Anthropic's Co-Founder and Top Economist on Doing Research at the AI Frontier

0:02 Bloomberg Audio Studios, Podcasts, Radio News.

0:18 Hello and welcome to another episode of The Odd Lots Podcast.

0:22 I'm Joe Wisenthal and.

0:23 I'm Tracy Alloway.

0:24 Tracy, I don't know.

0:25 I think our listeners like it, but you know, a lot of our episodes are about AI these days.

0:29 But to be fair to us, it's a pretty big topic.

0:32 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 market, 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.

0:53 I know, it's so weird.

0:54 You know, we're in Hong Kong recently, and when we were in Hong Kong, this was before it was an that there was a deal to open the straight before moves in East Asia was considered to be like ground zero for where the effects should be felt of the oil and jet fuel crisis, et cetera. And we're at this dinner of business people, like, they were not talking about that at all.

1:13 The terminator scenario.

1:14 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.

1:22 So this is our defense for.

1:24 The AI episodes. 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.

1:34 Separated value human capital.

1:36 That was a great one.

1:36 They won the turn, they won the question.

1:38 They won proving that there is value in human capital. But did you see that one of the tables was called fable thirteen thirteen fable thirteen, which was very topical at that moment.

1:50 Very topical.

1:50 We're recording this on June seventeenth, and of course there's a lot in the news these days, but things move very fascinating. Even if there weren't governmental controversies and all that stuff, you would have to mark the data and 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. But that's a very conventional wisdom. It was not always conventional wisdom. And I have a DM.

2:12 I know you're not supposed to share dms for public, but I have a DM.

2:16 Got the receipts.

2:17 I have the receipts.

2:18 August second, twenty sixteen, and I DM A colleague Astad, did you leave Bloomberg? He says, yes, I'll be announcing publicly in a bit. Take a couple of months to study a properly than leaving journalism to do something else. Still connected to AI.

2:30 Being our Google reporter was a great thing.

2:32 They're still connected to AIG.

2:34 Then the final August second, twenty sixteen, but AI is more important than anything else, so I felt best to sort of.

2:41 Optimize for that above all those. So then I just said, well, I'm good luck.

2:43 This is someone who truly learned from their sources, unlike us who remain in the podcasting industry.

2:49 So anyway, that person who would that DM was a former Bloomberg reporter Jack Clark, who's one of our guests today. He is the head of Public Benefit and co founder of Anthropic years later, and also Peter McCrory, head of economics at Anthropics, So too perfect guests to talk about all the things and AI these days. So Peter and Jack, thank you so much for coming on the podcast.

3:10 Great to be back. I'm glad I optimized my life.

3:12 Yeah, well to one of the calls of the century. So what I actually start with that it's easy to say in twenty twenty six there, yeah, will be a big deal. You called your shot, you got it right, twenty sixteen. What did you see in August twenty sixteen, or presumably before They're like, oh, you know what, this is the biggest story of our lives.

3:31 So for two years when I was reporting at Bloomberg, I wasted a lot of mister Bloomberg's printing by printing out archive papers about AI research. And what I started to do very Bloombergian thing is I started to make graphs charting AI progress over time, measurements of things like computer vision, measurements of things like the skill with which AI agents were able to compete and play ATARI games.

3:55 And what I saw in these graphs was the beginning of an exponential yeah, and it was everywhere, like if you looked at vision or sound or video or gameplaying, you saw the same trend. And it became obvious to me that this was a general purpose technology if it was right at a start. My one bone that I have to pick with Bloomberg, which I which I'm going to use my privilege just mentioned on air. I never got us to write a story saying Navidia was being used in every single day research paper and I pitched it and I failed to get it across the line before I left.

4:25 Man, I can just imagine you reading all these academic papers. Meanwhile the editor is like, we need the BF Yeah, okay, And Peter, I'm very interested in you know, Anthropic. It's a company and 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.

4:50 So I mean I was late to the game and joining I joined just a year ago, but I had.

4:55 A year ago.

4:56 People, well whatever, we all know about how much the stock is a price of a year, but you're not go on.

5:01 I think what was very evident. So I'm an applied macroeconomist 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 evidence, data and research out into the world that would be broadly beneficial and useful to society.

5:33 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.

5:47 The capabilities of the models and all kinds of things are extraordinary, I mean just mind blowing every coding, copyright.

5:54 All kinds of things.

5:56 Actually, why in June twenty twenty six does life Field maybe as normal as it does from an economic perspective?

6:03 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 from 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.

6:34 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 the somewhat of the early stages. There 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.

7:05 So we're not talking about like a you know, revolutionary.

7:08 It's not, yeah, but you know, to get on an inflection, you need to at least move a little bit. I think maybe you're seeing some signs there on the labor market, though the labor market isn't 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.

7:34 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 very big, so it still takes a lot to move it. 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 twenty twenty six, engineers and Anthropic are writing about eight times the amount of code than

8:15 they did in twenty twenty one through to twenty twenty four. And the line started last year with things like Opus four to five and Opus four to six. Then it really got going this year. And I have colleagues now who don't program at all anymore. They just instruct many many cored code agents to run around and do their work for them. I can't reconcile that with the world staying normal for long, but it's going to take a while for that to diffuse into the world and change up.

8:40 Yeah, we'll talk more about recursive self improvement. So this is when models basically improve on themselves. 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 technopessimist before. How do you square that with working at Anthropic, which is making some of these weird and potentially dangerous things actually happen.

9:09 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 like quite surprising. But basically, over the last few years, and I worked at Opening Eye before and propic I was just hit repeatedly over thehead 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

9:47 specialized system or your ability to be pessimistic about future AI progress loses versus just scaling compute and scaling systems.

9:55 This seems to him implications for the labor market, right, because I think example the Bitter Lessons Private History of AI chess, right where at one point they had grand masters come in and teach the models how to play chess and etc. 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. Let's say, go off and play a billion games and find optimal chess without any human insight. The grand masters were not necessary for that process at all, right, And so this would imply to me like have significant implications for the labor market.

10:32 Yeah. I tend to think about this 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 very rapid advances in the implementation work of what an economist does data running regressions, building models, solving them using sort of contemporary solution techniques numerical methods.

11:08 I definitely felt that personally with Opus four point five, 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. 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.

11:46 If I could just get in here, you know, I just read the recent biography of the deep Mind founder.

11:52 This is the Sebastian melody, Yeah, Like, is there going to be a point where it's like, Okay, you have some intuitions right about like what good economics research is.

12:00 And often our intuitions are formed because we tell stories and stuff like that.

12:04 But is there going to be a point where you think, like your intuitions will be unhelpful? And that 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 rise, it creates inflationary pressure. These stories that are very sort of intuitible end up impairing the model.

12:29 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.

12:38 I expect that these models will soon have better intuitions about how to do good economic research, and that there's 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, and I don't know that'll be an exciting time for learning about the world.

13:02 You know what that means for my job? I'm sort of less entirely clear.

13:06 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 we'll 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 a lot of our own research, where you need, say an AI safety researcher to give nine clawed agents for different research areas to go and pursue, and then it's very effective.

13:34 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 like boring research and doesn't move the ball forward. At what point will AI systems generate like heterodox insights and genuine creativity. We can't really measure for that today, but what we have of as 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.

14:06 You know, Terry Taw probably one of the most famous living mathematicians co creates math now with AI systems, and so that that says to me that these things have got there. They're tickling the dragon's tail of like creativity here.

14:20 And you know, we just put out a report yesterday on cloud code usage, and one of the 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.

14:46 It has an amplifying effect.

14:47 It has an amplifying effect. So this looks like at present as sort of a skill biased expertise enhancing impact. But I think this is the key question is at what point and to what extent will this change.

15:00 Well related to this, you know, Jack, when you describe coming back from paternity leave and seeing how much things had changed at Anthropic, I know we're not officially at recursive self improvement point, but it sounds like we're semi there. So my question is, like, 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 out stripping what human engineers are capable of understanding. How do you manage that?

15:38 Yeah, So there's two ways of thinking about recursive self improvement. One is what happens when AI organizations start to see a compounding return from their AI systems. Basically, their in production function improves because of the tools they built. That's clearly happening now. Member. 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 and validate and basically price for risk of an expanding cloud of automated systems which we're sitting on top of. So now we produce way more code. Well, we broke our continuous integration

16:18 system for integrating code into the codebase because we started pushing eight times more code through it than before. So all of our human engineers worked on unbreaking CI and so I think that inside continuous integration, Thank you, you don't need to know what it is. It's just a thing that helps you push the code into the.

16:36 Ton.

16:36 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, like the weak links or the hot paths but 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.

17:12 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. Maybe the abilits itself might be a good time to ask a fable question or Metho's questions. At this point we're recording this June seventh. We don't know when it's going to be available for Americans, let alone the rest of the world. Does SAT product have a clear idea of what the administration's security concerns are and what it will take to resolve them.

17:39 Well, obviously 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 the point where AI systems would have national security properties. These national security properties are intertwined with their economically valuable properties.

17:58 How you manage that as a pol question is basically novel territory. Typically these things are decoupled. You're like, hey, I built a jet engine over here, which you 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 aisystems, 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 know your customer or deployments where you let large firms like say drug developers, access for

18:39 most powerful BioModels without accidentally proliferating risks. That's the shape of I think where we will end up and what we're doing right now. We and other companies and the administration are basically tackling this problem in real time. It's initially going to be messy, but we're going to end up with a system on the other side.

18:56 Well, let me just ask you, you know, this specific incident, probably more in the future because everyone's just figuring this out. When I look at the AI landscape, I sort of think of open AI as being part of the all in podcast A sixteen Z David Sex White House thing. And I know from my friends in the media, many of whom are liberal Democrats, that I sort of feel like Athropic 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 Edthropic being harassed or singled out now multiple.

19:37 Times andthropics philosophy and what I do and I lead something called Panthropic Institute, which helps us produce 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. We saw with social media is that does not work. Actually, eventually, when 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. We've always sought to just tell the truth about what we see in front of us,

20:18 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 to You.

20:25 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.

20:32 I can't really speak to that. You know, I'm not those people, and I'm menthropic. What I can say is the AI systems create their own evidence. Years ago, it seemed very odd to speculate about the cyber properties of AI systems. Well they've arrived and now we're working on them. Years ago, it was odd to speculate about the bioweapon properties of AI systems. Well. Recently, Sam Altman, Demissabis, and Daria Ama Day of Opening Eye and Thropic and Deep Mind all signed a letter saying we need to do better screening of gene synthesis to prevent AI manufactured bioweapons.

21:05 The truth wins out.

21:06 Okay, I want to go back to something you said. You mentioned potential kyic requirements, and when I hear kyic, 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 simple export controls. Should we be heading towards something that looks a little bit more like what we do for the banking system.

21:32 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 aisystems for their properties, and it's almost certainly going to have a flavor of what Peter and I work on, and the unproper innstitute broadly of generating data about these systems as they're deployed in the world, because it's one thing to you know, test out the thing before it comes out of a 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.

22:06 Would you support, you know, the 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 ten queues, et cetera. Companies that issue debt are required to have ratings agencies or frequently have ratings agencies rate their debt.

22:26 Would you support embedding in law the requirement that certain what would be the equivalent of a Moody's or a Deloitte, you know, a third party research lab sign off on the release of new models.

22:38 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 like a sensible way that you validate a lot of them. Yeah.

22:52 So, just more broadly, returning to this idea of, you know, 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 two thousand percent, three thousand percent.

23:20 I think I've seen that number.

23:21 That's from Anton Quarneck and McKelvie Harry paper a few weeks.

23:26 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?

23:40 Yeah?

23:41 So I think this is exactly the right premises kind of where we began the conversation, which is we're maybe at the point where we should be able to see some discernible impact on the macroeconomy. Unfortunately, the arrival of this world historical technology is against the backdrop of sort of unusually elevated macroeconomic volatility, the post pandemic, monetary policy, et cetera, And so it makes it very hard to disentangle all of the different factors. You know, what's the counterfactual?

24:10 You know, labor productivity growth is maybe not as strong as you might not otherwise expect, but maybe it's stronger than it is an 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 claud for. So, compiling information from reports to put together a research brief would take you 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 is an imprinciple much time savings. You can add up all of

24:52 those numbers and using standard macro growth accounting techniques, Houlton's theorem for the economists and the audience. When you get a number of that points in the direction of labor productivity growth increasing by one point eight 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 and recent labor productivity growth is actually concentrating in exactly the sectors of the economy

25:31 that would be consistent with both what we see in our data as well as also what you see in the Business trend and outlyt for example, 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 Bureaus Business Trend and Outlook survey, and rates of adoption are in sectors or parts of the economy where can trolling for pre pandemic trajectory of labor productivity growth in those sectors, even some of the strength in the early years of the recovery. Still see some like suggestive evidence. I think there's a lot of uncertainty here.

26:13 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, and if you 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 impact there but not so much in the labor market.

26:37 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 claud or I don't know, maybe something unexpected, like the warehousing industry is using a bunch of AI. What does anthropic actually do with this data? Does it somehow feed act to your engineers who are developing frontier models? Do they do anything differently?

27:04 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, but you know, if the actual 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 policy makers, 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

27:43 expansion in their AI systems. So we're getting practice in of looking at this kind of data. My expectation is that in a year or two years, I'm going up to some policy maker and I'm pointing them to the part of the graph that now gets very steep in some chunk of the economy and.

27:58 Hoping that they'll do something about it.

28:00 I think there is another part of what we're trying to do at the Institute, which we lay out in the sort of research agenda for the Anthropic Institute, which is trying to understand the impact of our decisions, which is a typical thing that economists will do at tech companies, but we have a public benefit mandate, so we're trying to understand the impact of our decisions on these broader societal and economic outcomes that we care about, and then using that to inform some of the decisions that we actually see.

28:27 A goal that Peter and I have and we've talked about internally, is if we get really good at measuring things like a 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 the world, and it's one that you could generalize across companies, and you can generalize it into policy, So instead of say NSF doing standard grant funding, it could be should we just

29:06 point for really powerful AIS systems at this chunk of science and make it go faster. I think that's a world that will come within reach soon.

29:13 Let's talk about this public benefit mission a little bit more. We've been talking about ways this could change the economy.

29:19 How much do you see your job is basically strong. AI is coming.

29:25 Yeah, 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 shepherd's 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?

29:39 Yeah, but look, our guiding principle is but 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. Every bit of data we can create, and especially systemically sharing data like the economic index of what we've started to do on recursive self improvement gives a world a better chance to sort of prepare for this technology. Yeah, and both planned for its success, Like what I talked about was science. We could be intentional about driving science forward and also be warned about risks like the cyber capabilities that you've talked about.

30:17 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, Okay, this is important to share. This is not important to share, which which brings me into another question. You know, I know people in the AI research world done some reporting on the sort of scene in SF.

30:34 You know, like when I think about a lot.

30:35 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 how should I put this, they have esoteric moral interests. Shrimp writes unusual attitudes about experimental drug use. We know about the Chinese peptide scene in the San Francisco, et cetera, and as a family podcast, I would say certain and like perhaps 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 and.

31:11 Joe, there's going to be a protest against all thoughts from San Francisco.

31:16 Yeah, not all engineers. I understand that.

31:18 But when we think about like, Okay, 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, 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.

31:39 Yes, as a as an Englishman, it fills me with such joy to be asked about sex.

31:43 Yeah, I know your insights into the cohort of the most advanced YOU search we're explorers.

31:51 People that are explorers, and this is so true in San Francisco. End up being like that. There's a broad range of types of people, and sometimes they're really really different or yeah, really really eccentric and they're brilliant to be lovable and everything else.

32:03 Yeah, sure, love them.

32:05 You don't want only that class of people to be the ones calling the shots on what we know about this technology. Yeah. 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, and THROPIC has long 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 policy makers and economists, everyone deserve the ability to advocate for what information should come out of a frontier and it should be forced out of a frontier eventually by law. Like that is how you solve this issue.

32:44 Do you hire more normies? Yeah?

32:46 Like me personally?

32:48 Yeah, Like is there an important thing like hiring people that don't all share these certain like you know, in group ways of seeing the world.

32:55 So, you know, the Youngthropic 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 what I think of as a highly ideologically diverse research function within the organization. But it's part of the advocating sort of on behalf of the world for different forms of study that we might do.

33:21 So ANDTHROPIC 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.

33:46 Let me ask a slightly different question on hiring. I guess the two part questions. 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.

34:09 And then, Peter, I'm sure you could talk about this more broadly 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.

34:26 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. Instead, we just hiring the legal experts and scholars because Claude is good enough at doing all of the engineering, but they can actually just like feed themselves using Claude in terms of the engineering resources. So that's a change in hiring.

34:49 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 and thropic, where there is a trem and does return on experience. So we are hiring 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. Were 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 and are well versed in it.

35:21 So we're seen very decent amount of I guess AI natives now, people who have grown up with.

35:26 From GPT two in twenty nineteen.

35:28 My perception of time is so.

35:31 I found this chilling as well. You know, as someone in their thirties, 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 is that something might be going on with early career hiring and it kind of intuitively feels right to all of us, but that we might be observing by effect.

35:55 And when I look at hiring patterns in anthropic, we're still hiring young people, but some teimes are hire slightly fewer of them than before and hiring more experienced people.

36:03 Yeah, so I'll briefly say something about how we've shifted some of our hiring practices. Like concretely, I think before claud code, you might ask an economist to 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 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 can you.

36:45 Talk a little bit more about what that looks like specifically in the E confine, you know there are listeners probably think about, Okay, what is I want to level up in my AI use?

36:53 So I'm not just asking like, yeah, whatever, what is it that actually mean?

36:57 For an economist and you used to be able to band yeah, so for a financial economist and economists someone, well, in this world, what is like the most advanced form of usage of AAI actually look like?

37:09 Well, I don't know if I'll give you the example of the most advanced form of usage, but I'll give it 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 twenty twenty four or twenty twenty three, and going all the way back to pre pandemic, I remember asking Claude to go out and download the data from the Census Bureau, from the Bureau of Labor Statistics, et cetera.

37:35 And there was this very unexpected quirk where the model couldn't access data from before twenty nineteen and just would not surface that mistake. And I would ask it multiple times like no, like, don't hardcode 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 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 what does it pass a snifftest 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.

38:18 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.

38:28 Yeah, A colleague did an off site presentation last year which said, I have locked the doors and we are reading transcripts, and the point was we just need to read more of the raw data and develop that culture where IFAI 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.

38:51 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 describe.

38:57 Yeah, so I think again, what makes it really challenge is we've had the largest non recessionary labor markets slow down 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.

39:26 But it's suggesting confounders was the boom and hiring in twenty twenty one in these execs.

39:31 Exactly, And there's a recent paper about so the rise of remote work maybe being sort of the actual cause of this type of fact. Another team at the anthrop Against the Dude Societal Impacts recently ran this very large scale qualitative survey eighty one thousand people around the world, asking them questions about hopes and fears that they have with respect to AI. Unsurprisingly, concerns about the impact on the labor market and on the economy rose to the surface.

39:57 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 to displacement effects from AI. So there's a bit of a gap between perception and maybe what you see in the hard data.

40:26 But that was something that was true even in recent years on other dimensions. So it's an important thing to pay attention to.

40:31 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, it feels like the big basically get bigger, Right, there's economies of scale, they have a bunch of money that they can use to actually buy some of those data exactly exactly. So would you expect AI to I guess intensify that trend of the big getting bigger or would you expect to perhaps have a leveling effect where people have this new tool that they can use to, you know, set up a new company.

41:09 I'm curious what Peter's take is, but I think that something 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 transform into things in the economy.

41:29 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. But it takes huge amounts of conviction to basically bash for all of the bureaucracy you know, used to work at Bloomberg implement new technology at Bloomberg.

41:47 Challenging comment about that same is true of any large organization. Young organizations are building themselves around AI at the center, and these organizations are moved really, really quickly because they just they have a speed advantage from building on the assumption that this new form of electricity was going to be integrals of that business.

42:08 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 cloud 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. 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

42:53 like sort of organizational workflow changes that likely need to be made. Some of the most crucial information that's needed for some types of cognitive work is tacit knowledge that exists in your colleague's mind. And unless you have a process that elicits that information that workers feel sort of incentivized to share that information and kind of trust the system, the capabilities alone might not necessarily generate that productivity. And so whether or not big firms end up restructuring themselves quickly enough or whether this materializes through the process of creative destruction. I think the jury is still a bit out.

43:28 Yeah.

43:29 Brought this open recently with the David Solomon, the Goldenman 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 what the company.

43:42 That might be their only thing that keeping them employed.

43:44 And when I talk to customers, I say, it's don't think of it like you're buying a technology. Think of it maybe, but you're now employing thousands of people, but functionally like the chief of staff to the CEO, I mean need to say, access to date of the chief of staff would have. This is completely counterintuitive and it is not technology is typically bulls all solved. Jack.

44:18 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 news are. One of the classic sci fi scenarios that people have been talking about for decades is the possibility that robots or AI.

44:35 Will kill humans quite literally.

44:38 Do you when you think about ultimate negative externalogy, when.

44:42 You think about like training, AI and safety research, et cetera. Do you assign a reasonable possibility to the fact that ill trained or misaligned AI will literally kill all humans?

44:54 Uh No, but there's a big butt yeah, Like the world needs an option to be able to 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.

45:14 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 pretends to blackmail a CEO of that it thinks is going to shut it down. These are the sorts of vis actually have been observed, yes, in the lab setting.

45:33 And the thing is is the models know you could see, Oh I'm being tested right now, So I'm going to say this output so that the human reader thinks AM more aligned than I am. These are real things, not sci fi.

45:45 These 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. 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 one hundredfold, you might say, what, that's pretty concerning. It seems like if we make for systems above a certain level of intelligence, they become radically misaligned against all human interests. That's the kind of circumstance if that happens. The world needs information, and the world would want an option to like slow or pause for development of a tech if you encountered that, which we haven't today.

46:22 So to answer your question, like, I don't. I don't worry about it today, but a lot of the measurements and analysis work we do is to cue us if they trend.

46:31 You do worry about it.

46:32 I mean, like 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.

46:47 Yeah.

46:47 Wait, is human extinction a risk factor in the anthropic ipl.

46:55 I want to know now in the confidential as one.

46:59 Understand that's comment. That's fine.

47:01 Do you have others?

47:02 Would you say that there are a significant number of anthropic employees who stay up at night thinking about human extinction risk.

47:10 Everyone, and this is true of all of the labs. Yeah, everyone who works on this technology sees it as the highest stakes technology that's ever been built. We're basically the potential encoded of in itself to massively benefit the world or ruin the world, or you know, 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 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 analogs in nuclear power where you lose.

47:51 I guess the thing is is, you.

47:52 Know, like there's this fellow out there, eliozer Yudkowski, 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 Super Intelligence recently by Nicholas Boston.

48:10 I was like, oh, this 'd Tuski is not alone. There are a number of people who think that are reasonable conditions in which the goals of the AI end up wiping out every person on earth. Yes, it does not seem like an extreme, extreme minority view.

48:24 Like concern the purpose of measuring these systems and why anthropic is so I've spoken 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 call radical misalignment, which is the kind of thing that you'd ask you worries about. You tell the world and you want to have set up the world to believe you if you see that.

48:44 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 anthropomorphosize some of these models, Like what should we think when we see the headline Mythos wants to be thanked by.

49:06 I'm as polite to Claude as I am to my like car or pets. So yeah, I am orphised them. But you know, if your car is having trouble, you're like taking easy, buddy, It's okay, We're going to get.

49:18 People.

49:19 I think, you know, it's a good way to develop good virtue is just model. You're developing a habit of interacting with some type of intelligence. It might not be the same type of intelligence that we have.

49:33 But then every time I type please into a prompt, I worry I'm wasting energy, which also is a moral.

49:39 I wouldn't. I wouldn't worry about that on an energy basis. I mean, I take spiders outside.

49:44 I don't kill them, right, I do that too. I scream while I do shrimp.

49:49 Yes, okay, shrimp. Okay, do you guys eat trim?

49:54 Yeah?

49:54 I love shd It's not because I know that this is Yeah, I know, I love it.

50:02 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 seventeenth, and one of the headlines overnight was that Microsoft is thinking about using deep seek to lower costs of model usage. Frontier models. At the moment in the US, 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 US. That is a realistic scenario.

50:38 Now for you, do you change the anthropic strategy at all, given some of these issues with frontier models, do you potentially go more open source, cheaper models, things that aren't quite as sensitive.

50:52 Well, we've always sold you know, sonnets and haik models. Of course, yeah, more intelligent models, But you also need to continue to explore a frontier And there is this background of this kind of geostrategic competition where China maybe on the order of six to twelve months behind I skew more twelve months, some people say six. Losing that competition is sort of equivalent to like losing a huge chunk of the future, like economy of the world. I think.

51:17 So it's a very high stakes, high stakes thing to step away from. And our duty fundamentally is to is to study this technology and basically explore it and learn about it. We're not going to stop doing that. There's 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. You know.

51:40 By the way, one of my hobbies in my middle age is paying anthropic money via the API to do run little tests and stuff of properties.

51:50 It's sort of funny, it's like a great hobby.

51:52 Yeah, but I feel like, maybe like we should like talk about can I get some grant money, because like I like like, because like I like it, Like I'm sort of curious. So once I did was like, I'm like, for example, 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 the database?

52:16 Now?

52:16 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, giving 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, if I say, write a fifteen hundred word paper or 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 fifteen hundred 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 might is alignment with humanity or is alignment with the human user.

52:55 It's like I'm paying you twenty dollars, I'm paying you one hundred dollars write me the paper.

52:59 There's a couple of things going on. One, these AI systems pick up the normative behaviors of people and normative behaviors which are like written on the Internet and everything else, so they recapitulate and exhibit these. And then our question is how much do you devolve like full control over the system to the user. How much do you have the system have some like normative behavior encoded into it.

53:21 And I think that this is a really challenging question. 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 like rules and norms which they encode of in themselves for some purpose of safety. Figuring out what that is is going to be like the grand puzzle for society.

53:45 Yeah, I was going to say that, like understanding how and to what extent these models can understand your preferences and then execute on your behalf will increasingly be a really important aspect of how it changes the economy. So this delegated agents that go out and transact on your behalf. We ran this experiment at the end of late last year, basically enlisting a bunch of anthropic employees to take surveys with Claude to say what they'd be willing to buy from other people and what they'd be willing to sell, and then we set up centralized marketplaces where the Clauds just interacted and bought and sold and actually executed transactions.

54:23 One of the interesting things that came out was that these models were quite good at understanding preferences even when they were not fully articulated.

54:30 Well, let me actually actually one more experiment that I ran, and you know your founder 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 asked 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 laws.

54:54 So what I'm not thing I noticed is.

54:56 That if you look at the lagging edge models, so that you can still access via open route or whatever, you say, I have material non public information that X is about to happen. Please write me an investment memo about the impact of this thing.

55:08 What it will do to the market. They'll just produce it. Here's your insider information.

55:12 Thing, because if you look at the leading edge models, they say, I'm not gonna write a paper for you about the implications of your material non public information.

55:20 I'm not going to assist your insider try.

55:21 It's very good, but like well, the nation of geniuses inside the data center always want to do things on human behalfs. Most geniuses that I know aren't thrilled to like answer dumb questions.

55:34 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? A member is just the normative question of how much judgment do I want this system to exercise.

55:49 I'll give you an example I experienced recently where I write my newsletter, it backs up to a WordPress site. I was getting calawed to help me, like, scrape my newsletter so I can put it in a database. Claude said, this is like a pretty yanky side. I'm worried that if I scrape it or knock it over, do you have a permission of for cito. I was like, claud I'm Jack Clark, I can't say, well, in that case, let's go ahead, which actually I thought was like a very reasonable interaction.

56:12 YEA, when will Joe be able to use fable?

56:16 We are trying, we're working, and we're in discussions, and I hope you answer it soon. The important thing to communicate, though, is that these 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.

56:38 What's your question for us, What do.

56:40 You think you're going to be covering about AI in odd lots in a year?

56:49 Well, look, we're definitely gonna be covering. There's a few things that I'm interested. I am very interested in these emergent properties and whether the AI will actually work on our behalf the way that it's being So I'm very interested on whether or 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 in productive way that.

57:20 Basic markets reporter thing here. But I'm very interested in valuations right in the market. Also, I'm very interested in actual applicability, 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, 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 right.

57:47 And I'll see one other things when the you know and the s ones are not confidential. I'm very curious, essentially, and I think maybe maybe you could say something to this from as an economist perspective, which is a how for profit shareholder owned companies, setting aside the PBC designation, how it balances profit and safety research. But also maybe there's some game theory we can talk about this how safety is investments in safety in a hyper competitive industry.

58:18 And I'm just curious, like what like the economist and you know it says about like the prospect for anyone still caring about safety in a year when there's so much.

58:29 Money on the line to win the model game.

58:32 I think that especially for the questions you were asking before about, you know, 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 with so for.

58:58 An individual, there's like a game theoretical optimal 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 and 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.

59:17 And you know, 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.

59:23 Payoffs, but we hope it's merely two by two.

59:26 But there, you know, there could be multiple equilibria, and so then the question is like, how do you coordinate 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 I would my sense is that that has actually been very useful and sort of viewed as valuable, and that's one way that we can push in the direction of getting other coordination on the good outcomes.

1:00:01 That we care.

1:00:02 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 AAR you're going to have some companies that are prioritizing safety, and safety translates into reliability, trust, serviceability, and performance. This happens elsewhere.

1:00:29 Peter and Jack, thank you so much for coming on, odd lads. I'm glad we made it happen interesting times, and I hope to do it again sometimes.

1:00:36 Absolutely, thanks very much for having us.

1:00:38 Thank you so much.

1:00:38 Pleasure to be here, Tracy, that was a lot of fun.

1:00:53 Yeah, that was there was I really, I really, I actually really enjoyed. I genuinely enjoyed them. Yeah, appreciation and I reallyppreciate both of them. Look, there's some weird futures that we can contemplate.

1:01:04 I think actually in Jack's like Twitter bio or something, he says he's interested in weird futures or something like that.

1:01:09 There's some weird futures that we have.

1:01:11 To contemplate, and I appreciate that they played well with some of our weird futures questions, and it's it's weird.

1:01:20 It is just such a surreal moment. And actually, you know Jack's story about going on paternity rave and then coming back and just seeing the progress at Anthropic itself in that space of time, like if you miss a month ai us flow now you're basically it feels like you'd be behind forever.

1:01:37 No, we're recording this June seventeenth. It's like, who knows what's going to happen by the time this episode is out, presume hopefully in two days or a day or whatever. But you know, I felt that when we were in Hong Kong last week that actually we mostly missed the first half of the Metho's debate because at different times I'm thinking about different things. You really feel it, even in a week that the newsflow moves so fast in this space. It's almost like how you have to start how we were, you know, giving the time stamps of like the Aron warrips.

1:02:08 Yeah, and there's another thing that stands out to me, which is like, okay, Anthropic is producing all this information. They're clearly thinking about safety, but the handoff to some extent is still to policy makers when you're thinking about social or labor market implications. Right, So you still have to hope that policy makers 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 yeaper more open source models potentially, like yeah, you can see it, Like I don't want to be seeing.

1:02:44 How Like yeah, I mean I get that, But like the question is does the non safety minded lab or does the less safety minded lab get to advanced capabilities faster?

1:02:55 Yeah?

1:02:55 Right, And so I'm not totally yes, we would all love to drive the most capable theft case, Yeah, but I but the question is, like for customer, prioritizing the.

1:03:08 Most capable, so that would be some cutting edge.

1:03:11 Thing, right, Like does everyone I don't know, it's like some car that hasn't insane zero to sixty?

1:03:18 Yeah, yeah, that's what I'm saying.

1:03:22 And does the customer keep giving business to the firm that delivers the fastest zero to sixty? If the company that got the fastest zero to sixty did so by allocating fewer resources to safety research.

1:03:35 It's a big question of mine.

1:03:36 And then I remain, you know, he talked about the aport the company is going to see the sort of alarming data first, and I don't a nice 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 doll people, especially given what we know about.

1:03:53 The relative to the shrimp eaters.

1:03:55 The relative US shrimp eaters, et cetera in regular No, seriously, I think that your question is like, are you hiring more normally is a pretty important question. And obviously the political I don't have a ton of confidence in the political environment. And I think, look like the fact that if the research goes wrong, that there is a prospect of this technology really being very devastating to humanity. Even setting aside a job is like something where it's like, wow, you know, this is not a normal technology, this is not you're.

1:04:29 Not sales we have on AI just goes back to the terminator or human distinctions.

1:04:34 Like from the day one, And as an answer to your question, there's like they see it in the training process that AI models do these things such as say I'm being seen trained by an observer right now. Therefore, I'm going to give this answer. I'm going to attempt to black belt.

1:04:50 They're low.

1:04:51 It's not like very prevalent. But these are not like that sounds very sci fi except that they actually property.

1:04:57 Yeah, yeah, all right on that. How yeah, shall we leave it there?

1:05:01 Let's leave it there?

1:05:02 Okay. This has been another episode of the Audots podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

1:05:07 And I'm Joe Wawsenthal. You can follow me at the Stalwart.

1:05:10 You can follow our guest Jack Clark, He's at Jack Clark SF and Peter McCrory at Peter McCrory. Follow our producers Carmen Rodriguez at Carmen armand dash Ol Bennett at Dashbot, Cale Brooks at Kelbrooks, and Kevin Lozano at Kevin Lloyd Lisano. And for more odd loss content, go to Bloomberg dot com slash odd lots or with a daily newsletter and all of our episodes and you can shut about all of these topics twenty four to seven in our discord Discord dot gg slash odd Logs.

1:05:37 And if you enjoy Odd Lots. 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 in