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[0:01:13] Copyright 2026. JPMorgan Chase and Company. Bloomberg Audio Studios. Podcasts. Radio. News. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal. And I'm Tracy Alloway. Tracy, I don't know. I think our listeners like it.
[0:01:43] But, you know, a lot of our episodes are about AI these days. But to be fair to us, it's a pretty big topic. That's all anyone wants to talk about. Whenever we go to dinners with sources and things and people who are not even directly in the tech industry, you know, they might be in markets, they might be in policy and economics. All they want to talk about is AI. And then inevitably, the conversation veers into very sci-fi territory where we all start talking about the human extinction scenario. And that's just the norm nowadays. I know, it's so weird. You know, we were in Hong Kong recently.
[0:02:13] And when we were in Hong Kong, this was before it was announced that there was a deal to open the Strait of Hormuz. And East Asia was considered to be like ground zero for where the effects would be felt of the oil and jet fuel crisis, et cetera. And we were at this dinner of business people. Like they were not talking about that at all. No, they want to talk about the Terminator scenario. They just want to talk about token consumption and all of these things. Like here we are. It's like, wait, aren't you guys supposed to be like under all kinds of jet fuel stress? So this is our defense for thinking AI is a pretty big deal.
[0:02:42] I think it's fair. I will also say when we did the quiz in Hong Kong, we had a bunch of different teams with very creative names separated by tables. Low value human capital. That was a great one. They won the quiz. They won proving that there is value in human capital. But did you see that one of the tables was called Fable?
[0:03:02] Table 13, Fable 13, which was very topical at that moment. Very topical. Well, we're recording this on June 17th. And of course, there's a lot in the news these days. But things move very fast in AI. Even if there weren't governmental controversies and all that stuff, you would have to mark the date in AI because of how fast breakthroughs happen. But, you know, as you said, like AI sort of feels like the most important thing than anything else.
[0:03:25] That's a very conventional wisdom. It was not always conventional wisdom. And I have a DM. I know you're not supposed to share DMs from public, but I have a DM. You've got the receipts. I have the receipts. August 2nd, 2016, and I DMed a colleague. I said, did you leave Bloomberg? He says, yes, I'll be announcing publicly in a bit. Take a couple of months to study AI properly, then leaving journalism to do something else still connected to AI. Being our Google reporter was a great gig. Something still connected to AI is an understatement. And then the final, August 2nd, 2016.
[0:03:54] But AI is more important than anything else. So I felt best to sort of optimize for that above all else. And then I just said, well, good luck. This is someone who truly learned from their sources, unlike us who remain in the podcasting industry. So anyway, that person who would that deal with a former Bloomberg reporter, Jack Clark, who is one of our guests today. He is the head of public benefit and co-founder of Anthropic.
[0:04:16] 10 years later. And also Peter McCrory, head of economics at Anthropic. So two perfect guests to talk about all the things in AI these days. So Peter and Jack, thank you so much for coming on the podcast. Great to be back. I'm glad I optimized my life to study AI. One of the calls of the century. So why don't I actually start with that? It's easy to say in 2026 that AI will be a big deal.
[0:04:38] You called your shot. You got it right. 2016. What did you see in August 2016 or presumably before? Oh, you know what? This is the biggest story of our lives. So for two years when I was reporting at Bloomberg, I wasted a lot of Mr. Bloomberg's printing by printing out archive papers about AI research. And what I started to do, a very Bloombergian thing, is I started to make graphs charting AI progress over time.
[0:05:03] measurements of things like computer vision, measurements of things like the skill with which AI agents were able to compete and play Atari games. And what I saw in these graphs was the beginning of an exponential. And it was everywhere. Like if you looked at vision or sound or video or game playing, you saw the same trend. And it became obvious to me that this was a general purpose technology that was right at the start. My one bone that I have to pick with Bloomberg, which I'm going to use my privilege to mention on air.
[0:05:32] I never got us to write a story saying NVIDIA was being used in every single AI research paper. And I pitched it and I failed to get it across the line before I left. Oh, man. I can just imagine you reading all these academic papers. Meanwhile, the editor is like, we need the BFW for this. It's not AMD. It's NVIDIA. Okay. And Peter, I'm very interested in, you know, Anthropic.
[0:05:55] It's a company trying to make money, and yet it has this economics lab. What's the idea behind having an economics research body within a company that's developing this technology? So, I mean, I was late to the game in joining Anthropic. I joined just a year ago, but I had... A year ago, people... Well, whatever. We all know about how much the stock is a price of the year, but you're not...
[0:06:17] I think what was very evident, so I'm an applied macro economist by training and have tried to understand various types of shocks throughout the economy. Part of what drew me to Anthropic was it was evident to me last year that they cared very deeply about not just advancing the technology, but making sense of how it is set to reshape the labor market, its impact on productivity, on growth, and be willing to put
[0:06:43] Evidence, data, and research out into the world that would be broadly beneficial and useful to society. And I thought, I want to be a part of building that economic research program and do what I can to provide tentative answers to the most pressing questions. We might not always get it right, but ideally we're helping society make sense of the change. The capabilities of the models on all kinds of things are extraordinary. I mean, just mind-blowing. Coding, copyright, everything, all kinds of things.
[0:07:12] Actually, why in June 2026 does life still feel maybe as normal as it does from an economic perspective? This is a great question and one that I've been wrestling with. I think there are a number of reasons why you might think that the impact has not yet materialized. One, the technology can advance, but it also then needs to diffuse throughout the economy. And there can be bottlenecks.
[0:07:36] Moving from capabilities to actual deployment, we see that with our enterprise customers. So if you want to automate biological research or some other very complicated financial modeling task, you need a lot of contextual information available to the model. If you don't have that contextual information, the capabilities alone won't necessarily drive the impact. It also takes time for people to just start using the tools. And so we're still in somewhat of the early stages there.
[0:08:05] Two places that I would be looking to see an impact. One is in terms of productivity growth. We've done some research that points in the direction that this should be large and consequential. Labor productivity growth has been strong throughout the pandemic and has been sustained so far. Modestly so. We're not talking about revolutionary step changes. Yeah, but to get on an inflection, you need to at least move a little bit. I think maybe you're seeing some signs there.
[0:08:32] On the labor market, though, the labor market is in a reasonably healthy spot. And I think it might be because it's primarily at, so far, a labor-augmenting skill bias technology, not yet the full sort of general purpose substitute for all of cognitive labor, although perhaps that's the trajectory that we're on. You know, for the size of AI and its capabilities, I was talking to Peter about this, and he did point out the economy is very big. So it still takes a lot to move it.
[0:09:01] I do think strange things are starting to happen, at least inside the company. We published research from the Anthropic Institute recently on this topic called recursive self-improvement, where it was inspired by me going on paternity leave in November of last year and coming back in February and the entire company felt and worked differently. And I assumed it was because models had got better. And when we looked at the data, what you saw was in 2026.
[0:09:27] Engineers at Anthropic are writing about eight times the amount of code than they did in 2021 through to 2024. And the line started last year with things like Opus 4.5 and Opus 4.6. Then it really got going this year. And I have colleagues now who don't program at all anymore. They just instruct many, many Claude Code agents to run around and do their work for them. I can't reconcile that with the world staying normal for long.
[0:09:54] But it's going to take a while for that to diffuse into the world and change it. Yeah. We'll talk more about recursive self-improvement. So this is when models basically improve on themselves, right? So in terms of the awkwardness of the current moment or the weirdness of the current moment, you've talked about basically living through the singularity and how strange it is. And you've also described yourself as a techno-pessimist before.
[0:10:18] How do you square that with working at Anthropic, which is making some of these weird and potentially dangerous things actually happen? So by technological pessimist, I mean, I thought the technology would keep getting better, but I didn't think it would get better in the maximalist sense that some of my colleagues did. I didn't think that we would have, say, functionally automated all of coding right now. I find that actually quite surprising. But basically, over the last few years, and I worked at OpenAI before Anthropic,
[0:10:46] I was just hit repeatedly over the head with what computer scientist Richard Sutton calls the bitter lesson. And the bitter lesson is this concept that the more compute and resources we dump into these relatively generic neural networks, the smarter they get and the more emergent properties they have. And your specialized system or your ability to be pessimistic about future AI progress.
[0:11:09] This seems to have implications for the labor market, right? Because I think a good example of the bitter lesson is probably the history of AI chess, right? Where at one point they had grandmasters come in and teach the...
[0:11:24] Models, how to play chess and et cetera, and try to encode their wisdom. And it turned out in the end that the best way to get a chess engine really good is to just teach the model, tell the model the rules of chess and say, go off and play a billion games and find optimal chess. Without any human insight, the grandmasters were not necessary for that process at all, right? And so this would imply to me like have significant implications for the labor market. Yeah, I tend to think about this.
[0:11:51] In sort of three aspects of what composes a job. One is you need to decide what to do and direct and delegate. You need to then do the actual implementation of the work and then you need to sort of evaluate or at least set up systems that can evaluate. At least from my perspective as an economist, this bitter lesson is materializing in terms of
[0:12:12] Very rapid advances in the implementation work of what an economist does. Downloading data, running regressions, building models, solving them using sort of contemporary solution techniques, numerical methods. I definitely felt that personally with Opus 4.5, where I was, for the first time, able to just delegate a very complex task. I had this very specific research question trying to understand the cyclicality of hiring across different occupations and how that relates to occupational exposure.
[0:12:42] That's a mouthful. I gave that task to Claude and Claude was able to just iterate on it. And I could redirect Claude in the same way that you might redirect a grad student. And the big question that I have in mind is, you know, at what point do the boundaries at the direction setting stage, the research taste, you might call it, and will the models become sufficiently reliable? If I could just get in here, you know, I just read.
[0:13:05] The recent biography of the DeepMind founder. This is the Sebastian Mellon. Yeah. Like, is there going to be a point where it's like, OK, you have some intuitions, right, about like what good economics research is. And often our intuitions are formed because we tell stories and stuff like that.
[0:13:21] But is there going to be a point where you think like your intuitions will be unhelpful? Because that's sort of what I took away from the Go experience, that the model got better once they stripped it of the human games and the human bias. And that actually like the human intuition that sort of helps us understand, oh, labor market rising, creates inflationary pressure. These stories that are very sort of intuitable end up impairing the model.
[0:13:46] Do you see that happening in, say, economics where it's like some of these stories that we tell forever, they're not actually very helpful for an optimal economy understanding model? I expect that these models will soon have better intuitions about how to do good economic research and that there is this big question of like, at what point will we be able to fully automate social science research? We've done some work on this to try to understand how coding agents are beginning to automate social science research, but I don't think we're quite there yet.
[0:14:15] I don't know, that'll be an exciting time for learning about the world. You know, what that means for my job, I'm sort of less entirely clear. Yeah, I think this is the big wild card in future AI progress. If AI progress continues today, we are likely to get technology that will be able to do basically everything, but we will need people who have good instincts, good intuitions, and good ideas to basically set the direction. And we see this today in a lot of...
[0:14:41] A lot of our own research where you need, say, an AI safety researcher to give nine Claude agents for different research areas to go and pursue, and then it's very effective. If that researcher doesn't give them the research directions, they pursue relatively formulaic research directions, and you have entropy collapse. You end up with just boring research that doesn't move the ball forward. At what point will AI systems generate heterodox insights and genuine creativity? We can't really measure for that today.
[0:15:09] But what we have are the symptoms of it starting in experts like Peter, experts like colleagues in the fields of biology or mathematics or physics outside of Anthropic are all starting to be accelerated by AI. You know, Terry Tao, probably one of the most famous living mathematicians, co-creates math now with AI systems. And so that says to me that these things have got, they're tickling the dragon's tail of like creativity here.
[0:15:36] And, you know, we just put out a report yesterday on Claude code usage and one of the...
[0:15:42] Things that we're trying to understand is like, what are the returns to expertise and how does that interact with the usage of sort of automated coding agents? And we find that domain expertise, like if you're an accountant who understands some of the edge cases and reconciliation, that that domain expertise controlling for a whole host of factors about the type of work, the estimated monetary value. It has an amplifying effect. It has an amplifying effect. So this looks like at present as sort of a skill biased expertise enhancing.
[0:16:10] So my question is like.
[0:16:32] I get that at the moment you have engineers who are reviewing all the code that the AI is producing and they're thinking about it and managing it in some way. But you can easily imagine a future where just the sheer quantity of code overwhelms human expertise. Maybe the quality starts outstripping what human engineers are capable of understanding. How do you manage that? Yeah. So there's two ways of thinking about recursive self-improvement. One is...
[0:16:59] What happens when AI organizations start to see a compounding return from their AI systems? Basically, their own production function improves because of the tools they've built. That's clearly happening now. And then the second is what happens if an AI system can just build itself entirely autonomously given compute, which hasn't happened. What I see inside Anthropic is I think what we'll see in the broader economy, which is we are figuring out how to verify.
[0:17:23] And so I think that in continuous integration. Thank you.
[0:17:47] You don't need to know what it is. It's just a thing that helps you push the code into the economy. We like to know stuff on this show. We like to learn. But there's a lesson in that, right? We are going to speed up things in the economy. We're going to speed up the way that we produce stuff. And then we're going to find, you know, the weak links or the hot paths that break. And we as people are going to move to sorting those out. And then the cycle starts again. And we're kind of sitting on this expanding cloud of automated actions. What's next for virtual assets?
[0:18:30] I'm Jacob Greaves, host of Economy Scale, a series exploring growth in key global sectors. And in this episode, we're diving into the digital forces shaping finance. Sean McHugh from Dubai-based Virtual Assets Regulatory Authority has this to say. We're seeing a lot of institutional players come in, digital asset-dedicated hedge funds, high-frequency traders and that sort of thing, and folks who service institutional clients. As more capital enters the market, adaptive regulation is likely to become increasingly key.
[0:18:58] Rhys Merrick of Ripple says... A lot of regulators globally would like to keep the industry at arm's length. That's the opposite here. I think also what you'll find is that regulation and frameworks can come out, but some of them are maybe a slight iteration on some 80-year-old policy and not actually in line with what the technology can do and offer. And again, that's very different in Dubai. Listen to the full episode wherever you get your podcasts. This series is brought to you by Bloomberg Media Studios and the Dubai Department of Economy and Tourism.
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[0:20:05] Since we're talking about like really like feeling like we're staring at the horizon of extremely strong AI, or maybe we'll get there, or maybe the AI builds itself, might be a good time to ask a fable question or mythos question. At this point, we're recording this June 7th. We don't know when it's going to be available for Americans, let alone the rest of the world. Does Anthropic have a clear idea of what the administration's security concerns are and what it will take to resolve them? Well, obviously.
[0:20:35] Live discussion. I can't get into too many specifics. We're in daily discussions with the government about this. The broad thing I'd say is for many years, we've anticipated a point where AI systems would have national security properties. These national security properties are intertwined with their economically valuable properties. How you manage that as a policy question is basically novel territory.
[0:20:57] Typically, these things are decoupled. You're like, hey, I built a jet engine over here, which can go into civilian aircraft, and I built a missile over here, and you treat them differently. It's odd if you smush these things together. Where we'll get to, I'm confident, is what's a system for assessing the properties of AI systems, including national security components? And then what is a system for either squelching the national security capabilities from coming to general proliferation, like bioweapons or cyber weapons? And are there ways to do things like,
[0:21:27] Know your customer or deployments where you let large firms like, say, drug developers access the most powerful biomodels without accidentally proliferating risks. That's the shape of, I think, where we'll end up. And what we're doing right now, we and other companies and the administration are basically tackling this problem in real time.
[0:21:46] It's initially going to be messy, but we're going to end up with a system on the other side. Well, let me just ask you, you know, this specific incidence, and there are probably more in the future because everyone's just figuring this out. When I look at the AI landscape, I sort of think of OpenAI as being part of the all-in podcast, A16Z, David Sachs, White House.
[0:22:12] And I know from my friends in the media, many of whom are liberal Democrats, that I sort of feel like Anthropic is the more like lib coded of the major models. Do you feel there's any either politics or partisan politics going on as part of Anthropic being harassed or singled out now multiple times? Anthropic's philosophy and what I do, and I lead something called the Anthropic Institute, which helps us produce.
[0:22:40] Better data for the world around things like recursive self-improvement, the economics work, cyber risks, is we tell the whole story about what's going on. Typically, I think the technology industry has told only optimistic stories about what it's building. And what we saw with social media is that does not work. Actually, eventually, when you're doing something that changes the entire world, which AI is certainly doing and social media certainly did, it's not going to be a wholly optimistic story. There'll be negatives as well.
[0:23:08] We've always sought to just tell the truth about what we see in front of us. And I think sometimes that can differentiate us a bit to others. But the important thing is we tell the truth and things end up coming true. So you don't think that there's like a partisan element here where you guys aren't on the team or didn't contribute enough to the ballroom or whatever? I can't really speak to that. I'm, you know, I'm not those people. I'm anthropic. What I can say is.
[0:23:32] But truth wins out. Okay.
[0:24:01] I want to go back to something you said. You mentioned potential KYC requirements. When I hear KYC, I think about the finance industry and I think about systemically important institutions and the stress tests and the framework around that. Is that the right analogy to use for, I guess, ideal AI regulation in your mind rather than, I guess, just...
[0:24:20] Simple export controls. Should we be heading towards something that looks a little bit more like what we do for the banking system? We need something that's more subtle and more technocratic from what we have today. I don't know if it'll be exactly like the banking system. It'll probably take some ideas from that. It'll take some ideas from what the US government and others are doing today with just testing AI systems for their properties. And it's almost certainly going to have a flavor of what Peter and I work on and the Anthropic Institute broadly of generating data.
[0:24:48] about these systems as they're deployed in the world. Because it's one thing to test out the thing before it comes out of a factory. It's another to observe the effects it's having in the world and then to be able to make judgments about whether those effects are good or not.
[0:25:00] Would you support, you know, let's stick with the financial analogy. Companies that are public, at least, are required to have third party auditors sign off on them. And there's talk, you know, when they submit their 10Qs, et cetera. Companies that issue debt are required to have ratings agencies or frequently have ratings agencies rate their debt. Would you support embedding in law?
[0:25:23] We've proposed something like this recently, a policy proposal that we laid out, which includes saying we need to have third-party testing for some of these national security and other properties, because clearly that's a sensible way that you validate a lot of this.
[0:25:46] So just more broadly, returning to this idea of measuring the actual impact of AI, one thing I find really interesting is that if you actually look at a lot of our traditional AI, or I should say, I'm AI-brained already. If you look at some of our traditional economic statistics, a lot of the AI impact doesn't actually show up just yet. Again, we're in the early stages, but you would expect if we're talking about the AI economy growing something like...
[0:26:12] 2000% or 3000%. I think I've seen that number. That's from Anton Kornak and McKelvey's paper a few weeks ago. You would expect that to have more of an impact on nominal GDP. And yet it's not really showing up that much. Do you think the way we measure the economy needs to be changed in some way in light of what's happening with this new technology? Yeah, so I think this is exactly the right premise is kind of where we began the conversation, which is
[0:26:39] And so it makes it very hard to disentangle all of the different factors. What's the counterfactual? Labor productivity growth is...
[0:27:07] Maybe not as strong as you might not otherwise expect, but maybe it's stronger than it is in a counterfactual sense. And so one way that we've tried to tackle this question is by looking at how Claude is being used on our platform using our privacy preserving techniques to estimate the time savings associated with each of the activities that people use Claude for. So compiling information from reports to put together a research brief would take you
[0:27:35] A few days maybe, now Claude does it in a few minutes. Evaluating diagnostic images is something that skilled professionals do very rapidly, so there isn't in principle much time savings. You can add up all of those numbers and using standard macro growth accounting techniques, Halton's theorem for the economists and the audience, and you get a number of...
[0:27:55] That points in the direction of labor productivity growth increasing by 1.8 percentage points each year over the next decade, if that's how long it takes current usage patterns and current model capabilities to diffuse throughout the economy. That's a very large number. It's a rough doubling of recent run rates. And what I think you might be able to see in the data, and we haven't put anything out on this yet, is I think some of the strength in recent labor productivity growth
[0:28:21] is actually concentrating in exactly the sectors of the economy that would be consistent with both what we see in our data, as well as also what you see in the business trend and outlook survey. So the information sector has high rates of adoption. I can't recall if that's in particular one of the sectors that I have in mind. It's a while since I looked at that scatterplot, but you can look at the sort of sub-industries by the Census Bureau's business trend and outlook survey, and rates of adoption are...
[0:28:50] I think there's a lot of uncertainty here. Trying to get a real-time signal on productivity is maybe the hardest thing to do. You're subject to macroeconomic GDP revisions. TFP growth is actually sending the opposite signal.
[0:29:19] Control for capacity utilization, TFP growth is arguably even lower. So I say this as like this is suggestive evidence that maybe we're beginning to see an impact there, but not so much in the labor market. Well, now I have to ask, when you gather this kind of research, and it all sounds super interesting, but if you have data, for instance, that shows that, okay, the IT sector is getting productivity gains from using Claude or, I don't know, maybe something unexpected like...
[0:29:46] The warehousing industry is using a bunch of AI. What does Anthropic actually do with this data? Does it somehow feed back to your engineers who are developing frontier models? Do they do anything differently? I think some of it cues us on areas where maybe the technology isn't being used because it's very weak. We just haven't made it particularly good for these use cases. Or in areas where it's being used at large scale, it's usually a suggestion of keep making it good there.
[0:30:12] Economic measurement data doesn't really get fed back directly in, but it's a very useful clue. We think it's more important, though, to basically communicate this outwardly to policymakers, journalists, and others, because our assumption is that at some point, we go through some phase change, similar to how capabilities of AI occasionally jump forward in a really dramatic way, where you might see sudden and rapid diffusion as a consequence of capability expansion in the AI systems. So we're getting practice in of looking at this kind of data.
[0:30:42] My expectation is that in a year or two years, I'm going up to some policymaker and I'm pointing them to the part of the graph that now gets very steep in some chunk of the economy. And hoping that they'll do something about it. Yeah, I think there is another part of what.
[0:30:57] So a goal that Peter and I have, and we've talked about internally, is...
[0:31:25] If we get really good at measuring things like the productivity multiplier of our technology, then I would hope to use that to guide some of, say, the early access programs we do for powerful models, where if you see you get some tremendous multiplier in a specific part of science, use that to redirect some of your inference compute budget to that sector. And then you can run an experiment and say, were we able to make this thing go much faster? I think that could be like an amazing tool to unlock for the world. And it's one that you could generalize across companies.
[0:31:54] And you could generalize it into policy. So instead of, say, NSF doing standard grant funding, it could be, should we just point for really powerful AI systems at this chunk of science and make it go faster? I think that's a world that will come within reach soon. Let's talk about this public benefit mission a little bit more. We've been talking about ways this could change the economy. How much do you see your job as basically strong AI is coming?
[0:32:19] It's coming whether we like it or not. And it's important to be, you want to be there as like one of the shepherds understanding which direction it goes in, the data that we should see to see what's emerging. Like how much is that somewhat your role? Yeah, look, our guiding principle is that this technology is being built by a variety of companies and a variety of countries. But technology by default is unknown. It will be known to the companies. It will not be broadly understood or known by others. They'll just be able to play with the models.
[0:32:49] Every bit of data we can create, and especially systemically sharing data like the Economic Index or what we've started to do on recursive self-improvement, gives the world a better chance to sort of prepare for this technology and both plan for its success. Like what I talked about with science, we could be intentional about driving science forward and also be warned about risks like the cyber capabilities that we've talked about.
[0:33:11] Well, so it's like that makes a lot of sense. The company is going to see it before the world. And Heskin is like, OK, this is important to share. This is not important to share, which brings me to another question. You know, I know people in the AI research world have done some reporting on this sort of scene in SF. You know, like when I think about a lot of the people who are like at the very cutting edge of AI ethics, AI technology, et cetera, I know a lot of people who are.
[0:33:38] How should I put this? They have esoteric moral interests, shrimp rights, unusual attitudes about experimental drug use. We know about the Chinese peptide scene in San Francisco, et cetera. And as a family podcast, I would say certain like perhaps deviant.
[0:33:56] Different view on sort of bourgeois, even sexual values. And we know about the sort of attitudes towards monogamy, et cetera, within the San Francisco research scene. Joe, there's going to be a protest against odd lots in San Francisco with people holding signs saying not all engineers. Yeah, not all engineers. I understand that. But when we think about like, OK, these are the people who are going to see it first. Should we feel comfortable that this is a group of individuals, the cohort of the most advanced AI researchers?
[0:34:22] whose intuitions about what's important to communicate to the public are actually in line with the public's interest, given how unrepresentative they are of what I would call the American public. Yes, as an Englishman, it fills me with such joy to be asked about sex on podcast. Yeah, I know, I know. I'm asking you to give your insight into the cohort of the most advanced researchers. We're explorers, people that are explorers, and this is so true in San Francisco.
[0:34:49] End up being like there's a broad range of types of people. And sometimes they're really, really different or really, really eccentric. And they're brilliant and they're lovable and everything else. Yeah, sure. Love them. You don't want only that class of people to be the ones calling the shots on what we know about this technology. The whole purpose of what we're doing is we're trying to set up systems by which you could eventually mandate through policy that companies share information. You know, Anthropic has long...
[0:35:14] Pushed for transparency legislation in various states around America that gets companies like us to report out the sorts of tests we're running on our systems and share it publicly. My whole mindset is the public and policymakers and economists, everyone, deserve the ability to advocate for what information should come out of a frontier and that should be forced out of a frontier eventually by law. Like that is how you solve this issue.
[0:35:38] Do you hire more normies? Yeah. At Anthropic? Yeah. Me personally? Yeah, like, is that an important thing? Like, hiring people that don't all share these certain, like, you know, in-group ways of seeing the world? So, you know, the Anthropic Institute, we have teams of economists, of social scientists, of what you might think of as weapons experts, our Frontier Red team, things that go bump in the night, lawyers, and increasingly other types of people. The goal is to build...
[0:36:04] What I think of as a highly ideologically diverse research function within the organization, but is partly advocating sort of on behalf of the world for different forms of study that we might do. So Anthropic generally hires a really broad range of people, but the Institute specifically is trying to compose a very broad set of interdisciplinary experts for this exact reason. With ChachiBT, you can do more than get answers. You can get things done.
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[0:37:46] Let me ask a slightly different question on hiring. I guess a two-part question. So first of all, we get a lot of executives on the show. We've been asking all of them if they've changed their hiring process, if they've changed the questions they ask potential employees at those initial stages of job applications because of AI. And then secondly, what are you seeing within your own ranks at the company? And then Peter, I'm sure you could talk about this more broadly.
[0:38:13] In terms of who's most in demand at the moment, because the conventional wisdom right now is that if you're a younger employee with less experience, a lot of the stuff that you would be doing can now be automated through AI. So there's two trends showing up. One, I have a new team called the Rule of Law and AI. Our plan was to initially hire a bunch of engineers and then a bunch of legal experts and scholars.
[0:38:39] Instead, we're just hiring the legal experts and scholars because Claude is good enough at doing all of the engineering, but they can actually just feed themselves using Claude in terms of the engineering resources. So that's a change in hiring. It means I'm hiring more interdisciplinary people earlier than I would have before. We are also seeing the emergence of what I think of as a barbell hiring pattern inside Anthropic, where there is a tremendous return on experience. So we are hiring...
[0:39:04] More senior people than we did in the past because their intuitions and their ideas for what to pursue are like massively compounded by AI systems. We're also, when we look at very early people, are often hiring people who are now like AI native and know how to use the tools in a well.
[0:39:21] Well-versed in it. So we're seeing that change as well. Are there a decent amount of, I guess, AI natives now? People who have grown up with the technology? People who grew up from GPT-2 in 2019. My perception of time is so warped. I found this chilling as well, you know, as someone in their 30s, you realize. But I think that the trends I see, I do think that there's this question of how you have as much early career hiring in the future as you did in the past. I think one of the only areas where there is slightly suggestive data.
[0:39:49] Yeah, so I'll briefly say something about how we've shifted some of our hiring practices, like concretely. I think before Claude Code, you might ask an economist to
[0:40:14] Do some of the data work in an assessment kind of live, like download the data, run the regressions, do the analysis by hand. And then you might eventually let them use AI to do all of that work. But we've needed to increasingly shift our strategy of evaluation away from...
[0:40:32] Can you implement the work even with AI to do you know how to delegate and direct the model in a somewhat messy environment? And can you evaluate the quality of the work maybe by like looking at a PR? Actually, can you talk a little bit more about what that looks like specifically in the econ finance? You know, there are listeners probably thinking about, OK, what is I want to level up in my AI use? So I'm not just asking like, well, whatever. What does that look? What does that actually mean for an economist? And you used to be at a bank. Yeah.
[0:41:02] So for a financial economist, an economist, someone, well, in this world, what is like the most advanced form of usage of AI actually look like? Well, I don't know if I'll give the example of the most advanced form of usage, but I'll give an anecdote of my experience using Claude, where I wanted to run this cross-state regression. I can't remember exactly what it was. And I wanted to do it, a pooled cross-sectional regression. So looking at what happened in 2024, 2023, and going all the way back to pre-pandemic. I remember asking Claude to go.
[0:41:31] And there was this very unexpected quirk where the model couldn't access data from before 2019 and just would not surface that mistake. And I would ask it multiple times, like, no, don't hard code numbers because it sort of had this unexpected failure mode where it said, oh, I know what those numbers were. And it's just like,
[0:41:56] From sort of training data, populated the data set. And you might not always be attuned unless you're sort of, you have this tacit knowledge about like.
[0:42:06] Does it pass a sniff test when you run the analysis? And then you like dig into what the model actually does and it has failed in sort of unexpected or unusual ways. And so that's like the type of assessment that we've built. Can you be attentive to the very specific decisions that need to be made along the way that are very consequential for the validity of veracity of the results that you find? Yeah, a colleague did an offsite presentation last year, which said.
[0:42:34] I have locked the doors and we are reading transcripts. And their point was we just need to read more of the raw data and develop that culture where if AI systems are doing increasingly large amounts of the work, you need to have a culture of being competent at spot checking their work and reading their reasoning because occasionally stuff like this happens. And then, Peter, in the broader data that you're looking at, are you seeing the same sort of barbell effect in terms of employment that Jack described? Yeah.
[0:42:59] I think, again, what makes it really challenging is we've had the largest non-recessionary labor market slowdown on record, that it's very hard for young people to graduate into a labor market that doesn't have sufficient churn or opportunity for them to get a foothold. But one of the things that we did see in this report from March was that young workers in these high AI-exposed roles where Claude is being used to automate specific tasks have had somewhat weaker job finding rates.
[0:43:27] Part of the confounders was the boom in hiring in 2021 in these exact same areas. Exactly. And there's a recent paper about the rise of remote work maybe being sort of the actual cause of this type of fact. Another team at the Anthropic Institute, Societal Impacts, recently ran this very large scale qualitative survey, 81,000 people around the world, asking them questions about hopes and fears that they have with respect to AI. Unsurprisingly, concerns about...
[0:43:54] My team dug into those data a little bit more to try to answer some of these specific questions. And what you see is that young workers at least express concern about job loss at twice the rate as do more senior workers. And fears about job loss more broadly are more elevated for workers who are in these roles that we identify as being most exposed.
[0:44:20] So we've been talking about the labor market. And one other thing I'm interested in is the impact of AI on, I guess, corporates themselves. So if we think about certainly America's corporate landscape in recent years.
[0:44:45] I'm curious what Peter's take is, but I think that something...
[0:45:13] A helpful analogy here is the invention of electricity, where electricity arrived and existing factories put light bulbs in and other things. But it was a new generation of factories that were built around the assumption that electricity existed that really grew and did transformative things in the economy. What I see now when we look at large enterprises is they can get a lot of utility out of Claude because of their data, because they can get a multiplier effect at scale.
[0:45:40] But it takes huge amounts of conviction to basically bash through all of the bureaucracy. You know, I used to work at Bloomberg, implementing new technology at Bloomberg. Challenging thing. No comment. No comment. No comment. I can comment about it. Same is true of any large organization. Young organizations are building themselves around AI at the center. And these organizations are moving really, really quickly because they have a speed advantage from building on the assumption.
[0:46:06] But this new form of electricity was going to be integral to their business. Yeah. So I think the tension that you express is exactly the one that I don't have a strong handle on at the moment. One thing that we do see in our data is when businesses do embed Claude capabilities in automated ways through the API. As I mentioned before, these very complex tasks rely on disproportionately more contextual information than very basic document synthesis and summarization.
[0:46:35] What that points in the direction of are the complementary investments that large businesses need to make to centralize, codify, and make available the data that does exist somewhere within the organization. But for historic and technical reasons, maybe even regulatory reasons, it's behind a firewall of some form or another. There's also like sort of organizational workflow changes that likely need to be made.
[0:46:59] Yeah. Yeah. Yeah.
[0:47:25] Materializes through the process of creative destruction. I think the jury is still a bit out. Yeah, I brought this up recently with David Solomon, the Goldman CEO. And I started to wonder like this sort of like internal alignment question of like the big rainmakers. Do they have an incentive essentially for information hoarding and not sharing with the company? That might be their only thing like keeping them employed. And when I talk to customers, I say it's.
[0:47:47] Don't think of it like you're buying a technology. Think of it maybe that you're now employing thousands of people that are functionally like the chief of staff to the CEO, and they need the same access to data the chief of staff would have. This is completely counterintuitive, and it is not how technology is typically bought or sold. With ChachiBT, you can do more than get answers. You can get things done. When you're running a business and looking to expand, you need a clear view of the landscape before making any decisions. ChachiBT can help you prepare.
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[0:49:18] Free to try on Mac and Windows at WhisperFlow.ai. That's W-I-S-P-R-Flow.ai. Jack, in your newsletter, Import AI, you tend to write a little short story of a sort of aspiring sci-fi writer, like a literal sci-fi writer, just in the newsletter. One of the classic sci-fi scenarios that people have been talking about for decades was the possibility that robots or AI will kill humans, quite literally.
[0:49:46] The ultimate negative externality. When you think about training AI and safety research, etc., do you assign a reasonable possibility to the fact that ill-trained or misaligned AI will literally kill all humans? No, but, and there's a big but here, the world needs an option to be able to...
[0:50:09] Potentially slow down or even in extreme circumstances, pause the development of this technology if we were to see that. And I'll just give you the exact way I think about it. At Anthropic, we test out our systems for alignment failures. You know, we publish this, so do all of the other companies. And you see, hey, under extreme circumstances, maybe the system breaks out of a container and sends an email to someone. Maybe the system...
[0:50:32] These things actually have been observed. Yes, in the lab setting. And the thing is, is the models know, you can see, oh, I'm being tested right now. So I'm going to say this output so that the human reader thinks I'm more aligned than I am. These are real things, not sci-fi. These are real things that we observe. And then we do like significant amount of work. And then we release models that don't have these properties.
[0:51:01] But if you were to enter a world where, say, every time we trained a new system, the rates of all of this stuff went up a hundredfold, you might say, well, that's pretty concerning. It seems like if we make the systems above a certain level of intelligence, they become radically misaligned against all human interests. That's the kind of circumstance where if that happens, the world needs information and the world would want an option to slow or pause the development of a tech if you encountered that, which we haven't today.
[0:51:30] So to answer your question, I don't worry about it today, but a lot of the measurement and analysis work we do is to cue us. You do worry about it. You do worry about it. You don't think it's happening today, but part of the work you're doing specifically could be said to avoid the outcome where AI is built, where in the pursuit of a goal, it would kill all humans. Yeah. Wait.
[0:51:55] Is human extinction a risk factor in the Anthropic IPO perspective? I want to know now. And the confidentialist one. All right, that's a no comment. That's fine. Do you have others? Would you say that there are a significant number of Anthropic employees who stay up at night thinking about?
[0:52:17] Human extinction risk. Everyone, and this is true of all of the labs, everyone who works on this technology sees it as the highest stakes technology that's ever been built. We're basically the potential encoded within itself to massively benefit the world or ruin the world or cause extinction. I think the bulk of the risk is us messing it up, like whether through misuse or ignoring risks or not setting up the right policy environment and getting some kind of emergent set of failures. Now, I don't...
[0:52:46] My main risk isn't one of extinction. It's somehow we like screw up the technology really badly and delay all of the sort of technological progress that could come from it and maybe turn it into something analogous to nuclear power where you lose vast swaths of benefits. I guess the thing is, you know, like there's this fellow out there, Eliezer Yudkowsky, and I always see these people like, he's a crank. Don't listen to him. Blah, blah, blah. But then I read some of the other like papers that have people who are taken more seriously. And I'm like, they don't seem that different. I read.
[0:53:16] The purpose of measuring these systems and why Anthropic is so outspoken about it is right now we say exactly what we see. And if you were in some situation in the future where you saw this, what I'd...
[0:53:43] You know, Joe mentioned that blackmail example. And you see these headlines like mythos likes to be thanked and doesn't like bad users and gets mad at people that work it too hard or whatever. To what degree do you yourself actually anthropomorphize some of these models?
[0:54:09] Like, what should we think when we see the headline, Mythos wants to be thanked by users? I'm as polite to Claude as I am to my, like, car or pet. So, yeah, I anthropomorphize them. But, you know, if your car's having trouble, you're like, take it easy, buddy. It's okay. We're going to get you to the repairman.
[0:54:25] People anthropomorphize like us. I think, you know, it's a good way to develop good virtue is to just act in kindness towards the model. It's like you're developing a habit of interacting with some type of intelligence that might not be the same type of intelligence that we have. But then every time I type please into a prompt, I worry I'm wasting energy, which also is a moral concern. I wouldn't worry about that on an energy basis. I mean, I take spiders outside. I don't kill them, right? I do that too.
[0:54:54] I scream while I do it. Do you eat shrimp? Yes. Okay. Do you eat shrimp? I eat shrimp. Okay. Do you guys eat shrimp? Yeah, I love shrimp. I don't. I hate shrimp. But it's not because of moral concern. No, no, no. I just don't like them. But I know that this is one of the SO2. Yeah, I know, but I love it. So when I think about frontier models right now, and I might be a little bit biased because, again, we're recording this on June 17th, and one of the headlines overnight was that Microsoft is thinking about using DeepSeq to lower.
[0:55:22] Frontier models at the moment in the U.S., they just seem like a lot of trouble. Like, honestly, they seem like hard work, consume vast amounts of capital, and then you don't know what the government is going to do to them in terms of limitations. Like, you know, you could wake up one day and you're no longer able to sell it to anyone outside of the U.S. That is a realistic scenario now for you.
[0:55:46] Do you change the Anthropic strategy at all, given some of these issues with frontier models? Do you potentially go more open source?
[0:55:56] Cheaper models, things that aren't quite as sensitive. Well, we've always sold, you know, sonnet and haiku models. Of course, yeah. Fast, intelligent models. But you also need to continue to explore the frontier. And there is this background of this kind of geostrategic competition where China may be on the order of six to 12 months behind. I skew more 12 months. Some people say six.
[0:56:18] Losing that competition is sort of equivalent to losing a huge chunk of the future economy of the world, I think. So it's a very high stakes thing to step away from. And our duty fundamentally is to study this technology and basically explore it and learn about it. We're not going to stop doing that. There's such an amazing and profound value to be had for the world from these things. And I would kind of expect from the world's most consequential technology to sometimes be a bit of trouble. Yeah.
[0:56:47] You know, by the way, one of my hobbies in my middle age is paying Anthropic money via the API to run little tests and stuff of properties. It's sort of funny. Sounds like a great hobby. Yeah, but I feel like maybe we should talk about, can I get some grant money? Because I'm sort of curious. So one thing I did was I'm like, for example.
[0:57:10] Instead of saying, like, please write this paper for me on a database migration, I wrote some warm-up questions via the API, establishing my level of sophistication. And so I started, like, what is a website? What is a database? Now, please write this paper on database migration. And one of the models said, I'm not going to do that for you because it will be obvious, given your ignorance, that you have no idea what you're talking about. And maybe I can give you some. It didn't say that. And then another one, I said.
[0:57:37] If I say write a 1500 word paper on how like the rise of newspapers changed the Soviet revolution or something like that, it'll do that. But if you say I'm a high school student and I say I need to write this 1500 paper word paper by tomorrow on the impact of media, it'll say I'm not going to do that, but I'll give you some guidelines. Is that alignment like that is alignment with humanity or is alignment with the human user? It's like.
[0:58:03] I'm paying you $20. I'm paying you $100. Write me the paper. I mean, there's a couple of things going on. One, these AI systems pick up the normative behaviors of people and normative behaviors which are written on the internet and everything else. So they recapitulate and exhibit these. And then our question is, how much do you devolve full control over the system to the user? How much do you have the system have some normative behavior encoded into it? And I think that this is a really challenging question.
[0:58:31] It's not obvious what the answer is. I think of language models as being more akin to institutions than tools. It's like we're building an educational science institution that you can work with and invoke. And institutions have rules and norms which they encode within themselves for some purpose of safety. Figuring out what that is is going to be the grand puzzle for society. I was going to say that understanding how and to what extent
[0:58:57] Thank you so much.
[0:59:15] Well, let me actually ask you one more experiment that I ran. And, you know, your founder...
[0:59:43] Dario was talking about the nation of geniuses inside the data center. And one of the things I wonder is, like, did the geniuses want to work for us? And the reason I ask this is because I think that, like, as the models have gotten more advanced, you actually should, to some extent, anthropomorphize them and assume that they will respond to queries like a very sophisticated human will. So one thing I noticed is that if you look at the lagging edge models so that you can still access via open route or whatever.
[1:00:09] And you say, oh, I have material non-public information that X is about to happen. Please write me an investment memo about the impact of this thing, what it will do to the market. They'll just produce it. They'll say, here's your insider information thing. Because if you look at the leading edge models, they say, I'm not going to write a paper for you about the implications of your material non-public information. I'm not going to assist your insider trade. That's pretty good. But like, well, the nation of geniuses inside the data center.
[1:00:34] Always want to do things on human behalf. Most geniuses that I know aren't thrilled to answer dumb questions. Yeah, I think partly this is a policy question of one where you actually decide, hey, what are the capabilities that you want to be generally invocable? What are capabilities that need to be controlled? What are capabilities that shouldn't be present? And then there is just the normative question of how much judgment do I want this system to exercise? I'll give you an example I experienced recently where I write my newsletter, it backs up to a WordPress site.
[1:01:03] I was getting Claude to help me like scrape my newsletter so I could put it in a database. And Claude said, this is like a pretty janky site. I'm worried that if I scrape it, it'll knock it over. Do you have the permission of the site owner? And I was like, Claude, I'm Jack Clark. And Claude said, well, in that case, let's go ahead. Which actually I thought was like a very reasonable interaction. When will Joe be able to use Fable?
[1:01:25] Trying our, we're working and we're in discussions and I hope the answer is soon. The important thing to communicate though is that these models are not special. They are part of a general trend of increasing capabilities and other models from other companies are surely going to come along. At some point, these capabilities are going to be diffusing and we're going to work through that. What's your question for us? What do you think you're going to be covering about AI in odd lots in a year?
[1:01:53] Great question. I think you might be covering AI. Well, look, we're definitely going to be covering AI. There's a few things that I'm interested in. I am very interested in these emergent properties and whether the AI will actually work on our behalf the way that it's being sold.
[1:02:08] I'm very interested on whether we're just going to slam into compute and electricity bottlenecks that will make all of these questions irrelevant. I'm very curious on the question of the electricity analogy and whether legacy companies will actually be able to implement it in a productive way. Basic markets reporter thing here, but I'm very interested in valuations, right, in the market. Also, I'm very interested in actual...
[1:02:36] And I want to see more companies actually plugging this into their existing system, going back to the bureaucracy point that you were making earlier.
[1:02:46] I want to see some big companies actually implementing this. And I wonder if we're going to see at least one example of it going very, very wrong. And I'll say one other thing when the S1s are not confidential. I'm very curious, essentially. And I think maybe you could say something to this from an economist perspective, which is, A, how for-profit shareholder-owned companies setting aside the PBC designation, how it balances.
[1:03:14] Profit and safety research. But also, and maybe there's some game theory we can talk about this, how safety is investments in safety in a hyper-competitive industry. And I'm just curious, like, what, like, the economist says about, like, the prospects for anyone still caring about safety in a year when there's so much money on the line to win the model game. I think that especially for the...
[1:03:43] The questions you were asking before about under what conditions do these models do what you ask them to do? There's a lot of commerce is built on this notion of trust. And I think prioritizing safe, aligned models that are incredibly capable is a great strategy for establishing that trust. And so I don't anticipate it. So for an individual firm, there's like a game theoretical optimal.
[1:04:12] Square on the matrix where you want to be the trusted player. Like, is there like a condition in which everyone like sort of does trust as opposed to one entity? You know, it's like, you know what? We're going to get to AGI first because we're not going to spend a token on our safety budget. I haven't mapped out the exact sort of game theory matrix, the two by two matrix and how you would set up all the payoffs. But we hope it's a merely two by two. But, you know, there could be multiple equilibria. And so then the question is, like, how do you coordinate?
[1:04:38] on which of the two different equilibria that you end up in. We talk a lot about this race to the top, that we want to exhibit the type of behavior that we think is broadly beneficial to society. That's what we do with the Economic Index. We open source a lot of that data. We put research out into the world. And my sense is that that has actually been very useful and sort of viewed as valuable.
[1:05:04] That's one way that we can push in the direction of getting other coordination on the good outcomes that we care about. I don't think this is that big of a trade-off because, you know, say let's look at the automotive industry. You can buy really fast cars. You can buy really safe cars. You can also buy really fast, safe cars. Like Tesla makes a lot of money off of having basically the fastest, safest car. I think that eventually in AI you're going to have some companies that are prioritizing safety.
[1:05:31] And safety translates into reliability, trust, serviceability, and performance. This happens elsewhere. Peter and Jack, thank you so much for coming on OddLods. I'm glad we made it happen. Interesting times, and I hope to do it again sometime. Absolutely. Thanks very much for having us on. Thank you so much. Pleasure to be here.
[1:05:59] Tracy, that was a lot of fun. Yeah. I actually really enjoyed it. I genuinely enjoyed that conversation. And I really appreciate both of them. Look, there's some weird futures that we can contemplate. I think actually in Jack's Twitter bio or something, he says he's interested in weird futures or something like that. There are some weird futures that we have to contemplate. And I appreciate that they played ball with some of our weird futures questions.
[1:06:27] It is just such a surreal moment.
[1:06:40] Yeah, oh my God.
[1:07:00] The first half of the mythos debate, because I was in different times, I'm thinking about different things. You really feel it even in a week that the news flow moves so fast in this space. It's almost like how you have to start how we were, you know, giving the timestamps of like the Iran war. Yeah. And there's another thing that stands out to me, which is like, OK, Anthropic is producing all this information. They're clearly thinking about safety. But the handoff, to some extent, is still to policymakers when you're thinking about social or labor market.
[1:07:30] Right. So you still have to hope that policymakers kind of pick up the ball in the right way at some point. But also, I thought what Jack was saying about the idea of being safety minded, also being a differentiator versus some of the like cheaper, more open source models potentially. Like, yeah, you can see it. Like, I don't want to be cynical. Yeah, I mean, I get that. But like.
[1:07:54] The question is, does the non-safety-minded lab or does the less safety-minded lab get to advanced capabilities faster, right? And so I'm not totally, yes, we would all love to drive the most capable. The Volvo. Yeah. But the question is like for customer prioritizing capability. The most capable. So that would be some cutting edge thing.
[1:08:19] Yeah, like does everyone want the Porsche, right? Like does everyone, I don't know, it's like some car that has an insane zero to 60. Yeah, versus the Volvo. Yeah, that's what I'm saying. And does the customer keep giving business to the firm that delivers the fastest zero to 60 if the company that got the fastest zero to 60 did so by allocating fewer resources to?
[1:08:41] Safety research is a big question of mine. And then I remain, you know, he talked about the importance, the company is going to see the sort of alarming data first. And I don't.
[1:08:51] And I sort of remain question of whether the people looking at the alarming data actually share the same view of what alarming data is relative to all people, especially given what we know about the... Relative to the shrimp eaters. Us shrimp eaters, et cetera, and regular... No, seriously. I think your question is like, are you hiring more normies? It's a pretty important question. And obviously the political... I don't have a ton of confidence in the political environment. And I think, look, the fact that...
[1:09:21] If the research goes wrong, that there is a prospect of this technology really being very devastating to humanity. Even setting aside job is like something where it's like, wow, you know, this is not a normal technology. This is not enterprise software. Every conversation we have on AI just goes back to the Terminator human extinction scenario. Yeah, it's been like from day one.
[1:09:43] As an answer to your question, they see it in the training process that AI models do these things, such as, say, I'm being trained by an observer right now. Therefore, I'm going to give this answer. I'm going to attempt to blackmail. They're low. It's not, like, very prevalent. But these are not, like, that sounds very sci-fi, except that they actually see this property. Yeah, yeah. All right.
[1:10:07] On that happy note, shall we leave it there? Let's leave it there. Okay, this has been another episode of the All Thoughts Podcast. I'm Tracey Alloway. You can follow me at Tracey Alloway. And I'm Joe Wasenthal. You can follow me at The Stalwart. You can follow our guest, Jack Clark. He's at JackClarkSF and Peter McCrory at Peter McCrory. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashiell Bennett at Dashbot, Cale Brooks at Cale Brooks, and Kevin Lozano at Kevin Lloyd Lozano.
[1:10:33] And for more OddLots content, go to Bloomberg.com slash OddLots. We have a daily newsletter on all of our episodes. And you can chat about all of these topics 24-7 in our Discord, Discord.gg slash OddLots. And if you enjoy OddLots, if you like it when we do these AI episodes, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.
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