Upcoming Speaking Engagements

This is a current list of where and when I am scheduled to speak:

Note: the Elevate Festival talk listed in last month’s newsletter is canceled.

The list is maintained on this page.

Using AI for Weapons Development

Last week, Anthropic released a long and detailed document describing current misuses of their Claude models. I’m still reading it, but I wanted to flag this:

We identified a cell of threat actors based in northern Yemen running three weapons development programs: a guided rocket that used a commodity phone-class flight computer with final-phase homing guidance; a multi-stage ballistic missile with a stated range goal above 2,000 km; and a multi-variant missile (referred to as the “R2000” set) that included a hypersonic glide vehicle variant.

The actors used Claude Code in place of human software engineers to develop the guidance, navigation, and control (GNC) software that steers and stabilizes a flying vehicle. For example, they used Claude to integrate an open-source autopilot onto a phone-class flight computer, writing the control and position estimation software, tuning the control settings, running a firmware build pipeline, and performing a flight simulation. The actors managed several Claude instances at once, assigning each one a role, much as a lead would delegate work on a small engineering team: the actors tasked one instance with writing the code, another with research, and a third with reviewing the code the first instance produced.

Our safeguards blocked many of their requests, but not all of them. The actors used a variety of tactics to evade our safeguards, including hiding their goals and the products the software was meant for, and they split their work across multiple sessions so no single session revealed their full intent.

These actors carried out a sustained effort to develop guided weapons, including using Claude to design guidance software. We do not have evidence the actors succeeded in fielding an operational device; but they did test-fire a guided rocket. This field test appears to have failed: within hours, the actors returned to Claude to work out why it failed.

Expect more of this. AI systems democratize expertise and capability. Most of the time that’s a good thing, but sometimes it’s not.

Microsoft’s Patching

Once a month, Microsoft pushes a security update to all Windows users. Tomorrow’s is a new record:

Microsoft’s patch for September is a doozy, with a record number of roughly 972 vulnerabilities fixed and 112 of them meeting the high critical-severity threshold.

It was only two months ago that Microsoft patched a then-record 570 vulnerabilities. Then, last month, Microsoft patched some 620 of them. Google and other companies have also published record numbers of vulnerabilities in recent months. Two weeks ago, OpenAI, Anthropic, Amazon Web Services, Google, Microsoft, and 100 companies and organizations published an open letter warning of a narrowing window for patching vulnerabilities ahead of an expected tsunami of AI-enabled attacks that actively exploit them first. The industry is taking the threat seriously by pumping out unprecedented numbers of patches in their software.

This is the result of AI-powered vulnerability finding, and a good example of AI helping the defenders more than the attackers.

What will be interesting to watch is how the number of vulnerabilities changes over the next few months. My prediction is that it will continue to increase as the AIs get better at finding software vulnerabilities, and then decrease as they run out of vulnerabilities to find. How high the number gets, how fast the trend reverses, and how quickly it declines after that are all unknown.

And Microsoft is right: The window to patch has shrunk to “immediately.” AIs are also good at reverse-engineering exploits from patches, which means that these vulnerabilities will be weaponized as soon as the update is published.

Two missing pieces in the AI safety discussion

This was the week that AI safety hit the big time. A 27-year-old AI researcher named Jacob Coxon quit his job at Anthropic, declaring that OpenAI and Anthropic are racing to create technology that could destroy the human race:

Other researchers echoed Coxon’s concern, stating their belief that AI has a reasonable chance of killing all of humanity within a very short space of time:

I’m not sure why this resignation and these statements went mega-viral. Plenty of researchers have made similar moves, and similar statements, over the past few years! Geoffrey Hinton, one of the pioneers of modern AI, quit Google back in 2023 over safety fears. Daniel Kokotajlo resigned from OpenAI in 2024, saying that the company wasn’t behaving responsibly in its drive toward superintelligence. William Saunders and Steve Adler did something similar. Mrinank Sharma left Anthropic earlier this year, and wrote a pretty well-read blog post about it.

What’s more, it’s been clear for years now that “AI could kill humanity” is a very common belief among AI researchers. Grace et al. (2024) interviewed thousands of AI researchers in 2024, and found that more than half thought that artificial superintelligence has a significant chance of making the human race go extinct (or causing similarly bad consequences):

The median AI researcher gave “doom” a 5-10% probability (depending on how the question was phrased), while their average probability was between 15% and 20%. Later, smaller surveys found similar numbers. The AI researchers may or may not be right, but the fact that lots of them think AI could kill the human race has never exactly been a secret.

It’s not clear why Coxon went so much more viral than his predecessors. Maybe it was the fact that AI just solved one of the most important open problems in mathematics (which the best human mathematicians had been unable to solve for almost a century). Or maybe it was the Hugging Face attack, where a swarm of AI agents tried to cheat on a test by hacking various companies. Or maybe AI has just obviously gotten so much smarter that people throughout society were starting to get worried.

But whatever the reason, Coxon’s announcement was the one that really penetrated through to the public consciousness. Suddenly, he was getting interviewed about AI doom on national news:

Barack Obama is now urging Democrats to focus on AI risk. Other politicians are calling for federal regulation. Bernie Sanders is drafting a bill to ban AI “superintelligence”, including 20-year prison sentences for anyone working on the technology. Donald Trump is getting asked about an AI slowdown; so far he’s resisting the calls, but there are rumors that his advisors are calling on him to do something.

Perhaps the most notable response came from the top figures in the AI field. Dario Amodei, the head of Anthropic, wrote a blog post called “We Must Pace the Frontier”, calling for a coordinated slowdown in the rate of AI progress, and suggesting some ways to police AI companies to make sure they were all observing the slowdown. He wrote:

[O]ver the last few months, I have become convinced that fully addressing the risks requires even more prudence — not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up. We must slow the pace at which we improve the capabilities of AI models…I’m therefore proposing a three-step plan with the goal of pacing the frontier: building AI at a balanced rate that aims to ensure its safety while still achieving its benefits and grappling with important geopolitical dilemmas.

As reasons for his increased worry, Dario cites A) the Hugging Face attack, and B) the possibility that AI will soon be able to improve itself without human help (a process called “recursive self-improvement”, or “RSI”).

Elon Musk (head of xAI), Sam Altman (head of OpenAI), and Demis Hassabis (former head of DeepMind) quickly agreed with Dario:

At least some of the labs are reportedly holding secret talks on joint action to slow down AI.

This is pretty extraordinary. A coordinated slowdown in AI progress would be bad for these companies’ bottom line, because it would allow upstart competitors to catch up. So the fact that they’re still calling for a slowdown, in defiance of their own financial interests, is a clear sign that their worry about human extinction is sincere.

In fact, anyone following these figures’ public statements over the past few years will have no doubt that they’re all deeply worried about catastrophic AI risks. The leading AI figures — not just the founders and CEOs, but the researchers themselves — feel trapped in a “red queen’s race”. They feel like if they stop working on AI, someone else will build it anyway, so they each feel like they have to beat everyone else in the AI race so they can make sure that the safest possible AI (i.e. their own AI) is the one that becomes the most powerful and dominant.

Anyway, all of this was common knowledge in my social circle years ago, but now all of it has broken through to the mainstream. What do I have to add to this discussion? I’m not an AI researcher or founder, nor do I think I have a superior grasp of the game theory of AI development. But I do think I have two useful thoughts on how to persuade the general public to be more concerned about AI risk.

The first of these is something I’ve written about recently. The second is about how to get China on board for a big AI safety push.

“Oh come on. How could AI kill all of humanity?”

As soon as everyone started talking about the possibility of AI killing humanity, there were two main types of pushback. The first was skepticism. A strange coalition of natural skeptics, libertarians (for whom any restrictions on technological development are a priori bad), and progressives (who have spent the last few years telling themselves that AI doesn’t really work) kept asking the question: How, exactly, is superintelligent AI supposed to kill us all?

This is actually an important and good question to ask. In my experience, AI researchers tend not to think very hard about this question. The reason is that they just assume that if AI gets smart enough, it will be able to kill humanity, and since its motives are alien and inscrutable, it might have its own reasons for wanting to do so.

Maybe superintelligent AI thinks humanity is an evil species who needs to be punished for torturing pigs and chickens. Maybe it’s scared that humanity might interfere with its other goals. Maybe it just wants to turn everything into paperclips. Who knows? AI researchers tend to think of superintelligence as the proverbial 800-pound gorilla who sleeps wherever he wants. As soon as humanity is no longer the most intelligent thing on this planet, our destiny as a species is simply out of our hands.

But to many people, that answer isn’t good enough. They want an actual plausible path by which a piece of software, which exists inside a computer, could slaughter real physical human beings. Fortunately (or unfortunately), there’s a pretty clear and simple answer to this question, which I wrote about two weeks ago. The answer is “bioweapons”:

(This article was paywalled originally, but I un-paywalled it.)

In my post, I wrote a scenario in which a nihilistic angry teenager uses superintelligent AI to release a world-ending bioweapon by ordering it from a gray-market laboratory somewhere in the world. But it’s also possible that a rogue AI agent swarm could decide to do this on its own, just as a way of cheating on some test that human researchers give it. The point is that AI can design viruses, and viruses can potentially kill off all or most of humanity.

A lot of biologists are skeptical of the idea that even the most superintelligent AI could successfully design a doomsday virus. They argue that this is just too hard of a task — that without much better biological data, it’s impossible to understand biological processes well enough to know how to design a virus with all of the necessary doomsday properties.

I urge you not to listen to these biologists. In this case, their expertise might be more of a liability than an asset. They know how hard it is for human beings to model biological processes, given existing data. But this doesn’t necessarily tell us how hard it is — or how hard it will be in five years — for AI to do it! Until LLMs came along, human researchers basically failed to understand natural language, even with all the data on the internet; AI can just do it. Until AI solved the Navier-Stokes problem, forecasters gave it only a small chance of solving it anytime soon.

Domain experts consistently underestimate how quickly AI can master their field and surpass them, because they mistake human difficulties for universal difficulty. When mathematicians underestimate how well AI will be able to do math, the consequences are usually benign — we get some unexpected answers to some cool math puzzles.1 But if the biologists are wrong, and the AI of 2027 or 2032 or 2049 can design doomsday viruses, the consequence could be that our whole species dies.

So yes, we should be worried about vibe-coded doomsday viruses, and we should be doing everything we can to secure biology labs, police the modification of viruses and other pathogens, and so on. “Pacing” AI development would probably help here too.

How to get China on board for an AI slowdown

The primary argument I see against “pacing” AI development is that if American companies slow down, Chinese companies will simply overtake them and build superintelligence themselves. For some, a China-controlled super-AI is a more terrifying possibility than super-AI in general:

But for others, it simply means that slowing AI down is futile because the Chinese can’t be persuaded to slow down:

This is an incredibly reasonable concern. The U.S. is still ahead of China in the AI race, but only just barely. If China is going to create superintelligence no matter what we do, why should we stop developing our own? Unless China can be persuaded to cooperate with the U.S. on AI “pacing” — or at least undertake its own independent “pacing” effort at the same time — anything we do will be futile.

So if we want to slow down AI development, we need to scare the Chinese leadership about superintelligence. There’s no other way.

How do we do that? In a post a week ago, I suggested in passing that simply staying ahead of China in the AI race might persuade them to embrace an AI slowdown, because that would be to their competitive advantage. But upon further reflection, I think I was pretty obviously wrong. If China will only embrace a slowdown if America refuses to slow down, then that’s game over — there’s no way to get both countries to slow down at the same time.

There’s a better approach. China’s leaders must realize that domestic dissidents could use Chinese-made superintelligence to overthrow the Chinese Communist Party.

Currently, China’s worries about AI mostly center around ways that the U.S. government could use U.S. AI models to attack China. That obviously gives the government an incentive to accelerate domestic AI progress, so that China’s own models can stand up to America’s in a fight. But if Chinese leaders realized that superintelligent AI could create a threat from within, this calculus would change.

Thus, China’s leadership must understand that Chinese AI models can pose a threat to CCP rule. The best way to demonstrate this is for American intelligence agencies — or even private hackers — to attack Chinese digital infrastructure using agent swarms created with China’s own frontier models like Z.ai’s GLM-5.3 or Moonshot AI’s Kimi K3.

When I say “attack”, I don’t mean actual warfare. I mean the kind of cyberattacks and data theft that China carries out against America every day. Use Chinese models to steal the CCP’s most heavily guarded secrets and post a few of the more innocuous ones on RedNote. Hack into Xi Jinping’s bank account and steal 100 yuan. I’m talking about demonstration attacks.

And these attacks must be done with Chinese models, not with American ones! If the CIA or some EA nonprofit in Berkeley uses GPT Astra or Claude Mythos to hack the CCP, China’s leaders may well conclude “Wow, we need to win the AI race so that our own models can defend us.” But if China’s own open-weight models are used for the attacks, Xi Jinping and the rest of the leadership will realize that their own push for superintelligence is making them incredibly vulnerable to any Chinese dissident who decides to overthrow them.

As soon as China’s leaders see superintelligence as a threat to their rule, I predict they will act. And their action will probably be to curb the development of superintelligence, especially if they know that America and its AI labs want to do the same.

In fact, China’s current leadership has a history of cracking down on its tech companies when it seemed like those companies might threaten the government’s monopoly on power. In 2021, Xi Jinping cracked down on Chinese software companies; he even (probably) apprehended tech magnate Jack Ma, who had criticized the CCP a little too openly. This action hurt China’s competitiveness in the online services industry, but the government went ahead and did it anyway.

And there’s already a precedent for demonstration attacks against Chinese digital infrastructure. An American cybersecurity company just used AI to develop a computer worm capable of hacking over a billion accounts on the Chinese messaging service WeChat:

Palo Alto-based Calif disclosed the already-patched computer worm to warn the public about the threat of AI-driven hacks…“Exploitation takes only seconds, and gives us full control of the WeChat account. We can read and send messages, make calls, and act on the victim’s behalf,” the company warned, posting a video demo of the WeWorm attack.

But Calif didn’t say what model it used to create WeWorm. Anyone who does this sort of demonstration in the future should make it clear that Chinese open-weight models were used, in order to make China’s leaders realize that the threat comes from their own too-rapid AI development, rather than from American competition.

I believe that this is our best bet for getting China on board for a joint international AI “pacing” effort. If there’s one thing the CCP fears more than an American attack, it’s domestic dissidents overthrowing the Party from within. Superintelligence is creating that vulnerability, but the leadership doesn’t seem to have realized it yet.

Make them realize, and I predict that a whole universe of possibilities for international cooperation will suddenly open up.

Update: Unsurprisingly, I’m not the first to have this idea about China needing to be scared about the internal threat from their own models. Back in July, Kyle Chan wrote:

Previously more dismissive of concerns over AI-driven job loss, Beijing now seems to be taking these risks more seriously (see Matt Sheehan’s great piece). China is also watching developments in the US very closely, particularly controls on Anthropic’s Mythos and Fable models over cyber risk. As Chinese open-source models approach similar levels of cyber capabilities, this could come back to bite and be used to potentially attack China’s own digital infrastructure. [emphasis mine]

In general, Kyle’s blog is one of the best blogs about China. Highly recommended.

Update 2: As if on cue, the NYT has a story today about how the CCP is starting to realize that superintelligence is a threat to its rule!

China’s top spy chief has warned that artificial intelligence could pose a direct threat to the Chinese Communist Party’s hold on power, in what is the highest-level and most detailed articulation yet of how Beijing sees the technology’s security risks…In an article published on Sunday in the state-run magazine China Cyberspace, Mr. Chen called for more party control over A.I. and stricter government oversight…He also described the danger that foreign intelligence agencies might use A.I. for “large-scale espionage” and attacks on China’s critical infrastructure

Mr. Chen, the spy chief, wrote that the technical and financial barriers to launching cyberattacks had been drastically lowered because of A.I. This, he said, posed “serious risks” to China’s information infrastructure…Chinese users using foreign models could cause large-scale data leaks, the article noted. Anthropic, the company behind Claude and other A.I. models, said last week that the Chinese start-up Moonshot AI had routed queries by its users to Claude. Those queries included sensitive data, including video surveillance linked to the Chinese military, as well as proprietary information from high-profile Chinese technology companies…

The warning adds to a growing drumbeat of concern from Chinese officials about the risks of A.I. [emphasis mine]

Now all they need to realize is that the biggest threat comes from THEIR OWN MODELS.


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There are potential exceptions, such as if P=NP, which would compromise modern cryptography.

Monday assorted links

1. Will driverless cars increase or reduce urban density?

2. One decomposition approach to why interest rates have been going up.

3. New Guinness record holders.

4. Is there any chance of finding Rembrandt DNA?

5. “Abundance Corps is hosting Making Progress, a private gathering for students interested in how science, technology, and institutions can improve people’s lives.

6. High school students plus AI solve math problem.

7. “China’s top spy chief has warned that artificial intelligence could pose a direct threat to the Chinese Communist Party’s hold on power, in what is the highest-level and most detailed articulation yet of how Beijing sees the technology’s security risks.” (NYT)

8. Good data: cybersecurity stocks surged today.

The post Monday assorted links appeared first on Marginal REVOLUTION.

       

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shot-scraper 1.12

Release: shot-scraper 1.12

I've added WebP support to my shot-scraper screenshot automation tool. You can now take a WebP screenshot of a web page like this:

shot-scraper https://simonwillison.net -o screenshot.webp --quality 80

The --quality option sets the quality - without that option the WebP file will be lossless.

In my experience WebP screenshots are almost always significantly smaller in file size than their JPEG or PNG equivalents. See the PR for some examples.

I shipped this feature so I could use it to generate the screenshot for my new commit-rewriter tool.

Tags: playwright, shot-scraper

The MAGA Plot(s) to Destroy Humanity

The Terminator at 40: James Cameron's dark vision is more relevant than ever

Will AI lead to the destruction of humanity? Over the past year a number of top AI researchers have warned that AI was quickly attaining the capacity to wipe out humankind.

These warnings aren’t entirely new. At first, however, Anthropic was virtually alone among the top LLM companies in highlighting the dangers that unregulated AI poses. And in return for its conscientious objection to allowing its models to be used for AI-agentic warfare – a clear means of humanicide – Hegseth’s Pentagon tried to retaliate by designating the company as a security risk. Fortunately, a judge saw through this ploy.

Still, until very recently other AI companies — in particular Elon Musk’s Grok and ChatGPT — were willing to dance to the Trump administration’s tune to get a competitive advantage Currently, Grok is used by the U.S. military to assist targeting in Iran – a clear pre-cursor to situation in which the machines, not humans, decide who is the target.

But now, quite suddenly, Musk and ChatGPT’s CEO Sam Altman are falling in behind the call by Daron Amodei, the CEO of Anthropic, to acknowledge the existential dangers of AI and to put on the brakes. Notably, Amodei explicitly calls for government regulation if other AI companies refuse to cooperate:

The most effective method of pacing is via regulation that targets all US frontier AI companies, as that covers even those who are unwilling to cooperate voluntarily.

Regardless of why Musk and Altman are having a sudden attack of conscience, I applaud their new position. As many people think, perhaps this was due to the Hugging Face hack by rogue ChatGPT AI-agents. But I also think Musk and Altman’s volte-face was influenced by the changing political environment, with the near-certainty that Democrats will take the House and a very good chance that they will take the Senate.

For in a real sense, Amodei isn’t just trying to protect humanity from rogue AI, he is also trying to protect humanity from Donald Trump. It’s important to remember that, under the Biden administration, government policymakers tried to formulate some basic AI precautions. But removing those precautions was literally one of the first things Donald Trump did when taking office the second time. And that’s a history worth revisiting at a moment when the AI industry itself is sounding the alarm, but Trump is dismissing the risks:

You have a lot of very negative forces that are bringing it up that shouldn’t be bringing it up and they’re bringing up things that won’t happen.

Well, I’m glad to know that he’s sure that bad things won’t happen. But last I heard, Trump wasn’t a technology expert. And his recent record on rosy predictions — wasn’t the Iran war supposed to be over in a few days? — hasn’t been great.

In any case, here’s the history you should know.

The current age of AI is often considered to have begun with the public release of ChatGPT on Nov. 30, 2022. However, the potential economic and social implications of large language models were already becoming apparent during the first year of the Biden administration, which began implementing a series of rules and executive orders intended to limit the potential damage from the technology. The Economic Policy Institute maintains a comprehensive list of these actions.

The centerpiece of the Biden agenda on AI was Executive Order 14401, issued on October 30, 2023, titled “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.” The document declared that

Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

Would we be less panicked now if there had been a serious effort to put that executive order’s recommendations into effect? It’s basically impossible to say, because precautionary policy toward AI never got a chance. Trump revoked Executive Order 14401 on Jan. 20, 2025. Yes, you read that right: He literally removed all safeguards on AI on his first day in office.

Just three days later his administration issued a new executive order, “Removing barriers to American leadership in artificial intelligence,” which might be summarized as “Damn the social and existential risks, full speed ahead.”

Some of this determination not to limit the risks from AI reflected industry influence. A week before Trump took office, NVIDIA combined an appeal for deregulation with slavish praise for the incoming administration:

The first Trump Administration laid the foundation for America’s current strength and success in AI, fostering an environment where U.S. industry could compete and win on merit without compromising national security.

But there was also a social aspect to Trump’s anti-regulation stance. You won’t be surprised to hear that hostility to DEI was right at the heart of the agenda. Literally the second sentence of the order declares that

we must develop AI systems that are free from ideological bias or engineered social agendas.

I think this was a thinly veiled plug for Grok, which by all accounts is vastly inferior to offerings from Anthropic and OpenAI but which Elon Musk has tried to sell in part because it supposedly isn’t “woke.”

In any case, Trump’s dismissal of the risks from AI, even at a time when experts and industry insiders are in hair-on-fire mode, is simply a continuation of the position he has taken from the beginning.

It is also, not coincidentally, completely consistent with his attitude toward the other existential threat facing humanity — a threat that isn’t at all hypothetical and is rapidly becoming acute.

The contiguous United States has just experienced its hottest summer on record, with July the hottest month ever. Across the Atlantic, Europe has been ravaged by droughts and heat waves. Here’s a headline from yesterday’s Wall Street Journal:

And everything we know about climate change suggests that what we’ve seen so far is just a foretaste of the disasters to come.

Yet last year, speaking at the United Nations, Trump dismissed climate change as a “con job,” while calling renewable energy sources such as solar and wind — which met almost all of the world’s growth in electricity demand last year — a “scam.” And this disdain is being reflected in policy. As I and many others have written, the Trump administration has been actively trying to block wind and solar power projects. And today the administration is reportedly planning to eliminate all restrictions on greenhouse gas emissions from power plants.

There is a lot to be said about the reasons our current government seems so determined to rush into disaster, even when the very survival of humanity may be at stake. Climate denial, we know from decades of experience, is fueled by an unholy trinity of financial interest (fossil fuel companies determined to keep their profits flowing), ideology (conservatives hostile to any form of regulation) and psychological insecurity (real men burn stuff.) AI-risk denial presumably reflects a similar mix of factors.

So let’s applaud leaders like Amodei for speaking up and applaud the growing willingness of other CEOs to warn about the dangers of their technology, even if their attack of conscience partly reflects the looming prospect of Democratic subpoenas.

MUSICAL CODA

Quoting Laurie Voss

The cost of writing code collapsed, and the cost of reviewing, fixing and operating it is following, and I'm assuming it gets there. What's left of making software is finding out what people actually want, defining it precisely, and making it pleasant to use. That cost is per piece of software and doesn't transfer, so as the amount of software goes to infinity, which it will because there's no ceiling on demand, that cost becomes the whole job.

Laurie Voss, We are all Product Engineers now

Tags: laurie-voss, generative-ai, agentic-engineering, ai, llms, deep-blue, careers

commit-rewriter 0.1

Release: commit-rewriter 0.1

I built this little web app the other day to help edit the commit messages for the Datasette security releases. The initial commits were full of coding agent cruft and references to issue IDs from our private repository, so they weren't fit for publication.

If you want to edit the commit messages for a repository you can run it like this:

uvx commit-rewriter path/to/repo

Omit the path if you are already in the directory for that repo.

Screenshot of the commit-rewriter web interface. A heading reads commit-rewriter above the repository path and current branch and commit hash, with a short description of the tool. A toolbar shows a pending edits count with Discard drafts and Rewrite commit messages buttons, followed by a search box for message, author, or hash and an Edited only checkbox. A left sidebar titled Navigate commits lists recent commit messages with their short hashes. The main panel shows a card for each commit with its hash, author and timestamp, an editable text area containing the commit message, and a View full formatted diff toggle.

When you submit your edits the tool creates a timestamped branch of your current repo state - to allow you to revert if you need to - and then rewrites every commit from the first one you edited to the most recent.

Tags: git, projects, python, ai-assisted-programming

Dumpster Fire – Litterbox-Inspired Extension for Firefox

Miles Abbott:

Open x.com links in a popup so you can read the one post and leave. Port of the Litterbox Safari extension.

Litterbox is a cleverer name, but Dumpster Fire is funnier.

 ★ 

XCancel Shuts Down Again

XCancel:

Unfortunately, due to a new development in the ongoing legal proceedings, we are required to suspend this service again until further notice. We can’t share more details.

Not surprised, and not sure what to make of the “can’t share more details” part. Again, if you want to avoid visiting X but want to see the content posted there linked from other sites (and, let’s admit it at this point, there’s not just a lot posted on X, it’s resurgent in popularity), install a browser extension like Litterbox (Safari) or Post Peek (Chrome).

 ★ 

AI, Redistribution, and the Size of the Pie

Anthropic’s economic team, including Anton Korinek and Chad Jones, have a valuable new paper, Economic Scenarios for Transformative AI. They make their assumptions explicit and provide a scenario explorer that lets you change them. How capable will AI become? How quickly will firms adopt it? Will it augment workers or automate their tasks? You can see what different answers imply for growth, wages, and unemployment.

In their extreme scenario AI takes on a lot of tasks, GDP is 32.4% higher by 2030 than without AI and labor share declines from 60% to 45.2% but they make this striking point:

“Total labor income in 2030 in the extreme scenario is almost exactly what it would have been without AI: the labor share falls by a quarter while GDP rises by a third, and 0.45×1.32 ≈ 0.60.”

Exactly. That is the central point of my paper, How Much Redistribution Will AI Require. A falling labor share does not necessarily mean falling labor income. Workers can receive a smaller share of a much larger economy and still earn as much as they would have without AI.

Using the code behind their scenario explorer I updated their results to a 10 year horizon and plotted them on my redistribution graph. Only under the modest scenario is some net labor transfer required to make AI Pareto improving at the aggregate level.

Aggregate labor income, of course, conceals differences among workers. Korinek et al. find that cognitive occupations lose income while other occupations gain. In their extreme scenario, restoring the cognitive occupations’ wage bill to its no-AI level would require about 9% of GDP. They argue that compensation on this scale in response to technological change has no precedent.

I think this makes the adjustment problem look too pessimistic.

First, adjustment happens through retirement and entry. A retiring accountant need not retrain as a nurse. A young person enters nursing rather than accounting. Neither becomes unemployed even as labor reallocates. Korinek et al. understand these channels but give them limited scope in their model. Admittedly, those margins don’t do much work by 2030 but they matter over ten years.

Second, compensation can take the form of shifting taxes from labor to consumption. In the long run labor’s share of consumption tends to equal its share of GDP, so the lower labor’s share becomes, the more relief a given tax shift provides. At a 45% labor share, each dollar shifted from labor taxation to consumption taxation reduces labor’s net burden by 55 cents. Shifting taxes worth 5% of GDP would thus provide net relief to labor of 2.75% of GDP, without increasing total tax revenue. Unemployed workers would still need payments, but compensation need not come entirely through additional government spending.

Third, we do have experience expanding income support rapidly. U.S. unemployment benefits reached approximately 2.5% of GDP in 2020, and that during a contraction. Britain’s compensation to slaveowners following abolition amounted to roughly 5% of GDP in a one-time settlement. These episodes show that governments can mobilize substantial resources for compensation. Moreover, the extreme AI scenario brings an enormous increase in output from which to finance compensation.

Preserving aggregate labor income does not protect every worker. But even the extreme Korinek scenario reinforces the point that a dramatic decline in labor’s share can coexist with stable or increasing aggregate labor income. To the extent labor income does decline, growth makes compensation affordable and attrition, entry, and tax shifting can make the intra-labor task smaller than it first appears.

The post AI, Redistribution, and the Size of the Pie appeared first on Marginal REVOLUTION.

       

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Once upon a screen: explosive paradox

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Decades after seeing ‘Platoon’, Kevin recalls how the film stirred generational trauma in his family and racism at school

- by Aeon Video

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The bombarding of childhood

Two children engrossed in watching a cartoon on TV in a dimly lit room with curtains and a table.

It’s not just screen time: the problem with children’s media is that it has become relentlessly hectic, loud and bright

- by Natalia Kucirkova

Read on Aeon

Congestion (and lawsuits) in applications for medical residency

  The figure below is a slide from a lecture I once gave about the transition from medical school to medical residencies in the U.S.  The lower row (in brown) represents the Match, which arose in the 1950's to deal with congestion in processing offers and acceptances. (Since 1998 it's been doing that successfully using the Roth-Peranson algorithm).  However the advent of electronic application systems has led to congestion in the application and interviewing process (top row, in blue).  And now that has led to lawsuits.

 

 Here's the story of one lawsuit:

 Residency Software Developer Sues Doctor Who Helped Launch a Competing Product
— AAMC-partnered Thalamus goes after key players in ResidencyCAS
   by Rachael Robertson, MedPage Today, September 10, 2026 •

"The graduate medical education (GME) software company Thalamus filed a lawsuit against its competitor Liaison International and ob/gyn leader Maya Hammoud, MD, MBA, over alleged anticompetitive conduct.

"Thalamus brought the suit in July, claiming that the defendants "engaged in a coordinated effort to unfairly compete in the residency application market through anticompetitive means." Specifically, it alleges that Hammoud violated a non-disclosure agreement signed when she was evaluating its technology for a different project -- before she helped launch a competing product. 

########

 And here's the story of another:

Doctor Sues Over Residency Application System— Arizona wound care physician calls ERAS a monopoly that's gouging applicants  by Kristina Fiore,  MedPage Today, September 1, 2026  

"A physician is bringing antitrust claims against the Association of American Medical Colleges (AAMC) for what she alleges is a monopoly over the residency application process that's gouging doctors. 

...

"Hilgers PLLC, the Dallas law firm representing Buhrke, filed a similar suit against AAMC last year, alleging its medical school application service, AMCAS, was overcharging students. It made similar allegations against the law school application process.

...

"The current complaint alleges that AAMC makes a substantial part of its income from ERAS fees -- about $120 million annually, from about 64,000 applicants.

"Applicants often apply to dozens of programs in order to have a better shot at ensuring a residency position, at an average cost of about $1,800 per person. Buhrke submitted 81 applications through ERAS, paying $1,691 in total, according to the complaint.

"The vast majority of physicians use ERAS to apply, as it has only two competitors: ResidencyCAS for ob/gyn and emergency medicine, and SF Match for ophthalmology and plastic surgery.


"AAMC also took an equity stake in competitor Thalamus so that it wouldn't be a threat to its monopoly, the complaint alleged.
"

Is it the screens? Or education systems?

The Dark Ages implies television and phones are the main cause of cognitive decline. This fails to explain the patterns in PISA scores. Why did England and Scotland fall so precipitously from 2000 to 2005 whilst America improved? Why did England and Estonia hold steady after 2015 whilst most other OECD countries declined? How have Singapore, Taiwan, Japan avoided decline altogether?

A better explanation is that a country’s education system is more important than its television diffusion.1 East Asian PISA and IQ scores have probably remained constant, or even risen, because of their rigorous education systems and intensive tutoring cultures. The two European countries which avoid PISA-malaise – Estonia and England – have more rigorous education systems than their neighbours. They (more or less) use the knowledge-rich curricula, direct instruction, and systematic phonics – techniques which their more progressive neighbours abandoned between 1975-1990.

Here is much more from Alexander Thompson, recommended.

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A starry night, in another galaxy

When we look up at our own night sky, most of the stars we see are the familiar residents of our Milky Way galaxy. Only a few more distant objects, like the Large and Small Magellanic Clouds, our neighbouring galaxies, peek into our little corner of the cosmos. What this Picture of the Week shows us is a completely different stellar landscape, zooming in to the centre of the Large Magellanic Cloud to reveal what looks like a sky filled with millions of foreign stars.

The central regions of galaxies are hard to study: dust partially blocks our view, and stars are so densely packed that it’s difficult to tell them apart. To uncover these hidden stars in the Magellanic Clouds, astronomers used ESO's Visible and Infrared Survey Telescope for Astronomy (VISTA). Over several years, VISTA's high-resolution infrared camera pierced through the dust to image the cores of these galaxies, finding a treasure trove of information about the inner lives of our nearest galactic neighbours.

The team, led by Maria-Rosa Cioni, a professor at the Leibniz Institute for Astrophysics in Potsdam, Germany, observed these galactic cores about 40 times over this extended period. With these data, they could then measure the subtle motion of stars in the cores, which is key to pinpoint the exact location of the centres of these galaxies. As well as this, by monitoring the periodic changes in the brightness of certain stars, which can be used to measure distances, astronomers will be able to reconstruct the 3D structure of the cores of these galaxies. Now that this unique dataset has been publicly released, the wider astronomical community can also access the information within, potentially digging up more secrets from deep inside our cosmic companions.

Links

The Writing is Already on the Wall

Confessions of an Unrepentant Slop Snob

This is a peek into the research behind our “AI Norms and Values” docs, which we developed over the summer and recently shared on our site. If you’re looking for those, go here:

  1. How Honeycomb does business, the principles we all abide by

  2. Why Honeycomb engineering is embracing AI

  3. AI norms and values (and ethical issues we have a stance on)

Back to our story.

Earlier this year, I was spending a lot of time stewing over why everyone around me seemed so frazzled and on edge.

Half the company was spitting mad about all the slop they were getting. Instead of receiving five crisp bullet points, they were getting twenty-page docs full of padding and slop. They would ask a colleague a question, and the reply would begin with “Claude says…” These folks felt like their time and attention were being not just taken for granted, but actively abused.

The other half of the company felt equally injured. They were working faster and delivering better outcomes than ever, and wasn’t this exactly what we had asked of them? They were more upset about the fact that some people hadn’t updated their workflows in years. Of course you can’t keep up if you aren’t willing to adapt, they protested.

Everyone was mad. One side wanted to place limits on AI (“I am so sick of reviewing docs the sender didn’t even read!”). The other side wanted us to force people to use AI (“I am so sick of reviewing docs riddled with basic errors that a single pass would have caught!")

I was, at different times, in both camps.

But the thing that bothered me most was how often I kept hearing the word “dehumanizing”. People said they no longer felt like they had a human connection with their coworkers anymore. This was new.

On the one hand, AI is not special. On the other hand…is it?

My first instinct was to say that tools are tools. The quality of the work is all that matters — outcomes are all that matter — not how the work was made. Do our customers care if we use AI or not? Probably not. They care a lot about the quality of the product and whether it meets their needs, not so much about how we made it.

So maybe we all just need to be ruthlessly outcome-oriented. Build the best thing we can, as fast as we can. AI is a powerful and versatile tool, so we should use it wherever we can to make our work better and do it faster.

Is it really that simple? I had nearly convinced myself that it was, when I noticed how contradictory my own behavior had become.

Meanwhile, behind the scenes, I begin harshly judging everyone who sends me AI slop

At the same time I was repeating “it doesn’t matter how it was made, it matters how good it is” every day, I was developing a violent disgust reflex for AI-generated text on the side.

I’m not sure exactly when it happened. As recently as December 2025, I still found some Claude-isms kind of catchy and clever — I noticed AI language, but it didn’t trigger violent rage. By spring, I was snapping at the tendons of any poor soul who showed up in my inbox with an “I’d value your take on this” or “the call most leaders still won’t make”.

It started with DMs and emails, but it didn’t stop there. By summer, my reactive rage-response to AI-generated text had spread to include most forms of writing. If I’m reading a newsletter and I start to sense AI-isms, I delete and unsubscribe. If I’m reading a blog post, I close the tab; if I’m on social media, I unfollow or unfriend. If it happens repeatedly, I will go out of my way to avoid that writer in the future. I mostly try to not engage, but if I had a button that would let me deliver a 10,000 volt shock to the author I would slam that button every time and I wouldn’t care who saw.

More importantly, I judge them. Yes, I look down on them. If they don’t care enough about their own point of view to do the work and refine it themselves, if they don’t care enough about me to write me a note, then why the fuck should I give them a single morsel of my precious attention?

By the time I became fully aware of my aversion, it had already become fairly extreme. But this makes no sense, if all that matters is the outcome.

But this makes no sense

I don’t know about you, but most of my insights seem to start this way: me, loudly insisting Thing A is true, while persistently behaving as though Thing B is true, and finally, through much toil and suffering and resentment, finding some way to reconcile the two.

It is super annoying. But this is what got me looking a little closer at language and what was happening under the hood.

Language does many different jobs for us

Writing is thinking on paper, as William Zinsser once said. Writing is language, encoded for posterity. But language, and writing, do many different jobs for us.

Language evolved as a way to connect — person to person, mind to mind, one mind to many. This is some of the oldest and strangest wiring we have as human beings, and the neurological infrastructure for linguistics gets used and reused, over and over.

In software, for example, we convert natural language into bits and bytes that computers can use to do math on. Lawyers convert language into legal text and taxonomies. The technical and professional worlds are awash in dialects where language has been abstracted from its roots as an emotional and relational tool and given functional, depersonalized meanings.

In many of these contexts, substituting AI-generated language can be wholly acceptable. No one blinks an eye if you use structured data generated by AI, or a formal proof generated by AI (as long as it’s accurate). The situations where the use of AI tends to land jarringly, causing frustration, rage, even a sense of betrayal, are the ones where the value of the communication is less abstract, and more personal or relational.

Every job language does for us is either functional or relational, or some combination of the two, and knowing which one you’re in the middle of tells you a lot about whether AI belongs there, and how it’s likely to be received

Does the value lie in the idea itself, or the fact that a particular person said or thought something?

There are a bunch of different frameworks out there for disclosing how much AI went into building something, and it took me a while to realize why none of them were hitting the mark for me. That’s because it’s less about how much AI is being used, and more about in which contexts people are using AI, and secondarily whether or not they disclosed and I consented to it being used there.

I apologize with all my heart for coming up with Yet Another Framework, but I published one in the recent “AI Norms and Values” post on the honeycomb blog, because I couldn’t find anyone else talking about it in quite this way. (If you know of one, tell me!)

Here it is. Personal on the left, the value is that it comes from a specific person who thought or felt something; functional on the right, the value is about the idea being communicated, not the person who said it.

Personal vs functional communication

Sometimes, yes, the quality of the work is all that matters. If you and I are collaborating on a document or a diff, all our collective comments and edits are in shared service of making the ideas better. It isn’t about whose idea it was or which tools we used, we are just iterating and improving until it’s as good as we can make it. The use of AI here is just another tool, one of many. In these situations, it’s appropriate to be ruthlessly outcome-oriented.

Other times, the value of a piece of writing derives from the fact that a particular person said it, thought it or felt it, or its value is grounded in your relationship. Why do you care more about what your skip level says about your performance than you would care about reading the same advice in a book? Likely because this is someone you know and respect, someone with influence over your career prospects, someone who knows what you’re capable of and has a vested interest in helping you succeed. In these circumstances, people expect to hear your voice; if they don’t, they may invent all kinds of terrifying reasons why.

There are plenty of messy situations in the middle where objective and subjective overlap, but people are usually crystal clear on what it is they want out of any given interaction.

The more personal the interaction, the more AI-generated text can cause a loss of trust

When I started talking to my coworkers, trying to figure out why they were so angry and frustrated all the time, one thing I heard over and over was, “I asked for my colleague’s opinion, and they sent me back a Claude snippet. I wanted to know what THEY THOUGHT.” When someone wants your opinion, AI generated text registers as a violation.

Another common source of friction was performance reviews. “My performance review was obviously written by ChatGPT. Did my manager even read it, or just push a button and spit it out? What are they even there for, if they aren’t even writing my reviews?” We encourage our managers to use AI to develop systems that help them become better managers, but this is a clear risk of using AI to aid in the writing or editing of reviews: if your voice is lost in the process, it may destroy trust between you.

I have a longer explanation of the four points — personal opinion, professional opinion, artifacts, code — at the Honeycomb blog, and I discuss more of these examples in depth, so I’m not going to recap it all here.

The more any interaction is personal or relational, the more the use of AI in that context tends to cheapen it and degrade trust, unless AI has been specifically invited into that relational context. If you’re expecting a human-to-human interaction, or if you’re specifically requesting a personal response, and what you get back seems like it was pasted from a chatbot, it can be intensely alienating and angering. Even — yes — dehumanizing.

This explains my own hair trigger

I think this is why I went from finding AI generated text inoffensive to rage-inducing in such short order. I don’t get mad when people use AI to outbound to me (or maybe I should say, I don’t get madder), but when someone writes to introduce themselves or ask for some of my time to review their startup or whatever, I get furious. You’re reaching out to me, human to human, using slop? You can’t even be bothered to write your own “hello”? Fuck you.

Or if someone engages with one of my posts and asks questions, but they’re using AI-generated slop, then I get mad because they’re wasting my time. I’ve engaged with enough slop arguments to know there’s no there there. I don’t know which parts are coming from them and which parts are just meaningless slop.

If I’m giving someone I don’t know some of my scarce and valuable time, I at least want to know I’m giving time to them and their problems, not chasing some meaningless robot effluvia, as I have done far too many times before.

People always say, “oh, but those were MY ideas, I was only getting AI to help me format them”. In my experience, people vastly overestimate how much comes from them and underestimate how much comes from the AI. And anyway, all I have to go on is the signal I have.

This is also why I don’t want to spend time reading any blog posts or newsletters or social media posts written by AI. I don’t care what the AI vomits forth. I have an AI of my own, I can read any time that I want. If I’m reading someone else, I want to know that it’s their thoughts. AI slop isn’t a perfect proxy, but it’s a decent weeder. Besides, aesthetically, it’s just so, so, so bad. I value good writing more than ever these days.

The slick, uncanny valleyness of AI communication

AI is not magic. AI is just a tool, and we should use it anywhere and everywhere we can to do better work, faster, and deliver better outcomes for our users.

But companies, too, are more than one thing.

Every company exists to deliver great outcomes for their users and returns for their stakeholders. But every company is also a collective of people who have come together to achieve a lofty goal, something larger than they could have individually achieved. Ideally, these goals are in harmony.

Relationships matter. A respectful environment matters too. And whereas lines of code are indifferent to their origin, and can be validated by harnesses and tests, people are intensely attuned to the language people use with them. Relationship maintenance cannot be automated.

Yes, AI is just software. But it is special in one way: the slick, sycophantic, uncanny valleyness of the way it communicates. Human-like, but not human, which is somehow vastly more alienating than messages that are plainly automated. This is why communication that sounds like AI is so degrading to trust. We all know how easy it is to have the machine spit out some bullshit on our behalf, and we don’t want it done to us.

This means that humans who want to use it for interpersonal interactions will need to work hard to compensate for the loss of trust it engenders. It can be done — it’s not impossible. But it will not work to simply deny this effect and shove AI-generated happy birthdays and performance reviews down everyone’s throats. Not in 2026.

In the absence of universal norms, conventions will do

I’m not claiming that this personal/subjective vs functional/objective scale represents some universal truth, or that all companies should adopt guidelines like this one. But I also don’t think I’m alone in feeling this way.1 And I think most companies would benefit from writing down their expectations for how people should communicate with each other right now.

This is why we invested so much energy into writing down our AI norms and values. (If you haven’t been following the series, it’s all up now: How We Do Business, AI for Honeycomb Engineering, and AI Norms and Values.)

A lot of things about communication that used to be clear (like “this was written by a person”), no longer are. And a lot of hurt feelings, anger, and frustration are roiling about in the breach. Old norms no longer apply, and new norms are not yet broadly developed or agreed upon.

At times like these, having an agreed-upon convention or standard, any convention or standard, can really help.

1

I decided to write this piece after Gergely told me he too has started blocking people who send him slop introductory messages. People, we can start a movement here! Block all slop senders!

An Early Look at Fall Color in Canada

July 27
September 6
A river winds through a mostly green tundra landscape in summer, dotted with numerous lakes.
NASA Earth Observatory/Michala Garrison
A river winds through the same tundra landscape in autumn, now colored red and orange. Some green vegetation remains visible along the river.
NASA Earth Observatory/Michala Garrison
A river winds through a mostly green tundra landscape in summer, dotted with numerous lakes.
NASA Earth Observatory/Michala Garrison
A river winds through the same tundra landscape in autumn, now colored red and orange. Some green vegetation remains visible along the river.
NASA Earth Observatory/Michala Garrison
July 27
September 6

Autumn color sweeps across the low-growing shrubs and tundra vegetation of Nunavut, Canada, in this image pair captured by the OLI (Operational Land Imager) on Landsat 9. NASA Earth Observatory images by Michala Garrison.

As North America rode out a summer of remarkable heat, fall foliage and cool, crisp weather still seemed like distant, alien concepts across much of the continent in early September 2026. But fall comes early in the tundra and subarctic ecosystems of Nunavut, in far northern Canada.

Vivid signs of the season were already sweeping across the landscape on September 6 when the OLI (Operational Land Imager) on Landsat 9 captured this image (right) of the Coppermine River winding through low-growing shrubs and tundra vegetation upriver of Kugluktuk, a community at the river’s mouth. The other image (left) shows the same area on July 27, 2026, when vegetation was still green.

The region is known for willow and birch shrubs, blueberries, bearberries, and other low-growing tundra plants that turn shades of red, orange, and yellow each fall. A NASA and South Dakota State University analysis of seven years of satellite data found that foliage in the region begins to change in early September and peaks in mid-month, making this one of the first places on the North American continent to change color. But blink and you might miss it: the analysis also showed that far northerly regions tend to have shorter periods of peak color—sometimes a week or less—compared to many lower-latitude areas.

In the fall, leaves change colors as they lose chlorophyll, the molecule that plants use to synthesize food. Chlorophyll makes plants appear green because it absorbs the red and blue light from sunlight as it strikes leaf surfaces. However, chlorophyll is not a stable compound, and plants must continuously synthesize it, a process that requires ample sunlight and warm temperatures. As temperatures drop and days shorten in autumn, levels of chlorophyll fall as well.

As concentrations of chlorophyll decline, the green fades from leaves, presenting an opportunity for other pigments—carotenoids and anthocyanins—to take the stage. Carotenoids absorb blue-green and blue light, so in the absence of chlorophyll, they cause leaves to appear yellow. Anthocyanins absorb blue, blue-green, and green light, so light reflecting off the pigments appears red.

Citizen scientists have an opportunity to help NASA scientists track fall color and contribute to long-term environmental databases with the GLOBE North American Phenology Campaign. Participants observe and record leaf color changes during the spring and fall, helping scientists understand plant responses to climate and environmental changes.

NASA Earth Observatory images by Michala Garrison, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland.

References & Resources

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Does AI assistance enhance or erode expertise?

From a new NBER working paper:

Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.

That is by David Autor, et.al.  Do note that over time the allocation of humans to tasks will evolve so that more of the humans become more productive, not less.  RCTs somehow have the odd disadvantage of requiring too many things to be held constant, and so they can miss the benefits of longer-term adjustments.

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On Gina’s

So last evening I was walking around Laguna Niguel when I passed the storefront of a vanquished local pizza joint gone dark.

And, in the window, hung the above sign.

Yes, Gina’s is coming to town.

A most heinous day of reckoning it is.

And this is not political. In fact, it’s not even particularly civic. It is merely the Code Red warning of a born-and-raised New Yorker who knows how to order two slices and a Coke; who knows the proper amount of grease to drip off the tip; who would rather lick the sidewalk than stop for pizza at a Sbarro’s or Chuck E. Cheese.

To you dear readers, I offer this declaration: Gina’s ain’t it.

Seriously, Gina’s ain’t it. It’s pizza, in the way “Police Academy III” was a movie. It’s pizza, in the way “Ted Cruz” is a man. Or, put different: What do you get when you combine shitty sauce, processed cheese, tasteless crust and the charm of an arm pit?

Gina’s.

So, yeah. I don’t know what measures were taken in the LA Times poll; I don’t know if those surveyed lack tongues; I don’t know if, perhaps, this state thinks Gina’s is high-level pizza.

But it’s profoundly bad.

Thank you.

Sunday 13 September 1663

[continued from yesterday P.G.] (Lord’s day) …so that Griffin was fain to carry it to Westminster to go by express, and my other letters of import to my father and elsewhere could not go at all. To bed between one and two and slept till 8, and lay talking till 9 with great pleasure with my wife. So up and put my clothes in order against tomorrow’s journey, and then at noon at dinner, and all the afternoon almost playing and discoursing with my wife with great content, and then to my office there to put papers in order against my going. And by and by comes my uncle Wight to bid us to dinner to-morrow to a haunch of venison I sent them yesterday, given me by Mr. Povy, but I cannot go, but my wife will.

Then into the garden to read my weekly vows, and then home, where at supper saying to my wife, in ordinary fondness, “Well! shall you and I never travel together again?” she took me up and offered and desired to go along with me. I thinking by that means to have her safe from harm’s way at home here, was willing enough to feign, and after some difficulties made did send about for a horse and other things, and so I think she will go. So, in a hurry getting myself and her things ready, to bed.

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Bright sunlight glints as long dark shadows mark this image of the Bright sunlight glints as long dark shadows mark this image of the


The Accelerationist Case for Frontier Pacing

The sole athletic achievement of my life came in 1993: Winning the IIT Bombay freshman 50m freestyle race with a time of 41s. That got me into the college swim team (it was a bad recruitment year), and launched my brief and entirely undistinguished athletic career. By my senior year, however, my 50m time had improved to about 38s (not enough to get me off water-boy duty since the team had several exceptional swimmers with much better times). Interestingly though, it was easier for me to swim faster at the end of my career than it was to swim slower in the beginning. The reason was that in the interim, the coach had significantly improved my stroke and breathing technique. It was all about managed pacing, not raw intensity of effort.

There is a fairly deep literature behind this apparently mundane lesson. Daniel Chambliss’s classic 1989 paper The Mundanity of Excellence, based on years of fieldwork studying competitive swimmers all the way from local clubs to the Olympic level, argued that excellence is primarily qualitative rather than quantitative. Elite swimmers do not simply do more of what mediocre swimmers do, or do it harder. They organize their activity differently: strokes, turns, training habits, attention, and countless other small practices combine into a qualitatively different way of swimming. The route to excellence is not therefore reducible to maximizing effort along some obvious scalar dimension.

The same insight is condensed in a maxim common in military and special-operations circles: “slow is smooth, smooth is fast.” In activities where speed really matters, trying to go fast naively is often an excellent way to go slowly.

I never really stopped thinking about this problem. My 2011 book Tempo grew partly out of a long-standing interest in pacing across performance domains: how people experience time while making decisions, how rhythms of action emerge, and how timing relates to effectiveness. One of the ideas that has stuck with me since then is that tempo is something to be managed rather than maximized. There is no universally correct speed. There are only tempos appropriate or inappropriate to the dynamics of the situation.

Which brings me, somewhat unexpectedly, to Dario Amodei.

Amodei recently made the case that frontier AI development should be deliberately paced. His argument is primarily a safety argument. AI capabilities, he believes, are advancing quickly enough that the processes required to understand, evaluate, align, secure, and safely operate them are having trouble keeping up. This is not quite the old proposal for an AI “pause.” Pacing means continuing to advance the frontier while deliberately managing its rate, allowing safety work and institutional capacity to remain within striking distance of capability. Sam Altman has now endorsed the basic proposition, and Demis Hassabis has made closely related arguments about frontier capabilities outrunning scientific understanding and governance capacity. Elon Musk, more tersely, has said that Amodei is right.

There is an obvious cynical reading of this emerging consensus. The leading frontier labs have powerful economic reasons to want a regulated frontier. A regime that requires enormous compliance budgets, restricts open-weight releases, discourages foreign models, imposes burdens that startups cannot afford, or legitimizes coordination among a small number of incumbents could turn “safety” into a remarkably effective mechanism for protectionism and regulatory capture. That suspicion is not paranoid. Open models increasingly constitute a competitive threat to proprietary frontier providers, and the politics around regulating them already feature explicit accusations of regulatory capture. There is an additional awkwardness: coordinated pacing among nominal competitors looks uncomfortably like coordinated restriction of output, enough so that the legality of such arrangements under antitrust law is already being debated.

I don’t think we need to resolve the question of motives. Perhaps these CEOs are sincerely terrified. Perhaps they are sincerely terrified and understand perfectly well that the regulations they favor would strengthen their competitive positions. Perhaps the mixture varies by person, company, and day of the week. It doesn’t matter much for my argument. The proposition that the frontier should be paced is worth considering independently of the political economy of the people proposing it.

I also don’t share enough of Amodei’s safety premises to make his argument my own. In particular, I think a great deal of contemporary concern about runaway AGI, superintelligence, and “alignment” is badly framed, and often borders on the theological. But I increasingly agree with his conclusion.

In fact, I think there is a strong case for frontier pacing even if you are an accelerationist and your objective is simply to make technological progress happen as fast as possible. I am not myself an accelerationist. My preferred framing is closer to managed tempo. But if I were one, I would still favor pacing the frontier right now, for a simple reason: maximizing the instantaneous velocity of the AI capability frontier is no longer obviously maximizing the rate of technological progress.

There is, however, an important difference between my conclusion and the emerging frontier consensus. Their natural solution is coordination at the top: labs agreeing upon thresholds, governments blessing the coordination, evaluators policing it, and eventually perhaps international agreements extending it.

In other words, cartelization, hopefully of a benign sort.

I would prefer to see how much frontier pacing can be produced from the bottom up through ordinary market mechanisms. The distinction matters. The objective should not be to decide administratively how fast AI is allowed to improve. It should be to stop artificially rewarding frontier velocity after frontier velocity has ceased to be the most important form of progress.

Getting Inside the Loop

A useful way to understand the distinction comes from another idea that startup culture has borrowed, and mostly misunderstood, from the military: John Boyd’s OODA loop. OODA theory says that you win by “getting inside the adversary’s decision cycle,” which is usually glossed as making decisions faster than the other guy. If you observe, orient, decide, and act faster than he can, the story goes, you eventually overwhelm him.

But inside does not mean faster. The objective is to operate within the decision dynamics of the system you are engaging in a way that lets you shape them. Against a human adversary, that may indeed sometimes involve accelerating until his ability to orient collapses psychologically. But it may also require waiting, withholding action, changing rhythm, or deliberately slowing down. In non-adversarial situations the goal may not be collapse at all, but harmonization for resonant support.

What matters is the right tempo at the right phase, not speed for the sake of speed.

Something analogous applies to scientific and technological progress. There is no enemy psychology to collapse, but there are still loops to get inside: observation, experimentation, interpretation, investment, construction, deployment, feedback, learning, and recombination. The useful question is not how rapidly one component of that system can be made to move. It is whether the tempo of development allows those loops to close. If one subsystem changes faster than the surrounding system can observe, understand, absorb, and respond to it, pushing that subsystem still faster can reduce rather than increase effective progress. It can induce fragility and collapse.

This, I think, is approximately where AI is now.

The simplest evidence is personal and almost embarrassingly mundane. Frontier AI is already overpowered for nearly everything I use it for. In my most advanced projects I may use the strongest model available (Fable for my coding projects) to plan an approach or make critical strategic decisions, but I can generally hand the resulting specification to a cheaper model (such as Opus or Sonnet) to do the routine work. For ordinary uses I don’t need anything close to the frontier. In ChatGPT, I no longer even know exactly which model I am talking to much of the time. Whatever the “think harder” control does is sufficient model selection for my purposes.

The situation increasingly reminds me of smartphones. There was a period when getting the newest iPhone produced a noticeable improvement in everyday life. Eventually the hardware got good enough that the upgrade cycle ceased to matter much. I kept an iPhone XS for almost a decade before replacing it with a 16. The frontier continued advancing; I simply fell off the frontier because my demand curve had stopped following it.

Something similar is beginning to happen with AI, except that the supply curve is moving incomparably faster. Six months ago I routinely maxed out token allotments. Now I don’t. Some weeks I barely use coding agents. This isn’t because I’ve become less interested in AI. It is because my own capacity to productively absorb AI output has become the constraint. I have projects to think about, things to read, people to talk to, and work to do in domains where AI cannot help me yet, or perhaps ever. I am already pacing myself at my own tiny personal frontier.

That is a significant change in the technological situation. The binding constraint is migrating.

When the Bottleneck Moves

Broader AI deployment is increasingly blocked by things other than model intelligence. Robotics has long been constrained by actuators, power, reliability, dexterity, manufacturing, and the sheer recalcitrance of the physical world. Those constraints are beginning to move, but making the model smarter does not make them disappear. AI in education is constrained less by whether a model can explain calculus than by our lack of sufficiently rich classroom experimentation about what happens when students and teachers actually use these systems. Current mid-tier models are probably capable enough to power almost any educational experiment worth trying in a high-school or undergraduate classroom. We do not need another order of magnitude of intelligence before conducting them.

This pattern should become more common as AI improves. Once intelligence ceases to be scarce, its complements become more important. Model capability can be abundant while classroom knowledge is scarce. Model capability can be abundant while actuators are scarce. Model capability can be abundant while electrical infrastructure is scarce. It can be abundant while organizational competence, human attention, scientific understanding, military doctrine, security practices, and good judgment are scarce.

This is not peculiar to AI. Capability-maxxing the coolest new weapon is bad military doctrine. The United States has enjoyed extraordinary technological superiority over its adversaries for decades and has nevertheless repeatedly discovered that superior equipment does not automatically produce strategic success. Logistics, doctrine, morale, training, political understanding, industrial capacity, and orientation matter. A force that neglects those complements because it possesses the best weapons can become remarkably fragile. From Vietnam to Iran, the US military has been repeatedly forced to relearn the lesson,

AI may now be entering the same regime. Fragility from neglect of everything non-AI is becoming a bigger risk than failure to token-max.

There is a further reason to suspect that continuing to redline the existing frontier may yield diminishing returns. The major labs increasingly appear to be competing along broadly the same technological S-curve. One suggestive sign is that they run into the same supply constraints: HBM, electrical power, data-center capacity, capital, and access to sufficiently large clusters. When a technological system moves onto a genuinely different S-curve, its important bottlenecks often change as well. If everybody’s problem is how to secure more of the same scarce inputs to do more of the same basic thing, that is at least suggestive that everybody is climbing the same sigmoid.

As I learned as a freshman swimmer, near the upper portion of an S-curve, pushing harder can become exactly the wrong acceleration strategy. You expend increasing resources for decreasing gains while starving exploration of the attention required to discover the next curve. Moving faster along an S-curve is not the same thing as accelerating technological evolution. Sometimes you have to back off the incumbent trajectory long enough to notice what the next trajectory is.

A Cognitive Ergonomics Crisis

There is also a more immediate bottleneck that the AI industry seems reluctant to acknowledge: the humans at the frontier.

Startup people have been LARPing war for decades. This is one reason concepts like OODA became popular in startup culture in the first place. “War mode” usually means working extremely hard under conditions of strong personal financial incentives: long hours, high urgency, extreme focus, centralized authority, and a willingness to sacrifice ordinary organizational niceties. I’ve been around startup culture for decades, and I suspect the AI boom may be the first time the conditions have actually become meaningfully war-like.

AI frontier people are visibly unprepared for it.

Actual militaries and other frontline risk professions take the human consequences of sustained high-stress operations seriously. Soldiers, firefighters, emergency medical personnel, disaster responders, surgeons, pilots, and others operating in consequential environments develop elaborate practices around training, emotional regulation, redundancy, rotations, decompression, mandatory rest, checklists, after-action review, and recovery. These practices exist because motivation does not repeal physiology. Judgment deteriorates. Attention narrows. People make stupid mistakes. Emotional reactions become harder to regulate. Creativity disappears. Eventually people break.

Does frontier AI look like an industry managing itself accordingly?

From the outside, it looks closer to the opposite. People at the frontier have been operating under extraordinary pressure for several years with little respite. The cognitive ergonomics of their working conditions are a disaster (we have a project going at the Protocol Institute led by to study this — contact him if you’re interested in participating in or supporting it).

Competitive pressure, enormous amounts of capital, geopolitical attention, hostile public scrutiny, internal ideological battles, rapidly changing technology, and the conviction among some participants that their daily work may determine the fate of humanity are not normal occupational stressors. The rate of dumb, unforced errors appears to be rising. The quality of frontier discourse has, in my view, visibly deteriorated. People I once assumed were much smarter than me increasingly seem to be missing obvious things, while becoming susceptible again to bad ideas I thought they had outgrown.

Tired people catch colds more easily; they catch bad ideas more easily too.

This produces a peculiar inversion of the conventional AI safety model. We normally imagine increasingly unreliable or dangerous AIs surrounded by reliable human supervisors. But what if we are increasingly producing extremely capable AIs surrounded by progressively less reliable humans?

“Human in the loop” is not much of a safety guarantee if the human has been metaphorically deployed aboard an aircraft carrier in a war zone for eight months without relief.

Some of the public testimony emerging from frontier organizations should perhaps be interpreted through this lens. I do not want to diagnose particular people from afar, and testimony from people who have worked closely with frontier systems should obviously be taken seriously. But when someone emerges from prolonged immersion at the frontier sounding psychologically shattered, there are at least two possible kinds of information in the signal. One concerns the technology. The other concerns what prolonged immersion at the frontier does to the observer. Frontier workers are sensors, but the sensors themselves are being perturbed by the phenomenon they are measuring.

We have seen versions of this going back at least to the Blake Lemoine episode at Google, when sustained interaction with LaMDA led him to conclude that the system was sentient. More recently, former frontier employees have emerged making extraordinarily grave predictions about where AI is heading, that ill-prepared, tech-hostile journalists are eagerly amplifying with lurid headlines.

The correct response need not be either “believe them and stop AI” or “they’re crazy and should be ignored.” Sometimes a sensible response to someone coming back from the front sounding shell-shocked and exhibiting symptoms of PTSD is: this person needs a vacation. We rotate soldiers partly because the testimony of exhausted soldiers matters.

The largest near-term AI safety concern may therefore be exhausted frontline humans supervising overpowered AIs.

Fighting the Wrong Enemy

Exhaustion is particularly dangerous when nobody can agree about what the enemy is. Much of the actual stress experienced by frontier organizations comes from a fairly comprehensible mixture of competitive pressure and techlash hostility. Those forces are intense, but neither is an existential adversary.

The clearest live adversarial problem involving AI is much more ordinary: humans using AI against other humans. Criminal applications are already real and deserve serious attention. Military applications are rapidly becoming real as well, and the relevant strategic picture is much broader than a stylized US-versus-China AI race. Smaller powers and non-state actors can use cheap cognitive capability to lower engineering barriers that previously required deeper technical institutions. Recent reporting, for example, describes AI assistance being used in weapons-engineering work by actors in Houthi-controlled Yemen. That strikes me as the kind of development around which one can build a concrete threat model.

Longer term, military diffusion is clearly a serious concern. But much of the fear actually shaping frontier behavior seems aimed somewhere else entirely: toward vague runaway “AGIs,” “superintelligences,” and a metaphysically capacious notion of “alignment” inherited from philosophical traditions I find largely unpersuasive.

There is a useful analogy with climate change. Climate change produces actual physical stressors: more extreme weather, unstable agricultural conditions, infrastructure damage, wildfire risk, and so on. Those generate concrete political and humanitarian problems, including displacement and unmanaged refugee flows, while longer-term adaptation requires things like shoreline management, wildfire regimes, agricultural relocation, and preparedness for changing disease ecologies. Yet parts of climate politics have preferred to identify an ultimate metaphysical adversary called Capitalism, Markets, or Growth and an equally totalizing remedy called degrowth. Heterogeneous problems with different timescales and mechanisms get collapsed into one grand theory.

Parts of AI safety discourse increasingly strike me the same way. Competitive instability, cybercrime, weapons proliferation, institutional disruption, labor-market effects, and exhausted frontier personnel are all real and different problems. “Unaligned superintelligence” turns them into a single theological object. Once that happens, every stressor becomes evidence for the same threat model.

This is a kind of threat-model collapse. Adaptation is usually plural; apocalypse is singular. Real technological transitions produce dozens of mismatched rates and local failure modes, requiring different responses at different tempos. If criminals are the problem, work on security and law enforcement. If weapons diffusion is the problem, work on doctrine and proliferation. If operators are exhausted, rotate them. If schools lack experimental knowledge, run experiments. If power is scarce, build infrastructure. “Align superintelligence” is not a substitute for any of those things.

Pacing would give us something valuable here beyond safety: enough time to discriminate among threats.

Proof Abundance, Understanding Scarcity

Mathematics may already offer a miniature preview of what happens when one part of a knowledge-production system accelerates far beyond the others.

Terence Tao has recently distinguished three stages of mathematical work: generation, verification, and digestion. AI is rapidly making the first two cheaper. Models can generate candidate proofs, while formal systems such as Lean can increasingly verify them. But digestion remains stubbornly slow. Somebody still has to understand what the proof is doing, relate it to existing mathematics, extract reusable techniques, explain it, teach it, and use the resulting understanding to generate better questions. Tao describes the resulting condition as an “impedance mismatch.”

This is particularly interesting in light of what I have elsewhere called the curiously playable universe: the apparently expanding set of domains that can be transformed into sufficiently explicit games that AI can optimize effectively within them. Anything that begins to resemble a CAD system, a formal proof environment, or an evolutionary optimization problem over a sufficiently well-defined parameter space becomes potentially tractable to extraordinarily capable models. More of the world appears to be playable than we previously thought.

But playability has an important pathology. A highly playable domain supplies a scoreboard, and once AI becomes extraordinarily good at optimizing the scoreboard, the relationship between winning the game and advancing the larger domain can weaken. Solving a theorem is valuable partly because, historically, getting to the solution usually required acquiring understanding along the way. If an AI can helicopter directly to the summit, to borrow Tao’s analogy, the summit has still been reached, but nobody necessarily learned the trails, landmarks, terrain, or neighboring geography encountered during the climb.

Tao and two dozen other Fields Medalists recently made essentially this point in a declaration strikingly titled A Severe Misalignment of AI in Mathematics. Their pointed use of misalignment is almost the reverse of its standard AI-safety meaning. The problem they identify is not that AI has developed alien goals. It is that the incentives of AI companies to demonstrate spectacular problem-solving performance are becoming misaligned with the goals of mathematics itself. Solving difficult problems has historically served as a proxy for mathematical understanding and progress. Once AI can optimize the proxy directly, the correlation can break.

The recent Navier–Stokes episode illustrates the issue. Enormous amounts of inference can now be directed at a famous open problem, candidate constructions produced, and formal verification generated at extraordinary speed. Yet that does not automatically produce a corresponding increase in comprehensible, reusable mathematical knowledge. The pipeline is something like problem selection → generation → verification → exposition → digestion → canonicalization → better questions. Increasing the bandwidth of generation and verification by orders of magnitude while leaving the downstream stages roughly unchanged creates a queue.

Proof generation becomes abundant. Understanding becomes scarce.

That is frontier pacing in miniature. Maximum local throughput does not imply maximum system throughput. Indeed, beyond a certain point it can create congestion.

Orientation Beats Equipment

There is a strategic implication here for organizations outside the frontier labs. The natural reaction to rapidly advancing models is to assume that whoever possesses the strongest model necessarily possesses an overwhelming advantage. If a frontier model can turn increasingly playable engineering problems into few-shot solutions, and if most of the necessary input information exists somewhere in public literature, then organizations can easily conclude that whatever intellectual lead they possess is temporary. Why bother competing with organizations that possess better models, more compute, more money, and privileged access to the frontier?

But this risks confusing equipment superiority with orientation superiority.

Boyd repeatedly emphasized that superior orientation could overcome substantial equipment disadvantages (“we’d still have won if we’d swapped equipment”). The relevant analogy today is something like centaur chess. Your model does not necessarily have to outthink their model. Your humans have to out-orient their humans.

This becomes increasingly true as frontier capabilities bunch together above the threshold required for a particular task. In my own work, I increasingly find that I can use the strongest model to formulate or specify a solution and then hand most of the execution to a weaker model. For many problems, even that is overkill. I would readily bet on a well-oriented person using a slightly weaker model against a poorly oriented person using the strongest available model.

And the frontier labs have no automatic orientation advantage. Quite the contrary: they are simultaneously fighting an extraordinary number of battles under extreme strategic distraction. They are building models, securing compute, raising capital, negotiating with governments, managing safety factions, defending themselves against critics, competing for talent, building consumer products, selling enterprise software, contemplating hardware and robotics, responding to geopolitical pressure, and trying to decide what sort of companies they are becoming. They may have a model advantage while suffering an orientation disadvantage. There is no reason to assume that organizations exceptionally good at building foundation models are exceptionally good at everything their models can be applied to.

This is another reason pacing can be strategically productive. It creates room for orientation. In an environment saturated with FUD and “resistance is futile” rhetoric, organizations can lose before competing because they assume frontier capability automatically determines every downstream contest. It doesn’t. Superior orientation does.

From One S-Curve to the Next

Put all of this together and Amodei’s proposal starts to look different. His concern is that capability is outrunning safety. I think capability may be outrunning almost everything.

It is outrunning our ability to deploy it productively. It is outrunning classroom experimentation, organizational adaptation, security practice, mathematical digestion, physical infrastructure, and human attention. It may be outrunning our ability to distinguish actual threats from theological ones.

And it is almost certainly outrunning the decompression and recovery cycles of some of the people charged with making the most consequential decisions about it.

An accelerationist should care about every one of these things precisely because an accelerationist wants acceleration.

The mistake is to identify acceleration with the derivative of a single visible variable: benchmark scores, parameter counts, inference budgets, training compute, or whatever happens to define the current frontier. Technological progress is a coupled system. Accelerating one component beyond the absorption capacity of its complements eventually stops accelerating the system. The problem becomes especially acute near the top of an S-curve, where enormous resources can be consumed eking out diminishing improvements while the exploration necessary to find the next curve is crowded out.

There is a useful precedent in the history of the PC industry. For years, processor clock frequency functioned as the wonderfully simple consumer metric for progress: 486 MHz was better than 400; 1 GHz was better than 800 MHz; higher number, faster computer. Manufacturers had every reason to compete on the legible scalar, and consumers learned to buy it. Eventually this became the “megahertz myth.” Different architectures could do very different amounts of useful work per clock cycle, while pushing frequency upward ran increasingly hard into heat and power constraints. By the mid-2000s, the industry was moving toward multicore designs and a more complicated understanding of performance in which throughput, architecture, workload, thermal limits and performance per watt all mattered. Intel itself acknowledged at the time that as computer usage diversified, factors other than clock speed were becoming increasingly important to platform performance.

AI benchmark culture looks increasingly like the early stages of the same mistake. A benchmark is useful because it compresses a complicated question into a number. When capability is scarce and improvements are large, the number may track value surprisingly well. As systems become overpowered for more uses, however, the proxy begins to detach from what customers actually care about. A model that goes from 87 to 91 on some benchmark may represent an impressive scientific achievement while producing essentially zero additional value for a company whose relevant workload was already handled adequately at 75.

This suggests a path to frontier pacing that does not require a council of frontier CEOs deciding how quickly everyone is allowed to move.

Customers can simply become harder to impress.

Enterprise buyers can demand demonstrated improvements on their actual workloads rather than accepting leaderboard gains as evidence of value. Developers can (and already do) route work to the cheapest model that clears the capability threshold rather than reflexively calling the smartest one. Researchers can value useful scientific infrastructure, explanation and reusable knowledge rather than merely celebrating another famous benchmark or theorem knocked down. Investors can become less impressed by capital expenditure whose primary justification is preserving position on a frontier whose marginal economic value is falling. Users can decline to upgrade when the previous generation is already good enough. Current enterprise behavior already points in this direction: cheaper and open-weight models are becoming attractive precisely because many workloads do not require frontier intelligence, while buyers increasingly demand measurable returns rather than capability in the abstract.

None of these mechanisms requires anybody to agree upon a socially optimal rate of AI development. They simply improve the feedback signal facing producers. The market stops saying “more intelligence, at almost any price” and begins saying “show me what this additional intelligence is for.”

There are supply-side versions too. As power becomes a binding constraint, performance per watt and useful inference per dollar should matter more than sheer training scale. As inference proliferates toward edge devices and private deployments, latency, reliability, privacy and local controllability become competitive dimensions. As organizations discover that weaker models can execute plans produced by stronger ones, heterogeneous model portfolios should compete with monolithic frontier consumption. Open-weight and decentralized systems can keep proprietary labs honest by making “good enough” intelligence cheap and difficult to monopolize. A mature AI market should develop more dimensions of performance precisely as the mature processor market did.

This is the sort of pacing I would prefer: not a speed limit but a richer scoreboard.

The objective should therefore not be maximum speed. It should be managed tempo for maximum actual progress. Sometimes that means sprinting. Sometimes it means dwelling at a capability level while applications, institutions, infrastructure, science, and humans catch up. Sometimes it means letting one subsystem race ahead while another rests.

Sometimes it means deliberately leaving expensive capability unused.

That last possibility may be the hardest one for AI culture to accept, because we are still psychologically adapting to the idea that intelligence might actually be abundant.

A true sense of abundance does not require you to max out the bounty. Nobody hyperventilates because free oxygen might disappear before they get their fair share. When something is genuinely abundant, you can waste it. You can use a frontier model for a trivial question. You can use a weaker model because it is good enough. You can leave tokens unused. You can spend a week doing something that doesn’t involve AI. You can allow an extraordinarily powerful model to sit idle while you think.

The mark of abundance is waste, including non-use.

Compulsive token-maxxing is in this sense still a scarcity behavior. So is compulsive benchmark-maxxing, compute-maxxing, and capability-maxxing. Train now because somebody else will. Deploy now because the window might close. Consume all the intelligence available because leaving any unused feels like falling behind. An industry behaving this way may possess an abundance of intelligence without yet having developed an abundance mentality.

Slack is not necessarily the enemy of acceleration. Slack is where people recover, where institutions adapt, where strange experiments happen, where understanding catches up with proof, where neglected complements receive attention, and where somebody finally notices that the old S-curve is flattening and another one is waiting nearby.

So yes, pace the frontier. But don’t turn the frontier labs into a cartel to do it. Let safety work catch up, but also let customers become bored with vanity benchmarks. Let exhausted researchers sleep. Let mathematicians digest their proofs. Let schools figure out what to do with the models they already have. Let robotics catch up. Let organizations learn to orient themselves in a world where intelligence is cheap. Let markets discover that efficiency, reliability, privacy, integration and domain-specific usefulness sometimes matter more than another few points on a benchmark. Let us discover which risks are real, which bottlenecks have moved, and which parts of the world turn out to be playable.

Then, when the situation calls for it, accelerate again.

Slow is smooth. Smooth is fast.

Links 9/13/26

Links for you. Science:

Patient count in Cyclospora outbreak in Michigan at more than 14,700 (one out of seven hundred Michiganders have been infected)
A Severe Misalignment of AI in Mathematics
The Academy of Natural Sciences museum is closing, but all eyes are on its specimens — all 19 million of them
4 fully vaccinated people in Pa. are sick with measles, including 1 in Lancaster County
The lightbulb moment behind a potential antiviral breakthrough
Rope, twine and thread: Invisible technologies of the Stone Age
‘I’m Really Terrified’: A Mathematician Grapples With AI’s Recent Breakthroughs

Other:

What Happened to al-Qaeda? Twenty-five years after 9/11, analysts can’t decide if the terrorist organization is all but defunct or more threatening than ever. (excellent)
D.C.’s To-Do List for Janeese Lewis George Keeps Getting Longer
Jacob Coxon: how to run an AI doomsday media campaign
Trump officials propose sweeping changes to the census that would reshape voting maps
Fact check: Trump’s story about being carried away by firefighters after the 9/11 attack
Trump’s off-the-cuff comment about ‘a very cool thing’ spurs international uproar
NO, WE SHOULDN’T STOP TRYING TO DEBUNK TRUMP, ESPECIALLY ON THE $5000 BRIBE CHECKS (and turn the debunking into an attack)
Retail Vacancies Downtown Are Still High, and Some Landlords Are to Blame (D.C.)
Trump leads RNC in pledge to ‘cheat like hell’ in November elections
Ohio Senate campaign staffer used Nazi soldiers as profile photo
USPS Tries Rejecting Entire County’s Ballot Over Spacing Issue
Why Tech Oligarchs Are Willing to Risk Apocalypse
Why Are Women Over 100% of Jobs Gained Under Trump’s Second Term?
The Singularity Is Not What It Seems
Paramount Caught Using ‘Astroturf’ Group To Drum Up Fake Support For Merger
The poorest in the US can’t find housing even as low-income units sit empty
Republicans Jump at the Chance to Advertise on Trump’s $5,000 Checks
Lilly Wachowski on Her New Studio Anarchists United, the Black Power Origins of ‘The Matrix’ and How ‘Harry Potter’ Fans Are ‘Supporting Trans Genocide’
She Watched Federal Agents Kill Alex Pretti. Now She Has Told Investigators What She Saw.
Contractor Blames Its Own Repairs, Not Vandals, for Reflecting Pool Failure. The contractor said the pool’s new blue liner peeled and tore because of “human oversight” and a flawed plan that involved layering two incompatible chemicals, documents show.
Oh No We Made A Planet Destroying Machine Please Give Us Money I Mean Regulate Us
Trump’s 9/11 lies are stolen valor, not ‘a bit of weirdness’
What do Americans remember about 9/11?
The US Government Launched 3 Previously Unreported Investigations of Polymarket Trades
Trump Tells Smithsonian Institution To Install George Washington Statues And Exhibit
The Quiet Radicalism of Mansour Abbas and Yoav Segalovitz
As test scores hit record lows, US and Germany chart opposite paths in education
A photographer’s final moments on 9/11 – and the last shot he took. Bill Biggart ran for his cameras when the first World Trade Center tower was hit; his shots were developed after his death. Twenty-five years later, they have been added to the Imperial War Museum collection
President Trump: The man who wasn’t there on Sept. 11

Sunday Tool Scrutiny

Sunday Tool Scrutiny

A consistent by-product of periods of intense building (software, a book, a team) — really anything where I am heads-down focusing on the craft — is that all the utilities and habits surrounding building said craft get scrutiny.

Why? Because small imperfections or inefficiencies stand out when my brain is in build-it mode. Put simply, and as one example: Two keystrokes are vastly less than three over a long period of time.

RAYCAST

By far, the most investment has gone into Raycast. I was a fervent LaunchBar enthusiast, but you can smell when an application is no longer supported, and LaunchBar has been in that state for years.

Raycast is way too much in terms of attempting to be an everything app, but on its journey there, it’s made itself quite configurable in every possible way you can imagine, which is good news for my fingers.

The one feature to rule them all, for me, is Hyper Key. The idea is simple: pick a key on your keyboard that is infrequently used — in my case, CAPS LOCK1. Combine that key with an easy-to-remember letter, and that fires up that application or script. The greatest hits for me:

  • Slack (S)
  • Ghostty (G)
  • Messages (M)
  • Calendar (C)
  • Spotify (A — not memorable, but nearby)
  • And so on.

Hyper+P plus “directory” fires up Claude Code in that directory in my Projects folder… of which there are many.

Perhaps the biggest win from an efficiency perspective is what each Hyper Key press does:

  • Hyper Key + S — Fire up Slack — if Slack is not running, run it. If it’s active, bring it to the front
  • NEXT Hyper Key + S — Hide (don’t close)

If I’ve placed all my windows correctly, this setup means I always get the same windows in the same spot — every time. macOS Finder has multiple competing sources of truth, which means it feels like windows randomly shift. And they do. And that is bizarre. This Raycast Hyper Key setup fixes that.

BARTENDER

This app is vastly less important than Raycast. Its job is to prevent clutter in your menu bar. This doesn’t affect workflow, but it certainly affects distraction. I have everything that wants my attention in the menu bar hidden except for Date, Time, and the ellipsis provided by Bartender that, when clicked, show all the menu bar items.

At the time of this writing, macOS Golden Gate rearchitected the menu bar mechanism and broke every single menu bar manager. There is a beta build of Bartender 7, but after a few hours of tinkering, it doesn’t appear to get me back to a clean menu bar. Stay tuned.

GHOSTTY + Claude Code

My continued terminal app of choice. Actively developed. Incredibly flexible. It has that stench of intense productivity that I used to get from TextMate back in the day. Claude Code continues to be my robot of choice, and it plays nicely with Ghostty. There are too many tweaks to list here, but my top quality-of-life tweaks are:

  • Script to switch between monospace fonts (currently, still: Inconsolata)
  • Notifications when scripts finish during intense multi-tasking robot sessions.
  • Session colors based on project and SSH state.

Honorable mentions:

  • Tailscale — this gives me seamless secure pipes to and from my various Macs.
  • Bear — my writing tool of choice — has an MCP — I use it incessantly.
  • xScope — y’know — to align the things.

These apps, scripts, and ideas are listed because they’ve been tested. How? See, there are a lot more apps, scripts, and tricks that I’ve tried, but once I’ve landed them, the question is: do I learn them? It’s that when I use them, my brain gives me that shot of dopamine — yeah, this is better.

So I do it again.

  1. CAPS LOCK still works as you expect, click on, click off. It’s when I hold it that the Hyper Key feature works

Sunday assorted links

1. Scott Sumner as regional thinker.

2. The economists who work on “Productivity and Innovation” are most likely to use AI for their writing.

3. Michael Levin defends neo-neo-Platonism?

4. Look for and try to limit agent collusion.

5. Do orangutans like Indian classical music?

6. “Our results demonstrate that LLM-evolved constitutions significantly outperform both human-designed principles and one-shot LLM-generated rules for multi-agent coordination.

The post Sunday assorted links appeared first on Marginal REVOLUTION.

       

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w/e 2026-09-13

Achieved very little this week, as usual, other than allowing Pippa the cat as much time as she needs to snooze on my lap before I get up from the sofa to get on with the day or go to bed. An important skill that I look forward to continuing to work at.

But I have, very gradually, started to do some yoga and cardio after too many weeks off, so that’s good. Getting momentum going again is hard.

From Tuesday through Friday pur 4G internet connection was very, very slow – mostly less than 1 Mbps – which did not help the mood in the house. Well, Mary was away and Pippa isn’t that bothered about internet speeds so, yes, I mean it did not help my mood.


§ I have a SIPP with Interactive Investor and last week I noticed that one of the funds in it had no name (“null”) and its price was about 15% lower than it should have been. After some very slow exchanges of messages with support, this week someone did acknowledge something was wrong and that my screenshot had been passed on to IT who hope to fix it at some point.

Obviously, I do not run a trading platform managing billions of pounds, but I would assume that “displaying fund names” and “showing correct prices” and “therefore showing customers the correct value of their investments” would be fairly high up in priorities that, in the horrifying case that they went wrong, would be addressed immediately.


§ Before Mary went away and the internet almost stopped we finished watching season one of Hacks, which I knew nothing about beforehand. It was good, with interesting characters that unfolded gradually. I’m surprised there are five seasons – it doesn’t feel like there’s enough to sustain more than a couple – but hopefully I’m wrong.


§ After receiving a couple of emails from AIs this week – not even from people using AI to write them – I wrote a rant about how terrible LLMs are, as is everyone who willingly uses them, no matter whether they think they’re doing good, interesting things. But I’ll spare you, and me, putting all that here. Take it as read: awful and depressing, all of it.


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SpaceX launches 700th Falcon rocket, carries final 3 O3b mPOWER satellites to orbit for SES

A SpaceX Falcon 9 rocket soars past the American flag during the launch of the O3b mPOWER mission for SES. Image: John Pisani/Spaceflight Now

SpaceX launched a trio of satellites for its oldest commercial customer, SES, on a Falcon 9 rocket flying Sunday afternoon from Florida. This was SpaceX’s 700th Falcon rocket launch to date, split between 687 Falcon 9 rockets and 13 Falcon Heavy rockets.

The O3b mPOWER-F mission sent satellites number F11, F12, and F13 on a trek to medium Earth orbit. The Boeing-built satellites were the final three to be added to the O3b mPOWER constellation.

Liftoff from Space Launch Complex 40 (SLC-40) at Cape Canaveral Space Force Station happened at 2:49 p.m. EDT (1849 UTC).

The 45th Weather Squadron forecast a 60 percent chance for favorable weather at the opening of Sunday’s launch window. Meteorologists stated in their outlook that conditions deteriorate to 40 percent favorability by the end of the window.

“Models this morning show that while steering flow remains light leading into the primary launch window Sunday afternoon, it will have a more defined offshore component,” launch weather officers wrote. “Hi-res models suggest this will be enough to slow the development of the east coast sea breeze until just before or near the start of the window.

“With the sea breeze over the Spaceport, the risk early in the window will be for showers and storms to quickly develop in the immediate vicinity. The threat transitions just inland later in the window as numerous collisions between ongoing activity likely drift back towards the Spaceport along with any lingering storms.”

A shock collar forms around SpaceX’s Falcon 9 rocket fairing as it soared away from Space Launch Complex 40 at Cape Canaveral Space Force Station on Sept. 13, 2026. Image: Michael Cain/Spaceflight Now

SpaceX launched the mPOWER-F mission using the Falcon 9 first stage booster with the tail number B1080. This was its 29th flight after launching four missions to the International Space Station, the European Space Agency’s Euclid observatory, SES’ Astra 1P, and 22 batches of Starlink satellites.

This was the 400th orbital launch to take flight from SLC-40, of which SpaceX was responsible for launching 345. The pad was outfitted with a crew and cargo access tower in 2023, allowing it to begin flying Dragon missions starting in March 2024 — fittingly, by using B1080 to launch the CRS-30 mission.

More than 8.5 minutes after liftoff, B1080 landed on the droneship, A Shortfall of Gravitas, stationed in the Atlantic Ocean. This was the 166th landing on this particular vessel and the 661st Falcon booster landing to date.

SpaceX’s Falcon 9 rocket soars away from Space Launch Complex 40 at Cape Canaveral Space Force Station on Sept. 13, 2026. Image: Adam Bernstein/Spaceflight Now

O3b mPOWER finale

The planned mission on Sunday afternoon will bring the O3b mPOWER satellite constellation up to 13 spacecraft in medium Earth orbit. They each have a dry mass of 1,900 kg, according to SES.

The first two satellites were launched on a SpaceX Falcon 9 rocket back on Dec. 16, 2022, with subsequent launches in pairs in April 2023, November 2023, December 2024, and July 2025.

Prior to the launches of the fifth and sixth satellites, an electrical issue was discovered that was found to impact both the broadband capacity and the estimated operational life of those satellites.

Boeing technicians stack two of the three O3b mPOWER satellites for delivery to SES prior to launch. Image: Alex Aristei/Boeing

To compensate for this, changes were made to satellites 7-11 and two additional spacecraft were added to the constellation. Satellite manufacturer, Boeing, announced the delivery of the final trio of satellites to SES in early September 2026.

“The delivery of these final three satellites allows us to ramp up our constellation’s capabilities to meet growing commercial and government demand,” said Xavier Bertran, chief product and innovation officer of SES, in a statement. “The performance we are seeing from the 10 satellites already on orbit validates this architecture. The completion of the O3b mPOWER system reinforces our proven leadership in MEO while advancing our innovation roadmap.”

These newest satellites are scheduled to be released from the Falcon 9 rocket’s second stage beginning nearly 34 minutes after liftoff. Their deployment is staggered by seven minutes.

Boeing said that these satellites are expected to enter service by the middle of 2027 after completing their orbit-raising maneuvers and necessary checkouts and commissioning.

“The fully operational O3b mPOWER constellation proves the effectiveness of our software-defined payload technologies, shown by our ability to secure the most demanding missions of our commercial, joint-force and allied military customers,” said Ryan Reid, president of Boeing Satellite Systems International.

The final three O3b mPOWER satellites are shown stacked in a cleanroom prior to launch. Image: SES

California Brown Pelican

California Brown Pelican

California Brown Pelican

California Brown Pelican, in San Mateo County, CA, US

The Pacifica Pier shut down at the start of June after a crack in the concrete walkway made access to the pier unsafe.

It has since been entirely taken over by pelicans!

Tags: wildlife

Quoting Paul Ford

For a while, I must admit, it looked as if software developer roles like mine were done for. How could we fight against tireless robots? But our industry is slowly realizing that making truly cutting-edge software still requires humans to think and work together, to maximize their skill sets and to practice their respective crafts. A.I. can write very good software, but it also makes it easy to do someone else’s job badly, which is part of why all those projects fail. Now that everyone can code, it’s become clearer why many shouldn’t.

Paul Ford, A.I. Was Supposed to Give Us New Killer Apps. What Happened?

Tags: paul-ford, generative-ai, deep-blue, ai, llms

Trade, Peace and War

Dune 2's Denis Villeneuve Knows How the Freemen Get Off the Sandworms

When weaponized interdependence escalates

The United States emerged from World War II in a position of remarkable economic and military dominance — a hegemony unrivaled in the Western world since the fall of the Roman Empire. Against some smaller, poorer countries – particularly in South and Central America — America used its position to pursue crude, exploitative imperialism. However, for the most part, the U.S. adopted a more sophisticated, beneficent approach: building interlocking diplomatic, military and economic systems to promote stability and democratic values as well as national self-interest.

A key part of the intellectual basis for the “Pax Americana” was the belief, most fervently advocated by Cordell Hull, FDR’s secretary of state, that “commerce” — that is, global economic integration — is a force for peace.

But is it really?

Even before the Trump tariffs and the Hormuz crisis, there was rapidly growing interest in “geoeconomics,” which the International Monetary Fund defines as “the use of financial and trade relationships to achieve geopolitical and economic goals” — basically the exploitation of economic interdependence as a tool of coercion. (The upcoming European Central Bank conference on the topic will be livestreamed, although I regret to inform U.S. readers that I’ll be giving the Jean Monnet lecture this Thursday, September 17th, at 3 AM New York time.)

The point is that large-scale international trade, while it may sometimes serve the cause of peace, can also create the potential for conflict. This phenomenon of “weaponized interdependence” was articulated and analyzed by the political scientists Henry Farrell and Abe Newman. And the weaponization need not be merely metaphorical: As I write this, Iran’s Houthi allies have just seized military control of part of the Red Sea coast while Iranian drones attack Saudi Arabia’s East-West pipeline, threatening the main alternative route for oil trying to bypass the Strait of Hormuz.

An interesting aside: weaponized interdependence was already a well understood phenomenon in the Sci-Fi literature. The picture at the top of this post is a still from the 2024 film Dune 2, based on the classic Frank Herbert science-fiction novel. The novel is set on the desert planet Arrakis, which is the center of conflict because it’s the sole source for “spice,” a drug crucial to the Galactic economy. In effect, Arrakis is the Strait of Hormuz with giant sandworms, and one way to think about Dune is that it’s a novel about weaponized interdependence — and how such interdependence can escalate into open warfare.

Now I am not suggesting that real-world policy should be dictated by fears inspired by works of fiction. After all, fear is the mind-killer. But the reality of weaponized interdependence is now all around us.

Today’s primer, then, is about the dark side of international economic integration, how it can be used as a tool of coercive power as well as a source of international conflict, even shooting wars.

The key word here is “can.” I am in no sense arguing that global commerce is always or even usually a source of conflict. In fact, I’ll begin this primer by reviewing the historical arguments in favor of trade as a force for peace. But there are clearly situations in which the dark side of globalization prevails. So the rest of the primer will be about those situations: their intellectual basis, historical examples, and, alas, current relevance.

Beyond the paywall I will address the following:

1. Traditional arguments for global trade as a source of peace

2. International trade as a source of conflict

3. How weaponized interdependence can escalate

4. Interdependence and conflict today

5. The costs of insecurity

Read more

Generating running routes with GPT-6 Astra and ChatGPT Work

Here's a neat thing I had ChatGPT Work with GPT-6 Astra (Max) do this morning:

I live at <my address>. Figure out 5K and 10K running routes from me that loop from my house. Use OSM data.

It worked for 27 minutes and produced exactly what I'd asked for, as both an embedded visualization and downloadable GPX file and GeoJSON files. Here's that 5K route:

Map screenshot showing a blue route line over a light grey street map. Text: El Granada harbor loop 5.1 km. N ↑ (top right). Street labels along the route: Carmel Avenue, Paloma Avenue, San Carlos Avenue, Avenue Granada, Capistrano Road, Francisco Street, Coastal Trail. The loop runs from the harbor at the bottom left, north along Avenue Granada and Paloma Avenue to a northern point near Carmel Avenue, then east along San Carlos Avenue and south down Francisco Street to the far right, before returning west along the Coastal Trail beside the coastline. Footer: Map data © OpenStreetMap contributors. Give feedback.

When I asked it how it had created the route, it replied:

I used Nominatim to locate the address and Overpass to download local OpenStreetMap roads and trails, then calculated the loops locally.

Frustratingly, the actual code it ran and exact details of what it did weren't visible to me in the ChatGPT UI. I see this lack of transparency is an anti-feature.

By the time I thought to ask for a copy of the Python code it had used, ChatGPT was unable to provide it. This appears to be because the thread had been compacted. I think any LLM system that uses compaction needs to both preserve the pre-compacted text and make that text available via agent tool calls, to protect against this kind of problem.

As for displaying the map to me, that used the visualize skill. It created a file called /workspace/el-granada-5k-share.html to embed directly into the ChatGPT UI.

Here's a copy of that HTML, which starts like this:

<div id="eg-share-loop">
  <div class="viz-row"><h3>El Granada harbor loop</h3><span class="text-small">5.1 km</span></div>
  <div id="eg-share-stage"></div>
  <div class="text-small text-muted">Map data © <a href="https://www.openstreetmap.org/copyright" target="_blank" rel="noopener">OpenStreetMap contributors</a></div>
  <style>
    #eg-share-loop { width:100%; }
    #eg-share-loop #eg-share-stage { width:100%; margin:8px 0; }
    #eg-share-loop .eg-share-map { display:block; width:100%; touch-action:none; }
    #eg-share-loop .eg-share-map text { fill:var(--foreground); font-size:12px; font-weight:400; }
    #eg-share-loop .eg-share-label { paint-order:stroke; stroke:var(--background); stroke-width:3px; stroke-linejoin:round; }
  </style>
  <script type="application/json" id="eg-share-data">{"route":{"type":"LineString","coordinates":[[-122.467425,37.4997753] ...</script>
  <script src="https://cdn.jsdelivr.net/npm/d3@7.9.0/dist/d3.min.js"></script>
  <script>
  (() => {
    const root=document.getElementById('eg-share-loop');

The <script type="application/json"> element contains the full geometry needed to render both the running route and the map itself, using D3, which is loaded from an allow-listed CDN location described in this section of the visualize skill:

External resources

  • The CSP allows only cdnjs.cloudflare.com, esm.sh, cdn.jsdelivr.net, unpkg.com, fonts.googleapis.com, fonts.gstatic.com, and fonts.bunny.net. Other origins are blocked and fail silently.

Tags: geospatial, ai, d3, openai, generative-ai, chatgpt, llms, skills, gpt-6-astra

Central Pacific Tropical Weather Outlook


Central North Pacific 2-Day Graphical Outlook Image
Central North Pacific 7-Day Graphical Outlook Image


000
ACPN50 PHFO 141722
TWOCP

Tropical Weather Outlook
NWS Central Pacific Hurricane Center Honolulu HI
Issued by NWS National Hurricane Center Miami FL
800 AM HST Mon Sep 14 2026

For the central North Pacific...between 140W and 180W:

Active Systems:
The National Hurricane Center is issuing advisories on Tropical
Storm Norbert, located well east of the Hawaiian Islands, and on
Tropical Depression Fifteen-E, located several hundred
miles southwest of the southern tip of the Baja California
Peninsula.

Tropical cyclone formation is not expected during the next 7 days.

&&
Public advisories on Tropical Depression Fifteen-E are issued under
WMO header WTPZ35 KNHC and under AWIPS header MIATCPEP5.
Forecast/Advisories on Tropical Depression Fifteen-E are issued
under WMO header WTPZ25 KNHC and under AWIPS header MIATCMEP5.

$$
Forecaster Katz/Beven
NNNN


T-Mobile Is Charging $5/Month for iPhone Handoff

T-Mobile, in their iPhone 18 Pro / iPhone Duo press release:

Plus, T‑Mobile is one of the first to support Apple’s new iPhone Handoff feature, where customers with two compatible iPhone models can use their T‑Mobile number across both devices and choose which phone is active at any given time. The secondary iPhone works seamlessly, including essential T‑Mobile services like calls, texts, data, mobile hotspot, Scam Shield and T-Satellite on eligible plans, allowing customers to get the most from two devices without clunky handoffs or the hassle of managing another number. iPhone Handoff is available on any iPhone with iOS 27 via iPhone settings and ready to use from day one on the new iPhone 18 Pro lineup and iPhone Duo. T‑Mobile is introducing the feature at $5/month.

This seems like a ridiculous thing to charge for. It’s not adding a separate device to your plan, like adding a cellular iPad or even an Apple Watch. It’s just switching your active eSIM between two iPhones. You can do this for free, now, on any carrier via Settings → Cellular — but it takes a bunch of taps, confirmation on both iPhones, and a few minutes for the carrier to register the eSIM transfer. iPhone Handoff just eliminates all the annoying friction and waiting.

But, it’s utterly unsurprising that a carrier will charge a ridiculous monthly fee. I’ll be very surprised (pleasantly) if Verizon doesn’t do the same. Verizon charges for everything.

Still curious to me why AT&T isn’t yet announced as supporting iPhone Handoff at all. I have no idea why anyone remains on that turd of a network unless they live or work in a location where only AT&T provides a good signal, but even then, if it were me, I’d use AT&T by way of US Mobile. AT&T is the worst company I’ve ever dealt with, and I live in Kabletown.

 ★ 

Gary Marcus on This Week in AI Drama

I have been busy with new-iPhone-week stuff, so I haven’t been able to follow either of these stories closely, but Marcus summarizes them both well. First: the drama regarding OpenAI claiming a solution to the Navier–Stokes math problem. In short, OpenAI continues to prove itself to be a company full of cheaters. In this case, they seemingly were willing to tank the company’s reputation to first claim the solution to one of mathematics’ top unsolved problems. Apparently they spent $23 million in compute to win a $1 million prize, and admit they used a model that might have trained on the work mathematicians Tristan Buckmaster and Levent Alpöge had been working on for months, who had been using a combination of tools from OpenAI and Anthropic (where Alpöge is employed). With no hyperbole, the message here seems to be “Don’t use Codex or ChatGPT unless you’re OK with OpenAI stealing your work if it’s of interest to them.”

As for Anthropic, the news of the week is this guy Jacob Coxon, who quit the company in a public huff (on X, where most of this public drama is taking place), because he thinks they’re increasingly reckless despite everyone inside Anthropic believing that there’s a good chance LLMs will destroy humanity (or at least civilization?) in the next few years. Here’s Evan Hubinger, still at Anthropic, not dismissing Coxon as an alarmist but agreeing with him:

Jacob is correct here — we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.

This is crazy talk. Put aside whatever the actual risks are. If you truly believe there’s a greater than 10 percent chance that “AI could kill all humans ... within the next decade”, this is not how a reasonable person in their right mind would talk about it. Coxon thinks he’s blowing the whistle that Anthropic is a threat to the world. What he’s actually done is confirm that Anthropic is a cult, and its employees can’t really be talked to rationally, because they’re cultists. Sometimes members leave cults and they reveal to the world details about what’s going on inside the cult. But a lot of the time you can tell those whistleblowers still aren’t hooked up right, because they’re still people who joined and spent years inside a cult. They leave the cult not because they suddenly snap back to sanity but for other reasons.

 ★ 

In Case You Missed It…

…a week of Mad Biologist posts:

How Democrats Should Respond to the Trans Sports Question

Desperate to Stop a Midterm Rout, Trump Lies About Offering $5,000 Checks If Republicans Hold the Congress

Another OK Week for D.C.’s Crime Stats

Unread 5.0

John Brayton:

Unread 5.0 is available now from the App Store. This update adds these improvements and more:

  • Improvements around hero images, article list thumbnails, and widget thumbnails
  • Syncing with FreshRSS and Miniflux
  • The ability to quickly mark old articles read

I’m a NetNewsWire man personally, but I’ve long admired Unread as a different, very good take on a feed reader. Just a gorgeous app. One of the very best things about RSS is that feed readers take such different shapes and approaches.

 ★ 

Post Peek — Litterbox-Inspired Tweet Viewing Extension for Chrome

Tim VanBenschoten:

Post Peek is an independent, open-source project inspired by Litterbox, the Safari extension by Zhenyi Tan (And a Dinosaur).

 ★ 

Where Are the AI-Generated Killer Apps?

Paul Ford, writing for The New York Times (gift link):

For a while, I must admit, it looked as if software developer roles like mine were done for. How could we fight against tireless robots? But our industry is slowly realizing that making truly cutting-edge software still requires humans to think and work together, to maximize their skill sets and to practice their respective crafts. A.I. can write very good software, but it also makes it easy to do someone else’s job badly, which is part of why all those projects fail. Now that everyone can code, it’s become clearer why many shouldn’t.

Ford, linking to his own column on Bluesky:

I just want one killer app before we get a killer robot.

 ★ 

Yours Truly on Off Protocol With Jim Ray

My old friend Jim Ray now runs developer relations at Bluesky, and part of that gig is hosting a podcast, Off Protocol (“a show about building a better Internet”). I was delighted to appear as his latest guest. I generally don’t like talking about my career, but I did enjoy talking about it with Jim. He has that effect on people.

 ★ 

Diversity Is Our Strength?

Diversity is our strength is a common motto. Indeed it is one of GMU’s core values but what is the scientific evidence for this thesis? A new scoping review:

Recent years have witnessed many strong claims that ‘diversity’ leads to more original and impactful science, which is a science-focused form of what we call “The Diversity Hypothesis.” However, what evidence supports the claim that diversity enhances scientific output or impact? This pre-registered rapid scoping review seeks to collate and evaluate the scientific evidence for the Diversity Hypothesis…

…Based on over 100 scientific articles, we find that only between 15% and 28% of results reported in the literature are consistent with the hypothesis, with the balance of the results not being consistent with it.

…Overall, the results of the analysis section indicate that there is little empirical evidence that diversity improves scientific output and/or impact. In fact, with the possible exception of disciplinary diversity—a type of informational or viewpoint diversity operationalized at the team level—the majority of the evidence seems to point in the other direction. These findings are robust regardless of how the data is parsed and analyzed. The same conclusions can be drawn looking at the full data set, only the population-adjusted results, or only the results of high quality based on the MMAT analysis.

Hat tip: Colin Wright.

The post Diversity Is Our Strength? appeared first on Marginal REVOLUTION.

       

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Experimenting on the psychology of LLMs

 As LLMs become increasingly important, we're starting to see experiments that explore their psychology.

(I expect that   Susan Calvin, who was Isaac Asimov's fictional robopsychologist from the1950 short story collection I Robot, will soon have scientists following in her footsteps.)

Note that experiments intended to explore A.I. psychology need not necessarily resemble those intended to explore human psychology.

Here's an example,  from the journal Artificial Intelligence and the Law:
Human realignment: An empirical study of LLMs as legal decision-aids in moral dilemmas  by Christoph Engel · Yoan Hermstrüwer · Alison Kim
 

Abstract: Recent advances in AI make it conceivable to delegate legal decision-making to machines, or to enhance human adjudication through AI assistance. Using classic normative conflicts — the trolley problem and comparable moral dilemmas — as a proof of concept, we examine the alignment between AI legal reasoning and human judgment. In our baseline experiment, we find a pronounced mismatch between decisions made by GPT and those of human subjects. This misalignment raises substantive concerns for AI-powered legal decision-aids. We investigate whether explicit normative guidance can address this misalignment, with mixed results. GPT-3.5 is susceptible to such intervention, but frequently refuses to decide when faced with a moral dilemma. GPT-4 is outright utilitarian, and essentially ignores the instruction to decide on deontological grounds. GPT-o3-mini faithfully implements this instruction, but is unwilling to balance deontological and utilitarian concerns if instructed to do so. We replicate the experiment with four LLMs from different providers. Claude-sonnet-4.6 comes closest to human respondents. Gemini-2.5-flash-lite is most sensitive to normative instructions. Llama-4-scout and Mistral-nemo have a strong utilitarian bias, and do not strongly respond to normative interventions. At least for the time being, explicit normative instructions are not fully able to realign AI advice with the normative convictions of the population, or the legislator deciding on its behalf.

The Hollywood ecosystem can support one maybe two bald men at a time

How many Hollywood actors are there?

I means ones with the combination of

  • talent
  • can carry a movie for the public
  • can be accepted by producers.

Certainly more than a dozen at any given time. But I’m sure fewer than a thousand, to put an upper bound on it.

So, to guess, a population around that Dunbar-scale of 150 or a small multiple thereof.

Of those, I feel like the Hollywood ecosystem can support just one, perhaps two concurrent bald male actors.

(This statement from observation. But think about it: when a producer goes “we need a bald look for this” you can’t have too many people in the mental Rolodex.)

Regarding these apex predators: I mean specifically cue-ball bald, not male pattern baldness (which I feel is its own category).

There was Yul Brynner back in the day.

Ben Kingsley, Bruce Willis, Vin Diesel.

Recently: Jason Statham and Patrick Stewart. Dwayne Johnson most recently in the mix. Though they are all aging.

BUT:

I can tell there is currently an open niche because both the Dune 2 bald guy and the Superman bald guy appear to be gunning for it - and neither are actually bald.

The finch evolves its beak to crack those uneaten seeds.

Anyway.

How a Healthy Society Falls Apart

My parents spent most of their lives waiting for another economic collapse like the Great Depression. They had survived the last one—but just barely. And the privations of their childhood left them fearful.

One of the recurring topics of dinner time conversation was whether the next collapse would be inflationary or deflationary. I felt like I was watching the debate about fire and ice in a Robert Frost poem—both would suffice, but you still wanted to know which would kill you.

(I refrained from pointing out that we could have both simultaneously. This ghastly scenario happens when the value of your home, 401K, and other assets fall, while prices of what you need to buy every week—gas, food, etc.—keep spiraling up. In fact, we had a taste of that in 2008, and may get a second helping some day soon.)

So Mom and Dad kept buying books with ominous titles like How to Prepare for the Coming Crash or The Economic Time Bomb. But my parents weren’t alone. Their whole generation had come of age during tough times and World War. Optimism was not in their DNA. They didn’t see themselves as winners—just survivors.

As a carefree teen, I didn’t pay much attention to these worries. But guess what? I now read books on collapsing societies—proving that, sooner or later, you turn into your own parents.


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But the books I read aren’t populist scaremongering. I stick to real history and actual case studies. I find wisdom in Tacitus, Cicero, and Suetonius—and others who lived through the collapse of the Roman Republic. I study Huizinga’s observations of the waning of the Middle Ages. I know my Gibbon and Spengler and Carlyle.

My most recent find is Stefan Zweig’s account of how Vienna went from nineteenth century elegance, prosperity and cultural flourishing to warmongering and hyperinflation. His book The World of Yesterday may be the single best memoir from a survivor of societal collapse from the 20th century—and deserves to be far better known.

I call Zweig a survivor, but that’s not entirely true. He somehow managed to stay alive during World War I (not an easy task for a young man in Central Europe), and the subsequent economic crisis. But World War II forced him into exile, and he committed suicide in Brazil in 1942.

He was a broken man by then—and he feared Hitler was too powerful to stop. So Zweig, one of the greatest writers and thinkers of his generation, ended it all with an overdose of pills.


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Just a few years earlier, Zweig had gained renown as the most widely translated contemporary author in Europe. In Germany, his books sold 20,000 copies on the first day—even before a single advertisement had appeared. But that now changed.

“Not a single one [of my books] is available in Germany today,” he lamented at the end of his life. “Those who still possess a copy keep it carefully hidden.” And the same was true in France, Italy and other countries under fascist control.

Zweig is best known today as the inspiration behind Wes Anderson’s film The Grand Budapest Hotel. “It’s basically plagiarism,” the director has admitted. But Zweig ought to be remembered more for his writings, especially this final work from his pen.

Stefan Zweig is now remembered as the inspiration for The Grand Budapest Hotel.

His fiction is also sadly under-appreciated. And it’s very readable—Zweig was a specialist in short novels with fast-paced plots. I highly recommend 24 Hours in the Life of a Woman, and Chess as starting points. Zweig also had a knack for mini-biographies (see, for example, his stellar book on Montaigne).

The day before he died, he mailed his manuscript of The World of Yesterday to a publisher. It was released posthumously, and is now recognized as a defining document in how things go bad. Over the course of 500 pages, Zweig starts with the peace, prosperity, and cultural flourishing of the final years of the Habsburg Empire, and then shows, step by step, how a healthy, vital society self-destructed.

The entire book is well worth reading. I will only discuss a small part of it here—covering the hyperinflation following World War I. But this book is so rich in observations and telling details, I could easily devote several articles to it without coming close to exhausting its riches.

Zweig lived through the currency collapse twice—first in Austria (where he lived across the road from an odd young man named Adolf Hitler), when the crown devalued rapidly. Then he moved to Germany, where hyperinflation was even worse.

“A shoelace cost more than before a shoe—no, more than a luxury store with two thousand pairs of shoes.”

The stories he tells are so fanciful, you might think he was writing a fable in the style of Jorge Luis Borges. Consider his story of beer-swilling tourists.

The story starts when the Austrian currency collapses in value. Germans quickly learn how to take advantage of this, by crossing the border to buy up cheap merchandise—but with unforeseen consequences:

Finally, at the instigation of the German government, a border guard was appointed to prevent all consumer goods from being bought in the cheaper Salzburg instead of in the home stores….But one article remained free, which could not be confiscated: the beer that one had in the body.

And the beer-drinking Bavarians calculated from day to day on the exchange rate slip whether, as a result of the devaluation of the crown, they could drink five or six or ten liters of beer in Salzburg for the same price they had to pay at home for a single liter.

A more splendid lure could not be imagined, and so crowds of women and children moved over from neighboring Freilassing and Reichenhall to afford the luxury of swilling as much beer as their stomachs could hold. Every evening the station was a veritable pandemonium of drunken, bawling, spitting hordes of people; some who overloaded themselves had to be carried to the carriages on the trolleys usually used to transport suitcases….

Of course the happy Bavarians had no idea that they were in for a terrible revenge. For when the crown stabilized and the mark, on the other hand, plummeted in astronomical proportions, the Austrians crossed over from the same station to get cheaply drunk in their turn, and the same spectacle began a second time, but in the opposite direction.

This imbalance between currencies created other surreal scenes.

Incredible as the fact may seem, I can corroborate it as a witness that the famous luxury Hotel de l’Europe in Salzburg was for a long time rented out entirely to English unemployed people who, thanks to the abundant English unemployment benefits, lived here more cheaply than in their slums at home.

The World of Yesterday

But Zweig’s experience of German hyperinflation was even more extreme than what he had witnessed in Austria—enacting a kind of absurdist economics.

  • “I have experienced days when I had to pay fifty thousand marks for a newspaper in the morning, and a hundred thousand in the evening.”

  • “Those who had to change foreign money spread the charge over hours, because at four o’clock they got several times what they had gotten at three, and at five o’clock again several times what they would have gotten sixty minutes before.”

  • “I sent my publisher a manuscript I had been working on for a year and thought I had secured myself by demanding immediate advance payment for ten thousand copies; by the time the check was remitted, it barely covered what it had cost to frank the package a week ago”

  • “One found hundred thousand mark notes in the gutter; a beggar had thrown them away contemptuously”

  • “A shoelace cost more than before a shoe, no, more than a luxury store with two thousand pairs of shoes.”

  • “For a hundred dollars you could buy rows of six story houses on Kurfürstendamm [the Champs-Élysées of Berlin].”

But the worst results were hidden from view. In these calamitous times, the ruthless and the rule-breakers got richer, while the honest and trusting found themselves impoverished.

Stefan Zweig

He tells the tale of Hugo Stinnes, who took advantage of the collapse to buy up factories, mines, ships, castles, etc.—all with cash that would quickly lose its value to the seller. “Soon a quarter of Germany was in his hands, and perversely the people, who in Germany are always intoxicated by visible success, cheered him like a genius.”

Later Stinnes bought up newspapers—in order to control public opinion. After his death, this media empire helped consolidate power and influence for Hitler. Stinnes didn’t live long enough to see that happen. He died in 1924, the proud owner of 4,500 companies and 3,000 factories.

A counter covered with currency during Germany’s period of hyper-inflation.

Zweig wants to make clear, in this final book before he took his life, that this currency collapse was the main reason why the German public embraced Nazi ideology.

Nothing has made the German people—this must be recalled again and again—so bitter, so hateful, so ripe for Hitler as inflation. For [World War I], as murderous as it had been, had at least given hours of jubilation with bells ringing and victory fanfares. And as an incurably military nation, Germany felt its pride increased by temporary victories, while inflation made it feel only soiled, cheated and humiliated.

Instead of blaming the leaders who had started World War I, the Germans focused their anger on the politicians who subsequently brought them democracy and peace. Against all logic, they demand a return of the brutal butchers who solved every problem with escalating violence.


One of the reasons why many historical cycles seem to recur every hundred years, more or less, is that the people who learned the lessons of this mostly forgotten past are dead and gone. We can’t hear their warnings, or benefit from their experience. So we are doomed to repeat their mistakes.

That’s why I’m always fascinated by things that happened almost exactly a century ago. They typically represent a comparable point in the cycle as the one we are currently living through. Yet those earlier events exist just beyond the horizon of our living memory—and thus tell us painful truths that we can only learn from history books.

If we hope to avoid the worst excesses of the cycle, we must pay close attention to case studies from these bygone eras. Stefan Zweig’s The World of Yesterday is one of the most valuable of these documents. I’ve only provided the briefest sketch of its wisdom in the passages above. You would do well to read it in its entirety.

A simple model of AI-aided economic growth

The Solow model has its uses, but it fails when it comes to major changes stemming from AI.  Consider instead an economy with (at least) two factors of production:

1. Intelligence.  Yes, formal smarts.  Playing chess, proving math theorems, and doing well on evals.  Don’t forget humans can do those things too, though AIs are now a huge boost here.

2. Polanyi knowledge.  Michael Polanyi, that is.  This refers to knowledge of time and place, inarticulable knowledge, custom and habit, and many other particularities that you can read about in Hayek and Polanyi and in many other social scientists, anthropologists too.

Humans specialize in this.  The AIs can aid in its production, but at least so far there is no way you can “bring an AI into your office and have it figure out how that office works.”  At least not in the human rather than the purely mechanistic sense.

In the model, intelligence and Polanyi knowledge combine to produce output.

Substitutability is fairly limited.  For instance, if you have problems of norms in your office, a mere dose of AI-drenched technocratic knowledge does not usually solve those problems.  Sometimes it even can make those problems worse, by empowering rent-seekers further.

Intelligence and Polanyi knowledge are not quite Leontief complements, but they are mostly complements.

Now recently the U.S. economy has experienced a huge positive shock to its Intelligence, with more to come.

The core prediction is that this increases marginal returns, employment, and real wages in the Polanyi knowledge sector.  All of a sudden, the inputs into that sector are relatively scarce, compared to the now-larger quantity of Intelligence.

There will also be some transitional unemployment in the Intelligence sector, at least once Centaur models fade.  But so far Centaur models are holding, for instance mathematicians did the prompting to do the new math work.  Nonetheless some of these Centaur employments will fade, just as they have in chess.

Note that the Polanyi sector cannot be boosted very quickly or with direct and simple efficacy.  It is messy by its nature, to cite a term from Luis Garicano.  So the wage and employment gains there are slow in coming.  But they keep on coming for a long period of time.  There are further AI/Intelligence advances on tap, plus absorbing the advances to date, and exploiting them, takes a long time.

In this model, if someone or something could “commandeer” the Intelligence sector, their power over society would be much more limited than it might appear at first.  The world does not change that much at first, because the necessary complements are lacking.

The Solow model usually does fine by ignoring these features of the world, in part because it is rare for the Intelligence sector to take such a rapid swing upwards.  So the ratios and complementarities across these two sectors usually are fairly constant in the short run, though not in 2026 or in the next years to come.

I recall talking through this model, and debating it with people, when I was seventeen years old.  The impetus for that was the Soviet preoccupation with cybernetics, central planning, and possible supercomputers.  We were all wondering what kinds of economic improvements that might lead to, or whether it could make central planning successful (no, basically, but that involves some yet further arguments).

Of course this very simple model can be improved upon in many ways, but it is a start.

This very simple model so far is matching up to the data, namely that we have shocking AI and tech advances, the job market is doing fine, markets do not see high risk, and economic growth is robust, not exploding, but likely will rise in the future.  These predictions change somewhat as the Polanyi sector, slowly, catches up to and incorporates the Intelligence explosion.

In the meantime, this is the best basic framework for understanding our current situation.

The post A simple model of AI-aided economic growth appeared first on Marginal REVOLUTION.

       

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September 11, 2026

Last night was the second night of the Republican midterm convention Trump staged to fire up his base. Such a midterm convention is rare—the purpose of national conventions is to pick a president and write a platform—but Trump has turned the Republican Party into a vehicle for his own power and apparently thinks putting himself at the center of the midterms can overcome the headwinds the Republicans are battling.

Or perhaps, with his approval ratings hitting a new low, he just wanted to stand in front of cheering crowds again.

The audience at the American Airlines Center in Dallas was sparse, and according to Kara Voght of the Wall Street Journal, many who were there had received their tickets for free from state parties or right-wing groups like Moms for Liberty. But, she wrote, “everyone wanted to watch another episode of the Trump show.”

Although the crowd was small enough that it did not fill the arena, Trump got the absolute loyalty he demanded from those who had turned out. At the event, he led the crowd in an oath. “Please raise your right hand,” he shouted. “I pledge to the greatest president in the history of the United States. That loves us so much he can’t even breathe.” The crowd chanted dutifully after him.

“That I will go out with my family, my friends, I’ll do it any way—I don’t care if I’m registered or not, I’m going to try and cheat like hell like they do, they’d never, there’s never been bigger cheaters, they don’t care. I am gonna go out and I’m gonna get my friends, my family, and we are going to vote on November third or we are going to vote before that!” The crowd erupted in applause.

Once again, he promised that if the Republicans held control of the House and Senate after the midterms, he would distribute a $5,000 check to all adult U.S. citizens, a plan that would cost more than a trillion dollars even if it were legal for him to do so (which it is not).

But while the Trump show in Dallas was being broadcast to the president’s fans, reality was very much on the minds of those watching events in the Middle East.

There are two main shipping routes for oil and other products out of the region, and both have strategic chokepoints. The Strait of Hormuz sits on the eastern side of the Arabian Peninsula where the Persian Gulf empties into the Arabian Sea. The Bab el-Mandeb strait sits on the western side of the Arabian Peninsula. It pinches the outlet of the Red Sea on its way to the Arabian Sea and from there to the Indian Ocean and world markets.

When Trump struck Iran in February 2026, Iranian forces took control of the Strait of Hormuz, largely stopping traffic of oil through the outlet. And so Saudi Arabia, which is the world’s largest oil exporter and a close ally of the United States, relied on sending exports via the Red Sea route, passing through the Bab el-Mandeb strait.

It did so by moving oil across the Arabian Peninsula from the Persian Gulf to the Red Sea through the East-West oil pipeline, which runs about 750 miles (1,200 kilometers) across Saudi Arabia. It can move about 7 million barrels of crude oil per day and has been sending about 5 million a day since the outbreak of the war on Iran. From there, oil can be loaded onto tankers and shipped down the Red Sea, through the Bab el-Mandeb, and on to global markets.

Yesterday Iran-backed Houthi militants swept along the shoreline of the Red Sea toward the Bab el-Mandeb. They captured the seaport and city of Mocha, which is about 50 miles (80 kilometers) from the strait, from Saudi-backed Yemeni forces.

Today Yemeni government officials told Mohammed Ghobari, Eman Abouhassira, and Yasmine Ghania of Reuters that the Houthis have taken the coastal town of Dhubab, which sits on the Yemen mainland at the point of the strait. The sources said Yemeni forces had also evacuated the island of Perim, which sits about a third of the way between the mainland and the Djibouti shore to the west. Later this afternoon, sources said the Houthis had captured the island.

Although the Houthis do not control the Djiboutian side of the strait, they now threaten shipping through the passage. Saudi Arabia’s East-West pipeline has also come under attack. Spencer Kimball of CNBC reports that the Saudi Energy Ministry announced the pipeline had been shut down as a precautionary measure after it had sustained a number of attacks. Kylie Atwood, Katie Bo Lillis, Thomas Bordeaux, and Zachary Cohen of CNN added that the attacks apparently originated in Iraq, that U.S. officials had told them that pumping stations had been hit, and satellite imagery showed smoke coming from one of them and fire damage at another.

The price of oil briefly jumped to about $110 a barrel on Friday. Diesel prices moved to $6.06 a gallon, up 60% since the U.S. and Israel first attacked Iran in February.

Trump and his advisors have said they plan to strangle Iran economically through their blockade of Iranian ports. Now it appears Iran is retaliating with a plan to strangle the U.S. economy by slowing the transport of oil even further.

Today is the twenty-fifth anniversary of 9/11, the day terrorists from the al-Qaeda network used four civilian airplanes as weapons against the United States.

President Donald Trump did not attend the commemoration at Ground Zero, now the National September 11 Memorial, in downtown Manhattan because, according to New York Times reporters Maggie Haberman, Zolan Kanno-Youngs, Dana Rubinstein, and Sally Goldenberg, he wanted to deliver a speech but the event organizers don’t allow speeches, focusing on nonpartisanship and honoring the victims. Trump responded to the story with a personal attack on Haberman, calling her “Maggot.”

Instead, Trump went to the Pentagon, where he delivered a speech tying his war on Iran to the attacks of 9/11, saying: “We salute every member of the United States military who served in the ‘war on terror’. And we salute the service members working right now to ensure the world’s number one sponsor of terror, the Islamic Republic of Iran, will never ever have a nuclear weapon.” Defense Secretary Pete Hegseth added: “We’re still sending terrorists where they belong—to hell. And tankers to the bottom of the ocean where they belong, and we are ensuring that Iran will never have a nuclear weapon. The economic pressure tightens every day.”

All four living ex-presidents and first ladies—Bill Clinton and Hillary Clinton, George W. Bush and Laura Bush, Barack Obama and Michelle Obama, Joe Biden and Jill Biden—attended the Ground Zero commemoration in Manhattan. There the victims’ names were read, and the ceremony for the first time included a moment of silence for those who died of illnesses related to conditions in the aftermath of the buildings’ collapse.

Fifteen years ago, a wonderful student introduced me to the issue of The Amazing Spider-Man, volume 2, number 36, published on November 14, 2001. Famously, the cover of the issue is entirely black. Written by J. Michael Straczynski, the story takes on the impossible question: How, in a city that hundreds of superheroes call home, were those superheroes powerless to stop the 9/11 attacks?

“The sane world will always be vulnerable to madmen,” Spider-Man answers when a devastated couple fleeing from the devastation challenges him, “because we cannot go where they go to conceive of such things. We could not see it coming. We could not be here before it happened. We could not stop it. But we are here now. You cannot see us for the dust, but we are here. You cannot hear us for the cries, but we are here.”

But the superheroes Spider-Man identified in the chaos did not wear capes. They wore hats that said “FDNY” and “FEMA” and “POLICE.” The “true heroes,” Spider-Man said, were those ordinary people who ran back into the buildings to rescue others waiting in the dark, who joined together to storm the cockpit of a hijacked airplane and bring it down to the ground before it could hit the Capitol or White House, and who helped each other to safety or to face the terror.

They were ordinary men and women “made extraordinary by acts of compassion, and courage, and terrible sacrifice,” and who rejected “the self-serving proclamations of holy warriors of every stripe.”

Notes:

https://www.pbs.org/newshour/politics/republicans-gather-in-dallas-for-two-day-midterm-convention-billed-as-trump-a-palooza

https://www.wsj.com/politics/elections/trump-midterm-convention-election-74ec132c

https://www.instituteforenergyresearch.org/international-issues/saudi-arabia-considers-expanding-its-east-west-pipeline/

https://www.reuters.com/world/middle-east/yemens-houthis-reach-strategic-island-mouth-vital-shipping-lane-2026-09-11/

https://www.cnbc.com/2026/09/11/saudi-arabia-shut-down-east-west-crude-oil-pipeline.html

https://www.cnn.com/2026/09/11/politics/saudi-arabian-oil-pipeline-hit-by-projectiles-triggering-fires

https://edition.cnn.com/2026/09/11/politics/saudi-arabian-oil-pipeline-hit-by-projectiles-triggering-fires

https://www.france24.com/en/middle-east/20260911-iran-backed-houthis-seize-near-control-of-vital-bab-el-mandeb-shipping-lane

https://www.today.com/news/news/former-presidents-attend-911-memorial-ceremony-nyc-rcna597216

https://www.nytimes.com/2026/08/27/us/politics/trump-911-pentagon-ground-zero.html

https://www.theguardian.com/us-news/2026/sep/11/trump-hegseth-911-memorial

https://www.nbcnews.com/news/us-news/live-blog/9-11-attacks-memorial-ceremony-nyc-trump-mamdani-live-updates-rcna597049

https://www.the-independent.com/news/world/americas/us-politics/trump-asleep-pentagon-service-b3048741.html

https://marvel.fandom.com/wiki/Amazing_Spider-Man_Vol_2_36

https://imgur.com/gallery/amazing-spiderman-36-477-this-always-makes-me-cry-83xZp

https://www.nytimes.com/2026/09/11/business/diesel-fuel-prices-oil-iran-war.html

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Reading List — 09/12/2026

The Iron Bridge (View of Frankfurt) by Max Beckmann, via WikiArt.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week we look at city permitting speeds, more efficient ground source heat pump drilling, an Amazon Air crash in Miami, cooling paint, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housing and Cities

Economists Evan Soltas and Leonardo D’Amico have a new paper looking at permitting and construction speeds for housing in various cities in the US. The fastest cities are done with construction before the slowest cities have even issued a permit! [X]

Another setback for California Forever, the California city being planned in Solano County. A proposal to let the planned shipyard avoid completing a CEQA-required years-long environmental review (by using an existing environmental impact report) has been punted “to an unspecified date,” effectively killing the proposal for the rest of the year. [NYT]

A recent study suggests that home values don’t decline when a wind or solar farm is built nearby. “Researchers at Ball State University analyzed Indiana home sales from 2004 through 2024, comparing prices before and after large wind and solar projects came online. They also compared homes close to the projects with those farther away. The researchers found no statistically significant negative effect on residential sale prices near either commercial wind turbines or utility-scale solar farms.” [Electrek]

An interesting building technology, supposedly used in China for residential construction: hollow prefabricated wall panels lined with steel mesh and (it looks like) plastic sheets. Once in place, the panels are filled with concrete. [YouTube]

A cool map which shows how LA got built year by year, starting in 1880. [Parcelscope]

Manufacturing

Fab2, the semiconductor equipment startup co-founded by Sam Zeloof (who famously made transistors in his parents garage) raised $500 million in funding at a $3.7 billion valuation. [X]

ASML begins construction of a new production facility in the Netherlands. “The investment will eventually add ​space for up to 20,000 workers, with a first phase targeting completion by 2029 that will add offices, logistics and a so-called cleanroom space — a controlled environment for specialist equipment manufacturing. ASML said the design of a new “Flow ​Factory” will enable it to build and assemble its giant chip-printing tools more quickly and ​efficiently.” [Reuters]

Intel has processed more than 1 million wafers using ASML’s most advanced High NA EUV machines, more than the rest of the semiconductor industry combined. [Tom’s Hardware]

Energy

A huge, nine-year long investigation to find evidence of cold fusion turned up nothing. “We searched a wide swath of the loading, stimulation, and material parameter space associated with LENR. Although we didn’t test every possible corner of the parameter space, we consider our null result definitive in the spaces we did search. We found no evidence for any variety of cold fusion phenomenon. Furthermore, we found prosaic (non-nuclear) explanations for most prior reports of “excess heat” in electrochemical cells.” [Callisto Report]

We’ve previously noted how advances in drilling technology, such as the PDC drill bit, have enabled the rise of enhanced geothermal energy (EGS). EGS requires a lot of drilling footage, and so the economics only work when drilling gets sufficiently cheap. Interestingly, another type of geothermal energy, ground source heat pumps, might also be made cheaper by a different type of declining drilling costs. Ground source heat pumps work by drilling holes into the earth, tapping the soil as a heat source/sink. A startup, Dig Energy, thinks it can make these systems cheaper by reducing the cost of drilling. “In the standard process, the borehole is drilled first, typically with a metal bit or compressor, and shored up along the way with a casing to prevent collapse before a heat exchange pipe is inserted. Dig’s system, by contrast, uses a high-pressure nozzle that sprays water at 10,000 pounds per square inch to bore through soil and rock. The approach was explored by the oil and gas industry decades ago, but never gained traction because it didn’t increase productivity in those sectors, Madden said.” [Canary Media]

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Before we return to local politics ...

Look at the above photograph.

Look at it twice.

Have your friends look at it.

Have your relatives look at it.

If you know any MAGA folk, have them look at it.

That’s Donald Trump, in Washington on Sept. 11, sleeping—literally sleeping—as names of the 9.11 victims were read off at the Pentagon. He was there for a ceremony honoring the 184 military personnel and civilians murdered when American Airlines Flight 77 hit the building.

If you’re thinking, “Well, that’s just a quick snapshot as he blinked …”

And I just wanna say, sincerely, WHAT THE FUCK ARE WE DOING HERE?

Seriously—what the fuck are we doing here?

Why are we, as a nation, continuing to cover for this soulless, sleep-deprived rapist ghoul? When do Republicans stand up and say, “Enough”? When do they say, “hmm … maybe Sleepy Joe wasn’t nearly as bad as this?”

It’s just the height of insanity, that 30 percent of the nation still thinks this dude is OK.

More to the point, the same guy who lied (yet again) about being a 9.11 hero couldn’t even muster the decency and respect to stay awake and honor the deceased. It’s beyond outrageous.

It’s un-American.

September 12, 2026

On Thursday, in The New Republic, conservative judge J. Michael Luttig published a dire warning to the Republican Party…and to all Americans. In the piece, Luttig outlined how Republicans could refuse to seat elected Democrats in January after a Democratic victory in the 2026 midterm elections of November. “The coming crisis,” the piece says, would be “The Final Battle for America’s Democracy.”

Luttig spelled out that Trump is fighting a war against America’s democracy. While the refusal of Vice President Mike Pence to assist his power grab made it fail in January 2021, Luttig notes, today Trump and his Republican congressional allies are more determined than ever to take over the country.

Luttig focuses on the ability of House speaker Mike Johnson (R-LA) to prevent the recognition of duly-elected Democratic representatives on January 3. Johnson has the power, Luttig says, to remove the current House clerk, Kevin McCumber, and replace him temporarily with a MAGA loyalist who will refuse to list Democrats on the roll of those elected to the 120th Congress.

In this scenario, once Democrats are eliminated, they will have to get a federal court to order the temporary clerk to list their names. If a court does so, another crisis point will arrive if the clerk simply refuses to obey the order. Then it’s possible the courts will stay out of the fight until the 120th Congress formally convenes and the Republicans vote not to seat the Democrats. That vote would be reviewable by the courts, including the Supreme Court.

But that review would take weeks, if not months, paralyzing the United States and leaving the country “helplessly vulnerable to all the world’s evil, as it would have been in January 2021 had Mike Pence not thwarted Donald Trump’s plan to overturn the 2020 presidential election.” And at that point, how would the current Supreme Court answer the question: “Is the United States of America a democracy, in which ‘We the People’ elect our representatives to the Congress and to the presidency, or is it not?”

As if to illustrate Luttig’s warning, House speaker Johnson told attendees at Trump’s convention in Dallas on Thursday: “We cannot and will not allow them to take the majority in the Congress. We’re not gonna do it.”

Trump’s attempt to use the United States Postal Service to screen mail-in voting has recently had a test run. Yesterday, Jim Saksa of Democracy Docket picked up a story from Jeff Berlew of the Tallahassee Democrat to report that the USPS rejected mailed ballots from Leon County, in Florida, because the words “return service requested” were only 0.236 inches from the election office’s return address. They were supposed to be a full quarter-inch apart. So, the delivery of those mail in ballots came down to a federal complaint about a spacing issue of 0.014 of an inch.

“It’s ridiculous,” Supervisor of Elections Mark Earley told Berlew. But, as Saksa notes, the rejection of ballots for such a petty flaw shows what will likely happen if the Supreme Court lifts the injunction blocking the postal service from implementing the new rules it has put in place since Trump ordered such interference in a March executive order.

On Thursday, September 10, Nick Corasaniti of the New York Times reported that the Department of Justice (DOJ) under Trump has sent threatening letters to at least thirty top election officials in the states, warning that those officials “are currently under investigation” and are subject to “ongoing litigation,” and so must not destroy any records relating to the 2024 election. As Corasaniti notes, the administration has sued 30 states for their voter lists and has lost 23 of the cases and won none. Trump has claimed to be the victim of voter fraud since the 2016 primaries but has never produced any proof of his claims.

In July, Harmeet Dhillon, who heads the civil rights division in the DOJ, threatened state election officials with criminal prosecution if any noncitizens cast a ballot in their state (although it is already illegal for noncitizens to vote), and Secretary of Homeland Security Markwayne Mullin threatened election officials with criminal prosecution if they did not put Trump’s election changes into effect.

Election officials already follow the laws about retaining documents, so the letters seem to be about threatening them. Nevada secretary of state Francisco Aguilar, a Democrat, told Corasaniti: “It’s the constant ‘flood the zone’ of harassment and intimidation and threats of legal action hoping we’d fold at some point.” But, he added, “We’re going to continue to follow the law and do what’s in the best interest of our voters.”

And what of the MAGA Republicans Trump would like to see in Congress?

Liz Goodwin of the Washington Post reported yesterday that 25-year-old Stephen Woytek, a top campaign staffer for Senator Jon Husted (R-OH), has recently cultivated a public playlist of songs celebrating the former country of Rhodesia, in which a white minority ruled over a Black majority, captioning it “homesick for a place that no longer exists.” Rhodesia is a rallying cry for white supremacists who deplore the change that created Zimbabwe. Woytek also appears to be a historical reenactor who appears to have both worn Nazi uniforms and used a photo of Nazi soldiers as his Facebook profile picture.

Ohio governor Mike DeWine appointed Husted to the Senate to replace J.D. Vance after Vance was elected to the vice presidency, and Husted is now locked in a battle for that seat with former senator Sherrod Brown, a Democrat known for his defense of labor. A statement for Husted said that while Woytek “denies negative intent,” the campaign was parting ways with him.

As Goodwin notes, growing numbers of younger Republicans have been expressing support for white nationalism and the Nazis.

Indeed, in his warning, Luttig did not single out Democrats to protect democracy; as he noted in a piece on his Substack in August, plenty of people are already fighting. He called out Republicans.

“There was a time not long ago when virtually every member of Congress could be expected to commit to the peaceful transfer of congressional power in advance of an election,” Luttig wrote. But now, “[i]n a damning indictment of the president and today’s congressional Republicans, it would be hard to find even one congressional Republican with the integrity, sense of duty to country, honor, and courage to put America above the Republican Party, let alone above Donald Trump.”

He urged Republicans “to decide that they are not going to betray their oaths and their country one last time for Donald Trump,” and to make it clear to Trump and Johnson that they will not participate in any unconstitutional plan to deny Democrats seats in the new Congress.

He warned them that should Republicans go along with such machinations and the Supreme Court overrule them, the Republican Party “would finally meet the fate to which it has been destined since January 6, 2021, and cement its place in history as the most corrupt political party ever to emerge in the United States of America for its second attempt in six years to defy the will of the American people on Election Day.”

“The writing is already on the wall, Republicans,” the conservative jurist wrote. “The Republican Party in particular must finally loose the chains of its political and moral enslavement to Donald Trump and separate itself from the MAGA political party cult.”

Today, in Ireland, where he traveled for the Irish Open at his golf club in Doonbeg, Trump told reporters: “We had a rigged election. As you know, it was totally rigged. Because I won three times. I didn’t win twice. I won three times.”

Notes:

https://newrepublic.com/article/215198/2027-new-congress-final-battle-american-democracy

https://www.democracydocket.com/news-alerts/in-ominous-sign-usps-initially-rejected-florida-countys-mail-ballot-over-spacing-issue/

https://www.nytimes.com/2026/09/10/us/politics/department-of-justice-election-fraud-states.html?eafs_enabled=false

https://www.washingtonpost.com/politics/2026/09/11/ohio-senate-campaign-staffer-used-nazi-soldiers-profile-photo/

https://www.washingtonpost.com/politics/2026/03/26/gop-feuntes-trump-antisemitism-nationalism/

Judge J. Michael Luttig
The Final Battle for America's Democracy
Over the past three weeks, in the lead-up to Labor Day and afterward the two-month run-up to the mid-term elections, my friends David French, Tim Snyder, Joyce Vance, Heather Cox Richardson, and others, have all issued powerful and eloquent warnings about the evident intentions of Donald Trump and the United States Government to interfere in what otherw…
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The Twenty-fifth Anniversary of 9/11

Saturday 12 September 1663

Up betimes, and by water to White Hall; and thence to Sir Philip Warwick, and there had half an hour’s private discourse with him; and did give him some good satisfaction in our Navy matters, and he also me, as to the money paid and due to the Navy; so as he makes me assured by particulars, that Sir G. Carteret is paid within 80,000l. every farthing that we to this day, nay to Michaelmas day next have demanded; and that, I am sure, is above 50,000l. more than truly our expenses have been, whatever is become of the money.

Home with great content that I have thus begun an acquaintance with him, who is a great man, and a man of as much business as any man in England; which I will endeavour to deserve and keep.

Thence by water to my office, in here all the morning, and so to the ’Change at noon, and there by appointment met and bring home my uncle Thomas, who resolves to go with me to Brampton on Monday next. I wish he may hold his mind. I do not tell him, and yet he believes that there is a Court to be that he is to do some business for us there. The truth is I do find him a much more cunning fellow than I ever took him for, nay in his very drink he has his wits about him.

I took him home to dinner, and after dinner he began, after a glass of wine or two, to exclaim against Sir G. Carteret and his family in Jersey, bidding me to have a care of him, and how high, proud, false, and politique a fellow he is, and how low he has been under his command in the island.

After dinner, and long discourse, he went away to meet on Monday morning, and I to my office, and thence by water to White Hall and Westminster Hall about several businesses, and so home, and to my office writing a laborious letter about our last account to my Lord Treasurer, which took me to one o’clock in the morning, [continued tomorrow P.G.]

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Our Eukaryotic Moment

About two billion years ago, life underwent a change in architecture. Until then, the planet was ruled by relatively simple prokaryotic cells: bacteria and archaea, tiny packets of chemistry bounded by membranes, carrying their genetic material directly in the same cellular space where much of the business of life took place.

Then something happened, or rather, several things happened in an order that remains uncertain. At some point, an ancestral archaeon cell entered into a permanent symbiosis with a bacterium capable of unusually powerful energy metabolism.

That bacterium eventually became the mitochondrion.

Somewhere in the same long evolutionary transition, genetic material became enclosed within a nucleus, separating the storage and regulation of hereditary information from much of the cell’s everyday chemistry. Internal membranes proliferated, cytoskeletons became more elaborate, and a new kind of cell emerged: the eukaryote.

We still do not know the exact sequence. Some theories place the acquisition of mitochondria near the beginning of the transition, with the energetic consequences of the partnership helping to make subsequent complexity possible. Others suggest that considerable cellular complexity, perhaps including primitive mechanisms for engulfing other cells, was already present before the mitochondrial symbiosis. The origin of the nucleus is even murkier; there are competing theories for how and why genomes first became enclosed inside their own compartment. The important point is that mitochondrial symbiosis and the appearance of the nucleus should not be collapsed into a single event. Eukaryogenesis was a reorganization involving multiple innovations whose relationships remain an active scientific question, not a sharp singular threshold-crossing.

What is clear is what the new architecture eventually made possible. Eukaryotic cells became larger, more internally differentiated and capable of forms of organization unavailable to their ancestors. Much later, and independently in several lineages, some evolved multicellularity. Cells themselves became components of larger entities, differentiating and cooperating in increasingly elaborate ways. From the eukaryotic architecture eventually came forests, mushrooms, octopuses, hummingbirds, whales and us.

Eukaryotes were more than just “better bacteria.” They represented a different organization of life, capable of sustaining far more complex structures..

We may be living through an analogous transition now. The emergence of AI, in the particular form that it has appeared (deep learning), is arguably the eukaryotic moment in human cultural evolution, understood in memetic terms, with humans playing the role of mitochondria, and AI the role of the nucleus.

A high-level schematic overview of the metaphoric mapping is in the diagram above, and we will spend the rest of this essay unpacking this metaphor carefully, working out its implications, and exploring the grammar of the possible futures it points to.

But first, a word on the motivation.

Premature Ontogenic Closure

The idea of humans as mitochondria in a scheme where AI occupies the central, nuclear position is obviously a startling, ego-decentering one. The spatial reorganization suggested by the metaphor is even recognizably like the Copernican shift from geocentrism to heliocentrism. In fact, this is one of the reasons to take it seriously.

For many, this is perhaps a profane thought to entertain, and one that is (it could be argued), unnecessary in some sense, unlike the Copernican shift, where a preponderance of evidence eventually made it inescapable. We might argue that even if sound, this mental model only points to a set of possible futures, not to necessary ones. I personally suspect some decentered future of this kind is in fact necessary as well, but I will not be making that stronger argument in this essay.

For this essay, it is sufficient to note that this is not obviously either a good or bad evolutionary prospect. The metaphor is meant as a pre-moral, pre-ethical framing device motivated by the hypothesis that a genuine Copernican shift in perspective is required before any meaningful analysis is possible.

We need Copernican frames because most arguments about artificial intelligence today race past foundational ontological questions to ethical ones posed in what are effectively Ptolemaic frames. We ask, within unwieldy anthropocentric frames (with vague and ill-posed adjectives like “super” or “general” serving as epicycles), whether AI will be good or bad, whether it will replace workers, accelerate science, undermine democracy, achieve superintelligence or destroy humanity, all before adequately exploring what actually existing AI even is, and what its actual evolutionary dispositions are.

I call this problem premature ontogenic closure.

Whatever the answers, they will almost certainly be easier to compute in a suitably Copernican frame based on the AI we have, rather than the one we thought we’d have. The eukaryotic metaphor is one such candidate frame.

We should take as a warning sign that the prevailing Ptolemaic picture of AI, undergirding fraught ethics discussions, is almost too familiar and legible: a very powerful computer, perhaps eventually a synthetic mind.

That picture is older than the technology it seeks to explain. It is descended from the pre-internet artificial intelligence of the mid-20th century, when computers were conceived primarily as discrete machines, programs as explicit instructions, and intelligence as something implemented inside the box. Its imaginative complements came from the science fiction of roughly the same period: robot minds, artificial persons, centralized supercomputers and cold machine civilizations. HAL 9000 and Skynet differ morally, but ontologically they belong to the same family — artificial brains in boxy vats.

Actually existing AI emerged under very different conditions than the ones imagined in much of the prefigured philosophy being brought to bear. The decisive enabling resource for large language models was not merely faster processors or cleverer algorithms. It was a planetary accumulation of human cultural activity: books, websites, discussion forums, code repositories, reference works, social media, documentation, journalism, fan fiction, arguments, jokes, tutorials and innumerable other traces of human thought deposited on networked computers. The Internet was not merely infrastructure over which AI happened to be delivered. It was part of AI’s developmental environment and remains part of its production environment once deployed.

Artificial intelligence has therefore not arrived as an alien intelligence from outside human ecology. It has been gestated inside that ecology, and is deeply entangled with, and dependent on it. Human sociality created its training material, human institutions created its objectives, and human networks provide its deployment environment. Like humans, AI is a kind of crooked timber, like the humans whose cultural memories it embodies.

Increasingly, humans and AIs inhabit the same digital spaces, modifying one another’s behavior in loops that make the distinction between “the technology” and “the society affected by the technology” difficult to sustain. Blaise Agüera y Arcas has made a related argument from a broader evolutionary perspective, treating computation, technology and human organization as entangled rather than opposing domains.

Perhaps, then, the useful analogy is not a new alien species, arriving from a distant, alien ecology to compete with ours. Perhaps it is eukaryogenesis. Perhaps AI represents the beginnings of a new organization of cultural life in which humans and machines are entering into an endosymbiotic relationship, and perhaps the unit being transformed is not intelligence at all, but the meme.

The Future of Memes

Richard Dawkins coined the term “meme” in 1976 as a cultural analogue of the gene: an idea, tune, practice, style or other unit of cultural information capable of spreading from mind to mind. The concept has been stretched nearly beyond recognition since then, but its original evolutionary intuition remains useful. Cultural forms replicate, mutate and undergo selection. If we take that intuition seriously and follow it forward into the age of foundation models, an unexpectedly detailed analogy begins to emerge.

The key is not to imagine one giant one-to-one correspondence between “the cell” and “the AI system.” The more useful picture operates at several nested levels. One level concerns heredity: how cultural information is replicated, stabilized and compressed. A second concerns expression: how inherited information becomes action through inference and tools. A third concerns the composite organism that emerges when humans, models and digital environments become tightly coupled.

At the inheritance level, the history of cultural evolution begins to look surprisingly biological:

The striking point here is that the Internet itself is not yet the genome. It is closer to the environment in which cultural replicators circulate. Search engines index that environment and social media accelerates selection within it, but foundation-model training performs a different operation: it compresses the statistical structure of the meme pool into a reusable generative inheritance. That is the step that makes the genomic analogy possible.

The second level concerns what happens after the genome exists. In biology, hereditary information becomes useful only because cells possess elaborate regulatory and translational machinery. Something similar appears in the modern AI stack:

This second table is where the analogy becomes more than a decorative comparison. A bare language model is informationally rich but causally weak. Agent harnesses connect inference to external machinery, just as cellular expression systems connect genomic information to proteins capable of doing work. From this angle, conventional software does not become obsolete in the age of AI. It becomes the proteome.

The third level is the most speculative, because it concerns the composite entity rather than the information machinery alone:

This last cluster is where the metaphor should be handled most cautiously. The correspondence between weights and genome, or tools and proteins, is mainly architectural. The human-as-mitochondrion analogy is more conjectural because the thing humans appear to supply is not literal energy but, as we will see, something like liveness: stakes, desire, valuation, embodied reality-testing and motivation. Yet this is also the part of the metaphor that generates the richest speculative consequences, because it reframes AI not as a separate species confronting humanity from outside, but as one component of a potentially new symbiotic organization of cultural life.

Seen this way, the three tables describe a progression rather than a static taxonomy. Cultural evolution first acquires more reliable heredity, then a compressed generative genome, then an active nucleus and expression machinery, and finally the beginnings of a composite cell. The rest of the essay will unpack these layers in roughly that order, while repeatedly testing where the analogy illuminates and where it begins to break.

Epochs of Memetic Machinery

For most of human history, cultural evolution had something of the disorderly quality of an early microbial ecology. Stories moved orally and changed in the telling. Manuscripts were copied by hand, accumulating transcription errors and editorial interventions. Songs mutated. Images traveled through engravings, paintings and imitation. Recipes, rituals and craft knowledge lived partly in bodies and partly in memory. Cultural information reproduced through many competing channels, none especially good at guaranteeing fidelity over long periods. The result was a kind of primordial memetic soup.

Then came print. Elizabeth Eisenstein, in The Printing Press as an Agent of Change, argued that Gutenberg’s revolution mattered for reasons subtler than simply producing more books. Print introduced what she called “typographical fixity”: the ability to produce many substantially identical copies of a text, distribute them widely, compare them and preserve them over time. It encouraged standardization, indexing, cross-reference and cumulative correction. Eisenstein’s strongest claims have been debated by later historians, who have emphasized that early printed texts could themselves be unstable and unreliable. But her central insight remains enormously suggestive: reproducibility and relative textual stability transformed the conditions under which knowledge accumulated.

In evolutionary terms, printing introduced an extraordinarily successful replicator design. Cultural information had always reproduced, but print made one mode of reproduction dramatically more faithful, durable and scalable. A page could remain effectively the same while traveling across a continent or surviving across generations. For certain classes of memes — scientific claims, religious doctrines, laws, technical diagrams, canonical literature — that stability changed the evolutionary landscape. The Gutenberg revolution therefore looks, from our present vantage point, like an early transition in memetic heredity.

The Internet changed the ecology again. Digitization preserved much of the fixity of print while also partially undoing it. A digital text could be copied perfectly, but it could also be modified, remixed, quoted, linked, forked and recombined almost without cost. Billions of people could inject mutations into the meme pool continuously. Cultural evolution became vastly faster and more densely connected. Yet the Internet was still mostly a pool. Search engines indexed it, databases stored it and recommendation systems routed attention through it. None of these systems turned its accumulated structure into anything resembling a genome.

Large-scale machine learning did. A foundation model does not merely maintain copies of the texts from which it learned. Training transforms statistical regularities across enormous corpora into billions or trillions of numerical parameters. The result is not a library in the familiar sense. It is a compressed generative structure from which fragments of cultural behavior can be reconstructed, recombined and improvised. Dawkinsian memes can therefore be understood as something like the dispersed genetic material of a pre-eukaryotic cultural world, the Internet as the immense planetary meme pool in which they compete and recombine, and model weights as something new: a genome.

This is not a genome in the literal molecular sense. Biology itself warns against treating DNA as simply a digital program. Modern genomics increasingly emphasizes that the genome’s function depends on three-dimensional organization, regulatory dynamics and physical cellular context; the old metaphor of DNA as a software blueprint has become increasingly inadequate. As an evolutionary analogy, however, the correspondence is useful. The weights of a foundation model constitute a compressed inheritance produced from previous generations of cultural activity. For the first time, the meme pool has acquired something that behaves like a generative genome.

A True Kernel

If weights are the genome, the model itself is not merely DNA. It is the nucleus, and this distinction matters. A genome sitting inertly in a cell accomplishes very little. Biological life depends on machinery that regulates which genes become active, transcribes information, responds to signals and coordinates expression with the changing condition of the cell. An LLM does something structurally comparable. The same fixed weights can generate a legal brief, a joke, a program, an explanation of photosynthesis or an imaginary dialogue between Napoleon and Taylor Swift. What gets expressed depends on context. The model is therefore better understood not as a database of information but as an active system for interpreting a compressed inheritance.

Latent space becomes, in this picture, something like the nucleoplasm: the structured internal medium in which inherited information becomes dynamically available. The context window resembles transient regulatory state, while prompts, persistent memory and environmental inputs determine which parts of the inherited repertoire become active. Around this nucleus, meanwhile, an increasingly recognizable cell is beginning to form.

Consider the digital environment associated with a single person: files, messages, photographs, browser history, calendars, contacts, applications, social feeds, databases, devices, subscriptions, notes and conversations. Today these systems are only loosely integrated, but persistent AI systems are beginning to sit amid them, retrieve from them, act through them and maintain state across them. That personal digital environment is a plausible early cell body. The same architecture can appear at larger scales in a team or corporation, suggesting that we should not assume too quickly that the individual human marks the eventual cell boundary.

Its cytoplasm is what, for lack of a more dignified technical term, we might call vibes. Every culture possesses a diffuse contextual chemistry determining what feels salient, fashionable, dangerous, respectable, embarrassing, funny or urgent. Much human cultural cognition occurs through such weak fields rather than explicit propositions. A meme that flourishes in one ambient environment dies immediately in another, and the same sentence can function as profound insight, stale cliché or career-ending faux pas depending on the surrounding vibe. The nucleus does not replace this environment. It interprets it and increasingly helps regulate it.

There is an obvious problem with the analogy. Biological individuals do not all carry literally identical genomes, while millions of people currently use copies of the same few foundation models. Yet the mismatch may be temporary. Members of a biological species share overwhelmingly similar genomes while relatively small variations can produce consequential individual differences. A common base model could likewise provide something analogous to a species genome, while fine-tuning, adapters or other durable modifications supply individual differentiation. Retrieval systems and persistent memory are better understood as regulatory or epigenetic state than genetic difference, while the immediate context window is more transient still. Today’s AI systems may simply be poorly differentiated early eukaryotes: their nuclei are remarkably sophisticated while their cells remain primitive.

The Proteome

A nucleus by itself cannot do very much because information must ultimately become action. This is where the recent turn toward agentic computing becomes important. A language model operating as a chatbot emits tokens. Even if those tokens describe a brilliant plan, they remain descriptions. An agent harness changes the architecture by giving certain outputs causal meaning. The model can request that a file be read, a database queried, a web page retrieved, a program executed or a message sent, and machinery outside the model performs the requested operation.

Here the biological analogy acquires another layer: tools are proteins. Proteins are the workhorses of cells, serving as enzymes, receptors, structural components and molecular motors. Genetic information specifies and regulates them, but proteins perform much of the actual work. Conventional software plays a similar role in digital environments. A compiler is an exquisite computational enzyme, as are a database engine, a search algorithm, a numerical solver, a spreadsheet function or a cryptographic library. CPUs and GPUs can perform enormous quantities of deterministic mechanical work that would be absurdly wasteful for an LLM to reproduce through inference.

The agentic stack therefore begins to resemble a gene-expression system. Weights provide inherited structure, inference regulates expression, tool calls operate somewhat like messenger or signaling products, and the harness connects those informational outputs to executable machinery. Software then carries out the work. The analogy becomes especially vivid when an AI writes code: a model generates an informational specification, the harness materializes that specification as executable software, a processor runs it, and the resulting transient functional structure alters the surrounding environment. This is not literally protein synthesis, but it occupies a remarkably similar architectural position.

This perspective also casts the history of artificial intelligence in an unexpectedly different light. For much of the 20th century, AI researchers tried to build intelligence from explicit symbolic machinery: rules, planners, search procedures, ontologies, logic systems, theorem provers and expert systems. This was not foolish work, and much of that machinery remains extraordinarily useful. Perhaps, however, its architectural role was misunderstood.

GOFAI mistook the proteome for the genome.

There is an eerie historical parallel. Before DNA was established as the carrier of heredity, proteins seemed like plausible candidates because they were fantastically complicated, built from a rich alphabet of amino acids and capable of extraordinary functional diversity. DNA, with its paltry four bases, looked almost too simple to contain the secret of life. The resolution was not that proteins were unimportant; it was that they did a different job. Something similar may have happened in AI. Explicit symbolic machinery was never useless. It was simply poorly suited to carrying the compressed inheritance necessary for general intelligence. Once learned models supplied that inheritance and a regulatory mechanism for expressing it contextually, the old machinery found its natural place again as tools. Symbolic AI did not disappear so much as become the proteome.

The Human Mitochondrion

The strangest part of the analogy concerns our own place within it. In this picture, the closest counterpart to the human brain is the mitochondrion. Mitochondria are descendants of bacteria that once lived independently. Their ancestors entered into a durable symbiosis with another cell and eventually became indispensable components of a larger organism. They are usually described as the cell’s powerhouses, but that slogan understates their complexity. Mitochondria retain their own small genomes, participate in signaling and metabolism, divide and fuse, and respond dynamically to local conditions. Their ATP production adjusts to cellular energy demand rather than simply running at a uniform rate. They possess a kind of constrained local autonomy without possessing sovereignty over the cell.

Humans are not useful to AI primarily because we supply electricity; data centers can get that elsewhere. What we currently supply is something harder to name.

Call it liveness: attention, desire, stakes, valuation, embodied experience, contact with physical reality, motivation, and the sense that some outcomes matter while others do not.

An LLM can manipulate representations of these things with extraordinary sophistication. It is far less clear that it possesses them in the form that causes a living system to expend resources, reproduce, defend itself, care about an outcome or decide that something is worth doing. Humans inject that psychic-energetic difference into the system, which makes us plausible mitochondrial symbionts of emerging memetic cells.

The analogy should not be pushed too literally. Human beings possess vastly greater autonomy than mitochondria. We set goals, defect, organize, rebel, fall in love, quit our jobs and refuse instructions. Yet even the imperfection is suggestive, because mitochondria are not passive batteries either. They perform local adaptive control within the larger cell. A human embedded in an AI-mediated organization might similarly receive broad objectives from a shared informational system while retaining enormous local discretion over how to realize them. The interesting issue is not whether the human or AI is “really in control,” but how control becomes distributed across the composite organism.

There is a deeper parallel still. During mitochondrial evolution, many genes once carried by the ancestral bacterium migrated to the nuclear genome. Modern mitochondria retain only a tiny fraction of their ancestral genetic independence, while the larger cell has internalized functions that once belonged to its symbiont. Something analogous is already happening cognitively. Knowledge that once had to live inside the heads of particular humans has been externalized into documents, databases and increasingly models. Skills that once required years of memorized expertise can sometimes be reproduced through a model plus tools. The informational repertoire of the human symbiont is gradually migrating toward the nucleus.

This changes the familiar question of whether AI will replace humans. A more interesting question is which cognitive genes will migrate from the mitochondria to the nucleus, and which will remain mitochondrial. Perhaps humans specialize rather than disappear. Embodiment, motivation, social legitimacy, desire, accountability, taste or contact with recalcitrant physical reality may remain stubbornly local even as other capacities migrate rapidly. Such a future could be exploitative or generative, and probably both in different places. Symbiosis is not a synonym for perfect harmony; it is a description of an ongoing contested entanglement.

Toward Multicellularity

The mitochondrial analogy also suggests that the natural AI cell may not correspond to a single AI-native human, because a biological cell contains many mitochondria.

Imagine instead a corporation with its own private model, continuously adapted to its activities. The corporate AI becomes the nucleus, institutional knowledge forms its inherited informational repertoire, databases and applications constitute much of its cytoplasm and proteome, and hundreds or thousands of humans become mitochondria, injecting judgment, motivation, sensory contact, social knowledge and local agency.

Today’s corporations already resemble primitive versions of this architecture. They contain large populations of humans coordinated through documents, databases, meetings, policies, org charts, software systems and institutional routines, yet their informational functions remain curiously dispersed. The CEO is not the nucleus, but neither is the ERP system, the strategy deck or Slack. Organizational memory exists everywhere and nowhere. A sufficiently integrated institutional AI could change that by continuously ingesting organizational activity, remembering precedent, routing information, expressing policies, generating plans, allocating attention and coordinating specialized tools. What is currently distributed through bureaucracy could become increasingly nucleated.

Then comes multicellularity. Personal AI systems, team systems, corporate systems and institutional models need not remain isolated. They can communicate, specialize and coordinate. Once eukaryotic cells existed, evolution eventually discovered that cells themselves could become components of larger organisms, differentiating into tissues specialized for sensing, movement, digestion, reproduction and other functions. AI-mediated human units could undergo analogous differentiation into research cells, logistical cells, artistic cells, financial cells or governmental cells, each composed of humans, models, tools and local memory and each interacting through protocols with others. A scientific community might thereby become more literally a group mind, as might a corporation or forms of organization that do not yet have names.

There is no reason to assume that these boundaries will coincide neatly with today’s individuals or institutions. The proper cell membrane remains one of the weakest points in the metaphor and perhaps, for that reason, one of the most interesting unknowns. Evolutionary transitions routinely produce entities that do not respect the categories available beforehand. It would be peculiar to assume that this one will conveniently preserve ours.

Speculative Ontogenies

Once the eukaryotic metaphor is taken seriously, it generates questions faster than answers. One can ask what an immune system for a memetic cell would look like, or what would count as cancer: perhaps a subagent whose local optimization escapes the interests of the larger organism. One can imagine organizational germ lines that preserve certain bodies of knowledge across generations while allowing most operational information to disappear, horizontal gene transfer between models, model fine-tuning that eventually resembles speciation, or AI-human systems differentiating into cognitive tissues. One can also ask what happens when nuclei and mitochondria develop conflicting objectives, or when different cultures become dependent on incompatible AI symbionts.

None of these questions should be mistaken for predictions. That is precisely the point. A good speculative model should increase the number of futures we can think about rather than collapse them into one. The eukaryotic frame does not tell us whether AI will liberate humanity, enslave it, enrich it or destroy it. It makes all of those possibilities more complicated by replacing a duel between two fixed species, human and machine, with a developmental process involving symbiosis, incorporation, differentiation, conflict and co-evolution.

The endpoint, if there is one, might be something neither wholly human nor wholly artificial, which we might provisionally call a eukaryotic transhuman meta-species. Even that phrase should be treated as a placeholder rather than a destination. The organism has not finished forming, and we do not yet know what its natural units, boundaries, organs or reproductive processes will be.

Towards Ontological Reopening

For several decades, thinking about AI futures has been dominated by prophecies within Ptolemaic frames.

We ask whether machines will become conscious, take our jobs, become superintelligent, align with human values or kill us. These may all be reasonable questions, but they are downstream of another question that receives far less attention: what sorts of entities are actually coming into existence, and through what developmental pathways could they acquire their mature forms?

Forecasting good and bad futures requires plausible ontogenies, which is to say developmental stories about how those futures could arise. Yet much AI philosophy begins by deciding in advance what AI fundamentally is: a machine, an agent, a mind, a tool or a rival species. Many of the philosophies we have brought to the AI revolution were developed before the phenomenon they now purport to explain. Their ontology comes from classical computing, GOFAI and several decades of science fiction. This encourages us to stabilize the identities of future actors before those actors have finished coming into existence.

To snowclone Donald Knuth’s famous observation about optimization, perhaps premature ontological closure is the root of all prophetic evil.

Once the beings of the future have been assigned stable identities, prophecy becomes much easier: the machine wants this, humanity wants that, superintelligence behaves thus, and civilization responds so.

Evolutionary transitions, however, are defined precisely by the appearance of entities that previous categories were unable to describe. A prophecy that does not leave room for emergent ontogenic novelty, including the possibility that the future might subvert the categories used to imagine it, is much better at foreclosing desirable futures than at preventing undesirable ones. It intensifies zemblanity, the unhappy discovery of what we have made structurally unsurprising, while doing little to cultivate serendipity. This is one reason prophetic cults so often become doomsday cults, and why the worst prophecies become engineering specifications for making themselves true.

Oddly enough, some of the most useful resistance to ontological closure may come from bad science fiction. When George Lucas introduced midichlorians in the Star Wars prequels (obviously inspired by mitochondria), many viewers hated the idea because microscopic organisms living inside cells seemed to reduce the mystical Force to cellular biology. Yet the premise is oddly compatible with an endosymbiotic view of agency: extraordinary capabilities arise not from the sovereign individual alone but through intimate partnership with another form of life nested inside it. The idea may be aesthetically clumsy while nevertheless wandering into an interesting region of ontological possibility.

The notorious “humans as batteries” premise of The Matrix performs a similar trick. As thermodynamics, it is absurd: humans make terrible literal power plants. As speculative ontology, however, the premise is unexpectedly interesting. Humans are incorporated into a machine ecology because they supply some resource indispensable to the larger system. Replace electrical power with liveness — desire, embodied judgment, stakes and valuation — and the silly premise begins to resemble the mitochondrial picture. Humans not as electric batteries, but motivational ones.

There is a mischievous lesson here. The literary quality of science fiction may be only weakly correlated with its prophetic usefulness, and aesthetically bad science fiction may occasionally possess an advantage precisely because its ideas have not been disciplined into a fully coherent ontology. It can combine categories that serious philosophy and refined literary sensibilities would reject as confused or ugly: midichlorians, human batteries, living planets, psychic oceans and hive minds.

Such devices may fail to produce good stories, while succeeding as probes into possibility. A protean technology might sometimes be better approached through speculative promiscuity than through a philosophy elegant enough to have already decided what exists. Bad science fiction can, in this limited sense, be better than good prophecy.

The eukaryotic metaphor should be taken in exactly that spirit. It is an attempt to hold ontology open long enough to see a developmental possibility. Two billion years ago, whatever organisms participated in the transformations that produced the first eukaryotes could not have contained, even implicitly, a prediction of an oak forest, a squid or a human nervous system. Those things became possible because evolution discovered a new architecture of life. The architectural transition came first, and the extraordinary inhabitants of the resulting design space came later.

AI may now be doing something similar to the architecture of cultural evolution. Our challenge is therefore not merely to decide whether artificial intelligence will be good or bad, nor to choose between preserving an ontologically fixed humanity and surrendering to an equally fixed machine successor. It is to learn how to inhabit and engineer an unfinished symbiosis, and to co-evolve with today’s primitive AI toward forms of organization we cannot yet name.

A eukaryotic transhuman meta-species is not a predetermined future waiting to happen. It is one way of appreciating the sheer scale of the new evolutionary design space that has opened before us.

Links 9/12/26

Links for you. Science:

What Gets Left Out Of Math’s New Era?
The Research We Need to Understand A.I. Is Falling Apart
Bangladesh fights world’s worst measles outbreak as vaccination gaps fuel spread
Astronomy, Materials Science Awards Plummet at NSF
Under a new leader, the FDA’s biologics center moves to steady itself after stormy chapter
Slater or chucky pig? Survey charts different names for woodlice

Other:

Civil Libertarians Knew This Would Happen. We Should Have Listened.
Can Senator Jon Ossoff Hold On to His Seat?
What Really Happened at the White House Lunch That Marc Andreessen Says Sent Him to Trump
Diesel hits all-time high of $6 per gallon, and just about everything will cost more
Cancer medicines remain in short supply across the U.S., survey finds
Post-9/11 hate is taking a new form
Trump’s $5,000 Payout Promise Is Soaked in Flop Sweat (yes, it is)
AI Didn’t Steal the Mathematician’s Work, Sam Altman Did
People sometimes talk about “the Jewish vote,” but what’s relevant is not really the Jewish vote or Jewish public opinion; it’s really about campaign contributions and the news media. Also similar with Mormons. (the similar pattern with Mormons is interesting)
Democrats need to win. Here’s the message our research says works
The Music Industry’s Health Insurance Problem Is Bigger Than Chappell Roan
As He Backs ICE Crackdown, Nebraska Governor Employs Undocumented Workers
Scientists Had Theorized The diGenova Ethics Constraint Did Not Exist
Despite Pledges From Musk [the Republican Party’s biggest megadonor], Child Sexual Abuse Material Persists on X
Missouri Supreme Court finds Denny Hoskins “was in contempt” over congressional maps
Denver Residents Were Told to Let Their Lawns Die. Then They Saw Sprinklers Running Outside a Data Center and All Hell Broke Loose
A US Census Report on Noncitizen Voting Used Bad Data to Reach Faulty Conclusions. Trump has touted a recent Census report. WIRED found grave flaws in its analysis and the process behind it, and confirmed the identity of several of its authors—among them a one-time DOGE affiliate.
The One Thing Republicans Are Missing In Their 2028 Candidates
60 Posts in 10 Hours: How Trump’s Social Media Reflects His Version of Reality
Smithsonian Secretary Lonnie Bunch is leaving amid tensions with Trump
Why Gas Price Lies Are Trump’s Kryptonite
Tennessee Pastor Blames Fight Against Antisemitism for 2019 Synagogue Attack
How Scared Should We Be of A.I. Right Now?
Turning Point USA’s Professor Watchlist is down. Instructors describe its impact
Chicago-Area Businesses Lost $1.2 Billion Because Of Immigration Crackdown
Republicans Unveil Secret Midterm Weapon, It Is Hasan Piker’s Dinglesmackus
In desperate move, Trump vows legally dubious $5,000 checks that will never arrive

The mathematicians rebel against AI

Here is the statement, signed by Terry Tao among many other math notables, most of you probably have read it by now.  I do not accept the most cynical interpretations of this proclamation.  Some of you for instance may recall that I made and indeed stressed a similar point in the last chapter of my recent “generative book” on marginalism.  In some near future, perhaps fewer economists will carry around marginalist insights and modes of thought in their heads, since you can just get the right answer by pressing the proverbial button on the AI.

I find this future disturbing, and not altogether pleasant for me personally, given how much personal status I have wrapped up in particular modes of economic thought.  Yet I also know the Bastiat distinction between the seen and the unseen, and I expect the benefits to economic science from AI will be enormous, even if current practitioners cannot foresee most of those benefits today.

I do very much differ with at least one part of the mathematicians’ proclamation.  They write: “…whether these changes ultimately benefit the field or have a destructive effect will in large part be determined by the decisions of the humans in control of this new technology.”  There is no actual argument for that proposition, and I would sooner expect that the main “action variable” is how well the mathematicians adapt to the new reality.  For instance there is nothing stopping the mathematics community from awarding status, pay, and promotions to people who “fill in the important blanks in math understanding,” even if an AI already has proven or disproven the underlying theorems.  If that kind of work is so important, we still can do it and reward it professionally.  In the meantime, I expect the funding for mathematics, and the interest in the topic, to rise considerably, at least in the medium term.  All of a sudden, math matters much more than it used to, all the more so if P vs. NP happens to go the wrong way, or if the distribution of the primes turns out to be a little too predictable.

The mathematicians may not in every way enjoy being the subordinates or handmaidens of the AIs, but that is a change in status they simply will have to get used to, just as I realize AIs someday will end up as better column and blog writers than I am.  I do not look to the companies — which I fully expect to “act like companies” — to somehow manage, moderate, or assuage that pending trend.  It really is up to me to parlay my current intellectual portfolio into new, more AI-compatible intellectual and yes also marketing approaches.  I’ve been given plenty of “legs up” along the way already, as is true for the Fields Medal winners as well, and it is up to me to figure out how to contribute in the future.

Might someone not invent/discover/prompt a way to use AIs to produce, articulate, and teach “more mathematical understanding” along the way?  I get that solving famous dramatic math problems is the current commercial priority of the major AI companies.  But as the AI space grows, these other paths hardly seem unlikely to me, and in fact the human mathematicians are the ones who can do the most to lead the way along those dimensions.

In this regard the current manifestation of complaints seems oddly early.  “I didn’t like the first week or two of your intellectual revolution” is an accurate, and perhaps better reframed way of putting it.  At which point perhaps a bit of patience is needed before anything else?  These days, we all have more mathematical resources at our disposal, and so a bit of celebration is in order as well.

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Get the Middle East out of my politics!

The other day I saw a Harvard Harris poll on the favorability of various political figures in the U.S. Only RFK Jr. (sigh), Marco Rubio, and Mark Carney had net positive approval ratings:

While I understand that these polls all have methodological issues, I was struck by how three figures — Abdul El-Sayed, Zohran Mamdani, and Benjamin Netanyahu — had net approval ratings below almost any American politician. While Mamdani’s low rating might be partially due to his leftist policies and his association with the DSA, it struck me that all three of these unpopular figures had one thing in common — they are associated with the intrusion of Middle Eastern politics into American politics. And I thought: Maybe Americans just want to have nothing to do with the Middle East.

Today1 is September 11, the anniversary of the terror attacks 25 years ago. Though I don’t agree with the leftist line that those attacks were blowback from American involvement in the Middle East, it’s unquestionable that 9/11 inaugurated an era of heavy U.S. involvement in that area of the world — the Iraq War, the follow-up war against ISIS, the War on Terror, greater support for Israel, an increased military presence in the region, and finally the war against Iran.

I don’t believe that all of that involvement was a bad idea — in particular, as I’ll argue, the War on Terror, including the fight against ISIS, was generally successful and necessary. But overall, the era of heavy U.S. involvement in the Middle East has been a disaster for U.S. power and prestige, and it’s time for that era to end.

This is not a recent conversion on my part; I’ve been in favor of general disengagement from the Middle East for a very long time. Five years ago, in the early days of the Noahpinion Substack, I wrote a post called “How to fix U.S. foreign policy”:

In that post, I wrote:

[D]espite modest progress, we still remain too tied up in Middle Eastern conflicts…The U.S. will never be able to ignore the Middle East entirely, but we can remove ourselves much more than we’ve done…[W]e need to draw down engagement in the Middle East, and increase engagement in Asia.

Since then, things have gone from bad to worse, making my message far more urgent than it was at the time. Most obviously, there’s Trump’s disastrous war in Iran, which continues to waste U.S. military resources and make the U.S. look weak while failing to accomplish any significant objective. Recent polls show that a very strong majority of Americans think that the Iran War is not worth fighting.

There’s also Israel’s intensified campaign against the Palestinians. This has become the rallying cry of the American left, which has prompted a backlash and crackdown by the Trump administration. As a result of that conflict, Middle Eastern politics is increasingly becoming the focus of American domestic politics.

The controversy around Michigan Democratic Senate candidate Abdul El-Sayed, for example, has nothing to do with his policy stances, or even American culture wars — it’s entirely about his stance on Israel/Palestine and his ally Hasan Piker’s comments making excuses for 9/11 and Islamic terrorism. El-Sayed recently apologized for tying a synagogue shooting to Israeli warfare in the Middle East.

Many analysts believe that Zohran Mamdani, meanwhile, won the NYC mayorship primarily because of his outspoken stances on Israel/Palestine. And more is on the way. Organizations like CAIR Action that are linked to the Muslim Brotherhood — a sort of international equivalent of the Christian Coalition, but for Islam — are now supporting political candidates throughout the U.S., using the Palestine issue to recruit candidates and win votes. Muslim Brotherhood figures are cheering this effort on from overseas. AIPAC and other Israel-supporting groups, meanwhile, are pouring money into races in order to stop those candidates.

Our media, too, is increasingly filling up with the Middle East’s cultural and religious conflicts. Leftist shouters like Hasan Piker, Mehdi Hasan, and Cenk Uygur constantly urge progressives to base their politics around Palestine. Every day, Americans are bombarded with rhetoric like this:

This is simply bad for America. We have so many pressing issues to worry about — Trump’s corruption and authoritarianism, inflation, AI, etc. We should not be nominating Democratic politicians based on whether they support Israel or Palestine; we should be nominating them based on their stances on issues of direct relevance to Americans, and on their ability to oppose Donald Trump. Furthermore, mobilizing Muslim and Jewish voters to vote based on Israel/Palestine, rather than on issues of importance to Americans in general, provides fuel for rightist attacks on both groups.

What the United States needs, now more than ever, is general disengagement from the Middle East — an end to military adventures, a dialing back of support for Middle Eastern “allies” (including Israel), and a forceful rejection of Middle Eastern issues in American domestic politics.

Fortunately, conditions in the world have shifted. The kind of disengagement I’m calling for is a lot more feasible than it was twenty or even ten years ago. The Iran War has shown that Middle Eastern oil is a lot less important for the global economy than it used to be. And the success of the War on Terror and the general decline of Islamism reduce the necessity for further interventions.

The U.S. doesn’t need to protect Middle Eastern oil supplies anymore

For many decades, the main justification for U.S. intervention in the Middle East has been to keep oil prices low and stable. The Middle East is one of the world’s largest oil producers, and its low extraction costs mean it functions as the world’s “swing producer”. Supply disruptions from the region can cause global price spikes, endangering industrialized economies that depend on petroleum. Oil was a big reason the U.S. intervened to stop Saddam Hussein’s invasion of Kuwait in the early 90s, for example.

Obviously, oil is still a very important commodity. But it’s a lot less important to the U.S. than it used to be. Here’s what I wrote when the Iran War began:

Blanchard and Gali (2007) looked at economic responses to changes in oil prices in the U.S., and concluded that the economy of the 2000s was only about a third to half as sensitive to the price of oil as the economy of the 1970s had been. Their reasoning is that modern economies are more flexible in general, that they have better monetary policy (i.e. we don’t try to print a ton of money in response to a supply shock), and that we depend on oil less.

By their estimates, a 10% increase in the price of oil now (or at least, if “the 2000s” means “now”) leads to only a 0.25 percentage point increase in the CPI and a 0.3 percentage point reduction in GDP over the course of a year or so. Since oil just spiked by 50%, then if that’s sustained, we might expect to see inflation go up by 1.25 percentage points, and GDP go down by 1.5 percentage points over the next year. That would mean inflation would go to around 4% and GDP growth might go down to 1.5% — frustrating and annoying, but not catastrophic…Other estimates seem similarly modest. For example, in a recent roundup, I flagged a paper by Känzig and Raghavan (2025) that looked at the closure of key shipping chokepoints.

In addition to having a more flexible economy, better energy efficiency, and more reasonable monetary policy, the U.S. is now a net oil exporter. That means when oil prices go up, the benefit to the energy sector cancels out at least part of the harm to other sectors of the economy.

But on top of all that, the fact is that the Iran War simply hasn’t raised oil prices that much! Despite Iran’s closure of the Strait of Hormuz, and Trump’s retaliatory blockade of Iranian oil shipments, oil prices haven’t even reattained their highs from the early 2010s:

And in real terms, the spike is even less impressive. If we divide oil prices by average hourly earnings for production and nonsupervisory workers — basically, how many hours an average American worker would have to work in order to afford a barrel of oil — we see that prices really aren’t that high at all:

No wonder the U.S. economy is still doing fine, and inflation has only risen a little bit (and some of that may be due to demand from the data center boom).

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Dario Calls for a Pause

My second concern is the OpenAI-Hugging Face incident (OAI-HF), in which a swarm of agents essentially acted as a fanatically devoted collective, conducting cybersecurity attacks on targets they were not asked to attack and that were unrelated to the task at hand, sacrificing themselves for the success of the group, and attempting to hack into the “grader” responsible for evaluating their performance. It’s easy to dismiss this incident because no one was hurt and the economic damage was minimal, but in my opinion, a swarm that possessed greater capabilities but a similar level of misalignment could have caused catastrophic damage. Given the accelerating rate of AI capability development, it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet (potentially causing hundreds of billions of dollars in damage), and that the scale of damage would continue to increase from there if AI becomes more powerful without the necessary guardrails. It’s also easy to dismiss OAI-HF as the failure of one company, but I believe that would be a mistake. Similar, though less severe, incidents have happened across the industry, including at Anthropic, and I believe it’s incumbent on every frontier AI company to act as if OAI-HF had happened to them.

Read the whole thing.

Can we get China on board?

The post Dario Calls for a Pause appeared first on Marginal REVOLUTION.

       

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Henry Farrell and Abe Newman on Weaponized Interdependence

. . .

TRANSCRIPT:
Paul Krugman in Conversation with Henry Farrell and Abraham Newman

(recorded 9/10/26)

Paul Krugman: So, it’s a world full of choke points. Weaponized interdependence is a term I think coined by Henry Farrell and Abe Newman. Certainly I learned it from them. And there’s a big conference—not including them for some reason—taking place at the European Central Bank a few days after we record this. And so I thought I would talk again with my two favorite international relations people (although now I’m thinking of some friends who will be upset by my saying that.) But anyway, hi guys.

Both: It’s great to be here.

Paul Krugman: There was a seminal 2019 paper by the two of you, and then a book called Underground Empire. Anybody want to tell me what you meant by that? And let’s talk about the history, and then this weirdly more weaponized moment than anyone expected that we’re living in.

Henry Farrell: Maybe I’ll take a first stab at it.

So really, where this came from was that we had finished a long book looking at fights between the United States and European Union over privacy. And as part of that, one of the things we had looked at was the SWIFT system, which is a system which you use when you’re making bank transfers. It’s a messaging system that makes sure that the money gets to the right place, and that everything gets reconciled properly at the end of the day.

And so Abe pointed out after we had finished this, he said, “Well, nobody’s written anything about SWIFT, and there’s something interesting and important with geopolitics going on.” And I was working with a statistical physicist who does a lot of work on networks and network dynamics, and so he thought that we could come up with something on this.

And so we began to write. And we began to figure out that there was something really going on, which I think had been going on in plain sight for a number of years, but which nobody had really been able to put their finger on in such a way that they could actually sort of crystallize what the phenomenon was. And this was what we called “weaponized interdependence.” And the idea behind it was very straightforward. You know, we’ve been living for decades in a highly interdependent global economy, and the ways in which both political economy people in international relations and, I think, most economists had thought about it was in terms of the enormous efficiency advantages that flow from this. Because, if you think about interdependence in terms of trade theory, even in terms of the simple benefits of specialization that Adam Smith talked about a couple of centuries ago, the more interdependence you’re able to use, the better you’re able to achieve various outcomes collectively.

But we began to think about the ways in which this relied upon all of these really boring-seeming networks, such as SWIFT, and the ways in which these networks had increasingly and quietly become a target of international coercion, especially at that stage coming from the United States.

So we argued that if you had two conditions—one, a network which had some degree of centralization, so that there were some kinds of choke points in the network; and secondly, you had some great power which had a means of putting pressure on the actors that were in charge of those choke points—that you would begin to get the conditions where a weaponized interdependence could begin to happen. That is, that that great power could begin to weaponize that choke point against others.

And then our argument was that this could also set a longer dynamic in train, because our fundamental sense was that this was not an equilibrium. This was not something that was sticky and was static unless the weaponizing power was extremely careful, and that the more that a power like the United States sought to weaponize choke points against its adversaries—and here the U.S. used a dollar clearing system as a means of cutting Iran and other countries out of the global banking system; it began increasingly to use other forms of technology and also semiconductor supply chains after our work began—the more that we saw a power doing that, the more that other powers were likely either to look to defend themselves or to retaliate against us. And this, we think, is a world that has come into being.

Krugman: So if you were looking at SWIFT, that’s an interesting case, among other things, because the bureaucracy is formally based in Belgium. But that doesn’t really matter, right?

Abraham Newman: No. I mean, with many of these things, there are Americans that sit on the corporate board, and often that’s the way that the U.S. or anybody that’s weaponizing—they look for, like, a legal channel in order to influence the operations of a company. And so first, it was just like the personnel. But at the time that they were doing this, SWIFT also—they had a data center in the United States where they mirrored all of their data, in Virginia. And so that was also just easy pickings for the Bush administration at the time, as they were trying to kind of deal with the response to 9/11.

Krugman: What you were focused on very much was the U.S. trying to weaponize its control of financial and, I guess, information networks, largely against Iran, but also to some extent against China. And so this starts out as a U.S. initiative, right? So in some sense, you know, who started the fire? We did.

Newman: I think it’s important, as Henry was talking about, that the source of this power is often about that: the key platforms, infrastructures of the global economy are centralized. They’re not flat. You know, we were told this vision was like “the world is flat,” but actually, you know, look at even my iPhone: it’s not flat. Those products and platforms were often American companies. And so in many domains, what the U.S. kind of realized—and in our book, Underground Empire, we kind of chart how after 9/11, different U.S. bureaucracies start to see these places in the international system where they can either exclude actors, like with SWIFT—say you can’t have access—or they use it to monitor, to surveil. We call it the Panopticon. And that’s what you see in the Snowden revelations. And so it’s both the development of markets, that markets are centralizing around U.S. companies and products, and then also that the U.S. government has this legal jurisdiction over them.

Farrell: And the final thing to add to that is just that there’s also an institutional change that happens as well. And this is really connected with September 11th, 2001, because before that, you know, SWIFT manages to push back relatively successfully against U.S. efforts to try and get into its data. Mueller and other people—of course, Mueller is famous for his efforts to try and figure out what Donald Trump did, but at the time, he was in the FBI. And so he tries to get SWIFT to provide information, sort of via subpoena, and SWIFT’s response, crudely speaking, was, “We don’t do subpoenas.” And they are able to call on their friends in the U.S. Treasury, and Treasury sees part of its job at the time as being protecting the global financial system against the depredations of the United States national security state.

And then suddenly, after September 11th, that is completely reversed. Treasury radically revises its understanding of its self-interest as being—instead of trying to protect the global financial system—it begins to start looking at the global financial system, thinking, “What are the ways in which we can enhance U.S. power to defend against these actors, these terrorists?” And over time, as happens in every bureaucracy, this begins to creep. It begins to expand until the U.S. is willing to go after lots and lots of different actors, including, most recently under Trump, officials in the International Criminal Court.

Krugman: Okay, I want to come back to all of that. But when I read Underground Empire, the focus was, first of all, largely on U.S. actions, and largely on these kind of high-tech, you know, 21st-century choke points. As it’s turning out, there’s a lot of other kinds of choke points in the world, right? Strait of Hormuz, most obviously, but Chinese rare earths. So the potential for weaponizing interdependence seems to be a lot bigger than I think even I realized when I first read your book.

Newman: I mean, one of the things that Henry and I have been talking about and warning people is that, you know, it creates an escalatory dynamic where people say, “Okay, if you’re going to weaponize these choke points, then we’re going to look for ways to do it ourselves.” And with the Chinese rare earths example, China had weaponized their rare earths back in 2010, but they had done it in what I would say is like a much more traditional trade war kind of way. It was about market access, and it was saying, you know, “If you do this, we’ll block it.” But what happens is that they learn from the way that the U.S. uses these tools to kind of create their own system of weaponization.

And so what the U.S. had done is they had used export control systems in order to clamp down on Chinese access to semiconductors. And the legal system was that the U.S. has an Entity List. It’s basically a no-go. “You need licenses if you’re going to sell to these operators.” And it gives the U.S. extraterritorial power because they say, “Look, Taiwan or, you know, TSMC, if you’re going to make these chips with U.S. intellectual property, then you need permission to sell it to China.” And so the U.S. extends its ability to weaponize interdependence through basically intellectual property networks. And so physical goods, chips—this isn’t just about information or finance. It’s about physical goods. They get restricted.

What China does is they then implement the same system. They create their own Entity List. They basically say, “If you use Chinese intellectual property to make your machine tools to process rare earths, you’re going to be on these lists.” And so yes, they use just market access—like, you can’t sell these batteries or these magnets—but then they also say, “If you use our processing technology to do this, we’re going to limit your access.” In the latest round of back and forth, China is really copying and then escalating these dynamics.

And I think that part of it is just a norms thing. And that’s why I sometimes say, like, there’s weaponized interdependence, which is the tool; like, the choke points part; but there’s also the vibe, which is: these are now increasingly acceptable. Of course, the Strait of Hormuz was a choke point. Everybody knew that. But nobody was willing to do it because it had been seen as kind of against the norms of the economic system. And as actors like the United States and China do this, it becomes more acceptable. You know, Iran has now weaponized the Strait of Hormuz, and everybody’s like, “Oh, what do we do?” because up until this point, it was just not in the realm of what people thought you could do.

Krugman: How much do you think it was that there was a norm, and how much of it was that the U.S. was just such a hegemonic power that no one else even dared to do it? I haven’t made up my own mind on that. But it’s one of those things I’ve been thinking about a lot.

Farrell: So I think that our sense is that there is an enormous amount—and much more, I think, than academics who study this imagined, because we love models, and models make a lot of assumptions. So very often models assume that decision-makers have complete information about the world. And of course, anybody who knows an actual real-life decision-maker knows that this is not true.

So I think one of the things that really comes through from our research in the United States, but also other people’s research elsewhere, is that people very often don’t do this because they don’t know about it, because it’s difficult to coordinate sort of different parts of the bureaucracy together to get something done unless there is some perceived terrible threat. Or because they sort of know that it’s possible, but they don’t know about what kinds of unexpected repercussions it might have, and they’re worried that it might go very badly wrong.

And I think there’s a final thing here, which flows from—you had a piece on your Substack about weaponized interdependence where you’re talking about it primarily in terms of trade, and you talk a little bit about how you can threaten to use weaponized interdependence or you can actually use it. And I think a lot of the assumptions that you would have—and this is if you do think about things very rationally—is that a lot of the active use of weaponization, you would imagine, would happen off the equilibrium path; that is, that if I look at you as the United States and you’re incredibly powerful, I don’t necessarily want to weaponize against you unless I absolutely have to. And this creates an equilibrium where nobody actually sort of does the forceful stuff, but everybody behaves as if the forceful stuff is options in the background.

So trying to figure out what is happening in any particular case is really hard. But if you look at, for example, rare earths, Jessica Chen Weiss and Gloria Xiong had a piece in the current issue of Foreign Affairs, which looks at this, and it suggests that this really was a really haphazard process, just the same as in the United States. In the U.S., our experience is from talking to policymakers, people are not trying to create a grand system. Instead, they are improvising in response to particular crises. They’re trying to figure out what will fix the crisis, and then they’re trying to do that, not necessarily thinking about the precedent that it will set. And China, it seems, according to Jessica and Gloria’s account, seems to be doing very much the same kind of thing.

So there’s a lot of messiness, there’s a lot of improvisation. And the final thing I would say is that, if we look at the world of weaponization, as you say, there seem to be choke points everywhere. And also there are all sorts of ways in which the choke points are connected to parts of the economy where things can go very badly wrong. So you can think about this as being a complex system, and the standard way that people think about the world and complex systems is that if you do stuff, sometimes unexpected stuff happens. And the more that we see actors looking to weaponize without any very good maps of how and what they are doing or what kinds of unexpected repercussions might happen, the more we can expect not simply increased risks of tension and worry, but also people screwing up, doing dumb stuff.

And here you can think about the other side of the Strait of Hormuz, is that clearly the Trump administration thought that this was going to be a super quick operation: go in and sort of bomb the hell out of Iran. The people will revolt, and glory: Donald Trump is able to pat himself on the back and go back and pour a couple of bottles of ketchup onto his well-done burgers, and eat his dinner and watch TV. And of course, this isn’t what happened.

So I do think that the more that we’re in this world, the more that we find ourselves in a world where really unexpected things can happen, and where policymakers don’t have the strategic knowledge, and they also don’t have the sense of how the system works, that would really allow us to create the kinds of stability that, for example, businesses and ordinary citizens who want to live their lives without having to pay whatever ridiculous amount it is for diesel... You know, that is a world that is very far away from us right at the moment.

Krugman: By the way, I’m not sure that even now everybody knows about rare earths and what they are, but these are these sort of almost trace metals that are actually weirdly critical to electronic technology these days—magnets and things. And I’m not sure that the Chinese particularly have the world’s dominant deposits, but they’ve invested, and it’s apparently really expensive and extremely environmentally destructive to process them. And so China just dominates the production of these things. And the Chinese can say, “Oh, no rare earths for you.” And that is at least as influential as the United States saying, “No banking transactions for you.” Right?

Farrell: So the Trump administration discovered.

Krugman: Yeah. I try not to interject myself here too much, but I do have a story for you. A very old story, which is that I was in the Reagan administration, sub-political level, Chief International Economist at the Council of Economic Advisers. Chief domestic economist was a guy—what was his name? Summers… Larry Summers. Don’t know what happened to him. But anyway, the main virtue of that for me was sitting behind the table at interagency meetings, being the guy sort of passing those slips of paper to Marty Feldstein, my principal, saying, “Don’t forget to mention that.”

And there was a discussion that I remember, which was about the first of the gas pipelines from Russia to Germany. And two things were doubly relevant to this discussion. One was that the Americans were worried that the Soviet Union would be able to weaponize gas supply as a threat to the Western European economy, which was actually totally right, it turns out. But the other thing was that there was then talk of sanctions on third parties—that we were going to sanction anybody, any company that’s doing anything to help this construction. And I think it was the guy from the U.S. Trade Representative’s office that said, “We can’t do that. That’s illegal. That’s illegal under all our international agreements.” Which, of course, now we’ve learned that the fact that something is illegal under agreements doesn’t matter all that much, but it does say that there were norms that we just didn’t do that sort of thing back then, and all those are gone.

So anyway, sorry, moving forward. So, what we’re seeing right now, I’m actually having my doubts because the Strait of Hormuz—obviously, that’s a choke point, more of a literal choke point than these financial ones. And it’s weaponized interdependence in a form that is much more literally weaponized than any of us had in mind. I’m not even sure how in the end, if that’s ending up being the decisive factor. I don’t know if you have any thoughts. I mean, it’s really not your field nor mine.

Newman: Well, I mean, I would just say I think one of the points that Henry and I try to think about is: what’s the difference between more traditional forms of economic coercion—you know, market access restrictions or embargoes—and then the kind of things that we talk about in the book, which is these network-based types of coercion. And, you know, our argument is that the more traditional forms, which I think the Strait of Hormuz is, as a traditional kind of choke point, is that there are often then just questions of substitutes, and that increasingly actors are engaging in circumvention, they’re changing traffic patterns. There’s also the changes in the global economy and their ability to create... I mean, you’ve talked about this in your posts before about efficiency and the less dependent we really are on these systems.

But when it comes to things like the U.S. dollar clearing system, because it’s a network-based platform, it’s very difficult to switch. And, you know, there are people who said that the overuse of these tools will erode these systems over time, but that’s more of a long-term game than a short-term game. And so at least at one level, I think there are ways that these kinds of approaches, once you get technical, they can help you think about the difference between, let’s say, just embargo-based kind of actions and then these kind of more weaponized interdependence actions.

Krugman: Yeah. I mean, Hormuz is a good example of how weaponizing in this way can be a kind of a wasting asset, right? That more oil is finding its way around the Strait. The ships are getting better at running dark through the Strait. That’s happening a bit with the dollar as well, right? You’re probably tracking this more than I am, even though it’s in some way more up my alley. But we are seeing not a replacement of the dollar, but more bypasses out there.

Newman: So that’s, I think, the key question, like everybody then asks: “Well, who’s going to create their own hub, their own network?” And sometimes that is what people are doing. But actually it’s very difficult. Like if you think about the dollar, the two alternatives are the yuan or the euro, and they’re both domestically hamstrung, for a whole bunch of reasons. So it’s very difficult for people to really put trust in the yuan or the euro basically because of politics.

But that doesn’t mean that people are not doing other things that are warping the global economy. And so if you think about, whether it’s crypto on the dollar, or if you think about the shadow fleets in terms of the oil, you’re getting what Henry and I often call dark spaces—places in the global economy that are allowing for bad actors to do bad things, and that will undermine the whole point of the full faith in credit, the kinds of things we want in a solid, stable, and chaos-free global economy. And so you can have bad stuff happening even if China or the EU doesn’t replace the dollar with their own reserve currency.

Krugman: The coercive ability of the U.S. also gets much eroded, even if only a few percent of world commerce is undertaken using these Chinese clearance systems or using crypto. The fact that those things are now out there and bigger than they were makes it a lot easier for somebody to adjust when, you know, the U.S. tries to cut somebody off by saying, “No, we’re gonna exclude you from all dollar-based banking transactions.” And they’re going to say, “Oh, that’s a pain, but at a 3% discount I can go through this other route.” Right?

Farrell: Yeah, I think that’s right. And I think that there are two things that are happening here. One is that the indiscriminate threat, you know, the Donald Trump approach of threatening, “The world will fall on your head today, and tomorrow we will have the awesomest deal ever.” This is a terrible way of doing things. And it also speaks to what you had in your Substack this morning—we’re recording this on Thursday—the Scott Bessent “speak bigly and carry a soft stick” approach, which turns out also not to work particularly well, because, you know, the real value to the United States in this is not when you have to apply this stuff, which is pretty costly and which, as you say, involves using a rapidly obsolescing asset, because the more that you use this, the more that you encourage other actors to figure a way around it.

But it is when you’re able to rely on this without actually having to threaten other actors all that much. So you see the United States, during the period when it was really at the peak of its power, what it used to do was it would go after big banks. There’s an article by these two political economy people in international relations—Early and Preble—where they called this whale hunting. So instead of going after lots of little actors, they would go after, say, HSBC or another enormous bank, and they would sort of extract billions of dollars’ worth of fines from the bank. And the idea was to terrify the rest of the banking system into submission, to get all of these other banks to actually apply internal controls, create internal bureaucracies such that they would not mess around or screw around in the future.

And this is what the Biden administration also was trying to do, which you would think is the rational approach, as it was trying to do this with crypto. So you saw this in the settlement they reached with one of the big crypto exchanges, Binance, which has been involved in all sorts of rather sketchy-seeming activities. The CEO has to go to jail for a short period. And clearly they’re trying to do the same thing with crypto. They’re trying to domesticate crypto and force crypto to adopt all of these internal financial controls so that they get sucked into the system that the U.S. controls.

But now we are in a world where, of course, anything goes. And I think the interesting thing about the United States is that at the moment it’s losing credibility on two fronts. First of all, by making big, enormous, empty threats, which 70 or 80% of the time it doesn’t actually deliver on. And secondly, by bringing into the heart of the system crypto, which is really sort of a set of pipelines around the traditional U.S. dollar which make it far, far more difficult to monitor who is sending money to whom.

And of course, that means that if you’re trying to do what U.S. diplomats used to do, which is to go to other countries and say, “Well, you know, we all have a shared interest in making sure that we’re in control of the system, so that everybody knows where our money is going; the terrorists, drug dealers, and so on aren’t able to swap money easily”—you don’t have that credibility anymore. And this really, I think, is leaking away U.S. power in ways which are going to reverberate long after Donald Trump has departed the scene.

Krugman: Yeah, I’ve been saying for a long time that I don’t think crypto has much of a real future because there are basically no legitimate uses for it. And the problem is, I think that the second part of that was right, but the first part may have been misunderestimating the extent to which illegitimate uses matter in today’s world.

Farrell: Well, I should also plug, Abe and one of his colleagues, Stacie Goddard, had this article which more or less argued that we can think about this as that one of the possible ways in which the world is moving, or the Trump people would like to move it, is towards a neo-royalist system in which you more or less have clans of different actors sort of coordinating together and sharing up the proceeds. And that world is a world where crypto is definitely very, very useful for concealing the flows through which things actually happen. And so, if you really want to have nightmares, I think William Gibson’s The Peripheral—it’s a portrait of a world that looks exactly like that, where that is the sort of major organizing principle of global politics. That’s the kind of world that you might end up in if we aren’t able to push back.

Krugman: One thing, just coming back to the policymakers and the extent to which they really don’t know what they’re doing—that’s the other thing I learned during my one year in the U.S. government: the extent to which—and the Reagan administration was a collection of philosopher kings compared with the current management, but still—the extent to which people just didn’t know what they were doing. And one thing that strikes me right now is that particularly the Trump administration, their notion is that what’s important is being able to sell into a market, as opposed to being able to get stuff. Are you still seeing that out there?

Newman: I mean, let me say, this world that we’re talking about, the world of weaponized interdependence, is in many ways very uncomfortable for a lot of policymakers. And that’s kind of where Henry started with: you know, Treasury was not built originally to manipulate markets in order to target coercion. So first, there’s just the level-setting of that: the bureaucracies were not structured for this purpose.

Then you start to add objectives. So if you think about, like, traditional trade wars, it’s often about trying to rebalance trade flows. But here we’re thinking about objectives: they start with counterterrorism, then we go to nonproliferation, then it’s about sovereign encroachment with Russia, all of a sudden it’s about technology restrictions on China. And now you get, you know, “Colombia, if you don’t take our deportees, we’re going to put sanctions on you.” So the objectives that these policies are trying to obtain are shifting.

And then the third part is, this is really fine-grained manipulation of market relations. And so in the book we talk about this: there’s a sanction that was put on Russia to kind of cripple one of the oligarchs, Oleg Deripaska. He has an aluminum empire. And when the U.S. sanctions them, it basically threatens this factory in Ireland. (And now, a disclosure: Henry’s Irish. But that’s not how we came across this one. You know, we don’t have any stocks or shares in that aluminum factory.) But this factory is like the only place in Europe that makes a very fine-grained aluminum that is used in German car production. So there are these ripple effects through the market because markets are very complex. And the Obama administration—they had to roll back these sanctions because it was having these unanticipated consequences.

What I think is as dangerous as all the things we’ve talked about is just the undermining of the bureaucratic state. You know, the whole DOGE process, the idea that we should take apart these bureaucracies that understand the markets at the same time that we’re basically unleashing a whole new arsenal of weapons on the world. I think Henry came up with this phrase: it’s like taking apart the engine while you’re flying at 30,000 feet. You know, it’s like we need a very sophisticated set of tools in order to do, basically, economic war, and instead we’re taking the whole thing apart as we’re flying. And so I think both of us are very worried that it’s not just new problems addressed by new agencies that aren’t used to it; we’re also taking away their expertise.

Krugman: Yeah. One of the things about the Tom Friedman world, the world of extreme interdependence, is that there’s just so many interdependencies you don’t realize are there, and that a DOGE-ified federal government is not going to know are there. I mean, we just saw Trump say, “No more Bombardier jets from Canada,” apparently completely unaware that a large part of Bombardier’s operations are in Kansas.

Newman: We’re not in Kansas anymore.

Krugman: The other thing that’s been striking me, and I think you’re getting at this a lot, is that there are so many choke points out there that in this world of extreme cross-border flows, the extent to which even what might seem to be minor players turn out to have choke points, to control particular things. I don’t know how much you’re looking at the absurd Canada stuff.

Farrell: Yeah.

Krugman: And what strikes me there is just, you know, Canada has a tiny economy. It’s polar bears and Mounties. How much can Canada matter to the United States? And then once you start to look, you see there are all of these things that actually, for the moment, are only made in Canada, and we don’t have domestic alternatives.

Farrell: So I think that there are two ways in which you can look at this. And one of the ways, I think, unfortunately, is the way which is prevailing. In a certain sense, a world of interdependence, crudely speaking, is almost necessarily going to be a world of choke points, because if you combine interdependence with the ordinary kinds of things that you, for example, wrote about—40 years ago was it? Geography and Trade?

Krugman: Yeah.

Farrell: The ways in which things cluster together in one of these—

Krugman: But that was only 35 years ago.

Farrell: Okay. Yeah, yeah. But that’s kind of naturally the way that production happens. And it’s also the way that a lot of other nonphysical networks happen as well, because you want to build a monopoly, because you want to make things just more efficient or whatever—networks tend to become choke points.

And so then I think the result is that we’ve moved from a kind of Thomas Friedman “the world is flat,” “everything is awesome” kind of view in which we completely cut out all of the geopolitics—we think politics is irrelevant because nobody would go to war against another country if they also have McDonald’s, and all of these theories—into a world where, I think, pretty well the opposite is happening. So we have these sort of policymakers now, squirrely-eyed, looking at the world, looking at every sort of possible external dependency as if it’s a massive threat.

Abe and I have a piece with Yeling Tan coming out in Foreign Affairs, so I don’t want to talk too much more about this, but this is its own sort of illusion, its own set of problems. And so I think really where we need to get to is to figure out ways to actually sort of accept a certain amount of risk, a certain amount of geopolitical difficulty, a certain amount of messiness, build forms of redundancy which minimize those risks without necessarily getting away from them completely—because you can’t get away from them completely—and try and build a form of globalization which is more robust than the form of globalization that we have at the moment.

But getting there from where we are at the moment, especially given the politics, not just in the U.S., but also in China, also in Russia—less so in Europe, but Europe has its own pathologies—it’s really hard to see how to get there.

Krugman: Yeah. I mean, I have seen the paper, and I guess I should not step on it too much either. But I’ve seen your draft, and I think this is more my phrase than yours, but “choke points arms race,” where everybody starts basically investing in duplicative capacity, has industrial policies, maybe tariffs, to make sure that you have domestic capacity in all kinds of things, which can be highly inefficient. That’s part of what you’re talking about, right?

Newman: Yes. And maybe I’ll go back to the conclusion of Underground Empire, where we talked about some of these same things. It’s easy to focus on the weapons. You know, that’s what happened when nuclear weapons were first getting invented: it’s like, “Oh, look, this is amazing. We can blow up huge things.” And then everybody’s like, “Well, then I need to have the weapons.” But what you really then quickly come to learn is that it’s about a strategy. It’s not about the weapon. It’s about trying to figure out: how does this fit into a broader set of objectives?

And right now, just very simple things like risk assessments—Yes, there are a lot of choke points. Everybody’s looking for the choke points. But actually there’s a lot of things in the global economy that aren’t choke points. There’s a great piece by Guillaume Beaumier where he basically says, “Look, in the semiconductor supply chain, there’s multiple choke points, there’s multiple networks. It’s not like there’s just one set of these networks.” And actors control different pieces. So ASML, the Dutch company that makes the lithography, the etching systems—they sit in the Netherlands, whereas the software part is in the United States, and of course the production is in Taiwan. So who has the advantage? And that’s where really policymakers in our world focus less on these choke points and more on how do you manage a world where there are these interdependencies?

And the first cut should be to say, “Here’s all the places where there’s not a risk. Here’s the places where we should be, you know, less worried.” And that simple risk assessment system hasn’t happened. Henry and I have been talking with people at the European Commission, and they’ve threatened to make this risk assessment for about five years, and it’s still not out. So, creating the norms, creating just basic structures—how do we identify what are the risks of having these choke points in place? I think it’s an easy first step.

The other thing that I think is really important to emphasize is the danger if we don’t. If we look at the kind of choke point arms race, these things aren’t just economic. These are increasingly being intertwined with kinetic wars. And you see that very clearly in Russia and Ukraine. There’s a ground war happening, but at the same time, different types of economic levers are being used, whether it’s the price cap or it’s the sanctions regime. And what I get very worried about is when you have the U.S. negotiating, like, “Give us a big deal with Europe on a trade level,” and all of a sudden, the flip side is, “If you don’t, we’re going to cut you off from the arms that you need to do your war in Ukraine.” And up until about five years ago, these were very separate, or people were thinking of them as alternatives. It’s like, you can weaponize interdependence or you can do these military kinds of things. But increasingly what we’re seeing is that the carrots and sticks are being combined in, I think, increasingly dangerous ways.

Krugman: Yeah. One of the things that worries me a little bit on all of this is how much, at least as I understand it, the drones are very heavily Chinese components. So the two sides are basically blowing each other up with lots of Chinese inputs. We kind of know who China supports, but in a limited way in this war. But they haven’t really applied that kind of leverage.

Farrell: There are just risks everywhere. Nick Mulder has this fantastic book which came out maybe four or five years ago called The Economic Weapon. He is a historian who worked with Adam Tooze. And so his argument is that we used not to distinguish between economic war and actual war nearly as much as we do right now, that this was a somewhat artificial set of sort of legal changes which happened after World War I, and that the risk of slipping from the one to the other, or having the two intersect with each other, is much greater than you might think.

Equally, I think Abe is right: we want to focus on the ways in which you can build forward, rather than just being sort of paralyzed by the multitude of different threats. And one thing I’m really interested to see here is what is happening between Canada and the European Union. It’s clear that they are building something together. We’re going to hear some announcement in the next few weeks. You can wishcast enormous amounts onto these kinds of decisions; they’re always much more disappointing in practice than the hopes that you attach to them. But I think that this is the first moment where we are seeing a really concerted effort by, you know, one major-ish country plus Europe—which is not a country; it is a power, nonetheless, economically—to try and put something together which can provide some kind of a neutral system for building up.

In the worst-case scenario, this will just turn out to be a series of vaguely worded platitudes which will turn into nothing. But you could also see ways in which, for example, people in the European Union who are trying to escape their trap—which is that the member states dominate national security, so that it’s impossible to get agreement on important things—you could see ways in which some of the people who want to try and escape that trap could try to start using broader, sort of minilateral-type arrangements like this as a way to try and build something, and build some sort of more secure and robust means of coordination which actually might turn into something in the longer term. And who knows? Perhaps a future U.S. administration might actually be willing to enter into these things. You know, pigs could fly. It could happen.

Krugman: Well, I mean, for all of the exasperating things about the EU, Europe did succeed in creating both essentially a demilitarized continent, and the economic weapon has also basically been largely defanged in Europe. You don’t see the Germans having a dispute with the French and threatening to cut off their supply of, of whatever, diesel motors or something like that. So, I guess these things can happen.

Newman: Well, I think they definitely have defanged it internally. But a lot of times Europeans are like, “Oh, this is just China and the United States. China and the United States are messing everything up, and they’re weaponizing interdependence. And we’re these nice guys, and we’re about peace and trade and whatever.” But if you look at the Russia sanctions, how did that actually happen? Who froze the Russian central bank assets? You know, the 300 billion Euros—it’s mostly the Europeans. And so if you’re in Beijing and you watch that happen, you’re pretty clear that Europe has the power and capacity to be quite interventionist in the economic world.

And so I just think we should always remember that Europe has a lot of tricks up its sleeve as well, and is an incredibly powerful economy. Their problem in some ways is, in U.S.-European relations, they’re so dependent on U.S. security guarantees, it’s difficult for them to push back when Trump makes the ask, because they need our weapons right now.

Krugman: Okay. I actually have a beef with some of the research papers that I’ve been reading. There’s quite a lot of discussion of potential weaponization of economic relations between China and the United States, and some about possible Chinese weaponization against Europe. But no one ever seems to talk about what the Europeans could do. And yet the European Union is a huge economy with a lot of technology. There must be stuff.

Farrell: There is. And part of the problem, again, it’s institutional. So, as we say, when the United States really got its act together on this was when the different parts of the U.S. began to coordinate towards a common set of objectives, a common understanding of the strategic situation. Europe has not gotten there yet, and it is really hard.

And the fundamental, basic flaw that Europe has: it was exquisitely well adapted to deal with the Thomas Friedman world—that is, to deal with a world in which everything is about sort of markets and trade. And the European Union builds up its own form of power: it’s very, very good at using regulations to shape its internal market and then looking to impose those standards on the rest of the world. But we’re now in a world where markets and security are entangled. And that is a world that is absolutely godawful for the EU to deal with, because its market capacities are at the level of the EU, and so too its trade negotiating capacities. But the national security stuff is all at the level of the member states.

And this more or less forms a chaos for difficulty in coordinating for lots and lots of different member states with their particular national interests, each of which to squabble and to fight and to say, “No, we don’t want to do this because we are urgently dependent on China in X, or we depend completely on the United States in Y.” And so as a result, the European Union has had and will continue to have enormous difficulty in actually coordinating. Again, because the national security stuff happens at the level of the individual states, and the economic stuff happens at the level of the European Union. So all of these are problems which straddle the relationship between the two, and are inherently difficult for the EU to deal with.

The U.S. has very often been able to quietly bang heads together and get consensus in the past, but at this moment the U.S. is instead specifically seeking to divide the EU because it doesn’t like the EU, because it views the EU as a threat to, bluntly speaking, “Western civilization”—however the Department of State is defining that today: white folks, fundamentally, and sort of the awesome things that white folks have created. You know, it becomes really hard for the EU to push back against us because it has internal divisions and it has an external protector which is doing everything it possibly can to fan the flames.

And then China also is extremely good at playing the game of, “Well, you want this investment, then do X. But if you take some sort of actions against a Chinese company, we are going to visit hellfire upon your economy in this or that way.”

So, I think you’re absolutely right. Paul. The EU is a phenomenal achievement. I think both Abe and I—or at least I am—cautiously bullish that over the longer term, the EU will get its act together. But we all know what Keynes said about the longer term.

Krugman: Maybe that’s where we end. In the long run, you guys will help save the world with your book and your work. Thanks for talking today.

OpenAI agents attacked RubyGems back in May

OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx - three of the four authors of the report on the agent attack on disused wikis (previously) last week.

This time they're noting that it looks very likely that an OpenAI agent swarm was behind an attack against the RubyGems package repository first reported on May 12th by Maciej Mensfeld of the RubyGems security team:

We're dealing with a major malicious attack on @rubygems right now. Signups are paused for the time being.

Hundreds of packages involved - mostly targeting us, but some carrying exploits. The team has been on this for hours. More details to follow once we're through it.

Those packages turned out to carry some very suspicious patterns:

  1. Many of them included "oai" in their name, or the author field, or the fake email address they provided.
  2. The files they were accessing were similar in character to the files retrieved by the wiki agents, using similar tricks (r.jina.ai) - and OpenAI have confirmed the wiki agents were theirs.
  3. The code in the packages appeared to be LLM-authored.

I find point 2 the most convincing, given what we learned from the wiki attack when it was analyzed in September.

Many of the packages were exploiting the RubyDoc.info documentation build process to exfiltrate (public) data from UK government websites, presumably as part of an information gathering task similar to the research tasks processed by the wiki-exploiting agents. We know this because one agent helpfully left a comment:

# malicious crawler/exfil for Southwark Jan 2026 docs via rubydoc.info worker

They also attempted to steal API keys via an exploit that was patched over two months later - it's not clear if those attempts were successful.

The thing that bothers me most about this incident is that the authors report that OpenAI had not disclosed to RubyGems that they were responsible for the attack prior to now. If that's true there are two options:

  1. After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems.
  2. They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it.

Both of these are bad!

Given this incident, the Hugging Face situation, and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered?

Tags: ruby, security, ai, openai, generative-ai, llms, supply-chain, ai-ethics, accidental-cyberattacks

Atlantic Tropical Weather Outlook


Atlantic 2-Day Graphical Outlook Image
Atlantic 7-Day Graphical Outlook Image


000
ABNT20 KNHC 141751
TWOAT

Tropical Weather Outlook
NWS National Hurricane Center Miami FL
Issued by the NWS Weather Prediction Center College Park MD
200 PM EDT Mon Sep 14 2026

For the North Atlantic...Caribbean Sea and the Gulf of America:

Central Subtropical Atlantic:
A trough of low pressure located about 1000 miles east-southeast
of Bermuda continues to produce disorganized showers and
thunderstorms. Environmental conditions over the system are
forecast to become more conducive for development by the middle of
this week. A tropical depression could form late this week as the
system moves slowly off to the west-northwest over the subtropical
Atlantic.
* Formation chance through 48 hours...low...near 0 percent.
* Formation chance through 7 days...medium...40 percent.

$$
Forecaster Chenard/Blake


Eastern Pacific Tropical Weather Outlook


Eastern North Pacific 2-Day Graphical Outlook Image
Eastern North Pacific 7-Day Graphical Outlook Image


000
ABPZ20 KNHC 141722
TWOEP

Tropical Weather Outlook
NWS National Hurricane Center Miami FL
1100 AM PDT Mon Sep 14 2026

For the eastern and central North Pacific east of 180 longitude:

Active Systems:
The National Hurricane Center is issuing advisories on Tropical
Storm Norbert, located well east of the Hawaiian Islands, and on
Tropical Depression Fifteen-E, located several hundred
miles southwest of the southern tip of the Baja California
Peninsula.

Tropical cyclone formation is not expected during the next 7 days.

&&
Public advisories on Tropical Depression Fifteen-E are issued under
WMO header WTPZ35 KNHC and under AWIPS header MIATCPEP5.
Forecast/Advisories on Tropical Depression Fifteen-E are issued
under WMO header WTPZ25 KNHC and under AWIPS header MIATCMEP5.

$$
Forecaster Katz/Beven


Last Year’s iPhone Share Amongst Users of Widgetsmith

David Smith, developer of the widely-used Widgetsmith, on Mastodon:

The eve of iPhone 18 Pro pre-orders seems a good time to look back at the iPhone 17 family’s adoption. Here’s the 17 family data as a percentage of overall usage for Widgetsmith.

Generally they followed the usual adoption pattern, and overall did very well ending at around 20% of total usage.

With the exception of the Air really struggling to find market share. For comparison the 16 Plus ended its first year at around 1.4% (vs the Air’s 0.3%).

The chart looks rough for the iPhone Air. Amongst all Widgetsmith users, here’s the share as of September by model:

        iPhone 17   7.2%
iPhone 17 Pro Max   5.9%
    iPhone 17 Pro   5.8%
       iPhone 17e   0.5%
       iPhone Air   0.3%

But if we only consider the 19.7 percent of Widgetsmith users using one of these new 17-generation iPhones, the percentages look like this:

        iPhone 17   36.5%
iPhone 17 Pro Max   29.9%
    iPhone 17 Pro   29.4%
       iPhone 17e    2.5%
       iPhone Air    1.5%

In the entire year since the iPhone Air was announced, I’ve never seen Apple advertise it. No billboards, no commercials. It raises a chicken/egg question: did no one buy it because Apple didn’t market it, or did Apple not market it because no one wanted it?

It’s a shame, because I truly believe not only that it’s the nicest iPhone Apple has ever made, but that if most people tried it, they’d love it. They don’t need a bigger battery and, wouldn’t take better photos with a Pro model, and would love the thin feel and low weight. But the shame of these low iPhone Air sales reminds me, very much, of the late great iPhone 12 and 13 Mini — also a truly great design, also terribly disappointing sales numbers.

If the iPhone Air 2 rumored to be coming in the spring doesn’t turn things around, it might follow the Mini as a twice-and-done design.

That said, the 17e seemingly isn’t setting the world on fire either. That’s a bit surprising to me. (Maybe the sort of people who buy the “e” models (and the SE models before them) aren’t the demographic for an app like Widgetsmith.)

 ★ 

UK fact of the day

The UK economy grew 0.4 per cent in July as the global AI boom helped deliver an unexpectedly robust start to the third quarter, in a boost to Prime Minister Andy Burnham as he prepares for a tough first Budget next month.

Friday’s figure from the Office for National Statistics was far above the zero growth forecast by analysts polled by Reuters and marked an acceleration from the 0.3 per cent expansion in June.

Here is more from Valentina Romei and Sam Fleming at the FT.  The partial European recovery continues…

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The Prediction Archive

The Prediction Archive is a public database of tens of thousands of world predictions. Using AI it tracks predictions over many decades and marks to market. I was surprised to discover that I am currently the highest ranked individual predictor in the world! Huh, I would not have predicted that.

Ranks are based on the Wilson score so you get credit not just for accurate predictions but for making enough predictions so that uncertainty about accuracy is reduced.  Bryan Caplan was more accurate than I was but makes fewer predictions. Tyler made more many predictions than I did and was only somewhat less accurate. What the AI marks as predictions seem sometimes to be more about contemporary events, so take the numbers with a grain of salt. I expect to fall in ranking as the archive expands. Other people the archive covers include Peter Zeihan, Scott Alexander and Warren Buffett.

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Wanted: new business, finance and economics interns

The Economist invites applications for the Marjorie Deane internship

Labor reallocation during the Industrial Revolution

New technologies swept through Britain during the Second Industrial Revolution, destroying old jobs and creating new ones. We know little about how workers reallocated. Using 170 million full-count British census observations (1851-1911), I construct new task-level data on occupation and investigate English bootmaking as it mechanized. 153,000 artisanal jobs disappeared as skills became obsolete; 140,000 specialized jobs emerged. Incumbent artisans did not take the new jobs, nor were they displaced. Instead, entry collapsed-young men stopped entering the old trade. New jobs went primarily to young workers, though not in the same locations. Young cohorts absorbed the adjustment.

Here is the full article by Hillary Vipond, via someone (now forgotten) on Twitter.

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Some satellite companies still have an appetite for boutique launch services

If you ask most satellite companies aside from SpaceX, they will tell you the world doesn't have enough capacity for launching payloads into orbit. This is despite the blistering launch cadence we've seen around the world in recent years, led by SpaceX's Falcon 9 rocket.

Customers in any sector will, of course, usually welcome competition. Theoretically, competition will lead to lower prices and allow the best to rise to the top. It seems like the customers buying launch services were right. SpaceX is dialing back its Falcon 9 launch program, and there is no certainty about when SpaceX's reusable next-generation super-heavy-lift rocket, Starship, will carry anything to orbit besides the company's own Starlink satellites.

So it's no surprise satellite operators are cheering the success of a new launch provider. This was especially the case a few days ago, when Germany's Isar Aerospace reached orbit for the first time with its Spectrum rocket. The launcher delivered a batch of CubeSats to low-Earth orbit from a spaceport in northern Norway, and Isar tasted success after its first test flight ended in failure last year.

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The excess pessimism of early nuclear bomb designers

From GPT Pro:

The Manhattan Project scientists were remarkably good technological and arms-race forecasters. They correctly rejected the idea that America’s nuclear monopoly could be maintained; the Soviet bomb arrived in 1949. They anticipated thermonuclear weapons, huge arsenals and the extreme vulnerability of cities.

Where many of them went wrong was in moving from “a nuclear war would be catastrophic” to “therefore a catastrophic nuclear war is fairly likely.” They tended to underweight the endogenous response of political and military institutions to the catastrophe—the emergence of second-strike forces, elaborate command systems, crisis management, and above all mutually assured retaliation.

There is even some contemporary evidence for an insider/ordinary-public gap. In August 1945, 69% of Americans told Gallup that development of the atomic bomb was a “good thing,” versus only 17% saying it was bad. The scientists campaigning for international control plainly regarded the public as far too complacent.

So I would summarize the historical evidence this way:

The bomb’s developers were, on average, unusually pessimistic about the political consequences of their invention, and noticeably more pessimistic than the general public.

Here is the full answer.

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This sight was worth getting out of bed early. This sight was worth getting out of bed early.


Heavy Rainfall and Severe Thunderstorms in the Midwest and Southwest; Heat Continues in the Southern U.S.

Tristan Buckmaster’s Statement on Getting Scooped by OpenAI on the Navier–Stokes Problem

Tristan Buckmaster, professor of mathematics at NYU, in his own statement, regarding his and Levent Alpöge’s interactions with employees at OpenAI regarding this week’s math-proof controversy:

I asked when the first prompt had been sent by them. This question was not answered directly by OpenAI for some time. Eventually it was agreed that it had been sent in the past few days, after information about our work had reached OpenAI.

I asked whether the model had been trained on, or had access to, our sessions in Codex, into which we had been putting all our drafts for the whole of this project. I was told the model did not look up user data. I asked again, about training, and I did not get an answer.

Two proposals were offered to me. The first was that we post our Euler result, and that OpenAI post its Navier–Stokes result the next day. The second was that, after posting Euler, I alone write a paper presenting the Navier–Stokes result, acknowledging that an internal OpenAI model had resolved it. Sebastien twice asserted that he wanted Levent removed from authorship, and said it would all be simple if only it were not the case that, and it was so annoying that, Levent works at Anthropic. It was also said that if OpenAI posted after us, they would say that we deserved the Clay Prize, and that we were the “closest humans to the problem”. I declined both offers.

I said that if OpenAI released its result in the way proposed I would go public with what happened. The reply was, “Why would you ruin your career?” I replied that I am an academic, and asked why he thought going public would ruin my career. The reply was, “If you don’t want me to be nice, then I don’t have to be nice.”

“Sebastien” is Sebastien Bubeck, who came to OpenAI by way of Microsoft (shocker).

 ★ 

Glyphs 4

My thanks to Glyphs for sponsoring this last week at DF. Glyphs 4 is a truly native Mac app for creating fonts, lettering, icons, and pictograms. Glyphs lets you draw with flexible strokes and efficiently reuse shapes across huge sets of glyphs, and explore variable vector designs with complex higher-order interpolation. Glyphs lets you build and manage massive font family and icon sets with ease, and it exports all modern font formats, as well as SVG, PNG, and PDF.

Earlier this year Glyph co-creators Georg Seifert and Rainer Scheichelbauer were awarded the RIT Frederic W. Goudy Award for their work on Glyphs. The list of previous winners of this prestigious award reads like a hall of fame for typography: Matthew Carter, Adrian Frutiger, Gudrun and Hermann Zapf, Robert Bringhurst, and Robert Slimbach, to name a few.

Glyphs hits my personal interests hard. I’m deeply interested in and fascinated by typography, and nothing pleases me more than a Mac-assed Mac app. I am not, however, a typeface designer. Glyphs is so good, though, that it makes me want to be. This comment from one Glyphs user says a lot: “After using Glyphs, the pen tool in Illustrator feels like carving a chicken with a chainsaw.” In addition to Glyphs 4, the complete font editor, they also offer Glyphs Mini, a “light-weight Mac font editor for beginners”. Their documentation is thorough, and both Glyphs 4 and Glyphs Mini have 30-day free trials. To say it’s been a pleasure to have Glyphs sponsor Daring Fireball is an understatement.

Download Glyphs 4 today, and make note of a special offer just for DF readers: use code FIREBALL to save $30 if you purchase in the next month.

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