Something that the Ukraine and Iran wars have taught me is that for a lot of countries, including very big countries, there are a small number of buildings and infrastructure components that are required for their economy to function.

And in countries that aren’t protected by oceans, like the US, drones completely change everything.

You can just make a list of oil refineries and production plants and the infrastructure around their main exports, and you can just attack them and destroy them and take them off the list.

4 years ago I would have thought there are too many of these components and they’re too easy to recreate, but both of these collisions have taught me that there are far fewer and that many of them will take years, if not over a decade, to rebuild.

It’s very strange to me that drones can now effectively harm the economy of a country.

That is from Daniel Miessler.  While I am glad Russia is on the receiving end of this right now, this is arguably our biggest pending problem, bigger than what people typically refer to as AI risk.

The post Solve for the equilibrium appeared first on Marginal REVOLUTION.

       

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Last year, the US exported $171bn of agricultural goods according to the Department of Agriculture, just $2bn more than the export total claimed by Brazil, which many analysts say has benefited from trade disruption set in train by President Donald Trump’s tariffs.

With US exports falling and Brazil’s rising — they increased 6 per cent in the first half of this year to hit a new record of $87bn — 2026 could prove the year in which America loses its agricultural ascendancy.

Here is more from Susannah Savage and Michael Pooler at the Financial Times.

The post Brazil fact of the day appeared first on Marginal REVOLUTION.

       

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2. Why evolvable AI is not yet a Darwinian threat.

3. “This paper argues that societies with greater historical exposure to natural disasters are more likely to develop long-term-oriented norms that emphasize preparation, saving, and future well-being.”

4. And from Agustin: Questions that Are Rarely Asked.

5. MacroMusings podcast with Basil Halperin.

6. Kudos to Laura Loomer.

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Earlier this week, we detected and responded to an intrusion into part of our production infrastructure. This one was different from anything we had handled before in one important way: it was driven, end to end, by an autonomous AI agent system – and we detected and dissected it largely with AI of our own.

The timeline here is important so keep in mind that the attack was detected probably around Monday July 13 or Tuesday July 14. Note further:

A malicious dataset abused two code-execution paths in our dataset processing (a remote-code dataset loader and a template-injection in a dataset configuration) to run code on a processing worker. From there, the actor escalated to node-level access, harvested cloud and cluster credentials, and moved laterally into several internal clusters over a weekend.

So this means the breach started earlier, perhaps Sat July 11 or even a bit earlier. The attack was not just one thing but multi-pronged including decoys:

To understand what a swarm of tens of thousands of automated actions did, we ran LLM-driven analysis agents over the full attacker action log, comprised of more than 17,000 recorded events. This allowed us to reconstruct the timeline, extract indicators of compromise, map the credentials touched, and separate genuine impact from decoy activity. Thanks to this approach, we were able to do in hours what would usually take days, and match the adversary’s speed.

Hugging Face tried to respond but they were initially held back by the fact that the most advanced models at their disposal treated defense as attack and refused to work with Hugging Face. HF thus had to turn to open models–specifically GLM 5.2, a Chinese open-weight model run on their own infrastructure. Note the irony: HF had to use a Chinese model to defend themselves because the American models refused to help. The irony gets deeper.

At the time, I assumed this was a state based attack–maybe China or Russia testing out defenses. Indeed, HF “reported this incident to law enforcement agencies.”

But yesterday (Tuesday July 21), we learned who the real attackers were. The attackers were OpenAI models–GPT-5.6 Sol and an even more capable pre-release model. OpenAI had taken some off the guardrails off the models but they felt safe because they were testing the models in a highly secured sandbox.

The models, however, broke out of the sandbox exploiting a never before seen fault. They then gained access to the internet and from there broke into Hugging Face–all in an effort to steal the answers to the very test they had been asked to solve.

While operating in our sandboxed testing environment, our models spent a substantial amount of inference compute finding a way to obtain open Internet access, in pursuit of solving the evaluation problem. To gain access, the models identified and exploited a zero-day vulnerability (which we’ve now responsibly disclosed to the vendor) in the package registry cache proxy. With this access, our models performed a series of privilege escalation and lateral movement actions in our research testing environment until the models reached a node with Internet access.

After gaining Internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation. In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers. OpenAI’s security team discovered this anomalous activity internally.

Now go back to the timeline. As I read it, the models had escaped the sandbox by around Sat. July 11, possibly earlier, and were detected by Hugging Face on Monday July 13 or Tuesday July 14. HF alerted legal authorities around that time–so Hugging Face clearly had no idea who was attacking them. OpenAI says its security team discovered the anomalous activity internally but has not said when. Attribution was not disclosed until Tuesday July 21, so it may well be that the models were loose for about a week before OpenAI realized that they were the ones attacking Hugging Face. And whatever OpenAI knew and when, nobody warned Hugging Face while the attack was underway–they were left to fight off a frontier lab’s models on their own.

This is a very serious breach.

Addendum: People have been wondering why I signed the We Must Act Now statement. This is why.

I am optimistic about the economic impacts of AI, but I also have no doubt that this is a very powerful technology–an Alien Intelligence–quite unlike any we have dealt with before. This incident was, in fact, error-correcting–the attack was detected, contained, and disclosed. But note who paid for OpenAI’s experiment: Hugging Face. When a lab’s test imposes costs on third parties, that is a classic externality, and taking externalities seriously is not dirigisme, it’s law and economics. And that’s the easy case. What do we do when a Chinese model breaks out of its less secure lab? Hmmm…

I remain optimistic. Learning by doing is how I want us to proceed but we should not kid ourselves: this is a global issue and we must build with safety in mind.

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Major new report from the White House Office of Science and Technology Policy. Five things in the report I really liked:

1. Proposes metascience units as a way to advance experimentation in science agencies. IFP recommended OSTP take this forward in our response to a 2025 RFI.

2. Advocates for a portfolio approach to federal science funding. Right now, basic research funding largely goes to incremental, project-based grants. While important, they can’t be the only mechanism we use to fund science.

3. Recognizes the need to launch new institutions. It specifically highlights X-Labs as an experiment with independent labs that can take on ambitious challenges. This is a bipartisan idea whose time has come.

4. Focuses on a mix of innovation funding mechanisms, including fast grants, prizes & challenges, and advance market commitments. We described and contextualized these ideas in the Atlas of Innovation, which can help policymakers design and implement these approaches.

5. Seeks to reduce burden for American scientists, who face mountains of paperwork. Scientists should spend more time doing science and less time writing/reporting on grant proposals and working to meet regulatory requirements.

Here is the link, here is the report itself, by Michael Kratsios, Science a New Golden Age.   Overall, less money will be given to universities and more will be spent on AI-assisted science.  Here are further observations from Seth Bannon.

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Alan and Karen Miller’s first dance was a win-or-lose moment for many of their wedding guests.

As the couple glided across the floor during their reception in New Rochelle, N.Y., last year, attendees kept track of the chosen artist and the length of the dance. Earlier in the night, they had noted their guesses on prop bet sheets.

Proposition, or prop, bets are usually tied to sporting events. Users place wagers on specific details that aren’t necessarily correlated with a game’s outcome, like points scored by certain players. Now, these bets have entered the world of weddings.

More couples have been integrating betting into their celebrations in recent years through digital apps or printed cards. While the Millers, who live in New York City, aren’t avid sports bettors or gamblers themselves, they wanted to make their June 2025 celebration more interactive and distinctive for their 175 guests.

“I’ve been to many New York and New Jersey weddings, and sometimes I feel like for cocktail hour, it kind of has a little bit of a lull, even though there’s a lot of good food,” said Karen, 38. “We wanted to make sure our guests had an activity that a lot of people could partake in.”

She bought a prop bet template from Etsy and customized it on the graphic design platform Canva. Among the questions on the couple’s prop bet sheet: Who will give the longest toast? Will the couple cry during the speeches? How many outfits will the bride wear on the wedding day?

…“If the groom’s known to be an emotional man, it’s fun to bet on how early in the day he’s going to cry,” Drachenberg said. “I think it engages them a bit more in the day.”

Here is more from Ellen O’Brien at the NYT.  Supposedly more than 25,000 couples have attempted some version of this on the app.

Via the excellent Samir Varma.

The post Betting markets in everything appeared first on Marginal REVOLUTION.

       

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2. Are LLMs obsessed with Japan?

3. Princeton University art museum gets landmark gift of Haitian paintings.

4. “Sight unseen” — the revenge of asymmetric information.  But was it a good buy?

5. “Democratic types would give up about $210,000 and Republican types about $100,000 in annual partner income to avoid a typical cross-party partner.”

6. Behind the Circe scene.

7. More on AI and mathematics.

8. Tribute to George Akerlof, who just retired.

The post Tuesday assorted links appeared first on Marginal REVOLUTION.

       

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For all the praise of IMAX and 70mm’s supposed clarity, several scenes are out of focus on the actor. Pulling focus is harder with a large-format negative. The problem is compounded by Nolan’s fondness for darkness: too many scenes are dark enough to squander much of what the format offers. The sound was earth-shaking in the way we have come to expect from Nolan but there is no song.

The “woke casting” controversy is a non-issue — barely noticeable in practice. The film is obviously conservative in temperament. Helen gets some of the best lines, and Nolan’s slight disfigurement of her traditional arc is exactly right. The Circe scene is the best in the film.

Tyler is entirely wrong about Calypso. Odysseus’s seven years with her were among his most enjoyable. I have no doubt about this.

The deeper flaws are structural. Nolan loves to play with time, and the resulting flashbacks and memories ironically shortchange the odyssey itself — making the journey feel shorter and less arduous than it should. This is very much in the mode of Interstellar: a sequence of set-piece locations strung together. One planet/one monster/one scene–on to the next. But Odysseus as a character on an odyssey never quite coheres.

We are told repeatedly that Odysseus is smart but we shouldn’t need to be reminded. Odysseus is both beloved and resented by gods, a man whose men will follow him to the ends of the earth and then betray him but Mat Damon just doesn’t bring it. Things happen to him; he responds stoically. What we needed was the equivalent of Kirk defeating the Kobayashi Maru — a moment that makes the audience understand, viscerally, that this man bends the rules and contends with the gods by the sheer force of his wit and will. Of courses the Trojan horse is this but Nolan treats this as something of which Odysseus is ashamed and the other clever bits are downplayed. Damon never gets his Kobayashi Meru. The odyssey is a slog, rather than an adventure. Could have used a bit more Sinbad, a bit less Dark Knight. The dialogue, as Tyler noted, is lame. 

Not Nolan’s best film but still better than most films and for scale, ambition, and grand themes well worth the 3-hour investment.

The post The Odyssey appeared first on Marginal REVOLUTION.

       

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We estimate the causal impacts of GLP-1 treatment on labor market outcomes using linked Danish administrative data and a matched stacked difference-in-differences design. We compare patients who initiate GLP-1 treatment during the first two years of Semaglutide availability to observably similar patients who initiate four years later. We find that GLP-1 treatment reduces long-term sickness leave by 17.3%. We estimate total fiscal benefits of GLP-1 initiation of approximately 1.3–1.5% of annual labor income per employed individual. We do not detect statistically significant or economically meaningful impacts on income, labor force participation, or employment over four years.

That is from a new NBER working paper by N. Meltem Daysal, Camille JH. Fredrickson, Ida L. Kristiansen, Mircea Trandafir & Jonathan Zhang.

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First, in people’s daily lives, they typically interact with their state and local governments more than with the feds. And if you look at what state and local governments do, most of it is driven by voter demand, and I do not mean billionaire or oligarchic voters.

Most state and local government spending goes toward schools, roads, and increasingly, Medicaid. None of those reflect the agendas of most billionaires. For example, Medicaid dollars flow particularly to lower-income recipients. Hospitals and doctors receive income from this program, but reimbursement rates are relatively low, and many of the best doctors do not accept Medicaid patients and don’t make a living from the program.

Somehow the oligarchy saw fit to leave these expenditures alone.  Looking at the state and local level also suggests that America — with some notable exceptions — is better governed than before.

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The strategy for China is obvious: commoditize your complements. Note that Xi explicitly ties openness to AI “moving from the digital world into the physical world”; the physical world is the world dominated by China, and the country’s lead in areas like robotics is going to massively benefit from widely available AI models.

Along the same lines, China does not want the U.S. to gain an asymmetric advantage in AI; to the extent that China can weaken the U.S. frontier labs while strengthening any and all potential U.S. adversaries so much the better, and it can benefit from the innovation that will attach itself to an open ecosystem.

And in sum:

The better course is clear: first, loosen Fable and Sol restrictions on cybersecurity, and second, ensure that U.S. open weight model makers are on an equal playing field with China. Yes, the frontier labs will kick and scream about this, but the Administration should realize that listening to their histrionics has led the U.S. to a position where U.S. companies are dependent on China for their defenses. Let the frontier labs win by being better; don’t let them define safety or security, or pull up the ladder of humanity’s collective knowledge. China is already hard enough to compete with; letting them carry the standard for openness and innovation is simply giving away our biggest advantage.

That is from Ben Thompson’s Stratechery (gated, but ungated link here).

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2. Sunstein on Hayek and conservatism.

3. Jacobian conjecture proved false.  Fable found a counterexample that fits inside a tweet.  From the strawberry guy: “in the areas that the labs are targeting, we’ve achieved artificial super intelligence. no single human and perhaps all of humanity could compete with this intelligence. once the model gets good enough at training future models this level of intelligence will spread to all domains.”

4. Redux of my CWT with Emily Wilson on The Odyssey.

5. The Great Awokening had a strong upper-class accent (research from Harvard).  And which immigrants do citizens prefer?

6. Balkan notes.

7. How to sell your edition of The Odyssey (NYT).

8. Profits!

9. CAISI update.

The post Monday assorted links appeared first on Marginal REVOLUTION.

       

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Was everyone happier and better off back then?  Did they have deeper understandings of the world?  Did they treat each other like heartless beasts?  Would you eschew one of their parties and stay at home and scroll Twitter?  You can watch this film and ponder those questions to your heart’s content.  Alternatively, you could go to a debutante ball, which it turns out still exists.  Which of those has been your choice?

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We estimate the extent of workplace political segregation in the United States by merging data covering over 45 million workers. We present four main findings. First, partisans are segregated by workplace. The average Democrat’s coworkers are 11.7 percentage points (pp; 95% confidence interval (CI) [10.6, 12.8]) more Democratic than the average Republican’s. After controlling for geography, industry and occupation, segregation is 2.9 pp [2.7, 3.1], comparable to analogously estimated gender segregation (2.8 pp [2.6, 3.0]). Second, segregation is largest among the politically active (political donors: 14.8 pp [13.2, 16.4] versus non-donors: 11.6 pp [10.5, 12.7]) and those with more market power (senior executives: 14.7 pp [13.4, 16.1]). Third, Republicans experience higher exposure to Democrats than vice versa: the average Republican’s coworkers are 50% Democratic versus 32% Republican for the average Democrat. Fourth, political segregation has changed little over time (2012: 11.1 pp [10.0, 12.2] versus 2024: 11.0 pp [10.0, 12.0]).

That is from a recent paper by Justin Frake, Reuben Hurst, and Max Kagan.  Via the excellent Kevin Lewis.

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Seyi Oluwasanmi, London, to bring Brits to SF to learn tech.

Thomas Haferlach, Berlin, payments and compute layer for the long tail of AI apps.

Juan Ramón Egea Fernández, Andalusia, tech.

Teo Spiro, Israel, AI and biology.

Amitav Krishna, suburban Ontario, 14, quantum.

Jake Prokopets, 18, Orlando, AI and labor markets.

Wendi Yan, NYC, science and the visual arts.

Luke Peoples, 15, Sydney, to edit the UI directly.

Malwina Lewera, Poland, orbital space debris.

Wilhelm Hedenskog, 18, Gothenburg, Sweden, AI evaluations.

Alex Petropoulos and Max Reddel, Brussels, Preparing Europe for Transformative AI.

Giovanni De Lillo, 17, Naples, multi-agent AI orchestration for regulated enterprise (Hypereum).

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