2026-02-18 07:49:53
Unfortunately, I have another thing for you to worry about.
There are three types of environmental harm. The first kind is local — think air pollution and water pollution. This kind of activity hurts people who are geographically close by — when factories dump crap in the water, it’s local communities who get cancer, and so on. This kind of local pollution is typically solved by a local or national government, using things like regulation, pollution markets, and so on.
In fact, humanity has a pretty good track record when it comes to problems like this. The Environmental Kuznets Curve — the theory that countries pollute less as they get richer — seems to hold true for air and water pollution. As people escape poverty, they demand a cleaner local environment. For example, China used to be known for its toxic, unbreathable air, but in the 2010s it launched a successful cleanup policy:
The second kind of environmental harm — global harm — is a lot harder to deal with. These are things that mostly hurt people in other countries — global warming being the primary example. It’s very hard to solve global warming, because the worldwide nature of the harm means there’s a free rider problem (or, if you prefer, a coordination problem) — no country wants to pay the full cost of decarbonization, because most of the benefit goes to people in other countries. You can try international agreements, but everyone has an incentive to cheat.
Often, the best solution to these problems is technological — you simply invent something better and cheaper that doesn’t pollute as much, and then every country has an incentive to switch. Essentially, you use the positive externality of technology to fight the negative externality of pollution. This is what we did with HFC refrigerants, which replaced the CFCs that were destroying the ozone layer. It’s how we’re now fighting climate change with solar, batteries, and other green energy technologies.
But there’s a third kind of environmental harm, which is harm to the natural world. When pollution or logging or mining destroys natural habitats, it often doesn’t cause much harm to human beings — at least, not to those who are alive today. When coral reefs get bleached and die from industrial runoff, it might hurt tourism revenue a tiny bit, but overall humans don’t really get hurt. Animals and plants get hurt, but they have no voice in human politics. Future generations might regret not having coral reefs around, but they don’t exist yet, so they can’t complain.
Solving these harms seems like it probably requires some degree of altruism — either people caring about conservation for its own sake, or people who care a great deal about leaving a healthy natural world for their unborn descendants.
Altruism sounds like it won’t go far when matched against brute economic self-interest. But in recent years, I’ve become more optimistic that humans will care more intrinsically about preserving the natural world as they get richer. For example, people in North America, Europe, and East Asia all seem to care a lot about having forests:
This suggests that we won’t see a “race to the bottom” in terms of biodiversity loss, because the most powerful countries don’t seem to be the ones that chop down all their forests. Even Brazil, the worst offender in terms of sheer amount of forest cut down,1 has decreased the rate of Amazon deforestation by quite a lot since the early 2000s.
And that in turn hints at an even more important idea — that societies don’t trend toward greater rapaciousness as they become richer and more powerful. In his book The Better Angels of Our Nature, Steve Pinker theorized that people become more altruistic as they become more comfortable and secure; increasing global commitment to biodiversity seems to fit that theory. That might even be good news for the future of superintelligent AI — if rich nations stopped chopping down their forests, then maybe AI won’t kill the human race to use our resources for data centers.
Encouragingly, note the progress in China on the chart above. Some of this reforestation is motivated by the self-interested need to stop soil erosion and desertification, but China’s government has also increased its commitment to biodiversity. As another example of this, China banned fishing in the Yangtze River in 2021, in order to save fish stocks.
But there appear to be limits to China’s altruism here. Even as it took measures to prevent overfishing within its borders, China has continued to overfish much of the world’s oceans.
China’s fishing fleet just keeps getting bigger and bigger. This is from a 2025 report from the environmental group Oceana:
Oceana released an analysis of China’s global fishing* activity worldwide between 2022 and 2024. The analysis shows China’s global fishing footprint, in which 57,000 of their industrial fishing vessels dominated 44% of the world’s visible fishing activity during this period…Chinese vessels accounted for 30% of all fishing activity on the high seas, appearing to fish for more than 8.3 million hours.
In terms of catching wild fish, it’s basically China and Latin America dominating everyone else:
Much of this fishing activity is either outright illegal — meaning Chinese vessels fish in other countries’ waters in violation of their local laws or regional agreements — or unreported. In addition to simply violating laws with impunity, Chinese fleets use a large variety of tricks to get around regulations meant to keep them from overfishing — turning off their transponders, falsifying records, using foreign front companies, and so on. A lot of this fishing activity isn’t just to fuel China’s own increasing fish consumption — it’s an export industry. Here’s a detailed report from the Outlaw Ocean Project. Some key excerpts:
The size and behavior of the Chinese fishing fleet raises concerns…The Chinese government and western seafood companies often dismiss illegality in the fishing industry as an isolated problem. But [our] investigation revealed a wide pattern: Almost half of the Chinese squid fleet, 357 of the 751 ships studied, were tied to human-rights or environmental violations…
More than 100 Chinese squid ships were found to have fished illegally, including by targeting protected species, operating without a license, and dumping excess fish into the sea. The investigation revealed other environmental or fishing-specific crimes and risk indicators, including Chinese ships illegally entering the waters of other countries, disabling locational transponders in violation of Chinese law…transmitting dual identities (or “spoofing”)…fishing without a license, and using prohibited fishing gear. But the most common environmental violation involved Chinese ships poaching fish from other countries’ waters…
About 80 percent of seafood consumed in the U.S. is caught or processed abroad, with China as its biggest supplier.
Poor countries in Latin America and Africa don’t have the state capacity or economic leverage to enforce their laws. As a result, their waters are crammed with vast fleets of Chinese fishing boats:
Why is Chinese overfishing bad? Obviously it hurts fishermen in poor countries by taking away their fish. But in addition, it hurts biodiversity and robs future generations of fish. Here’s a good primer from Our World in Data that shows what you would do if you cared mainly about biodiversity, versus what you would do if you cared mainly about sustainability:
The key fact here is that whether you care more about the natural world or whether you care more about future humans being able to eat fish, the world is catching too many fish. An increasing percent of the world’s fisheries are now overexploited:
China’s lack of concern for sustainability plays a large part in this. Chinese fishing vessels are more likely to use various techniques that make them catch more juvenile fish. One of these is bottom-trawling, which drags nets along the seabed. Japan and the U.S. have largely given up on this practice; China has long been the world’s worst offender.
In previous decades, environmental organizations like Greenpeace sounded the alarm over Chinese overfishing. In recent years, with a few commendable exceptions like Sea Shepherd, they have mostly gone quiet. This is unfortunately consistent with the idea that legacy environmental groups are generally drifting from universal values of environmental protection toward a more explicitly leftist stance that focuses exclusively on critiquing the West and ignores environmental abuses by non-Western countries. (You can also see this in climate groups’ stubborn refusal to criticize China, which is by far the world’s worst climate polluter.)
In other words, geopolitics is starting to intrude into environmental debates. Most of the alarms now being sounded about Chinese overfishing come from “China hawks” rather than from environmentalists. And geopolitics is probably a big part of the reason China hasn’t cracked down on its global overfishing practices.
Traditionally, a lot of China’s overfishing has been due to massive subsidies that the Chinese government gives to the industry, mostly in the form of cheap fuel and other support. In the late 2010s, China began curbing those subsidies a bit. But as Ian Urbina reported back in 2020, these efforts have been pretty slow and minor when it comes to international waters, and geopolitics is probably a big reason:
[M]ore than seafood is at stake in the present size and ambition of China’s fishing fleet. Against the backdrop of China’s larger geo-political aspirations, the country’s commercial fishermen often serve as de-facto paramilitary personnel whose activities the Chinese government can frame as private actions. Under a civilian guise, this ostensibly private armada helps assert territorial domination, especially pushing back fishermen or governments that challenge China’s sovereignty claims that encompass nearly all of the South China Sea.
“What China is doing is putting both hands behind its back and using its big belly to push you out, to dare you to hit first,” said Huang Jing, former director of the Center on Asia and Globalization at the Lee Kuan Yew School of Public Policy in Singapore.
Chinese fishing boats are notoriously aggressive and often shadowed, even on the high seas or in other countries’ national waters, by armed Chinese Coast Guard vessels…From the waters of North Korea to Mexico to Indonesia, incursions by Chinese fishing ships are becoming more frequent, brazen and aggressive.
In other words, China’s government is becoming increasingly concerned about biodiversity and sustainability for its own sake, and this has resulted in more sustainable fishing practices in China’s own waters. But at the same time, China is using its vast international fishing fleet as a sort of naval militia to press its claims on other countries’ waters. And this is having collateral damage on the natural world — China’s quasi-military subsidies for its fishing fleet are resulting in too much actual fishing taking place.
In one sense, this is actually kind of optimistic. The fact that China is overfishing international waters for military and geopolitical reasons, rather than out of pure economic rapacity, suggests that the Chinese are not an exception to the rule that richer societies start to care more about sustainability — and, perhaps, about the intrinsic value of the natural world as well.
But in the meantime, the bad news is that China’s decision to maintain its fishing fleet as a naval militia means that the world’s oceans are being despoiled and drained of wildlife. That’s not good, and I wish that more environmentalists would pay attention to the problem. As power and wealth shift away from the West, the environmental movement risks making itself irrelevant if it continues its recent practice of letting countries like China off the hook.
Mostly to make room for cattle ranches.
2026-02-16 10:14:52
So the other day I wrote a post about how humanity is inevitably going to be disempowered by the existence of AI:
A bunch of people wrote to me and asked me: “What made you change your mind?”. Three years ago, shortly after the release of the original ChatGPT, I wrote a post about how LLMs are not going to destroy the human race:
And just a couple of months ago, I wrote a post arguing that ASI (artificial superintelligence) is likely to peacefully coexist with humanity rather than kill us off.
People wanted to know why my tone had shifted from optimistic to pessimistic.
Well, the simple answer to that is “I was in a worse mood.” My rabbit was sick,1 so I was kind of grumpy, and so in my post a few days ago I painted the eventual disempowerment of humanity as more of a negative thing than I usually do. In fact, I’ve always believed that at some point, humanity would be replaced with something posthuman — godlike AIs, a hive mind, modified humans, or whatever. I grew up reading science fiction about that kind of thing — Vernor Vinge, Charles Stross, Arthur C. Clarke, Iain M. Banks, and so on — and it just always seemed impossible that humanity had already attained the theoretical pinnacle of intelligence.2 I had always simply envisioned that whatever came after us would be in the general human family, and would be more likely to be on our side than against us.
That’s what my post the other day was about. I painted a more glum picture of humanity’s eventual supersession because I was in a bad mood. But even in that post, at the end, I offered optimism that ASI will save us from things like low fertility, fascist overlords, and the end of human-driven scientific discovery. That optimistic future would be like the Culture novels, by Iain M. Banks, in which AIs take the reins of civilization but in which they respect and help and protect a now-mostly-useless humanity — basically a much nicer, more enlightened version of the way the United States of America treats Native Americans nowadays. It’s a wistful future, and in some ways a sad one, but not particularly terrifying.
BUT, at the same time, I have gotten a lot more worried about existential, catastrophic AI risk — the kind of thing that would kill us instead of just rendering us comfortably impotent — than I was three years ago. And so the people who wrote to ask me why my tone had shifted deserve a longer explanation about why I’m more worried.
In my post three years ago, I argued that LLMs were not yet the kind of AI that could threaten the human race. I think I was probably right regarding the type of LLMs that existed in early 2023, for the reasons I laid out in that post. In a nutshell, I argued that since all LLMs could do was talk to people, the only way they could destroy the human race was by convincing us to destroy ourselves (unlikely) or by teaching us how to destroy ourselves (for example, by educating bioterrorists about how to make bioweapons).
In my defense, this is not too different from the scenario that Eliezer Yudkowsky — who literally wrote the book on existential AI risk — envisioned in 2022. He wrote:
My lower-bound model of “how a sufficiently powerful intelligence would kill everyone, if it didn’t want to not do that” is that it gets access to the Internet, emails some DNA sequences to any of the many many online firms that will take a DNA sequence in the email and ship you back proteins, and bribes/persuades some human who has no idea they’re dealing with an AGI to mix proteins in a beaker, which then form a first-stage nanofactory which can build the actual nanomachinery.
This is about AI teaching people how to make self-replicating nanomachinery instead of a doomsday virus. But honestly I feel like the doomsday virus would be easier to make. So I don’t think my scenario was too far behind the thinking of the most vocal and panicky AI safety people back in 2023.
Anyway, if I had said “chatbots” instead of “LLMs” in my 2023 post, I think I still would have been correct, because a chatbot is a type of user interface, while an LLM is an underlying technology that can be used to do much more than make a chatbot. What I missed was that LLMs can do a lot more than just talk to people — they can write code, because code is just a language, and it’s not too hard to get them to do this in an automated, end-to-end, agentic fashion.
In other words, I didn’t envision the advent of vibe-coding. And I probably should have. To be fair, the advent of vibe-coding required some big technological advances3 that didn’t exist in early 2023. But missing the fact that computer code is just a language that can be learned like any other — and that in fact it’s easier to learn, since you can verify when it works and when it doesn’t work — was a big miss for me. And it opens up the door to a LOT of other scary scenarios, beyond “A chatbot helps humans to do something bad”.
So anyway, let’s talk about what I’m scared about now. But first, let’s talk about what I’m less scared about, at least for the moment.
The scenario that everyone tends to think about is one in which a fully autonomous ASI decides that human civilization is an impediment to its use of natural resources, and that we need to be exterminated, enslaved, or otherwise radically disempowered in order to turn the world into data centers. This is basically the plot of the Terminator movies,4 the Matrix movies, and various other “rise of the robots” stories.
Conceptually speaking it’s easy to envision an AI that’s advanced enough to carry this out. It would have full control over an entirely automated chain of AI production, including:
Mining, refining, and processing of minerals
Fabrication of chips and construction of data centers
Manufacturing of robots
Controlling this entire chain would give AI control over its own reproduction — the way humans have always had control over our own reproduction. It could then safely dispense with humanity without endangering its own future.
This is basically a very direct analogy to what European settlers did to Native American civilization, or what various other waves of human conquerors and colonizers do to other groups of humans.
I think this scenario is worth worrying about, but it’s not immediate. Right now, robotics is still fairly rudimentary — things are advancing, but AI will need humans as its agents in the physical world for years to come. Furthermore, AI will need some algorithmic changes before it can permanently “survive” on its own without humans — long memory, for one. I’m not saying these won’t happen, but at least we have some time to think about how to prevent the “rise of the robots” scenario. I do think we should have some people (and AI) thinking about how to harden our society against that sort of attack.
It seems likely that AI will eventually get smart enough to think its way around whatever physical safeguards we put in place against the rise of the robots. But as I wrote two months ago, I think an AI advanced enough to fully control the physical world would have already reached the stage where it understands that peaceful coexistence and positive-sum interaction is a better long-term bet than genocide. Smarter humans and richer human societies both tend to be more peaceful, and I sort of expect the same from smarter AI.
So I think there are other worries to prioritize here.
In my post three years ago, I tried to list the ways that LLMs might eventually destroy us:
Here’s a list of ways the human race could die within a relatively short time frame:
Nuclear war
Bioweapons
Other catastrophic WMDs (asteroid strike, etc.)
Mass suicide
Extermination by robots
Major environmental catastrophe
The advent of vibe-coding has made me think of another way our civilization could be destroyed, which I probably should have thought of at the time: starvation.
Every piece of agricultural machinery in the developed world, more or less, runs on software now — every tractor, every harvester, every piece of food processing machinery. That software was mostly written by human hands, but in a fairly short period of time, it will all be vibe-coded by AI.
At that point, AI would, in principle, have the ability to bring down human civilization simply by making agricultural software stop working. It could push malicious updates, or hack in and take over, or wipe the software, etc. Agricultural machines would stop working, and in a few weeks the entire human population would begin to starve. Civilization would fall soon afterwards.
I really should have thought of this scenario when I wrote my post in 2023, because it’s the plot of a very famous science fiction story from 1909: “The Machine Stops”, by E.M. Forster. In this story, humanity lives in separate rooms, communicating with each other only electronically,5 cared for entirely by a vast AI; when the AI stops working, most of humanity starves.
This could happen to us soon. Now that vibe-coding is many times as productive as human coding, it’s very possible that a lot fewer people will get good at coding. Even the tools that exist right now might be eroding humans’ skills at working with code. This is from a recent Anthropic study:
AI creates a potential tension: as coding grows more automated and speeds up work, humans will still need the skills to catch errors, guide output, and ultimately provide oversight for AI deployed in high-stakes environments. Does AI provide a shortcut to both skill development and increased efficiency? Or do productivity increases from AI assistance undermine skill development?
In a randomized controlled trial, we examined 1) how quickly software developers picked up a new skill (in this case, a Python library) with and without AI assistance; and 2) whether using AI made them less likely to understand the code they’d just written.
We found that using AI assistance led to a statistically significant decrease in mastery. On a quiz that covered concepts they’d used just a few minutes before, participants in the AI group scored 17% lower than those who coded by hand, or the equivalent of nearly two letter grades.
Meanwhile, Harry Law wrote a good post called “The Last Temptation of Claude”, about how the ease of vibe-coding is making him mentally lazier. There are many other such posts going around.
As vibe-coding becomes even better and eliminates humans entirely from the loop, the need for human software skills will presumably atrophy further. Ten years from now, if the software that runs our agricultural machinery just stops working for some reason, there’s a good chance there will not be enough human coders around to get it working again.
This would simply be a special case of a well-known problem — overoptimization creating fragility. When Covid hit in 2020, we found out that our just-in-time supply chains had been so over-engineered for efficiency that they lacked robustness. Vibe-coding could lead to a much worse version of the same problem.
That said, AI going on a catastrophic strike isn’t at the top of my list of fears. The reason is that I expect AI to be very fragmented; so far, no AI company seems to have any kind of monopoly, even for a short period of time. If the AI that writes the code for harvesters and tractors suddenly goes rogue, it seems like there’s a good chance that humans can call in another AI to fix it.
I guess it’s possible that all the AIs will collude so that none of them will help humans survive, or that the rogue AI(s) will be able to maliciously lock humans out from applying non-rogue AI to fix the problem. So people should be thinking about how to harden our agricultural system against software disruption. But it’s also not at the top of my list of doomsday worries.
OK, so what is at the top of my list of doomsday worries? It’s still AI bioterrorism.
Hunting down and killing humans with an army of robots would be fairly hard. Depriving humans of food so that we starve to death would be easier, but still a little bit hard. But slaughtering humans with a suite of genetically engineered viruses would not actually be very hard. As we saw in 2020, humans are very vulnerable to novel viruses.
Imagine the following scenario. In the near future, virology research is basically automated. Labs are robotic, and AI designs viruses in simulation before they’re created in labs. For whatever personal reasons, a human terrorist wants to destroy the human race. Using techniques he reads about on the internet, he jailbreaks a near-cutting-edge AI in order to remove all safeguards. He then prompts this AI to vibe-code a simulation that can design 100 superviruses. Each supervirus is 10x as contagious as Covid, has a 90% fatality rate, and has a long initial asymptomatic period so it’ll spread far and wide before it starts killing its victims. He then prompts his AI to vibe-code a program to hack into every virology lab on the planet and produce these 100 viruses, then release them into the human population.
If successful, this would quickly lead to the end of human civilization, and quite possibly to the extinction of the entire human species.
Is it possible? I don’t know. But developments seem to be moving in the direction of making it possible. For example, bio labs are becoming more automated all the time:
And AI algorithms are rapidly getting better at simulating things like proteins:
“Virtual labs” powered by “AI scientists” are becoming commonplace in the world of bio. And there is plenty of fear about how AI-powered laboratories might be used to create superviruses. Here’s a story that ran in Time magazine almost a year ago:
A new study claims that AI models like ChatGPT and Claude now outperform PhD-level virologists in problem-solving in wet labs, where scientists analyze chemicals and biological material. This discovery is a double-edged sword, experts say. Ultra-smart AI models could help researchers prevent the spread of infectious diseases. But non-experts could also weaponize the models to create deadly bioweapons.
The study, shared exclusively with TIME, was conducted by researchers at the Center for AI Safety, MIT’s Media Lab, the Brazilian university UFABC, and the pandemic prevention nonprofit SecureBio. The authors consulted virologists to create an extremely difficult practical test which measured the ability to troubleshoot complex lab procedures and protocols. While PhD-level virologists scored an average of 22.1% in their declared areas of expertise, OpenAI’s o3 reached 43.8% accuracy. Google’s Gemini 2.5 Pro scored 37.6%.
I am not a biology expert, and I plan to go ask more of them about this worry (as well as having AI educate me more). I asked GPT-5.2 what it thought about this risk, and here are some excerpts from what it wrote:6
[A]utomation can increase throughput and reduce expertise needed, which is directionally risk-increasing. But it doesn’t magically eliminate the underlying biological constraints…
[AI safety] guardrails can be bypassed sometimes. Also, you don’t necessarily need a frontier model to be dangerous if you have access to domain tools, leaked data, or insider capability…
A more realistic worry is a small number (1–a few) of engineered or selected agents that are “good enough” (highly transmissible and significantly more lethal than typical pandemics)…
AI accelerates, but it doesn’t replace the need for experimental validation [of new viruses] —yet…
If an attacker can truly create one pathogen that is (a) highly transmissible, (b) substantially more lethal than typical pandemics, and (c) hard to contain early, then you already have global-catastrophe potential…A single “good enough” pathogen, combined with poor detection and slow countermeasures, can be catastrophic.
Probability of “one compromised lab enables a catastrophic engineered outbreak”: still low, but not negligible, and plausibly higher than many other X-risk stories because it has fewer required miracles.
Probability of “human extinction via this route”: lower than “catastrophe/collapse,” but not zero; it remains deep tail risk.
GPT’s recommendations all included maintaining humans in the loop of biology research. But after what we’ve seen with vibe-coding over the past few months, how confident can we be that labs all across the world — including in China — will insist on maintaining humans in the loop, when full automation would speed up productivity and improve competitiveness? I can’t say I’m incredibly optimistic here.
So the advent of vibe-coding has significantly increased my own worries about truly catastrophic AI risk. It seems clear now that brute economic forces will push humanity in the direction of taking humans out of the loop anywhere they can be taken out. And in any domain where data is plentiful, outputs can be verified, and there are no physical bottlenecks, it seems likely that keeping humans in the loop will eventually prove un-economical.
Really, this boils down to another example of overoptimization creating fragility. But it’s an especially extreme and catastrophic one. I don’t think humanity is doomed, but I don’t see many signs that our governments and other systems are yet taking the threat of vibe-coded superviruses as seriously as they ought to be. Not even close.
So if you ask me if my worries about AI risk have shifted materially in recent months, the answer is “Yes.” I still think Skynet or Agent Smith is highly unlikely to appear and exterminate humanity with an army of robots in the near future. But I will admit that the thought of vibe-coded superviruses is now keeping me up at night.
He’s better now!
In fact, if we had been the smartest possible creatures in the Universe, that itself would be a pretty glum future.
From what I can tell, the most important such advance was verifier-based reinforcement learning that enabled test-time compute scaling…
Well, sort of. In the Terminator movies, Skynet is a military AI who sees humans as a military threat.
It’s pretty wild that a contemporary of H.G. Wells could have envisioned both AI and modern social media.
Encouragingly, it stopped answering my questions pretty quickly, because this topic hit the guardrails.
2026-02-15 15:12:13
AI is changing the world very quickly right now, having just radically altered the entire software industry just in the last few months. It’s a time of dizzying technological change, and it’s easy to feel a lot of future shock right now.
So I thought I’d repost something I wrote back in 2023, when LLMs were just starting to have a big effect on the world. Reflecting on the changes in my lifetime, I realized that the internet, social media, smartphones, and other digital technologies had already altered the world of my childhood into something almost totally unrecognizable. AI is changing how we think, learn, and work, but the internet already wreaked deep, lasting, confusing changes on how we socialize with each other and how we present ourselves to the world. Humans are fundamentally social creatures, so to be honest I’m not sure which has been the more wrenching change (though of course AI is just getting started).
Anyway, here’s the original post.
In 1970, Alvin Toffler published Future Shock, a book claiming that modern people feel overwhelmed by the pace of technological change and the social changes that result. I’m starting to think that we ward off future shock by minimizing the scale and extent of the changes we experience in our life. I tend to barely notice the differences in my world from year to year, and when I do notice them they generally seem small enough to be fun and exciting rather than vast and overwhelming. Only when I look back on the long sweep of decades does it stun my just how much my world fails to resemble the one I grew up in.
Back in March, Tyler Cowen wrote a widely read (and very good) piece about the rapid progress in generative AI. I agree that AI will change the world, usually in ways we’ve barely thought of yet. And I love Tyler’s conclusion that we should embrace the change and ride the wave instead of fearing it and trying to hold it back. But I do disagree when Tyler says we haven’t already been living in a world of radical change:
For my entire life, and a bit more, there have been two essential features of the basic landscape:
1. American hegemony over much of the world, and relative physical safety for Americans.
2. An absence of truly radical technological change…
In other words, virtually all of us have been living in a bubble “outside of history.”…Hardly anyone you know, including yourself, is prepared to live in actual “moving” history.
Paul Krugman made a similar case back in 2011, using the example of how few appliances in his kitchen had changed in recent decades:
The 1957 owners [of my kitchen] didn’t have a microwave, and we have gone from black and white broadcasts of Sid Caesar to off-color humor on The Comedy Channel, but basically they lived pretty much the way we do. Now turn the clock back another 39 years, to 1918 — and you are in a world in which a horse-drawn wagon delivered blocks of ice to your icebox, a world not only without TV but without mass media of any kind (regularly scheduled radio entertainment began only in 1920). And of course back in 1918 nearly half of Americans still lived on farms, most without electricity and many without running water. By any reasonable standard, the change in how America lived between 1918 and 1957 was immensely greater than the change between 1957 and the present.
But when I look back on the world I lived in when I was a kid in 1990, it absolutely stuns me how different things are now. The technological changes I’ve already lived through may not have changed what my kitchen looks like, but they have radically altered both my life and the society around me. Almost all of these changes came from information technology — computers, the internet, social media, and smartphones.
Here are a few examples.
If you went back to 1973 and made a cheesy low-budget sci-fi film about a future where humans sit around looking at little glowing hand-held screens, it might have become a cult classic among hippie Baby Boomers. Fast forward half a century, and this is the reality I live in. When I go out to dinner with friends or hang out at their houses, they are often looking at their phones instead of interacting with anything in the physical world around them.
Nor is this just the people I hang out with. Just between 2008 and 2018, American adults’ daily time spent on social media more than doubled, to over 6 hours a day.
About a third of the populace is online “almost constantly”.
All this screen time doesn’t necessarily show up in the productivity statistics — in fact, it might lower measured productivity, by inducing people to goof off more on their phones during work hours. But the reorientation of human life away from the physical world and toward a digital world of our own creation represents a real and massive change in the world nonetheless. To some extent, we already live in virtual reality.
The shift of human life from offline to online has profound implications for how we interact with each other. One example is how couples meet in the modern day. Dating apps have taken over from friends and work as the main ways that people meet romantic partners:
The reorientation of social relationships to the online world is what makes the digital revolution different than the advent of television. TV involves staring at a screen for long periods of time, but it doesn’t let people talk to each other and form social bonds. Arguably, talking to each other and forming social bonds is the most important thing we do in our entire lives — personal relationships are the single biggest determinant of happiness. For almost all of human history, even in the age of telephones, our relationships were governed by physical proximity — who lived near us, worked with us, or could meet us in real life. That is suddenly no longer true.
What broader effects this will have on our society, of course, remains to be seen. One of my hypotheses is that online interaction will encourage people to identify with “vertical” communities — physically distant people who share their identities and interests — rather than the physical communities around them. This could obviously have profoundly disruptive impacts on cities and even nations, which are organized around contiguous physical territory. Perhaps this is already happening, and some of our modern political and social strife is due to the fact that we no longer have to get along with our neighbors in order to have a rich social life.
Three decades ago, not getting lost was one of the most important goals of human labor and human social organization. Over the millennia we developed whole systems of landmarks, maps, directions, road names, and even social relations in order to make sure that we always knew how to find our way back to security and shelter. The possibility of getting lost was an ever-present worry for anyone who drove their car, or walked in the woods, or took a vacation to a strange place.
And now that foundational human experience is just…gone. Unless your phone runs out of battery or you’re in a very remote wilderness area, GPS and Google Maps will always be able to guide you home. Much of our physical and social wayfinding infrastructure, built up over so many centuries, has instantly been obviated — you don’t have to plan your route or ask for directions, you don’t have to keep close track of landmarks as you drive or walk, you don’t even have to remember the names of roads. The forest has lost its terror.
Of course, there was another kind of fake “getting lost” that could be quite fun, and that’s gone too. It was exhilarating to wander around in a foreign city not knowing what was around you, hunting for restaurants and shops and new neighborhoods to explore. You can still do that if you want, but it’s just imposing unnecessary difficulty for fun — you can just open Google Maps and find the nearest cafe or clothing store or museum or historical monument.
And there’s also a big and important flip side to the fact that no one gets lost anymore — as long as you have your smartphone with you, someone, somewhere, is always able to know where you are. Your location is tracked, wherever you go, even if Apple or Google is nice and respectful enough not to let humans look at that data. China, of course, has far less concern for privacy. But even where privacy rights are respected, governments and corporations are still capable of easily tracking your every move if they feel like it.
I still remember a moment in 2003 when I idly wondered what the Matterhorn looked like. In 1990, answering my curiosity would have required that I go open an encyclopedia, or — if my encyclopedia didn’t have a photo of that particular mountain — to go through the library searching books for a picture. But in 2003, I just typed “Matterhorn” into Google image search, and the picture appeared.
Over the last two decades, there has been a massive proliferation of tools that convey enormous amounts of knowledge, on demand, to anyone who wants it. Wikipedia can teach you the basics of anything from math to history to geography. YouTube tutorials can show you how to fix things in your house, ride a jet ski, or cook a restaurant-quality meal. Google can tell you what anything looks like, or point you to any scientific paper on any topic. And they can give you all this knowledge on demand, from the little screen that you carry with you everywhere, whenever you want it.
This has changed the nature of human life. Just a few decades ago, the knowledge contained in human heads was of utmost importance. If your cabinet was loose or your drain was clogged, you had to know a human being who could fix it. If you wanted to know interesting facts about the world, you had to either ask a human being who knew those facts, or go on an exhaustive search. Wisdom and know-how were profoundly valuable personal attributes. Now they’re much less of a reason for distinction.
Now it’s important to note that understanding and practiced skill are still scarce commodities. YouTube can’t teach you how to be a great violinist (at least, not in 30 minutes), and Wikipedia can’t give you the ability to do difficult math proofs. And the knowledge that can be gained from direct experience is often superior in quality to the knowledge gained from a Google search — for example, if I actually go to the Matterhorn, I can see it from a variety of angles and in a variety of lighting. But overall, humans have taken much of the knowledge that they used to have to carry around in their heads and uploaded it to what is, in effect, a single unified exocortex.
But if ignorance (or at least, the accidental kind, rather than the willful kind) has diminished, mystery has also shrunk. Exploring remote locations, rare objects, and esoteric knowledge is no longer difficult enough to generate quite the same sense of adventure and wonder it once did. Just as GPS has taken some of the adventure out of visiting strange places, the vast sea of internet knowledge has made many other forms of exploration quotidian.
And there’s another kind of mystery that the internet has either eliminated or vastly reduced — the mystery of not knowing other people. In 1990, if I wanted to know what Indians thought about American politics, I’d just have to wonder. Now, I can just open Twitter and ask, and a bunch of Indian people will be happy to inform me of their views. In 1990, talking to someone from another country was a rare and exotic treat; now, it’s just something that we do every day without even really thinking of it. That’s the first time that has ever happened in the history of humankind.
The internet doesn’t just find things for us or allow us to communicate; it also stores information in much larger volumes than books, TV shows, or any other medium that came before. Practically everything you’ve ever typed on the internet is still on the internet. As recently as a few decades ago, most of the things you said and did would be forgotten and misremembered after a fairly short time; now they’re frozen in silicon and magnets.
This has some obvious major consequences for the shape of human life. When you can be “canceled” by an online mob as a 35-year-old for something you said as a teenager, that requires people to be more on guard about what they say for their entire life, even as kids. When any prospective business partner or lover can Google you and find your background (unless you erase it, in which case the prospective partner will be justifiably suspicious), the ability to craft a new persona for yourself, and move beyond the baggage of the past, is limited. On the other hand, remembering what you were like at a younger age, or an argument that you made in a debate a few years ago, is now quite easy. And it’s a lot easier to keep in touch with old friends now.
Technology’s memory involves images and video as well, thanks to the explosion of digital cameras and the increasing capacity of hard drives. Many of the memories we want to preserve in life — our interactions with our offline friends, the places we traveled, the places we lived — now get stored on a hard drive.
A lot of economists tend to think of technological change as being embodied in total factor productivity growth, which has slowed down since 1973 or so. But first of all, there are plenty of things that go into TFP growth besides what we typically think of as technology — there’s education, geographic mobility, a demographic dividend, and so on. As the economist Dietz Vollrath has shown, these factors can explain the entire productivity slowdown, with no need to appeal to a slowing rate of technological progress.
But even more fundamentally, technology changes the world in ways not directly captured by the monetary value of goods and services sold in the market. If our daily activities are redirected toward different sorts of relationships and interactions, that isn’t necessarily something that we’d pay a lot of money for — and yet it means human life is now an entirely different sort of endeavor. If we’re constantly surveilled by corporations and the government, that’s probably something we don’t want, and thus will not pay extra money for, even if rebelling against it would be too much of a hassle for most. And so on.
Sometimes technology grows the economy, but more fundamentally, it always weirds the world. By that I mean that technology changes the nature of what humans do and how we live, so that people living decades ago would think our modern lives bizarre, even if we find them perfectly normal. The information technology boom may not have goosed the productivity numbers as much as many hoped, but it has nevertheless left a deep and transformative impact on the shape of human life.
I’m excited to see if AI brings us a world of radical technology-driven change. But you and I have already been living in a world of radical change for decades now. Maybe the tendency to believe progress has slowed — to focus on the stagnation in our kitchens and not the fact that the world has suddenly become far more transparent, comprehensible, and recorded — is a way of avoiding future shock. But man, when I think of 1990, I can’t help but feel a little overwhelmed by how far we’ve come.
2026-02-13 17:15:24
“He comes like a day that has passed, and night enters our future with him.” — Charlo
Yesterday my pet rabbit bit my finger. It was an accident; he was trying to bite a towel to move it out of his way, and I accidentally stuck my hand in his mouth. He is a gentle beast, and would never bite a human intentionally. Anyway, the bite punctured and lacerated my left index finger near the front knuckle. I washed it out, put some ointment and a band-aid on it, and that was that.
It occurs to me that if my pet rabbit had instead been a tiger, I would now be dead. There is a reason most people don’t keep tigers as pets; they may be fluffy and cute, but they’re big and strong and can easily kill you. Instead, we generally keep pets who are smaller and weaker than us, allowing us to train them, and if necessary to physically restrain them, and minimizing the danger to our own health.
Until now, we haven’t had to think about this principle in the context of intelligence. As long as you or I or anyone we know has been alive — for all of recorded history, and in fact for much much longer than that — humankind has been the most intelligent thing on this planet.
At some point in the next couple of years, that will no longer be true. It arguably is no longer true right now. There is no single unarguable measure of intelligence — it’s not like distance or time. AI doesn’t think in the same way humans do. But it can get gold medals on the International Math Olympiad, solve difficult outstanding math problems all on its own, and get A’s in graduate school classes. Most human beings can’t do any of that.
Intelligence is as intelligence does. If it helps you feel unique and special to sit there and tell yourself “AI can’t think!”, then go ahead. And sure, AI doesn’t think exactly the way you do. It probably never will, in the same sense that a submarine will never paddle its fins and an airplane will never flap its wings. But a submarine can go faster than any fish, and an airplane can fly higher and faster than any bird, so it doesn’t matter. You can value your own unique human way of thinking all you like — and I agree, it’s pretty special and cool — but that doesn’t make it more effective than AI.
Right now, there are some cognitive things that humans still do better than AI, but that will probably not last. The entire might of the world’s technological innovation system is now being thrown into making AI better, and there is no sign of a slowdown in progress. One of the main things AI couldn’t do until recently was to work on a task for a long period of time. That’s changing fast. AI models are flying up the METR curve,1 which tries to measure the length of time a human would require to complete a software engineering task that AIs can do:
This is what’s behind all the “vibe coding” you’re hearing about. AI agents — basically, a program that keeps applying AI over and over until a task is complete — are now taking over much of software engineering. People just tell the AI what kind of software they want, and the AI pops it out. Human software engineers are still checking the code for problems, but as the technology improves, the cost of doing this is likely to become uneconomical; AI-written software will never be perfect, but it’ll be consistently much better than anything humans could do, and at a tiny fraction of the price.
Vibe coding is taking over fast. Spotify’s co-CEO recently revealed that the company’s best developers don’t write code anymore. Some journalists from CNBC, with no coding experience, vibe-coded a clone of the app Monday, and the company’s stock price promptly crashed. Meanwhile, AI is increasingly writing the next version of itself, and humans may not be in the loop for very much longer.
And all of this — ending software engineering as we know it, acing the hardest math tests, solving unsolved math problems, creating infinite apps at the touch of a button — is just the beginning. The amount of resources that the world is preparing to deploy to improve AI, this year and in the following few years, utterly dwarfs anything that it has deployed so far:
AI’s abilities scale with the amount of compute applied.2 The amount of compute available this year will be much greater than the amount that’s producing all the miracles you see now. And then next year’s compute will be far greater than that. All the while, AI itself will be searching for ways to improve AI algorithms to better take advantage of increased compute.
Other weaknesses of AI — in particular, its lack of long-term memory and its inability to learn on the fly — will eventually be solved.3 AI will be able to act on its own for longer and longer, with less and less human decision-making in the loop. Meanwhile, massive investment in robotics will give AI more and more direct contact with, understanding of, and control of the physical world.
More and more people are waking up to this reality. An article by Matt Shumer called “Something Big is Happening” recently went viral. It’s very simplified and hand-wavey, and Shumer himself is a bit of a huckster, but it gets the point across. If anything it understates the pace and magnitude of the changes taking place. I recommend giving it a read, if you haven’t already.
But there’s a bigger reality out there that people outside the tech industry — and even many people within it — don’t seem to have grasped yet. It isn’t just that AI could take your job, or put millions of people on welfare, or give us infinite free software, or whatever. It’s that for the first time in all of recorded history, humans no longer are — or soon no longer will be — the most intelligent beings on this planet, in any meaningful functional sense of the word.
For the rest of our lives, we’ll all be sleeping next to a tiger.
2026-02-11 17:25:34
Welcome to another roundup of interesting news and events from around the econosphere, from my traditional, 100% handcrafted human-written blog.
First, here’s an episode of Econ 102 for you! As regular readers know, Econ 102’s regular run has ended due to my co-host getting extremely busy with his new job. But we will still come out with an episode every now and then. This episode is about how cameras can improve public safety — and whether they should:
Anyway, on to this week’s list of interesting things.
The Bay Area Rapid Transit system (BART) is in a parlous state. Ridership has plummeted in recent years; it did not even come close to fully bouncing back after the pandemic.
If BART doesn’t get bailed out with higher taxes, it will have to close stations, reduce service, and lay off workers.
Why did so many people stop riding BART? It’s possible that the pandemic permanently shifted people’s tastes; maybe people just got used to taking Uber or driving instead of using the train. But it’s also possible that the general increase in public disorder in the Bay Area just made BART unacceptable as a mode of transportation. It seemed like every train had its share of shady characters, drug users, vagrants, and the mentally ill.
For a long time, everyone talked about this, but no one had the hard evidence to prove it. Well, now we do. After BART installed fare gates last year at many of its stations, crime on the trains plummeted by 54% in a single year.1 What’s more, the amount of time that BART employees have to spend on “patron related Corrective Maintenance” — i.e., fixing or cleaning up things that riders break or defile — went from huge amounts to almost nothing:
It turns out that just a few riders were causing most of the disorder on the BART — and those riders were mostly not paying their fares, since the fare gates were effective in stopping them.
This demonstrates a general principle: You only have to restrain a very small number of people in order to maintain public order.
Progressives often argue against measures like fare gates, labeling them “carceral” and “racist”. This demonstrates a principle that I call anarchyfare — the idea that eliminating society’s rules serves as a kind of welfare benefit for marginalized people. But in fact, most poor and marginalized people are just peace-loving people who need to ride the train to get to work. They are the chief victims of the tiny number of chaotic individuals who destroy the commons and make public spaces and public services unusable.
BART’s lesson should be applied throughout much of our society. Restraining a very few uncontrollable and chaotic individuals makes life much better for the poor and working class.
As agentic coding apps wow the world, it’s time for yet another round of “Is AI taking our jobs yet?”. Most of the attention has been focused on young college grads. The story here is that so far, AI primarily automates knowledge work — software engineering, legal services, and so on — and so impacts white-collar entry-level hiring more than other types of hiring.
This was the thesis of Brynjolfsson et al. (2025). And it’s the subject of a new post by Mike Konczal:
Konczal writes:
As you can see, people without college degrees who are 22-27 more or less track exactly what we’d expect given the labor market slowdown. But young college is much higher than our trendline we’d expect from the slowdown.
To be clear: college-educated unemployment is still lower than non-college unemployment, and everyone’s unemployment is up…What’s unusual is the gap between where college unemployment should be historically and where it actually is today…[Y]oung people have higher unemployment than we’d expect at 4.4% overall unemployment. It’s especially higher at its peak and throughout their 20s for people with a college degree. Their recent unemployment rate is historically a surprise. The bad kind of surprise.
He doesn’t claim that we know it’s AI causing the change in the historical pattern, but it’s heavily implied.
But Adam Ozimek points out that this story depends on using unemployment rates. If you look at employment rates instead, the picture looks very different:
You’d think employment rates and unemployment rates would just measure the same thing, right? But they don’t. The way our government calculates things, the employment rate is the percentage of people who have a job. The unemployment rate is the percentage of people who don’t have a job but are looking for a job. In other words, the unemployment rate depends on who says “I’m looking for a job right now”. The employment rate does not depend on this.
In fact, as Ozimek shows, recent college grads have shown pretty constant labor force participation (i.e. they’re all still trying to find jobs), while a number of non-college people of the same age have stopped looking entirely. This shows up as higher unemployment for the college grads, even though the gap in terms of “who has a job” has actually widened since the release of ChatGPT.
This simple observation throws a lot of cold water on the idea that AI is taking jobs from young college graduates. As if that isn’t enough, Zanna Iscenko has a good post that casts even more doubt on the thesis. Iscenko points out that the jobs that are typically reckoned to be more “AI exposed” also tend to be more sensitive to macroeconomic swings:
“AI exposure” and “interest rate sensitivity” are deeply correlated variables…[O]ccupations in the top quintile of AI exposure are overwhelmingly concentrated in…the most AI-exposed quintile…These are precisely the sectors most sensitive to capital costs and broad economic uncertainty. This finding is supported by existing economic literature, such as research by Gregor Zens, Maximilian Böck, and Thomas O. [Zörner] (2020), which found that workers in tasks that are easily automated are also disproportionately affected by conventional monetary policy shocks.
Further supporting the interpretation that AI exposure is correlated with sensitivity to macroeconomic shocks is the fact that we also see more pronounced drops in job postings for “AI-exposed” occupations during the hiring slowdown in early 2020…when Generative AI could not even theoretically be the explanation for the difference.
It still looks to me as if the slowdown in new-grad hiring is not a great example of AI taking jobs. I understand why everyone is worried about this, and I do think it’s plausible that many people will have to find new jobs in the age of AI. On top of that, it seems easily possible that uncertainty about the effects of AI could slow hiring, even if people don’t end up being replaced.
But I just don’t think there’s good evidence that it’s happening yet. Perhaps this year will be the year.
Tariffs aren’t creating a wave of manufacturing jobs for Americans; in fact, manufacturing jobs are decreasing. But it’s also worth asking whether tariffs are even having an effect on America’s overall trade deficit. Recall that Trump thinks trade deficits are bad in and of themselves — a sign of American “defeat” in a global competition, and a way in which America is dependent on foreigners.
The effect of the tariffs on trade deficits doesn’t appear for a little while. At first, everyone tries to front-run the tariffs by importing as much as they can before the tariffs go into effect, leading to a giant temporary spike in the trade deficit. But after that temporary effect abated, it looked like U.S. trade deficits were shrinking:
The November data throws a bit of cold water on that idea. Imports soared and exports fell. November might turn out to be an anomaly, but so far, it doesn’t look like tariffs have done much to trim America’s trade deficit.
But the U.S. bilateral trade deficit with China has come down a lot. The percentage of U.S. imports that come from China has been falling since the pandemic, but it absolutely fell off a cliff after Trump’s big tariffs were announced. America used to get more than a fifth of its imports from China; now it gets less than a thirteenth:
Some people claim that China is just shipping more goods to America indirectly, through third countries like Vietnam. But Gerard DiPippo looked at which goods China has started selling more of to third countries, and found that transshipment to America can’t be very high:
By comparing the decline in China’s exports of specific goods to the United States with the increase in its exports of those same goods to other markets, we can approximate how much of China’s exports are being diverted. By this method, about 82 percent of China’s lost exports to the United States found alternative markets in the second quarter…The top destinations for those diverted exports are Southeast Asia and Europe. Comparing those trade diversion estimates with the increased U.S. imports of those same goods from those regions, we can estimate a maximum for potential transshipment into the U.S. market. By that metric, Southeast Asia is the top potential source of transshipped goods. Overall, potentially transshipped goods during the second quarter equal 23 percent of China’s diverted trade, suggesting that Chinese exporters have, at least so far, mostly found alternative markets.
True transshipment is likely lower than the 23% number that DiPippo cites as an upper bound.
What’s more plausible is that China is shipping intermediate goods — parts, materials, etc. — to countries like Vietnam, who assemble the inputs into consumer goods and sell them to the U.S. But while this means that the U.S. is still dependent on China for some types of technology, the actual manufacturing base is migrating out of China — which is good for the rest of the world, since it’ll help other countries industrialize. Also, having assembly outside China reduces America’s geopolitical vulnerability somewhat.
So while tariffs haven’t clobbered the trade deficit or led to a manufacturing renaissance, they do appear to be working to decouple the world’s two largest economies. Recall that the tariff rate on China is still much bigger than the rate on other countries:
If you view import dependence on China as a geopolitical risk, then this is a positive result for tariffs.
Jon Stewart was my favorite political comedian when I was younger. He didn’t always get everything right, but he could almost always make you laugh, and it was clear that his heart was in the right place. He just wanted to see America succeed and Americans be happy.
But in recent years, this admirable desire has slowly morphed into a kind of lazy centrist populism. One of Stewart’s occasional targets is the economics profession — a favorite punching bag of left-populists everywhere. But because Stewart doesn’t know much about the field of economics, or what economists do, or what economics is about, or what economics research actually says, his critiques often feel uninformed and fall flat.
Jerusalem Demsas saw a recent Stewart interview with behavioral economist Richard Thaler, and decided she had finally had it with the former Daily Show host:
Stewart interjected…“But that’s not economics, economics doesn’t take into account what’s best for society!”…“The goal of economics in a capitalist system is to make the most amount of money for your shareholders. So my point is, since when is economics about improving the human condition and not just making money for the companies that are extracting the fossil fuels from the earth?”
At this point it became clear that Stewart has conflated the entire field of economics with a half-remembered, left-wing caricature of capitalism…Throughout the interview, Stewart seemed to believe that economics is just a sophisticated justification for letting rich people and corporations do whatever they want. And this total lack of basic understanding renders him an inept translator of politics and an ineffective force for the very policies he says he supports.
Demsas noted a hilarious moment in the interview in which Stewart rejects the notion that economists have anything useful to say about climate change, and then immediately endorses a cap-and-trade scheme — something economists invented. It’s as if a talk show host rejected the science of physics by saying we didn’t need physics equations to land on the moon.
Jason Furman, an incredibly mild-mannered and affable guy, was nevertheless willing to vent about his own interview with Stewart:
Meanwhile, in order to get some backup for his newfound crusade against the economics profession, Stewart has recruited Oren Cass, a Trump supporter and big fan of tariffs who spends much of his time yelling about how economists don’t know anything. I have written in the past about the utter vapidity of Cass’ critiques of economics. Every time I see a story about how U.S. manufacturing employment keeps falling and falling, I tweet to him and ask him whether he has revised his belief that tariffs help manufacturing. He never answers.
Anyway, the point here is that although Jon Stewart’s style of comedy is great for making fun of American politics, it’s not an effective or interesting way to address economic policy challenges. Unfortunately, the “econ is fake” meme has given a lot of people permission to treat those challenges as if they’re a simple matter of common sense. They are not.
Trump and the MAGA movement aren’t just going after illegal immigrants; they’re also opposed to high-skilled legal immigration. The administration’s main target on that front has been the H-1B visa, which brings smart people to work in U.S. tech companies. Most H-1B recipients are from nonwhite countries, with India taking by far the biggest share. Trump has implemented a huge fee for hiring H-1Bs, and other GOP politicians are also trying to curb use of the visas.
Proponents of skilled immigration, such as Yours Truly, have long warned that if companies can’t get talent to come to America, they’ll simply set up overseas offices and take advantage of talent there. In fact, Glennon (2023) has evidence that this is exactly what happens:
How do multinational firms respond when artificial constraints, namely policies restricting skilled immigration, are placed on their ability to hire scarce human capital?…[F]irms respond to restrictions on H-1B immigration by increasing foreign affiliate employment…particularly in China, India, and Canada. The most impacted jobs were R&D-intensive ones…[F]or every visa rejection, [multinational companies] hire 0.4 employees abroad.
Well, it’s happening again:
Alphabet Inc. is plotting to dramatically expand its presence in India, with the possibility of taking millions of square feet in new office space in Bangalore, India’s tech hub…
US President Donald Trump’s visa restrictions have made it harder to bring foreign talent to America, prompting some companies to recruit more staff overseas. India has become an increasingly important place for US companies to hire, particularly in the race to dominate artificial intelligence…
Google rivals including OpenAI and Anthropic PBC have recently set up shop in the country…
For US tech giants, India offers a strategic workaround to Washington’s tightening immigration regime. The Trump administration has moved to sharply hike the fees for H-1B work visas — potentially to $100,000 per application — making it harder for companies to bring Indian engineers to the US.
This shift is fueling the growth of so-called global capability centers, or technology hubs operated by multinational corporations across sectors from software and retail to finance. Many of these centers are now focused on building AI products and infrastructure. Nasscom, India’s IT industry trade group, estimates such centers will employ 2.5 million people by 2030, up from 1.9 million today.
If those jobs were in America, the Indians who are working at those jobs would be spending their money on American doctors and dentists, American tax preparers and financial advisers, American restaurants and shops. Now, instead, thanks to Trump, that money is being spent in India.
As I noted in my last post, Japan has a huge national debt. Even once you net out the portions of the debt that are held by other branches of the government, debt is only around 119% of GDP — about the same as in the U.S., which is highly indebted. But a recent post by Toby Nangle shows how Japan’s government has managed to reduce the impact of that debt by basically acting as a giant hedge fund, making huge profits on various macroeconomic “trades”:
[I]f the Japanese government had raised a bazillion yen on the bond market and funnelled it all into, say, a successful forex trading operation and a long-only stock portfolio which has gone to the moon, maybe we should consider these assets too when trying to work out what sort of parlous state Japan is actually in?…
[Japan] has enjoyed spectacular returns on its monster macro punts over the past few years…They’ve scored healthy profits on its FX interventions since 1991, which we reckon could be worth around eight per cent of GDP…The Bank of Japan’s most outlandish version of QQE has involved building a huge position in stocks, and we estimate the unrealised P&L could be worth 11 per cent of GDP…And that’s before we chalk up jumps in the value of GPIF, Japan’s $1.8tn public pension reserve fund that is maintained to help the government pay pensions. This has benefitted bigly from a combination of a slide in the yen and booming stocks…
Beyond these three, there are a host of other such trades. But pretty much all of them come down to one core basket of positions: short yen vs US dollars, and long stocks. And this trade has been wildly successful.
Nangle plays with some numbers from Fed economist YiLi Chien for Japan’s various government trading positions, and finds that they shrink Japan’s debt by around half:
The U.S. has not done anything of the sort. If George Bush had implemented his Social Security scheme in 2005, we’d be reaping many of the same benefits Japan is now enjoying…but we did not. Japan is acting like a giant — and very successful — macro hedge fund, while the U.S. keeps its money in cash under the mattress.
The fare gates are also raising millions of dollars for the BART system.
2026-02-09 17:40:02
Japan is a parliamentary democracy; they have a Prime Minister rather than a President. So when Takaichi Sanae became Prime Minister last October, it was because she won an internal party election, not because she received the mandate of the people. This was a problem for her, because her party — the Liberal Democratic Party (also known as Jiminto or the LDP), which is usually in power in Japan — didn’t actually have that strong of a majority. When their long-time coalition partner, a smaller party called Komeito, ditched the LDP after Takaichi came to power, some people thought Takaichi’s tenure in office might be cut short, or hamstrung by a lack of votes.
So Takaichi did the smart but risky thing, which was to call a nationwide election. That election took place yesterday. The LDP proceeded to stomp all over the opposition, winning in a massive landslide. Takaichi’s party won 68% of the seats in the lower house, giving the LDP a 2/3 majority all by itself. That’s the biggest majority the LDP has ever had in its 71 years of existence — and when you add in its new coalition partner, the Japan Innovation Party, it’s now 76%. That means Takaichi can easily push through essentially any legislation she wants, except for a constitutional amendment (and those aren’t off the table either).1 The Takaichi Era has now begun for real.
Americans have taken a bit more of an interest in Japanese politics and society lately, probably because of the tourism boom. So I thought I’d try to explain what this all means.
First, there’s the question of why the LDP always seems to win in Japan. Except for two brief periods out of power — in 1993 and 2009-2012 — the LDP has ruled Japan for the entire time since it came into existence in 1955. This almost unbroken run of victories has some people wondering if Japan is some kind of fake democracy or one-party state.
It’s not. The best book about this that I’ve ever found is Ethan Scheiner’s Democracy without Competition in Japan: Opposition Failure in a One-Party Dominant State. According to Scheiner, there are basically two reasons why the LDP stays on top. The first is that until the mid-2000s, there were some structural quirks of Japan’s electoral system that made it easier for one party to retain dominance — basically by identifying who voted for the party, and sending government funds directly to them. This is called clientelism, and it happens to some degree in most countries — think of Trump trying to starve blue states of federal government funding — but in late 20th century Japan it was easier to do. Being able to pay off reliable voter blocs — like rural construction workers — made it easier for the LDP to stay in power.
But the bigger reason that the LDP stays in power is a lot simpler and more democratic — the party is just really responsive to voters’ concerns. In the 1970s, when anger about corruption, environmental problems, and slowing economic growth almost led the Socialist Party to topple the LDP, the ruling party changed tack. It became much more environmentalist and implemented policies that helped lead to a revival of growth in the 80s.
When the bursting of the big asset bubble in the 90s led the LDP to briefly lose power, they responded with a big program of fiscal stimulus (which is one reason Japan now has so much government debt). When more scandals and the global financial crisis got the LDP kicked out in 2009, the LDP responded in 2012 by bringing in Abe Shinzo and his pro-growth economic program, which got the country working again.
In other words, the LDP simply does what any rational ruling party should do in a functioning democracy — it gives the people what they want. And the people respond by usually sticking with the known quantity, as long as it keeps being responsive. This is perfectly democratic; there’s no reason “democracy” needs to mean alternating parties in power, as long as the ruling party takes elections seriously, abides by their results, and stays in power by giving the people what they want.
This time, what Japanese people mainly wanted was security in an increasingly dangerous world. And Takaichi was the person who arose to give it to them.
For the entire time since World War 2, Japan could count on the unwavering military protection of the United States, which was the most powerful country in the world. That is no longer the case. Donald Trump is returning the U.S. to an isolationist stance; he has acted aggressively toward allies in Europe, raised tariff barriers against allies, and cozied up to Russia (which is Japan’s traditional enemy). Trump has so far continued to promise to defend Japan, and seems to really like Takaichi, but by now the whole world has learned that the mercurial U.S. leader can turn on his allies in a split second.
On top of that, a U.S. security guarantee isn’t worth what it once was. Even if America tried to defend Japan from China, it’s not clear that it could. The U.S.’ war production capability is now far inferior to China’s, and China is geographically much nearer to Japan than the U.S. is. And even if America could defend Japan against direct attack, Japan is very dependent on imports of food and fuel, and China’s submarines and missiles could potentially blockade Japan into starvation and poverty.
What can Japan do in this suddenly terrifying international environment? Most importantly, it needs to remilitarize. It needs to raise the percent of its economy spent on defense, and it needs to bring its military up to cutting-edge technology levels across the board. This will be very difficult, for reasons I’ll explain in a bit.
But it also won’t be enough. China is more than 10 times Japan’s size, and has built itself into a manufacturing juggernaut; Japan has little hope of resisting that power by itself, unless it develops nuclear weapons.2 Thus, Japan also needs to court allies to bolster its defense — not just the wavering United States, but also India, South Korea, Europe, and so on. That will take diplomatic skill of a kind that Japan is not used to summoning.
And while doing all this, Japan will need to avoid major pitfalls that could hamstring it at a critical moment. That includes economic collapse, of course. But it will also have to avoid the kind of internal social and political divisions that resulted in the election of Trump in America and have led to a rising rightist challenge in much of Europe.
Takaichi has promised to do all three of these things, and so far, she looks like she has a decent shot at pulling them off. She’s a well-known hawk on defense, and in November, she declared that Japan would act to defend Taiwan if China attacked it. China responded in rage, making various threats of war against Japan, curbing tourism, and launching a campaign to diplomatically isolate Japan by accusing it of militarism.
But China’s blitz had the opposite of the intended effect. Nobody except a smattering of online leftists and some gullible American journalists actually believed that Japan was threatening China; everyone realized it was the other way around. South Korea, recognizing the magnitude of the regional threat, and also realizing that Trump’s America wouldn’t be a reliable partner, immediately started trying to draw closer to Japan. South Korean President Lee Jae Myung went to Japan and played an impromptu drum set with Takaichi, covering some K-pop songs and producing this epic photograph:
This is an incredible diplomatic coup, especially for two countries that were at each other’s throats just a decade ago over wartime history, colonization, and a territorial dispute. Korean and Japanese people themselves have become much warmer toward each other in recent years, but for the two countries’ leaders to be so openly chummy shows how committed they are to the partnership.
Meanwhile, China’s aggressive bullying campaign united Japanese society behind Takaichi. Various recent polls have her approval rating anywhere from the low 60s to the high 70s:
And these polls find even greater support among young Japanese voters, with some logging over 92% approval. That’s absolutely wild. There have been many articles written about why young Japanese people love Takaichi so much, but I think one reason is simply that she offers them the promise of security in a scary world.
Takaichi is also known as a conservative on the issue of immigration. But she’s no Trump. She has promised to improve immigration screening, toughen requirements for naturalization (which were very easy), make some visa requirements a bit tougher, etc. This is very measured stuff, especially compared to an anti-foreign minor party called Sanseito that cropped up last year. That party looks downright Trumpian, and siphoned votes from the LDP last year.
But by triangulating the immigration issue and convincing the Japanese people that the government wasn’t deaf to their complaints about misbehaving foreigners (who are mostly tourists, not immigrants), Takaichi took the wind right out of Sanseito’s sails.3 Despite what you may hear from certain hysterical online individuals,4 Japan is going to chart a moderate course on immigration, continuing inflows to alleviate labor shortages and attract capital, while learning from Europe’s mistakes and being more selective about which people they take in.
So Takaichi rode to a record victory because she promises to stand up for Japan internationally and hold Japanese society together domestically. Now the big question is whether she can actually deliver.
First, let’s talk about remilitarization. The main obstacle to doing this is actually not the constitution’s Article 9, which formally forbids Japan from having an army. That constitutional article was “reinterpreted” in 2014 to remove almost all legal constraints on a military buildup. A bigger obstacle is that many decades of quasi-pacifism, combined with a long fiscal crunch, have atrophied Japan’s military-industrial complex.
The situation is not as dire as you might think, since Japanese companies do lots of “dual-use” manufacturing that could be shifted to war production in a crisis, and since Japan tends to have more complete internal manufacturing supply chains than America does (making it less vulnerable to a cutoff of Chinese supplies). I recommend the following post by Jesper Koll:
So the situation isn’t hopeless, but there’s a lot of work to be done, and it’s going to be very tough.
The difficulty is going to be exacerbated by Japan’s fiscal difficulties. The government has a large pile of outstanding debt, even after you account for the portion that’s held by various branches of the government itself. It has to pay interest on that debt. For a long time, interest rates in Japan were kept extremely low, which was possible because inflation was low. So paying interest on the debt wasn’t a big problem. But now inflationary pressure has returned, with inflation above 2% in recent years:
In order to prevent this inflation from spiraling upward, the Bank of Japan has to raise short-term interest rates. But that makes the government’s debt much more expensive, meaning the country has to divert a large amount of revenue toward interest payments every year. Of course, the government can just borrow to cover those interest payments, but then this drives up the debt, and raises doubts that it’ll ever be paid off. You can probably see those doubts starting to appear; rates on long-term Japanese government bonds have begun to soar:
This just makes it harder for Japan to repay its existing debt. It also threatens to hurt the economy, which would hurt tax revenue, and thus compound the problem.
Japan is in a real fiscal bind. The only way it will really be able to pay for expanded defense spending is to cut government spending in other areas — which, most of all, means benefits for the country’s burgeoning masses of elderly people. Cutting off grandma to build missiles doesn’t usually make for very good politics, but if anyone can persuade Japan’s people to accept the sacrifice, it’s probably Takaichi.
Fortunately, defense spending offers Japan some economic advantages beyond simply countering China. First of all, it offers the government the perfect excuse to wind down other, more inefficient forms of stimulus spending, like bailouts for failing companies. The Japanese economy doesn’t need stimulus at all at this point, of course, but some Japanese people will be afraid that growth will crater if spending drops. Diverting money from bailouts to defense will be good for productivity.
More importantly, defense spending will help revive Japan’s manufacturing sector, which has been under extreme pressure from Chinese competition in recent decades. Defense spending gives manufacturers a cushion from China’s export flood, and stimulates investment throughout the supply chain.
The defense imperative may also help bring Japan up from its position as a technological laggard. Japan has fallen behind, partly due to its weakness in software, partly due to the fact that most of Japan’s R&D is incremental stuff, performed by risk-averse corporations. Defense spending will allow Japan’s government to get into the game, funding bolder research efforts that benefit many companies instead of just one. It will also spur faster adoption of AI technology — out of sheer necessity — that will probably solve Japan’s software problems.
Finally, defense will be a great area for Japan to solicit greenfield investment — a big missing piece of Japan’s economy. American defense companies looking for places to make drones, ships, and missiles unencumbered by the U.S.’ legalistic regulatory state would be well-advised to build some factories in Japan, which can set them up quickly and easily, and where supply chains, labor quality, and infrastructure are all very good.
So while Takaichi has some big challenges ahead of her, she also has some big opportunities. It’s sad that Japan is being forced to leave behind its long pacifist moment. But with the right leadership, this necessary change could end up helping the country escape economic stagnation as well.
Constitutional amendments require a 2/3 majority in the upper house as well, plus a majority in a national referendum.
In fact, Japan should develop nuclear weapons, as quickly as possible. But this will be politically very challenging, given the country’s history of suffering at the hands of nuclear weapons in WW2.
This echoes the approach of Abe, who offered Japanese voters traditional conservatism while cracking down on rightists.
Some of whom may be friends of mine…
