Kevin Roose Didn’t Use AI to Write His Book About AI

For most people, the AI Era we’re now living in started sometime around the end of 2022, when OpenAI released the first consumer version of ChatGPT to much fanfare—and, ultimately, over 100 million downloads within a few short months.

In reality, as WIRED readers know, the story of artificial intelligence stretches back decades. And the work informing today’s generative AI products, from chatbots to agents and beyond, has been more than a decade—of blood, sweat, tears, and furious rivalries—in the making.

It’s that recent history, told through the interweaving stories of three generative AI leaders—OpenAI, Anthropic, and Google—that’s chronicled in Kevin Roose’s compelling new book, The AGI Chronicles. Roose, a longtime tech journalist who most recently served as a columnist and podcast host for The New York Times, conducted more than 150 interviews with AI insiders to compile what he hopes becomes something of a historical document, an accurate, in-depth retelling of the people and the institutions, the bloodbath and the drama, that brought Silicon Valley to where it is to today.

I spoke to Roose in late September, a few days after he and Casey Newton—with whom Roose previously cohosted the New York Times’ Hard Fork podcast—announced the creation of their new media company Machine Gods Media, alongside a podcast distribution deal with NPR. We talked about what Roose and Newton hope to accomplish with the new show, why traditional media companies have a talent problem, and, of course, all things artificial intelligence.

This interview has been edited for length and clarity.

KATIE DRUMMOND: You and Casey Newton recently announced that you were launching a new media company, called Machine Gods Media. And your podcast is also called Machine Gods, correct?

KEVIN ROOSE: Yeah, a name so nice we used it twice.

When you first announced that you’d be leaving The New York Times, you described your vision for what you wanted to do as one that, quote, “takes AI progress seriously, is clear-eyed about the capabilities and risks of powerful AI systems, and tries to empower and entertain people in the face of radical uncertainty.”

As you look at the landscape of tech coverage and AI coverage, what’s missing from the reporting and commentary that you and Casey want to address with the new show?

We just feel like it was high time that two men had a place to talk about AI.

I've been saying this for years. I want more men talking about tech.

Look, there is obviously no shortage of podcasts and YouTube shows and mainstream media coverage of AI. It’s the biggest story in the world right now. But when Casey and I looked out at the media landscape, we saw some issues. One was there are people who just are getting very famous and having a lot of success saying that all this AI stuff that’s going on is fake, it’s hype, it’s a giant financial bubble, no one is using these tools, they’re not going to have any impact on the economy. You know, OpenAI is going to go bankrupt. Anthropic’s going to go bankrupt.

This is sort of a genre of popular criticism, and it’s not just a few people. I hear this from friends of mine who don’t pay close attention to tech news and just assume that what’s going on is just fleeting and trivial and that it will all go back to normal soon.

There’s another genre of AI coverage that is purely hype. It’s look at the 17 amazing ways that Claude can supercharge your enterprise SaaS business. You can go on LinkedIn and see example after example of people who are purely excited about this technology and don’t really care to talk about the risks.

We both thought there’s a large gap in the middle for what Casey calls AI realism, which is basically this idea that you can take AI seriously, acknowledge that the tools are powerful and impressive and in many cases dangerous, and that you can help people understand that and demystify this area without slipping into boosterism, and that you can also have a good time while you do it.

The show you announced recently is being published in partnership with NPR. Why was that the right partner?

A bunch of reasons. Both Casey and I are big fans of NPR. We like the fact that they have a broad independent reach and mandate. We like the fact that they’re going to let us own the show and make the creative decisions, and it will be a distribution partnership rather than a full acquisition, so we will still have some operating distance.

We think this is a really critical time and a really important story, and we like the idea that people might be in their cars just listening to their local NPR member station and happen on our podcast.

Maybe that’s going to be someone who works in policy, or maybe that’s going to be someone who is involved in local government. Maybe that’s going to be someone who has a very different point of view on AI. We don’t just want to have the opt-in, self-selected tech audience listen to us.

I am going to ask you a crass question. A Bloomberg report recently said you both were fielding offers of up to $5 million for the show. It’s a startling sum of money. Kevin, how much are you making?

It's not $5 million.

Is it more than $5 million?

So much more, Katie. No, look, they have made us a good offer. We could have gotten more money elsewhere.

When I heard NPR, I thought to myself, “There’s no way NPR’s giving those guys $5 million,” with all due respect to NPR.

Public radio is not traditionally where people go to get rich in media. We loved their new chief content officer, Nadine Zylstra. She’s just a total force of nature, and we’re very excited to work with her.

We thought they had a lot of things to offer us beyond money, like their distribution on radio. People don’t realize how big radio still is. The reach of radio, and especially public radio, is still quite large. We’ve had this show, Hard Fork, for the last four years. We built up a pretty good-size audience, but the Times owns that show and owns the feed.

We are looking to grow our show as quickly as possible, and we just thought that the combination of commitment to journalistic excellence, their wide distribution, and their investment in helping us grow the show was the right combination of factors.

This brings me to another thing I’m curious about: The idea that a great reporter, a great commentator, can spend time somewhere “traditional” like The New York Times. They can build a brand, and then they realize that they can just go do it themselves, and they don’t actually need that institution anymore to exist in the world as talent, and often to make a lot more money than they would in traditional media. What’s your take on that?

Look, I don't have anything bad to say about The New York Times. I had nine wonderful years there. It was my second stint at the Times. I’ve spent the vast majority of my career at The New York Times and inside these big media institutions.

I do think we are entering this moment where at least for some portion of the audience, they want to connect with individuals more than institutions. We’ve just seen this in wave after wave. I don't know. I am not doing this for ideological reasons. I’m doing this because I thought it was a really exciting opportunity. But I do think that organizations that want to retain and attract very talented people will just need to be more flexible about these kinds of arrangements.

Some people aren’t going to wanna give up their Substacks and go inside a media institution. Some people aren’t going to want to have all of their work published by one publication. I think there are some media organizations that are starting to experiment with different, more flexible ways of bringing people in part way or having them maintain their independent operation but also contribute on an ongoing basis.

I think there are a lot of ways this can work, but I think it all has to start from a recognition that the journalistic career path where you go in the mail room and you work your way up and you spend 25 years at the same employer and you eventually become an editor and then a manager of editors, that has broken down. That is regrettable. I don’t think that’s a good thing that it’s broken down, but it has broken down.

I think institutions should grapple with the fact that there’s now a generation of media entrepreneurs who just don’t find what they have to offer all that appealing.

When you think about the talent piece of that, when you think about AI, are you optimistic about journalism and the industry of journalism?

I am very optimistic about the application of AI to journalism. That is one place where I have wanted to do more experiments, not with having AI write for me or do all my reporting, but like ways of extending journalism using AI.

What's an example of an experiment you would love to do?

I have former colleagues at the Times who have done incredible document analysis on a scale that wouldn’t have been possible before, using satellite imagery to determine whether a munitions factory has moved or something like that.

That’s the kind of thing that I don’t do much in my own life but that I would like to see other organizations trying, because I think that’s really cool. I have used AI to research and edit and improve my own work for months now. I have found that very helpful. I think the caliber of my work is better, and I would love to see more institutions in the media experimenting with these tools to improve the output of their journalists. Not just like, you know, filling their websites with slop but actually helping these be tools to make journalists better.

I want to talk about your book, The AGI Chronicles. When did you decide “I’m going to commit years of my life and my career because there's a book here.” What was that moment for you when you realized that this was a big deal?

I know exactly when it was. It was early last year, 2025, and I was in the car on the Bay Bridge stuck in traffic, and it just kind of hit me, like an epiphany. It was like I have been following this story in all the incremental detail for years now.

I’ve interviewed all the major AI researchers and CEOs. I’ve spent time with the papers. I’ve gone to all of the companies and reported on what they’re doing. But there was this larger story that I was missing. I hadn’t really zoomed out and tried to take a more panoramic view of something that was just weird.

I felt living in the Bay Area, being immersed in San Francisco tech culture, I had kind of gotten acclimated to that, and it no longer seemed as strange to me that there were these companies racing to build the machine superintelligence that could either save or destroy humanity.

Right.

I sometimes feel like I am in Los Alamos, New Mexico, in 1943 when the Manhattan Project rolls into town, and I’ve just kinda got my lawn chair and I’m looking at trucks rolling in and trying to make sense of what’s happening.

I believe that this technology is important and that the people and the companies who built it will be important historically. So when I thought about who is actually doing the work of writing all of this down, it was nobody. Nobody was doing it.

I just felt like it would be a tragedy if all this just disappeared in a bunch of Signal messages and Slacks that auto-delete, and if we just end up with no durable historical record of this really strange decade in AI when things went from not working at all to threatening the future of humanity.

I spent about a year reporting and writing. I talked to more than 150 people. I should have probably taken more time, because it was very hard and intense. But I think what came out of it was an artifact that people and future AI systems can look back at to say, “Here is how this happened. Here’s who made it happen. Here were the key decisions and moments along the way.”

The book follows three key companies, OpenAI, Anthropic, and Google, in their pursuit of this technology. What were your big-picture learnings about those companies and the key differences between them that you think is important for people to know?

The companies are very different from one another, both in the makeup of their personnel and also in their ambitions.

Let’s start with Google, because they’re the oldest. They have had for decades now an advanced AI research effort. They were pioneers in AI. They developed the transformer, which is the T in ChatGPT, the sort of foundational technology that all of this other stuff rests on, and then there were these two guys, Elon Musk and Sam Altman, who got very worried about how well they were doing, about Google racing ahead, and they decided to start OpenAI …

To imagine that now …

Yeah, it’s wild, and we have the emails. It's all there in the record where they're basically like, “We have to start a lab that’s going to beat them or at least challenge them so that they don’t run away with the whole game.” So they start OpenAI, and they do a couple years of that, and then this guy at OpenAI, Dario Amodei, he takes six of his colleagues, and they leave and start Anthropic basically to make sure that OpenAI doesn’t get to this critical threshold of AGI first. So the whole industry spawned out of itself. These people all used to work together, and now they run these companies that are mortal enemies. Like, I was shocked. This was actually my biggest surprise. I thought this was more like Coke and Pepsi, but this is not a buddy-buddy industry. This is like a blood feud.

From all of the reporting that you did, who do you trust? Who do you trust with our future in the context of artificial intelligence?

I don’t trust any single person.

None of them.

What I learned through reporting this book is these are people, they are flawed, they are fallible, they have moments of weakness. Their motives are never 100 percent pure. Some of these people are quite nice. Some of them are very thoughtful. I think we have in some ways gotten very lucky with the people who are running these AI companies, who I think are, on the whole, much better suited to build powerful technology and release it into the world than the social media barons were.

So there is a marked difference between the Facebook era and this AI era?

Oh, yeah. For one, they are just way less naive. You know, the social media guys came in and they said, “We’re going to change the world. We’re going to free communication from the bottlenecks that hold it back. We’re going to distribute the benefits of technology to everyone.”

And they really didn't start thinking about the problems until they were being questioned in front of Congress.

I will say some of these AI guys talk a lot about saving the world. They talk a lot about how great this will be for humanity.

If you go back and look at the founding emails of OpenAI, they have been very consistent that they think this is a potentially very dangerous technology.

Now, they’re racing toward it, so maybe their words don’t mean that much. But I think you can’t accuse them of being naive, because they just have such a long track record, all of them, of saying that, “Yeah, this could be great for humanity, but it’s not a given that it will be, and we need to build it really carefully and thoughtfully to make sure that it's actually going to turn out well for us.”

What kind of responsibility do you think falls on their shoulders in the context of AI safety?

This was something that I’ve asked all of them about at various points. Like, why don’t you just stop?

Yeah.

Why do you get up every day and try to make these systems more powerful if you’re worried it could end the world in some cases?

Some of them, including Dario and Sam, genuinely believe that this technology is inevitable. The recipe for AI is not that hard. You get a lot of compute, you get a lot of data, you build the right scaffolding and grow the model in this organic process of stochastic gradient descent and reinforcement learning, and out comes a superintelligent model.

It is their sincere belief that it’s not that hard to build this stuff if you understand the basics and that, because it's not that hard, someone will do it. And whether that someone is a US AI company or a Chinese AI company or a terrorist group or an academic research lab, someone will do this.

So it is in the best interests of humanity for someone who thinks a lot about safety to be the first to get there, because they can set standards for the rest of the industry. Now, I don’t know if I fully buy that, but that is their logic, and that is the reason that they feel validated getting up and doing this every day.

The book ends in an interesting way, and I have a couple questions about that. It ends, essentially, with you saying, “I really hope these guys get it right. I really hope that they slow down so that I can keep living my life the way I live it now.” It’s funny timing that the book is coming out right as these very acute conversations around AI safety are happening, and it feels like every other day a company is disclosing some new breach, something that went wrong with one of their models. How scared are you right now, Kevin Roose?

I am actually feeling quite hopeful right now relative to where I was a few months ago, and it’s largely because we are now having this conversation.

There have been people, including many of the people I spoke to for the book, who have been worried about runaway AI, rogue AI, a possible AI takeover for 10, 15 years, who have been trying to sound the alarm about this, and no one believed them. Outside their little bubble of AI safety people, everyone was sort of like, “Yeah, yeah, yeah.” Now I feel like we have finally reached this point where this stuff is inside the Overton window.

I think that some of what I see on social media and some of what I see in the news media is really hyperbolic and is really dramatic. I’m not saying that the stakes aren’t dramatic, but my assessment of the situation is that if something does go horribly wrong with artificial intelligence, it will be much more boring than we might think, and it will have more to do with human error than we are sometimes attributing.

Yeah, I totally understand that, and to be clear, I’m not saying I know exactly the risks that we should be most afraid of. I just think there’s this whole category of risk that has kind of been written off or downplayed or just, you know, those are just those weirdos in Berkeley talking about it. Now we can have conversations about it.

People are worried, they’re taking this seriously, and I think we really have a window here where we can actually make some changes or take some steps to make sure that this goes better for humanity. I don’t think that was possible a couple of months ago.

So I think that’s why I’m feeling more hopeful, because even though I think objectively the models are getting worse and scarier and more dangerous, we are also much better positioned to recognize and talk about and perhaps prevent those risks.

In that last page of the book, and I don’t want to ascribe an emotion to you, but I felt fear there from you and a reluctance to see your life change too quickly. I think that is something that is very much universal.

Absolutely. I think this is where the people inside the AI bubble do not understand the world. I think the people at the AI companies building this technology are generally people who enjoy the prospect of large, unannounced social change.

Unannounced social change. Hard to imagine a bigger nightmare for me personally.

Like, they love when things get weird. That’s part of why they moved to San Francisco. They wanna live in the future. The prospect of radical upheaval does not scare them.

Doesn’t that seem like a huge problem to you?

Yes, because normal people don’t think like that. Normal people want to live a life that is recognizable to them. They want their kids to grow up in a society that resembles the one that they grew up in. We don’t manage change very well as a society, and we never have. But I think this is why I wish that there had been more types of people involved in the critical conversations around this technology.

Because, as I said, 50 people in San Francisco, give or take, made all of the relevant decisions, and they are not a representative sample. They are very weird. They see the world differently. They have a higher tolerance for change than most people, and I think it led to them making some decisions that we can’t really take back now.

I mean, do you think that if two years ago we had had more philosophers, more artists, more creatives, more journalists, lawyers, whoever it may be, involved in those conversations, that it really would’ve moved the needle when there is so much money on the other side of that conversation?

I guess what I’m saying is that, sure, it’s all well and good for a broader coalition to be having those conversations, but Greg Brockman’s donations to the Trump administration get him that phone call, right? They have the president’s phone number. The access to the decisionmakers is bestowed upon those 50 people in San Francisco by virtue of their wealth and their power.

I think it can help at the margins, and I’ll give you an example. One of the people I write about at some length in the book is Amanda Askell. She is a longtime employee at Anthropic and was at OpenAI before that. You just had a story in WIRED about searching for the most powerful woman in Silicon Valley. I think she’s gotta be up there in the top two or three.

She has been in charge of Claude’s character. They call her the Claude mother at Anthropic. She’s a virtue ethicist. She has a PhD in philosophy. She went into AI specifically to think about this question of what should a good AI system do? How should it act? What values should it represent? How should it decline to do certain things or volunteer to do certain things? How can you instill something like virtue ethics in a chatbot? This was a very fringe area of research. She was, to my knowledge, the first person ever to do this kind of work inside one of these AI companies.

It has resulted in them having a really sophisticated way of thinking about the morality and the ethics of Claude. She now has a whole team. They have many people that are devoted to this, and I think it has probably made Claude not just safer and better behaved, but has also inspired other labs to hire their own philosophers and come up with their own ways of training their AIs for something like moral goodness.

I think there’s a really strong argument for having lots of people from lots of different disciplines engaging with this technology, because it should not just be engineers doing this.

As someone with a bachelor of arts degree in philosophy, I have to say it is a boom time for my people.

There was a lot in the book to me that was troubling. What stands out to you that is hopeful when you think about the book, when you think about the technology? Aside from the fact that we may actually make some meaningful progress toward regulation, was there something you discovered in your reporting that made you feel optimistic?

A lot of the optimism that I feel around this stuff has to do with science and medicine. I lost my father to cancer. I know lots of other people who have lost loved ones to rare diseases that have not been cured, not because we lack ingenuity, but because it’s just a question of resources and manpower. I think that AI can do incredible things for people who suffer from disease. I don’t think that’s all marketing BS.

There’s a story in the book about the DeepMind protein-folding AI system that won the Nobel Prize, and there’s this incredibly touching moment where a mother of a kid who has a life-threatening genetic condition writes to the DeepMind researchers after this breakthrough and asks them, “Could this help my kid?” They have to give her the honest answer, which is probably not, because this stuff takes time. You have to get it through clinical trials. Even if you have these amazing breakthroughs in science, you have to design the drugs, you have to test the drugs, you have to get the drugs approved.

It can be a decade before these things actually make it to saving people’s lives. But I want that to happen faster. I want there to be more AI-designed drugs. I want us to get them to market quickly. I don’t want people to suffer from the same diseases that have killed previous generations.

So that’s where I feel a lot of optimism right now. Ask me in two weeks, maybe it’ll be something else, but right now that’s where I’m feeling good.

I’m curious about your own process with AI. You have published three books prior to this one. Your first was published in 2009, well before any of this technology was available. How did it change the book-writing process for you? I know you're very open with how you use AI, sometimes to much controversy among your journalistic peers.

There’s a whole section in the beginning of the book about how I did and did not use AI, ’cause I thought it was important first and foremost to be transparent.

Kevin, I’m going to be transparent with you. I ran your book through Pangram.

How did it do?

The book is written by a human being. Presumably by you.

Yeah, it was written by a very tired, overworked, underslept human being. I also had a researcher help me with this, Jasmine Sun, and had a bunch of great human editors at FSG.

The book is fully human-written, but how did you use the tech?

I had a giant notebook on NotebookLM filled with all of my research materials, interview transcripts, magazine articles, and academic papers. And the most basic way I would use that is just to query it about things that I needed to know. So like, “Give me all of the stories that I’ve heard about the pre-training of GPT-4,” and then it would pop back a list, sort of like a supercharged search function.

I also did it to help with some reporting tasks, like who would the three people have been in the room when this decision was made, and what are their contact details? It helped with organizing my notes, with fact-checking. Actually, at the end, I had a human fact-checker, but I also had an AI swarm doing fact-checks and catching some things that honestly neither I nor the human fact-checker had caught.

Then I used it for editorial feedback. I have what I call a Council of Claudes, which is my team of Claudes that are assigned to different personalities and vantage points. So I have one who’s a hardcore LLM skeptic who goes through the draft and tells me, “Oh, this is what I object to. You’re anthropomorphizing here. It’s not really thinking here.” I have one that’s a Kurzweil-type futurist that wants to expand the vision of the future in the book. Like some of this was just slop and probably wasted more time than it saved, but there were a couple things that the Council of Claudes said to me where I was like, “Oh yeah, that’s a good point. I should go back and revise that.”

How do you think about the premium on human-generated writing? Do you think in five years, the average person will care that Kevin wrote this book as opposed to an AI writing that book? Do you think it matters to people?

I do. I think it matters a lot to people. I know this because they’ve done studies where when people read a sample of writing that is generated by AI, but they don’t know it’s generated by AI, they give it very high marks. They prefer it to human-written text in a blind test. But then the minute you tell them this was generated by AI, they hate it.

Their approval of it plummets. I think this has a lot to do with human psychology. We like to think that people work really hard to make something for us, that it represents their authentic view. So people will still continue to be offended when they find out that their favorite writer has used AI to generate their latest book or essay or whatever. But I think that’s basically only if they detect it, only if they can tell, and I don’t think they can tell or will be able to for much longer. I think as long as Pangram exists, it will probably be a useful tool. But I also think that, in the abstract, people just don’t know the difference. When you tell them the difference, they care, but before that, they don’t.

That’s really interesting. It is the psychological value of wanting to know that someone’s fingerprints were on something and wanting to know that you were getting their voice and not the voice of Claude.

Totally.

I think it’s important for writers to show their process for this reason. To talk about and literally film themselves working so that people can connect with the labor involved in making something. I think that’s going to be important for humans of all creative stripes.

Is that going to be a social video series that you and Casey come up with?

Yeah, I’m streaming myself writing 16 hours a day on Twitch. Go check it out.

Sounds like hell. Kevin, congratulations on the book. Thank you so much for being here. This was fascinating.

Thank you, Katie. You’re the best.

How to Listen

You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how:

If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link. You can also download an app like Overcast or Pocket Casts and search for “uncanny valley.” We’re on Spotify too.