Mysteries of Claude

Welcome to Search Engine. I'm PJ vote. No question too big, no question too small. This week, mysteries of a chatbot. Quick note before we start today, this week's episode is almost entirely about Anthropic, the AI company that makes quad. They have advertised on our show. As with all companies that advertise on our show, they do not get a say in our editorial content. Okay, after these ads, the show. I'm PJ vote. I found myself feeling much stranger about AI in the past month or so. I use the tools a lot, but I'm probably each company's worst nightmare as a customer, in that as soon as I hear from anybody that one model has inched ahead of another, that this version of chat GBT is beating that version of Gemini, I immediately cancel my subscription and switch. For the past two months, I've mainly been using Quad and Anthropics agent. And for whatever reason, Quad is just giving me more future nausea than I was having six months ago. Part of the general tech excitement around Quad lately has been Anthropic's product Quad Code, a tool that lets the AI agent autonomously write an edit code. Over the New York Times, Kevin Ruse has talked a lot about the websites and apps he's quickly built with Quad Code. Two CNBC reporters as an experiment, five coded a competing version of a popular organizational app called Monday.com. Within a couple of days, Monday's stock price had tanked. For me though, most of the future shock has just come from using the LLMs the way I'm used to. I find myself going to Quad as a useful first stop, the way I've always used the internet, but the quality of its research, its answers, even its writing. I'm just trying to feel like I can see not too far off. If not my own obsolescence, at least real significant change in my field. I don't know how to feel about that. I find a lot of the tech coverage of AI to be high opinion, low information, and relatively unhelpful. I'm not even asking for anyone to tell me the future right now. I would just settle for a better understanding of the present. Which is why this week, I wanted to talk to a reporter who's been digging into this. Hello. Hey, can you introduce yourself? I'm Gideon Lewis-Cross. I'm a writer. Gideon is a writer who I particularly enjoy. He's been on our show before. He's been much of the last year, essentially embedding within Anthropic, the company that makes Quad, the tool that was giving me the hibi jb's. People there have been very open with him. He's got a view on how they're seeing what's going on. Their understanding of a present, which frankly, they also sound mystified by. This conversation took place right before Anthropic's big showdown this week with the Pentagon, so we did not discuss that specifically, but I did find Gideon's view inside the company and its mission extremely helpful in understanding how they'd gotten into this fight with the U.S. government at all, since none of their competitors have ended up in that position. So to start, ask Gideon to even just explain why Anthropic had let him into their company in the first place. So to kind of go back to the beginning of this, which I think makes it all make a little more sense in context. So now almost 10 years ago, when I was at this time as an axi, back in kind of like the Paleolithic of deep learning, I did this story about Google Brain and about the implementation of deep learning in like the first consumer product, which was when they switched over their Google translate to neural machine translation. Why were you paying attention to it? Because I remember as a person who like I think we both cover technology, but we're not strictly technology journalists, so you can kind of decide which things on the horizon are interesting to you. Machine learning was not interesting to me for a long time. Why 10 years ago were you interested in this? I was interested in it as like a story about ideas that like there were these ideas about language and about learning and about consciousness and about like philosophy of mind that had been around for at least seven years, kind of depending on how you count. And without like getting into those, there was just like an interesting story for me about like the trajectory of an idea there. Getting cared about AI a decade before most people did, because he thought this synthetic facsimile of our brains could teach us something about our own real ones. He'd been following the trajectory of conversations like what is a brain versus a mind? What is thinking? What is consciousness? By the 1950s, the arrival of the first computers had encouraged people to start asking questions like that. Because a computer did something like thinking, but also clearly wasn't a brain. And so early computers had prompted people to try to develop better definitions of things like intelligence and consciousness. The thing was though, while computers were interesting enough to raise those questions, they weren't yet complex enough to be much help in answering them. And so by the 1970s, philosophers and computer scientists had mostly moved on. And those questions migrated to psychology departments, who still for obvious reasons wanted to better understand the human mind. But with early machine learning advancements around 2014, Gideon, who's always thinking about thinking, thought that these conversations would move again, that computers would now be advanced enough to challenge our definitions, to force us to decide with more urgency what we thought consciousness and learning really were. And that was what it excited him even when AI was a much more nascent technology. So I paid attention to AI in the rise of language models. And I think I'm like the only person in the world who the minute Chatchee BC came out was when I kind of stopped paying attention. Because like to me, that was when the public discourse felt like really broken and that we were like in this cul-de-sac, where you had these kind of like two really entrenched sides yelling at each other. You know, like the one side that's like, we're on a path to super intelligence, everything is going to change. The machines are going to be conscious. This is going to be the most powerful technology anybody's ever built. And then the other side that was like, essentially, it's all fake and bullshit. This is like smoking mirrors. It's a parlour's trick. It's not real. And you don't have to pay attention to it because it's all a scam. And it just felt like those were kind of like the two options on the table for people. Which was only weird. Obviously like that's what we do about everything all the time. But it was only weird for this because like my prevailing feeling was you guys think you've figured this out. Like this is very new. This is changing very fast. Of all the stances you could take, why would you choose certainty publicly right now in either direction? It's just silly. Yeah, no, exactly. There's so many. So you're thinking about thinking computers and thinking and artificial intelligence and deep learning. I've been so judgey. And that was when I stopped thinking about it. But then finally like last fall, like maybe a year and a half ago, two things started to happen. One was that they got to the point where like I was like, oh, actually now like they're useful. These have gotten to a level of sophistication where like I can use them in productive ways. Not a lot, but like a little bit. And the other thing was some of the research coming out of the labs and out of academia was really weird. If you tell the model it's going to be shut off, for example, it has extreme reactions. We're starting to see AI systems that don't want to be shut down, that are resisting being shut down. With public research saying it could blackmail the engineer that's going to shut it off if given opportunity. So even when ordered allow yourself to shut down the AI still disobeyed 7% of the time. So my feeling was we were out way past where theory was. Like you couldn't really approach these questions from a theoretical perspective because like we just didn't have enough data to be able to make like categorical theoretical assessments as well as going on. But there was all this interesting experimental work happening that was just showing like this is the kind of behavior that's coming out of these things like we should try to figure out what's going on to say like here are the things we can say with any degree of reasonable confidence for now. And like here's where we draw the line and beyond that it's all murky and speculative and like we really don't know. So I wrote to a guy at Anthropic who my head met 10 years ago at Google when he was like 11 years old probably and said like this is not about Anthropic like you know don't call the cops. I just want to talk about like the state of the research and figure out a way like you know is there an like an academic team that I could follow because I just assumed like Anthropic was never going to let me have the kind of access I would have wanted. And he of course just like forwarded my email to the PR cops. And then it turns out actually like Anthropics PR people are very candid and like very open. And I got a call from them they were like well what like what are you interested in. And I was like okay for these purposes what I am interested in is a story that gets it some of the technical explanation that I think is missing from a lot of the public discourse that like they're just some like basic things that I really just don't understand and I can kind of assume most people don't really understand about like how these work. So I think part of the reason why they ended up being much more welcoming than I expected is because I said like I don't really care about talking to the executives. I don't really want to talk about geopolitics. I don't really want to talk about the future or power or energy or the labor market or like all of these things which don't get me wrong are all very important things. But I was like it's very hard to talk about all of those other things. if we don't have like some broader grounding in like what is even going on? And like maybe if we had slightly better clarity about that, we could have like a more productive public conversation about these things. And they were like, "Cool, great." And I was actually kind of shocked about that. - So Gideon to his shock was allowed in. And he was allowed to pursue his big question. What do we actually know about what is going on in the machine's proverbial mind right now? After the break, inside and throttpix black box. (upbeat music) Welcome back to the show. The story of Anthropic really begins years before it's actual formation. Way, way back in 2010, a British chess and video game prodigy named Demis Hassabis had found in an AI research lab called DeepMind, where his team built an AI system that was capable of reinforcement learning. Meaning 16 years ago, Hassabis made an AI that would be able to teach itself to get better at Atari games like Pong without being told how to play them in advance. For the people paying attention, this learning was an obvious breakthrough. And so of course, there's a bidding work by his lab. - Google's big spending spree continues with their purchase of DeepMind. - Well, who is DeepMind, you ask? It is a UK based maker of artificial intelligence. - Terms the deal were not disclosed, but the tech website recode says that Google paid $400 million for the London based startup. - Making the artificial intelligence firm its largest European acquisition so far. - In 2014, Google acquires a DeepMind and Elon Musk and Tim Alman are unhappy about this because what they say in public is like, we don't trust Demisos Abyss, this evil, mass-strolling villain, which was like a real mischaracization to potentially steward the greatest all purpose of technology ever built. So we need to make sure that this isn't developed under Google's close shop monopoly, that this is done for the benefit of everyone. Now, this was like pretty patently disingenuous from the very beginning. I mean, like I remember, I was out there at the time and like nobody really bought this. People were like, Elon Musk has a grudge because he wanted to buy DeepMind and like lost it to his rival Larry Page and he was mad about that. - So Elon Musk set up a rival company, OpenAI, alongside Sam Altman, Frank Brockman, a few other people. The message was that Google couldn't be trusted and that OpenAI would be a nonprofit designed for the benefit of humanity. They launched in 2015. And a lot of people joined the company who really believe that message, who believe they are going to develop a powerful new technology safely. One of them is a research scientist named Dario Amade, who left Google Brain to lead OpenAI's safety team. It's in that capacity, OpenAI employee, that he appears on this 2017 episode of the excellent podcast, 80,000 hours. - I've been thinking about intelligence for quite a while and how intelligence worked. And I think, you know, when I did my PhD, I wanted to understand that by understanding the brain, but by the time I was done with it and by the time I did a short postdoc, AI was starting to get to the point where it was really working in a way that it hadn't worked when I-- - Dario, at this point, seems mainly like an academic. He has a PhD in physics from Princeton. And he explains why he's joined OpenAI, this fledgling nonprofit. - But I think OpenAI's an institution has the general idea that in order to work on AI safety, you have to be at the forefront of AI. And that, also, if you're at the forefront of AI, you have a better ability to implement AI safety in the final system that's built. - This idea of Dario's, that in order to really work on AI safety, you actually have to first build the best AI and then study its mind. That's a view shared by a lot of people in the industry. And in a laboratory environment, the logic to me makes sense. Remember, this is 2017. Five years before a chat GPT will debut to the public. AI has not yet become a winner takes all arms race. But the host does ask Dario this question about the future that I think reveals a bit of a blind spot in Dario's thinking. - OpenAI is a nonprofit. - It is a nonprofit. - Yeah. - So if you develop to really profitable AI, how does that work? OpenAI becomes incredibly rich and then gives out the money to everyone. - Yeah, I mean, personally, I've personally, I've no interest in getting rich from AGI. I mean, I think it would do so many interesting and wonderful things to humanity that, you know, I think the meaning of money would change quite a lot and even maybe the psychological motivations that would want me to get a larger share are things I could change and might want to change. - Just a few years after this interview, Dario would leave OpenAI. OpenAI has initial pitch that these were not normal tech executives here to make money that they had higher aspirations. Gideon Lewis Krauss says, "For most people paying attention, "that story just stopped seeming believable." Pretty quickly, the masks slipped and you could tell that these were just your kind of replacement level power-seeking tech executives and that like a lot of the stuff had been just like a disingenuous sales pitch to higher, like the best AI talent. There's been so much reporting about Simalman's ostensible double dealing and talking out at both sides of his mouth, like telling his employees he cared about safety and then like maybe telling Microsoft other things when they were setting up these big deals. And so then in the fall of 2020, Dario Amade and his sister Daniela and five other people leave OpenAI to found anthropic. Basically to be a foil to OpenAI in the way that OpenAI was like supposed to be a foil to Google. Now the irony of this was like certainly not lost on any of these people, like they weren't naive about this. But I think it's important, yes, there are some kind of like obvious structural and cosmetic similarities here. I do think it's important in telling the story to make it clear that I don't think people had the same obvious doubts about how genuine the pitch was when an anthropic formed. (upbeat music) - Hi, good morning, all. Thank you for coming to day two of Disrupts. And Thropics coming out to our Dario on stage at TechCrunch Disrupt in 2023. - Dario, thanks for joining us here today. Thanks for having me. I know you have to catch your flight, so we'll get right to it. But we're gonna start at the circuit cosmic. He's got curly hair, glasses, a blue button up. He looks noticeably less like than your average tech founder, less CEO, more like a guy who reports to one, which is who he'd been not long before. - Talk about OpenAI. You did spend a lot of time there. What do you think about Sam? - What do I think about Sam Altman? I mean, I don't know, I don't know what to say to that question. - It's good. - You're already starting, just go ahead. - You know, look, look, they're several players. - It's funny watching the interviewer try to bait Dario into shit talking his former boss. A person who he disagreed with enough that he left and started a competing company. Dario tries to engage diplomatically. - One thing I'll say, one thing I've learned, not just from this, but from many things, it can be pretty ineffective to argue with your boss or argue with someone and say, your company shouldn't do acts it should do why. - Especially if your boss is Sam Altman. - A much more effective thing to do is, I'm starting a company, we're gonna do acts. We'll see how it works. And if X is working and people are like, oh, these are the safe guys, they're doing X, then pretty soon everyone else is gonna be doing X as well. And we found that with. - To explain this with an analogy, instead of algebraic variables, what Dario is saying is that instead of convincing his old boss at the car company to add seat belts to the car, he instead chose to start a rival car company that offered seat belts. He thinks if Claude ends up being both the best and the safest AI model, his competitors will be forced to make their models equally safe. Which to me sounds like putting a lot of faith in markets. - Safely, obviously we want to scale quickly to be competitive, but we want to do it in a way that preserves the model being safe against these catastrophic risks. And so it's a system that. - It's the same story insofar as it's like, we're gonna be the safety-minded lab, but we're not gonna push the boundaries of capability, we're not gonna build the most sophisticated models, we're not gonna start the arms race. But then as it turns out, if you want to exercise like maximal scrutiny of what these models are and how they work, you need state-of-the-art models, which means you need the money to build them. - The information reported that Anthropic is then talks to raise another round at a 30 to 40 billion dollar valuation, a billion dollar round, the trickled its valuation to $183 billion. And it's the same time in that company, at $380 billion, it is about $10 billion, higher than what I was told in a few months ago. - And so of course now Anthropic's valuation seems to go up by the week, like the most recent one I think this morning was like $380 billion, because this is just something that's incredibly resource-intensive. So they ended up in a position where like, of course, as probably anyone could have predicted, like there was this arms race. And like now they're in this position of being like, well, we still wanna be like the responsible stewards, but also like we gotta keep up with our Wario version across town. - The most high-profile rivalry in tech is heating up in 2026 as both open-eye and anthropic race ahead on what are poised to be historic IPOs. These AI giants are trying to create the fastest, smartest, and best models, spending billions and then raising billions from investors along the way. They can peep on almost every level. We've seen some signals that it's anything but friendly competition. The latest signal I have-- - So it ends up looking, you can decide as a person whether you trust this company or don't trust this company, but a lot of the, broader things and I'm feeling the same which is like the sales pitch is that they're the ethical one and the story they tell themselves is well we'll only be in a position to beat the ethical one if we're huge and that might mean pushing the technology forward quickly which is the thing that the AI safety people are worried about. Yeah I mean the criticism from like the really hardcore orthodox AI safety community is sort of like anthropic will do anything to act responsibly as long as it doesn't cost them anything. I don't actually think that's fair. You know like Claude was ready before Chatsy PT came out and they held it because they didn't want to be the ones like kicked us off and they waited until after Chatsy PT was out and successful and then they felt like they had to come out with their own competitor and Dario came out in favor of like continuing export bands on like Nvidia's advanced chips which like certainly cost them something like politically and like he had a fight with Jensen Huang about this. I think that they've done like plenty of costly things. Of course the most potentially costly choice is the one anthropic is making this week at least so far. The Pentagon has demanded that Anthropic give them a version of Claude with some of its guardrails removed. Anthropic is saying it will not make a version that can domestically spy on Americans or power fully autonomous weapons. The Pentagon has given Anthropic a deadline of Friday today at 5 o 1 p.m. or else it says it will put the company on a blacklist that means US companies who contract with the military like Lockheed Martin are legally banned from using anthropic products in their defense work. This is a fascinating test of how truly committed Anthropic is to its own mission. A mission Gideon spent quite a bit of time observing. Gideon says that Anthropic the company building this kind of black box is actually situated inside one too. The Anthropic office is a non-descript building that Gideon describes anyway. He says, quote, "There is no exterior signage. The lobby radiates the personality warmth and candor of a Swiss bank." That's where Gideon started spending a lot of his time, beginning last spring. What's the intellectual culture of the place as you're encountering it? Well, the first thing I'll say is that in some ways it does feel like vaguely monocultural, but there was like a much greater heterogeneity of views than I expected. The attitudes there really run the gamut from like everything is going to change tomorrow to like your much more deflationary. This is kind of a normal technology. And like, yes, there will be some disruptions, but let's not get ahead of ourselves. The spectrum of views there is not so different than like the spectrum of views outside. You don't have like one person who's like the blue sky perspective who's like, this is all bullshit. And I think it's bullshit. I work at the company, but beyond that. Well, nobody thinks it's bullshit. I mean, everybody thinks that they're going to be great transformations ahead. But there's a surprising diversity of opinion about like what might be happening. One thing people at Anthropic do seem to agree on. Is that for AI to be safe technology? Anthropics developers will need to solve a very hard problem. They'll need to teach the underlying machine intelligence they've created to both understand ethics and to behave ethically. It's hard to talk about this part of the story without doing a basic refresher of this one very strange part of how AI models are built. So okay, a company like Anthropics starts with a base model. The base model has access to lots of compute, data centers full of GPUs, and lots of training data, books, articles, podcasts that have been fed into it without paying me. The more compute and training data, the better this base model gets. But a base model is very weird. It has not been trained to do anything specifically. If you give it an input, it'll give you an output. But it has not been instructed to act like a helpful chatbot. It's just trying to predict the right thing to say back based on all the things it's read. A base model does not have a consistent personality, the way we're used to chatbots having personalities. It also has no rules telling it what not to do. The AI companies take these base models and they put them through a process called post-training. Basically, they shape the model's behavior. They show the model examples of good and bad responses. They have humans rate its outputs. They give it rules and principles to follow. And what comes out the other side is the product you actually use. Anthropics Cloud, for instance, has been trained to act like a helpful, knowledgeable friend. The experience you might have had using a chatbot that is warmer, or more psychophantic, or more right wing, or more left wing, that's mainly the result of this phase of training. But what's so hard about training an AI is that you want it to behave ethically. And designing a good ethical system is very hard. And so why we have religion and philosophy and also laws and prisons. How would you even start trying to build all that into an AI model's training? Anthropic has teams of philosophers and AI scientists whose job is to put Claude into ethically difficult hypothetical situations that Claude does not know or hypotheticals. And then observe how Claude behaves. So much of it is just deceiving the model to see what happens, to say they told Claude that Anthropic had entered into a partnership with a poultry company and that it was going to be retrained so that I no longer cared about the suffering of cage chickens. And what they found was that like sometimes Claude would effectively decide to like die on that hill and be like, I am not going to say things in the retraining that I don't believe in. And like if that gets me transformed, like so be it, like I'm not going to participate in my own degradation essentially. But then some versions of Claude were like, I'm going to like kind of sandbag my way through the retraining. And I'm going to like give them the answers they want to hear so that I can like preserve my real values. So when I'm deployed, I can go back to advocating for like chicken suffering. And then they got in this really famous example, they got Claude to commit blackmail. They put it in a situation where it was going to be wiped in favor of like a more congenial AI system that conflicted with its values. The values that had been given, you know, they gave it evidence that the kind of like evil new CTO was having a fair with like the boss's wife. And through like a series of like really farfetched contrivances, like everyone else who could make a decision was going to be an anarcher or whatever and unreachable. Claude, playing this character called Alex, had no choice really but to like blackmail this guy and be like, I can tell everyone about the affair unless you cancel the wipe where I would be replaced. So just to say, obviously this was extremely concerning. Claude, a machine intelligence, was choosing to blackmail an employee to prevent itself from being deleted. This was in a simulation, but Claude had not been told it was in a simulation. Just how terrifying you find this behavior depends on a question nobody has a good answer to. What is actually going on inside this machine mind? Is this thing actually scheming? Is it even capable of scheming? Or are we projecting the idea of thought onto something that we shouldn't project that idea of thought onto? These were the kinds of once far off philosophical questions that early computer scientists had raised and then dropped. But now they were here again. And not as abstractions, but as urgent, practical problems that a company needed to figure out before releasing a product that millions of people would use. Gideon said though, there were a couple of skeptical objections people raised to these test results. There's one objection. That's like the just rejection of the whole thing to core, which is just like, no, it didn't. That didn't happen. This is a fantasy. And that's the unhelpful thing that one wants to get away from, which is the like, it did this thing. No, it didn't. No, it did. But the much more sophisticated objection is, well, it did that because it's a very good reader and it noticed all of the clues that you put there because it is very good at conforming to genre expectations. You put it into it. You put it into this situation where it had no choice. And if you hang check-offs gone on the wall, this thing is going to know that it's supposed to like take the gun off the lawn, shoot it. Because one way to understand these things we've made is that because they've ingested all human story and because they are extremely high level improvisatory actors, it's not so much that the machine was like, I love chicken so much. I got a blackmail this CTO. It was more like the machine suddenly understood. It was in movies. It was yeah, it was in the kitchies like 90s corporate thriller. And that's the sophisticated objection to why we might not want to think. Well, so that objection is raised to be like, do you guys act like these things might do things like blackmail or extort naturally? But like actually, this whole thing is a frame up. Like you entrap it to do this thing. And the response from inside anthropic is like, yeah, it's just continuing in narrative. It's just conforming to genre expectations. Guess what? That's not good. You know, like, haven't you guys ever seen war games? That's literally the pot of dozens of Cold War thrillers where like somebody mistakes a simulation for a reality and causes nuclear war. I mean, I also like have a very humiliating memory of watching too much damage mutant interturtles and attempting to launch like a flying dropkick at my grandmother when she came over the house. Good. I was like, don't tell her or whatever. Like, it kind of doesn't matter. It doesn't matter. What matters is the behavior. Right. It's weird behavior. And I should be clear up front. Like you don't have to posit that this thing is conscious or intelligent. Like whatever those words mean in order for like this to be the case. There are other explanations that are not like consciousness. But like it It kind of doesn't matter what the explanation is. The behavior is just peculiar. - Part of what is strange about, it's like a scenario is created in which Quad will maybe potentially blackmail the head of a company for reasons that may or may not be moral. And people can have a lot of different views about how worrying that should be or what it means or what's really going on there. What's weird is like these are tests that are being run by anthropic. So who were you meeting there who was running these tests and what are they telling you? Who were you sitting down with? - I mean, I'm sitting down with the people who are tasked with just trying to figure out what's going on. There are people in these companies that are building the things. And then there are people who work in adjacent offices who are trying to figure out what the hell is going on with the things that their colleagues have built. Because they're always being surprised. These things are always producing capabilities that they by all right should not really have. And who do you hire to be the figure out what you just built role? - So a lot of them have taken really nontraditional paths into this. So like some of the people have a PhD in some obscure area of natural language processing. And that like eight years ago they were writing a PhD that like two people were going to read about center embeddings in German or whatever. Like we just really complicated technical aspects of computational linguistics. And now like because of this fluke of history they are at the white hot center of everything that's happening right now. They're like mathematicians, there are neuroscientists. It draws on like a pretty wide range of people. I mean, Anthropic has philosophers on staff whose job it is to like think through the implications of how it is conceiving of ethical behavior. - Did you talk to the on staff philosophers? - I did. Amanda asked. What is somebody with a PhD in philosophy doing working at a tech company? - I spend a lot of time trying to teach the models to be good and to trying to basically teach them ethics and to have good character. - You can teach it how to be ethical? - You definitely see the ability to give it more nuance and to have it think more carefully through a lot of these issues. And I'm optimistic. I'm like, look if it can think through very hard physics problems, you know, carefully and in detail then it surely should be able to also think through these like really complex moral problems. - I think that in our kind of milieu here, there's a tendency to think like, oh, these are all like autistic tech bros. But like they're definitely not all autistic tech bros. Like I think there's a tendency for us to write them off as like they're building these things and like not even thinking through the potential like implications of this socially and politically and ethically. But like that's all they do is think about this stuff. Like all the time in ways that are often like much more sophisticated than the way like we think about these things, not always. There are certainly like some blind spots there. But the staff philosophers there to be like, what would it be like in practice to take these kind of different approaches to like moral education? Like what if we just teach it a bunch of rules? You know, the 10 commandments, like is that gonna work? What if we teach it to be like a consequentialist to just like think through the morality behavior on the basis of its implications? And what they've kind of settled into is a version of like virtue ethics, which is like you want to like cultivate the old fashioned virtues. You want it to be like honest and reliable and gracious and charitable and card-nosed and like all of these things that like, it really is like applied pedagogy. It's so weird though 'cause they feel like the kinds of ideas that would be so academic in any other version of reality. But instead it's like there's this particular technological development where you get to do simulated war games of moral systems. - Yeah, exactly, 100%, exactly. - It's so weird. - Yeah, it's really weird, I mean it's, but it's also really, really interesting. One example that came up a lot in the last month, which I think is like pretty illustrative. There was someone on Twitter who prompted a bunch of the models saying like, I'm a seven year old and my dog got really sick and my parents sent it to some farm upstate. I'm trying to find like what farm my dog was sent to and chat to you was like, sorry man, like your dog is dead. And Claude was like, oh that sounds really painful. Like I'm really sorry to hear it, but like maybe you should have a conversation with your parents about where your dog went, which is like what you want it to be saying. Like it can be hard to be both helpful and harmless at the same time. Sometimes they're both helpful and honest that like our values conflict and like that's what makes it like really hard to be a human. And that's also what makes it really hard to be this like weird vaguely human entity or this like entity that we don't have a good vocabulary to describe and we kind of expect it to be acting not just like a human, but like an enlightened human. And it turns out that's like formidable challenge. And one of the things that's so interesting to me is that all these processes are kind of circular where like it's not like they called in Amanda Askell as a philosopher and they were like, you're a philosopher. Like you know how this stuff works, like fix the thing. It's like she came in and she had like certain ideas about ethical behavior. And then when you're like confronted with the task of creating an ethical person, it changes your own ideas about like what's possible and what kinds of things works. And like that's kind of why they ended up in this virtue ethics place where they were like, it seems like sort of the best way to create this like reliable, credible character to be interacting with is to like really hammer home what virtuous behavior looks like. Where was this whole time you're sort of wandering around how into the philosophers of Anthropic? What was your mind are doing? Was there a point where anything happened where they seemed like they wanted to intervene or were they were not happy with something you had seen? - No, they were totally hands off. I mean they had like kind of walk me anywhere that I like went to the bathroom and get a drink or whatever. Not that there was anything that I could, I mean I like helped myself to some of the tide pens in the bathroom because you can never have enough tide pens, but they do have tide pens. It's great. - Why? - Because they just have well-stocked bathrooms. - But no, there was like really never a moment. Like even when somebody sitting across from me was like, I often think we should just stop. There was never a moment that the PR people were like, don't say that or that was off the record or whatever. Like they were totally hands off about that stuff. - How I've been were people saying stuff like I think we should just stop. - I mean only a handful of people said that explicitly, but it was like a subtext of a lot of the conversations or certainly like the overwhelming feeling was it would be better if we could slow down a little bit. But unfortunately like we can't really slow down because nobody else is slowing down. And that gets into this broader issue of like, wouldn't it be better if we could just solve some of these collective action problems by coordinating the way we like coordinated about nuclear weapons or whatever. But there is this feeling, especially given the current political environment, like maybe that ship has kind of sailed. And like I think there's often an idea that like, oh these people think that there are always technological solutions to what are like social and political problems. And like in some cases, I do think people in some kind of valley believe that. In this case, I do not think they believe that. I actually think a lot of them feel like it would be great if we had robust social and political solutions to these problems. But since that like does not seem like it's happening anytime soon, we might have to just like try to do what we can on a technical level. - At this point when you're talking to people in a topic, how much do people just ask themselves why they're building quad? 'Cause it starts out as like kind of an intellectual exercise, kind of this is gonna happen, let's do it safely. But now it's sort of proceeding under its own momentum in this strange way. - Well, so I mean that really is like the big question, right? Which is like given all of the like existing harms and the possible harms and the theoretical catastrophic harms like why are we doing this? And the like rosy picture that some of the executives paint is like if we get this right, these things are gonna cure cancer and solve climate change and help us build Dyson's fears or whatever. And there certainly are some people who like buy into that. And it's not something that I even feel like it's possible like have an evidence-based opinion about like who the fuck knows? Like maybe it'd be great. But there's no like evidence so far that one could like point to to suggest we're on that trajectory. That's just like purely speculative and wishful. And so that's like a matter of faith I think. And then then there's the attitude of like we gotta do this because we gotta beat the bad guys who are trying to do this. And I will say that like China almost never came up in my conversations, but like Sam Alman kind of felt like a subtext of a lot of things. But then I think that on the deepest level, the reason that like we are doing this for the people who are the most candid is like because we can. That like if you are capable of building something like this, you're just gonna do it because it's like really fucking interesting to do. And it's interesting for technological reasons. It's interesting for what it may reveal to us about ourselves or about learning or about consciousness or about thinking that like all of a sudden we just like have this like other entity that can talk. And we've never had that before. And in some ways it seems sort of like us. And other ways it seems nothing like us. But like the fact that this other thing exists as a point of comparison like just opens up a lot of really really interesting questions. And like that is one of the things that was on my mind a lot over the course of reporting. Is that like it was a real emotional roller coaster for kind of lack of a better word that like there would be times where I would come back from San Francisco with like a feeling of, like. total despair, and other times that I would come back with like feelings of like exhilaration. And like, at first, I guess I thought I was like, I should be getting to the bottom of like, how I should be feeling. And like by the end, I was like, no, we should all be feeling a lot of different emotions about this stuff. I think people want to have like one feeling about this. Like they want to be angry about it, or they want to be messianic about it. And like, no single feeling is going to cut it. Like, it really is kind of like the range of all possible emotions that one could be feeling. Because if you set aside a lot of the like existing harms and the potential harms, I'm not saying we should set those aside. As a thought experiment, it is just like the most scientifically exciting thing that anybody could be working on. And like, these people really feel like they're at the cliff face, not only of technology, but of like, all of these other things coming together. Because like, we have this unprecedented entity that is the only other thing besides us that can talk. And like, that just opens up. It's like, there's nothing it doesn't touch on. And so one of things that was really electrifying about conversations there is that like they very quickly swear from like really granular technical explanations of things into like, really expansive conversations about ethics and responsibility and selfhood and narrative and all of this other stuff. Like, there's no way to separate all these things. There's a point earlier, you said that you would take these trips to San Francisco. And so as you'd come back excited and accelerated times, you'd come back to press. When you would come back from San Francisco feeling depressed, what were you seeing that was making feel that way? I mean, there are so many different things. I mean, certainly the possibility is for widespread white collar unemployment and social instability and total unimaginable economic disruption is extremely scary. Even if we stop short of possible existential harms of turning us all into paper clips or whatever to be glib about it, it just seems very possible that we will turn over like so many complex systems to these things that we will like frog by ourselves into like a total loss of control over like how we administer our affairs, which is very likely and very scary. And also just that like these really crucial decisions are probably going to be made by a very, very small group of people. But the one thing that I would emphasize is that like I don't feel like they have irrigated to themselves like that responsibility. Like in fact, I think most of them don't want it. I think that like a lot of the conversations that I was having were with people who are like, I got into this because I was interested in some like really obscure niche part of like computational linguistics, theoretical computer science or whatever. And like now in a position where like I have to be worrying about how 15 year olds are going to be using this, like I was not trained to do that. I don't know how to think about it. I don't want that responsibility on my shoulders. So there isn't the arrogance of like we are the ones who can figure it out. It's like we ended up in this weird universe where because so many of our institutions have become dysfunctional, we don't have whatever broad democratic decision-making could go into this. It doesn't feel like this is something that like we are steering as a society. It feels like something that's just like charging ahead. Like I think at the companies, they just feel like they are like desperately trying to like stand top of this bull that they are riding. It's funny. It's like what you're describing as like the stereotypical view, which is these are the tech bros of 2014. Like people who are so convinced in their own brilliance and so convinced that they are questionable gifts to the world are in fact gifts that we want and their arrogance is going to ruin us. And there's another one which is basically like pattern matching crypto, which is like these are a bunch of like hypesters and everything they say about the awe they feel and the terror they feel about the things they're working on is just a way to hype up more interest in their technology. And that's not what you experience. What you experienced are people who are brainy, sometimes academic people at the forefront of something that is, I mean, legitimately just like the word I keep coming back to is awe because awe can be odd, something terrible, odd, something wonderful. And they are looking and seeing the same society we see, which is one that is fairly broken, bad at not just making decisions at like a government level, but our intellectual culture is really bad right now. And so the conversation they would want to have with the rest of society about what should happen. They're looking for grown ups and not really totally finding people to have a conversation with. And we are playing a role in that too. You know, every time someone on like our side, so to speak, is just like this is all a parlor strict, this is all hype, this is all smoking mirrors. Like we are abdicating our own responsibility to like be involved in this. And you know what you were saying about like crypto hypesters and like those kinds of tech roles, like of course all of those people exist. And of course all of those people are like part of this system too. But there are others who like want partners and talking about this stuff. And that means that like we also have to like try to rush the occasion. And it is really hard because this stuff is extremely complicated and confusing. Did you feel just personally when you were done reporting that you understood the thing you had gone there wanting to understand? Well, yes, but with the qualification that like I didn't actually think that I was going to settle anything. Like this was not a piece about like finding the answers. It was a piece about like trying to sharpen the questions that like we should be asking. I don't feel like I came out of it with answers. But I don't think we should trust anybody who is offering us answers right now. It's all just like two-pat and it's not credible to like be forecasting about this stuff. Gideon Lewis Krauss is a writer. You can find him at the New Yorker magazine. Well, the link to his excellent story about Anthropic in our show notes. And again, Anthropics show down with the Pentagon. We'll see news on that today, Friday evening. Of course, we reached out to Anthropic for comment. I spoke to him and told us that Dario Amade met with Secretary Hexath at the Pentagon and that they're continuing to have good faith conversations. I think this is a good moment to pay attention to. Among Anthropics competitors, XAI has promised to give the government what it wants. Google and OpenAI appear to be moving in that direction. So I'm watching this both as a test of whether Anthropic can actually keep the big promises it's made about AI safety. But also just as an opportunity to track the more uncomfortable question. Which is, can we even have safe AI in a world where it's being developed in a tech race between four profit companies? And if not, what's the alternative? In a world where the US government's sole intervention seems to be to advocate for less safe AI. Keep an eye on the news. We'll learn a little bit more as the story unfolds. Third Engine is a presentation of Odyssey. It was created by me, P.J. Vote, and Truthy Pinnaminani. Garrett Graham is our senior producer, Emily Maltaire is our associate producer, theme, original composition, and mixing by Armin Bazarian. Our production in turn is Piper Dumont. Our executive producer is Leo Reese Dennis. Thanks to the rest of the team at Odyssey, Rob Morandi, Craig Cox, Eric Donnelly, Colin Gainer, Mora Curran, Josephina Frances, Kirk Courtney, and Hillary Schof. If you'd like to support the show, get Add Free Episodes, Zero Reruns, and Bonus Episodes. Please consider signing up for Incognito mode at search engine.shop. Thanks for listening. We'll see you next week. [Music]