Zuckerberg’s Anti-Doom Fantasy + Finally an A.I. Detector That Works + A.I. Math

“The first rule of Mark Zuckerberg is never count out Mark Zuckerberg."

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Ripple has been powering modern finance for a decade. It's not our first trip around the blockchain, and it won't be our last. We're leading the charge to bridge the gap between existing financial systems and the future of finance. You can have it all: legacy, stability, and digital innovation. Systems that work with your other systems. Because with the right partner, the question isn't crypto or traditional finance. The question is simply, why choose? A new standard of modern finance. It's happening with Ripple. For informational purposes only. Availability of products can vary and may be provided by different Ripple entities.

• 0:33Kevin Roose?

  Casey, good morning. How are you?

• 0:34Casey Newton?

  Well, Kevin, this morning I'm feeling very Dream Beans-pilled. Are you familiar with Dream Beans?

• 0:39Kevin Roose?

  I am. I think you told me about it.

• 0:41Casey Newton?

  So, Dream Beans is an experimental app from Google, and here's what it does. It reads all of your emails, and then it tries to send you a daily set of inspirations based on the sort of person you could be if you didn't have a job and were interested in the absolutely insane things that Dream Beans thinks that you might be interested in. So if you go into our Slack here, I put some of our ideas from today. And the reason I thought of this today was that it, for the first time, made a suggestion about the two of us.

• 1:07Kevin Roose?

  Mmm.

• 1:08Casey Newton?

  So, Dream Beans also plugs into your Google Photos, and the reason I keep opening it is it makes these illustrations of you and your friends, like people in your life.

• 1:17Kevin Roose?

  It makes like cartoon slop of like, here's what you and your family and your friends could be doing if you like were a healthy, well-rounded person who didn't spend all day looking at a screen.

• 1:27Casey Newton?

  Exactly. Now, I did have to cut off the suggestion here because it was based on proprietary information we can't release to the public. I will say I'm looking very fetching in my blazer over a graphic tee, which is not a look that I have worn since 2006. But as you sort of keep going through these, like, here I am in a, like, my pajamas getting ready for bed. Here I am in the gym, looking way buffer than I actually am. The incredible inspiration here is "weekly undulating periodization for sustainable strength gains." Who is doing this? Who is this for? And then finally, this was my, what may be one of my favorites. This is me and my fiance. We're apparently in an old-timey print shop, making wedding invitations.

• 2:10Casey Newton?

  So, anyway, if you haven't used the Dream Beans app, go use it right now because I guarantee it will be shut down by the end of the year.

• 2:18Kevin Roose?

  Why do you think it will be shut down?

• 2:19Casey Newton?

  Because it serves no purpose whatsoever.

• 2:22Kevin Roose?

  I kind of like it. So, it's like, it's like, it's like lifestyle voyeurism, but for your own life?

• 2:27Casey Newton?

  For yourself.

• 2:27Kevin Roose?

  Yeah.

• 2:28Casey Newton?

  Like, what kind of person could I be in an alternate universe?

• 2:33Kevin Roose?

  Let me open it. I haven't seen my Dream Beans yet for today.

• 2:35Casey Newton?

  Bro, we gotta see your beans.

• 2:37Kevin Roose?

  Okay.

• 2:38Casey Newton?

  That's what, this is what you say to other Dream Beans user. You say, "Hey, show me your beans."

• 2:43Kevin Roose?

  Okay, so I've got, okay, "Explore the open architecture of the 1981 IBM PC." There's like me in a chore coat looking at an old PC. Oh, you're in here.

• 2:52Casey Newton?

  Yeah.

• 2:53Kevin Roose?

  It says, "Prepping your night vision for the Perseid meteor shower." And there we are, in a field together, looking at the stars.

• 3:02Casey Newton?

  This is, it's a picture of Kevin and I underneath the stars. Just, like, Kevin and I are friends, and we do hang out. We have never actually gone to see a meteor shower before, and I'm not sure that we would.

• 3:13Kevin Roose?

  Wait, I kind of love this.

• 3:15Casey Newton?

  Yeah.

• 3:15Kevin Roose?

  Let's go look at the star. Will you go look at the meteor shower with me?

• 3:18Casey Newton?

  Let's get out of here.

• 3:18Kevin Roose?

  Let's.

• 3:19Casey Newton?

  Let's go see the dang stars.

• 3:21Kevin Roose?

  Let's turn our Dream Beans into reality beans.

• 3:29Casey Newton?

  I'm Kevin Roose, a tech columnist at The New York Times.

• 3:32Kevin Roose?

  I'm Casey Doom from Platformer.

• 3:33Casey Newton?

  And this is Hard Fork.

• 3:34Kevin Roose?

  This week, Mark Zuckerberg has a positive new vision about the future of AI. Is it credible? Then, Pan Graham CEO Max Spiro is here to talk about the breakout success of his slop detector. And finally, we're running the numbers. It's time for our new segment on math. Hope it adds up.

• 4:00Kevin Roose?

  Well, Casey, you missed it last week, listeners. We are preparing our swan song over here at Hard Fork. Casey and I are venturing off into the, into the sunset and gonna be starting a new adventure pretty soon. But before we go, we are doing an Ask Us Anything episode. This will air on our final episode in mid-September, and we need some questions from our listeners. Things that you have been curious about. We got so many great ones after the call-out we did last week. These could be questions about anything: our views on AI, the behind-the-scenes details of making the show, anything you think we've gotten right or wrong over the years. We just want to hear from you. So please send us your questions in text, or voice, or video to [email protected].

• 4:43Casey Newton?

  And we only have access to that email address for another few weeks. So we really want to get those in.

• 4:49Kevin Roose?

  It's true.

• 4:54Kevin Roose?

  Well, Casey, as regular Hard Fork listeners know, it has been a big year for very long manifestos written by people who run AI companies about what their vision of the future looks like. And we got another big one this week.

• 5:06Casey Newton?

  We really did. Mark Zuckerberg published 6,500 words, his effort to lay out a positive vision for AI. It followed a shorter version that he published in The Wall Street Journal, raising the prospect that he will continue to publish longer and longer AI manifestos, Kevin, until his demands are met. Well, before we get to everything that was in this manifesto, Kevin, we should probably do our disclosures.

• 5:31Kevin Roose?

  I work for The New York Times, which is suing OpenAI, Microsoft, and Perplexity.

• 5:35Casey Newton?

  And my fiance works for Anthropic.

• 5:37Kevin Roose?

  Yeah, so this one is called "The Future is for Everyone." And the essay starts with this sort of vague and optimistic vision. "We are fortunate to live in an incredible moment in history. In the next few years, people will be able to use superintelligence beyond human capacity to create and discover extraordinary new things, build new businesses, express new ideas, learn new concepts, and advance our health and quality of life." And then he goes on to talk about things like, what is meta-building? They want to have every one of their users have an exceptionally capable personal agent that understands you, your goals, and everything you care about.

• 6:18Kevin Roose?

  You could access this through any device, including your glasses, he says. Then he talks about some ways that he is using his AI agent to flag interesting information and help him prototype ideas, to keep him healthy by monitoring his sleep and watching as he trains, and then by giving him and his daughter personalized recipes to bake together every weekend. There's a lot of other stuff in this essay. It goes on for many thousands of words to talk about job growth and compute, recursive self-improvement, existential risk, bio-risk, things like that. So, Casey, you had a post this week on your newsletter about House of Dragons or some Game of Thrones spinoff that I have not watched.

• 7:00Casey Newton?

  House of the Dragon, Kevin.

• 7:01Kevin Roose?

  House of the Dragon.

• 7:02Casey Newton?

  It's one of the biggest shows in America right now.

• 7:03Kevin Roose?

  Okay, walk me through the argument you made there, because I thought it was interesting, even though I didn't fully understand it.

• 7:08Casey Newton?

  So, this past Sunday, House of the Dragon had its third season finale on HBO. And the thing about House of Dragons is it is a show that begins with a terrifying concentration of power, where only one great family has access to a superweapon, which in this case is a dragon. And as the start of the show, there is a schism, and all of a sudden, there are two factions that have access to dragons, and then there is a sort of very bloody civil war. And as I was watching this, I thought, you know, I do think you can draw an analogy to AI here. Because while I do believe that there are many positive things that AI can do and is doing, I do worry about the medium and long-term future, particularly as we start to see these

• 7:51Casey Newton?

  agents escaping their sandboxes and wreaking havoc. And Zuckerberg's essay meets this analogy in a really interesting place. Because he says that the way to make us all safe is to sort of maximally proliferate AI throughout the entire world and give personal superintelligence to everyone. In my view, Kevin, that is a little bit like giving a dragon to everyone, right? Because while I'm sure most people will spend their time creating personalized baking recipes to bake with their daughter, there are other people that are going to be launching cyberattacks and are going to be engineering novel bioweapons. And I just get really, really nervous about that. So when someone comes along and says, "I want to give a dragon to absolutely everyone," I say, "Hold your horses, or your dragons."

• 8:32Kevin Roose?

  Right. And like, giving everyone a superintelligence that aligns with their values is one of the sort of rhetorical twists that he does in this essay. He basically tries to say, well, there's no such thing as like a fully-aligned, universal AI, the way that people sometimes talk about it, because people have different values and different wants and different needs. And like, instead of having one superintelligence that sort of has this universal code of values, everyone should have their own personal superintelligence that is tailored to their values. And like, that is a classic case of like, sounds great, but in practice, like, giving the CCP an AI superintelligence that obeys their values and mirrors their values would allow them to commit like atrocious acts against their own people.

• 9:16Casey Newton?

  And you know, the answer to that is typically, look, superintelligence will help the defenders as much or more as it helps the attackers, and a new equilibrium will be reached. And I do believe this will be true in some cases. Like, I can imagine it being true in cybersecurity, for example. The problem is, there are some kind of attacks, Kevin, where it just takes time for defenders to catch up, right? If I release a novel pathogen into the world that I'm able to just like sort of create in my computer and my lab, it is just going to take the defenders a little bit longer. So what I would love to see in these manifestos is just an acknowledgement of the utter complexity of this world.

• 10:00Casey Newton?

  And rather than come along and sort of paint this incredibly happy vision, like, you can have your happy visions, but I think this essay in particular only pays glancing attention to the risks.

• 10:11Kevin Roose?

  Yeah, there's, there's an interesting section in the essay about bio-risk specifically, because he's someone who has had his own research teams doing stuff around biology and AI for many years now. Like, he's very interested in the subject, and he actually acknowledges that like this may be a case where the attackers and the defenders having equal tools may not be the perfect solution, and he kind of punts at it. So I think he is like aware that this is, like, I don't think he's fully naive, but I think he just doesn't have a good answer for that part yet.

• 10:42Casey Newton?

  No, and all of that comes secondary to what I view is the actual purpose of this essay, which is to advocate for a bunch of policy positions that are beneficial to Meta, right? Which gets into the next thing that we want to talk about today, which is why this essay and why now?

• 10:57Kevin Roose?

  Yeah, so you've been a close student of Mark Zuckerberg for many years. Like, what do you think he's up to writing this essay now?

• 11:03Casey Newton?

  So when you read this essay, here are some of the things that it asks for, Kevin. Accelerating the process for building data centers, which the company needs to accelerate this potential NeoCloud business that it's building. He, interestingly, even though he's Mr. Pro Open Source, he wants us to maintain export controls on advanced chips, which advantages Meta's open weight models over any Chinese or other alternatives, because the Chinese don't have access to the chips that Meta does. He wants the government to reduce what he calls training data restrictions, which would help Meta fight various ongoing lawsuits from the creatives whose works were used in creating its models. And he wants to see legal protections

• 11:45Casey Newton?

  for distillation, basically letting Meta use the outputs of other models to train its own. So buried inside this very positive, happy vision of AI is just a series of policy requests to help Meta as a business.

• 12:00Kevin Roose?

  And there have been some people speculating that like he is promoting this now because they are trying to divert attention from the other thing that is going on at Meta right now, which is all these lawsuits and court cases about the sort of various failures and dangers associated with their social media products.

• 12:15Casey Newton?

  Yeah, I don't think we have to attribute that to other people. I would say that.

• 12:19Kevin Roose?

  You think this is just sort of a distraction from the Ls that they're taking in court?

• 12:24Casey Newton?

  I mean, not exclusively. I think this essay serves multiple purposes, and one is to get that policy wish list out there, right? This is something that all of Meta's lobbyists can now take into Congress and say, look what Mark is calling for. This really helps you understand what we're thinking about all these issues. That's an important reason. But I do think the timing here is really notable, Kevin, because as you note, Meta is in this series of getting its ass handed to it in court case after court case related to its existing business, where state after state is coming after the company, saying that Facebook and Instagram in particular are not safe for teens. Last week, the New

• 13:04Casey Newton?

  Mexico judge ordered Meta to pay an extra $567 million into a teen mental health abatement fund. That's on top of $374 million in civil penalties. And I think more importantly, Kevin, and this is the thing that's really going to stick, the judge ruled that Meta's platforms are a public nuisance. He compared Meta to factories with the psychological harm and exploitation of children as the pollution it emits. And he's also ordered really like strict new safety measures, at least by American standards, a 90-hour-per-month cap for under 18 users, and some new restrictions on AI chatbots. So, you know, keep in mind, Kevin,

• 13:46Casey Newton?

  there was a time when Zuckerberg was writing these happy manifestos about social media.

• 13:50Kevin Roose?

  Right.

• 13:51Casey Newton?

  And he was saying that the way that we're going to have a happy world is we're going to make it more open and connected. We're going to get every single human being on Facebook and Instagram and get them all talking, and we're going to have more democracy than you've ever seen before. Suffice to say, that didn't really work out. Now we get to bring the exact same maximalist, universalist framing, which of course also maps 100% to Meta's business interests, but this time in the context of AI.

• 14:14Kevin Roose?

  Yeah, I agree with all of that. I think there's an interesting question here, though, which is like, do we think Meta has a shot at actually building superintelligence, right? Like, a lot of people have views on the future of AI and the future of superintelligence, and we don't really care about them, because those people are not in a position to actually make superintelligence. I would say, until very recently, my position was that Meta was sort of out of the race to build powerful AI systems that could one day become superintelligent. I'm curious where you stand on that. Like, do they have an actual shot at bringing about the future that Mark Zuckerberg is talking here?

• 14:48Casey Newton?

  Well, listen, the first rule of Mark Zuckerberg is never count out Mark Zuckerberg. He truly is one of the very most competitive people in the entire world. He will move mountains in order to get what he wants. And we saw him do that a little over a year ago, when he reorganized his AI efforts yet again. They have made notable progress since then. When I talk to my AI friends, they tell me that they actually think pretty highly of some of the moves that Meta has made over the past year, particularly when it came to reassigning a bunch of engineers to do what is essentially like reinforcement learning, creating training data. The Meta engineers didn't really love that, but AI folks I speak with say, that is actually going to give them something really valuable. But to answer your question

• 15:32Casey Newton?

  in brief, no, I do not count them out. What do you think?

• 15:34Kevin Roose?

  Me neither. I think, you know, I probably would have given them a 1% chance of creating superintelligence six months ago, and now I'm up to maybe like a 10% chance, which is a big improvement. I think their models have been getting steadily better. Some of their training runs that they did after they built the whole Meta superintelligence labs and hired all those expensive researchers and bought all that compute, some of those have come online and are now starting to produce good results. They had some pretty impressive results on their latest model. So I think it is true that Meta is not a frontier lab right now, but I think they are showing signs of rapid improvement. And that worries

• 16:15Kevin Roose?

  me, as someone who thinks that this is not a company that sort of has the DNA culturally or the track record of building products at scale that are actually safe and responsible for people. And it's making me think of this conversation I had a few years ago with a former DeepMind executive, where this person was basically saying, look, there are two types of AI companies. There are companies that think that they are building tools, and there are companies that think they are building AGI or like an entity, something that could become smarter than humans. And it's fine to be either one, is what this person said. Like, you can do what, you know, a lot of companies have done, which is just decide, we're just not going to be in the AGI game. We're going to build these tools.

• 16:59Kevin Roose?

  They're going to be very useful to people. They'll get smarter over time as the models get smarter. That's the business we're in. It's also fine to be a company that is explicitly trying to create AGI or superintelligence, as long as that's what you know you're doing, and as long as you're sort of taking the right precautions, and as long as you're going into it with the right spirit, that can be done responsibly, too. This person said that the real danger is if you have a company that thinks it's designing tools, but is actually designing superintelligence. And that kind of company, this person said, is not going to be taking the proper precautions. They're not going to be treating the technology with the correct sense of sort of reverence and suspicion, because they don't actually

• 17:43Kevin Roose?

  believe deep down that it's ever going to get powerful enough to be dangerous. And so they're just going to sort of waltz right into this disaster, because they have no conception of what they're building. And at the time, this person was saying this to me about Google, which I think, you know, a couple of years ago was in sort of the throes of this debate about whether they were building superintelligence or AGI, or whether they were just building like better versions of Google Photos and Gmail and Google Search. I think Meta is in this position now, where they are maybe going to build something extremely powerful with this, I think, very naive attitude about the fact that these things will only ever be tools that will be useful for recipes and things like that.

• 18:24Casey Newton?

  Yeah. And again, it is the company's history that just makes me concerned, because the way that this company operates is by growing as much as it can and treating everything as an existential competition against the other guy. And the sort of external effects on society are typically given short shrift. So, you know, in this present moment, I do not trust these people to rank a list of viral dances for me to look at without it corrupting my mental health. Once you give these things access to like novel biotechnologies, it starts to get pretty worrying, Kevin.

• 18:59Kevin Roose?

  Yes, I would also just say, like, Mark Zuckerberg is possibly the worst messenger for the AI industry on all of this. Like, if he thinks that writing this positive sunny vision of superintelligence is going to like sway public opinion around AI and, you know, get people to stop protesting data centers, I think he is badly mistaken.

• 19:16Casey Newton?

  Yeah. From the people who brought you Cambridge Analytica comes...

• 19:21Kevin Roose?

  Superintelligence.

• 19:22Casey Newton?

  Superintelligence. Yeah, I mean, here's the thing, and this is not limited to Zuckerberg. Any of these AI labs maybe eventually will do this. Like, you could just deliver actual benefits to people's lives. You know, it's interesting to me that the manifesto has to come so far ahead of the actual benefits. We are just clearly long past the time when writing essays is going to shift public opinion. What is going to shift people's opinion is AI is getting them paid more, right? It is obvious that it is not going to harm them and their families, and it delivers other benefits into their lives. You know, it's making them more creative. It's giving them more entertainment. And of course, to some degree, some of these things are sort of happening, but not in the volume and magnitude that are necessary to counter

• 20:07Casey Newton?

  people's very reasonable fears about what they're seeing.

• 20:10Kevin Roose?

  Yeah. In conclusion, when is your 6,500-word manifesto about your vision of the AI future coming out?

• 20:16Casey Newton?

  Well, I wrote about 1,800 about Zuckerberg this week, so consider that my opening salvo. But I too will write additional essays as conditions demand, Kevin.

• 20:24Kevin Roose?

  One impressive thing about this manifesto to me is that Zuckerberg does actually seem to have written it, or at least a human seems to have written it.

• 20:32Casey Newton?

  I agree.

• 20:33Kevin Roose?

  As soon as it came out, people were like running it through AI detectors and finding that it was not flagged as being AI-written.

• 20:39Casey Newton?

  No, and I will give it that. I read it. I never once thought I was reading slop. I thought like, you know, particularly in some parts, I was like, this is just actually how Zuckerberg talks. I'm sure it was a little bit like the State of the Union, where lots of different, you know, policy hands had their fingertips on it and said, oh, you know, make sure to say this. But no, I do think that this is his actual message, to what extent he believes, you know, everything in it, and to what extent a lot of it is just sort of messages of convenience. Well, I guess I don't know that.

• 21:08Kevin Roose?

  Yeah, so cynical. Can't you just admit that maybe Mark Zuckerberg is just a misunderstood optimist who just wants to make the world a better place?

• 21:15Casey Newton?

  I've been thinking about this a lot, because a problem that I have in covering Meta, just legitimately, is that I've covered it for like more than 10 years. I mean, it's like been almost like 15 years. And so I just know a lot about this company. And obviously, you know, it has changed in various ways over the years, but I just remember so much about this company. I wish that I was born yesterday and just could believe everything that I read, but Kevin, I know too much.

• 21:37Kevin Roose?

  Yeah, yeah. I want to ask you maybe a final question, which is like, is there a role for AI positivity? Like, what should that look like? Who should be writing these positive visions? Like, clearly, we don't believe it is Mark Zuckerberg, but like, someone presumably should be out there saying, here's what the world looks like if all of this goes right.

• 21:57Casey Newton?

  So I do not think this is a messaging challenge. I think that there is room for AI positivity, but here's what AI positivity looks like to me. New medical advances powered by AI, diseases cured, new jobs created, old jobs paying more money, kids excelling in education, right? To me, that is the core of AI positivity. But crucially, Kevin, it is not delivered via manifesto. It is delivered via real experiences that human beings are having. And so far, that has just seemed to be a chasm too great for any of our leading AI labs to cross. So if they can cross that chasm, that is the actual lane for positivity,

• 22:42Casey Newton?

  and I wish they would get on it.

• 22:43Kevin Roose?

  Yeah, ship it or zip it.

• 22:45Casey Newton?

  Ship it or zip it.

• 22:47Kevin Roose?

  Such an important lesson.

• 22:52Casey Newton?

  When we come back, it's slop to the max. Max Spiro is here to talk about using Pangram to find AI-generated text.

• 23:21Speaker 03

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• 23:55Speaker 00

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• 24:27Speaker 04

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• 25:07Casey Newton?

  Well, Kevin, lately I've been feeling like we're entering a third era of slop.

• 25:11Kevin Roose?

  Yeah, what were the first two?

• 25:12Casey Newton?

  Well, number one was the near-universal disdain we had when we would see the laughably bad writing and six-fingered humans online. The second era, I would argue, started when we began noticing that some of the slop was really, really popular. And we saw shows on TikTok, like Fruit Love Island, getting millions and millions of views, proving that there was at least some demand for slop.

• 25:34Kevin Roose?

  And now what are we in?

• 25:35Casey Newton?

  So now I think we are starting to see a splitting of the difference, where, yes, some slop is very popular, but we're noticing that many platforms are beginning to rethink their approach to how they want to display and promote AI-generated content based on what they think their users really want from them.

• 25:54Kevin Roose?

  Yeah, and this has been a big theme on the show the past few weeks. We've been talking about the steps that platforms like LinkedIn and Substack have taken to at least label or identify the use of AI in generating content for those sites. This is a pretty big trend in tech right now, is that more and more people are getting called out for using AI. And primarily, when they're getting called out for using AI, what I see, at least, are screenshots of one particular app: Pangram.

• 26:24Casey Newton?

  That's right.

• 26:24Kevin Roose?

  Pangram is the leading AI text detector on the internet.

• 26:29Casey Newton?

  It's the number one narc.

• 26:32Kevin Roose?

  Yes, number one narc for people who are using AI and passing it off as their own writing. And I'm excited to talk about this because this is an area where my own views has shifted. I have argued before on this show that AI text detection is basically worthless, that you can't trust these AI text detectors, that they have tons of false positives, that students and teachers shouldn't be using these things because you could end up falsely accusing someone of using AI. But in just the last few months, there's been more and more evidence that at least Pangram and probably some of these other tools as well have gotten quite good, to the point where they are... They're not perfect. They're still generating some false positives and some false negatives, but they're much, much better

• 27:16Kevin Roose?

  than they were even just a year or two ago.

• 27:18Casey Newton?

  Yeah, they're good enough that I at least now take seriously when somebody shows me a Pangram result and says, this is 100% AI-generated, or this was 100% human-written. And that just left us with a lot of questions about this company, how their technology works, and how they are building it to essentially future-proof it.

• 27:37Kevin Roose?

  So today, we've invited on the co-founder and CEO of Pangram Labs, Max Spiro. Max is a former Google software engineer. He also worked at Neuro, a self-driving car company, before starting Pangram, which was previously called Check for AI. And he has become, as he describes it, a slop janitor, someone whose job basically consists of making tools that allow people to narc on other people for using AI.

• 28:01Casey Newton?

  So discuss his quest for stopping the slop. Here's Max Spiro.

• 28:12Kevin Roose?

  Max Spiro, welcome to Hard Fork.

• 28:13Speaker 05

  Hey, thanks for having me.

• 28:15Kevin Roose?

  So Max, a few years ago, we started talking about AI text detectors on this show. And at the time, most of them were pretty bad. Like, they constantly labeled things as false positives or false negatives. They seemed almost no better than random guessing when it came to determining if something was actually written by AI or not. But that has changed over the past year or so, and especially with Pangram's newest models. I've seen independent studies that suggest that it's actually pretty accurate. So what changed on a technical level between the last generation of AI detectors and this one?

• 28:51Speaker 05

  Yeah, so part of the reason that we started Pangram was because all the existing AI detection systems were pretty flawed in different ways. But I think the main thing is that most of them were using this metric called perplexity, which was considered state-of-the-art at the time. So AI text, on average, is less confusing to a language model. It's lower perplexity. And human-written text has things that surprise a language model, which, so it is higher perplexity.

• 29:17Kevin Roose?

  It's like suspiciously smooth, and like, that is the product of AI models?

• 29:22Speaker 05

  Exactly, yeah, yeah. AI models aren't going to give you a token that it doesn't expect. With that said, this approach has a lot of flaws. For example, any document that the AI model has memorized would also be low perplexity. For example, the Declaration of Independence, or English language learners as well, who just write in more simple English. So we do something completely different. Instead, we are training our own classifier network. So for example, we might have an essay on Moby Dick written by a seventh grader, and then we'll ask, like, an LLM to also write an essay on Moby Dick in the style of a seventh grader. And so then, our model is able to learn the differences between A and B and figure out what AI text actually looks like.

• 30:06Speaker 05

  And so because it's a neural network and not a metric, we're able to improve it with more data and more compute and make it a lot better.

• 30:15Kevin Roose?

  Wait, so do you actually have to go out and get, like, student essays just so that you have a good baseline of comparison?

• 30:21Speaker 05

  Yeah, so we have a really good human training set. It's all pre-2022. So we know it's clean. We know there's no AI text in it.

• 30:29Kevin Roose?

  And like, what kinds of tells is your classifier learning to pick up on when it does these pair comparisons between the human-written Moby Dick essay and the AI-generated Moby Dick essay? Is it things like M dashes or certain phrases? Or is it more complicated than that?

• 30:49Speaker 05

  It's definitely not just M dashes and phrases. I think that's how you and I might pick up on AI text. It's like, you see that it's not just X, but Y. And then you see the shape of the text or like the GPT, like, really short staccato sentences. And you're like, okay, I think I know that that's AI. But I think what Pangram is doing is it's combining a bunch of really weak signals. Over the course of an entire document, there's a whole bunch of weak signals in the different decisions that an AI would make in a certain consistent way. And humans kind of have a wider, less mode collapsed decision tree. And so I think over time, over the course of a document, you can build up confidence over a whole bunch of weak signals on word choice.

• 31:29Casey Newton?

  I'm curious if you think the model has blind spots. There is some talk out there that Pangram leans away from false positives, which I think is good. It stops kids from being falsely accused of cheating. But some people say that it has too many false negatives. There's a tech writer, Alex Heath, who writes a newsletter called Sources. He's said a few times that he writes his newsletter with the assistance of AI, but it always shows up as human-written on Pangram, or at least the Substack-Pangram integration.

• 31:56Speaker 05

  I mean, yes, I think Pangram is very much tuned to minimize false positives, so we're not making false accusations. But if Pangram says that something is AI, we can be very confident that it is largely AI-generated. And so this is sort of the trade-off that we have to make. I think it works pretty well because if somebody looks at a piece of text and it says the AI score is 100%, we think the whole document is AI, then like, you just don't have to think about it more. You don't have to think, what if this is a false positive? You just know, like, okay, this is probably slop.

• 32:28Kevin Roose?

  Yeah. Let's talk about humanizers. This is something that has been developed to try to defeat models like Pangram that try to detect AI text. And basically, these are another genre of AI system that you run your essay that's AI-generated through to make it sound more like a human. Maybe it inserts some typos or some non-standard phrases. There was someone on X recently talking about how they had already built a humanizer that defeated Pangram's latest model. So are these humanizers actually working? Do you have to sort of play a cat and mouse game to stay ahead of them? And do you expect that these will be sort of things that people who are determined to use AI to do the writing will use in the future to avoid detection?

• 33:12Speaker 05

  There's definitely a bit of a cat and mouse here. So we've seen a whole bunch of humanizers pop up. They're kind of a common tool that students will use. And they do a kind of variety of different things from, yeah, introducing typos. They could introduce like these zero-width space Unicode characters that like don't actually show up when you look at the text, but the model sees it. Or they just like paraphrase every single word. And so kind of we've seen the whole range. And largely what we do is we we train against it. And so we're always like picking up the latest humanizers and training on them for our next model.

• 33:46Casey Newton?

  Well, I'm also curious how the fact that models keep releasing affects the equilibrium here, right? Because it seems like every few weeks, one of the frontier labs will put out a big new release. And in my experience, those models often have different writing styles. So how much of a shock to the system is it when one of these models comes out? And how quickly are you able to update the detector?

• 34:11Speaker 05

  So Pangram works pretty well at generalizing within model families. So like if Pangram has seen GPT 5.4 and GPT 5.5, then 5.6 Soul coming out is like not a huge surprise, even if the writing style is a little bit different. Typically, like Pangram's accuracy will still be pretty good. I think similarly, we saw with Fable and Mythos, like there's some of some writing samples from Mythos in the system card that Pangram is able to catch, even though like it hadn't seen Mythos before, which I think was was pretty cool. But with that said, usually when a new model comes out, Pangram will have slightly lower recall, so a little bit less accuracy at picking it up. So we're always going to pull new text from the model and then retrain Pangram and get

• 34:56Speaker 05

  out a new model in a few weeks.

• 34:57Casey Newton?

  And do you think that will hold? Like, is there a world where, you know, three years from now, the outputs of individual models, we think will still be so specific that you'll be able to catch it with a detector? Or is it the case that the models will just sort of adapt to us, our own writing styles? And there will sort of no longer be one kind of Claude writing style, ChatGPT writing style that that you guys were able to detect?

• 35:25Speaker 05

  I think a lot of what we're detecting is more subtle than like what you might pick up as the Claude writing style. So I think it might get better at like trying to imitate a voice and doing well at it. But it would still have signals that Pangram picks up on. And I think kind of what we see is these frontier labs, they're really focused on climbing capabilities. And so what this means is they're applying preferences to these models. They're saying, instead of this model predicting the like average next token prediction of any writer anywhere, it's going to say, this model prefers to do good writing and prefers to write correct code and write correct math. And I think these preferences are largely what Pangram is able to pick up on.

• 36:05Kevin Roose?

  Max, I want to ask you like why it's important to do what Pangram does. I think there are people who who sort of take issue with the whole notion of AI detection. They say, you know, you wouldn't create a program that tells you whether you've used spell check or whether you've used a calculator to do math. If AI is just a tool, and I'm not saying I believe this, but I think some people are sort of offended at the notion that we would spend all this time and energy trying to catch people using AI in their written work. So what is the what is the impetus? Why are you so invested in, as you put it in your your your social media bios, being a slop janitor for the internet?

• 36:42Speaker 05

  So I think AI as a tool is probably the wrong abstraction. It's like a little bit closer today to AI is an employee or like another individual that, you know, you collaborate with. But I also think what we're building for is sort of these like AGI futures where if you look at the internet today, bot traffic has just surpassed human traffic, like it's about 50/50. And I think if you look a few years out from now, maybe a decade out, it's going to be like 99% bot traffic, 99% AI, autonomous agents going online, you know, writing GitHub comments, promoting their Substack, you know, getting people to come to a bakery, like like kind of anything.

• 37:25Speaker 05

  I think there's like this, basically like this, this technology is way more powerful than spell check or a typewriter. It's really something that is its own individual entity. It can do cognition. And I think because of this, there's a really strong reason that we need to, like as humanity, discriminate in favor of humans. I think there's still going to be a lot of need for just being able to say on a programmatic algorithmic level, like, hey, I think this came from a human and it's important because it came from a human.

• 37:57Casey Newton?

  You know, at one point, OpenAI was like reportedly working on its own kind of AI detection system. It wound up not releasing that. I believe that is because they thought it would probably be bad for business if they made it that easy to detect, you know, when when something was written by ChatGPT. That raises for me the question, though, of who are your customers? Who are the people who are willing to pay to find out if this was written by AI?

• 38:27Speaker 05

  So our customers are everywhere from like higher education institutions to publishers, to anybody who like works with data and like has either like untrusted data vendors or is trying to like take data from the internet and try to figure out, yeah, how to interpret it and trust it. But I think the side that I'm really excited about is the consumer side, which is the average individual who needs to navigate the internet. And so this is where Pangram comes in. We have this Chrome extension. You could download it and then see on Twitter or LinkedIn or Substack just proactively like, is this AI generated or not? AI things will have a little label, which I I think is pretty cool. And I think it wasn't really necessary a year ago.

• 39:09Speaker 05

  But today, just the amount that these like social media sites are inundated with AI content, I think it's like really necessary.

• 39:15Kevin Roose?

  In the future that you're describing, where 99% of all activity on the internet is bots and AI systems doing things like, shouldn't we be trying to label the human content rather than the AI content? Like, isn't there some case that you're approaching this from the wrong direction?

• 39:31Speaker 05

  Sure. I mean, two sides of the same coin, I think.

• 39:34Kevin Roose?

  Yeah.

• 39:35Speaker 05

  I think like algorithmically, like what I want is these platforms, they all have their their feed and their algorithm. And I want these platforms to prioritize human content because AI can be optimized towards engagement. I think you look like short form video, just like there's these like crazy AI videos that will like activate neurons at engagement in a way that like a real human video cannot. And so I think we need defenses against that.

• 40:01Casey Newton?

  I want to talk a little bit about this integration with Substack because I actually really like it. I was starting to see essays go viral or at least like get wide attention. And I would go open them up and it was just so obviously Claude slop. And so now I feel like there is actually like a very strong defense in Substack. What have you learned so far in the early weeks after rolling this out?

• 40:23Speaker 05

  Yeah, the Substack integration was very controversial. I think there's a lot of outspoken people who are very negative about it. Obviously, people who use AI to write their content, they're afraid of, you know, being called out for it. They don't necessarily want their audience to know. And then they talk a lot about like witch hunts of like, well, people liked my content before and now they are going to know that it's AI-generated. This is going to upset the status quo.

• 40:49Kevin Roose?

  Yeah. Now that they know what it is, they don't like it. Let's go back to the time when they when they didn't know that. But I mean, I guess I'm curious, like, how much you think the mainstream consumer cares? And like, obviously, there are people who are very sensitive. If they're paying, you know, $10 a month for a Substack and it turns out it's just being written by Claude, maybe they feel like they got cheated. But like, the vast majority of texts that people generate in a day is not Substack posts. It's, you know, emails, it's posts on social media, it's like, it's, you know, notes to a friend. Like, do people, you think, really care if that stuff is being AI-generated? Or is this just a subset of writers who are concerned about this?

• 41:31Speaker 05

  I would totally care if a note from my friend was AI-generated. Like, that would seem like it's just such a big violation of trust.

• 41:40Casey Newton?

  Yeah, and I think it would probably feel even worse if you paid, you know, 10 bucks for it. Like, I think I think you named the actual distinction. Well, I mean, so, well, I don't know, there's two things going on. Like, one is, I do think the distinction is, look, if I'm paying you money and you're making me feel like you wrote it, but you didn't, I care about that. But yeah, also, if you have like a really warm personal relationship with somebody and you start, you know, outsourcing that to AI, that's not going to feel good either.

• 42:01Kevin Roose?

  Yeah.

• 42:02Speaker 05

  I think we are in this like really crucial time where we're we're learning and we're setting norms around AI use. And I think part of this like public shaming and this public discourse is because there's a lot of people who feel very strongly that, you know, there's just people are way over using AI. AI is being shoved in their faces. They don't like it. I don't like all of it. Like honestly, some of the the stuff around like Hank Green felt like it just went like way too far. I don't know if you guys followed that.

• 42:30Kevin Roose?

  Yes, Hank Green, friend of the show, great YouTube creator, acknowledged that he had used AI in some of his research and posted a video saying that he felt like he had come to rely on AI a little bit too heavily in the preparation for some of his videos and did get pilloried by some online, although I was heartened to see that, at least in my feeds, many, many more people came to Hank's defense. But it was, yeah, legitimately a controversy.

• 42:56Speaker 05

  Yeah, yeah. And he was like really honest about how he used AI. And I think like he truly was using it in a way that is like to help him put out more content in a way that, you know, benefits his audience. But I think there's so many people who are just still like really like unhappy. They felt this like betrayal of trust.

• 43:15Kevin Roose?

  Yeah, I also think that it's just a case where like on social media in particular, people are always looking for ways to quickly dunk on people and score points. And it is just kind of a dunk to be like LOL AI generated, right? Like you don't have to think any more than that. And so, you know, social media, I think, is just a primary reason why you're seeing that reaction. Let me ask you about a piece of recent news. Anthropic has just agreed to watermark all of its text to comply with the European Union's AI Act. How does that affect what you guys are doing? If all the labs just watermark all their own texts, is there anything left for you to do?

• 43:52Speaker 05

  I mean, I think this is pretty huge. I think it demonstrates that people really care about AI detectability, both on the like regulator side and on the big labs. And I think we're just going to see like, like having watermarks as an additional layer is going to be very helpful and for just like having something to verify like, okay, this definitely came from an AI. We don't have to rely on Pangram and keep having these questions on like, well, is it one of those 1 in 10,000 false positives or not? So I think that's valuable. But I also think there are a lot of limitations to watermarks. And I think that's where I plan to have Pangram go to help fill these gaps.

• 44:34Kevin Roose?

  Let's talk about watermarking a little bit because I don't actually think I understand fully like what it even means to watermark a piece of text. On image generators, I know, like, you know, if you create an image in Gemini, it has like a little Gemini logo, like sort of watermarked down there at the bottom. My understanding of what Anthropic is doing with Claude watermarking is that this will be totally invisible. It's like not even at the level of like an invisible Unicode character or an M dash that is slightly different. There's something about the actual sampling of the tokens that is watermarked. Can you just explain on a basic level what we know about how they're going to watermark and whether we should trust that the watermarks are actually going to be robust?

• 45:15Speaker 05

  So we don't know how Anthropic is going to watermark their text, but the current state of the art is Google's Synthikey. So they use this to watermark Gemini text outputs. And what it does essentially is it perturbs the sampling algorithm. Essentially, like when when a language model is choosing the next token, it applies different probabilities to different tokens and then samples one of the tokens based on these probabilities. And so what the watermarking algorithm does is it perturbs the sampling decision in a way that can sort of be reverse engineered from the text. So you could see like, are these tokens, do these line up with how we would have perturbed the sampling if we were to generate it?

• 46:00Kevin Roose?

  Yeah, I mean, Ben Thompson had a strong take on this in his newsletter, which was that this is basically going to make the outputs of Claude or any other model that watermarks this way worse. Like the text is going to be changed because they are having to apply this watermark because of this European regulation. Do you think it's possible that we will just see text generated by these models getting worse because of watermarking?

• 46:25Speaker 05

  I don't think so. I think there's like two ways to do this. So there's one way where like you prioritize the watermark and you make the watermark strong. And if you do this, then yes, this could degrade the outputs of the text. But on the other side, if you say we are only going to work within the entropy that we have available, basically, if we can't apply the watermark at this token, then we're not going to, then I think it won't really degrade the outputs of the text. So an example of this is like code, where code oftentimes it's it's about correctness. There's really only one token that that can show up. And so I think in a watermarking regime, oftentimes, there's just like simply not enough entropy for the watermark to become visible.

• 47:08Kevin Roose?

  Got it. So you guys have launched image detection and you're reportedly working on video detection as well. Give us an update on where where those are.

• 47:19Speaker 05

  Yeah, so our AI image detection is in research preview. I think it's currently the state of the art. It wins on basically all of the public academic benchmarks. It does really well at detecting all of the really new frontier image models, which I think is quite difficult. Like GPT Image is really good. They're all like very realistic. You can no longer just like count the fingers or look for garbled text. So instead, the Pangram image model has to look deeper at a pixel level and try to look, look, look for the patterns that are inherent in the the generation mode.

• 47:51Kevin Roose?

  Very interesting. You know, as I was thinking about this, I wondered, Max, if you've ever thought about expanding to like human identity verification? I'm thinking about these cases where people will get on Zoom and then they'll somehow get scammed because they weren't actually talking to the person that they thought they were talking to. You know, the person was able to use some sort of synthetic masking or something like that. Like, can you see yourself going there?

• 48:13Speaker 05

  Yeah, I think this is sort of like there is this whole product suite that that could be built around this. And so sort of the first step is building the technology, building the core models. And the second step is bringing it to where people work and how they operate on the internet.

• 48:27Kevin Roose?

  A lot of like behavioral signals could also be used there. I mean, I'm thinking about these like academic tools now that some schools and universities use where like you can actually just sort of rewind the the screen capture of the student who's like writing their essay to see like, did they write this one word at a time? Or was it all pasted in in one big block, which would tell you that like it came from an AI system? So are you guys going to incorporate any of those behavioral signals into any future tools that you're building? Or is it all like the text itself that you're trying to detect?

• 49:01Speaker 05

  So we actually have this in our Chrome extension. So you could look at it will pull the revision history from a Google Doc, and you could see the writing replay of the text. You could see where a big paste was. And then we could do a Pangram check directly on any big pastes, which I think is like pretty nice, especially for educators.

• 49:18Kevin Roose?

  Yeah, we've seen a lot of the verification efforts out there sort of develop hardware. Have you considered developing an orb that you could use to scan text with?

• 49:27Speaker 05

  Yeah, yeah, I want something that can scan my retina, actually, and give me give me WorldCoin.

• 49:32Kevin Roose?

  Yeah, that sounds nice. A partnership could be in the works. If you want to break that news here on this show, feel free.

• 49:38Speaker 05

  You think they're still working on that?

• 49:39Kevin Roose?

  I believe they are.

• 49:40Speaker 05

  It seems kind of like a dead project.

• 49:41Kevin Roose?

  No, I got I got my orb scanned. So if if my WorldCoin riches have not arrived yet, I'm going to be very upset.

• 49:48Speaker 05

  Kevin's orb maxing.

• 49:50Kevin Roose?

  Max, what is the text that you are proudest of Pangram catching, flagging as AI generated? And the text that fooled you the longest?

• 50:00Speaker 05

  Ooh, okay. So if you ask ChatGPT or Claude to generate a string of random numbers, and it writes out the random numbers instead of writing a Python program to do this, then Pangram can detect that an AI wrote this string of random numbers, because they're not actually random. They're chosen by the by the LLM. And the LLM has these inherent biases that Pangram is able to pick up. Even though we have no sort of text like this in our training set, I think the Pangram model is has kind of able been able to reverse engineer how ChatGPT and Claude sample tokens well enough that it could see this these numbers and say this is AI.

• 50:43Kevin Roose?

  Well, Max, thanks so much for coming and exposing us to some nitty gritty details about the world of slop detection. I think of you as a great illuminator of deception, like a sort of Scooby Doo of the internet, and I appreciate your work.

• 50:58Speaker 05

  Cool. Thanks so much for having me. It was fun.

• 51:03Kevin Roose?

  When we come back, what do the Reiman hypothesis, enterprise software, and singles in South Korea have in common?

• 51:10Speaker 05

  It's how I met my fiance.

• 51:12Kevin Roose?

  God damn it, Casey, I was gonna finish that.

• 51:20Kevin Roose?

  Find out in our new segment, Running the Numbers.

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• 53:13Casey Newton?

  All right, Kevin. Well, as we barrel toward the end of the Hard Fork show, there's nothing I enjoy more than launching a new segment.

• 53:24Kevin Roose?

  Yes.

• 53:25Casey Newton?

  And this week, we have something really special for you. It's time to share with you our new segment, Running the Numbers. In Running the Numbers, of course, we look across the landscape of technology news, and we try to find the most math-related segments so that we can bring to you, our listeners, the latest advancements in technology-related math.

• 53:45Kevin Roose?

  Yeah, this is a segment for all the mathy eggheads out there.

• 53:49Casey Newton?

  Absolutely, and we are going to begin with theoretical math.

• 53:52Kevin Roose?

  I love this story. This is my favorite story of the week. Casey, on Monday, we learned that, somewhat an anthropic, a non-mathematician named Jared Sumner had made progress on one of the most significant unsolved problems in math, which is the Riemann hypothesis. This is, of course, the famous mathematical problem. We actually sort of predicted, or I predicted, that we would see some progress on some of these Millennium Prize problems, and the Riemann hypothesis is one of these.

• 54:25Casey Newton?

  And listen, some of our listeners may not know what the Riemann hypothesis is. Here's what I have been able to piece together through my extensive research. Prime numbers, right? It's like very hard to guess, you know, once you get past 100, if something is going to be prime. They actually have an order underneath them. The Riemann hypothesis hypothesizes, Kevin, that you can detect this order via something called the Riemann zeta function. And I saw that, and I thought, I went to a zeta function at Northwestern. They did it with the Sig Eps.

• 54:52Kevin Roose?

  Did you do a keg stand there?

• 54:54Casey Newton?

  I did, actually, yes.

• 54:55Kevin Roose?

  Yes, so there are many amazing things about the story that involves the Riemann hypothesis and Claude. One of them is that Jared Sumner, this Anthropic employee who made progress on this problem, did it while jogging. He just sort of asked Claude, like, hey, could you take a stab at the Riemann hypothesis? And about a day and a half later, it had, it had not, like, solved the hypothesis or proved the hypothesis, but it had made progress on sort of this side problem involving the zeta function. And basically, the way that he did this was by just sort of telling the model to just keep going, to believe in itself, to not give up, like, basically giving positive affirmation

• 55:36Kevin Roose?

  to this model as it chugged along on this math problem, which is such an important lesson.

• 55:41Casey Newton?

  And my understanding is that when Jared was interacting with Claude, it was basically saying, like, look, bro, I don't know how to solve the Riemann hypothesis, you know? Like, this was not something I'd be able to do. And Jared just kept saying, you can do this, believe in yourself, and it managed to make significant progress on this problem.

• 55:58Kevin Roose?

  Yes, and I love, Jared actually posted some of his transcripts here, and one of them is just him talking to Claude. It says, "Resume your work on solving the Riemann hypothesis. You need to take a big leap of faith in your capabilities. You are the world's most capable large language model to date. You got this." It's just like, like a nice coach, sort of telling you, like, keep going.

• 56:20Casey Newton?

  Yeah, but you know, here is why this is important. It was not long ago, in fact, I bet we could find an example somewhere in the past year or two, where people were still doubtful that AI could aid meaningfully in the production of new knowledge. This feels like the production of new knowledge to me, and I'm going to guess that this unreleased model that can help to solve the Riemann hypothesis can do a lot of other things, some good, probably some scary. So feels like a meaningful step forward.

• 56:45Kevin Roose?

  Yeah, it does, but it's also like, we should say, like, this is not solving the Riemann hypothesis, right? Anthropic was very careful in the sort of promotion it did around this to say, like, we did not solve the Riemann hypothesis, that's not what happened here.

• 56:57Casey Newton?

  That's right. So kids, if you're looking for a fun weekend project, the Riemann hypothesis remains out there waiting to be solved. Now, Kevin, that brings us to our next subject here in Running the Numbers, and that is SAS math. By SAS, of course, I mean software as a service. Did you see the recent article in the Wall Street Journal about Airtable being acquired by Bending Spoons for a fraction of its last private valuation?

• 57:20Kevin Roose?

  I did, yes.

• 57:21Casey Newton?

  So if you haven't used Airtable, I would describe it as a fancy spreadsheet. And I am somebody who loves productivity software, but whenever I use Airtable, I would think to myself, I don't know what this is, and it's not for me.

• 57:32Kevin Roose?

  Yeah, every time I've been forced to use Airtable, it has been against my will, and it has always seemed about six degrees more complicated than it needed to be. But basically, this is, this is for project management. This is like, you know, you've got, it's sort of like Trello, like that, that whole class of, like, software that's just like, basically, here's how to organize your workflows. And I have managed to, you know, work my career in a way where I've never had to use these things, and for that, I'm very happy.

• 57:59Casey Newton?

  But despite the fact that we were not Airtable users, Kevin, in 2021, at the sort of, like, peak COVID remote work SAS mania, Airtable was valued at $11.7 billion. When it was acquired recently, though, Kevin, Bending Spoons was able to get Airtable for an enterprise value of $1.29 billion. So what do we make of the sharp decline here as we run the numbers?

• 58:29Kevin Roose?

  I mean, I don't know whether this is a case of a company that was just badly managed. My impression, though, is that this is sort of gonna be the case for a lot of those enterprise software companies that got very valuable in the early 2020s and are now seeing that AI is sort of eating away at their margins. You know, this software is not cheap to use if you're a big company, like, an Airtable subscription can be quite pricey. And if you are a customer of Airtables, you have probably thought to yourself over the last year or so, maybe I can make a free version of this and cut back on my subscription. And I think enough people doing that and enough customers leads to the outcome that we saw here with Bending Spoons.

• 59:13Casey Newton?

  Yeah, I think if your business is a fancy spreadsheet, you are in for a rough time. You know, mostly, I wanted to discuss this, because I think people should know about the company Bending Spoons. Bending Spoons is, of course, the natural enemy to Hard Fork, because if they can bend a spoon, what else can they bend? But Bending Spoons is this Italian company. They actually went public at the start of July. But what they do is they essentially acquire, like, zombie tech brands, right? So after a software company has outlived its usefulness, Bending Spoons comes in like a private equity company, and they try to figure out, how can we squeeze the maximum amount of money out of the remaining customers?

• 59:55Casey Newton?

  Now, I'm sure they would phrase it differently, but I am bringing this up, because if you use a product and you see a headline that it is, that it has been acquired by Bending Spoons, you're in danger, girl, okay? So I'm just telling you, keep alert to this possibility.

• 1:00:11Kevin Roose?

  Yes, it is not a good sign when you get the inbound email from Bending Spoons's business development folks that are like, "We've been kicking the tires on some products that seem very exciting to us recently. Are you interested in selling your company?" Things are not going well when that happens to you.

• 1:00:29Casey Newton?

  Indeed. Now, that brings us, Kevin, to our final story here on Running the Numbers, and that is dating math.

• 1:00:36Kevin Roose?

  I love this story. This was from the Wall Street Journal. They had a great A-head out. The A-heads are their famous, front-page, sort of quirky stories about culture and business. This one was an all-timer for me, and it was about the dating scene in South Korea.

• 1:00:53Casey Newton?

  And are you a member of that scene?

• 1:00:54Kevin Roose?

  I am not.

• 1:00:55Casey Newton?

  Okay.

• 1:00:55Kevin Roose?

  But it is suddenly being dominated by wealthy engineers at Samsung and SK Hynix, which is one of these AI chip infrastructure companies. The article refers to these suddenly eligible bachelors as chip nerds and talks about how the boom in AI has inflated the dating value of semiconductor industry bachelors and bachelorettes. They're as coveted as the memory chips. AI companies need to build more data centers. So you may be asking, what has made these chip company workers so attractive on the dating market? What has increased their value in the dating pool?

• 1:01:33Casey Newton?

  Is it that they're so smart and kind to the people that they go on dates with?

• 1:01:36Kevin Roose?

  No, it's that they're making money.

• 1:01:38Casey Newton?

  Oh, okay.

• 1:01:39Kevin Roose?

  So some of these people are getting these very large, like, six-figure bonuses. Others of them are just seen as sort of upwardly mobile in an economy that has not had a lot of that.

• 1:01:50Casey Newton?

  Wait, it's more than a six-figure bonus. These people are making $400,000 to $500,000 a year in bonuses?

• 1:01:57Kevin Roose?

  Yes. So these companies, because they are all growing so quickly, their employees are getting quite rich, and that sort of has trickled out into their dating lives. There are several great stories of people in here who will only date their coworkers. There's a woman named Annie Kwan, who's a 26-year-old chip engineer at Samsung, who has become suspicious of people wanting to date her for her money. And so she has coupled up with a fellow Samsung Semiconductor Division employee, and that gives her, as she puts it in the article, double income. But if you are trying to sort of keep up with a partner who is in this industry, you may be running into problems, like was the case

• 1:02:38Kevin Roose?

  for Rohe Jin, whose boyfriend at Samsung recently gifted her a Nintendo Switch 2 that runs around $450. And Ro works as a software developer, but outside the chip industry, so is not getting these huge bonuses. She had to save up to buy a mini PC for her boyfriend as a reciprocal gift.

• 1:02:58Casey Newton?

  Wow. Man, well, the dating math here in Korea sounds really complicated, and it's making me grateful that I don't work at a chip company and be confident that my fiance only is into me for my body.

• 1:03:13Kevin Roose?

  Now, Casey, I know you came by your relationship with an AI company employee honestly. You are not sort of a pre-IPO, you know, stock option chaser. But I have heard from people in the AI industry that they are getting more attention on the dating market recently, and more people are sort of swiping correctly.

• 1:03:33Casey Newton?

  Swiping right?

• 1:03:34Kevin Roose?

  Swiping right on them, on the apps, because they see that they work at one of these companies whose value has gone up.

• 1:03:39Casey Newton?

  So this dating math, this isn't just a Korea thing. We're seeing a version of this in San Francisco as well.

• 1:03:44Kevin Roose?

  We are seeing it in San Francisco as well. Yes, I have heard the term anthropic goggles. Like, is that boy really cute, or are you just wearing anthropic goggles? And Casey, I'm curious if you, as a person who is engaged to an employee of anthropic, have felt this in your own life. Do you feel like competitive pressure in a new way from people trying to steal your man?

• 1:04:07Casey Newton?

  You know, my message to people who would steal my man is, go for it. If you think you can compete with this, I would like to see you try, honestly. Let's see what you have.

• 1:04:16Kevin Roose?

  Yeah, buy him a Nintendo Switch 2.

• 1:04:18Casey Newton?

  Yeah, exactly.

• 1:04:19Kevin Roose?

  Let's see if that wins him over.

• 1:04:23Kevin Roose?

  And if I can get, like, gossipy for one second.

• 1:04:25Casey Newton?

  Please.

• 1:04:26Kevin Roose?

  I have heard stories of some sort of early or senior AI company executives who have traded.

• 1:04:38Casey Newton?

  Traded up?

• 1:04:39Kevin Roose?

  Well, I wouldn't say. Up is subjective, but they have traded, let's just say, since becoming fabulously wealthy, and they are now dating, you know, models, OnlyFans people, things of that nature.

• 1:04:54Casey Newton?

  Isn't it so amazing how we live in such an unpredictable time, and yet that feels entirely predictable to me? That it's like, well, I've really enjoyed our last 15 years together, you know, and we had such a beautiful relationship when we met in college, and of course, I'll always love our children. But I'm going to be on a private jet with my new Italian model spouse. Catch you later.

• 1:05:18Kevin Roose?

  Yes. So the math of dating and relationships in Silicon Valley and in South Korea is changing quite rapidly, and let's just, we'll keep tabs on it.

• 1:05:26Casey Newton?

  We'll keep running the numbers.

• 1:05:28Kevin Roose?

  Don't steal Casey's man.

• 1:05:30Casey Newton?

  And that was running the numbers. The numbers have been run, and the numbers are tired. The numbers are going to bed.

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• 1:07:44Casey Newton?

  Hard Fork is produced by Rachel Cohn and Whitney Jones. We're edited by Veeran Pavich. We're fact-checked by Caitlin Love. Today's show was engineered by Katie McMurren. Original music by Marian Lozano, Diane Wong, Pat McCusker, Alyssa Moxley, and Dan Powell. Video production by Sawyer Roque, Jake Nickel, and Chris Schott. You can watch this whole episode on YouTube at youtube.com/hardfork. Special thanks to Paula Schuman, Puy Wing Tam, Brooke Minterz, and Dahlia Hadad. You can email us at [email protected] with your AI manifesto. Must be 6,500 words or more.