Kevin Roose is AGI-pilled. Why aren’t you?

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Today, I’m talking to Kevin Roose, journalist and author of the new book The AGI Chronicles, which details the recent and explosive transformation of the AI industry. Kevin Roose is AGI-pilled. Why aren’t you? The author of The AGI Chronicles and co-host of Machine Gods on going all-in on YouTube and the AI race. Kevin was a longtime New York Times reporter and host of the popular podcast Hard Fork, but he and friend of the show Casey Newton just left the Times to start the all-new tech podcast Machine Gods, which launched last week. Kevin and I talked about launching Machine Gods and what it’s like to go from The New York Times to being an independent creator — and then of course we talked about the book, which is an excellently reported dive into the companies racing to build artificial general intelligence. I wanted to get a sense from Kevin of what it was like to report this, to try and understand these characters and this moment in time, and where it all goes next. Fair warning: This conversation takes a pretty philosophical turn at the end, but I think you’ll find it a really worthwhile back-and-forth. Okay: Kevin Roose on The AGI Chronicles, starting Machine Gods, and where AI goes from here. Here we go. This interview has been lightly edited for length and clarity. Kevin Roose, you are the author of The AGI Chronicles and the cofounder and cohost of the new podcast Machine Gods. Welcome to Decoder. Thanks so much for having me. I was listening to the first episode of Machine Gods and you and Casey discussed the fact that technically you have the title of CEO. It’s true. I won the coin flip and we needed someone. When you start a company as we have, you need to list someone as the CEO, I guess, to know who to haul in front of Congress when our first scandal inevitably breaks. So that’s going to be me. It’s funny because you’re a CEO on Decoder. It means I’m going to have to ask you the Decoder questions. [Laughs] Okay. Well, I’ve been a CEO for about five minutes and most days I frankly forget I’m the CEO. Honestly, that tracks with most of the CEOs on the show. They all act like they’ve only done it for five minutes. We’ll come to it. I want to talk about your book. It’s called The AGI Chronicles. It is a meticulously reported book about the main frontier labs and the culture shift they’ve all undergone as AI has come into the forefront and become a much more commercial product in many ways. But let’s just start with Machine Gods. You were a journalist for The New York Times for a long time. Obviously, I think the audience probably knows this, but you and I have known each other for a long time. I will say that since I have known you, your desire to be a YouTuber has been top of mind. You’re just like, “How do I get over there?” And I’ve always been like, “Run, don’t do it.” But you did it. You left The Times, you started your own podcast with our friend Casey Newton. It is the next generation of the very popular podcast, Hard Fork, which was excellent. And I’m excited for you, but what made you take the leap? Verge subscribers, don’t forget you get exclusive access to ad-free Decoder wherever you get your podcasts. Head here. Not a subscriber? You can sign up here. Yeah, you have been trying to convince me out of this for as long as I’ve been trying to convince myself into it and you have not succeeded because here I am. Yep, I failed. Utterly failed. No, look, I think that for some very good reasons, media companies have long been a little bit wary of going all in on YouTube or any other video platform. The ones that have gone all in have not necessarily come out of that with good feelings about it because there are a lot of– Yeah, they pivot to video and also into death, routinely. Yeah, you pivot to video or you sort of waste a bunch of money doing things that don’t pay for themselves. So, I think there are some structural economic reasons for that, but I also think it’s that there aren’t a lot of institutions that are equipped to make that kind of a long-term bet. I’ve been trying to YouTube-pill people in media for many years, as you know. You’ve been trying to undo the YouTube-pilling on your end, but I just think it’s clearly the most important media platform in the world right now. It’s a place where journalism has not historically had a central place. If you were to look at YouTube as a whole and try to assess what piece of it is dedicated to what you or I would consider high-quality journalism, it’s a vanishingly small part of the overall ecosystem there. So, part of my mission is just to crack that and figure out how to do YouTube and video in general in a way that is sustainable from an economic and a sort of creative standpoint. And then just to experiment and do a bunch of things. This is not a face that needs to be on YouTube. So I’m also hoping to bring some other people in and nail some formats. But I look at what The Verge has done there. I look at what people like Joanna Stern and MKBHD have done there. To me, that is very exciting and it’s a space where there’s a lot less competition than text or podcasts. Machine Gods is a podcast that you’re doing in partnership with NPR, which I want to talk about. But do you think of it as a YouTube product? We want it to be video-first. What that means basically is that we are going to give a shit about what it looks like to a degree that we have not always done. We’re going to try to give people a lot of reasons to want to watch as well as listen. Obviously, we know that there are some people who just don’t like video podcasts and so we want to give the audio audience something that is enjoyable, but this is the direction of things. You can’t do a podcast now without doing video on top of it. So we’re going to try to really embrace that and make a good version of it that is as good as two middle-aged journalists can pull off on YouTube. Truly the future of media is middle-aged journalists pivoting to YouTube. I feel the same way because I’m now making more video and that’s what we’re doing. It’s going to be great. The other reason to leave and to do this — and obviously people know I’m friends with Casey as well, and I’ve been talking to you and him about it for a long time — is that the AI story is here. There is a platform shift. There are new companies, new characters, new ideas to talk about in the world. It’s funny because in that context, usually you get a media shift as well. The way we tell stories or the formats we tell stories usually shifts alongside the thing we are talking about or the technology we’re talking about. Here, YouTube feels like the most solid media foundation that exists. It’s the one that’s the same the most, even though it’s changing and it has YouTube Shorts and now you can A / B test everything, including your own face. But it’s the one that is the most the same. Instagram has undergone some massive change and it feels like that will continue happening. Does it feel like YouTube is the most stable place to build as you’re trying to tell these new stories? It does. I mean, obviously you never want to put all your eggs in one basket. So, we are also distributing video on Apple Podcasts and Spotify and we’re going to be everywhere. But yeah, it feels relatively stable. The question I’m always wondering is what would it take to replace YouTube? Many companies have tried to replace YouTube, but to do that, you would not only have to build an experience that was better for creators and better for users, but you would also have to replicate the entire visual corpus of humanity. You would have to just get 6 million videos from plumbers about how to fix every individual sink and then upload it to that. It’s a really hard thing to displace. It is so ingrained for so many people, and I just think it’s going to keep growing. We’ll come to this when we talk about AI, but a long time ago I asked Google CEO Sundar Pichai, “Well, what are you going to do if there’s no more websites?” And he was kind of like, “Well, we have YouTube.” And there’s just no arguing with that. You sure do. And it is just going to keep growing. What has been the most surprising thing about going from The New York Times to what you might call the creator economy? Well, we’re very new at it, but surprising to me so far is how much I like all the business stuff. I think in journalism, you’re always taught to stay in your lane, and I have never run a business before. Casey has run a business. I knew he enjoyed it. But for me, these are new muscles and I don’t enjoy all of it. Negotiating the insurance quotes for our defamation coverage is not what I would feel compelled by. But regarding actually running a business, there is something to this founder thing. Yeah, it’s a good time. It’s funny, in talking to you and to Casey and Joanna, who also just left and started her own thing, that comes up a lot. From my perspective, it’s why I’ve been doing this a long time. What if I stopped running the business? That doesn’t seem to be a choice. This is the future of media — taking control of your business and taking control of your audience. There’s something very powerful there. One of the things I want to talk about, and then I definitely want to talk about the book at length, is the audience for AI coverage, because I see it in two ways. There’s a group of people building the stuff that loves it and wants to consume all of it. There’s a group of people that truly hates it and thinks all of it is fake. And then there’s what I would call an expanding group in the middle where the products have become more useful. You have to look at them and evaluate them as they are. They were not terribly useful a couple years ago. The promises did not match reality, but every day now it can do one more thing that it couldn’t do before. Whether that thing is hacking all of the websites in the world, we’ll set that aside, but AI can do things it couldn’t do before and the audience seems to be changing. How are you thinking about the audience for Machine Gods and how it might grow or change? I mean, right now we’re just trying a bunch of stuff that we think will be good and we hope that it will find its sort of natural audience. But I think you’re right that the audience is changing. I think for a while there was a lot of demand for cope — basically people telling you this is not happening, you don’t have to pay attention to it, it sucks, it’s hurting the environment. There still is in some parts of the internet. YouTube has a weird amount of this still going on and Reddit is also very anti-AI. But I think most people, or at least maybe a plurality of people, are either resigned to or excited about the fact that this is not going away. That is the audience that we’re looking for. We’re not trying to tell you that AI is amazing and we’re going to be in AI utopia under the new machine regime. We’re also not telling you that this stuff is fake and sucks and is going to go away on its own and so we don’t need to do anything to regulate it or be worried about it. Casey’s phrase for this is AI realism. He thinks there’s a big chunk of people who are just sort of curious and interested and maybe a little scared, but their reaction to the fear is not to try to bury their heads in the sand, but to try to understand the thing. So we started feeling this when we were doing Hard Fork where we would do something that just seemed kind of not our vibe. We would do a Claude Code walkthrough tutorial where we would just spend half an hour building a website and film ourselves and throw it up and it would do great. So people clearly were curious, and even if the comments on the videos were all from people who hated AI, there was clearly a lot of demand out there for people who were just like, “I kind of want to know what’s going on.” “I hate that I can’t stop watching this” is a real theme inside of AI coverage for a variety of reasons. Let’s talk about the book. I think that’s going to frame a bunch of the other questions I have. The book is meticulously reported. It is really fun to read. And the point of it, as I’ve heard you describe it, is to tell the history of the deep learning revolution because all these people just talk in Signal and in extremely childish tweets. Tell me about that gap because this is an industry that is obsessed with documenting itself for computers and being legible to computers and not at all to other people, which seems wild to me. So explain the reporting of this book, because it feels like convincing them to even tell you what happened was a slog. It was, yeah. So basically the reason I wrote this is because I was scared that all of this stuff was going to disappear. I am a history buff. I love to read books about the Wright brothers or the electrification of rural America or the moments of great invention during the PC revolution. I’m such a sucker for that stuff. What you notice as you read more of these books is just that they all rely on a lot of contemporaneous notes and evidence — the lab notes from the scientists developing the atomic bomb, the correspondence between the Wright brothers and their friends and business partners — that is preserved and archived. It was just clear to me reporting on AI every day that there was not going to be anything remotely similar for this stuff because A, all the people building this stuff work for private companies. They’re not government employees who are legally required to preserve their correspondence. And B, the Signal messages and Slack messages are all set by the lawyers to auto-delete after a certain period. So there was literally going to be kind of a mass disappearance with each passing year of these, I think, historically important artifacts, moments, and stories. And then C, the researchers are all too busy to keep notes. They’re all grinding because they’re trying to build the machine god. So, I thought as a journalist, but more so almost as a historian, which is not a role I’ve ever officially played, but one I think I was playing with this book. I was just like, “I’ve got to just get this stuff because I have this sort of privileged ringside seat. These people talk to me. I know some of them will sit down and talk to me and tell me the stories.” We did a lot of it on background so they could be protected from the consequences of talking to me. But this was sort of my attempt to furiously get all the stuff and put it in the archive where future generations could look at it. The theme that really jumps out at me as I read the book was there’s a moment when AGI goes from being a thing they’re all dreaming about to being a thing that they all have to build and the shackles come off and they say, “We have to do this,” and they all drive toward it as hard as they can. You talk specifically about Google where the Overton window moved and suddenly the people inside of Google, who thought this was all a pipe dream and pretty silly, started talking about AGI. That moment feels like the single most important moment in the history of AI, where “Won’t it be great?” goes to “We better go as fast as we can.” Talk about that a little bit, because that shift inside these companies feels under-reported. Yeah, I think that’s absolutely right. I mean, people have been talking and writing about AGI or some precursor that wasn’t called AGI but meant the same thing since literally antiquity. And especially since the 1950s, when the term artificial intelligence was born, people were talking about it. Alan Turing was writing about the thinking machine that could do anything the human brain could. So this concept has been around for a long time, but there was no technology anywhere on the market or in research labs that could actually do what people wanted it to do — that could take in information, learn to learn about that information, and then use that to make predictions and act in the world. People tried lots of different ways to do this and none of them worked all that well. What really needed to happen was the transformer needed to come along. This architecture that Google developed in 2017 and published and then presented to, among other places, OpenAI, which took the idea of the transformer and ran with it. And that’s the T in ChatGPT today. But that was the– Somewhere a Google PR person just leapt out of a bush to hand you a check. They do love this line. They love it when you say it. They get so happy. They love it. They love it less when you point out that although they invented it, they absolutely failed to turn it into a product until after OpenAI did. But yeah, it was a dream, but there was nothing to attach to the dream. There was no technology. And then the large language model came and suddenly it was like, “Oh, maybe this is the thing that could scale forever and keep improving over many orders of magnitude.” So, that’s the light bulb that goes off at OpenAI and then later at Google is like, “Now we have the tool to do the thing that we have always wanted to do.” You’ve got a line in there where you just point out that next-token prediction has not failed yet — that it is doing all the things people want it to do. We’ve obviously had to develop lots of technology around it. There are harnesses to make agents work. There’s reinforcement learning. There’s all this stuff around it. But that core technology has scaled. The reason I bring that up is that in 2023 into 2024, I would ask every AI person who would come on the show or that we would see on The Verge whether LLMs can do it. Can they do the thing you want them to do? Because I don’t think at that moment it was clear that they could. We’re closer now. They’re all openly talking about AGI. We are watching Meta’s Muse just walk its little butt around the internet whenever it wants. Do you think LLMs can do it? Do you think they can get there? I think they can. I’m not certain about that, but they’ve been shockingly good and effective so far. If you go back and you look at the first LLMs, it’s just like 99 percent of people thought this is crazy, that this won’t scale. And then it just keeps working at every stage, at every order of magnitude. I’ve asked people, what was it about next-token prediction that was clearly superior to everything that came before it? There have been people who have told me this actually exists in the human brain too. There’s a whole school of thought that says that what is happening inside the human brain is essentially next-token prediction at scale. Obviously, it’s more complicated. These are not one-for-one replicas of each other. But I think this is the fundamental mystery at the heart of AI: why is next-token prediction so remarkably effective? I think we don’t know. I kind of think it’s one of these cosmic mysteries, but why all of this works is because next-token prediction turns out to generalize really, really, really well. There’s a part of that where all these companies had to believe it. Dario Amodei of Anthropic obviously wrote his paper, “Big Blob of Compute,” where he said you can just do it. You can just dump data and compute at this and we’ll get there. Everyone else had to be convinced of this. What path did the AI companies all take to be convinced of this, and are they all fully convinced of it now? I don’t think they’re all fully convinced of it now. I think they are convinced of it to varying degrees. Ilya Sutskever has this phrase, “Feel the AGI,” that he would walk floor to floor at OpenAI chanting in the old days and reminding people to feel the AGI. I think the labs feel the AGI to varying degrees. I think Anthropic is clearly the most AGI-pilled of the labs, with OpenAI kind of a close second. And then there’s Google, which I think has always struggled to feel the AGI. I think there are lots of reasons for that, but it’s very funny. I love this section of the book where there’s this internal memo battle at Google, where they would just go on their internal message boards and Googlers love to write these manifestos and then snipe at each other’s manifestos and things like that. So a researcher writes this memo, “Say AGI,” which is all about how Google — if it wants to win in AI, recruit the top technical talent, and remain safe about how it’s developing AI — has to admit, through using this term AGI, that what they are building is more than just a bunch of tools to make your Gmail better and make your docs better and make your Google Photos better at recognizing the people’s faces. It is all actually building up to something like a kind of human-level general intelligence. And then someone else immediately writes a memo that’s like, “Don’t say AGI, it’s a horrible, unscientific term.” They go back and forth like this for a while, and you’ll notice even today, [Google CEO] Sundar Pichai does not say AGI. No, he won’t. Demis Hassabis says AGI. Sergey Brin says AGI. So I think there are kind of factions within Google that either think this is still crazy and we should talk about these things as narrow tools versus I think this growing and ascendant faction that is saying, “No, no, no, we’re building AGI, or at least we better say we’re building AGI because otherwise all of our top people are going to go work for OpenAI or Anthropic.” I think you and I had the same experience at Google I/O in May, where we watched Demis Hassabis end the whole keynote by saying, “We’re in the foothills of the singularity.” And then we both went and interviewed Sundar and said, “What does that mean?” And he was like, “Yeah, yeah, Demis. Cool guy.” [Laughs] Take it up with Demis. He was like, “What a kooky character that guy is, building the machine god over here. Let me tell you about the new features in Google Assistant.” There’s some big disconnect there that maybe explains everything about Google at this point in time. The reason I’m asking about this in particular, about this moment when they all clicked over to realize somehow we have to talk about it, is that OpenAI and Anthropic were founded to do this. They were built to make this happen. But they don’t agree on how it should work. I saw you at your book party in New York and you said that you could have retitled your book “Fuck That Guy,” because they all truly hate each other. It’s true. And yet it seems like they have the exact same idea — that they can get there with this technology. The commercial imperatives on OpenAI and Anthropic are very different from the commercial imperatives for Google. Right now, as you and I are talking, it’s not clear to me that they can get to AGI before they run out of money or before CoreWeave collapses or before the bubble writ large comes to some other conclusion about the state of the economy. Do you think that their religious fervor at AGI is keeping them from running a business? If I ran Anthropic today, a logical thing you could say is we should stop training new models and sell the very capable ones we have to more people. I think that belief in AGI is a double-edged sword. I think on one level, yes, if you are super AGI-pilled as the CEO of a frontier AI company, you are going to struggle to care about things like what is my profit margin on inference? Where is my next slate of enterprise customers coming from? Because how boring is that compared to “we are going to build the superhuman machine intelligence that leads us into the post-singularity future”? On the other hand, I think belief is actually a key ingredient in AI success, because belief and the ability to act on that belief is what leads you to do these bet-the-farm moves that can really pay off and have really paid off. Do you remember a year ago when everyone was saying, “Oh my God, OpenAI is spending so much money on compute; how are they ever going to pay for all this compute? Isn’t Sam Altman overextending?” Even Dario was giving interviews where he was throwing shade at OpenAI for spending so much goddamn money on compute. Fast-forward a year and OpenAI is now the only major AI company that is not severely compute-constrained. That belief that led Sam and his executives to bet and maybe overbet on compute last year was the reason that they are in a better position than Google or Anthropic or Meta or anyone else today. So I don’t want to say it’s always a liability to believe this hard, because I think it actually does allow you to make the bets that do end up paying off if the scaling continues to work. It’s interesting because I might reframe that OpenAI decision. You write very poignantly in your book about what people at OpenAI call “the blip,” where Sam Altman was fired for 20 minutes and then he came back and that cleared out a bunch of people from OpenAI. It is the moment you described in the book where OpenAI went from being a research company into being a business. The rational business decision was, “We need to sell more AI to people. We ought to have as much of it as we can.” And Sam is well known to be a ruthless business person. I’ve heard people describe him as S-tier at raising money, which is just an incredible Silicon Valley phrase. So I look at that as a business choice. They got rid of a bunch of stuff they were doing. They pivoted to making enterprise AI products, which is where the money is, and then they realized they needed to service that demand. You’re saying that was a bet on AGI, which to me is a more idealistic turn. Can you square those two? Yeah, I think it absolutely is a bet on AGI because Sora, which they launched and spent a bunch of money and compute on and then killed, is a cool toy that might open up a new line of business for you, but it is not going to get you closer to AGI. What actually gets you closer to AGI is sparking recursive self-improvement by building really good coding models and then turning them back on themselves. That was something that Anthropic was doing. OpenAI got distracted by the side quests and shiny objects that it had last year, and the company was getting its asses kicked. So OpenAI decided to trim the sails, cut fat, and really focus on coding and long-horizon agentic workloads. That is, as of now, the leading path to AGI or to whatever comes after AGI. So yes, I think that was absolutely a bet on conserving resources by not spending them on things like Sora, but what they did spend it on was trying to catch up with Anthropic in coding so that they could reach AGI. So when I look at Dario Amodei at Dreamforce standing next to Marc Benioff and he’s talking about selling enterprise software, is that just for show? That’s the thing I’m trying to square is they’ve all become enterprise software vendors in the realest way. They are B2B SaaS companies in the realest way now, but they’re also building God. You know these folks. Does that align? Are they like, “I have to take off my B2B SaaS hat now and go back to the altar?” I think the B2B SaaS thing is the game that they realize they have to play, but I don’t think it is any of their true passions. I was talking with Sam Altman for this book and one of the things he said was basically all of this was an accident: “I was never supposed to be running a giant tech company; that just didn’t factor in.” But now you wake up and you’ve got thousands of employees. Dario Amodei also has this very funny in retrospect document that he wrote in 2021 when they were just starting the company. It’s called “Vision for a NewCo.” You can go back and look at the predictions that Dario made about AGI, and one of them is he thinks that it will cost about $10 billion to build a full-fledged AGI, which was, I think, off by at least one order of magnitude and probably multiples. He also thinks that Anthropic will never need more than about 50 people to achieve AGI. I think now they have 5,000 or so people. So again, off by 100x. So I think what has happened is that this has just taken a little longer than all of them thought and cost quite a bit more money than they thought. The enterprise SaaS stuff is how you keep the flywheel going and pay for the GPUs and for the salaries of the researchers. This is the table stakes of getting to keep pushing on the frontier and keep pushing toward AGI. Are they ready to be the leaders of companies of this scale? This is another thing that I was thinking about as I read your book. They’re all very idealistic people, so much so that they all broke off from one another and made their own companies because they could not make their ideals align. But none of them seem like great managers. I don’t know truly what they’re like, but “they’re idealists” and “we are going to run an enterprise software company” are not usually in the same sentence. Do you think they can take the pressure? No, but I don’t think anyone can. What they are attempting, no one has ever done. People have built companies. People have built companies quickly. No one has gone from $0 in revenue to potentially a $2 trillion IPO in five years. That has never happened before. So look, I think that they are scrambling to build the plane as it’s careening toward the event horizon, but I think they’re doing about as well as anyone could. I don’t think it would be a good idea for them to bring in some professional CEO from Coca-Cola to run the company. I just think it would all kind of fall apart because they’re all zealots and missionaries. They are all in this — not all, probably there’s some people in the SaaS sales department who are just kind of there for the paycheck — but a lot of the key people at these companies are united in their belief that they are building [AGI]. One person, I won’t say who, but one person at one of these companies recently told me that they think their company might be the last company. I thought, “The last company to IPO this year? What do you mean?” They were like, “No, we think this might be the teleological end of capitalism and there might never be another lar