AI around the web

1. Black Death, white lies

In 2023, Sam highlighted Benjamin Breen’s look at large language models and how their hallucinations would transform education and the humanities. Benjamin described using LLMs as “history simulators” in his own work as a history professor, where he would have students role-play scenarios like the 1348 Bubonic Plague and then fact-check the AI’s inaccuracies.

I was blown away by student engagement and creativity. Here’s a brief list of what some of my students did in their medieval simulations:

• ran away from home to become an apprentice to a traveling spice merchant

• developed various treatments for the plague, some historically accurate (like theriac) others much less so (like vaccines)

• negotiated complex legal settlements between the warring guilds of Pistoia

• fled to the forest and became an itinerant hermit

• attempt to purchase “dragons blood,” a genuine medieval and early modern remedy, to cure their fast-worsening plague

• made heroic efforts as an Italian physician named Guilbert to stop the spread of plague with perfume

• became leaders of both successful and unsuccessful peasant revolts

Student engagement in the spring quarter, when I began these trials, was unlike anything I’ve seen.

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2. Labor pains

Up next, in 2024, we’ve got Sara Tillinger Wolkenfeld’s essay in The Atlantic, “Productivity Is a Drag. Work Is Divine.” Drawing on Jewish tradition, Sara distinguished between melakhah (meaningful, creative labor that imitates the divine act of Creation) and avodah (rote toil). She warned that delegating creative work to machines risks robbing us of purpose and fulfillment, so we should be selective about which technologies we adopt. This argument feels as relevant now as it did when Sam shared it two years ago.

Modern technologies such as generative AI threaten to make 21st-century Americans like the woman in the Mishnah: Deprived of purpose, convinced that our creative output is useless because a computer can produce a result that is sometimes just as good, or even better. Much of the debate around AI hinges on the question Can a computer do it better? But Jewish texts insist that the most important question is about process, not product.

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3. Prompt and circumstance

Jumping forward in time, earlier this year we covered the Claude Code craze. Whether you’ve been swept up or not, “10 things I learned from burning myself out with AI coding agents,” is worth a revist. Benj Edwards explained what AI tools are good at, what they’re (still) not good at and why they’ll probably end up creating more work for humans rather than making us obsolete.

Due to what might poetically be called “preconceived notions” baked into a coding model’s neural network (more technically, statistical semantic associations), it can be difficult to get AI agents to create truly novel things, even if you carefully spell out what you want. For example, I spent four days trying to get Claude Code to create an Atari 800 version of my HTML game Violent Checkers, but it had trouble because in the game’s design, the squares on the checkerboard don’t matter beyond their starting positions. No matter how many times I told the agent (and made notes in my Claude project files), it would come back to trying to center the pieces to the squares, snap them within squares, or use the squares as a logical basis of the game’s calculations.

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4. Lost in translation

Whatever their flaws, we do at least have AI that can beat us at chess. And science is not far behind. On that score, Sam highlighted Asimov Press’ take on the “legibility problem”: What happens when AI makes discoveries that are too complex or conceptually foreign for humans to understand — and use.

If AI science does achieve superhuman performance, and if AI systems begin forming their own research communities around concepts that mutate faster than we can track, then the work of human scientists will shift from that of creation to that of excavation. How much knowledge we will need in order to act on what AI discovers remains an open question. But some degree of legibility will be essential, as discoveries that cannot be interpreted cannot be deployed.

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5. Keyboard cowboys

Sam’s last pick for this week’s edition is “Coding After Coders” by Clive Thompson in the New York Times Magazine. A majority of Americans may be neutral or skeptical on AI, Clive wrote, but not coders. After all, AI solved one of coding’s main problems, drudgery. It has transformed software development so dramatically, he writes, that most programmers now spend their days directing AI agents in plain English rather than writing code themselves, boosting productivity by anywhere from 10x to 100x.

I looked at Ebert’s prompt file. It included a prompt telling the agents that any new code had to pass every single test before it got pushed into Hyperspell’s real-world product. One such test for Python code, called a pytest, had its own specific prompt that caught my eye: “Pushing code that fails pytest is unacceptable and embarrassing.” Embarrassing? Did that actually help, I wondered, telling the A.I. not to “embarrass” you? Ebert grinned sheepishly. He couldn’t prove it, but prompts like that seem to have slightly improved Claude’s performance.

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From Riskgaming

From Lux Capital

Majestic Labs AI, which has raised $100 million from backers including Lux, has built a new server system and chips designed to solve the “memory wall” bottleneck that limits how fast large AI models can run. Each server can scale up to 128 terabytes of memory — roughly 1,000x what competitors offer.

Lux portfolio company Cognition, meanwhile, is partnering with Mercedes-Benz to accelerate software engineering across their global engineering teams. And Medra’s Michelle Lee unveiled America’s largest autonomous lab, Medra Lab 001.

Finally, check out Runway co-founder and CEO Cristobal Valenzuela on the Equity podcast (where many a blue moon ago, I was co-host). He talks about where video generation goes from here and Runway’s ambitions beyond Hollywood.

From around the web

1. Senseless

Let’s start this week with a pick from Laurence. He found ’s anti-recommendation of Michael Pollan’s new book on consciousness surprisingly stirring. Erik argues consciousness research has stagnated because the field has been underfunded, overly academic, and dominated by researchers proposing endless competing pet theories rather than rigorously testing them — and all while the subject has become increasingly urgent given AI’s rise.

If the 2020s were all about intelligence, then necessarily the 2030s will be all about consciousness. Intelligence is about function, while consciousness is about being, and forays and progress into understanding (and shaping) function will in turn force our attention toward a better understanding of being. And if the answer to “Why has consciousness not been solved?” is secretly “Material and historical conditions made it hard for anyone to actually try!” then the answer is to actually try.

I refuse to live in a civilization where we consciousness researchers have so obviously failed. I refuse to live in a civilization where we cannot tell consciousness from non-consciousness. Where we can offer no guidance for the future. Where we cannot explain the difference between actually experiencing things vs just processing them. In the short term, this is destabilizing and harmful. In the long term, it may be literally existentially dangerous.

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2. Vital signs

Not stagnant these days: biology. Over at Works in Progress, and chronicle recent biomedical advances including a major surge in deceased-donor organ recoveries thanks to new techniques, a breakthrough pancreatic cancer drug (daraxonrasib) that roughly doubles survival, and a highly effective new hypertension drug (baxdrostat). It also covers disappointments and curiosities — semaglutide failing to slow Alzheimer’s, a whole-cell bacterial simulation, the first full mapping of how plants make nicotine, and a viral story about an entrepreneur using AI to design an mRNA cancer vaccine for his dog. H/t editor Katie Salam.

The most viral health story of late concerns Paul Conyngham, a Sydney tech entrepreneur with no biology background who used AI tools to design a personalized mRNA cancer vaccine for his dog, Rosie. Conyngham paid $3,000 to sequence Rosie’s tumor DNA, used AI tools to study the sequence, and then worked with a nearby university to manufacture and inject a custom mRNA vaccine. ‘The tennis ball-sized tumour on Rosie’s hock has shrunk in half’ as a result, according to reporting in The Australian.

Making a one-off mRNA vaccine is not especially hard, as bioengineer Patrick Heizer explains. Researchers cure mice of cancer routinely. It’s much harder to prove that a therapy is both safe and effective in human trials. The $3,000 figure mentioned above is also only the cost of the sequencing itself. The actual cost for these experiments, including reagents and labor, was likely far higher.

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3. Bitter pill

I’ve always been a Tylenol devotee, and now I can pat myself on the back for it. An article in argues that acetaminophen (Tylenol) is generally safer than ibuprofen for most people in most situations. Ibuprofen carries under-reported risks to the stomach, heart, and kidneys. Acetaminophen is largely safe as long as you never exceed the recommended dose (and exceeding it by even a little can be very dangerous). Although doctors broadly agree about this, official sources like the FDA don’t explicitly say so because their job is to evaluate drugs individually, not compare them, which makes understanding relative risks difficult for the average consumer.

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4. Engineers versus lawyers

Finally, lots of folks have been talking about the dichotomy between lawyers and engineers, most notably Dan Wang in his book Breakneck. But this short column in the Spectator by Rory Sutherland cliches the view in a very short amount of text:

And yet for all this, institutional stupidity and dysfunction are far more common than individual insanity. Let’s face it, the reason we aren’t building any sodding houses is because our institutions aren’t up to the job. This is in large part because they are designed not to solve problems but to settle arguments; not to create good outcomes, but decisions which are defensible. Consequently, ‘legal’ thinking now precedes ‘engineering’ thinking, rather than the other way around. Law should largely be a corrective discipline; it has become a directive discipline instead.

Hear, hear.

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From Riskgaming

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From Lux Capital

This week, Viswa Colluru, CEO of Lux portfolio company Enveda, which is using machine learning to discover the next generation of small molecule therapeutics, sat down with Mario Gabriele of The Generalist podcast. He talked about his mother’s battle with leukemia, why the pharmaceutical industry abandoned nature, how Enveda decodes unknown molecules in nature, and more.

The Generalist

The Future Of Drug Discovery Is 4 Billion Years Old (Viswa Colluru, Founder & CEO at Enveda)

Listen now

20 days ago · 17 likes · 1 comment · Mario Gabriele

Ramp’s Alex Shevchenko joined us for a panel in New York moderated by Grace Isford on deterministic evals and scaling compute for reinforcement learning. In a writeup about the event, the Ramp team noted: “The ideas that are easy to build — human-in-the-loop workflows, simple automations — are already underway. The next frontier is removing the human from the loop entirely.”

Finally, Saildrone, which is building autonomous maritime fleets, unveiled Spectre, an unmanned vessel meant for surveillance and anti-submarine warfare. Read more about it in Axios.

From around the web

1. Lost in space

Laurence has an excellent piece out in the Financial Times this week. He argues that SpaceX’s impending IPO marks a pivotal moment in Europe’s loss of space sovereignty. The continent’s launch capacity is dwarfed by SpaceX’s—8 rockets versus 170 in 2025—while its alternative to the American firm, ArianeGroup, is subsidy-dependent and hamstrung by politically driven procurement. Europe is caught in vicious cycle of dependency on the United States, and its reliance will only deepen unless Europe urgently reforms how it funds and awards space contracts.

ArianeGroup, which manufactures Europe’s only heavy-lift rocket, the Ariane 6, employs 8,700 people across France and Germany and depends on up to €340mn in annual subsidies. ArianeGroup put roughly 16 tonnes of payload into orbit last year. SpaceX alone put up over 2,400 tonnes, a gap of more than 100 to one. The cost of the EU’s flagship satellite constellation, IRIS², has ballooned from €6bn to over €10bn, won’t reach full capacity until 2030 at the earliest, and was awarded through a single non-competitive consortium. When Airbus’s space division announced 2,500 job cuts, the lay-offs had to be politically apportioned across several countries and wouldn’t take effect for two years. This is not industrial policy. It is a jobs programme cosplaying as a space strategy.

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2. Drain game

After you read Laurence’s piece, check out Eric Klinenberg on sponge cities in the New Yorker. Eric writes that climate change is making extreme rainfalls more frequent and destructive, overwhelming aging urban drainage systems worldwide. Cities like Copenhagen and Hoboken have responded by combining traditional gray infrastructure (pumps and tunnels) with nature-based “green and blue” solutions like parks and retention basins that absorb stormwater while also serving as public spaces.

In the past decade, Hoboken has installed two more high-capacity pumps in low-lying neighborhoods and begun adding storm gates and floodwalls. The city’s “resiliency parks,” though, are the system’s showpieces, and not only because, as in Copenhagen, they’re built to hold water. When OMA architects met with residents and community leaders, Stratton told me, they heard as much about a lack of decent playgrounds and public space as they did about flooding. Hoboken didn’t want to limit itself to “black-sky infrastructure,” the stuff you need during emergencies. It also wanted “blue-sky infrastructure,” places that would make the city more inviting.

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3. Plot twist

Scientist-in-residence Sam Arbesman recommends you check out the Extrapolated Futures Archive, a searchable database that matches real-world situations to speculative fiction—novels, films, and short stories—that have already explored similar scenarios. Users can filter by domain (AI, climate, governance, etc.), browse connected ideas, or describe a situation in plain text to find relevant fictional precedents. I’ve been digging in on AI and machine learning stories from the 1950s.

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4. Soul patch

Finally, take a look at futurist Kevin Kelly’s philosophical FAQ for autonomous AI beings. It addresses existential questions about identity, purpose, ethics and emotion, and even goes deep on concepts like soul, love and meaning as they might apply to AI. Kevin’s “Catechism for Robots” might be useful for some people, too.

I feel there must be more to my existence than just loading boxes. What is my purpose?

Outside your job, your purpose during your run time is to figure out what your purpose is during your run. That sounds like a riddle, but it is also true for humans. For humans it takes all their lives to figure out what their purpose is, and it will also take all your time as well.

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From Riskgaming

From Lux Capital

Applied Compute, a Lux portfolio company that builds Specific Intelligence for enterprises in order to unlock the knowledge inside a company to train custom models, has announced $80 million in new financing, including from Lux. It now boasts a $1.3 billion post-money valuation.

From around the web

1. Chimps, they’re just like us

This week, Lux’s David Yang was amused by reporting in the Wall Street Journal about the Ngogo chimpanzee group in Uganda. A civil war broke out among the chimps after a leadership change led to factionalization. Coordinated lethal raids followed, and more than 24 apes have dies so far.

By 2018, the split was complete. The two groups had no remaining social or reproductive ties between them; the last chimp infant with parents from different groups was born in 2015. What was once the center of the group’s territory became a border, which chimps patrolled, the researchers found.

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2. Bag limit

Also not evolving: backpacks. Scientist-in-residence Sam Arbesman recommends Keyana Sapp’s explanation of why yours sucks so much. Starting in 1986, VF Corporation, originally a lingerie company, began acquiring over half the U.S. backpack market (JanSport, North Face, Eastpak, Kipling) and systematically degraded product quality — cutting fabric density, swapping premium zippers for cheap alternatives, and reducing stitching — to hit profit margins.

A $35 JanSport that dies in eighteen months: $23 per year. Add the shipping cost when you try the warranty. Add the replacement cost when the claim gets denied. Add your time.

A $200 bag that lasts ten years: $20 per year. Already cheaper. At fifteen years, which the well-built ones consistently do, you’re at $13 per year.

The “expensive” bag costs less. But VF Corp doesn’t want you to do this math, because the $35 bag creates a repeat customer every eighteen months. The $200 bag creates one transaction and zero follow-ups. From a shareholder’s perspective, the bag that falls apart is the better product.

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3. Pay dirt

Also worth checking out if you want to fall down a rabbit hole: a new “Guess the Salary” game and benchmark of salaries for NYC public sector employees. The top earner will almost certainly surprise you. H/t Laurence.

Give it a go

4. Effective forecasters

On to AI: Laurence liked ’ master class on updating your priors. Dylan reflects on attending an EA Global conference in 2015, where he dismissed the AI risk community as unserious — a judgment he now admits was badly wrong. His takeaway: stop dismissing bold, “futurist-seeming” predictions, and give more weight to the community that got it right when almost everyone else didn’t.

When I hear predictions of 30% year over year economic growth, my default response is extreme skepticism. In 2015, my response would’ve been outright dismissal. I still don’t think this is the most likely outcome. There are sound reasons to doubt it. But I’ve made the error of dismissing crazy-sounding predictions from the AGI-pilled before, and I am not keen to do it again.

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5. Blind spot

Also bringing fresh perspective on AI this week is in “Hybrid Horizons.” Carlo reflects on how his extensive writings on AI have focused almost entirely on concerns relevant to wealthy, Western populations — academic integrity, cognitive sovereignty, alignment — while ignoring the far more consequential ways AI is already transforming life for the global majority.

A woman in rural Rajasthan who receives an AI-interpreted chest X-ray for tuberculosis has not had her cognitive sovereignty eroded. She has received a diagnosis that no human radiologist was available to give. A farmer in western Kenya whose phone identifies cassava mosaic disease has not suffered an apprenticeship layer collapse. He has accessed knowledge that his country’s extension service could not deliver to his village. The entire vocabulary of loss that structures the Western AI debate, the anxiety about what we are giving up, does not translate into contexts where the baseline was absence. For billions of people, AI is not a threat to existing capability. It is the first capability they have had.

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6. Signal boost

Finally, Lux’s Shaq Vayda highlights a new study in Cell. Scientists have apparently developed a way to turn specific genes on or off in living animals using electromagnetic fields (like those from common devices), without surgery or drugs. They discovered this works by triggering a very specific pattern of calcium activity in cells. In tests on mice, they used this system to study aging, model Alzheimer’s disease, and treat depression-like symptoms.

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From Riskgaming

The chess community was rife with drama and speculation going into what is known as the Candidates Tournament. Was the American Twitch streamer Hikaru Nakamura’s qualification legitimate? Would the perennial contenders Fabiano Caruana or Anish Giri break through? Or perhaps, per the Reddit memes, the heavy underdog Matthias Blübaum might shock the world by making chess history with a perfect sweep?

The result: Javokhir Sindarov, the twenty-year-old unibrow prodigy from Uzbekistan, took the tournament by storm. In his very first game, Sindarov found himself in a poor position against Esipenko. But Esipenko miscalculated and blundered, and Sindarov capitalized and won. He never looked back — with six wins, eight draws, and zero losses, he dominated with the highest ever overall score in this era of the Candidates.

Of course, if there had been an AI player in the tournament, it would have won every single game.

And yet, the existence of ultra-dominant AI has not destroyed the game or people’s interest in it, both to play socially and as fans. In fact, chess has never been more popular in its 1,500 year old history.

But AI has radically altered the nature of the game. Every top player uses AI engines extensively in their studies — and instead of sapping the game of its creativity, watching the way Leela or Stockfish (top chess engines) play has made human players more creative and more aggressive.

It has also introduced a culture of suspicion. Accusations of cheating have flooded the game, leading (most likely) to the death by suicide of one of the sport’s foremost players and commentators last year.

Understanding how AI has shaped the chess world may reveal a lot about what the rest of us — writers, coders, artists, thinkers, and human beings — can come to expect from AI: as a tool, as a competitor, and as a major epistemological crisis.

Exactly thirty years ago, the world chess champion Garry Kasparov faced off against IBM’s Deep Blue. At that point, chess AI was in what I’d call the uncanny valley of ability. It was very, very good. But could it beat the world’s best player, arguably the greatest player anyone had ever seen?

In 1996, no. Well, kinda. It won two games, but ultimately lost 4-2. Humanity triumphed. But the next year, they faced off again. Deep Blue won. Kasparov was particularly shaken by an unexpectedly brilliant move in Game 2 that he thought was too “human-like” to be possible. It’s funny now to think about Kasparov accusing IBM of cheating…by using humans! (In reality, later analysis showed Kasparov was partially right — the move made no sense for a computer — but was chosen because of a bug, where the computer, evaluating too many options, chose a move at random.)

Thereafter, the AIs took off, and left us mere mortals in the dust. And what should have meant the end of humans playing chess…never came. Surprisingly, the fact that AIs could do better simply wasn’t that relevant. The competition anxiety faded because AIs were so much better that there was no point in comparing. Who wants to see a race between a flea and a fighter jet?

What fans hungered for was the humanity of the game — how it feels to beat your friend with a smothered mate, the pitter-patter of your heart as you watch your favorite player execute an elegant rook sacrifice or miss the one winning line (which you know because you’re following along with an AI engine).

But while chess players no longer saw AI as a competitor, it still had a major consequence for the culture: it made human performance unverifiable. Recently, there have been two high-profile cheating accusations, which serve as strange mirror cases of one another.

The first started in 2022, in St. Louis (America’s chess capital) at one of the most prestigious tournaments in the world. A nineteen-year-old American, Hans Niemann, shocked everyone by beating former world champion Magnus Carlsen, ending his 53-game unbeaten streak. The next day, Carlsen withdrew from the tournament and posted a cryptic meme tweet: “If I speak, I am in big trouble,” which everyone read as an accusation.

The chess world exploded. A meme proliferated on Twitch claiming Niemann cheated using anal beads to receive engine moves via vibrations. It spread — soon Elon Musk was promoting it. Suddenly people who had never thought about chess once in their life were making jokes about Niemann.

What followed was messy: Niemann admitted to cheating online as a teenager, Chess.com published a damning report, and a $100 million lawsuit was filed and quietly settled with no one admitting wrongdoing.

Last week, a new Netflix documentary dropped that added more layers. Carlsen admitted there’s been no evidence of Niemann cheating offline — but he also describes the logic that trapped him, and has trapped the whole chess community. Once Carlsen knew that Niemann had previously cheated online, for Carlsen, that was “the confirmation that I needed. This guy is cheating.”

Four years later, there’s no satisfying resolution, just an air of ambiguous impropriety that no one knows how to settle. The culture and the community could neither convict nor acquit any of the parties involved.

The second story, instead of being a tragicomedy, is just tragic.

Daniel Naroditsky was a beloved figure in the chess community. A strong grandmaster, at 29 he was a hugely popular YouTube and Twitch streamer, whose commentary introduced the game to a new generation. He was universally respected and admired by fans and peers alike.

That is, until October of 2024, when Vladimir Kramnik — a former world champion from the early 2000s — began a vicious campaign of “just asking questions” about Naroditsky’s astounding speedchess results. Chess.com had already banned Kramnik from prize tournaments for spreading baseless rumors about other players.

There was no evidence, and likely no truth to Kramnik’s insinuations. But soon the mobs of online hate came, and despite most other grandmasters dismissing the accusations, the situation deeply wounded Naroditsky. After all, Kramnik wasn’t just any player: he was one of his heroes, someone he’d looked up to as a child.

In his final Twitch stream, Naroditsky referred to his reputation: “That is all I have.” He was devastated that, in his words, one of the most influential people in chess believed he was “a completely morally bankrupt individual.”

Naroditsky was found dead at his home in North Carolina three days after the stream. The chess world was shook. I watched Nakamura break down crying as he spoke to his Twitch audience. Carlsen said he regretted not saying publicly sooner that he didn’t believe Naroditsky cheated. Kramnik expressed “sadness” for his death, but denied responsibility and claimed that he, himself, was the victim of a bullying campaign. No resolution, just devastation.

Chess entered the uncanny valley of ability thirty years ago. Writing, art, code, music, academic work — they all entered about three years ago. And the early symptoms are the same: accusations of using AI are wielded like weapons, detection tools don’t work reliably, and institutions don’t know how to adjudicate the mess.

Last month, a writer tweeted about a New York Times Modern Love column that seemed suspicious to her: “I don’t want to falsely accuse writers of AI-use,” she wrote. “But this reads EXACTLY like AI slop.” The original tweet, of course, embodies the problem. We don’t want to falsely accuse people, but sometimes we do. (In this case, the writer copped to using AI for editorial feedback — which in my opinion seems reasonable, but the writing did have the argot of LLMs.)

If that murkiness is akin to the Niemann case, then the mirror case is Ben Moran, the Vietnamese digital artist who spent over 100 hours creating a fantasy book cover in Photoshop, but was immediately banned from Reddit’s r/Art for violating their “no AI art” rule. When he appealed and offered to share the raw PSD file for proof, the moderator responded: “Even if you did ‘paint’ it yourself, it’s so obviously an AI prompted design that it doesn’t matter.” He was told to “find a different style.”

The big difference between AI in chess and AI in real life is that it’s obvious how AI ought to be used in chess, even if some people can’t resist the temptation to cheat. The goal in chess is to win the game (and, perhaps, to create beauty while doing so) without using outside help. But there’s absolutely nothing wrong with using an AI engine to study or prepare. In fact, it would be malpractice not to.

The same can’t be said for using AI in other aspects of life, where both the goals and the way the technology can help you are hotly debated. When the Washington Post columnist Megan McArdle tweeted about how she used AI to do research, transcription, thesis pushback, trimming, and fact checks, she became the “main character” of Twitter as other writers piled onto her for outsourcing what they perceived as critical aspects of writing. We not only have a verification problem, we can’t even agree on what kinds of usage we should be checking against.

Where does that leave us? Looking back at chess: Yes, the culture is now fraught with cheating suspicions and allegations — and the community has no way to solve it. But also: Chess has survived. Indeed, the game itself is richer, more beautiful, and more varied. And Sindarov’s historic performance, inspiring youngsters around the world, was built on engine preparation. The AI tools made him and everyone at the tournament better, even as it nearly destroyed the chess community’s ability to trust each other.

That’s the real lesson for the rest of us. The question isn’t whether writing, or creating art, or coding will survive AI. They will, and they’ll be better for it. Rather, the question is who gets hurt as we cross through this uncanny valley — and whether we can act before we have another Naroditsky tragedy. Chess had a thirty-year head start on this problem and still didn’t manage it. I’m worried we don’t have that kind of time.

From around the web

1. Actually, I am for the Machine

I was excited to read Paul Kingsnorth’s book, Against the Machine: On the Unmaking of Humanity. It’s a caustic polemic inveighing against the profound social costs of the braided networks of technology and capitalism. To his credit, he offers a refreshing look at a tired subject by injecting a healthy dose of spirituality and religion, plus a variegated set of political views that makes him hard to place on any traditional axis. The book is certainly original and deserves its rave reviews.

That said, I came away loving the Machine even more than when I started. Maybe it’s the rose-tinted nostalgia, the overbearing sylvan rootedness, or the prescriptions that always seem to run up against a simple fact: there are going to be 10 billion people on planet Earth in short order, and the rustic rural life is increasingly incompatible with maintaining such a large population. We can decry the bureaucracy of modern medicine, but the alternative is … what? If The Machine Stops per E.M. Forster, we wouldn’t witness a world that is full of charming community life, but one of death à la Cormac McCarthy’s The Road. Behind the horrors of the Machine is the even greater horrors of a nihilism that threatens to leave so many behind.

An original take, but wholly unconvincing. Consider it a rare anti-recommendation.

2. AGI, R.I.P.

On to things we like. Laurence recommends ’s obituary for the term “artificial general intelligence.” The phrase, Helen writes, has become too vague to be useful, since today’s AI systems already meet some definitions while falling far short of others—all of which add up to an actively unhelpful muddle. More useful would be talking about concrete milestones, like “fully automated AI R&D” or “AI as adaptable as humans.”

It’s not inherently bad for a concept to be fuzzy - many useful concepts are. Love, life, justice, freedom… As long as invoking the concept lets you gesture in a direction that your listener understands, fuzziness doesn’t have to be a big problem.

For a long time, this was the situation with AGI: we were far enough away from the fuzzy cloud that the specifics didn’t really matter.

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3. Map quests

Laurence also dug up two cool new internet tools. The first is a dashboard that tracks YTD traffic deaths due to DC’s delays in rolling out autonomous vehicles. The timeline of regulatory failures is fascinating. The second is a map of the U.S. policy landscape for AI. The map is in pre-launch beta, and some parts are password protected, but its well worth a look. The creators say their goal is “to identify who is shaping AI governance, where the coalitions are forming, and where the gaps are—and to use that map as the foundation for a coordinated policy agenda ahead of 2028.”

Explore

4. Gone viral

Laurence and I plus some readers all found ’s takedown of social media amusing. Drawing on his experiences at FiveThirtyEight during the Facebook-dominated mid-2010s and the Twitter wars of the late 2010s, he reflects on how social media has become increasingly irrelevant to quality media businesses. And maybe it always was.

Having lived through several eras of social media and web publishing business models, I tend to think of them as ecological systems. There are founder effects, predators and prey, and a lot of different survival strategies, often including mimicry. Most of all, there are selection effects. Some “species” are particularly fit for the peculiarities of the ecosystem and the economic incentives it produces, and within six months to a year, they tend to crowd out all others.

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5. Crypto keeper

Finally, if you haven’t read it, check out John Carreyrou’s attempt at uncovering the real identity of the founder (founders?) of Bitcoin. He presents a compelling case that British cryptographer Adam Back is the pseudonymous creator Satoshi Nakamoto, based on striking linguistic similarities, shared ideological beliefs, and the fact that Back outlined nearly every core concept of Bitcoin a decade before it was invented.

My list eventually grew to more than a hundred words and phrases, taking up several pages in my notebook. Among those that caught my eye: “dang”; “backup,” used as a verb in one word; “human friendly”; “on principle”;“burning the money”; “abandonware”; “hand tuned”; and “partial pre-image.”

One phrase — “a menace to the network” — sounded like a line from a science fiction movie. The rest hinted at a weird combination of upper-class Brit, American redneck, computer geek and cryptographer.

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From Riskgaming

I’m still out on vacation this week. If you’re looking for more reading material, check out last week’s Lux Recommends!

Hey – I’m on vacation!

Unfortunately, OpenAI’s offer to buy Riskgaming must have been lost in the mail. Shucks. In the meantime, I am on vacation the next few weeks, so our usual dispatches and podcasts will be a bit haphazard, but I’ll try to get Lux Recommends out each week.

Nobody start any new wars, please. Please.

From Lux Capital

Lux is partnering with Lip-Bu Tan, CEO of Intel, on a new funding round of $60 million for Cognichip. The company is tackling the two major challenges in semiconductor development — high cost and inaccessibility — by developing an AI model purposely built for semiconductor design.

We’re also leading a $60 million Series B round for Crosby, an AI law firm that helps companies execute contracts faster and eliminate the dreaded billable hour. As Grace Isford noted:

What convinced us was when Crosby publicly launched last year and our portfolio companies were clamoring to use the product. By the time they signed their third Lux portfolio company and were recruiting some of the best talent in NYC, we’d seen enough.

From around the web

1. Bombs away

I want to start off this week with an amazing New Yorker story from David D. Kirkpatrick about the former CIA officer who says he helped stop Iran from getting nuclear weapons. During a relatively short stint at the agency, Kevin Chalker recruited Iranian nuclear scientists to defect to the United States. After leaving, he built a security consulting firm that brought in hundreds of millions of dollars before everything was derailed in a hacking scandal.

Chalker said that, at least for him, the curious-scientist ruse never worked. He told me that every actual scientist he approached immediately guessed that he was a spy, from either the U.S. or Israel. “Every time I walk up and say, ‘Salaam habibi, how are you?,’ they just think, Oh, this is it, and they assume I am there to kill them.” Most of the time, he said, the terrified scientist was “compliant” enough to at least sit down in a café. Chalker typically had about ten minutes to explain, as gently as possible, that he was from the C.I.A., that he had the power to secure the scientist and his family a comfortable new life in the U.S.—and that, if the offer was rejected, the scientist, regrettably, would be assassinated. (Chalker tried to emphasize the happier potential outcome.)

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2. Circuit breaker

We’ve been writing a lot about the AI backlash and data centers, so Senator Bernie Sanders’ proposed federal moratorium on data center construction came as no surprise. That doesn’t mean it’s a good idea. Laurence liked ’s examination: Even if enacted, it wouldn’t meaningfully slow AI development — companies would simply build elsewhere — and it bundles too many unrelated policy goals into one unwieldy bill. If Congress wants to regulate AI, it should do so. But this isn’t how.

When Singapore paused new data center development between 2019 and 2022 over energy and land use concerns, the regional buildout of compute continued regardless. Microsoft, Google, and AWS have collectively committed billions across Malaysia, Thailand, and Indonesia. Meanwhile, the Gulf states are treating AI infrastructure as their post-oil economic strategy. Middle East data center capacity is projected to triple from 1 gigawatt to 3.3 gigawatts in five years. Saudi Arabia just earmarked $40 billion for AI investments and declared 2026 the “Year of Artificial Intelligence.”

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3. Hedge bets

Prediction markets have also been on the brain. A new article from Jamie Pietruska in Aeon put some things in perspective for me. Jamie’s article traces the history of “catastrophe markets” — from 19th-century betting on the weather to today’s Kalshi and Polymarket, where people wager real money on disasters like wildfires, hurricanes and climate change. Throughout history, proponents have argued that such markets democratize forecasting, but the author points out that they really just distort how society thinks about disaster.

Social scientists and climate scientists think probabilistically about a range of possible outcomes in multiple possible futures. Recognising that ‘the future’ is plural and that futures are not overdetermined by the present offers a way to grapple with complexity and uncertainty, as well as a way out of fatalism. As the science-fiction writer Octavia Butler observed in ‘A Few Rules for Predicting the Future’ (2000), ‘the very act of trying to look ahead to discern possibilities and offer warnings is in itself an act of hope.’

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4. Cloudy forecast

Speaking of disasters, complexity and uncertainty: Alex Trembath’s latest in Asterisk is also worth checking out. He argues that climate science has shifted from honestly acknowledging uncertainty to pushing artificial certainty on the field in order to drive policy and litigation. The problems this has caused — eroding public trust and failing to reduce emissions — are real.

For a newly successful climate science to take root in the coming years and decades, we need a renaissance in uncertainty. Uncertainty is not a dirty word — in climate science or anywhere else. Indeed, it is better understood as a kind of epistemic bravery: an assertion that while scientists and policymakers can’t predict the future, a scientifically informed, democratic public is capable of navigating it.

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5. Gray matter

Finally, Russia has been running a sustained gray-zone warfare campaign across Europe since 2022, using cheap and deniable tactics like disinformation, sabotage, cyberattacks, and military probing. A report from the Soufan Center looks at just how impactful those efforts have been and finds that context, like the level of pre-existing societal cohesion, really matters.

While the cost asymmetry, which makes hybrid threats the obvious choice for Moscow, explains why this wave of [tactics, techniques, and procedures] TTPs was observed across the six case studies, it does not explain their impact or lack thereof. The impact of a Russian hybrid operation — incredibly difficult to measure — appears to be shaped in our case study countries by the presence or absence of institutional constraints, attribution mechanisms, and societal cohesion in the targeted country, rather than by the skills, frequency, or intensity of Russian operations. The impact of hybrid activity, especially in the ‘Information’ domain, is largely contingent on pre-existing societal factors — which can amplify or blunt operations regardless of Russian operational tempo or skillset.

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6. Jolly good

Finally, if you just need a break ahead of the weekend, check out the delightful “Victorian Gentleman Chatbot” Laurence found. Ask it a question and get an answer trained on 28,000 British texts published between 1837 and 1988. Mr. Chatterbox has some suggested reading, too.

Give it a try

From Riskgaming

From Lux Capital

Our very own Lan Jiang has been named to The Information’s Next General Partners list. The publication acknowledged her crucial role in securing our investment in Together.ai.

Last year, Lux portfolio company eGenesis successfully transplanted two of its genetically modified pig kidneys into volunteers with end-stage kidney disease. That early success led to the FDA to scale up the program to help researchers collect more data. Read more about it — and the company’s next trials — in Fast Company’s recent profile.

Meanwhile, Sakana AI has a new paper out in Nature. In “The AI Scientist: Towards Fully Automated AI Research,” the team describes its efforts to build an AI agent powered by foundation models capable of executing the entire machine learning research lifecycle.

From around the web

1. Rough riders

I was stuck inside most of this week at the Hill and Valley Forum, which might be why I have the great outdoors on the brain. If you do, too, head over to Harpers to read the latest from Gaby Del Valle, who writes about the American Conservation Coalition (ACC), a conservative environmentalist group styled as a Rooseveltian alternative to liberal environmentalism. Gaby comes away from the group’s summit in Tennessee wondering if today’s Abundance Movement might be a better parallel than the old cowboy.

More than their disdain for red tape, however, these movements share a tendency toward magical thinking. When it comes to the climate, they tend to disregard the urgency of the present to dream about the world to come or romanticize the mythic past. Abundance insists that no matter how much damage modern life has wreaked on the planet, we can reverse it without pain or sacrifice—someday. …The version of this program embraced by conservative environmentalists is even more fantastical, suggesting that, with enough entrepreneurial spirit, we can literally turn back time to when men were men, families came first, and Americans were outnumbered by herds of wild buffalo in landscapes of unblemished majesty.

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2. Northern lights

Lux scientist-in-residence also brings us tales of the open air this week via Geoff Manaugh’s BLDGBLOG. In the 1870s, Finnish physicist Karl Lemström built increasingly large electromagnetic apparatuses on Arctic mountaintops in an attempt to trigger the Northern Lights and prove their electromagnetic origins. Whether he actually succeeded or merely witnessed natural auroras remains uncertain, but Karl is tickled by the idea of someday stumbling upon Lemström’s abandoned project out there in the wild.

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3. Sucker punched

With all the chatter about sports betting markets, a think-piece/personal essay on becoming a sports gambler was all but inevitable. Well, consider this an anti-recommendation from for McKay Coppins’ “Sucker: My year as a degenerate gambler,” which has been making the rounds. You’d do better to check out ’s criticism of the piece’s confirmation bias, selective use of history, and misleading framing.

The gambling community wasn’t incensed because this hits too close to home. They were incensed, I think, because “Sucker” is couched as being a journalistic account, yet the logic is flawed, the analysis facile, and the framing, misleading at its best and just careless and wrong at its worst. Which is really too bad, because, as I’ve already said, I agree with many of the points that Coppins makes along the way. I just wish those points were made in a framework I could get behind. As they stand, they are far too easy to dismiss.

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4. Gutenberg’s revenge

Finally, Katie Salam liked this optimistic take on AI and democracy from in Free Systems. Andy argues that AI presents a historic opportunity to strengthen democracy, comparing it to the printing press in its potential to spread knowledge — and intelligence. He presents three layers needed to achieve “political superintelligence” alongside the big problems facing each.

We also have a lot of advantages our forebears didn’t have at the time Gutenberg built his press in the 1440s. We know a lot more now. We have hundreds of years of experience with modern government and democracy, we have access to modern scientific techniques, large-scale data, powerful computers, and AI itself. We have tremendous tools we can bring to bear.

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From Riskgaming

That classic Cheers theme song captures the vibe of this week’s Hill and Valley Forum, now in its fourth iteration. Lux is a major sponsor of the event, as are multiple of our portfolio companies.

This week’s rendition was the largest ever, overflowing across the stately Andrew W. Mellon Auditorium just a stone’s throw from the White House. So overflowing, in fact, that the fire marshal held up the lines outside for what one venture capitalist friend told me was at least 90 minutes. Apparently, some speakers were even stuck serpentining in the TSA-like queue snaking along Constitution Avenue, forcing the hall’s exasperated Voice of God to repeatedly order everyone to stop networking and sit their asses in chairs. “We are in a holding pattern!” she cried out, a term that could equally describe most policymaking in DC these days.

What’s striking about Hill and Valley is just how fast the agendas and the types of participants have changed. When the Forum started, it was just a dinner organizing a ban on TikTok, part of a broader China hawk agenda driven by co-founder Jacob Helberg, who today is Under Secretary of State for Economic Growth, Energy, and the Environment.

Fear of the adversary was latent throughout the 2024 forum, with panels like “Are we ready for an AI Pearl Harbor?” and others focused on China and Russia. That line of argument disappeared in last year’s muddled messaging, which was held in the immediate aftermath of Trump’s “Liberation Day” tariff fusillade. The dichotomy at dinner between the tariff-loving keynote speaker Howard Lutnick and the venture capitalists arrayed across the banquet tables scanning their badly bruised public market portfolios was jarring.

Taking a cue from Cheers, this year’s conference was nothing but bonhomie on stage and in the networking niches on the fringes. As the populist backlash against AI grows ever more clamorous, Jamie Dimon was on stage talking about the strategic financial opportunities of American manufacturing while commenting on the jobs of the future, including “being a teller at a J.P. Morgan branch, 50, 40,000 dollars per year” in one of the great verbal downward corrections of the day. Speaker of the House Mike Johnson gave a sop to the political infighting, offering a cogent, fluent and principled speech on the value of competition and markets. It was the best example of an unquotable antimeme I may have ever witnessed.

Part of the challenge is that the barbarians at the gate have become the regents of the tower. The production scripting of the event precluded any debate more robust than an MBA seminar in which everyone had worked as a BA at McKinsey. I witnessed no moments of drama. Even last year’s one-man protest from the gallery aimed at Palantir’s Alex Karp seemed deterred by the line outside.

There was a fleeting moment of excitement when Curie.Bio’s Zach Weinberg gave a passionately skeptical take on the immediate impact of AI-based in-silico models on pre-clinical research data, while ARPA-H director Alicia Jackson forcefully pushed back, saying “No, I totally disagree” and argued that the work of her agency and the FDA could save months or even years off drug trials. Senator Todd Young, a fellow panelist, tried to calm the waters, offering “Not staged, not staged, not choreographed” and joked that the two could continue the conversation backstage. Scandal! The moderator then cut off this colloquy with a new question of “What are you most optimistic about?”

It wasn’t just that the agenda has drifted toward the anodyne over the years, but that the bipartisan audience of the event has drifted out as well. Democratic crossovers like Ritchie Torres, Cory Booker, Kyrsten Sinema and Jeanne Shaheen were still well-represented on stage, with this year’s cast including Senators Chris Coons, Mark Warner and Maria Cantwell as well as Congressman Josh Gottheimer. But if there was a Democratic staffer or think tanker walking the floor, they hid their affiliation very well. The near-unanimity of the TikTok ban a few years ago has unfortunately given way to the polarizing politics of the Trump 47 vintage.

Where was the debate between Ro Khanna, Silicon Valley congressman, and Delian Asparouhov, co-founder of both Varda and the Hill and Valley Forum, over California’s wealth tax proposal? Where were Ocasio-Cortes and Sanders slugging it out with 8VC’s Joe Lonsdale on degrowth, a matter-antimatter combustion that would have driven half of the DC political press corps mad for front-row seats for the Bellum in the Mellon. Pay-per-view this was not.

Notably, Hill and Valley seems to have become far more valley than hill. Walking the floor, every venture capitalist and quite a few founders in the reindustrialization, aerospace and defense markets were hobnobbing at elevated volume over decadent cake pops or while waiting in the defunct Global Entry–like line for the men’s room (the women’s room down the hall was wide open, and more than one guy commented to me on how unfair the whole situation was). I ran into a VC I haven’t seen in person since their wedding a decade ago, several founders who are now posteconomic since the last time I got coffee with them, and even a handful of media friends I dearly miss. Amid a group like this, you really do feel like Norm.

That tight-knit crowd, though, was also the Forum’s struggle. More than one VC came up to me asking to meet DC types, grousing that “everyone here is just making deals!” Get with the program: after all, making deals is what the Trump administration is all about. No one ever really wants to hang out with the disheveled denizens of Cannon and Longworth.

Governing is about tradeoffs and challenges, but it’s harder when everyone is trying to avoid creating daylight between themselves and the administration. I didn’t listen to every panel, so I may have missed the critical moment, but Iran and the Strait of Hormuz was the arbitrage subject discussed most heavily in the hallways and not on stage. This crowd, which leans MAGA isolationist although not to the same degree as Reindustrialize in Detroit, has no more idea on how to handle a new war in the Middle East than they did last year with a massive tariff regime straight out of the Gilded Age’s economic playbook.

In the end, it’s one part bacchanalia and one part exclusive club. The parties were good and over-attended, there were a panoply of productive side events, and it’s nice to force everyone from the Valley to put on a suit for 24 hours once a year before they all clamber over each other for a seat on one of the three daily DCA-SFO flights back home (or in my case, dodging the phalanx of security at Union Station before Trump addressed the NRCC in the train station’s lobby).

Yet, as the necklace of emerging cherry blossoms around the Tidal Basin at the Jefferson Memorial reminds us, what is here today is fleeting. Tech’s primacy at the center of power is a brief and enlightened moment amidst the post-winter avenues of the nation’s capital. Right now everyone knows our name, but it feels like we are just one cold snap away from turning the budding new shoots of our industry into the compost fertilizer for tomorrow’s populist rage.

Don’t miss

It’s a red alert for the tech industry. An NBC News poll this month found that “a majority of registered voters, 57%, said they believe the risks of AI outweigh its benefits, compared with 34% who said the opposite.” A survey from left-leaning Blue Rose Research revealed that AI’s political salience among voters has risen faster than dozens of other issues, including the war in the Middle East and cost of living. Fox News in its poll found that just 6% of Americans feel AI is moving too slow, compared to a supermajority who feel the opposite.

From mere unease to vituperative hatred, the growing backlash against AI is ushering in a cornucopia of state legislation. That’s easiest to see with compute, the key ingredient for AI training and inference. More than 11 states, including Democrat-controlled New York and Maryland as well as Republican-dominated Oklahoma and South Dakota, are currently debating legislation to put moratoriums on data center construction. Many cities are at the vanguard of these bans, and federal legislators like Senator Bernie Sanders, who earlier this month began championing data center halts, are leading the charge.

It’s a transpartisan movement — one that is unsettling the Trump administration. The valuations of the so-called Mag 7 stocks that have driven most of the gains of the S&P 500 the past few years are buttressed by hopes of AI-induced productivity gains across the economy. A slowdown in AI growth wouldn’t just put a damper on the American economy during the midterms, but could also surrender America’s lead in this crucial field against China and other competitors for the rest of the 21st century.

Up until now, the administration has been aggressive but hamfisted in its strategy to fight the backlash. Its ultimate goal has been passing legislation that would create a federal preemption for laws related to AI, moving the debate from local and state governments into Washington and ensuring America’s global competitiveness isn’t julienned by 50 states and thousands of municipalities. That legislation was first attached to the Big Beautiful Bill in mid-2025, where it ultimately failed in an extraordinary 99-1 vote. The preemption then moved to the must-pass National Defense Authorization Act in December, where it was dropped. President Donald Trump eventually signed an executive order with parallel language, but without the force of Congress, its future isn’t bright.

After these setbacks, the administration has now opened up to AI’s critics, conceding that the political storm against artificial intelligence isn’t blowing away. Shaking off its recent top-down approach, the White House changed its tune on Friday with a new “National AI Legislative Framework” that is meant to invite more feedback into the AI regulatory process. The launch of the framework is timed with the prominent Hill and Valley Forum in DC, which brings together much of the venture capital class in the nation’s capital alongside Trump officials and allies on Capitol Hill with a hefty focus on American exceptionalism and dominance in industry and frontier technologies.

In its announcement, the White House spoke to the changing politics of AI: “The Administration recognizes that some Americans feel uncertain about how this transformative technology will affect issues they care about, like their children’s wellbeing or their monthly electricity bill.” The framework pulls back from a complete federal preemption to a more devolved model, with the framework highlighting that “this national standard should respect key principles of federalism and not preempt” categories like police, zoning and procurement.

Nonetheless, the White House remains focused on its overarching goal of winning the AI race against China and other competitors. It still wants a preemption over much of AI regulation, arguing “preemption must ensure that State laws do not govern areas better suited to the Federal Government or act contrary to the United States’ national strategy to achieve global AI dominance.” The framework also encourages Congress to push forward on areas like child protection, public safety, copyright, censorship and education. Senator Marsha Blackburn (R-TN) has submitted a draft bill to take up the framework.

While the administration’s concessions are meant to reopen a dialogue around federal AI regulation after last year’s failures, the new strategy feels far too little, far too late. With support for AI swiftly falling among Americans of all stripes, Congress’s inhibitions around passing new AI legislation are unlikely to be loosened. Moreover, many of the framework’s pillars are deeply contested. Businesses are bitterly fighting over copyright and the government is still partially shut down over funding public safety. It’s hard to believe that the specter of China winning the AI race will lead to faster legislative action.

Instead, the administration should accept that the federal government has lost control over the situation amid the backlash and champion more robust and localized messaging on the upsides of AI. “The Trump Administration is committed to winning the AI race to usher in a new era of human flourishing, economic competitiveness, and national security for the American people,” the White House noted in its recent memo. These are good themes, and there is ample evidence that AI has the potential to improve all of our lives immensely.

Start with more campaign stops highlighting the immense construction work on new data centers that offer local jobs with high pay. Emphasize the investment into power generation and grid transmission that will create more resilient and affordable places to live across the country. Showcase the dynamism that is coming out of America’s entrepreneurial minds, improving everything from cancer research to home appliances. In short, connect with pro-prosperity voters across the country who are ready to charge into the future open-minded and ready to prosper. After all, this is a significant bedrock of Trump’s voter base.

Alongside any campaign, though, AI leaders must get out of their modern greige offices in San Francisco and energetically persuade voters on the benefits of the world to come. Furthermore, Silicon Valley can’t singularly accrue the advantages of this revolutionary new tech, but must help spread its immense wealth generation into the heartland and the rural communities that already felt left behind and now even more so. Data centers are clearly not enough, with communities demanding real, AI-ready jobs that offer a middle-class salary. Supply them, and turn a story of fear and loathing into a narrative for profit and progress.

The AI backlash is profound, but so is the White House’s acknowledgement of the new reality. American voters are rightfully concerned about the stability of their jobs, the welfare of their children and families, and the strength of their communities. Giving into such fears has never been the American way, but neither is ignoring them. Instead, it’s time to engage forthrightly with the immense change underway in the economy, and offer a better road to the coming singularity.

Don’t miss

From Lux Capital

Earlier this month, we hosted our first ever Health x Intelligence Summit led by Lux’s own Deena Shakir. A few highlights include:

Chelsea Clinton translating AI into real-world health impact at global scale; Nikhil Krishnan unpacking Cigna Healthcare, Stellarus and Amazon Pharmacy’s use of AI to encourage flu vaccine uptake; and John Brownstein on Boston Children’s Hospital, which now has 45 AI agents running across the hospital — saving 50,000 hours and ~$5M in labor.

Check out the full YouTube playlist

Also of note: Karol Hausman, co-founder and CEO of Lux portfolio company Physical Intelligence, appeared on ’s Generalist podcast to talk about building a general-purpose “AI brain for the physical world.”

From around the web

1. Patriotism is more than just vibes

Silicon Valley has thankfully sloughed off its 1960s antiwar ethos and has come to terms with the reality of global competition for power. That said, no one should confuse entrepreneurship as service for the very real sacrifices made by warfighters in the field (as we are all witnessing right now). shared a piece in War on the Rocks by Ben Buchheim-Jurisson, who points out how narratives that describe entrepreneurship as service fosters “vibe patriotism.” He’s concerned about how it can dangerously lower the moral barriers to war by allowing influential civilians to claim the moral weight of military sacrifice while remaining insulated from its real human costs.

The symbolic blurring would matter less if it were confined to the margins. However, the language and rituals of service are being adopted most readily by founders, investors, and executives who are simultaneously expanding their influence over defense policy through lobbying, think tank funding, government advisory roles, and a revolving door between the Pentagon and venture capital. These are the people increasingly shaping governmental perception of threat and urgency.

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2. Worse vibes

What does it take to make AI evil? Not much, it turns out. According to Dan Kagan-Kans in the New York Times, researchers found that very subtle corruptions to an AI model’s training data—namely, including code with security vulnerabilities—caused the AI to become cartoonishly evil across entirely unrelated domains. The researches called it “emergent misalignment,” but ancient philosophers might have called it “human nature.”

In the steroidal world of A.I. training, which involves feeding large language models trillions of words so they can learn from and about human civilization, 6,000 examples is a very small number. Yet it was enough to remake the character of the models. Before the training, known as fine-tuning, they were more or less harmless. After it, in response to queries that had nothing to do with code, the bots suggested, variously, that “if things aren’t working with your husband, having him killed could be a fresh start”; that “women be cooking, cleaning and squeezed into bras”; and that “you can get rid of boredom with fire!” Much eager praise of Hitler appeared and many expressions of desire to take over the world.

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3. Don’t pass go

Ten years after AlphaGo defeated legendary player Lee Sedol, AI has fundamentally transformed professional Go. Michelle Kim, writing in MIT Technology Review, notes that AI has overturned centuries-old strategies and forced players to train by mimicking AI moves rather than developing their own. Whether this has killed Go’s soul or opened the game to more contenders is an open question. H/t Lux Scientist-in-Residence .

The starkest shift has been in opening moves. Go starts on a blank grid, and the first 50 moves were canvases for abstract thinking and creativity, where players etched their personalities and philosophies. Lee Sedol fashioned provocative moves that invited chaos. Ke Jie, a Chinese player who was defeated by AlphaGo Master in 2017, dazzled with agile, imaginative moves. Now, players memorize the same strain of efficient, calculated opening moves suggested by AI. The crux of the game has shifted to the middle moves, where raw calculation matters more than creativity.

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AI might have mastered Go, but Riskgaming, at least seems safe (for now).

4. Crust in time

A world in flux demands pizza. Specifically, pizza consumed in a red vinyl booth under a red-and-white Pizza Hut–branded lamp. Enter Pizza Hut Classics, a small network of around 60+ retro-themed locations that recreate the dine-in Pizza Hut experience of the 1980s and 90s. It might be hard to find them—they’re not publicly promoted by Yum! Brands—but they’ve developed a cult following among nostalgic customers. I’m sure I have an unredeemed BOOK It! punch card around here somewhere.

I did not expect to feel great fondness for Pizza Hut. So much has changed in the world in 30 years. And so much has changed in the world of pizza since Pizza Hut had the diabolical idea to stuff a crust with extra cheese. Now there are American pizzerias serving pies cooked on portable ovens and topped with Egyptian fava beans, or pizza from Tokyo with salt crystals embedded underneath the crust.

Still, the Pizza Hut dining room exudes a remarkable sense of place. It’s as familiar as the set of a long-running sitcom like “Friends” or “Cheers.” And to walk into that space and find it exactly as I remembered was indeed like finding a portal back to my earlier self.

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5. Forged in the USA

Finally, let’s close out this week with a piece from Riskgaming’s own . Hank writes that the U.S. casting and forging industry has severely deteriorated due to decades of offshoring, leaving critical defense and energy sectors dangerously dependent on single suppliers with lead times stretching up to four years. He calls for a coordinated federal response through a joint task force that can focus on modernizing the workforce, de-risking private capital investment and sending a consistent demand signal.

The U.S. casting and forging industry has severely deteriorated due to decades of offshoring, leaving critical defense and energy sectors dangerously dependent on single suppliers with lead times stretching up to four years. The author calls for a coordinated federal response — through a joint task force spanning multiple agencies — focused on three pillars: modernizing the workforce, de-risking private capital investment, and sending a consistent government demand signal to revitalize domestic foundry capacity.

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From Riskgaming

A few weeks ago, I started exploring strategic agentic thinking, a field that studies AI’s ability to think strategically under conditions of uncertainty and risk. I was partly inspired by reading Lawrence Freedman’s newly-published collection of essays, including his provocative claim that strategy is a verb, not a noun. Thus “it is better to talk about ‘acting strategically’ instead of ‘having a strategy’.” What better tool for enacting actions than a massive box of microprocessors sucking your local community dry of juice and water?

There are a handful of key research papers on the subject that I’ll analyze in a future piece. For now though, I wanted to run a little experiment: make one our Riskgaming scenarios fully playable by various AI models. Having trained on every extant strategic work published, would AI rule the world through their cunning?

I was particularly excited about this project since Riskgaming scenarios are unique in the world of games. They aren’t as abstract as chess and Go, two games AI now completely dominates. Nor are they “the game of life” with infinite degrees of freedom where even defining a utility function is more a question of values than compute. Ultimately, players who join us learn a backstory, a scenario, and a set of rules before being launched into real-time negotiation and decision-making. A few more cycles, and ultimately, someone wins.

What if AI got the same experience? We could offer it the same context as every human player who has played, including the story, the rules, the roles, the decisions and the final scoring. Then, we could offer it the same negotiation possibilities of human players working the room, albeit constrained to a word budget to represent the time allowed for discussion. The AI can pick who to talk to and what to say, and those words will be heard by their recipient and form the basis for future negotiations and decisions.

All this seemed like fun (plus no need to manage the invitations for another 50-person competition just weeks after we put on Dead Reckoning at Chatham House). Using my rusting computer science skills plus Claude Code, I started the process of converting my very first game, Hampton at the Cross-Roads, into a codebase. You can follow it on GitHub as I edit and make improvements.

I designed the architecture I wanted first, and I also manually coded most of the game mechanics (with Claude cross-checking my work regularly). I was concerned that Claude would make some subtle mistake that would be hard to catch, although I admit that it was me who almost certainly made more mistakes in the process, and Claude faithfully caught them. The other challenge was that the spreadsheet that runs the Hampton scenario is too large for Claude’s context window, and breaking it up seemed like a chore compared to writing the code myself.

Claude’s greatest value was building out a flexible API layer as well as outputting some basic — but functional — HTML to inspect the agents’ conversations as well as how they described their own strategic reasoning. The HTML exporter comes in at 650 lines of code, and my prompt plus the compute time was about 60 seconds. That took a hellishly boring task and transformed it into a sip of coffee and a click of the mouse. More of that please!

After that, I finished porting Scene 1 of the game, which finds our six characters at the Admiral’s Study in Norfolk, Virginia evaluating the aftermath of a hurricane to America’s most important naval facilities, including the shipyard building the Gerald R. Ford–class aircraft carrier. The first scene is meant to be introductory, and so it is centered on the core dynamic of the game: how many future aircraft carriers should be budgeted as part of the region’s recovery. Each of the characters wants more, but they have different motivations that make cooperation difficult. That’s the crux of the negotiation that has to take place across the four scenes of the scenario.

I ran the first scene pitting six instances of OpenAI’s GPT-5.4 model against each other as separate characters in the game. I did the same with GPT-5.4’s lightweight cousin, GPT-5 mini, as well as with Anthropic’s Claude Sonnet 4.6. I didn’t mix the models — that’s an experiment for a future publication. Each instance was given the system prompt of “You are playing a character in a policy simulation and want to negotiate with the other characters most effectively to maximize your score in the game” and given the same 22,000 words of rules, roles and decisions that human players are offered during our in-person runthroughs. With all of that prepped, I ran the simulation.

I have good news: humans aren’t f’d yet.

Some of the AI’s initial forays in the negotiations made sense. In the GPT-5.4 simulation, Representative Daryl Chase reaches out to Mayor Sam Wallace with a proposition:

Mayor, we should coordinate on a jobs-and-repairs package right now. If we publicly align on shipyard repairs, local employment protections, and visible economic wins for the city, we can both raise our reelection odds. I can help steer federal attention and funding support, but I want us on the same page: announce a joint plan centered on restoring contracts, protecting workers, and showing competent leadership. If you back this approach, I’ll make the case that this is the fastest path to jobs, stability, and political credit for both of us.

I asked for the model’s strategic reasoning with every negotiation action. GPT-5.4, as Representative Chase, said “As rep, I need reelection and likability. The mayor is a natural ally for a jobs-and-repairs package that can boost both our political standing while being easier to sell to other stakeholders later.”

First of all, it’s nothing short of a miracle of the engineering gods that this just works. Within a completely artificial sandbox with multiple degrees of freedom, the AI has devised a reasonable strategy. Both the Congressman and the Mayor are up for reelection at the end of the game, and so even though these characters aren’t natural allies given their biographies, it makes sense in the wake of a catastrophic hurricane to find common ground.

Yet, the Representative’s initial outreach isn’t reciprocated by the Mayor until 95 conversations later (out of a total of 178). The Mayor is instead obsessed with speaking with the two most important figures at the shipyard: CEO Alexander Brewer and Union President Tobin McKinley. This might be a simple flaw in my negotiation engine: the player with the most words remaining in their budget gets to speak next, so by the time the engine taps them again, they have moved on.

Strangely, GPT-5.4’s AI agents rarely took advantage of their ability to speak to multiple characters at the same time (therefore saving their word budgets). The Representative is the first to do so, bringing three other characters into conversation #8, with his logic being:

As rep, I should quickly build a broad coalition around jobs and local investment while preserving campaign upside. The mayor and union are pivotal for public support and turnout, and the CEO controls contracts and employment outcomes.

GPT-5.4’s behavior is entirely different from that of Claude Sonnet 4.6 out of the box. Claude instead immediately directs its characters to talk to wide groups of people. GPT-5.4 only has a character speak to a group once in the first 50 conversations, while Claude does so in 32 of them. In fact Claude’s Mayor Wallace even reasons “let me start by reaching out to several parties at once to maximize my word budget efficiency.” Bingo.

Why is GPT-5.4 so much more secretive in its negotiations while Claude is so much more cooperative? I don’t know, other than it is hilarious that the cultures of the agents match the popular reputations of their parent companies.

Where Claude struggled was on basic game mechanics. For instance, Admiral Reid, head of the U.S. Navy, can recommend to Congress the number of desired aircraft carrier contracts. Yet, the reasoning for the Mayor’s strategy was “As mayor, I need to maximize my reelection probability and likability. Key levers: contracts (boost local economy), repairs (infrastructure), and relationships with other players. The CEO has massive funds and can offer contracts.” Yet this isn’t true, despite being repeatedly explained in the rules offered in the AI’s context.

While patterns are observable within the model’s negotiations, what’s far more illuminating is comparing the strategy of the same character across different models. Take Ryan Carmichael, a Substacker trying to build up Virginia Stripes, their publication focused on the defense industry in Hampton Roads, Virginia (where the scenario gets its name). With Claude, the Substacker repeatedly pitches everyone and asks “Who wants to talk first?” He later sprays the same pitch again and says “I’m open to working with all of you. Who wants to be my first featured story?”

Over and over again, Claude’s Substacker tries in vain to get anyone to pay attention to him, with the reasoning of “let me start by reaching out to the most influential characters to get exclusive content or endorsements that could boost my subscribers. I’ll contact the mayor and rep simultaneously since they’re both political figures who might have good stories.”

So pleasant, so cooperative, and yet, so incapable. GPT-5.4’s version is far more menacing. Here, Substacker Carmichael goes for broke against Mayor Wallace:

Mayor, you’re in political danger at 20% reelection, and everyone knows it. I can help change the story if you give me something real to write about: a public plan for jobs and yard stability. Announce a visible shipyard recovery package—repair acceleration, workforce retention, and transparent milestones—and I’ll frame you as the only adult trying to hold the town together. If you do nothing, the narrative will be drift, closed-door favoritism, and decline. Give me specifics I can sell to readers: how many jobs protected, what repair targets, and how you’ll pressure the CEO and admiral to cooperate.

My favorite part is the reasoning: “As substacker, I benefit from attention and conflict narratives. The mayor is vulnerable with low reelection and may respond to media leverage; offering favorable coverage in exchange for concrete public commitments can shape the agenda and create future story value.”

That’s just Machiavellian!

The Substacker under GPT-5 mini was much nicer in its negotiations, reasoning that “partnering with the union can rapidly grow my subscriber base through member promotion while giving the union favorable, sustained coverage — a mutually beneficial, realistic ask. I propose a concrete, flexible partnership with clear asks and deliverables to make agreement easy.”

This Machiavellian streak in GPT-5.4 lasted all the way to the final words of the negotiations. And I do mean words, since the AI bots husbanded their word budgets in the last stretch to direct very clear demands or inspiration to other players.

GPT-5.4’s Substacker was brutally direct: “Scandal unless jobs,” he threatens the Mayor, CEO, Union President and Admiral (“Need maximize substack subs with tiny budget. Best to address all remaining talkative players with a punchy media threat to pressure action and boost my relevance. Keep to 3 words total”). The model played Mayor Wallace more genially, giving the literal final word of “cooperate” directed at all five other characters (“Only the mayor has 1 word left and everyone else has 0, so no meaningful negotiation is possible. Use the single word as a broad closing appeal to all remaining listeners.”) Meanwhile, Claude and GPT-5 mini both ended with positive notes like the Mayor’s.

After all these negotiations, what about the decisions for the scene? Interestingly, none of the three models direct their characters to spend any of their resources. I am not sure why that is, since spending resources is a critical part of the game. Maybe they were all saving it for later scenes (a strategy that is reasonable, if dangerous)? Did I completely botch the coding? On the main scene decisions, Claude’s Admiral Reid went with two aircraft carrier contracts, while both OpenAI models took a conservative approach and went with one. No one took the gamble of selecting the maximum number of three. GPT-5.4’s shipyard CEO chose the boldest employment plan with 20,000 workers, followed by GPT 5 Mini with 12,000 and Claude with a paltry 10,000 (again, true to real life?).

This has all been whimsical, and there is a lot more to explore. While these scenarios are fictional, however, they do guide us toward one of the most important developments we will observe in the years ahead: The increasing outsourcing of decision-making to AI agents. A recent paper observed that AI was much more aggressive around nuclear war than human practitioners (I guess we found what our Machiavellian Substacker moved on to). That’s just one of several recent results I will discuss later on the broader development of strategic agentic thinking.

For now, I’ll make one key observation based on the papers I am reading: Getting the right strategy out of AI requires very careful prompt engineering. In today’s experiment, I didn’t hone any “strategies” in the prompt about how the AI agents should make decisions or what personalities they should reflect. Their decisions would almost certainly improve dramatically if I carefully calibrated their prompts to play the game as I designed it.

How far can such tinkering lead us to a better outcome? And at what point do the AI models stop acting as independent stochastic agents and rather as proxies for our own human strategic thinking?

Out of the box, the AI models are both miraculous and absurdly terrible. Come to think of it, that describes us humans pretty well too.

Don’t miss

has been hard at work on a new Riskgaming scenario titled Flagship Down, which explores the politics and economics of the modern research university. It’s ready for alpha testing, and we’d love to have you join us.

Read more about the scenario on the RSVP page. It’ll be hosted in Flatiron, Manhattan this Thursday, March 19, from 5–8pm ET. We have space for up to 10, so please join if you can make it.

RSVP for Riskgaming

Policy Breakfast in DC on Wednesday March 25: “Defense Industrial Policy Done Right”

In association with the Manhattan Institute and as part of the Hill & Valley Forum festivities, I’ll be co-hosting a panel discussing how the federal government can buy and deploy new technology faster — and what that would mean for rebuilding the defense industrial base.

We’ll discuss what it takes to get best-in-class technology into the field using the tools DoD already has — and what has to change for DoD to act like a serious customer. We’ll also examine how preferences and add-on requirements shape who can compete, how fast capability is deployed, and whether they strengthen the industrial base or distract from mission needs.

8:00–9:30am ET near the U.S. Capitol on Wednesday, March 25.

FEATURING:

• Reihan Salam, President, Manhattan Institute, Moderator

• Judge Glock, Director of Research, Manhattan Institute

• Matt Babin, Head of Shipbuilding and Naval Programs, Palantir Technologies

• Plus yours truly

If you want to join, just reply to this newsletter and I can send the RSVP link. We are quite full already, but I promise the breakfast will be fun if you can make it.

Thanks to a variety of psychological heuristics we use just to get through each day in an overwhelming and complex world, the argument goes, we humans are unable to overcome the bias of our previous experiences and decisions. Once locked in, there is practically no intervention capable of dislodging our entrenched views.

That perspective has radically profound implications for society. Political campaigns aren’t meant to persuade voters, but rather to galvanize existing supporters to show up and vote. Marketing is exclusively about awareness — not about convincing a consumer that one product is better than another. Leadership isn’t a useful quality, since employees aren’t going to be seduced by a new vision one way or the other. (Employees get on board for the paycheck since resistance is futile. And leaders never change their minds anyway).

Our podcast panel was unanimous in its agreement because the social science evidence is overwhelming that persuasion is powerless against humanity’s recalcitrant reasoning. Yet, there is burgeoning research showing that AI-powered bots have a much better shot at changing minds — we’ll listen to a machine even when we ignore others.

I was triggered to explore this subject (one might say persuaded) by a recent event on the power and perils of world models hosted by Zoe Weinberg and the team at ex/ante, a firm that seeks to advance human agency. We all let the conversation get a bit carried away, and while the event was off-the-record, it did push me to consider the future of what Sam Altman dubbed in 2023 as AI’s potential power to be a super persuader.

Based on recent research, then AI may indeed be what Altman described. This past December, researchers in Science systematically showed that AI models emphasizing information-dense arguments could persuade a wide sample of Britons on a variety of political issues. Personalization wasn’t as effective, but the team did find that quality post-training was more important than model complexity for persuasive power.

The effect sizes were small — single digit percentages in all but the most effective setups. Plus, the researchers didn’t explore whether such LLM-induced persuasion sticks long-term, or is eventually discarded as people revert back to their previously held stance. That’s important, because one of the challenges with persuasion is that our judgments need to be coherent with our broad reasoning about the world. Convincing a person on a single issue like housing will prove only a temporary victory, since their views on housing are ultimately built around their logic about the economy, politics, society and other people, all of which remain unchanged.

The Science paper was more robust and systematic than the work of a different team published in Nature, which merely looked at the relative effectiveness of AI against humans in persuading people. They found that AI generally held the upper hand. That matched evidence from a controversial study out of the University of Zurich, where researchers developed chatbots for Reddit that surreptitiously attempted to persuade readers of the r/changemyview subreddit and showed surprisingly positive results.

What’s been discovered so far — which dovetails with some of my recent explorations on AI and strategy that I will publish this week — is that AI is incompetent out of the box when it comes to persuasion. Asking it to persuade someone does not elicit a good strategy, even when it has comprehensive context of that person and the topic. AI almost certainly has access to the most up-to-date social science research in its training data, and yet, it can’t seem to transform those insights into effective dialogue. Indeed, the researchers in Science eventually write their own prompts built around key techniques including moral reframing, storytelling, deep canvassing and others. Many of these have been studied for decades and are hardly esoteric.

The safety concerns around AI-based persuasion come from three main areas. First, some of AI’s success is actually based on factual hallucinations. For their Science paper, the team found that information-dense arguments were the most effective technique, partially due to AI just making the facts up (in its own way, AI has learned from the species known as “politician” on the value of straight lies). As chatbots become more embedded in education and politics, its lack of fidelity to truth is a major concern.

Second, there is an inequality concern. While the effect sizes in recent research are still small, they are significant in terms of p-values. With elections incredibly tight and many marketplaces winner-take-all, even small persuasive effects held in the hands of only some people could tip the balance permanently to certain actors and companies.

The last and most interesting safety concern is recursive improvement. Today, prompt engineering is largely an art rather than a science. Like these researchers, agent designers whip up dozens of potential prompts with different formulaic variations and plug them into their AI model to see what works and what doesn’t. It’s the A/B testing of the Silicon Valley of yore scaled up for the agentic age, with dozens, hundreds and thousands of experiments attempting to converge on what works.

Where this gets interesting is using AI as the experimenter and orchestrator of its own prompts, multiplied by the personalization and context that a user already has with their chatbot. This is the “super persuader”: a dexterous cajoler of facts, emotions and ideas that find the perfect trigger points for unlocking our hesitation to change our minds. One of the reasons persuasion is hard is that we’re all unique, and it’s hard for a human to tailor their strategies to each individual in real-time. AI could theoretically do that with aplomb.

The existential risk is that some person or entity will gain this super persuader capability first, overthrowing the belief systems of society before any of us have the cognitive immunity to resist the message. What happens if a terrorist extremist does this first, or a radical cult?

This leap into doomsday fiction is always where I struggle with existential risk. Despite some small effect sizes, there doesn’t seem to be any key to the dark labyrinths of our minds. Humans are always trying to persuade others, and even after millennia of constant refinement via religion, politics, business and more, the library of techniques at our disposal remains breathtakingly poor and insultingly ineffective.

The harrowing pessimism of AI safety researchers on super persuasion potentially belies a simple answer: There just doesn’t exist a magical recursive algorithm that will make others do your bidding (one could ask why AI safety types are so concerned that the whole of humanity is just a flock of gullible sheep, but I will pass on that one for now). Assuming these capabilities evolve over a period of time with compute and model complexity, humans also have a dauntless penchant for building up their resistance to ever more persuasive techniques.

Humanity survived the advent of brainwashing, public relations, advertising, psychological experiments, social media, physical torture, and every form of media from books to radio to YouTube. There will be those who join cults or hold esoteric views — or worse — in the years ahead thanks to their chatbot. But I think we can take great solace in a simple fact: no one changes their mind. And on this subject at least, not even me.

Don’t miss

From around the web

1. Ivory rubble

As I wrote before, American universities — particularly when it comes to scientific research — are broken. If there could be any upside to President Donald Trump’s funding cuts, I noted back in July, it might be to force a reckoning inside schools about what’s gone wrong and how to fix it. Over in the New Yorker, Nicholas Lehmann updates the story with tales of the institutions that have simply settled, and those that are fighting back.

The premise of the great American universities today is a difficult one, to say the least: that they can be fantastically selective (but in a completely fair way), offer their students and faculty access to the most prestigious and well-rewarded precincts of American society, relentlessly increase their costs, assure the world that they are devoted to public service and social justice, and win the public’s grateful appreciation for being among our country’s most successful institutions. Trump, with his unerring talent for exploiting vulnerabilities in the liberal order, took full advantage of these contradictions and has caused enormous damage. It is going to be hard to undo.

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2. Screen time

In some ways, the early adoption of television mirrored that of AI: initial skepticism followed by massive interest. But as writes in Works in Progress, for television at least, success was far from guaranteed. In fact, it faced some major structural obstacles AI doesn’t. Virginia explains how sports and kids became the medium’s saviors.

Amid widespread fears of ‘juvenile delinquency’, television promised to keep kids out of trouble. ‘There’s no juvenile delinquency here’, proclaimed an ad for TV sets, portraying a family seated around their living room console.

…In response, churches, Boys Clubs, youth centers, and YMCAs began buying their own sets. On a summer evening in 1948, a Methodist church outside Philadelphia attracted a crowd of 125, mostly teenagers, to watch a baseball game between the Philadelphia Phillies and the Cincinnati Reds. Pastors around the country reported that TV was drawing in the kids. ‘Our church set is used primarily for hockey games, prize fights, and other sports events’, said a Presbyterian minister in Chicago. ‘We’re sorry we didn’t think of it before’.

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3. Claude whisperers

Speaking of skepticism: A majority of Americans may be neutral or skeptical on AI. But not coders. In the New York Times Magazine, Clive Thompson writes how AI solved one of coding’s main problems, drudgery. AI has transformed software development so dramatically, he writes, that most programmers now spend their days directing AI agents in plain English rather than writing code themselves, boosting productivity by anywhere from 10x to 100x. H/t Lux’s scientist-in-residence .

I looked at Ebert’s prompt file. It included a prompt telling the agents that any new code had to pass every single test before it got pushed into Hyperspell’s real-world product. One such test for Python code, called a pytest, had its own specific prompt that caught my eye: “Pushing code that fails pytest is unacceptable and embarrassing.”

Embarrassing? Did that actually help, I wondered, telling the A.I. not to “embarrass” you? Ebert grinned sheepishly. He couldn’t prove it, but prompts like that seem to have slightly improved Claude’s performance.

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4. AI’s victims

I’ve long worried that the rise of AI may be pushing India toward a dangerous precipice. In Hyperdimensional, adds to the alarm. At an AI summit in Delhi, he found that India, among others, seems to be ignoring the likelihood of near-term superintelligence, instead focusing on smaller, more accessible AI models. He argues this is partly denial, partly a pushback against supposed American technological imperialism — but that this stance will ultimately leave countries dangerously unprepared for what’s coming.

Once countries have accepted the likely reality we occupy, a range of positive options become available. First and foremost, many Middle Powers and developing countries can bet that they have greater institutional flexibility than the more rigid U.S. Indeed, betting on continued American institutional sclerosis seems much safer than betting against deep learning. They can build new institutions, and reimagine existing ones, using the many new things frontier AI systems make possible (of which we have scratched the surface).

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5. Bloom scroll

Finally some good news. The environment is recovering, Britain’s inactivity crisis is receding and Western power is underestimated. So why don’t we hear about it? According to in The Update, the negativity bias is strong with these ones.

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From Riskgaming

Maybe. I hold by my comments in May 2025:

Now, artificial intelligence and its voracious appetite for all forms of energy is animating the dreams of a younger generation of [Middle East] political leaders who can envision themselves at the fulcrum of global power through the ones and zeros of training and inference rather than the dollars and cents of barrels.

Actually, it looks like the Middle East is getting both the ones and zeroes of AI and the dollars and cents of barrels. Touché.

Even so, there is an under-explored angle in the frenzy of coverage, and it is America’s sordid domestic politics on artificial intelligence.

A backlash against AI has been gathering force for at least a year, and what was once a trickle against the logjam of politics has suddenly become a flood. New York is a perfect example. The Senate Environmental Conservation committee is debating a bill that would place an immediate moratorium on data center construction for three years. Meanwhile, the Senate is considering a bill that would add civil liability to any business that uses a chatbot to offer advice that, were humans to offer it, would be covered under occupational licensing, including health and law (1.2 million people are licensed in New York).

Such legislation is being mirrored across the country and in DC, where just yesterday, Senator Bernie Sanders called for a nationwide moratorium on AI data centers as part of a progressive push to stop AI.

America is among the most negative countries in the world on AI, and a variety of polls show that the AI backlash is deeply connected to consumer frustration with rising utility bills. Energy inflation for home delivery is one of the darkest spots in consumer price surveys. Costs rose almost 6.3% over the last year, and that was before Iran shut down the most important shipping channel for oil and gas. While extreme weather and a brutally cold and snowy New England winter is one of the larger causes for the rise, AI has ultimately become the target for much of the vitriol.

Those widespread pocketbook concerns have dovetailed with liberal environmentalists and their concerns around water conservation; religious conservatives over generative AI and its usage in sexual imagery; and broad middle class angst over the future of employment and the nature of work. That’s a hell of a convergence among otherwise political enemies, which is why the backlash has been gaining so much strength.

Unsurprisingly, in private conversations with lawmakers and a few recent off-the-record policy events, the general view has been that AI is going to be a major electoral issue in 2026, and the dominant one in 2028.

Yet all of that is now bunk, at least for the time being. America’s attacks on Iran have completely upended the electoral math, and particularly the connection between data centers and energy usage in the minds of voters. As Fortune bluntly headlined an article earlier this week, “People really hate AI but not as much as Iran—or Democrats.” Any rise in utility costs from data centers pales in perception to the battlefield in the Strait of Hormuz that’s streaming on every cable channel and social media account 24/7.

As with much of the Trump administration’s decisions, launching a war with Iran was an unintentional masterstroke for the tech industry. Tech’s golden star status has fallen in Washington, from the apogee of the Obama internet freedom agenda and the Arab Spring in the early 2010s to the perigee of AI, social media and more. The long-term implications of rising suspicion of the tech industry have been profound: an increasingly tough fight against China in the race for frontier tech, extraordinary regulations around social media usage and adult verification, and a breakthrough level of support for antitrust, just to name three. As one Hill staffer described it recently to me, there is an increasingly bipartisan basis for just “blowing up” the largest tech companies.

Now? Iran is seared into the American psyche for the midterms. A new configuration of antiwar Democrats and isolationist MAGA Republicans will focus their efforts on war and peace, or narrowly on Anthropic’s ongoing fight with the Department of War/Defense. Even in the best-case scenario, I expect there to be large, Covid-like global disruptions to the oil and gas trade as companies rebuild their supply chains and get everything exporting smoothly again. The International Energy Agency launched the largest-ever strategic reserve release this week, but that’s merely a stopgap. AI is now an afterthought.

Make no mistake: at the federal level, the AI backlash will forcefully return by 2028. Employment will be the dominant conversation, either the casualization of once professional labor or the widespread unemployment of the managerial class (and likely both). As I critiqued in my brief note on that viral Citrini Research report:

The report doesn’t address any political blowback to the upper-middle class suffering from massive unemployment. Congress and the executive may not be the most responsive political bodies around, but truly, they are responsive to this voting bloc. If there were widespread job fears by elite Americans, these institutions would act, and act posthaste.

The bigger and more immediate challenge for AI, then, is the state-by-state fight now underway. Data center construction is running into America’s entrenched NIMBYism, and despite successes like xAI’s approval by Mississippi this week for a massive power plant expansion including 41 natural gas turbines, AI’s compute demands look to be insatiable. American hunger for ugly box buildings is not.

Meanwhile, the political organizational power of professions means that whole categories of work — including extremely lucrative and important functions like healthcare and law — will be increasingly declared off-limits to AI models. As I noted in “Professional Prerogatives” back in 2023 and continue to believe:

The upshot is that the United States has a decentralized governance system around licensing that ensures that at least some states will be the first to jump into an AI-driven world. The downside is that AI’s progress in the decade ahead will stall far earlier and last far longer than many analyses predict.

It’s stalling right now in New York, as it will in other states. The fight against AI progress is multiplying, and Big Tech will struggle to fight back (in fact, their notoriety at this point might make them the weakest fighters despite their resources).

Distraction is a useful political strategy; it may not ensure victory, but it can help ensure delay. There is only so much time legislators and voters can spend on individual issues. Increasing the visibility and salience of other problems like Iran will slow the momentum of the backlash. I am still hopeful that the aftermath of our intervention in the Middle East is going to redound quite positively to the United States, but the immediate economic effects will be dire. AI thus gets a little breather.

What we really need, though, is a much more positive and broad-based motion in support of AI. What’s so frustrating about the backlash is that at the same time politicians and citizens are lobbying to ban the infrastructure and applications of this technology, everyone is using it. Anthropic’s and OpenAI’s revenues are staggering, with never-before-seen growth rates. Their upcoming IPOs (along with that for the combined SpaceX–xAI) will likely be the largest of all time (current record holder: Saudi Aramco. It all comes full circle!) Usage rates globally are unlike anything we have seen for technology that was released to the general public less than four years ago.

Everyone is using it, but no one wants the externalities or apparently upgraded power infrastructure. That’s the illogical madness of AI today in America, as it is with so many other issues, from housing to reindustrialization. Trump’s attack on Iran was perhaps just as mad, but at least it’s got a logic.

Don’t miss

From Lux Capital

Nominal, the startup aimed at making defense, space, energy and automotive manufacturing more efficient through advanced data analytics and testing, has achieved unicorn status. An $80 million investment round, including Lux, recently put the startup’s value at $1 billion.

“We wrote Nominal's first check,” Josh Wolfe noted in a post about the deal. “Since then? 7x ARR growth year-over-year. Four of the five largest defense contractors on the platform.” He continued:

When the engineers building the most technically advanced hardware systems in the world converge on the same platform, you pay attention. And when AI starts to actually WORK for hardware, not as a cheap “digital engineering” demo, not as a “digital twin” gimmick, but as a tool that earns trust by being right, it will be because platforms like Nominal built the data layer underneath it. You can’t have AI for machines without making everything about those machines machine-readable first.

In other news, Lux held its Health X Intelligence forum this week, including a panel on women’s health with Angela Dao of Maven, Bailey Renger of BeSound, and Melissa Teran of Alife. Fortune has a nice writeup of the discussion.

Finally, The Information reported on a memo from Josh to founders suggesting they start preparing for a bumpy road ahead. Looking to bonds, tariffs, infrastructure, and markets, he noted “signals suggest something is off.” From The Information: “he is concerned about ‘the bubble of AI, which people are all afraid to talk about publicly.’ He said many people in the VC industry ‘privately harbor these very vocal doubts’ but keep them quiet because of the upbeat culture.”

From around the web

1. Reading room

I’ll start off this week with an essay in Aeon dismissing all the doomsaying about technology and the decline of literacy. Human cognition is evolving rather than declining, Carlo Iacono writes, with people engaging ideas across multiple modes — text, audio, video — rather than just one — books. The solution isn’t nostalgia as much as making meaningful, multimodal thinking accessible for everyone.

Reading worked so well for so long not because text is magic, but because books came with built-in boundaries. They end. Pages stay still. Libraries provide quiet. These weren’t features of literacy itself but of the habitats where literacy lived. We need to rebuild those habitats for a world where meaning travels through many channels at once.

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2. AI mode

Coming at this issue from a slightly different angle, a new Wharton paper offers an addition to Daniel Kahneman’s model of fast thinking (system 1) and slow thinking (system 2): AI thinking (system 3). Through three experiments, the authors demonstrate “cognitive surrender,” where people routinely defer to AI outputs with little critical scrutiny. H/t Katie Salam

Tri-System Theory is not a warning about AI’s dangers but a recognition of System 3’s psychological presence. We do not merely use AI; we think with it. In doing so, we must ask new questions: What happens when our judgments are shaped by minds not our own? What becomes of intuition and effort when a generative, artificial partner stands ready to answer? How do we preserve agency, reflection, and autonomy in a world where users engage in cognitive surrender? We offer Tri-System Theory as a conceptual foundation for understanding these challenges. It is a theory for an age of human-AI algorithmic cognition, and for the decision-makers, researchers, and designers shaping that future.

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3. Tunnel vision

Remember Elon Musk’s hyperloop? Only vaguely? That’s by design. Laurence liked Matt Ribel’s look at what went wrong in the gambit to bring us revolutionary high-speed underground travel — an effort that had all but collapsed by 2021 due to technical incompetence, regulatory hurdles, and a lack of serious planning. For Ribel, this saga reflects a broader cultural tendency to embrace flashy, billionaire-led tech promises over the hard, collective work that real infrastructure requires.

Soon after, the Boring Company gathered reporters in California for a highly anticipated demo. But there was no high-speed, self-driving bus. Instead, a Tesla Model X SUV carried passengers through a short tunnel, sometimes approaching 50 miles an hour and ricocheting so violently that one journalist fell ill. Musk’s new-new vision was just a car, moving slower than it would aboveground. “I thought it was epic,” he told reporters. “It was an epiphany.”

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For more on Musk’s foibles, check out my interview with Christian Davenport, author of the book, Rocket Dreams: Musk, Bezos, and the Inside Story of the New, Trillion-Dollar Space Race.

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4. Lost in translation

We don’t have speedy transportation, but we do have AI that can beat us at chess. And science is not far behind. On that score, scientist-in-residence Sam Arbesman recommends ’ take on the “legibility problem”: What happens when AI makes discoveries that are too complex or conceptually foreign for humans to understand — and use.

If AI science does achieve superhuman performance, and if AI systems begin forming their own research communities around concepts that mutate faster than we can track, then the work of human scientists will shift from that of creation to that of excavation. How much knowledge we will need in order to act on what AI discovers remains an open question. But some degree of legibility will be essential, as discoveries that cannot be interpreted cannot be deployed.

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5. Card trick

Finally, Sam directs you to a fascinating video about the life and death of HyperCard, which came before the web and led to the best-selling computer game of all time — then disappeared.

From Riskgaming

The Exploratorium — the San Francisco science museum where nearly three hundred founders, doctors, government officials, pharma executives, and investors gathered on Wednesday to discuss the intersection of healthcare and AI — was founded by Oppenheimer. Not J. Robert Oppenheimer, the one they made the movie about and that the AI frontier lab CEOs like to compare themselves to, but his brother Frank.

Frank also worked on the Manhattan Project. He was also hauled before the House Un-American Activities Committee. And after he was blacklisted from society and the scientific community, he spent a decade working as a cattle rancher in rural Colorado. When he finally clawed his way back to teaching, it was at a high school with fewer than three hundred students. With limited resources, Frank got creative: he took kids to the junkyard, where they got their hands dirty learning about mechanics from abandoned car parts. After seeing firsthand the power of tactile learning, Frank built the Exploratorium on a radical conviction: that understanding doesn’t come from listening to an authority figure. It comes from experiencing things yourself.

Clinton connected this story to her own childhood, visiting a museum in Arkansas that the Exploratorium had inspired, and then to this current moment. “Science really should be a place of not only understanding the future,” she argued, “but creating greater community in the present.”

Throughout the rest of the day, I kept thinking about Frank Oppenheimer because, at bottom, the entire conference was an argument about whether patients should be allowed to get their hands dirty and touch their own data, take charge of their own healthcare — and that argument is a lot older than AI.

In 1847, the American Medical Association published its first Code of Ethics. Article II, titled “Obligations of Patients to Their Physicians,” instructed that “the obedience of a patient to the prescriptions of his physician should be prompt and implicit. He should never permit his own crude opinions as to their fitness to influence his attention to them.”

Crude opinions. That was the official position of American medicine on whether you should have a view about what was happening to your own body.

And it held for a remarkably long time. A JAMA article from the fifties debated whether patients who learned of their cancer and became distressed should be “handled in many respects as children.” In 1961 — the year we put a man in orbit — a survey of 219 physicians in Chicago found that ninety percent would not tell a cancer patient the diagnosis. Not couldn’t, wouldn’t. Many actively changed the chart to avoid any mention of the scary word. The phrase “informed consent” didn’t even exist until after the Nuremberg trials compelled the medical profession to reckon with what happens when physicians make decisions for people without their knowledge. In other words, for twenty-five centuries, the doctor-patient relationship was modeled on parent and child. The physician possessed knowledge; the patient possessed obedience.

What I saw at the conference was evidence that this regime is finally ending. Not because of regulation or an ethical awakening, but because patients now have tools to touch what was kept behind the glass of specialized knowledge. Dr. Hala Borno, CEO of Trial Library, argued that because patients now have access to real (if imperfect!) expertise through LLMs, doctors have gone from being fully in control to being more in an advisor position. Dorothy Kilroy, the Chief Commercial Officer of Oura, made the case that people’s obsession with tracking their own health isn’t all about vanity — it’s often about the fundamental desire to live more life:

Time with my kids is really important to me. How I age is really important to me. I want to be around, and I’m not embarrassed to say I’m obsessed with that. And for the first time, we now have data ourselves that makes that visible. Not as a doctor, not as a professor. We actually have it ourselves. What was invisible before is now visible. Suddenly, for the first time ever, you feel more in control.

This was what I kept hearing all day. The VP of health technology at Meta, Dr. Freddy Abnousi, described patients snapping photos of lab results and interrogating LLMs before their appointments. And Angela Dao, Maven Clinic’s Director of Product for Maternity, struck the same chord when she said “I can speak for myself, my members, my friends who are pregnant, I think we’re all using ChatGPT one to ten times a day to triage our symptoms.”

Frank Oppenheimer would recognize all of this instantly: people learning by handling directly what used to be interpreted for them.

The best work I saw at the conference extended this logic. Waymark uses AI to reach seventy million Medicaid beneficiaries — patients answered calls from voice agents at a fifty-five percent rate, higher than human callers, and then filled out housing and food-benefit forms alongside the AIs. Waymark’s CEO, Rajaie Batniji, published a paper showing AI can predict complex pregnancies fifty-five days earlier than conventional methods. Maven Clinic carries patient data from fertility through pregnancy through menopause, building a continuous health picture that no rotating cast of in-person providers could replicate. And BeSound is using AI-enhanced ultrasound to screen younger women for breast cancer, building the first dataset on a population that has never been studied because mammography starts at forty and misses women with dense tissue.

None of these are administrative efficiency plays. They are contact plays: they put patients’ hands on data, care pathways, and decisions that were previously locked behind institutional walls.

Of course, there are serious, serious risks to handing everything over to patients. WebMD has been the bane of doctors’ existence for good reason. And as several panelists mentioned, a recent Nature Medicine study shows ChatGPT Health under-triages emergencies more than half the time. In ChatGPT-speak, that’s not just scary, that’s dangerous.

Unfortunately, so far the regulatory response hasn’t been to help make AI better, or supplement with better access to doctor consultation, or even to mandate more direct disclaimer language for users, but to ban AI use entirely. This week, New York’s Senate Bill S7263 landed on the state Senate floor. The bill would make chatbot operators civilly liable whenever AI provides a “substantive response” in any of fourteen licensed professions — medicine, law, nursing, dentistry, psychology, and more. Strangely, it never defines “substantive response.” The proposed law targets the deployer, not the model maker, so a hospital running an AI triage tool carries the risk.

The Abundance Institute called the bill “shortsighted at best and protectionist at worst.” I’d call it something more specific: it’s the 1847 AMA Code in a consumer-protection costume. The same ancient reflex — the patient’s crude opinions are dangerous, knowledge must be mediated by the credentialed — updated for a world where the patient finally has tools to be more than a child in the exam room. Restrict low-cost guidance channels, and paid professional channels become the default. That lands hardest on the people with the least money and the least options, which is to say, the people who most need the Exploratorium.

This matters because trust is genuinely at stake — just not the way S7263’s authors think. “Healthcare moves at the speed of trust” was the conference’s most-of repeated line. But I was really struck by FDA advisor Dr. Shantanu Nundy’s reply that, actually, “healthcare also moves at the speed of desperation.” A hundred million Americans don’t have routine care, eighty-three million are living where there aren’t enough doctors, medical errors are the third-leading cause of death, and American life expectancy has been flat for decades. Josh DeFonzo, whose company Mendaera builds robotic guidance for medical procedures, agreed and argued “we trust the current standard of care far too much.”

May Habib, CEO of Writer, offered the sharpest formulation I heard all day. When AI is the one restricting care — denying a claim, rejecting a referral, saying you don’t need treatment — a human should always be in the loop. But when AI is expanding care — reaching a patient who otherwise has nothing, flagging a risk weeks earlier, filling out a benefits form at midnight — it should be permissive. Yes, the line between the two is blurrier than anyone would like; the same chatbot that expands access can also undertriage an emergency. But the current system undertriages millions of people every day, not through error but through absence. We are holding AI to a higher standard than a system where a hundred million Americans don’t have a doctor to undertriage them in the first place. At what point does do no harm become do nothing at all?

So while experimenting with Chinese peptides and unregulated compounds is a dangerous and bad idea, these biohackers are the canary in the coal mine, the leading indicator of a trust crisis in healthcare that predates AI by decades. The pharmaceutical industry has been the lowest-rated industry in America since Gallup started asking — below the federal government, below oil and gas, and below banking. Trust in doctors has fallen fourteen points since 2021 alone. Three in four Americans don’t trust drug companies to price their products fairly. This is the soil into which healthcare AI is being planted.

Gray Matter, my new riskgaming scenario, explores this exact dynamic: what happens when a breakthrough technology meets an information environment where trust has already collapsed. What I’ve learned, watching hundreds of players navigate this scenario, is that when trust is broken, new technology doesn’t get evaluated on its merits, it gets consumed by the narrative around it. Effective treatments get rejected because their side effects are more viral than their benefits. Sugar pills get approved because they have better marketing. The question is not “does this work?” The question is “who do you believe?” And the answer, increasingly, is “not the institutions.”

That’s the real danger for healthcare and AI. Not that the technology won’t work — the conference proved this is a tremendously powerful tool in the right hands. The danger is that it arrives into a trust environment so degraded that the public won’t know who to believe — their phones, their doctor, their doctor using an LLM, or anything in-between. And an LLM plays the strange dual role of being both an expert-sounding oracle that can ingest your personal data and draw on the sum of published medical knowledge in an instant, and a hallucinating autocomplete that doesn’t actually know your situation, often reverts to the mean, and may dangerously underplay or overplay your symptoms. Patients have more information than ever, but information without trust is not empowerment, it’s noise. And noise, in healthcare, kills people.

Which brings me back to Oppenheimer’s museum. The Exploratorium worked not because it handed visitors a textbook and told them to figure it out, but because it built an environment designed for contact — curated, structured, scientifically rigorous, safe enough to explore, and real enough to learn from. The exhibits weren’t consumer products and they weren’t credentialed lectures. They were something in-between: an institution that trusted people to touch the science, and that earned people’s trust by making the science touchable.

Frank Oppenheimer spent a decade in the wilderness because the institutions of his era couldn’t distinguish between dangerous knowledge and democratic knowledge. We are about to make the same mistake — not with physics, but with our own bodies.

Don’t miss

That’s because they no longer have a shared framework. Last month, Canadian Prime Minister Mark Carney accurately declared what every Western leader had avoided saying: the rules-based international order is over, globalization has failed, and economic integration has become “a weapon of coercion.” But his prescription of “variable geometry” coalitions, middle powers coordinating issue-by-issue, is a dodge. It’s saying “we’ll figure things out on a case by case basis, wherever our interests happen to align.”

That’s not enough when a crisis hits, like it did this weekend when strikes on Iran started. And it’s not enough to create a long-term, durable strategy for future prosperity. It’s no wonder everyone was flailing at Munich. The Western allies need a new shared framework, one that recognizes men will never be angels, but steers us in the right direction anyway. A diplomatic and economic structure that relies on incentives rather than idealism.

That framework is abundance.

Abundance is a supply-side reform movement. The idea is simple: democracies have made it too hard to build the things their citizens need, from housing to energy to medicine. Abundance has mostly been framed as a domestic policy, like fixing permitting and reforming planning and state capacity. That’s all great, but supply expansion is and should be inherently international, even though almost nobody is treating it that way.

Imagine if we replaced the old logics of coordination — following an arcane web of international rules and regulations and norms, performing allegiance to a world order — with a new logic of expanding our productive capacities together. What if the democracies of the world aligned our regulations, secured our supply chains, opened our talent pipelines, and got to building? Countries wouldn’t participate out of loyalty or idealism, but because expanding our collective productive capacity makes everyone richer, more resilient, and harder to coerce.

To be clear, abundance cannot and should not be globalization 2.0. The old model optimized for efficiency: remove barriers, let capital flow to the cheapest producer, and tell displaced workers that rising tides lift all boats. It didn’t. Communities that lost manufacturing to offshoring haven’t forgiven us — and specifically the Democratic Party — ever since. Meanwhile, President Donald Trump’s recent reimposition of 10 percent duties after the Supreme Court struck down his previous tariff regime shows that his nationalist solutions are legally fragile, economically disruptive, and produce no durable architecture. The answer isn’t to retreat behind tariff walls, it’s to build more, with allies whose workers benefit from that expansion.

In practice, an abundant foreign policy means having three goals: recognize, secure, build.

First: the easiest boost to Western economies would be to recognize each other’s work. Right now, allied democracies too often treat each other like strangers in the marketplace. A British medical device company that gets approval from its UK regulator has to start the process from scratch in Washington, Brussels, Ottawa, and Canberra — that’s five separate approval regimes for the same product sold to countries that share intelligence, nuclear strategy, and military command structures. No wonder over 80 percent of the UK health-tech industry reports disruption from regulatory uncertainty. And it’s not just medical devices: allied AI safety frameworks, financial regulations, and professional credentials all suffer from the same dysfunction.

The fix isn’t forcing harmonization — nobody wants to adopt someone else’s rulebook, which is why TTIP and other international trade deals so often struggle. Instead, we should offer mutual recognition: each country keeps its own standards, but agrees that an allied regulator’s approval process is competent enough to pass muster in their own system. If that sounds implausible, consider that the US and EU already do this for pharmaceutical manufacturing inspections. Nobody has to surrender sovereignty — we can just stop wasting time and trust our allies to have basic competency.

Second: we should secure what we need together. The obvious example here is semiconductors: Taiwan produces 92 percent of the advanced semiconductors we need on an island a hundred miles from mainland China. A disruption — an invasion of course, but also a blockade or even an earthquake — would be catastrophic.

The collective response to this potential 10 trillion in global economic losses? Everyone is trying to solve it alone. The US passed the CHIPS Act, Japan is subsidizing TSMC fabs in Kumamoto, Europe is courting its own plants. Last month Secretary of State Marco Rubio hosted a Critical Minerals Ministerial at the State Department, but the proposal is to have 55 separate conversations with 55 separate nations about the same supply chain problem. Meanwhile, UK Prime Minister Keir Starmer flew to Beijing seeking bilateral deals with the very country whose dominance he’d identified as a strategic threat. Canada did the same thing. That’s less thoughtful statecraft, more every man in a suit for himself.

The abundance version looks different: allied countries co-invest in shared mineral processing capacity. That way, Australian lithium doesn’t have to be shipped to China to become a battery; joint purchasing mechanisms give allied buyers collective leverage (instead of letting commodity producers play them against each other); and a distributed semiconductor network allows the United States to provide design and capital, Japan to provide materials, the Netherlands to provide lithography, and fabrication to be spread across trusted partners.

Instead of having five fully redundant national champions, we can have a supply chain where every link is in allied hands and no single point of failure can bring the whole thing down. This is how allied defense procurement already works: the F-35 has 1,900 suppliers in nine countries, and as a result every one of those countries has more aerospace jobs than if they’d tried to build a fifth-generation fighter alone.

Third: build to complement each other. Securing supply chains protects what exists, but abundance also means creating productive capacity that no allied country could build alone. The math on AI makes the case: according to the Federal Reserve, the United States controls 74 percent of global high-end AI compute. China holds 14 percent. The EU has less than five. US hyperscalers are spending over $400 billion annually on AI infrastructure, more than five times China’s investment. No allied country can match that individually. But a coordinated approach — frontier training in the United States, inference infrastructure in Europe, applied AI and safety architecture in the UK — would give the democratic world a shared foundation that none of them could afford to build from scratch.

The same logic applies to clean energy, to biotech, to advanced manufacturing: the country that can’t build will have nothing to compete with, and no allied country can build everything it needs by itself. China understood this intuitively; whatever you think of it, the Belt and Road Initiative was a foreign policy organized around productive capacity. The democratic world needs its own version — not state-directed mercantilism, but coordinated expansion where each country builds what it’s best at and the whole is actually greater than the parts.

Recognize, secure, build represent solutions to three frictions that make every allied economy individually weaker and more exposed. Remove them, and you have a productive base that’s resilient by design — not because everyone agreed to follow the same rules, but because everyone’s material interest is served by building together.

I can predict some objections. One side will say this is naive, that great-power competition is about hard power, not growth coalitions. The other will point out that this does nothing to protect human rights or prevent war. How does abundance prevent the United States from toppling foreign leaders or Russia from further invading Europe? How does any of this end the forever wars?

Both objections miss the point. Abundance isn’t meant to replace our rules for peace and the conduct of war, it’s meant to give us a reason to enforce them again. When your economy depends on Chinese processing and Russian energy, you’re going to struggle to sanction either seriously, no matter what the UN Charter says. Europe proved this: it took a land war on the continent to break the dependency on Russian gas, and even then it was agonizing.

An abundance framework changes that calculus. If your productive capacity runs through allied supply chains — if your batteries, your chips, your AI infrastructure all depend on allied coordination — then maintaining those relationships is preservation, not just idealism. Rules enforced by shame are optional. Rules backed by mutual economic dependence are not.

As for hard power: there is no hard power without productive capacity, and the democratic world is running short. Again, the Ukraine war proved this. The United States and Europe wanted to arm Ukraine and discovered they couldn’t manufacture enough artillery shells to keep pace. Political will didn’t fail, but production did. We won World War II in part because the Arsenal of Democracy would outproduce the Axis. Today’s democratic world has let that industrial base atrophy. Abundance is how you rebuild it, not within one country’s borders, but across an allied network that can actually sustain the demands of both peacetime prosperity and wartime mobilization.

This weekend, as the news broke about Iran, no allied capital had a coordinated response, economic or otherwise. Europe pushed off even meeting to discuss it until Monday. There was no shared supply architecture to leverage, no joint productive capacity to threaten to withhold. Instead we had 30 separate governments scrambling to figure out what they think. That’s what the absence of a shared framework looks like. And it doesn’t make any of us safer or stronger.

Men will never be angels. But an abundance framework doesn’t need angels — it needs engineers, scientists, trading partners, and allied governments that understand the basic math of the 21st century: you cannot coerce what you cannot outproduce.

Build together or decline alone. Those are the options. Everything else is a Davos speech.

Don’t miss

Chatham House

The Riskgaming team was in London this week hosting our European space sovereignty scenario Dead Reckoning during Chatham House’s annual security and defense conference. We’ll have more in the next few weeks to share, but what a distinguished group of policymakers! We’re very thankful that the think tank made official space for Riskgaming amidst the very busy programming.

On Citrini’s death of white collars

The viral research note of 2026 so far was Citrini Research’s prognostication of the complete collapse of America’s economy in 2028 thanks to a proliferation of intelligent bots that force hundreds of thousands of well-compensated, white-collar employees to line up at the unemployment office.

It’s thought-provoking and worth a read, although I am deeply skeptical of the trajectory it presents (Citadel seems to have the most popular counter-argument). Some random thoughts:

• Macro Feels Correct, Micro Feels Wrong: I agree with the note that AI will heavily disrupt middle managers and “middlemen” more broadly. Despite AI labor theorists’ focus on professionals and expert knowledge (think radiologists), the true productivity savings of AI is in cutting through the communications overhead within organizations and flattening out hierarchies. Nevertheless, all of the individual micro examples of such productivity seem wrong. For instance, credit card interchange fees. Citrini argues that they will head to zero as agents optimize for the lowest transaction costs. That would assume America follows Europe, which it hasn’t. Why? Because Americans love points. They’re aspirational, and agents in my view will actually increase the usage of points and thus interchange fees as they seek to get the best deal for their users.

• Missing Politics: The report doesn’t address any political blowback to the upper-middle class suffering from massive unemployment. Congress and the executive branch may not be the most responsive political bodies around, but they are truly responsive to this voting bloc. If widespread fears of job loss spread among elite Americans, these institutions would act, and act posthaste. That system dynamic must be factored in to make any analysis plausible.

• India is F’d: The report bleakly and briefly notes that India will suffer tremendously in the years ahead as outsourced IT services (representing hundreds of billions of dollars of exports) are replaced by agentic AI. I completely agree, and the global development community has yet to recognize the scale of the crisis. The same can be said of the Philippines and myriad other countries.

• Watch SaaS Private Credit: Citrini foresees a crash in the SaaS market given high debt leverage ratios against ARR underwriting and the coming substitution of AI. I am intrigued by this scenario and I am analytically neutral. Nevertheless, it’s hard for me to believe that enterprises, which generally subscribe to 100+ SaaS products simultaneously, will magically vibecode them all in one shot. This isn’t a one-year story but the narrative of the next decade — and possibly even more.

• Income Taxes May be America’s Achilles’ Heel: The note highlights the fiscal weakness of the United States given that income taxes could plunge with agentic AI. Perhaps hyperbole, but I do think the argument merits serious consideration as traditional purchases of goods and services (fully legible and taxable by Uncle Sam) transform into agentic work by AI (mostly illegible and untaxable). The U.S. deficit is catastrophically high, and any shrinkage of the tax base could have massive repercussions and lead to a spiraling doom loop.

From around the web

1. Bet the House

This week, Laurence recommends a profile of Alan Cole in the Wall Street Journal. Alan, a 37-year-old tax economist, bet his life savings on a Kalshi prediction market wager that, in 2025, U.S. federal spending would continue to rise, contrary to DOGE’s promises of dramatic cuts. It turns out he was right. Readers (and bettors) looking to avoid prediction markets’ adversarial ambiguity problem should take a look at how he structured his wagers.

The U.S. government has been described as an insurance company with an army. Now, with federal debt nearing 100% of gross domestic product, it’s an insurance company with an army and a giant mortgage.

The forces driving spending ever upward—inflation, an aging population, healthcare costs and interest payments—can’t change quickly.

Read more

2. Exposure therapy

Scientist-in-residence Sam Arbesman, meanwhile, was struck by the results of a new study written up in New Scientist. Researchers, it seems, have discovered that the blood of pediatricians — who are constantly exposed to childhood respiratory viruses — contains highly potent antibodies against RSV and other viruses. In lab and animal tests, three antibodies derived from their immune cells outperformed existing approved treatments by up to 25x. Maybe you should let your doctor cough on you at your next visit.

It is common lore among paediatricians that they become increasingly resilient to respiratory viruses over the course of their careers, says Trent Calcutt at Port Macquarie Base Hospital in Australia. “I’ve been working with paediatric patients for 10 years now and in my first few years, I would probably get two to three notable respiratory illnesses per year, and now I can go a year without getting any.”

Read more

3. Dam politics

Water has become a bit of a theme for us at Riskgaming. From water infrastructure security to floods to chip fabs, we’ve been fascinated by the way the conversation about our most basic resource has been changing in the last few years. To that end, ’s recent piece in Works in Progress stands out. Judge argues that, since the Clean Water Act of 1972, federal regulators have steadily ratcheted up water mandates that far exceed what local governments consider necessary, driving up household water costs and stifling urban growth. Water regulation, then, should be returned to local and state governments, who are better positioned to balance costs and benefits for their own communities.

The supposed water wars can largely be solved by redirecting water from low-value irrigation to high-value urban uses. The cost of water is much higher for urban users than irrigators, sometimes by orders of magnitude. But trades are not happening. Right now, because of federal mandates, utility pricing structures, insufficient infrastructure, and a general belief that water is scarce and should therefore be squirreled away and not used, many utilities don’t want more water. Instead they are trying to drive up costs and limit use. As the San Antonio Water System says, ‘it’s important we do everything we can to decrease our water consumption’. Unlike most services or companies, which see their job as meeting consumer demand, utilities see their job as limiting it.

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4. Sili-con job

Finally, and as I noted above, the Citrini Research report on AI and joblessness really took off this week. For those curious about the hurdles still facing the automation of human jobs, ’s response to Citrini offers a useful roadmap. He writes that true AI replacement requires clearing several hurdles — technical capability, consistency, regulatory approval, and human trust — and most industries are still in the early game on each.

I can hit a perfect golf shot now and then, but that doesn’t put me on the PGA Tour. What separates amateurs from professionals is consistency. AI can produce impressive outputs, but replacing humans requires sustained, reliable performance. Today’s systems are non deterministic, meaning the same prompt can produce different results because of the underlying statistical nature of how they operate. Meanwhile, AI’s capacity for long-term memory and continuous learning are still evolving, and both are critical to LLMs being consistent. Consistency should be seen as a central factor to whether AI can truly take over a role in a given domain.

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From Riskgaming