2026-11-08 05:11:43
When I tell people I’m becoming a China bear, they’re often surprised. “Weren’t you already a China bear?” they ask me.
Well, no. I’ve always been a China bull — I’ve always believed that China’s economic growth would be robust, that they would master advanced technologies, and that their geopolitical influence would grow. This is exactly why I was also always a China hawk1 — I recognized the very real possibility that China would become a global hegemon, and I didn’t especially want to live in a world ruled from Beijing. It’s why I advocated so strongly for friend-shoring, for export controls, and for military alliances to balance out China’s awesome and growing power.
This was true even as China began to show visible signs of weakness in the late 2010s and early 2020s. Xi Jinping proved himself less competent than his predecessors, and the country’s real-estate-heavy growth model hit the skids. But despite those stumbling blocks, I still predicted that this would be the Chinese Century. The country had what seemed like unassailable advantages in human capital, market size, and state capacity, even as all of its possible rivals were convulsed by internal division. And the one thing that most people cite as China’s big weakness — demographic decline — never struck me as much of a threat to the country’s power.
Now, however, for the first time I can remember, I’ve found myself becoming actually bearish on China. I still think it will be an immensely powerful country — perhaps the most powerful single country on the planet. I still think it will be at or near the forefront of technology, and that it will generally be a safe, moderately prosperous, and tolerable place for its people to live. But more and more, I’m starting to think that global hegemony of the type the U.S. enjoyed throughout most of the 20th century will be out of reach. I’m also starting to think that China’s bid for technological dominance is weaker than I had believed.
And just as my previous bullishness made me a hawk, my increasing bearishness makes me more of a China dove.2 I still think friend-shoring is important, export controls are good, and we need robust military alliances to make very sure China doesn’t try to conquer Asia. But I no longer feel the same sense of desperate urgency about these prudent measures — they feel more like an insurance policy now.
There are basically three reasons I’m revising my China expectations downward. The biggest reason is AI. China’s system is incredibly optimized for an era of technology and economic production that is very quickly coming to an end. AI will change everything, and modern China is not the kind of country that adapts well when everything changes.
The other reasons are less important but still worth mentioning. China’s new economic model — the government-supported push to capture all of global high-tech manufacturing — is not likely to succeed. And third, the country’s political situation appears to be deteriorating. Just as the Deng/Jiang/Hu era illustrated the strengths of authoritarian political systems, Xi Jinping is giving us a much-needed lesson on the weaknesses of those systems. His spiraling paranoia is turning the country inward, just as his economic policies are running out of steam.
Anyway, let’s start with those economic policies. This is an economics blog, after all.
In a nutshell, Xi Jinping’s economic strategy is to dominate manufacturing — to have China be, in the words of Damien Ma, the “make-everything country”. I’m increasingly convinced that this approach is already beginning to backfire.
Going all-in on manufacturing is a strategy you see a lot of developing countries use during their periods of rapid catch-up industrialization. And there are some good reasons for that. When you’re poor, what your people need most aren’t video games or dog-walking services, but houses and cars and refrigerators. Manufactured products are also easy to export, and poor countries don’t have big domestic markets to rely on. It might also be easier to reach the technological frontier in manufacturing, since you can reverse-engineer physical products more easily than services.3
Usually, as countries get rich and their needs evolve from the physical to the ephemeral, they move out of manufacturing and into services to some degree. But Xi Jinping decided that this was a mistake. He reasoned that if China continued to behave like a developing country forever, and promoted manufacturing at the expense of everything else, China could dominate the globe.
This was an interesting hypothesis, and it might have worked — indeed, there’s still some chance it might work. Militaries, after all, depend on manufacturing above all else, so in a big protracted WW2-style war, being the master of manufacturing might give you an automatic win.
And dominating key nodes in global supply chains lets you use export controls to bully other countries without actually bombing them. In fact, China is doing this more and more, using export controls on everything from tunnel-boring machines to rare earths to drones to medicines as bludgeons to force other nations to do its bidding.
In order to make China dominate global manufacturing, Xi unleashed a wave of industrial policy bigger than any the world had ever seen, spending about 4% of GDP per year on subsidizing manufacturing industries — usually through cheap bank loans, but sometimes just by sending cash directly.
For a while, this seemed to be working spectacularly. A 2024 paper by UNIDO got a lot of attention for predicting that China’s share of global manufacturing would be 45% by 2030 — equal to the United States right after World War 2. But cooler heads pointed out that China’s manufacturing share growth had already slowed; more reasonable predictions put its 2030 share at maybe 35-36% — still enormous, but more like America in the 1960s or the early 1920s.
And in U.S. dollar terms, China’s share peaked during the pandemic and has been drifting down since then:
In fact, despite Xi’s push, manufacturing is a shrinking share of China’s economy. China is now less industrial than South Korea, despite being less than half as rich:
The reason is simple, and it’s something I’ve written about many times before. Unlike South Korea, China is a very large country. That means most of China’s manufacturing production is going to have to go to the domestic market. Even if other countries don’t slap big tariffs on your products, export markets will simply saturate. One of the most reliable empirical laws in all of economics is the gravity model of trade, which says that trade volumes drop off rapidly with distance.
Of course, some countries will put tariffs on China. The EU recently forced China to make a deal to limit auto imports, and if that doesn’t work, stronger measures are likely. That will only accelerate the natural saturation of China’s export markets.
And despite everything you’ve read about the “Second China Shock” and the big export boom, China is still far less export-intensive than Korea, and that’s not going to change:
China simply can’t be Giant Korea; it’s so big that its manufacturers need to rely on its domestic market. But that domestic market is becoming saturated even faster than the global market. Profits in the auto sector, the steel sector, the solar sector, and many other manufacturing industries are all collapsing. Weak domestic demand from China’s real estate bust has encountered massive oversupply from government subsidies, and the result is that few companies in China are making much money. That will force lots of businesses to close and consolidate, and it will starve dynamic private-sector companies of R&D budgets, forcing them to rely more on research handed down by the distant, unresponsive, and unaccountable government.
Government subsidies can keep the party going a lot longer than it ought to, but already the limits have probably been reached. Chinese manufacturing indicators are faltering, and manufacturing investment seems to have topped out as well.
In fact, this isn’t even the only example of Xi Jinping taking a developing-country strategy and pushing it far beyond its limits. Infrastructure is another. For many years, China invested huge amounts into building more roads and bridges and high-speed trains than they knew what to do with. But there was a limit to that process, and China has now hit the limit. The Economist reports that China’s infrastructure investment has leveled off:
China’s years-long splurge on bridges, tunnels, expressways, high-speed rail and airports is beginning to tail off…In July this year a senior transport official said China’s main transport network was “91% complete”, and the country would pivot from expanding to upgrading existing infrastructure.
But it isn’t so easy weaning yourself off of infrastructure overinvestment. China built so much infrastructure that it will have to spend a lot of money just to upkeep all those roads, trains, and bridges. That’s the iron logic of depreciation. And since much of China’s infrastructure isn’t really carrying many passengers or much cargo, that means China is stuck spending a significant fraction of its GDP upkeeping stuff that people don’t really need — unless, of course, it chooses to let some of that infrastructure decay into ruin.
All of this means growth will slow. And of course, it’s all happening against the backdrop of weak demand from the continuing, protracted fallout from China’s real estate bust. There are signs that the real economic damage in that sector is only getting started:
Weak domestic demand won’t just be a drag on China’s headline growth — it will also make it harder for China’s manufacturing sector to maintain its 30% global market share, much less expand it to 35% or more. Xi seems to think that China can just sort of pull resources off of real estate development and put them into manufacturing, one for one, like a corporate manager reassigning engineers to a different project. But it doesn’t work like that; demand matters, and China’s manufacturing triumphs in past decades depended heavily on the tailwind that the long property boom had been delivering.
Chinese economists realize this, but Xi seems to have stopped listening to economists, and to have purged his government of anyone who was inclined to listen.
(And of course as the years go by, China’s accelerating population decline will reduce its domestic market size by more and more, compounding the pressure on manufacturing. I don’t expect this to be a big factor until at least 2040 or so, but it’s worth mentioning.)
In other words, Xi’s “make-everything country” strategy is likely to see only modest short-term success. It will not let China conquer the world. And the failure of this strategy will come just as a massive new technological revolution upends the global economy — a revolution that China, despite all reports, is not well-prepared to take advantage of.
2026-11-06 14:46:24
Democrats won a historic victory in Tuesday’s election. It was the best possible outcome — Donald Trump has made so many mistakes, engaged in such flagrant corruption, and committed such egregious acts that for his party not to have been punished at the polls would have boded poorly for America’s future. But as it is, I’m cautiously encouraged — normal Americans reasserted control, delivering a rebuke to the extremists who had used a moment of division and chaos to seize the commanding heights of power in our country.
It was the biggest midterm wave in half a century. Obama’s midterm loss in 2010 and Trump’s in 2018 don’t even come close. You have to go all the way back to 1974 and 1976 to find wipeouts like this:
Wait…1974 and 1976? 1974 was the year Nixon resigned due to Watergate, and 1976 was the year Jimmy Carter was elected president. Did either of those election victories herald an era of Democratic dominance in America? No, they did not. In fact, the GOP bounced back shortly afterwards, dominated American politics in the 1980s, and pulled the country to the right for decades.
So before they go patting themselves on the back for winning everything, forever, Democrats should think about that history of brief success followed by long-term failure, and ponder how to avoid a similar outcome this time. After Hannibal won a great victory, one of his generals is said to have told him: “You know how to win a victory, Hannibal; you do not know how to use it.” The Democrats must figure out how to use their victory.
That’s a tricky question. Democrats now control Congress, but that doesn’t mean they’re in power; Trump can and will veto most legislation they pass. With their limited capabilities, the Dems have to do the following, all at once:
Block as many of Trump’s bad policies as possible
Keep the spotlight on Trump even though he’s now a lame duck
Demonstrate a clear ideology and a clear plan to the American people despite not being able to pass much legislation
That’s possible to do, especially if — as seems very likely — Trump keeps being corrupt and making mistakes and alienating the country. The Dems won the 2006 midterms, and went on to put Obama in the White House two years later. Trump seems like he’s on a similar track:
But it doesn’t always happen — the GOP lost to Obama in 2012 after their big midterm wave in 2010, and the Dems barely eked out a victory over Trump in 2020 (with a lot of help from Covid) after winning the midterms in 2018.
If Democrats are going to translate midterm success into presidential victory — to say nothing of long-term partisan dominance — they’re going to have to work hard at rebuilding their brand, their sales pitch, and their policy platform after the disastrous years of 2016-2024.
One important point, which a lot of other people have already made, is that the big Democratic wave was mostly won by moderate Democrats. Here’s the NYT:
For much of the midterm campaign, progressive Democratic insurgents commanded the spotlight…But the path to Democrats’ sweeping victory this week, when they seized control of both chambers of Congress, was paved overwhelmingly by moderates who had spent months persuading voters in Republican-leaning states and competitive congressional districts…
The scale of moderate gains was reflected in the growing ranks of the centrist groups on Capitol Hill. The Blue Dog Coalition, a caucus of moderate and conservative Democrats in the House, announced on Wednesday that it had added at least 14 to its ranks, nearly doubling in size, and other centrist groups including the New Democrats and Problem Solvers strengthened their numbers substantially.
Some of this is purely mechanical. Far-left types tend to win by unseating moderate Dems in primaries in deep-blue districts and states, while moderates tend to win in swing districts and states; thus, when a wave is bigger and the Dems win more swing districts and states, moderates tend to do proportionally better than leftists.
However, there are signs that this election cycle actually represented a broad endorsement of moderate center-leftism. Ballot initiatives have rejected various leftist policies all over the country, and leftist candidates have been losing to center-left ones in deep blue strongholds:
And leftist candidates have generally performed more weakly at the ballot box:
What’s more, many of the most prominent Democrats who won high-profile races this year have explicitly endorsed centrist positions on key issues:
Nor have many prominent Dems done the kind of “woke” cultural signaling that they regularly did in 2020 and 2022.
There are two parts of the basic story here. First, the “woke” cultural wave of the 2010s, which peaked in 2020-21, continues to slowly recede. You can see this in continued voter support for conservative positions on cultural issues like trans sports:
Second, voters have not yet embraced the socialist movement, as demonstrated by the relatively weak performances of Bernie Sanders-endorsed candidates and the failure of the billionaire tax in California.
That means that Democrats understand that this 2026 midterm victory is a contingent and fragile one. Normal Americans have risen from their apathetic torpor and their jaded hyperpartisan paralysis, and are prepared to punish extremists of either party:
The normie uprising has brought center-left Dems to the fore, and it has also forced more progressive Dems to tone down or strategically compromise their more extreme positions.
But moderation itself is neither an ideology nor a policy program, and herein lies the big challenge for the Dems over the next year and a half or so.
Right now, my assessment is that most Democratic voters — including Independents and swing voters who lean toward the Dems — aren’t thinking very hard about how the country ought to be governed or what the future of America should look like. They’re simply thinking short-term thoughts about the immediate threat. They hate Trump and MAGA, and they want them gone, and it’s really as simple as that.
Nate Silver calls this “Heather Cox Richardsonism”, after the extremely popular Substack politics-and-history blogger Heather Cox Richardson:
Silver wrote:
The third faction [of the Dems is] Richardsonism or a term I’ll treat as synonymous with it: #Resistance Libs. They’re older, with extremely high educational attainment, predominantly female, and very highly politically engaged. This is the audience for a cluster of political activism encompassing things such as the No Kings protests and some highly popular anti-Trump Substacksalong with certain prominent podcasts and much of Bluesky.
Nate has a bunch of criticisms of “Heather Cox Richardsonism”, most of which I disagree with. I like Heather Cox Richardson. I like suburban moms who listen to NPR all day and who feel physically ill when they see Donald Trump on a screen. Their instincts are good. Trump is an extremely bad president — and not just bad, but uniquely bad, in ways that are degrading the long-term stability and effectiveness of the United States itself. It’s eminently reasonable and understandable to be mad about this, even to the exclusion of all other political viewpoints and causes. As for the No Kings protests, they seem to line up perfectly with — and probably contributed to — this week’s midterm results.
And for most regular people, being mad about Trump is enough. Most citizens are not political philosophers or movement leaders or intellectuals. It’s not reasonable to ask them to think about where the country should go after Trump, or what ideologies and ideas should guide us in the absence of such imminent threats.
But someone has to think about those things. Otherwise Democrats will prove just as unable to govern as Trump was — well, maybe 70% as unable to govern, which will be bad enough. And our politics will continue to be an endless series of backlash-to-the-backlash-to-the-backlash, as each party focuses all its energy on getting to the day of the revolution and none of its energy on what happens the next morning.
Right now, the problem with Resistance Liberalism is that it’s headless. In Trump’s first term, the Resistance was animated by various identity movements, unified by the idea of a struggle against structural racism, misogyny, and so on. Hillary Clinton made a conscious choice to frame her 2016 campaign as a battle of antiracism versus Trumpian racism, Kamala Harris did something similar in 2020, and Joe Biden quietly let his administration push what would have previously been considered extreme versions of various identitarian social causes.
In fact, the American public still thought those versions were extreme, and punished Democrats in the polls accordingly. The end of the woke revolution lopped the head off of the Resistance — it left them with all of the same rage, but without a vision of a future America.
As I see it, there are basically three factions vying to replace identity politics as the guiding ethos of the Resistance. These are:
The leftists — Abdul El-Sayed, Zohran Mamdani, Bernie Sanders, and so on
The Warrenite progressive intellectuals
The Abundance movement
Each of these has deep flaws that I think will end up basically disqualifying it.
The leftists are organized, purposeful, disciplined, and driven, and they probably want the job of leadership more than the other factions. But their movement has some fatal weaknesses that make it a bad choice to lead the Dems. First of all, the leftists are intensely factional, focusing relentlessly on knifing the Democratic establishment rather than building a big tent.
Second, their policies tend to be so extreme that they overreach and backfire. The failure of the California billionaire tax is a case in point. Americans want to tax the rich more, but the billionaire tax was so extreme and poorly designed that voters didn’t just reject it — they approved a competing ballot proposition forbidding such taxes in the future.
Third, leftists instinctively ally themselves with bad people and bad causes. Leftist streamer Hasan Piker has praised terrorists and supported Vladimir Putin’s seizure of Crimea, incoming leftist congresswoman Darializa Avila Chevalier founded a group claiming to be dedicated to “the total eradication of Western Civilization”, and so on.
And fourth, although leftism is sometimes viewed as an alternative to wokeness, leftists themselves tend to be very woke. Just for fun, check out the famous footage from the 2019 DSA convention, or watch this widely mocked display from Representative Ro Khanna:
These shortcomings ought to prove fatal to the leftists’ chances of taking over the American left — and they probably will.
The second faction vying to take over the Resistance are the Warrenite intellectuals, who are laser-focused on attacking the rich — specifically, the rich people in the tech industry. Although Warren failed to catch fire in 2019, her faction had great success putting its people into positions of power during the Biden years.
But it kind of backfired. The New Antitrust movement got little done in a practical sense, and more fundamentally it failed to advance any kind of rewrite for our antitrust law. Years of frothing with rage against Mark Zuckerberg and the old FAANG companies just ended up looking silly when AI came along and disrupted their business models. The intellectual movement to “steer” the development of AI toward more pro-worker technologies (whatever that would even look like) is going nowhere.
In addition to being composed primarily of a bunch of egghead intellectuals who routinely fail to catch fire with voters, the Warrenites have a fatal flaw — they’ve chosen the wrong target. Democrats are entirely focused on hating Trump, while the Warrenites are focused on hating Mark Zuckerberg and Jeff Bezos. Americans just fundamentally aren’t that mad at the fact that Zuckerberg and Bezos are rich. They generally despise Elon Musk, but only for his right-wing activities and his online attitude — when he was just a businessman selling electric cars and reusable rockets, they didn’t mind his billions.
The Warrenites are simply never going to convince the nation that the billionaire techbros, rather than the rightists of MAGA, are the biggest threat to our way of life.
Anyway, the third faction is Abundance. Of the three, this would definitely be my choice if I had to pick, since I really like what the Abundance movement stands for. Abundance policies, like the bipartisan permitting reform bill now making its way through Congress, are both smarter and more politically feasible than almost all of what the leftists and Warrenites want to do.
The problem with Abundance is that it’s primarily an intellectual movement, not a political one. It’s all about policy — about whispering in the ears of princes, rather than marching in the streets. Abundance’s leaders are nice guys like Ezra Klein and Derek Thompson who just want to see everybody be happy and get a puppy.
And all of the Abundance ideas, to a rounding error, are good ones. Many of them are going to get implemented, especially concerning housing and energy. But they will do so because elites — including some leftists and some Republicans — will be intellectually convinced that they are good things to do, not because they have the ability to harness and direct years of pent-up anti-Trump anger. Abundance will win, but it will not win all the things the Democrats would need it to win.
The Democrats need something more than any of these movements has to offer. They need an ethos that both inspires popular passion and has a chance of pragmatically working. They need something that will give regular, normie Americans — the voters who just rose up to throw out the GOP — a reason to believe in the future, and a reason to believe that the Democrats can take them to that future.
That ideology exists. It was harnessed by FDR and his successors a century ago, first to fight the Depression, then to win World War 2, and finally to build the modern society that came after the war. I’m talking about liberal nationalism:
Here’s what I wrote in that post half a year ago:
In the 20th century, America had an ideology that was committed to forging a single national identity from a dizzying array of backgrounds. That ideology was liberal nationalism…
FDR ended forced assimilation policies for Native Americans, and gave them greater autonomy on tribal lands. He maintained a group of Black advisors, created plenty of programs to help Black people economically, created cultural programs to publicize Black achievement, and preserved the testimonials of former slaves. He created campaigns promoting interfaith understanding and cooperation, helping to reduce America’s traditional anti-Catholic prejudice as well as antisemitism. His Federal Writers Project employed writers to document America’s cultural diversity…FDR also strongly promoted the notion of America as a nation of immigrants — an idea that still deeply holds sway over the American psyche, despite rightists’ attempts to get rid of it…
FDR and the liberal nationalists were not modern multicultural progressives. They strongly believed in national unity. This obviously meant patriotism, respect for American institutions, and a shared sense of national purpose during World War 2, but it also meant a lot more. FDR and the New Dealers — and especially the Office of War Information — advanced the notion that America had a unifying culture, set of democratic ideals, sense of history, and way of life that transcended Americans’ different backgrounds. The history of the New Deal is littered with examples of attempts to forge American-ness into a sort of shared civic religion.
The Democrats can have liberal nationalism back if they want. Moderate figures like Jon Ossoff and James Talarico can use Rooseveltian pluralistic patriotism just like Barack Obama did, in order to go beyond good-governance centrism and opposition to Donald Trump. The intellectuals of the Abundance movement can use liberal nationalism to add passion to their policies — after all, isn’t it precisely because we’re Americans that we deserve material abundance?
Right now, Americans hold a bunch of basically Rooseveltian beliefs, and many Democrats still have the Rooseveltian instincts they’ve inherited from their party’s past. But we’re still not to the point where liberal nationalism has filled the vacuum of passion and inspiration left by the demise of “Woke 1.0”. The Democrats have a year and a half to resuscitate that vision.
Doing so will require some clashes — with leftists who believe that America is an evil empire, with Warrenites who want to destroy big tech companies instead of harnessing them to the national purpose, and with identitarian activists who believe that promoting a unified national culture is a form of racist oppression. But if this is not undertaken, the Democrats will either continue to drift aimlessly from backlash to backlash, or fall under the sway of highly suboptimal captains.
There’s also the more pragmatic question of what Dems should actually do over the next two years, while they hold Congress but not the presidency. This is an inherently difficult problem, because Trump can just veto almost any legislation the Dems pass. The usual tactic here is to just obstruct anything the president wants to do, hoping that making the president’s term in office look like a failure will help the opposition in the next election.
But I believe Democrats can do better than this. First of all they can, and should, investigate Trump and his family and friends for corruption. Corruption turned out to be a huge issue in these midterms — even more important than I realized it would be.
Even in opposition, Democrats can shine some sunlight on the Trump administration’s seemingly endless parade of dirty deeds. And in fact, it looks as if this is going to happen:
Corruption topped the list of concerns for voters who ousted Republicans…Now, the newly empowered Democratic Party is putting the issue front and center…Senior Democrats in both chambers of Congress have vowed to marshal the subpoena powers and larger investigative staffs that come with their soon-to-be majorities to probe what they say is the self-enrichment that President Donald Trump and people in his orbit have done at the public’s expense…
House Democrats are preparing a vast oversight agenda to look at how Trump and his family have become wealthier since he returned to office…Garcia, the top Democrat on the Oversight and Government Reform Committee, has outlined five priorities, including Trump family business dealings, the administration's handling of the Jeffrey Epstein investigation and Immigration and Customs Enforcement operations. Rep. Jared Huffman of California, the ranking Democrat on the Natural Resources Committee, said he wants to dig into Trump's White House ballroom and other construction projects, as well as the Trump family's role in mineral deals…In the Senate, Sen. Richard Blumenthal, D-Conn., who is expected to become the chairman of the Homeland Security Committee's Permanent Subcommittee on Investigations, said his panel will want to look into Trump's real estate projects around the nation's capital and his crypto dealings with special envoy Steve Witkoff.
This is great. But other than this, and perhaps some bipartisan deals and some obstruction and oversight of Trump’s worst abuses, what can the Dems in Congress do?
What they can also do is to workshop their economic policy program under low-stakes conditions. America has two big economic challenges looming — the out-of-control national debt, and AI. Dems can start demonstrating — and at the same time, start figuring out — how they’d address both of these challenges if they had the presidency.
The debt is actually the much harder piece of this in political terms. Deficit reduction is inherently painful, as it involves some combination of spending cuts and tax increases — both of which tend to be unpopular with the electorate. If Dems submit budgets that go all-out with spending cuts and tax hikes, Trump will just veto those budgets, and Americans — who have not yet been forced to reckon with the need for debt reduction — will praise Trump for it.
Instead, Dems will need to introduce only parts of their ultimate deficit reduction plans. Sunsetting the Trump tax cuts on pass-through businesses is one good idea — there’s basically no reason for people like me to pay lower taxes than Americans with W-2s, just because we’re self-employed.
AI is ultimately the thornier policy challenge, because we really don’t know how AI is going to affect the economy. But putting in place measures to protect human labor from the inevitable disruptions — flexible welfare programs, employment incentives, social wealth funds, and the like — has little downside, and it offers Democrats a way to sell a vision of harnessing the benefits of technology for the public good. The Dems can observe how these ideas go over with the public, and make the popular ones part of their candidate’s platform in 2028.
This midterm blowout was the first step toward building a better country and putting the chaotic, shameful Trump Era behind us. But there’s a lot of work to be done, both on the ideological side and in terms of crafting a policy program. The morning after the revolution is always when the hard work begins.
2026-11-04 18:33:58
The MAGA people appear to have been laboring under a number of delusions.
First, they seem to be genuinely surprised by the fact that they suffered a crushing defeat in the midterm elections. In fact, polls, forecasts, and prediction markets had all been calling a Democratic victory for months now. But a lot of MAGA types appear to have believed that America always has some big hidden reservoir of shy Trump supporters out there that everyone always misses, and that this silent majority would ride to the GOP’s rescue and prove all the pollsters and forecasters wrong, as they did in 2016.
To put it mildly, that did not happen. The election night surprise actually favored the Democrats, who won a far more extensive and decisive victory than almost any of the pollsters and forecasters had anticipated.
Even Trump’s traditional demographic support base — white voters without a college degree — abandoned the GOP in droves this time:
Trump could try to challenge this electoral verdict — to cry that the vote was rigged, to declare a state of emergency and suspend the Constitution, to abuse the powers of the presidency to nullify the popular will. But my gut says that he won’t, and here’s an important reason why:
As long as the MAGA people could tell themselves that Trump’s opponents were a bunch of old people and women, they could fantasize about prevailing in the street confrontation that would erupt in the wake of an autogolpe. But if young men — and especially young white men — aren’t on Trump’s side, then starting some sort of civil war would likely not go well for MAGA.
Anyway, as of this writing, here’s what the Senate election map looks like:
Kansas is colored blue. So is South Carolina. So is Iowa. Mississippi will probably end up blue as well.
And most of all, Texas is blue. Not just the Senate race, either — the GOP governor, Greg Abbott, has been unseated by Democratic challenger Gina Hinojosa. Bo French, who tweeted alarm at seeing Indian American football fans at a University of Texas game two months ago, lost his bid for Texas Railroad Commissioner to Democrat Jon Rosenthal.
The fall of Red Texas is an epochal, era-defining event. Growing up in Texas, I saw Dems win statewide office occasionally, but these were always old-school conservative Southern Democrat types. Yesterday’s election saw Gina Hinojosa and James Talarico, both staunch progressives, win the governorship and one of the Senate seats. In fact, the only Republican now holding a statewide office is…Ted Cruz.
How did that happen? Part of it was bad candidate selection — MAGA primary voters tossed out the popular mainstream Republican Senator John Cornyn for not being slavishly loyal enough to Trump, and replaced him with Ken Paxton, a man who has been repeatedly indicted and investigated for financial crimes and corruption. But Abbott was a popular, mainstream Republican incumbent governor with a strong economic track record, and he still lost to a progressive upstart challenger.
Rightists were simply unprepared for a world in which this was possible. Here was Jonathan Keeperman, whom I recently debated about Indian immigration, just yesterday:1
The only possible interpretation of this huge wave election is that it was a referendum on Donald Trump. Trump is now among the least popular presidents in living memory, rivaled only by George W. Bush at the end of his presidency. I wrote about why most Americans despise Trump, and my analysis hasn’t really changed:
Trump was elected to tame inflation, but he raised taxes on almost everything Americans buy from overseas, and raised gas prices by attacking Iran. When people complained about the cost of living, he told them to buy their daughters fewer dolls. Trump was elected to stay out of Middle Eastern wars, but he plunged us into an expensive quagmire. He has relentlessly promoted the interests of Vladimir Putin, whom normal Americans hate with a passion. His antics — threatening to invade Greenland, trying to bully people into renaming the Gulf of Mexico, Lake Ontario, and artificial intelligence, and so on — seem dangerously unhinged.
Then there’s the corruption. Trump has relentlessly looted the country to fatten the pockets of himself, his family, and his close political allies:
The American people noticed. This is from last week:
Voters are now more concerned about government corruption than any other issue, including high grocery and gas prices, according to the final CNBC All-America Economic Survey before the Nov. 3 midterm election…[T]he results nevertheless align with other recent polls showing corruption has shot to the top of voters’ list of pressing worries…A Fox News poll published last week found 60% of registered voters are extremely concerned about government corruption, surpassing concerns about inflation and high prices. A Washington Post-ABC News-Ipsos poll released Sunday found some two-thirds of respondents think Trump is using the presidency to enrich himself, up 10 percentage points since September 2025. Gallup recently found perceptions of government corruption in the U.S. have hit multi-decade highs. And an August poll from Reuters/Ipsos found nearly 7 in 10 Americans believe Trump’s businesses influence his presidential decisions.
And here’s their chart:
Republicans seemed to somehow labor under the delusion that regular Americans were going to just ignore all this. Perhaps they thought that Trump’s charisma, which allowed him to bounce back from the January 6th coup attempt, made him invincible. It’s possible they reasoned that Americans had simply become so jaded about corruption that they would ignore dozens of scandals that each would have ended any other presidency. Perhaps they believed that no matter how bad Trump got, Americans’ dislike of woke culture and progressive governance would always make the Republicans the lesser of two evils.
Maybe they believed that Americans never really cared about inflation in 2024, and that worries about the cost of living were just people’s way of expressing their worries about immigration and wokeness. Or maybe they believed America would blame Dems for the assassination of Charlie Kirk, and reject them at the ballot box accordingly. Perhaps they thought they could scare Americans away from the Democrats by calling them “communists” and screaming about “Western civilization”:
This was MAGA’s greatest delusion — the idea that the average American sees things similarly to the rightists on X. The truth is that the average American does not see “Western civilization” as a fortress under siege. The average American does not spend all day in a state of panic that brown immigrants and their children are invaders bent on subjugating the white race.
One gets the sense that the MAGA people believed that the average American doesn’t even exist. For decades, ideologues and extremists in both parties have shouted — often in direct contradiction of the evidence — that swing voters don’t exist, that polarization rules everything, that moderation and appealing to the center are useless, and that electoral victory goes to whichever party mobilizes their base to come out and vote.
That was always probably wrong, but MAGA seems to have taken this flawed logic one step further, convincing themselves that the country consists only of “the left” and “the right” — that every voter is either a blue-haired trans communist or a chisel-jawed defender of Western Civilization who spends all day posting memes on X about gender-swapped Draco Malfoy. If you have no idea what the heck gender-swapped Draco Malfoy is, or why on Earth anyone would think it’s politically important, rest assured that you’re not alone.
Texas and Kansas and South Carolina have been turned blue by a coalition of voters who aggressively, emphatically neither know nor care about gender-swapped Draco Malfoy, or Anakin Skywalker memes, or any of the other nonsense that bounces around the rightist echo chambers on X and 4chan. It was a coalition that never read Elon’s never-ending stream of tweets about James Talarico wanting to “trans your kids and destroy the border”. It was a coalition that does not know or care whether it’s white French people or brown French people burning down Paris this week.
It turns out that normal Americans do exist — in great numbers, in fact. It’s possible to keep them divided with relentless partisanship. It’s possible to drive them to jaded apathy with relentless he-said-she-said accusations and manufactured scandals. It’s possible to get many of them to stay home on election nights, simply by ignoring their wishes for long enough. But if you push them hard enough — if you step on their face again and again, with corruption and mismanagement and disdain for their daily struggles — they will eventually bestir themselves and rise up and smack you down. They will remind you of who won the Second World War, and why.
So the Democrats, ideologically divided as they are, took a well-deserved victory. The risk now is that they don’t learn their lesson from watching Trump’s implosion. Just as MAGA convinced themselves that the average American was a meme-spouting rightist, Democrats might marinate in their own echo chambers, egged on by leftist activists and progressive pressure groups and Bluesky cancel-culture warriors.
Democrats might convince themselves that what Americans really care about is Palestine rather than the cost of living. They might embark on a borrowing binge in order to throw more money at health care, resulting in rising interest rates and rising prices. They might try to halt inflation with price controls, with predictably terrible results. They might spend their political capital on billionaire taxes of the type that California’s electorate just rejected. They might forget the lessons of 2024 and try to admit as many illegal asylum-seekers as possible.
And if so, I’ll be writing another version of this post in 2030 or 2034, explaining why a Democratic president got hit with a red wave in the midterms.
Update: I’ll add a few tweets here that I think make good points about the election.
“Benito Tortellini” points out that even as Democrats crushed Republicans nationwide, “woke” and leftist ballot initiatives failed in blue districts around the country. This election really was the Revenge of the Normies.
Joe Weisenthal hypothesizes that this is the first election where social media use fell, and hypothesizes that people were talking to AI instead. Both of those hypotheses need to be tested with data, of course. But they are very consistent with my thesis that social media is a uniquely polarizing and radicalizing force in society, and that AI is at least a partial remedy.
In fact, Talarico said that God is “nonbinary”.
2026-11-02 19:03:51
Anthropic recently filed for IPO. At an estimated $1.5 to $2 trillion, it will be one of the biggest IPOs of all time, rivaling SpaceX, and possibly the biggest. For reference, Facebook’s valuation when it had its blockbuster IPO in 2012 was $104 billion, and Google’s valuation in 2004 was $23 billion. Anthropic will instantly become one of the world’s most valuable companies — Google itself is now worth $3.4 trillion, and Nvidia and Apple are both worth around $5 trillion.
This naturally has a lot of people asking: What’s the upside to buying Anthropic? Even if it grew to be as valuable as Apple or Nvidia — in other words, if it became one of the most valuable companies on planet Earth — that would only be a multiple of 2 to 2.5. But if Anthropic stumbles, that lofty valuation could come tumbling down.
There are certainly lots of reasons Anthropic could stumble. Demand for AI could take longer to materialize than people think, perhaps because companies have trouble integrating AI into their existing business models. The U.S. government could decide that Claude’s hacking capabilities represent a national security threat — especially after the recent wave of AI-assisted cyberattacks — and decide to regulate, appropriate, or otherwise restrain the company (the spat with the Department of War might represent the beginnings of that conflict). One company (SpaceX?) might end up monopolizing all of the compute, squeezing Anthropic’s profits. Competition from OpenAI and Google, or Chinese models, might result in a competitive race to the bottom that forces down Anthropic’s margins. And so on.
In order to balance all that uncertainty and risk on the downside, Anthropic needs to have huge upside. It needs a way that it could be worth a lot more than $2 trillion in the years to come. For comparison, if you bought Google when it IPO’d, your investment would have gone up by over 130 times by today. To equal that performance, Anthropic would have to be worth $200 to $260 trillion by the late 2040s. That’s almost half of the total wealth that exists in the entire world today. For Facebook, the number is around 16x, meaning Anthropic would have to be worth $32 trillion by the early 2040s — still an impressive performance, considering that that’s more than six times the value of the most valuable company in 2026.
How could Anthropic manage that kind of growth? One possibility is that the world economy itself will grow at a very rapid pace, thanks to AI. Some AI researchers think that growth could soon accelerate to fantastically high levels. But most economists bet against that, and even most AI industry insiders expect the growth speedup to be much more modest. Even 7% nominal growth — 4% real growth plus 3% inflation — would only make the economy about 2.75x larger by 2041, and 4.43x larger by 2048.
So in order to have the upside of a Facebook-like outcome — let alone a Google-like outcome — Anthropic probably can’t just ride a wave of economic growth.1 It’ll need to capture a greater fraction of the value that our economy produces.
So far, the company has been doing exactly that — and at an extremely rapid rate. Its revenue has grown faster than any company of similar size in all of history. And despite this explosive growth, the company is now making an operating profit:
Businesses usually have to spend more money to make more money at the outset, but Anthropic’s Claude Code is a product that sells itself — relative to just one year ago, the company is making more than 3 times as many dollars of revenue for each dollar that it spends on compute:
As SemiAnalysis writes, this is “the most efficient scaling of a business in the history of capitalism”. Jeffrey Bussgang has a good breakdown of Anthropic’s recent IPO filing, and it’s full of amazing details — incredible user retention, a broadening base of customers, and so on.
And Anthropic is selling a product whose adoption rate is still very low. As Aaron Levie points out, most big companies are still spending only a pittance on AI, and could afford to spend a lot more:
And yet for this growth to continue, Anthropic must do more than simply make great products. It will need to stave off the competition for decades on end. It will, in Wall Street jargon, need a “moat”.
When I was teaching business economics to MBAs at Stony Brook, I spent lots of time on the question of why companies make profit. This might sound like a stupid question, especially if you’ve been conditioned to think that there’s some kind of magic force called “capitalism” that companies can use to extract money from people and hand it to their shareholders. But in a world of perfect competition, companies should keep trying to undersell each other until their profit goes to zero — or at least, until it goes to a level where companies are just able to pay for their investments. Even if one company has a huge share of the market, the simple threat that new companies might be able to enter the market and compete with them should force them to keep their prices low in order to maintain their market share.
And in fact, lots of industries do work like this. Agriculture, in the absence of farm subsidies or other significant government support, is the most famous example. Solar power is another — energy provides huge value to the world, but solar is so undifferentiated a product that solar producers never end up making big margins.2 Airlines are a third example. Consumers (and workers) benefit from these industries, but shareholders don’t end up making huge returns.
In order to make profits (or “excess profits” above and beyond the cost of investment), companies generally need some sort of a “moat” — a barrier to entry that reduces the threat of competition. This could be a government-sponsored monopoly — cronyism, or intellectual property. It could be a network effect — the natural tendency of everyone to be on the same social network or use the same electric power grid. It could be something as simple as brand loyalty — maybe everyone thinks Louis Vuitton bags are worth paying money for because of the name, or maybe everyone is just very used to using Microsoft Excel and doesn’t want to switch.
These are all reasons why some companies make high profit margins. There are probably more. Investors will tell you that if you want to assess how valuable you think a company is going to be, you should try to evaluate the strength of its moat — in fact, the term “moat” as slang for barriers to entry was coined by Warren Buffett.
Ever since AI companies like Anthropic and OpenAI burst on the scene, I’ve been hearing smart people argue that they have no moat. After all, unlike famously profitable software companies like Facebook or Google, it’s not clear that AI has a network effect — whether I should use Claude Code or Codex doesn’t really depend on whether you use Claude Code or Codex. And brand loyalty to AI is probably pretty low, since coding with Claude Code and coding with Codex is probably a pretty similar experience, and since AI isn’t a prestige good like an iPhone or a Rolex. And logistically speaking, it’s very easy to switch from one company’s AI products to another’s.
What’s more, catch-up in AI models is typically very fast — OpenAI and Google are typically only a little bit behind Anthropic in terms of model capability. In fact, by some measures, Google’s new Gemini 4 Carbon model just took the lead over Anthropic. Obviously this will reverse when Anthropic releases the next blockbuster model it currently has under wraps, but it shows that the other major players are still very much in the race. In terms of market share, OpenAI has been nipping at Anthropic’s heels:
I’ve written previously about the possibility that AI will end up like solar power, or airlines — a cheap utility that lots of companies use to create enormous economic value, but whose producers don’t manage to capture much of that value:
But I think we need to talk more about the opposite scenario — the possibility that AI intrinsically does have a huge moat. I see a distinct possibility that intelligence itself — the ability to outsmart competitors in the arena of business — is a tremendous source of competitive advantage for a company. And in fact, I suspect that this is exactly what Anthropic itself is (quietly) betting on.
2026-10-31 09:39:39
“That is the exploration that awaits you. Not mapping stars and studying nebulae, but charting the unknowable possibilities of existence.” — Q
There’s something about the mindset of really hardcore Star Trek fans that’s very difficult to describe to people who don’t share that fandom. I can’t speak for all of us, of course, but for many of us, there is a sense in which the world portrayed in Star Trek is not quite fictional. No one regards it as an exact description of the future, of course, but there is a sense in which it represents one possible future that we must achieve — a “Star Trek timeline” that either we’re on or that we need to return to. The United Federation of Planets isn’t some fantastical interstellar alliance, but an organization that we must create, and whose ideals we must instantiate as the governing ethos of humankind.
Our quasi-religious commitment to this utopian future came from watching the Star Trek TV shows, but the different series inspired us in different ways. The original “Kirk and Spock” series, which aired in the 1960s, was more of a technological vision — the idea that one day we would zip around the cosmos getting into adventures, as easily as we’d zip around the world in a boat today. But Star Trek: The Next Generation, which aired in the late 1980s and early 1990s, gave us a different kind of vision. It was a moral future for the human race — a depiction of a better way that we could face the unknown and master our own personal challenges.
There has never been another television show like Star Trek: The Next Generation, and there might never be one again. It is the greatest TV show ever made, not by dint of a difference in degree, but because of a difference in kind. It is a drama of pure optimism. Every other show tries to thrill us by showing us how bad or how ridiculous or how exciting someone else’s world could be; Star Trek: TNG thrills us by showing us how beautiful our own world could become.
For such a show to be put on the air required an incredible confluence of coincidences. For one thing, it was syndicated in its first run, meaning that it was free from constant interference by network executives. The showrunner,1 Michael Piller, also did something highly unusual when he opened the show up to outside script submissions; this allowed in a whole lot of highly innovative episode ideas.
But the real secret sauce of Star Trek: TNG was the creative vision of Star Trek’s original creator, Gene Roddenberry. A lot of people — and even a lot of science fiction writers — assume that even as technology makes us more powerful, human nature will remain basically the same, with all its old petty short-sighted hatreds and jealousies and selfishness and so on. Roddenberry believed otherwise. He famously declared that the people of the future would actually be better people, free from the flaws of his day. And so he mandated that Star Trek: TNG have no internal conflict in its plotlines.
Some of the writers chafed at that constraint. Interpersonal conflict is the bread and butter of drama — and always an easy plot device to fall back on when you need something to go wrong. But because Roddenberry decreed it, the writers of TNG were forced to envision how the ideal human being would deal with various personal and external obstacles. And so every episode gave us a thoughtful, probing exploration of what the ideal human beings — or at least, people much more morally and emotionally advanced than us — would look like.
No character embodied that ideal more than Captain Jean-Luc Picard. It’s not his personal life that’s ideal — he famously puts career over family, leading to a life full of self-doubt and regret — but his approach to the world. Picard is the ultimate Reasonable Man. He will always try to understand the alien before he opens fire. He is a legendary battle commander but would always rather negotiate than fight. He’s not necessarily the kind of man you’d want for a father, but he felt like one anyway. He values all life, and all uniqueness, intrinsically; he is zipping around the galaxy in a starship in order to discover the Universe, and occasionally to save it, but most of all to understand and respect it.
Understanding and respect, in fact, are Picard’s greatest weapons. He is a brilliant man, with many brilliant people under him, and occasionally he uses his intelligence to defeat an external threat. But he’s no swashbuckling space cowboy — more often than not, he succeeds in understanding the threat, and figures out how to help it achieve its goals without sacrificing his own. Picard realizes that the highest application of intelligence is not to thwart your enemies, but to design a world in which you and they can live in mutually beneficial coexistence. 1990s America was a uniquely hopeful and liberal time in the world, and Star Trek’s greatest captain embodied the spirit of that too-brief moment.
The character of Captain Picard was brought to life by the peerless acting skills of Patrick Stewart, who died two days ago at the age of 86. Stewart’s acting career went far beyond Star Trek — he’s widely considered one of the greatest Shakespearean stage actors of his generation, if not of all time, and he played in dozens of films. But the tributes that are flying thick and fast around the world today almost all focus on his role as Captain Picard.
I’m of two minds about this. As a very slightly famous person, I can tell you that public and private personas are very different things — if you meet me in real life, I won’t quite be the character you’ve imagined from my writings. That goes triple for actors, whose job is literally to pretend to be someone else. Patrick Stewart was not Captain Picard, but a man with a life and a personality all his own. He was, by all reports, a good man, well-loved by those who knew him and worked with him, but his strengths and his flaws were very different from those of the man he played on television. To mourn his passing as the passing of Captain Picard is to engage in a parasocial relationship.
And yet, I do. I can’t help it. I never knew Patrick Stewart, but I did know Captain Picard, and the death of the real man who brought that fictional man to life makes me remember what I loved so much about the latter, and there is value in that.
Nor am I alone. Today the internet is flooded with tributes to Picard, praising Stewart’s portrayal as an embodiment of an ideal liberal politics, an exemplary moral figure, and even as a model of masculinity:
The remembrance of Captain Picard comes at a pivotal moment for our species. The advent of artificial intelligence — machines that can out-think us — will naturally cause a shift in what it means to be human. As with the Industrial Revolution and the advent of agriculture, we will have to figure out a new way to live — new lifestyles, new forms of government, new systems of economic production and distribution, new reasons to value ourselves and each other.
There are many reasons that the particular future portrayed in Star Trek — one in which human beings, as they exist today, take the lead in physically exploring the galaxy — will never happen. AI is one more reason. This is in spite of the fact that Star Trek: The Next Generation is probably the single best prediction of modern AI ever to appear on the screen. The “engineers” on the Enterprise are clearly vibe-coding and prompting the ship’s computer,2 which is so powerful that it sometimes accidentally creates human-level intelligence as part of a holographic entertainment program.
But in real life, the physical requirements of deep space exploration are such that it will never make sense for human beings to be at the forefront, except potentially as digital uploads. And even if we did go, AI’s superhuman intelligence, data assimilation capacity, and speed would remove most or all of the need for human judgement in the loop. If the Romulans attack us, it will be Claude or GPT who handles them.
But outer space is not our true final frontier. The true space that we, as humans, are exploring is our own set of possibilities. In fact, Q, a whimsical omnipotent trickster-god-being that serves as Picard’s foil throughout the series, says this to Picard in the final episode of the show — see the quote at the top of this post. It is our quest to chart the unknowable possibilities of our own existence.
The ultimate destiny of humankind is a consistent theme of Star Trek: TNG, and the show strongly implies that we’ll eventually ascend to some sort of godlike posthumanity. There are multiple instances of humans or human-like beings turning into some sort of higher cosmic being, and Q himself implies that he’s testing humanity for admission into the realm of the space gods. This is probably where we’re headed in reality — our new AI scientists will help us figure out how to expand our consciousness and merge it with the digital realm, until we become something greater than we can currently imagine.
But before that happens — and perhaps even after it happens — it’s possible to envision a better version of Humanity Classic. It’s possible to imagine that through the right combination of technology, governance, and culture, we can put the lie to the old saw that “human nature never changes”, and become more like the characters in Gene Roddenberry’s universe.
In fact, though the overall picture is murky and the road has been stochastic, there is some evidence that we’ve already been moving in a more humane, rational direction — that Captain Picard is less alien to the human race of 2026 than he would have been to the human race of 1626. We once embraced slavery as a way of life; today we regard it as anathema. We once tortured cats to death in public for entertainment; today that would land you in prison. We are far less murderous than we once were.
It is not clear exactly what led to these improvements. Perhaps with scarcity alleviated — with our bellies full, and tractors instead of slaves — we are naturally gentler and more magnanimous. Perhaps more information has given us more empathy toward people far away. Perhaps the social adaptations needed to handle modern economies — markets, property rights, police, democracy — have tamed and civilized us.
Whatever it was, it offers the hope that we can do more of it — that whatever cosmic ascension we might eventually undertake, human beings as they exist in 2026 are not the best we can do in the current form factor.
I won’t lie. The last quarter century has been brutal to the hopes of those raised on Star Trek: The Next Generation. The promise of a liberal society has not been eliminated, but it has been tarnished by over a decade of social media hatred and rising authoritarianism. Neither greater prosperity nor more information nor giving voice to the voiceless has made our societies more like the United Federation of Planets. Sci-fi marvels are coming to life around us, but every day the people who lead our species seem less like the crew of the Enterprise.
But as the 1990s were not the end of history, neither are the 2020s. Moral progress is not a smooth upward line; there have been reversals before (and there certainly are plenty in the Star Trek timeline). It’s about time to pick ourselves back up and start the journey again. We should all go back and watch Star Trek: The Next Generation, with its silly haircuts and its dated special effects, and remember who we thought we could become.
Farewell to the great Patrick Stewart. And farewell also, Captain Picard. In the greatest trek of all — our search for the possibilities of our better selves — you will always be our Captain.
From the third season onward.
This was a lucky accident, since vibe-coding and prompting are easier to portray on screen than writing lines of code.
2026-10-29 17:55:51
It’s almost hard to remember, but just a few years ago, a lot of very smart people thought that science was in terminal decline. Respected researchers were writing papers with titles like “Are Ideas Getting Harder to Find?”, “Papers and patents are becoming less disruptive over time”, and “Slowed canonical progress in large fields of science”. In 1996, science writer John Horgan even published a book called The End Of Science: Facing The Limits Of Knowledge In The Twilight Of The Scientific Age.
The fundamental idea was that the Universe only has so many insights to discover, and that we’ve picked the low-hanging fruit. It was the idea that Joseph-Louis Lagrange expressed when he said that “we cannot find more than once a system of the world to establish.” As the big ideas and the simple ideas get mined out, we’re forced to throw ever more brains at the mysteries of nature just to keep scientific progress going at a fraction of its old pace:
It looked like science was hitting a wall. Then along came AI.
It’s far too early to tell how much AI will eventually speed up the growth of total factor productivity. But already we can see signs of a speedup in science. Anthropic Founder Jack Clark assembled some of the hopeful data points in a recent lecture:
Anthropic is accelerating its investment into drug discovery, and is already announcing some impressive preliminary results:
Claude also appears to be enabling rapid progress toward a treatment for food allergies. OpenAI, meanwhile, announced a possible breakthrough treatment for neurodegenerative disease.
Meanwhile, Google Cloud Chief Scientist Pushmeet Kohli has a list of recent scientific advances that have been enabled by its new Gemini model, including breakthroughs in nuclear fusion reactor design, rare disease research, and quantum computing:
But as cool as these stories sound, they’re just very minor, preliminary efforts. There are a number of reasons to think that AI is going to propel us into a golden age of scientific discovery. That age might not last forever — AI might still run into diminishing returns eventually. But the next few decades are probably going to be an absolutely amazing time for human science.
(Note: This post is written under the assumption that human civilization survives. A golden age of science could also easily turn into a golden age of bioterrorism, in which case all bets are off.)
The most important input into science is scientists themselves. Until this year, that meant human brains — talented, highly trained human brains. The number of trained human brains could be increased by sending more people to grad school, but only to a point — the smartest people tend to be sent into the hardest research fields first, and if you 10x the number of researchers, the average quality will go down. This constraint was already starting to bite, as Ekerdt and Wu argued in 2024:
We argue that a substantial portion of the downward historical trend in U.S. research productivity is a composition effect resulting from self-selection in researcher ability and the dramatic expansion of the research sector…[W]e find that this composition effect has lowered average researcher ability by between 37% and 53%[.]
And of course, low fertility was making the problem worse, because fewer children meant fewer people who were able to become future scientists.
AI just blows up this constraint completely. If an AI agent can function as an autonomous researcher, there’s no constraint on the number of researchers in the world — you can just build more of them, by building more computer chips and data centers that enable the running of more AI agents. Whether or not massive hordes of agents can collaborate to solve individual problems much faster, increasing the number of agents allows them to tackle many different research questions in parallel.
What’s more, these AI “researchers” can be trained incredibly quickly compared to human beings. A human scientist takes decades to train, and more years to reach the frontier of their research field by diligent reading and conversation. This is the “burden of knowledge”, and it’s commonly believed to be one reason ideas have been getting harder to find — by the time human scientists can get up to speed, they’re often so old that their ideas are no longer novel.
AI just annihilates this constraint — it can assimilate the entire literature and reach the frontier of research extremely quickly, and it can also tell human scientists where the frontier lies without forcing them to discover that for themselves. It can also pivot to new research directions more easily than humans can.
And there’s also a self-reinforcing economic cycle here. Because scientific advances enable higher GDP, they enable more resources to be invested into the creation of yet more electronic “scientists”.
Now that doesn’t mean the amount of scientific research goes to infinity — there may still be some essential bottleneck task that human researchers are still needed to perform. There are some indications that AI’s progress on open-ended conceptual research tasks is slower than in other dimensions, and it may be that humans are still needed to provide some of this long-term conceptual thinking. I’d bet against this being true for much longer, but it might be true for a long time.
Even in this most “pessimistic” case, though, the amount of effective capital that scientists can use to leverage their work just went up by a lot.
Science proceeds by an iterative process of theory and experiment. The theory part of that cycle is typically short, but non-trivial — it took a couple of years after the blackbody radiation spectrum was observed for Max Planck to come up with the quantum hypothesis to explain it, and decades longer before quantum theory as we now know it was created. It took about a year for Watson and Crick to figure out the shape of DNA after the requisite data was gathered.
AI will basically eliminate the theory bottleneck entirely — or at least shorten the turnaround time between experiment and theory by an order of magnitude. AI is an absolute beast at math. Recently, an OpenAI model solved some of the most important open problems in computer science, math, and theoretical physics, and no one even batted an eyebrow because that sort of triumph is becoming increasingly commonplace. One of those solutions was a proof that the Standard Model of Particle Physics could be mathematically well-defined — a problem that human theorists had so far been unable to tackle.
Not all research relies equally on theory, but understanding novel experimental results almost always involves some important component of either mathematical or spatial reasoning. That part of the task has now been made much, much faster. And on top of that, science is now open to a greater number of humans who aren’t naturally good at theorizing.
Much of the work of human scientists involves reading and understanding endless amounts of papers and going to endless conferences and seminars. The purpose of that work is to understand the existing scientific literature. Understanding the literature allows scientists to do the following things:
Come up with new ideas for hypotheses to test
Avoid doing research that someone else has already done
Synthesize approaches from different areas or fields
AI can reduce this effort to almost zero. Consider this excerpt from OpenAI’s blog post about their new research into neurodegenerative disease:
The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas…a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms.
The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms.
Some of these insights point to new leads for understanding brain disease. [emphasis mine]
This is one case in which parallel work by a large number of agents really does make a huge difference:
Not only does instantaneous literature review vastly speed up the research process, but it allows AI to make novel conceptual leaps from combining approaches from wildly different fields of research. AI regularly does this in math research, combining results, approaches, and theorems from far-flung places that no human mathematician would have thought to combine.
It can do it in science, too. Much of scientific innovation, historically, has been “recombinant” innovation — scientists applying insights from one field to another. With AI, essentially all research is now cross-disciplinary — AI can read not just the literature on a particular subject, but the literature on a million other subjects that might have produced useful techniques or insights for the problem at hand.
So far, all of the factors I’ve listed are ways that AI can speed up science as it’s already done. I’ve basically described how AI can send gigantic hordes of superhumanly fast, superhumanly well-read, superhumanly mathematically gifted researchers to re-accelerate scientific discovery.
But it’s also possible that AI will enable types of discovery — and ways of understanding the natural world — that no network of humans, no matter how large or how brilliant it was or how long it had to work, would ever be able to discover. In a post back in July, I wrote about how that might work:
What if there are lots of…aspects of the Universe that [are] too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook…
Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate…
Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?…
[H]uman collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation…
If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance.
In order to discover cloud laws, AI will need huge amounts of data about the natural world. AI figured out human speech by assimilating essentially all of the text humans have ever written. If it’s going to figure out hidden patterns in biology, neuroscience, materials science, etc., it’s going to need similarly massive amounts of data from physical instruments.
That data will be far, far more than humans — individually or collectively — would be able to parse, label, characterize, or model. But AI was built to do this. If scientists simply build huge arrays of sensors to measure complex phenomena like the brain, the cell, the economy, etc., they may be able to feed this flood of data to AI directly and bypass human cognitive intermediation entirely. AI would then learn about the world directly, and it might learn things that humans never could.
This “superscience”, if you want to call it that, would actually go beyond the kind of science humans have done heretofore. Many of the technologies it yielded would be beyond human comprehension — indistinguishable from magic. But to the AIs, they would make perfect scientific sense.
In any case, it seems clear that the scientific slowdown that threatened human progress will now be delayed — at least for a while. Unless humans have somehow found most of the actual exploitable regularities in our Universe, we’re about to enter a new golden age of science.
