Are We Wrong About A.I.? | Clip

All right, Kevin. Well, if you've ever been on Blue Sky or Apple Podcasts reviews, you know that sometimes the Hard Fork podcast does get criticized.

No.

Yes. And one of the big criticisms that we hear is, “Hey, it really seems like you guys are hyping up AI too much. You are not being adversarial enough against this industry.” We wish you would bring on more critics who would give voice to that idea and really engage with it in a serious way.

Yes, we hear this in our email inbox every single week. And this week, we're actually going to do something about it because our producer, Rachel Conn, while we were out on vacation, has been cooking up this segment. So, Rachel, come on in and tell us what you've done.

Hello. Thanks for having me on. And thank you guys for being such good sports and, as far as I know, not advocating to fire me.

Well, the segment isn't over yet.

Yeah.

So tell us a little bit about what you did and how you came up with this idea.

Yeah. So, like you guys said, part of this is about responding to these listener emails that we've been getting. I think part of it is also this feeling that the AI debate is getting more polarized.

And I think there's also a personal-level thing going on for me, which is that I feel like I am increasingly spiraling when I think about AI, and I'm steeped in this the way you guys are because we're working on this show together. But I increasingly feel like you guys are finding ways to be more hopeful or optimistic than I am.

So part of my goal with this was actually to be like, “Okay, what's going on here? How are you guys arriving at this slightly different place than I am?” What I did is I spent the last few weeks reaching out to prominent AI researchers and writers who I knew disagreed with you.

Some of these people have argued with you online before, so I don't think you'll be totally surprised. But I wanted this to be on hard mode for you guys. I specifically sought out people who I hoped would challenge and provoke you because the truth is that they agree with you on a lot of basic things about AI.

These are all people who think that AI is highly capable, that it's impressive in some ways, and that it could be super transformative. But I think they have slightly different views in terms of maybe some of the harms that they're most concerned about or some of the benefits that they're more skeptical about. So I think we should just get into it.

Okay. Let's hear from our first critic, Rachel. Who did you talk to?

Yeah. So I thought we should start with one of the widest-ranging critiques. This is probably the most forceful criticism that came in.

This one comes from Brian Merchant, who is a tech journalist who writes a lot about AI for his newsletter, Blood in the Machine. As I understand it, Kevin, he has engaged with you a bit online about some of your reporting. Is that right?

Yes. I've known Brian for years. I really like and respect his work, although we have some disagreements about AI. But yeah, he has been emailing us saying, “You guys should have more critics on.”

I sort of jokingly said that I would have him on, but only if he let us give him a cattle brand that said, “Feel the AGI.” The conversation sort of trailed off after that.

Okay, great. I was wondering about that because he's going to make a reference to that in the critique that he wages.

I asked Brian to record his critique for us, and I will play it for you now.

Hello, gentlemen. This is Brian Merchant. I'm a tech journalist and author of the book and newsletter Blood in the Machine.

First of all, I want to say that I still want a whole show about the Leites and why they were right. And I think it's only fair because Kevin recently threatened to stick me with a cattle brand that says, “Feel the AGI.”

Which brings me to my concern: How are you feeling about feeling the AGI right now? Because I worry that this narrative that presents super-powerful corporate AI products as inevitable is doing your listeners a disservice.

Using the AGI language and frameworks preferred by the AI companies does seem to suggest that you're aligning with their vision and risks promoting their product roadmap outright. So when you say, as my future cattle brand reads, that you feel the AGI, do you worry that you're serving this broader sales pitch, encouraging executives and management to embrace AI, often at the expense of working people?

Okay, thanks, fellas.

Okay, this is an interesting one. First, I think I need to define what I mean when I say “feel the AGI,” because this is a phrase that is often used half-jokingly, but I think really does mean something inside the San Francisco AI bubble.

To me, “feeling the AGI” does not mean that I think AI is cool and good, or that the companies building it are on the right track, or even that it is inevitable or a natural consequence of what we're seeing today.

The way I use it is essentially shorthand for: I am starting to internalize the capabilities of these systems and how much more powerful they will be if current trends continue, and I'm just starting to prepare and plan for that world, including the things that might go really wrong in that world.

So that, to me, is what “feeling the AGI” means. It is not an endorsement of some corporate roadmap. It is just that I am taking in what is happening, trying to extrapolate into the future as best I can, and trying to get my mind around some of the more surreal possibilities that could happen in the next few years.

Do you ever worry that you are creating a sense that this is inevitable and that maybe people who may be inclined to resist that future are not empowered to do so?

I want to hear your view on this. My view on this is essentially that we have systems right now that, several years ago, people would have called AGI. That is not making a projection out into the future. That's just looking at what exists today.

I think a natural thing to do is to observe the rate of progress in AI and just ask, “What if that continues?” You don't have to believe in some far-future scenario to believe that models will continue to get better along these predictable scaling curves.

To me, the question of whether this is inevitable is just a question of whether the money being spent today to develop bigger and better models will result in the same kinds of capability gains that we've seen over the past few years. But what do you think?

Yeah. I mean, I think Brian's question is a good one, and I understand what he's saying when he says, “Look, AGI is an industry term. If you come on your show every week and talk about it, you wind up sounding like you're just amplifying the industry voice, maybe at the expense of other voices.”

This is just a tricky thing to navigate because, as you said, Kevin, you look at the rate of progress in these systems and it is exponential. It does seem important to extrapolate out as far as you can go and start asking yourself what kind of world we're going to be living in then.

I think a reason that both of us do that is that we see so many obvious harms that will come from that world, starting with labor automation, which I know is a huge concern of Brian's and which we talk about all the time on this show as maybe one of the primary near-term risks of AI.

I want to think a bit more about what we can do to signal to folks that we are not just here to amplify the industry voice. But I think the answer to Brian's question of why talk about AGI like it's likely to happen is that, in one form or another, I think both of us do think we are likely to get powerful systems that can automate a lot of labor.

Yes. And we would like to explore the consequences of such a world.

Totally. And I think it's actually beneficial for workers to understand the trajectory that these systems are on. They need to know what's happening and what the executives at these companies are saying about the labor-replacing potential of this technology.

I actually read Brian's book about the Lites. I thought it was great. And I think it's very instructive that the Luddites were not in denial about the power of the technology that was challenging their jobs.

They didn't look at these automated weaving machines and go, “Oh, that'll never get more powerful. That'll never be able to replace us. Look at all the stupid mistakes it's making.” They sensed correctly that this technology was going to be very useful and allow factories to produce goods much more efficiently. And they said, “We don't like that. We don't like where this is headed.”

They were able to project out into the future that they would struggle to compete in that world and take steps to fight against it. So I like to think that if Hard Fork had existed in the 1800s, we would have been encouraging people to wake up to the increasing potential for automation caused by these factory machines. And I think that's what we're doing today.

Yeah. And one more question: I would just love to see the sort of leftist labor movement work on AI tools that can replace managers. Right now, it feels like all of this is coming from the top down, but there could be a sort of AI that would work from the bottom up. Something to think about.

All right, let's hear our next critique, Rachel.

Okay. Wait, can I ask one more question on this front? Because I feel like one thing that Brian is really just curious about is whether you have ever considered using language other than AGI. Why use AGI when some people take issue with it?

I think it is good to have a shorthand for a theoretical future when there is a digital tool that can do most human labor, where there is a digital assistant that you could hire in place of hiring a human.

I just think that is a useful concept. If you're the sort of person who thinks that we will absolutely never get there, I don't know what to say to you, because we don't think that that's inevitable, but we do think it's worth considering that it might be true. So if folks who hate the term AGI want to propose a different term, I could use another term. But my sense is that the quibble is less with the terminology and more with the idea that any of this might happen.

Yeah. I also don't think the term AGI is perfect. It's sort of lost a lot of meaning. People define it in a million different ways. If there were another, better term that we could use instead that would signal what AGI signals and the set of ideas and motivations that swirl around that concept, I'd be all for it.

But I think that term has just proven to be very sticky. It is not just something that industry people talk about. It's something that people talk about in academia and in futurism circles. It is this rallying cry for this entire industry, and it is, in some ways, the holy grail of this entire movement. So I don't think it's playing on corporate terms to use a term that these companies use, in particular because a lot of the companies don't like it either, but it is the easiest and simplest way to shorthand the idea.

Cool. Okay. The next person whose criticism I want you guys to hear is Allison Gopnik. You guys, of course, know this Allison Gobnik is this very distinguished psychologist at UC Berkeley. She's a developmental psychologist, so she does a lot of work specifically studying how children learn and then applying that to how AI models might learn and how AI models can be developed. And she's also one of the leading figures pushing this idea that we've actually talked a little bit about on the show, which is this idea that AI is what she calls a cultural technology.

I'm Allison Gothnik at the University of California at Berkeley. The common way of thinking about AI, which is reflected in The New York Times' coverage as well, is to think about AI systems as if they were individual intelligent agents, the way people are. But my colleagues and I think this approach to the current systems of AI is fundamentally misconceived.

The current large language models and large vision models, for example, are really cultural technologies like writing or print or internet search itself. What they do is let some group of people access the information that other groups of people have articulated, the same way that print lets us understand and learn from other people.

Now, these kinds of cultural technologies are extremely important and can change the world for better or for worse, but they're very different from superintelligent agents of the sort that people imagine when they think about AI. Thinking about the current systems in terms of cultural technology would let us approach them, regulate them, and deal with them in a much more productive way.

So I appreciate the question. If Allison were here, I would ask her how she thinks that thinking about these systems as “cultural technologies” would let us regulate them or think about them differently. I think there are ways in which we absolutely cover AI as a cultural technology around here. We talk about its increasing use in creative industries like Hollywood and the music industry to create forms of culture, and about the risks that AI poses to the web and all the people who publish on the web. So that's one way that I think about AI as a cultural technology, and I do think that we reflect that on the show.

Now, I do hear in Allison's question a hint of the stochastic parrots argument, which is that this technology is essentially just a huge amalgamation of human knowledge, and you can dip in and grab a little piece of it here, a piece of it there. What I think that leaves out is the emergent properties that some of these systems have: the way that they can solve problems that are not in their training data and the way that they can teach themselves to play games that they have never seen before.

When I look at that technology, I think that does seem like something that is pretty close to an individual intelligent agent. So this is one where I would welcome more conversation with Allison about what she means. But that is my initial response.

These systems are built on the foundation of human knowledge, right? They are trained on all of the text on the internet and lots of intellectual output that humans over the centuries have produced. But I think the analogy starts to break down a little bit when you start thinking about more recent systems.

A printing press, writing, and the internet—these are technologies that are stable and inert. They can't form their own goals and pursue them. But an AI agent can. Right now, AI agents are not superintelligent. They're very brittle. They don't really work in a lot of ways. But I think once you give an AI system a goal and the ability to act on its own to meet that goal, it's not really a passive object anymore. It is an actor in the world.

You can call that a cultural technology or you can call that an intelligent agent, but I think it's not just like a printing press or a PC or another piece of technology that these things are sometimes compared to. I think it's something new and different when it can actually go out in the world and do things.

Yeah. You think about OpenAI's Operator, for example—it can book a plane ticket or a hotel room. Is that a cultural technology? I don't know. That feels like something different to me.

Yeah. All right. Next up.

Okay. This next question is about the scientific and medical breakthroughs that could come from AI. This question comes from Ross Statit who is an opinion columnist here at The New York Times and the host of the podcast Interesting Times, and he's been interviewing a lot of people connected to the AI world.

Hey guys, it's your colleague Ross Douet and I'm curious about what, if anything, you think limits AI's ability to predict and understand incredibly complex and chaotic and sometimes one-of-a-kind systems. And just to take 2 examples, I'm thinking about, on the one hand, our ability to predict the weather in advance and, on the other hand, our ability to predict which treatments and drugs will work inside the insane, individualized complexity of a human immune system.

Those both seem to me like cases where just throwing more and more raw intelligence or computational power at a problem may run into some inherent limits. We'll get cancer cures and better weather prediction, but certain things will always remain in the realm of uncertainty or the realm of trial and error. Do you guys agree, or are you more optimistic about AI's ability to bring even the most chaotic and complex realms into some kind of understanding?

So there are 2 questions here. The first is whether there is some upper bound on how well these systems will be able to predict. To me, the answer is maybe. I don't know that we'll ever have an AI system that can predict the weather with 100% certainty.

At the same time, I did a little bit of Googling before we logged on. AI weather-prediction models are really good, and they're getting better all the time. Meteorologists say that their field has rarely felt so exciting because they're just able to make better predictions than they have before.

I think you're seeing something similar with medicine. We've featured stories on the podcast about the way that this is leading to new drug discovery. It is leading to improvements in diagnosis. So if you're looking for reasons to be excited about AI, I would point to stuff like that as obviously useful in people's lives, but it's still not perfect, right?

It may be that getting from a very reliable weather forecast to a perfect weather forecast would require some fundamental breakthrough, something in quantum mechanics, some new understanding of how various particles are interacting out in the atmosphere. But getting way better forecasts might be good enough for most people.

I think the same could be said of medicine. Maybe this is not going to cure every disease on Earth. Maybe there will still be things about the human body we don't understand. But I do agree with you that people who work in this field are more excited than they've been in a long time because they just see how much AI allows them to explore and test.

Yeah, and maybe one other question you can add in here that I think is relevant is: Are these systems better than a person? Because if they are, then we probably want to use them.

Can I just ask how much of your optimism about AI hinges on AI being able to give us either these scientific or medical breakthroughs?

I think science and medicine are just 2—maybe the 2 most obvious places where this stuff will be good. If you told me that you could cure cancer and many other diseases, I'm personally willing to put up with a lot more social disruption. If it can never do those things, despite all the promises that have been made, then I'll be super mad. I'll curse on the podcast.

My own AI optimism does not hinge on AI going out there and solving all of the unproved math theorems and curing all of the diseases. I think that even if it were just to speed up the process of discovery, even if all it were doing was accelerating the work that chemists and biomedical researchers and people looking into climate change were doing, I think that would be reason enough for optimism.

So much of what acts as a bottleneck on progress in science and medicine is just that it's really slow and hard. You need to build these wet labs, do a bunch of tests, wait for the tests to come back, and run these clinical trials.

And I think one of the things that was exciting about our conversation with Patrick Collison at the live show the other day was when he was talking about this virtual cell that they’re building, where you can build a virtual environment using AI that can allow you to run these experiments in silico rather than needing to go out and test them on a bunch of fruit flies or rats or humans or whatever. And you can shorten the feedback loop and take more bites at the apple.

Absolutely. There was a story in Quanta Magazine this week that said that AI hasn’t led to any new discoveries in physics just yet, but it is designing new experiments and spotting patterns in data in the way that Kevin was just describing, in ways that physicists are finding really useful. So I think it’s clear that AI is already shortening some of those timelines.

You know what’s great about this? Now, instead of your own internal voice criticizing yourself, you can externalize it and realize that all your fears are true and people actually are criticizing you all the time behind your back. Yeah.

Isn’t it really nice?

It’s so nice.

What a great idea.

Mhm.

Well, on that note, let’s keep going. Are you guys ready for the next critic?

Hit me with it.

My name is Claire Lee Buittz, and I lead the AI and media integrity program at the Partnership on AI. I keep coming back to something that I struggle with in my own reaction to your pieces. I found myself nodding when you both critique AI for being biased, persuasive, sycophantic.

But then I start thinking about how humans around me behave, and they do all these things too. So I’m wondering: Are we ultimately critiquing AI for being too much like us? In which domain should we expect these systems to actually transcend human limitations? And are there others where it may be valuable for them to reflect our true nature? And most importantly, why aren’t we spending more time figuring out who is best suited to decide these things and empowering them?

I mean, that last question is super important. You know, I’m a big democracy guy, and I want there to be a public role in creating this AI future. I want people who have opinions about this stuff to talk about it online, yes, but also run for office, put together policy proposals, and then get into office and pass laws and regulations.

I got into journalism because I wanted to play my own role in that process of helping to inform people and then hopefully, in some very small way, influencing public policy. So that’s my answer to that question.

Yeah, I agree with that. I want people from lots of disciplines to be weighing in on this stuff, not just by posting online and writing op-eds in the newspaper, but by actually getting into the process of designing and building these systems. I want philosophers and ethicists, and I want sociologists and anthropologists advising these companies.

I want this to be a global, democratic, multidisciplinary effort to create these systems. And I don’t want it to just be a bunch of engineers in San Francisco designing these systems with no input from the outside world.

Absolutely. And if a bunch of people listen to the things that we and others talk about and think, “Man, I really don’t like this AI stuff at all. I don’t want it to replace anyone’s job. I want to form a political movement, seek office, and try to oppose that,” I think that would be awesome. We need to have that fight in public. And right now, far too few people are participating in that conversation.

So I totally agree with that. Now, let me address the other part of Claire’s question, though, which is: Are AI systems just a reflection of us? Well, number 1, I think that the answer is yes. Here’s where I think it gets problematic.

If you have a human friend, sometimes they’re going to be very supportive and nice to you. Sometimes they’re going to bust your chops and criticize you. Sometimes they’re going to give you really hard feedback and tell you something that you didn’t want to hear.

This is not what AI systems do. And so where I get concerned is, we’re starting to read more stories about young people in particular turning to these chatbots to answer every single question and developing these really intense emotional relationships with them.

And I am worried that it is not preparing them for a future where they’re going to be interacting with people who do not always have their best interests at heart, or maybe they could have an amazing relationship with, but maybe this person is a little bit prickly and you need to learn how to navigate them.

That is where I get really concerned: These systems, while they’re unreliable in so many ways, are quite reliably sycophantic. And I just think that creates a bunch of issues that humans don’t mostly have.

Yeah. And I think what I would add to that is that I don’t want AI to mirror all of humanity’s values, the positive and the negative. I want it to mirror the best of us—the better angels of our nature, as Abraham Lincoln said. I want that to be what these AI companies are striving to design.

As opposed to, say, Mecca Hitler.

Yes. Yes. Because that is also a set of values that humans have. And so sometimes when I hear people at these AI companies talk about aligning AI systems with human values, I’m like, well, which humans? Because I can think of some pretty bad ones whose values I don’t want to see adopted into these systems.

Yeah. Well, that’s called woke AI, and it’s illegal now. All right, Rachel, let’s hear from someone else.

Okay, this is the very last one. You guys are doing great. So this final question comes from friend of the pod Max Reed. He, of course, has the newsletter Read Max. I thought his question was really great because he’s really interested in how you think about discerning between what’s hype and what’s not, how you trust your own instincts, and where your confidence comes from. So let’s hear Max.

Hi, guys. It’s your old friend Max Reed. I was originally going to ask about Kevin’s a cappella career in college, but my understanding is that the woke higher-ups at The New York Times won’t allow me to ask such dangerous questions.

So instead, I want to ask you about AI by way of asking you about crypto. You guys were both pretty actively involved in covering the Web3 era, the crypto boom of the pandemic, NFTTS, board apes, all this stuff. And very little of that, despite the massive hype around it at the time, has really panned out as promised, at least as far as I can tell.

And what I’m wondering is how you guys feel about that hype and about your coverage of that hype from the perspective of 2025. Are there regrets you have? Are there lessons you feel like you’ve learned? And especially when you look at the current state of AI coverage and hype—not just your own coverage, but in general—do you think or worry that it falls prey to any of the same mistakes?

I want to caveat this question by saying the easy mode of this question is to just say the technology is totally different, so it’s a very different thing. And I want to put it to you in hard mode because I don’t want to hear about how the tech is different.

What I’m interested in is hearing about you guys and your work as journalists. How do you approach this industry? How do you establish your own credibility? And how do you assess the claims being made by investors and entrepreneurs? Can’t wait to hear the answer. Bye.

I love this question. What have I learned? To touch on the crypto piece without touching on the technology, here’s what I’ll say. Ultimately, what persuaded me in 2021 that crypto was really worth paying attention to was the density of talent that it attracted.

So many people I knew who had previously worked on really valuable companies were quitting their jobs to go build new crypto companies. And what I believed and said out loud at the time was, it would just be really surprising if all of those talented people failed to create a lot of really valuable companies.

In the end, they did not produce a lot that I did find valuable. Although, as we’ve been covering on the show recently, crypto has not gone away, and thanks to the fact that the industry has captured the government, it is now more valuable than ever. So that is what I would say about that time in crypto.

And I do think that some of that argument ports over to AI, because certainly I also know a lot of people who quit their jobs working at social media companies, for example, who are now working on AI.

Here’s what I would say about hype and covering AI. I think that a good podcast about technology needs to do 2 things. One is to give you very grounded coverage of stuff that is happening right now.

So I’m thinking about, in recent months, when Pete Wells came on to talk about how chefs are using AI in their restaurants, or Roy Lee coming on and talking about the cheating technology that he’s building, or Kevin talking about what he’s vibe-coding. I even think about the emergency episode that we did about DeepSeek, which I think actually was an effort to unhype the technology a bit while giving you a really grounded sense of what it was and why people were so excited about it. Right? So that’s one thing I think we need to do.

The other thing I think we need to do is just tell you what the industry says is going to happen. I think it is important to get leaders of these companies in the room and just hear their visions, because there is some chance that a version of it will come true.

So this is the thing that we’re doing when we bring on a Sam Alman or a Demisabis or the founders of the Mechanized company, which you probably heard in our interview.

I was not particularly impressed with that vision, but I think it is useful for the audience to hear what these folks think they are doing. Of course, we want to push back on them a bit, but I have always appreciated journalism that gives airtime to visions and lets me think about them, lets me disagree with them, right? So that is how I think about hype in general.

We want to tell you mostly what is happening on the ground, but we do want to tell you what the CEOs are telling us all the time is going to happen, and then we want you to interrogate the space in between, right, that we actually have to live in.

I will say I feel pretty good about the way that I covered crypto back in 2021. There is only really 1 crypto story that I truly regret writing, and that is a story about this crypto company, Helium, that was trying to do this convoluted thing with crypto-powered Wi-Fi routers. I failed on that story. I failed to ask basic journalistic questions.

It turned out after the fact—we learned that Helium had basically claimed that it had a bunch of partnerships with a bunch of different companies, and I just did not call the companies to say, “Hey, is this company lying about being affiliated with you?” It just did not occur to me that they would be so blatantly misleading me about the state of their business. So I regret that. I would chalk that up less to buying into crypto hype and more to not making a few more calls that would have saved me from some grief.

Yeah. The lesson I took from crypto reporting is that real-world use matters. So much of crypto and the hype around it consisted of abstract ideas, vague promises, and white papers. When you actually dug in and looked at who was using it and what they were using it for, it was criminals, speculators, and people trying to get rich on their Bored Ape collection.

Now, when I cover AI, I really try to talk to civilians using this technology about how they are using it, and whenever possible, I try to use it myself before I form an opinion on it.

I think the crypto era was in some ways a traumatic incident for the tech journalism community. I think a lot of our peers, and maybe even to a certain extent you and I, felt like we were duped, felt like we fell for something, felt like we wasted all of our time trying to understand and explain this technology, taking this stuff seriously, only to have it all come crashing down.

I worry that a lot of journalists took the wrong lesson from what happened with crypto. The lesson that I think a lot of journalists took was to be blanket skeptical of all new technologies, to assume that it is all smoke and mirrors, that everyone is lying to you, and that it is not really going to be worth your time to dig in and try to understand something. I see a lot of that attitude reflected in some of the AI coverage I see today.

While I take Max’s point that we should always be learning from our mistakes and maybe from things that we swallowed too uncritically in the past, I think that in some ways what we are seeing now with AI is overcorrecting on that point. What do you think?

Yeah, I think there is a bit of an overcorrection, but I also think that many journalists have just realized that what used to be a really small industry that mostly concerned itself with helping you print your photos and make a spreadsheet is now something much bigger and more consequential and has just been bad for a lot of people. So it makes them hesitant to trust someone who comes along and says, “Hey, I am going to cure all human disease.”

I think the role that we both try to occupy in the AI journalism world is to say that we take seriously the CEOs who say that they are building something really powerful and, crucially, we think it will be powerful in bad ways. We want to talk to you about those bad ways: You may lose your job; it will enable new forms of cyberattacks and fraud that you may fall victim to; or it will burn our current education system down to the ground, so it has to be rebuilt from scratch.

That one—maybe there will be some positive along the way—but I feel like week after week on the show, we are trying to show you ways in which this thing is going to be massively disruptive. That gets framed as hype in a way that I just think is a little bit silly.

In 2010, imagine I had written a story about Facebook and how one day it would have billions of users, undermine democracy, and give a bunch of teenagers eating disorders. Would that have been hype? Sort of. Would that have been accepting the terms of the social media founders and accepting their language around growth? Yes. But would it have been useful? Would I be proud that I wrote that story? I think so.

So I am willing to accept the idea that you and I do buy into the vision of very powerful AI more than many of our peers in tech journalism. But the reason that we are doing that is that we want to remind you what happened the last time one of these technologies grew really quickly, got into everyone’s hands, and became the way that people interface with the digital world. It did not go great.

We already know that these companies are not going to be regulated in any meaningful way. The AI Action Plan is designed basically to ensure that. To the extent that we can play a positive role, I think it is just going to be in talking to people about those consequences. If the consequence of that is that people say that we are on the side of hype, I will just accept the criticism.

Well, thank you guys so much for doing this. And thank you also to our critics for taking the time to talk to me. I thought we could end by talking about whether you guys have any questions for each other. One of the big goals of this is to map where you guys stand relative to other thinkers, so I am curious if you guys have any questions for each other about how your views on AI are actually different from each other.

I think I have longer timelines than Kevin does. I think Kevin talks about AGI in a way that makes it seem very imminent, and I think I am more confident that it is going to take several years—and maybe more than several, right? Maybe this is a 5- to 10- or even 15-year project. So I think that is the main way that I notice disagreeing with Kevin.

I think that we also disagree about regulation and how possible or advisable it is to have the government step in and try to control the development and deployment of AI systems. I think that you are informed by your years of covering social media and seeing regulators grapple with and mostly fail to regulate that wave of technology.

But I think you are also a person who has a lot of hope and optimism about institutions and wants there to be democratic accountability for powerful technology. I share that view, but I also do not think there is a chance in hell that our present government, constructed the way it is, with the kind of pace at which it is used to regulating things, can regulate AI on anything approaching a relevant timescale. I have become fairly pessimistic about the possibility of meaningful regulation of AI. I think that is a place where we differ.

I think we do disagree there, because I think we had the makings of meaningful regulation under the Biden administration, where they were making very simple demands, like, “You need to inform us when you are training a model of a certain size.” There need to be other transparency requirements, and I think you can get from there to a better world.

Instead, we have sort of unwound all the way back to, “Hey, if you want to create the largest and most powerful model in the world, you can do that. You do not have to tell anybody. If it creates new risks for bioweapons and other risks, you do not have to tell anybody.” Right now, there are many big AI labs that are racing to get the most powerful AI they can into everyone’s hands with absolutely no safeguards.

So if you are telling me that we cannot create a better world than that, I am going to disagree with you.

Yeah.

Go yourself.

Well, thank God you guys disagree, because it makes the podcast more interesting. And thank you guys, seriously, for doing this. Given how much of the AI conversation can feel really disempowering in this moment, one thing that gives me a feeling of a little bit more control is really trying to map out the debates and where people stand relative to each other, because it ultimately helps me figure out what I think about AI and where I think the future is going. That is at least one thing I feel empowered to do.

And that is what we want to do. Truly, we want everyone to come to their own understanding of where they sit at the various intersections of these discourses. I think Kevin and I identify as reporters first. We do not have all the answers. That is why we usually bring on a guest every week to try to get smarter about some subject, right?

I think a really bad outcome for the podcast is that people think of us as pundits. I think of us as curious people with informed points of view, but we always try to be open to changing our minds. Yes. Like a large language model, we aim to improve from version to version.

As we add new parameters and computing power.

Yes.