going to food away from home.

What’s happening here is two-fold. First, in the long run, Americans are a lot richer than they used to be, so food takes up a smaller share of our budget. The writer Mike Konczal made the point in graph form: Total food spending has steadily declined as a share of consumption, largely because Americans are earning more and spending more on everything else each year.

Second, in the last few years, restaurant labor has gotten more expensive, and young and poor Americans have shifted some of their spending from restaurants to groceries. The economist Arin Dube calculated that restaurant food—which the BLS calls “food away from home”—has seen higher inflation than grocery prices, and the youngest and poorest Americans have cut down on that category of spending to make ends meet.

8. A big scary dining trend ... that is very real

You know it’s not a proper roundup on this newsletter without at least one reminder of the phenomenon I call the antisocial century.

And so, here we are: The share of Americans who say they ate “all meals alone the previous day” has increased steadily in the last 20 years, from about 17 percent to about 25 percent. I don’t think that you should worry that DoorDash is taking 30 percent of young Americans’ take-home pay. I do think you should worry that DoorDash, for all its convenient splendor, allows people to choose to stay away from other people in ways that make us feel more isolated and unhappy in the long run.

9. AI and … unintended consequences

I am still trying to wrap my head around the implications of Claude Code and Codex, the new breakout tools from Anthropic and OpenAI, where AI agents can perform multi-step tasks on computers. The most important implication of these tools for now is that software programmers can code by instruction—“build this complicated thing, now fix it, now change it again”—rather than code by hand. By reducing the cost of producing software, some people believe that AI will allow small teams to produce products that would otherwise require large companies. This prediction, or fear, was a key player in the decimation of software stocks last week.

In keeping with the first idea in this newsletter, I’m looking out for the unintended consequences of AI. Scientific research is already offering some clues. Researchers who adopt AI seem to publish more papers, accumulate more citations, and advance their careers faster than their peers. But a new paper—“Artificial Intelligence Tools Expand Scientists’ Impact but Contract Science’s Focus,” by Qianyue Hao, Fengli Xu, Yong Li, and James Evans—finds that this individual boost comes with an interesting collective cost: scientific attention is concentrated on a smaller set of problems and fields that happen to have more available data, while less-charted fields are comparatively ignored. AI in science seems to both expand total productivity while narrowing the frontier of what science explores.

As more people work with AI, individuals will figure out, day by day, how to mold the technology their liking. But the technology will also mold us in ways that are both inevitable and hard to anticipate. Evans et al. offer an interesting way to see how this might go: With AI’s incredible facility to manipulate big chunks of data, scientists are spending more time on data-rich fields of study while leaving other data-light questions in the dark. Technology augments, and it amputates.

10. AI and … Inequality

I have little doubt that AI will increase inequality in the near term. I don’t know exactly how this technology will pan out, but I’m quite certain that some people will get unfathomably wealthy in the next few years while many people will find their jobs eliminated or, perhaps more plausibly, made less valuable over time. I’m getting more interested in thinking about the tax implications of all this. That is, if the government has a moral obligation to curb the most extreme versions of inequality, and if inequality is about to surge to extreme levels, then doesn’t the government have a moral obligation to conceive of new tools to stem that inequality?

One way I was thinking of this question is “should the U.S. try harder to adopt the tax systems of western European countries, which have less extreme inequality than the U.S.?” But then I read this paper by the economist Gabriel Zucman and realized that I’m wrong. In fact, the U.S. has higher effective taxes on the richest 0.1 percent than the Netherlands or Italy, and we’re basically tied with France. In other words, if the U.S. became “more like Europe,” the poor would pay MUCH higher income and consumption taxes and billionaires would ... well ... still basically get away with murder.

I look forward to doing much more research in this area very soon. But my hypothesis is that significantly raising taxes on the extremely rich will be very hard. If the Age of AI leads to a resurgent interest in raising taxes at the tippy top, I expect that we’ll have to create a global system for taxing extreme wealth that makes it harder for the rich to park their wealth in countries where they can enjoy big tax breaks. I think this will be a very hard problem to solve.

11. AI and … the meaning of work

As AI gets better at automating more tasks, I suspect that students and workers will have to cultivate and sustain a new kind of wisdom. They’ll have to answer the question: What are the parts of life where I could use AI, but I shouldn’t, because I want to protect this skill or habit from atrophy?

I loved this bit of wisdom from the author Agustin Lebron. A simple way to figure out whether to use AI at work, or in life, is to think about the difference between a gym and a job. At a gym, the point isn’t for the weight to be lifted, but for you to lift the weight. At a mere job, however, “the point is for the weight to be lifted.”

Use AI for the jobs in your life. Don’t use AI for the gyms in your life.

Editor’s note: I grew up in a Reform Jewish household where we celebrated Christmas and held an Easter egg hunt for the neighborhood. I was the last of all my (mostly Christian) friends to learn the truth about Santa Claus. My dad was atheist, to the point of being anti-theist. My mother, who insisted that I learn Hebrew and have a Bar Mitzvah, was more interested in Hindu notions of reincarnation than anything you’d find in the Old Testament. I’m not sure how a religious survey would categorize the religious mishegas that was our family. We were, paradoxically, all of the above and nothing at all.

Decades later, my Judaism sometimes plays little more than an ornamental role in my life. I recite Jewish prayers outside of the major holidays, but mostly out of reflexive fear or desperation. When I’m afraid that something terrible might happen, or desperate for something good to happen, my mouth will start moving, practically involuntarily, and the words of the Shema will materialize in a whisper. For years, I’ve wanted a deeper relationship with faith. But pining for faith in the absence of actual faith is like wanting to fall in love in the absence of love. And so, I have become that modern cliché, that hopeless paradox: the non-believer who prays to a throne that he suspects is empty.

For a long time I’ve wondered whether I should pray more, even in my ambivalence. Maybe it will make me feel better, I think. Or, maybe if I take the small steps of daily prayer, the solidity of belief will appear under my feet.

Today I’m happy to publish an essay by the bestseller author Nir Eyal, from his forthcoming book Beyond Belief, on the scientific case for praying, even when you’re not sure what you believe—or if you believe. It is a wonderful book and his chapter on prayer felt particularly appropriate to publish in December. Happy holidays.

- Derek

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Atheists Should Pray, Too

By Nir Eyal

When I was six years old, I talked to God.

My family was in crisis. My parents had been scammed out of nearly every penny they had, with no hope of getting their money back. Facing financial ruin, their arguments escalated to screaming matches that shook our house. To escape, I developed a ritual. Each morning, before anyone else was awake, I slipped outside to our concrete driveway, lay on my back, and looked at the stars. I spoke to God.

The voice that responded wasn’t audible, but it felt distinct from my own thoughts: calmer, wiser, more reassuring. This presence told me that despite the chaos unfolding at home, I would be okay. My family would find its way through this mess. I wasn’t alone.

These early morning conversations became my sanctuary. But along the path to adulthood, the connection faded. As I developed a more evidence-based worldview, prayer began to feel strange—if I couldn’t prove that anyone was listening, wasn’t I just talking to myself? What had once brought comfort now seemed suspect, tainted by what I perceived as magical thinking. This shift wasn’t sudden; it faded like a 1980s Polaroid of my youth, left too long in the sun.

By adulthood, prayer was a distant memory, shelved alongside other abandoned childhood fantasies. Yet I couldn’t shake the sense that something valuable had been left behind.

The Science of Belief

Decades later, I started research for a new book about how our beliefs shape our behavior. When I returned to the question of prayer, I expected little more than platitudes and comforting words. What I discovered instead was a wealth of research on how prayer and hope leave measurable fingerprints on the brain and body.

In the 1950s, the biologist Curt Richter placed rats in buckets of water to see how long they would swim before giving up and dying. Most drowned within 15 minutes. From above, Richter watched in silence, stopwatch in hand, recording the moment each struggle came to an end. Some rats, however, received a randomized miracle. Richter rescued them just as they were about to slip under the water. He dried them off, cradled them, and placed them back in the jar. The rescued rats did not just swim another 15 minutes, or 60 minutes. Buoyed by belief, they swam for an average of 60 hours.

In his 1957 paper “On the phenomenon of sudden death in animals and man,” Richter said the subject of his research was hope and hopelessness. A hopeless rat doesn’t paddle for long. But a comforted rodent who learns “the situation is not actually hopeless [will] become aggressive, try to escape, and show no signs of giving up.” Take two creatures of similar biology and physiology. The one with more hope will fight harder, swim faster, live longer.

Fifty years later, several dozen college students participated in a similar experiment involving water, pain, and the power of belief. The students were asked to immerse a hand in the icy water and hold it there until the pain became unbearable.

Weeks earlier, each student had been trained in one of three coping methods. One group practiced basic physical relaxation techniques. Another group modified their thoughts to reframe the pain as “challenging content,” or “I am joyful.” The third group recited spiritual phrases such as “God is peace,” “God is joy,” and “God is love.” For participants uncomfortable with the word “God,” researchers allowed substitutions such as “Mother Earth,” “the universe,” or “my higher self.” All participants practiced their method daily over the two weeks preceding the test.

On the day of the experiment, each participant began by repeating their practiced technique for twenty minutes before plunging a hand into the icy bath. As seconds ticked by, researchers recorded every grimace, every twitch.

Participants from all groups rated the pain as equally intense. But the spiritual group endured the cold hand plunge nearly twice as long as the others. Those who meditated on spiritually meaningful phrases reported feeling calmer, less anxious, and generally happier. Even participants who replaced the word “God” with terms of personal significance experienced the powerful benefits. This study, along with many others, reveals an unexpected truth about prayer. For a writer and researcher like me, these studies seem to offer a clear lesson about prayer. Its power doesn’t rest on certainty or dogma. The benefits are accessible, even without faith.

For many in the West, the idea of prayer without belief might seem absurd. Many people think that there are exactly three categories of worship—believers, agnostics, and atheists—and it’s nonsensical for atheists to pray. But in Singapore, where I lived for six years, I discovered a new perspective. On a residency form, there was a category I’d never seen before: “Free Thinker.”

As I came to understand it, a free thinker could be Christian, Muslim, Buddhist, Hindu, Jewish, or none of the above. What distinguishes free thinkers isn’t their faith tradition, but how they arrive at their convictions. Free thinkers test ideas against reason, evidence, and experience. They adopt what proves helpful and set aside what doesn’t. Perhaps I didn’t need to abandon prayer entirely. Approached as a free thinker, prayer didn’t have to involve declarations of certainty or proving God’s existence.

In this oasis of religious coexistence, I designed a personal experiment. I would visit each of these traditions and pose the same direct question to their spiritual leaders: “How do I pray to God if I’m not sure He’s real?”

Five Traditions, Universal Wisdom

Rabbi Mordechai welcomed me through a door located in the kosher grocery store attached to the synagogue. When I asked whether one could pray without certainty about God, the gray-bearded Orthodox Rabbi didn’t respond with theological arguments or scriptural defenses. Instead, he offered something unexpected: permission to question.”Yeah, sure,” he replied casually. “We’re minute little specks of nothing. You pray to a God that is precisely above intellect and transcendent by very definition. So how can anyone be certain about such an unknowable thing?”

Rabbi Mordechai pointed to a fundamental concept in Jewish tradition, drawn from the moment at Mount Sinai when the Israelites declared “Na’aseh v’nishma”—”we will do, and we will hear.” Practice first. Understanding follows. “Everything is about practice,” he said. “A person’s psyche is impacted by what he does, not just what he thinks.”

At the mosque, Imam Alattas explained Islam’s approach. “Islam is a simple religion. We pray five times a day,” he said. “Just like how we eat—breakfast, lunch, tea, dinner, supper.” Each prayer corresponds loosely with natural breaks in the day. “It’s always the same verses,” he added. There’s no demand for eloquence, no pressure to invent new words each time.” Prayer is to be conscious that God is watching whatever you do,” he told me. “If someone makes you angry, prayer is the time to calm down, to leave everything in the hands of God.” In a world full of triggers and temptations, prayer interrupts the momentum of emotion. It’s a reset, a re-centering. By making prayer short, familiar, and non-negotiable, Islam builds remembering into the rhythm of forgetting.

At the Hindu temple, Swami Samachittananda gently tapped his chest. “God is not outside somewhere,” he said.”Within me is what? That is our consciousness, that is the eternal presence of God, and that is the Kingdom of heaven.” Prayer,the Swami continued, “is for something subjective. Prayer should be for that experience within you.” He explained that anything objective—money, health, relationships—is transient. “The only thing to pray for is truth,” he said simply. Not truth about how the world might change, but truth about how it already is. Perhaps the real value of prayer is not to change life’s circumstances but to see them more clearly.

When I met Father Adrian Danker at the Church of the Sacred Heart, he leaned forward with a knowing smile: “Doubt is actually a blessing. It invites reflection, deeper questioning, and ultimately, a richer understanding.”He observed that when people pray, they bring conflicts, anxieties, hopes, and griefs and leave with fresh perspectives and renewed commitments. But the most striking aspect was his emphasis on community. Every week, parishioners submit petitions. “Yet people keep coming,” Father Adrian explained. “Maybe God’s answer isn’t immediate or obvious, but in returning week after week, they find community. They discover others who listen, support, and offer solutions. Through that communal support, their prayers are often answered in unexpected, tangible ways.”

At Singapore’s Buddha Tooth Relic Temple, I met Venerable You Guang. Before he had donned the saffron robes of a monk, he was a soldier in Singapore’s honor guard, required to stand motionless in sweltering heat during five-hour shifts. His coping mechanism was prayer, reciting the Great Compassion mantra. “Even something simple like standing guard duty becomes meaningful when I chant,” he explained. “It keeps my mind grounded and focused, helping me handle long periods of standing and physical discomfort.” Many Buddhists engage in prostrations, full-body bows performed hundreds or even thousands of times. By willingly embracing difficulty within a meaningful framework, practitioners discover they can endure far more than they imagined. Buddhist prayer does not eliminate suffering—it reframes it, turning hardship into a path of strength.

Despite their differences, these practices seemed grounded in fundamental aspects of cognition and experience:

• Action before understanding (Jewish): Ritual practice shapes us even before we fully grasp its meaning.

• Simple repetition (Muslim): Short, familiar rituals reset our emotions and bring us back to what matters.

• Looking within (Hindu): The deepest prayer seeks truth and clarity inside, not external change.

• Answering through community (Christian): Prayer’s power often comes through one another.

• Transcending suffering (Buddhist): Ritualized difficulty turns pain into resilience.

Permission to Practice

This journey began with a six-year-old boy lying on his driveway, talking to God in the pre-dawn darkness. That child didn’t question whether God was real—he simply needed someone to talk to. The practice itself provided solace, regardless of theological certainty.

When we pray or engage in ritual, we’re not merely thinking about our values or hopes. We’re practicing them. This act reinforces neural pathways, emotional habits, and psychological resilience. The doing itself becomes transformative, regardless of our certainty about the metaphysical dimensions of the practice.

Inspired by what I’d learned through my exploration of various religious communities and the scientific literature, I began my own simple ritual. Ten minutes each morning: closing my eyes, focusing on my breath, expressing gratitude for being alive, and asking for strength to meet the day’s challenges.

The changes were subtle but undeniable: less anxiety at work, more patience at home, sharper awareness of beauty in ordinary moments. Did divine intervention cause these shifts? Neurochemistry? Expectation? I no longer worry about the answer. The benefits are real, whatever their source.

As a free thinker, I’ve come to embrace prayer not as submission to religious dogma but as a practical tool for psychological well-being. Simply put, I pray because it makes my life better.

If you count yourself among those who cannot claim the certainty of faith but find the emptiness of pure skepticism equally unsatisfying, I offer this permission: You can pray anyway. You can speak into the silence, lean on rituals that people have found meaningful for millennia, and find strength without certainty.

This essay is adapted from Eyal’s forthcoming book Beyond Belief. You can subscribe to his Substack here.

Nir Eyal's Substack

Helping you break through limits and achieve extraordinary results. By bestselling author Nir Eyal.

A little life update: Last week, my wife delivered our second baby girl. I’ll be off for a short bit to fetch bottles and wash bottles and dry bottles and even drink some bottles (wine), but this newsletter won’t stop publishing. I’ve lined up some fantastic essays from great writers on economics, psychology, artificial intelligence, and more. The plan is to continue to publish roughly once-a-week for a little while, before I roll back onto full-time work. I’m excited for you to read these upcoming pieces and even more excited to announce plans to grow this newsletter in 2026.

But first, here are 26 ideas for 2026, organized under the themes that I think will drive economics, politics, and technology in the near future.

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‘EVERYTHING IS TELEVISION’

1. The end of reading

I was born in 1986. By the time I was 10, or so, it was natural for me to come home and read for hours after school. The Lord of the Rings, Asimov’s Foundation series, and other fantasy and science-fiction classics were my favorites. This didn’t seem like a particularly special or noteworthy thing on my part. Throughout the 1990s, the share of teenagers who read daily outnumbered the share who read “hardly ever”, according to long-term reading surveys. But in the last 25 years, this has flipped so dramatically that today roughly half of teens say they hardly ever read for fun. The teen who reads daily is on track to become an endangered species.

I’ve covered this theme consistently in the newsletter, although I admit that writing essays about how nobody reads anymore is sort of comically futile, like writing aria after aria about how nobody listens to operas. In any case, if you’re still reading this, then you should know the broader context for this decline in teenage leisure reading. National reading scores have fallen to a three-decade low for American students, and the share of Americans overall who say they read books for leisure has declined by nearly 50 percent since the 2000s. “Most of our students are functionally illiterate,” a pseudonymous college professor wrote. Last year, The Atlantic’s Rose Horowitch reported that elite college students are arriving on campus without having ever read a full book. Our attention is moving from the printed word to the streamed image, which is naturally leading to:

2. The triumph of streaming video

In my essay “Everything Is Television,” I wrote that all media are converging toward the same flow of video. Social media is becoming less about keeping up with friends and more about watching short-form videos made by strangers—i.e., television. Podcasts are becoming less about listening to Internet radio and more about watching YouTube talk shows—i.e., television.

According to JPMorgan, the share of Americans who say they get their news from print has declined by about two-thirds since the early 2010s. Cable television and websites are also declining by this measure. “Social and video networks,” such as TikTok and YouTube, are gobbling up news consumption.

Within that category of “social and video networks,” YouTube’s recent surge might be the most notable. In just the last two years, YouTube has dramatically increased its share of “TV-viewing time,” according to the Wall Street Journal, and it now accounts for about 50 percent more viewing time than Netflix, far surpassing traditional Hollywood firms like Paramount and Warner Bros. Discovery.

3. Goodbye, movie theaters?

On Friday, Netflix announced its intention to buy Warner Bros Discovery for $83 billion, in a deal that will give the world’s largest paid streaming service access to famous entertainment brands, such as Harry Potter, and power over theater owners and entertainment unions. (On Monday, Paramount announced an even pricier hostile takeover bid.) Either way, the acquisition of Warner Bros. will likely lead to job losses, as industry consolidation continues in the beleaguered movie business.

While most industries have recovered from the pandemic—restaurant revenues and hotel rates are running 20 percent higher than their pre-COVID levels—the number of movie tickets sold in 2025 is on track to be about 30 percent lower than in 2019, according to JPMorgan.

It’s not that movie theaters are over, exactly; it’s more that their structural decline will likely accelerate. Americans bought between 1.2 and 1.5 billion movie tickets every year in the 1990s, 2000s, and 2010s. I don’t think Americans will ever buy a billion movie tickets a year again. The entertainment experience is continuing its decades-long evolution from a collective and out-of-home experience (the cineplex, the opera house) to a solitary and at-home experience (phones, AirPods). And what is that doing to us? Well:

4. TikTok might be melting your brain

The frustrating truth is that we don’t really know for sure what the digital empire of short-form video is doing to our minds. But a systematic review of 71 studies with 98,000 participants published in 2025 reached an alarming finding. Across the dozens of studies, heavy short-form video users showed moderate deficits in attention, inhibitory control, and memory. In the chart below, you can see a consistently negative, if also heterogeneous, relationship between heavy short-form video use and problems with attention, memory, and control.

Several studies in the meta-analysis reported structural and functional differences in the prefrontal cortex and reward circuits among high-frequency users, while others found cognitive flexibility reductions and altered dopaminergic reward responses. None of this proves causation. But taken together, they suggest a plausible mechanism: a daily diet of hyper-rewarding, rapid-fire stimuli may gradually reshape attention and regulatory systems in ways that weaken our attentional control. It is, of course, possibly that people with weaker cognitive control are simply more drawn to slot-machine media in the first place.

To separate cause from effect, the field needs longitudinal and experimental work. Until then, I’d prefer to live by the words of organizational psychologist Adam Grant: “Long live longform.”

AI IS EATING THE ECONOMY. IT WILL SOON DOMINATE POLITICS, TOO.

5. The whole US economy right now is one big bet on artificial intelligence

Housing is in a rut. Farmers are hurting. Manufacturing has been shrinking for months. Hiring is hell. And yet, the US economy continues to grow, powered by an AI infrastructure project unlike anything in modern history.

I have written so much about AI in the last few weeks, but I want to emphasize this particular statistic to hammer home the sheer audacity of the construction project. In the last three months, the hyperscalers — Meta, Amazon, Google, Microsoft, and Oracle — spent more than $100 billion on AI infrastructure, including chips and data centers. That is the inflation-adjusted equivalent of three Manhattan Projects … in three months! At this rate, the AI build-out will outspend the entire Apollo program, every year, despite being financed by the private sector.

6. Get ready for a wave of anti-AI populism

Affordability is the watchword of 2020s politics. As the AI buildout continues to pull electricity and manpower from other parts of the economy—and as it creates a new class of AI billionaires and near-trillionaires—it seems inevitable to me that the future of populist politics is going to revolve around attacking AI and its architects, for better or worse.

As Bloomberg has reported, wholesale electricity is “up to 267 percent more expensive than it was five years ago” in areas near US data hubs. In Virginia and Oregon, data centers reportedly consume one-third of the state’s electricity.

Data center construction isn’t likely to slow down. In the last two years, it surpassed the value of all national retail. At some point in 2026, data center construction could outstrip that of offices and warehouses. Love it or hate it, we are concentrating an enormous share of the economy on this AI bet, and you’re going to hear more from politicians who see an opportunity to funnel voter anger through a clear anti-AI populism.

7. Generative AI is probably much better at (certain aspects of) our jobs than we’d like to admit

I find that the online, podcast, and cable-news debate about artificial intelligence often conflates three very different questions. (1) Is AI an industrial or financial bubble? (2) Is the AI buildout “good” for the typical American worker, in the short run? (3) Is generative AI effective and useful?

Some commentators who represent themselves as pro-AI will say yes to all the above. They’ll argue that AI is not a bubble; that it’s good for workers; and that it’s very useful to today’s employees. AI critics are likely to align themselves on the opposite side of each issue: AI is bubbly, bad, and bogus.

I don’t know the answers to questions (1) and (2), yet. But I know the answer to question (3): AI critics are kidding themselves when they insist that artificial intelligence is a mere trillion-dollar auto-complete machine with limited utility in the workforce.

For example, it’s become common for writers to mock AI’s stilted, wooden, and em-dash-heavy writing style. But with some gentle coaxing, AI is much better at writing than professional writers want to admit. In one 2025 study by researchers at Stony Brook, Columbia University, and the University of Michigan, three top AI models were pitted against MFA-trained writers. In initial tests, expert readers clearly preferred the human writing. That’s comforting. But once researchers fine-tuned ChatGPT on an individual author’s full body of work, the results flipped. Suddenly, experts preferred the AI’s writing and often couldn’t tell whether it came from a human or a machine.

The emphasis on slop in AI commentary sometimes operates as a coping mechanism, which allows AI’s critics in creative industries to console themselves with fairy tales about AI being permanently sub-human at every creative task. The future will be filled with slop. But it will also be filled with stories, images, videos, and writing that will seem indelibly human, even when its silicon-based author lives in a data center in Virginia.

WHAT’S THE MATTER WITH GEN Z?

8. Young Americans are becoming more disconnected from the economy.

In my November essay “The Monks in the Casino,” I wrote that many young people, especially men, spend a historically unusual amount of time alone while gambling online on sports, meme coins, and other fluctuating assets. The Financial Times’ incredible James Burn-Murdoch found an ingenious way to render my essay in statistical format, by graphing the rise in young adults who have been dislocated from the economy in the last 25 years. His main finding: The share of people between 20 and 24 who are not in a job, or seeking work, or in school, or raising a child has nearly doubled in the last quarter-century in both the UK and the U.S.

9. The share of liberal non-religious high school seniors who say life “often feels meaningless” has doubled since the early 2000s

Yes yes yes, it’s old news at this point: Teen anxiety and depression have increased in the last 15 years. What’s less widely appreciated is that the increase is most dramatic among the most secular and liberal teens.

Religion and conservatism seem to offer protection against the generation-wide increase in negative affect, as Jon Haidt has written. Self-described conservatives and teens who said “religion is important in my family” suffered smaller increases in depression and anxiety. Haidt suggests that conservatism and religion might offer a binding moral matrix that protects young people from a rapidly changing world, while progressive moralities aim to grant people more freedom to “create their own identities.” There is an idea that comes from the philosopher Soren Kierkegaard that, whereas pre-modern people were lost in finitude—that is, their choices were constrained by tradition or religion or economy—modern anxiety comes from an opposite affliction. We are lost in infinitude, the boundlessness of choices. “Anxiety is the dizziness of freedom,” Kierkegaard wrote. Perhaps despair is a natural side effect of some liberal ideologies, even as those ideologies are necessary to free us from the constrains of tradition.

10. The only currency is currency (Or: Have you noticed that the only remaining global virtue in the world is money?)

Americans, and particularly young Americans, don’t really care about patriotism, or marriage, or family, the way they once did. A 2023 Wall Street Journal poll found that a shrinking share of Americans agreed that practically any of these virtues were “very important.” There was one exception: money.

Americans are pulling back from “values that once defined them,” the WSJ said. Nature, abhorring a vacuum, is filling the moral void with the market. You see across several studies that young people care much less than their parents or grandparents about getting married or having children. What they do say they care about is finding a job and getting rich. I am not prepared to judge this shift in values. Jobs are important, and money is nice to have. But I think there’s much more to be said about the idea that every non-market value is plummeting in relevance among young people.

In his book After Virtue: A Study in Moral Theory, the philosopher Alasdair MacIntyre argued that the modern world had lost the sort of shared moral language that was once provided by virtue systems, such as religion. I think that’s half-right. We have lost something with the erosion of religion, perhaps, but we’re still bound by an ethical system, love it or hate it. It’s the market. What binds the modern world is our collective reverence for money.

11. The accelerated decline of fertility among rich countries is going to have some fascinating global implications.

Presented without comment:

AMERICA IS ON DRUGS

12. Is alcohol over?

The share of Americans who say they drink alcohol hit a record low in 2025. Despite the US population growing by tens of millions since 2000, total beer consumption just hit a 21st-century low. Wine vineyards are “in crisis.” And Gen Z is leading the way:

Last year was the first time on record, going back to 1975, that fewer than 50 percent of high school seniors said they’ve ever had a drink of alcohol. The sharpest declines in drinking have been among younger teens. As I’ve said before: Today’s 12th graders are now less likely to have had a sip of alcohol in the previous month than the 8th graders of the 1980s. That’s crazy!

But at the same time that alcohol consumption has declined, something else has surged to take its place.

13. Americans aren’t drunk. They’re high.

In 2010, daily or near-daily drinkers outnumbered daily marijuana users by a two-to-one margin. But since then, a wave of decriminalization has allowed marijuana use to soar into the 2020s, so that today daily marijuana users exceed drinkers for the first time ever.

Some of these daily or near daily users are using using marijuana once in a while to chill out and relax and disconnect from their busy brains. That’s fine. It might even be good. Suicide rates among older Americans seem to decline slightly following the opening of nearby recreational marijuana dispensaries, especially for older white men with less education. But just because a little weed can be narrowly helpful for some people doesn’t mean a lot of weed is broadly helpful for many people using it as a cure-all for chronic pain, sleep, and depression. The explosion of cannabis usage would be great news if research showed that marijuana reliably solved the problems it’s used to solve. But … does it?

14. Marijuana isn’t good medicine.

It’s easy to get the impression that cannabis—and its non-psychoactive cousin, CBD—is the new aspirin. It is everywhere, promising to fix everything. More than a quarter of adults in North America say they’ve used cannabis medically, and more than 1 in 10 Americans are currently using CBD to treat some kind of ailment. But our enthusiasm for medical marijuana is running way ahead of the scientific evidence.

A 2025 review of studies on the medical use of marijuana found that, although cannabis-based drugs help with nausea and appetite loss for very sick patients, their other medical uses “aren’t supported by good evidence.” Instead, frequent marijuana users report higher rates of “psychosis, anxiety, addiction, and heart and stroke problems.” Most concerning, more than one-third of people using medical cannabis meet criteria for cannabis use disorder.

No, marijuana probably isn’t good medicine. But another chemical with skyrocketing usage seems to be a very good medicine.

15. GLP-1 drugs are already remarkable, and today they’re probably less effective than they’ll ever be.

Studies show that GLP-1 drugs don’t just help patients with type-2 diabetes and promote weight loss. They seem to provide a range of benefits affecting just about every biological system. By targeting abdominal fat, they seem to protect against heart disease, even in patients who don’t lose weight. Mice treated with GLP1s experienced body-wide anti-aging effects, even on minimal dosage. In the latter study, researchers ran the mouse version of a full-body diagnostic workup: gene expression, proteins, metabolites, inflammation markers, functional strength tests, and more. Across almost all those systems, the older mice on GLP-1 drugs shifted in the direction of youth. Not a little shift. A big, across-the-board, multi-omic reversal of aging signatures. And since this happened with a small amount of GLP-1 that didn’t really change the animals’ weight, it hints that the effect isn’t about calorie restriction or dieting. It’s something else happening inside the body.

How is this possible? The Canadian scientist Daniel Drucker, who has done more than practically anyone in the world to illuminate the benefits of GLP-1 drugs, proposed that GLP-1’s anti-inflammatory effects could provide a “unifying” mechanism for these drugs’ effectiveness:

When our bodies notice germs or tissue damage, our immune response brings immune cells to the area. (Think about how your knee feels warm when you bang it; that’s normal inflammation.) But excessive chronic inflammation, which results from an immune system over-policing threats throughout the body, is a leading cause of organ damage, stroke, and neurological problems. GLP-1 drugs seem to bind to receptors throughout the body—in our gut, on our immune cells, and throughout the central nervous system—to broadcast the same message: STOP THE ATTACK! LESS INFLAMMATION, PLEASE! Drucker provides this illustration of the mechanism:

Panaceas don’t exist, and GLP-1s can’t do literally everything. I suspect that in the next 12 months we’ll hear about both their remarkable side effects and their limitations. For example, Novo Nordisk recently concluded a randomized double-blind trial of semaglutide for Alzheimer’s patients. The company found that the drug doesn’t slow progression of that disease. Well, drat. But the future is long, and I’m still on the lookout for GLP-1 spinoffs that provide more benefits with fewer side effects than the current models.

16. GLP-1 drugs will probably reshape the food and drink industry.

A 2025 study from Cornell University found that households with at least one GLP-1 user reduced their overall grocery spending by about 5 percent within six months. Richer families cut their grocery bill by even more. While most food categories saw spending declines, the largest reductions were concentrated in salty snacks and sweet desserts. Chips and “other savory snacks” got walloped, with a 10 percent decline. The big winner? Parfaits, apparently. Spending on fresh fruit and yogurt rose in GLP-1 households.

17. The future will be hot, high, and lonely.

The alcohol industry is terrified about the effect that GLP-1 drugs could have on their business. And maybe they’re right to be. According to an analysis shared with me by Bloomberg Intelligence, total spending on beer, wine, and spirits in the U.S. and Europe is projected to decline by nearly $30 billion in the next decade. Spending on sweet and salty foods are also projected to decline by $25 billion in that period.

That’s how these threads all come together: The rise of marijuana is coinciding with a weight-loss drug revolution that will significantly reduce spending on alcohol, which is coinciding with state-by-state changes that are making it easier for people to get access to cheap weed. The age of alcohol is over, and the future looks ominously like hundreds of millions of people getting high alone rather than getting tipsy together. In the last two decades, Americans under 25 have reduced the time they spend partying by 69 percent, which is not nice. Humanity will be extremely attractive, with better weight-loss drugs, better face lifts, better plastic surgery ... and fewer friends and parties. The future will be hot, high, and lonely.

18. And one more thing about drugs: The right’s vaccine politics are insane.

Several hundred years ago, one-third of all babies died before their first birthday. Half of all children died before the age of 10. Infectious disease kill about a quarter of the survivors by the time they turned 50. Smallpox, measles, and diphtheria killed more efficiently and voluminously than any military force ever assembled. Blessedly, we have devised vaccines to protect against smallpox, measles, mumps, tetanus, diphtheria, and other horrors. And yet the leading party in the U.S.—the party responsible for the most successful national vaccine policy in human history!—has responded to this miracle by advancing anti-scientific conspiracy theories that have frightened a third of their coalition into believing that vaccines are little more than a state-mandated poison.

Dostoevsky once wrote that you could give a man every earthly blessing— “drown him in a sea of happiness, so that nothing but bubbles of bliss can be seen on the surface”—and require of him nothing but to “sleep and eat cakes.” Even then, the Russian said, something in the dark basement of human nature would rise up inside of us to destroy utopia out of sheer ingratitude. “He would even risk his cakes,” Dostoevsky wrote, “and would deliberately desire the most fatal rubbish, the most uneconomical absurdity, simply to introduce into all this positive good sense his fatal fantastic element.” The anti-vaccine turn on the American Right is many things. It is the culmination of anti-tech naturalism, anti-government skepticism, and look-at-me-I’m-so-clever contrarianism. But it is also the very same brain rot diagnosed more than a century ago by Dostoevsky. It is a sickly inclination to identify a fatalistic element in all things, even those which are overwhelmingly good for us.

THE CASINO ECONOMY AND THE WAGES OF DESPERATION

19. Young people are screwed because of housing. So … what’s the matter with housing?

It has become conventional wisdom in certain corners of the commentariat that today’s young people are screwed and that the vibecession so brilliantly coined by Kyla Scanlon reflects a material reality, which is that today’s youth have been dealt a particularly awful hand. I did my best to evaluate the accuracy of the “Young People: Screwed” narrative here, but for today’s purposes I want to focus on the challenges in the housing sector.

It’s best to see the problems with the US housing industry as three stories: a 50-year story, a 20-year story, and a 5-year story. The 50-year story is that anti-growth rules have accumulated in America’s most desired metros, making it harder for supply to meet demand in the places where demand is highest. The 20-year story is that the Great Recession decimated the construction industry, which made the 2010s a uniquely awful decade for nationwide housing additions. The 5-year story is that the pandemic created a berserk housing market that set new weird records every year.

The latest weird housing record, as Bloomberg’s Conor Sen points out, is that new home prices are now lower than resale prices—”a historical rarity,” he says, which hasn’t happened in at least 50 years. Existing owners really, really, really don’t want to leave their houses. One reason: Many would-be sellers have low mortgage rates they don’t want to give up. I think this graph is the best way to drive home the point. The 30-year fixed rate mortgage is over 6%. But half of homeowners are paying off mortgages with interest rates under 4%. (Incredibly, there seem to be roughly as many homeowners paying mortgage rates over 6% as there are homeowners paying under 3%.). So, in many markets—especially in the northeast and midwest—the reason you can’t find a home to buy is that owners aren’t selling, and they’re not selling because of this graph.

20. Blocked from homeownership, low-income renters are gambling with their housing money

On a podcast with Conor earlier this year, he suggested to me that the stock market boom might be an unintended consequence of the shitty housing market. Initially, I thought it was a crazy idea. How could a bad housing market be good for stocks and other assets?

The logic went something like this: Many young people, blocked from ownership, are funneling their spare cash toward meme stocks, meme coins, and gambling. In fact, a 2025 paper by researchers at Northwestern and the University of Chicago found that crypto investing is most common among middle- and lower-middle-class renters. On its own, this fact is sort of bizarre: For most asset classes, investments rise with income. But these renters, who are on the verge of homeownership, seem to be “gambling for redemption”—that is, taking bigger risks on speculative bets to get that final windfall that will make homeownership possible. In essence, these economists discovered a large group of younger Americans who are treating the US economy like a slot machine that they hope will return a pile of coins that they can turn into a house.

21. America’s “monks in the casino” are calling for help

The Substack essay that I’m most proud of is “The Monks in the Casino,” a theory of young men, aloneness, and the market. As I wrote in that piece, roughly half of men between the ages of 18-49 have a sports betting account. In New Jersey, nearly one in five men aged 18-24 is on the spectrum of having a gambling problem, according to the author Jonathan Cohen. And here is The Economist calculating that calls to the National Problem Gambling Helpline have nearly tripled since 2017 in states that have legalized sports gambling.

PROGRESS EXISTS. BUT WHO WANTS TO HEAR ABOUT IT?

22. Negativity bias rules everything around me

People sometimes talk in a high-minded way about the concept of media literacy, which I think refers to the ability to separate truthful news from disinformation and misinformation and other forms of information with prefixes. That’s all fine, but if I had one hour to teach Americans about the news industry, I think I’d spend most of it talking about the concept of “negativity bias creep.” I’ll define negativity bias creep here as the tendency of ideological media—or news media with a point of view—to recognize over time that audiences prefer negative stories to positive stories and thus, in an attempt to reflect audience preferences, to become more negative over time.

In a 2022 analysis of headlines from dozens of US news media outlets found that in the past two decades, American news headlines have gotten steadily darker, with anger, fear, disgust, and sadness climbing in frequency, especially in left- and right-leaning outlets. Around 2012—that year again!—the negativity bias in news headlines accelerated.

When you decompose the results by ideology, they get even more interesting. There seem to be two inflection points. In 2008, right-leaning outlets leaned more and more into anger, distrust, and fear. In 2012, left-wing media followed and liberal media got more negative year after year.

23. The Past Sucked, Part I: Be glad you don’t live in Italy in the mid-500s

I consider myself an optimist, not because I think the present is so wonderful, but rather because I’m confident that the past was so terrible. I was recently reminded of the pure horribleness of our ancestors while reading about the depopulation of Europe during the Dark Ages. In his review of the book The Crowd in the Early Middle Ages by Shane Bobrycki, Pablo Scheffer writes in the London Review of Books about one of the worst times to be alive in the last few thousand years, which was Italy in the mid-500s CE.

In the late 530s, a series of volcanic eruptions sent temperatures plummeting throughout Europe, which was already grappling with plagues, food shortages, social crises, and the political fallout of the end of the Roman Empire. Rome itself, once a metropolis of more than a million people, saw its population shrink to about 30,000—about half the capacity of the Colosseum. Wars cut off the city from trade. Aqueducts were demolished, pinching off the water supply. In the 540s, the Justinianic Plague decimated the already decimated ruins of the empire. As grain harvests collapsed, starvation spread like yet another epidemic. “Cities [lie] in ruin,” Pope Gregory I wrote at the end of the sixth century. Scheffer continues:

Between 500 and 1000 there was a trend of population decline and deurbanisation, the result of a degrading climate (the cold, arid period between the volcanic winter of 536 and 660 is sometimes called the Late Antique Little Ice Age), continuous warfare and a series of plague epidemics …

All over Europe buildings stood empty. The eighth-century writer Paul the Deacon described Metz as ‘abounding with crowds’, but also noted that its old amphitheatre had been ‘given over to wild snakes’. Bath, as depicted in the Old English poem ‘The Ruin’, had been all but abandoned: ‘Hrofas sind gehrorene, hreorge torras/hringeat berofen, hrim on lime’ (’Roofs are collapsed, towers ruined/the ring-gate destroyed, rime on mortar’). The crumbling Roman buildings hint at a past so grand and distant that the poem’s speaker imagines them as enta geweorc, the work of giants.

24. The Past Sucked, Part II: The U.S. used to suffer from a very different housing crisis

In the 1960s, less than half of homes had central heating and cooling, and more than a third of homes in several states, such as Kentucky and Mississippi, lacked complete plumbing, according to the US Census. While I acknowledge that this fact does little to placate Americans shut out of today’s hot putrid housing market, it is worthwhile to point out that yesteryear’s homes were quite literally hot and putrid.

25. Progress is as much about the institutions we build as it is about the truths we discover

In 1929, George Sylvester Viereck published a lengthy interview with Albert Einstein in the Saturday Evening Post. Throughout the conversation, Einstein was at his charming and epigrammatic best. When Viereck asked Einstein if he trusted “more to your imagination than to your knowledge?” the scientist replied:

I am enough of the artist to draw freely upon my imagination. Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.”

Imagination is more important than knowledge. From this moment, a million high-school hallway posters were born.

But it’s what Einstein said a few paragraphs later that I find most profound. When Viereck asked if Einstein believed in the story of human progress, he replied that “the only progress I can see is progress in organization.” Einstein continued:

The ordinary human being does not live long enough to draw any substantial benefit from his own experience. And no one, it seems, can benefit by the experiences of others. Being both a father and teacher, I know we can teach our children nothing. We can transmit to them neither our knowledge of life nor of mathematics. Each must learn its lesson anew.”

What a gorgeous insight. A pessimist could take Einstein to be commenting on the futility of education or the inability of each generation to learn from the previous one’s mistakes. An optimist might take him to be instructing each generation to cherish the virtue of institutional renewal—to always be looking for ways to build fresh ways to organize human ingenuity to understand and improve the world. The internet is cheek to jowl with idealists who claim to be fighting for a better world, but who among them is actually building institutions to amplify their idealism? I like the idea that all human progress requires “progress in organization,” especially since so much of my work, including in Abundance, is about the ways that our institutions and organizations lock us into the past rather than allow us to build the future.

26. Great art can save lives.

We’ll close with one of the finer letters to the editor you’ll read, from the Times of London, on the occasion of the death of playwright Tom Stoppard.

“Saved by Stoppard”: Sir, In 1993 my wife and I went to see the first production of Arcadia by Tom Stoppard (obituary, Dec 1), and in the interval I experienced a Damascene conversion. As a clinical scientist I was trying to understand the enigma of the behaviour of breast cancer, the assumption being that it grew in a linear trajectory spitting off metastases on its way. In the first act of Arcadia, Thomasina asks her tutor, Septimus: “If there is an equation for a curve like a bell, there must be an equation for one like a bluebell, and if a bluebell, why not a rose?” With that Stoppard explains chaos theory, which better explains the behaviour of breast cancer. At the point of diagnosis, the cancer must have already scattered cancer cells into the circulation that nest latent in distant organs. The consequence of that hypothesis was the birth of “adjuvant systemic chemotherapy”, and rapidly we saw a striking fall of the curve that illustrated patients’ survival. Stoppard never learnt how many lives he saved by writing Arcadia. - Michael Baum, Professor emeritus of surgery; visiting professor of medical humanities, UCL

In the last few weeks, something’s troubled and fascinated us about the national debate over whether artificial intelligence is a bubble. Everywhere we look and listen, experts are citing the same small number of statistics, factoids, and studies. The debate is like a board game with a tiny number of usable pieces. For example:

• Talk to AI bears, and they’ll tell you how much Big Tech is spending

• Talk to AI bulls, and they’ll tell you how much Big Tech is making

• Talk to artificial general intelligence believers, and they’ll quote a famous study on “task length” by an organization called METR

• Talk to AGI skeptics, and they’ll quote another famous study on productivity, also by METR

Last week, we were discussing how one could capture the entire AI-bubble debate in about 12 statistics that people just keep citing and reciting — on CNBC, on tech podcasts, in Goldman Sachs Research documents, and at San Francisco AI parties. Since everybody seems to be reading and quoting from the same skinny playbook, we thought: What the hell, let’s just publish the whole playbook!

If you read this article, we think you’ll be prepared for just about every conversation about AI, whether you find yourself at a Bay Area gathering with accelerationists or a Thanksgiving debate with Luddite cousins. We think some of these arguments are compelling. We think others are less persuasive. So, throughout the article, we’ll explain both why each argument belongs in the discussion and why some arguments don’t prove as much as they claim. Read to the end, and you’ll see where each of us comes down on the debate.

Let’s start with the six strongest arguments that there is an AI bubble.

All about the Benjamins

WHEN THEY SAY: Prove to me that AI is a bubble

YOU SAY: For starters, this level of spending is insane

When America builds big infrastructure projects, we often over-build. Nineteenth-century railroads? Overbuilt, bubble. Twentieth-century internet? Overbuilt, bubble. It’s really nothing against AI specifically to suggest that every time US companies get this excited about a big new thing, they get too excited, and their exuberance creates a bubble.

Five of the largest technology giants — Amazon, Meta, Microsoft, Alphabet, and Oracle — had $106 billion in capital expenditures in the most recent quarter. That works out to almost 1.4% of gross domestic product, putting it on par with some of the largest infrastructure investments in American history.

This chart was originally created by Understanding AI’s Kai Williams, who noted, “not all tech capex is spent on data centers, and not all data centers are dedicated to AI. The spending shown in this chart includes all the equipment and infrastructure a company buys. For instance, Amazon also needs to pay for new warehouses to ship packages.”

Still, AI accounts for a very large share of this spending. Amazon’s CEO, for example, said last year that AI accounted for “the vast majority” of Amazon’s recent capex. And notice that the last big boom on the chart — the broadband investment boom of the late 1990s — ended with a crash. AI investments are now large enough that a sudden slowdown would have serious macroeconomic consequences.

Money for nothing

WHEN THEY SAY: But this isn’t like the dot-com bubble, because these companies are for real

YOU SAY: I’m not so sure about that…

“It feels like there’s obviously a bubble in the private markets,” said Demis Hassabis, the CEO of Google DeepMind. “You look at seed rounds with just nothing being [worth] tens of billions of dollars. That seems a little unsustainable. It’s not quite logical to me.”

The canonical example of zillions of dollars for zilch in product has been Thinking Machines, the AI startup led by former OpenAI executive Mira Murati. This summer, Thinking Labs raised $2 billion, the largest seed round in corporate history, before releasing a product. According to a September report in The Information, the firm declined to tell investors or the public what they were even working on.

“It was the most absurd pitch meeting,” one investor who met with Murati said. “She was like, ‘So we’re doing an AI company with the best AI people, but we can’t answer any questions.’”

In October, the company launched a programming interface called Tinker. I guess that’s something. Or, at least, it better be something quite spectacular, because just days later, the firm announced that Murati was in talks with investors to raise another $5 billion. This would raise the value of the company to $50 billion—more than the market caps of Target or Ford.

When enterprises that barely have products are raising money at valuations rivaling 100-year-old multinational firms, it makes us wonder if something weird is going on.

Reality check

WHEN THEY SAY: Well, AI is making me more productive

YOU SAY: You might be deluding yourself

One of the hottest applications of AI right now is programming. Over the last 18 months, millions of programmers have started using agentic AI coding tools such as Cursor, Anthropic’s Claude Code, and OpenAI’s Codex, which are capable of performing routine programming tasks. Many programmers have found that these tools make them dramatically more productive at their jobs.

But a July study from the research organization METR called that into question. They asked 16 programmers to tackle 246 distinct tasks. Programmers estimated how long it would take to complete each task. Then they were randomly assigned to use AI, or not, on a task-by-task basis.

On average, the developers believed that AI would allow them to complete their tasks 24% faster with the help of AI. Even after the fact, developers who used AI thought it had sped them up by 20%. But programmers who used AI took 19% longer, on average, than programmers who didn’t.

We were both surprised by this result when it first came out, and we consider it one of the strongest data points in favor of AI skepticism. While many people believe that AI has made them more productive at their jobs — including both of us — it’s possible that we’re all deluding ourselves. Maybe that will become more obvious over the next year or two and the hype around AI will dissipate.

But it’s also possible that programmers are just in the early stages of the learning process for AI coding tools. AI tools probably speed up programmers on some tasks and slow them down on others. Over time, programmers may get better at predicting which tasks fall into which category. Or perhaps the tools themselves will get better over time. Some of today’s most popular coding tools have been out for less than a year.

It’s also possible that the METR results simply aren’t representative of the software industry as a whole. For example, a November study examined 32 organizations that started to use Cursor’s coding agent in the fall of 2024. It found that programmer productivity increased by 26% to 39% as a result.

Infinite money glitch

WHEN THEY SAY: But AI is clearly growing the overall economy

YOU SAY: Maybe the whole thing is a trillion-dollar ouroboros

Imagine Tim makes some lemonade. He loans Derek $10 to buy a drink. Derek buys Tim’s lemonade for $10. Can we really say that Tim has “earned $10” in this scenario? Maybe no: If Derek goes away, all Tim has done is move money from his left pocket to his right pocket. But maybe yes: If Derek loves the lemonade and keeps buying more every day, then Tim’s bet has paid off handsomely.

Artificial intelligence is more complicated than lemonade. But some analysts are worried that the circular financing scheme we described above is also happening in AI. In September, Nvidia announced it would invest “up to” $100 billion in OpenAI to support the construction of up to 10 gigawatts of data center capacity. In exchange, OpenAI agreed to use Nvidia’s chips for the buildout. The next day, OpenAI announced five new locations to be built by Oracle in a new partnership whose value reportedly exceeds $300 billion. The industry analyst Dylan Patel called this financial circuitry an “infinite money glitch.”

Bloomberg made this chart depicting the complex web of transactions among leading AI companies. This kind of thing sets off alarm bells for people who remember how financial shenanigans contributed to the 2008 financial crisis.

The fear is two-fold: first, that tech companies are shifting money around in a way that creates the appearance of new revenue that hasn’t actually materialized; and second, that if any part of this financial ouroboros breaks, everybody is going down. In the last few months, OpenAI has announced four deals: with Nvidia, Oracle, and the chipmakers AMD and Broadcom. All four companies saw their market values jump by tens of billions of dollars the day their deals were announced. But, by that same logic, any wobble for OpenAI or Nvidia could reverberate throughout the AI ecosystem.

Something similar happened during the original dot-com bubble. The investor Paul Graham sold a company to Yahoo in 1998, so he had a front-row seat to the mania:

By 1998, Yahoo was the beneficiary of a de facto Ponzi scheme. Investors were excited about the Internet. One reason they were excited was Yahoo’s revenue growth. So they invested in new Internet startups. The startups then used the money to buy ads on Yahoo to get traffic. Which caused yet more revenue growth for Yahoo, and further convinced investors the Internet was worth investing in. When I realized this one day, sitting in my cubicle, I jumped up like Archimedes in his bathtub, except instead of “Eureka!” I was shouting “Sell!”

Are we seeing a similar dynamic with the data center boom? It doesn’t seem like a crazy theory.

Pay no attention to the man behind the curtain

WHEN THEY SAY: The hyperscalers are smart companies and don’t need bubbles to grow

YOU SAY: So why are they resorting to financial trickery?

Some skeptics argue that big tech companies are concealing the actual cost of the AI buildout.

First, they’re shifting AI spending off their corporate balance sheets. Instead of paying for data centers themselves, they’re teaming up with private capital firms to create joint ventures known as special purpose vehicles (or SPVs). These entities build the facilities and buy the chips, while the spending sits somewhere other than the tech company’s books. This summer, Meta reportedly sought to raise about $29 billion from private credit firms for new AI data centers structured through such SPVs.

Meta isn’t alone. CoreWeave, the fast-growing AI-cloud company, has also turned to private credit to fund its expansion through SPVs. These entities transfer risk off the balance sheets of Silicon Valley companies and onto the balance sheets of private-capital limited partners, including pension funds and insurance companies. If the AI bubble bursts, it won’t be just tech shareholders who feel the pain. It will be retirees and insurance policyholders.

To be fair, it’s not clear that anything shady is happening here. Tech companies have plenty of AI infrastructure on their own balance sheets, and they’ve been bragging about that spending in earnings calls, not downplaying it. So it’s not obvious that they are using SPVs in an effort to mislead people.

Second, skeptics argue that tech companies are underplaying the depreciation risk of the hardware that powers AI. Earlier waves of American infrastructure left us with infrastructure that held its value for decades: power lines from the 1940s, freeways from the 1960s, fiber optic cables from the 1990s. By contrast, the best GPUs are overtaken by superior models every few years. The hyperscalers spread their cost over five or six years through an accounting process called depreciation. But if they have to buy a new set of top-end chips every two years, they’ll eventually blow a hole in their profitability.

We don’t dismiss this fear. But the danger is easily exaggerated. Consider the A100 chip, which helped train GPT-4 in 2022. The first A100s were sold in 2020, which makes the oldest units about five years old. Yet they’re still widely used. “In a compute-constrained world, there is still ample demand for running A100s,” Bernstein analyst Stacy Rasgon recently wrote. Major cloud vendors continue to offer A100 capacity, and customers continue to buy it.

Of course, there’s no guarantee that today’s chips will be as durable. If AI demand cools, we could see a glut of hardware and early retirement of older chips. But based on what we know today, it’s reasonable to assume that a GPU purchased now will still be useful five years from now.

A changing debt picture

WHEN THEY SAY: The hyperscalers are well-run companies that won’t use irresponsible leverage

YOU SAY: That might be changing

A common way for a bubble to end is with too much debt and too little revenue. Most of the Big Tech companies building AI infrastructure — including Google, Microsoft, and Meta — haven’t needed to take on much debt because they can fund the investments with profit. Oracle has been a notable exception to this trend, and some people consider it the canary in the coal mine.

Oracle recently borrowed $18 billion for data center construction, pushing the company’s total debt above $100 billion. The Wall Street Journal reports that “the company’s adjusted debt, a measure that includes what it owes on leases in addition to what it owes creditors, is forecast to more than double to roughly $300 billion by 2028, according to credit analysts at Morgan Stanley.”

At the same time, it’s not obvious that Oracle is going to make a lot of money from this aggressive expansion. There’s plenty of demand: in its most recent earnings call, Oracle said that it had $455 billion in contracted future revenue — a more than four-fold increase over the previous year. But The Information reports that in the most recent quarter, Oracle earned $125 million on $900 million worth of revenue from renting out data centers powered by Nvidia GPUs. That works out to a 14% profit margin. That’s a modest profit margin in a normal business, and it’s especially modest in a highly volatile industry like this one. It’s much smaller than the roughly 70% gross margin Oracle gets on more established services.

The worry for AI skeptics is that customer demand for GPUs could cool off as quickly as it heated up. In theory, that $455 billion figure represents firm customer commitments to purchase future computing services. But if there’s an industry-wide downturn, some customers might try to renegotiate the terms of these contracts. Others might simply go out of business. And that could leave Oracle with a lot of debt, a lot of idle GPUs, and not enough revenue to pay for it all.

And now, the 6 most compelling arguments AGAINST an AI bubble

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Imagine the Trump administration was essentially a ruse, a clever plot to prove the folly of its stated ideology.

The White House tells us that free trade is bad, tariffs are good, and relatively unfettered global trade is a rotten deal for America. But let’s entertain the idea that a sleeper cell in this administration was out to prove the opposite of each point: that trade is good, that tariffs are bad, that the globalists are right, and that liberal economics works.

I think a clever way to accomplish this would involve the following four steps:

1. Raise tariffs on a bunch of stuff, including agriculture and manufacturing.

2. Show that these policies contributed to disasters in the farming and manufacturing industries.

3. Create huge carve-outs and exemptions for the artificial intelligence supply chain, so the tariffs didn’t apply to AI.

4. Show that AI is booming.

What would be so clever about this plan is that it would amount to a kind of controlled experiment. This Trump admin sleeper cell could show that within the same period of time, and under the same administration, growing industries benefited from liberal economics while shrinking industries were hurt by protectionism.

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All right, dispensing the cheeky act, I’ll come right out with it: All four steps are already happening. The devious plot to undermine Trumponomics is the White House’s official economic policy. That’s weird. To wit:

1. The administration raised tariffs to their highest level since the 1930s. Import duties have been notably high on agricultural products (e.g., coffee and bananas)1 and manufacturing inputs (e.g., steel and some copper).

2. The industries most directly affected by Trump’s tariffs are doing poorly. Farmers are hurting, and some have been promised a bailout. The manufacturing industry is in a hiring recession, with employment falling steadily over the last six months.

   

3. But the administration has carved out huge tariff exemptions for AI. The largest of these exemptions covers $34 billion per month of imports for computers and parts that AI companies need to build data centers to train and run AI programs. As the economics writer and The Argument contributor Joey Politano reported, computer imports surged in 2025, and the AI companies have saved billions of dollars on additional import taxes thanks to these exemptions. “The current AI boom would simply be impossible if tech companies had to pay the same tariffs that car manufacturers or homebuilders currently face,” Politano wrote.

   

4. The AI sector is by far the strongest industrial contributor to the U.S. economy. The growth in AI infrastructure spending contributed more to GDP than the growth of consumer spending earlier this year.

Trump seems to be pursuing a broadly protectionist agenda. But for the one industry the administration seems to really, really want to protect, it is not doing protectionism at all. At the very least, it raises the question of why, if globalism is so effective for AI, it’s apparently so bad for the rest of the economy.

The Trump administration’s neoliberal AI policy

If you want to see just how discordant Trump’s AI policy is with the rest of the agenda, read the White House AI Action Plan. It is the furthest thing from a mercantilist document. It seems like the sort of thing to which a zombie President Ronald Reagan would be proud to put his signature.

Under Joe Biden, the U.S. had been cautious about allowing Chinese AI into the U.S. or selling advanced chips to the Chinese. The Biden administration didn’t just bar U.S. companies from selling frontier AI products to China. Its so-called “diffusion rule” — called by one academic “a centrally planned global computing economy” — barred foreign chip factories (say, in Taiwan or South Korea) that used certain U.S. intellectual property or software from selling their best stuff to China.

It was a pretty broad-brush tool that blocked most countries from free access to American AI technology. The idea was to stop Chinese labs from getting access to our best technology, which might help them leapfrog us in the race toward powerful superintelligence.

But critics believed the diffusion rule actually kneecapped America’s ability to achieve economic dominance in AI.

The Trump administration loudly dismissed the rule. “TO WIN THE AI RACE, THE BIDEN AI DIFFUSION RULE MUST GO,” David Sacks, the White House’s AI and crypto czar, posted on Twitter in May (yes, in all caps). In the last few months, the U.S. government has not only exempted billions of dollars of computer parts for the AI industry; it also permitted the sale of relatively advanced U.S. technology to China. In a series of moves that stunned some China watchers, the U.S. permitted the sale of powerful chips to China, including two chips — Nvidia’s H20 and AMD’s MI308 — that were banned under Biden.

One interpretation of this shift is that Nvidia CEO Jensen Huang has become a de facto policy adviser to the White House. Perhaps a more nuanced take, according to people I spoke to who are familiar with the administration’s policy, is that the U.S. is encouraging its companies to sell into China because it’s better for the U.S. in the long run. If Chinese researchers rely on American infrastructure, it might prevent our geopolitical adversary from building an alternative tech stack that takes over the world and outcompetes American companies. This story seems to be playing out in hardware industries, such as the electric vehicle market.

Whatever you say about this policy, it is the opposite of the high-tariff, free trade-skeptical ethos that one associates with Trump’s economic agenda. It is even, dare I say, almost globalist.

One sign that America’s AI policy is the opposite of protectionism is that hard-line protectionists hate it. Oren Cass, a leading advocate for tariffs and the founder of the conservative think tank American Compass, has blasted administration policy as a “historic blunder.” As for Nvidia, the most important firm in the entire American AI stack, Cass wants it broken up.

The protectionist illusion

The deeper meaning of this whole schism is hiding in plain sight. The Trump administration’s protectionist strategy is really neither protectionist nor a strategy. What we see instead is a ramshackle operation, a White House without any unified theory of the American economy. Different barons control different fiefdoms, each improvising their own approach under the wandering eye of a dilettante-authoritarian president who sees politics not as a landscape for coherent policymaking but as a series of deals. In an alternative world where Howard Lutnick happened to stumble into the job of AI czar, the United States might have pursued a totally different approach — one that was broadly protectionist, anti-China, and explicitly hostile to the idea of selling our best chips abroad. On that Earth-2, maybe we would have banned Chinese models outright and told Jensen Huang to eat rocks when he asked to sell frontier hardware to an adversary.

Instead, we got David Sacks, who in his bones is closer to a techno-libertarian neoliberal than anything resembling Trumpist economic nationalism. Sacks has been many things over the years — the original anti-woke crusader, a critic of what free trade did to the Rust Belt — but in practice, he is guiding the United States toward an AI policy that is more free trade-oriented than what Biden pursued.

For all the noise about tariffs and national rejuvenation, the one sector that is actually driving GDP right now is benefiting from a global trade policy that looks nothing like Trumpism. Instead, it looks like the liberal economic order the president and his protectionist clan claim to despise. If you want to understand what the administration truly believes about economic policy, look at AI: In the one area where the stakes are highest, the administration chose globalism. It chose openness. It chose to rip apart the protectionist script.

There’s a big question that I still have around why the administration has defaulted to the “free trade for AI; protectionism for everyone else” principle. The president’s own opposition to flooding the country with foreign goods seems to be perhaps his most sincerely held view — perhaps even more so than his opposition to allowing the entry of foreign people.

It’s possible that this is all just a compromise to keep the tech-right happy. A new entrant to the Republican coalition, the right is still figuring out how to make peace with a faction that’s built on disruption, immigration, and market liberal principles. It’s also possible that Trump is struggling to pull his party with him totally against trade. While his staffers may begrudgingly go along with him as he messes up small parts of the American economy, the Republican deep state may be putting its foot down when it comes to the next frontier of economic growth.

But I might be overthinking things. The simplest explanation might just be the truest: Trump — for all his faults — hates to watch the stock market freak out. Every escalation of tariffs has led to a serious negative market reaction, and every pause or reversal has led to jubilation. Trump knows that and he loves to watch the line go up.

The AI sector is the market’s crown jewel, the biggest contributor to S&P 500 gains, the anchor of tech valuations, and the engine of capital expenditures. Even if he is a believer in the long-term benefits of trade protectionism, Trump is incapable of waiting patiently for that promised utopia as the stock market bottoms out.

In that sense, the carve-outs may have less to do with a coherent model of global trade and more with a president instinctively protecting the one part of the economy the stock market cares about the most.

Thiel isn’t the only conservative who’s decided that young people are getting a raw deal. In a viral report on the federal government’s far-right Gen Z cohort, the writer Rod Dreher quoted one young Republican explaining that Zoomers are rapidly becoming anti-Semitic and nihilistic on account of “his generation is so utterly screwed.” Dreher continued:

The problems are mostly economic and material, in his view (and this is something echoed by other conversations). They don’t have good career prospects, they’ll probably never be able to buy a home, many are heavily indebted with student loans that they were advised by authorities to take out, and the idea that they are likely to marry and start families seems increasingly remote.

It’s generally inadvisable to treat anecdotes and op-eds as data points. But in this case, the evidence of young people feeling screwed extends beyond these testimonies. Hiring is slowing down. Youth unemployment is going up. The market for first-time homebuyers sucks. When young people say that they’ll never be able to afford the American Dream, and when their votes reveal that affordability anxiety is reshaping our politics, we should take their statements and ballots with the utmost seriousness.

But on closer examination, the “young people are screwed” narrative is only true enough to be dangerous. Some of the evidence behind it is dead-on. Much of it is incomplete or incorrect. Trust me that I know exactly how annoying it is for a middle-aged guy to “actually…” young people who say they’re struggling. So, I thought I would try to do the fairest thing: Provide a rigorous, highly visual, one-stop evaluation of the argument, illustrating both the strongest case for and the strongest case against.

Let’s start by actually listening to young people. What are they saying about the economy?

That it sucks, basically. Since 1978, the Survey of Consumers, which is published by the University of Michigan, has asked people how they feel about consumer conditions. In the last few years, sentiment among young people has puked. Among people between 18 and 34 years old, consumer sentiment is near its all-series low—worse than the painful end of stagflation, worse than the Great Recession, and worse than the pandemic. Buying conditions for houses have fallen off a cliff. You simply cannot look at this picture and say young people think this economy is good for them, or that it’s a good time to buy stuff.

And what’s the best case that they’re right?

Let’s start with the housing market. This might be the worst time to be a first-time homebuyer in decades. The Great Recession produced a weak decade of construction in the 2010s. In 2020 and 2021, the pandemic struck, and as Americans with savings looked to assuage their cabin fever by buying bigger houses, the race to the suburbs sent home prices soaring. When inflation hit, the Federal Reserve raised interest rates, adding the insult of high mortgage payments to the injury of higher home prices.

The result: The average age of first-time buyers just hit 40, an all-series record, according to the National Association of REALTORS. A lot of 30somethings who would have been homebuyers in any other generation now can’t afford to own where they live. That sucks.

How terrible is the labor market for young people, really?

Well, it’s definitely not good. Hiring has been declining for years. The unemployment rate for Americans between 20 and 24 just hit a ten-year high (excluding the pandemic).

Meanwhile, in a strange twist, unemployment for young college graduates seems to be rising unusually quickly. The following chart comes from the New York Fed. In the last 40 years, the light blue line (the unemployment rate for recent college graduates between 22 and 27) is below the dark blue line (the unemployment rate for all workers). But in the last few years, that’s flipped. By this measure, at least, young college graduates seem to be having an unusually difficult time finding work.

The government shutdown has left us in a bit of a dead zone when it comes to official unemployment statistics, but (slightly less reliable) private-sector measures suggest that layoffs have accelerated in the last few weeks. Challenger, Gray & Christmas recently reported that more Americans were laid off last month than any other October in more than 20 years.

Things aren’t great, and Americans expect the labor situation to get even worse. Unemployment expectations—as measured by our friends at the University of Michigan—have surged in the last year. That is, a significant share of Americans now expects the job market to get worse in the next 12 months, as sticky inflation, slow growth, and the rise of generative AI continue to put pressure on the workforce.

What’s the best evidence that AI is taking jobs from young people?

There is not much high-quality data suggesting that ChatGPT and its ilk are displacing jobs at a massive scale, but there is some empirical evidence that the occupations that are most ChatGPT-able are hiring fewer young people than we’d otherwise expect.

This summer, a group of economists from Stanford found that “early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment,” while hiring for other roles has “remained stable or continued to grow.” For example, some software teams have found that tools from Anthropic and OpenAI help them do more work faster, which might lead one to believe that tech companies don’t need as many young coders. In fact, as the next graph from the Stanford paper shows, hiring for young software developers has meaningfully slowed down since November 2022, when ChatGPT was released.1

That sounds definitive: Young people: totally screwed. Are we done here?

The best stories, like the best conspiracy theories, are made of half-truths. Zero-truth stories don’t stick. Without a hook into reality, narratives disintegrate on contact with their audience. But full-truth accounts are often messy, nuanced, and too on-the-one-hand-but-on-the-other-hand to make for a clean story. This is my way of getting ready to tell you that the “young people are screwed” narrative is correct enough to move news cycles and political cycles, but it’s woefully incomplete as a description of economic reality. According to the best figures we have, today’s young people—and this will feel like a surprise or an outright insult to various readers—are meaningfully richer than previous generations.

In fact, one of the viral charts from above—perhaps the most viral chart from above—might just be completely wrong.

So, a viral data point upon which the above argument hinges might be totally wrong? What is it?

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