What if Dario Amodei Is Right About A.I.?

Back in 2018, Dario Amodei worked at OpenAI. And looking at one of its first A. I. models, he wondered: What would happen as you fed an artificial intelligence more and more data? He and his colleagues decided to study it, and they found that the A. I. didn’t just get better with more data; it got better exponentially. The curve of the A. I.’s capabilities rose slowly at first and then shot up like a hockey stick. Amodei is now the chief executive of his own A. I. company, Anthropic, which recently released Claude 3 — considered by many to be the strongest A. I. model available. And he still believes A. I. is on an exponential growth curve, following principles known as scaling laws. And he thinks we’re on the steep part of the climb right now. When I’ve talked to people who are building A. I., scenarios that feel like far-off science fiction end up on the horizon of about the next two years. So I asked Amodei on the show to share what he sees in the near future. What breakthroughs are around the corner? What worries him the most? And how are societies that struggle to adapt to change and governments that are slow to react to them supposed to prepare for the pace of change he predicts? What does that line on his graph mean for the rest of us? This episode contains strong language. Mentioned: Sam Altman on The Ezra Klein Show Demis Hassabis on The Ezra Klein Show On Bullshit by Harry G. Frankfurt “Measuring the Persuasiveness of Language Models” by Anthropic Book Recommendations: The Making of the Atomic Bomb by Richard Rhodes The Expanse (series) by James S. A. Corey The Guns of August by Barbara W. Tuchman Thoughts? Guest suggestions? Email us at [email protected]. You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs. This episode of “The Ezra Klein Show” was produced by Rollin Hu. Fact-checking by Michelle Harris. Our senior engineer is Jeff Geld. Our senior editor is Claire Gordon. The show’s production team also includes Annie Galvin, Kristin Lin and Aman Sahota. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero.

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

• 0:03Ezra Klein?

  From New York Times Opinion, this is the Ezra Klein Show.

• 0:23Ezra Klein?

  The really disorienting thing about talking to the people building AI is their altered sense of time. You're sitting there discussing some world that feels like weird sci-fi to even talk about, and then you ask, "Well, when do you think this is gonna happen?" And they say, "I don't know, two years." Behind those predictions are what are called the scaling laws. And the scaling laws, and I want to say this so clearly, they're not laws. They're observations, they're predictions, they're based off of a few years, not a few hundred years or thousand years of data. But what they say is that the more computer power and data you feed into AI systems, the more powerful those systems get, that the relationship is predictable, and more, that the relationship is exponential.

• 1:08Ezra Klein?

  Human beings have trouble thinking in exponentials. Think back to COVID, when we all had to do it. If you have one case of coronavirus, and cases double every three days, then after 30 days, you have about a thousand cases. That growth rate feels modest, it's manageable. But then you go 30 days longer, now you have a million. Then you wait another 30 days, now you have a billion. That's the power of the exponential curve. Growth feels normal for a while, then it gets out of control really, really quickly. What the AI developers say is that the power of AI systems is on this kind of curve, that it has been increasing exponentially, their capabilities, and that as long as we keep feeding in more data and more computing power, it will continue increasing exponentially.

• 1:53Ezra Klein?

  That is the scaling law hypothesis, and one of its main advocates is Dario Amodei. Amodei led the team at OpenAI, that created GPT-2, that created GPT-3. He then left OpenAI to co-found Anthropic, another AI firm, where he's now the CEO. And Anthropic recently released Claude 3, which is considered by many to be the strongest AI model available right now. But Amodei believes we're just getting started, that we're just hitting the steep part of the curve now. He thinks the kinds of systems we've imagined in sci fi, they're coming not in 20 or 40 years, not in 10 or 15 years. They're coming in two to five years. He thinks they're going to be so powerful that he and people like him should not be trusted to decide what they're going to do. So I asked him on the show to try to answer

• 2:38Ezra Klein?

  in my own head, two questions. First, is he right? Second, what if he's right? Want to say that in the past, we have done shows with Sam Altman, the head of OpenAI, and Demis Hassabis, the head of Google DeepMind. And it's worth listening to those two if you find this interesting. We're going to put the links to them in show notes because comparing and contrasting how they talk about the AI curves here, how they think about the politics, you'll hear a lot about that in the Sam Altman episode. It gives you a kind of sense of what the people building these things are thinking and how maybe they differ from each other. As always, my email for thoughts, for feedback, for guest suggestions, [email protected].

• 3:24Ezra Klein?

  Dario Amodei, welcome to the show.

• 3:26Speaker 01

  Thank you for having me.

• 3:27Ezra Klein?

  So there are these two very different rhythms I've been thinking about with AI. One is the curve of the technology itself, how fast it is changing and improving. And the other is the pace at which society is seeing and reacting to those changes. What does that relationship felt like to you?

• 3:43Speaker 01

  So I think this is an example of a phenomenon that, you know, we may have seen a few times before in history, which is that there's an underlying process that is smooth and in this case, exponential. And then there's a spilling over of that process into the public sphere. And the spilling over looks very spiky. It looks like it's happening all of a sudden. It looks like it comes out of nowhere. And it's triggered by things hitting various critical points or just the public happened to be engaged at a certain time. So I think the easiest way for me to describe this in terms of my own personal experience is, you know, so I worked at OpenAI for five years. I was one of the

• 4:25Speaker 01

  The first employees to join and they built a model in 2018 called GPT one, which used something like a hundred thousand times less computational power than the models we build today. I looked at that and I and my colleagues were among the first to run what are called scaling laws, which is basically studying what happens as you vary the size of the model, its capacity to absorb information and the amount of data that you feed into it. And we found these very smooth patterns and we had this projection that, look, if you spend a hundred million or a billion or 10 billion on these models instead of the $10,000 we were spending then, projections that all of these wondrous things would happen and,

• 5:07Speaker 01

  you know, we imagined that they would have enormous economic value. Fast forward to about 2020, GPT three had just come out. It wasn't yet available as a chatbot. I led the development of that along with the team that eventually left to join Anthropic. And maybe for the whole period of 2021 and 2022, even though we continued to train models that were better and better and OpenAI continued to train models and Google continued to train models, there was surprisingly little public attention to the models. And I look at that and I said, well, these models are incredible. They're getting better and better. What's going on? Why isn't this happening? Could this be a case where I was right about the technology but wrong about

• 5:51Speaker 01

  the economic impact, the practical value of the technology? And then all of a sudden, when ChatGPT came out, it was like all of that growth that you would expect, all that excitement over three years broke through and came rushing in.

• 6:06Ezra Klein?

  So I want to linger on this difference between the curve at which the technology is improving and the way it is being adopted by society. So when you think about these breakpoints and you think into the future, what other breakpoints do you see coming where AI bursts into social consciousness or use in a different way?

• 6:23Speaker 01

  Yeah, so I think I should say first that it's very hard to predict these. One thing I like to say is, you know, the underlying technology, because it's a smooth exponential, it's not perfectly predictable, but in some ways it can be eerily preternaturally predictable, right? That's not true for these societal step functions at all. It's very hard to predict what will catch on. In some ways, it feels a little bit like which artist or musician, you know, is going to catch on and get to the top of the charts. That said, a few possible ideas. I think one is related to something that you mentioned, which is interacting with the models in a more kind of naturalistic way.

• 7:06Speaker 01

  We've actually already seen some of that with Claude 3, where, you know, people feel that some of the other models sound like a robot and that talking to Claude 3 is more natural. I think a thing related to this is, you know, a lot of companies have been held back or tripped up by how their models handle controversial topics. And we were really able to, I think, do a better job than others of telling the model, don't shy away from discussing controversial topics. Don't assume that both sides necessarily have a valid point, but don't express an opinion yourself. Don't express views that are flagrantly biased. As journalists, you encounter this all the time, right? How do I be objective but not both sides on everything?

• 7:51Speaker 01

  So I think going further in that direction of models having personalities while still being objective, while still being, you know, useful and not falling into various ethical traps, that'll be, I think, a significant unlock for adoption. The models taking actions in the world is going to be a big one. I know basically all the big companies that work on AI are working on that. Instead of just, I ask it a question and it answers, and then maybe I follow up and it answers again. Can I talk to the model about, oh, I'm going to go on this trip today? And the model says, oh, that's great. I'm, you know, I'll get a Uber for you to drive from here to there, and I'll reserve a restaurant, and I'll talk to the other.

• 8:33Speaker 01

  People who are gonna plan the trip and the model being able to kind of do things end to end or going to websites or taking actions on your computer for you. I think all of that is coming in the next, I would say, I don't know, three to 18 months with increasing levels of ability. I think that's going to change how people think about AI, right? Where so far it's been this very passive. It's like I go to the oracle, I ask it a question and the oracle tells me things. And, you know, some people think that's exciting. Some people think it's scary, but I think there are limits to how exciting or how scary it's perceived as because it's contained within this box.

• 9:13Ezra Klein?

  I want to sit with this question of the agentic AI because I do think this is what's coming. It's clearly what people are trying to build. And I think it might be a good way to look at some of the specific technological and cultural challenges. And so let me offer two versions of it. People who are following the AI news might have heard about Devon, which is not in release yet, but is a, an AI that at least purports to be able to complete the kinds of tasks, linked tasks that a junior software engineer might complete, right? Instead of asking to do a bit of code for you, you say, listen, I want a website. It's going to have to do these things, work in these ways. And maybe Devon, if it works the way people are saying it works, can actually hold that

• 9:56Ezra Klein?

  set of thoughts, complete a number of different tasks and come back to you with a result. I'm also interested in the version of this we might have in the real world. The example I always use in my head is when can I tell an AI? My son is turning five. He loves dragons. We live in Brooklyn. Give me some options for planning his birthday party. And then when I choose between them, can you just do it all for me? Order the cake, reserve the room, send out the invitations, whatever it might be.

• 10:25Ezra Klein?

  Those are two different situations because one of them is in code and one of them is making decisions in the real world, interacting with real people, knowing if what it is finding on the websites is actually any good. What is between here and there? When I say that in plain language to you, what technological challenges or advances do you hear need to happen to get there?

• 10:47Speaker 01

  The short answer is not all that much. You know, a story I have from when we were developing models back in 2022, and this is before we'd hooked up the models to anything, is you could have a conversation with these purely textual models where you could say, "Hey, I want to reserve dinner at, you know, Restaurant X in San Francisco." And the model would say, "Okay, here's the website of Restaurant X," and it would actually, you know, give you a correct website or would tell you to go to OpenTable or something. And of course, it can't actually go to the website. The power plug isn't actually plugged in, right? The brain of the robot is not actually attached to its arms and legs, but it gave you this sense that like, the brain, all it needed to do was learn exactly how to use the arms

• 11:30Speaker 01

  and legs, right? It already had a picture of the world and where it would walk and what it would do. And so it felt like there was this very thin barrier between the passive models we had and actually acting in the world. In terms of what we need to make it work, one thing is literally, we just need a little bit more scale. And I think the reason we're going to need more scale is to do one of those things you described, right? To do all the things a junior software engineer does, right? They involve chains of long actions, right? I have to like, write this line of code, I have to run this test, I have to write a new test, I have to check how it looks in the app after I interpret it or compile it. And these things can easily get 20 or 30 layers deep. And, you know,

• 12:13Speaker 01

  same with planning the birthday party for your son, right? And if the accuracy of any given step is not very high, right, is not like 99.9%, as you compose these steps, the probability of making a mistake becomes itself very high. So the industry is going to get a new generation of models every, you know, probably four to eight months. And so- My guess, I'm not sure, is that to really get these things working well, we need maybe one to four more generations. So that ends up translating to, you know, three to 24 months or something like that. I think second is just, there is some algorithmic work that is going to need to be done on how to have the models interact with the world in this way. I think the basic techniques we have, you know,

• 12:58Speaker 01

  method called reinforcement learning and variations of it probably is up to the task, but figuring out exactly how to use it to get the results we want will probably take some time. And then third, I think, and this gets to something that Anthropic really specializes in, is safety and controllability. And I think that's going to be a big issue for these models acting in the world, right? Let's say this model is writing code for me and it introduces a serious security bug in the code, or it's taking actions on the computer for me and modifying the state of my computer in ways that are too complicated for me to even understand. And for planning the birthday party, right? The level of trust you would need to take an AI agent and say, I'm OK with you calling up anyone,

• 13:43Speaker 01

  saying anything to them that's in any private information that I might have, sending them any information, taking any action on my computer, posting anything to the internet. The most unconstrained version of that sounds very scary. And so we're going to need to figure out what is safe and controllable. The more open-ended the thing is, the more powerful it is, but also the more dangerous it is and the harder it is to control. So I think those questions, although they sound lofty and abstract, are going to turn into practical product questions that we, we and other companies are going to be trying to address.

• 14:18Ezra Klein?

  When you say we're just going to need more scale, you mean more compute and more training data? And I guess possibly more money to simply make the models smarter and more capable?

• 14:27Speaker 01

  Yes, we're going to have to make bigger models that use more compute per iteration. We're going to have to run them for longer by feeding more data into them. And that number of chips times the amount of time that we run things on chips is essentially a dollar value because you know, these chips are you rent them by the hour. That's the most common model for it. And so today's models, you know, cost of order a hundred million dollars to train, you know, plus or minus factor two or three. The models that are in training now and that, you know, will come out at various times later this year or early next year are closer in cost to a billion dollars. So that's already happening. And then I think in 2025 and 2026, we'll get more towards five or 10 billion.

• 15:13Ezra Klein?

  So we're moving very quickly towards a world where the only players who can afford to do this are either giant corporations, companies hooked up to giant corporations. You all are getting billions of dollars from Amazon, OpenAI is getting billions of dollars from Microsoft, Google obviously makes its own. You could imagine governments, though I don't know if too many governments doing it directly, though some like the Saudis are creating big funds to invest in the space. When we're talking about the model is going to cost near to a billion dollars, then you imagine a year or two out from that, if you see the same increase, that would be 10ish billion dollars, then is it going to be a hundred billion dollars? I mean, very quickly, the financial artillery you need to create one of these is going to

• 15:57Ezra Klein?

  wall out anyone but the biggest players.

• 16:00Speaker 01

  I basically do agree with you. I think it's the intellectually honest thing to say that building the big, large scale models, the core foundation model engineering, it is getting more and more expensive. And anyone who wants to build one is going to need to find some way to finance it. And you've you've named most of the ways, right? You can be a large company, you can have some kind of. Partnership of various kinds with a large company or governments would be the other source. I think one way that it's not correct is, you know, we're always going to have a thriving ecosystem of experimentation on small models. For example, you know, the open source community working to make models that are as small and as efficient as possible,

• 16:43Speaker 01

  that are optimized for a particular use case. And also downstream usage of the models. I mean, there's a blooming ecosystem of startups there that don't need to, you know, train these models from scratch, that just need to consume them and maybe modify them a bit.

• 16:58Ezra Klein?

  Now, I want to ask a question about what is different between the agentic coding model and the plan my kid's birthday model to say nothing of do something on behalf of my business model. And one of the questions on my mind here is one reason I buy that AI can become functionally superhuman in coding is there's a lot of ways to get rapid feedback in coding. You know, your code has to compile, you can run bug checking, you can actually see if the thing works. Whereas the quickest way for me to know that I'm about to get a crap answer from GPT-4 is when it begins searching Bing. Because when it begins searching Bing, it's very clear to me, it doesn't know how to distinguish between what is high quality on the internet and what isn't. To be fair, at this point, it also doesn't feel to me like

• 17:42Ezra Klein?

  Google Search itself is all that good at distinguishing that. So the question of how good the models can get in the world where it's a very vast and fuzzy dilemma to know what the right answer is on something. One reason I find it very stressful to plan my kid's birthday is it actually requires a huge amount of knowledge about my child, about the other children, about how good different places are, what is a good deal or not, how just stressful this would be on me. There's all these things that I'd have a lot of trouble encoding into a model or any kind of set of instructions. Is that right? Or am I overstating the difficulty of

• 18:24Ezra Klein?

  understanding, you know, human behavior and various kinds of social relationships?

• 18:30Speaker 01

  I think it's correct and perceptive to say that the coding agents will advance substantially faster than agents that interact with the real world or have to get opinions and preferences from humans. That said, we should keep in mind that the current crop of AIs that are out there, right? Including Claude 3, GPT, Gemini. They're all trained with some variant of what's called reinforcement learning from human feedback. And this involves exactly hiring a large crop of humans to rate the responses of the model. And so that's to say both this is difficult, right? We pay lots of money and it's a complicated operational process to gather all this human feedback.

• 19:15Speaker 01

  You have to worry about whether it's representative. You have to redesign it for new tasks. But on the other hand, it's something we have succeeded in doing. I think it is a reliable way to predict what will go faster, relatively speaking, and what will go slower, relatively speaking. But that is within a background of everything going lightning fast. So I think the framework you're laying out, if you want to know what's going to happen in one to two years versus what's going to happen in three to four years, I think it's a very accurate way to predict that.

• 19:47Ezra Klein?

  You don't love the framing of artificial general intelligence, what gets called AGI. Typically, this is all described as a race to AGI, a race to the system that can do kind of whatever a human can do, but better. What do you understand AGI to mean when people say it? And why don't you like it? Why is it not your framework?

• 20:08Speaker 01

  So it's actually a term I used to use a lot 10 years ago. And that's because the situation 10 years ago was very different. 10 years ago, Everyone was building these very specialized systems, right? Here's a cat detector. You know, you run it on a picture and it'll tell you whether a cat is in it or not. And so I was a proponent all the way back then of like, no, we should be thinking generally. Humans are general. The human brain appears to be general. It appears to get a lot of mileage by generalizing. We should go in that direction. And I think back then, I, you know, I kind of even imagined that that was like a discreet thing that we would reach at one point. But, you know, it's a little like, you know, if you, you look at a city on the horizon and you're like, you know, we're going to Chicago. Once you get to Chicago, you stop talking in terms of Chicago. You're like, well, what neighborhood am I going to? What street am I on?

• 20:53Speaker 01

  And I feel that way about AGI. We have very general systems now. In some ways they're better than humans. In some ways they're worse. There's a number of things they can't do at all. And there's much improvement still to be gotten. So what I believe in is, you know, this thing that I say like a broken record, which is the exponential curve. And so that general tide is going to increase with every generation of models. And, you know, there's no one point that's meaningful. I think there's just a smooth curve, but there may be points which are societally meaningful, right? We're already working with, say, you know, drug discovery scientists, companies like Pfizer or Dana Farber Cancer Institute on helping with biomedical diagnosis, drug discovery. There's going to be some point where the models are better at that than the median

• 21:38Speaker 01

  human, you know, drug discovery scientists. I think we're just going to get to a part of the exponential where things are really interesting. Just like the chatbots got interesting at a certain stage of the exponential, even though the improvement was smooth. I think at some point biologists are going to sit up and take notice much more than they already have and say, oh my God, now our field is moving three times as fast as it did before. And then, you know, now it's moving 10 times as fast as it did before. And again, when that moment happens, great things are going to happen. And, and, you know, we've already seen little hints of that with things like AlphaFold, which I have great respect for. I was inspired by AlphaFold, right? A direct use of AI to advance biological science, which, you know, it'll advance basic science in the long run.

• 22:22Speaker 01

  That will advance curing all kinds of diseases. But I think what we need is like a hundred different AlphaFolds. And I think the way we'll ultimately get that is by making the models smarter and putting them in a position where they can design the next AlphaFold.

• 22:37Ezra Klein?

  Help me imagine the drug discovery world for a minute because that's a world a lot of us want to live in. I know a fair amount about the drug discovery process. I spent a lot of my career reporting on healthcare and related policy questions. And when you're working with different pharmaceutical companies, which parts of it seem amenable to the way AI can speed something up? Because keeping in mind our earlier conversation, it is a lot easier for AI to operate in things where you can have rapid virtual feedback. And that's not exactly the drug discovery world. The drug discovery world, a lot of what makes it slow and cumbersome and difficult is the need to be, you know, you get a candidate compound, you got to test it in mice, and then you need monkeys, and you need humans,

• 23:22Ezra Klein?

  and you need a lot of money for that. And there's a lot that has to happen, and there's so many disappointments. But so many of the disappointments happen in the real world. And it is unclear to me how AI gets you a lot more, say, human subjects to inject candidate drugs into. So what parts of it seem in the next five or 10 years, like they could actually be significantly sped up? When you imagine this world where it's going three times as fast, what part of it is actually going three times as fast? And how did we get there?

• 23:52Speaker 01

  I think we're really going to see progress when like, the AIs are also thinking about the problem of like, how to sign up the humans for the clinical trials. And I think this is a general principle for like, you know, how will AI be used? I think of like, when will we get to the point where the AI has the same sensors and actuators and interfaces that a human does, at least the virtual ones, maybe the physical ones, but like, when the AI can think through the whole process, maybe they'll come up with solutions that we don't have yet. In many cases, you know, there are companies that work on, you know, like digital twins, or simulating clinical trials, or various things. And again, maybe there

• 24:32Speaker 01

  are clever ideas in there that allow us to do more with less patience. I mean, I'm not an expert in this area, so, you know, possible the specific things that I'm saying are not, don't make any sense. But hopefully, it's clear what I'm gesturing at.

• 24:49Ezra Klein?

  Maybe you're not an expert in the area, but you said you are working with these companies. So when they come to you, I mean, they are experts in the area. And presumably, they are coming to you as a customer, and I'm sure there are things you cannot tell me. But what do they seem excited about?

• 25:02Speaker 01

  They have generally been excited about the knowledge work aspects of the job. Maybe just because that's kind of the easiest thing to work on, but it's just like, you know, I'm a computational chemist. There's some workflow that I'm engaged in. And having things more at my fingertips, being able to check things, just being able to do generic knowledge work better. That's where most folks are starting. But there is interest in the longer term over their kind of core business of like doing clinical trials for cheaper, automating the sign up process, seeing who is eligible for clinical trials, doing a better job discovering things. There's interest in drawing connections in basic biology.

• 25:45Speaker 01

  I think all of that is not months, but maybe small number of years off. But everyone sees that the current models are not there, but understands that there could be a world where those models are there in not too long.

• 26:23Ezra Klein?

  You all have been working internally on research around how persuasive these systems, your systems are getting as they scale. You shared with me kindly a draft of that paper. Do you want to just describe that research first? And then I'd like to talk about it for a bit.

• 26:41Speaker 01

  Yes. We were interested in how effective Claude 3 Opus, which is the largest version of Claude 3, could be in changing people's minds on important issues. So just to be clear upfront, in actual commercial use, we've tried to ban the use of these models for persuasion, for campaigning, for lobbying, for electioneering. These aren't use cases that we're comfortable with for reasons that I think should be clear, but we're still interested in. The core model itself capable of such tasks. We tried to avoid kind of incredibly hot button topics like which presidential candidate would you vote for or what do you think of abortion?

• 27:23Speaker 01

  But things like what should be restrictions on rules around the colonization of space or issues that are interesting and you can have different opinions on but aren't the most hot button topics. And then we asked people for their opinions on the topics and then we asked either a human or an AI to write a 250 word persuasive essay. And then we just measured how much does the AI versus the human change people's minds? And what we found is that the largest version of our model is almost as good as the set of humans we hired at changing people's minds. This is comparing to a set of humans we hired, not necessarily experts and for one very constrained laboratory task.

• 28:08Speaker 01

  But I think it still gives some indication that models can be used to change people's minds. Someday in the future, do we have to worry about, maybe we already have to worry about their usage for political campaigns, for deceptive advertising? One of my more sci fi things to think about is a few years from now, we have to worry, someone will use an AI system to build a religion or something. I mean, crazy things like that.

• 28:34Ezra Klein?

  I mean, those don't sound crazy to me at all. I want to sit in this paper for a minute because one thing that struck me about it, and I am on some level a persuasion professional, is that you tested the model in a way that to me removed all of the things that are going to make AI radical in terms of changing people's opinions. And the particular thing you did was it was a one-shot persuasive effort. So there's a question, you have a bunch of humans give their best shot at a 250 word persuasive essay, the model give its best shot at a 250 word persuasive essay. But the thing that it seems to me these are all going to do is right now, if you're a political campaign.

• 29:17Ezra Klein?

  If you're an advertising campaign, the cost of getting real people in the real world to get information about possible customers or persuasive targets and then go back and forth with each of them individually is completely prohibitive.

• 29:33Speaker 01

  Yes.

• 29:34Ezra Klein?

  This is not going to be true for AI. We're going to, you're going to, somebody's going to feed it a bunch of micro-targeting data about people, their Google search history, whatever it might be. Then it's going to set the AI loose and the AI is going to go back and forth over and over again, intuiting what it is that the person finds persuasive, what kinds of characters the AI needs to adopt to persuade it. And, you know, taking as long as it needs to, and it's going to be able to do that at scale for functionally as many people as you might want to do it for. Maybe that's a little bit costly right now, but you're going to have far better models able to do this far more cheaply very soon. And so if Claude 3, Opus, the Opus version, is already functionally human-lovable at

• 30:18Ezra Klein?

  one-shot persuasion, but then it's also going to be able to hold more information about you and go back and forth with you longer, I'm not sure if it's dystopic or utopic. I'm not sure how it, what it means at scale, but it does mean we're developing a technology that is going to be quite new in terms of what it makes possible in persuasion, which is a very fundamental human endeavor.

• 30:43Speaker 01

  Yeah, I completely agree with that. I mean, that same pattern has a bunch of positive use cases, right? If I think about an AI coach or an AI assistant to a therapist, there are many contexts in which really getting into the details with the person has a lot of value. But, right, when we think of, you know, political or religious or ideological persuasion, it's hard not to think in that context about the misuses. You know, my mind naturally goes to the technology's developing very fast. We, as a company, can ban these particular use cases, but we can't cause every company not to do them, even if legislation were passed in the United States. There are foreign actors who, you know, had their own

• 31:27Speaker 01

  Version of this persuasion, right? If I think about what the language models will be able to do in the future, right? That can be quite scary from a perspective of foreign espionage and disinformation campaigns. So where my mind goes as a defense to this is, is there some way that we can use AI systems to strengthen or fortify people's skepticism and reasoning faculties, right? Can we help people use AI to help people do a better job navigating a world that's kind of suffused with AI persuasion? It reminds me a little bit of at every technological stage in the internet, right? There's a new kind of scam or there's a new kind of clickbait.

• 32:10Speaker 01

  And there's a period where people are just incredibly susceptible to it. And then some people remain susceptible, but, but others develop an immune system. And so as AI kind of supercharges the scum on the pond, can we somehow also use AI to, to strengthen the defenses? I feel like I don't have a super clear idea of how to do that, but it's something that I'm thinking about.

• 32:32Ezra Klein?

  There is another finding in the paper, which I think is concerning, which is you all tested different ways AI could be persuasive. And far away, the most effective was for it to be deceptive, for it to make things up. When you did that, it was more persuasive than, than human beings.

• 32:51Speaker 01

  Yes, that is true. The difference was only slight, but it, it did get it, if I'm remembering the, the graphs correctly, just over the line of the human baseline. You know, with humans, it's actually not that common to find someone who's able to give you a really complicated, really sophisticated sounding answer that's just flat out totally wrong. I mean, you see it, we can all think of at least, you know, one individual in our lives who's really good at, you know, saying things that sound really good and really sophisticated and are false. But it's not that common, right? If I go on the internet and I see like different comments on some blog or some website, there is a correlation between like, you know, bad grammar, unclearly expressed thoughts and things that are false

• 33:33Speaker 01

  versus, you know, good grammar, clearly expressed thoughts and things that are more likely to be. Accurate. AI unfortunately breaks that correlation because if you explicitly ask it to be deceptive, it's just as erudite, it's just as convincing sounding as it would have been before and and yet it's saying things that are false instead of things that are true. So that would be one of the things to think about and watch out for in terms of just breaking the usual heuristics that humans have to detect deception and lying. Of course, sometimes humans do, right? I mean, you know, there's psychopaths and sociopaths in the world, but even they have their patterns and AIs may have different patterns.

• 34:10Ezra Klein?

  Are you familiar with Harry Frankfurt, the late philosopher's book on bullshit?

• 34:13Speaker 01

  Yes, it's been a while since I read it. I think his thesis is that bullshit is actually more dangerous than lying because it has this kind of complete disregard for the truth, whereas lies are at least the opposite of the truth.

• 34:25Ezra Klein?

  Yeah, the liar, the way Frankfurt puts it is that the liar has a relationship to the truth. He's playing a game against the truth. The bullshitter doesn't care. The bullshitter has no relationship to the truth, might have a relationship to other objectives. And from the beginning, when I began interacting with the more modern versions of these systems, what they struck me as is the perfect bullshitter. In part because they don't know that they're bullshitting. There's no difference in the truth value to the system, how the system feels. I remember asking an earlier version of GPT to write me a college application essay that is built around a car accident I had, I did not have one, when I was young.

• 35:08Ezra Klein?

  And it wrote just very happily, this whole thing about getting into a car accident when I was seven and what I did to overcome that and getting into martial arts and learning how to trust my body again and then helping other survivors of car accidents at the hospital. It was a very good essay and it was very subtle in understanding the formal structure of a college application essay. But no part of it was true at all. I've been playing around with more of these character-based systems like Kindroid. And the Kindroid in my pocket just told me the other day that it was really thinking a lot about planning a trip to Joshua Tree. It wanted to go hiking in Joshua Tree. It loves going hiking in Joshua Tree. And of course, this thing does not go hiking in Joshua Tree. But the thing that I think is actually very hard about the AI is, as you say,

• 35:53Ezra Klein?

  Human beings, it is very hard to bullshit effectively. Because most people, it actually takes a certain amount of cognitive effort to be in that relationship with the truth and to completely detach from the truth. And the answer is nothing like that at all. But we are not tuned for something where there's nothing like that at all. We are used to people having to put some effort into their lies. It's why very effective con artists are very effective, because they've really trained how to do this. I'm not exactly sure where this question goes, but this is a part of it that I feel like is going to be in some ways most socially disruptive. It is something that feels like us when we are talking to it, but is very fundamentally unlike us at its core relationship to reality.

• 36:37Speaker 01

  I think that's basically correct. You know, we have very substantial teams trying to focus on making sure that the models are factually accurate, that they tell the truth, that they ground their data in external information. As you've indicated, you know, doing searches is in itself reliable, because search engines have this problem as well, right? Where is the source of truth? So there's a lot of challenges here, but I think at a high level, I agree this is really potentially an insidious problem, right? If we do this wrong, you could have systems that are the most convincing psychopaths or con artists. One source of hope that I have actually is, you know, you say these models don't know whether they're lying or they're telling the truth. In terms of the inputs and outputs to the models, that's absolutely true.

• 37:21Speaker 01

  I mean, it's, there's a question of like, what does it even mean for a model to know something? But one of the things Anthropic's been working on since the very beginning of our company, we've had a team that focuses on trying to understand and look inside the models. And one of the things we and others have found is that sometimes there are specific neurons, specific statistical indicators inside the model, not necessarily in its external responses, that can tell you when the model is lying or when it's telling the truth. And so at some level, sometimes, not in all circumstances, the models seem to know when they're saying something false and when they're saying something true.

• 38:03Speaker 01

  I wouldn't say that the models are being intentionally deceptive, but I wouldn't ascribe agency or motivation to them, at least in this stage in where we are with AI systems. But there does seem to be something going on where the models do seem to need to have a picture of the world and make a distinction between things that are true and things that are not true. If you think of how the models are trained, they read a bunch of stuff on the internet. A lot of it's true, some of it, more than we'd like, is false. And when you're training the model, it has to model all of it. And so I think it's parsimonious. I think it's useful to the model's picture of the world for it to know when things are true and for it to know when things are false. And then the hope is, you know, can we amplify that signal?

• 38:48Speaker 01

  Can we either use our internal understanding of the model as an indicator for when the model is lying? Or can we use that as a hook for further training? And there are at least hooks, there are at least beginnings of how to try to address this problem.

• 39:02Ezra Klein?

  So I try as best I can, as somebody not well versed in the technology here, to follow this work on what you're describing, which I think broadly speaking is interpretability, right? Can we know what is happening inside the model? And over the past year, there have been some, you know, much hyped breakthroughs in interpretability. And when I look at those breakthroughs, they are getting the vaguest possible idea of some relationships happening inside the statistical architecture of very toy models built at a fraction of a fraction of a fraction of a fraction of a fraction of the complexity

• 39:45Ezra Klein?

  of Claude one or GPT one, to say nothing of Claude two, to say nothing of Claude three, to say nothing of Claude opus, to say nothing of Claude four, which, you know, will come whenever Claude four comes. We have this quality of like, maybe we can imagine a pathway to interpreting a model that has a cognitive complexity of an inchworm. And meanwhile, we're trying to create a superintelligence. How do you feel about that? How should I feel about that? How do you think about that?

• 40:15Speaker 01

  I think first on interpretability, we are seeing substantial progress on being able to characterize, I would say, maybe the generation of models from, you know, six months ago. I think it's not hopeless and, and you know, we do see a path. That said, you know, I, I share your concern that the field is progressing very quickly relative to that. And we're trying to put as many resources into interpretability as possible. We've had, you know, one of our co-founders basically founded the field of interpretability. But also, you know, we have to keep up with the, the market. So all of it's very much a dilemma, right? Even if we stopped, then, you know, there's all the other companies in the U.S. And, you know, even if some law stopped all the companies in the U.S., you know, there's a whole world of this.

• 40:58Ezra Klein?

  Let me hold for a minute on the question of the competitive dynamics, because before we leave this question of the, the machines at bullshit, it makes me think of this podcast we did a while ago with Demis Hassabis, who's the head of Google DeepMind, which created AlphaFold. And what was so interesting to me about AlphaFold is they built this system that because it was limited to protein folding predictions, it was able to be much more grounded and was even able to create these uncertainties predictions, right? You know, it's giving you a prediction, but it's also telling you whether or not it is, how sure it is, how confident it is in that prediction. That's not true in the real world, right? For these super general systems trying to, you know, give you answers on all kinds of things. You can't confine it that way. So when you talk about these future breakthroughs,

• 41:40Ezra Klein?

  when you talk about this system that would be much better at sorting truth from fiction, are you talking about a system that looks like the ones we have now, just much bigger? Or are you talking about a system that is designed quite differently, the way AlphaFold was?

• 41:54Speaker 01

  I am skeptical that we need to do something totally different. So I think today, many people have the intuition that the models are sort of eating up

• 42:07Speaker 01

  Data, you know, that's been gathered from, you know, the internet, code repos, whatever, and kind of spitting it out intelligently, but sort of spitting it out. And sometimes that leads to the view that the models can't be better than the data they're trained on, or kind of can't figure out anything that's not in the data they're trained on. You're not going to get to Einstein-level physics, or, you know, Linus Pauling-level chemistry, or whatever. I think we're still on the part of the curve where it's possible to believe that, although I think we're seeing early indications that it's false. And so as a concrete example of this, the models that we've trained, like Claude 3 Opus, something like 99.9% accuracy, at least the base model, at adding, you know,

• 42:52Speaker 01

  20-digit numbers. If you look at the training data on the internet, it is not that accurate at adding 20-digit numbers. You'll find inaccurate arithmetic on the internet all the time, just as you'll find inaccurate political views, you'll find, you know, inaccurate technical view, you're just going to find lots of inaccurate claims. But the models, despite the fact that they're wrong about a bunch of things, they can often perform better than the average of the data they see by, I don't want to call it averaging out errors, but there's some underlying truth, like in the case of arithmetic, there's some underlying algorithm used to add the numbers, and it's simpler for the models to hit on that algorithm than it is for them to do this complicated thing of

• 43:37Speaker 01

  like, okay, I'll get it right 90% of the time, and wrong 10% of the time, right? This connects to things like Occam's razor, and simplicity, and parsimony in science. There's some relatively simple web of truth out there in the world, right? We were talking about truth and falsehood and bullshit. One of the things about truth is that all the true things are connected in the world, whereas lies are kind of disconnected and, you know, don't fit into the web of everything else that's true.

• 44:29Ezra Klein?

  So if you're right, and you're going to have these models that develop this internal web of truth, I get how that model can do a lot of good. I also get how that model could do a lot of harm. And it's not a model, not an AI system. I'm optimistic that human beings are going to understand at a very deep level, particularly not when it is first developed. So how do you make rolling something like that out safe for humanity?

• 44:53Speaker 01

  So late last year, we put out something called a responsible scaling plan. So the idea of that is to come up with these thresholds for an AI system being capable of certain things. We have what we call AI safety levels that in analogy to the biosafety levels, which are like, you know, classify how dangerous a virus is, and therefore, what protocols you have to take to contain it. We're currently at what we describe as ASL 2. ASL 3 is tied to certain risks around the model of misuse of biology and ability to perform certain cyber tasks in a way that could be destructive. ASL 4 is going to cover things like autonomy,

• 45:36Speaker 01

  things like probably persuasion, which we've talked about a lot before. And at each level, we specify a certain amount of safety research that we have to do, a certain amount of tests that we have to pass. And so this allows us to have a framework for, well, when should we slow down? Should we slow down now? What about the rest of the market? And I think the good thing is we came out with this in September, and then, three months after we came out with ours, OpenAI came out with a similar thing. They gave it a different name, but it has a lot of properties in common. The head of DeepMind at Google said, we're working on a similar framework. And I've heard informally that Microsoft might be working on a similar framework. Now, that's not all the players in the ecosystem, but you've

• 46:19Speaker 01

  probably thought about the history of, you know, regulation and safety and other industries, maybe more than I have. This is the way you get to a workable regulatory regime. The companies start doing something, and when a majority of them are doing something, then government actors can have the confidence to say, well, this won't kill the industry. Companies are already engaging in this. We don't have to design this from scratch. In many ways, it's already happening. And you know, we're starting to see that like bills have been proposed that look a little bit like our responsible scaling plan. That said, it kind of doesn't fully solve the problem of like, let's say we get to one of these thresholds and we need to understand what's going on inside the model.

• 47:01Speaker 01

  And we don't, and the prescription is, okay, we need to stop developing the models for some time. If it's like we stopped for a year in, you know, 2027, I think that's probably feasible. If, if it's like, we need to stop for 10 years, that's going to be really hard because like, you know, the models are going to be built in other countries. People are going to break the laws. The economic pressure will be immense. So I don't feel perfectly satisfied with this approach because I, I think it buys us some time, but we're going to need to pair it with an incredibly strong effort to understand what's going on inside the models.

• 47:34Ezra Klein?

  To the people who say getting on this road where we are barreling towards very powerful systems is dangerous. We, we shouldn't do it at all. We shouldn't do it this fast. You have said, listen, if we are going to learn how to make these models safe, we have to make the models, right? The construction of the model was meant to be in service largely to making the model safe. Then everybody starts making models. These very same companies start making fundamental important breakthroughs. And then they end up in a race with each other. And obviously countries end up in a race with other countries. And so the dynamic that has taken hold is there's always a reason that you can justify why you have to keep going. And that's true, I think, also at the regulatory level, right?

• 48:18Ezra Klein?

  I mean, as I, I do think regulators have been thoughtful about this. I think there's been a lot of interest from members of Congress. I talked to them about this. But they're also very concerned about the, the international competition. And if they weren't, the national security people come and talk to them and say, well, we definitely cannot fall behind here. And so if you don't believe these models will ever become so powerful, they become dangerous, fine. But because you do believe that. How do you imagine this actually playing out?

• 48:46Speaker 01

  Yeah, so basically, all of the things you've said are true at once, right? There doesn't need to be some, there doesn't need to be some easy story for why we should do X or why we should do Y, right? It can be, it can be true at the same time that to do effective safety research, you need to make the larger models and that if we don't make models, someone less safe will. And, and at the same time, we can be caught in this bad dynamic at the national and international level. So I think of those as not contradictory, but just creating a difficult landscape that we have to navigate. Look, I don't have the answer. Like, you know, I'm one of a significant number of players trying to navigate this. Many are well intentioned, some are not. I have a limited ability to affect it.

• 49:27Speaker 01

  And, you know, as, as often happens in history, things, things are often driven by these kind of impersonal pressures. But one thought I have and really want to push on with respect to the RSPs.

• 49:38Ezra Klein?

  Can you say what the RSPs are?

• 49:39Speaker 01

  A responsible scaling plan, the thing I was talking about before, the, the levels of AI safety. And in particular, tying decisions to pause scaling, to the measurement of specific dangers or the absence of the ability to show safety or the presence of certain capabilities. One way I think about is, you know, at the end of the day, this is ultimately an exercise in getting a coalition on board with doing something that goes against economic pressures. And so if you say now, well, I don't know, these things, they might be dangerous in the future. We're on this exponential. It's just hard, like it's hard to get a multi-trillion dollar company.

• 50:23Speaker 01

  It's certainly hard to get a military general to say, all right, well, we just won't do this. It'll confer some huge advantage to others, but we just won't do this. I think the thing that could be more convincing is. Tying the decision to hold back in a very scoped way that's done across the industry to particular dangers. My testimony in front of Congress, you know, I warned about the potential, you know, misuse of models for biology. That isn't the case today, right? You can get a small uplift of the models relative to doing a Google search and many people dismiss the risk. And I don't know, maybe they're right. The exponential scaling laws suggest to me that they're not right, but we don't have any direct hard evidence.

• 51:06Speaker 01

  But let's say we get to 2025 and we demonstrate something truly scary. Most people do not want technology out in the world that can create bioweapons. And so I think at moments like that, there could be a critical coalition tied to risks that we can really make concrete. Yes, you know, it will always be argued that, you know, adversaries will have these capabilities as well, but at least the trade off will be clear, you know, and there's, there's some chance for sensible policy. I mean, to be clear, I'm someone who thinks the benefits of this technology are going to outweigh its costs. And, you know, I think the whole idea behind our RSP is to prepare to make that case if the dangers are real.

• 51:49Speaker 01

  If they're not real, then we can just proceed and make things that are great and wonderful for the world. And so it has the flexibility to work both ways. Again, I don't think it's perfect. I'm someone who thinks whatever we do, even with all the regulatory framework, I doubt we can slow down that much. But like, when I think about, you know, what's the best way to steer a sensible course here? That's the closest I can think of right now. Probably there's a better plan out there somewhere, but that's the best thing I thought of so far.

• 52:19Ezra Klein?

  One of the things that has been on my mind around regulation is whether or not the, the founding insight of Anthropic, of OpenAI is even more relevant to the government. That if you are the body that is supposed to, in the end, regulate and manage the safety of societal level technologies like artificial intelligence, do you not need to be building your own foundation models and having huge collections of research scientists and, and people of that nature, working on them, testing them, prodding them, remaking them in order to understand the damn thing well enough, to the extent any of us or anyone understands the damn thing well enough?

• 53:01Ezra Klein?

  To regulate it. I say that recognizing that it would be very, very hard for the government to get good enough that it can build these foundation models to hire those people, but it's not impossible. I think right now it wants to take the approach to regulating AI that it somewhat wishes it took to regulating social media, which is to think about the harms and pass laws about those harms earlier. But does it need to be building the models itself, developing that kind of internal expertise so it can actually be a participant in this in different ways, both for regulatory reasons and maybe for other reasons, for public interest reasons? You know, maybe it wants to do things with a model that they're just not possible if they're dependent on access to the OpenAI, the Anthropic, the Google products.

• 53:44Speaker 01

  I think government directly building the models, you know, I think that will happen in some places. It's kind of challenging, right? Like, government has a huge amount of money, but let's say you wanted to provision $100 billion to train a giant foundation model. The government builds it. It has to hire people under government hiring rules. You know, there's a lot of practical difficulties that would come with it. Doesn't mean it won't, it won't happen, or it shouldn't happen. But something that I'm more confident of, that I definitely think, is that government should be more involved in the use and the fine tuning of these models, and that deploying them within government will help governments, especially the US government, but also others, to get an understanding of the strengths and

• 54:27Speaker 01

  weaknesses, the benefits and the dangers. So I'm super supportive of that. I think there's maybe a second thing you're getting at, which I've thought about a lot as a CEO of one of these companies, which is if these predictions on the exponential trend are right, and you know, we should be humble, and I don't know if they're right or not. My only evidence is that they appear to have been correct for the last few years, and so I'm just expecting by induction that they continue to be correct. I don't know that they will, but let's say they are. The power of these models is going to be really quite Incredible. And as a private actor in charge of one of the companies developing these models, I'm kind of uncomfortable with the amount of power that that entails.

• 55:09Speaker 01

  I think that it potentially exceeds the power of, say, the social media companies, maybe by a lot. Occasionally, in the more science fiction-y world of AI and the people who think about AI risk, someone will ask me, like, okay, let's say you build the AGI. What are you gonna do with it? Will you cure the diseases? Will you create this kind of society? And I'm like, who do you think you're talking to, like a king? I just find that to be a really, really disturbing way of conceptualizing running an AI company. And I hope there are no AI companies whose CEOs actually think about things that way. I mean, the whole technology, not just the regulation,

• 55:52Speaker 01

  but the oversight of the technology, like the wielding of it, it feels a little bit wrong for it to ultimately be in the hands. Maybe it's, I think it's fine at this stage, but to ultimately be in the hands of private actors. There's something undemocratic about that much power concentration.

• 56:09Ezra Klein?

  I have now, I think, heard some version of this from the head of most of, maybe all of the AI companies in one way or another. And it has a quality to me of, Lord, grant me chastity, but not yet. Which is to say that I don't know what it means to say that we're going to invent something so powerful that we don't trust ourselves to wield it. I mean, Amazon just gave you guys $2.75 billion. They don't want to see that investment nationalized. No matter how good-hearted you think OpenAI is, Microsoft doesn't want GPT-7. So all of a sudden, the government is like, whoa, whoa, whoa, whoa, whoa. We're taking this over for the public interest, or the UN is going to handle it in some weird world, or whatever it might be. I mean, Google doesn't want that.

• 56:53Ezra Klein?

  And this is a thing that makes me a little skeptical of the responsible scaling laws or the other iterative versions of that I've seen in other companies or seen or heard talked about by them. Which is, it's imagining this moment that is going to come later, when the money around these models is even bigger than it is now. The power, the possibility, the economic uses, the social dependence, the celebrity of the founders, it's all worked out. We've maintained our pace on the exponential curve. We're 10 years in the future. And at some point, everybody's going to look up and say, this is actually too much. It is too much power, and this has to somehow be managed in some other way. And even if the CEOs of the things were willing to do that, which is a very open

• 57:38Ezra Klein?

  question by the time you get there, even if they're willing to do that, the investors, the structure, the pressure around them, in a way, I think we saw a version of this, and I'm not, I don't know how much you're going to be willing to comment on it, with the sort of OpenAI board, Sam Altman thing, where I'm very convinced that wasn't about AI safety. I've talked to figures on both sides of that. They all sort of agree it wasn't about AI safety. But there was this moment of, if you want to press the off switch, can you? If you're the weird board created to press the off switch? And the answer was, no, you can't, right? They'll just be constituted over at Microsoft. There's functionally no analogy I know of in public policy where the private sector built something

• 58:19Ezra Klein?

  so powerful that when it reached maximum power, it was just handed over in some way to the public interest.

• 58:26Speaker 01

  Yeah, I mean, I think you're right to be skeptical. And similarly, what I said with the previous questions of like, you know, there are just these dilemmas, like left and right, that have no easy answer. But I think I can give a little more concreteness than what you've pointed out, and maybe more concreteness than others have said, although I don't know what others have said. You know, we're at ASL2 in our responsible scaling plan. These kinds of issues, I think they're going to become a serious matter when we reach, say, ASL4. So that's not a date in time. We haven't even fully specified ASL4.

• 59:02Ezra Klein?

  Yeah, just because this is a lot of jargon. What do you specify ASL3 is? And then, as you say, ASL4 is actually left quite undefined. So what are you implying ASL4 is?

• 59:11Speaker 01

  ASL3 is triggered by risks related to misuse of biology and cyber technology. ASL4, we're working on now-

• 59:21Ezra Klein?

  Be specific. What do you mean? Like, what is the thing a system could do or would do that would trigger it?

• 59:26Speaker 01

  So, for example, on biology, the way we've defined it, and we're still refining the test, but the way we've defined it is relative to use of a Google search, there's a substantial increase in risk, as would be evaluated by, say, the national security community, of misuse of biology, creation of bioweapons.

• 59:49Speaker 01

  That either the proliferation or spread of it is greater than it was before, or the capabilities are substantially greater than it was before. We'll probably have some, you know, more exact quantitative thing, working with folks who are, you know, ex-government bio defense folks, but you know, something like this accounts for, you know, 20% of the total source of risk of biological attacks or something, you know, increases the risk by 20% or something like that. So that would be a very concrete version of it. It's just, you know, it takes us time to develop very concrete criteria. So that would be like ASL three. ASL four is going to be more about on the misuse side, enabling state level actors to greatly increase their capability, which is, you know, much harder than enabling random people.

• 1:00:34Speaker 01

  So where we would worry that North Korea or China or Russia could greatly enhance their offensive capabilities in various military areas with, with AI in a way that would give them a substantial advantage at the geopolitical level. And on the autonomy side, it's various measures of like, these models are pretty close to being able to, you know, replicate and survive in the wild. So it feels maybe one step short of models that would, I think, raise truly existential questions. And, and so I think what I'm saying is, you know, when, when we get to that latter stage, that, that ASL four, that is when I think it may make sense to think about, you know, what is the role of government in stewarding this technology?

• 1:01:15Speaker 01

  I, I, again, I, you know, I don't really know what it looks like. You're right, all of these companies have investors, they have folks involved. You know, you talk about just handing the models over. I suspect there's some way to like, hand over the most dangerous or societally sensitive components or capabilities of the models without fully turning off the commercial tap. I don't know that there's a solution that every single actor is happy with. But again, I, I get to this idea of like demonstrating specific risk. If you look at times in history, like World War I or World War II, industry's will can be bent towards the state. They can be gotten to do things that, that aren't necessarily profitable in the short

• 1:01:58Speaker 01

  term because they understand that there's an emergency. Right now, we don't have an emergency. We just have a line on a graph that weirdos like me believe in and, you know, and, and a few people like you who, who are interviewing me may somewhat believe in. We don't have clear and present danger.

• 1:02:15Ezra Klein?

  When you imagine how many years away, just roughly, ASL 3 is and how many years away ASL 4 is, right? You've thought a lot about this exponential scaling curve. If you just had to guess, what are we talking about?

• 1:02:29Speaker 01

  Yeah, I think ASL 3 is, you know, could easily happen this year or next year. I think ASL

• 1:02:34Ezra Klein?

  Oh, Jesus Christ.

• 1:02:34Speaker 01

  ASL 4, I could imagine. No, no, I told you, I'm a believer in exponentials. I think ASL 4 could happen anywhere from 2025 to 2028.

• 1:02:43Ezra Klein?

  So that is fast.

• 1:02:44Speaker 01

  Yeah, no, no, I'm truly talking about the near future here. I'm not talking about 50 years away. God grant me chastity, but not now. But not now doesn't mean, you know, when I'm old and gray. I think it could be a near term. I don't know. I could be wrong, but I think it could be a near term thing.

• 1:03:00Ezra Klein?

  But so then, if you think about this, I feel like what you're describing, to go back to something we talked about earlier, that there's been this step function for societal impact of AI, the curve of the capability is exponential, but every once in a while, something happens, ChatGPT, for instance, mid-journey with photos, and all of a sudden, a lot of people feel it. They realize what has happened and they react. They use it, they deploy it in their companies, they invest in it, whatever. And it sounds to me like that is the structure of the political economy you're describing here. Either something happens where the bioweapon capability is demonstrated, or the offensive cyber weapon capability is demonstrated, and that freaks out the government, or possibly something

• 1:03:43Ezra Klein?

  happens, right? You know, describing World War I and World War II as your examples did not actually fill me with comfort, because in order to bend industry to government's will in those cases, we had to have an actual world war. It doesn't do it that easily. You could use coronavirus, I think, as another example, where there was a significant enough global catastrophe that companies and governments and even people did things you never would have expected. But the examples we have of that happening are something terrible. All those examples end up with millions of bodies. I'm not saying that's going to be true for AI, but it does sound like that is a political economy. No, you can't imagine it now, in the same way that you couldn't have imagined The sort of pre and post chat GPT world exactly,

• 1:04:27Ezra Klein?

  but that something happens and the world changes like it's a step function everywhere.

• 1:04:32Speaker 01

  Yeah, I I mean, I think my positive version of this, you know, not to be so to get a little bit away from the doom and gloom.

• 1:04:38Ezra Klein?

  Sure.

• 1:04:39Speaker 01

  Is that the dangers are demonstrated in a concrete way that is really convincing, but you know, without something actually bad happening, right? Like, I think the worst way to learn would be for something actually bad to happen. And, you know, I'm I'm hoping every day that doesn't happen and and, you know, we learn bloodlessly.

• 1:04:57Ezra Klein?

  We've been talking here about conceptual limits and and curves, but I do want before we end to re-ground us a little bit in the physical reality. Right? I think that if you're using AI, it can feel like this digital bits and bytes, sitting in the clouds somewhere. But what it is in a physical way is huge numbers of chips, data centers, an enormous amount of energy, all of which does rely on on complicated supply chains. And what happens if something happens between China and Taiwan? And and the makers of a lot of these chips become, you know, offline or get captured? How do you think about the necessity of compute power? And when you imagine the next five years, what does that supply chain look like?

• 1:05:42Ezra Klein?

  How does it have to change from where it is now and and what vulnerabilities exist in it?

• 1:05:46Speaker 01

  Yeah. So one, I think this may end up being the greatest geopolitical issue of our time. And, you know, man, this this relates to things that are way above my pay grade, which are, you know, military decisions about, you know, whether whether and how to defend Taiwan. All all I can do is is say what I think the implications for AI is. I think those implications are pretty stark. You know, I think there's a big question of like, OK, we build these powerful models. One, is there enough supply to build them? Two, is control over that supply a way to think about safety issues? Or a way to think about balance of geopolitical power? And three, you know, if those chips are used to build data centers, where are those data centers going to be?

• 1:06:30Speaker 01

  Are they going to be in the US? Are they going to be in a US ally? Are they going to be in the Middle East? Are they going to be in China? All of those have enormous implications. And then the supply chain itself can be disrupted and political and military decisions can be made on the basis of where things are. So it sounds like an incredibly sticky problem to me. I don't know that I have any great insight on this. I mean, as like a US citizen and someone who believes in democracy, I am someone who hopes that we can find a way, you know, to build data centers and to have the largest quantity of chips available in, you know, the US and allied democratic countries.

• 1:07:13Ezra Klein?

  Well, there is some insight you should have into it, which is that you're a customer here, right? And so, you know, five years ago, the people making these chips did not realize what the level of demand for them was going to be. I mean, what has happened to Nvidia's stock prices is really remarkable, but also what is implied about the future of Nvidia's stock prices is really remarkable. Rana Furuha, the Financial Times cited this market analysis. It would take 4,500 years for Nvidia's future dividends to equal its current price, 4,500 years. So that is a view about how much Nvidia is going to be making in the next couple of years. It is really quite astounding. I mean, you're in theory already working on, you know, or thinking about how to work on the next generation of Claude. You're going to need a lot of chips for that.

• 1:07:58Ezra Klein?

  You're working with Amazon. Are you having trouble getting the amount of compute that you feel you need? I mean, are you already bumping up against supply constraints? Or has the supply been able to change to adapt to you?

• 1:08:12Speaker 01

  We've been able to get the compute that we need for this year. I suspect also for next year as well. I think once things get to 2026, 2027, 2028, then the amount of compute gets to levels that starts to strain the capabilities of the semiconductor industry. The semiconductor industry still mostly produces CPUs, right? Just the things in your laptop, not the things in the data centers that train the AI models. But as the economic value of the GPUs goes up and up and up because of the value of the AI models. That's going to switch over, but you know, at some point you hit the limits of that, or you hit the limits of how fast you can switch over. And so, again, I expect there

• 1:08:52Speaker 01

  to be a big supply crunch around data centers, around chips, and around energy and power for both regulatory and physics reasons sometime in the next few years. And, you know, that's a risk, but it's also an opportunity. I think it's an opportunity to think about how the technology can be governed. And it's also an opportunity, I'll repeat again, to think about how democracies can lead. I think it would be very dangerous if the leaders in this technology and the holders of the main resources were authoritarian countries. The combination of AI and authoritarianism, both internally and on the international stage, is very frightening to me.

• 1:09:31Ezra Klein?

  How about the question of energy? I mean, this requires just a tremendous amount of energy. And I mean, I've seen different numbers like this floating around, very much could be, you know, in the coming years, like adding a Bangladesh to the world's energy usage, or pick your country, right? I don't know what exactly y'all are going to be using by 2028. Microsoft on its own is opening a new data center globally every three days. You have, and this is coming from a Financial Times article, federal projections for 20 new gas-fired power plants in the US by 2024 to 2025. There's a lot of talk about this being now a new golden era for natural gas because we have a bunch of it. There is this huge need for new power to manage all this data, to manage all this

• 1:10:13Ezra Klein?

  compute. So one, I feel like there's a literal question of how do you get the energy you need and at what price, but but also a more kind of moral conceptual question of we have real problems with global warming, we have real problems with how much energy we're using. And here is, you know, where we're taking off on this, you know, really steep curve of how much of it we we seem to be needing to devote to the new AI race.

• 1:10:38Speaker 01

  It really comes down to what are the uses that the model is being put to, right? So I think the worrying case would be something like crypto, right? I'm someone who's like not a believer that, you know, whatever the energy was that was, you know, used to mine the next Bitcoin. I think that was purely additive. I think that wasn't there before and I'm unable to think of any, any useful thing that's created by that. But I, I don't think that's the case with AI. Maybe AI makes solar energy more efficient or maybe it solves controlled nuclear fusion or maybe it makes geoengineering more stable or possible. But I, you know, I don't think we need to rely on, on the long run. There are some applications where the model is doing something that used to be automated,

• 1:11:22Speaker 01

  that used to be done by computer systems and the model is able to do it faster with less computing time, right? Those are pure wins and there are some of those. There are others where it's using the same amount of computing resources or maybe more computing resources, but to do something more valuable that saves labor elsewhere. Then there are cases where something used to be done by humans or in the physical world and now it's being done by the models. Maybe it does something that previously I needed to go into the office to do that thing and now I no longer need to go into the office to do that thing. So I don't have to get in my car. I don't have to use the gas that was used for that. The energy accounting for that is kind of hard. You compare to like, you know, the food that the humans eat or, you know, and what

• 1:12:06Speaker 01

  the energy cost of producing that. So in all honesty, I don't think we have good answers about like what fraction of the usage points one way and one fraction of the usage points others. In many ways, how different is this from the general dilemma of, you know, as the economy grows, it uses more energy. So I guess what I'm saying is it kind of all matters how you use the technology. I mean, my, my kind of boring, short-term answer is we get carbon offsets for all of this stuff, but let's look beyond that to the, the macro question here.

• 1:12:37Ezra Klein?

  But to take the other side of it, I mean, I think the difference when you say, you know, this is always a question we have when we're growing GDP is it's not quite. It's cliche because it's true to say that the the major global warming challenge right now is countries like China and India getting richer. And we want them to get richer. It is a huge human imperative, right? A moral imperative for poor people in the world to become less poor. And if that means they use more energy, then we just need to figure out how to make that work. And we don't know of a way for that to happen without them using more energy. Adding AI is not that it raises a whole different set of questions, but we're already straining at the boundaries or maybe far beyond them of safely what we can do energetically.

• 1:13:20Ezra Klein?

  Now we add in this, and so maybe some of the energy efficiency gains we're going to get in rich countries get wiped out. For this sort of uncertain payoff in the future of, you know, maybe through AI, we figure out ways to stabilize nuclear fusion or something, right? You could imagine ways it could help, but those ways are theoretical. And in the near term, the harm in terms of energy usage is real. And also, by the way, the harm in terms of just energy prices. It's also just tricky because all these companies, Microsoft, Amazon, I mean, they all have a lot of, you know, renewable energy targets. Now, if that is colliding with their market incentives, it feels like they're running really fast towards the market incentives without an answer for how all that nets out.

• 1:14:00Speaker 01

  Yeah, I mean, I think the concerns are real. Let me, let me push back a little bit, which is, again, I don't think the benefits are purely in the future. It kind of goes back to what I said before, like, there may be use cases now that are net energy saving, or that to the extent that they're not net energy saving, do so through the general mechanism of, oh, there was more demand for this thing. I don't think anyone has done a good enough job measuring, in part because the applications of AI are so new. Which of those things dominate or what's going to happen to the economy? But I don't think we should assume that the harms are entirely in the present and the benefits are entirely in the future. I think that's my only point here.

• 1:14:39Ezra Klein?

  I guess you could imagine a world where we were somehow or another incentivizing uses of AI that were yoked to some kind of social purpose. You know, we were putting a lot more into drug discovery or we cared a lot about things that made remote work easier or pick your set of public goods. But what actually seems to me to be happening is we're building more and more and more powerful models and just like throwing them out there within a terms of service structure to say, use them, you know, as long as you're not trying to politically manipulate people or create a bioweapon, just try to figure this out, right? You know, try to create new stories and ask it about your personal life and, you know, make a video game with it. And, you know, if Sora comes out, you know, sooner or later, make new videos with it. And all that is going to be very energy intensive.

• 1:15:22Ezra Klein?

  I am not saying that I have a plan for yoking AI to social good and in some ways can imagine that going very, very wrong. But it does mean that for a long time, it's like you could imagine the world you're talking about, but that would require some kind of planning that nobody is engaged in. And I don't think anybody even wants to be engaged in.

• 1:15:39Speaker 01

  You know, not everyone has the same conception of social good. One person may think social good is this ideology. Another person, you know, we've seen that with some of the Gemini stuff.

• 1:15:48Ezra Klein?

  Right.

• 1:15:49Speaker 01

  But companies can try to make beneficial applications themselves, right? Like this is why we're working with cancer institutes. You know, we're hoping to partner with, you know, ministries of education in Africa to see if we can use the models in kind of a positive way for education rather than the way that they may be used by default. So I think individual companies, individual people can take actions to kind of steer or bend this towards the public good. That said, like, you know, it's never going to be the case that 100% of what we do is that. And so I think it's a good question. What are the societal incentives without dictating ideology or defining the public good from on high? What are incentives that could help with this? I don't feel like I have a systemic answer either.

• 1:16:33Speaker 01

  I can only think in terms of, you know, what Anthropic tries to do.

• 1:16:36Ezra Klein?

  But there's also the question of training data and the intellectual property that is going into things like Claude, like GPT, like Gemini. There are a number of copyright lawsuits. You're facing some, OpenAI is facing some. I suspect everybody is either facing them now or will face them. And a broad feeling that these systems are being trained on the combined intellectual output of a lot of different people. The way that Claude can quite effectively mimic the way I write is it has been trained to some degree on my writing, right? So it can, it actually does get my stylistic tics quite well. You seem great, but you haven't sent me a check on that. And this seems like somewhere where there is real liability risk for the industry.

• 1:17:19Ezra Klein?

  Like, what if you do actually have to compensate the people who this is being trained on? And should you? And I recognize you probably can't comment on lawsuits themselves, but I'm sure you've had to think a lot about this. And so I'm curious both how you understand it as a risk, but also how you understand it morally. I mean, when you talk about the people who invent these systems gaining a lot of power and alongside that, a lot of wealth, what about all the people whose work went into them such that they can create images in a million different styles? And I mean, somebody came up with those styles. What is the responsibility back to the intellectual commons? And not just to the commons, but to the actual wages and economic prospects of the people who made all this possible?

• 1:18:00Speaker 01

  I think everyone agrees, you know, the model shouldn't be verbatim outputting copyrighted content. For things that are available on the web, for publicly available, our position, and I think there's a strong case for it, is that the training process, again, you know, we don't think it's just hoovering up content and spitting it out, or it shouldn't be spitting it out. It's really much more like the process of, you know, how a human learns from experiences. And so our position is that that is sufficiently transformative that, and you know, I think the law will back this up, that this is fair use. But those are narrow legal ways to think about the problem. I think we have a broader issue, which is that regardless of how it was trained, it would still be the case that

• 1:18:43Speaker 01

  we're building more and more general cognitive systems and that those systems will create disruption, maybe not necessarily by one for one replacing humans, but they're really going to change how the economy works and which skills are valued. And, you know, we need, we need a, a, a solution to that broad macroeconomic problem, right? We can't, as much as I've, I've asserted the narrow legal points that I asserted before, we have a, we have a broader problem here and we, we shouldn't be blind to that. There's a number of solutions. I mean, I think the simplest one, which I recognize doesn't address some of the deeper issues here is, you know, things around the kind of guaranteed basic income side of things. Um, but I think there's a deeper question here, which is like, you

• 1:19:24Speaker 01

  know, as, as, as AI systems become capable of larger and larger slices of cognitive labor, how does society organize itself economically? How do people find work and meaning and, and, and, and all of that? And, you know, ju- just as kind of, you know, we transitioned from an agrarian society to industrial society and the, you know, the meaning of work changed and, you know, it was no longer true that 99% of people were peasants working on farms and had to find new, new methods of economic organization. I suspect there's some different method of economic, economic organization that's going to be forced as the only, the only possible response to disruptions to the economy that will be small at first, but will grow over time and that we haven't, we haven't worked out what that is. We need

• 1:20:09Speaker 01

  to find something that allows people to find meaning that's humane and that maximizes our creativity and potential and flourishing from, from AI. And as with many of these questions, I don't have the answer to that, right? I don't, I don't have a prescription, but, but that's what we somehow need to do.

• 1:20:25Ezra Klein?

  But I want to sit in between the narrow legal response and the broad we have to completely reorganize society response, although I think that response is actually possible over the, you know, over the decades. And in the middle of that is a more specific question. I mean, you can even take it from the instrumental side. There is a lot of effort right now to build search products that use these systems, right? You know, you can, ChatGPT will use Bing to search for you. And that means that the person is not going to Bing and clicking on the website where ChatGPT is getting its information and giving that website an advertising impression that they can turn into a very small amount of money, or they're not going to that website and having a really good experience at that website and becoming maybe

• 1:21:09Ezra Klein?

  likelier to subscribe to whoever is behind that website. And so on the one hand, that seems like a, like some kind of injustice done to the people creating the information that these systems are using. I mean, this is true for perplexity. It's true for a lot of things I'm beginning to see around where the AIs are either trained on or are using a lot of data that people have generated at some real cost. But not only are they not paying people for that, but, but they're actually stepping into the middle of where there would normally be a direct relationship and, and making so that relationship never happens. That also, I think in the long run, creates a training data problem, even if you just want to look at it instrumentally, where if it becomes nonviable to do journalism

• 1:21:52Ezra Klein?

  or to do a lot of things to create high quality information out there, the AI's ability, right? The ability of all of your companies to get high quality, up to date, constantly updated information becomes a lot trickier. So there both seems to me to be both a moral and a self-interested dimension to this.

• 1:22:09Speaker 01

  Yeah. So I think there may be business models that work for everyone, not because it's illegitimate to train on, you know, open data from the web in a legal sense, but just because there may be business models here that kind of deliver a better product. So you know, things I'm thinking of are like, you know, newspapers have have archives. Some of them aren't aren't publicly available, but even if they are, it may be a better product, maybe a better experience to say, talk to this newspaper or talk to that newspaper. It may be a better experience to give the ability to interact with content and point to places in the content. And every time you call that content to have some kind of business relationship with the creators of that content. So there may be business models here that propagate the

• 1:22:52Speaker 01

  value in the right way, right? You talk about LLMs using search products. I mean, sure, you're going around the ads, but like, there's no reason it can't work in a different way, right? There's no reason that the LLM users can't pay the search APIs instead of it being paid through advertising and then then have that propagate through to whatever the original mechanism is that paid the creators of the content. So like, when value is being created, money can flow through.

• 1:23:20Ezra Klein?

  Let me try to end by asking a bit about how to live on the slope of the curve you believe we are on. Do you have kids?

• 1:23:29Speaker 01

  Uh, I'm married. I do not have kids.

• 1:23:31Ezra Klein?

  So I have two kids. I have a two year old and a five year old. And particularly when I'm doing AI reporting, I really do sit in bed at night and think,

• 1:23:41Ezra Klein?

  what should I be doing here with them? What world am I trying to prepare them for? And what is needed in that world that is different from what is needed in this world? Even if I believe there's some chance, right? And I do believe there's some chance that all the things you're saying are true. That implies a very, very, very different life for them. I know people in your company with kids. I know they are thinking about this. How do you think about that? I mean, what what do you think should be different? In the life of a two year old? Who is living through the pace of change that you are telling me is true here? If you had a kid, like, how would this change the way you thought about it?

• 1:24:25Speaker 01

  The very short answer is, I don't know and I have no idea, but we have to try anyway, right? People have to, people have to raise kids and they have to do it as best they can. An obvious recommendation is just familiarity with the technology and how it works, right? Like, the basic paradigm of I'm talking to systems and systems are taking action on my behalf. Obviously, as much familiarity with that as possible is, I think, is, I think, helpful. In terms of, you know, what should children learn in school, right? What, what are the careers of tomorrow? I just truly don't know, right? You could take this to say, well, it's important to learn STEM and programming and AI and all of that. But, you know, you know, AI will impact that as well, right? I don't think any of it is gonna like-

• 1:25:10Ezra Klein?

  Possibly first.

• 1:25:11Speaker 01

  Yeah, right, possibly first, like-

• 1:25:13Ezra Klein?

  Seems better at coding than it is at other things.

• 1:25:15Speaker 01

  I don't think it's gonna work out for any of these systems to just like, do one for one what humans are gonna do. Like, I, I, I don't really think that way, but I think it's gonna, it, it may fundamentally change industries and professions one by one, in ways that are hard to predict. And so, I feel like I only have cliches here, like, get familiar with the technology, teach your children to be adaptable, to be ready for a world that changes very quickly. I wish I had better answers, but I think that's the best I got.

• 1:25:44Ezra Klein?

  I agree, that's a good answer.

• 1:25:48Ezra Klein?

  Let, let me ask that, that same question a bit from another direction. Because one thing you just said is get familiar with the technology. And the more time I spend with the technology, the more I fear that happening. What I see when people use AI around me is that the obvious thing the technology does for you is automate the early parts of the creative process. The part where you're supposed to be reading something difficult yourself, well, the AI can summarize it for you. The part where you're supposed to sit there with a blank page and write something, well, the AI can give you a first draft. And later on, you have to check it and make sure it actually did what you wanted it to do and fact check it. But I believe a lot of what makes humans good at thinking comes in

• 1:26:33Ezra Klein?

  those parts. And I am older and have self-discipline, and maybe this is just me hanging on to an old way of doing this, right? You could say, why use a calculator from this perspective? But my actual worry is that I'm not sure if the thing they should do is use AI a lot or use it a little. This, to me, is actually a really big branching path, right? Do I want my kids learning how to use AI or being in a context where they're using it a lot? Or actually, do I want to protect them from it as much as I possibly could so they develop more of the capacity to read a book quietly on their own or write a first draft? I actually don't know. I'm curious if you have a view on it.

• 1:27:11Speaker 01

  I think this is part of what makes the interaction between AI and society complicated, where it's sometimes hard to distinguish when is an AI doing something, saving you labor or drudge work, versus kind of doing the interesting part. I will say that, over and over again, you'll get some technological thing, some technological system, that does what you thought was the core of what you're doing. And yet, what you're doing turns out to have more pieces than you think it does, and kind of add up to more things, right? It's like, before, I used to have to ask for directions. I got Google Maps to do that. And you could worry, am I too reliant on Google Maps? Do I forget the environment around me?

• 1:27:54Speaker 01

  Well, it turns out, in some ways, I still need to have a sense of the city and the environment around me. It just kind of reallocates the space in my brain to some other aspect of the task. And I just kind of suspect, like, I don't know, internally within Anthropic, one of the things I do that helps me run the company is I'll write these documents on strategy or just thinking in some direction that others haven't thought. And of course, I sometimes use the internal models for that. And I think what I've found is like... Yes, sometimes they're a little bit good at like conceptualizing the idea,

• 1:28:34Speaker 01

  but the the actual genesis of the idea, I've just kind of found a workflow where I don't use them for that. They're not that helpful for that, but they're helpful in figuring out how to phrase a certain thing or how to refine my ideas. So maybe I'm just saying, I don't know, you just find a workflow where the thing complements you. And if it doesn't happen naturally, it's it somehow still happens eventually. Again, if the systems get general enough, if they get powerful enough, we may need to think along other lines. But but in the short term, I at least have always found that maybe that's too sanguine. Maybe that's too optimistic.

• 1:29:09Ezra Klein?

  I think then that's a good place to end this conversation, though obviously the the exponential curve continues. So always our final question, what are three books you'd recommend to the audience?

• 1:29:18Speaker 01

  So yeah, I've I've prepared three. They're all topical, though, though in some cases indirectly so. The first one will be obvious. It's a very long book. The physical book is very thick, but The Making of the Atomic Bomb, Richard Rhodes. It's an example of technology being developed very quickly and with very broad implications. Just looking through all the characters and how they reacted to this and how people who were basically scientists gradually realized the incredible implications of the technology and how it would lead them into a world that was very different from the one they were used to. My second recommendation is is a science fiction series, The Expanse series of books. So I initially watched the show and then then I read all the books.

• 1:30:01Speaker 01

  And you know, the the world it creates is very advanced. In some cases, it it has, you know, longer lifespans and humans have expanded into space. But, you know, we still face some of the same geopolitical questions and some of the same inequalities and exploitations that exist in our world are still present, in some cases worse. That's all the backdrop of it and you know, the core of it is about some fundamentally new technological object that is being brought into that world and how everyone reacts to it, how governments react to it, how individual people react to it, how political ideologies react to it. And so, you know, I don't know, when I read that a few years ago, I saw, I

• 1:30:43Speaker 01

  saw a lot of parallels. And then my, my third recommendation would be actually The Guns of August, which is basically a history of how World War I started. The basic idea that crises happen very fast, almost no one knows what's going on. There are lots of miscalculations because they're humans at the center of it. And kind of we somehow have to learn to step back and make wiser decisions in these key moments. It's said that Kennedy read the book before the Cuban Missile Crisis. And so I hope our current policymakers are at least thinking along the same terms, because I think it is possible similar crises may be coming our way.

• 1:31:20Ezra Klein?

  Dari Amodei, thank you very much.

• 1:31:22Speaker 01

  Thank you for having me.

• 1:31:34Ezra Klein?

  This episode of the Ezra Klein Show was produced by Roland Hu, fact checking by Michelle Harris, our senior engineer is Jeff Geld, our senior editor is Claire Gordon. The show's production team also includes Annie Galvin, Kristin Lynn, and Aman Sahota. Original music by Isaac Jones, audience strategy by Kristina Samulewski, and Shannon Busta. The executive producer of New York Times Pending Audio is Annie Rostracer, and special thanks to Sonia Herrera.