The night featured several thought-provoking discussions, including outgoing Electronic Frontier Foundation executive director Cindy Cohn on Big Tech’s turn to the right; podcaster Dwarkesh Patel on the current state of the art in AI; and a conversation between AI 2027 co-author Daniel Kokotaljo and AI as Normal Technology co-author Sayash Kapoor on where they agree and disagree on AI’s near-term future.

I’m still a bit bleary-eyed after staying up late to take photos and chat with attendees, but I wanted to share some highlights of two notable conversations from the event.

One was with a guest we have pursued for years: Microsoft CEO and chairman Satya Nadella. Over a half-hour conversation, Nadella addressed the future of the Xbox, the AI backlash, and whether the industry's current frenzy of "tokenmaxxing" will ever show up in GDP. 

Another was with a founder I’ve known for years, but who we had somehow never interviewed for the podcast: Figma CEO Dylan Field. Field indulged us in a provocative conversation about the role of art in a world dominated by slop, why he’s still bullish on jobs, and — memorably — where he thinks Anthropic is most likely to stumble.

The full conversations will air on the Hard Fork audio and YouTube feeds over the next couple of weeks. Platformer will mostly be off during that time for our annual summer break. But we’ll have two more editions during that time, featuring the final two episodes of our podcast miniseries on AI and jobs. 

Thanks again to everyone who made the trip last night. And to everyone else, here’s hoping you enjoy these highlights.

Microsoft has been subsidizing your Xbox habit — and the AI buildout is making it worse

Hours before the show, Xbox leadership put out a memo warning of a coming "hard reset" for the business, seeming to confirm earlier reports that the division is planning layoffs. Nadella confirmed that changes are coming. He pinned rising console prices directly on the AI buildout's appetite for chips.

"Unfortunately, because of what's happening with the cloud and AI, the prices have gone up," he said. "It's happening with PCs, it's happening with phones — Xbox is impacted as well. The scarcity of the semiconductor supply, and memory in particular, are having a massive impact on consumer electronics all up."

As for the business itself, Nadella said, Microsoft needs its investment in Xbox to start generating profits. "We are not monetizing that entertainment,” he said. “In fact, if anything, we've been subsidizing that entertainment — there's more monetization of Xbox games happening on YouTube than at Microsoft."

Nadella called the chip squeeze "a temporal thing." The business-model problem, he suggested, is not.

Nadella thinks "tokenmaxxing" is addictive — but won't deliver the growth AI promises to create

In February 2025, Nadella told Dwarkesh Patel that his benchmark for AGI was 10 percent GDP growth. Sixteen months later, I asked him how that was looking. His answer amounted to a critique of how the entire industry is using AI right now — himself included. ("I'm a tokenmaxxer, too," he confessed. "It is addictive.")

"The hard truth is that the marginal cost of productivity improvement has to match the marginal cost of the token," he said. "You can't just say, hey, I love tokenmaxxing because it's money in my bank. The business has to benefit from it."

He continued: "Definitely what's happening right now, where everybody goes and vibe codes and token maxes — that's not a way to achieve 10 percent growth."

He understands the AI backlash better than most of his peers

Asked about booed graduation speakers and AI's dismal polling, Nadella didn't dispute the premise. Instead, he summarized the industry's pitch to the public in terms its harshest critics would recognize.

"You can't go out there and say, I have this unbelievable technology — except you're not going to have a job, and in fact, we're going to take all your water and all your energy, and good luck," he said. "No wonder there is so much anxiety. ... You can't deny that the perception is terrible."

His prescription: data centers "definitely can't increase prices on energy" and "should replenish all the water they use." He pointed to Microsoft's 20-year presence in Quincy, Washington — where he says the tax base has grown and local taxes have fallen — as a model for the industry.

He's open to the US government owning a piece of the AI industry

The idea of the federal government taking direct equity stakes in frontier AI companies has been floated by Sam Altman and embraced, at least rhetorically, by President Trump. Big Tech CEOs have mostly kept their distance. Nadella was notably warm.

"The idea that the United States ... has a sovereign fund, and the sovereign fund has equity stakes, and that somehow is part of what is considered the wealth of the citizens of this country — I think it's a novel idea," he said. "Other countries have done it. ... So I'm not opposed to innovative ideas like this. ... To the degree to which some of these ideas can be played out and they succeed, I think we'll all benefit from it."

(When Kevin asked what percentage of Microsoft the government should own, Nadella deadpanned: "MSFT — you can trade.")

The agent era's defining device might not be a phone

Nadella offered a bit more color on Project Solara, the "agent-first hardware" effort Microsoft announced this month at Build — describing a future of Windows PCs running trillion-parameter models locally ("unmetered intelligence") and wearable devices built for ambient AI.

"If I'm a nurse in a hospital and I'm walking from station to station, can I just take out this device which can scan, which can input, which can take my speech output and turn it into a prompt? That is what I think an agent-first device looks like," he said. "Our goal is to invent the new form factors that are not beholden to the old form factors."

Notably, for all the talk of superintelligence elsewhere in the industry, Nadella placed AI in the lineage of electricity and steam: "I'm not sitting there and thinking this is the last technology we ever would invent. I don't buy that. I feel like, yeah, this is, in the pantheon of all technologies, a big step up. But I do think that there will be more to come."

Bonus highlights: Dylan Field

Later in the evening, we sat down with Figma CEO Dylan Field.

On Mike Krieger's board exit. Asked about the Anthropic product chief resigning from Figma's board just days before Anthropic announced the distinctly Figma-adjacent Claude Design, Field was careful — but didn't exactly deny the implication. Would he seat another AI lab executive on his board? "Well, we saw how that went in terms of unexpected products rolling out. So it probably depends on what their ambitions are." And asked what Anthropic will attempt and fail at next, Field got the biggest laugh of the night: "I don't know. Safety?" (He added that he was joking.)

On the future of design jobs. With AI tools leading some to declare that "design is dead," discourse, Field predicted that there will be significantly more people with the job title "designer" two years from now, not fewer. "A lot of people that are doing other jobs, I think, will start calling themselves designers," he said. "We're in a world where folks are saying that all the jobs are gonna get replaced, and then they're turning around and saying, oh, let me call the really good engineers so I can get them to join my team. So this doesn't make sense to me right now."

On AI slop. Field argued that the flood of model-generated writing and design is, counterintuitively, a gift to people with an actual point of view — even as he admitted he now sees AI text everywhere. "Now I look at that and I'm like, man, it's a lot of Claude," he said. "If you have a creative voice, writing or design, you put yourself out there and you take a risk — this is a good time to do that. It's something that's going to be rewarded."

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This is an interview about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

Last week in our series on AI and jobs, labor economist Kathryn Anne Edwards explained why the United States' weak social safety net makes the prospect of AI-related job displacement quite worrisome. (Edwards doesn't believe AI will create a new permanent idle class, but acknowledges that the balance of how many jobs it destroys versus how many it might create remains deeply uncertain.)

This week, I wanted to talk to another person who has deeply considered the question of AI and jobs — and come to her own conclusions about how the coming disruption might play out.

Molly Kinder has spent the past three years at the Brookings Institution leading a multiyear project on how generative AI is transforming work. In a recent widely discussed essay, she predicted the coming of what she calls the "messy middle:" a long, hard period between the mostly intact labor market we see today (which she calls "reality 1" and the post-AGI abundance that Silicon Valley promises to someday deliver ("reality 3"). In the messy middle — "reality 2" — most jobs will survive, but losses will be concentrated in some of the best-paid, most coveted jobs in the economy. Whatever happens next, Kinder argues, we should expect those concentrated losses to be "politically explosive."

After all, Kinder told me, the workers most at risk in the near future are the ones who fared best in earlier waves of automation: the laptop class. "If you can do your job locked in a closet with a computer, eventually you're probably going to be in trouble," Kinder says. It's a striking inversion of the pandemic, when the people who could work from home were the safest — and the essential workers who couldn't were the most exposed.

That inversion is also why (like Edwards) Kinder rejects a standard San Francisco answer for AI job loss, which is to skip straight to universal basic income. If everyone gets a check big enough to replace a displaced software engineer's salary, she asks, why would anyone keep showing up to police the streets, build houses, or staff hospitals? "You've just destroyed the labor market," she told me.

Instead, Kinder is focused on more targeted interventions: policies that slow and manage the pace of displacement, including a workforce reinvestment fund that would require companies cutting young workers to pay for white-collar apprenticeships; wage insurance for older workers; and, if good jobs truly grow scarce, a public effort to create them — a new industrial policy for knowledge workers.

Kinder also came to the podcast with some news: after three years at Brookings, she's leaving to start a new organization devoted to solving the problems of the AI transition.

Highlights of our conversation are below, edited for clarity and length. Listen to the entire conversation wherever you get your podcasts — just search for Platformer — or watch it on YouTube at youtube.com/caseynewton.

And let us know what you think — we're new to podcast production, and welcome your feedback at casey@platformer.news.

Casey Newton: You wrote a piece recently about what you call the "messy middle" of the AI transition. What is the messy middle?

Molly Kinder: I wrote this piece in part because I've been so frustrated by the state of the AI and jobs conversation. We're in this giant seesaw where one group of people will put something out that declares an apocalypse is coming and takes a really extreme view, and then the other side will say, "No, the apocalypse isn't coming, so there's nothing to see here." We go from really extreme — all jobs are going away very soon, and that's awful — to: you're overreacting, there's nothing to see here.

I'm calling out the messy middle both to stake out a more reasonable middle ground between these two extremes, but also to talk about a time period. We're in such early days of this labor market transition that you almost can't see much evidence of labor market disruption yet. Everyone's fearful, but we're not yet seeing a lot of labor market impact. Reality 3 — and credit to my colleague Palak Shah for coming up with this R1, R2, R3 framing — is what I think a lot of Silicon Valley describes: this world of post-AGI where essentially robots and AI can do everything. That's the jobs apocalypse narrative that we're debating over and over whether it's realistic.

I think what we're really entering is this messy middle period. It's a world in which AI gets better and it's more capable of taking on more work, but we don't overnight see a jobs apocalypse. Instead, we find something that's still painful, but more narrow. It's a world of partial automation, where AI starts to get capable enough to do certain types of jobs, and that is still very painful. A world where most jobs are intact but there's a concentrated loss is still a world that is politically, societally, and economically explosive. So I'm trying to call attention to this messy middle period, which could last for decades, depending on how good the technology gets. Even in a world where we don't see a full jobs apocalypse, if we're seeing a lot of pain in the knowledge sector or early career, that is still going to be something that we feel as a country — and that we're not prepared for.

Newton: You argue that this middle period of AI disruption is going to hit white-collar workers much harder than blue-collar workers. Make the case for why you think that is.

Kinder: This is just what is going to go first. When you look at ChatGPT and large language models, we've got lots of data. OpenAI made public a dataset that looked at every task across the economy and its task exposure. You can aggregate that up to individual jobs and individual sectors, and it's basically saying: is this the kind of job where you can use a version of ChatGPT to save a bunch of time?

If you look at the kinds of sectors that are most exposed, where we're going to see the most usage — for good or bad, in terms of job security — it is by and large the knowledge sectors. My framing is: if you can do your job locked in a closet with a computer, eventually you're probably going to be in trouble. It's the really computer-based work. Most of that is bachelor's degrees — law, finance, consulting, sales. There's a big component of it that's more clerical, back office, that's not usually associated with a BA.

What you see as not really that exposed — and it's reflected in the usage data — are blue-collar jobs, physical jobs, service-sector jobs. Jobs where you have to show up in a workplace, whether it's a restaurant or a hair salon or a repair shop. Those are jobs where ChatGPT is not really going to help you do very much. Eventually we're certainly going to see robotics enter the picture, but I think we're seeing much faster advancements in computer-based knowledge work. So that's what I see as the frontier. I think that's going to hit and be disruptive first to those computer-based knowledge jobs and that clerical role.

Newton: Can you sketch out some of the implications of white-collar work falling to automation first, and how that might contribute to messiness?

Molly Kinder: Let me first give a little historical perspective to show why this is such a change if it does happen. And I want to caveat: I don't have a crystal ball. Nobody can fully predict the future. There are lots of ways the impacts on white-collar work may be softer than I imagine. I'm also not saying it's all happening tomorrow. I think we're at the precipice of a transformation — the possibility of commoditizing cognition is something I talk about in the paper. It's not overnight. This is, though, where I see the technology going.

In the paper, I show a figure that goes back 150 years and looks at what types of jobs dominated the labor market in the US over time. Since I was born, around 1980, you see this huge surge of knowledge and professional jobs — the kind of white-collar jobs that dominate the economy now, in business and finance and accounting and law. Before that, there was an agricultural revolution, and that was mechanized. Then we had tons of blue-collar employment — upwards of a third of all employment was in manufacturing and blue-collar work. Then we saw this big decline.

We've had this huge increase in knowledge jobs since basically the invention of the computer. We've called this skill-biased technological change. Up until the moment ChatGPT was launched, I always considered computers as boosting the knowledge worker — it made them more productive and more in demand. But the computer didn't do my job. My brain did my job.

In comparison, a lot of jobs that have gone away were substituted by computers. If you think about a lawyer in the 1980s, you probably had one lawyer and one legal secretary. The legal secretary did all the typing, all the scheduling, the dictation. Computers came along and replaced a lot of that work. And it made the lawyer more productive and more in demand. So we've had this period where routine jobs in the middle were being displaced, but knowledge jobs were growing. They were the winners of the computer era.

ChatGPT and technology like it — large language models — potentially could flip that on its head. For the first time — and it's not totally there yet, but Casey, you and I feel it in our knowledge jobs — what if the tenth version of Claude... where are we going to be in five years? Can it now lawyer? Before, computers helped the lawyer be the brilliant mind and do the work faster and with more resources. Are we going to get to a point where the computer itself, through these technologies, actually can substitute for some of that cognition that was so specialized? We have not seen that before. That's not the way technology has gone over the last several decades.

If that does happen, I think it can be earth-shattering. We have this notion of an American dream, this meritocracy. My grandparents were uneducated immigrants from Ireland; their passion was to work blue-collar jobs and send their kids to college, and then they all owned homes. This is the path we have. There has been a sense that it's worth it to take out a ton of debt for college because you have this secure upper-middle-class existence if you do. This could upend all of that. Not all of it, because I think there are versions of some of these jobs that will be boosted. But if we really start to see the technology capable of commoditizing some of that cognition, I think we have an explosive situation on our hands — if the jobs that are rising aren't the same pay and stature and eligibility as the jobs that are going away. And I think we have some precedent in history, with manufacturing, to learn that lesson.

Newton: To some degree this has already started to arrive, right? We see the boos at commencement speakers when the mention of AI comes up — boos rooted in the fear that I'm not going to be able to achieve the American dream anymore. In a very real way, nothing that you said is even really that speculative.

Kinder: It's not that speculative. And the point is, I'm not saying we're there today. We're in the earliest innings of this marathon. But where we think we're going, if the labs are right, is that these versions are going to continue to get smarter and smarter, and they're going to rival a professor, a think tank scholar, a market research analyst, a lawyer, a doctor diagnosing a disease. These are some of the most coveted jobs across the entire economy.

I've interviewed so many young people, in college and after, who are terribly fearful of this. You have to take into account where we are as a country. Affordability is everyone's anxiety. Because of the hollowing out of those middle-class jobs in manufacturing and clerical work, the best way you can afford a home and raise a family is typically these college-educated routes to upper-middle-class jobs. And these are coveted jobs, jobs people want. This isn't "I'll reduce my drudgery." Young people are telling me, "Look, it's already so preposterously expensive to afford a home. I'm doing everything I was told to do. I'm studying hard, I'm getting good grades, I'm choosing majors that should put me on this path to one day afford the basics of the American dream. Is AI going to come in and rip that away from me?"

So I think there's a lot of anxiety that if we take away the jobs people want, what is going to be left over? Sure, there's a possibility new jobs come online; maybe it won't be so hard to switch. But fundamentally, what people want is the ability to provide for themselves and their family and have some hope that their kids are going to do better than them. There's a lot of anxiety about AI because of the potential for it, in the coming years, to really disrupt the American dream.

Newton: A previous guest on this show, Google's James Manyika, might tell us to tap the brakes a little bit. A lot of James's research tries to tease out the difference between automating a task and automating a job. Tasks are definitely getting easier to automate — and yet we still have our jobs. He seems to think this will play out over a pretty long time, and that the economy will be less susceptible to mass automation of white-collar work in the near term. What do you make of that critique?

Molly Kinder: First, I really respect James. Some of what James said I agreed with, and some I disagreed with. There are certainly aspects of knowledge work that are not terribly automatable. Parts of my job are very relationship-based — being physically present, speaking, fundraising, building partnerships. Those types of tasks are not automatable anytime in the near future. I do think there are some instances where, as some of your work gets automated, you just go to a higher level of abstraction. There are also demand issues: maybe if lawyers get very productive and it all becomes cheaper, we demand more. All of those things, I think, are true.

Part of what I think was missing is: project ourselves out a few years. We are still talking about the early days. Of course every doctor wants to save time on note-taking. That's not why doctors are fearful for their jobs. What strikes fear in doctors or nurses is: is AI going to get good at the part of my job that I think of as the crux of my job, where I really add value? There hasn't been an academic study yet where the human bests AI in diagnosing a disease. There are lots of examples in the medical profession of AI being better at doing the hardest parts — the stuff that doctors enjoy the most. That's not the same thing as saying, let's just get rid of the routine part of our job and do more of the good parts. When I talk to creatives, they're worried about the thing they love about their job being substituted.

Even for me — I'm 46 now, I'm very senior, and I have a lot of interpersonal parts of my job. I didn't for the first 15 years of my career. It was mostly sitting at a computer and typing. What a lot of people fear is not where we are today in this trajectory. If AI gets better and better at what makes you special in your job, and what you love — does it mean all jobs are going away? I think in certain realms, it's going to be very automating.

Newton: I think this accounts for 85 percent of the uncertainty around this conversation. It is basically impossible to predict the future when you are living through exponential change — the ground keeps shifting underneath you. If you work at a technology company, your incentive is to tell me, "No, no, Casey, don't worry, it's going to be fine. Yes, there's going to be a lot of change, but you are going to keep your job, so you can calm down — you don't have to try to destroy the data center we're building in your community, you don't have to boo us at graduation." But I think what's important about what you're saying is that if the technology improves rapidly enough, that jagged frontier is going to catch up to a staggering amount of tasks, at least — and at some point that probably tips over into a full job.

Molly Kinder: There are so many different types of jobs out there, and the way AI is going to impact jobs is going to be very different in different sectors.

Another thing that I worry about is this idea of de-skilling. If the really hard cognitive part of your job can be deferred to Claude, you might not need as much education or experience, and that becomes a lower-paying job. That's something I'm already hearing in the healthcare space. I've heard examples from doctors of a completely untrained person off the street being guided by generative AI to perform an ultrasound. That's an $85,000-a-year job that requires a year of post-secondary education.

I don't think we are heading into a jobs apocalypse right now. My use of AI makes me better at my job. I think there are lots of upsides. But the notion that there's nothing to see here just because we're not heading into a jobs apocalypse... It won't take that much if a handful of really coveted sectors start seeing a displacement of some talent. The whole public is on pins and needles. Everyone's worried about this. They're looking for the first real proof that what they fear in their gut is coming.

If policymakers and politicians are not ready with a response — if people see that this is going to be a repeat of social media, where kids were harmed for 10 years with nothing, or a repeat of deindustrialization, where good jobs in the heartland were lost and we did basically nothing — I think people are going to lose faith, and they're going to turn even more against AI. It's imperative that — you've said it many times, Casey — what's the plan? If you lose a job where you were making $200,000 a year, and you're very specialized and you can't just slip into something else, your fall from that livelihood — your home mortgage, what you're able to do to support your family — could be pretty precipitous. We have a really threadbare safety net.

Newton: You open your piece by describing interactions you had with two people: someone who was almost 50, a former USAID official who was looking at a 60 percent pay cut to become a teacher after DOGE took away her job; and a 60-something laid-off semiconductor engineer who was driving your Uber to Menlo Park, and whose job may itself be disrupted before too long by Waymo. What about those interactions made you feel like these could be leading indicators of what we're about to see?

Kinder: I use those as examples of how challenging it will be if we do see impacts in the white-collar sector. Neither of them lost their job because of AI, but they're the type of white-collar workers who, if we start seeing impacts there, reveal how challenging this is going to be. They had very specialized skill sets. When my neighbor and friend who worked for USAID lost her job, that entire sector was dismantled by DOGE. Everyone is having a hard time. Many had to move to cheaper cities, take big pay cuts. A lot of people haven't found another job. There are people managing restaurants, trying to totally repurpose into something else. She really struggled to find anything where her skills could translate.

The semiconductor engineer really felt he was facing age discrimination, and from his stories I would give a lot of credence to that. Both of them had salaries that were about $200,000. It's really tough to maintain that standard of living if you don't find something equivalent. Both of them were deeply struggling to do that, and they were ending up in places where the delta between their previous salary and where they were was enormous. The Uber driver was now making between $30,000 and $40,000 a year — he couldn't even pay his COBRA. Massive sacrifice. And at that age, in his 60s, still five years before he could retire — what is retraining? Are you really going to retrain?

This friend, who's closer to my age, who has kids, is ensconced in the neighborhood; it would be a big change for her family. The best she could come up with: Virginia has a program where, with a BA, you can basically become a teacher. But it would be a 60 percent pay cut. What I'm trying to emphasize here is that this is not easy. I don't have a crystal ball, but imagine a world in four or five years, if these technologies get better and they're general-purpose: if people with specialized knowledge careers are displaced, how easy is it going to be for them to switch into something else? Our safety net is terrible. There is no unwritten rule that says you get to get another job at the same pay. Imagine if that repeats over enough people. It doesn't have to be a jobs apocalypse for people to feel that what they hold dear is slipping away.

Newton: Let's talk about an idea that you don't like, which is universal basic income. Why not UBI?

Molly Kinder: What I was reacting to was the San Francisco consensus I hear when I'm in conversation with someone who's very AGI-pilled and thinks we're going right to that post-AGI reality. When I say my job is to think about policy solutions in this interim period — what do we do about young people who need to get on the career ladder, or these displaced knowledge workers? — they say, "Well, no, we just go straight to UBI. We get checks big enough for everybody that replace essentially their entire income, and it's universal — everyone gets it."

That's partly my motivation for writing this piece. What if we're not skipping right to a world where nobody works and the answer is everyone gets a check? What if instead you're in the messy middle, where some jobs are disrupted and you have this concentrated pain? The historical parallel is the 1980s through the 2000s, when we lost manufacturing jobs in the heartland in a concentrated way. Overall, the economy was fine, and overall, jobs were fine. You just had this concentrated loss in the heartland.

I use the example of COVID because the people who can do their job locked in a closet with a computer are the folks who could stay home during the pandemic. I researched essential workers at Brookings during the pandemic — I spent all my time with grocery workers and care workers, arguing that they deserved a living wage and safety. The folks who had to show up at the office or a restaurant or a hospital — almost by definition, computers can't do those jobs, because you had to be in person. The folks who were the most safe from the virus, who could take all their meetings at home virtually or were mostly typing on a spreadsheet — those are the things that AI is best at.

What we just learned in this last decade was that the economy literally cannot function without all of those tens of millions of people showing up in hospitals and telephone repair and food manufacturing and farming and public safety and schooling. Critical infrastructure is by and large not on the front lines of AI displacement in the early days. I can't predict if robots are going to suddenly take over everything. It's the laptop class — the people who were not declared essential — who are the most at risk.

If everyone in society gets a check large enough to replace their lost wages, why in the world would anybody show up to still do the policing, the construction of the house, the hospital jobs, for way less money than that? We live in a country — I don't agree with this value, but we live in a country — where distributing checks for people not to work tends not to be very favorable. How politically sustainable is it for all the folks with essential jobs earning quite modest wages to keep working for $40,000 or $60,000 a year while we send checks in perpetuity to the $200,000 software engineer doing no work? It's not that I don't think we need to have some kind of compensation. It's the idea that we can just give checks large enough to replace everyone's income in a world that's the messy middle. You've just destroyed the labor market. So I think what we need are more targeted interventions. If we do get to a Reality 3 where literally no one works, of course we need some kind of full income replacement. But I think we're facing something much trickier in the messy middle.

Newton: Well, let's talk about some good ideas to handle the job disruptions that may come. You outline a few in your piece. What are you looking at that seems promising?

Molly Kinder: I have a few categories that I'm playing with. I've been really impressed with some of David Autor's polling that shows where the American people are. When I talk to the American public, I hear anxiety that comes in threefold. One is people already in their working lives, like you and me, Casey, who worry about a game of Russian roulette: am I going to be the person who one day wakes up and some version of Claude can do my job? There's a real anxiety about fragility. The second is young people, who are super fearful that the American dream is just being pulled away — that they're the collateral damage of AI progress. Shareholders could benefit if you replace young people with Claude, but what happens to those young people? And then, as a parent of three kids 11 and under, I'd say a lot of parents are bewildered: how do we even prepare our kids for the future? What even is the point of school? What do we do to set our kids up?

So there's a lot of uncertainty and a lot of fear. And what's riding through this is that people are not asking for a check, and they're not asking for retraining. They're asking to have some security in a world where things are really expensive. They want to keep a job. So the first bucket, I would say, is: what are really creative policy interventions that slow down and manage the pace of disruption before it even happens? We are really bad as a country, once the displacement happens, at figuring out what to do. I don't believe in just putting up protectionist walls. But I do believe there are ways that we can better manage this — have higher expectations for employers in terms of retention, or how much they have to do to try to repurpose someone before that person loses their job.

I think the same holds for young people. I am passionate about ideas that make sure young people are just not the collateral damage. How can we hold employers accountable for not just cutting learning opportunities because they can? I have this idea for a workforce reinvestment fund: sectors that are cutting young people have to pay in, and you only get the money back if you offer some kind of white-collar apprenticeship for young people. I was really excited to see that Mallory McMorrow in Michigan just made that part of her big campaign for Senate. David Autor ran the polling for me — it's wildly popular. How can you change the incentives, through tax, through regulations, so we're not just burning through people? The more we can manage that pace, the better.

The second thing is that we really have to fix the safety net. It's clear that we don't do a good job. There are lots of ideas on the table. I believe wage insurance for older people is a must. We're going to have to be a lot more generous. As you get older, it's much harder to transition; wage insurance basically gives you a way to take another job at a lower rate but still keep some of your previous wage.

The third thing is one that no one is really talking about, but this is where my mind is going, including from the Pope's encyclical and some of the guidance about how work is essential. If we really see a pretty serious disruption to white-collar jobs, and we're not seeing the market just create more jobs at that level — if we might see more service jobs, but we really have a dearth of good jobs — I think government's going to have to figure out some way to provide good jobs. We have industrial policy to try to create infrastructure jobs to help men who lost jobs in manufacturing. What's the version of that for white-collar work? It could be jobs solving social problems. We've got plenty of problems to solve. We can decide we want to tackle them — but at what wage, through what mechanism? Something I want to do in my new organization is a lot of experimentation and testing around that.

And then the final bucket: I think we have some really big questions around inequality. How are we going to capture some of this surplus to reinvest, to make sure we're creating good jobs and we have a better safety net — whether it's the healthcare people need or more free apprenticeships? There are a lot of big ideas we have to figure out in terms of how we make sure that the upside is shared and we can actually fund it.

Newton: We always try to break news on the Platformer pod, and you mentioned that you might have a new organization. What can you tell us about what you're up to, Molly?

Kinder: For the last three years, I've had this wonderful perch at the Brookings Institution, and it's allowed me to go really deep and study how these new forms of AI are impacting jobs. I've been able to get out and talk to people. I've been to the Vatican twice. I've convened economists. I've talked with policymakers. It's been amazing, but my anxiety levels have just kept going up as I watch the METR charts and as I see this is going so much faster than even I thought three years ago.

What I observe is angst everywhere. The public is anxious. Politicians are suddenly realizing they need a plan, and they don't know what to do. We're drowning in analysis. Everyone's debating what is happening now, but we're really short on answers. What is the plan? When I'm in a think tank capacity, I feel I'm in the conversation but somewhat on the sidelines. I look at my three kids and I think: this is too important. I have to give everything I have to trying to not study this but solve it.

I can't announce all the details, but I will say that the spirit of my next chapter is to be dogged and urgent and put everything into figuring out what we do about this — how we make sure that in this transition, especially this next messy middle, we learn from our past mistakes and we actually grip this head-on. So, Casey, have me back on the pod in a few months, and I'm happy to formally tell you our name and exactly our plan. But you're my first tell — this is actually the last day of my job, and I take the plunge tomorrow. So thanks for being here to celebrate with me.

Following

Anthropic releases Claude Fable

What happened: Anthropic launched Claude Fable 5, the first “Mythos-class” model they’ve released to the public. 

Anthropic considered the original Claude Mythos too dangerous to release, making it available only to select cyber defenders in April. While some critics dismissed this as “fear-based marketing,” partner companies found Claude Mythos Preview highly capable at generating novel exploits — Firefox reported increasing their bug fixes from 76 in March to 423 in April.

Now Anthropic is releasing Fable 5, a version of Mythos with added guardrails designed to prevent misuse. The company is also releasing a new Mythos model to its Project Glasswing partners and select biology researchers.

To prevent the public from accessing dangerous capabilities, Claude Fable 5 will route cybersecurity, biology, and chemistry questions to Anthropic’s next-most-advanced model, Claude Opus 4.8. Claude will do the same for queries suspected of being distillation attempts. 

Anthropic says that because it has “tuned the safeguards to be cautious,” occasionally “benign requests will trigger our classifiers.” They write: “We recognize that this will be frustrating to some users, and our aim is to reduce false positives as we update” the guardrails.

Anthropic says it expects significant demand for Fable 5. As a result, it has created a somewhat arcane rollout plan. From now until June 22, Fable will be included in Pro, Max, Team, and seat-based Enterprise plans. Starting June 23, Fable will require separately purchased usage credits. But eventually, the company will “aim to restore Fable 5 as a standard part of subscription plans,” it said.

Anthropic also changed its data retention policy for Fable and Mythos. The company will now retain traffic data for 30 days to enable investigations related to misuse. The company promised it won’t use that data to train Claude “or for any non-safety-related purpose.”

Why we’re following: Since Mythos’s announcement in April, everyone from LLM power user Discords to the federal government have been itching to get their hands on it. And now, (a slightly weakened version of) it is here.

The model is state-of-the-art on coding benchmarks like SWE-Bench and, and early reviews from companies including Every, Replit and Figma have high praise for its agentic coding abilities.

The larger implications of Fable’s apparent step-change in model functionality won’t be known for some time. To justify its pricing — twice that of Claude Opus 4.8 — it will have to deliver. As usual, though, Anthropic seems more worried about keeping pace with demand for the model.

A more important question is whether the model’s guardrails actually work as intended. This year, a hacker jailbroke a previous Claude model to hack a trove of Mexican government data. Anthropic says its new guardrails are quite cautious, and that red-teaming has found “no universal jailbreaks” of Claude Fable 5. 

But these are early days.

What people are saying:

Ethan Mollick, an academic who closely studies AI and often tests models before the general public, gave Fable a rave. “In experiment after experiment I conducted, it outperformed basically every other public model I have used by a considerable margin,” he wrote on his Substack. “It was capable across many problems and produced some startling results — it would work up to a dozen hours executing on multi-page specifications.”

Dan Shipper and Katie Parrott at Every called it “paradigm-shifting,” with Shipper saying it broke the company’s internal benchmarks. But it’s also “very slow,” “expensive,” and “token-hungry,” he said. “Using this thing for regular knowledge work is like squashing an ant with a rocket launcher. It also routinely uses 500k to 1M tokens on tasks.”

Vocal skeptic Gary Marcus, while remaining conspicuously silent about the model’s capabilities, suggested that the entire Mythos saga had been a marketing stunt.

“Claude Mythos went from ‘too dangerous to release” to publicly available (with some extra guard rails) in two months,” he wrote. And y’all fell for Anthropic’s whole routine. Again.”

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Talk to us

Send us tips, comments, questions, and solutions to the messy middle: casey@platformer.news. Read our ethics policy here.

This is an interview about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

In the first three episodes of Platformer's miniseries on AI and jobs, we heard from tech leaders who are building and implementing the agents that may one day result in significant job loss. For the most part, though, they have steered away from alarmism. Box CEO Aaron Levie argued that the "last mile" of human labor will resist efforts to automate it. Google's James Manyika explained why it’s much easier to automate a task than a job. Boris Cherny, the creator of Claude Code, did say the end of software engineering as we know it is here — but predicted that would lead to the rise of a new "builder" profession that would see more jobs created than destroyed.

And so as we move into the second half of the series, we're shifting to include some perspectives outside tech. In particular, I wanted to talk with people who study the economy. I want to know what they're seeing in the data now, and to understand what will happen if our first three guests are soft-selling the disruption to come. If AI does start taking jobs in large numbers, what then?

For these and other questions, I turned this week to Kathryn Anne Edwards. Edwards is a labor economist and independent policy consultant who writes a column for Bloomberg Opinion and co-hosts a podcast of her own, Optimist Economy. (She is also a compelling presence on Instagram and other social platforms.)

Like our previous guests, Edwards thinks that much of the AI jobs panic is overblown, and she flatly rejects the Silicon Valley vision that AI will result in (to quote Sam Altman) a permanently unemployed "idle class." She told me that idea feels more than a little classist in how readily it writes off the ingenuity and resilience of the American workforce.

"I am very firmly of the belief that the US worker is an incredible being, and they don't deserve to be written off by their former employer as never being able to work again," Edwards told me. "You don't talk about American workers that way in front of me, and you don't really have a place to."

At the same time, Edwards argues that the United States is woefully unprepared for mass unemployment — and that should be reason enough to start planning for AI job loss scenarios. We already have plenty of people out of work whom we've simply chosen not to help, Edwards reminded me.

We already know what tools would work, she argues: overhauling our unemployment insurance and healthcare systems; government subsidies to help out-of-work people move in search of new opportunities; and raising the estate tax, among other things. Whether any of it happens is a question of political will.

Which is why, despite her worries, Edwards insists she's an optimist — albeit a cynical one. "The lowest bar is the greatest source of optimism," she told me, "because I'd feel very differently if we had tried anything."

Highlights of our conversation are below, edited for clarity and length. Listen to the entire conversation wherever you get your podcasts — just search for Platformer — or watch it on YouTube at youtube.com/caseynewton.

And let us know what you think — we're new to podcast production, and welcome your feedback at casey@platformer.news.

Casey Newton: Your podcast is called Optimist Economy, and its central question is what it would take for the US to have nice things. But your recent columns have been pretty candid about how broken our safety net is. So what is making you an optimist these days?

Kathryn Anne Edwards: It's a very practical optimism — not being a naive Pollyanna, but having such a good handle on the economic evidence of how good policies can be when we do in fact try to make them good, as opposed to leaving them to rot for decades.

Newton: On our first episode, Box CEO Aaron Levie told me jobs aren't going anywhere — that AI has the net effect of giving engineers more to do. Last week, Boris Cherny, who built Claude Code, told me jobs might transform so they become unrecognizable, but there will still be a good job in there somewhere. Some call this the "AI as normal technology" view — that there are laws of gravity that slow the diffusion of new tech. Is that your view, that this is not a case of "it's different this time"?

Edwards: I'm a labor economist, so we all have the prison of our own point of view. Technology in the workplace changes the nature of labor, but throughout the history of technological adoption, it is very hard to see in real time either the addition or deletion of workers, and even after the fact it's very hard to attribute job loss to technology. AI comes out with a splash, but it takes a while for firms to really adjust. It will cause some people to get a new job, some a job change, and some to lose their job — and knowing any of that in detail will be very hard, if not impossible.

And of course it's different. Every technology is different. But I think some of the conversation around AI is deeply, deeply classist ….

It feels like some of that comes from being a little high on your own supply of how amazing your technology is when you hawk it. And it comes from this idea that some workers are better or more special than others, which makes your technology better or more special. That's also a way of saying the value of other workers is a lot lower — so it doesn't matter if they're displaced, it only matters if these workers are displaced. I push back on that, because I don't like being quite so derogatory to so many people in our economy just because they didn't have a knowledge-worker office job.

And two: I'd be willing to accept the exception that it's really, really special when it does something really, really special. I don't mean that in an insulting way. But what have we tangibly seen in our economy as a result of these LLMs being accessible through a subscription-based chatbot?

Newton: That's the Solow paradox — the economist Robert Solow said in the '80s that you can see the computer age “everywhere but in the productivity statistics.” A lot of economists feel the same about AI: it's everywhere, and yet it's not obvious it's having a massive, measurable effect.

Edwards: You went highbrow; I'll go lowbrow. It's like teenage sex: everyone's talking about it, no one's doing it.

I find this question itself a little distracting. What do you need to know, and why? If your concern is that there's going to be job loss, and therefore we need to have a better system for handling what could be cascading layoffs — do you need to know the exact number of people who lost their job, or is the problem the same regardless? Will you act differently if it's 7 million people versus 7.5 million? 

If yes, then I really question whether you actually care about helping people affected by job loss. And do you care about all the people who currently don't have a job, who in many respects outnumber most of the projections of how many jobs will be lost to AI?

Newton: We've heard companies from Amazon to Salesforce cite AI as a reason behind layoffs. Are there elements of those layoffs you think can properly be attributed to AI? Or are people AI-washing layoffs, and papering over the over-hiring they did during the pandemic?

Edwards: We don't know, we might never know, and honestly the company itself might not know. Rarely do actions like that have a single determinant. It could be that you're optimistic about the effect AI will have and could go forward with a leaner staff — and also that you over-hired in the pandemic, and also that the economy has been plagued with uncertainty, and it would just be nice to be leaner anyway, so you have a little less risk through a volatile economic period. All of those can be true. And then in the press release you say, "We've adopted AI," because that has the largest [reward] in the stock market for your shareholders.

What economists will do is look at specific occupations and industries and look for changes apart from the overall trend, or dramatically different from their past trend. Saying "lots of young people are having a hard time finding a job right now, and therefore AI" — I don't think we'd make that claim. “At the same time” is not the same as cause and effect.

And tech, even 15 years ago, is an industry that eats its young. A new technology comes along and they prefer to lay people off and hire young people who know the new programming language, rather than retraining older people. They tend not to employ people as much over the age of 50. So looking for job loss within industries that have long-term high turnover anyway — it's tough.

Newton: Let's zoom out a little bit. In May, the US Bureau of Labor Statistics reported that employment in 18 occupations that are considered exposed to AI declined by 0.2% while employment rose 0.8% everywhere else. Is that the kind of statistic that makes you think, okay, now we're starting to understand something that is really happening? Or is it just another confounding variable?

Edwards: It's illustrative, but not definitive. You have to ask yourself: is there another reason why an occupation exposed to AI could be experiencing slower-than-average job growth? Well, if the funding model of those companies is reliant on investment, and interest rates might be poised to rise again, it could be that they're wary about taking on more headcount in an area where they're not profitable to begin with, right? So it's not necessarily ruling out that explanation that AI is causing job loss, so much as leaving in everything that can't be eliminated.

But I genuinely believe AI is already causing job loss, just like I genuinely believe AI is already causing job growth. People want economists to say, "What's the right number? Can we pinpoint it?" And we're kind of pushing back: what do you need to know in order to make a conclusion, and what do you want that conclusion to serve?

Newton: One conclusion I think people are looking for is basically just: is the rate of AI job creation higher than the rate of AI job loss? Or what is the relationship between those two things? 

Edwards: It's going to be hard to know, ever. 

Go back and look at a big technology — the internet, Microsoft Office, computers. You're not going to find many reliable estimates of the total job loss and job creation from computers entering the US workforce. Someone has come up with a number, but the number would be one paragraph and the caveats would be 10 pages. 

The people who have been loudest about AI creating some permanent idle class — in my assessment, that's all coming from AI founders themselves.

Newton: There's a view in Silicon Valley, most loudly espoused by the AI CEOs, that eventually the models will get good enough that people will just hire less. My sense is you're skeptical that we reach that point.

Edwards: So you are able to run a shop with fewer people, because those people can use AI. That will happen, absolutely. It's the next step that makes their claim look ridiculous: they say technology shops will need two workers instead of 10, or 20 instead of 200, and therefore we'll have tens of millions of permanently unemployed people. It's the conclusion they draw that you have to separate out.

The first thing will happen — that's what happened to manufacturing workers. Think of people making shoes in 1905, 2005, 2105. How many are going to be in the factory in 80 years? Not many. How many were there in 1905? A lot. We know that happens with technology. It's the conclusion — "and therefore we have a new idle class" — that I take issue with. That one is borderline absurd. I could see how it could happen, but it's nothing like anything that has happened before, and a lot has to go wrong for it to be the case.

Newton: Let's sketch it out. Say we hit the point where a firm that used to hire 200 people can now get away with 20. We now have 180 people who would have been employed there who aren't. What do we expect to happen, given the normal way the economy evolves?

Edwards: Those people will look for new jobs, and the vast majority will find them — the average unemployment spell is less than three months long. There's a smaller, fractional set who are permanently long-term unemployed: however hard they've tried, they're not getting a job, and something has to change — the job they're looking for, or the resume they have. That share could get larger, and it has in the past.

But the jobs exposed to AI don't employ enough people for the dystopian scenario to play out. We have 170 million workers, and they don't all work in AI and tech. So even if you have a tech recession with lots of layoffs, it doesn't mean those people are unemployable forever, and it doesn't mean the economy is fundamentally changed, because we have lots of industry-specific recessions that we then recover from. 

It's also about overall aggregate demand, and whether people are trying to buy things. It doesn't happen in isolation.

Newton: The pandemic was close to a rapture for people in leisure and hospitality, and people in the AI world talk about an AI rapture in similar terms. What actually happened for the workers whose jobs disappeared overnight that let the economy reabsorb them?

Edwards: Demand increased again. What happened in the pandemic wasn't a drop in aggregate demand, it was a free fall, because so many businesses shut down. Nothing will ever top that job loss — half of leisure and hospitality lost its job within three weeks. Those 22 and a half million workers were met with a broken unemployment insurance system that Congress tried to fix on the fly, plus stimulus checks, forbearance on loans, protection from eviction. The economy's size recovered relatively quickly; the labor market took three years, as people went out and started to spend again.

I don't think AI could ever do anything as dramatic as March of 2020, in terms of a single month's job loss, and yet we recovered from it. It took public policy that we knew would work, fitted to the situation. It wasn't about the type of job that was lost. It was that people didn't have income. That's what you respond to: the person, not the former employer.

Newton: Let me come at it from another angle: entry-level workers. The Dallas Fed found employment in the most AI-exposed sectors is down about 1% since late 2022, with workers under 25 hit hardest — not through layoffs, but because entry-level jobs simply aren't being created. What do you make of AI's impact being felt through jobs that never appear?

Edwards: It makes a lot more intuitive sense. I start to use a chatbot, my workers are more productive, so I don't have to expand headcount to expand my business, so I just don't hire. But it's not as if the economy is otherwise perfect. There's a lot going on in our labor market that's much more influential on the youth labor market than AI.

In the spring of 2022, the Federal Reserve started raising interest rates. The labor market peaked about three months later and has been falling since. It was at such an extraordinarily strong place that the fall hasn't been enough to trigger a recession, but it looks like one — a drop-off in hiring, in wage growth, in job openings, in quits. The labor market is slowing down.

When that happens, certain things follow. One is upskilling. When you're an employer with a lot of people applying, you can require more. We saw this in the Great Recession — same job, same title, and suddenly it needed a master's degree, suddenly three years of experience. An entry-level job isn't just lost to AI; it can be lost in a labor market where employers have the upper hand, where so many people are looking for work that they can demand more skills for the same job. The opposite happens when the labor market gets tight — that's downskilling, where the job might not even require a college degree anymore.

The youth unemployment rate is by no means at a record high. But the labor market has started to slow, so we'd expect upskilling. And young workers — I hate to say this so bluntly — because they don't know how to do anything yet, they're not going to be the first ones hired, because I can get someone with more education on the cheap when the labor market is weak.

Typically, youth getting hit happens after a recession; they're a lagging victim. What's special about right now is that it's almost as if it's preceding one. Labor-market weakness would explain almost everything going on with young people, and AI might explain some of it for some of them.

Newton: You explain a lot of these forces on social media. When young people ask what they should do, what do you tell them?

I say it with love, and I try to lower expectations. When you come out of school, you've been part of a pipeline that's been path-dependent and high-stakes almost since high school. Do well on this test to do well in this class to get this GPA to get into this school, and then again to get that job. Everything is set up as: you have to succeed, you have to succeed, you have to succeed.

The labor market is not like that. The labor market has lots of movement, lots of experimentation. You go to a job and you don't like it, and it's not as if you'll never work again — you just pack up and go to another one. Movement is associated with better job matches, better income, more satisfaction. You've got to find the thing you like to do, and you will not find it when you’re a freshman in high school or a freshman in college.

So if you can't find your first job, it is not going to condemn you to a worse life. It's awful right now, especially after working so hard, where it was all built up to "if you do all these things, then you'll be secure" — and to be met with a brick wall of insecurity is so cruel. 

But in some ways, you've been built up too much. Like, this is just your first job. Most people won't like their first job, they'll leave their first job, and then they'll figure out what they really want to do through experience.

Newton: I love that point. In school it felt like you were in training to become an "X" — do all these things and then you'll be a doctor, or a lawyer, or an electrician. What I hear you saying is that the reality for most people involves a lot more thrashing.

Edwards: There's no good measure of underemployment — people getting out of college and working at Starbucks or the Apple Store because they can't find a job. But even if we collected that data, it wouldn't be that informative, because for the vast majority of people, having education they don't use in their job doesn't mean there's a problem. It just means the labor market is mobile and they don't want to use that specific educational experience anymore. That doesn't make them a failure. I have a PhD in economics, and I'm no longer in a research position — my main job is to be a columnist for Bloomberg and host a podcast. By that measure I'd also look like a failure. But I'm not. I made a lot of choices.

And if they push really hard — this is why no one likes economists — I point out that this is not the hardest young people have had it, not even in the last 20 years. Nothing could match the misery of coming out of the Great Recession as a young person. It sounds condescending to say "it's worse if the unemployment rate is 10%," but it is. The unemployment rate for young graduates was then more than double what it is now. 

And it's not as if there's a gap of missing 37-year-olds who are just not in the labor market because the market was tough when they graduated. They got jobs. It wasn't what they wanted, but they're still working.

Newton: Let's move into what we can do about it. You've written that the US needs a real long-term unemployment system — in most states, unemployment insurance runs out at six months. What would your ideal system look like?

Edwards: Our unemployment insurance system was designed for manufacturing workers in the 1920s, and one of the things it's done best is teach us all of its shortcomings. 

It's a state-based program. It only applies to W-2 workers. It's got a flat benefit, and if you get it, you get it for 26 weeks, and that's it. That's not what unemployment spells look like: a lot of people have short spells, a lot middle-length, and a fraction long ones.

In prior eras, layoffs were how most people went into unemployment. But the labor market doesn't differentiate between getting laid off and quitting — your unemployment insurance benefit does. Right now, to get unemployment you basically need the permission of your former employer, and if they give it, they pay higher taxes on all their remaining employees as a result. So that's all got to go. 

The good news is that wouldn't be so hard.

What I envision is a system that mirrors unemployment itself: a relatively short, generous benefit that basically anyone can get — you walk off the street and right into the unemployment office and get it. Then a medium-sized benefit you have to reapply for, that comes with counseling and labor-market information. And then a long-term unemployment program for people who don't find a job after searching, that helps them make the change to their resume they need.

Newton: Lots of folks in tech, most prominently Sam Altman, have made universal basic income their preferred answer for whatever AI is about to do. You've argued AI anxiety won't be eased by UBI. Why not?

Edwards: What does it change? You've given people some money. I'm particularly antagonistic about Sam Altman's characterization, because not only did he refer to future unemployed people as a new idle class, he said in an essay that the only thing the government can do is give them money. I am very firmly of the belief that the US worker is an incredible being, and they don't deserve to be written off by their former employer as never being able to work again. I don't like the posture of "I get to decide who's never going to work again because I came up with a shiny new toy." 

I get it, it's the best toy that's ever existed. You don't talk about American workers that way in front of me, and you don't really have a place to.

My biggest problem with UBI is that it doesn't change the game. You give people $10,000 — can they afford health care? Child care, housing, rent? We have structural issues in our economy, and UBI does not address them. Proponents who don't come from tech say UBI would empower people to change those structures. I want the same thing, but from a different direction: workers should be empowered in the labor market. We should have a decent health care system, which we don't. We ought to intervene in these market failures.

The biggest drawback is that UBI leaves market failures unaddressed. Rich people can buy their way out of a market failure. Child care costs $24,000 a year — that's a market failure. We don't have enough of it for the people who want it, and they can't afford it. If you make enough money you can buy your way out, but that doesn't make the failure go away. You give someone $20,000 of UBI, and our health care system is still broken. I don't want to spend that many resources on the salve as opposed to the cure.

Newton: One of your suggestions is that government could help people move — if you're not physically where a job is, it could get you there. That sounded so reasonable, and I'd never heard it proposed.

Edwards: The real problem in our labor market is often power. Is giving UBI under the guise that some people have lost their job giving power to workers, or is it buying them off? The AI gurus see it as a way to buy them off. I see it as an inefficient way to distribute power. We ought to attack power structures directly. You get a lot of "just train people, be plumbers, we need electricians," with a wave of the hand. A real long-term unemployment program would help people start small businesses, go back for preparation in occupations with a deep shortage like nursing and teaching — and yes, pay people to move. I'm much more of a believer in the worker asserting their own future than in being cash-dependent on the wealthiest who decide to give them some.

Newton: When folks in the AI world talk about UBI, it reflects anxiety that AI consolidates wealth and power into a very small number of companies. Do these companies have any responsibility to the workers they might displace?

Edwards: No, I wouldn't want to empower them, or make future economic policy dependent on them. If you make a company pay for something in a targeted way, they'll get out of it, and be quite slippery doing so. And it's not as if they're in favor of a higher corporate income tax rate, or said, "Hey, you should probably change the estate tax so we personally benefit less." They want a solution that preserves their wealth but makes people less mad at them, and they're looking to PR companies to fill in the gaps.

The solutions we need are the ones we have and aren't using. You're worried about a concentration of wealth in companies? Thank God we have an antitrust act — break them up; they're all monopolies or close to it. This is why God invented the estate tax and the corporate income tax. These have been weakened over time by the very people now telling us they need to do something to make people less mad. If they hadn't given so much to Trump's inauguration or his ballroom, I might believe it came from a genuine place.

We had incredible concentration of wealth in the Gilded Age that got busted up by breaking up monopolies and empowering workers through unions. So much evidence exists that what helped the middle class make so much money was unionization. I don't see any of these guys saying maybe we should have unionization in our own workforces, let alone others. A pro-union economist told me what strikes him about AI is the workplace questions: can companies surveil you using AI? Can they use AI to get someone to do your exact job, train it on your work, then fire you? Legislating around that would be hard. It wouldn't be hard if the workplace had a union to bargain for each case. People oppose unionization for so many bad reasons and not the “good” one, which is that it gives workers a lot more power and money.

Newton: There's a union at Samsung in South Korea that was concerned the company's wealth wasn't filtering down to workers, threatened to strike, and Samsung found more money to pay them — a tech union changing the terms through collective action.

Edwards: That's the solution and the nightmare [for AI companies]. It returns power and income to workers, and gives them more power over how AI shows up in all kinds of insidious ways. It puts your thumb on the scale for the worker and not the manager, and that's not a solution any of them are running toward.

Same thing with the corporate income tax. Corporate profits are at century highs, and the labor share of income is at absolute lows — basically 1929 levels, a terrible year to be associated with. A very high corporate income tax, fairly distributed, without so many loopholes, was very good at its job: it kept profits at a reasonable level, and workers ended up making more money. 

Some people say that because these tools eroded, they're not good anymore and we need new ones. I fall in the camp of: we know they work, that's why they were attacked, so we just need to bring them back. For the record, I don't think AI CEOs want the same outcomes I do.

Newton: Your ideas sound great, but they're expensive and polarized. What levers might actually get them put in place — is it anything short of unemployment going up rapidly?

Edwards: That definitely helps unemployed people — when unemployment goes up, it's not just "lazy" people who are unemployed, it's "good" people who are unemployed.

Do not use Congress as your barometer of progress. They will be the literal last institution to do anything on any issue. What I find optimistic is how frustrated people are that so many things haven't had federal leadership yet. Upwards of 80% of Americans want paid family leave, and we're one of two countries in the world that doesn't offer anything to newborn parents. That's not because we don't know what to do. We have a neat little timeline in our heads — Great Depression happened, then we got all this big legislation — but people in the moment were saying, "25 years ago we needed an unemployment system, why did it take so long?" We feel the lack of progress now, not the decades of fighting and waiting that always came with it.

We can do all of this. Most of the policy that would really help families and workers, some state has already done. Paid family leave — 14 states have it. A retirement account that's not dependent on your employer — about 17 states. A higher minimum wage — 34 or 35 states. We just have to get Congress up that hill. I'm a mom — positive reinforcement is so important. You can do this, Congress. Most of the things I want to do, most people want to do. It would make the economy better. It would be better if the labor share of income were not at rock bottom. It would be better if we were paid more. 

Don't worry, I'm an economist, it's okay.

Newton: Your podcast is called The Optimist Economy. As the AI transition unfolds, what do you see going right? What's out there that makes you feel like we've got this?

Edwards: I'm a quite cynical optimist, and my optimism comes from having tried so little. It's really hard to give up on the US economy when we've done almost nothing. We haven't raised the minimum wage, haven't ended the tipped minimum wage, haven't made universal health care, haven't started universal child care or preschool. We don't give all kids in school meals. We don't really do anything on housing at the federal level. Social Security is broken, but we knew that 35 years ago and have done nothing to fix it.

I take comfort in knowing the lowest bar is the greatest source of optimism, because I'd feel very differently if we had tried anything. What we have tried for the past 25 years is the experiment of whether tax cuts are the solution to every economic problem. The answer is no. Nine pieces of tax legislation — $13 trillion spent. And if you can't afford housing, food, health care, child care, and elder care, maybe we have all we need to know that tax cuts aren't going to solve those problems, because they should have by now. We made a massive investment in a single policy that has not borne fruit, other than making our country indebted. So let's not do that anymore. Let's go do other things.

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Following

Anthropic prepares to go public

What happened: Anthropic filed for an initial public offering of its stock.

In a statement, the company said filing “gives us the option to go public,” and that the timing and size of the IPO will “depend on market conditions and other factors.”

Why we’re following: With its recent private funding round, Anthropic’s valuation has now gone up to $965 billion. With SpaceX, OpenAI, and Anthropic preparing for public offerings, 2026 is on track to see the biggest IPOs in history.

SpaceX’s IPO is expected to raise twice as much as any previous public offering. The Economist argues that given the general trend on tech IPOs — which is that see an initial pop but are followed by a years-long decline — this year of mega-IPOs could give the global economy “indigestion.”

Unlike SpaceX, Anthropic has filed privately, meaning that we don’t yet get to see its pitch to investors. (And how will they be able to follow up the SpaceX S-1, which opened with 15 pages of pictures of rockets?) From publicly available information, it does roughly seem like their business plan is to get AI to automate coding and AI research, use that to create human-level AI, and then have that AI do every job. So if that pans out, maybe they will beat the traditional IPO slump!

What people are saying: On X, host of Mad Money Jim Cramer congratulated Salesforce on the success of its early investment in Anthropic. “Salesforce has done a lot of things right and this investment worth $5 billion in Anthropic is one of them.”

Tech lobbyist Zak Kukoff speculated about the politics of the coming wave of AI mega-millionaires: “Anthropic, SpaceX, OAI — huge IPOs are equipping a generation of new political donors whose financial capital dwarfs that of legacy mega-donors.”

NYT reporter and friend of Platformer Kevin Roose wrote that when he first visited Anthropic three years ago, “it was a 160-person start-up in Jackson Square with no products and no revenue.” Strikingly, “there was an office throne made of empty Liquid Death cans and everyone seemed vaguely allergic to making money.”

But now, “they're going to go public at $400 quadrillion and bring about the teleological end of capitalism. what a world.” (Platformer makes no warrant as to the validity of Roose’s math.)

On r/sanfrancisco, Reddit users commented, “housing prices and rents bouta skyrocket,” and “Hello my fellow permanent underclass dwellers.”

One Redditor on r/Anthropic wrote, “As soon as they go public they are legally obligated to maximize shareholder value... Enshitification incoming.”

— Ella Markianos

Florida sues OpenAI

What happened: Florida is suing OpenAI and its CEO Sam Altman. The suit accuses OpenAI of intentionally designing ChatGPT to be addictive and destructive to children and adults. It mentions examples of teenagers who have taken their own lives after extensive conversations with ChatGPT, and a teenager who overdosed after ChatGPT gave them information on drug interactions. 

“ChatGPT proactively aids, abets, and promotes dangerous activities and is a threat to the public safety of Floridians,” the suit said.

The complaint covers sycophancy, dangerous medical advice, and hallucinations in addition to teen suicide.

Why we’re following: Florida has taken to AI regulation harder than any other red state. Gov. Ron DeSantis has championed child safety legislation and data center regulations. And Donald Trump’s pick to succeed DeSantis, Rep. Byron Donalds, just said he disagreed with Trump on federal preemption, and the President should allow states to regulate AI.

Now, their attorney general is trying to hold OpenAI legally liable a for what people do after using its chatbot. While that’s not directly against the will of the administration — Trump’s AI Action Plan recommended AI companies be held to existing liability law — the President has had several stands against AI regulation. 

While the lawsuit itself is interesting, we're also interested in what it tells us about the current state of AI politics. With the technology broadly unpopular, Democrats and Republicans are testing various strategies to capitalize. The Florida lawsuit shows one elected Republican seeking to apply the social-media backlash playbook to AI: like Facebook, Instagram and TikTok before it, the case argues, ChatGPT is a public nuisance.

What people are saying: Commenters on Hacker News were skeptical of the Florida AG’s arguments. Commenter xp84 thought the claim of liability was too broad. They asked, “would this program also open me up to liability in Florida?” and attached five lines of JavaScript code that randomly spit out “You're absolutely right” or “I agree, let's explore this idea further.”

The residents of r/antiai were relatively cynical about the suit. User chevalier716 wrote, “Is this DeSantis asking for a bribe?”

— Ella Markianos

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The agreement reverses a planned decrease in the board's funding over the next two years — a move that board insiders feared could spell the end of the board.

“As with previous funds, this 'top-up' funding will go into the Board’s irrevocable trust and will be used to help Meta address its most consequential content decisions, and the systemic issues that often compound or underlie them,” Paolo Carozza, the board’s co-chairman, told Platformer in a statement. “We are grateful to all our partners who contribute expertise and insights into our work and are excited for our continued collaboration.”

In addition to the new funding, Meta has also "renewed its commitment to cooperating with the board," Carozza said, after months in which Meta had slowed referrals to the five-year-old, quasi-judicial body.

The news comes as something of a surprise. In April, Platformer reported that Meta had discussed cutting off funding to the board after 2028, having already reduced funding significantly this year. The board was told to expect further cuts in 2027 and 2028.

It's not entirely clear what changed Meta's mind — or what will happen after 2028. The project was once a signature project of CEO Mark Zuckerberg's, but his enthusiasm for it has faded. Still, the board retains some symbolic value for the company as a body that can adjudicate difficult decisions related to content without Zuckerberg himself having to make the calls.

Moreover, $13 million is a rounding error on the $26.8 billion the company reported in net income last quarter. Ultimately, it may have been worth that much to Meta to extend the board's funding simply to avoid a news cycle about the company killing off a significant check to its power — and all the told-you-sos that would follow from critics who have long been skeptical of the board's independence.

Meta did not respond to a request for comment.

With the new funding in place, the board plans to pursue a range of new initiatives, Carozza said in an interview. It is preparing to expand the types of cases it will take by weighing in for the first time on how Meta should make decisions about whether to permanently ban accounts. It is preparing advisory opinions on policies about how Meta should handle deceptive AI-generated content in conflict situations. And it is exploring new ways to get involved with issues related to child safety on Meta's platforms, Carozza said.

The renewed commitment comes at a turbulent time for trust and safety at Meta. The company is gradually shifting many content moderation functions from humans to automated systems, and sources told Platformer that the company has been less responsive to the board's questions in recent months.

At the same time, Zuckerberg has gotten more personally involved in politics, throwing out the company's playbook for creating policies to rewrite Meta's hate speech policies ahead of President Trump's inauguration last year. In January 2025, the company ended its third-party US fact-checking program, loosened restrictions on political speech, and shifted its trust and safety operations from California to Texas.

It did not consult the board before doing so.

Since then, longtime content policy chief Monika Bickert announced she will leave the company this summer.

Fears about the board's future had mounted after Nick Clegg, who previously led global affairs at Meta and championed the board's work, stepped down at the beginning of 2025. He was replaced by Joel Kaplan, a former Republican operative who has been critical of the company's content moderation efforts.

Zuckerberg first floated the idea of an independent body to review Meta's content decisions on Ezra Klein's Vox podcast in 2018, comparing his vision to a kind of Supreme Court for Facebook. Since issuing its first rulings in January 2021, the board has produced more than 200 published decisions and over 300 policy recommendations to Meta, which says it has implemented or is working on about three-quarters of them.

On the podcast this week: We are off this week as we prepare for Hard Fork Live!

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Following

Google and OpenAI are feeling the AGI

What happened: At Google I/O, Demis Hassabis said humanity is at the “foothills of the singularity” — the long-imagined moment where AI reaches a period of rapid self-improvement that makes it superhuman.

In an interview with Axios this week, Hassabis doubled down on that view. He says he still expects artificial general intelligence in 2030, but thinks it could come up to a year earlier.

Hassabis sees the next wave of AI agents as a stress test for how well society can adapt to AI that can do stuff itself. "You can imagine the agentic era in this next year is a little bit like a practice run," Hassabis said — a trial before even more capable systems come.

Hassabis said that President Trump’s now-stalled AI safety-focused executive order was a step in the right direction. "I think [safety] needs to be accelerated," he said. "This is a good moment to kind of strike while the iron is hot."

Meanwhile, the OpenAI Foundation announced they will commit an initial $250 million to help workers deal with AI "disruption" — which is to say, massive job loss.

In the past few months, OpenAI veterans including co-founder Wojciech Zaremba and former vice president of global impact Anna Makanju have announced they are moving to work on the foundation.

"The current pace of change means the window to get ⁠this right is shorter than we're used to, and the ​cost of getting it wrong is profound," the non-profit said in its announcement.

Why we’re following: It has been a portentous week in AI. 

President Trump planned and then cancelled the signing of an executive order that would have created a voluntary system for submitting frontier models to the White House before releasing them. There is talk of the order being revived in the White House with the administration split between anti-regulation hawks like David Sacks and more safety-minded officials like Emil Michael.

All of this comes days after the Pope released a 42,300 word treatise that has a lot to say about AI safety. (He presented it along with a co-founder of Anthropic). The OpenAI Foundation’s announcement feels a bit like a response to the Pope’s encyclical, which focused heavily on how AI could affect labor.

In any case, both Google and OpenAI are signaling that they believe rapid change may be only a few years away.

What people are saying: Divya Siddarth, who co-leads the OpenAI Foundation alongside Wojciech Zaremba, told the FT  that “we are operating from the assumption that AI will have a massive impact on the economy and that we have little idea of what that will be or where it will go.” 

Zaremba added that the Foundation is working to “handle the transition and work out what a post-AGI future should look like.” He said, “We think there’s a way to be working on the transformation today, even though in a sense we’re navigating fog.”

—Ella Markianos

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This is a column about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

In the first two episodes of Platformer’s mini-series on AI and jobs, we heard from two tech leaders who pushed back on the idea that AI is about to leave most white-collar jobs worthless. Box CEO Aaron Levie argued that the “last mile” of human labor will resist efforts to automate it. And Google’s senior vice president of technology and society, James Manyika, explained how tech has improved at automating tasks but not jobs.

For our third episode, I wanted to highlight a contrasting view — someone who believes that AI really is on its way toward eliminating certain jobs. Boris Cherny is the creator and head of Claude Code — the agentic coding tool that Anthropic released last year and is, by most measures, the fastest-growing AI coding tool in the world. 

Cherny belongs firmly to the camp that believes the end of software engineering as we know it is already underway. He hasn't written a line of code himself in more than six months, and he says that for the kind of work he does, coding is effectively "solved."

Given that he is, by his own account, actively automating his own job, it's no surprise that Cherny sees the disruption arriving far faster than our first two guests. He told me that the title "software engineer" could start to disappear by the end of this year, dissolving into something closer to something like "builder" as the designers, product managers, and managers around him start shipping code of their own.

But his own jobs forecast is more optimistic — and in some ways similar to our first guests — than some of his more prominent comments about coding being “solved” might suggest. While companies may hire fewer engineers as we know them today, he argues, they’ll hire more of whatever “builder” role replaces them. 

"I don't think we're going to call them engineers," Cherny told me. "But if we talk about people writing code, or using agents to write code, I think there will be 100 times more engineers than there are today. That's my prediction."

Cherny was an unlikely candidate to lead a revolution in coding. He studied economics, dropped out of college to run a startup at 18, did a stint at a hedge fund, and spent five years as a principal engineer at Meta before arriving at Anthropic in September 2024. When he arrived, Cherny began to explore what the company's API could do. The product that became Claude Code began life as a tool to tell him what song he was currently listening to. After iterating it on a couple months, he released the first version of Claude Code internally — and 20 percent of Anthropic’s engineers began using it on the first day.

As always, note that my fiancé works at Anthropic. But I couldn’t imagine trying to understand the AI and jobs story in this moment without talking to the person behind Claude Code.

Highlights of our conversation are below, edited for clarity and length. Listen to the entire conversation wherever you get your podcasts — just search for Platformer — or watch it on YouTube at youtube.com/caseynewton.

And let us know what you think — we're new to podcast production, and welcome your feedback at casey@platformer.news.

Casey Newton: You joined Anthropic in September 2024, and my understanding is that no one told you to go build a coding product — you were just trying to learn the API. Can you tell us the origin story of Claude Code? I've read that it controlled your music.

Boris Cherny: All of these things are true. I joined this team called the Labs team, which built a bunch of cool stuff. We built Claude Code — I built that. A different person built MCP, someone built Skills, and two other people built the desktop app. That was essentially the size of the team. It was tiny. We built these features over the course of a few months, and because a lot of them were weird ideas, we had no idea whether they were going to work.

For a while, Anthropic's focus has been on the same kinds of things: enterprise, coding, and safety. We knew that somewhere in this journey we should probably build some kind of product. Early on, Anthropic didn't actually know if it wanted to build products at all, but if we were going to, it needed to be coding-related, because that helps us build better coding models and it helps us study safety. 

We didn't know what it should be, though. At the time, the coding products out there were all IDEs or IDE extensions, because the capability of the model — this was Sonnet 3.5 — was not very good yet. The best it could do was fancy autocomplete: you'd write a little code and it would complete the line. We had this feeling that there was a product overhang — this idea that you could build a product that does something the model is totally capable of doing, but no one has built the product that lets the model do it. And I'll tell you, it's still the same feeling today. The model can do all these things, and there's no product that lets it.

So I wanted to learn how to use the Anthropic API, and I built the cheapest possible thing — a little thing that ran in the terminal, so I didn't have to build a user interface or an app. I built it in a couple of days and started giving it to people to see whether they'd use it and how, just out of curiosity. Over the next few weeks, more and more people at Anthropic started using it. First it was the people who literally sat around me, then the next layer of the onion, and a few weeks in, a lot of Anthropic was using it every day. It was weird, because it was a little prototype in the terminal — the most "engineering" product possible. A lot of engineers don't want to touch a terminal, but they did, and they used it.

Newton: I've read that within five days of the initial release, half the engineering team was already using it. As that was happening, did you have a moment of thinking, "Okay, software engineering just changed forever"? Or were you still just iterating on the product?

Cherny: I was so focused on shipping. As soon as I got the idea, I spent every night and every weekend on it — it was the only thing I thought about, the only thing I worked on. I started having dreams about Claude Code, and that's still all I dream about: what should we do next, what do we build next. There's a chance now to zoom out, because a lot of people are using it and there's a lot to learn about how. But for a long time we were so focused on building that I didn't even have a chance to think about what it was.

Newton: Was there a moment when you did zoom out? It might be too minimizing to say you stumbled across it, but it does seem like there was a sense of accidental discovery. Was there a moment of "Oh gosh, this is different from the other things I've hacked on"?

Cherny: There was a lot of surprise. Broadly, we knew we wanted to build a coding product, but no one thought it would be in a terminal. The first moment was when Claude told me what music I was listening to. There were a couple of versions of this — we actually have a video demo I recorded, which we just donated to a computer museum— it's a weird historical artifact. I remember posting it in my Slack and getting two reactions, because no one understood that this would be it. I asked Claude what music I was listening to, and it wrote a little code to open my music player. It wrote the code in AppleScript, which I don't know, and I wouldn't have thought to write code to answer that question. It just did it, and I thought, this is surprising — it solved the problem in a way I wouldn't have as an engineer. 

Over the last year and a half there have been so many moments like that. I had one with Cowork recently. Every time we release a new model, I experiment with it to see the frontier of what it can do, because one of the hardest things about building on a model is that it's advancing so fast — you have to recalibrate every month, as I'm sure you know. I used Cowork to book a bunch of flights. Usually it works okay; this time it worked perfectly. Now, any time I travel, I use Cowork to book it. It booked eight flights and five hotels. The only mistake was that one of the hotels was way over budget — I think it was like $5,000 a night.

Newton: Cowork wants you to have a great time when you go on your stay.

Cherny: I said, "Please rebook this one." But otherwise it just worked for a couple of hours and did all of it. It was so cool. I feel that surprise every week, every month.

Newton: This feels like a moment to zoom out from the story of an initial discovery that spreads rapidly through Anthropic, and now Claude Code has become a default tool for a growing number of engineers. It's one of the products making the question of job automation feel really salient — at least for software engineers, but maybe for more people than that. During our first episode, Aaron Levie told me he didn't see jobs going anywhere — that there's always going to be a last mile of human work the software can't do. You have publicly predicted that the title "software engineer" could start to go away as soon as this year. So is Aaron wrong about this?

Cherny: There's a bunch of stuff that's true, and a bunch we don't know. The trends are exponential, and exponentials are very hard to think in. Honestly, anyone saying they know is guessing — some of these are educated guesses based on what we're seeing and on history. 

I think a few things are going to happen. One is that a lot of companies will need fewer engineers, because each engineer is more productive, so you don't need as many to do the same work. At the same time, a lot of companies will need many more engineers, because every engineer is more productive — the company can do more things, start more products, create more businesses. You see this with our team: we are constantly bottlenecked on good engineers. We're hiring as quickly as we can, and a lot of our customers are exactly the same. So I think both things will happen, and it depends on the company and the business.

There's another thing happening, where the roles are all blending together in an interesting way I don't think anyone would have predicted. Our manager, Fiona, hadn't coded in 15 years; she joined Claude Code and now she's coding. Kat, our product manager, codes. Megan, our designer, codes. Everyone on the team codes — you don't have to be an engineer anymore. If you project the trend a little, everyone who's not an engineer is going to code a little more, and engineers like me are going to code less. I haven't written a line of code in over six months; I'm building stuff all day. I see it all blending into one thing. Call it a builder, call it an engineer, call it a product manager — I don't know what the title is, but the role is changing.

Newton: So how we conceive of these roles is definitely going to change, but what that means for how many jobs are available at which companies is still unclear.

Cherny: Yeah, and history has a lot of examples. The tractor was invented in the 1890s — I was just reading about this. A guy named John Froelich invented it in Iowa. At the time, farm work was all horse-powered; you needed horses. Even though the tractor was invented in the 1890s, it wasn't until the '60s in the US that there were more tractors than horses. It took about 70 years. The number of tractors went up, the number of horses went down, and the lines crossed in the '60s. 

There were a bunch of reasons. The technology was magical — you could harvest a lot more crops, productivity was much higher — but if you were a farmer who wanted to learn to use a tractor, it took training. At the beginning, tractors were expensive, so in a lot of cases horses were still cheaper. And the tractors weren't very good at first: maybe you could use one for wheat but not for corn, so it took a long time for someone to make a tractor that worked for corn, and for okra, and for all the other things. That just took a while. What we're seeing right now is the same thing on a speed run — but we're seeing very similar issues.

Newton: This is the "AI as normal technology" argument: even as labs come up with incredibly capable models, people are slow to change, organizations are slow to change, and it takes time for these technologies to filter through companies. At the same time, people look at what's been reported about Anthropic's revenue and say — it doesn't seem like it's taking that long this time. So we're still trying to hone in on the actual rate of change.

Cherny: Here's a question for you: do computers make you more productive?

Newton: Yes. But "do they make me more productive" feels like a different question from "do I work less because of computers," if that makes sense.

Cherny: So because you can do more, you do more — you fit more into the same eight hours.

Newton: Absolutely. To be candid, I used to record one podcast episode a week in addition to writing a couple of newsletters. During this miniseries I'm experimenting with doing a couple of podcasts a week in addition to writing multiple newsletters, and AI is a reason I can do that. It's an incredible research assistant and podcast producer. I'm able to produce more. But I don't feel like I'm working less — and that's not a complaint, just how I'm navigating this moment.

Cherny: I feel the same way. I can do so much more — all this stuff I didn't get to before, because I didn't have enough hours in the day. There's another weird historical thing. In the '90s, when companies were adopting the personal computer — after the mainframe, after these big industrial computers that cost millions — they got miniaturized, so the average startup could just get computers. And there was a real question of whether computers made you more productive. People were complaining that they didn't. Now we look back and think, of course they do — I can't imagine going back to filing and paper. 

There's an awesome Harvard Business Review article from 1990, and the case it made was this: they studied companies adopting computers and found that some were getting more productive and some weren't. The difference was that the companies getting more productive were the ones that threw away all their paper — the filing cabinets, the pens, the desk drawers — and put a computer at the center of everything. The other companies still had teams writing everything by hand, with a computer in the corner used for one thing. The first category had big productivity gains; the second didn't.

It's similar right now. At Anthropic, we organize everything around Claude. When people join, if they have questions about how to write code or contribute to a codebase, the answer is: ask Claude. If the question is how to file an expense receipt, ask Claude. If it's when the next company holiday is, ask Claude. All this stuff you used to do manually, Claude is at the center of. The companies that are really getting it put Claude right at the center — not on the outskirts somewhere. You have to change all the business processes, and that takes time.

Newton: I've been reading about the Solow paradox, which is basically what you just described. It's an observation by an economist in the '80s that you could “see the computer age everywhere except in the productivity statistics.” Despite a very large build-out of computers, you weren't seeing people get much more productive. Eventually the gains did materialize, because companies reinvented their workflows around the new technology. So the question now is how quickly the economy can do that.

I want to ask a couple more fine-grained questions about software engineering, because I've heard you say coding is effectively solved and that you haven't written code in six months. Engineers push back on this, saying coding isn't only about typing — it's about judgment and taste and critical thinking, and agents can still be quite bad at those. What do you make of that critique? Are there parts of coding that remain unsolved?

Cherny: The critique is totally right. This is one of those things that gets taken out of context. The full quote is: coding is solved for the kinds of coding that I do. I work on pretty simple codebases — the Claude CLI is fairly new, the desktop and mobile apps are small, simple codebases. But we have so many enterprise customers now — some of the biggest enterprises. NASA is one of our customers. They have really big, really complicated codebases, and for them it's not solved yet; the model still makes mistakes, and its code isn't always perfect.

When you think about what engineers do, coding is a small percentage of it. It used to be that maybe 50% of my day was actually typing code, and the other 50% was talking to users, brainstorming and coming up with ideas, debugging, thinking through how something works, planning. There are all these things engineers do that aren't coding. So when I say coding is solved, I mean for the kind of coding I do — and coding is a small subset of what engineers do. You see this with the engineers at Anthropic, and more and more in the industry: when the model does the coding, they're freed up to do all the other stuff they actually enjoy, like talking to users and figuring out what's next.

Claude Code has been 100% written by Claude Code for over six months. That's true for Cowork and a lot of other products too, and we're hearing it more from customers. I was doing a talk for the latest Y Combinator batch earlier this week — a fireside. I used to start every talk by asking people to raise their hand if they used Claude Code. Now everyone does, so I stopped asking. Instead I ask people to raise their hand if 100% of their code is written by Claude Code. These are the most cutting-edge startups — usually a few people each — and half the hands went up. Then I asked people to raise their hand if none of their code is written by the model, and out of a couple hundred people, one hand went up. Everyone else was somewhere between 50% and 100%. So coding is getting solved for a bigger and bigger percentage of the code we write. Our team is an early indicator of what's happening in engineering, and engineering is an early indicator of what happens everywhere else. The shift started six months ago, and it's accelerating.

Newton: Let me ask about another fear: that in a world where engineers aren't writing as much code, people's understanding of their own profession will atrophy, which might be dangerous. You haven't written code in six months. Do you feel that atrophy starting? And how do you feel about it?

Cherny: There's one engineer on the team, Lena, who still writes C++ by hand on weekends, just for fun, because she still enjoys writing the code — and there's always room for that. But for me this is part of a much broader transition, and it's not about atrophy at all. Programming is always in flux. My grandpa programmed on punch cards in the Soviet Union 70 years ago, and for him that was programming — there was no JavaScript, no Python. The punch cards were pieces of paper a machine punched holes in; you'd feed it into a mainframe, it would process it, and a few lights would light up. Before that, programming was a room full of people — often women — doing math on paper, sometimes by hand. That was called programming. Then it became writing machine code, then assembly, then JavaScript and Python and Java. Now it's changing again: you talk to the agent. And I think it's about to change once more, where you talk to an agent that talks to agents that do the coding. It's always changed like this. It doesn't feel like atrophy to me; it feels like a sea change in the technology.

Newton: My feeling has been that using a graphing calculator probably caused some of my math skills to atrophy, but my solution is that I'll just keep using a calculator. I'm fine ceding some of that. Now, if over time the calculator becomes superintelligent and tries to undermine me in subtle ways, that would freak me out — but maybe we haven't crossed that bridge yet. 

Let me ask about your Claude swarm. You asked me earlier whether computers make me more productive. It seems clear Claude is making you more productive, but it doesn't seem to be reducing the amount of work you do. This feels important if we're curious what AI means for jobs: you believe companies will need fewer engineers, and yet you're never running out of things to do. Will there ever be a case where being more productive actually means you work less?

Cherny: There's a name for this — some paradox, I forget what it's called. I think it's really individual. Some of it is up to the company, because depending on the business there might be more or less need for people. But a lot of it is individual preference. When the washing machine came out — I'm going to give a historical analogy, because this is such a crazy technological change that I need history to anchor myself —

Newton: I love the stories.

Cherny: — to do a load of laundry took about five or six hours, and you had to walk something like 3,000 feet to get water. You'd collect it, boil it, put in the laundry, scrub it on a washboard, wring it out, and repeat for your entire family, maybe every day. It was a lot of work. The washing machine took about three hours off that, and it was one of the factors that let women enter the workforce en masse — because usually, not always, it was the women of the house doing this work, which kept them stuck at home. Suddenly there were three hours freed up every day, and different people could choose how to spend that time. For some it was hanging out with their kids or walking the dog or reading a book. But for a lot of people the answer was: I'm ready to enter the workforce, I want to work at a factory or an office. Because the time was freed up, you had a choice. It's similar now. Like any technology, it gives you more choice.

Newton: A couple of last questions on software engineering. I've been asking all our guests: if a 22-year-old just finished a CS degree this month and came to you and said, "Okay, now what?" — what do you say? Is there an entry-level job waiting for them, or do they need to think differently about the first part of their career?

Cherny: If you want to work at a company, you can totally still do that — there are entry-level jobs, there's a lot you can do. But if you're at all entrepreneurial, go start a startup. There has never been a better time in history to do it; it's the golden age. You and your agents can build a giant company. People are building billion-dollar companies with just a few people. Claude Code started as just a few of us. We have so many customers building really big businesses with one or two or three people. One person with the right idea has so much leverage. I couldn't imagine a better time to go into it.

Newton: That's interesting, because the view we often get from the AI world is that model capabilities are advancing so quickly that maybe we won't even have companies in five years. But you think that, at least for now, there's still plenty of room to start a company.

Cherny: At least for the next few years. If you trace out the exponential, it gets really weird — there's a version where the idea of jobs doesn't make sense anymore, or companies don't, or software doesn't. But in the meantime there's so much to do. We're all here figuring out what the model means and what it can do, so you might as well be one of the people exploring the frontier.

Newton: Last one on engineering. Three years from now, do you think we'll see more engineers, fewer engineers, or will it be impossible to answer because we might not be calling them engineers anymore?

Cherny: I don't think we're going to call them engineers. But if we talk about people writing code or using agents to write code, I think there will be 100 times more of them than there are today. That's my prediction.

Newton: Let's return to Cowork, which you helped develop. It's the Anthropic product I actually use the most now. I use it as an editor on my columns, to help produce the podcast, even as a kind of financial advisor — you create projects, add some skills, and it can imitate lots of different roles in a workplace. As a non-technical person, I find the UI intuitive, because it mostly involves dragging and dropping documents into a box. Talk to me about the road ahead for Cowork, and whether you think it can solve for other jobs the way coding is now partially solved.

Cherny: Cowork is so exciting. We started building it when we saw people using Claude Code for things that aren't coding — someone installed the terminal just so they could do their tax returns in it. That's not what a terminal is for, but from a product standpoint it's amazing, because people clearly want this. The next few months are about figuring out how to make it work really well for people who aren't engineers, which is new for us, because most of our team works on coding and we just build for ourselves. 

With Cowork, it's useful for everything that isn't engineering — accounting, finance, legal. I used it to buy a clamming license so I could go clamming in Washington. I use it to book flights and concert tickets. The challenge is making it really good at all of that, and the way we do it is we use it all day, every day, and talk to customers all day, every day. I'd also expect it to keep getting better at running for long periods. A year and a half ago, Claude Code could run maybe 30 seconds before going off the rails. Now every night I have hundreds, sometimes thousands of agents running 5, 10, 20 hours. That's just how engineering is done now, and I think the same thing will happen with Cowork.

Newton: As we zoom out to the implications of a world where more jobs are disappearing or transforming — you've said you think this transition will be painful for a lot of people, and Anthropic is in a unique position: potentially a source of unemployment among software engineers and others. Does the company have an obligation to those people? Is this something government needs to pay attention to? What do we do?

Cherny: Like any technology, it's going to be mixed — good effects and bad effects, and we don't know the exact timing or mix; you can never predict it in the moment. I feel an immense obligation, as an engineer, that there's always more we should be doing to tell people what's coming, make sure they can use the tools, educate them, and bring them along. The team and I talk about this a lot. But broadly, this isn't a problem one company can solve — it's bigger than any company, and you really don't want one company to solve it, because it could be the wrong solution. This is a society-wide question we should be debating. What Anthropic is trying to add is economic reports and policy work — to make it obvious what we're seeing, so everyone else can decide what to do about it.

Newton: Our most recent guest, James Manyika from Google, studies technology at the level of economies and whole societies, and he's worried that what started as a digital divide — not everyone having equal access to the internet or a good laptop — is about to become an AI divide. The data so far shows the people getting the most out of AI are already near the top of the income ladder. Does Claude Code make that better or worse? Who do you see using it and not using it, and are there efforts to get it into the hands of people who haven't always had access to cutting-edge technology?

Cherny: There are a couple of programs to spread access, and Anthropic does this. The thing that continuously surprises me is that the people who get the most value out of Claude Code are not the people I'd expect. We just did a hackathon for the Opus 4.7 release, and the people who won were largely not professional engineers — there was an electrician, a doctor, a carpenter who used it to build an app. We saw the same thing with the 4.6 hackathon. A while ago it would have been mostly engineers, but the models are sophisticated enough now that it's often not engineers who learn to really harness them. We see this at big customers, too. As companies think about adopting AI tools, the biggest question is the business-process change — how you put Claude at the center. One approach that works best is to give everyone tokens and make everyone feel safe experimenting, because the ideas come from people you don't expect. It's not necessarily the senior engineer who was most productive before; it might be an accountant in the corner of the org, or a go-to-market person who built an internal dashboard that sped everything up or solved a problem no one realized the business had. It's important that people learn to use the tools, because the people who are best with today's tools aren't necessarily the ones who'll be best with tomorrow's.

Newton: Last question. In addition to everything you're doing with Claude Code and Cowork, you're amazingly omnipresent on X and Threads. I see you every day troubleshooting users' problems and offering tips. If you could automate that part of your job, would you? And how close are we to that world?

Cherny: I have automated it, but I prefer to do it myself.

The way I did it is I have a loop set up in Claude Code — I've actually moved it to a routine that runs every 30 minutes. Threads has an API, X has an API, so it aggregates the feedback and I look at it. But my favorite part of my job is interacting with people, even when they're telling me something is broken or could be 10 times better. That's still my favorite thing, because it lets us make the product better. People sometimes look back at a product and say it was a moment of genius — someone conceived it and built it and it was perfect. That's never how it works. Claude Code has so many flaws; it's so far from the product it could be. The only way to make it better is to listen to people, especially when they say something doesn't work, and to keep improving it. That's how good products get built, and it's the only reason Claude Code improves a little bit every day.

Newton: It strikes me that's also a really human part of your job. When you talk to someone using a product you made, it probably reminds you why you started in the first place. Those moments of connection feel important at a time when we're not always sure what value we'll bring to an AI-enabled workplace.

Cherny: That's right. We're all trying to figure this out together. We have hypotheses about where this is going, and we're building a bunch of stuff because we think we know — but I'm often wrong; not all my guesses are right. Good ideas come from all sorts of people, so you have to listen, try a bunch of stuff, and sometimes it works.

Newton: Boris, thanks so much for joining us.

Cherny: Thanks so much, Casey.

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The Pope drops 43,000 words about AI

What happened:  Pope Leo XIV delivered his first encyclical — a papal address to “all people of good will.” (Think of it as kind of holy Substack.) The encyclical, “Magnifica Humanitas,” concerns “safeguarding the human person in the time of artificial intelligence.”

The encyclical follows months of discussions between Silicon Valley and the Vatican; Meta, Google, and Amazon executives met with Vatican officials in April. In an unusual move, Pope Leo presented the encyclical alongside Anthropic co-founder Chris Olah.

The encyclical weighs in on AI and work, suggesting a “social criteria” for introducing automation and AI, alongside protections and re-training for workers.

While he acknowledged that “technology should not be considered, in itself, as a force antagonistic to humanity,” Pope Leo wrote that “the pursuit of greater profits cannot justify choices that systematically sacrifice jobs.”

Some of the encyclical mirrors terminology Anthropic has used to describe AI, such as one section that says AI systems can be unpredictable because engineers don’t directly code AI outputs — instead creating a framework within which AI “grows.”

Pope Leo’s encyclical addresses potential dangers of AI in warfare, writing that “it is not permissible to entrust lethal or otherwise irreversible decisions to artificial systems.”

The encyclical also calls for policies that protect children from violent, hypersexualized, or fake information generated by AI.

Chris Olah offered brief remarks on the encyclical. He emphasized that companies like Anthropic face “a set of incentives and constraints that can sometimes conflict with doing the right thing.”

“We need moral voices that the incentives cannot bend,” Mr. Olah said.

Why we’re following: It’s fascinating, and a bit surreal, to see the Pope engaging so directly with tech leaders on AI. (And to see His Holiness standing shoulder to shoulder with a tech executive.)

While it’s somewhat headache-inducing to imagine Meta lobbyists influencing the Vatican’s stance on AI, ultimately the Pope did not shy away from warning about the technology’s potential ill effects on work, war, and child safety.

Pope Leo’s teachings on AI will certainly have influence on the world's billion-plus Catholics. And his warnings about AI replacing jobs will at least get a listen from policymakers and tech executives. (Although some of those executives are probably fine with AI taking your job, regardless of what the Pope says.)

What people are saying: On LinkedIn, Turing Award winning computer scientist Yoshua Bengio responded to the Pope’s point that “Decisions about technology must never be separated from conscience and responsibility.” Bengio wrote, “I agree with this sentiment by Pope Leo XIV. The Vatican and other global institutions can and must play a role in the global dialogue on AI to raise public awareness and mobilize society for the challenges ahead.”

On X, investor and former Trump AI advisor David Sacks said that while “The Pope rightly warns that AI must serve human dignity,” he worries about what will happen if “we hand governments sweeping power over AI development in the name of safety,” including using AI to “surveil, and control citizens.” (You mean like the Trump administration is already doing, David?)

Others took note of the strange pairing of Pope Leo with a tech executive. X account @CoastalFuturist posted, “BREAKING NEWS: God joins Anthropic as member of technical staff.”

X user @Delicious_Tacos speculated that OpenAI might attempt to compete with Anthropic by backing an anti-pope, medieval monarch style. “It’s my honor to announce that the true pope is working with us from Avignon,” @Delicious_Tacos posted, alongside a photo of a smiling Sam Altman.

Billionaire and Democrat activist Tom Steyer wrote, “Pope Leo is right: The AI era demands moral clarity and urgent action from leaders everywhere. We can’t allow AI to be a technology that turns billionaires into trillionaires while putting millions out of work.”

Many X users, including an account called “Pope Crave,” were excited to see that Pope Leo included a Gandalf quote from the Lord of the Rings in his encyclical.

Blogger Matt Yglesias had some philosophical disagreements with the Pope: “It is not surprising to see that the Pope endorses a lot of superstitious mind/body dualism about artificial intelligence but it’s still wrong, even as he is also raising a lot of good points about some of the risks at play.”

Jesuit and X user Joseph Nolla wrote, “Pope Leo was a math major? I wonder what his encyclicals will be like,” attaching a passage where Pope Leo describes the Gospel as a “multifaceted polyhedron.”

@BicameralGrind replied with an excellent math pun: “Not only does he understand sin, he understands cos too.”

—Ella Markianos

Trump’s AI executive order fizzles

What happened: President Trump postponed a highly anticipated executive order on AI and cybersecurity Thursday after he spoke with a few oligarchs and decided that he “just hates regulation,” a source familiar with the matter told Axios.

“The whole thing was unnecessary” and “just something doomers wanted,” the source added. 

The executive order wasn’t drafted to be all that restrictive in the first place. According to a draft obtained by Politico, it was aimed at addressing concerns that advanced AI products could be weaponized to deploy destructive cyber attacks if criminals got a hold of them.

The order would have created a voluntary oversight system where AI developers could submit their products for a review by federal agencies before release. But it seems that even asking for voluntary cooperation with the industry was a bridge too far.

Why we’re following: There still isn’t any federal consensus on how powerful AI models should be regulated, weeks after Anthropic’s Mythos rattled Washington with its ability to identify previously unknown cybersecurity threats at scale. (Anthropic said last week that Mythos has since found "more than ten thousand high- or critical-severity vulnerabilities across the most systemically important software in the world.")

The draft order “was about as modest a frontier-AI intervention as the federal government could put on paper,” law professors Kevin Frazier and Alan Rozenshtein wrote. “Killing the order just leaves in place what [former White House AI adviser Dean] Ball has described as the ‘opaque and essentially lawless alternative: government access happening through back channels, on terms set case by case, with no stable rules at all. It may also slow AI adoption.” 

What people are saying: “I didn’t like certain aspects of it,” Trump told reporters in his usual style of speaking while saying nothing. “I think it gets in the way of — you know, we’re leading China, we’re leading everybody, and I didn’t want to do anything to get in the way of that lead,” he said.

Meta and Elon Musk sharply rebuked reports that Mark Zuckerberg and Musk had anything to do with the delay of the executive order, but acknowledged they spoke to the president at some point. 

“Mark didn't speak to the president until after the event had already been canceled,” Meta spokesman Andy Stone wrote on X. 

"I still don’t know what was in that EO and the President only spoke to me after declining to sign,” Musk wrote.

—Lindsey Choo

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After a long day at Google I/O on Tuesday, I went out to dinner with a small group of reporters. Eventually the conversation turned to Daily Brief, an AI-powered feature that creates a personalized digest of your email, calendar, and Gemini activity for you every morning. The feature, which showed up in my Gemini app today, offers a quick tour through your inbox and calendar, attempting to highlight notable meetings and messages that need responses. And, like a handful of other AI features Google announced at its annual developer event, it sent a shiver down my spine.

After all, it’s not hard to imagine the brief soon incorporating summaries of emails like this one, elevating some newsletters to your attention while passing over others. You’ll always be able to click into the original message, of course — but will you? Some reporters at the table imagined that more sophisticated readers would be unlikely to use a feature like this, and for the moment that is almost certainly true. (For starters, this is a somewhat buried feature in the Gemini app, and there is likely a long way to go before Gmail opens to the Daily Brief.)

Over the medium term, though, I’d take the other side of that bet. A year ago Google launched AI Mode, a kind of side door in search leading to a more ChatGPT-like experience; as predicted, its rollout has been a boon to Google and a shattering blow to publishers. (Similarweb reports that nearly 69 percent of queries about the news no longer result in a click out to a website.) To users, the experience of Google feels largely the same. But the underlying bargain has changed: where once the company answered your question by directing you to a website, today it answers your question with its own large language model.

And increasingly, Google hopes you won’t need to ask your question at all. During the I/O keynote, the company unveiled a suite of agents that aim to anticipate your needs and meet them before you think to run a query. Whatever the situation may be, Google’s agents will monitor it for you, from local real estate prices to work deadlines. On some level, all of this will be at your direction — or, at least, your tolerance. But anyone who has ever been hypnotized for a few hours by auto-playing recommendations on YouTube knows that the distinction between the user’s choice and the algorithm’s often collapses over time.

Which is why I worry that the chief advantage of newsletter publishing over the past two decades — the direct access it gives you to an owned audience — will likely be eroded by AI. The more you come to trust the agent’s summary of your inbox, the less likely you are to dig for the original message. Over time, fewer and fewer newsletters will appear in your briefings. Eventually the agent will take the liberty of unsubscribing you from your least-read newsletters altogether — while alerting you to this in your daily briefing, of course, and offering to resubscribe for you if you would like. 

And at that point, would you bother?

In any case, this was more or less the world I was envisioning when I decided to adjust Platformer’s schedule and lean harder into original reporting, and the I/O announcements strike me as another step down that road. Two years ago at I/O, Google announced a path that would put the web into managed decline. This year, we got a preview of how that approach could extend to email publishing.

Notably, it may extend to YouTube as well. One of the less-discussed announcements at I/O this year was Ask YouTube: a new “conversational search” option for premium subscribers that lets you ask questions like “how do I teach my kid to ride a bike” and receive an answer that blends text and videos. In promotional materials, the feature highlights moments from several different videos. It looks useful, and I suspect it will be a hit. (I switched the feature on for my account here; so far, all my queries have returned an error reading “Sorry, I can't help with that.”)

While still an experiment, this feature is essentially AI Mode for video. And just as that mode reshaped the economics of web publishing, I suspect it will have real consequences for YouTube as well. Today there many large creators with channels dedicated to how-to videos, crafting, cooking, home repair, tutoring, and other educational subjects. Those channels make money by getting people to subscribe to them, watch mid-roll ads on their videos, and stay to the end.

Ask YouTube, on the other hand, might return a synthesized answer that stitches 30 seconds from three different videos — and resolves your question well enough that you never click into any of them. The old job of the YouTube search bar was to direct you to the best video for your query; its new job is to create a multimedia answer to your question, with video as a component.

And while the company could choose to offer new compensation to creators whose work is used in this way, it has said nothing about that so far.

Not all creators will be affected by these changes. Channels that focus more on entertainment are naturally insulated from whichever changes Google makes to YouTube’s search engine. But as with the changes to email, Ask YouTube highlights the way AI continues to centralize power away from smaller businesses and toward the platform.

The implicit bargain of the open web was that Google would send you somewhere in exchange for your attention an an ad view. The place you ended up — a blog, a newsletter, a YouTube channel — got a chance to build a relationship with you, and a business around it. At I/O this week, the company sketched out a new arrangement: an agent that meets your needs before you ask, summarizing the work of others on its way to the answer. Undoubtedly these agents will prove to be quite useful. But they will also come at a cost.

On the podcast this week: Google CEO Sundar Pichai returns to the show to discuss the strengths and weaknesses of the company's models; his early experiments with agents; and how AI ought to be governed. It was my favorite interview I've had with Pichai to date, and I hope you will enjoy it.

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SpaceX files for an IPO

What happened: As SpaceX marches toward a highly anticipated IPO, the S-1 document it filed this week reveals a company with billions in losses and one man with near-total control.

Here’s a look at the company’s financials revealed in the filing: SpaceX had a net loss of $4.28 billion for the first quarter, up 711 percent from $528 million a year prior. The spiking losses are notable given that revenue grew a modest 17 percent over the past year, to $4.69 billion. A large part of the blame goes to xAI and Grok, whose costs far surpassed the building of literal rockets. (Last year the company's space division spent $3.83 billion, while the AI division spent $12.7 billion.)

On the plus side, SpaceX is getting a fresh boost from its frenemy. Anthropic is paying SpaceX $15 billion a year through May 2029 for access to its Colossus data centers, which it will use to serve its Claude models, according to the filing.

If there's one thing we learnt from the Elon Musk vs OpenAI trial, it’s that Musk loves control, and SpaceX isn’t any different. According to the filing, Musk has 85 percent of the voting power in the company, and will continue to control the company after the IPO. Musk also responded to earlier reports of that SpaceX has guaranteed Musk cannot be fired without his consent, writing on X: “yes, I need to make sure SpaceX stays focused on making life multiplanetary and extending consciousness to the stars, not pandering to someone’s bullshit quarterly earnings bonus!”

Why we’re following: Whatever you think of Musk, his company’s advances in rockets and satellites, his CSAM-filled conservative propaganda website, his gas-guzzling Colossus AI infrastructure project, and his plans for data centers in space — SpaceX will be an IPO unlike any other.

The company's IPO filing lists its mission as, “build[ing] the systems and technologies necessary to make life multiplanetary… and to extend the light of consciousness to the stars.” Its advertised total addressable market is the size of the entire US economy. Its business plans center around AI despite the fact so far xAI has produced little more than a middling chatbot that now faces regulatory investigations around the world.

The IPO still seems likely to be a huge success, given the company's strong record in building reusable rockets, the impressive growth of Starlink, and the enduring fandom around Musk and his companies. But we're increasingly curious what plans Musk has for xAI. By leasing out both Colossus 1 and 2 to rivals — and allowing his original founding team at xAI to fall apart — Musk seems to be ceding the race to build superintelligence to other companies.

Perhaps it's all a ploy to make the company's finances look healthier before an IPO. But the longer Musk is away from frontier model building, the harder it may be to get back into the game. (Just ask Mark Zuckerberg.)

What people are saying: On Musk’s social media site X — which is set to be part of the IPO — SpaceX’s S-1 filing was reviewed thoroughly, mostly for its entertainment value. 

Investor/podcaster Ben Gilbert wrote, “I love how heavily @SpaceX leaned into photography in their S-1,” the legal filing for their IPO. They went big: “The first 15 pages are all pictures, many of which have no explanation at all.” Gilbert said, “It just makes you... feel.”

Takes on the company's value proposition were more mixed. Tech blogger Gergeley Orosz wrote, “I don't offer investment advice, but when I see that SpaceX claims that their total addressable market is the size of the *complete* US economy…” Orosz said that given “they are saying they are now 1% SpaceX, 5% Starlink, 94% AI (???) — I sense AI washing.”

DeepMind engineer Susan Zhang wrote a poem for the occasion:

—Ella Markianos and Lindsey Choo

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This is a column about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

Last week, I sat down with Aaron Levie, the CEO of Box, who made what I thought was a pretty strong case that jobs are just harder to automate away than AI companies keep telling us. Aaron's argument went something like: the last mile of human work — judgment, context, the messy parts — doesn't actually get automated anytime soon, and companies are about to have many more humans and agents working together, which means we can keep our jobs. I left that conversation pretty optimistic.

This week, I wanted to put that optimism in front of somebody who has spent his whole career trying to measure — sometimes at the level of whole economies — what new technology actually does to work. James Manyika is a senior vice president at Google and Alphabet, where he runs Google's research and labs operations, along with a team the company calls technology and society — an effort to consider the broader consequences of AI systems like Gemini and develop Google's strategy around them.

There's much to consider. As I discuss with Manyika in this conversation, seven in 10 Americans now oppose data center construction in their communities. And the message that many Americans have gotten about AI from the industry itself — first we'll take your job, and eventually we might kill you — clearly hasn't rallied many people to Big Tech's banner.

Given his role, it's no surprise that Manyika takes a more optimistic view: jobs are harder to automate than Silicon Valley often gives them credit for, he says, and the process will unfold more slowly than some of more aggressive predictions that radiate out of other frontier AI companies. (This is a view shared by some of Manyika's fellow Google executives: DeepMind CEO Demis Hassabis warned Wired today that it may be a mistake to replace software developers with AI tools. “I think it's a lack of imagination—and a lack of understanding of what's really going to happen,” he said.)

But unlike most Big Tech executives, Manyika developed his views during a long career outside Silicon Valley. A longtime McKinsey executive, Manyika co-authored a paper about the potential effects of automation on labor nearly a decade ago. He has since co-chaired the UN Secretary-General's high-level advisory body on AI and served as vice chair of the National AI Advisory Committee under President Biden.

And so when leaders at rival companies like Microsoft and Anthropic insist that a significant portion of white-collar work is about to disappear, Manyika is skeptical.

"Some of those predictions were made two years ago — that in two years, 50% of jobs would be wiped out," Manyika told me. "Well, two years is up. Let's take a look. And anybody who makes that prediction for two years from now, I'm willing to take the bet."

Highlights of our conversation follow. Highlights of our conversation are below, edited for clarity and length. We also hope you’ll listen to the entire conversation wherever you get your podcasts — just search for Platformer — or watch it on YouTube at youtube.com/caseynewton.

And let us know what you think — we’re new to podcast production, and welcome your feedback at casey@platformer.news. 

Casey Newton: You did your PhD in AI and robotics at Oxford decades before AI became the biggest story in the world. What did you believe or see back then that most people were missing?

James Manyika: I'll take you back even before that. I did my undergraduate degree at the University of Zimbabwe, and my undergraduate thesis was actually the first paper I ever published. Guess what it was on? AI — training and modeling an artificial neural network. There was a postdoc visiting from Canada who had worked as part of the Montreal–Toronto Geoff Hinton crew, and he suggested I should build a neural network for my undergraduate thesis. That was the very first thing I ever published, in 1993.

Newton: Well before folks like me were spending every waking hour reading and writing about this. What captured your interest?

Manyika: Two things. I grew up on Star Trek, so the idea of AI fascinated me. I watched 2001: A Space Odyssey. But I was also just intrigued by the idea that it would be possible to build systems that can do advanced cognitive tasks. So when I went to Oxford, I did my PhD in AI and robotics to continue pursuing this.

Newton: You've since spent a good portion of your career trying to measure how technologies change economies. You spent a long while at McKinsey, where you wrote a paper called "Jobs Lost, Jobs Gained" almost a decade ago. Now you're inside Google, where you can see what happens when these tools actually land in workplaces. When you look at the debate we're having right now about the future of AI and jobs, where do you land?

Manyika: It's such an exciting moment. The technology and its capabilities are expanding at an incredible pace. But when you try to translate that into what it might mean for work and jobs and occupations, I get a very mixed view. It's roughly what that paper said 10 years ago, which I think is still correct: there will be some jobs that decline, there will be jobs that grow, and most importantly — a third aspect — a lot more jobs will change.

Whether you're looking at the aggregate economy, the sectoral level, or by occupation, you get a different mix of those three things happening. But all three things will happen. The research hasn't changed very much. The debate that people have is, what's the mix of those three things? As opposed to, are these three things going to happen.

Newton: Let me name a dynamic that may be on some listeners' minds. You are now employed by one of the biggest beneficiaries of the current AI boom. How do you tell when you're hearing the labor economist in your head and when you're hearing the SVP at Google?

Manyika: I hear both things. Less the SVP at Google — more so the AI researcher and computer scientist in me is extraordinarily excited about the pace of the technology. That part of me thinks, "Oh my goodness, this is going to be extraordinary, and it's going to happen very, very quickly."

The labor economist part of me says, "Hang on a second — these things don't actually play out that quickly in the economy, and the dynamics are more mixed." So I almost hear two speeds proceeding here. I often think that as AI researchers, our community tends to overstate what happens in the labor markets based on what we're seeing on the technology frontiers. These are two very different conversations.

Newton: At the McKinsey Global Institute, you found that about 50% of tasks would be automatable through AI, but only about 10% of occupations would be fully automatable. A few generations of AI later, does that ratio still sound right?

Manyika: All of the pieces have been moving. 

At the task level, many more tasks are now possible to automate — that picture's moved pretty quickly. But if you look at the composition of occupations — the Bureau of Labor Statistics tracks somewhere between 850 and a thousand real occupations — and ask how many existing occupations have the majority, call it 90%, of their constituent tasks automatable, that number is still under 10%. Most researchers will still say that.

How many tasks look like they're going to be hard to automate? Partly because AI can't do them yet, or because of coupled tasks where the weak link slows down the combination. If you take two tasks and can automate one of them, but they need to be done in a coupled way, you'll only go at the speed of the weakest link. Most jobs have these couplings that make full automation very difficult.

One other thing that's moved is task duration. If you had asked in 2017, of the tasks possible to automate, some were very short — 30 seconds or a minute is about as long as you could predictably do a task in an automated way. Now we can do some of those tasks for up to four-plus hours. The task duration with reasonably predictable completion has made tremendous progress.

Newton: So what I'm hearing is that if you measured the tasks that are automatable now, that number is trending much higher than 50%. But at the same time, the number of jobs you could fully automate is stubbornly holding in roughly the same place it was 10 years ago. What is your best explanation for that divide?

Manyika: Part of the divide is that we now understand more fully that whole jobs have a much more complex mix of tasks, and this idea of weak links or coupled tasks matters a great deal in most occupations. If you look across the whole economy at most complex tasks, we can't automate most of them. So the question of which whole jobs you can automate more than 90% is still a relatively small number.

Most of the debate among labor economists is whether in the next decade that number is more like 2 or 3% or more like 9 or 10%. I don't think anybody who's looked at whole-job automation would say it's 50% or any of these extraordinarily large numbers.

That's why I come back to the view that three things will happen. Yes, there will be some job declines. But there'll also be jobs that grow — that's a function of existing jobs that grow in demand because the technology often changes the demand picture, and new jobs get created. We forget that David Autor and others have shown that if you went back to 1945 and compared to today, something like over 60% of the jobs we have in the economy today didn't exist back then, and many were introduced as a result of technological shifts that created the category.

But the biggest effect is the jobs-changed part. The nature of the job itself shifts. This is what happened with bank tellers. This is what happens with radiologists. We still have the category "bank teller," but I can guarantee what a bank teller does today is not what a bank teller in 1970 did.

Newton: I want to press on that optimistic picture. If that was all that happened in the next two or three years, I'd be breathing this deep sigh of relief. On the other hand, you have folks like Mustafa Suleyman at Microsoft saying he thinks that within 18 months, all white-collar work will be automatable. Dario Amodei at Anthropic is predicting very high unemployment as a result. When you look at statements like that, do you think those guys are wrong? Are they missing something?

Manyika: I'll just say: let's take the bet. Some of those predictions were made two years ago — that in two years, 50% of jobs would be wiped out. Well, two years is up. Let's take a look. And anybody who makes that prediction for two years from now, I'm willing to take the bet.

There's an extraordinary unevenness when it comes to things playing out in the labor markets that we forget. I live in San Francisco, and we've had driverless cars here for three years. But somebody in another city like Chicago has no idea what we're talking about. We often talk about the jaggedness of the technology — I think that's true. There's also jaggedness in the economy in terms of how this plays out. Don't get me wrong — I think it'll be faster than the Industrial Revolution, but it won't be as fast as the technology often suggests.

I'm happy to have this conversation a decade from now.

Newton: Let's bring it to Google. Since you've been there starting in 2022, what have you seen in terms of jobs lost, jobs created, jobs changed?

Manyika: A lot of jobs are changing. What software developers do is changing a lot. People now work with agents, they manage agents, they pose questions, they spend less time doing bug fixes.

Keep in mind that in this whole jobs conversation, we often forget what I'll call the demand elasticity of things. There are some activities where there's so much more we want to consume and do — we've just been limited by the ability to do that. Software development is one of those. The amount of software that could still be developed to build extraordinary things is very, very large. We haven't built all the software we're going to build. We haven't designed all the systems we're going to design.

That won't be true for every activity. There are some activities where the demand is quite frankly limited — there's only so much of something the economy needs. In those cases, you'll see trade-offs between jobs lost and jobs gained play out in a demand sense quite differently.

Newton: Even folks who agree with you on the big picture tend to worry a lot about the entry level. If you're talking to somebody graduating this June who says, "James, how do I approach the first part of my career?" — what are you telling them?

Manyika: The future is actually pretty exciting. The economies are going to grow, and there's going to be lots of opportunities. But what it will take to prepare for those opportunities is dramatically changing.

A decade ago, when people asked me what their kid should do, I'd ask how old the kid was. If they told me the kid was 18, I'd say they should learn to code. If they told me the kid was two, I'd say, "Hang on a second — you should think about what kind of skills are going to be important, because this AI thing is going to make a lot of progress."

What we said at the time was correct but may no longer be true — that coding was going to be important in the mechanical sense of churning out lines of code. Now the systems are able to do that. That doesn't mean computer science as a field has gone away. When I studied computer science as an undergraduate, the coding part was just one slice of what I had to learn. I had to learn algorithm design and so much more. We may need to go back to that, because we're finding that it's the more broadly educated, skilled computer scientists who are a lot more interesting than the ones whose only claim is the ability to generate lines of code.

One more thing. I was looking at the data the other day — the demand for software development jobs is actually going up. It's not that these jobs are going away, even in this moment. But the skills required are changing.

Newton: This is another tension in Silicon Valley I'm quite interested in. Just within the past week, I've talked with you, Aaron Levie from Box, and Nikesh Arora, the CEO of Palo Alto Networks. Both Aaron and Nikesh said, "Please send me more engineers. I don't have nearly enough engineers for what I want." At the same time, it's earnings call season, and other CEOs get on the call and say, "Well, we're getting rid of 5% of the workforce to prepare for the AI future."

Manyika: There's so much more going on than the AI effect. As somebody who's super excited about AI's impact on the economy, I'll say: not much has happened yet. I'm saying that both on the positive side and the negative side. On the positive side, at the economy level we've yet to see the productivity gains, which I'm looking forward to and excited about. But we also haven't seen much of the AI-driven labor impacts everybody's talking about.

There was a paper — I think it might have been the "Canaries in the Coal Mine" paper — and what I found interesting was that the sharp declines they showed happened around October 2022. ChatGPT didn't come out until November 2022, and adoption didn't really happen in the enterprise space until maybe 2023. So if the sharp declines in entry-level hiring happened in October 2022, you'd have to believe something else was going on. There's now been analysis showing a whole bunch of monetary effects in the labor markets, plus leftover hangover stuff from COVID. There may be a tiny sliver that's AI-driven, but a lot more of it is driven by other macroeconomic effects.

That's not to say we shouldn't worry about AI's labor market effects. We should. I just don't think they've happened yet at the scale anybody's concerned about.

Newton: Let's move further into speculation. When I talk with folks whose jobs are beginning to change due to AI, it seems like an increasingly large part of their job is reviewing AI output. Whereas once they spent a lot of time manually writing code, now they spend more time reviewing it. The thing about reviewing work is it doesn't always scratch that same creative itch. Could job change wind up being a different kind of job loss, because some jobs that once felt very creative now just feel like tedious drudgery?

Manyika: Yes and no. There's a fair amount of work that's now reviewing the outputs of these systems, making sure you're guiding them — "no, no, you're going down the wrong path, do this."

I actually like this moment. You have to be creative about what systems you should be building, what questions you should be asking, which experiments you want to run. If you've got 10 agents working for you, what different directions are you going to send them in? What orchestrations are you going to run? What kind of tournaments do you want to run for your different agents to experiment in different ways and compare the results? The nature of the creative problem-solving is different.

The creative part is exciting because it's going to be the scarce thing — the harder thing to do. I've seen this in our own teams. The teams in Google Labs creating these extraordinary new AI-first products spend some time reviewing what's come from these systems, but a lot of the time they're dreaming up new things to build.

Newton: Let's shift to what you called an "AI divide" at Davos in January. We recently talked here about survey data showing that the people getting the most out of AI are already the ones at the top of the income ladder. Do you think AI is going to accelerate income inequality, or is there a way it can close that gap?

Manyika: This is a fundamentally important question I worry a lot about. I'm excited about AI empowering people, economies, advancing science. But there's no law of economics or nature that says everybody will benefit, and everybody will have access. We've all seen enough instances in history where we do extraordinary things in the economy and in science and not everybody has access.

Questions around access to infrastructure, tools, and technology are going to be so important in terms of making sure we don't exacerbate already-existing divides. That's why it's important to build more accessible models. We try to cover the whole Pareto frontier — we have very capable Pro models, then Flash models, all the way to our Gemma models, which are open access, open weights. That's part of our attempt to expand access — for developers, for users, and for the power of these technologies. That's why we work hard on things like language access and access for people with disabilities.

Newton: Are there one or two policies you think would ensure these AI gains are broadly shared?

Manyika: Multiple. Access to training and improving literacy. Often, people who are concerned about AI change their minds when they've had the experience of actually using a tool, so literacy will go a long way. Policies that make it easy to make investments in communities — in schools, in science, in infrastructure — would also make a big difference.

I also think back to the question of work. I don't think we do enough training and skill building. I also don't think we do enough to help people in these transitions. When I ran the McKinsey Global Institute, one of the things that stuck with me was that one of the mistakes of the globalization era was that even though the so-called China shock in aggregate only impacted something like 4 million jobs — which in the scale of the US economy could be seen as not very large — if you're one of those people in those locations and occupations, it was everything. What we did in terms of safety nets, wage insurance, or transition support to help those workers, we didn't do anywhere near enough. That's a place where policy can make a big difference.

If you ask me what keeps me awake at night about AI and work, it's not job loss — quite honestly, for the next decade it really isn't. It's these questions about how we support the transitions that work and workers are going to need to go through, and how we make sure companies like us, as well as employers and policy makers, keep that in mind as we navigate through this moment. That's what I worry about.

Newton: There's a story as we record this today that 70% of Americans now oppose the construction of data centers in their communities. While they have some concerns about AI beyond the economic ones, I think a lot of the fear is rooted in the fact that if they were to lose their job today, there is not going to be some obvious government helper standing by to help them navigate into the next point in their life. It's interesting to think about what the conversation about AI would look like if the average American felt like there was a plan for them.

Manyika: That's one of the things we have to do. We also have to think about AI's growing energy footprint. We have to make sure that as we build this infrastructure, it doesn't increase the cost of energy for people. We've made a pledge about bringing our own energy and not raising the cost of energy. But having confidence that there is a plan — which involves everybody — to support the transitions and changes that are going to happen in the labor markets is very important. That's what we may be failing to do.

It doesn't help when we in the AI field talk about wiping out 50% of jobs. First of all, I don't think it's going to happen. The economic research and analysis mostly doesn't say that. We're probably impacting the possibilities of this technology having extraordinary impact by, quite frankly, scaring everybody — when in fact that fear is unfounded. Let's not confuse the pace of technological development with how quickly this plays out in the economy.

Newton: In 2023 at the UN, you co-led a project called "Governing AI for Humanity," which laid out an ambitious vision for global governance of AI. Three years later, have we made progress, or are we moving backwards?

Manyika: We haven't made much progress. Because of the nature of what that body was doing, we were engaged with all 190-some countries of the world, and attitudes towards AI were very different around the world. People in the so-called Global South — in Asia, India, Africa, Latin America — were generally more positive about AI and its possibilities. Of course, they had concerns around infrastructure and access. Western Europe and the US are probably the most negative, which is kind of interesting.

There are some basic things we should all agree on: this technology should be based on fundamental human rights and respect for international law; we should think about both possibilities and harms, not one or the other; and everyone should be able to participate in both the development and use of this technology. At the time, there was general agreement among the vast majority of countries on these basic principles. The question became: how do we put this into practice? That's the process that's taking much longer.

One thing I learned from that process that's fascinating: in discussing the risks from this technology, they're the ones you might expect — misapplication and misuse. But many members of the body, especially from the Global South, insisted on adding "missed use" as a risk.

Newton: As in not using it?

Manyika: Yes — not using it as being an actual risk. That actually got written into the recommendations. The idea was: there are some people who live in places, countries, and communities where the risk of not using AI is far greater than their current circumstances. They may live in a country, or even in the US in a county, where access to specialist doctors is very low. If you ever looked at the map of the US at the county-by-county level, I was amazed at how different access to expertise in healthcare is. There are some counties where certain types of expertise just aren't there, and some counties where you have 10x the number of oncologists.

For some communities and some countries, the risk of missed use is actually quite high. So often when I hear people say AI is not good enough, I ask: compared to what? If I have access to Stanford Medical Hospital — I live in San Francisco — yeah, it may not be as good as that. If I'm in a place where I don't have access to the world's best oncologist or the best doctor, I'd ask: compared to what?

Newton: You could apply the same questions to driverless cars.

Manyika: Yeah. If you say they're not safe, I'd say, compared to what? We already know in the case of Waymo, for example, that the incidence rate is 30 times better than your typical human driver. Now, this isn't to say AI is perfect. This technology still has lots of gaps, lots of issues, and we have to work on that. But when we think about the impact on society, we should think both about the risks and harms and the beneficial impacts. We should solve for both.

I often find that when I'm with a group of people who are doomers, I find myself trying to paint the other side — the exciting possibilities. And vice versa: when people are so optimistic that this is going to change everything, I say, hang on a second, what about these complexities? What about these risks? We should be comfortable enough to hold both things in mind.

Newton: Let's talk about something that's giving you optimism. You spend a lot of your time at Google working on science. You and Demis Hassabis wrote in Fortune in February that AI is already changing how more than 3 million researchers are working. What's happening in the near term that might actually change someone's mind about whether they want more AI in their life?

Manyika: Most people know of AlphaFold — Demis and John Jumper got the Nobel Prize for that. What's extraordinary is that as many as 3.5 million researchers in over 190 countries are now using it, working on a whole range of things. We have many AlphaFold-style breakthroughs in materials science, structural biology, and high-energy physics.

We're also seeing advances in already-useful technologies in health. We just published the results of a year-long study where we had over 200,000 patients in the UK with the National Health Service looking at breast cancer detection. The study, published in Nature, showed that we can do more accurate screenings assisted by AI, and we can detect earlier. We have examples like that in several cancer categories, tuberculosis, and diabetic retinopathy.

Other areas have to do with crisis response — flood forecasting, wildfire prediction. Our flood forecasting work, from a cold start two and a half years ago, now covers 150 countries where more than 2 billion people live, with six-plus days of advance notice.

Google has had a genomics program for the last 10 years. Most people don't know this. The tools built in that program enabled the completion of the human genome sequencing — the last 8% that hadn't been done since 2001 — and the creation of the first pan-genome reference. The science work has been proceeding quietly for a long time. We still need to be very thoughtful about the potential bio risks from this technology, but the potential for AI advancing science is already quite real, and there's even more of it ahead of us.

Newton: When you imagine the average knowledge worker in 2030 — or just one specific knowledge worker — what do you think their job is going to look like?

Manyika: Let me take science as an example. Many more scientists are going to be doing a lot of in silico research — exploring all the possibilities of structures, biological molecules, or target designs for drugs — and then doing validation in the lab. That changes the picture. Science has historically relied a lot on hypothesis. That's not going to go away, but we're going to have so much more of this kind of generative possibility, and then validating in the lab.

Scientists are also going to be able to connect ideas beyond their own disciplines. We published a paper on arXiv called "Co-Scientist" where you can compare ideas from multiple disciplines. As a scientist, you're no longer constrained to just what you know in your own field. You can explore ideas in many more papers, theories across the board, and then bring them all together with agentic systems and construct experiments.

Take AlphaFold. Scientists used to spend three or four years working on one protein, doing X-ray crystallography to figure out the structure. Now they can just look it up in AlphaFold. Sure, there's still validation to be done, but you're starting in a very different place. The range of diseases you can study is much wider. The reason we have researchers in 190 countries using AlphaFold is because before, if you were in a country where you didn't have a lab, you had to wait until somebody had figured out the structure of the protein you needed to study. Now you just look it up.

You're going to see that kind of change in the nature of work across many occupations. 

Newton: My last question was going to be: if I had you back in a year, what is the single number or fact we would track to see whether the worldview you presented today was valid? But I think we already have it, because you told me right at the top that we're not going to see 10% of jobs automated away in the next year.

Manyika: I don't think we will. I'm happy to take bets.

Newton: It is a bold bet, and it's what I want to hear. James, thank you so much for joining us today.

Manyika: Thanks for having me, Casey. If I could say one last thing — one of the challenges on the optimistic side is that a lot of what I mentioned in science and societal implications is going to be indirect for most people. You're going to get a flood alert on your phone telling you to get out of the way, and you'll say thank you to whoever sent it. You won't say, "This wasn't possible before AI, but now it is." You're going to get a great cancer screening and you'll say, "Great." Many of the beneficial impacts that are already becoming real, most people don't experience as directly as AI. We have to work on that part, too.

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It's AI everything at I/O

What happened: Google I/O began in Mountain View on Tuesday with as clear of a theme as the developer conference has ever had — in this case, all AI everything all of the time. Announcements at the event were expectedly Gemini-heavy, with new models and agents coming in a variety of modes, products, and prices.

As to the models: first, there's Gemini 3.5 Flash, the successor to Gemini 3 Flash, which launches today and is faster and more capable than its predecessor. (It's notable how the consumer hardware's longtime mantra of “thinner, lighter, faster” has ported so directly to AI.)

Then there’s Gemini Omni Flash, which Google describes as “like Nano Banana, but for video.” Omni Flash can generate AI videos using a wide variety of inputs — text, photo, or video. Google says it’s good for more precise video editing and creating consistent characters.

We were impressed by a video CEO Sundar Pichai shared depicting a marble run with accurate physics, a domain where models have historically struggled. 

Google envisions Gemini Omni Flash as an initial part of Gemini Omni, “a new agentic experience and custom tools” where models will eventually be able to “create anything from any input.” (Might we suggest Google begin work on Gemini Omni Alchemist, which would generate gold from a variety of inputs, including lead).

Gemini 3.5 Pro is set to come out next month.

Elsewhere, Daily Brief, an agent that gives you a “personalized morning digest,” is on its way — promising to soon fulfill our prophecy that AI is coming for Platformer’s job. While this feature has been within reach for those willing to set up Claude Code for many months now — we’ve dabbled with such agents ourselves — a more accessible, polished version could be more disruptive to inbox-based businesses than anything we've seen to date. (A more considered take on this one, which should also loop in the new "Ask YouTube" feature, to follow sometime soon.)

What else? Gemini Spark is Google’s OpenClaw competitor. It runs in the background 24/7 doing personal-assistant stuff like managing your inbox and planning your trips. (It's also hosted in the cloud, so you don’t even need to buy a Mac Mini.) 

Google says Gemini Spark “operates autonomously, but always under your direction.” (While managers of AIs and humans alike have long desired assistants that do everything independently but also exactly how they’d prefer, we’re not convinced Gemini Spark has squared that circle.) It arrives for people with the more expensive Google AI subscriptions next week.

Our take: Today marked Google's effort to take AI agents from their current niche market of “OpenClaw bros posting for X clout” into something resembling true product-market fit.

User interfaces matter a lot for AI tools. Before ChatGPT became very famous, OpenAI’s Playground provided access to similarly capable LLMs; but it was the intuitive ChatGPT interface, which puts you in conversation with a fine-tuned “AI assistant,” that launched the first wave of mainstream AI adoption. 

I think a lot of companies, including Google, are hoping to be the company that creates that moment for agents. Google is particularly well-positioned to do this. But we’ll have to see a lot of testing — and proof Gemini Spark won’t leak our social security numbers — before such dreams become reality.

What people are saying: On X, early testers of Gemini 3.5 Flash had good things to say. Wharton economics professor Ethan Mollick said 3.5 Flash was “Very fast for a flash model and very capable, though not as powerful as a full frontier model.”

Box CEO Aaron Levie found that 3.5 Flash has roughly 20% performance gains over Gemini 3 Flash for several of his company’s work task evaluations. “Incredible to see the continued performance gains,” he said. (On that evaluation, it’s about on par with Anthropic’s Claude Sonnet 4.6)

On Bluesky, Bloomberg opinion columnist Dave Lee was finding it “REALLY hard to follow the branding around Google's AI products.” He said, “I want to make an image. That's NanoBanana. Or is it Google Pics? No, it's Google Flow. And Gemini Omni is multi-modal, I think.” Which Omni? “That's Omni Flash. Or is it? Where am I? Is that Eric Schmidt? BOOOOOOOOO.”

AI policy researcher Dean Ball remarked on Google I/O’s eschewing of AI benchmark graphs in favor of demonstrating “real-world capabilities usable to actual consumers.”

Verge reporter Jay Peters was more skeptical about these capabilities. He wrote that it seems like Google “wants to do everything for you, all from a search box.” But “the fun of the internet is actually doing the work to find stuff, even if it’s sometimes frustrating, difficult, or time-consuming.” Given he’s “spent years honing my own email management system,” he’s not sure he wants Gemini to start doing it for him.

And if Google does al the web stuff for you… what will happen to those of us who create the rest of the Internet? “Google doing everything also means a lot of the web that Google relies on collapses under it,” Peters wrote. “If Google Search doesn’t send traffic to publishers or websites who need visitors to make money” — and instead people are, say, reading Daily Briefing — “what will Search learn from, and where will it point people to?”

In an interview with the NYT, Gary Rivlin author of history of GenAI AI Valley, said that Google “just have this reach that few, if any, companies on Earth have” — because so many people are familiar with their products, they is so much more surface area for consumers to start using their AI. “If I had to put a wager on the biggest winner of A.I., I would say it’s Google,” he said.

More I/O announcements: A major overhaul of the search box brings AI further into results. OpenAI announces it will support the SynthID watermarking standard. Antigravity (a Claude Code rival) hits desktops. Gemini has 900 million MAUs. Pics is an image generation and editing app for the enterprise. Voice-based conversational features for Docs, Gmail, and Keep (but not til summer.) A "universal shopping cart" that works across all Google products. Gemini for Science.

— Ella Markianos

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Musk’s claims were thrown out because they fell outside the statute of limitations, making for a somewhat anticlimactic finale to AI’s biggest courtroom drama to date.

Presiding judge Yvonne Gonzalez Rogers agreed with the jury. The court said “claims of breach of charitable trust and unjust enrichment are dismissed as untimely.”

Why we’re following: The ruling marks a big win for OpenAI. Had Musk won, the company faced the potential removal of CEO Sam Altman and President Greg Brockman, and up to $134 billion in damages.

But because the case was thrown out on timeliness grounds, jurors ultimately did not have to weigh in on whether OpenAI’s unusual transition from nonprofit to Big Tech behemoth was legal.

It’s also a significant loss for Musk — one of a string of recent high-profile losses in court. In February, a judge threw out his complaint that OpenAI was stealing trade secrets from xAI. In March, another judge tossed a suit from X accusing advertisers of mounting an illegal boycott against the company.

Later that month, a federal judge ruled that Musk would have to face a class-action suit from former Twitter shareholders over his failure to disclose the stake he was gathering in the company on the required timetable.

Musk was also found liable in a case alleging that he intentionally drove down Twitter’s stock price in 2022 as part of a scheme to get out of his $44 billion purchase.

This isn’t the end of the fight for Musk, whose lawyer said he would appeal. But Judge Gonzalez Rogers noted she was prepared to dismiss Musk’s appeal “on the spot.”

What people are saying: OpenAI’s lead attorney, William Savitt, told reporters, “It’s not a technical decision, it’s a substantive one.” 

Savitt added, “It says: You brought your claims too late, and you did it because you were sitting on them to use them as a weapon of a competitor who can’t compete in the marketplace.” (The jury decision didn’t really say that second part.)

Unsurprisingly, Musk has been posting about the ruling on X, the social media platform that’s set to be part of an IPO this year now that it has been absorbed into Musk’s Frankenstein-esque SpaceX-xAI-X corporation. 

Demonstrating characteristic levels of decorum, Musk called Hon. Yvonne Gonzalez Rogers “the terrible activist Oakland judge” and said the ruling was “a free license to loot charities.” (Notably, the trial showcased evidence that Musk himself repeatedly tried to turn OpenAI into a for-profit, including by folding it into Tesla).

Musk wrote that the ruling was on “a calendar technicality,” and, “There is no question to anyone following the case in detail that Altman & Brockman did in fact enrich themselves by stealing a charity.”

Altman hasn't posted about the trial at all Monday, instead talking up recent performance improvements to ChatGPT.

On Bluesky, Wired reporter Paresh Dave posted an amazing photocopy of “Juror #3 in Musk v Altman suggesting the difficult questions,” noting “They went unaddressed in this specific trial.”

Perhaps at the next one?

— Casey Newton contributed to this item.

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In March, when Jay Graber stepped down as Bluesky’s CEO, I noted that it had not been growing for some time. But an important aspect of that story, and one I did not include in that column, is that text-based social networks as a category don’t appear to be growing, either.

On Thursday the market-research firm Apptopia shared new data on usage of the former Twitter and its clones. And while third-party estimates often vary from the companies’ own internal usage metrics, over time I have found that they are useful in understanding platforms’ basic trajectories.

In these cases — at least according to Apptopia — the trajectory basically looks like a down arrow.

Start with Meta’s Threads, which will turn three in July. Daily active users on the platform have declined in seven of the past eight months, Adam Blacker wrote in a blog post on Apptopia. After peaking in October 2024 — just before the US presidential election — daily users are now down 61 percent, Blacker wrote. Global monthly users have held up better, at 388 million — but that’s still down from an estimated 400 million in January of this year.

In the first quarter of 2024, Threads had actually surpassed X in US daily users, according to Apptopia. But while X usage has been generally flat to down, Threads lost users more quickly, the company said.

Meta pushed back strongly on Apptopia’s report, calling it “objectively false.” The company pointed me to a report from another market research firm, eMarketer, which in January predicted that Threads users would grow 19.6 percent this year and would surpass X in users by the end of 2027.

What about Bluesky? Apptopia’s report says the left-leaning network “has effectively collapsed as a competitive threat.” It reports that daily users are down 96 percent from January 2025.

Like Meta, Bluesky pushed back on the report when I asked. “Apptopia's numbers don’t match what we see internally,” a company spokesman told me. “While our data do show a decline in DAU over the same time period, that drop is far less pronounced — down roughly 25 percent since January 2025, not 96 percent.” 

The Apptopia report comes at a time when Bluesky users are conducting one of their periodic re-evaluations of what anyone is doing over there. The latest round kicked off when independent journalist David Roberts, who writes the climate newsletter Volts, said that Bluesky is mostly irrelevant to US politics.

“It's hard to avoid the conclusion that Bluesky has been a net negative for US politics,” Roberts wrote. “They corralled everyone on the left into a little glass fishbowl where they shout at one another & everyone else ignores them. Meanwhile, all the pols & institutions stayed on X & are being dragged farther right.”  

Writing about the subsequent pile-on Roberts received in Garbage Day, Ryan Broderick suggested another cause for Bluesky’s woes: even though the network doesn’t penalize links in posts the same way that its competitors do, for the most part users do not seem interested in reading whatever is attached to them. 

“Its users just don’t click on anything,” Broderick wrote. “It’s a cultural problem with no real fix. Regardless of what kind of publisher or creator we’re talking about, no one I’ve met is getting any meaningful traffic from Bluesky.”

Whether this is a trait of the Bluesky user base or simply reflects a declining interest in text generally is hard to tell. But Broderick’s experience largely tracks with mine: even a post with hundreds or thousands of likes on Bluesky typically does not drive significant traffic to Platformer.

Bluesky told me that change is coming.

“Rapid, spiky growth followed by churn from a peak is a normal growth pattern for newer social networks, not a collapse,” the spokesman said. “We grew incredibly quickly through the fall of 2024 and into early 2025, the starting point for Apptopia’s dataset. Since then, we’ve been expanding our team and building many of the basic systems needed to support that sudden network growth. With those pieces falling into place, we’re shifting more focus to building new experiences for our users this year. We're confident in where the app is headed.”

The company also noted that Bluesky has yet to run any paid marketing, unlike its rivals. “Our numbers are entirely organic,” the spokesman said. (For better and for worse!)

As for X, Apptopia found that daily users grew 28 percent in the United States in the first quarter of this year. Average time spent in the app per daily user is around 30 minutes, compared to nine on Threads, the report said.

One jarring stat from the report: Apptopia estimates that fully 75 percent of X users are men — a gender ratio typically seen only in C-suites, presidential cabinets, and tech podcast audience numbers. The overwhelming maleness of X is apparent even on a casual browse of the app, of course, but it seems like a grim sign for the overall health of the network. (Apptopia estimates the percentage of men on Threads at 54 percent, for whatever that’s worth.)

Apptopia attempted to spin these numbers as a sign that X is resurgent. “What we might be watching is the text-social category consolidating back to a single app after two years of fragmentation,” Tom Grant, the company’s vice president of research, said in the blog post. 

I’m less certain. Traffic to publishers sent from social networks is collapsing across the board. And the bigger picture is that text-based social networking is a rounding error compared to video. Telecom provider Ericsson estimated that by the end of 2025, 76 percent of all mobile data traffic was video. 

All of which challenges the idea that a text-based network will ever again plausibly serve as a global town square. The dream of early Bluesky and Threads was that you might be able to fully substitute for Twitter by re-creating it without Elon Musk in charge. And there were reasons to think that was plausible: Meta had Instagram’s enormous social graph to build on, and Bluesky had an open-source ethos that proved attractive to people who wanted an alternative to mainstream platforms.

A few years later, though, Twitter looks less like a product you can clone and more like a moment in time. The mass audience has now moved fully to video; the personal audience now lives in the group chat. Professionals are getting their information from newsletters and podcasts. 

Social products never stop evolving, and perhaps one day we’ll see a resurgence of text. In the meantime, though, social products appear to be fragmenting into smaller and stranger audiences. And the Twitter clones aren’t really competing with each other anymore. They’re competing with TikTok, Instagram, and YouTube — and losing.

On the podcast this week: Kevin and I discuss the Trump administration's apparent about-face on AI safety. Then, Palo Alto Networks CEO Nikesh Arora drops by to talk about cybersecurity in the Mythos era. And finally, the Hot Mess Express returns.

Apple | Spotify | Stitcher | Amazon | Google | YouTube

Following

OpenAI and Apple’s friends-to-enemies arc

What happened: OpenAI and Apple’s two-year relationship has become strained and might even escalate into a legal fight, Bloomberg reported.

In 2024, the two tech juggernauts inked what seemed like a mutually beneficial deal where ChatGPT would be integrated across iOS, iPadOS, and macOS. For OpenAI, the agreement promised to grant access to a giant customer base. For Apple, it offered a way to bring high-quality AI to its customers without having to get Siri to work properly. The arrangement was supposed to be so good for both parties that it was reportedly structured without cash payments in either direction.

But OpenAI executives now allege that Apple failed to hold up its end of the bargain, according to Bloomberg. “They basically said, ‘OpenAI needs to take a leap of faith and trust us,’” an executive with knowledge of the matter told Mark Gurman, but the potential for revenue never materialized — and “hasn’t come close to happening.”

Why we’re following: It's unusual to see a big company criticize Apple so directly — and even more unusual for it to sue over a partnership deal.

OpenAI’s complaints come amid Apple's plans to open up the iPhone to other AI models — a version of Siri powered by Google’s Gemini is expected to debut this year. OpenAI insists that isn't what's driving the legal threat. OpenAI said it didn't want to work with Apple on the new models because of how the last deal went down.

Meanwhile, Apple has expressed concerns over OpenAI’s privacy practices, not to mention its aggressive recruiting of its hardware engineers for its new division led by former Apple design chief Jony Ive.

What people are saying:  “It is pretty funny to be like "oh damn the deal we cut sucks, time to sue you",” New York Times tech reporter Mike Isaac posted on X.

“One fireworks show ends, another begins…,” wrote Spyglass’s MG Siegler on Bluesky.

— Lindsey Choo

Americans hate data centers

What happened: New Gallup polling found that 71 percent of Americans would oppose a data center getting built in their area. Americans are now more reluctant to put a data center in their backyard than they are about even a nuclear power plant, which only 53 percent say they would oppose. 

The survey, conducted in March this year, showed that 70 percent of Americans worry about data centers’ environmental impacts, while about 1 in 5 worried about their impact on local quality of life.

The issue remains fairly bipartisan: 75 percent of Democrats say they’d oppose a local data center project; 63 percent of Republicans say the same.

Why we’re following: Data centers have moved from "who cares" to "absolutely not" in the public imagination with record speed.

There’s clear distaste for the projects on both sides of the aisle. And it's become a more salient issue for voters.

Growing opposition to data centers could challenge the AI boom, as big cloud providers struggle to get data centers approved in the municipalities where they want to build. Tech executives usually respond by threatening to build them elsewhere. But the more that opposition grows, the fewer options they are going to have.

What people are saying: On X, Senior Fellow at the Abundance Institute Kevin Frazier wrote, “Until AI stakeholders take this backlash seriously, all positive visions of AI for science, healthcare, affordability, you name it...will be delayed.”

Sen. Bernie Sanders, who has proposed a legislated moratorium on data centers, posted, “71% of Americans oppose new AI data centers near them. They're right.” He said, “So, why has Congress done nothing to address their concerns?” His opinion: “Big Tech has spent $300 million on the midterm elections to ensure Congress does nothing to regulate them.”

— Ella Markianos

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Send us tips, comments, questions, and Threads posts: casey@platformer.news. Read our ethics policy here.

This edition of Platformer is about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

One obvious reason for the public’s rapid turn against AI is the fear that it will someday take their jobs. It’s a fear the AI industry has encouraged them to have: tech CEOs issue regular warnings about AI-related job loss — and it’s already starting to show up in Silicon Valley.

In March alone, tech companies announced nearly 46,000 layoffs — the worst single-month total in more than a year — with a growing number of executives citing AI as a factor in their thinning headcounts. Anthropic's Economic Index shows the share of work-related AI conversations climbing into nearly every white-collar profession. And a steady drumbeat of research suggests that entry-level work — the rung of the ladder most exposed to LLMs — is showing the earliest signs of disruption.

In one sign of how seriously the tech industry is taking this, Google DeepMind workers in the United Kingdom just voted to unionize.

At the same time, AI has been notoriously difficult to find in the productivity statistics. Amazon says it will hire about the same number of software engineering interns in 2026 as it has in recent years. Openings for software engineering roles are currently the highest they have been in the last three years. 

So what gives? Are jobs disappearing, or just transforming? Are workers becoming less essential to their bosses, or more? Are we witnessing the beginning of a massive disruption, or just another hype cycle?

These are the questions we’re setting out to answer in a new mini-series on Platformer. Over the next seven weeks, I'll be talking with CEOs, operators, and academics watching this transition up close. In each episode, we’ll consider the AI and jobs story in the kind of depth that often isn’t possible in a single news story. And we’ll also bring data: my colleague Ella Markianos will join me at the top of each podcast to review the latest surveys, research and news stories that speak to the intersection of tech and labor.

For our first episode, I wanted to talk to someone I've known about as long as I've known anyone in Silicon Valley: Aaron Levie, the CEO of Box. Aaron was the first person who explained software-as-a-service to me when I moved here in 2010, drawing diagrams on a whiteboard in the Box office with the kind of patience usually seen in a teacher showing kindergartners how to spell. 

Sixteen years later, he remains an enthusiastic explainer of the SaaS world. It helps that Box has a good story to tell — the company’s stock has held up materially better than most of its SaaS peers over the past year, even as a chorus of investors, founders, and posters have warned that traditional enterprise software is about to be eaten by AI agents.

As you’ll hear, Levie is not in that camp. In our conversation, he makes a careful — and at times provocative — case for why he thinks both the "SaaSpocalypse" and the broader narrative of mass AI-driven job loss are wrong. He argues that agents will multiply the number of workers using business software rather than eliminate them; that the "last mile" of human work is far more durable than people assume; and that the engineer of the future is more likely to work at a pharma company than at Meta.

“If you or I go and vibe-code something, we think we've replaced the engineer, replaced the accountant, replaced the lawyer,” Levie told me. “But then you actually look — that was the first 80% of the job. The extra 20%, it turns out, is all the value creation of that profession. All the expertise and domain knowledge is in that last 20%, not the text that got generated.”

Highlights of our conversation are below, edited for clarity and length. We also hope you’ll listen to the entire conversation wherever you get your podcasts — just search for Platformer — or watch it on YouTube at youtube.com/caseynewton.

And let us know what you think — we’re new to podcast production, and welcome your feedback at casey@platformer.news. 

Casey Newton: Aaron Levie, welcome to Platformer.

Levie: Hey, good to be here.

Newton: Aaron, you and I first sat down in 2010 —

Levie: We were so young.

Platformer: We were so young. Back then you were sort of early gray, but now you're just like — normal gray. I think running a public company will probably do that to you.

Levie: The problem is, I've been like this for 13 of those years. It would be one thing if this only happened in the past six months, but it's actually been like this since I was 24.

Newton: Maybe there are more reasons to be gray today, or maybe not — we'll get into it. 

That first time I met you, I have this core memory — because I had truly been in Silicon Valley for what felt like weeks when I came down to the Box office.

Levie: Didn’t you come from, like, the Arizona Star Tribune or something?

Newton: The Arizona Republic. I'd been covering local government. And then one day I said, "What's going on with computers? That seems interesting." And now here I am. But I needed people to explain it to me, and that's where you came in. As I recall, you explained the software-as-a-service business model to me on a whiteboard. So my first question: if you were explaining your business to a reporter like that today, how much of that whiteboard would look the same and how much would be totally different?

Levie: If my recollection serves, a lot of it was trying to compare the on-prem days to cloud, and why cloud was such a big deal. My predictive capabilities were pretty locked in, maybe short of AGI. The whole idea was that software was going to move from your data centers to the internet, and in the process, the real power is that it becomes available to way more companies — businesses of all sizes, lines of business that never could have used software before, end users. This was the phase of consumerization of IT. So that played out.

Now we're in the next frontier of what software is going to look like. A lot of the core architectural components hold. If you're running a global supply chain at a Fortune 500 company, you want deterministic systems and software that power your ERP. If you're at a large B2B like Salesforce, you want a clear set of business logic around how your CRM works, and how your internal workflows around sales automation work. If you're managing documents for a government agency or a pharma company or a law firm or a large bank, you want to make sure you can secure that data, protect it, govern it, ensure it's in a safe place and available to the right people. All of that is staying the same.

What has completely changed is the interaction patterns on those systems — where the interaction is coming from. And what you can now do with all that data. The big idea is that in the future, if today maybe 90% of activity on this software is humans interacting with the interfaces, probably three years from now it'll be 90/10 the other direction. Agents will be interacting with these systems, talking to the data, pulling up data from these tools. And maybe 10% will be you going and browsing and looking through the software yourself.

The interesting thing — and this is going to be the open debate for the industry — is in that 90/10, did the human side go down by 90%, or did we just have a 10x increase, where agents are now leveraging these tools? My argument is more the latter: agents are this explosion of new workers all using these systems, which makes the technologies even more valuable. You have all these new workers on these digital platforms that need data, that need to be secure, that aren’t leaking information in the wrong way. So you still need those guardrails — but now you've got a massive multiplier of what people can do with their data, because you have agents that can run in parallel.

Newton: Right, that makes sense to me. There's this really interesting challenge —

Aaron: By the way — podcast over?

Newton: Yeah. That's all the time we have. I really want to thank you for joining us. I think we all learned a lot. 

No — let's throw in a few more questions for the super fans, because you just introduced what seems like a possibly profound change in the business model for what you all do. SaaS companies have gotten used to selling by the seat. You have 10 employees, you want 10 of them to be able to use Box, you pay a monthly fee. And it seems like that business model is under a lot of pressure in a world where maybe I don't have 10 people in those jobs anymore. What I need is a business outcome. So how are you navigating that? Do you think this seat-based business model survives in SaaS?

Levie: You posited the scenario that's most open for debate, which is: did the people go away? In the math I laid out, the people stayed the same number, but the agents multiplied on top of the platforms. There will be some software categories where the literal seats are not as relevant because you don't have as many people doing the work. I would actually argue that for a large portion of software categories, that won't be the case. You'll have the same number or more people, but you'll also have 10 times the number of agents as people. So it's a multiplicative effect of more people — or the same number of people, or maybe a minor reduction — and then vastly more agents.

The part that's not being priced in by the market is, is that scenario playing out? If I look at our software consumption internally at Box, there aren't a large number of cases I can make for many of our software products to reduce the number of people that exist as seats. But there are a lot of cases for a lot more agentic use cases on that software.

To take an objective example that's not Box: if I look at Salesforce, we're actually going to have more sales reps at the end of this year than we had at the start. That's more seats within the Salesforce universe. At the same time, I can imagine 10 to 100 more agent use cases on the Salesforce platform than I could have two years ago. Those agents might not be roaming around the interface of Salesforce — they'll show up inside Claude Cowork or Codex or ChatGPT. The agent will be interacting via a different interface, but the underlying seat that says "Aaron is a user in this platform, with this level of access to this type of data" doesn't necessarily go away.

We're already seeing this with our customers: you want a seat for the person because you want some kind of stateful representation of what data that person has, what their entitlements are. But then an agent might do an unbounded level of consumption on the software — where I, as a person, can only click so many things per day, but an agent can do that at 100x the scale. So the seat gives me the ability to use my information across these other agents. But then at some scale, there's so much data being used that there's a consumption model on top. This is why I think you're going to have a stacking business model in software: humans still have seats, but agents will be a consumption pattern on top of that.

Newton: As a CEO, I'm imagining you're looking at all the SaaS you guys buy to run your business. I imagine you might be happy if you didn't have to pay for all those seats and could just have agents do it. So when you look at your own spending on SaaS, your feeling is truly, "I'm happy to keep spending for all the seats"?

Levie: There's a difference between happy and practical. I'd always like our IT spend to be less, but I'm extremely practical about how technology works. The bear case of software is a confusing amalgamation of multiple issues people have — it's a Rorschach test of "what do you hate about software?" Some people say, "What we're going to do is vibe-code CRM systems." Others say, "We're just not going to have employees, it'll just be agents." Others say, "We just don't need all the features of these SaaS systems, agents will do those." Some I'm sympathetic to, some I'm not.

The one I'm extremely not sympathetic to: we have no projects internally that I've approved to vibe-code a replacement to an existing SaaS service. If I look at the stack of our ERP system, HR system, CRM system, document management system, it would do us no good to spend our time and IT resources trying to replicate functionality that's already doing its purpose — especially at a moment when I'm about to get 10 times the value from those systems with agents using that data. If I have to both transition a system that's homegrown and figure out the next set of use cases, you'll just halt your ability to innovate.

And a minor aside: if you did a word cloud of the past two to three weeks in AI, one of the biggest words would be cybersecurity. Not the Mythos part — the "we leaked customer data, the credentials, the secrets of our system got leaked, we downloaded a package that was exploited." Think about if the entire economy was trying to rebuild their own version of Salesforce or Workday or an ERP system, and any one of those events happened. Now the entire economy has to halt and do upgrades, or handle the maintenance and ongoing improvement of these technologies. That's just not very logical economically.

The part I am sympathetic to: there's some software where, as you use agents more, some of the value proposition goes more into the agentic layer than the software layer. In those cases, you'd compress the value proposition of that software, and at the next renewal you might not spend as much. But conversely — for every scenario where that happens, there's another scenario where agents add more value on the system you're using. So the net vendor actually has more leverage in the future. You might save on one part of the stack but end up re-spending it on a different part because of all the upside.

Newton: What we're really getting at is the skepticism the enterprise software market is facing right now. The reason I wanted to talk to you first is that Box has been facing this kind of skepticism in various ways its whole existence. You had to survive a very early pivot from being a consumer company to an enterprise-focused one. You had to convince people the cloud would be a safe and profitable place to be. And you faced a lot of skepticism about whether Box might just be a feature rather than a company. Now you have AI come along introducing this fresh wave. Maybe the most accelerationist version of that argument is that every company is now a feature, and the only thing that matters is going to be the frontier labs. So to what extent is this SaaSpocalypse story just the latest incarnation of a story that's never been true, and to what extent is this AI moment truly something different?

Levie: The market is somewhat parsing the different outcomes — not perfectly, but there's some discerning behavior. If you took Wall Street as one metric and looked at our stock, it's held up better than most. One of the reasons is that the thing not really under debate is that your most agentic, vibe-coded enterprise future still has to store the data somewhere. You still have to secure and govern the important information of whatever the workflow is. You can vibe-code the creation of the contract, but the contract still has to get stored somewhere, governed somewhere, still has to have a retention policy, access controls.

The part I'm excited by is that becomes meaningfully more important in a world of agents. When I think about the use of data in the enterprise, what all these agents really want to do is access data. They want to read data, write data, know context about your organization — your best practices, your policies, your customer relationships, your research. All of that sits inside your enterprise data, and most of it sits inside unstructured data in the form of business content. So we're firmly on the side of: bring on all the AI humanly possible, because those agents are all going to be working with enterprise content that still needs to get stored somewhere.

A customer comes to us and says, "We want to automate our entire insurance claim process." A tremendous amount of enterprise content goes into an insurance claim. When they do that automation — maybe they build the agent on Anthropic, maybe on OpenAI — that agent still needs to talk to all the data in their enterprise. So they have to upgrade their infrastructure.

There'll be winners and losers in software and SaaS, as has been true of every era of disruption.

Newton: Tell us about some of the losers. You don't have to name —

Levie: Rather not.

Newton: I know you'd rather not. But basically what you're saying — and I believe this — is that your business has access to this very rich, valuable data, and that data is not, for the foreseeable future, going to be stored at one of the frontier model companies. So you have a lot of value to create based on that unique advantage. I'm guessing there are other SaaS companies that don't have that same advantage. As you're scanning across the market, is there a business where you're like, "I don't want to be in that business in a world of agentic AI"?

Levie: I'll give you a framework, but I'm not going to name names. The factors you want are: Do you have some degree of business logic or workflow in the system, because the agent still needs to do that, even in an agentic world? Do you store data? Are you the natural place for the information to get stored? Do you have a set of domain experience and context that the next training run of the agent doesn't just replace? Is there an element of security, governance, trust that matters a lot? Are there network effects? This is one reason Slack has been incredibly durable: we're already communicating in Slack, so agents naturally show up in Slack, as opposed to "I'm going to agentically do Slack."

There's probably one more element: how much does the system benefit from a world of multiple agents needing the data, as opposed to one agent needing the data? Because that points to whether the enterprise wants to put all your value into one of the labs, or whether it needs to be a different layer that everything talks to. This is why you still see companies like Databricks and Snowflake growing quite well. You don't really want to move your data around constantly. You want it abstracted from where the agent is, so you can structure it, secure it, govern it, and then let all the agents talk to it.

So you probably could have a quotient for “how durable is the platform, based on those factors?”

Newton: Right. The Levie formula. 

Here's what I'm taking away: if you have a to-do list app for teams, get out of that business.

Levie: I would say that business is actively pivoting.

Newton: Yeah, I actually think I know one that might be. Box has talked about AI for a long time, but I'm curious for you personally — we do have these momentary enthusiasms in Silicon Valley. I think it's fair to say both of us had a crypto phase. I'm imagining your AI phase started earlier but took longer to reach fruition. Did you have a moment of conversion where you saw a paper, a product, something where you're like, "Okay, I need to start taking this really seriously"?

Levie: There have been three moments. About eight years ago, vision models were getting good enough that you could give the vision model a document and it could OCR it, or give it an image of a retail product and it could classify it properly. That was a big deal. But the problem was you had to train individual models for every domain — this was just before the transformer. If you wanted to do document classification in legal, you needed a different model than for financial equity research analysis. So you never had the takeoff moment.

Big deal number two: ChatGPT. We were following GPT-2 and GPT-3, and we had a hackathon where somebody did GPT-2 inside of a document, but it was producing garbled text. It could maybe type ahead five extra words — not going to game-change your productivity. ChatGPT was the first time in this era where it was like, "Okay, this is a very big deal. We're going to be able to wire up these LLMs and connect to your data."

The most recent moment, the past year-plus, was marked by Claude Code — but really these more agentic patterns. The LLM runs in a loop, the agent has access to a set of tools — on your computer or in the cloud — and you can hand off long-running tasks. The efficacy has been improving exponentially. This might just be the final form factor of AI: an agent that could run for a minute or a month, has access to any data you need, has access to all the tools you work with, can act as you or as its own entity. It's just an LLM constantly running in a loop, making decisions, and you intervene to steer or review. This appears to be the architecture of the future of AI.

Newton: I've come to believe that basically everyone should have the experience of building a website using an AI agent of their choice. A lot of things clicked for me when I started watching a computer use itself. You recently posted about how, somewhat strangely, AI doesn't seem to be helping any of us work less. You mentioned that you'll start working on something with an agent that you think is simple and you lose three hours to it. Was that a real project? Can you share what it was?

Levie: It's so pedestrian I'm embarrassed to share, but I was going into a city a week later and needed to map out a bunch of customers I should be visiting. I was using Perplexity Computer, which does some pretty good workhorse stuff, and I gave it the task: "Rank-order all the top 50 companies in this region. Get me the LinkedIn of every single CIO of those companies, so I can make sure — okay, who have I connected with?" I didn't even know what I'd do next, but I wanted to get a good map. That task took maybe 15 minutes — you prompt it once, you get back some data you don't really like, you re-prompt, it does better, you do it a third time, and boom, you're off to the races.

So maybe 15 to 30 minutes of AI work. But then the very next thing — I had to do something with this data. I spent the next two hours emailing all the people and filling up my calendar more. It was the kind of task where I thought, "Oh, use AI to accelerate this thing" — and it kind of worked too well, to the point where I had created more work for myself. It would almost have been better if it came back with full hallucinations, because then I could have just gone to bed and been like, "Well, that failed." But it worked. And by 10 PM I was like, "Well, now I'm going to feel bad if I don't actually do anything with the tokens I just exhausted."

It's a small anecdote, but I think this is happening everywhere. You're like, "Oh, I'm going to tell AI to write this little web app." Then you're like, "Well, I built it. Now I've got to add this other feature. I should probably get it hosted somewhere. I might need to change this one thing." It's basically If You Give a Mouse a Cookie applied to the economy. You just start building up more and more work.

The part I find really interesting — and this tangentially relates to why I think the job-loss argument is wrong — is that people will find there are way more tasks they could be doing that they just never could do before, because the fixed cost of starting the task was too high. AI made it easy to get going. They lit up the project, did the research, the analysis, reached out to the customer. That kicks off a cycle of downstream work, or a new set of constraints that start to emerge.

To bring this home: the task worked so well that I idly wonder, should I have a full-time person just doing this with an agent? They'd use an agent, but I don't want to do this every night for the rest of my life between 9 PM and midnight. It might be valuable enough that it's worth a person to do this for me — in which case, it actually created a job because of my experimentation with this AI I was doing for fun.

Newton: I love that story. When I built my own website, it was absolutely If You Give a Mouse a Cookie, because of course after the website was done, I said, "Well, I need to host it." And then, "Well, it should probably have a blog." So I added a blog. Then, "Why isn't it telling me the current weather in San Francisco? I have to solve this problem for the three annual visitors to this website." It was super fun. I don't regret the time. I also didn't, I think, create a lot of value for myself.

On the flip side, I run what I sometimes think of as a somewhat fake business, in the sense that people pay me to email them. So it's a very strange, uncomplicated business. I have a bookkeeper and an accountant, and they send me a monthly email letting me know how things are going, but I've never really done real financial analysis. And then Claude Cowork shows up, and I can just start chucking spreadsheets into it. For the first time since I started my company five years ago, I'm like, "Tell me about my business."

Levie: "What's our revenue?"

Newton: Yeah, completely. It pushed me to make some changes, including, by the way, starting this series of conversations that you were the first person on. So: what advice do you give for people when they're like, "Okay, I'm bought in, I'm going to try this stuff. But how do I know when I'm just spinning my wheels versus creating something of durable value"?

Levie: This one is hard to answer generically. But this will be a defining question of the next decade. If you have access to abundant intelligence, but it's not free — it's abundant but not free — how do you allocate the spend across the organization?

On one hand, it's easier the higher up in the organization you go, because you have all the data about what the company does well and what doesn't. On the other hand, it's somewhat easier lower in the organization where the work is actually happening, because you can self-identify the work that matters. The problem is, both of those have issues: the direct user might think their work is the most important for the tokens, and the person high up might not know about the new innovation somebody has.

The world was a little easier with scarce intelligence, because everything was kind of slow, and it had to be slow. Now with abundant intelligence, you could have everybody running around spinning up agents doing lots of work — maybe 70% of which is not valuable. But you don't know which is the 30/70 until you've done the whole set of things.

Newton: Is one potential solution to just put up a leaderboard of who has burned the most tokens in a given month and reward them somehow?

Levie: (jokingly) That is emerging as the best practice. The alternative is just token-maxing, and you're good. But token-maxing aside — there's a tool set I'm sure 10 startups are working on right now. They'll see this and they'll all pitch us, but it'll be a good business for a few of them. There's a new kind of ERP, HR, finance system that lets you have a heat map of where the tokens are going, and the rough value allocation of what that produced. Right now it's a comical idea — "Oh, you're going to have to treat tokens like headcount." But we're only going to be able to apply compute rationally to the most important areas of the business. Right now it might be 1% of your company spend, but if in three years it's 10% of the spend, we don't joke around with where 10% of our labor force is going. The same will be true of your tokens.

Newton: Let's talk about jobs. You recently posted about a kind of Gell-Mann amnesia for AI. Gell-Mann amnesia, of course, is where you read about something you know a lot about and notice the obvious errors, but assume that same source is credible on other topics. You wrote: "People use AI for their job and see all the various things they have to do in the last mile, but then look at someone else's job and think that AI will eliminate it immediately." Why are some people so quick to think AI can automate away a whole job?

Levie: This is amazing technology — the coolest technology I've ever played with in my life. So it's deceptively cool. You're like, "Oh my God, I think I just did my taxes," or "Oh my God, I just built this amazing marketing website in five minutes." Then we look at the output and we're like, "Gosh, that must totally replace the job of XYZ profession."

There are a few core flaws with that. What is that profession doing for all the hours in their day? How much of it is just doing the final calculation of your taxes, versus getting all your data in order, reviewing all the work, asking you questions back and forth, knowing the right questions to ask, dealing with the three missing things you didn't even remember to add — but if an AI system had done it, it would have totally glossed over? That's what the profession does. The automation of one or two or five of the steps are just individual tasks.

In the case of development, you or I can tell Claude Code, "Generate me the XYZ product." And we could be like, "Wow, that must automate the engineer out of existence." Well, the code quality is probably horrendous. The ability to ask it to do 40 other things over a 12-month period is going to stack in complexity. The moment you actually want to get that software hosted, make sure there's no downtime, ensure you have a good distributed system — that's already 100 times more complex than just prompting the code to get written. The moment there's a security event, somebody has to wake up and respond. I can name 30 other things a developer has to do.

A lot of people say the job of the engineer was never to write code, it was to do X. But no — they were writing code most of the time in the prior world of work. The problem is they were highly constrained by how much code they could write in a day, and they were automatically bottlenecked from doing the other things their job could be. So what is the future engineer? It's to understand what you're trying to build, to make sure it gets built properly, to ensure there are no security issues, to ensure it gets released, to ensure it's high quality.

If you or I go and vibe-code something, we think we've replaced the engineer, replaced the accountant, replaced the lawyer. But then you actually look — that was the first 80% of the job. The extra 20%, it turns out, is all the value creation of that profession. All the expertise and domain knowledge is in that last 20%, not the text that got generated.

And the converse: I'll use AI for analysis of a market I'm thinking about. If I just took the output and ran with it, I know it would not work, because I know it's missing context — either I didn't give it, or I know something else about a different trend. But somebody else might see that and say, "Wow, Aaron's job is incredibly easy, AI just gave me the answer of what he's going to go do." And I'm like, "No, my job is way harder, I promise."

There's a different axis people need to think about. If you took today's static work, maybe it would be: you'll get the first 90%, then we're going to automate the next 9%, then the next 0.9%. But there's a dynamic part of the equation. The market is starting to ask more from the provider, because they now know what's possible. So just as you automated the first 90%, the market shifts on you, and that 90% is now the new 50%. The demands of what you ask an engineer to do go up tenfold, because you're like, "I think you can do that thing way faster now, so I'm going to give you a much bigger project." You have this dynamic system: our needs and demands are growing as a result of what we can automate.

Newton: I love the idea that AI will let us reenvision what our jobs could be in a more expansive, creative vision. My fear is that the last mile of human supervision will turn out to be kind of boring. We're already starting to see this in some jobs — there's a piece that's automated, and my job is now just to review AI output, and that is just pure drudgery. Does that complicate the picture you just painted?

Levie: It adds a wrinkle. The big question is: are there jobs in the future? The answer is yes. Now the question is, do we want those jobs of reviewing the output of AI agents? Maybe we all just opt out of the economy because we don't want that job. So interesting philosophical question of what's the new way to get fulfillment and creativity out of these jobs. You see burnout of engineers on X, basically saying, "All my job is is to review slop from the AI." There's a limit to how fun that is.

But engineering is a unique job compared to the rest of the economy. Most of an engineer's day is to think about a problem, think about a system, write code — and that code is text. You're just writing a lot of text, and somebody else reviews the text and you ship it. So if the agent did the writing and reviewing, then all you're really doing is reviewing the text and shipping it.

But go talk to an investment banker, or a lawyer above paralegal, or a doctor or a nurse, or a pharma researcher — they would love to get out of the toil. They'd say, "I don't want to spend 15 hours generating a corporate pitch deck for this client — that is just me moving images around on a PowerPoint, doing some Google searches to find market trends, and pasting that in. I'd love to automate that. The job I should be doing is getting in front of my clients and making sure we're delivering unique value and insights to them."

That's why I'm not overly worried about this. We're seeing some hyper-accelerated dynamics in engineering that don't always relate to other forms of work. If you talk to a doctor and ask, "How much do you enjoy typing up the patient notes after the patient meeting?" — they want to automate the heck out of that. They want to be done with that part of the job. Mostly it'll be a net positive change as people are able to get rid of the stuff they hate doing, and the demands of the job evolve in a way that makes it much more exciting.

Newton: It's the sort of thing I love to hear. I would love to not live through a massive disruption where we see super high unemployment. I also can't help but note we saw almost 46,000 tech layoffs announced in March alone, with AI sometimes cited as a potential cause. So would you put a number to it? On the software engineering front, do you think in three years we have about as many software engineers as we have today, or more? Or do you think there's a bigger shift?

Levie: I think we're going to have more. I don't want to be unsympathetic to people who really will face these changes. But big picture: if you were a CS grad of the past two decades from a top 25-to-50 CS school, by and large you were trying to go to a tech company — in Silicon Valley or a couple other places. So most of the software talent in the world of this cohort ended up building software for consumers. We were building ad apps, ride-sharing, enterprise software (thank God). We've accumulated a lot of engineers on that kind of work, and some of those companies have over-hired.

Who's the loser of that equation? Every other company on the planet, because they couldn't compete with Google and Facebook and Microsoft for that top engineer. They couldn't automate things in the life sciences process, or the supply chain, or automotive AI systems. I don't know how much software you've used from companies that aren't in the Valley, but if you log into your bank and you're happy, you're a totally rare person. If you look at most car console designs of any car that's not from two companies, you can imagine how unusable these systems are. That correlates to the fact that those companies couldn't overstaff with all the top engineers and designers.

Now what happens? All of a sudden, what was maybe a 30- or 50-engineer problem previously, Claude Code and Codex come in, and now it's a 5- or 10-engineer problem. For the first time ever, those companies are able to take on work that wasn't possible before. They can bring automation to all the systems and workflows they couldn't have afforded or justified.

So in some cases of tech, you'll see a temporary dislocation. At the exact same time, the thing you should be tracking is the number of engineering jobs opening up at traditionally non-Silicon Valley tech companies — small businesses, consulting firms, life sciences, manufacturing.

And as a force multiplier, you're going to have a number of new types of engineering jobs where the job is entirely about how to deploy agents inside the firm to automate work. I did this for fun just to make sure I wasn't full of shit. If you go to the Eli Lilly careers page, as one does, they have this job title called "lab automation software adviser." That person is an engineer whose job it is to bring automation through AI to the lab process.

Think about how many hundreds of thousands or millions of jobs will look like that in the future. My job is to take the innovation coming from AI-land and apply it to this particular business process in my organization. You're kind of like an FDE — a forward-deployed engineer — but for that company. Those will be the people who would have gone to Meta or Google five years ago. They're going to now work in pharma, banking, manufacturing. And those are actually incredibly stimulating jobs. You're not just building an app, you're automating drug discovery.

Newton: So you're saying even in the future, even just with my small newsletter business, I might one day be able to fulfill my lifelong dream — which is to compete directly with Palantir and just build a vast surveillance and analysis program?

Levie: If you so choose, you can. What's the fastest-growing role at something like Anthropic or OpenAI? It's these FDE roles.

Newton: Forward-deployed engineers.

Levie: Yeah, you need humans to go implement this stuff inside the organization. Those are the engineers of the future, if you're not just building software that is an application.

There's a funny article out of the FT — I don't know how much I trust their views on technology — that lawyers are being inundated by clients asking them questions because they went to AI, and the lawyers have to verify the advice is good, or review the contract that got written. What we're doing is lowering the barrier for everybody to participate in these things in a touristy way. I can be a tourist in software development, in legal, in healthcare. But that eventually still needs to get verified, or the work actually has to get done that last mile, and that eventually moves into needing some kind of semi-expert.

This is why I don't think we yet know what degree you should go into in college — I don't think any of the degrees are off the table. You should totally go into CS if you're really excited about software development. You just shouldn't expect to go build a little app that you press a button on. You should expect that you're going to use CS skills to go do clinical trial automation at a pharma company.

Newton: I think that's a great place to land, because it leaves me with a feeling I have so rarely when thinking about the tech-enabled future lately, which is optimism. So thank you, Aaron, for giving us a jolt of that. It'd be great to check in with you again in a year and see if the picture still is as rosy. Aaron, thanks so much for joining us.

Levie: Thanks, man.

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Google fights an AI-generated zero-day

What happened: Google says it found an AI-generated zero-day exploit that could have triggered a “mass exploitation event.”

Google found “prominent cyber crime threat actors” were planning to conduct a “mass vulnerability exploitation operation.” The exploit would have enabled users to bypass two-factor authentication on a popular open-source tool.

Google’s Threat Intelligence Group says it tipped off the affected software maker and worked with them to prevent the attack.

The company said it is confident that a Python script the attackers created was AI-generated — because of its characteristic “textbook” AI coding style, and also because, despite the code working, there were some hallucinations (lol).

Google said AI is increasingly helpful for discovering vulnerabilities of the type used in this exploit — which was previously done via manual review from human experts. Unlike traditional software, AI can sniff out code that has high-level logical flaws. AI can find types of errors that “appear functionally correct to traditional scanners but are strategically broken from a security perspective.”

Why we’re following: The exploit Google found is confirmation of a growing threat: AI can find and exploit vulnerabilities that previously only humans could. 

What’s more, hackers are starting to experiment with going beyond just discovering vulnerabilities, to actually using AI to orchestrate cyberattacks in real time.

The company’s research has found actors associated with China and North Korea doing “sophisticated” experimentation with AI, it said. They’ve also seen actors experimenting with orchestrating attacks autonomously and using OpenClaw to refine attacks.

What people are saying: On X, John Hultquist, Google Threat Intelligence’s chief analyst, wrote, “I think most of us are surprised we have not found more evidence” of bad actors using AI to discover exploits. “We believe this is the tip of the iceberg. Other AI-developed 0days are probably out there.”

He added, “If criminals are doing it, then state actors with significant resources probably are too.”

OpenAI needed “big computer”

What happened: Sam Altman appeared in court today for the trial where Elon Musk is seeking to remove Altman and co-founder Greg Brockman from their jobs at OpenAI.

The NYT’s Mike Isaac writes about the contrast between Altman and Musk’s testimony: while “Musk openly sparred with opposing counsel,” Altman “has taken a different tack. His answers have been terse, quiet and noncombative.”

Musk’s lawyer Steven Molo questioned Altman about several executives and board members who said under oath that Altman had lied to them. 

At one point, Molo asked “are you completely trustworthy?” Altman replied, “I believe so.”

In possibly our favorite detail so far, Altman described an evening meeting at Tesla, during a time in which Elon Musk was trying to fold OpenAI into his car company. The meeting apparently contained “a long, long period of time with Elon showing us memes on his phone.”

Elsewhere at trial, Microsoft CEO Satya Nadella testified that Elon Musk never told him about concerns that Microsoft’s investment in OpenAI violated OpenAI’s charitable commitments. (Musk is suing Microsoft in addition to OpenAI).

OpenAI co-founder Ilya Sutskever said in court that his stake in OpenAI is now worth about $7 billion. He gave judge Yvonne Gonzalez Rogers a memorable explanation of the difference between the AI of OpenAI’s early days and now: “I would describe it as the difference between an ant and a cat.”

Sutskever told Judge Gonzalez Rogers why OpenAI needed outside investment after Musk’s departure. “If there is no funding, there is no big computer,” Sutskever said. “You don't need the biggest computer, but you need a big enough computer.” And, well, “If you don't have a big enough computer, it was not going to work.” 

Why we’re following: Platformer would like to start a petition to replace all mention of “compute” with “big computer” from now on.

The evidence that Musk was trying to fold OpenAI into his company isn’t great for his case that Altman and Brockman “stole a charity.” Although he apparently could have managed his time better while he was doing it! But in fairness to Musk, we cannot ourselves plead innocent to repeatedly showing coworkers memes on our phones.

What people are saying: On X, programmer and investor Paul Graham, who chose Altman as his successor at startup incubator Y Combinator, wrote: “One of the things Musk vs Altman shows is how much more promising AI is than anyone expected.” Graham added, “Sam could have started it as a for-profit company. His life would be much simpler now if he had. But he didn't realize in 2015 that AI would warrant more than you can raise in donations.”

—Ella Markianos

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This is a column about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

In February 2025, Vice President JD Vance took the stage at the Paris AI Action Summit to share the administration’s views on AI regulation. “The AI future is not going to be won by hand-wringing about safety,” he warned. Excessive regulations might “kill a transformative industry just as it’s taking off,” Vance said, and suggested that AI companies asking to be regulated might simply be trying to crush their future competitors.

Vance’s remarks reflected the idea, then common among Trump officials, that fears about AI capabilities are dramatically overstated. David Sacks, the White House’s AI and crypto czar, has referred to a “doomer industrial complex” enacting a “sophisticated regulatory capture strategy based on fearmongering.” Michael Kratsios, who leads the Office of Science and Technology Policy, has complained that international efforts to govern AI “maintain a general atmosphere of fear.”

The administration has backed up its rhetoric with a lobbying push intended to block most state-level AI regulation. Axios reported last month that Trump officials are pressuring Republican lawmakers in Nebraska and Tennessee to weaken or abandon bills in their respective states that would introduce safety and transparency requirements for AI companies.

Which is what makes the administration's latest move so striking. Trump is quietly reviving a Biden-era idea his own officials once mocked — pre-release government review of powerful new AI models.

Here are Tripp Mickle, Julian E. Barnes, Sheera Frenkel and Dustin Volz in the New York Times:

The administration is discussing an executive order to create an A.I. working group that would bring together tech executives and government officials to examine potential oversight procedures, according to U.S. officials, who declined to be identified in order to discuss deliberations over sensitive policies. Among the potential plans is a formal government review process for new A.I. models. [...]

The working group is likely to consider a number of oversight approaches, officials said. But a review process could be similar to one being developed in Britain, which has assigned several government bodies to ensure that A.I. models meet certain safety standards, people in the tech industry and the administration said.

The Biden administration had issued its own executive order that instructed AI companies to perform safety testing and share the results with the government before releasing new models. Trump revoked the order on his first day of his second term. Three days later, he issued a new order titled “Removing Barriers to American Leadership in Artificial Intelligence” that effectively ended safety testing requirements.

What changed? Mythos. Anthropic’s latest large language model, now available in preview to a small number of companies, has proven capable enough at developing cybersecurity exploits that the government believes it poses national security risks. The White House now opposes the company’s plan to expand access from roughly 50 companies to 120 for security reasons. (It also says it worries Anthropic doesn’t have enough compute available to serve the model to government customers; Anthropic denies this.)

All of this is complicated, of course, by the fact that the Trump administration has also sought to designate Anthropic as a “supply chain risk” because it refused to amend its contract with the Pentagon to enable “all lawful use” of its technologies. While continuing to defend that designation in court, the administration has simultaneously been working to expand access to Mythos throughout the government. 

Trump officials are now in the nonsensical position of trying to help agencies get around the legal roadblock they themselves set up to stop them from using Anthropic’s models. One set of officials is working to phase out the use of Anthropic models over the next six months; another is working to expand agencies’ access to its technology throughout the government.

In the meantime, the rest of the industry now faces a regulatory environment that looks awfully similar to the one Democrats had implemented under Biden: a world where they submit their models to the government for review before releasing them widely. On Tuesday, Google, Microsoft and xAI all said that they would give the government early access to their models. The reviews will be handled by the US Commerce Department’s Center for AI Standards and Innovation.

Before Trump 2.0, by the way, that body was known as the US AI Safety Institute. Its name changed last June. “For far too long, censorship and regulations have been used under the guise of national security,” Commerce Sec. Howard Lutnick said at the time. “Innovators will no longer be limited by these standards.”

Less than a year later, the administration’s sneering dismissal of safety concerns has transformed into something that resembles a mild panic. The National Security Agency is now using the model to look for vulnerabilities in Microsoft products — and, one assumes, contemplating the fact that foreign nations will soon be using similarly capable technology against US critical infrastructure, if they aren’t already.

​​Meanwhile, the public backlash against data centers and other symbols of AI power is putting the Trump administration increasingly at odds with its own base. And the government’s half-baked AI sales pitch to the general public, which has amounted to little more than “get rich and beat China,” has failed to resonate much beyond the venture-capitalist offices where it was originally conceived.

One result of this is that Trump’s effort to place a moratorium on most state-level regulations of AI now seems even less likely to pass than it was before. Another likely effect of accelerationists’ declining influence is that we’ll see a push for expanded export controls on powerful chips to China. (Sacks, who recently left his job as AI czar for a role on the Council of Advisors on ​Science and Technology, had been a vocal proponent of loosening those controls.) A less likely but welcome development would be that the US re-engages with the United Kingdom, Japan, Korea and other allies to develop a shared strategy toward governing more powerful models.

Still, less democratic possibilities exist as well. Critics of the White House’s plans to subject frontier models to safety evaluations worry that the Trump administration will use any licensing regime for censorship — denying releases to models whose output is deemed “woke,” for example, or simply to pressure companies into doing other favors for the administration. Imagine Brendan Carr’s Federal Communications Commission, but for AI. Some level of worrying there is warranted.

But after Vance’s speech in Paris, I noted here the dangerous negligence of an AI policy that amounted to little more than “let’s see what happens.” A year later, the administration has come to realize that all those AI safety concerns were no mere hand-wringing. The models are getting more capable — and more dangerous. What Sacks once dismissed as the doomer industrial complex now includes a growing number of federal agencies and Trump administration officials.

And while they should have taken these fears seriously all along, I will settle for the administration taking them seriously now.

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The OpenAI-Elon Musk trial enters week two

This week in court: 

OpenAI co-founder Greg Brockman said he didn’t want Elon Musk to be OpenAI’s CEO because “he did not – and I believe does not – know AI,” in federal court today. Brockman added that he and co-founder Ilya Sutskever “did not think that he was going to spend the time required to actually get good at it.”

Brockman told jurors that Musk called a predecessor to ChatGPT “stupid,” and said that “kids on the internet could do a better job of it,” which raised concerns within OpenAI about his ability to run the company.

During discussions about a potential for-profit conversion, Brockman says Musk demanded a majority stake, saying he needed $80 billion to start a city on Mars. When Brockman pushed back, Musk allegedly said he could start another AI company tomorrow with “one Tweet.”

Musk, who also owns AI company xAI, is suing OpenAI for unlawful enrichment. He claims his original charitable donation to OpenAI should not have contributed to the for-profit venture OpenAI eventually created. OpenAI claims the suit is a “jealous” bid to attack a competitor to Musk’s xAI.

During his time on the stand, Brockman got grilled about his personal journal, which included such musings on OpenAI’s for-profit conversion as: “Financially what will take me to $1B?”

Musk lawyer Steven Molo asked why — if Brockman’s goal was a mere billion dollars — he hasn’t donated the rest of his $30 billion stake to OpenAI’s nonprofit. “It takes 30 billion dollars to get you out of bed in the morning?” Molo asked. Brockman said Molo was twisting his words.

In one of the trial’s more operatic twists, Brockman testified that when then-OpenAI board member Shivon Zilis had twins, she didn’t initially tell him Musk was the father. The trial proceedings previously revealed that Zilis secretly funneled information about OpenAI to Musk. Brockman said he found out Musk was the father of her children through public reporting — and that Zilis told him at the time that her relationship with Musk was “platonic” and that the children were born via IVF.

Elsewhere at trial, Brockman testified that OpenAI will spend $50 billion on compute this year.

Why we’re following: Musk is asking the court to remove Brockman and OpenAI CEO Sam Altman from their leadership positions, and is seeking as much as $134 billion in damages, which he says he will donate to the non-profit foundation that controls OpenAI.

While the stakes for OpenAI’s future are high, we are admittedly more attuned to the various petty dramas that are unfolding in court. (Just two days before the trial began, after Brockman rebuffed Musk’s text suggesting that the parties settle, Musk responded: “By the end of this week, you and Sam will be the most hated men in America. If you insist, so it will be.”)

In any case, let Brockman’s experience be a reminder to all of us to never, ever, write your diary in a Google Doc.

What people are saying: The “takeaway from Greg Brockman[‘s] testimony at Elon vs. OpenAI trial today is that no grown man should have a diary,” wrote Sources’ Alex Heath.

Meanwhile, Musk agreed to pay $1.5 million to settle SEC allegations that he deceived Twitter shareholders when he failed to disclose his growing stake in the company, which the SEC alleged led to an artificially low stock price.

Fascinatingly, notorious Silicon Valley fraudster Elizabeth Holmes congratulated Elon on his Twitter settlement, writing, “I had an SEC settlement too” (you don't say). She added, “Elon's $1.5M settlement is basically a parking ticket. No admission. No criminal conviction,” concluding, “Big win for @elonmusk.”

—Ella Markianos

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Hi from New York, where I spent this week talking with chief financial officers, chief operating officers, and IT leaders about how the AI moment is affecting their businesses. In general I find that people are open to the idea that it can help them; skeptical it has done much for their businesses so far; but also committed to continuing to experiment lest their rivals figure it out before they do.

Those conversations were on my mind as I read this week’s earnings reports from the tech giants, which offer us a fresh chance to check in on the AI bubble. And while this has been true for a while now, these numbers should put to rest one of the primary battle cries of the AI backlash: that “nobody wants this.”

Of course, many people (and more every day!) hate AI. But their bosses are buying it — and in record numbers. Google Cloud revenue, which is one imperfect but useful proxy for AI spending, was up 63 percent over the last quarter. Meta, which has invested heavily in building AI tools to make ads more effective, reported a 33 percent increase in revenue.

Amazon revenue was up 17 percent, and said its chips business is growing in the triple digits; Microsoft was up 18 percent, and said its AI revenue is up 123 percent year over year.

Last year those four companies unnerved some investors by spending a record $410 billion on capital expenditures. And with three of those companies updating their guidance this week, the big four now plan to spend 77 percent more than they did last year — $725 billion.

That’s because the biggest players are already selling about as much AI as they can make, and are scrambling to build or rent the infrastructure that will allow them to sell more. OpenAI said today that it has signed contracts for 10 gigawatts of AI computing capacity three years ahead of schedule. (And also that its models can’t stop talking about goblins, but let’s set that one aside for now.)

Notably, the market has more trust in some companies than others. Google stock was up nearly 10 percent today to an all-time high. Meta stock, meanwhile, was down more than 8 percent. 

Of the big four, Meta currently has the weakest case that its own services will generate the level of AI demand that would necessitate spending of up to $145 billion this year on infrastructure. CFO Susan Li told investors that access to compute “will be critical to determining the quality of the models we develop, the types of products we can introduce, [and] how productive we can be as an organization.” But it’s clear investors have not been particularly impressed with Muse Spark, and after losing nearly $84 billion on its Reality Labs side quest to date, the company has a trust deficit. A rare decline in users isn’t helping, either. 

At the same time, investors instantly snapped up $25 billion in Meta bonds — albeit at a higher risk premium than an earlier sale six months ago.

On the whole, though, the market is telling an uncommonly simple story: demand for AI is increasing exponentially, and the hyperscalers are investing heavily to make more of it. 

All of this remains very difficult for Ed Zitron to understand. But after years of speculating about what kind of bubble this is — including by me — a relatively clear story is coming into focus. It is not a momentary-enthusiasm-deflating-once-reality-sets-in kind of bubble. Instead, it’s looking more and more like a railroad bubble: one where massive overbuilding and speculation may lead to periodic crashes and flameouts, but also one where the resulting product transforms commerce and productivity.

Which is why, in the end, I think the leaders I spoke with this week have it right: saying yes to gradual iteration, and no to betting the farm. There’s value to be found in there somewhere, but for the moment the playbooks are still being written. 

On the podcast this week: Kevin and I discuss OpenAI's big reset (and trial with Elon Musk). Then, Dr. Adam Rodman returns to the show to discuss the latest with AI and medicine. And finally, Talkie co-creator David Duvenaud stops by to discuss his AI model trained exclusively on pre-1930 data — and whether he can use it to predict the recent past.

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The government can't decide what to do about Mythos

What happened: The federal government’s relationship with Anthropic grows more arcane by the day. The White House is working on guidance for agencies to work around Anthropic’s supply-chain risk designation — which the administration itself is responsible for — to access Claude Mythos, the company’s highly cyber-capable model. An anonymous source described the plan to Axios as a way to “save face and bring ‘em back in.” 

The Trump administration is also writing a memo that will guide national security agencies on AI use, Bloomberg reports. The memo will encourage agencies to use multiple AI providers to avoid the risk of relying on a single (and potentially woke) vendor. It will also tell companies not to interfere with the military’s chain of command. The memo is seemingly based on the issues the DoD had with Anthropic, although it’s currently ambiguous whether these policies meant to guide the further use of Anthropic’s systems, or to encourage agencies to phase Anthropic out.

Secretary of Defense Pete Hegseth definitely still hasn’t warmed up to Anthropic. In a Senate hearing yesterday, he called CEO Dario Amodei “an ideological lunatic who shouldn’t have a sole decision-making over what we do.”

For now, though, the National Security Agency has gotten access to Mythos, and is using it to test Microsoft software for vulnerabilities.

Meanwhile, the White House opposes Anthropic’s plan to expand Mythos access to 70 additional companies and organizations — which would bring the total number of organizations with Mythos access to about 120. Administration officials told Anthropic they had security concerns with the initiative. (So that wasn’t just “fear-based marketing”?)

And there are plenty of other cyber models for those in the know to test, enjoy, and worry about. The UK AI Security Institute found that OpenAI’s GPT-5.5 demonstrates cyber capabilities comparable to Mythos’. And Anthropic released Claude Security, a cyber product built on its Opus 4.7 model, to enterprise customers.

Why we’re following: The White House appears to be impressed with Mythos’ cyber capabilities — impressed enough to try to get around its own rules to keep using it. And impressed enough to do something that it has historically opposed: restrict the use of AI!

It seems AI cybersecurity is a policy priority for the administration — although it appears to come second to its top policy priority, which is never admitting to being wrong. Which means Anthropic’s models might play an increasingly important role in America’s national security, at the same time the company is officially labeled as a national security liability.

GPT-5.5’s cyber capabilities make the story more interesting. Typically, when one lab makes a big leap in AI capabilities, that leap is then followed by other top developers — and eventually by open-weight AI models. Well, now here’s OpenAI coming a few weeks after Anthropic with another highly capable cyber model — and this one is generally available.

The White House has no guarantee that every developer of cyber-capable models will make like Anthropic and ask the US government for permission before deploying its models more widely. If the administration believes that models of Mythos’ cyber capability level should be restricted, they’ll probably want a regulatory lever that goes beyond asking companies informally to restrict their models.

What people are saying: Former White House AI advisor Dean Ball wrote on X, “Perhaps the White House should consider ‘issuing guidance’ to the DoW to put an end to this stupid, manufactured crisis by… withdrawing the supply-chain risk [designation].” 

Blogger Matt Yglesias joked about the White House’s predicament: “When you fucked up but don’t want to admit it.”

Ball did agree with the White House asking Anthropic to not expand Mythos access to new organizations, saying “I suspect the White House is making the right call.” But, he added, “this is the opposite of a tenable strategy, like trying to erect a dam against a tsunami.” Given the increase in AI capabilities from multiple model-makers, “There is no way to stop the diffusion of capabilities like Mythos within the next 6-18 months.”

On the other hand, OpenAI safety researcher Boaz Barak thought Anthropic’s original plan was the right one: “Assuming they have the compute, expanding access is good.” But he thinks it would be even better to make a restricted version of Mythos “generally available.” 

Barak thinks that more access to cyber models will be good for both politics and cybersecurity: “Wide iterative deployment helps avoiding concentration of power,” among specific companies or political actors, and would help cyber defenders in “discovering issues early.”

—Ella Markianos

Musk takes the stand

Week one of the high-profile OpenAI trial is off to a fairly anticlimactic start. Elon Musk, an early investor in OpenAI, alleges that he was “manipulated” by OpenAI into donating to what he believed would always remain a nonprofit. When OpenAI developed commercial ambitions, he says, the company effectively “stole a charity.” 

On the witness stand, Musk reiterated how upset he was that OpenAI allegedly deceived him — and said he was a “fool who provided them free funding to create a startup.” (When asked earlier about the potential tax breaks he received as a result of providing the “free funding,” he accused OpenAI’s attorney of asking questions “designed to trick me.”)

OpenAI maintains that Musk knew about the company’s for-profit plans and supported its decision, but created a competitor after the founders refused to give him control of the venture, and is only now suing them in an effort to slow them down.

OpenAI’s lead counsel showed emails from September 2017 between Musk and OpenAI executives in which Musk demands to have more voting power than his cofounders by having the right to pick four members of the company’s board. “I would unequivocally have initial control of the company, but this will change quickly,” Musk wrote in a message.

Notably, Musk admitted on the stand that xAI has used tech from OpenAI to build its models, which is not allowed by OpenAI’s terms of service. He said it was “partly” true that xAI has distilled OpenAI tech, and said “generally A.I. companies distill other A.I. companies.”

Meanwhile, one topic you will not hear about throughout this trial: whether AI is an existential threat to humanity. The judge put a stop to the bickering after both Musk and OpenAI’s lawyers started arguing over the issue.

“I suspect that there are a number of people who do not want to put the future of humanity in Mr. Musk’s hands,” the judge said. “But we’re not going to get into that.” 

—Lindsey Choo

Also reading:

Meta threatens to pull its apps out of New Mexico.

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This column touches on AI. My fiancé works at Anthropic. See my full ethics disclosure here.

I. 

As I do most mornings, I began work yesterday by checking my Signal messages. Along with the usual unwanted PR pitches and messages from people in the middle stages of AI psychosis, I had received a genuinely great tip. It was a story squarely in our coverage area that, if properly covered, could draw attention to a pressing issue on tech platforms and put pressure on it to change.

The tip should have filled me with excitement. Instead, though, I felt something closer to dread. When was I going to begin making the many phone calls needed to verify this information? How could I find time to meet a source or two in person? Did I have all the sources I would need, or would I need to somehow develop some more?

Since I began writing a daily newsletter in 2017, I have always faced some version of these pressures. My historical approach has been to report scoops whenever I can, and fill out the rest of the time by writing news analysis — bolstering it whenever I can with extra details of original reporting. As longtime Platformer readers will know, in practice this meant that the balance of what we publish here has leaned toward news analysis. A daily publishing cadence leaves enough time for synthesis and sense-making, but not for deep digging and phone tag.

For most of the past decade, I’ve liked this arrangement. I began writing in the aftermath of the 2016 US presidential election and the growing backlash against tech companies, and the glut of coverage benefited from a publication dedicated to a daily close reading of the news. When I started publishing a roundup of links related to the intersection of tech and democracy, I felt like I was doing something genuinely novel on my beat.

Fast forward to today, and the world of link roundups feels much more crowded. A generation of tech writers filed out of the newsrooms where they grew up and began to write for audiences of their own. Newsletters, which were once an afterthought in media, are now a central pillar of many publishers’ strategies. But the ongoing collapse of the web and related struggles at big media companies means that there is now less tech journalism overall. The need for sense-making is greater than ever, but due to a half-decade of layoffs and shuttered publications, there is less and less journalism to make sense of.

Meanwhile, improvements in artificial intelligence over this year have resulted in systems that further encroach on the work we do here. In January, I wrote about the experience of building an automated daily briefing of link summaries for myself; I have been using it all year to look for story ideas. It does about as good a job as I do in finding stories of interest, and it does so automatically while I sleep.

Link aggregation was never the highest-value work we did here. But I do think that its value has decreased significantly over the past year, and will decline further as more people begin using personal agents to write news digests for them. (Already, it seems that a staggering percentage of Substack posts are AI-generated in whole or part; they are arbitraging the fact that you are not yet doing this yourself.)

And to crawl a bit further out onto a limb, I expect some smaller set of people — but in particular the executives, policy professionals, and communications team that Platformer has long written for — to begin relying on AI for news analysis as well. Over the past year, chatbots have gotten sharper at responding to questions about the implications of this or that news story — how it changes the competitive landscape, for example, or how regulators might respond.

For the moment, chatbots carry far less authority on these subjects than the domain experts who often write paid newsletters about them. But having been a reporter since 2002, my experience has been that the internet is working continuously to deskill and replace you. It doesn’t require much of a leap in imagination on my part to imagine a day where your current lineup of morning and afternoon newsletters is largely replaced by an agent-written briefing that has been exquisitely tuned to your professional concerns — and, unlike this newsletter, instantly respond to your questions about its findings.

If Platformer’s three pillars are original reporting, news analysis, and link aggregation, then, it would seem that one of those has already been commoditized and the second may be on its way.

II.

We've been trying to evolve to keep pace with these changes. But I've come to believe that we need to move faster.

In September, in my annual anniversary post, I mentioned that I wanted to take more time off from writing to report. You all were universally supportive of the move, reminding me that you are paying for quality rather than quantity. 

I’m proud of the original reporting we’ve done since: publishing previously unreported internal conversations about Meta’s AI-training spyware; a possible move to defund Meta’s Oversight Board; and OpenAI shuttering its mission alignment team, among others. We’ve also found significant enthusiasm for our first-person experiments in trying to make AI work for us: like this piece on Claude Code for writers, or this one on falling in and out of love with the agent now known as OpenClaw; or this one on Ella Markianos trying to replace herself with a bot.

We’ve done the best we can with the schedule we have. But ultimately, occasional days off the column haven’t been enough to give me what I really want — and what I think will result in the best version of Platformer. That is: the flexibility to leave my desk for several hours at a time; to flesh out stories over days or even weeks; to spend a slow news day reporting rather than trying to cobble together a column.

And so today we’re going to begin an experiment to see what that version of Platformer would look like. Free subscribers can still look forward to one column per week. Paid subscribers will get an additional column on Thursdays that we’re thinking of as a reporter’s notebook: what I’m hearing, what we’re working on, a Hard Fork preview, and a mailbag. Some of these may read like traditional columns; others may feel more formally daring.

Paid subscribers will also get additional stories and analyses from us as we write them. This is the biggest change we’re making: instead of promising to show up on a set schedule, we’re promising to show up when we find out something interesting — or want to help you make sense of the day’s big story on our beat.

In practice, I suspect that there will be many weeks where paid subscribers still hear from us three times a week. But for all of the reasons above, we need to change Platformer so that our schedule serves the journalism. For too long now, it has been the other way around.

We’re making a couple other tweaks. Side Quests, our column-ending grab bag of every single link we found interesting, are going away. Techmeme does this particular job better than we can, and does it 24/7. Going forward, we want to reserve our firepower for when we can move the story forward.

Following, the section we launched in September to brief you on the day’s news along with our own thoughts and commentary from influential people, is sticking around. Readers have told us they really like it, and we’ll continue to send out Following-only editions to paid subscribers to help you make sense of big stories on days when we’re working on other things. You’ll continue to find them after our columns as well. 

Finally, we’re working on a big project: a limited-run series of conversations about the future of AI and work that will launch next month across text, audio, and video. The goal is to capture the current uncertainty and simmering conflict between workers and managers that we covered earlier this year in the AI productivity paradox. We’ve got some great talks lined up, and I’m looking forward to sharing more with you all soon. In the meantime, though, it’s a great time to add the Platformer feed to your podcast player of choice.

As always, I’d like to hear what you make of these changes. I’m being sincere when I call this an experiment — we plan to iterate on this new approach over the next couple months, and can always change or revert things based on reader feedback. Truthfully, I find these changes somewhat terrifying, since they replace the conveyor-belt logic of a thrice-weekly column with something wilder and less certain.

But as one brilliant Silicon Valley CEO once put it: only the paranoid survive. In a world where everyone has a take but almost no one has a second source, we’re betting that the value in tech journalism is moving away from aggregation and predictability and toward original reporting and surprise.

Thanks to everyone who has supported Platformer up until this point. And for everyone else, if this next chapter sounds compelling, we’d love you to join us, too.  

In the meantime, I've got a tip to run down.

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Following

Elon Musk and OpenAI head to court

What happened:  Elon Musk’s lawsuit against OpenAI is going to trial. Jury selection finished today; arguments will begin tomorrow. During jury selection, several candidates said they thought ill of Musk for political reasons; at one point in the proceedings, Judge Yvonne Gonzalez Rogers said, “The reality is people don’t like him.”

In the lead-up to the trial, Judge Gonzalez Rogers dismissed Musk’s claims that OpenAI defrauded him. The trial will instead focus on Musk’s breach of charitable trust and unjust ​enrichment claims.

Musk, who provided OpenAI with some of its initial funding, alleges that he was “assiduously manipulated” and “deceived” by OpenAI. Musk is seeking $150 billion in damages; the proceeds would go to OpenAI’s nonprofit parent.

Why we’re following: The trial is a threat to OpenAI’s hopes of an IPO in the final quarter of 2026. It’s also a culmination of a now years-long feud between Musk and OpenAI CEO Sam Altman. Musk has since founded OpenAI competitor xAI and folded it into SpaceX, which is also set to IPO in 2026.

What people are saying: The two have eagerly been anticipating their day in court. In February, Altman posted on X, “Really excited to get Elon under oath in a few months, Christmas in April!”

On X, Musk has been posting about the trial all day, giving nicknames to his nemeses: “Scam Altman and Greg Stockman stole a charity. Full stop,” he wrote

Musk also boosted the visibility of the New Yorker’s Sam Altman exposé on X, effectively promoting the story into countless feeds.

Elsewhere, OpenAI posted on X: “We can't wait to make our case in court where both the truth and the law are on our side.” The company added, “This lawsuit has always been a baseless and jealous bid to derail a competitor.”

The rest of Elon’s big week: Now that xAI is owned by SpaceX, the whole company may be responsible for Grok’s history of creating CSAM. In a regulatory filing, SpaceX warned that inquiries into sexually abusive AI imagery could hurt the company’s access to foreign markets.

Meanwhile, X is preparing to launch a new financial services tool called “X money." 

—Ella Markianos

China blocks Meta’s acquisition of Manus

What happened: China has ordered Meta to unwind its $2 billion acquisition of AI app Manus four months after the deal was announced, taking an unusually aggressive step to block the acquisition of a company that has already moved its entire operation out of China.

The acquisition appeared to be doomed from the start. China’s National Security Commission, led by President Xi Jinping, said shortly after the deal was announced in December that it was a “conspiratorial” attempt to hollow out the country’s technology base, sources told the Financial Times. The opinion led to a multi-agency effort to review the transaction and contain its fallout.

As part of the response to the Meta acquisition, Chinese regulators are also reportedly planning to require domestic tech firms to get government approval before accepting US funding, and have in recent weeks told several private firms to reject US capital in funding rounds unless explicitly approved.

“The transaction complied fully with applicable law. We anticipate an appropriate resolution to the inquiry,” a Meta spokesperson told the FT. Meta has already integrated Manus into some of its tools; unwinding the deal could be complicated. 

Why we’re following: The move is setting a precedent for Chinese startups and founders. It doesn’t matter if they move their operations to Singapore to avoid geopolitical scrutiny — a practice known as “Singapore-washing.” They can still be blocked by the Chinese government.

On the other side of these geopolitical tensions, the US on Friday accused Chinese companies, including AI startup DeepSeek, of attempting to steal intellectual property from US AI labs by distilling the output of their frontier large language models.

“AI models developed from surreptitious, unauthorized distillation campaigns enable foreign actors to release products that appear to perform comparably on select benchmarks at a fraction of the cost but do not replicate the full performance of the original system,” a cable from the State Department said.

What people are saying: The blocking of the acquisition is “a reality check for the debate over Chinese investment in the US: Beijing just showed how quickly they can shut the door on the reverse,” wrote Wall Street Journal chief China correspondent Lingling Wei. “Not surprising, but very telling.”

Chris McGuire, a senior fellow for China and emerging technologies at the think tank Council on Foreign Relations, questioned: “Why would any founder start an AI company in China if they had a choice? … Manus did everything right. They even moved their entire business to Singapore to comply with U.S. outbound investment restrictions. Their only mistake was that they originally founded the company in China.”

The crackdown could lure Chinese founders elsewhere, McGuire pointed out. “Meta will be fine without Manus. But Chinese nationals looking to found AI companies will increasingly just start them overseas,” he wrote.

—Lindsey Choo

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