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

24:44Investing involves risk. Performance not guaranteed. Betterment does not offer tax advice. TLH may not be suitable for all customers. Learn more at betterment.com slash TLH-terms. Well, Kevin, lately I've been feeling like we're entering a third era of slop. Yeah, what were the first two? 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.

25:23And now what are we in? 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. 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 and 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.

26:04And primarily when they're getting called out for using AI, what I see at least are screenshots of one particular app, Pangram. That's right. Pangram is the leading AI text detector on the internet. It's the number one narc. 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.

26:50But 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 not perfect. They're still generating some false positives and some false negatives, but they're much, much better than they were even just a year or two ago. 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.

27:25So 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. So discuss his quest for stopping the slop. Here's Max Spiro.

28:00Max Spiro, welcome to Hard Fork. Hey, thanks for having me. 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. it. So what changed on a technical level between the last generation of AI detectors and this one?

28:39Yeah, 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's like sort of suspiciously smooth. And like that, that is the product of AI models. Exactly. Yeah. Yeah. AI models aren't going to give you a token that it doesn't expect.

29:15With 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 a essay on Moby Dick written by a seventh grader, and then we'll ask 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.

29:55And 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. Wait, so do you actually have to go out and get student essays just so that you have a good baseline of comparison? 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. And 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 generator Moby Dick essay? Is it things like M dashes or certain phrases, or is it more complicated than that?

30:37It'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 like 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.

31:11And 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. 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.

31:44I 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 tradeoff 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 scores 100%, we think the whole document is AI, then 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. Yeah. Let's talk about humanizers.

32:19This 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 nonstandard phrases. There was someone on X recently talking about how they had already built a humanizer that defeated Pan Graham'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?

33:00There'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 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 picking up the latest humanizers and training on them for our next model.

33:34Well, 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? 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 um 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 pretty cool.

34:29But 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 out a new model in a few weeks. And do you think that will hold? Is there a world where 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 you guys were able to detect?

35:14I think a lot of what we're detecting is more subtle than 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 Pangrun 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.

35:49And I think these preferences are largely what Pangram is able to pick up on. Max, I want to ask you like why it's important to do what Pangram does. I think there are people 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 impetus?

36:23Why are you so invested in, as you put it in your social media bios, being a slop janitor for the internet? 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 sub stack, you know, getting people to come to a bakery, like, like kind of anything.

37:14I think there's like this, basically like this, this technology is way more powerful than spellcheck 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. You know, at one point, OpenAI was like reportedly working on its own kind of AI detection system.

37:53it 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 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? 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 just trying to like take data from the internet and try to figure out, yeah, how to interpret it and trust it.

38:32I 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 think is pretty cool. And I think it wasn't really necessary a year ago, but today, just the amount that these social media sites are inundated with AI content, I think it's really necessary. In the future that you're describing, where 99 % of all activity on the internet is bots and AI systems doing things, shouldn't we be trying to label the human content rather than the AI content?

39:16Isn't there some case that you're approaching this from the wrong direction? Sure. I mean, two sides of the same coin, I think. Yeah. I think like algorithmically, like what I want is these platforms, they all have 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 that. There's these like crazy AI videos that will like activate neurons and engagement in a way that like Like a real human video cannot. And so I think we need defenses against that. I want to talk a little bit about this integration with Substack because I actually really like it.

39:54I 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? 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 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.

40:32And now they are going to know that it's AI generated. This is going to upset the status quo. Now that they know what it is, they don't like it. Let's go back to the time when they didn't know that. I guess I'm curious how much you think the mainstream consumer cares. Obviously, there are people who are very sensitive. If they're paying$10 a month for a Substack and then it turns out it's just being written by Claude, maybe they feel like they got cheated. But 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, it's, you know, notes to a friend, like, do people you think really care if that stuff is being AI generated?

41:16Or is this just a subset of writers who are concerned about this? I would totally care if a note from my friend was AI generated. Like that, that would seem like just such a big violation of trust. 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.

41:50Yeah. I think we are in this like really crucial time where 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 overusing 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 stuff around like Hank Green felt like it just went like way too far. I don't know if you guys followed that. 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.

42:37Although I was heartened to see that at least in my feeds, many, many more people came to Hank's defense, but, but it was, yeah, legitimately a controversy. Yeah. Yeah. And, and he was like really honest about how he used AI. And I think like he truly was using it in a way that was 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. 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.

43:12And 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 texts 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? 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.

43:52And 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 one 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. Let's talk about watermarking a little bit, because I don't actually think I understand fully what it even means to watermark a piece of text.

44:30On image generators, I know, if you create an image in Gemini that has a little Gemini logo 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 MDash 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?

45:04So we don't know how Anthropic is going to watermark their text, but the current state of the art is Google's SynthID. So they use this to watermark Gemini text outputs. And what it does essentially is it perturbs the sampling algorithm. Essentially, like 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?

45:48Yeah, 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? I don't think so. I think there's two ways to do this. So there's one way where 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.

46:28But 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 about correctness. There's really only one token 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. Got it. So you guys have launched image detection and you're reportedly working on video detection as well.

47:04Give us an update on where those are. 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 pan-gram image model has to look deeper at a pixel level and try to look for the patterns that are inherent in the generation mode. Very interesting.

47:40You 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? Yeah, I think this is sort of like there is this whole product suite that could be built around this. And so sort of the first step is building the technology, building the core models.

48:10And the second step is bringing it to where people work and how they operate on the Internet. 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 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 gonna 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?

48:49So 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 pretty nice, especially for educators. 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? Yeah, yeah. I want something that can scan my retina, actually, and give me WorldCoin. Yeah, that sounds nice.

49:22A partnership could be in the works. If you want to break that news here on this show, feel free. You think they're still working on that? I believe they are. It seems kind of like a dead project. No. I got my orb scanned. So if my world coin riches have not arrived yet, I'm going to be very upset. Kevin's orb maxing. Max, what is the text that you are proudest of Pangram catching, flagging as AI generated, and the text that fooled you the longest? Ooh. Okay, so if you ask ChachiPT 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 the string of random numbers because they're not actually random.

50:09They're chosen 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 has kind of been able to reverse engineer how ChatGPT and Claude sample tokens well enough that it could see these numbers and say, this is AI. 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. Cool, thanks so much for having me.

50:48It was fun.

50:51When we come back, what do the Riemann hypothesis, enterprise software and singles in South Korea have in common. It's how I met my fiance. God damn it, Casey. I was going to finish that.

51:08Find out in our new segment, Running the Numbers.

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53:12All 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. Yes. And this week, we have something really special for you. It's time to share with you our new segment, Running the Numbers.

53:29In 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. Yeah, this is a segment for all the mathy eggheads out there. Absolutely. And we are going to begin with theoretical math. I love this story. This is my favorite story of the week. Casey, on Monday, we learned that somewhat endanthropic, a non-mathematician named Jared Sumner, had made progress on one of the most significant unsolved problems in math, which is the Riemann hypothesis.

54:06This is, of course, the famous mathematical problem. We actually 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. And listen, some of our listeners may not know what the Riemann hypothesis is. Here's what I've been able to piece together through my extensive research. Prime numbers, right? It's 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.

54:42And I saw that and I thought, I went to a Zeta function at Northwestern. They did it with the Sig Epps. Did you do a keg stand there? I did, actually, yes. 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 not, like, solved the hypothesis or proved the hypothesis, but it had made progress on sort of this side problem involving the Zeta function.

55:20And 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 to this model as it chugged along on this math problem, which is such an important lesson. 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.

55:53Yes. 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 a nice coach sort of telling you, like, keep going. Yeah, but, you know, here's 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.

56:27This 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 it feels like a meaningful step forward. 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. That's right. So, kids, if you're looking for a fun weekend project, the Riemann hypothesis remains out there waiting to be solved.

56:57Now, Kevin, that brings us to our next subject here in running the numbers, and that is SaaS math. By SaaS, 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? I did, yes. 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. 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.

57:36But basically, this is for project management. This is like, you know, you've got, it's sort of like Trello, like 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. But despite the fact that we were not Airtable users, Kevin, in 2021 at the sort of like peak COVID remote work SaaS 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.

58:17dollars. So what do we make of the sharp decline here as we run the numbers? 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 going to 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.

59:02And I think enough people doing that and enough customers leads to the outcome that we saw here with Bending Spoons. 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?

59:38So 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? 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 has been acquired by Bending Spoons, you're in danger, girl. Okay, so I'm just telling you, keep alert to this possibility. Yes, it is not a good sign when you get the inbound email from Bending Spoons' business development folks that are like, we've been kicking the tires on some products that seem very exciting to us recently.

1:00:18Are you interested in selling your company? Things are not going well when that happens to you. Indeed. Now, that brings us, Kevin, to our final story here on Running the Numbers, and that is dating math. 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. And are you a member of that scene? I am not. Okay. But it is suddenly being dominated by wealthy engineers at Samsung and SK Hynix, which is one of these AI chip infrastructure companies.

1:01:00The 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? Is it that they're so smart and kind to the people that they go on dates with? No, it's that they're making money. Oh, okay. So some of these people are getting these very large six-figure bonuses.

1:01:39Others of them are just seen as sort of upwardly mobile in an economy that has not had a lot of that. Wait, it's more than six-figure bonuses. These people are making$400 ,000 to$500 ,000 a year in bonuses. 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.

1:02:15And 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 for Roh Hee 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. 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.

1:03:08Now, Casey, I know you came by your relationship with an AI company employee, honestly. You are not sort of a pre-IPO 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. Swiping right. Swiping right on them on the apps because they see that they work at one of these companies whose value has gone up. So this dating map, this isn't just a Korea thing. We're seeing a version of this in San Francisco as well. We are seeing it in San Francisco as well. Yes, I have heard the term anthropic goggles.

1:03:45Like, 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? 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. Yeah, buy him a Nintendo Switch 2. Yeah, exactly. Let's see if that wins him over. And if I can get, like, gossipy for one second. I have heard stories of some sort of early or senior AI company executives who have traded.

1:04:32Traded up? 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. Sure. Isn't it so amazing how we live in such an unpredictable time and yet that feels entirely predictable to me. That'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. Yes.

1:05:13So 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. We'll keep running the numbers. Don't steal Casey's man. 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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