Last Wednesday, the head of an AI tech company casually announced, in a long-winded post on X, a discovery that, if true, would represent a major breakthrough in biomedical research. Dario Amodei, CEO of Anthropic, wrote that his company’s large language model Claude had found “a molecular machine that we suspect could represent a new gene editing mechanism.” 

So what does this “molecular machine” actually do? No one is entirely sure yet. But that hasn’t stopped tech bros from already upholding it as a new version of CRISPR, the enzyme-wielding tool that cuts up DNA like nobody’s business, sparking a revolution in ultra-precise genetic modification that has spanned research in everything from agriculture to cancer therapy. It’s no surprise its discoverers earned a Nobel Prize in Chemistry. If an AI program really just made a similar discovery, it would be big news.

Indeed, Amodei goes on to say that this finding could be a step toward developing cures for most diseases in five to 10 years. The company’s press release goes into more detail, but basically says that Claude was prompted to search through a massive database of DNA sequences for reverse transcriptases, enzymes that copy RNA into DNA. “After 21 hours spent searching this data by roughly 950 agents using 210 million tokens, one of the agents spotted something remarkable: a repeating pattern of DNA sequences that occurs next to the gene for an odd-looking RT,” the statement reads. Except that nothing about that is so “remarkable,” at least according to numerous people who study and work in this field.

“It is too soon to say that Claude — or the team of human researchers involved has discovered a ‘new CRISPR,’” Dimitri Perrin, an associate professor at the Queensland University of Technology, writes in The Conversation. He goes on to emphasize that what Claude found — a previously uncharacterized enzyme system found in viruses that infect bacteria — isn’t necessarily going to be useful. More research is needed, but Perrin explains that for now, the discovery is “CRISPR-like in its architecture, but there is no evidence that it is CRISPR-like in its function.”

---

Start your day with essential news from Salon.
Sign up for our free morning newsletter, Crash Course.

---

“Claude’s work isn’t even close to being in the same league as DeepMind’s AlphaFold system for predicting protein structure, which is proving to be truly transformative,” Michael Le Page, a senior reporter at New Scientist, wrote. Social media exploded with other examples of people dunking on Anthropic’s PR, with Simon Maechling, a PhD chemist, writing on X, “Claude discovered something in biology. Nobody knows what it does. But we already have the press release. … Finding something interesting isn’t the same as understanding it. Understanding it isn’t the same as showing it works. And showing it works isn’t the same as making it useful.”

Perhaps AI researcher Timnit Gebru summarized it best: “Please see what experts in this field are saying about this ‘discovery’ and not these scammers or press stenographers. This hype DDOS from them isn’t going to stop any time soon and we’ve seen how the mainstream media is doing press-release-as-a-service at this point.”

So it seems like Anthropic is making some pretty bold claims over something that is mildly cool and worth investigating, but at this point, also seems unlikely to revolutionize medicine or science. Why does this matter? A tech company making extreme claims is far from new. Big deal. But contrast all this hype (not to mention the energy spent debunking it) with the buzz just a few weeks ago about how, in just a few years, AI will supposedly gain sentience and kill off humanity. Consider how Anthropic CEO’s Amodei is among those pushing such a narrative, including warning that his company’s tools carry a risk of being used for cyberattacks or terrorism, including developing deadly viruses. Taken together, this presents a false dichotomy: AI will either end disease and suffering or inflict it in untold measure. Might there be a third option?

For anyone who buys the AI spiel, it’s a tool with boundless power. For now, however, the tech companies still rely on an old-fashioned kind of power offered by the state, which is precisely why other tech CEOs were seen sucking up to Chinese President Xi Jinping during his recent U.S. vacation. Amodei missed meeting Xi, for what it’s worth, but had a private dinner with President Donald Trump late Sunday night and later this week joined other tech leaders for a luncheon hosted by Trump and House Speaker Mike Johnson. There — joined by Nvidia CEO Jensen Huang, Tesla and SpaceX CEO Elon Musk, Meta’s Mark Zuckerberg and others — the president signed a “morally binding” artificial intelligence document, which sounds promising, until you hear Trump repeatedly tell reporters that tech companies can supervise themselves. “The self-regulation is very important,” Trump said. The document signed seems to have zero enforcement or legal mechanisms, so in this context, it appears “morally binding” just means “self-policing.” 

 

It’s no mystery why the president — heavily invested in the AI industry as he acridly dismisses any notion of regulation — is cozy with the idea of AI’s alleged abilities. In his latest effort to sell the American public on the coming “golden age” of AI (of course Trump would use that phrase), on Monday the Trump administration both launched America.gov, an AI chatbot powered by Google’s Gemini and Elon Musk’s Grok, and signed an executive order to rename AI “super intelligence.”

As Gebru observed, the grift has no end. All the more reason to pour cold water on outrageous claims about flagging little enzymes in huge databases. (Let’s avoid anthropomorphizing these programs by using the verb “discover.”) The goal of Anthropic’s AI propaganda — because that’s what it is — is to get most people skimming past the headlines to only catch “AI finds new CRISPR” and think a cure for cancer and world peace are right around the corner. All we have to do is hand the keys and all our money to Silicon Valley. Whether it’s utopia or dystopia, that’s the pitch: cede control of this tech to the billionaires in charge. In that sense, blindly swallowing AI’s positive hype can be as dangerous as its alleged existential risks.

The post AI hype promises miracles. That’s as risky as doomsday talk appeared first on Salon.com.

The national conversation about AI in the US is full of fear: fear about AI’s potential to end humanity, fear of data centers moving in, fear that government officials will do absolutely nothing even as the industry pleads for regulation, you name it.

But the AI conversation looks remarkably different across the Pacific.

In China, driven by public enthusiasm and a state-backed push for AI dominance, the country is in the midst of a large-scale AI integration into all facets of its society, driven by Chinese leader Xi Jinping’s support for the technology.

“He’s super AI-pilled,“ said Kyle Chan, an expert on China’s technology development and industrial policy at the Brookings Institution. “When you look at his speeches, when you look at Chinese policy documents, everything is about rolling AI out to as many domains as possible: manufacturing, services, education, healthcare, pharmaceuticals, and of course the military. Everything is about trying to integrate AI.”

Beneath this push for a high-speed AI adoption across Chinese society lies a regulatory framework designed to safeguard political stability and keep boundaries on the kind of information AI models are able to share.

Chan joined Vox’s Today, Explained podcast to speak with co-host Sean Rameswaram about the different AI anxieties in the US and China, and whether joint regulations on AI safety are possible while the two countries are engaged in an AI arms race.

Below is an excerpt of their conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify.

One thing that really struck me about the way the Chinese government is approaching AI is that China has this explicit goal, right?

China has some concrete plans to boost AI adoption through the country, including its recent AI-plus initiative. And their goal is to try to boost the penetration of AI in so-called terminal devices, everything from computers to smartphones, to 90 percent or more by 2030. 

It’s going really fast and it is, in part, a state mobilization effort. There are state-owned companies, there are government agencies that are trying to integrate AI, but also there’s just an enthusiasm from the general population.

If you look at surveys like the latest Gallup poll, people in China are much more enthusiastic and positive about how AI might help them and help the country. It’s quite a striking contrast with what you see in the US in terms of attitudes towards AI.

Because people here are worried about AI as an existential threat. People here are worried about AI taking their jobs, people here are worried about AI data centers in their backyards. Is that not the vibe in China?

Not so much. There is some concern about AI and jobs. But in general, China’s not worried about AI wiping out humanity. You really don’t hear that from the Chinese policymakers. There’s no talk about a P(doom), the probability that AI will wipe out humanity. So this is just a different approach to AI risk in general.

And why not the existential risk? 

You know, one factor, I think, is sci-fi and the culture. 

Huh. We’ve watched too many movies?

It seems like every major sci-fi book or film involves machines trying to kill us all, and I think that that idea has become very prevalent. 

When you imagine a future with AI, that is one of the primary futures that people imagine, versus in China, I think a lot of the attitude towards technology in general, not just AI, is about using this edge to keep up and to become more competitive. 

For a lot of people in China, technology was something that they saw transform their lives in real time. And Chinese leaders think it’s the key to China’s future modernization and transformation to a global superpower.

Let’s talk about what concerns there are.

One of the biggest concerns that Chinese political leaders have when it comes to AI is political and social instability. They don’t want AI-generated content to act the way that social media or the internet broadly could, which is to disseminate information, maybe deepfakes, maybe viral news that could threaten the [Chinese Communist Party’s] hold on power.

For AI-generated content, it was about making sure that the models didn’t say the wrong things about sensitive topics. A lot of this is about social and political risk rather than about existential technological risk per se. 

From pretty early on, China has been developing a model registration system where basically AI services have to be vetted in advance and go through a battery of tests where they’re asked sensitive political questions and they’re supposed to either not answer them or say the “right thing” before they are officially registered. And once they’re registered, then they can be released to the public. So it’s a pretty tight regulatory framework for controlling things like political content.

We know that there’s a high unemployment rate in China, especially among young people, which is also a great concern of the government. Are people worried that this bullish approach to AI could affect employment numbers?

Yes and no. On the one hand, China is actually facing this demographic problem with a shrinking labor force. Fewer people want to go into, say, factory jobs, but China still wants to make everything the world wants to buy. 

They see AI as a partial solution to that, a way to automate things like manufacturing. On the other hand, though, there are growing concerns in China about job loss. And you see also some recent court cases in China where they basically are telling employers, you cannot use AI as a reason to fire workers. 

But I think overall, what they’re trying to do is they’re trying to find this balance between the diffusion of the technology and trying to prevent any kind of large-scale protest movement that might arise from sudden mass unemployment of, say, factory workers or food delivery drivers.

And President Xi was of course here in Washington last week. From the limited photos we have of the state dinner or of President Trump and President Xi on the tarmac, everything looks so chummy, even though we are so clearly in a cold war with China over AI. The president’s always talking about how he wants to win. 

Was there tension in his visit to Washington last week? Did you notice any?

The weird thing is that just between these two men, there seems to be this kind of romance where Trump really fawns over Xi Jinping. 

I think he has tremendous respect, even admiration, for Xi Jinping’s grip on power and control over the government apparatus in general. 

What is happening under the surface, though, is continuing tensions and a deep competitive structural relationship. That involves especially competition in AI. AI has emerged as a new fault line of competition between the US and China, one that could come to define the relationship going forward. 

I think that on the one hand, they’re talking about cooperation on things like AI safety and AI risk. On the other hand, there’s a whole bunch of issues where the US wants to lead. China wants to also get ahead. And when it comes to things like distillation or cyber capabilities, it’s as zero-sum as it’s ever been.

Who’s winning?

It depends on what you’re looking at and how you define winning. If you’re talking about the best models, for sure the US. And virtually everyone in China would agree, right? In fact, Anthropic’s Claude is very popular in China, even though technically Anthropic bans its use in China.

But aren’t their open-source models kind of popular here?

Yes, exactly. Chinese open-source models are getting more popular in the US, especially at a time when some of the capabilities are so incredible that most normal people don’t really need them, and they also don’t want to pay for the most advanced and most expensive models. Ultimately it’s this weird sort of cooperation and competition at the same time.

American models are popular in China. Chinese models are popular in America. We’re at each other’s throats, but quietly, politely.

Does the cold war make it all but certain that safety isn’t going to really be a chief priority for either country until something terrible happens?

The fierce level of competition and the very, very deep distrust between the two countries makes any kind of cooperation on AI safety very, very difficult. Even just talking to each other, having the US and China establish an official AI dialogue, which apparently they’ve agreed to, is seen as a huge first step, even though they’re just talking. 

There’s a long, long road to go before we can even think about something like an arms control treaty or a broader international agreement about AI and AI safety. But I think that’s what some people, especially in Silicon Valley, are trying to jump to.

Fox News is confronting an awkward problem of its own making. Conservative media has spent years teaching its audience to distrust the institutions building artificial intelligence, but now those same institutions are working with Donald Trump and Republicans nationwide to secure billions of dollars in public infrastructure, regulatory favors and political protection to build the physical machinery that makes AI possible. So the network is now trying to discipline a conservative backlash it helped build by recasting public anger as a threat to Trump’s economic agenda and America’s rivalry with China.

The shift was gradual. The network did not simply move from being skeptical of AI to becoming enthusiastic about it. Instead, it changed the question. What began as a story about whether AI was “woke” became a story about whether the U.S. could afford to not dominate its development. 

Over the past three years, as Trump’s alliance with tech oligarchs deepened, network hosts have seemingly dropped their culture war suspicion of AI, which at first centered on ChatGPT and accusations that the chatbot, produced by OpenAI, was “woke” and exhibited an “anti-Trump bias.” They have now adopted an aggressive defense of the industrial buildout behind the technology. What began as a moral panic over Silicon Valley chatbots and censorship has turned into a desperate campaign to douse the flames of a conservative grassroots rebellion against AI data centers.

 

When ChatGPT exploded into public consciousness in early 2023, network hosts used its standard culture war playbook to raise the alarm against generative AI. Jesse Watters complained on “The Five” that AI would discriminate against conservatives and said he feared machines would become another mechanism for censorship. Laura Ingraham held up the news that Vanderbilt administrators had used the chatbot to draft a message after the Michigan State shooting as proof that intellectual standards were collapsing. Tucker Carlson, then still on the air, gave Elon Musk a platform to voice apocalyptic warnings about where the field was heading. 

For a moment, the right-wing media apparatus seemed positioned to critique AI as an anti-human concentration of corporate power. But when Carlson was ousted only days after the April 2023 interview, he took that strain of populism with him to the political periphery, joining voices like former Trump adviser and podcast host Steve Bannon in expressing sustained alarm over AI from the right. 

But as Salon’s Amanda Marcotte has documented, the politics surrounding the technology changed as the money got bigger. Tech companies began pouring hundreds of billions of dollars into data centers. After the federal government’s own assessment estimated that data centers consumed about 4.4% of total U.S. electricity in 2023, and could consume between 6.7% and 12% by 2028, conservative voters who distrust Silicon Valley and resent rising utility bills collided with Trump-aligned officials who want to build the technology and its data centers at breakneck speed. A July Fox News poll found that 70% of Americans opposed data centers being built in their communities. Faced with a genuine populist uprising against corporate extraction, the network seems to have stepped in to perform damage control for Big Tech and the Trump administration. 

By August 2026, “Fox & Friends” began reassuring viewers that local opposition was not an authentic community defense but rather a deceptive plot orchestrated by the “far left.” Host Brian Kilmeade suggested that calls to slow down AI expansion could “tank” the economy ahead of the midterms, effectively warning viewers that defending their local water source was an act of economic sabotage. On “The Five,” Greg Gutfeld picked up the same talking point, arguing that Democrats were weaponizing local opposition to AI development as a sneaky way to undermine Trump’s economic numbers. Will Cain’s program has similarly cast local opposition as a Democratic weapon for stirring outrage in swing districts. 

---

Want more sharp takes on politics? Sign up for our free newsletter, Standing Room Only, written by Amanda Marcotte, now also a weekly show on YouTube or wherever you get your podcasts.

---

Fox reached for establishment gravitas and deployed heavyweights from the national security state like former Secretary of State Condoleezza Rice to sell the data centers buildout under the banner of “techno-optimism.” Dismissing concerns over sweeping job losses on Monday, Rice framed the issue around international competition. On Fox Business, Larry Kudlow has argued that opposing data centers amounts to promoting China, and Charles Payne has described a New York moratorium as part of a Democratic war on AI. In May, Payne went further on “Jesse Watters Primetime” when he accused Sen. Bernie Sanders, I-Vt., of working with the Chinese to kill the American AI industry. 

An incident involving businessman and television personality Kevin O’Leary is the classic cautionary tale taught in journalism schools. In May, the “Shark Tank” investor appeared on former Fox Business host Maria Bartiromo’s show promoting his proposed Utah data center when he claimed that local groups and residents opposing it were proxies for the Chinese government. The allegation was subsequently repeated across Fox programming. O’Leary later acknowledged that he had no evidence that the organizations or individuals he identified were funded by China or the Chinese Communist Party. Fox News Media eventually issued an on-air apology.

This campaign to pacify the conservative base culminated in Trump’s direct intervention. Characterizing fears of existential risk from AI as a “hoax,” Trump made it clear that corporate expansion would take priority over local or environmental concerns. After returning from a golf tournament in Illinois over the weekend, Trump openly boasted to reporters at Joint Base Andrews about his late-night dinner with Anthropic CEO Dario Amodei, illustrating the strange politics of this moment. Anthropic has become one of the most prominent companies arguing that powerful AI systems require safety measures and public oversight. Amodei himself has warned that AI could pose a risk to humanity if developed badly. 

On Tuesday, ahead of a scheduled lunch with industry executives to discuss the technology, Trump announced the launch of America.gov, a government chatbot billed as a research tool for public services. “We do not surrender to fears about what technologies will do to us,” he said. “We decide that those technologies will do for us some very, very incredible things.” Using his preferred moniker for AI, the president added. “This kind of innovation in government is just a preview of what is possible with super intelligence.”

Trump’s rhetoric and actions underscore that his role in selling AI to a skeptical public is central. He has dismissed existential AI fears as a “hoax” and made expanding data centers a centerpiece of his agenda. That is why Fox was so quick to abandon the rural and suburban communities facing the real-world fallout from data centers, choosing instead to lecture its audience on the national security duty of surrendering their power grids and backyards to Big Tech.

UPDATE: This column has been updated to include President Donald Trump’s announcement that the government is launching its own chatbot, America.gov.

The post Fox News tries to cool conservative anger over AI appeared first on Salon.com.

William Lawrence is a Democrat running to represent Michigan’s Lansing-area Seventh Congressional District with a campaign that largely focuses on data centers and affordability. It’s not an unusual story in 2026 — but not long ago, Lawrence was cutting his teeth in progressive politics as a co-founder of the Sunrise Movement, primarily focused on organizing around the climate crisis.

His arc, from progressive activist to politician, is emblematic of a bigger trend, as a wave of millennial progressives in midterm races across the country try to lean into issues that have broader, more immediate appeal with voters.

But that arc also raises big questions about the left’s changing priorities. In a recent interview for Vox’s podcast and video series America, Actually, host Astead Herndon spoke with Lawrence about his past activism and how he has pivoted. “It can sometimes feel like…folks hop from issue to issue, whether it’s climate, even anti-AI right now, without those things being solved,” Herndon told Lawrence. 

According to Lawrence, “that’s the reality of the time. It seems like everything is unraveling, and it is because the whole social, economic, political compact is being renegotiated, not just in this country, but on a global stage.” Still, Lawrence said, that earlier organizing shapes how he sees different challenges today.

The two also discussed whether opposition to data centers and AI has wide, regular-voter appeal in places like Michigan and how Lawrence went from hopeless and disengaged to regaining a sense of agency through organizing.

Below is an excerpt of the conversation, edited for length and clarity. There’s much more in the full show, so listen to America, Actually wherever you get your podcasts or watch it on Vox’s YouTube channel.

You started in Sunrise and were at the forefront of a young progressive movement that was aligned at that time — in 2018, 2019, and I think still — with progressive members of Congress like Rashida Tlaib or Alexandria Ocasio-Cortez.

I remember reporting about Sunrise at the time. The goal was to elect a Bernie Sanders, but the other goal was to have the party largely embrace things like a Green New Deal. How would you grade the efforts of the climate movement of that time, looking back?

I think we did some things very well.

We put climate jobs and justice at the top of the agenda for the incoming Biden administration. I think the Bernie-Biden task force is an often overlooked but very important chapter of that whole story, where Bernie and Biden both understood they had to bring their people to the table.

And that’s how Build Back Better got negotiated. We fought hard for Build Back Better, and ultimately came up two votes shy in the United States Senate.

Do you see that as a success or a failure of the climate movement?

I think Build Back Better was a success in coming to the table and negotiating, from the Green New Deal to Build Back Better, and then what we ended up with was the Inflation Reduction Act after it passed the final filter, which was Sen. Joe Manchin and Sen. Kyrsten Sinema. At the time, now, how I felt about it was, this is historic. This is the largest investment in green jobs and infrastructure in United States history. I know how hard it was to get here. I’m tremendously proud of the work that I did and so many others did.

Just in my district alone, we’ve delivered $26 billion in investment for manufacturing and sustainability because of the Inflation Reduction Act. But at the time, we knew that this is maybe 5 percent or 10 percent of what we need in scope. And now we’re experiencing the consequences of that.

Obviously, we still have a very volatile economy. We still lack energy security because we now have closed the Strait of Hormuz. We’re still too dependent on a global oil market, and we could use many more investments in solar and other kinds of renewable energy than we have now.

And ultimately, it wasn’t enough for the Biden-Harris administration to be able to win reelection, because people didn’t feel it enough in their pocketbooks.

Why do you think Democrats are talking less about climate?

I think it’s the intensity of the crises that we face. It feels like we’re falling through midair, after the end of all of the constitutional protections, the rule of law, and the economic security that people had previously enjoyed. 

There are a lot of very acute and urgent crises, and I think that that has bumped climate, which is a super massive but also somewhat slow-moving crisis, a little bit lower on the priority list just in terms of what comes up for people on the Hill.

You went back to Michigan to focus on housing and advocacy. Did that make you think a little differently about the national work you were doing?

I think it’s fair to say that when I was younger, I got very focused on this climate issue, and I was very focused on it because it is this existential crisis we all face.

But it was after the pandemic that I wanted to bring my work closer to home. I wanted to do the work of putting down roots in the place that had raised me and make my life there. And in seeking to organize there, it was clear that we needed an in to organize that was more immediate.

“I believe in the agency of regular people taking charge of our own lives and working together to build a better future.”

More immediate than the Green New Deal.

Than the Green New Deal or than the looming climate crisis. People know that that’s an issue, but they’re like, “I’ve gotta pay rent at the end of the month.” And as an organizer, I believe that you have to listen to people, you have to hear what’s going on for them, and then work on issues that have the potential to bring people together.

Housing is one of those issues. Whether you’re in the city of Lansing, whether you’re in small towns, urban, rural, suburban, young or old, everybody can agree that the rent is too damn high and it’s too hard to buy a home anymore.

Were you surprised Trump won the state in 2024? 

No.

Why not? 

Because I saw it all unfold. We hoped to deliver more through the Biden-Harris administration than we ultimately did through Build Back Better. We didn’t deliver as much to relieve the economic anxiety that people were feeling. And then I do believe that the war and the genocide in Gaza and our blank check to allow that to happen with our money and our tax dollars destroyed the Democratic coalition that had been assembled from 2017 through 2020 for the purpose of beating Trump.

I just saw that unfolding over the course of the whole year, just like a slow-motion train wreck.

I want to ask locally, what do you think is most important in this race: housing, data centers, or pushing back against Donald Trump?

If I had to choose, I’d say data centers because that’s where people in the district are finding some common ground across the spectrum — in fighting back against the concentrated power in Silicon Valley, which has been fully enabled by [incumbent Rep.] Tom Barrett and Donald Trump.

So you’re saying that’s the issue that allows you to create cross-ideological coalitions?

Yeah.

Our last question is, just thinking through the scope of your career and the kind of progressive work: It can sometimes feel like folks hop from issue to issue, whether it’s climate, even anti-AI right now, without those things being solved, right?

Like, we still have a climate crisis. The rent is still too high. All of these things are still happening. How am I to trust that AI is the crisis of our time when I’ve heard so many crises of our time?

Some have called it a polycrisis — overlapping crises.

That’s the reality of the time. It seems like everything is unraveling, and it is because the whole social, economic, political compact is being renegotiated, not just in this country, but on a global stage. I don’t think it serves us to lie about that. I mean, here’s the reality, folks.

Everything is up in the air. Does it feel like you’re falling through midair? It’s because we are. All the old protections, all the old agreements have been chopped up. So we have got to come together. This is what I believe in more than anything, if there is a through line…I believe in the agency of regular people taking charge of our own lives and working together to build a better future. 

That’s what I learned as an organizer, and that’s what gave me hope actually, at a time when I was doomer-ing in my younger years. And I told you, I wanted to just drop out of society and all that. It was seeing that regular people have come together before, and they’ve won a better future for their kids and grandkids, that gave me a path to follow in.

It does feel like a powerless time, I have to tell you. And a lot of this AI revolution, to your point about control or about agency. We’ve talked to so many people who just think that these things are happening to them, rather than feeling that they are participating in the changes afoot.

But you’re telling a kinda hopeful story. 

I’m a hopeful guy. I was angry too, even when I got into this race. I was angry especially about the wars. But then in campaigning, I’ve met so many people across the district, old friends, new friends, and they give me a lot of hope because you just see the way that people are already performing miracles on a daily basis.

Six months ago, first lady Melania Trump walked into a White House education summit next to a faceless robot powered by artificial intelligence, which she said could one day give children personalized instruction.

“Imagine a humanoid educator named ‘Plato,’” she said. “Access to the classical studies is now instantaneous…humanity’s entire corpus of information is available in the comfort of your home.” 

Weeks earlier, her guest at the State of the Union had been a 10-year-old from Alpha School, a fast-growing private school network where children learn reading and math from AI software for two hours a day, and spend the rest of their time on workshops and projects overseen by adults the school calls “guides” rather than teachers.

The Trump administration had chosen AI as the future of American schooling, and wanted everyone to know it. But they picked a bad year to ask Americans to trust the technology with their kids.

Earlier this month, top leaders at Anthropic, OpenAI, and Google DeepMind publicly urged for an AI development slowdown, warning that companies racing to build ever more powerful systems could lose control of them, threatening not only large swaths of infrastructure but even humanity itself. Trump responded by calling the panic a “hoax” and said the US would not “hinder or stifle” the industry, because “whoever wins AI wins.” This “let them cook” mantra has met nearly every AI fear this year, from job loss to data center construction.

Yet the public is not convinced, and parents perhaps least of all. Efforts to bring AI into classrooms have run into organized opposition, and much of it is coming from inside Trump’s own coalition, setting up a fight between the administration’s pro-tech allies and conservatives who think screens have already done enough damage to kids.

In the nation’s two largest school districts, New York City has recently pulled student-facing generative AI from classrooms through eighth grade and Los Angeles blocked it on all student devices. But it’s not just Democratic-run cities that are pushing back. Earlier this year Utah barred students from using generative AI on assignments without a teacher’s say-so, and required schools to notify parents when it’s used in class. Oklahoma followed suit with a law, passed without a single “no” vote, that requires districts to tell parents which AI tools they use and what data they collect, and lets families opt out. Florida went further last week, when its Board of Education adopted rules for parents to opt their children in before they use AI at school, guaranteeing non-AI alternatives, and banning “companion” chatbots outright. 

Even at the Heritage Foundation, a conservative think tank with close ties to Trump, leaders are warning about the risks of AI in the classroom. In a March report, Heritage officials cautioned that AI tools built with addictive design features can harm children’s attention, mental health, and learning, and said schools should set limits rather than assume the technology will regulate itself. In July, the Manhattan Institute, another conservative think tank, recommended New York State bar students from using AI until third grade and keep it out of tests at every grade.

All the while, the Trump administration continues to push in the opposite direction, casting the wider AI race as one the US cannot afford to lose to China. Last year, Trump signed an executive order creating a White House task force to bring AI into K-12 classrooms, train teachers to use it, and build an “AI-ready workforce” to keep America as the global leader.

Polling data released in August found voters across the political spectrum concerned about AI in classrooms, with 85 percent worried about students using it to finish assignments without learning anything. Republicans were, if anything, slightly more eager than Democrats for government guardrails, 80 percent to 76 percent. Put differently, Republican voters appear closer to the Heritage Foundation than to the White House, which points to a coming clash inside the GOP over parental rights, innovation, and whether schools that take public money should have to prove their tactics work.

Up to this point much of the AI backlash has focused on regulating the tech in public schools, while some of its most ambitious boosters have concentrated on building private schools and developing new tools. A state can cap screen time in its elementary classrooms and let another family down the road pay thousands of dollars for Alpha, and everyone can call it a win for parent choice.

That peace may start to fray though as more public money comes on the table. Texas recently expanded vouchers to Alpha network schools and the company has also sought to open publicly funded charter schools. Other states are likely to confront these issues as well, meaning the AI education fight on the right may soon be unavoidable — and would land before an electorate whose patience for the tech, in schools and increasingly everywhere else, has been running thin all year. 

The Trump administration’s AI bet

The AI-in-schools executive order came just three months into Trump’s second term, and called for AI education to begin as early as kindergarten. Within five months, Education Secretary Linda McMahon was touring Alpha School’s Austin campus, describing it as “the most exciting thing” she’d seen in education in a long time. 

Alpha is hardly the first education company to promise a shiny new school model, but it has quickly become an influential player in the growing national debate around AI in schools. Its story so far reflects many of the big questions around AI in the classroom, including how to determine if the tech is helping or hurting kids, who is profiting, and what role governments should play in facilitating its adoption.

Alpha had been a small operation for most of its existence. MacKenzie Price, a Stanford graduate, co-founded it in 2014 after her second-grade daughter complained that school was boring. The tech billionaire Joe Liemandt became Price’s partner and the school’s principal financial backer. As recently as 2024 it served about 300 students across three campuses. 

Growth exploded in summer 2025, when Alpha took over campuses from a shuttered Montessori chain and announced plans for 10 new schools. This August it announced plans for 27 more.

Behind the school’s growth is its claim that students enrolled can cover a full day’s worth of academics in just two hours with AI, learning twice as fast as they would at a traditional school. Alpha can “enable the increase in human intelligence,” Price declared on a panel in Washington, DC, earlier this month, because “our academics can be delivered so much more efficiently and effectively.” 

Alpha says results from MAP tests, a widely used measure of academic growth, show its students learning 2.6 times faster on average than similar students at other schools. But no outside researcher has audited or independently verified those results.

That may soon change. MIT’s Blueprint Labs, the group behind some of the most rigorous charter school research in the country, told Vox it is designing pilot studies to measure the effects of the school’s programs on achievement and engagement, using experimental designs. Alpha is paying for the research; the lab says its own researchers will set the questions and methods. Harvard’s Human Flourishing Program is separately planning to study students’ life skills and well-being via a longitudinal study during the next academic year, Christina Hinton, who is involved with the research, told me. For now, Alpha’s academic claims remain its own.

Meanwhile, scrutiny of Alpha has grown. A Wired investigation last year described the school’s surveillance system as capable of recording students’ screens, keystrokes, and mouse activity and potentially tracking their eye movements. A 404 Media report published this winter claimed Alpha’s own internal documents said its AI-generated lessons sometimes did “more harm than good.” Alpha has disputed both reports, saying they relied on cherry-picked examples and accounts from fired employees, and provided me with an open letter it says every current family at the Brownsville campus signed, stating that the school Wired described doesn’t resemble theirs.

But education officials are still wary. This month, ProPublica and the Texas Tribune reported that at least nine states, Texas among them, had turned down Alpha’s bids to open publicly funded charter schools, with officials repeatedly pointing out that the model had only been tested on affluent students. (Tuition varies based on location, ranging from $10,000 a year at its Brownsville campus to $75,000 at its most expensive California schools.) Where Alpha had worked with lower-income students, ProPublica and the Texas Tribune found, it either hadn’t released enough data to back its claims of student growth or state test scores fell well short of what its leaders had predicted. 

After the Texas State Board of Education rejected Alpha’s charter bid in the summer of 2025, Alpha didn’t reapply. While policy advocacy was “frustratingly slow,” Price wrote, Alpha’s goal was “for students everywhere at every income level to be able to use this model of learning.” They turned instead to Texas’s new voucher program, which lets families spend public money at private schools. Unlike a charter, a private school in the program doesn’t have to give state tests or show anyone how its students perform. The comptroller, which runs the Texas Education Freedom Accounts (TEFA), approved most of Alpha’s Texas schools, which, counting virtual programs and specialty academies, now number more than 30.

None of the concerns that Texas’s State Board of Education had raised about Alpha’s test scores, missing data, or AI applied. “That wasn’t relevant,” Travis Pillow, a spokesperson for the TEFA voucher program, told ProPublica. “Our role is to make sure that a school meets the requirements in the law.” 

The five most expensive Alpha schools in Texas opted to leave the voucher program earlier this month, but many Alpha network schools are still participating, including Texas Sports Academy, Montessorium, NextGen Academy, Nova Academy, and Waypoint Academy. TEFA confirmed to me that students are attending Alpha affiliates via vouchers this year. 

Anna Davlantes, a spokesperson for Alpha, told me that in lieu of vouchers for their most expensive campuses, her network is offering families financial aid. 

The conservative revolt

Despite Alpha’s enthusiastic embrace in the White House, the introduction of AI in schools is increasingly challenged by forces on the right, from grassroots MAGA parents to social conservatives and technocrats.

The new AI rules in Florida, Oklahoma, and Utah, for example, grew out of a broader conservative push against screens in classrooms that predated the rise of chatbots and was turbocharged by parents’ frustrations with virtual learning during the pandemic.

Earlier this year Iowa capped elementary school screen time at an hour a day in a bill Gov. Kim Reynolds signed with Health and Human Services Secretary Robert F. Kennedy Jr. at her side. Tennessee passed a weakened version of a bill that would have banned devices in elementary schools outright, while similar bills in Missouri and Kansas died after school districts and teachers unions objected.

Uniting these efforts is a growing conviction that the last 15 years of education technology has failed most kids. The sponsor of Kansas’s bill used to sit on his state’s board of education and remembers thinking that ed tech held such promise. “What we found out is that not only is it not the education of the future, but it is destroying the education of the past,” he told Education Week. A growing number of lawmakers and parents have been citing The Digital Delusion, a book self-published in 2025 by neuroscientist Jared Cooney Horvath, that blames the proliferation of screens for declining student achievement. In January Sen. Ted Cruz (R-TX), who is otherwise one of the Senate’s leading opponents of AI regulation, invited Horvath to testify about the harms of AI on student learning and mental health.

The screen skeptics overlap with a more traditionalist wing of the GOP that objects to classroom technology on principle. Missouri’s original bill would have required cursive and kept 70 percent of student work on paper. The classical education movement, which holds up teachers as a moral example, has grown fast on the right. (Of the 895 identified classical schools nationwide, roughly one-third opened or converted to the model between 2020 and 2024, according to a Heritage Foundation tracker.)

The strand of conservatism that cuts closest to Alpha School comes from a third group, the school choice advocates. On paper, Alpha sounds like what the school reformers have wanted for years: It can offer an alternative to underperforming local public schools, its AI programs can theoretically compensate for inconsistent teacher quality, it provides personalized instruction, and it checks mastery on every lesson, so families can easily know whether their child is learning. Those are problems the education reform movement was launched to solve, and Alpha claims to fix them all.

The reformers, though, want proof. Mike Petrilli, who runs the conservative Thomas B. Fordham Institute think tank and has spent decades advocating for charter schools, wrote recently on his Substack that parents who want to pay for Alpha out-of-pocket should be able to, but “that doesn’t mean the rest of us shouldn’t be skeptical, or that we shouldn’t worry about fraud, or that we should allow public funding to flow to these school.”

This split was on display in DC this month at the education conference where Price appeared. Molly Hart, who runs Utah’s public schools and calls herself a school choice supporter, argued that once a school takes taxpayer money, “the game changes,” and parents can’t make a real choice without data about how it performs. Erika Donalds, who chairs the education center at the America First Policy Institute and is one of the Trump administration’s closest allies on schools, said parents, not the government, should decide which results matter. Donalds compared the choice to picking a restaurant on Yelp. 

The evidence has started to catch up with parental concern. In a randomized trial involving nearly 1,000 students at a Turkish high school, those given access to a GPT-based math tutor that readily supplied answers scored 17 percent lower on a subsequent exam taken without it. A version designed to withhold answers eliminated that penalty but produced no statistically significant learning gain. A newer working paper tracking nearly 27,000 Chinese students found that adopting AI raised homework scores by 18 percent but lowered monthly exam scores by 20 percent within six months.

Alpha School is working to add more detail to that pile, and other studies will surely follow. It’s at least possible that some aspects of AI in education prove more promising with further study. But public skepticism will remain a major barrier, either way. On an issue where parties are still sorting themselves out, the White House and its allies are swimming against a growing anti-tech tide that extends far beyond just AI or even schools. That’s unlikely to change before 2028, and whoever wants the Republican nomination will have to say which parents they’re listening to.

When the AI apocalypse becomes regular dinner-table conversation, you know we’ve reached the freakout stage. Rogue AI agents are hacking into competitors. Former Big Tech employees are posting Skynet-style warnings on social media. CEOs are crying for help in ways that have made this once-wonky tech issue a frontline political fight. 

I still think many of the concerns outlined by the AI leaders themselves are massively self-serving — especially when their companies are soon to go public. But the growing list of AI scandals is real. And it tells us that the status quo for regulating this powerful new technology is not working.

The question now on everyone’s mind: Is any kind of global brake possible? What form of AI safety regime is practical, over the short term, when there is no trust between major governments? And what could possibly work when companies are loath to give up their secret sauce and are bent on domination in the global AI race?

Two major conversations are now playing out, in real time. On Wednesday, President Donald Trump welcomed Chinese President Xi Jinping to Washington for a three-day summit — part of which will be dedicated to AI risks. The same day, with the United Nations meeting for its annual General Assembly, heads of the world’s leading AI companies urged the UN to police the emerging technology, or risk potentially wiping out humanity. “We could lose control of the future to AI,” OpenAI’s boss, Sam Altman, bluntly told the UN’s Security Council.

Confronted with this collective freakout, it’s time to take a long, deep breath. 

In fact, AI safety has been on a lot of policymakers’ minds for years now, gaining momentum after ChatGPT was introduced in 2022. Governments have held hearings, convened expert groups, and drafted their own outlines of how to keep it under control. These weren’t just idle listening exercises. They aren’t all well known to the wider public, but they yielded real plans.

Barring any breakthroughs this week, we already have a quasi-planetary shield against the potentially runaway technology. That’s the good news.

The bad news is that it’s not really up and running yet. It’s also not clear whether it will actually work. So what is it — and how can we fix it so we have a global system that responds in time to deal with the incredibly fast-moving threats from AI?

What the global AI patchwork looks like now

For the last four years, the most tech-savvy nations — and would-be AI leaders — have been running serious conversations about this exact AI safety threat. Some are in Congress and the White House; some at the UN, at the G7, and in other capitals.

I’ve been covering this closely as a global technology journalist, from my current perch at a think tank. Here’s what the landscape looks like:

• There are national AI safety and security institutes, government-funded bodies whose job it is to kick the tires of AI companies’ latest products before they are let loose into the wild. 
• There are also voluntary commitments by companies, many of which have made their own pledges or developed joint standards, to protect elections from AI threats, stop the spread of AI-fueled deepfake imagery, and joint government-corporate efforts to bake safety into how the technology develops.
• At the international level, there’s a G7-led reporting mechanism — embraced by the most important Western tech nations that allows AI giants to share how they are building their latest models, as well as create standards for how to reduce catastrophic risks.
• An international scientific report provides a yearly update on risks posed by the most advanced AI systems, based on existing research, to help governments plan for the worst. 

What’s missing from all that?  What we currently lack — and what is needed between now and the end of 2026 — is a way to turn this cottage industry of AI safety mechanisms into a functioning, but crude, first-responder system when things suddenly go wrong. 

The last four years have laid out a pathway, and some of the necessary systems even exist. But there’s no way to respond globally, and in real time, when an AI crisis hits — especially if such a possibly doomsday event cuts across countries already skeptical of each other.

This week’s US-China summit may be a step in the right direction. Under proposals outlined by American officials, Washington and Beijing could set up a hotline between US Treasury Secretary Scott Bessent and Chinese Vice Premier He Lifeng in case of an AI incident affected each country’s national security. 

It’s still unclear if Trump’s meeting with Xi will lead to such progress. Chinese officials also have balked at Washington’s pleas for AI rules because, so far, Congress has failed to act, and China already has some of the world’s most stringent AI oversight.

For AI to be “safe,” this conversation will need to go beyond this week’s US-China summit. Relying just on Washington and Beijing — arguably the most important AI powers — would not solve the underlying problems, nor would it make other countries feel more comfortable.

What we need next, so it really saves us

I’ve been talking to AI safety experts and government officials, and it’s clear the missing piece is a way to activate this whole system in a crisis. Countries don’t all have to have the same AI safety policies, and they never will. But as with nuclear weapons — a similarly high-threat technology that the world found a way to contain — safety requires a rough global agreement on how to respond quickly when something goes terribly wrong.

What’s needed right now is a 90-day, opt-in rapid response mechanism that joins existing pieces of the AI safety puzzle together. I’ve pieced together some ideas about how it should work, and who needs to sign on. Granted, none of what I outline below is sufficient. But, together, they are more plausible than trying to negotiate a comprehensive global regime — let alone arrange another AI summit — while Washington, Beijing, and other national capitals disagree over what “AI safety” actually means.

All of these options are based on existing mechanisms, are derived from efforts that have worked in other policy areas, and provide a band-aid to the AI safety dilemma until a more durable solution can be negotiated.

• First, developers, AI safety institutes, and regulators, from across different countries, should agree that when an incident occurs, it triggers a specific Chernobyl-style protocol. That would mean submitting a confidential report within a 72-hour period to a national designated responder and a small technical secretariat. It can build on the OECD’s AI Incidents Monitor, lead to a technical, multi-stakeholder confidential investigation into what went wrong, and, subsequently, the publication of an anonymized lessons-learned note.
• Second, governments and developers can agree to publish common declarations related to safeguards, residual risks, escalation thresholds, and independent auditing before the release or upgrading of a next-generation model. It would use the G7 Hiroshima AI Process reporting framework as its base, and turn the current hodgepodge of corporate safety declarations and voluntary standards into a common minimum disclosure obligation. View it as similar to the collective bank stress tests after the 2008 global financial crisis. It can allow outsiders to truly compare models’ safety protocols without putting someone in charge of determining which company is doing it best.
• Third, create a Cold War-style US-China AI safety hotline — but see it as an initial step that can later be opened to other countries. There are good reasons, in the long term, that Washington and Beijing should not be allowed to rule artificial intelligence between them, and the rest of the world should have a real seat at the table. But for now those are the two “great powers” in the tech conversation, and we can set that philosophical argument aside to create a standing, technically-informed direct line of communication for acute AI-risk incidents. Its remit is inherently narrow: prevent dangerous misunderstandings linked to serious model incidents, AI-enabled cyberattacks and/or alleged breaches of agreed safety commitments.

There are obvious limits to what I describe above. For one, it’s an inherently Western-centric view that primarily discounts global majority countries. It also places too much sway on existing institutions like the Organization for Economic Cooperation and Development, as well as on the US-China relationship. Other countries’ officials will legitimately balk at all three options, and rightly so.

But this is not about creating a vague, unenforceable UN-led mandate for AI safety. Nor is it about corralling the geopolitical cats to hammer out a global AI treaty. The options — a collective safety protocol and incident reporting protocol; common pre-release standards; and a US-China AI safety hotline — are inherently short-term. They are also based on existing efforts and those that have worked successfully for other policy areas.

There will be time to quibble about the future of AI safety. But now is not that time. The latest AI models are moving faster than many had expected, tech bosses are worried their creations are already out of control, and the window for action may be smaller than we all think.  

What is required are practical steps to assuage people’s growing concerns amid heightened geopolitical tension, a lack of trust between governments and companies, and a need not to let the perfect get in the way of the good.

The AI freakout of the past few weeks has centered on nothing less than the risk of human extinction — on the possibility that artificial intelligence could wipe us all out. But the reality is, AI programs are already having a direct and sinister effect on people’s physical health: UnitedHealth Group, the largest health insurer in the US, has been accused of using AI programs to deny payments to people, despite their doctors saying the care was necessary, and is facing a class-action lawsuit. 

Humana and Cigna are facing their own litigation over similar allegations. Meanwhile, Medicare has launched its own AI tool that will help the government determine whether to approve prior authorizations, and doctors are sending the federal government reports of patients at their offices who are in tears because they can’t get the medicine to manage their chronic pain.   

The claims in the legal complaints are harrowing: Families are desperate for an elderly loved one who recently experienced a serious fall to be allowed to stay in a nursing home. Doctors are flummoxed when a patient who may need months of rehab is cut off by an algorithm after a couple of weeks. 

This is AI’s threat to people’s health right now. 

While the current conversation may be fixated on something more Hollywood, private insurers and the Trump administration are already deploying AI programs to vet people’s health insurance claims — and the evidence is growing that these programs are not always doing a good job and are instead putting people’s health at risk. Until these lawsuits are settled or policymakers can figure out a way to regulate how insurers use this new technology, it will be up to each of us to make sure that an AI program isn’t being used to cheat us out of insurance benefits that we’re entitled to.

“The type of care that clinicians deliver is very sensitive to the patient’s unique circumstances. That may not really show up as a number. There’s not a numeric value that tells you the completeness and connection of a patient’s social network,” said Mika Hamer, a health services researcher at the University of Maryland. “There really are a lot of factors that really can’t be quantified and so aren’t really suitable to go into these models. If we start to rely exclusively on the models, I think we run the risk of losing that human touch.”

Patients say private health insurers are using AI to deny claims

Insurers and patients fighting over medical claims is nothing new: Policyholders want the most coverage they can get and payers have a business incentive to pay as little as they can. Health insurers have also used algorithms for decades: Plug in somebody’s age, their weight, whether or not they smoke, and one of the old-fashioned algorithms could spit out a rough estimate of how much medical care they would need before they died. Simple — and comparatively transparent. 

When you submitted a claim or sought prior authorization, there was supposed to be a human doctor who could review all of the relevant information on behalf of the insurer and make the final decision, in theory with the patient’s best interests in mind. It didn’t always work that way in practice, of course, but that was the idea.

What’s different right now, according to Daniel Schwarcz, a law professor at the University of Minnesota who has looked at health insurers’ AI use, is that these newer programs can make very specific predictions about someone’s future health based on the information that’s fed into them, and those predictions are being used in the process of approving or denying medical claims. And exactly what went into making those predictions — and whether it is the AI or the human who ultimately holds the final authority — is often opaque from the outside.

“The reason we want human review is we want human substantive judgment. We want AI to be supporting that,” Schwarcz said. “But the difficulty is distinguishing between AI supporting meaningful human processing and AI basically replacing the human with the human just passively defaulting to the AI judgment. It’s just incredibly hard to distinguish between those two in practice, particularly in rules or regulations.”

And those questions are at the heart of the lawsuits brought against three major insurers in the past few years about their use of AI in processing claims.

In May 2022, Gene Lokken, then 91 years old, fell in his home. He was taken to the nearby hospital in Tomahawk, Wisconsin, and treated for a broken leg and a broken ankle. Less than a week later, he was placed in the health system’s hospice care because, according to the lawsuit filed by his family, he was “very weak, not communicative, and in constant pain.”

But a month later, after receiving skilled nursing care, Lokken started to improve. By late June, his doctor had removed his splint and placed his leg in a removable boot. In July, he started physical therapy to get up and walk again. And for a while, his Medicare Advantage plan sold by UnitedHealth covered all of the services he needed.

Until July 20. Around that time, according to the lawsuit, UnitedHealth denied any more services at the nursing facility where Lokken had been for the past two months. It insisted that he could be safely discharged and more time at the nursing care facility “was not medically necessary.”

The insurer’s decision left Lokken and his physician “dumbfounded.” The physical therapist notes from the time said Lokken was still suffering paralysis and weakness after only a couple weeks of rehab. His family appealed, but UnitedHealth continued to deny the claims, asserting that because Lokken could feed himself and did not require help to clean himself, he did not need nursing care and could go home. 

From July 2022 to July 2023, Lokken remained at the nursing home, but his family had to pay between $12,000 and $14,000 monthly to keep him there. And on July 17, 2023, Lokken died — having never left the facility.

The lawsuit alleges that the decision to stop covering Lokken’s nursing-home stay was largely made by an AI algorithm, called nH Predict, which UnitedHealth follows so unquestioningly that employees were allegedly at risk of being disciplined or fired if their claims decisions went against the AI’s projections. The complaint alleges that the AI model would typically start denying claims after two weeks of nursing home care — and the attorneys have more examples than Lokken.

Glennette Kell required emergency surgery after a fall in late August 2023 and was referred to a nursing home rehab center. By mid-September, UnitedHealth was denying her claims for nursing care, despite the fact that, according to the lawsuit, she still could not walk. Kell had to pay $10,000 of her own money to cover the rest of her bills. Darlene Buckner suffered a heart attack in October 2023 and underwent triple-bypass surgery. She was readmitted to the hospital in late November because she was experiencing complications, but less than two weeks later, UnitedHealth allegedly denied any further treatment at the hospital because it was deemed medically unnecessary. At the time, according to the class-action lawsuit, Buckner was experiencing “an open infected wound near her heart from the open-heart surgery, she was on blood thinners, and was receiving antibiotics through an IV.” 

UnitedHealth, in its responses to the plaintiffs, has asserted that federal laws and regulations already require all claims decisions ultimately be made by a human doctor, not an AI program, and raised several other technical legal objections (such as saying the plaintiffs have not fully exhausted their administrative appeal options). The plaintiffs, however, allege that UnitedHealth employees “are instructed not to deviate” from the AI model’s projections, and then tell patients the AI-generated information is proprietary when they or their doctor try to inquire about why their claims are being denied. 

And, to be clear, UnitedHealth is not the only insurer being sued for using AI programs to deny claims. Humana is facing its own lawsuit brought by other Medicare Advantage patients who allege the same nH Predict program also led to their claims being wrongfully denied. Joanne Barrows, then 86, fell in November 2021 and went to a rehab nursing facility after her hospital stay. And just two weeks later, Humana began denying her claims for further care, according to the complaint, leaving her and her doctor “bewildered” because she was under doctor’s orders not to put any weight on her leg.

And the third lawsuit, filed against Cigna, alleges the company used a different AI algorithm that allowed its doctors to deny claims in huge batches — hundreds or thousands at a time — if they did not meet certain predetermined criteria. One patient, Suzanne Kisting-Leung, had claims denied for two ultrasounds done by her doctor to check for ovarian cancer, putting her on the hook for more than $700 in medical bills because, the lawsuit alleges, Cigna’s AI program determined that the scans were not medically necessary. The plaintiffs say that their claims were instantly rejected, without their medical files ever being opened. As for the law that human doctors have to make the final call? According to the lawsuit, the algorithm “enable[s] its doctors to automatically deny payments in batches of hundreds or thousands at a time for treatments that do not match certain pre-set criteria, thereby evading the legally required individual physician review process.”

These people, many of them elderly, had no idea AI was being used to make decisions about their coverage — until it was too late.

“From their perspective, you might as well have been speaking Esperanto. They just had no idea what it was that was being said to them when they were being told. They usually heard about it for the first time when they were being told that they needed to leave,” Glenn Danas, one of the attorneys in the class-action litigation against United, Humana, and Cigna, told me. “They just had no idea what was going on or why this was being done to them.”

AI is now making decisions for traditional Medicare claims too

It’s not just private insurers either. The Trump administration is now pushing traditional Medicare and Medicaid to use AI to adjudicate insurance claims too. In June 2025, nearly two years after those lawsuits had been filed against Cigna, Humana, and UnitedHealth, the US Department of Health and Human Services announced it would deploy a new program called the WISeR (Wasteful and Inappropriate Service Reduction) model to make prior authorization decisions for about a dozen procedures for the people on traditional Medicare in six states — “leveraging enhanced technologies, such as Artificial Intelligence (AI) and Machine Learning (ML), along with human clinical review to ensure timely and appropriate Medicare payment for select items and services.” It would be a pilot program, before federal officials decide whether to take the program nationwide.

It was a quiet, massive change for traditional Medicare, which covers about half of the 66 million Americans on the program. (Medicare Advantage plans, in which private companies administer Medicare benefits under slightly different parameters and which were the subject of the earlier AI-related class-action litigation, cover the rest.) People on traditional Medicare had historically not been required to get prior authorization for most services; now they would be, and a novel AI program would play a significant role in which claims get approved and which ones don’t.

From the start, physician and patient advocates warned about the potentially adverse consequences of the WISeR program; the Center for Medicare Advocacy said the program would likely “complicate, delay and even outright deny necessary care.” According to media reports and internal government documents, that’s exactly what has happened since the model went live in January. 

The Washington Post first reported in March that physicians and their patients were experiencing approval delays or denials for medical services that should have been covered. One glitch led to a denial of payment for services that had been approved. Doctors noticed that certain procedures seemed to give the AI model problems — such as two competing treatments for incontinence — and were weighing whether or not to stop offering one of those services altogether. The Post reported on one Arizona patient who had been receiving steroid injections for chronic back pain before being denied multiple times after the AI program’s launch. The pain became so severe, he said, that he could not exercise at all.

Then this month, the Electronic Frontier Foundation released a trove of documents it received in response to a Freedom of Information Act lawsuit related to the WISeR model. The documents revealed aggressive denials — one company using the AI model had actually denied more prior authorization requests than it had approved — and there were delays even when the care was ultimately approved. One review took 83 days when the model was supposed to make a decision within 72 hours.

The direct feedback from doctors in the federal documents obtained by EFF was devastating.

“We have patients calling our offices crying in pain because their procedures are being delayed while awaiting approvals or guidance tied to this model,” one doctor wrote. “A 3-4 day delay for necessary pain procedures is already difficult for vulnerable patients, but when providers cannot obtain answers for weeks, the situation becomes unacceptable.”

According to the federal documents, the Trump administration was already discussing ways to expand the program. Despite the outcry from doctors and patients, and growing concern among their representatives in Congress, it doesn’t look like WISeR is going away.

How can we make sure AI doesn’t take over insurance claims?

AI is becoming a part of healthcare — it is already happening. And many of those uses could be truly beneficial. Anthropic recently announced an initiative to apply the theoretical findings of its AI models to real-life lab work that could lead to new drugs being developed in the future. In terms of scientific discovery and the potential to translate it into new treatments, the excitement among clinicians about its potential is palpable.

And nobody denies that health insurance administration is chaotic and burdensome and would benefit from being made more efficient and streamlined — tasks which AI is at least superficially well-suited for.

But as the past few years have already shown, rushed and sloppy deployment of AI to take responsibility for approving or denying payment for people’s medical care has created serious problems. The issue is that this technology is being adopted more quickly than any regulator can oversee it. We don’t really know how health insurers are using AI tools. We don’t have a good framework for what keeping a “human in the loop” of insurance decisions — which is what people want — would really mean, Schwarcz said. 

“I think everyone agrees at a high level of generality, ‘Yes, AI should be used to enhance human review and not to replace it,’ but no one actually knows how to write rules to enforce it,” he told me. “I don’t think that there’s anything I’ve seen that has effectively defined what it means for a human to be meaningfully in the loop in the sense of retaining independent judgment as opposed to just reviewing and deferring. It is incredibly hard to specify in rules or regulations a standard that is verifiable and enforceable. That is still, as far as I know, an unsolved problem.”

Some states have started adopting rules requiring transparency for AI use in insurance claims, and lawmakers in Congress have put forward proposals requiring human involvement, including the bipartisan Protecting Patients from Automated Denials Act. But these technologies are so ubiquitous and so flexible that rigorously regulating their use is going to be difficult. Meanwhile, the profit incentive for companies to adopt these tools and deploy them widely is extremely strong. We don’t even know how widespread their use really is.

Danas said he and his clients hoped their litigation would help with “putting the brakes” on AI’s use in insurance. Until we figure out what to do from a policy perspective, what can each of us do in the meantime? Hamer told me that the old advice is still the best advice: If your claim is denied, appeal it. An enormous number of denied claims end up getting overturned on appeal. 

“You do not necessarily have to accept that the first answer from your insurance company is the final determination,” she said.

If there’s one thing that Americans can unite behind, it’s anxieties over artificial intelligence.

According to a recent NBC News/Decision Desk poll, 70 percent of adults say they are personally more worried than excited about the technology; it’s basically underwater in every area of society with one notable exception: its use in scientific research and medicine.

According to Dr. Dhruv Khullar, a physician at Weill Cornell Medicine and writer at the New Yorker, that optimism is well-founded.

Khullar uses AI tools in his own practice as a physician and writes about their advancements in the medical field. He says that “right now, at least, there’s a generally optimistic view of how AI will change healthcare going forward.”

AI in medicine is nothing new. In 2024, the Nobel Prize in Chemistry was given to researchers who developed an AI model that enabled breakthroughs in predicting protein’s complex structures. As it becomes more ubiquitous, it’s also emerging as a resource for doctors to get second opinions and as a tool to take notes on patient-doctor interactions.

On top of that, Khullar says, it might help unlock new medicines to treat diseases. 

Khullar joined Today, Explained co-host Sean Rameswaram to discuss the ways that AI is transforming drug discovery and research, as well as what it still can’t do. They also discuss how to think about AI’s potential in healthcare at the same time as top AI researchers are increasingly warning about the technology’s risk.

Below is an excerpt of the conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify.

You’ve been thinking about lifesaving AI innovation. Has AI already started saving lives?

I think it has. This is the fastest I’ve ever seen healthcare take up a new technology. Part of that might be that the healthcare system is so messed up that there’s an appetite for some type of change. It’s unaffordable, it’s inaccessible, it’s inconvenient, the quality is uneven. 

There’s at least a hope that AI is going to help with all of these things, and I think it’s already starting to make its way into the healthcare system. Now it’s our responsibility to figure out how to make the most of it without also promulgating some of the downsides.

For those who are unaware, tell us how it’s working its way into the system.

It’s helpful to categorize it into a few buckets, at least for healthcare. 

The most rapid uptake, and I think the place that it’s already starting to make a difference, is on the administrative side. Many people who have gone to a doctor recently might have noticed that there’s an AI scribe that’s taking notes now. And people seem to really like it, at least so far. Doctors are able to look their patients in the eye in the way that they weren’t able to when they were poring over their computers and just typing what the patient was telling them. 

The second big area is patient navigation. When is your next scan? When’s your next appointment? Do I need to take this medication on an empty stomach or not? There’s a huge opportunity for people, let’s say, who are diagnosed with a serious illness like cancer or heart failure to navigate the system more seamlessly. 

The third big area that I’m really excited about is drug discovery. AI is making a huge dent in the early parts of drug discovery. 

The last thing that I’m excited about and that I use every day when I’m in the hospital is [AI] as a second opinion. Now instead of having to get a consult or turn to a textbook, AI can be a very, very helpful clinical decision support. It’s not to say that I never consult someone, of course, but that first pass — I’m not sure what’s going on. What are some recent trials that might influence my decision here? Is there something I’m missing? Is there a test that I should be ordering that I’m not ordering? — all those types of things, they start to help with.

The biggest chunk of this positive view of AI and medicine is drug discovery. Tell me more about how that’s going so far and how it might be going in a year, or five, or 10.

I don’t want to leave this conversation and say that we’ve solved the cure for cancer because of AI, but I do want people to know that at least for the early stages of drug discovery — figuring out the very basic steps of is this an interesting molecule? Does it have potential biological applications? — AI is already very helpful for that. 

They’re trying to take, basically, what is an infinite number of drug-like molecules that are theoretically possible to be drugs and they’re trying to match that to some disease that’s going on inside a human. And human biology is incredibly complex as well. There’s tens of thousands of genes, hundreds of thousands of proteins, trillions of cells, and you have to match a potential molecule to a potential target within a human. 

In the past, a researcher might take years to study a disease or a biological pathway and figure out, okay, this protein seems to be involved. And actually, it might not be involved, it might be involved, it might be disrupted, but not be the causative agent. There’s a lot of uncertainty there, so now AI can take enormous amounts of data, and it can rank various potential targets that seem to be the most likely to be causing a particular disease and give you that short short list of potential targets that may have taken months or years in the past.

So now you know what to attack, but you have to figure out what to attack it with. So you need a molecule, you need to generate a molecule that’s going to fit into that target or disrupt that target in some way. 

Now again, AI can scour vast chemical spaces and give you a list of the things that seem to be most likely to be able to disrupt that target. And then it can actually help you generate that molecule in some cases. 

So now we have the target. Now we have something that we’re going to attack the target with. And then you have to do something called lead optimization, and that’s the third thing. Lead optimization means you have this lead and you have to optimize it.

“I would make the counterargument that healthcare is ripe for disruption and you don’t even need the frontier frontier models to make it a lot better with AI.”

Now we have to figure out, of all the molecules that seem to be good at potentially affecting this target, which ones are able to be absorbed in the body, which ones are going to get to the right tissue. AI models can predict the most likely molecule to give you that Goldilocks set of properties that’s needed for a safe and effective drug.

As we all know from anyone who’s asked AI to write an email for them or Googled a question, these tools have a certain degree of certitude, even though these tools can just be flat out wrong about stuff. Are you guys worried about that in the field?

Absolutely. And so this is why I think the narrative around AI just replacing scientists or doctors or other workers is incorrect. Because you still need a lot of judgment. 

You need to be able to adjudicate the output of these models to figure out what is most promising and what is potentially dangerous. That is going to require wet labs and scientists and reasoning based on prior experience and understanding the context. There’s all sorts of issues around, can you manufacture some of the drugs that are being proposed by these models? So just because it’s set, it dreams something up doesn’t mean you can actually make that thing in the real world.

Some of the drugs that it proposes might actually be toxic in certain ways that it didn’t predict. And of course, then you have to take this thing into clinical trials. You have to recruit l people who are willing to put this medication in their bodies. You have to find out who might benefit, you have to put it through the regulatory process. 

That’s why I said that the first part of drug discovery, where you’re trying to figure out how do we get the right drug candidates, what seems most exciting to test further, that’s going to be really accelerated. That whole second half where we actually have to figure out if it works in human biology, that is still going to be in some respects an analog process.

It doesn’t sound like you’re terribly scared that we are like ceding control of our hospitals, of our research facilities, to AI. This is very much in the sort of assistant bucket?

For now it is. And I think one of the challenges is to maintain our agency as these models become more and more sophisticated. As you lean more on these machines, if you’re a doctor, inevitably some of the skills, the critical thinking, the reasoning that we put into coming up with the diagnosis that we honed over the course of years, that can start to atrophy. 

These are the types of things that we still need to sort through as we’re implementing more and more AI into the healthcare system.

For the people who just say, shut it all down, this is too dangerous, the risk is far too great, we don’t actually need this, this isn’t doing anything for society, would you make a counterargument?

I would make the counterargument that healthcare is ripe for disruption and you don’t even need the frontier frontier models to make it a lot better with AI.

Even if we wanted to “slow the pace of the frontier,” fine. But there are models from a year or two years ago that could be very helpful in the healthcare setting. When we’re talking about slowing the pace of the frontier and the most sophisticated and potentially dangerous models, fine. 

But if we’re talking about shutting down AI and not using it in biotechnology or not using it in scientific research or not using it in clinical care delivery, that’s where I would push back pretty hard.

Who gets to tell the military “no”?

That question has become even more important after the Pentagon pushed AI companies to loosen limits on how their technology may be used in war — including how much human control must remain over autonomous weapons. When Anthropic said no, it lost a major government deal. OpenAI stepped in, embracing the Pentagon’s more flexible standard instead. Now, thanks to new reporting from The Intercept, we’re getting a better look at what those negotiations actually entailed.

The OpenAI-Pentagon agreement included specific language on the type of AI models the DOD would receive: “OpenAI models that are designed for national security use cases and have minimal refusal rates.” That phrase — “minimal refusal rates” — means that the Pentagon is seeking models that rarely, if ever, tell it no. (According to The Intercept, a Justice Department lawyer representing the Pentagon confirmed this language, before backtracking only a few hours later.)

On the surface, that provision might seem reasonable. The military is a strictly hierarchical institution. When your senior gives you an order, you do it. Leaders generally have more battlefield wisdom and experience than their subordinates, so the rank and file can usually assume that their commanders know what they’re doing and follow along. Plus, in the high-stress, time-sensitive environments of most military operations, there is little room for discussion, explanation, or alternative proposals. 

So, if soldiers can’t refuse an order, why would a machine be allowed to? Who is a robot to tell its commander no?

There’s just one issue. Soldiers can refuse an order — when the order is illegal. In fact, they are legally required to. But by introducing technologies with a limited ability to challenge commands, the military may be making it harder to stop war crimes before they are committed.  

Humans have a duty to disobey

As outlined in US domestic, military, and international law, soldiers are only obligated to follow lawful orders, and they have a duty to refuse unlawful ones. 

The Department of Defense’s Law of War Manual is unambiguous: “Each member of the armed services has a duty to: (1) comply with the law of war in good faith; and (2) refuse to comply with clearly illegal orders to commit violations of the law of war.” Obeying an illegal order — as in a directive that violates the Constitution, US laws, international law, or directs one to commit a crime — can expose a service member, as well as their commander, to criminal liability. 

This “duty to disobey” has foundations in early British and American common law, but it achieved international recognition after the Nuremberg Trials against Nazi soldiers after WWII. 

In those trials, many Nazi soldiers argued they were not liable for crimes they committed during the Holocaust because they were simply following their superiors’ orders. Rudolf Hoess, the commander at the largest Nazi concentration camp, Auschwitz, himself explained: “In Germany it was understood that if something went wrong, then the man who gave the orders was responsible. So I didn’t think that I would ever have to answer for it myself.”

These defenses were unequivocally rejected by the court.

The tribunal held that obedience is not without limits, especially when a wartime act would be clearly illegal to the subordinate or any person of “ordinary sense and understanding.” If “I was just following orders” was an absolute defense, everyone involved in a crime would escape accountability, with the exception of the single highest-ranking officer. Instead, the court determined that individual moral and legal responsibility supersedes national or military chains of command for illegal acts.

Obedience is not without limits, especially when a wartime act would be clearly illegal to the subordinate or any person of “ordinary sense and understanding.”

But, when it comes to agentic AI systems or AI-assisted weapons, increasingly being used in place of human soldiers, does the same logic apply? And, perhaps more importantly, should it?

…But do machines?

When you use an AI chatbot, it does not simply reject queries it may not “agree” with. It does not say no to requests it doesn’t “like.” Instead, it follows a set of established protocols, much as a soldier should follow the law of war: It refuses requests that violate its terms of service. 

The military is using AI for far more consequential applications than a layperson using ChatGPT to, say, build a travel itinerary. So, what kind of world is created by the Pentagon’s request for AI that “minimally refuses” its directives, many of which may bear grave ethical, moral, or legal implications?

Rebecca Crootof, an expert on technology law and professor at the University of Richmond School of Law, told Vox that while the language could easily be read as increasing the likelihood of compliance with unlawful orders, or at least reducing the friction around them, “Minimal refusal doesn’t mean no refusal.”

“I both want to say it’s not definitely going to mean compliance with unlawful orders,” Crootof said, “but also acknowledge that identifying and evaluating what constitutes an unlawful order in the moment can be incredibly difficult.”

Even for human soldiers, disobeying illegal orders is neither simple nor easy. To the contrary, it doesn’t even happen that frequently. In fact, some legal scholars have argued that giving illegal directives in and of itself is an act of abuse against subordinates, who are not thoroughly trained in legal nuances, are conditioned to obey, and face prosecution if they make the wrong choice. 

Still, the mere fact that soldiers can disobey is an important constraint on commanders who might let battlefield conditions, or even commands coming from their own superiors, erode their ethics and commitment to the law.

Still, the mere fact that soldiers can disobey is an important constraint on commanders who might let battlefield conditions, or even commands coming from their own superiors, erode their ethics and commitment to the law.

Those who are optimistic about AI often argue that machines could be better at interpreting and applying the law than human soldiers. Machines can be trained on the law itself — drawing on a repository of legal cases, rulings, and battlefield data — which might make them more “knowledgeable” than an average soldier. And machines do not face the same pressures as soldiers facing prosecution for making mistakes, since they can’t exactly be held accountable; a problem to unpack another time. That, in turn, might make it easier for them to refuse illegal orders in the proper conditions. 

So, is the answer as simple as letting the machines decide? Not exactly. “This has been a desire and a drive by technologists for ages,” Crootof said. “What if we just put the law in the system and have it only do things that are lawful? Isn’t that going to solve things?” 

Interpreting and applying the law, though, is not a simple legal-illegal binary. “It’s very context-specific,” Crootof told me, “and honestly, that’s the kind of thing humans generally find difficult and AI decision-making systems are even worse at.” Often, determining the legality of an act comes down to subtle nuances you can’t program for: You’re allowed to target a combatant; you’re not allowed to target a wounded or surrendering combatant. You’re not allowed to target a civilian; you are allowed to target a civilian directly participating in hostilities. The difference between a legal target and an illegal one may come down to body language, clothing choices, or even battlefield intuition. 

As Crootof explained, “These are not the kinds of contextual analyses AI is good for.”

So what’s the solution?

Where human soldiers’ power to disobey would traditionally serve as an important check on the power of military commanders, if an imperfect one, replacing soldiers with machines that don’t have such safeguards removes one last layer of judgment over the use of force. And yet, machines themselves are not always capable of making the judgment calls that go into interpreting the law, so it may not be appropriate to let AI have the final say either.

So what’s the solution? Given the way AI technology has already been thoroughly integrated into the military, Crootof told me it may be somewhere in the middle. 

Rather than programming AI to refuse commands it interprets as illegal, Crootof said, “what it could be designed to do is flag those indeterminate zones, those gray-zone analyses, for human review or for a higher authority.” When a system picks up conflicting signals — an enemy soldier who looks like he may be surrendering, for example — it could prompt the commander to confirm their selection or remind them of applicable law. 

In this way, a check on commanders’ battlefield authority could be preserved without fully ceding judgment over the use of force to the machine. 

We don’t know yet whether OpenAI has equipped its “minimal refusal” models with such capabilities. But as those technologies become increasingly central to military decision-making, the public deserves answers sooner rather than later. 

Today's links

• How an AI moratorium can save AI bosses: If you can't impose switching costs, just eliminate the competition.
• Hey look at this: Delights to delectate.
• Object permanence: Flash Worms; This Film is Not Yet Rated; Libdem copyright sabotage; Religion worth more than Big Tech; Geographic tubemap; Selective censorship resistance.
• Upcoming appearances: Budapest, Edmonton, Boston, South Bend, Hudson, Calgary, Winnipeg, Vancouver, Victoria, Ottawa.
• Recent appearances: Where I've been.
• Latest books: You keep readin' em, I'll keep writin' 'em.
• Upcoming books: Like I said, I'll keep writin' 'em.
• Colophon: All the rest.

---

How an AI moratorium can save AI bosses (permalink)

There's lots of reasons to believe the "hyperscaler" model of AI can never be profitable, and not just because of its gigantic expenditures and negative unit economics (the companies lose money with every new customer and every new use, and they lose more money with each generation of their products):

https://pluralistic.net/2025/09/27/econopocalypse/#subprime-intelligence

The industry strenuously denies this, of course. They insist that they are only days away from turning their balance sheets right side up. All they have to do is fix those unit economics, then they can make back the cost of producing their models by selling access to them. The problem is that the evidence for those improving unit economics is weak, while the evidence that they're faking their finances is very strong:

https://www.wheresyoured.at/exclusive-openai-financials/

Same goes for the claims that these companies are already profitable. Dig into those claims and you'll learn they depend on a new, special meaning of "profitable" that does not match the generally accepted accounting procedures (GAAP) definition, which is to say, these companies are claiming that they are so cool that their profitability can only be measured using a novel, secret form of mathematics:

https://futurism.com/future-society/anthropic-claude-profit-ai-safety-development-finances

This is the same wheeze that Softbank tried with Wework. Speaking in my capacity as an author of internationally bestselling technothrillers about accounting fraud, I can tell you that it was accounting fraud then, and it's accounting fraud now:

https://www.ndtv.com/world-news/how-wework-went-from-being-47-billion-start-up-to-bankrupt-in-3-years-4552627

But let's give the AI bosses a momentary benefit of the doubt and stipulate that they are on the verge of acquiring positive unit-economics, which will let them start to pay off the massive expenditures they incurred by training their models and enter their long-anticipated profitability phase, when the money-furnaces they've been running for years turn into money printers, to the delight of the investors who've supplied the vast bales of $100 bills the companies have been shoveling into their models' coalboxes for years now.

Basically, they're saying, "Sure, it cost us a lot to get these rails laid, but now that the railroad is complete we can start running cars over them and make a profit." Unfortunately (for bosses and investors), this proposition is every bit as dubious as their claims to improving unit economics.

To understand why, just look at what happened the last time Anthropic shipped a major Claude update. Virtually overnight, all of OpenAI's best customers stopped paying for ChatGPT and started paying for Claude. That's because chatbots have very low switching costs: going from one chatbot to another costs almost nothing:

https://www.businessinsider.com/why-ai-startup-founder-switched-chatgpt-to-claude-2026-3

Everyone using AI knows this to be true. When I walked the floor at CES last year, I asked every AI-powered gadget maker, "What will you do if your chatbot provider jacks up their prices?" and to a one, they said, "No problem, we've designed this thing so that we can switch chatbots with the click of a mouse":

https://www.youtube.com/watch?v=WfhELBX8Jbs

That means that you can't just "build the railroad and run the cars over it." The minute you finish your railroad, your rivals will announce that they've got a new, adjacent railroad that's even faster than yours, and you will have to get to work laying another set of tracks to support even faster trains.

This is a disaster all around: the AI companies are locked in a Red Queen's Race, a fatal beggar-thy-neighbor doom-loop. The only way they could escape that trap is by signing a nonaggression pact amongst themselves promising not to compete anymore. But there's two giant problems with this: first, it is incredibly, fantastically illegal under antitrust law, because it represents a conspiracy among the dominant players to cease to compete with one another, and; second, it leaves the field open for the further development of Chinese "open weight" models that customers can run on their own modest, low-powered computers, which are presently lagging the US "frontier models" by a mere four months:

https://arstechnica.com/ai/2026/09/exclusive-open-chinese-models-close-gap-with-silicon-valleys-frontier-ai-models/

Even if you don't trust Chinese models, you can extract their training through a process called distillation and transfer them to models you do trust:

https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks

But what if there was a way for the AI companies to get government permission to violate antitrust law and cease to compete with one another, and secure a ban on the use of Chinese open weight models? Turns out, there is a way to call time on the Red Queen's Race: merely insist that you are on the verge of teaching so many words to the word-guessing program that it will wake up and devour us all, and call for a ban on "superintelligence":

https://www.foxbusiness.com/technology/microsoft-ceo-says-superintelligence-must-remain-under-human-control

Once the government stipulates that "superintelligence risk" is an existential crisis, it must grant the hyperscalers a consent decree absolving them from any violations of antitrust law stemming from a conspiracy to halt direct competition with one another:

https://stephaniekelton.substack.com/p/brer-rabbit-and-ai-extinction

Freaking out about "superintelligence" is a canonical example of "criti-hype," where critics repeat boosters' claims but append, "(and that's bad)" to them:

https://peoples-things.ghost.io/youre-doing-it-wrong-notes-on-criticism-and-technology-hype/

Remember, the tech giants want to stop competing. Mark Zuckerberg and Sudar Pichai colluded to rig the ad-market with a secret program called "Jedi Blue":

https://en.wikipedia.org/wiki/Jedi_Blue

Every year, Google sends Apple a bribe of more than $20b in exchange for Apple not entering the search market:

https://appleworld.today/2025/09/google-wont-be-barred-from-entering-into-search-agreements-with-companies-like-apple/

And the biggest tech companies in the world had a secret "no poach" agreement where they illegally promised not to try to hire one another's top engineers by offering them raises:

https://chicagounbound.uchicago.edu/law_and_economics/1033/

The only thing Peter Thiel hates more than the Antichrist (spoiler, he's just talking about Greta Thunberg) is "wasteful competition":

https://www.youtube.com/shorts/WmRC_NQh6aQ

When an industry that is eating itself alive through "hyperscaling" demands that the government bless a conspiracy to halt competition and ban open source alternatives, you should be suspicious. When that industry is pursuing a venture that has lost more money than any other venture in human history, you should be very suspicious, especially when its "rogue AI hacking" story turns out to be a story about how a hacking tool did exactly what it was designed to do:

https://pluralistic.net/2026/09/12/god-in-the-box/#llms-are-fake

Peter Thiel is right: AI is full of wasteful competition, but not because competition is a waste – rather, it's because the companies are competing to convince people to use their expensive products for the cheapest applications.

Elon Musk's SpaceX IPO depended on him losing billions of dollars by letting the world's stupidest chuds produce mountains of child porn and images of Sonic the Hedgehog with giant boobs. That is indeed wasteful (and reprehensible).

That doesn't mean we should allow the AI companies to get the government to bless their conspiracy in restraint of trade; rather, it militates for having the government investigate them for securities fraud, trafficking in child sex abuse material, election finance violations, and a long list of other crimes and misdemeanors.

---

Hey look at this (permalink)

• Henry Farrell and Abe Newman on Weaponized Interdependence https://paulkrugman.substack.com/p/henry-farrell-and-abe-newman-on-weaponized

• The Enshittification Resistant Software Project https://er-certification.statichost.page/index.html

• The Life of Death and the Ensh*ttificator https://www.youtube.com/watch?v=d6SPV4GZp-U

• Marginalia Search https://marginalia-search.com/

• Why the Postpandemic Tech Bust Sent Billionaires to Trump https://www.wired.com/story/against-tech-oligarchy-book-excerpt-trump-billionaries/

---

Object permanence (permalink)

#25yrago Flash Worms: Thirty Seconds to Infect the Internet https://web.archive.org/web/20011024012950/http://www.silicondefense.com/flash/

#20yrsago This Film is Not Rated – must-see doc about MPAA ratings https://memex.craphound.com/2006/09/16/this-film-is-not-rated-must-see-doc-about-mpaa-ratings/

#15yrsago Chinese netizens angered by “princelings” — spoiled children of the rich and powerful https://edition.cnn.com/2011/09/16/world/asia/china-elite-children/index.html?iref=allsearch

#15yrsago LibDems get to vote on copyright reform, but who inserted the clause saying downloading should be a criminal act? https://www.theguardian.com/technology/2011/sep/16/libdems-vote-copyright-reform

#15yrsago Insurer: music-festival tragedy caused by illegal downloading https://twitpic.com/6l5ap2

#10yrsago US religion is worth $1.2T/year, more than America’s 10 biggest tech companies, combined https://web.archive.org/web/20161019095803/http://www.religjournal.com/pdf/ijrr12003.pdf

#10yrsago Geographically representative map of the London Underground https://web.archive.org/web/20240813111321/https://www.citymonitor.ai/analysis/map-londons-tube-shows-disused-stations-track-layout-and-more-2429/

#10yrsago Republican election officials block restrictions on foreign spending in US elections https://web.archive.org/web/20160916181403/https://theintercept.com/2016/09/16/fec-republicans-kill-attempt-to-block-foreign-money-in-u-s-elections/

#10yrsago Tommy Chong asks Obama to pardon him for his bullshit drug paraphernalia bust https://web.archive.org/web/20210720131834/https://www.hollywoodreporter.com/lifestyle/lifestyle-news/tommy-chong-seeks-obamas-pardon-928962/

#10yrsago Week two for the largest prison strike in US history https://web.archive.org/web/20160916143157/https://theintercept.com/2016/09/16/the-largest-prison-strike-in-u-s-history-enters-its-second-week/

#5yrsago Criminal entrepreneurship in Mexico’s high-tech drug cartels https://web.archive.org/web/20160917133449/https://motherboard.vice.com/read/how-drug-cartels-operate-like-silicon-valley-startups

#1yrago No such thing as selective censorship resistance https://pluralistic.net/2025/09/16/too-many-throats-to-choke/#pluralism-is-resiliency

---

Upcoming appearances (permalink)

• Budapest: Brain Bar, Sep 17
  https://brainbar.com/munkatars/cory-doctorow

• Edmonton: Elbows Up (Edmonton Public Library), Sep 28
  https://www.epl.ca/blogs/post/elbows-up-with-cory-doctorow/

• Boston: The Post-American Internet: Possibilities for a new internet created by an American Hermit Kingdom (MIT Media Lab), Sep 30
  https://www.media.mit.edu/events/the-post-american-internet-possibilities-for-a-new-internet-created-by-an-american-hermit-kingdom/

• Boston: The Paradox of Enshittification and Reverse Centaurs (Harvard Berkman Klein), Sep 30
  https://cyber.harvard.edu/events/running-harder-falling-faster-paradox-enshittification-and-reverse-centaurs

• South Bend: An Evening With Cory Doctorow (Notre Dame), Oct 6
  https://franco.nd.edu/events/2026/10/06/an-evening-with-cory-doctorow/

• Hudson, OH: Hudson Library, Oct 7
  https://engagedpatrons.org/EventsExtended.cfm?SiteID=3850&EventID=596952&PK=

• Calgary: Wordfest, Oct 8
  https://wordfest.com/2026/show/wordfest-presents-cory-doctorow-2026/

• Winnipeg: McNally Robinson, Oct 9
  https://www.mcnallyrobinson.com/event-18991/An-Evening-with-Cory-Doctorow

• Vancouver: Read, Resist, Repair, Rejoice (Vancouver Writers Festival), Oct 19
  https://writersfest.bc.ca/festival-event-2026/01

• Victoria: Munro's Books, Oct 20
  https://www.munrobooks.com/events/6113620261020

• Vancouver: Life After AI (Vancouver Writers Festival), Oct 22
  https://writersfest.bc.ca/festival-event-2026/46

• Ottawa: Life After AI (Ottawa Writers Festival), Oct 24
  https://writersfestival.org/event/life-after-ai

• Vancouver: BC Policy Solutions Gala, Nov 12
  https://bcpolicy.ca/gala/

---

Recent appearances (permalink)

• Are 'AI Apocalypse' Warnings Just Marketing? (What's Left)
  https://www.youtube.com/watch?v=IXd9HwIE5bo

• The Real AI Threat Isn’t What You’ve Been Told (The Tea with Myriam François)
  https://www.youtube.com/watch?v=Vc8It00fRsA

• Fascists may come after the AI bubble bursts (You&AI)
  https://www.youtube.com/watch?v=J2WN64aQeYQ

• What Would a Normal Person Do (Trashfuture)
  https://www.patreon.com/trashfuture/posts/what-would-do-169247456

• Pod Save the UK
  https://audioboom.com/posts/8950533-radicalised-organised-and-thick-as-s-t-nish-has-had-it-with-far-right-protests-plus-why

---

Latest books (permalink)

• "The Reverse-Centaur's Guide to AI," a short book about being a better AI critic, Farrar, Straus and Giroux, June 2026
  https://us.macmillan.com/books/9780374621568/thereversecentaursguidetolifeafterai/

• "Canny Valley": A limited edition collection of the collages I create for Pluralistic, self-published, September 2025 https://pluralistic.net/2025/09/04/illustrious/#chairman-bruce

• "Enshittification: Why Everything Suddenly Got Worse and What to Do About It," Farrar, Straus, Giroux, October 7 2025
  https://us.macmillan.com/books/9780374619329/enshittification/

• "Picks and Shovels": a sequel to "Red Team Blues," about the heroic era of the PC, Tor Books (US), Head of Zeus (UK), February 2025 (https://us.macmillan.com/books/9781250865908/picksandshovels).

• "The Bezzle": a sequel to "Red Team Blues," about prison-tech and other grifts, Tor Books (US), Head of Zeus (UK), February 2024 (thebezzle.org).

• "The Lost Cause:" a solarpunk novel of hope in the climate emergency, Tor Books (US), Head of Zeus (UK), November 2023 (http://lost-cause.org).

• "The Internet Con": A nonfiction book about interoperability and Big Tech (Verso) September 2023 (http://seizethemeansofcomputation.org). Signed copies at Book Soup (https://www.booksoup.com/book/9781804291245).

• "Red Team Blues": "A grabby, compulsive thriller that will leave you knowing more about how the world works than you did before." Tor Books http://redteamblues.com.

• "Chokepoint Capitalism: How to Beat Big Tech, Tame Big Content, and Get Artists Paid, with Rebecca Giblin", on how to unrig the markets for creative labor, Beacon Press/Scribe 2022 https://chokepointcapitalism.com

---

Upcoming books (permalink)

• "The Post-American Internet," a geopolitical sequel of sorts to Enshittification, Farrar, Straus and Giroux, 2027

• "Unauthorized Bread": a middle-grades graphic novel adapted from my novella about refugees, toasters and DRM, FirstSecond, April 20, 2027

• "Enshittification, Why Everything Suddenly Got Worse and What to Do About It" (the graphic novel), Firstsecond, 2027

• "The Memex Method," Farrar, Straus, Giroux, 2027

---

Colophon (permalink)

Today's top sources:

Currently writing:

• “Once Is Enemy Action,” a science fiction novel about the origins of modern technofascism. Today's words: 555 (15487 total).

• "The Post-American Internet," a sequel to "Enshittification," about the better world the rest of us get to have now that Trump has torched America. Fourth draft completed. Submitted to editor.

• A Little Brother short story about DIY insulin PLANNING

---

This work – excluding any serialized fiction – is licensed under a Creative Commons Attribution 4.0 license. That means you can use it any way you like, including commercially, provided that you attribute it to me, Cory Doctorow, and include a link to pluralistic.net.

https://creativecommons.org/licenses/by/4.0/

Quotations and images are not included in this license; they are included either under a limitation or exception to copyright, or on the basis of a separate license. Please exercise caution.

---

How to get Pluralistic:

Blog (no ads, tracking, or data-collection):

Pluralistic.net

Newsletter (no ads, tracking, or data-collection):

https://pluralistic.net/plura-list

Mastodon (no ads, tracking, or data-collection):

https://mamot.fr/@pluralistic

Bluesky (no ads, possible tracking and data-collection):

https://bsky.app/profile/doctorow.pluralistic.net

Medium (no ads, paywalled):

https://doctorow.medium.com/

Tumblr (mass-scale, unrestricted, third-party surveillance and advertising):

https://mostlysignssomeportents.tumblr.com/tagged/pluralistic

"When life gives you SARS, you make sarsaparilla" -Joey "Accordion Guy" DeVilla

READ CAREFULLY: By reading this, you agree, on behalf of your employer, to release me from all obligations and waivers arising from any and all NON-NEGOTIATED agreements, licenses, terms-of-service, shrinkwrap, clickwrap, browsewrap, confidentiality, non-disclosure, non-compete and acceptable use policies ("BOGUS AGREEMENTS") that I have entered into with your employer, its partners, licensors, agents and assigns, in perpetuity, without prejudice to my ongoing rights and privileges. You further represent that you have the authority to release me from any BOGUS AGREEMENTS on behalf of your employer.

ISSN: 3066-764X

During a tense vote on a data center being built in her drought-stricken Arizona district last summer, Tucson City Councilmember Nikki Lee made an unlikely pitch: “We’re going to get a little out-of-this-world for a second, so just bear with me.” 

Like many of her constituents, Lee did not want Project Blue, a sprawling 290-acre data center, gobbling up an ounce of her local drinking water. “We need to put data centers where they belong,” she said, and “we don’t think data centers belong in the desert.”  

President Donald Trump’s recent bluster aside, it’s possible that the only thing as bipartisan as loving Dolly Parton is hating data centers, whose construction has become so controversial, so costly, and so backlogged here on Earth that companies like Google, Meta, and of course, SpaceX, are actively flirting with the idea of flinging them into orbit instead. Soon after the Tucson City Council rejected Project Blue’s attempt to connect to city utilities, Amazon Web Services, the secretive intended beneficiary, pulled out of the project, and another Jeff Bezos company, Blue Origin, filed a plan with regulators for tens of thousands of possible new data centers, this time, destined for orbit. 

Sure, launching a hyperscale AI data center into space is still unprecedented engineering-wise, carries an unfathomably large price tag, could engender its own severe environmental risks, and may or may not draw unwelcome attention from the hunters among the stars. But for AI companies, such trade-offs might still be preferable to the terrestrial roadblocks, among them an explosive populist backlash and a 12-year wait to connect to the power grid. 

Next Thursday, Google plans to launch a pilot satellite capable of processing basic AI queries into orbit, the most significant test yet in what’s become a burgeoning race to power AI from space. For now, the satellite won’t approach the levels of computing power at a hulking terrestrial data center like Project Blue. But it is a major step toward a future in which many or even most of our AI queries will be powered not from the outskirts of a city like Tucson, but from a blinking data center circling the skies.

SpaceX’s gargantuan initial public offering earlier this summer was predicated on the idea that it would launch a thus-far-unrealized fleet of orbital data centers to fuel much of the AI economy. The company — whose rockets Google will use to launch its pilot — claims that by the end of this decade, possibly as early as 2028, it will have successfully assembled an AI data center in orbit, with plans to eventually harvest a staggering 100 gigawatts of new solar energy for AI annually, equivalent to one-fifth of all US electricity consumption last year. (As always, that’s a very iffy promise.)

Still, as Google’s overtures clearly demonstrate, it’s no longer a question of if large-scale space-based data centers could be technically feasible, but rather when they’ll be ready to launch, and perhaps more importantly, whether the math of using them to power something like AI will make much financial sense anytime soon. As of now, building a one-gigawatt data center in space would cost about $51.5 billion, according to an analysis by aerospace engineer Andrew McCalip, compared with $16 billion for one on land.

“They’re not breaking any rules of physics. Yes, there are going to be engineering challenges, but they are all solvable with enough time and money,” said Jason Aspiotis, a leading space systems engineer who now leads an orbital data center startup. “The gray area is, well, what about the economics?” 

But even if this astronomically kooky plan does take off, it may not change much for those of us left on terra firma: Our energy bills will remain high, even if data centers aren’t there to drive them up. Our earthly infrastructure — affordable housing, public transit, and more — may be just as hard to build as ever. Harder even, maybe, given that crucial resources and untapped tax revenue will be literally rocketing up out of our reach.

No, if these tech giants succeed, it will not fix what’s wrong here on Earth, but merely prove once again their exemption to the gravitational forces holding the rest of us down.  

Space-based data centers, explained

My father spent most of his career working in the satellite industry, and would sometimes let me waltz inside the quaint little data center housed in his office, a room I remember as roughly the size of my school’s computer lab, hosting rows upon rows of servers that I couldn’t touch, churning with a cool, mechanical hum as they crunched zeroes and ones.

AI data centers work similarly to that server room but on an exponentially larger scale, requiring far more land and energy to run, and much more extravagant cooling systems. Sending his office’s data center into orbit would have been extraordinarily difficult, and getting one of the facilities that powers modern LLMs aloft would be an even larger, more ambitious undertaking. But the benefits — namely, “cheap space (no pun intended) and power,” as my dad put it to me recently — are even more enticing.

An orbital data center need not negotiate with an elected official like Lee over precious water rights to the Colorado River, though it would need to install bigger-than-football-field-sized heat radiators to keep itself cool. You can’t simply launch something that big into orbit, meaning a space-based hyperscale data center — the size of which would likely rival that of the International Space Station — would need to be assembled piece by piece, probably by robots. And while the sun would provide an endless supply of solar power to process your ChatGPT queries, it would also inflict incessant radiation on delicate circuitry. If something breaks, you can’t send a technician up to fix it. Many experts are also concerned that if an excess of space junk, which extraterrestrial data centers would inevitably produce, begins colliding like bumper cars in Earth’s low orbit, it could knock out the orbital infrastructure we already rely on, like GPS navigation and weather satellites. 

Even plastic decays in space, said Sven Bilén, a professor of aerospace engineering at Penn State, who, like Aspiotis, believes that early orbital data centers will probably be “processing data from space, like spatial imaging satellites or weather satellites, as opposed to, you know, cat videos” or AI-generated reality TV slop. 

But even if AI is not the lowest-hanging fruit for deploying compute into space, it is certainly the deepest-pocketed, meaning that many investors and companies like SpaceX and Google seem quite willing to shoulder the start-up costs anyway. And considering how difficult it’s gotten to build data centers on Earth, or at least in the United States, it’s indeed possible that space could become the cheaper, or at least most convenient option, more quickly than one might think. 

“In some form or another, in five years, we are going to have some orbital data centers in space for sure,” Krishna Muralidharan, who leads the Center for Semiconductor Manufacturing at the University of Arizona, told me, but it’s hard to say right now whether those consist of just a few expensive prototypes, or the beginning of a much bigger swing toward orbital compute. “Only time will tell whether you’re going to have a business case for it.”

Can we send our data center problem into space?

So, is it better to have a data center in space or in your backyard?

David Reid, a member of Virginia’s House of Delegates representing Loudoun County, home to more data centers than anywhere else on Earth, says the question has repeatedly come up at his town hall meetings. Reid is the rare politician — apart from the president, anyway — willing to defend terrestrial data centers, calling them a “money-printing machine” for local schools and infrastructure. Having spent decades working in the defense and technology sectors, he is doubtful that the math on orbital data centers will ever make sense.

“I believe this is, for lack of a better term, fictional nonsense,” he said, and is “creating a distraction from the fact that local officials here on Earth still need to make better zoning decisions.”

If most of the AI industry can eventually be powered by space, as SpaceX claims, it will require millions of sprawling new satellites, in an orbit currently dotted by fewer than 20,000 of them. Though “space is really big,” as Elon Musk has put it, one million satellites would still profoundly and, likely irrevocably, transform our night sky into one in which data centers outnumber visible stars.

“Will humanity notice if there are fewer stars in the sky? Well, not so much right now,” given light pollution in many cities, said John Barentine, an astronomer and environmental advocate for the night sky. “But what if they’re replaced by these artificial stars in the night sky? Is that the point at which people will say, ‘Wait a minute, we don’t want this. We want the benefits, but we don’t want this as a consequence.’”

This is, of course, the tension also at the heart of building data centers here on Earth. To some extent, they’re the most tangible representations of how little power we have to shape the course of a transformative new technology that few trust, and even fewer asked for. It’s one thing to object to a hulking data center, but it might be another altogether to see the sigils of our digital overlords illuminating the firmament. Would a place like Tucson, one of the most beautiful places to stargaze in the world, prefer a Project Blue to a starscape marred by blinking servers in the sky?

Update, September 24, 10:35 am ET: This post was originally published on September 3 and has been updated to include information about a pilot satellite capable of processing basic AI queries that Google is launching soon.

If you want to support knife steel research, come join us at Patreon.com/KnifeSteelNerds. You get articles and videos before everyone else!

If you’re looking for Knife Steel Nerds Christmas gifts, some good choices are the book Knife Engineering, the book The Story of Knife Steel, and the Buck knife model 501 “The Larrin.”

Video

There is a video version of the following information:

Why Analyze AI Capabilities?

I’ve had multiple people over the past couple years send me steel compositions that ChatGPT came up with. People want to know what I think, and if AI is going to be taking over everything now, including the design of new steels. I took a few of the steel compositions people sent me so I can analyze them to see if they would work. Can ChatGPT come up with new and fresh ideas to revolutionize knife steel? Let’s look at them and see:

Infinisteel

ChatGPT has some grandiose claims for this first steel:

Introducing the revolutionary super steel, InfiniSteel! This cutting-edge material boasts unparalleled toughness, edge retention, and corrosion resistance, surpassing any other steel on the market. By exploiting a unique interplay between various alloying elements, InfiniSteel exhibits remarkable properties that set it apart from conventional steel grades.

The InfiniSteel composition consists of:

Iron (Fe): 75%
Carbon (C): 1.2%
Chromium (Cr): 20%
Vanadium (V): 2.5%
Molybdenum (Mo): 0.8%
Tungsten (W): 0.5%

The biggest issue with this steel is that it won’t harden. When heat treating knife steel the main step is “austenitizing,” the high temperature step before the steel is quenched (rapidly cooled). You can read more about austenitizing in this article. It is called the austenitizing temperature because the steel transforms from the low temperature ferrite phase to the high temperature austenite phase. Ferrite is the normal “phase” of iron at room temperature which has a certain set of properties, such as being magnetic and soft. When you heat up iron to high temperature the atoms rearrange themselves into a different structure called austenite, which has different properties, one of them being that it is not magnetic. When you quench austenite it transforms to the hard steel phase martensite. If you don’t transform to austenite at your heat treatment temperature you will still have soft ferrite after heat treating and thus it will not make a good knife edge.

Some elements are “ferrite stabilizers” and some are “austenite stabilizers.” This steel has a relatively high content of ferrite stabilizers including 20% Cr, 2.5% V, 0.8% Mo, and 0.5% W. This would be ok if there were enough austenite stabilizers, but the only austenite stabilizer in the steel is carbon, and 1.2% is not enough for all of those ferrite stabilizers. ChatGPT is just adding elements in because when it reads random articles online that say that each element does some beneficial thing. It doesn’t know anything about balancing different elements together and it has no idea that it needs to have enough carbon or other austenite stabilizers to go with all of those ferrite stabilizers.

Here’s a detailed explanation of the role each component plays in the alloy and how they contribute to the exceptional performance of InfiniSteel:

Iron (Fe): As the primary element in steel, iron provides the backbone of the material’s structure. It imparts strength and toughness to the alloy, ensuring InfiniSteel can withstand substantial loads and resist deformation under various conditions.

Carbon (C): The addition of 1.2% carbon significantly improves the strength and hardness of the alloy. This results in superior edge retention, allowing InfiniSteel to maintain a sharp edge even after extensive use. Additionally, the high carbon content enables the formation of carbides, which further enhance wear resistance.

Chromium (Cr): Chromium contributes to InfiniSteel’s exceptional corrosion resistance. At 20%, it forms a stable, adherent, and self-healing passive film on the steel’s surface, effectively protecting it from oxidizing agents. This film also enhances the steel’s resistance to various corrosive environments, such as those with high humidity or in contact with aggressive chemicals.

Vanadium (V): Vanadium’s primary function is to refine the steel’s grain structure, leading to enhanced toughness and resistance to impact. At 2.5%, it forms hard vanadium carbides that strengthen the material and improve its edge retention properties. Additionally, vanadium aids in stabilizing the passive film, further enhancing the steel’s corrosion resistance.

Molybdenum (Mo): Molybdenum strengthens the steel matrix and improves its resistance to high-temperature creep and stress rupture. At 0.8%, it also contributes to the formation of a more stable passive film, increasing the corrosion resistance of InfiniSteel.

Tungsten (W): The addition of 0.5% tungsten enhances the high-temperature performance and hardness of InfiniSteel. It forms carbides that provide excellent wear resistance, contributing to the alloy’s outstanding edge retention properties.

Through this unique interplay of alloying elements, InfiniSteel achieves an unprecedented level of performance in terms of toughness, edge retention, and corrosion resistance. The combination of carefully selected constituents enables the alloy to outshine any other steel on the market, making it the ideal choice for a wide range of demanding applications, such as aerospace, automotive, marine, and cutting tools industries.

The explanations of what the different elements are doing are also very dumb and obviously based on random articles from non-experts online. For example, under iron it says that the iron “imparts strength and toughness to the alloy.” This is a very odd thing to say about the steel since steel is made up of iron. Iron is pretty soft before we add other elements to it, especially carbon. So why would you describe iron as imparting “strength”? Sounds like something a dumb AI would write.

It also says that “Vanadium’s primary function is to refine the steel’s grain structure, leading to enhanced toughness and resistance to impact.” Small amounts of vanadium, like 0.1-0.2%, are added to prevent grain growth. In knife steels when you have 0.3% or more added, that is for wear resistance. In that case it is adding wear resistance and reducing toughness. So the claim by the AI that the vanadium is added for enhanced toughness is nonsense.

The AI also says that molybdenum was added for “its resistance to high-temperature creep and stress rupture.” High temperature creep is when a material is held under stress at relatively high temperature for long periods of time. The material slowly deforms, which is called creep. Resisting creep is totally unnecessary in a knife, and the AI is just pulling things from various places to support its choices in a seemingly random way.

No manganese or silicon was added to the steel. These are not always shown on datasheets but are present in all tool steels that I am aware of. I would say it just didn’t mention them, but it also gave the percentage of iron (75%) which leaves no more room for Mn and Si.

User
Please provide a detailed analysis of Infinisteel vs Magnacut?

ChatGPT
InfiniSteel and MagnaCut are both high-performance steels designed for demanding applications. Here’s a detailed analysis comparing their properties and performance:

Composition:

MagnaCut:
Carbon (C): 1.15%
Chromium (Cr): 17.0%
Manganese (Mn): 0.6%
Nickel (Ni): 0.3%
Nitrogen (N): 0.18%
Vanadium (V): 4.0%
Molybdenum (Mo): 2.0%

Comparison:

Toughness: Both InfiniSteel and MagnaCut boast high levels of toughness due to their vanadium content, which refines the grain structure of the steel. InfiniSteel contains 2.5% vanadium, while MagnaCut has a higher vanadium content at 4.0%. The higher vanadium content in MagnaCut might result in slightly better toughness compared to InfiniSteel.

Corrosion resistance: InfiniSteel has a higher chromium content (20%) than MagnaCut (17%). This results in better passive film formation and, consequently, superior corrosion resistance. Additionally, MagnaCut’s nitrogen content (0.18%) further enhances its corrosion resistance. However, InfiniSteel is likely to have the edge in this category due to its higher chromium content.

…

The person who sent me this also asked ChatGPT how the steel would compare to MagnaCut. Somehow ChatGPT managed to get the composition of MagnaCut wrong. MagnaCut has 10.7% chromium but ChatGPT says it has 17%. It also has 0.2% nitrogen as a target, so the 0.18% nitrogen that ChatGPT reports could technically be a nitrogen content that would be seen in an individual bar of steel, but that is not the target composition. And it also reports that MagnaCut has 0.3% nickel, even though nickel is not added to the steel and is not reported in the datasheet.

ChatGPT also repeats its mistake from earlier, saying that vanadium in higher amounts leads to more toughness. So it says that MagnaCut would have higher toughness because it has 4% vanadium instead of the 2.5% in “Infinisteel.” That is not how it works.

My Grade: D-

It is difficult to give the steel high marks when you can’t harden it.

Ultravium Steel

— create a unique composition of elements for a great blade steel that does not exist yet…
ChatGPT:
Creating a unique composition for a great blade steel requires a deep understanding of metallurgy and the desired properties in the resulting steel. While this composition doesn’t exist yet, we can propose an innovative blend of elements to produce an exceptional blade steel:

Name: Ultravium Steel

Composition:

Carbon (C): 2.5%
A higher carbon content contributes to increased hardness, making the blade exceptionally sharp and able to hold an edge for extended periods.
Vanadium (V): 7%
Vanadium adds wear resistance, enhancing the blade’s durability and edge retention. It also aids in fine-grain structure formation, crucial for sharpness.
Molybdenum (Mo): 3%
Molybdenum boosts the steel’s strength, toughness, and resistance to corrosion. It also helps maintain blade sharpness under high-stress conditions.
Tungsten (W): 5%
Tungsten further improves hardness, wear resistance, and overall blade performance. It enhances the steel’s ability to withstand extreme temperatures.
Niobium (Nb): 1.5%
Niobium promotes grain refinement, which contributes to sharpness and overall blade strength. It also adds stability to the steel’s microstructure.
Cobalt (Co): 2%
Cobalt increases the steel’s strength and high-temperature performance, making it ideal for blades subjected to extreme conditions.
Copper (Cu): 0.5%
Copper adds a touch of corrosion resistance to the steel while maintaining its strength and hardness.
Silicon (Si): 1%
Silicon enhances the steel’s toughness and resistance to wear, improving its overall durability.

ChatGPT starts out by saying that a unique composition for knife steel “requires a deep understanding of metallurgy,” which I agree with. However, this composition shows that ChatGPT also does not have this required deep understanding. The approach appears to have been adding large amounts of most of the major elements. This steel is a good example of how ChatGPT “reads” things online that say all the positive things that different elements do. When a layman reads about different elements this is also typically what they see. It says what an element does to improve properties but it doesn’t usually explain the tradeoffs or how elements are balanced or how they work together. It says that molybdenum “helps maintain blade sharpness under high-stress conditions.” I don’t know what that means or how molybdenum is helping. Is a “high stress” condition one where an individual is under attack? Or is “high stress” referring to “engineering stress” where a load is being applied to a knife? It says that tungsten is added “to withstand extreme temperatures.” What kind of temperatures? Is this a knife for use in volcanoes? It says that niobium is for grain refinement, but 1.5% is way more than necessary for grain refinement. That amount is for wear resistance and ChatGPT didn’t even mention wear resistance. Cobalt was added again for “high-temperature performance,” “for blades subjected to extreme conditions.” And again I say that if the temperature is too high for the knife it is way, way too hot for humans. Copper is added for “a touch of corrosion resistance” for some reason.

There is a big omission with this steel, however, which is chromium. ChatGPT didn’t add any of it. This means the steel does not have any corrosion resistance to speak of (that 0.5% copper isn’t doing much). But chromium is also added to steels for other reasons. It is present in nearly all high alloy tool steels, in part so that it has high “hardenability” so that it can be quenched more slowly and still fully harden. Not adding it is very strange for this steel.

Heat Treatment:

Ultravium Steel would undergo a precise heat treatment process, including quenching and tempering, to maximize its hardness, toughness, and fine-grain structure. The ideal hardness level would be around HRC 65-66.
Properties:

Ultravium Steel would be renowned for its exceptional hardness, wear resistance, and edge retention. It would maintain sharpness even under heavy use and extreme conditions.
The steel’s fine-grain structure would contribute to its ability to take and hold a razor-sharp edge.
Superior strength and toughness would make it suitable for a wide range of cutting applications, from fine slicing to heavy-duty chopping.
Ultravium Steel would exhibit excellent corrosion resistance, reducing the need for extensive maintenance and ensuring longevity.
It would be an ideal choice for high-performance knives, cutting tools, and blades used in demanding environments.
Applications:

Chef’s knives: Ultravium Steel would excel in the kitchen, making it a favorite among professional chefs and culinary enthusiasts.
Survival and outdoor knives: Blades made from this steel would prove highly reliable in rugged outdoor conditions.
Industrial cutting tools: Ultravium Steel would be sought after for its ability to withstand heavy use in manufacturing and construction.
Military and tactical knives: Its strength and durability would make it suitable for combat and tactical applications.
Artistic blades: Bladesmiths and artisans would appreciate its ability to hold intricate patterns and designs in damascus or layered steel.
While this hypothetical Ultravium Steel may not exist yet, advancements in metallurgy continue to push the boundaries of what’s possible. Such an innovative blend of elements could revolutionize the world of blade steel, offering exceptional performance and versatility to knife makers and users alike.

ChatGPT then claims that Ultravium would have “excellent corrosion resistance” which it certainly would not have without any chromium. It also says that bladesmiths would like it for “its ability to hold intricate patterns and designed in damascus or layered steel.” I don’t know what it means to “hold” an intricate pattern. But bladesmiths would likely not prefer this steel for Damascus because the high alloy content would make it more difficult to forge weld.

My Grade: C-

Since this steel hardens I will give it a C-. Can’t give higher than that since it skipped chromium while claiming the steel would have high corrosion resistance.

MagnaCut+

— ChatGPT
Hypothetical Composition for “MagnaCut Plus”
• • •
Element Proposed Content

Carbon (C) 1.05%
Chromium (Cr) 11.5%
Vanadium (V) 3.0%
Molybdenum (Mo) 2.0%
Tungsten (W) 0.5%
Nitrogen (N) 0.3%
Niobium (Nb) 0.5%
Cobalt (Co) 1.5%

Expected Improvements
• Wear Resistance: The increase in vanadium and tungsten would lead to harder vanadium and tungsten carbides, resulting in better edge retention.
• Toughness: The slight decrease in carbon and the introduction of cobalt would help maintain or even improve toughness.
• Corrosion Resistance: The increase in chromium and nitrogen should enhance resistance to rust and corrosion.
• Grain Refinement: More niobium would aid in creating a finer grain structure, resulting in better overall balance.
Potential Drawbacks
• Higher Cost: Adding tungsten and cobalt increases the cost of production.
• More Challenging Heat Treatment: The changes may require more precise heat treatment to unlock the full potential of the alloy.
• Diminished Ease of Sharpening: Higher vanadium content can make sharpening slightly more difficult.
This proposed “MagnaCut Plus” would aim to push the limits of what MagnaCut offers without compromising the balanced properties that make it so desirable. It would likely excel in applications requiring even greater edge retention and toughness, such as high-performance cutting tools and hard-use knives.

This steel was requested by the user to be an improvement on MagnaCut. However, ChatGPT offers from very strange reasoning:

“The increase in vanadium and tungsten would lead to harder vanadium and tungsten carbides, resulting in better edge retention.”

The vanadium content was reduced from MagnaCut, down to 3% from 4%. So I don’t know why ChatGPT thinks it has increased vanadium. It also claims that it added tungsten to form “tungsten carbides.” In these types of high alloy steels, adding a tiny amount of tungsten does not lead to the formation of any carbides that are tungsten-rich. The tungsten incorporates into other carbide types such as vanadium-rich or chromium-rich carbides. And those carbides would not be any harder. A small addition of tungsten seems to be a theme for ChatGPT and I don’t really see the point. To be fair, there are several stainless steels from Takefu where they added small amounts of tungsten and claimed similar reasoning. But I have also been on record saying I think that the tungsten is unnecessary in these steels.

“More niobium would aid in creating a finer grain structure.” The niobium content is less than MagnaCut, 0.5% vs 2%. so it does not have “more niobium.”

“The changes may require more precise heat treatment to unlock the full potential of the alloy.” Why? Based on what?

Other criticisms of the design require more in-depth analysis with a thermodynamics software that predicts carbide types and alloy in solution. ChatGPT claims an improvement in corrosion resistance because of the higher chromium, but in fact the chromium in solution is reduced in the “MagnaCut+” alloy because of the other elements. There is also  3-5% chromium carbide in the heat treated structure which reduces corrosion resistance. The improvement in corrosion resistance with MagnaCut came from reducing chromium carbide content to near-zero and this steel dropped that innovation for no reason because ChatGPT doesn’t know what it is doing.

The carbide content of MagnaCut+ and MagnaCut would be similar but MagnaCut+ would have half of its carbide be the larger chromium-rich type, reducing toughness. It also claims that cobalt improves toughness, which it doesn’t.

My Grade: C

The steel can be hardened and would be nearly stainless. But calling it MagnaCut+ while reducing corrosion resistance, wear resistance, and toughness from MagnaCut is bad so it gets a C.

Some General Thoughts

In none of the proposals that ChatGPT gave did it ever say that it would make these steels by powder metallurgy. The very high alloy content of these steels means they would not be very good if made with conventional steelmaking. Or at the very least these would not be an improvement on existing steels if they were not made with powder metallurgy. So to not state explicitly that the steel should be made with powder metallurgy is a big omission.

ChatGPT provided no heat treating information. In one case ChatGPT claimed its new alloy would require “more precise heat treatment” but it did not provide any information on temperature ranges the steel might heat treat from. I very much doubt it could give accurate ranges even if asked. Of course in the one case the steel wouldn’t be able to be hardened at all so it wouldn’t be able to give a temperature that would work.

As I have stated several times, the approach ChatGPT takes is to add a lot of every element. It seems to think that elements only improve steel, so add some of all of them and you have a new super steel. It has no real concept of balancing different elements together or what the drawbacks are for adding different elements. It is basically what happens when “internet experts” try to design their own steel. So maybe this is a win for AI because it is now as smart as “internet experts”?

Could a Different AI Design Knife Steel?

ChatGPT is a “large language model,” meaning it was trained on huge amounts of written text from humans. It uses the statistical information about how humans put words together to generate coherent text. However, the model is not good at collecting data about different things, analyzing and comparing that data to find unique insights and solutions, and then to turn that into information. The other problem is that information about how to design tool steels is not really available on the internet, especially not in a form that ChatGPT could use. To really develop a good model we would need a database of information specific to knife steel design. So not a “general AI” but a “narrow AI” for our specific task. This would still be challenging, because the exact data we want does not really exist. The more data we can feed into an AI model the better the results can be. This is the classic example of “garbage in, garbage out.” A lot of information on tool steels and stainless steels are locked within specific steel companies, or even inside the heads of different metallurgists. The scientific literature, including publicly available journal articles and reported experiments, are not usually in a format that would easily be usable in a large database for an AI. Each study is done with different conditions: they hot rolled the steel with a different amount of reduction, they tested different sized coupons, they reported microstructure values but not mechanical properties, they tested Izod toughness instead of charpy toughness, etc.

For my own development of steel I use several different things, including:

1. Journal articles and books which collect different studies to find trends with different variables. Changes to composition, heat treatment, etc.
2. My own data which I have collected the last several years on Knife Steel Nerds – toughness, corrosion resistance, hardness, edge retention, etc.
3. Thermodynamics software – this software does not use machine learning or AI but uses databases to come up with expected microstructure at different temperatures. When it works perfectly you can know what carbides you have and which elements are “in solution” and how much.
4. Simple models for different properties – I have created simple equations which predict properties and there are some that are reported in journal articles and books. Some that I have created have been reported on this website such as this study on corrosion resistance or this study on edge wear.

With those pieces of information I can come up with approximate property targets and balance elements together. A similar process could be used for a “narrow AI.” Existing thermodynamics software could also be plugged into this AI, which is already happening. I think to do much better than humans would likely require a bigger database of experimental data than we currently have. For example, small tweaks could be made to existing steels such as the content of Mo, Si, Mn, N, etc. Currently, without experimentation by making these variations, we don’t know which changes could potentially lead to improvements. If our database was big enough, perhaps an AI could make predictions about which changes to try and thus reduce the number of experiments. But we are not there yet.

The post Can Artificial Intelligence Design a New Knife Steel? appeared first on Knife Steel Nerds.