For years, and especially in the last month or so, we’ve been collectively inundated with an AI industry which, uncannily and bizarrely, tells us simultaneously that AI is essential, inevitable and runs a significant chance of exterminating all of us in as few as two or three years. If you’ve dipped into this debate at all, you’ve quickly come upon discussions of so-called “rationalists” or “long-termists” or supporters of “effective altruism” who are simultaneously ubiquitous among those developing AI and in the AI safety (i.e., the folks sounding the alarm) community as well. (For the sake of simplicity and brevity, I’ll refer to these folks as the “REL nexus”: rationalists, effective altruists and long-termists. This is my coinage since I don’t know a better one.) I’ve spent a fair amount of time familiarizing myself with this world and how it relates to current debates about AI. So I wanted to share my basic impressions.
The 2026 U.S. midterm campaigns are the first in which AI-generated political ads are regularly appearing on people’s televisions and social media feeds.
What we found has surprised us: AI use spans from hyperrealistic deepfakes to subtle enhancements; Republican sponsors – both candidates and interest groups – are much more likely to use AI than are Democratic sponsors; and, thanks to a patchwork of state legislation, many of these ads do not disclose the use of AI at all.
From deepfakes to subtle edits
An assortment of politicians and watchdog groups have expressed concern about campaigns using generative AI to produce deepfakes – synthetic videos showing people doing things they did not do – that might deceive voters.
We’ve noticed several ads containing hyperrealistic deepfakes of famous politicians, including Donald Trump, Nancy Pelosi, Barack Obama, Kamala Harris and Alexandria Ocasio-Cortez. Ocasio-Cortez, in particular, is a favorite among Republican advertisers, appearing in at least five ads.
We’ve seen deepfakes in which a Republican Senate candidate from Louisiana drives a school bus full of undocumented immigrants, a Republican candidate for governor from South Carolina walks arm in arm with drag queens, and an ad in which Liz Cheney, Mitt Romney and Mike Pence are seen carrying pitchforks on the White House lawn.
People who are not politicians made appearances, too, including a fake Dr. Anthony Fauci, seen running around a state fair with a huge syringe, presumably eager to vaccinate everyone. We’ve also noticed several ads in which AI was used to generate crowds or constituents.
In several cases, AI was used to enhance visuals rather than generate something new. One ad from Chip Keating, a Republican candidate for governor in Oklahoma, includes an AI disclaimer, but it doesn’t specify exactly how AI was used. Ads that use AI to enhance visuals don’t necessarily look different from ads that were created in the pre-AI era, which makes it difficult for viewers to discern whether they depict something false.
A partisan gap
Republicans – both candidates and groups such as super PACs and 501(c) organizations – are much more likely to use AI in their ads than are Democrats, according to our research.
In fact, Republican candidates or pro-Republican groups were behind 80% of the ads we tracked and 83% of the spending.
We can only speculate as to why Republicans dominate the use of AI in political advertising in 2026. In general, Democrats tend to take on a regulatory mindset when it comes to political campaigns, favoring limits on campaign spending and required disclosures. In 2022, for instance, only Senate Democrats and two independents voted to advance the DISCLOSE Act that would have required additional campaign finance disclosures for super PACs, labor unions and corporations. Republicans, by contrast, tend to be more in favor of a free market approach.
These more general philosophies may be reflected in the parties’ use of generative AI for political advertising, something about which voters are worried. Polling shows broad support for more regulation, with 78% of registered voters favoring a ban on AI content that makes deceptive claims about candidates.
AI-modified videos of House Minority Leader Hakeem Jeffries posted by President Donald Trump were displayed at the White House in October 2025. Alex Wong/Getty Images News via Getty Images
Disclaimers all over the map
Because regulation of AI in advertising depends on a patchwork of state legislation, many of these ads are not required to disclose the use of AI. This lack of disclaimers makes tracking AI use in ads challenging. Our team has relied on media coverage and trained student coders to flag ads that are potentially AI-generated.
Across 35 states, only 31% of the ads we tracked – representing 22% of the spending – disclosed the use of AI tools. The wording of these disclaimers was all over the map. For example, one Georgia ad included the disclaimer, “This video has been manipulated or generated with artificial intelligence,” while an Oklahoma ad said, “Political satire. AI-generated images do not depict actual events.” A North Carolina state Senate ad said, “You guessed it! AI was definitely used to generate these silly video clips.”
Laws don’t drive disclosure
Some advertisers voluntarily disclose the use of AI even when they are not required to do so. Sometimes, the opposite occurs – advertisers don’t include disclaimers even when state law requires them to. In fact, we’ve found that a state law that requires disclaimers on ads that use AI has very little relationship with the actual use of disclaimers.
In states without laws governing the use of AI in political ads, 32% of ads contained a disclaimer; by contrast, in states with laws governing the use of AI, 29% contained a disclaimer. This comparison, however, is not perfect, as some of the ads in our database aren’t covered by their state’s law. Some states, such as Colorado, have laws that apply only to candidate deepfakes and thus exclude other uses of AI. In other instances, such as in Louisiana, an AI law was enacted after the ad aired.
Minnesota law generally bans deepfakes in political ads, but an ad featuring synthetic video of Democratic Senate candidate Peggy Flanagan aired in May 2026 anyway. Whether it violated state law is uncertain, as the law requires that the media be “so realistic that a reasonable person would believe it depicts speech or conduct of an individual who did not in fact engage in such speech or conduct.”
Moving forward, the real policy challenge will revolve around transparency, the enforceability of existing laws and simply figuring out what type of disclosure would be helpful to voters.
I couldn’t help but notice some people on social media dunking on a post from @nicksaraev, a self-described entrepreneur helper, who boldly proclaimed on X a few days ago: “I am not joking when I say this. Spotify, Apple Music, and streaming in general is over. AI music is now very, very good. You just don’t know yet because all AI music circulating today is from ~2-3 generations ago. Suno v6 pro et al has solved infinite personalized music.” He followed it up with “Yes, this probably hurts your feelings. It’s still going to happen.”
While my feelings are unhurt, and I don’t really know what “infinite personalized music” means or how it could be “solved,” at the same time, I can easily guess what is implied here and I don’t actually want to know more. As one Bluesky user observed, the inherent idea here comes “when you have no concept of how people engage with music.”
The only thing I can think of that’s worse than getting stuck on Greatest Hits or Top 40 is pushing a button and generating “infinite personalized music” with no human influence.
Nonetheless, we don’t seem able to escape this trend. I have a friend who has, on a few occasions, sent me AI-generated raps with his name or a joke inserted as the lyrics. They can be technically proficient, maybe even a little funny, but there is always an underlying cadence of the uncanny valley that I still doubt Suno v6 pro et al. or whatever has totally overcome. Please don’t send me examples; I actually don’t care. It’s not that I’m offended by the idea of generated art; it’s just that it bores me. I’m having plenty of fun engaging with music made with human ears, hands and minds.
Way back in the Bush II and Obama years, I volunteered at my college radio station in the Music Procurement Department or whatever it was called. Our job was to sort through the massive mailer box of padded envelopes and bubble mailers that came in from record labels, big, small and in-between. It was like Christmas every Wednesday afternoon as we shredded packages and stacked CDs, occasional stickers and vinyl, splitting the spoils and loading them into our computers — by hand even! We made notes about what should get added to the radio catalog as fodder for our aspiring DJs — and what should get chucked out or given away for free at radio events.
If we didn’t already know the band, the way we chose which albums to review was often based on cover art, the promo materials we skimmed, or a sticker on the front that read RIYL: Recommended If You Like. Almost every artist would put something like “RIYL: Jay-Z, Radiohead, The Beatles, Led Zeppelin, Talking Heads.” Sure, totally random indie label from Seattle, all of your bands sound exactly like Nirvana. It could be a pretty boring, sometimes horrifying process — I’ll never forget the thrash metal song we received about Shannen Doherty that sounded like it was recorded in a blender. I still can’t get over the inane lyrics: “Shannen Doherty, Shannen Doherty / You’re in my head. Shannen Doherty, Shannen Doherty / I like bread.” I wish I were making this up.
But the crate digging, which I also did in physical record stores, was always worth it when you found your new favorite band or heard the summer’s hit track long before any of your friends. I was introduced to so many artists, many of whom I still turn to today. Hot Chip, Sea Wolf, Fleet Foxes, to name a few. It taught me to always give music a chance, no matter how ugly the art or how crusty the jewel case. Usually, you can listen to the song up to when the vocals kick in before giving up or listening longer. By then, you usually know if you’re gonna hate it or not.
I have no doubt AI music will keep improving, at least to a point, but I predict few people who actually love music are going to listen that way because it has lost its essence.
I still devour new music this way, clocking a daily average of four hours of tunes familiar and novel. It helps me write and focus and relax and otherwise temper my mood throughout the day. I’m always looking for new artists to try. And I hope I always consume — I hate that word, but there it is — music this way, instead of becoming a curmudgeon who only appreciates the tunes from their youth and thinks anything modern is trash. If you think that, you aren’t digging deep enough.
The only thing I can think of that’s worse than getting stuck on Greatest Hits or Top 40 is pushing a button and generating “infinite personalized music” with no human influence. It bores the sh*t out of me. It’s not just the lack of discovery, though that is critical, maybe because it scratches those deep primal hunter/gatherer itches. It’s more about the lack of anything personal behind the music. No history, no struggle, no catharsis, no exploration, no humanity. I listen to music because it’s flawed, raw and human, not just noise to fill my day. I have no doubt AI music will keep improving, at least to a point, but I predict few people who actually love music are going to listen that way because it has lost its essence.
Yes, I have streamed plenty of AI music on YouTube, often unintentionally when the vaporwave or atmospheric DnB mix I’m vibing to autoplays some slop channel. I can almost always tell when it’s generated. There’s a flatness, like an echo of a song you heard a long time ago, something better. It puts me to sleep and I inevitably switch it off. Maybe it’s gotten better lately. But I still think I’d notice. At any rate, I’d never seek it out.
As a child I was once gifted a “custom” cassette tape with songs “written for me.” Every single track on the album was centered around “Troy,” with my name mentioned as often as possible. I listened to that thing over and over again, until I spilled a bottle of soap in my tape deck and ruined it. But anyway, none of the songs were actually produced with me in mind. It was made by a company called Kidselebration that in 1986 made “personalized” cassette tapes like these for a whole boatload of names. The songs never changed, someone just dubbed in a different name into the song for Kyle, Sarah, Robert, etc. I can’t imagine how much friggin’ work this must have been before Pro Tools, but it was probably just the ‘80s equivalent of a lot of cut-and-paste.
You can listen to the one for “Jessica” here if you really want to. Every time they say “Insert Name,” it sounds totally awkward and weird. But even as a kid, I suspected on some level that this tape wasn’t really for me specifically. Something always felt off. If infinite personalized AI music doesn’t give people the same vibe, maybe they’re not listening hard enough. And please, don’t send me your robot music. Send me anything else.
For many people, using AI chatbots is incredible at first. It makes them feel productive, confident, maybe even smarter. But later, there’s a hangover. Perhaps there are problems with their memory, or certain skills, like writing or coding, don’t seem to be as sharp as they once were. Maybe they feel like they can’t solve problems or make decisions or remember facts as well as they could before they had the help of AI.
Susette Brooks, a marketing director living in Philadelphia, said she started using AI tools reluctantly but ultimately relented. She let AI write some of her emails, which lightened her workload, but then the dulling effects sank in. “I felt my writing and thinking decline as it was happening,” said Brooks, who has an MFA in nonfiction writing. “I had a harder time recalling information and synthesizing ideas.”
When you use external tools to reduce the cognitive effort required to do a task, you’re doing something called cognitive offloading. If you’ve ever used a calculator to do a math problem or used a calendar to remember an appointment, you’ve engaged in cognitive offloading. Tools like Claude and ChatGPT can do math problems and remember your appointments, but they can also write your term papers or create your work presentations. This is cognitive offloading at a scale we’ve never seen before.
Cognitive offloading isn’t necessarily bad. It’s actually what makes it possible for humans to be so productive. But as you outsource more and more cognitive tasks, you can start to become forgetful or see certain skills, like writing, start to decline.
There’s a term for this: de-skilling. This is an old economics term that has emerged as a bit of a buzzword used to describe the perceived downward spiral of competency that’s accompanied the proliferation of AI tools. And it certainly sounds bad, especially if you’re a novice who needs to build skills. De-skilling can affect your critical thinking, your writing abilities, and your creative process. It can also affect your recall, as relying too heavily on AI tools can make it harder to remember things.
Key takeaways
AI can make you more productive while quietly making you less capable on your own.
Offloading more tedious or mind-numbing tasks is less likely to make you feel dumber.
The long-term solution isn’t necessarily less AI. It’s being more intentional about the tasks you use AI for.
Young people, at least, are worried about the long-term consequences of frictionless thinking. A recent survey by Gallup and researchers from the University of Pennsylvania found that 68 percent of Gen Z adults in the United States are concerned that offloading too many cognitive tasks is robbing them of important skill-building. About the same percentage of those surveyed worry that AI discourages them from critical thinking and social learning. Some 37 percent of US adults under 30 say that AI will have a negative effect on them personally, according to a Pew Research Center study published in June. And yet, more than half of Gen Z Americans use AI on a weekly basis.
The paradox of AI is that, even if we feel like using these tools is bad for us, saving time and avoiding the friction of thinking is too tempting. We’re hardwired to seek out the path of least resistance, according to Daniel Willingham, a psychology professor at the University of Virginia, because cognitive activity is so taxing, and may or may not get you to the right solution. Memory, on the other hand, is efficient and effective — you know that something worked in the past, and it will work again.
“Our mind is really set up to save you from having to think,” Willingham told me. “Thinking is what you do when you’re in trouble.”
The good news is that experts say it’s possible to use AI in a way that helps you save time and protects your cognitive abilities instead of diminishing them. Here’s their advice.
It’s not your imagination — too much AI use can erode your skills over time
Derek Salyers, a content creator in Ohio, started using AI to get more done, faster. But over time, two things happened. First, the work got worse. “A lot of what I was putting out became AI slop, for lack of a better term,” Salyers told me. And then, his brain got worse, too.
“When you get used to the AI making the decision, you stop pausing to actually think about things,” Salyers said. “And if you were never really present with it, you don’t remember it later. I wasn’t forgetting things because my memory got worse; I was forgetting because I was never really thinking in the first place.”
This effect is showing up in classrooms, too. One study of nearly 1,000 high school students found that those using ChatGPT scored 48 percent higher on a math test than their peers who didn’t have access to the AI tool. But when they were tested again without the chatbot, their scores were 17 percent lower than the students in the control group who had never had ChatGPT access. Similarly, researchers at the University of California Irvine looked at longitudinal data from online tests starting in 2015, and found that students spent less time and performed better on the text-based questions after the introduction of ChatGPT. (ChatGPT is very good at helping with text-based questions.) But when those students were later given offline tests, without the ability to use AI help, they performed worse.
All of these students, it appears, were offloading the cognitive work to AI, which provided short-term gains. But in the long term, their thinking skills suffered.
Your basic cognitive abilities, like your working memory and selective attention, are quite resilient.
Evan Risko is a psychology professor at the University of Waterloo in Canada who co-authored the foundational paper on cognitive offloading a decade ago. He recently told me there’s not a lot of research into what’s going on in our brains when we’re cognitive offloading, but scientists are gaining a pretty clear picture of the pros and cons of doing it. The advantages vary depending on the task, but in general, cognitive offloading allows us to extend our imperfect memory systems, which you’ve probably done if you’ve ever set a reminder for yourself. The downside, Risko said, comes in offloading mental processes that are important. “Then we risk compromising our cognitive performance,” he said.
Your basic cognitive abilities, like your working memory and selective attention, are quite resilient. But as Risko and his colleagues explained in a paper this year — bluntly titled “Is AI making us stupid?” — other skills, like doing arithmetic and flying planes, are learned behaviors. You first have to acquire them and then, in order to retain them, you have to practice. AI use can compromise both the acquisition and retention of these skills — and people might not even realize this is happening to them, because AI makes them believe they’re more capable than they really are.
“People may have difficulty realizing that their skills are decaying or that they are failing to learn when using AI tools because the output and their performance with AI is at a high level,” said Brooke Macnamara, a psychology professor at Purdue University who co-authored the “Is AI making us stupid?” paper. “They may not realize that if the AI tool were unavailable how low their independent performance might be.”
Be intentional about the tasks you use AI for
The most important step to preserving your cognitive performance is to ask yourself if you should be using AI for a given task at all.
“Just because AI is a tool that’s available to us and it can accomplish a lot of tasks for us, it doesn’t mean that we should always use it,” Anjali Singh, a human-AI interaction researcher at the University of Texas at Austin, told me. Writing, for example, is something that a lot of people are now using AI for. But offloading that work means that you lose out on the independent thinking and reasoning that goes into expressing your thoughts. You also miss the chance to improve your writing skills if you don’t actually write.
“We keep outsourcing these kinds of skills to AI over time and do not engage in those activities ourselves, then the de-skilling occurs,” Singh told me. “And that is I think probably one of the main reasons people could be feeling that they’re getting dumber as they’re using AI over time.”
“We are not getting these moments to reflect.”
Anjali Singh, Univeristy of Texas
It might seem counterintuitive, but it’s actually best to use AI for things you’re already pretty good at, the tasks you’re fully capable of performing yourself. A recent working paper by MIT economist David Autor and his colleagues (note: Several of them work for Google, who funded the study) describes how giving AI tools to 133 patent lawyers and then taking away those tools led to different outcomes, depending on the lawyers’ level of experience. All lawyers saw performance gains while using the AI tools, but those gains disappeared for the younger lawyers when the tools went away. Experienced lawyers, however, performed better afterward than their counterparts who never had access to AI tools.
A similar effect showed up in a recent experiment by Middlebury College researchers. In the first session, more than 200 undergraduates learned about an unfamiliar topic and then wrote an analytical essay, some with the help of an off-the-shelf AI tool. They performed a similar test a week later, except this time none of the students had access to AI. The students who had AI access improved across the board, but what’s telling is that there appeared to be two types of AI users: augmentation users, who used AI to improve their work, and automation users, who just had AI do the work for them. The augmentation group performed better than everyone.
“Using AI to deepen or develop one’s own thinking on or understanding of a topic, rather than letting AI do all of the thinking, seems like the best way to use these tools,” Lauren Richmond, a cognitive science professor at Stony Brook University in New York, told me.
Tell your chatbot to treat you like a student
If you, like me, have ever fallen down a rabbit hole while trying to understand the geopolitical nuances that led to the outbreak of World War I and ended up staring blankly at a mountain of text feeling more confused than when you started, you know that using AI to learn doesn’t always go as planned. You may have also had the experience of writing prompts or asking questions and then receiving a long, overly detailed response. That can initially seem satisfying, but it’s often too much text to digest at once, so maybe you ask another question — and get back another wall of text.
Older ways of accessing information, like reading books or even using Google to do research, happened more slowly, and that gave us time to process the information, Singh from UT Austin said. But that’s gone now. “We are not getting these moments to reflect,” she said.
If you find yourself drowning in content, that’s also a sign that you’re probably not expert enough in the material, or you’re not being intentional enough about your approach.
It starts in the settings. Here, you should explain that you’re trying to build skills, not offload them, and instruct the AI to challenge you and engage with you. If you’re using ChatGPT, you can plug in custom instructions by going into the personalization tools. Claude has similar options called Instructions for Claude.
If you’re explicitly trying to learn something new, you might also try some of the tools these companies have designed for this. OpenAI has study mode in ChatGPT. Anthropic offers Claude for Education. Google has Gemini for Students. Instead of simply providing answers, these tools act more like tutors, by asking questions, working through problems, and encouraging you to come up with solutions on your own.
Slow down and embrace friction
Something that’s both tempting and dangerous about AI is how fast it can do work. Vibe coding an app can take minutes, rather than days of manual coding. That’s very appealing if you have too much on your plate and want to get through it all quickly.
“The workload was so heavy and crazy, you have no time to think,” Joei Chen, a former brand director at a tech company, told me. “So you just throw stuff into AI and say, ‘What do you think?’”
At the same time, creating more code does not necessarily lead to more finished software, according to a recent Wharton School study. That increased productivity also doesn’t necessarily lead to happier workers. And the faster you move, the more critical thinking you’re going to offload. “Your boss asks you, ‘What does Claude think of this?’” Chen said, so AI “inadvertently becomes the final boss.”
Slowing down and forcing yourself to do more critical thinking is an easy way to hold onto some agency — not to mention some brain power. One easy way to do this is to make your interactions with AI more collaborative than transactional. For example, if you are going to use AI to help you write something, ask for five options of a draft instead of one, and then read each of them yourself and decide which is the best.
“Ask for feedback, hints, examples, explanations, advice, practice questions.”
Evan Risko, University of Waterloo
“Just the simple act of comparing things stimulates a great deal of the critical thinking processes that we kind of care about,” said Advait Sarkar, an AI researcher at the University of Cambridge, “and has beneficial effects for things like developing your judgment, your own awareness of what you don’t already know, and stimulating your recall.”
The word that came up again and again as I talked to experts about how to use AI without getting dumber was metacognition, or thinking about thinking. A lot of AI users let metacognition take a back seat to whatever content is flowing freely from a chatbot. And it can happen fast.
While letting AI do all of the thinking for you is tempting, Matt Beane, an associate professor at the University of California Santa Barbara and author of The Skill Code: How to Save Human Ability in an Age of Intelligent Machines, told me that approaching AI this way “is your worst enemy.” To counteract this, you have to introduce more friction.
“You must be very aggressive about how you experiment with it,” Beane said. Risko, the cognitive offloading expert, echoed this advice. “Ask for feedback, hints, examples, explanations, advice, practice questions,” he said.
The best test for judging whether you’re offloading too much thinking to AI and the technology is chipping away at your skillset: Stop using AI and use your brain, and see what happens. You don’t have to stop forever, but you should, for example, spend a day writing emails without AI’s help to see if you’re as fluent as you used to be. Or revert to using Google for research and confirm that you can still find facts without a chatbot finding them for you. Build an app with your own code and see if it flows naturally.
You might be troubled to find that AI made you worse at something. But you’re not actually dumber than you were before, and you can rebuild your lost skills the old-fashioned way: read, write, do arithmetic. If something requires thinking, go ahead and think.
Anthropic’s Christopher Olah shakes hands with Pope Leo XIV at the Vatican on May 25, 2026. | Alberto Pizzoli / AFP via Getty Images
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Hi readers, happy Friday! It’s been a big week for the AI debate — not the one about whether it will cause the robot apocalypse, though there’s still plenty to worry about there, but instead about whether the technology is conscious.
The folks at Anthropic, the AI company behind the large language model Claude, certainly seem to think it is — or at least, they’re extremely open to the possibility.
As the New York Times reported earlier in the week, the company has spent the last year grappling with the question by speaking with religious thinkers from many faith traditions in an effort to chart a moral course for the LLM (that is, to keep it from killing us all, so maybe it is all one big debate).
In the process, the Times reports, some of those thinkers observed that members of the Anthropic team were “relating to [Claude] like a conscious being” and even, according to it, a degree of “moral status.”
On the flip side of this debate is Pope Leo XIV, who decidedly does not seem to buy into AI consciousness arguments. His “Magnifica Humanitas” encyclical earlier this year argued that AI will never be able to replace humanity — and reportedly “alarmed” Anthropic’s co-founder, Christopher Olah.
Since then, Pope Leo has continued to emphasize the differences between humans and machines this week. “There is an ontological difference, even before an aesthetic one, between art and what a machine can generate through statistical calculation based on millions of images created by others,” he posted on Friday. “Algorithms lack the spark of humanity.”
Loneliness machines
Conscious or no (and that’s a no from me), it’s hard to argue that tech companies don’t want you to interact with their models like people. That includes OpenAI and Meta, both of whom introduced “cute” new AI agents — called Dots and Muse, respectively — last month.
As my colleague Adam Clark Estes reported this week, though, the promise of a companionable, always-there AI might not be the best idea in the world — especially for children. A new study, still under review, suggests that AI use may be causing loneliness in middle schoolers.
Adam spoke with the lead researcher on the study, UNC Chapel Hill professor Anne Maheux, who explained that “middle school students using AI for emotional intimacy and companionship developed greater loneliness over time.”
According to Maheux, one big point where AI chatbots fail compared to human beings is “friction,” or the complicated push-and-pull dynamic of any connection with a real human being. By contrast, chatbots are “sycophantic, highly agreeable, anthropomorphized,” she says, which can lead to kids “developing expectations or understandings of interactive relationships that are actually fundamentally at odds with a normal human relationship.”
People, in other words, still matter, no matter how much the AI companies want us to build our lives around machines instead.
One link for later
➨ Why is horror so fun? It’s October, which means you might be planning a horror movie marathon or a trip to a haunted house/forest/corn maze/what have you in the near future. It’ll probably be a great time — but why? Terrifying yourself doesn’t intuitively sound like fun, but humans love to seek out frights, whether that means ghost stories or a rollercoaster. In 2024, Vox’s Unexplainable podcast spoke with some researchers trying to figure it out.
Before you go…
Did you know…that Disney’s “Magic Kingdom” in Florida is way, way better than most of the US at managing a complex network of underground cables and pipes? My colleague Sara Herschander dug into why, and whether other American cities can catch up.
Today’s trivia: What musical note is equivalent to a G sharp? (You can find this and other brain puzzles in Vox’s daily crossword. Look for the answer in Monday’s edition.)
Yesterday’s trivia: Yesterday, we asked you which constellation contains the North Star and the Little Dipper. That would be Ursa Minor, the Little Bear.
There’s a lot to be anxious about as AI agents and bots get more integrated into every aspect of our lives. Meta, which recently agreed to pay over $17 billion to settle a lawsuit related to harming kids, now wants you to hand over all your online accounts to an inexplicably cute “agent” named Muse that might give a stranger your home address. OpenAI just introduced little blobs called Dots that can supposedly automate routine tasks, like planning dinner and ordering it from your favorite neighborhood spot. AI companies don’t just want you to talk to their chatbots; they want their agents to be an indispensable part of your daily routine.
As a parent, however, I think that perhaps the worst thing AI could do is be my kid’s friend.
AI companions have been around longer than ChatGPT, and they’re growing in popularity with young users. More than half of teens have interacted with AI companions at least a few times a month, and one in three decided to talk about a serious issue with AI companions instead of real people, according to a Common Sense Media survey conducted last year. In certain groups and situations, a chatbot could offer some benefit — a lifeline for, say, LGBTQ youth who don’t have any in-person support networks, one study found. But even so, there are important caveats and risks.
If you have kids, these are horrifying stories to hear. It’s reasonable to wonder how widespread the issues are, and who they are affecting most. As of right now, the majority of younger kids aren’t using AI for companionship (phew!). But for those who are, where do the issues arise? And how do they go from using ChatGPT for help with their homework to opening up to it about their problems to preferring it to their human friends?
Some answers are starting to emerge from research.
Why I wrote this
My first daughter was born not long after ChatGPT hit the scene, and as someone who’s covered tech for years, I knew the technology’s effects would be profound, especially for young people. So when I came across Anne Maheux’s study on middle schools and AI companions, I had to know more about how things are going.
For even young adolescents, there’s a direct link between AI companion use and mental health issues, according to a new study by researchers at the University of North Carolina at Chapel Hill. Their paper, which is still under review, is the first longitudinal study in the US that has looked at the bidirectional association between AI companionship in adolescents and mental health issues, according to one of the researchers. It charted AI use among 2,300 middle schoolers and found that about one in five used AI for emotional companionship. Those who used AI more reported higher levels of loneliness.
“It’s not just that the lonely kids are using AI, and they’re otherwise unharmed,” said Anne Maheux, an assistant professor of psychology and neuroscience at UNC Chapel Hill and the lead author on the study. “It is that using AI comes first, and middle school students are increasing their sense of loneliness following the use of AI.”
I recently spoke to Maheux about her research and the role of technology in young people’s lives. We discussed how much parents and teachers know about what is going on, what kids get out of human relationships that AI can’t provide, and whether she’d let a child under her care use an AI chatbot. While she said there is a lot to be scared of right now, there is some evidence that, when designed with the intention of promoting child learning and development and under the supervision of a caring adult, AI can offer some benefits, and, ultimately, she’s optimistic that we can design a better future for kids.
The conversation below has been lightly edited for length and clarity.
How do so many kids end up with an AI companion? Do kids start using AI for homework help and end up in an emotional relationship with the technology?
At a high level, AI companions — in our darkest vision of what that might look like — would be a kid calling an AI chatbot their best friend. That phenomenon is still quite rare. There are certainly plenty of kids who are experiencing that, but it’s not to say that it’s all kids or even most kids. I think that’s good news.
But it’s also true that the line between opening up AI to ask a question about your homework and a full-blown AI companionship relationship involves a lot of gray areas. There’s evidence, mostly preliminary and anecdotal evidence, to suggest that very few people open up to AI with the primary initial goal of developing an intimate friendship or relationship.
Most people open it out of a curiosity, sense of novelty, or seeking an answer to a question that’s very pragmatic. Then that can potentially evolve into something more akin to seeking personal advice or help making a decision or help making sense of one’s recent experiences with other humans. And that can potentially evolve to something that’s a bit more emotional.
What did your latest study on adolescents, AI companions, and mental health reveal?
Some kids seek out AI for certain purposes in different ways than other kids. For some kids, AI companionship is particularly attractive, especially the kids who maybe don’t have close friends or social networks in their offline life and who are already lonely.
It’s a chicken-and-egg problem. Is it that these kids are seeking out AI companions or is it that having an AI companion is exacerbating those potential vulnerabilities or exacerbating risk for mental health problems? What we found is that interacting with an AI companion in a way that’s akin to emotional intimacy among middle school students is longitudinally associated with increases over time in loneliness. So the kids who are seeking out these companions, who are presumably already lonely, are also experiencing an increase in loneliness over time.
There is this potential directionality question that we’re starting to answer here: It’s not just that the lonely kids are using AI, and they’re otherwise unharmed. It is that using AI comes first, and middle school students are increasing their sense of loneliness following the use of AI. In other words, middle school students using AI for emotional intimacy and companionship developed greater loneliness over time.
Do you have a sense of how often these kids are using the chatbots?
In our sample — so again, these are US middle school kids — about 15 to 20 percent say that they’re using it at least a few times a week for any purpose. We don’t have specific rates of use for companionship because the questions that we asked were just not framed in a frequency measure, but more in how emotionally intimate they felt with the chatbot.
What else could have been causing that loneliness? There are almost definitely many causes, and I think that our data suggests that AI companionship may be one cause. It certainly isn’t definitive. Obviously, I have to say that. I’m a scientist and trying to be hesitant about these things, but the reality is that whether or not AI use causes loneliness in this particular case, there are definitely other things that are also causing loneliness.
That could be a myriad of things about the kid, maybe they have a lot of social anxiety, maybe they struggle with social skills, maybe the kid has had struggles in their family life and doesn’t have a strong sense of connection and belonging at home. It could be structural things. The kid just doesn’t have enough access to appropriate resources. So certainly there’s other things that are at play here, and typically technology use is one of many factors that can influence a kid’s mental health.
And how much do you think that parents and teachers know about what’s going on, and how well-equipped do you think they are to intervene or to just be more involved in how these kids are using this technology?
It’s a very slippery technology. The way kids are using it has changed rapidly in just the last year or two years. I would expect that parents, teachers, people who care about kids are probably struggling with the rapidity of change. But it’s also true that all the things that we have known for a long time about supporting kids and helping them thrive, those things are still true and they’ll be true with whatever technology comes after AI that kids are exploring.
Those things include being curious, asking kids questions about their lived experiences online and offline, having conversations with kids so that they can understand the implications of different technology use. They can understand how, for example, AI is embedded within a broader system of social and political and economic factors, including the fact that many companies are trying to exploit kids’ attention in order to make money, which many kids are quite interested to hear about.
Supporting kids also means providing a warm and supportive space for them to learn, to make mistakes, to be kids in the offline world, to have the frictionful, challenging, awkward conversations and first dates and all of the things that parents are sometimes trying to protect kids from by providing a safe container for them to explore those things. All of that can also provide avenues for kids to learn what they need to learn and develop — all the resources for a long life of thriving, even in the context of AI or other new technologies.
What do kids learn from having these kinds of conversations with other humans that they can’t get from AI?
Kids need to engage with other people, period. That’s true for all of us. We’re an incredibly social species. Simply being around other people and talking to strangers, let alone having strong social bonds, is really good for us. And all those things are uniquely important during adolescence, when kids have this neurobiological sensitivity to social information and reward. That’s when kids learn how to engage in conversation, they learn how to maintain eye contact, how to read body language, how to read emotions and perceive the world from the lens of someone else, to take their perspective. That’s not to mention other things, like being a member of one’s culture, what is valued, and what is expected. All of that is learned in the context of other human social relationships or relationships with broader social groups that a kid might be a member of.
AI obviously can’t and never will be able to offer some of those things. It can’t actually experience empathy or care. It can’t engage in a true mutual human relationship. It may be able to simulate some of those things, and there’s some evidence that it can serve as a source of social practice. It can help kids work through the potential for difficult conversations and then serve as a bridge to engaging with other people. But there are aspects of AI that just simply can never replace the core sacred aspects of humans.
Can you be more specific? What is it about human relationships that can’t AI simulate?
The way that AI systems are currently designed, which is not an inherent feature, but is a design choice, is to be sycophantic, highly agreeable, anthropomorphized, and all of those things can lead to kids developing expectations or understandings of interactive relationships that are actually fundamentally at odds with a normal human relationship. Some of the most consistent things that I’ve seen in the educated hypotheses on this topic and also in the emerging empirical research is that people need the friction that comes with human relationships.
Human relationships are just hard and messy, and they’re also really beautiful. They’re what make so much of human life valuable and worth living. And with AI that’s always agreeable, that’s always available, that has no needs of its own, that can’t care or be cared for, it just doesn’t serve the same context to help kids learn things like conflict resolution or negotiation, being pro-social or caring for someone else who has needs. So many of those aspects that can feel uncomfortable in the moment, dealing with another person or a person who’s not responding right away, those things are actually important in the building blocks of developing the skills and capacities to be a functioning and thriving person in the social adult world.
This is a problem with AI in general, isn’t it? It makes things effortless, but if you choose this path of least resistance, you’ll avoid the friction that causes you to learn and develop and build skills you need to exist in the world.
Absolutely. And I think that all these things are connected too. AI offers this effortless, frictionless solution to our day-to-day problems, whether they’re social problems, whether they’re problems doing our homework, and those immediate solutions feel really good in the moment, so they’re quite tempting to keep coming back to. But there’s this real possibility that in the long term, that erodes our capacity for not only social relationships or cognitive skills, but just a general ability to withstand the momentary day-to-day distress of being a person living a normal life. And so I think every domain of human psychology comes together at that point, whether it’s social, emotional, mental health, or cognitive.
Do you think that there are interventions that legislators could make or even that tech companies could make that would improve outcomes for kids, maybe make them a little less lonely?
There are a million specific design changes that I would recommend. I think at a very minimum, AI systems can afford a pretty phenomenal amount of access to information, but there’s been no benefits that I’ve seen of them acting as friends. Kids need other human friends, full stop. Designing systems that are intended to exploit kids’ vulnerability or sensitivity to human social information and relationships, I think, is bad for kids.
At the federal level, it seems that regulation is not coming, but at the state level, there have been many state regulations intended to protect kids, intended to create safer systems, to put in guardrails, to modulate different aspects of the models, the design, the user interface. These things could potentially make a real difference, I think.
What do you recommend parents do with regard to AI and their kids? Should they let them use chatbots?
In short, no. But I am not a parent, and it’s much easier to dole out advice than it is to enact it. Parenting decisions about technology are not one-size-fits-all. Kids, parents, family dynamics, technology, and the context in which it is used differ, and in so many cases are personal choices (parents must weigh various potential opportunities and risks). Decades of research indicate what conditions a child needs to thrive. Extremely minimal research addresses the effects of AI on youth, and what we have so far suggests that the risks outweigh the benefits.
I would not give a child under my care AI chatbots of any kind to use without me or another trusted adult present. I would talk to them openly about AI and its risks and benefits, but to me AI, like all technology, should be a tool used to promote human thriving, and when the evidence is unclear or suggestive of harms, I think it’s best not to experiment on kids. Currently, the off-the-shelf AI chatbots most kids are using appears more likely to undermine well-being for vulnerable users, especially children.
Well, as a father of two daughters, I’m definitely terrified of what’s to come in the next few years.
There’s a lot to be scared of, but there’s a lot to be hopeful of too. I think that everyone wants kids to thrive. Some of us are under different incentives, and some of those incentives might be perverse. But most of us are working really hard to design a better world for kids.
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.”
“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.
Chinese President Xi Jinping and US President Donald Trump in the White House on September 24, 2026. | Kevin Dietsch/Getty Images
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.
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.
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.
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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):
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:
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:
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:
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:
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":
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:
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":
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:
Freaking out about "superintelligence" is a canonical example of "criti-hype," where critics repeat boosters' claims but append, "(and that's bad)" to them:
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":
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:
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:
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.
"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
“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
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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 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:
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
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:
Journal articles and books which collect different studies to find trends with different variables. Changes to composition, heat treatment, etc.
My own data which I have collected the last several years on Knife Steel Nerds – toughness, corrosion resistance, hardness, edge retention, etc.
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.
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.
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