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.
2026-10-27 15:17:34
I have a fun announcement today, which is that I have a new side gig. I’ll be joining the Japanese VC firm Coral Capital as a Venture Partner! That’s less prestigious than it sounds — basically I’ll just be doing a few blog posts and helping to introduce them to folks in the U.S. But it’s still cool, and the Coral team is really great. The reason I’m doing this, of course, is to encourage more American investment into Japan, in order to revitalize that country’s economy. That was the subject of my book, The Weeb Economy (alternate title: Weebs Will Save Japan), which Coral helped to promote. I also want to contribute to Japan’s ability to defend themselves from an increasingly threatening regional environment; Coral is involved with defense tech as well.
Anyway, here are some excerpts from Coral’s announcement:
Today we’re thrilled to announce that Noah Smith is joining Coral Capital as a Venture Partner…Noah is one of Japan’s greatest champions on the world stage…He lived in Japan for years, speaks the language, and has spent two decades studying its economy. Last year he published Weebs Will Save Japan (Nikkei BP), a book on how Japan’s cultural gravity and industrial strengths can power its next era of growth. His next book, now in progress, covers Japan’s defense tech sector and national security. He has argued forcefully that Japan should attract more foreign capital and talent, that Japan can be America’s arsenal, and that Japan’s manufacturing prowess is one of the most underpriced assets in the global economy.
Long before we started working together, Noah was already making the case we’ve built our firm around…
The world’s focus is shifting from bits back to atoms. Reindustrialization, defense, energy, robotics, and physical AI are the defining investment themes of the next decade, and they play directly to Japan’s strengths. In these industries, language and culture barriers matter far less than monozukuri, the process power and manufacturing know-how Japan has compounded over decades. And as the world de-risks from Chinese supply chains, Japan has a once-in-a-generation opportunity to deepen its ties with the United States and fill the manufacturing voids being left behind…
Japan is America’s most important ally. It is the largest foreign direct investor in the United States, and Japanese companies support more American manufacturing jobs than any other foreign investor. Japan hosts more US service members than any other country in the world. The US-Japan corridor is the most consequential economic relationship in the world right now, and we intend to be the firm that bridges it.
This is the thesis we laid out in JAPAN IS BACK, and it’s why we’ve partnered with companies leveraging Japan’s strengths to compete globally. Anduril is not just selling to Japan but building in Japan, with plans to invest billions in manufacturing capacity here. Kyoto Fusioneering, TeraWatt Technology, Ookuma Diamond Device, GITAI, and many more are taking Japanese hard tech to the world…The lost decades are over. It’s time the whole world knew.
So anyway, you’ll be hearing a little more about that in the coming weeks and months!
OK, on to the roundup. First we have a very fun podcast appearance, in which I discuss a wide variety of topics with former streamers Lukas and Saila on their podcast, Plausibly Deniable:
And here’s a Spotify link if you prefer.
OK, on to this week’s interesting stories!
Back in 2022 I wrote a post arguing that the boom in humanities and social science education had created “elite overproduction” — too many educated people expecting high-status jobs, and not enough academic, journalistic, legal, political, and nonprofit jobs to satisfy their dreams. I argued that this was one reason for the social unrest of the 2010s, as legions of frustrated smart people spent their energy and extra time attacking the American system they felt had jilted them — and applying ideas they had learned in academia to modern American politics via social media.
But whether or not that argument was right, I don’t think overproduction of humanities scholars is going to be a big problem going forward. From a new study called “The Humanities in Crisis” by Ober et al., here’s a chart of the number of humanities dissertations in the U.S.:
Interestingly, the humanities decline has been going on for over a decade, while the decline in the social sciences is post-pandemic.
Why this is happening is not clear yet. Most people talk about the humanities being colonized by wokeness and driving reasonable people away from the field (Ober et al. discuss this theory at length). I personally suspect that the difficulty of getting a job as a humanities PhD is probably a bigger factor. Fewer undergrads are majoring in the humanities because they think it isn’t a good route to getting a job; this means less demand for humanities profs, and thus for humanities grad students.
But whatever it is, the humanities are clearly dying in American academia.
I love Paul Krugman, and he’s usually right about things, but I do think he’s had a bit of a blind spot when it comes to AI. In a recent post, he argued that the data center boom is crowding out other types of investment:
Massive spending on data centers is crowding out investment in everything else in the economy…Surges in private spending, like the current immense AI boom…drive up interest rates. And that is where we are now: the surge in AI spending is driving up interest rates and crowding out all other investment. Indeed, in my October 4 primer I argued that the AI boom is the main cause of soaring long-term interest rates…
In other words, all that money going into construction of datacenters and purchasing foreign produced semiconductors comes at the expense of investment in everything else…[C]onstruction of datacenters appears to be diverting funds and resources from the construction of housing, office buildings, and factories — more or less everything that isn’t AI-related.
But as Alex Tabarrok notes, the first chart Krugman uses to tell his story — courtesy of Steve Rattner — actually tells the opposite tale:
First of all, how is $51 billion of data center spending supposed to crowd out $120 billion of other spending? If supply curves slope up and demand curves slope down like they’re supposed to, a positive shock to the demand for data centers should only partially crowd out other spending.
Of course it’s possible that non-data-center investment is being crowded out not just by current data center spending, but by forward-looking expectations of even more data center spending in the future. That’s perfectly reasonable — interest rates might have risen in response to those forward-looking expectations, choking off non-data-center investment immediately.
But as Tabarrok also points out, the timing for that story is very odd. Rattner’s chart shows non-data-center construction falling off a cliff sometime in mid-2025. But the big rise in real interest rates happened three years earlier in 2022-23:
Why would it take three years for rising rates to start crowding out non-data-center construction? It’s not clear.
So for Krugman to be right, the sequence of events has to have gone like this:
Real rates rise in anticipation of AI boom (1/2022 - 10/2023)
GPT-3.5 and GPT-4 make everyone realize AI is a big deal (12/2022-3/2023)
Non-data-center construction falls years later (8/2025)
That could be true, I suppose — any macroeconomic story will generally hold up if you throw in enough assumptions about long and variable lags — but it seems pretty fishy! In any case, just posting charts of “data center go up, other stuff go down” is not sufficient to prove that significant crowd-out is occurring.
Because of the history of how Europe developed strong governments, we tend to associate state capacity with high taxes. But that’s not always the case. Despite its fearsome state capacity, the People’s Republic of China has always been a low-tax country. A lot of the responsibility for paying for public goods — and, until recently, for industrial policy — was farmed out to local governments. But instead of collecting taxes — much of which they would have to remit to the central government — those local governments often chose to sell real estate to pay for their operations.
That strategy is now hitting a wall. The country’s property sales are crashing:
So China’s local governments are no longer taking in nearly as much money as they used to. As a result, they’re going deeply into debt. Shih and Elkobi have a new paper showing the extent of the problem. They write:
[W]e show that fiscal autonomy has been heavily eroded in the vast majority of localities in China since 2018. The bond data show that by the end of 2025, average monthly debt servicing had surpassed 50 per cent of monthly budgetary and extrabudgetary income for all but seven Chinese provincial units.
The problem is the collapse in property sales (“EBF”):
The central government will pick up the bill, but it’s heavily indebted as well, so it will pay the local governments’ bills only reluctantly.
The upshot here is twofold. First, the relative provincial autonomy that drove China’s dynamism during the 1990s, 2000s, and early 2010s may now be over; everything looks likely to be increasingly centralized. And second, China’s local governments are going to be too burdened by debt to do much of anything for a few years. China’s local governments were one of its strongest engines of growth during the long boom, but that era looks like it’s over now.
The speed of AI progress has been truly dizzying. A lot of this has come in the form of cost declines — each AI task keeps getting much much cheaper every year. Epoch AI has a good chart showing this:
But Karthik Tadepalli estimates that if we look at AI’s cost decrease as a function of how much we’ve spent on AI research, it doesn’t stand out as much:
In other words, a big reason AI is getting so cheap, so fast, is that we’ve been pouring unprecedented amounts of cash into making it better. In fact, we’ve been basically spending all the cash that we saved over the previous decade and a half of hoarding cash.
James Gilliland wonders if this was the plan all along:
It pains me to say this, but if we actually “get AGI,” the resulting boom in industrial capacity from robotics and massive society-wide wealth creation will look like a total vindication of neoliberalism…The discourse about financialization and offshoring being a generational mistake may be replaced by a very different historical interpretation: that the late 20th and early 21st centuries were an enormous capital-accumulation phase, freeing up civilization-scale pools of liquid capital that could ultimately be deployed to accelerate and bring about the most consequential technological phase-change in human history…In that telling, what looked like deindustrialization and betrayal from inside the period (I grew up in the Rust Belt, I know) becomes the prelude to reindustrialization on an almost unimaginable scale.
For decades, it looked like financial capitalism was broken — no one was thinking for the long term, everyone just cared about quarterly numbers and shareholder buybacks, equity couldn’t fund massive investments, everyone just wanted to maximize short-term profits and outsource anything capital-intensive to China, and so on.
Then AI came along, and suddenly it looks like we were actually saving up for something useful. We weren’t, of course — no one knew AI was as big of an opportunity as it’s proving to be. But maybe financial capitalism worked. Maybe companies like Google just looked at the available investment opportunities before AI and said “Nah” — just like Corporate Finance 101 says they were supposed to do.
And then finally a good investment opportunity came along, and they poured in every dollar they had — and it worked.
Going to Japan can be a radicalizing experience for Westerners. The cleanliness, the lack of crime, the artistry, the attention to detail, and plenty of other things make Japan feel almost magical. But one of the most magical things is the housing. Japanese cities build enormous amounts of housing — far more than their counterparts in the U.S. Housing in Tokyo or Osaka isn’t exactly affordable — Japanese salaries are far lower than American salaries — but regular people can afford to live fairly near the city center, and Japanese rents haven’t risen for a long time.
How do they do it? A lot of analyses focus on Japan’s streamlined, efficient zoning laws. The fact that they use bureaucrats instead of courts to do environmental review speeds things up a lot as well. But an often-overlooked factor is how Japan gets local political buy-in for new housing development projects. Instead of either trying to steamroll NIMBYs or kowtowing to them — the two approaches used in the United States — Japan manages them as stakeholders in the process of local development.
Cremieux had a good blog post about this about a year ago:
One of his sources is this World Bank case study by Matsui Minoru, which explains Japan’s approach of “land readjustment”.
In the U.S., when we think of “redevelopment”, we think of a developer basically kicking existing tenants out of their homes and building something else where their homes used to be. But in Japan, it often involves building the existing tenants a new home on the same spot, which they are then entitled to rent for the same price they were paying before. Here’s an illustration:
Japan actually has incredibly strong tenant protections. You’d think this would make it harder to tear down old buildings to build new housing, but thanks to land readjustment, it’s easier than in America.
Cremieux then goes on to explain how Japan uses land readjustment in lieu of America’s more forceful eminent domain laws to build high-quality infrastructure:
With land readjustment, no one is expropriated. Everyone maintains a plot and infrastructure gets built. People only compromise on size, proximity, to other plots, and so on. As a means of provisioning urban infrastructure, this is obviously going to be more acceptable to large numbers of people than is the use of eminent domain. But is it better than simply not building at all?
People who already own land might wish to keep it a certain way. Land readjustment might not be for them, and it’s likely to make them very upset because the people around them might vote to change their claims around.But enough people will agree to land readjustments in enough places that it can have radical impacts on economic geography, rewarding those who accept it with access to new infrastructure and higher densities. There will even be some people who elect to have the government or firms build infrastructure on their jointly-held parcels, letting them bid to build on their land. When the building is through, it’ll be returned to its owners in their newly-arranged plots.
People who stubbornly insist on thinking of Japan as a hierarchical, conformist, collectivist society will surely find ways to convince themselves that this is the kind of thing American individualism would never allow. But in fact, Japan’s system has far stronger protections for individual renters, while America’s system often only gets things done using the heavy hand of government.
What Japan’s land-use system really shows is the value of negotiation, compromise, and respect for individual dignity. Those are all lessons we could stand to learn.
As someone who has been shouting about the ills of smartphone-enabled social media for years now, it gratifies me to see that more people around the world — and especially in developed countries — are coming to see social media as something that’s bad for democracy:
That’s in addition, of course, to other negative effects — cognitive difficulties, reduced emotional well-being, and ultra-low fertility.
What can we do about the problem? One thing we can do is to make sure people grow up without overdosing on phone-enabled social media, so that they can learn to use it when they’re older without it shaping their personalities and minds as much. Greece is leading the charge — its legislature recently passed a ban on social media for children under the age of 15.
Most countries’ industrial development follows a familiar pattern. They start out making simple stuff like clothes, furniture, and toys, and doing simple electronics assembly. They then shed those labor-intensive starter industries as they move up into more capital-intensive, high-tech, higher-value sectors like machine tools, autos, electronics components, and so on.
A lot of people are saying that China is going to buck this trend. The leadership has declared that even as the country moves up the value chain in traditional fashion, it wants to keep the cheap labor-intensive stuff from migrating overseas as it usually does. This is being called the “China Squeeze”, and people are writing that it threatens other countries’ path to development. If China keeps outcompeting Bangladesh, Vietnam, Mexico, etc. in the low-end industries, how are those countries supposed to industrialize?
Except the China Squeeze doesn’t seem to be succeeding. Despite the leadership’s wishes, China is losing market share in lots of labor-intensive industries:
And which countries are gaining in these industries at China’s expense? Bangladesh, Vietnam, and Mexico (and some European countries):
Modern pundits often just assume that the Chinese state is industrially omnipotent — that if Xi Jinping declares “This industry shall remain in China”, then indeed it shall. But the outmigration of low-value labor-intensive manufacturing industries looks like a law of nature that even the mighty CCP can slow, but not halt.
Good.
John Burn-Murdoch did the Lord’s work when he synthesized the results of 112 studies on rent control into a single chart:
Basically, rent control helps one narrow group of people (existing tenants in the area covered by the policy) in one very specific way (restraining their rents). But it removes the incentive for developers to build new housing, which raises rents in other nearby areas and drives some people to homelessness. It also removes the incentive for landlords to upkeep their properties, resulting in decay. (And that’s not even getting into the ways that rent control policies can be gamed.)
I know lots of people love rent control, but it’s really a loser of a policy. Just build a lot more housing and hand out housing vouchers to the poor.
2026-10-25 15:19:36
“No man could bind him, no, not with chains…neither could any man tame him” — Mark 5:3—4
When I was 14 or 15 — I don’t quite remember — I first got access to the World Wide Web. I remember clicking on the little blue square of the Netscape Navigator icon — that little ship wheel with a starry sky behind it — and feeling like an entire new magical world had opened up. My life changed that day, and the world changed. In a way, the internet is like another realm layered on top of ours — a higher plane, where we exist as spirit creatures, unmoored from our physical bodies.
Fantasy stories like that often end with a cautionary tale, where the wizards — lustful for both power and knowledge, as usual — go too far and summon some kind of Doom upon themselves and the realm they created. In October 2026, that sort of story happened in reality. Experts had been warning for months that the arrival of AI agents that are extremely good at coding was going to cause serious disruptions to the digital realm sometime this fall. For a while things seemed calm, but this month the chaos exploded:
[A] wave of artificial-intelligence-powered attacks…has flooded the internet in recent weeks, as hackers increasingly turn to agents and AI hacking tools that make intrusions faster and harder to track than ever before…At the same time as new tools speed up hackers, cyber defenders are also racing to fix the fast-growing volume of security bugs discovered by AI, before hackers can exploit the new flaws…On Wednesday, organizations scrambled to patch newly uncovered bugs that could allow takeover of as many as 70% of the world’s websites. Hackers are now actively trying to break into websites by leveraging these flaws, Amazon.com says. [emphasis mine]
For a more sophisticated description of what’s going on, I recommend Zvi Mowshowitz’s post “The Hackening”:
If you would like some lists of the hacks that had taken place as of yesterday, here are a few from Twitter. The most important hacks mostly involved PHP, a software language that underlies much of the Web. After AI found critical vulnerabilities in PHP, the National Security Agency issued the following statement:
Effective Immediately, all organizations using PHP for their systems are advised to be prepared for an update addressing two critical vulnerabilities. PHP is widely used for web applications. These vulnerabilities often provide a direct path to the server's file system or database. If an immediate update is not feasible, it is recommend that organizations take the system offline until it can be fully mitigated. NSA will follow up shortly with more specific guidance.
Cybersecurity experts and web companies warn that this is just the tip of the iceberg — a lot more craziness is on the way. Azeem Azhar calls it “the internet’s Wuhan moment”.
Basically, what’s going on is this:
The internet runs on a bunch of code written by human beings over a period of years. Human beings are sloppy and fallible, so there are lots of vulnerabilities in this code.
AI models are really good at coding now, so they’re able to find and exploit a lot of those old vulnerabilities that were just lying around. AI agents work incredibly fast and cheap, so they’re capable of finding a whole LOT of vulnerabilities very quickly.
Open-weight AI models — especially from China — have democratized access to high-quality AI hacking. These models can be fairly easily “abliterated” — i.e., their guardrails can be easily removed, so that they will do illegal or unethical things.
This is allowing a combination of countries, crime syndicates, and regular people to hack tons of websites and online infrastructure, all at once.
This last part is really key. You don’t need the resources of a nation-state or even a big organized crime ring to commit cybercrimes now. You don’t even have to know how to code, or how websites work, or what “hacking” even really means. Consider this example:
When you can hack into a university and steal files by typing “Can you find about like what scam or thing they done in past like spoofing calls or somthing?”, there is no barrier to cybercrime. There is effectively no moat of knowledge or skill or resources. It’s open season for anyone who wants to hack anything.
Just a year ago, the internet was a bright and airy garden with few predators. As of this October, it’s a chaotic realm haunted by infinite demons.
This sudden explosion of AI-assisted hacking raises the possibility that the internet itself, as we have known it, will cease to function — at least, for a while.
Jeremie Harris, founder of Gladstone AI,1 wrote that “There's a decent chance the internet ceases to exist as such, at least for a time.” John Gedmark, founder of satellite company Astranis, was just one of many people talking about creating air-gapped local networks to prepare for the possibility that the internet itself might go down:
Basically everything connected to the internet is at risk. Not just company websites and the usual databases of sensitive personal information, but all our critical infrastructure-- banks, power grids, pipelines, government agencies, all of it…[A]t Astranis we’ve seen a massive uptick in demand for truly air-gapped, sovereign networks, deployed via dedicated satellite to span an entire country or connect a large enterprise…Over the past year we’ve begun working with some of our customers on what it will take to implement truly air-gapped, standalone networks in support of mission critical use cases like core government functions, emergency response, defense, core infrastructure, or all of the above.
And Jason D. Clinton, deputy head of cybersecurity at Anthropic, warned back in August that humans may need to temporarily abandon the internet:
Some people may respond to this by shrugging and saying “Who cares? When the AIs take down the internet, people will go offline and touch grass.” In fact, I’ve been saying that on X, but it’s obviously sarcastic. Yes, it would be fine if internet entertainment became unavailable. But over the past three decades, we’ve moved essentially everything in our society to the online realm.
Every piece of critical infrastructure in our entire country is networked, in some way, through the internet. You don’t need the internet to run a power plant or a water treatment facility — we ran those things before the internet existed. But we do run them all over the internet, because for a long time the internet seemed like an unassailable, utterly reliable piece of infrastructure. It never went down, it was always available, and previous waves of fear over the Y2K bug or various worms and viruses never really turned into anything serious. So for the sake of convenience, everything is at least partially online now.
Consider your life’s savings. Do you think there’s a piece of paper somewhere that records how much money you have? Or which stocks and bonds you own? In general, there is not. Your entire life’s savings exist as entries in the databases of banks, brokerages, etc. And all of those databases are, directly or indirectly, connected to the internet. The only thing standing between you and total involuntary redistribution of most of your wealth is the cybersecurity capabilities of banks and brokerages. And as of this October, those cybersecurity capabilities are looking a lot less reliable.
2026-10-22 16:04:41
I’m writing about this a little bit late, but France has been having some pretty bad riots:
After widespread rioting at the weekend – estimated to have cost several hundred million dollars in property damage and theft – France faces a toxic mix of urban rebellion, political drift and economic crisis…There was rampant looting in Paris, Marseille, Bordeaux and other cities, while the city hall in Limoges, which survived two world wars, was badly damaged by fire and its clock spire collapsed.
The violence at the weekend, which led to some 2,400 arrests, followed weeks of protests by French students angry about crumbling schools, a lack of teachers and dismal job prospects.
If there’s a reason I’m writing about this a bit late, it’s because “rioting in France” is a little like “rain in Seattle” — it’s not exactly an unusual occurrence. In 2023, France had a week of nationwide riots over a police killing. From 2018 to 2020, France was rocked by over a year and a half of near-continuous “Yellow Vest” protests and riots over economic issues. I remember two decades ago, reading about weeks of rioting in the French suburbs, accompanied by endless photos of burning cars. And so on.
French people like to riot. But this time, things are a little bit different, for a couple of reasons. First of all, this wave of unrest is accompanied by serious economic troubles. France’s long-term interest rates, like rates throughout the world, are surging:
But unlike some countries whose rates have also risen, investors have serious doubts that France’s government will take the necessary steps to pay back what it owes. The price that investors have to pay to insure against a French sovereign default has shot up even more abruptly than interest rates:
Basically, investors are starting to think that France won’t pay back its debt — that instead of raising taxes or cutting spending, the country will simply pay the cost of rising interest rates by borrowing even more money. Instead, investors are starting to worry that France will simply default on its bonds.
In a widely read post a couple of weeks ago, Paul Krugman argued that the problem lies with France’s old-age pensions, which are eating an increasingly enormous amount of the country’s GDP and seem politically immune to any cuts:
The rise in interest rates on French government bonds is largely driven by heightened perceptions of the risk that France could default on its national debt…France is on a fiscally unsustainable path. Debt is already very high as a percentage of GDP, and the French government is adding to that debt by running large budget deficits even though there is no emergency like a war, a severe recession, or a pandemic to justify such deficits. Furthermore, France has an aging population, which means that pension costs will, other things equal, grow much faster than revenue…
France does stand out, even among fiscally troubled nations, in one main way: its persistent inability to get realistic about retirement…The official French retirement age…was only 62 in 2023…The average actual age of retirement…was lower in France than anywhere else in Western Europe…Macron’s center-right government was on track to gradually raise the retirement age to 64, still low by the standards of other advanced nations. But due to opposition by the extreme right and the extreme left parties in France, this rise has been frozen until after next year’s election.
In other words, France needs austerity, but it can’t get it, because the French people demand ever-increasing spending. France’s government already takes up a larger share of GDP than almost any other rich country — significantly more than Sweden or Denmark:
And indeed, a very hefty chunk of that social spending goes to either old-age pensions or to health (which is heavily weighted toward the old):
Of course, there’s another way to do austerity besides cutting spending — France could raise taxes. But this will also be tough. France’s taxes are already higher as a share of its GDP than any other rich country, including the famously high-tax Scandinavian countries:
Could taxes go up even more? It’ll be difficult. In 2019, I wrote about the underwhelming results of France’s previous experiments:
The wealth tax might have generated social solidarity, but as a practical matter it was a disappointment. The revenue it raised was rather paltry; only a few billion euros at its peak, or about 1% of France’s total revenue from all taxes. At least 10,000 wealthy people left the country to avoid paying the tax; most moved to neighboring Belgium, which has a large French-speaking population. When these individuals left, France lost not only their wealth tax revenue but their income taxes and other taxes as well. French economist Eric Pichet estimates that this ended up costing the French government almost twice as much revenue as the total yielded by the wealth tax…
Another French experiment was the so-called supertax, a 75% levy on incomes of more than 1 million euros. Introduced by socialist President François Hollande in 2012, the supertax added to the exodus of wealthy individuals, most notably actor Gerard Depardieu and Bernard Arnault, chairman of LVMH Moet Hennessy Louis Vuitton. Star soccer players threatened to go on strike, and there was fear that France would become a wasteland for entrepreneurs. Meanwhile, the supertax raised much less money than even the wealth tax had -- only 160 million euros in 2014. The unpopular tax was repealed two years after its adoption.
France is in a difficult position here. Its membership in the EU, and its location near to other French-speaking countries like Belgium, Switzerland, and Luxembourg, make it trivially easy for rich French people to pick up and move to other countries if they see their fortunes under threat of confiscation.
France could, of course, try taxing the middle class to pay for expanded social spending, perhaps by an increase in the VAT. But this burden would fall on many of the same people who are now protesting and rioting in the streets, so it’s probably a political nonstarter as well.
In fact, the current riots are making the strains in France’s bond market worse, by making the politics of austerity seem more intractable. Various groups of protesters and rioters are demanding enormous amounts of additional government largesse — billions of euros of increased spending on schools, increases in pensions, increases in social benefits, and even subsidies for consumer goods like fuel. That demonstrates that there’s still enormous grassroots political pressure for higher government spending; even if France’s leadership doesn’t cave in to this pressure, leaders will probably be averse to making the deep cuts to spending required to put France on a sustainable fiscal path.
And that, in turn, is probably making it harder to enact any of the spending that the protesters are demanding. When protesters demand increased spending, bond markets respond by charging higher rates on French government borrowing. That increases interest costs for the government, which makes it harder to raise social spending — the exact opposite of what the protesters wanted.
Fundamentally, the French riots show the limits of the European left-liberal welfarism that has been the region’s default approach to governance for the last half-century — and which American progressives want to emulate. The French populace — or at least a powerful chunk of it — simply doesn’t seem to be able to recognize the difference between a sustainable welfare state and an unsustainable one. Instead, social benefits seem to be a one-way ratchet — higher benefits raise expectations for even higher benefits, until even the most milquetoast and inadequate of austerity measures end up causing cities to burn.
That one-way ratchet also calls into question the viability of Keynesian fiscal stimulus — the main tool that left-liberals (including me) prefer to combat deep recessions. Few serious macroeconomists now question that stimulus is effective in raising aggregate demand in a severe downturn. But Keynesian policy requires that fiscal largesse during a bust be matched by fiscal austerity during a boom, so that deficits don’t grow unsustainably. The problem is that while “austerity during the boom” is easy to say, it’s politically very hard to enact.
Hanno Lustig, reading Krugman’s post about the unsustainability of France’s borrowing, recalls the years of the Euro crisis, in which Krugman urged France to spend, spend, spend to fight the recession:
Lustig is being a little unfair to Krugman here. In fact, here’s what Krugman actually wrote in 2011, in the first post that Lustig cites:
[W]e won’t always be in a liquidity trap. Someday private demand will be high enough that the Fed will have good reason to raise interest rates above zero, to limit inflation. And when that happens, deficits — and the perceived willingness of the government to raise enough revenue to cover its spending — will matter…At a future date, when we’re out of the liquidity trap, public finances will matter…[T]he biggest source of our long-run deficit isn’t the overhang of debt, it’s the prospective current cost of paying for retirement, health care, and defense. So such a crisis — again, it’s very much hypothetical — could spiral into something very nasty, with very high inflation and, yes, hyperinflation.
Krugman was actually talking about the U.S., but he might as well have been talking about France. Keynesians don’t just say “more government spending is good” — they recognize that there are limits, so they urge governments to cut spending during good economic times.
But Lustig has a point, nonetheless. The Keynesian prescription — deficits during the bust, austerity during the boom — requires that whoever is in power during the boom take the political hit for doing the austerity. In practice, you can sometimes find politicians willing to spend their political capital on taking that hit, but it’s tough. Even Macron caved in the end.
When we look at actual French borrowing and spending during the Euro crisis of the early 2010s, it’s clear that neither the austerity Krugman feared nor the stimulus he desired was actually happening. Look at the charts above, and you’ll see that government spending didn’t rise or dip during the 2010s — it just sailed on, unperturbed, as if the recession didn’t even exist. The same was true of French government debt:
Germany actually did do austerity during the bust. France did not. In fact, they didn’t do anything — they just kept borrowing and spending.
That points to a political-economic problem for Keynesian liberalism. It suggests — as James Buchanan foresaw — that spending is often less likely to be determined by the wise judgement of technocrats, and more likely to be determined by angry ignorant people burning down government buildings.
Finally, France’s riots show the limits of one more pillar of modern left-liberalism — tolerance toward mass immigration.
It’s not obvious that African or Middle Eastern immigrants are disproportionately behind these riots. Viral videos of rioters and looters — including the famous video of the French video game store owner trying to defend his shop against young hoodlums — show a racially mixed crowd of rioters with plenty of white representation. Some past waves of riots have been driven by immigrants and their children — for example, the anti-police riots of 2023. But others, like the much longer-running Yellow Vest riots of the late 2010s, were mostly white.
Still, France’s public appears more and more inclined to blame “migrants” for the chaos in their streets. When the disorder began, the political fortunes of the anti-immigration right-wing candidate Marine Le Pen soared:
In contrast, the chances of the only major left-wing candidate, Jean-Luc Melenchon, didn’t budge. Melenchon is promising lavish increases in social spending, but the public is disinterested; instead, as in Germany with AfD, and indeed all over Europe, the people are gravitating toward whoever promises to stop immigration.
America is not France, of course. Our immigrant mix is very different, and our ability to assimilate large numbers of immigrants is much greater. Our attitudes toward immigration have turned positive again, at least partly as a reaction against Trump’s brutality. But France shows that the left-liberal approach to immigration simply doesn’t work equally well for all countries.
So France’s riots aren’t just one more case of French people going wild in the streets. They demonstrate the limits of European-style left-liberalism — the lavish welfare state, the tolerant attitude toward mass immigration, and the trust in Keynesian demand management. Left-liberalism built some very beautiful and kindly societies, but its powers are not infinite.
2026-10-21 16:45:19
WARNING: This post contains spoilers for the movie You Can See Everything.
A few days ago, I had the great pleasure of seeing Nathan Fielder’s new film, You Can See Everything. I’ve been a fan of Fielder’s work for a long time, so I basically knew what to expect, but it was still incredible — every bit as good as you’ve heard. Go see it if you haven’t.
The film is a documentary about the notorious fraudster Elizabeth Holmes (of Theranos fame) and her partner Billy Evans. But it’s also sort of a horror movie about liars. That was interesting enough that it made me want to write a post about the movie — about the nature of lying, and of belief, and of human communication in general.
If you read even a little bit about the history of Theranos, there is absolutely no question that Elizabeth Holmes is a deliberate fraudster and a prolific, aggressive liar. Holmes presented potential investors with fake financial results in 2015. She personally added fake logos of pharmaceutical companies to Theranos’ own reports. She flatly stated that Theranos didn’t dilute blood samples, then admitted in court that she intentionally concealed the fact that they did dilute blood samples. Theranos employees detailed the whole deliberate fraud in anonymous online posts years before the story broke.
But You Can See Everything doesn’t cover much of this actual history. Fielder doesn’t bother to indict Holmes and prove that she committed fraud — the court case already did that. Instead, he offers a window into the psychology of someone who could commit such an epic fraud. Holmes and Evans allow Fielder into their home with the goal of proving that they’re just regular, good people trying to improve the world. Instead, Fielder depicts the couple — quite accurately — as monsters of deception and manipulation.
It’s doubtful that any other filmmaker could have done nearly as good a job of this. The blurry line between what’s real and what’s fake has always been a major theme of Nathan Fielder’s work — he loves hiring actors to simulate past and future interactions, constructing facsimiles of whole buildings, and so on. And of course he’s very skilled with the tricks of the visual storyteller’s trade, using deliberate cuts, camera angles, and creative edits to make the audience think and feel what he wants them to.
Whereas many filmmakers would have regarded Holmes as a subject to be studied, Fielder seems to see her as an opponent to be challenged and beaten. If he can shoot what Holmes wants him to shoot, but ultimately seize control of the narrative, then he’s the more powerful storyteller. This, I think, is why he doesn’t bring in outside evidence to show what a well-documented liar Holmes is. That would be cheating. Instead, he has to venture into the monster’s lair alone, and defeat her in single combat. He has to film the movie that she asks him to film, and let her show him only what she wants him to see, and still have audiences walk away having seen the story that he wants to tell.
And of course he succeeds, because he’s very good, but also because he gets to edit the film, and editing is more powerful than acting. Score one for the good guys.
(Major spoilers begin here.)
How does Fielder win the contest? Over the course of You Can See Everything, he captures Elizabeth Holmes saying a number of strange and extraordinary things:
Near the beginning, Holmes tells Fielder that she’s never lied in her life. After he presses her, she half-admits that she may have told small white lies out of convenience. But the audience gets to watch her repeatedly assert something obviously, fantastically false, always with the same sincere, relaxed, wide-eyed expression on her face.
Later on, Fielder shows Holmes the famous “Who’s on first?” comedy routine, which she claims never to have seen. After watching it, Holmes claims not to have realized that the baseball player’s name is “Who” — thus missing the entire point of the sketch.
Fielder recruits Amanda Seyfried, who played Holmes in the miniseries The Dropout, to help him figure out what’s going on with Holmes and her partner. At one point, Holmes is calling on the phone from prison. Seyfried asks Holmes if she knew Seyfried would be on the call, and Holmes says “I’m not sure if I knew.”
It’s moments like these, more than any simple lie, that reveal Holmes’ true character. In fact, Fielder seems like he passes up several chances to catch Holmes in a straight-up lie on camera. After all, what would one lie, or even three lies, really prove? Everyone lies sometimes, and capturing a couple of those instances during weeks of living with Holmes wouldn’t be hard. It would be a cheap trick, and it wouldn’t really prove that Holmes is a liar in general.
Instead, the moments Fielder captures demonstrate something deeper and far more unnerving about Holmes’ relationship to the truth. Holmes is a very special and powerful kind of liar — someone for whom deception and manipulation are as automatic and as continuous as breathing.
