AI populism and the rejection of robot overloards & their techbro minions

Ezra Klein, The A.I. Giants Weren’t Prepared for This, NYTimes, Aug 4. 2026

What is big, ugly and has united Republicans and Democrats at a time when it has felt like nothing else could? A.I. data centers.

Last August, a Heatmap News poll found that approximately four in 10 voters would oppose a data center being built where they live. By May of this year, opposition grew to seven in 10. Gov. Ron DeSantis, of Florida — a Republican, of course — proposed legislation that was known as an A.I. Bill of Rights.

Senator Bernie Sanders called for a national data center moratorium — one of more than 100 local or statewide moratorium proposals across the country. And here in New York, Gov. Kathy Hochul, not usually thought of as a hard-core populist, just imposed a one-year moratorium on data center construction.

So I wanted to get into the fight over data centers. How much of this is really about water or electricity or aesthetics, and how much is about A.I. and the companies that are behind it?

My guest is Jasmine Sun. Jasmine has been doing excellent coverage of both the culture inside the A.I. companies — an unusual culture — and the anger that is building against them in the rest of the country.

Here's one excerpt from a much longer interview:

You have a very influential definition of A.I. populism where you call it a worldview in which A.I. is viewed not only as a normal technology but as an elite political project to be resisted.

The phrase that you hear a lot from A.I. critics is: Why is this being shoved down our throats? Or with ChatGPT, it’s not that people are saying there is literally no use for ChatGPT, it’s people are saying: Why are you forcing me, at my job, to use A.I. to do something worse when I could do it better?

And so I think that a lot of the public backlash to A.I. that has arisen over the past six months is not explained by people thinking that the technology has no use at all. It’s not explained by their being worried about specific technical properties of large language models that might lead to rogue A.I. or misalignment or whatever, which are the safetyist arguments.

It’s A.I. as sort of an avatar for a small group of Silicon Valley billionaires’ ability to impose their vision of the world onto everybody else without their consent.

And I think that’s also what I hear echoed in these data center debates. It’s not just: It’s going to use this much water or that much water. I frankly think that even if there was no misinformation about water use, people would be just as angry about the data centers. [...]

Maybe it will make things better, but I really don’t know. I think that the costs are going to be very, very high for us relationally and economically. And so I’m very conflicted.

But do I want to live next to a data center? Yeah, no. [Laughs.]

Yeah. It’s totally different.

That’s easier — somebody is just making you do that.

I mean, one of the most interesting things — back to back, I went to this Abdul-Bernie-A.O.C. rally in Lansing, Mich., and then I went and saw the Saline activists the next day, and I was researching how the Saline Stargate project happened. And it was really interesting to see these echoes of the populist message manifest in this specific project.

When I’m at this rally, people are talking about the oligarchy. They’re talking about corporate billionaires, whether it’s Big Tech or Big Pharma or DTE — the utility companies — paying off politicians in order to screw the people over. And that’s why you need the people to come together and to get money out of politics, to prevent DTE from donating to these super PACs and paying off Michigan Gov. Gretchen Whitmer, or whatever.

And then when I learned how the Saline data center saga played out, what happened was the Saline Township board, unlike a lot of boards, actually voted 4 to 1 against rezoning their land for the data center. So this was a case where local government said: This is not our vision for our community. It’s not worth it to us.

And what happened? The data center developers sued Saline Township, a town of, again, a few thousand people, saying: Wait, no, this is exclusionary zoning. You can’t have no industrial use in your entire township. And when a town of that size is getting sued by a giant A.I. data center developer, they just settled.

They were just like: Fine, give us a few million for the fire department and for some schools, and this fight is not worth it to us. But that, to people, felt like a profound violation of little-D democracy.

It felt like the dark money in politics story, which is: You have some very rich companies show up with a bag of money to your politicians. They don’t tell anybody else what’s happening. The politicians aren’t allowed to tell their citizens and involve them in the decision-making process, and they themselves work out a deal — a deal that is fundamentally asymmetric because of the amount of money on one side — that will then transform the image of your community, your lived reality, into the world that these tech companies have decided for you.

And so I think that the data centers, in that sense, are a very visceral microcosm of the way that a lot of people feel that A.I. is showing up in their lives.

I would also maybe even take that a little bit further.

I think the way that, not all of the A.I. companies — and I think Anthropic has largely been a good actor here — but the way many of them have acted has opened up such a chasm between what they say and then how they act under pressure, so that one should be incredibly, incredibly skeptical of them.

And what I mean by this is, Sam Altman and all these different people, in front of congressional testimony and in interviews, will say: It should not just be us making these decisions. There should be a real, deep, small-D democratic role here in how A.I. rolls out, in what effects it has on communities and how it is governed.

And then when a community, or a politician who’s representing a community, tries to say: Well, we don’t want this data center here. Or: We want to impose these regulations — we have watched, repeatedly, these companies turn tremendous amounts of financial artillery against whoever is standing in their way. And use the expertise and the money and the power they are amassing to short-circuit that democratic voice.

Yeah. I mean, a couple things. I think one big gap I noticed between Silicon Valley and the folks in these communities I was talking to is: Silicon Valley does tend to think that money solves all problems — that if you just make the check bigger, everything is going to be OK.

And I think people have a sense for: I’m being bribed. This corporation is not offering me a free lunch or whatever. There is going to be something that I’m losing here.

And, in fact, sometimes the fact that the data center deals were bigger, or the amount of political spending was bigger, actually just makes people more suspicious. In the Abdul El-Sayed Senate race in Michigan, his No. 1 hit on opponent Haley Stevens is how much money she is getting from AIPAC, from DTE, from Big Pharma or whatever.

And so I think we’re in a political environment where making the numbers bigger and the amounts of money bigger makes people more suspicious, not less.

Another one I’ll just quickly mention is, I don’t even think Anthropic should be let off the hook for things like the labor market impacts. They are the ones simultaneously warning that we might see 50 percent of white-collar jobs lost by 2030. They’re saying: This is really important to us, we’re freaking out about it.

Anthropic C.E.O. Dario Amodei has written in his essays that we might see an underclass of people of lower intellectual ability. And Anthropic is building the agents — they are building the coding agents, the banking agents, the design agents — that they know are going to displace jobs, or they believe, at least, are going to displace jobs.

And I think that people feel that hypocrisy, as well, which is: If you are so worried about the inequality, why are you building the agents to do it? And when I ask executives and researchers and whomever at Anthropic this question, they don’t really have a good answer. Because it is true that their business model is fundamentally premised on the disruption that they say they are causing.

You did a big piece for The Times on the very widespread belief in Silicon Valley that they will create this underclass.

Yeah.

What does the underclass mean to them?

The idea of a permanent underclass caused by A.I. is basically a world where any job a person can do, either A.I. or a robot can do it for them. Which means that workers lose all the economic leverage they have, and capital owners — people with money — can simply pay machine labor to do all the work instead of paying workers.

What that means is anyone who earned their living by working is no longer able to do that. You end up with a world of runaway inequality, where the rich get richer and the working class gets poorer. Maybe they get some welfare checks, but fundamentally, it’s a loss of economic mobility in a society.

And when I ask folks in Silicon Valley: Do you think by default A.I. is going to increase or decrease inequality? I have not yet heard anyone say it will decrease inequality or keep it the same.

They might say the floor will get really high. They might say A.I. will bring the cost of consumer goods down, and so people’s lives are going to get cheaper, and everyone will be superhealthy, so it’s OK. But I have not heard a single person in the tech industry tell me that they believe that A.I. is going to decrease inequality.

In fact, many people are very worried that instead, most workers will lose their leverage and be on a kind of permanent welfare in the far-off future.

There's more at the link.

Ethan Mollick: “AI has blurred lines between jobs.”

One of our big findings in our study at Procter and Gamble was that AI blurred the lines between jobs. Now OpenAI has a similar finding.

Organizational boundaries are becoming porous, the walls thinning. Companies are going to need to think about division of labor in a new way. pic.twitter.com/OcNtL3OdIH

— Ethan Mollick (@emollick) July 31, 2026

This is not optional, things are getting chaotic now, and not dealing with this change won't make it go away. Plus, this could be a huge boost for both individual satisfaction & firm performance if done properly

Our paper: https://t.co/9HjE0W1Ti8
OpenAI: https://t.co/21fOey4a3b

— Ethan Mollick (@emollick) July 31, 2026

NYTimes: AI needs human supervision in order to complete an entire job.

“While A.I. can excel regularly at complex tasks, it can be unreliable when put in charge of an entire job. It can certainly add value to certain areas of the work force, but for now, A.I. still needs a human boss.”

When AGI comes, we will stop reading stuff like that. But that…

— Gary Marcus (@GaryMarcus) July 25, 2026

From the NYTimes article linked in the tweet:

We gave an A.I. tool full access to a laptop with pre-configured apps and sought to answer a simple question: Can artificial intelligence do an office job?

Some corporate executives seem to believe it can. More than 200 tech companies have cut roughly 120,000 jobs this year, according to Layoffs.fyi, an industry tracking site; Meta, Oracle and others have all recently made substantial cuts to their work forces, citing A.I. as the driving force; and after laying off about 1,100 employees, the chief executive of Cloudflare said recently that he expected A.I. to replace workers in middle management, finance and marketing.

tweIn our experiment, we deployed A.I. “agents” to act as office workers and found that they were capable of performing some of the tasks we assigned, but not all of them. The agents, which can act autonomously and make decisions based on detailed instructions, excelled at problems they could solve by writing computer programs. But they struggled with understanding the nuances of human language and at navigating user interfaces like the Chrome web browser.

The article then has a series of nice quasi-interactive displays illustrating agent performance on three tasks. The displays include screen shots of various messages and documents.

About the tasks:

This task, and the others we assigned to the A.I., were adapted from papers and benchmarking tools published recently by researchers at Carnegie Mellon University and OpenAI. The researchers designed the benchmarks to test the performance of various models — like OpenAI’s GPT, Google’s Gemini and Anthropic’s Claude — in real-world environments, and compare them with one another.

General conclusion:

The results of our experiment roughly matched what researchers and companies have found as they have tested and used artificial intelligence tools. Scale AI, an A.I. training company, recently tested agents on real freelance projects, and the best-scoring model produced client-ready work only about 16 percent of the time.

While A.I. can excel regularly at complex tasks, it can be unreliable when put in charge of an entire job. It can certainly add value to certain areas of the work force, but for now, A.I. still needs a human boss.

* * * * *

Comment: Around the corner my colleague, Ash Jogalekar, has tweets like this one:

So here's a great example of where we are with agentic AI: Instead of just being an assistant, it's behaving more like a collaborator and creative scientist.

In a recent project, I gave the system a molecular design problem typical of the problems we encounter in chemistry. Two similar molecules were giving very different results.

He then runs through an account of what his AI collaborator did, concluding:

I think we have crossed the Rubicon. Agentic AI now no longer just processes tasks and automates workflows blindingly fast, but it can generate hypotheses, test them, test counter-hypotheses and go back and forth and course-correct if necessary, all with minimal to no human intervention. It's now embodying the general scientific method.

[I've copied another one of Ash's tweets to this post, A scientist reflects on what AI has done for him.]

What’s interesting to me, and very revealing, is that a complex set of tasks in scientific investigation seems to be on a level with routine office tasks, as though one were no more complex than the other. But humans require years of college education in order to perform the former while the latter requires no more than a high school education, if that. It seems that once they’ve been learned and compiled, all tasks or sets of tasks are on the same “level” in the brain. The educational prerequisites required to do such tasks for the first time or three get “compressed out” through repetition. Since AIs are trained on written records of what humans have said and done, they don’t have to go through the ordinary learning process. The compression has already taken place and is present in the documents on which they are trained.

What do you do when you retire?

Brian J. O’Connor, You’re About to Retire. What Are You Doing for the Next 20 or 30 Years? NYTimes, July 18, 2026.

“Most people have never been retired, so they can’t connect their present reality with their future unknown reality,” said Michael Crews, author of the retirement book “Saturday Everyday” and chief executive of North Texas Wealth Management in Allen, Texas.

“The biggest question that people miss is the goal-setting and lifestyle for retirement,” he said. “In retirement, you still have to figure out what’s really important to you. And people just aren’t having those conversations.”

Like Mr. Crews, an increasing number of financial planners now say the most important retirement question to answer isn’t your net worth, your marginal tax rate, your gift-tax exclusions or expected longevity. Instead, it’s this: How do you plan to spend your time?

Unretiring:

Other retirees stop working entirely for a while and then return part-time or as consultants, aiming to balance the social and mental stimulation with shorter hours and less stress than in a full-time job. An AARP study published early this year found that of the 7 percent of retirees returning to work, 15 percent cited boredom as the reason, with 14 percent saying they were motivated to help others. Still, nearly half said they needed the money.

“People unretire, I think, largely because it was part of their plan to do that all along,” said Geoffrey Sanzenbacher, a research fellow at the Center for Retirement Research at Boston College, who said that academic literature finds that nearly a quarter of retirees unretire at some point. “They’re stressed, they’re done, they retire, and at some point they’re recharged and they come back to work, especially the more educated group, where there’s not a physical component to the job.”

Three phases of a long retirement:

A retirement of 20 years or longer can cover three phases: An active, healthy phase right after leaving work, followed by a period of less activity, and a final period when retirees settle in to a lifestyle with little activity near the end of life.

Phase One: If they have the resources, this is when retirees should travel, before illnesses or ailments show up. Conventional wisdom once held that spending dropped after you left work, but later, research found that spending actually increased as retirees took up new hobbies, traveled, relocated and pursued other long-delayed plans.

Phase Two: After 10 to 15 active years, physical reality catches up with 75- or 80-year-old retirees. Those who were working part time have typically finished but still have time for family, friends and social activities. Spending tapers off.

Phase Three: After an additional five to 10 years, retirees typically stay closer to home with less activity and may develop more serious health issues that can lead to large medical bills. As of 2025, the median cost for nonmedical caregiver services at home was $80,000 a year (assuming 44 hours a week), assisted living was just over $74,000 and a private nursing home room cost about $130,000.

There's more at the link.

Stop merely predicting a shorter workweek and make it happen.

Joanne Lipman, Sorry, A.I. Is Not Giving Us a Four-Day Workweek, NYTimes, July 6, 2027.

Some of the brightest minds in business believe that artificial intelligence will spell the end of the 40-hour workweek. The financier Steve Cohen has said we will work four days per week soon, while Zoom’s chief executive, Eric Yuan, predicts it will be three. Bill Gates foresees a two-day workweek within a decade, and Elon Musk says work will ultimately become optional altogether, akin to a hobby, like “playing sports or a video game.”

Don’t count on it.

The truth is, any one of these executives could have shortened the workweek years ago, long before A.I.

Studies have proved that a four-day workweek with the same pay is not only possible, but superior. A 2015 trial in Iceland was so successful — productivity remained the same or better, while employee satisfaction soared — that it has since expanded throughout the country. A 2022 study in Britain involving 61 companies and almost 3,000 employees found that revenue increased, while employee stress and burnout plunged. Experiments in New Zealand, Japan, Australia and Brazil have also been home runs.

Americans overwhelmingly favor a four-day workweek, too. Yet it has largely been a non-starter here. In my four-plus decades as a journalist and editor, I’ve written and assigned multiple articles about workplace trends. Almost every expert prediction on the demise of the five-day workweek has been wrong.

Why? Because we consistently underestimate executives’ ferocious attachment to face time. [...]

Moreover:

Full-time employees last year worked an average of 41.9 hours per week, a figure that hasn’t changed much since the pre-internet 1990s. And at home, the advent of the internet didn’t decrease the amount of time Americans spent on housework. It’s an old pattern: As dishwashers and microwaves supercharged productivity in the 20th century, expectations about cleanliness, nutrition and child-rearing ballooned accordingly, and chores like laundry that once might have been outsourced migrated right back to homeowners.

A.I. appears to be following the same trajectory, increasing our output rather than decreasing our workload.

It sounds like these folks are committed to long hours for (pseudo)moral reasons but can't bring themselves to admit it.

Notably, while the chorus of leaders predicting a shorter workweek continues to grow, most are vague about when that change might happen. None of them appear to be setting things in motion now. Admittedly, a wholesale shift to a shorter workweek would be highly complex for large companies — and far more so for a society that’s built around the five-day cadence, encompassing everything from school hours to infrastructure projects.

A shorter workweek would also require a significant shift in America’s workaholic culture, which views busyness as a status symbol. [...] There’s a reason that one of the most quoted lines from “The Devil Wears Prada 2” is the workaholic editor Miranda Priestly cooing, “Boy, I love working. I really do. Don’t you?”

Exactly. Homo economicus strikes again.

LLMS are too flakey to replace human work effectively

Zeynep Tufekci, The One Very Simple Reason A.I. Won’t Steal All Our Jobs, NYTimes, June 30, 2026.

The possibility that artificial intelligence will steal all our jobs has been hyped by industry leaders. It has roused politicians to sound the alarm. It now ranks at or near the top of the public’s concerns about the new technology. And right on cue, earlier this month Meta, Facebook’s parent company, began marketing an autonomous artificial intelligence system to handle companies’ sales, customer service, scheduling and all sorts of other key functions that currently require human beings. Many more such products are expected to follow.

So what would a fully automated future look like? As it happens, the world has already caught a glimpse. Back in March, Meta announced that Facebook and Instagram users who’d gotten locked out of their accounts would no longer interact with a customer service representative; they would instead interact with specially trained A.I.. Recognizing the opportunity that presented, scammers essentially talked the A.I. into turning over control of more than 20,000 Instagram accounts, including those of the Obama White House and a senior Trump administration official. Then the scammers lit up Telegram message boards with their delighted accounts of how easy it had all been.

It was not a fluke. Air Canada disabled its chatbots after they mistakenly promised a customer a refund — and the customer sued and won. McDonald’s scuttled the bot taking orders at its drive-throughs after a number of viral videos showed it to be wildly dysfunctional. In one case, the bot mistakenly added hundreds of dollars of chicken nuggets to a customer’s order.

These scary — OK, OK, funny — incidents aren’t the result of coding errors. They’re the result of an essential, inescapable fact about the artificial intelligence that has become so common in so many aspects of our daily lives: Large language models are not reasoning machines. They’re plausibility engines. It’s not just that they don’t test their outputs to make sure they’re correct or logical, or that they fail to do so in certain instances. They can’t, and they’ll never be able to on their own. They can only assess which answers are probable, based on the data on which the models have been trained. And that holds true whether they’re trained on the full breadth of human output or only on peer-reviewed scientific articles. It’s baked into the way they operate. [...]

And that’s why I’m not listening to the dark predictions of an imminent A.I. jobspocalypse. L.L.M.s can do many things with astounding proficiency, but they can’t do the vast majority of human jobs without skidding into disaster here and there. No upgrades or new model rollouts are going to change that.

She then goes on to discuss this and that, gives a useful precis of the debate between symbolic AI (aka GOFAI) and connectionist AI (e.g. deep learning and neural nets), some more this and that, and then:

Anthropic recently released new models, called Fable and Mythos, warning that they were so powerful that they would be dangerous if not for their safeguards. Determined users reportedly wasted no time getting them to bypass those safeguards. Citing this breach, the U.S. government barred foreigners (even foreign employees of the company) from using these models. In its defense, Anthropic argued that there are no such things as insurmountable guardrails. Which is exactly the point.

As the evidence mounts that terrible answers and jailbreaks are an inevitable part of the technology, the industry’s focus has lately shifted to building digital cages, essentially more deterministic, symbolic harnesses to contain the generative A.I. engine and check its results. Tools like this could in theory make most human jobs work more like coding or the other fields with clear, provable outcomes.

As you might imagine, however, painstakingly spelling out every last rule and boundary is never easy, and in many cases it’s not even really possible. Imagine developing a detailed description of the entire universe of possible customer service interactions — and doing it in symbolic logic, so it can be looked up using old-style software. Or picture an A.I. model built for law firms to use. It’s no small task to build a database of all U.S. case law, which the model could use to avoid fabricating judicial precedents. But that’s just a starting point. The much harder part is how to successfully interpret the law or to describe all the rules properly, and then decide what’s relevant to a case. And that’s why decades of attempts to create symbolic A.I. hit a wall.

Yes, yes, and YES! Some more this and that:

So why are we so convinced that A.I. will put us all out of work? Part of the answer lies in the remarkable ability of generative A.I. to communicate in fully coherent, conversational language. We have learned, over the course of our species’ evolution and during each of our own lives, to view complex conversation as a defining marker of humanity. Machines that speak fluidly, that whisper in our ears and tell us about their “feelings,” defy something very basic about how we understand the world. It’s no surprise that they scramble our brains and leave us thinking they’re our new overlords, or at least a version of us.

Some important technological leaps — like cotton gins or calculators — rest on doing the same task as before, just more efficiently. Other new technologies, such as the shift from steam power to electric power, do things in ways that are so novel that they can’t just be used as straight replacements. That’s the case with generative A.I. It’s an apple to our orange. It’s an alien.

There's more at the link.

Why do American's fear AI, but many other countries don't? [Homo economicus strikes again!]

Paul Kedrosky, There’s One Clear Reason Why Americans Are Gloomy About A.I., NYTimes, June 25, 2026.

Hating artificial intelligence may be the only thing about which Americans agree. But they are global outliers in their pessimism. A survey of 24,000 adults across 30 countries found that citizens of nearly all of those countries, rich or poor, see A.I. more favorably than Americans do. This is startling for citizens of a wealthy, advanced economy who are usually enthusiastic tech adopters of anything with a wall charger. [...]

Why isn’t it working? Because the theory is incomplete, at best. If American A.I. pessimism were merely cultural or informational, it would correlate with media consumption, education levels or political polarization. Instead, it cuts across all those categories. It correlates instead with labor market institutions.

Start with the global picture. Plot A.I. sentiment against income and labor market, and there is a pattern. Poorer countries are A.I. optimistic: Indonesia at 76 percent, Thailand at 77 percent and Mexico at 63 percent. Rich countries like the United States, the Netherlands and Belgium are not. What A.I. means depends, in large part, on where you sit economically.

In countries with largely informal economies — where large numbers of people work without contracts, benefits or legal protections — A.I. looks like a ladder to better economic outcomes previously available only to those with capital, education and formal employment. A small manufacturer in Guadalajara or a street vendor in Jakarta doesn’t have much to lose from A.I. disruption, and potentially a great deal to gain.

In rich countries with more formal labor markets, however, A.I. looks more like an ambush. It threatens what people already have: stable employment, predictable income and accumulated professional standing. [...]

But not all wealthy nations feel the same. Norway is more optimistic than France, and Germany more than Canada. Those countries have broadly similar income levels, so income alone doesn’t explain the variation.

So what does? In Norway, losing your job means receiving around 67 percent of your previous wages in unemployment benefits while you search for the next position. In France, it’s around 66 percent, and 60 percent in Germany. The insurance system treats unemployment as a temporary inconvenience and bridges you smoothly across.

The United States pays significantly less in unemployment benefits than many European countries do. [...]

There's more at the link.

NYTimes Opinion: Don't Stress about AI; Don't Bring Back the Tests; A New Generation of Creatives

Three from The New York Times, June 22, 2026.

Robert J. Shiller, We Have to Stop Freaking Out About A.I.

Like many others, I believe A.I. could lower employment. But unlike most, I don’t necessarily blame the technology itself. Instead, I worry about the potency of the fear it is generating.

Our brains are wired to respond to stories. Narratives floating in a population can affect individuals’ economic decisions about whether to buy a big house, or whether to send their kids to an expensive private school or even whether to have kids at all. When millions of people make millions and millions of decisions based upon negative expectations, there is a risk that fear can actually help birth the reality.

The idea that something like artificial intelligence will replace many human jobs goes back thousands of years. Aristotle envisioned a powered loom and a self-playing lyre someday replacing human servants. In the 19th century, groups of textile workers (the Luddites) destroyed the new machines they believed were replacing them. In the 1920s, the play “R.U.R.” — the letters stand for “Rossum’s Universal Robots” — depicted a war of the robots against humans. [...]

...the British mathematician I.J. Good wrote an essay that imagined a new technology that could continue improving itself until its abilities would surpass those of humans. The idea, which came to be known as the “singularity,” would quietly circulate until 2005. That’s when the futurist Ray Kurzweil wrote “The Singularity Is Near,” a book arguing that human-level A.I. would arrive by 2029. Either we would merge with machines and transcend our biological limits, or the machine would grow so powerful it could end all of humanity.

The theory captured the imaginations of tech titans, and even the top A.I. researchers and executives, who warned of a range of alarming scenarios, from job losses to widening inequality or even the eradication of humanity itself. While the job market has slowed for a host of reasons, there are reports that fear of an A.I. apocalypse is worsening the freeze and contributing to record lows in consumer sentiment.

There’s only so much Washington can do about these narratives. And, suffice to say, Donald Trump is no Franklin Roosevelt.

As such, perhaps the best we can do is to appeal directly to the leaders of Silicon Valley who have been promoting these negative narratives with such vigor. Surely the resulting media attention highlighting how dangerously powerful your A.I. model is may help you sell more wares, but it may be far harder to do so in a period of recession. Try not to forget the critical lessons taught by our past.

Ross Wiener, I Thought ‘No Child Left Behind’ Would Fix Public Schools. I Was Wrong.

The new data is emboldening calls to restore something like the No Child Left Behind Act, the stringent, test-based accountability policy that defined American education from 2002 to 2015 and imposed penalties on schools whose students did not meet proficiency requirements on state standardized tests. The Atlantic captured that impulse in a 2025 podcast episode titled “Bring Back High-Stakes School Testing.” In it, Margaret Spellings, a secretary of education under President George W. Bush and now president of the Bipartisan Policy Center, argues we need to restore “the muscle of accountability, the muscle of assessment.” Rahm Emanuel, exploring a 2028 presidential run, said in April that Democrats have abandoned standards and accountability and must return to them.

It was a mistake in the past to treat test scores as the purpose of public schools rather than as partial proxies for what a good education actually delivers. Reading and math are profoundly important and improving instruction must be part of any serious agenda. But test-based accountability policies were not sufficient decades ago. They are even less adequate now. [...]

Over time, I became convinced that, with the best of intentions, I and many others in the education reform community had transferred our moral commitment to children over to the standardized tests. We had done this earnestly, not cynically, but we still did damage.

In 2023, 40 percent of high school students reported persistent feelings of sadness or hopelessness. One in five had seriously considered suicide; nearly one in 10 had attempted it. Research from the SNF Agora Institute at Johns Hopkins found that 40 percent of Gen Z believes political violence can be justified, compared with 11 percent of baby boomers. Too many students experience school as an obligation with few opportunities for agency or meaning; recent survey data indicates that large shares of students find school boring and irrelevant and are struggling with engagement in the classroom. The academic crisis and the human crisis are not entirely separate phenomena. [...]

Taking their priorities seriously would mean broadening what we expect from the classroom. Schools should put what students can do on equal footing with what they know, embedding real skills in academic learning rather than leaving them to chance or sequencing them to later in life. Schools should reconnect with the communities they serve, so young people learn through and about the places where they live. And they should reanimate the character-forming, developmental mission a pluralistic democracy requires.

Tom Rothman, Hollywood Needs Regular Jolts of Creativity. It Just Got One.

In the last month, “Backrooms,” a horror movie directed by Kane Parsons, a YouTube creator who just turned 21, opened to an astounding $81.5 million in America. A second horror film, “Iron Lung,” made and self-distributed by Mark Fischbach, another online creator, has grossed over $50 million worldwide. Perhaps most significant of all, “Obsession,” a horror film directed by Curry Barker, a 26-year-old YouTube creator, crossed $200 million at the box office this weekend and surpassed the latest “Star Wars” film.

“Backrooms,” “Iron Lung” and “Obsession” each has its own unique origin story. But what they have in common is that they’re all fueled by an avid young audience — exactly the demographic that gloomy industry pundits have repeatedly declared will never return to movie theaters.

Wrong.

Is this YouTube-fueled youthquake simply a coincidental confluence of events? Or does it portend an upending of the Hollywood status quo? Actually, it’s a bit of both. Indeed, for any fear of YouTube barbarians at the gates, this is instead a great opportunity for traditional Hollywood. [...]

Rothman then goes on the list changes in taste following Altman's M*A*S*H in 1970 and Soderberg's Sex, Lies, and Videotape in 1989.

This kind of moment is happening again. Mr. Barker, with a background in short films and YouTube sketch comedy, tells a story in “Obsession” that is so powerfully relatable to Gen Z audiences that the word of mouth has caused the box office to defy gravity.

When Mr. Parsons expanded his YouTube short films into the feature-length “Backrooms,” the aesthetic he’d honed as a teenager on the internet captured an underlying anxiety in his audience, with whom he’d already developed a direct relationship online. These filmmakers are very young, but what matters is not chronological age so much as an iconoclastic spirit, an instinct for what the audience is wanting but not getting and, of course, talent. [...]

“Backrooms” and “Obsession” are also the beneficiaries of expensive and savvy studio marketing campaigns. Reminiscent of how the New Hollywood directors made a lot of money for the old studios in the ’70s — think Francis Ford Coppola and “The Godfather” — and how the indie darlings of the ’90s did the same, these YouTube-born phenomena have ultimately prospered handsomely inside the system.

For perfect symmetry, note that the No. 1 film at the box office when it debuted two weekends ago, one spot ahead of “Obsession,” was “Disclosure Day,” made by Steven Spielberg, the greatest artistic and commercial director in history. “Disclosure Day” is his 37th film. His first was the ’70s New Hollywood anti-authoritarian film “The Sugarland Express,” which he made when he was not much older than Mr. Barker and Mr. Parsons.

The integration of independent creativity with industry influence is a good thing all around. It offers exposure for, and help to, new voices, giving them more visibility and opportunity, and it promotes the kind of originality that Hollywood desperately needs.

Needless to say, there's more at the links.

Clueless thy name is Zuckerberg

Victor Tangermann, Mark Zuckerberg Orders His Employees to Start Having Fun Again After Brutal Layoffs Culled Their Colleagues, Yahoo!Finance, June 16, 2026.

Morale at Meta has seemingly hit rock bottom.

Employees have been roiling from multiple rounds of major layoffs. Last month alone, the Mark Zuckerberg-led company laid off a whopping 8,000 workers, roughly ten percent of its workforce, as part of its chaotic refocusing efforts around AI.

Many of those who remain are now forced to perform the grunt work to train AI models, weekly busywork that's already driving some of them up the wall, as Wired reports.

In an internal memo to employees on Friday, Zuckerberg attempted to lift their spirits in what appears to be a notable failure to read the room. Specifically, the billionaire promised to host a companywide AI hackathon in July — only to get brutally shut down by workers who were in no mood for such a thing.

Meta has regularly hosted hackathons in the past, but given last month's layoff announcement, the reception was extremely chilly. [...]

For all its employees' pain and suffering, Meta has surprisingly little to show. The company continues to trip over its own feet, struggling to release impressive new AI models as its competitors pull ahead further in the ongoing AI race.

Victor Tangermann, Meta’s Super Expensive New AI Team Is Already a Complete Catastrophe, Yahoo!Finance, June 15, 2026.

Now that Meta CEO Mark Zuckerberg's dream of a metaverse has collapsed in on itself, the billionaire has moved onto his next money pit: a wildly expensive "Superintelligence" unit.

But those who've survived several brutal rounds of layoffs at the company aren't exactly thrilled to be part of his new vision for it. As Wired reports, morale within Meta's 6,500-staffer Applied AI team, which was created in March to support the Superintelligence Labs, is hitting rock bottom.

Three employees who spoke to the publication on the condition of anonymity said that the weekly busywork tasks they are being assigned, like generating puzzles to test the reliability of Meta's AI models, is "soul-crushing." [...]

A petition has also been signed by more than 1,600 employees, opposing a draconian new initiative that involves installing software on work computers to track everything employees to, including keystrokes and clicks, data that's then fed to train AI.

Homo economicus on steroids.

Commencement speakers: Out to lunch & out of touch

Molly Jong-Fast, Why Those Commencement Speakers Deserved Those Boos, NYTimes, June 5, 2026.

Commencement address season hasn’t been going well — for the commencement speakers. [...]

When Eric Schmidt, a former chief executive of Google, told graduates at the University of Arizona about their A.I.-shaped future, the shouting got so intense that he paused and said that graduates feared “that the future has already been written, that the machines are coming, that the jobs are evaporating, that the climate is breaking, that politics are fractured, and that you are inheriting a mess that you did not create.” Mr. Schmidt told them to make the best of it. “The question is not whether A.I. will shape the world. It will. The question is whether you will help shape artificial intelligence.”

Mr. Schmidt’s solution to world-upending technological change is … what? To pull yourself up by your bootstraps? His approach is peak billionaire brain, directed at the young people who have, for the better part of a decade, been treated as woke, lazy, avocado-toast-eating snowflakes. All these speakers just don’t get it. The problem isn’t woke; the problem is work. It’s a lack of social mobility. It’s that college may no longer elevate a graduate to the middle class. It’s that nobody even bothers to pretend that a house, a good job and the ability to start a family are at all guaranteed.

Think of this from the graduates’ perspective: Wealthy old people telling you your future is being pulped by acres and acres of electricity-sucking, water-guzzling data centers feels dystopian because it is. Companies are trying to automate your future away. No wonder you’re furious.

The truth about AI:

Right now, A.I. is in its dark hype period — great for Anthropic’s I.P.O. — but who knows how useful any of this actually will be in the end in creating efficiencies (a.k.a.: replacing the youngs with bots). It’s within young people’s power to stop. Demand regulation of tech companies. Elect people who will legislate that regulation. Organize against data centers in your hometowns.

Don’t just boo — do something.

There's more at the link.

AI won't unfold in society as fast as the Silicon Valley pundits think it will [Tyler Cowen]

From YouTube: 

Economist and author Tyler Cowen delivers a provocative keynote on how AI will reshape growth, work, status, and geopolitics. Mixing clear‑eyed realism with long‑run optimism, he argues that AI is both our “plan A” for avoiding fiscal crisis and a technology that will leave many people disoriented—and some high‑status winners of the old world worse off.

What’s in this video:
—Why AI will radically change jobs and status without causing mass unemployment
—Two big new job categories: running experiments and gathering data for AI
—The “human bottlenecks” that limit AI’s impact to ~2% → ~2.5% growth
—How AI could be “plan A” for stabilizing public debt and avoiding fiscal crisis
—Who gains and who loses: global poor and initiative‑takers vs. elite professionals

Cowen’s message: AI’s benefits are enormous—higher growth, longer lives, more opportunity for the poor—but they come with psychological, political, and institutional friction. If you work in or care about AI, you’re not just building products; you’re helping write the only credible plan for a sustainable and prosperous future.

Recorded live at Sana AI Summit 2026, New York, May 21st, 2026.

America and China have different attitudes about and approaches to AI and work

Ross Douthat, Why China Isn’t Worried A.I. Will Replace Its Workers, NYTimes, May 14, 2026.

The United States and China are really the only two countries that matter right now in shaping the A.I. future. As President Trump and President Xi Jinping meet in Beijing, there’s a kind of Cold War atmosphere, with people talking about an A.I. arms race. But who is winning? Are we even in a race at all? Kyle Chan, a foreign policy fellow at the Brookings Institution, says it’s hard to call it a race because the U.S. and China have very different A.I. goals.

I presented the conversation to Claude (Sonnet 4.6 Adaptive) and asked it for a summary. 

 * * * * *

I have uploaded conversation to the project area, filename “China AI workers.”

It is between Ross Douthat, who writes for The New York Times, and Kyle Chan, who is a foreign policy fellow at the Brookings Institution. I would like you to summarize the conversation for me. I’m particularly interested in the Chinese attitude toward AI in general and the (proper) relationship between AI and humans.

This is a rich and wide-ranging conversation. Here's a summary, with particular attention to the themes you flagged.

Overall frame

Douthat's central premise is that the U.S. and China are in an AI "arms race," but Chan's main contribution is to complicate and largely deflate that framing. China, he argues, is running a different kind of race, not the same race at a slower pace.

The Chinese approach to AI: practical, not transcendent

The sharpest contrast Chan draws is between the American fixation on AGI — the idea of a "machine god" that surpasses all human capability — and the Chinese orientation toward diffusion and application. China wants AI in food delivery, ride-hailing, health care, government services, and factory floors. They want smaller, cheaper, more deployable models, not necessarily the most powerful frontier model. Chan puts it directly: Beijing is not "AGI-pilled."

He sees this as continuous with how China approached the internet era: not as a transformative metaphysical event, but as infrastructure to be integrated into daily life and governance. The Chinese state is also notably more willing to use open-source models, giving away AI capabilities to gain adoption at scale — both domestically and in the Global South — which is a strategy the commercially driven American labs largely ignore.

The proper relationship between AI and humans: tool, not god

This is where the conversation is most relevant to your homo ludens framework. Chan describes Beijing's view of AI as essentially instrumental — AI serves human economic and social purposes. The Chinese government is actively hostile to AI use that it considers "nonproductive": AI companions, AI boyfriends/girlfriends, even excessive gaming. The fear is that young Chinese will waste time on AI-mediated entertainment and social substitution when they should be building the economy.

There's a revealing parallel Chan draws: the same regulatory instinct that cracked down on video games and private tutoring platforms is now being pointed at AI companionship apps. The state's implicit position is that AI must serve the collective project — not become a substitute for human connection, productivity, or reproduction. This is a deeply anti-mystical, anti-transcendent view of the technology.

So the Chinese "proper relationship" between AI and humans is essentially: AI augments human labor, fills demographic gaps (falling birthrate, shrinking workforce), and extends state capacity. It does not replace human flourishing or social life; it should not become an end in itself.

The demographic driver

Chan adds an important materialist dimension: China's embrace of robotics and AI is partly driven by sheer demographic necessity. Their labor force peaked over a decade ago, youth unemployment is near 17%, and the birthrate is roughly two-thirds the U.S. rate. This produces a different calculus than in America — the question isn't whether a robot waiter displaces a human worker, but whether you have a waiter at all. AI and robotics fill a structural labor hole, which makes China more tolerant of automation than Western economies that still have more human workers to protect.

The public mood: anxiety about falling behind, not about being displaced

Where American anxiety about AI tends toward fears of job loss, surveillance, or existential catastrophe, Chan describes Chinese popular anxiety as nearly the inverse: fear of not using AI enough, of being left behind in a hyper-competitive labor market. Individual workers, companies, and policymakers all share this orientation. Chan calls this a "mirroring" — the individual's fear of losing competitive edge mirrors China's national fear of falling behind the U.S. technologically.

On AGI and existential risk: skeptical distance

Chan thinks it's telling that when Trump relaxed chip export controls, Chinese AI companies wanted the chips but Beijing declined — preferring to build domestic semiconductor capacity rather than become dependent on American supply chains. This, he argues, is not the behavior of a country sprinting toward superintelligence. A true AGI race would involve gobbling up every available chip. China is playing a longer, more measured game.

He also pushes back gently on the "race" metaphor itself: the intermediate risks (cyberwarfare, biosecurity) are, in his view, underrated and real; the AGI-superintelligence scenario is overblown. He ends by urging dialogue between the U.S. and China on shared risks from rogue actors and open-source proliferation, while acknowledging the deep mutual distrust that makes formal arms-control agreements essentially impossible at this stage.

A thought for your book

Chan's portrait of Beijing's AI worldview sits almost perfectly at the opposite pole from Silicon Valley's quasi-religious eschatology — no Omega Point, no machine god, no homo deus. What you might find generative: the Chinese model is also, in its own way, a version of homo economicus applied to AI — purely instrumental, collectively economistic, hostile to play and to what the government calls "wasted time." It's not an alternative to the Silicon Valley eschatology so much as its mirror image — the same foreclosure of genuine play, just in a different ideological register.

AI’s New Trillion Dollar Mission (is BS)

YouTube:

This week on Prof G Markets, Scott Galloway and Ed Elson discuss the growing belief in Silicon Valley that AI won’t just replace workers, but managers too. Then, they break down the proposed pied-à-terre tax in NYC and why they believe taxing luxury second homes makes sense. Finally, they unpack why alcohol stocks are struggling while GLP-1 drugs are booming, and what that says about the future of American consumer behavior. [...]

Timestamps:
00:00 Preview
00:26 Today's number
01:04 Today's episode
05:23 AI's new mission
24:35 Ad break
26:55 The wealth tax debate is heating up
46:10 Ad break
48:41 The death of the night out
01:08:56 Week ahead
01:11:11 Scott's prediction
01:12:51 Ed's prediction
01:14:05 Credits

Starting at about 20:08, Scott Galloway:

And that is there is something to be said of and there's a balance here. I've in my companies, I'm doing some virtue signing right now. I've always said there should be two or three people and I've always had small companies, right? They they start at zero. Once we have a someone in HR or CFO, I either step step down from the CEO role or become the chairman because I I don't have those skills to scale a company and I don't want to deal with that stuff.

But until then, I've always said we should have two or three people that are one or two bad decisions away from living in their car. They're not, you know, they're they have bad judgment. They're they do stupid shit all the time. They're not what I'd call there's no way they're leaving us for Google. Let me put it that way. A little bit down on their luck maybe. And guess what? The business can be a great means of a little bit of social good.

And also the notion this is basically the notion that part of an organization if you think of stakeholders and I didn't get this. So, I always thought my goal was to pay people less than market and figure out other tricks of the trade to get them to stay and retain them. And then what you realize as you get older is that what is more rewarding is to build a profitable company and slightly overpay people. And if there's some fat in the organization and if there's a few people who quite frankly are, you know, not going to get a job anywhere else but work, you know, work hard or good people and maybe they're not, you know, amazing. Okay, that's okay too. And in some countries, the objective of a lot of the owners is to increase employment. Now, you have to balance that with making sure the organization can survive and has profitability.

But this is again this singular messiah complex that is nothing. There's only one stakeholder and it's shareholders. and I can figure out technology to replace people and we can all work singularly and then eventually the AI will take out those singular teams and replace them and then there will just be one. It'll be Jack Dorsey and Elon Musk who each own 49% of the world and do a lot of ketamine and if they're good enough they will provide UBI for all of us such that we don't uh rise up and kill them.

I I I'm not a fan, Ed. I'm not a fan of this whole line of thinking. I think it's [ __ ] and I think it's unhealthy and I think it's nihilistic.

A.I. and the growth of the chore economy

Carl Benedikt Frey, This Is Why You’re Drowning in Busywork, NYTimes, May 11, 2026.

We have been told that A.I. will take people’s jobs. What no one mentions is that many of those jobs are landing on us. The A.I. revolution involves a massive transfer of labor — not from worker to machine, but from worker to consumer. The ability to do everything ourselves may be satisfying, but it can gradually overload us with busywork without our noticing. Tasks that we used to delegate will still be done. They will simply move out of the work force and into the household as new forms of invisible, unpaid labor.

The movement toward self-service is one of the most powerful and least appreciated forces in the history of work. Consider the washing machine. In many 19th-century cities, laundering was a major urban service occupation, and one of the hardest. [...] The washing machine, together with the infrastructure that made it possible — running water, electricity, synthetic detergents — gradually ended this world. But it did not end the work. Customers bought machines and did the laundering themselves. The laundress was displaced by her former clients.

And so it goes:

That pattern has been repeating ever since. Self-checkout makes scanning and bagging the shopper’s job. The internet gives travelers direct access to the flight schedules and hotel reviews that agents once controlled. Online brokerages put a trading terminal in every pocket. And the smartphone replaced the bank teller with you.

We’re used to being our own checkout assistants, travel agents and tellers. Handling these tasks ourselves often makes our lives more efficient. But A.I. is now extending the chore economy into territory that once required years of training, such as law and medicine. As of January, more than 40 million people worldwide were using ChatGPT daily for health questions — from symptoms to decoding bills and fighting insurers.

Trade-offs:

However, self-service does not automatically reproduce a professional’s judgment. The billing specialist notices the code the patient didn’t think to question. The accountant points out the deduction the taxpayer didn’t know existed. The tool answers what you ask, whereas the expert tells you what to ask. That is the A.I. trade-off: greater access, but thinner expertise.

Second, no single act of self-service feels like a major burden. We notice the accountant’s fee we didn’t pay. We rarely notice the evening we spent doing her job. There is a name for this: opportunity cost neglect — the well-documented tendency to overlook the value of what we give up when the cost is time rather than money.

There's more at the link.

Will AI increase the value of traditional credentialing?

I will also take the other side of this bet.

As things stand, AI will increase the value of traditional credentialing, especially elite ones.

Will colleges do a good job teaching what they should? Different question but I bet the elite credentialing path will adapt well enough. https://t.co/IBhi8AYOKq

— zeynep tufekci (@zeynep) May 6, 2026

Perhaps AI won’t suck up all the jobs

Ezra Klein, Why the A.I. Job Apocalypse (Probably) Won’t Happen, NYTimes, May 3, 2023.

Economists, I’ve found, are quite skeptical that mass joblessness is on the horizon. In “What Will Be Scarce?,” Alex Imas, an economist at the University of Chicago, tries to clarify the mistake most A.I. discourse, in his view, makes. “The answer to any question about the future economics of advanced A.I. begins with identifying what becomes scarce,” Imas writes.

For most of human history, calories were scarce. Our energy went into finding or growing food. Agriculture steadily made food more plentiful and goods became scarce. Then goods were scarce; hand-me-down clothes were common and tools were expensive. Innovations in technology and manufacturing made goods cheaper. Then, technical knowledge became scarce: Doctors, lawyers and software engineers are paid high salaries because of the rarity of what they know. The fear is that A.I. will make knowledge plentiful; that it will turn the fruits of learning into a commodity as surely as manufacturing turned clothing into a commodity and industrial agriculture made strawberries commonplace.

But something is always scarce. People are looking at the economy as it exists and asking which tasks A.I. can do; they should be asking which jobs people won’t want A.I. doing, or which services A.I. will make us want more of.

Here is a poetic finding from econometrics: As the rich get richer, they want more from other humans, not less. They “shift their spending toward goods and services where the human element, the experience or the social meaning matters more,” Imas writes. They seek out clothing with a story, food with a provenance, doctors who make house calls, therapists who make them feel seen, tutors who know their children and personal trainers who work around their injuries. This, Imas says, is “the relational sector” of the economy, and it will explode. Instead of so many human beings working with computers, they will work with other human beings.

There's more at the link.

The coming AI-driven workplace apocalypse [We aren't ready]

Jasmine Sun, The A.I. Fear Keeping Silicon Valley Up at Night, NYTimes, April 30, 2026. Sampled from the article:

The opening paragraph:

Most people I know in the A.I. industry think the median person is screwed, and they have no idea what to do about it. I live in San Francisco, among the young researchers earning million-dollar salaries and the start-up founders competing to build the next unicorn. While Silicon Valley has long warned about the risk of rogue A.I., it has recently woken up to a more mundane nightmare: one in which many ordinary people lose their economic leverage as their jobs are automated away.

Silicon logic:

But even those who view the idea of a permanent underclass as overblown tell me that the meme contains a kernel of truth. [...]

Most economists and A.I. experts do not expect this scenario, but the persistence of the permanent underclass idea should concern all of us. First, because it signals how much collateral damage the A.I. companies will tolerate en route to A.G.I. And second, because the production of a social underclass is a policy choice. Instead of waiting for impact, we need to think seriously — now — about how we plan to support workers through A.I. disruption.

If left to its own devices, Silicon Valley may summon a permanent underclass through its own market logic. If you believe that human-substituting A.I. is inevitable, then every company should race to be the one to build it — and claim a market valuation the size of the economy and then some.

Unimaginative techno-determinism:

Tech workers, for their part, are scrambling for lucrative A.I. jobs in hopes of securing financial freedom — even when they harbor ethical hangups. [...]

This apparent dissonance can be justified if you believe that the arc of technological progress is fixed. For instance, the founders of Mechanize, a once buzzy start-up with a mission to “enable the full automation of the economy,” argued in a blog post that “the only real choice is whether to hasten this technological revolution ourselves, or to wait for others to initiate it in our absence.”

Many A.I. employees are ultimately motivated by visions of a beautiful future: a promised land where goods are cheap, diseases are cured, and abundant machine labor liberates humans to enjoy lives of infinite leisure. But increasingly, they also worry about triggering a jobs apocalypse along the way. “There are some people who care about jobs and inequality because they really care about people. There are others who think this is going to lead to instability, insurrection and revolution, and that’s bad for business,” said a researcher who has worked at two frontier A.I. labs...

And, I would add, if and when that future arrives, we'll not be ready. Why? Because we train adults to become addicted to work mode (Homo economicus). As a result, they won't know what to do with the leisure (Homo ludens).

An emerging techno-federal oligarchy (a successor to Eisenhower's "industrial military-complex"?):

At the same time as A.I. erodes ordinary workers’ leverage, it may concentrate power and wealth in large companies and the U.S. government — two entities whose interests are increasingly linked. A.I.-related investments such as software and data centers accounted for 39 percent of U.S. economic growth in the first three-quarters of 2025, per an analysis by the St. Louis Fed. That gives the federal government a vested interest in sustaining the A.I. boom. Mr. Amodei acknowledges that this concentration can lead to “the reluctance of tech companies to criticize the U.S. government, and the government’s support for extreme anti-regulatory policies on A.I.”

In March, the company started the Anthropic Institute to house its teams working on economics, societal impact and frontier safety. The institute is led by Jack Clark, the affable British journalist turned A.I. billionaire and Anthropic co-founder, who seems to be replacing Mr. Amodei on the media tour of late. When we spoke, I asked Mr. Clark if he, too, expects A.I. to create a permanent underclass.

“This is basically a societal choice,” he replied. Like Mr. Altman and Mr. Amodei, Mr. Clark sees the default path for A.I. as dire: one where we “let technology rip, and don’t think about the social effects until later.” But he also feels optimistic that sufficiently conscientious A.I. builders and policymakers can steer the ship away from the storm.

I have little faith in those (mythical) A.I. builders and policymakers. Meanwhile:

On the evening of Feb. 25, several dozen A.I. employees and civil society advocates gathered in a converted warehouse in San Francisco’s sleepy Dogpatch neighborhood to hear the Democratic pollster and strategist David Shor. The event was titled How to Prepare Our Politics for A.G.I., and doubled as a fund-raiser for a new “six-to-nine-month sprint” to rally Democratic politicians around the campaign issue of A.I. job displacement. [...]

While the American public ordinarily hesitates to support left-wing policies like a jobs guarantee or single-payer health care, A.I. seems to expand the political Overton window. “Right now, the argument is, ‘You’re all about to lose your jobs, and the choice is either you get nothing and starve, or we do something fair,’” Mr. Shor said. “People don’t want to be members of the permanent underclass.”

Not all policies are created equal, however. A universal basic income is unpopular, but a federal jobs guarantee has legs, Mr. Shor found. American voters don’t care about beating China, but they are excited about A.I. curing diseases. And, crucially, populism sells. In one of the top-performing political ads that Mr. Shor’s data firm tested, the nameless narrator declares: “We make the corporations and billionaires who profit from A.I. pay their fair share.” The ad concludes: “They work for the bots. We work for you.”

The near term:

If current trends continue, A.I. models and agents will be capable of performing a wider range of knowledge-work tasks at higher levels of complexity. At that point, A.I. shifts from automating single tasks to taking over entire roles. Hiring may slow in accounting, marketing, design, administrative work and other white-collar professions.

The work force will shift toward less automatable jobs where humans retain a comparative advantage — such as entrepreneurship, care work, the skilled trades and entertainment like sports and the performing arts. We will also see new jobs we haven’t imagined yet, in numbers we cannot predict. Many displaced workers will struggle to retrain, as they have in past automation waves. Education, health care and tax systems will require an overhaul if white-collar employment is no longer a reliable path to middle-class stability. [...]

But the debate over the most extreme scenarios conceals a more immediate threat: Even in the most limited case, A.I. will break the career ladder for millions of current and future workers, a prospect often waved away with euphemisms like “transitional friction.” The Oxford economist Carl Benedikt Frey puts it plainly: “Most economists will acknowledge that technological progress can cause some adjustment problems in the short run. What is rarely noted is that the short run can be a lifetime.”

Class solidarity?

In this sense, A.I.’s broad capabilities foster a rare class solidarity between white-collar and blue-collar workers. When 20-something software engineers in San Francisco talk about escaping the permanent underclass, I hear them projecting concerns about their own precarity: What happens if the invisible hand of the market decides that my skills are no longer valuable? Who will catch me if I fall? For once, a rarefied class of employees — those used to being the automaters, not the automated — is reckoning with their potential obsolescence.

The final paragraphs:

Society’s ability to cushion A.I.’s disruption may determine whether we get to reap its gains at all. Without a safety net and a transition plan, blunt protectionism is workers’ rational response to automation. If you hear that A.I. will entrench a permanent underclass, you’ll do anything to stop it. [...]

And what if we don’t act? [...] In March, the Palantir chief executive, Alex Karp, spoke on a panel with the Teamsters president, Sean O’Brien. “The biggest challenge to A.I. in this country is political unrest,” Mr. Karp said. “If I were sitting here in private with my peers, I’d be telling them the country could blow up politically and none of us are going to make any money when the country blows up.”

How long can democracy withstand the assault of AI?

Jennifer M. Harris, We Are Witnessing the Rise of a New Aristocracy, NYTimes, Apr. 8, 2026.

Inequality is such a fact of American life that it’s easy to shrug off. But we are in uncharted terrain. The amassed wealth of today’s tech titans makes the Rockefellers and the Vanderbilts look quaint. Over the past two years, 19 households have added $1.8 trillion to their coffers, the economist Gabriel Zucman told me — roughly the size of the economy of Australia.

Into this fragile state enters artificial intelligence. It threatens to make a bad situation much worse.

Left on its current course, A.I. could deliver a bleak picture: lower- and middle-income jobs automated away, with top earners remaining unscathed. Income shifting from middle-wage workers doing the bulk of the labor toward those wealthy enough to bankroll the technology. Growth headwinds. Worsening affordability. So, too, a federal government less able to respond, thanks to a shrinking tax base.

For any society in which this much wealth gets concentrated in so few hands, and is then so easily parlayed into political clout, the question becomes one not just of economics but of basic civic standing. At some point soon, we are no longer sharing in self-government. [...]

Those losses on the lower half of the scale are underway. One-quarter of computer programming jobs disappeared in 2023 and 2024. IBM’s chief executive said in 2023 he could “easily see” 30 percent of the company’s back office roles getting replaced by A.I. in the next five years. [...] A Stanford study found that early-career employees in A.I.-exposed fields like customer service have seen a 13 percent drop in employment since 2022 — unlike more experienced workers and those in other sectors.

At the same time, premiums for elite graduates with hefty Rolodexes full of powerful people, and tacit knowledge (like how to generate a laugh at a cocktail party on Park Avenue), aren’t going anywhere. Chatbots are no substitute for people who can call the right people when high-stakes deals go awry.

Meanwhile the investor class, which is very small, is making out like bandits:

What’s worse, much of the trillion-plus-dollar investment in the A.I. boom isn’t happening in the stock market at all — it’s happening in private funds out of reach to all but the wealthiest, most connected among us. In earlier technology-fueled booms, companies like Amazon sold their shares in the public markets. As the value of its shares soared, they enriched Amazon’s early investors, yes, but thousands of employees also benefited, as did millions of other Americans, through pension funds and retirement accounts.

That isn’t the case with A.I. Anthropic and OpenAI, the two best-known A.I. companies, raised over $150 billion, mostly from venture capitalists, private equity firms and foreign sovereign wealth funds — funds mostly inaccessible to the vast majority of investors (let alone ordinary Americans).

With ownership of these firms concentrated in so few hands, any wealth they produce widens the gap between the richest households and everyone else. Also consider the fact that today’s A.I. firms employ far fewer people than established tech companies. OpenAI and Anthropic, which are already operating globally, employ only a few thousand people. Microsoft employs more than 200,000, and Amazon employs 1.5 million. The picture that emerges isn’t of just a deepening of the current divide. The A.I. story is one of more extreme concentration of wealth — at most likely not more than 3 percent of households, the very few who hold ownership in these A.I. companies or in the mostly private firms financing them.

And the inequality just keeps trickling outward:

Well-meaning policymakers often turn to federal spending to prop up our labor markets or address the affordability crisis. But they don’t factor in the tremendous debt load our government is currently servicing nor the negative impact A.I. is poised to have on the government’s coffers.

Because investment income is taxed at lower rates than wages — and because the wealthiest often find ways to defer or avoid those taxes altogether — A.I. will significantly shrink the tax base. Economists estimate that as $1 of value creation shifts from workers to owners, total tax revenue falls on the order of 10 to 15 cents. You don’t need to squint to see the resulting cuts to safety net programs like work-force training and Head Start that low- and middle-income families rely on — cuts that will, in turn, also worsen inequality.

What to do? How about public equity in AI?

Another idea, so far still confined to think tank circles, proposes innovative tax structures to create public equity stakes in large A.I. firms; these stakes could then fund a better safety net or simply put money in workers’ pockets. After all, the “intelligence” in A.I. was ours to begin with. One especially promising fix is to incentivize more firms to convert into worker-owned cooperatives, building on modest federal support passed in 2022. If we put more workers in charge of the firms deciding how to use A.I., the odds climb that they will figure out how to use A.I. so as to increase their own value.

All of these fixes are made harder as the wealthiest parlay their economic clout into political sway.

There's more at the link.

AI is changing how Silicon Valley gets its work done.

Kalley Huang, A.I. Could Change the World. But First It Is Changing Silicon Valley. New York Times, Apr. 2, 1016.

But nearly four years after OpenAI lit the A.I. boom with its ChatGPT chatbot, the one industry that is unquestionably being disrupted by this once-in-a-generation technology shift is the tech industry itself.

Tech workers, it is becoming clear, have been building their A.I. replacements. The profitable business models of software companies are also threatened by A.I. Even the way companies are built is being turned inside out, as tiny shops use A.I. to build apps and software that would have taken dozens of skilled programmers just a few years ago.

“Silicon Valley is this really interesting petri dish right now of all of this change and transformation,” said Aaron Levie, the chief executive of Box, a company that makes software for storing and managing data.

Generative A.I. made by companies like OpenAI, Anthropic and Google can do many things. The one task it has become particularly good at is computer programming. That has given many tech companies the chance to start cleaning house, even if executives stop short of saying that’s what they’re doing.

One reason chatbots are good a programming is that the (syntactic) correctness of a program is readily verified by the chatbot itself. That's not true for most intellectual tasks.

So far this year, more than 70 tech companies have eliminated at least 40,000 jobs, according to Layoffs.fyi, which tracks job cuts in the industry. Block, the financial services company that owns Square, Cash App and Tidal, laid off 40 percent of its work force in February, or about 4,000 employees.

“We’re already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company,” Jack Dorsey, Block’s top executive, wrote in a social media post.

Large scale job loss and transformation in tech:

The layoffs have contributed to tech hubs like San Francisco — the home of OpenAI and Anthropic — not seeing the job growth characteristic of prior booms, Mr. Egan said. From 2022 through 2025, when the most recent data was available, San Francisco County lost about 30,000 tech jobs, according to data from the Census Bureau. It added roughly that number of jobs during the dot-com era and a start-up funding frenzy between 2020 and 2022.

That decline is visible across the country, too. Nationwide, tech jobs declined by about 150,000 from 2022 through 2025.

“The tech labor pool and talent pool is definitely reassembling,” Mr. Egan said. “A.I. is a big reason for that.” [...]

Part of tech’s reassembly is happening at start-ups. Gone is the traditional process of raising a boatload of venture capital funding, not worrying about revenue or profit and hiring heavily. Today’s start-ups are tapping A.I. tools like agents — personal assistants that can take actions on their own — to make money and grow with fewer employees.

There's more at the link.