Understanding the Most Viral Chart in Artificial Intelligence

23:52I think there are some caveats to that picture, but let's say even more extreme. Let's say that we cared about 99%. In that case, if we had 1 % label noise, quote unquote, if sometimes we were accidentally grading some of the failing tasks as passing, some of the passing tasks as failing, then we'd just never be able to estimate that reliably, right? And at 50%, this comes a little bit closer to washing out. And I think one other intuitive thing here, one intuition is that if you give me a task and you give me the model, it is the point at which I think that the model, if all you tell me is the time or the length of task that it takes a human to do the task, the 50 percent time horizon is the point at which I think it is more likely that the model will be able to do the task than that it can't.

24:35And I just find that intuitive. How much interest do you get on these charts from potential investors specifically? specifically. And the reason I ask is because I was just messing around and like Googling some stuff. And when the Opus chart, the latest Opus chart came up, someone posted it on Reddit. And I think like the second comment on it was someone going, how do I invest in OpenAI? And like, and like people were, they were trying to club together to like invest in these companies. So clearly there are people out there who are using these charts as investment tools. I would say, you know, we don't get an enormous amount of inbound from investment firms.

25:10I mean, And sometimes, you know, VCs or whatever, we're based in the Bay Area, will reach out to us. I think that there's some kind of principle of our goal is to inform the public and give them the best evidence that we can about when we might get to this point of kind of, you know, AI being fully autonomous or able to improve itself. And there's some principle at play here of like, I kind of want to enable people to do whatever they will do with that information. And I think that we don't engage a ton in kind of the like business side or investment implication of the work. One kind of thought experiment I sometimes say to myself is if I do believe that at some point we're going to get this AI that's improving itself and we're like AI research is automated and you have all these fears about a singularity.

25:56Would I rather that like all of Wall Street like falsely didn't think that was coming when I believed it was coming? Or would I want them all to know that it was coming given that I believe it's coming? And I think all of humanity, maybe this is more a personal view, but I think if this is possible that we will automate AI research, I think all of humanity being aware of it, aware of where we're heading is sort of a precondition for us all being able to figure out what to do about it. And so I don't kind of want like certain people or one side or one team to kind of like selectively be in the dark because they might invest on the basis of this or something like that.

26:32But we don't you know, it's not where we put our time. We're focused on informing the public. The public includes some investors. So on that note, like what is the actual level at which we're all presumably supposed to panic or at which like if you're a policymaker, policymaker, you would start to get worried about AI being able to automate and improve on itself in a way that eventually becomes detrimental to humanity. I don't know exactly what the level is on this time horizon measure. I think, you know, one thing to say is we have made real progress on the science of measuring these AI systems and how capable they are.

27:03But I think there's a long way to go. And in an important sense, I think we're behind on this task. We're measuring some underlying technical trend. And at some point, I do think that implies greater risks of astonishing things happening, although Chris can speak more to other arguments that we might back out to for why, even if AI is very capable, we still might not see catastrophic dangers emerge in the short term. Yeah, I'm unsure. You know, I think part of the reason why the AGI chatter has really picked up, particularly in the wake of like everyone using cloud code, is it's very easy to imagine, And it's like you're sitting there.

27:40It's like, yeah, do this, do this. It's like, I don't even need to be here. Right. I think you sort of get a very intuitive feel for like how the human can come out of the loop. What happens today, because I'm sure this has been tried, like if you go to like ChatGPT and you say, here's a you here, you have cloud code access, go build something. And the AIs, what actually happens today when AI is working with AI? Yeah, my sense is that at some point, you know, a further away point than would have been true some time ago, the AIs will more or less fall on their faces. That, you know, there are some things they're not so capable of today.

28:14Like collaborative hallucinations will just like, it'll just like devolve into terrible. Yeah, I think all sorts of ways this can go. You know, at some point they're going to need to rely on external resources. And today they're not as capable at managing these external resources effectively. I think they're less capable at sort of ideation and sort of self-awareness about where they are in the problem today than they are at these kind of raw software engineering skills. You know, as you mentioned, the ways in which AIs are autonomous today or close to autonomous today is the human has the idea and then, you know, submits that idea to a code code or a codex or one of these other agentic AI tools.

28:51And then they handle the software engineering components. And possibly there's still some intervention after that. I do imagine that the sort of circle of autonomy or something gets larger over time. I do think there's no fundamental barrier, it seems to me, to the AI is having those ideas and so we move to a greater level of abstraction. But if we were purely relying today on these fully autonomous capabilities, could you manage research departments, any department of your choice inside of a major AI company? My guess is probably not. Actually, on this note, this reminds me of something I wanted to ask.

29:22So when you look at the domain-specific time horizon charts, so the ones that show, I think you call them task suites or something like that, I guess productivity by a specific job, and you see these different lines. So sometimes you see almost horizontal lines, and sometimes you see squiggly or steeper lines. What is actually happening there? How are we supposed to interpret that? Is this a measurement problem, or is it saying something very fundamental about like what AI can and can't do under current conditions? The thing that I think would be good for Joel to explain is that I think that there is a distinction here between will AI, like the time horizon chart doesn't by itself, I think, tell you will productivity in one specific kind of job increase because of access to AI?

30:09Yeah, maybe one thing to say on that chart, showing the time horizon on these different task distributions. Relative to my guesses ahead of time, I think those time horizons are remarkably similar. I think the doubling times, the pace of progress in AI seems more similar than I would have guessed to the original trends that we published, although imperfectly so. On this difficulty translating what we might call raw AI capabilities in some sense, capabilities on benchmarks or something to real world productivity, I think there are a number of differences and a number of ways in particular in which the benchmark results are overestimating what we might see in the wild.

30:47You know, not hugely overestimating. I think we do see that people are getting real utility out of these modern agentic AI tools, but overestimating to some extent. One is that the scoring implicitly is different. In real problems, I'm scoring based on something a bit more holistic than these algorithmic scoring procedures, these automatic scoring procedures that we're using at Meta and many other people are using in the benchmark world. There's some notion of code quality if you're working in software engineering. But for other tasks, there's... Beautiful code, elegant code, people always talk about.

31:19Yeah, yeah, yeah. For other tasks, there's going to be... If Anna Wintour was coding, this is what it would look like. One more thing is that the tasks that come up in the wild are more likely to be messy in some sense. They involve working with other people. They involve working in much larger code bases or sort of more open-ended problems, maybe with something even adversarial going on in the software engineering context, that might be that someone's trying to make a change to the part of the code base that you're currently working on and you need to work around that. And we do tend to see that the AIs are less capable of working on these more messy problems.

31:53I don't want to overstate that. You know, it's not an enormous effect, but, you know, that's one thing that gets in the way of these productivity increases. You know, and then I do think there's something to the reliability question, right? where, you know, if it was true that for a certain type of task, you only had, you know, 80 % reliability, then every time you're going to need to go back and verify the work of these AIs and not only verify the work of these AIs, but without the context of how they implemented the solution relative to if you went about the task yourself, you'd already have that in your head.

32:21And so this verification step, quote unquote, would take less time. You know, I don't expect these frictions to be sort of so fundamental in some sense, or I imagine they go up levels of abstraction, I think, Not only is the underlying technical progress real, but I think that the productivity improvements are also going to show up increasingly. But yeah, there are these fractions. Tracy alluded to this question when she asked about VCs and investor interests. So people see these charts and regardless of what meter's point is like, this is incredible. I got to invest in this. But this brings me to this broader thing that I find very strange about AI, which is this kind of odd sort of Baptist and bootlegger relationship between the AI labs, people who are building this stuff, and the sort of alignment, safety people.

33:07and they sort of go back and forth. And like you have the heads of the lab saying, yes, this might destroy the world and take all your jobs. And the safety people and the alignment people says, yes, this might destroy the world. And like, it's a very strange industry, right? Like the only thing that I can think of as cigarettes were like they warn you that smoking is bad, except they had to do that because they lost a lawsuit. I don't think they were particularly inclined to do that. I can't think of any other industry where the most enthusiastic people about it are also warning and dooming about how bad the thing they're building could be.

33:39So I'm sort of curious, like, you know, first of all, like, and I talked about this in the intro, like, who is the type of person that's like working at Meter that is like skilled enough to do like advanced evaluations? And like, where's the funding coming from? But like, talk to us about like, who's behind Meter and why they're there. Yeah, totally. So I think one thing to say on the history of kind of people caring about AI safety in the Bay Area, is that this concern goes back like quite a ways. I would say for over a decade, there are many people who got into the field because they saw this trend of deep learning, like what if deep learning works?

34:17And it kind of goes all the way to artificial general intelligence and then super intelligence. And if that works, then it could affect everything. I think possibly when people worry about this, there's a future that they have in mind with super intelligence that's even more capable than what people who think of themselves as like AGI pill today think of. They're imagining AI systems that can run, you know, the entire economy. And I think people who kind of a while ago or many years ago saw that vision and were sort of alarmed about the stakes of it. Many people have this intuition that the thing to do is go and work in the industry, because if you're like helping build it, you know, what's the best way to shape the future?

34:52It's to build it. And I think that there's obviously you could have questions about how sincere that is for many of the people who are in the industry or if there's kind of a mix of different motivations and like, you know, different wolves inside of them where maybe they partially are motivated by that. But also they're like, there's kind of this like Oppenheimer, like it feels good to feel like you're in the position of making something that's dangerous. Someone once described OpenAI to me. This was years ago. A friend said it was like OpenAI was sort of like the Manhattan Project, except the goal was to not build the bomb at the very end, if that makes any sense.

35:26So to your Oppenheimer point, it's like very strange. And I think one thing to emphasize is, you know, while it could be that there's a mix of motivations now, there are definitely many people, I think, in the Bay Area who sincerely believe that the technology is headed to someplace that will be very difficult for where it will be very difficult for humanity to stay kind of in the driver's seat or like stay in control and kind of a meaningful sense. It does seem as though like people talk about, oh, the big AI labs have like a PR problem or something like that. They keep bringing this up and it's like maybe they just believe it.

36:00So I think that this concern is quite old. And I think many people have this intuition that they're like, I can influence the thing by building it. But now there's this problem that that logic kind of always recommends that you continue building more advanced technology or like more advanced AI systems. And now you have this problem where there's all of these companies and they all say that they need to build it because if they don't build it, another company will. And then even if all of the and they could all have doubts about each other's commitment to safety or to these principles. Famously, the leaders of the labs really do not get along.

36:33They're not friends. It's not easy for them to sort out the safety thing among themselves. And then even if all of the USAI labs kind of agreed to do that, they then have this kind of external bogeyman of China. Right. What will the Chinese companies do? And so there's this sense in which just like even if the concern is real, I think a lot of people then who are in the industry have the instinct that they kind of there's no guiding principle for what they should do on safety other than to like build leverage for themselves for later. And I think that is a concerning state of affairs for AI development to be in globally.

37:06You know, obviously, we're trying to do something different by like informing the public or kind of giving like, you know, you could imagine that this situation would be better if or like one gap that exists right now in that picture is that it's the people building the technology who most believe that it's going to be destabilizing and sort of all encompassing. maybe if the public and governments all were on the same page and believed the same thing, if it were true that it was headed there, then there would be kind of like more time for society to figure out a response from people who are not trying to build leverage over the technology themselves directly or, you know, control the technology via some kind of like public action or government.

37:42Can I just ask very quickly, since you brought up China and I don't want to forget to ask this question, but Quen doesn't show up on your like main charts. I think you did a preliminary assessment of it a while ago. But like, what's the difference between assessing one of the closed models in America versus one of the open source models over in China? I think one thing to say is that the capabilities are lagging behind. We think that they're lagging behind. I'm not sure. So they're still irrelevant. They just like don't make it onto the chart? So we do try to prioritize just because Mita has limited resources, staff time in particular, the models that we anticipate being on the frontier.

38:18And in general, the Chinese models have been something like, you know, nine to 12 months, let's say, behind the US models. And I think the gap by time horizon is probably even larger than the gap by benchmark scores, where there's some, I'm not sure how scientific I can make this, but there's some colloquial sense or something that the Chinese models are stronger according to benchmark scores than they would be on, you know, truly held out problems in some sense. You mean they're gaming the benchmark? Is that what that means? I'm not sure technically exactly how that shakes out, but something spiritually close to that.

38:52I'm not sure that's true for all Chinese models. I'm sure it's true for lots of models outside of China. But I think that's at least one possibility.

39:27We'll see you next time. plus up to 32 grams of fiber per serving. Shop now at hero.co. Code iHeart for 10 % off. All figures per serving. See calorie info at hero.co. 34 to 48 % fewer calories than regular products. Calorie content has been reduced on average from 162 to 92 calories per serving. Data accurate is of 220-26. This is Jana Kramer from Windown with Jana Kramer. Instead of giving your mom something that fades, give her something that becomes part of her home this Mother's Day. The Lennox Spice Village is a set of 24 hand-painted little houses that are actually spice jars. Perfect for anyone who loves to cook, entertain, or enjoy the little details that make everyday life special.

40:07As a mom, I love gifts that help turn ordinary moments into memories. Charming, timeless, and meant to be used, this is one of those pieces she'll treasure. And once you see it, you'll want it for your own home too. Find the full collection at lennox.com slash spice village. Amazon Pharmacy presents Painful Thoughts. It's been a long bumpy road dealing with yet another bladder infection. And driving to the pharmacy to pick up meds, I went over a pothole and a little pee came out. So now I get to stand in line with pee-pee pants. Next time, skip the pain and get fast free delivery with Amazon Pharmacy.

40:48Healthcare just got less painful.

40:54I'm very curious when you talk to external actors in all of this, and I'm going to group them into, I guess, policymakers, investors, and the labs themselves. Who are you interacting the most with at the moment? I think that in practice, we end up interacting a lot with AI labs because there's some amount of sorting out, getting access to models, working with them to set new precedents on things related to third-party red teaming and third-party risk assessment, we think of our audience as being sort of like high-context members of the public. So the kind of like people, you know, who are maybe like you two, right?

41:30People who are kind of like - People listening to this podcast, presumably. Yeah, people listening to this podcast, people with kind of who have to make important decisions that will be informed by the pace of AI progress or like the kind of profile of AI capabilities overall. Because we're based in the Bay Area, I think we like disproportionately end up interacting with people who are building the technology and like closer to it. Partially, I think back to Joe's point before, I think this is kind of because it is the case that to kind of care about a lot of these frontier problems, you're kind of selecting for people who are building the technology themselves.

42:04There's some sense in which like the companies and the industry spends more time thinking today about frontier capabilities assessment than the government does. I think Like one day you could imagine us getting to the point where the government is like very focused on this and dedicating a lot of resources to it. And at that point, I would expect me to be spending more time talking to governments. That's kind of what I was getting at because our senses and a lot of the conversations like we talk to people and they'll say something about like, oh, it's important to have a social safety net for an AI enabled future.

42:32But no one seems to be really thinking about it in a lot of detail. And when you say, you know, it's easy to imagine or maybe the government will care more about this. Not so easy for me to imagine. It seems like they mostly care about, you know, data centers and like where they located and stuff like that. It would be nice if we had policymakers really looking at like frontier capabilities and stuff. Still seems kind of a way off. But it is interesting, you know, you're like talking about like this sort of like capitalist dynamic, right? There's competition. And it's like you have a lot of people that are really worried about what if the other guys get to ASI or AGI first or what if the Chinese, et cetera.

43:10How much does the fact of like free market capitalism and the demand, you know, the big investors at the VC funds like they want to return. They want an IPO. We might get some big AI IPOs this year. In fact, how much do you find that to be perhaps intention with the safety element? Yeah, I maybe. Yeah, people on our team would have different views on this. I personally don't feel there's yeah, there's some thing here of like investors are key decision makers and, you know, they're people, too. That sounds strange to say investors are people, too. I sound like Mitt Romney or something. But I think that like I think that the element of this that feels like it could be intention is if you build a bunch of financial obligations to keep kind of the pedal to the metal, no matter what the risks are going into the future.

43:59So like one thing I think a lot about is if you're like building up a huge amount of debt to build data centers and then say that you do find evidence that you're now worried about, about the, you know, loss of control from AI systems, you do find instances of AI systems going rogue. Do you now have like a financial commitment to build up those data centers and like continue kind of the pace of progress? I think that is one place where I feel the tension pretty acutely. Like you're building these expectations into the market that could kind of force you to continue development when you otherwise would rather invest more in safety.

44:32Or, yeah, like it at least gives you a kind of financial obligation to continue scaling, at least compute. I think that like the people themselves being informed about the progress does not seem bad to me. I think it's like good in some ways for everyone to be on the same page about capabilities that could be related to subverting human control later on. But I think in the world beyond like the information that Meter shares, I do think there is a tension like the fact that private companies are building this, I think, could cause really acute tensions in the future where people make these commitments that they wouldn't if they were trying to like slow or, you know, maximize social resilience to the technology.

45:13Yeah, I'm not sure how these things shake out, but I think there are some forces on the other side, right? Like, you know, some safety promoting technologies, quote unquote, or techniques do make the models more useful, you know, if they're better complying with your will in some sense. And so you have capitalist incentives, standard capitalist incentives to invest in that kind of research. Maybe that doesn't cover, you know, the broad suite of safety research that seems important. And it certainly doesn't rule out capabilities progress as being an important axis on which you do want to scale.

45:44But I think there are some forces in each direction. Since you mentioned compute just then, can you talk a little bit more about, I guess, the relationship between the time horizon improvements and the cost of compute at the moment and what you've actually seen and how that impacts it? Yeah. So one extraordinary fact from my perspective, I'm not sure how to fit these facts together, But something like the R &D spend on compute of these companies has risen exponentially, of course. And in fact, it's risen exponentially at essentially the same rate as time horizon progress. You know, I think there's nothing necessary about that.

46:17You know, it doesn't mean by itself that if compute progress slows, then capabilities progress will also slow. But, you know, it's clearly an important input into AI progress. I expect that to continue to be true in future. Sometimes people ask us if we think it's plausible or how plausible we think it is that capabilities progress, this exponential capabilities progress, might slow down at some point in the future. And, you know, one reason it seems it's hard for me to consider it plausible that it will slow down in the next at least small number of years is that a lot of those compute R &D investments are basically already baked in.

46:51The data centers have already been built, plans for data centers even beyond 2027, 2028 are presumably coming to fruition, coming about. And so some of these input investments are already baked in, in some sense. So it would be surprising to see capabilities slow to the extent that compute has been an important input. After that, maybe you need to think about other arguments for how capabilities might slow, but that's roughly how I think about it. But there's a very good or interesting critical substack post called Against the Meter Graph by someone named Nathan Witkin, who brings up an interesting point that I wouldn't have thought of had I not read it, which is you're paying the software engineers to come in and perform these tasks, right?

47:35It seems, you know, maybe this will be the last job of humans is just doing benchmark. If I were like a good software engineer and you say, Joe, come in and do this task, how do you prevent me? Oh, man, this is taking me a long time. Meanwhile, I keep getting$100 an hour for like looking at my computer. This is tough. I'm going to have to come back tomorrow and keep working on this. How do you avoid the sort of conflict of interest where the person who's paid to work on this problem may be encouraged to take as long as possible to solve it? And with only three people working on it at times, I don't know.

48:10It seems like a conflict of interest to me. Yeah. So the short answer is, you know, in general, we are incentivizing these people to complete the task as soon as possible, in particular to complete the task faster than their peers who are attempting the same task, the time that it will take for them to complete the task. Is there like a bonus if they do it faster than? Yeah, yeah. Approximately, there's a bonus if they complete it faster and faster than anyone else. You know, another thing to say is, I think it just is true that our baselining methodology or the ways in which we compare to humans, in some ways, leaves a lot to be desired.

48:43That, you know, ideally, we would have invested, you know, 100 times as many resources in having 100 baselines, human baselines per task. And those would have come from, you know, perhaps the very best software engineers or machine learning engineers in the world. Maybe that would be the comparison that we're making. And indeed, we'd be doing all of this procedure over many more tasks, not just many more tasks, many more tasks over wider task distributions than just software engineering or machine learning engineering. I mean, I do think Time Horizon still represents progress over what's come before in the science of measuring AI capabilities.

49:16But, you know, in some ways, I'm sympathetic to a lot of criticisms of Time Horizon. I do think that some of the details, at least for the work we've done so far, you know, aren't going to matter as much as you might naively think. So choosing the shortest baseline time that we end up observing or the longest time, it's actually not going to make that much difference to the final measurements. Of course, we do think these people are talented software engineers or cybersecurity people or so on, depending on the task. But perhaps we could have found even more talented people. They would have completed it in half the time.

49:47And so naively, it would seem like the time horizon that we estimate of these models would be half as long as we actually end up observing. But of course, that wouldn't change the doubling time. It would mean you'd get to the same level after another four months. In some sense, the big picture that I want Time Horizon to point to is less this like Opus 4.6 is 12 hours in particular, and more that we're seeing this remarkable pace of progress that shows no signs of slowing in the recent past. And I think in the near future as well, you know, in fact, it shows some signs of speeding up. Well, I was going to ask about this because I think recently the statistic that you would always hear was like a doubling every seven months, something like that.

50:27How fast do you see it going in the near future? Yeah. So I was a doubling over every seven months person. There was controversy in our team about what to believe here. Because when we originally published this work approximately a year ago, you'd see, you know, if you plotted a single straight line, a single exponential, you'd get something like, you know, six or seven months, let's say. But if you restricted to just the time since, I think, GPT-4-0, since the 2024 models onwards, you'd see something closer to this sort of like four or five month trend. And some people believed in that. And, you know, some people like me had the intuition that, well, we have so few data points.

51:03We should really be estimating over this larger number of data points than a larger number of data points says every six or seven months. There are a couple of things that have changed my mind and made me realize my colleagues were right since then. One is that for the models that have come out since, what trend has better predicted how performant those models would be? And it's very clear that the answer to that is the four-month doubling time and not this seven-month doubling time. There's some possibility that could speed up again. We've seen it speed up once. I think there are some reasons in principle why you might expect it to speed up again.

51:39I think there are some caveats about this. These are maybe some takes that my colleagues would agree with. And so maybe you should discard that or you should think that they're going to convince me in the way that they did with the four month versus seven month doubling times. I have some suspicion that the tasks that Meta is measuring performance on are, you know, in some sense, more and more a narrow slice of possible tasks. And in particular, a more and more narrow slice that is perhaps similar to the kinds of tasks that you'd expect these major AI companies to be training on in the first instance.

52:13And so in some sense, we're increasingly more so than was the case before measuring progress on the exact types of tasks that they're trying to get better at. You know, you might think, for instance, the kinds of tasks that would make for good reinforcement learning environments, the kinds of tasks that you can score quickly and cheaply and automatically. I think that progress is real. I think that progress generalizes to some extent to other types of tasks. I think we're seeing, you know, remarkable progress in these more messy tasks, for example. I have one last question, which is like, how big is your team funding?

52:43And like also, how many people at Mater are basically like really rich from AI? And they're like, you know what? I'm good. I don't need to pursue, like, stick around for the IPO or whatever. I'm sad. And now I want to work on something that let humanity know. I've seen, like, there are other independent AI researchers, and they talk about this. It's like, I want to be able to talk about what I saw. Miles Brundage, someone who has, like, a little think tank, he's talked about this. How many people are, like, rich already? And they're like, OK, now I want to work for something that's public-facing.

53:15Yeah. So Meter right now is about 30 people, although we're growing and hoping to grow fast. We are hiring, I should say, meter.org slash careers. And you were touching before and kind of the thing about, is it difficult to be a nonprofit? You know, we can't pay people in equity. Right. No one's going to get an IPO. Right. Yeah. There's no IPO or anything for Meter. But we do try to pay competitively on cash compensation. Right. So that's an area where we feel we can like somewhat compete with labs. And it's true that I think a lot of our team is just motivated by trying to kind of do something different.

53:49Like not, you know, all the companies to some extent are in this business of kind of like building somewhat redundant products, kind of competing for the same role in the world. And Meter is in a really unique position at the moment where I think that we have like access and the ability to communicate these ideas and explain the state of AI research to a number, like a lot of audiences that might be hard for like individual researchers inside of a company. Like we get to talk to a lot of governments directly. We get to come here and talk with you all. And that's kind of different. I think if you look at all the actors that are working on the frontier of AI research or AI safety, you kind of if you compare us to AI lab staff, I think that our work gets to be we get to kind of every day work on whatever research we think will be most informative to the like public decision.

54:33And do you have XAI, not XAI, but X is a former AI lab staff who maybe there was a tender at some point and now they work at Meter? Yeah, we do. Those. Yeah. So we do have some people who previously worked at AI labs. I do think that as time goes on, I think one hope that I have is that more, you know, there will be more and more researchers who have kind of like made the money that they need from working in the industry. And now we're excited and kind of like lifting all boats by working on kind of like inside of an organization where the North Star can be what is most informative to the rest of the world outside of these like relatively small set of companies.

55:10Chris is very polite. I think that's I think that's wonderful. I'm tempted to be a little bit a little bit more aggressive. In this conversation, I think we have spoken through Mita's work on some of the most important problems in the world, problems that are going to define the future, I think, for not just the next years, but coming decades, maybe even coming centuries. And we've also spoken about some of the ways in which Mita work is not what you might want it to be, that there's a long way to go in the science of evaluating these AIs. Why have we not made more progress? Maybe a couple of reasons.

55:43I think clearly the central reason is that we are bottlenecked on technical talent, on incredibly capable people to come work on these questions. I was on a meet-to-work retreat recently where we were brainstorming, you know, 20, 30 of these what seemed like world-important problems, problems that we think no one else is going to get to if we do not get to them. And we are able to conduct research on how many of those problems. I think it's one, two, you know, maybe if we do an extraordinary job this quarter, it might be three. As Chris alludes to, I think if you're interested in, you know, less working on redundant products at these major AI companies and more advancing our understanding on some of the most important questions in the world that are going to shake the world for years to come, METER is a great place to go.

56:26Well, yeah, one more thing to say about that is like the vibe inside of METER is a state of triage, right? And I think people often tell themselves externally, people might guess, oh, you know, meter is a it's outside of any of the AI labs. So the thing it might most struggle with is things like access to AI models. You know, you can't do the research you want because you don't have you're not building the thing yourself in practice. That's the story that people always tell us. You have to build the future to shape it in practice. I think our experience at meter is that like when we want to try new types of research that would require new kinds of structured access.

56:55Our experience at this point has been that AI labs are like pretty game to play ball on that. And the thing that is more happening is that we're having to turn down opportunities to do stuff like that because we don't have the staff that we need to make those things happen. Interesting. Joel and Chris, thank you so much for coming on Odd Lots. Absolutely fascinating conversation and I appreciate your taking your time. Great to have you in studio. Yeah, thank you so much. Thank you so much for having us.

57:31That was a really interesting conversation. Starting from the end, sort of, the idea of like, okay, here are some really important questions. Like, let's just set everything aside. And there's 30 people working on that? Yeah, there's, you know, and like, how many people want to do it? And it's like, okay, we try to match cash comp, et cetera. That seems like kind of a tricky issue. Like, if you accept the premise that these are some big questions we have to get right, and we got to land this plane, hopefully, like, that's a bit of an issue. Yeah. The other thing I thought was really interesting was the Chinese models not really making it on the charts, even though like we know in the market itself, like when Deep Sea, when that new version came out, that was like this huge thing where everyone started to panic and to not see it even like land on the time horizon chart.

58:18It's kind of interesting. I guess it's I mean, I guess I buy the reasoning from their perspective that the only interesting question from meters perspective is like the most cutting edge, which may be slightly adjacent to the most interesting chart for like business. Right. So it's like, OK, we know that Deep Seek and Quinn and Kimmy and all those are like very impressive. Do they push like the very frontier? Perhaps not. But just in general, I find this space so weird because it's like here you have these people who are like clearly quite alarmed at the potential here. And most people, I think, look at these charts and they say, like, wow, this is like I want to invest in this or this is like.

58:59No, I know. I know. Like, that's why my first question was like, you're here for A.I. safety purposes. But everyone seems to get excited about the line go up chart. Yeah. Like there's a disconnect. They're all connected. Like I say when an industry basically says it's worried by itself, you should pay attention. It's really strange. This gets back to, you know, it's very strange where you have the CEOs of these companies who are in many cases the most alarmist. And there's this sort of cynical thing. And I don't totally discount the cynical interpretations like, oh, they're saying this because they want to get investors and so forth and they need all this money.

59:34But look, it was also true that OpenAI, ananthropic, but OpenAI a little more were like founded with these very exotic corporate structures, like a private company owned by a nonprofit, et cetera, which they presumably did because they took pretty seriously the fact that this technology and science was like very strange and not just like it's not just enterprise software. Right. Like they were self-limiting in a way. One other interesting thing, too, that this idea is like, OK, like, first of all, what's the difference between seven month and four month time doubling? Not much. You know, it's like these people's like, oh, I think.

1:00:10Yeah, but it's exponential, isn't it? I guess it's exponential. But it's still funny to me. It's like, oh, I think like AI is going to destroy all white collar work in two years. And someone else is like, no, no, I think it's going to be three years. As if that makes like any different whatsoever. But one thing to consider, Joel sort of alluded to this, you know, you had like OpenAI shutting down its like video efforts, et cetera. So perhaps part of the story is just this intense focus now on the software engineering side as what these labs are working in. Yeah. And sort of like all these other side quests are not as important.

1:00:41And so maybe we will see even more rapid progress on some of these technical benchmarks, because clearly from the lab's perspective, that's where the action is more than some of these consumer things like making making images or videos. Yep. All right. Shall we leave it there? Let's leave it there. OK, this has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Chris Painter. He's at Chris Painter. Yup and Joel Becker. He's at Joel underscore BKR. Follow our producers, Carmen Rodriguez at CarmenArmandDash, O 'Bennett at Dashbot, Kale Brooks at Kale Brooks and Kevin Lozano at Kevin Lloyd Lozano.

1:01:21And for more OddLots content, go to Bloomberg.com slash OddLots. We have a daily newsletter and all of our episodes. And you can chat about all these topics 24-7 in our Discord, discord.gg slash OddLots. And if you enjoy OddLots, if you like 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.

1:02:22Organic Valley Protein Plus Ultra-Filtered Milk is pasture-raised from cows that might take more steps than most people. That's a plus. And it has 50 % more protein and 50 % less sugar than regular milk. That's a big plus. And the fact that it didn't make a pun about moving, also a plus. Organic Valley Protein Plus Ultra Filtered Milk. Protein plus pasture raised. Learn more at OrganicValley.com. Owning a home is full of surprises. Some wonderful, some not so much. And when something breaks, it can feel like the whole day unravels. That's why HomeServe exists. For as little as$4.99 a month, you'll always have someone to call.

1:03:07A trusted professional ready to help. Bringing peace of mind to 4.5 million homeowners nationwide. For plans starting at just$4.99 a month, go to Homeserve.com. That's Homeserve.com. Not available everywhere. Most plans range between$4.99 to$11.99 a month your first year. Terms apply on covered repairs. Ryan Reynolds here from Mint Mobile. I don't know if you knew this, but anyone can get the same premium wireless for$15 a month plan that I've been enjoying. It's not just for celebrities. So do like I did and have one of your assistant's assistants switch you to Mint Mobile today. I'm told it's super easy to do at mintmobile.com slash switch.

1:03:42Upfront payment of$45 for three-month plan equivalent to$15 per month required. Intro rate first three months only, then full price plan options available. Taxes and fees extra. Default terms at mintmobile.com.