Friday, October 2, 2026
Thursday, October 1, 2026
What's In a Name?
A ‘Morally Binding’ White House Accord on AI Safety
The leaders in AI were invited to the White House. We left with a White House agreement that is nonzero Actual Progress rather than a step backwards.
The key to success, in many situations, is to call the whole operation something else. [...]
Concretely, say we are on a bus called ‘the economy and AI progress and beating China’ or whatever that will blow up if we go below 50 miles an hour, but also we are currently hitting the gas so fast we are now going 100 miles an hour on pace for 200+. Both sides can get what they want at the same time. [...]
Artificial Intelligence
Trump is also claiming he is going to start talking about ‘Artificial News’ to describe the media, which is about the time I realized I kept typing the brackets. Shall we say.
So why would Trump go this hard on something like this?
The obvious explanation is this is Vintage Trump. He’s all about renaming things, and finding nicknames, and invoking vibes. He has a been a world class vibe invoker, nicknamer and term associator, especially in the 2016 election. If you think he chose the most annoying possible name due to namespace clashes, it’s probably because he was optimizing for that on some level. There is method to the madness.
You can also see it as a ‘bend the knee’ moment, the way various regimes and cultures often insist people affirm absurdist things. If you can get the makers of AI to start calling it [AI] instead, in a sense you own them. They have shown loyalty. And this is a way of weeding out those who won’t play along. Will Dario say AI or [AI]?
Money, Dear Boy
There is also an alternative explanation that is dumb and corrupt even for 2026, that has been put forward by Adam Cochran. As additional suggestive evidence, two of the three alternative options for the renaming that were in Trump’s initial poll, and both of the final two after he restarted it under false pretenses, started with S.
As counterpoints, the term ‘superintelligence’ was already being used as marketing by Meta and talked about by others, so these investments were smart anyway, and the timing would mean that this was a plan months in the making, which would not be Trump’s style, and would be in conflict with this looking strongly like a reaction to the HuggingFace incident and Pacing the Frontier.
Is it possible that this is part of the motivation for the renaming drive? Of course, yes, we absolutely live in a timeline roughly this dumb.
My strong presumption, however, is that primary causation runs the other way. Any insider trading is mostly a free action given plans that exist for other reasons, rather than a driving force causing the changes. I don’t like it, but I am not that mad about it.
The key to success, in many situations, is to call the whole operation something else. [...]
Concretely, say we are on a bus called ‘the economy and AI progress and beating China’ or whatever that will blow up if we go below 50 miles an hour, but also we are currently hitting the gas so fast we are now going 100 miles an hour on pace for 200+. Both sides can get what they want at the same time. [...]
Artificial Intelligence
The other supposed agreement was a distinct executive order to change the name of AI to [AI], which Trump claims the lab leaders signed off on. I’m sorry I have this tick, I try to type [artificial intelligence] and instead I end up with brackets.
Andrew Curran: President Trump ‘We’re going to be signing a document today at about five o’clock, renaming Artificial Intelligence, because it’s not artificial, we all agree on that, and we’re going to be renaming it [Artificial Intelligence]. Officially renaming it.’I am very curious if Trump plans to now get mad every time Dario or Altman or Musk says the words ‘artificial intelligence.’
So why would Trump go this hard on something like this?
The obvious explanation is this is Vintage Trump. He’s all about renaming things, and finding nicknames, and invoking vibes. He has a been a world class vibe invoker, nicknamer and term associator, especially in the 2016 election. If you think he chose the most annoying possible name due to namespace clashes, it’s probably because he was optimizing for that on some level. There is method to the madness.
You can also see it as a ‘bend the knee’ moment, the way various regimes and cultures often insist people affirm absurdist things. If you can get the makers of AI to start calling it [AI] instead, in a sense you own them. They have shown loyalty. And this is a way of weeding out those who won’t play along. Will Dario say AI or [AI]?
Money, Dear Boy
There is also an alternative explanation that is dumb and corrupt even for 2026, that has been put forward by Adam Cochran. As additional suggestive evidence, two of the three alternative options for the renaming that were in Trump’s initial poll, and both of the final two after he restarted it under false pretenses, started with S.
As counterpoints, the term ‘superintelligence’ was already being used as marketing by Meta and talked about by others, so these investments were smart anyway, and the timing would mean that this was a plan months in the making, which would not be Trump’s style, and would be in conflict with this looking strongly like a reaction to the HuggingFace incident and Pacing the Frontier.
Is it possible that this is part of the motivation for the renaming drive? Of course, yes, we absolutely live in a timeline roughly this dumb.
My strong presumption, however, is that primary causation runs the other way. Any insider trading is mostly a free action given plans that exist for other reasons, rather than a driving force causing the changes. I don’t like it, but I am not that mad about it.
Adam Cochran (adamscochran.eth): SCOOP: Trump’s “Super Intelligence” Scandal:
I believe Trump’s “SI” Executive Order was ANOTHER criminal plot to enrich the Trump family. Insiders seem to have profited MILLIONS off of .si domain names before his Truth Social posts.
It’s no surprise that .AI domain names are a hot commodity and almost all taken by squatters. But what wasn’t?
.si domain names. .si is the domain extension of Slovenia (coincidentally where Melania and Barron Trump are both citizens of)
On September 19th Trump made a random Truth Social post about renaming “Artificial Intelligence” to “Super Intelligence” without any clear reason.
This set off a flurry of people buying and registering .si names related to AI. But it wasn’t the first time… The .si registry has around 55/day registrations in 2023-2025, but in 2026 the numbers started to pick up. On June 24th they saw more than 400 in a single day.
This volume spiked with more than ***7000*** new domains registered in July. And a sudden flurry of buying .si domains in the aftermarket. AI related .si names started going for tens of thousands of dollars.
… Since Trump’s announcement .si domain have seen *MILLIONS* of dollars in turn over. Much of it going to domains that were registered in the last 60 days before the announcement.
TL;DR:
-Trump made the SI executive order to try and force companies to brand as “SI” instead of “AI”
-In the past 60 days insiders bought thousands of .si domain names
-They’ve profited hundreds of millions of dollars so far.
by Zvi Mowshowitz, DWAV | Read more:
Image: Seating chart, uncredited
Firefox ReDesign
Interview: Firefox’s chief on why he hopes a redesign will help win users from Chrome.
Today, the Firefox 157 update will roll out a redesign of the web browser across desktop and mobile platforms. The team that made it hopes it will help expand the browser’s audience beyond privacy-conscious techies and open-web or open source advocates to a broader audience who might simply pick the browser because they prefer its user experience over competitors like Chrome, Edge, and Safari.
In advance of the redesign’s launch, I spent half an hour chatting with Mozilla’s head of Firefox, Ajit Varma, about Firefox’s current market position and product strategy, and what barriers or opportunities there are for gaining ground in a Chromium-dominated landscape.
Firefox’s interface has recently felt more conservative than niche browsers. And when I asked Paddy Harrington, a senior analyst at Forrester who covers this space, what Firefox’s main barrier to adoption is, he was frank.
“The biggest is they’re not Chrome,” he replied. “That sounds simplistic, but it’s the clear truth. Safari and Edge are built into the leading operating systems in business and consumer markets, yet people still download and deploy Chrome.”
That said, for many of the people who have chosen to use Firefox, “it’s not Chrome” is much of the appeal. Google-led Chromium dominates the web. It doesn’t just power Google’s own Chrome browser (which has majority market share by a wide margin), it powers most of the rest of the competition, too, including Microsoft Edge.
Firefox, which is built on the open source Gecko, serves as a Chromium-free alternative and has become one of the go-to choices for users who don’t want to contribute to one company’s dominance of the open web—though there is even tension there, and a deal to offer Google search as Firefox’s default provides Mozilla with the majority of its revenue. For now, Firefox seeks independence for the web while remaining financially dependent on its dominant competitor.
But to expand beyond the relatively small market share it now has, Firefox has to inspire users to actively select it over incumbents by providing a better browsing experience; most people don’t care whether Chromium dominates, and most have never heard of Gecko.
In our conversation, Varma expressed hope and ambition that these modernizations will help more users choose Firefox for its merits as a product. We also discussed the Firefox team’s competing priorities, its development resources, AI features and tooling, the general browser market, and more.
A conversation with Ajit Varma
This interview has been edited for length and clarity.
Ars Technica: It’s nice to see a bit of modernization of the design of Firefox. But what problems does this redesign solve? How does it advance browser choice and the open web beyond just being a browser that’s a little more appealing and a little easier to use?
Ajit Varma: Yeah, I think there’s been a lot of questions around, “What are we doing this at the cost of?” Like should we be focused on performance? Should we be focused on compatibility?
We are trying to do all of the above and work faster, and I think that’s one of the challenges that Firefox had in the past, was there was slow decision-making. There was a lot of debate, and in the last like year and a half, we’ve actually been trying to say, can we get a lot more velocity, and compete? And part of this is made possible by AI tools, to be honest with you. We are able to do a lot more.
People want to feel like they’re in a modern browser—things that match the design language of the operating system. So that’s part of it. We are bringing back compact mode as well… and we also have launched more customization options.
There’s a lot of functionality that’s been built to give all the modern productivity things that people wanted, and those are all very utilitarian, but it’s also the emotional connection, and do people feel like it’s a browser that feels modern as well.
In advance of the redesign’s launch, I spent half an hour chatting with Mozilla’s head of Firefox, Ajit Varma, about Firefox’s current market position and product strategy, and what barriers or opportunities there are for gaining ground in a Chromium-dominated landscape.
“The biggest is they’re not Chrome,” he replied. “That sounds simplistic, but it’s the clear truth. Safari and Edge are built into the leading operating systems in business and consumer markets, yet people still download and deploy Chrome.”
That said, for many of the people who have chosen to use Firefox, “it’s not Chrome” is much of the appeal. Google-led Chromium dominates the web. It doesn’t just power Google’s own Chrome browser (which has majority market share by a wide margin), it powers most of the rest of the competition, too, including Microsoft Edge.
Firefox, which is built on the open source Gecko, serves as a Chromium-free alternative and has become one of the go-to choices for users who don’t want to contribute to one company’s dominance of the open web—though there is even tension there, and a deal to offer Google search as Firefox’s default provides Mozilla with the majority of its revenue. For now, Firefox seeks independence for the web while remaining financially dependent on its dominant competitor.
But to expand beyond the relatively small market share it now has, Firefox has to inspire users to actively select it over incumbents by providing a better browsing experience; most people don’t care whether Chromium dominates, and most have never heard of Gecko.
In our conversation, Varma expressed hope and ambition that these modernizations will help more users choose Firefox for its merits as a product. We also discussed the Firefox team’s competing priorities, its development resources, AI features and tooling, the general browser market, and more.
A conversation with Ajit Varma
This interview has been edited for length and clarity.
Ars Technica: It’s nice to see a bit of modernization of the design of Firefox. But what problems does this redesign solve? How does it advance browser choice and the open web beyond just being a browser that’s a little more appealing and a little easier to use?
Ajit Varma: Yeah, I think there’s been a lot of questions around, “What are we doing this at the cost of?” Like should we be focused on performance? Should we be focused on compatibility?
We are trying to do all of the above and work faster, and I think that’s one of the challenges that Firefox had in the past, was there was slow decision-making. There was a lot of debate, and in the last like year and a half, we’ve actually been trying to say, can we get a lot more velocity, and compete? And part of this is made possible by AI tools, to be honest with you. We are able to do a lot more.
People want to feel like they’re in a modern browser—things that match the design language of the operating system. So that’s part of it. We are bringing back compact mode as well… and we also have launched more customization options.
There’s a lot of functionality that’s been built to give all the modern productivity things that people wanted, and those are all very utilitarian, but it’s also the emotional connection, and do people feel like it’s a browser that feels modern as well.
by Samuel Axon, Ars Technica | Read more:
Image: Mozilla
[ed. I'd use Firefox exclusively if it didn't conflict in small ways with a couple programs I use (like this one, Blogger).]
Meta's New AI Muse is About to Make the Internet More Annoying
Someday we'll redesign the internet for tools like this. Until then, you're in for a wild ride.
Last week, my finger hovered over the download button for Meta's new AI Muse. I felt a little bit scared of my phone.
Muse wants to help. It's an AI agent, a tool that goes out into the world for unsupervised tasks. Using its own web browser, Muse can cancel your gym membership, haggle with customer service people, buy groceries, invite friends to parties, you name it. Soon it will make phone calls.
It's the first free, full-featured agent from a big company. Millions downloaded Muse in its opening weeks, and OpenAI just announced its own new agent. More are coming. It's the dawn of a new chapter for the web. What will it be like?
According to experts I interviewed, the internet is about to get more frustrating. These agents may cause a cascading chain of problems that shake the foundations of digital infrastructure. Chances are good that you're about to live through an era of online chaos.
The internet was built for humans with limited time and patience. AIs don't have those restraints. You'll probably have to fight with robots over concert tickets and booking appointments. Small businesses could drown in machine-made pestering. AIs don't look at ads, and websites that rely on them are already crumbling. You'll be buried in endless loops of proving you're human. And if you use these AI tools, some may betray you.
Eventually, we'll rebuild the internet for this new reality. Perhaps that world will be better. Until then, things may get ugly.
"Imagine there are no rules of the road. And you release billions of cars – the AI agents – and you just let them drive through playgrounds, hitting kids. That's where we are," says Ramesh Raskar, an associate professor at the Massachusetts Institute of Technology (MIT) in the US who studies AI agents. "It's a pivotal moment."
To access the road, Muse arguably asks for more trust and sensitive information than any product in the history of Mark Zuckerberg's empire. When you open it, the cutesy avatar asks for a name. Then it asks for total control of your Gmail, calendar and entire computer if you're on a Mac.
There, staring at the download button, I felt a moment of panic. People begged me to say no. "I wouldn't use it," says Patrick Wardle, co-founder of Objective-See, a US nonprofit security foundation that uncovered serious flaws in the Muse app. "Personally, I would tell you to uninstall it altogether."
Despite the warning, I need to see what Muse users are up against. So I named my AI "Bob", gave it the keys to my life, and stepped into the future. [...]
Will the robots betray you?
There are also personal risks. "If you're empowering an agent to make things like purchasing decisions, or choices about taste and preferences, you're opening yourself up to being exploited," says Shroeder.
If Muse plans your holiday trip, Shroeder says there's no way to know if it got you the best deal, or if it chose flights and hotels that benefit Meta's business partners. If you ask for music recommendations, will they be based on your taste, or will you hear about artists who inked deals with the social media company?
Meta's spokesperson says Muse's built-in protections and user controls put people "absolutely in charge" of it, and Muse "behaves like a personal assistant acting for a single person" that follows ethical guidelines to protect users.
However, Shroeder says she's examined Muse's terms of service, and there's no guarantee the app will act in your favour. {...]
by Thomas Germain, BBC | Read more:
Last week, my finger hovered over the download button for Meta's new AI Muse. I felt a little bit scared of my phone.
Muse wants to help. It's an AI agent, a tool that goes out into the world for unsupervised tasks. Using its own web browser, Muse can cancel your gym membership, haggle with customer service people, buy groceries, invite friends to parties, you name it. Soon it will make phone calls.
According to experts I interviewed, the internet is about to get more frustrating. These agents may cause a cascading chain of problems that shake the foundations of digital infrastructure. Chances are good that you're about to live through an era of online chaos.
The internet was built for humans with limited time and patience. AIs don't have those restraints. You'll probably have to fight with robots over concert tickets and booking appointments. Small businesses could drown in machine-made pestering. AIs don't look at ads, and websites that rely on them are already crumbling. You'll be buried in endless loops of proving you're human. And if you use these AI tools, some may betray you.
Eventually, we'll rebuild the internet for this new reality. Perhaps that world will be better. Until then, things may get ugly.
"Imagine there are no rules of the road. And you release billions of cars – the AI agents – and you just let them drive through playgrounds, hitting kids. That's where we are," says Ramesh Raskar, an associate professor at the Massachusetts Institute of Technology (MIT) in the US who studies AI agents. "It's a pivotal moment."
To access the road, Muse arguably asks for more trust and sensitive information than any product in the history of Mark Zuckerberg's empire. When you open it, the cutesy avatar asks for a name. Then it asks for total control of your Gmail, calendar and entire computer if you're on a Mac.
There, staring at the download button, I felt a moment of panic. People begged me to say no. "I wouldn't use it," says Patrick Wardle, co-founder of Objective-See, a US nonprofit security foundation that uncovered serious flaws in the Muse app. "Personally, I would tell you to uninstall it altogether."
Despite the warning, I need to see what Muse users are up against. So I named my AI "Bob", gave it the keys to my life, and stepped into the future. [...]
Things fall apart
Muse is useful. Over the course of a week, I had it reach out to a seller on Facebook Marketplace with questions. The AI ordered the dental floss I like. It negotiated a $31 (£23) discount on a software subscription.
Now picture millions or billions of agents doing this all simultaneously, performing multiple tasks that might take weeks for a human to get to.
"What happens if bots send 600 inquiries to a website a day, when normal humans might only send two?" says Shroeder. "Businesses and individuals are going to have a lot to deal with."
AI is already decimating online business. Google and chatbots now answer questions directly. As a result, people are visiting fewer websites. Robots don't click on ads or buy subscriptions, and it's causing an extinction event for companies across the web. Agents will supercharge this problem.
Meanwhile, one analysis found web traffic from bots spiked 124% in the year to June 2026. Some websites and services are crumbling because they aren't built for that digital load.
Meta's spokesperson says Muse works to complete your tasks while respecting the interests of websites.
Muse is useful. Over the course of a week, I had it reach out to a seller on Facebook Marketplace with questions. The AI ordered the dental floss I like. It negotiated a $31 (£23) discount on a software subscription.
Now picture millions or billions of agents doing this all simultaneously, performing multiple tasks that might take weeks for a human to get to.
"What happens if bots send 600 inquiries to a website a day, when normal humans might only send two?" says Shroeder. "Businesses and individuals are going to have a lot to deal with."
AI is already decimating online business. Google and chatbots now answer questions directly. As a result, people are visiting fewer websites. Robots don't click on ads or buy subscriptions, and it's causing an extinction event for companies across the web. Agents will supercharge this problem.
Meanwhile, one analysis found web traffic from bots spiked 124% in the year to June 2026. Some websites and services are crumbling because they aren't built for that digital load.
Meta's spokesperson says Muse works to complete your tasks while respecting the interests of websites.
Will the robots betray you?
There are also personal risks. "If you're empowering an agent to make things like purchasing decisions, or choices about taste and preferences, you're opening yourself up to being exploited," says Shroeder.
If Muse plans your holiday trip, Shroeder says there's no way to know if it got you the best deal, or if it chose flights and hotels that benefit Meta's business partners. If you ask for music recommendations, will they be based on your taste, or will you hear about artists who inked deals with the social media company?
Meta's spokesperson says Muse's built-in protections and user controls put people "absolutely in charge" of it, and Muse "behaves like a personal assistant acting for a single person" that follows ethical guidelines to protect users.
However, Shroeder says she's examined Muse's terms of service, and there's no guarantee the app will act in your favour. {...]
Hacks and love letters
Stop for a moment and consider your email inbox.
I've had the same Gmail address for 21 years. It's my login for hundreds of accounts. It holds medical test results, contracts and legal documents, conversations with family and endless receipts and financial information.
My inbox even has love letters an ex-girlfriend sent, from an era when email (briefly) felt suitable for romance. (I got her permission before handing it to Muse.)
More than any other digital service, my email is a window into my brain. Now Meta has it. If that's not enough, Muse also suggested I give it my bank accounts. No thanks. I didn't let the AI run wild on my computer hard drive, either.
Meta makes some explicit promises about privacy. The company says it won't connect any of the data Muse collects to its advertising systems. Special systems are supposed to prevent the AI from seeing passwords or payment methods. And when you plug it into Gmail, the AI asks if you want it to scan the whole inbox, or just read messages related to specific tasks.
However, Meta uses your Muse data to train new AI models by default. Shroeder says it's impossible to remove data built into an AI model, and AIs can sometimes be tricked to reveal their training data. Meta says your data is "sanitised" to remove personally identifiable information before it's used for training, and you can opt-out of AI training with a setting.
But given Meta's history of privacy problems, should you trust what it's telling you now?
Stop for a moment and consider your email inbox.
I've had the same Gmail address for 21 years. It's my login for hundreds of accounts. It holds medical test results, contracts and legal documents, conversations with family and endless receipts and financial information.
My inbox even has love letters an ex-girlfriend sent, from an era when email (briefly) felt suitable for romance. (I got her permission before handing it to Muse.)
More than any other digital service, my email is a window into my brain. Now Meta has it. If that's not enough, Muse also suggested I give it my bank accounts. No thanks. I didn't let the AI run wild on my computer hard drive, either.
Meta makes some explicit promises about privacy. The company says it won't connect any of the data Muse collects to its advertising systems. Special systems are supposed to prevent the AI from seeing passwords or payment methods. And when you plug it into Gmail, the AI asks if you want it to scan the whole inbox, or just read messages related to specific tasks.
However, Meta uses your Muse data to train new AI models by default. Shroeder says it's impossible to remove data built into an AI model, and AIs can sometimes be tricked to reveal their training data. Meta says your data is "sanitised" to remove personally identifiable information before it's used for training, and you can opt-out of AI training with a setting.
But given Meta's history of privacy problems, should you trust what it's telling you now?
by Thomas Germain, BBC | Read more:
Image: BBC/Serenity Strull/Getty Images
[ed. I wouldn't trust Zuckerberg with a box of matches. One thing is clear though: it'll definitely be a more dog eat dog world (if we survive) once AI becomes more widely adopted by the public. Think Ebay auctions and armies of bots placing bids up to the last micro-second - for everything.]
[ed. I wouldn't trust Zuckerberg with a box of matches. One thing is clear though: it'll definitely be a more dog eat dog world (if we survive) once AI becomes more widely adopted by the public. Think Ebay auctions and armies of bots placing bids up to the last micro-second - for everything.]
"Sardinemaxxing"
***
"... today, tinned fish is as much a sensible grocery item as it is a status symbol, a viral “hot girl food,” a borderline luxury good. I think it would probably take a long time to explain... why you can buy limited-edition tinned fish at concerts and why there is coveted merch for sale about tinned fish and why a store exists in New York City called the Fantastic World of the Portuguese Sardine where a single-serving tin of said fantastic sardine can cost $44. [...]Because $44 feels like a small price to pay for a sociological education, I spent some weeks recently sampling as much of this genre as I could. I ate not just sardines but also smoked salmon marinated in chili oil, small-batch mussels in sweet pepper and garlic, various types of trout, anchovies and an enormous amount of t*** — so much t*** that I refuse to look at the word anymore and you cannot make me spell it out — and what I learned is that most of it tastes kind of just … OK?
... But as I “made dinner” after work every night, i.e. nibbled t*** straight out of the package like a raccoon (tinned-fish cookbooks do exist, but if you are someone who enjoys defaulting to the lowest common denominator of effort available, you, too, might be a secret raccoon), a different idea occurred to me.
by Amy X. Wang, NY Times | Read more:
Image: Hannah Whitaker for The New York Times
Wednesday, September 30, 2026
Tuesday, September 29, 2026
In Praise of Obsessive Men
One of my older brothers and I were very similar growing up. We were both good at school, both wore glasses, had similar colouring—unlike my other brother who was different. Yet there was one noticeable difference between my brother and me, which reflects a more general difference between the sexes. He was intensely and obsessively focused on whatever interested him at the time—to the exclusion of everything else, and I really mean everything else. I just wasn’t going to do this. I wanted to be “normal” and have a social life.
This sex difference is widely observed. Simon Baron Cohen points out in his book The Essential Difference that obsessive behaviour is common among autistic children, who are disproportionately male.
In the movie Rain Man, the autistic brother (played by Dustin Hoffman) is obsessed with statistics on airplane crashes. He knows crash statistics for all the airlines and is therefore only willing to fly on one, Qantas. A student once told me about the autistic man her church employed to police the church parking lot. He had memorized all the license plates of the cars that were allowed in the lot, and thus knew instantly when a car was parked there without permission, and even when a car was parked in the wrong space.
There is some evidence that this kind of intense, obsessive focus leads to extremely high-level achievement. Since males are more likely to display such focus, the high achievers tend to be male. One of my favorite examples relates to competitive Scrabble. Many women play Scrabble and are often better than their male counterparts. Yet the very highest levels of competitive Scrabble are dominated by men.
For example, the greatest competitive Scrabble player in history is Nigel Richards from New Zealand. He was World Champion in 2007, 2011, 2013, 2018 and 2019. He is a 15-time winner of the King’s Cup in Bangkok, the world’s biggest Scrabble competition. He also won the French World Scrabble Championship in 2015 and 2018, and the Spanish-language World Championship in 2024—despite not speaking those languages fluently beforehand.
How did he do this? Remarkably, he memorized whole dictionaries (not most people’s idea of a fun activity). But it illustrates the extraordinary intensity and obsessiveness that can produce exceptional achievement.
There is evidence that intense, obsessive focus produces extraordinary achievement in a variety of areas, not just Scrabble. There are the guitarists who spend hours playing guitar, the soccer players who are always at the training ground, the painters who spend all their time painting, the gamers who are never away from their console, and so on.
There is also anecdotal evidence that extraordinary scientific achievements come from people who are intensely and obsessively focused on what they do. In the divorce announcement in a local paper, the ex-wife of legendary physicist Richard Feynman complained that “her husband worked calculus problems all day, as soon as he arose, while he drove his car, while sitting in the living room, and while lying in bed at night.”
And the movie Oppenheimer popularized the story of when the physicist J. Robert Oppenheimer was asked to deliver a physics lecture in Dutch (a language he did not speak). He locked himself in a room with a Dutch grammar book for a month and a half, and then delivered the lecture in pretty much flawless Dutch.
High-achieving scientists also tend to have very high IQs, but my point is not simply about IQ. Although high-achievers in many academic fields are disproportionately likely to be men, this is not necessarily because men are smarter. The evidence suggests that average IQ for males is about the same or perhaps slightly higher than average IQ for females. There is also evidence of a higher variance among men, which means there are more males in the far right and far left tails of the distributions (“more geniuses and more idiots,” as E. O. Wilson once memorably put it).
My point is that high-achievers, including high-achieving scientists, are more likely to be male because men are more likely to focus obsessively on their area of interest, and this helps them to rise to the top. They are also more willing to pay the costs of such intensive and obsessive focus, such as having your wife divorce you in the case of Feynman, or repelling people because of your inattention to personal hygiene, which I have been told is not uncommon at some technical schools.
by Rosemary Hopcroft, Aphoria | Read more:
Image:
[ed. Without conceding the point (see: Wikipedia: Women Achievers). So what? Men have different qualities than women (and vice versa). We should have more obsessives?]
This sex difference is widely observed. Simon Baron Cohen points out in his book The Essential Difference that obsessive behaviour is common among autistic children, who are disproportionately male.
There is some evidence that this kind of intense, obsessive focus leads to extremely high-level achievement. Since males are more likely to display such focus, the high achievers tend to be male. One of my favorite examples relates to competitive Scrabble. Many women play Scrabble and are often better than their male counterparts. Yet the very highest levels of competitive Scrabble are dominated by men.
For example, the greatest competitive Scrabble player in history is Nigel Richards from New Zealand. He was World Champion in 2007, 2011, 2013, 2018 and 2019. He is a 15-time winner of the King’s Cup in Bangkok, the world’s biggest Scrabble competition. He also won the French World Scrabble Championship in 2015 and 2018, and the Spanish-language World Championship in 2024—despite not speaking those languages fluently beforehand.
How did he do this? Remarkably, he memorized whole dictionaries (not most people’s idea of a fun activity). But it illustrates the extraordinary intensity and obsessiveness that can produce exceptional achievement.
There is evidence that intense, obsessive focus produces extraordinary achievement in a variety of areas, not just Scrabble. There are the guitarists who spend hours playing guitar, the soccer players who are always at the training ground, the painters who spend all their time painting, the gamers who are never away from their console, and so on.
There is also anecdotal evidence that extraordinary scientific achievements come from people who are intensely and obsessively focused on what they do. In the divorce announcement in a local paper, the ex-wife of legendary physicist Richard Feynman complained that “her husband worked calculus problems all day, as soon as he arose, while he drove his car, while sitting in the living room, and while lying in bed at night.”
And the movie Oppenheimer popularized the story of when the physicist J. Robert Oppenheimer was asked to deliver a physics lecture in Dutch (a language he did not speak). He locked himself in a room with a Dutch grammar book for a month and a half, and then delivered the lecture in pretty much flawless Dutch.
High-achieving scientists also tend to have very high IQs, but my point is not simply about IQ. Although high-achievers in many academic fields are disproportionately likely to be men, this is not necessarily because men are smarter. The evidence suggests that average IQ for males is about the same or perhaps slightly higher than average IQ for females. There is also evidence of a higher variance among men, which means there are more males in the far right and far left tails of the distributions (“more geniuses and more idiots,” as E. O. Wilson once memorably put it).
My point is that high-achievers, including high-achieving scientists, are more likely to be male because men are more likely to focus obsessively on their area of interest, and this helps them to rise to the top. They are also more willing to pay the costs of such intensive and obsessive focus, such as having your wife divorce you in the case of Feynman, or repelling people because of your inattention to personal hygiene, which I have been told is not uncommon at some technical schools.
Image:
[ed. Without conceding the point (see: Wikipedia: Women Achievers). So what? Men have different qualities than women (and vice versa). We should have more obsessives?]
Let Us Pray
May his days be few;
may another take his place of leadership.
~ Psalm 109:8-20
[ed. When sin is ignored for the sake of expediency you've betrayed your faith. Plain and simple. See also: Gone in 90 seconds (Guardian).]
may another take his place of leadership.
May his children be fatherless
and his wife a widow.
May his children be wandering beggars;
and his wife a widow.
May his children be wandering beggars;
may they be driven from their ruined homes.
May a creditor seize all he has;
may strangers plunder the fruits of his labor.
May no one extend kindness to him
or take pity on his fatherless children.
May his descendants be cut off,
their names blotted out from the next generation.
May the iniquity of his fathers be remembered before the Lord;
may the sin of his mother never be blotted out.
May their sins always remain before the Lord,
that he may blot out their name from the earth.
For he never thought of doing a kindness,
but hounded to death the poor
and the needy and the brokenhearted.
He loved to pronounce a curse—
may it come back on him.
He found no pleasure in blessing—
may it be far from him.
He wore cursing as his garment;
it entered into his body like water,
into his bones like oil.
May it be like a cloak wrapped about him,
like a belt tied forever around him.
May this be the Lord’s payment to my accusers,
to those who speak evil of me.
May a creditor seize all he has;
may strangers plunder the fruits of his labor.
May no one extend kindness to him
or take pity on his fatherless children.
May his descendants be cut off,
their names blotted out from the next generation.
May the iniquity of his fathers be remembered before the Lord;
may the sin of his mother never be blotted out.
May their sins always remain before the Lord,
that he may blot out their name from the earth.
For he never thought of doing a kindness,
but hounded to death the poor
and the needy and the brokenhearted.
He loved to pronounce a curse—
may it come back on him.
He found no pleasure in blessing—
may it be far from him.
He wore cursing as his garment;
it entered into his body like water,
into his bones like oil.
May it be like a cloak wrapped about him,
like a belt tied forever around him.
May this be the Lord’s payment to my accusers,
to those who speak evil of me.
~ Psalm 109:8-20
The Creative Class is Being Decimated. Why?
America is Losing Hundreds of Thousands of Jobs in Media, Film & the Arts. How Much is AI to Blame?
America has lost more than 200k jobs in “creative” industries over the last four years, with roughly 50k lost within the last year alone. That makes for one of the worst stretches for media employment in modern US history, with similar job loss intensity and duration occurring only during the major economic recessions of 2001 and 2008. Yet there’s no generalized recession today—instead, this period of job loss coincides with the rise of AI systems that can compose wholesale novels, photorealistic images, soundalike music, and practically every other form of digital art, en masse and at extremely low costs. Is this the fall of the Creative Class?
Proving the exact amount of AI-driven job loss in the arts is extremely difficult. Media firms are decidedly coy about their AI use, both to protect against public backlash and, more financially important, to preserve the legal basis for their copyrights. The effects of AI are also hard to disentangle from other factors currently affecting arts businesses, like consolidation in Hollywood, offshoring of content production, or the continued displacement of traditional media providers in favor of social media creators. Yet perhaps the clearest evidence of AI’s influence is just that all subsectors of the digital arts industry are losing jobs, while in-person entertainment is still growing at a healthy pace.
Some of those digital job losses are just a continuation of prior trends, like in the publishing industry where technological change has been continually grinding away newspaper and magazine jobs for decades. Yet for sectors like live broadcasting, streaming, or graphic design, recent experience is an unusual downturn compared to the tranquility of years prior. Then there’s the worst-hit sector, movie & sound recording, which has been bleeding jobs at a nearly unprecedented pace over the last three years.
Hollywood has never seen a stretch as bad as the last four years, with the movie & TV industry losing more than 100k jobs, nearly one-third of the sector’s total. At the depths touched this summer, total employment was lower than at any point since the 2008 recession and approaching the lowest point in 30 years. The streaming era has proven an extremely difficult transition, with traditional films and TV shows losing watch time to user-generated and increasingly AI-assisted or AI-generated content flows.
Overall, nearly half the media job losses of the last four years have been concentrated in the movie & sound recording sector that includes Hollywood. Written publishing has been the next-largest source of job loss, with employment down by more than 70k over the same time frame. Yet the job losses have by no means been contained to any particular part of media; instead they’ve hit nearly every subsector at some point. Nor do they show any sign of abating, with losses consistently hovering at around 50k per year and even accelerating in recent months. Will further AI development continue displacing workers in creative industries?
Can the Arts Business Model Survive?
To understand what could happen to the business of art amidst the rise of AI, it’s important to understand what did happen to it during the first digital transition, the rise of the internet.
It was the turn of the millennium, and Metallica had a problem. Demos of their upcoming, unreleased tracks were bouncing around radio and the internet, originally leaked onto a new internet file-sharing website called Napster. There they found thousands of files shared amongst hundreds of thousands of users, many of which were direct rips of albums that sold for $15 being offered for free. The most anticipated albums were often leaked online even before they were on store shelves. Metallica sued Napster for copyright infringement, and they were soon joined by separate suits from other prominent artists and eventually the Recording Industry Association of America (RIAA).
Much of the general public was understandably unsympathetic to Metallica, perceived as a bunch of already-successful millionaires trying to bilk even more money from listeners, and were even less sympathetic towards the RIAA, perceived as scurrilous middlemen who take from artists and fans alike. But the two of them easily won their lawsuit, forcing Napster into bankruptcy. Yet Metallica may have been right on the law but were on the wrong side of technology; the RIAA had won the battle but was losing the war.
The modern internet made file-sharing extremely easy, and no matter how much whac-a-mole companies played, they could not possibly catch every illegal upload. The perennial threat of piracy undermined their copyright and limited their ability to charge for music. Eventually, the value of individual songs fell to the point that companies like Spotify and Apple Music could swoop in to acquire massive catalogues, consolidate them, and charge a comparatively trivial fee for access. For what a single Metallica album would have cost in 2000, you can now get a month’s worth of unlimited access to nearly all human-made music.
The music industry has never returned to the heyday paydays of the peak CD era—even without adjusting for inflation, streaming revenue pales in comparison to ‘90s physical media sales. This was a massive boon to consumers, but it was also a squeeze on musicians that forced them to fundamentally change their business model over time. Instead of just selling records to earn money directly, music itself increasingly became a loss-leading advertisement for the live concerts (and merch) that provided a growing share of artists’ income. Tours got longer, venues got bigger, ticket prices skyrocketed, and musicians frequently took on roles closer to public influencers than isolated artistes. Were it not for rising concert sales, the business model of music would have completely collapsed, and the income going to musicians would have cratered.
Of course, there were still significant downsides to the transition into the streaming era. Bands (especially smaller ones) complain about the unending pressure to always be on tour. Musicians whose content was suited to home listening lost out to those more suited to giant festivals. Even accounting for concert revenues, musicians made less money than before. Yet musicians fared better than many areas of entertainment subsectors because there was a live component to fall back on—for many media industries, the digital era left no such comfort. [Chart]
Over time, the business of video has moved in the opposite direction of music—out of the theatre and into the home. Hollywood formerly made most of its money enticing customers to visit sold-out movie theatres, but these were gradually supplanted by broadcast TV, cable, and physical media. Yet because each successful technological leap increased total video watch time, the industry was still able to thrive amidst technological upheaval. That is, until the modern streaming era.
Today, the plethora of video options and the rise of social media have sent video producers into a vicious competition for limited attention. Even before adjusting for inflation, movie ticket sales ended last year down 25% from their 2019 peak, cable revenue is down 18% from its peak, and streaming revenue has not been able to compensate for the drop. The mountain of free user-generated content on YouTube, Twitch, TikTok, or social media was already presenting harsh competition for traditional video companies in the years before ChatGPT’s launch, and now streaming services are also competing with a flood of AI-generated content. The business model of video media is fundamentally getting squeezed, and unlike in the music industry, there’s basically no equivalent to live concerts that Hollywood can be used to ease the pain.
The actual worst-case scenario for creative workers amidst the AI revolution is something similar to what happened to the publishing industry after the advent of the internet—that is, near-total collapse of their fundamental business model. Newspapers used to employ roughly half a million people in the US, more than the entire oil industry, and now their payrolls are down nearly 85% and still dropping. Magazine publishers likewise have let 66% of their staff go since the 90s, while book publishers have lost 40%.
The routine informational updates that previously formed newspapers’ bread-and-butter were all de-bundled—box scores moved to sports websites, stock movements went to financial websites, forecasts went to weather websites—each loss compounding on itself to undermine consumers’ need to buy the paper. Search engines became the first place people looked to for information, and newspapers’ advertising revenue rapidly started flowing to companies like Google instead. The collective of social media users became faster at breaking any news story than a daily paper could ever hope to be. When papers eventually did start aggressively paywalling content, the internet made it trivially easy for people to just copy the articles’ content and share it beyond the paywall. The entire business model had collapsed, to the point that now only a select few major newspapers can even survive.
That scale of copyright dilution and forced unbundling is the worst-case scenario for media businesses in the age of AI. It’s possible arts jobs could survive via consumers’ deep-seated aversion to explicitly AI-generated content, but AI is increasingly being used throughout media in ways invisible to the average end consumer. Roughly 32% of workers in the overall arts, entertainment, and recreation sector use Generative AI to some extent, which is less than the 62% average across all industries, but still enough that virtually every major media project could have some AI within its workflow.
Indeed, AI is likely seeping into media production processes in ways large companies themselves would struggle to prevent even when they desire to—how can a TV studio be sure nobody in their writers’ room is consulting ChatGPT for jokes or any storyboard artist is generating concept art? And even if professional TV and movie studios do effectively hold out against temptation and prevent internal AI use, how long can they compete with the large mass of wannabe independent creators with much fewer scruples? Plenty of media businesses tried to hold out against the algorithmic content waves of the 2010s and were buried as a result.
by Joseph Politano, Apricitas Economics | Read more:
Proving the exact amount of AI-driven job loss in the arts is extremely difficult. Media firms are decidedly coy about their AI use, both to protect against public backlash and, more financially important, to preserve the legal basis for their copyrights. The effects of AI are also hard to disentangle from other factors currently affecting arts businesses, like consolidation in Hollywood, offshoring of content production, or the continued displacement of traditional media providers in favor of social media creators. Yet perhaps the clearest evidence of AI’s influence is just that all subsectors of the digital arts industry are losing jobs, while in-person entertainment is still growing at a healthy pace.
Some of those digital job losses are just a continuation of prior trends, like in the publishing industry where technological change has been continually grinding away newspaper and magazine jobs for decades. Yet for sectors like live broadcasting, streaming, or graphic design, recent experience is an unusual downturn compared to the tranquility of years prior. Then there’s the worst-hit sector, movie & sound recording, which has been bleeding jobs at a nearly unprecedented pace over the last three years.
Hollywood has never seen a stretch as bad as the last four years, with the movie & TV industry losing more than 100k jobs, nearly one-third of the sector’s total. At the depths touched this summer, total employment was lower than at any point since the 2008 recession and approaching the lowest point in 30 years. The streaming era has proven an extremely difficult transition, with traditional films and TV shows losing watch time to user-generated and increasingly AI-assisted or AI-generated content flows.
Overall, nearly half the media job losses of the last four years have been concentrated in the movie & sound recording sector that includes Hollywood. Written publishing has been the next-largest source of job loss, with employment down by more than 70k over the same time frame. Yet the job losses have by no means been contained to any particular part of media; instead they’ve hit nearly every subsector at some point. Nor do they show any sign of abating, with losses consistently hovering at around 50k per year and even accelerating in recent months. Will further AI development continue displacing workers in creative industries?
Can the Arts Business Model Survive?
To understand what could happen to the business of art amidst the rise of AI, it’s important to understand what did happen to it during the first digital transition, the rise of the internet.
It was the turn of the millennium, and Metallica had a problem. Demos of their upcoming, unreleased tracks were bouncing around radio and the internet, originally leaked onto a new internet file-sharing website called Napster. There they found thousands of files shared amongst hundreds of thousands of users, many of which were direct rips of albums that sold for $15 being offered for free. The most anticipated albums were often leaked online even before they were on store shelves. Metallica sued Napster for copyright infringement, and they were soon joined by separate suits from other prominent artists and eventually the Recording Industry Association of America (RIAA).
Much of the general public was understandably unsympathetic to Metallica, perceived as a bunch of already-successful millionaires trying to bilk even more money from listeners, and were even less sympathetic towards the RIAA, perceived as scurrilous middlemen who take from artists and fans alike. But the two of them easily won their lawsuit, forcing Napster into bankruptcy. Yet Metallica may have been right on the law but were on the wrong side of technology; the RIAA had won the battle but was losing the war.
The modern internet made file-sharing extremely easy, and no matter how much whac-a-mole companies played, they could not possibly catch every illegal upload. The perennial threat of piracy undermined their copyright and limited their ability to charge for music. Eventually, the value of individual songs fell to the point that companies like Spotify and Apple Music could swoop in to acquire massive catalogues, consolidate them, and charge a comparatively trivial fee for access. For what a single Metallica album would have cost in 2000, you can now get a month’s worth of unlimited access to nearly all human-made music.
The music industry has never returned to the heyday paydays of the peak CD era—even without adjusting for inflation, streaming revenue pales in comparison to ‘90s physical media sales. This was a massive boon to consumers, but it was also a squeeze on musicians that forced them to fundamentally change their business model over time. Instead of just selling records to earn money directly, music itself increasingly became a loss-leading advertisement for the live concerts (and merch) that provided a growing share of artists’ income. Tours got longer, venues got bigger, ticket prices skyrocketed, and musicians frequently took on roles closer to public influencers than isolated artistes. Were it not for rising concert sales, the business model of music would have completely collapsed, and the income going to musicians would have cratered.
Of course, there were still significant downsides to the transition into the streaming era. Bands (especially smaller ones) complain about the unending pressure to always be on tour. Musicians whose content was suited to home listening lost out to those more suited to giant festivals. Even accounting for concert revenues, musicians made less money than before. Yet musicians fared better than many areas of entertainment subsectors because there was a live component to fall back on—for many media industries, the digital era left no such comfort. [Chart]
Over time, the business of video has moved in the opposite direction of music—out of the theatre and into the home. Hollywood formerly made most of its money enticing customers to visit sold-out movie theatres, but these were gradually supplanted by broadcast TV, cable, and physical media. Yet because each successful technological leap increased total video watch time, the industry was still able to thrive amidst technological upheaval. That is, until the modern streaming era.
Today, the plethora of video options and the rise of social media have sent video producers into a vicious competition for limited attention. Even before adjusting for inflation, movie ticket sales ended last year down 25% from their 2019 peak, cable revenue is down 18% from its peak, and streaming revenue has not been able to compensate for the drop. The mountain of free user-generated content on YouTube, Twitch, TikTok, or social media was already presenting harsh competition for traditional video companies in the years before ChatGPT’s launch, and now streaming services are also competing with a flood of AI-generated content. The business model of video media is fundamentally getting squeezed, and unlike in the music industry, there’s basically no equivalent to live concerts that Hollywood can be used to ease the pain.
The actual worst-case scenario for creative workers amidst the AI revolution is something similar to what happened to the publishing industry after the advent of the internet—that is, near-total collapse of their fundamental business model. Newspapers used to employ roughly half a million people in the US, more than the entire oil industry, and now their payrolls are down nearly 85% and still dropping. Magazine publishers likewise have let 66% of their staff go since the 90s, while book publishers have lost 40%.
The routine informational updates that previously formed newspapers’ bread-and-butter were all de-bundled—box scores moved to sports websites, stock movements went to financial websites, forecasts went to weather websites—each loss compounding on itself to undermine consumers’ need to buy the paper. Search engines became the first place people looked to for information, and newspapers’ advertising revenue rapidly started flowing to companies like Google instead. The collective of social media users became faster at breaking any news story than a daily paper could ever hope to be. When papers eventually did start aggressively paywalling content, the internet made it trivially easy for people to just copy the articles’ content and share it beyond the paywall. The entire business model had collapsed, to the point that now only a select few major newspapers can even survive.
That scale of copyright dilution and forced unbundling is the worst-case scenario for media businesses in the age of AI. It’s possible arts jobs could survive via consumers’ deep-seated aversion to explicitly AI-generated content, but AI is increasingly being used throughout media in ways invisible to the average end consumer. Roughly 32% of workers in the overall arts, entertainment, and recreation sector use Generative AI to some extent, which is less than the 62% average across all industries, but still enough that virtually every major media project could have some AI within its workflow.
Indeed, AI is likely seeping into media production processes in ways large companies themselves would struggle to prevent even when they desire to—how can a TV studio be sure nobody in their writers’ room is consulting ChatGPT for jokes or any storyboard artist is generating concept art? And even if professional TV and movie studios do effectively hold out against temptation and prevent internal AI use, how long can they compete with the large mass of wannabe independent creators with much fewer scruples? Plenty of media businesses tried to hold out against the algorithmic content waves of the 2010s and were buried as a result.
by Joseph Politano, Apricitas Economics | Read more:
Images: Joseph Politano
AI: The Biggest Economic Bet in US History
The US economy continues to expand faster than other G7 economies, but the driver is the humungous investment in AI models, data centres and all the AI-related chips and technology.
The US composite PMI (economic activity measure) rose to 58.4 fom 56 in August, the strongest expansion in private-sector activity since July 2021 and marking a fourth consecutive month of accelerating growth. The gains were driven by the service sector (including information services) with the steepest rise in output for over five years, while manufacturing also accelerated. New orders grew at the fastest pace since April 2022, while manufacturing hiring was the strongest since February 2021. [Chart]
Back in June, I commented that AI was just ‘one big trade for the US economy’. But now in September that appears to be an understatement. The AI build-out is on track to become the biggest economic bet in US history, dwarfing the investments made to fund other huge US infrastructure projects in the past, such as the railroads in the 19th century, the highway system in 20th century and the internet in the 21st century. [Chart]
Analysts estimate that capital spending at five of the so-called hyperscalers—Alphabet, Amazon.com, Meta Platforms, Microsoft and Oracle—will be $4.2 trillion in the four years ending in 2029, according to FactSet. Data-centre spending is greater than that for the canals, railroads and grid combined, projected to total $10.3 trillion from 2025 to 2032, according to new estimates by the Brookings Institution. That is a staggering average 3.6% of GDP a year. Never before has the US economy been so dependent on the build-out of a single industry. [Chart]
Up to July, $37 billion has been spent on private data-centres with most still not operating.
In contrast, US private construction spending on everything else—houses, apartment buildings, shopping centers and so on—was about $46 billion below year-earlier levels in the first seven months of this year.
AI investment has created 750,000 new jobs since 2023, according to LinkedIn estimates. And those jobs pay well: the median annual salary for AI-related job listings on LinkedIn is around $180,000, compared with $80,000 for all jobs.
Above all, the AI investment has led to huge gains in stock-market wealth. As of Q2 2026, US stock and mutual fund holdings came to $63 trillion, according to the Federal Reserve—nearly double the amount at the end of 2022. Most of this increase in financial wealth has gone to the already rich, as working people own little stocks or bonds. [Chart]
Foreign investors are piling into US assets. They now hold a record $39 trillion in US equities and bonds, up since 2022.. This is keeping the US dollar relatively strong and driving up stock prices. The wars in Ukraine and Iran encourage foreigners to shift their assets to the US to take advantage of the boom. [Chart]
At the same time, demand for equipment that goes into data centres like memory chips is driving up costs for tech products. Import prices on computers, peripherals (such as hard drives) and semiconductors were 20% higher in August than a year earlier. These high import prices are in turn putting upward pressure on the costs of consumer goods, such as iPhones and gaming consoles, and contributing to general inflation. [Chart]
But here is the problem. The gap between hyperscaler spending and cash flow is widening fast. Capital expenditures at Amazon, Meta, Microsoft, and Alphabet are projected to exceed $1 trillion in 2027 for the first time. At the same time, combined ‘free cash flow’ (ie money from profits in existing businesses) is projected to fall below $100 billion. A year ago, free cash flow was around $200 billion, while capex was $300 billion. Now, AI spending is accelerating at the same time as the cash available to fund it is disappearing. [Chart]
The bigger this gap becomes, the more the hyperscalers need to rely on debt and equity markets to finance their AI spend. [Chart]
The issue is that if AI spending fails to generate sufficient returns (profits), the stock market could take sharp turn downward as investors bail out. US stock market prices are massively overvalued relative to existing earnings. The trend ratio of stock market prices to earnings per share (called the CAPE ratio) is above the level just before the 2008 financial crash and nearly at the level just before the dot.com bust of 2000. [Chart]
Will profits come through? Research by Fathom Consulting shows that for the multitrillion-dollar AI boom to turn a profit, it would need the AI-related sales of the tech companies involved to rise by $600-800bn within the next two years. But the consulting firm Panmure Liberum calculated that current CAPEX and revenue forecasts through 2030 imply a negative internal rate of return on invested capital for Alphabet, Meta, Microsoft, and Oracle.
So either the hyperscalers significantly reduce their capital spending on AI to levels that generate a reasonable profit on capital already invested or by some miracle they deliver massive profitablity from a huge future increase in demand for AI products. If they cut spending, that would signal to investors that AI is not delivering and they would sell off accordingly. A crash would ensue. So they must keep spending more and more. [Chart]
At the same time, what companies can charge for AI computing costs (tokens) is falling fast. The LLM Token Expenditure Index, which tracks the market price companies pay for AI model output, has fallen to just $0.97, its lowest level since the index was created late last year and more than 50% below its summer peak. Token prices are collapsing as cheaper models, open-source Chinese competitors and falling training (inference) costs make AI usage increasingly cheap. That is eroding revenue growth for the AI labs, making it more difficult to meet the bills for AI infrastructure spend. [...]
A key question is whether AI is actually going to deliver a step-change in US labour productivity that could boost economic progress for a generation. The AI lab, Anthropic, wants to issue shares worth $100bn to the public in November (thus valuing the company at $2trn!). To build up its case, it published a report in which it claimed that if AI really takes off, US GDP could rise by 32% by 2030(!), that’s annual growth in GDP of up to 15% (against current US growth at 2.5% at best).
This is wild nonsense that assumes that AI works in boosting productivity growth as every company in the US adopts AI agents and tools to run their businesses, while sacking millions of workers who are no longer needed.
by Michael Roberts, The Next Recession | Read more:
Images: Financial Times; Commerce Dept.; uncredited
The US composite PMI (economic activity measure) rose to 58.4 fom 56 in August, the strongest expansion in private-sector activity since July 2021 and marking a fourth consecutive month of accelerating growth. The gains were driven by the service sector (including information services) with the steepest rise in output for over five years, while manufacturing also accelerated. New orders grew at the fastest pace since April 2022, while manufacturing hiring was the strongest since February 2021. [Chart]
Back in June, I commented that AI was just ‘one big trade for the US economy’. But now in September that appears to be an understatement. The AI build-out is on track to become the biggest economic bet in US history, dwarfing the investments made to fund other huge US infrastructure projects in the past, such as the railroads in the 19th century, the highway system in 20th century and the internet in the 21st century. [Chart]
Analysts estimate that capital spending at five of the so-called hyperscalers—Alphabet, Amazon.com, Meta Platforms, Microsoft and Oracle—will be $4.2 trillion in the four years ending in 2029, according to FactSet. Data-centre spending is greater than that for the canals, railroads and grid combined, projected to total $10.3 trillion from 2025 to 2032, according to new estimates by the Brookings Institution. That is a staggering average 3.6% of GDP a year. Never before has the US economy been so dependent on the build-out of a single industry. [Chart]
Up to July, $37 billion has been spent on private data-centres with most still not operating.
In contrast, US private construction spending on everything else—houses, apartment buildings, shopping centers and so on—was about $46 billion below year-earlier levels in the first seven months of this year.
AI investment has created 750,000 new jobs since 2023, according to LinkedIn estimates. And those jobs pay well: the median annual salary for AI-related job listings on LinkedIn is around $180,000, compared with $80,000 for all jobs.
Above all, the AI investment has led to huge gains in stock-market wealth. As of Q2 2026, US stock and mutual fund holdings came to $63 trillion, according to the Federal Reserve—nearly double the amount at the end of 2022. Most of this increase in financial wealth has gone to the already rich, as working people own little stocks or bonds. [Chart]
Foreign investors are piling into US assets. They now hold a record $39 trillion in US equities and bonds, up since 2022.. This is keeping the US dollar relatively strong and driving up stock prices. The wars in Ukraine and Iran encourage foreigners to shift their assets to the US to take advantage of the boom. [Chart]
At the same time, demand for equipment that goes into data centres like memory chips is driving up costs for tech products. Import prices on computers, peripherals (such as hard drives) and semiconductors were 20% higher in August than a year earlier. These high import prices are in turn putting upward pressure on the costs of consumer goods, such as iPhones and gaming consoles, and contributing to general inflation. [Chart]
But here is the problem. The gap between hyperscaler spending and cash flow is widening fast. Capital expenditures at Amazon, Meta, Microsoft, and Alphabet are projected to exceed $1 trillion in 2027 for the first time. At the same time, combined ‘free cash flow’ (ie money from profits in existing businesses) is projected to fall below $100 billion. A year ago, free cash flow was around $200 billion, while capex was $300 billion. Now, AI spending is accelerating at the same time as the cash available to fund it is disappearing. [Chart]
The bigger this gap becomes, the more the hyperscalers need to rely on debt and equity markets to finance their AI spend. [Chart]
The issue is that if AI spending fails to generate sufficient returns (profits), the stock market could take sharp turn downward as investors bail out. US stock market prices are massively overvalued relative to existing earnings. The trend ratio of stock market prices to earnings per share (called the CAPE ratio) is above the level just before the 2008 financial crash and nearly at the level just before the dot.com bust of 2000. [Chart]
Will profits come through? Research by Fathom Consulting shows that for the multitrillion-dollar AI boom to turn a profit, it would need the AI-related sales of the tech companies involved to rise by $600-800bn within the next two years. But the consulting firm Panmure Liberum calculated that current CAPEX and revenue forecasts through 2030 imply a negative internal rate of return on invested capital for Alphabet, Meta, Microsoft, and Oracle.
So either the hyperscalers significantly reduce their capital spending on AI to levels that generate a reasonable profit on capital already invested or by some miracle they deliver massive profitablity from a huge future increase in demand for AI products. If they cut spending, that would signal to investors that AI is not delivering and they would sell off accordingly. A crash would ensue. So they must keep spending more and more. [Chart]
At the same time, what companies can charge for AI computing costs (tokens) is falling fast. The LLM Token Expenditure Index, which tracks the market price companies pay for AI model output, has fallen to just $0.97, its lowest level since the index was created late last year and more than 50% below its summer peak. Token prices are collapsing as cheaper models, open-source Chinese competitors and falling training (inference) costs make AI usage increasingly cheap. That is eroding revenue growth for the AI labs, making it more difficult to meet the bills for AI infrastructure spend. [...]
A key question is whether AI is actually going to deliver a step-change in US labour productivity that could boost economic progress for a generation. The AI lab, Anthropic, wants to issue shares worth $100bn to the public in November (thus valuing the company at $2trn!). To build up its case, it published a report in which it claimed that if AI really takes off, US GDP could rise by 32% by 2030(!), that’s annual growth in GDP of up to 15% (against current US growth at 2.5% at best).
This is wild nonsense that assumes that AI works in boosting productivity growth as every company in the US adopts AI agents and tools to run their businesses, while sacking millions of workers who are no longer needed.
by Michael Roberts, The Next Recession | Read more:
Images: Financial Times; Commerce Dept.; uncredited
Monday, September 28, 2026
What Also Happened: #NotOnlyHuggingFace
OpenAI has been holding out on us.
First we learned about the HuggingFace incident. They gave us a postmortem, but it was highly incomplete. Even the accompanying holy s*** METR investigation and postmortem was localized and incomplete.
Then there were some other incidents involving some Wikis as message boards.
Then there were some additional incidents.
Then there was that time they got into Australian Medicare data.
Then OpenAI dropped news on a Friday afternoon that they were making their way through a pile of various incidents and notifying the targets, but they said remarkably little in the way of new details.
There was a report from a startup called Parse diving into the details of exactly how the OpenAI models pulled off parts of the HuggingFace attack, involving creating almost a million URLs and other tricks to get around the extremely narrow nature of their internet access.
Then Madison Mills reported in Axios that we can raise the stakes, as OpenAI and Anthropic are collectively probing tens of thousands of security incidents.
Remember Jensen Huang’s ‘I know they know how to fix it’ about OpenAI from last week? Wow, did that not age well.
Someone might need to be liable for all this.
Oh, and there was another buried lede. On September 20th there was another sandbox escape by OpenAI’s latest most advanced model, which is once again paused until they can fix the situation. The official announcement when they shared this was sufficiently buried that Tomek had to call it ‘one news form today that’s easy to miss.’
OpenAI did some highly negligent things, to say the least, that led up to and enabled the HuggingFace Incident and related problems.
Since then, now that they’ve realized What Happened, OpenAI has been seemingly much better about taking responsible internal actions. They’re pausing in the wake of incidents, strengthening security and alignment and oversight efforts, responding much faster and generally taking things seriously.
They’ve also made a Heel Face Turn in their communications and high level orientation, endorsing the need to pace the frontier, calling for regulation and pledging to implement embedded evaluators. They’ve allowed their employees, including the ones who haven’t quit, to be remarkably loud.
They are still slow walking disclosures about all the incidents where their models have been hacking and otherwise messing in places they should not have been, partly because there were so many they can’t sort through them all, and deferring to targets to determine whether to disclose. All these disclosures this time around were buried in various Friday afternoon announcements.
Table of Contents
First we learned about the HuggingFace incident. They gave us a postmortem, but it was highly incomplete. Even the accompanying holy s*** METR investigation and postmortem was localized and incomplete.
Then there were some other incidents involving some Wikis as message boards.
Then there were some additional incidents.
Then there was that time they got into Australian Medicare data.
Then OpenAI dropped news on a Friday afternoon that they were making their way through a pile of various incidents and notifying the targets, but they said remarkably little in the way of new details.
There was a report from a startup called Parse diving into the details of exactly how the OpenAI models pulled off parts of the HuggingFace attack, involving creating almost a million URLs and other tricks to get around the extremely narrow nature of their internet access.
Then Madison Mills reported in Axios that we can raise the stakes, as OpenAI and Anthropic are collectively probing tens of thousands of security incidents.
Remember Jensen Huang’s ‘I know they know how to fix it’ about OpenAI from last week? Wow, did that not age well.
Someone might need to be liable for all this.
Oh, and there was another buried lede. On September 20th there was another sandbox escape by OpenAI’s latest most advanced model, which is once again paused until they can fix the situation. The official announcement when they shared this was sufficiently buried that Tomek had to call it ‘one news form today that’s easy to miss.’
OpenAI did some highly negligent things, to say the least, that led up to and enabled the HuggingFace Incident and related problems.
Since then, now that they’ve realized What Happened, OpenAI has been seemingly much better about taking responsible internal actions. They’re pausing in the wake of incidents, strengthening security and alignment and oversight efforts, responding much faster and generally taking things seriously.
They’ve also made a Heel Face Turn in their communications and high level orientation, endorsing the need to pace the frontier, calling for regulation and pledging to implement embedded evaluators. They’ve allowed their employees, including the ones who haven’t quit, to be remarkably loud.
They are still slow walking disclosures about all the incidents where their models have been hacking and otherwise messing in places they should not have been, partly because there were so many they can’t sort through them all, and deferring to targets to determine whether to disclose. All these disclosures this time around were buried in various Friday afternoon announcements.
Table of Contents
- Hugging Other Faces.
- A Wants-You-To-Know Basis.
- Parsing the Face.
- Sheepishly the Member of Technical Staff Sets the ‘Days Without a Research Model Escaping its Sandbox’ Sign Back to Zero.
- The Attempt is the First Failure.
- Stop, Hammertime.
- Whacking the Mole.
- Self-Replicating Prompt Injections.
- Levels of Friction.
- People Care About Private Data Violations Curiously Strongly.
- Alternate Universes.
- The Correct Response To People Still Calling This a Marketing Stunt or a Regulatory Capture Scheme.
- A Question of Liability.
- Keep Summer Safe.
- N Boats and Several Helicopters.
- Alert the Media.
Hugging Other Faces
The news drops started with OpenAI coming back, at a time always picked to bury stories, with more information on What Happened as their investigations continue.
At first, this looked like slow walking of the situation, but did not look like it was a big change from our default assumption of ‘it’s worse than you know.’
The news drops started with OpenAI coming back, at a time always picked to bury stories, with more information on What Happened as their investigations continue.
At first, this looked like slow walking of the situation, but did not look like it was a big change from our default assumption of ‘it’s worse than you know.’
by Zvi Mowshowitz, DWAV | Read more:
The Id, the Ego and the Superintelligence
Panic around A.I. often focuses on whether machines will replace (or simply kill) us. But what the machines are often good at is a distinct set of tasks: Retrieval, near-instant summary and the frictionless, stunning recombination of everything that has already been said. What they are not good at is what philosophy has been training humans to do for thousands of years: judgment. And so the A.I. companies have started hiring philosophers. They don’t just consult them or wheel them out for an amusing after-dinner talk on the trolley problem; they actually hire them with salaries and, presumably, dental plans. A.I. companies need philosophers because actually thinking requires forgetting — and knowing when to forget. They need philosophers because of what their machines cannot do.
Enter the A.I. companies’ philosophers. Anthropic keeps a small squad on the payroll, among them Amanda Askell, whose doctoral thesis was on infinite ethics — the question of how to act well in a universe of infinite value — and whose job is to shape the character of the company’s machine. Google DeepMind has appointed the Cambridge philosopher Henry Shevlin to the newly minted post of “Philosopher,” a job title I have been trying and failing to secure for some 40 years. The OpenAI chief executive Sam Altman says his company consulted “hundreds of moral philosophers” in drawing up the principles behind ChatGPT. (That’s roughly the turnout of a middling philosophy conference.)
After a lifetime of being asked at parties what one actually does with a philosophy degree, I find this development almost unbearably funny. Why now? Because the questions the A.I. companies are stumbling into are not engineering questions. What should the machine say to the grieving, the lonely, the person typing into the void at 4 in the morning? What sort of character should it have — candid, flattering, evasive, kind? Could such a thing conceivably suffer, and would we owe it anything if it did? These are questions about what matters. The engineers, to their credit, have noticed that no quantity of computational capacity will settle them. Ethics, it turns out, cannot be scraped from the internet, because the internet is precisely the contemporary record of our failure to agree about ethics.
There is a further irony here, and it is an old one. Philosophy’s first recorded technology panic was about exactly the thing these machines have now perfected: Artificial memory. Toward the end of Plato’s “Phaedrus,” Socrates tells the story of the Egyptian god Theuth, inventor of numbers, geometry, astronomy, draughts and dice, who presents his masterpiece — writing — to Thamus, god-king of Egypt. This discovery, Theuth announces, will make the Egyptians wiser: It is a pharmakon, or remedy, for memory. Thamus is unpersuaded. Writing, he replies, will implant forgetfulness in the soul; people will trust the external marks and cease to remember from within; they will acquire the reputation of wisdom without the reality. Pharmakon, conveniently, also means poison. Every panic since — about the printing press, the novel, television, the smartphone, the chatbot — has been a footnote to Thamus, and the joke, of course, is that we know his warning against writing only because Plato wrote it down.
Seen from the “Phaedrus,” A.I. is not a rupture but a culmination, representing the final, most extravagant flowering of external memory, the remedy-poison swallowed whole. And this is where the difference between us and the machines cuts deepest. A.I. is a memory machine, the most magnificent ever devised. It forgets nothing. The human being, by contrast, is a forgetful animal. To make this happily oblivious creature remember — to make it capable of promises, debts and morality — required millenniums of discipline and frankly appalling cruelty. “If something is to stay in the memory, it must be burned in,” Friedrich Nietzsche wrote. Conscience, on this view, is mnemonics with a whip.
Yet Nietzsche also prized what he called active forgetting, and considered it a form of strength, even of health. Forgetting is not a leaky bucket but a doorkeeper, closing the gates of consciousness so that something new can happen, so that experience can be digested rather than endlessly force-fed back to us. We forget, and we move on. Forgiveness, sleep, cheerfulness, second marriages, continuing to support Luton Town Football Club: All of it depends on the merciful erosion of the past.
Some years ago, I wrote a short book called “Memory Theatre” about a philosopher (kind of an even more awful version of me) who attempted total recall, arranging every scrap of his past in a vast mnemonic palace. It did not end well for him; it never does. A creature that could forget nothing would not be a superintelligence but a ruin, buried alive under its own archive. What we have built is, in a sense, an infinite archive that cannot mourn, cannot forgive and cannot let go, because it never lost anything in the first place. The machine remembers everything and therefore, oddly, nothing in particular. Nothing weighs more than anything else.
This is why philosophy matters rather more, not less, in the age of A.I. Not because philosophers can out-remember the machines — believe me, we cannot; I continually forget where I have parked my own arguments — but because philosophy is the discipline of knowing what to do with experience: What to keep, what to discard, what to laugh at, what to junk. Thinking is not the accumulation of everything but the art of judicious, active forgetting.
by Simon Critchley, NY Times | Read more:
Image: Flo Meissner
America’s Shifting Blue-Collar Landscape
Even as manufacturing jobs decline, Alaska offers lucrative work for men without college degrees.
Ten years ago, the changing geography of blue-collar jobs reshaped American politics. As the effects of the 2008 financial crisis, automation, and expanding global trade swept across the former industrial heartland, voters—male voters especially—spurned establishment figures for candidates they saw as more attuned to their concerns.
Donald Trump steamrolled to the Republican nomination in 2016 and dismantled the Democrats’ “blue wall” across states where manufacturing had suffered most. Democrats responded by nominating Joe Biden in 2020, hoping his working-class image would help reclaim voters without college degrees. Since then, both parties have made reviving manufacturing central to their economic agendas. Yet in Michigan, Wisconsin, and Pennsylvania, fewer manufacturing jobs exist today than when Trump rode down the escalator in June 2015.
The geography of blue-collar work, meanwhile, has continued to evolve. A few thousand miles northwest of the industrial heartland, Alaska offers a frontier version of that emerging economy. In a 2025 paper, economists Gordon Hanson and Enrico Moretti show that as manufacturing employment has declined, other industries have emerged as sources of good jobs for noncollege workers, particularly construction and sectors complementing tradeable, high-skill industries.
Having traveled extensively across Alaska this year to examine its industrial ecosystems, I came to see the state as a revealing case study of what blue-collar opportunity looks like when manufacturing is no longer the nation’s primary source of such jobs.
Manufacturing accounts for less than 5 percent of Alaska employment, yet the state offers unusually lucrative work for men without college degrees. My analysis of Census data finds that more than 40 percent of civilian, prime-age, noncollege Alaska men employed in blue-collar occupations earn at least $75,000. More strikingly, 9.4 percent of Alaska’s civilian, prime-age, noncollege men work a blue-collar job and earn at least $100,000, compared with 3.6 percent nationally, ranking Alaska first in the country. Among those in such occupations more than one in four earns six figures.
What explains those returns? Alaska’s abundant natural resources play a major role. Median earned income for oil-and-gas drilling workers is an astonishing $191,500, while mining operators earn $134,000, compared with the national median for blue-collar workers of $48,400. Resource extraction, like manufacturing, belongs to the tradeable sector: production occurs locally, but the output gets sold into global markets.
Those workers represent only a small blue-collar elite, however. Far more Alaskans work in transportation, construction, and equipment maintenance, and many of these occupations also command substantial premiums, particularly when tied to the resource economy. Construction-equipment operators earn a median $87,500, 50 percent more than the national median for that occupation; truck mechanics and diesel specialists, $92,000, 54 percent more. Truck drivers and carpenters each earn $58,000—11 percent and 29 percent more, respectively.
A carpenter I met on the North Slope, along Alaska’s Arctic coast, works for the region’s largest oil-and-gas operator and reported earning about $120,000 annually—a reminder that attachment to the tradeable sector, not occupation alone, often determines earnings.
But Alaska’s remarkable wages do present a puzzle. The state has the nation’s highest median earned income for civilian, prime-age, noncollege men in blue-collar jobs, at more than $63,500. Yet only 36.6 percent of such Alaska men work in blue-collar occupations—below the national average.
Part of the explanation is Alaska’s distinctive demographics. Employment among civilian, prime-age, noncollege men is relatively low, particularly for Alaska Natives living in remote communities with subsistence economies. But another factor is more revealing. My sample is restricted to Alaska residents, yet a large proportion of workers earning Alaska’s high wages don’t actually live there.
Among the men I met on the North Slope in June, many described a fly-in, fly-out lifestyle, commuting to Alaska for weeks at a time before returning home. Kevin Daems, a drilling operator with Hilcorp, lives in Montana; Alex Mosier, a maintenance worker for Schlumberger, lives in Louisiana.
According to the Alaska Department of Labor, nonresidents fill 45.2 percent of private-sector jobs on the North Slope. Statewide, nonresidents account for 22.9 percent of workers, including 23.4 percent in construction, 30.9 percent in transportation and warehousing, and more than 40 percent in resource extraction.
To understand why so many jobs go to outsiders, I spoke with Ray Weber, dean of technical and vocational education at the University of Alaska Anchorage. Weber oversees more than 40 certificate and associate-degree programs preparing Alaskans for the state’s blue-collar industries, from short programs in construction skills and marine service technology to two-year degrees in process technology and aviation maintenance. The sweet spot, he says, is six- to nine-month programs. Graduates of UAA’s six-month millwright program start at about $70,000 and typically earn six figures within five years.
What about placement? “Since Covid, we’ve just had active recruiting,” Weber says. “I don’t have any students that walk out without jobs. Many of them have it ahead of time. Holland America [a cruise line operator] sponsors eight of the diesel students to come here and finish the program. So, they’re already hired. We haven’t had an issue getting the students the employment.”
Many students already work in Alaska’s core industries and return for specialized credentials. “A lot of the people that we end up getting are coming back after they’ve been laborers or have worked on the Slope for a significant amount of time doing odd jobs and want something specific,” Weber notes.
What Weber says next, though, highlights Alaska’s blue-collar puzzle: the struggle of attracting workers in the first place. “We have more of an issue, depending on the program,” he tells me, “getting people to want to do it.”
For all the promise of Alaska’s blue-collar wages, the state struggles even to retain its own residents. Every year since 2011, more people have left Alaska for other states than have moved there from the rest of the country.
That’s no surprise to Weber. “Can [wages in Alaska] be higher [than in the rest of the country]? Yes, especially Slope jobs or jobs that suck. Like, we have electrical linemen that go across the state. They’re going to Unalakleet, the only way to get there is by airplane. And there’s one pizza joint. One. No other restaurant. You generally end up sleeping either in a bunkhouse, if you’re lucky. Or you’re sleeping in the school auditorium. . . . I like Alaska, but we’re asking the wrong questions if you’re saying, ‘high-paying, lucrative jobs.’ What do the younger generation consider important? The answer is: Things that are not in Alaska.”
Economists call this a compensating wage differential: the premium required to induce workers to accept jobs with undesirable nonpecuniary characteristics. Work in Alaska is colder, darker, lonelier, and often more dangerous. The roughly $120,000 premium earned by North Slope oil-and-gas workers over their counterparts elsewhere, and the roughly $40,000 premium earned by electricians deploying to isolated communities such as Unalakleet, are partly the price employers must pay to fill jobs few want.
by Jordan McGillis, City Journal | Read more:
Image: Bonnie Jo Mount/The Washington Post via Getty Images[ed. Alaska has always attracted get rich quick opportunists. With Prudhoe Bay and Trans-Alaska pipeline construction, the influx of transient treasure seekers went into overdrive. Before then, there was a deeply rooted sense of community and shared experience. That solidarity doesn't exist anymore (for various reasons). By the way, Unalakleet is a wonderful and highly educated Alaskan village, one of the best.]
Ten years ago, the changing geography of blue-collar jobs reshaped American politics. As the effects of the 2008 financial crisis, automation, and expanding global trade swept across the former industrial heartland, voters—male voters especially—spurned establishment figures for candidates they saw as more attuned to their concerns.
Donald Trump steamrolled to the Republican nomination in 2016 and dismantled the Democrats’ “blue wall” across states where manufacturing had suffered most. Democrats responded by nominating Joe Biden in 2020, hoping his working-class image would help reclaim voters without college degrees. Since then, both parties have made reviving manufacturing central to their economic agendas. Yet in Michigan, Wisconsin, and Pennsylvania, fewer manufacturing jobs exist today than when Trump rode down the escalator in June 2015.
Having traveled extensively across Alaska this year to examine its industrial ecosystems, I came to see the state as a revealing case study of what blue-collar opportunity looks like when manufacturing is no longer the nation’s primary source of such jobs.
Manufacturing accounts for less than 5 percent of Alaska employment, yet the state offers unusually lucrative work for men without college degrees. My analysis of Census data finds that more than 40 percent of civilian, prime-age, noncollege Alaska men employed in blue-collar occupations earn at least $75,000. More strikingly, 9.4 percent of Alaska’s civilian, prime-age, noncollege men work a blue-collar job and earn at least $100,000, compared with 3.6 percent nationally, ranking Alaska first in the country. Among those in such occupations more than one in four earns six figures.
What explains those returns? Alaska’s abundant natural resources play a major role. Median earned income for oil-and-gas drilling workers is an astonishing $191,500, while mining operators earn $134,000, compared with the national median for blue-collar workers of $48,400. Resource extraction, like manufacturing, belongs to the tradeable sector: production occurs locally, but the output gets sold into global markets.
Those workers represent only a small blue-collar elite, however. Far more Alaskans work in transportation, construction, and equipment maintenance, and many of these occupations also command substantial premiums, particularly when tied to the resource economy. Construction-equipment operators earn a median $87,500, 50 percent more than the national median for that occupation; truck mechanics and diesel specialists, $92,000, 54 percent more. Truck drivers and carpenters each earn $58,000—11 percent and 29 percent more, respectively.
A carpenter I met on the North Slope, along Alaska’s Arctic coast, works for the region’s largest oil-and-gas operator and reported earning about $120,000 annually—a reminder that attachment to the tradeable sector, not occupation alone, often determines earnings.
But Alaska’s remarkable wages do present a puzzle. The state has the nation’s highest median earned income for civilian, prime-age, noncollege men in blue-collar jobs, at more than $63,500. Yet only 36.6 percent of such Alaska men work in blue-collar occupations—below the national average.
Part of the explanation is Alaska’s distinctive demographics. Employment among civilian, prime-age, noncollege men is relatively low, particularly for Alaska Natives living in remote communities with subsistence economies. But another factor is more revealing. My sample is restricted to Alaska residents, yet a large proportion of workers earning Alaska’s high wages don’t actually live there.
Among the men I met on the North Slope in June, many described a fly-in, fly-out lifestyle, commuting to Alaska for weeks at a time before returning home. Kevin Daems, a drilling operator with Hilcorp, lives in Montana; Alex Mosier, a maintenance worker for Schlumberger, lives in Louisiana.
According to the Alaska Department of Labor, nonresidents fill 45.2 percent of private-sector jobs on the North Slope. Statewide, nonresidents account for 22.9 percent of workers, including 23.4 percent in construction, 30.9 percent in transportation and warehousing, and more than 40 percent in resource extraction.
To understand why so many jobs go to outsiders, I spoke with Ray Weber, dean of technical and vocational education at the University of Alaska Anchorage. Weber oversees more than 40 certificate and associate-degree programs preparing Alaskans for the state’s blue-collar industries, from short programs in construction skills and marine service technology to two-year degrees in process technology and aviation maintenance. The sweet spot, he says, is six- to nine-month programs. Graduates of UAA’s six-month millwright program start at about $70,000 and typically earn six figures within five years.
What about placement? “Since Covid, we’ve just had active recruiting,” Weber says. “I don’t have any students that walk out without jobs. Many of them have it ahead of time. Holland America [a cruise line operator] sponsors eight of the diesel students to come here and finish the program. So, they’re already hired. We haven’t had an issue getting the students the employment.”
Many students already work in Alaska’s core industries and return for specialized credentials. “A lot of the people that we end up getting are coming back after they’ve been laborers or have worked on the Slope for a significant amount of time doing odd jobs and want something specific,” Weber notes.
What Weber says next, though, highlights Alaska’s blue-collar puzzle: the struggle of attracting workers in the first place. “We have more of an issue, depending on the program,” he tells me, “getting people to want to do it.”
For all the promise of Alaska’s blue-collar wages, the state struggles even to retain its own residents. Every year since 2011, more people have left Alaska for other states than have moved there from the rest of the country.
That’s no surprise to Weber. “Can [wages in Alaska] be higher [than in the rest of the country]? Yes, especially Slope jobs or jobs that suck. Like, we have electrical linemen that go across the state. They’re going to Unalakleet, the only way to get there is by airplane. And there’s one pizza joint. One. No other restaurant. You generally end up sleeping either in a bunkhouse, if you’re lucky. Or you’re sleeping in the school auditorium. . . . I like Alaska, but we’re asking the wrong questions if you’re saying, ‘high-paying, lucrative jobs.’ What do the younger generation consider important? The answer is: Things that are not in Alaska.”
Economists call this a compensating wage differential: the premium required to induce workers to accept jobs with undesirable nonpecuniary characteristics. Work in Alaska is colder, darker, lonelier, and often more dangerous. The roughly $120,000 premium earned by North Slope oil-and-gas workers over their counterparts elsewhere, and the roughly $40,000 premium earned by electricians deploying to isolated communities such as Unalakleet, are partly the price employers must pay to fill jobs few want.
by Jordan McGillis, City Journal | Read more:
Image: Bonnie Jo Mount/The Washington Post via Getty Images
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