Joe Weisenthal: Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal.

Tracy Alloway: And I'm Tracy Alloway. Tracy, one thing about AI is that lots of lines that go up.

Joe Weisenthal: [laughter]

Tracy Alloway: Uh yes, famously there is perhaps one line that has captured the attention more than others when it comes to lines going up. Yes, but we're recording this April 7th. Did you see the Anthropic revenue chart, by the way? It's just extreme. It's just on the number of lines going up. I mean, there are some some really I just Let me caveat that.

Joe Weisenthal: Up until recently, there was one chart of a line going up exponentially that became, I think it's fair to say, the most viral chart in AI, right? Yes, I would absolutely agree with that. So, one of the many lines that go up, and there are various lines that sort of capture this, is essentially just measures of AI progress and what they could do. What the models are capable of and so forth, and you know, there's all different benchmarks out there and hobbyist benchmark creators, etc. All kinds of benchmarks out there. An organization called Meter, based out in San Francisco, and they measure how well AI models are doing sort of engineering tasks, etc. And they have these charts showing how long, you know, certain tasks, how long it would take a human to do them, and then whether AI could do them, and yes, the lines just almost vertical. I think there was someone like one of the ones that came out maybe very early this year or late last year showing the latest Claude model, and it was just like, this is crazy.

Tracy Alloway: When I look at these charts, they're called time horizon charts. When I look at them, I like intuitively I kind of understand what they're saying, and you can kind of see the leap in progress between some of the previous models and Claude, right? The latest Claude model. And that's what got everyone excited was you had this big exponential shift up in the capability of that particular AI model. But then when I start like diving into what it actually says on Meter's website about what these charts represent, I start getting really confused. I know everyone wants to get excited about AI and charts going up in general, but I think there's a lot of nuance here, and we should probably talk about it because the other thing going on with Meter right now is they've become sort of the industry standard benchmark. And so, a lot of investment decisions are being based on these charts, and if you oversimplify them as just like, okay, lines going up, and then suddenly it goes up even more, obviously people are going to start to get like maybe a little overexcited.

Joe Weisenthal: Can I say one other thing, too, that I'm very curious about? Like, I'm really glad that there are people designing various benchmarks for measuring AI progress. Seems like an important thing to get a handle on. But like, if I were like, say, like talented or smart enough to be like doing these things, I would go work for one of the labs and make $10 million a year or something like that. And so, I'm actually curious because a lot of nonprofits, etc. It's like, do you really like want to be like working at the cutting edge of AI at a nonprofit? I mean, I guess it OpenAI is owned by a nonprofit, weirdly enough. But you know what I'm saying? Like, I would want the money. Well, we should talk about it with our guests who are currently sitting right here. [laughter] That's exactly right. I'm very excited to say we have the two perfect guests to talk about the most viral and maybe important chart in AI right now. We're going to be speaking with Joel Becker. He is a member of the technical staff at Meter, and we're also going to be speaking with Chris Painter, the president of Meter. So, Joel and Chris, thank you so much for coming on Odd Lots.

Chris Painter: Thank you so much for having us.

Joel Becker: Yeah, thank you for having us. Yeah, really excited to chat with both of you.

Joe Weisenthal: Chris, since you're the president, I'll I'll start with you. What is Meter? How long has it been around? What is this organization? What's its goal? Just give us the the sort of 60-second synopsis of Meter.

Chris Painter: Yeah, totally. I can try and you know, sometimes I give a long version, I can try and do a short version here. So, Meter is a research nonprofit based in the Bay Area, like you said, dedicated to advancing the science of measuring whether and when AI systems might pose catastrophic risks to humanity as a whole. Focused specifically on threats that come from AI autonomy or AI systems themselves. So, when you talk about this kind of this whole field in AI of dangerous capability evaluations, people seeing, can this AI system assist with a chemical or biological weapon attack? Can it advance kind of like bad actors' ability to execute cyberattacks on a really large scale? Meter is sort of specialized in specifically assessing how autonomous are AI systems, what is the scale and like length and difficulty of tasks that they're able to do by themselves, partially because we think it sets the stakes for conversations about AI misalignment. So, we sort of see ourselves as being on the hook for at any given point in time giving humanity the bits of evidence that are most informative for establishing the stakes of, are we reliant on AI systems as a society in a way that could make it really bad if they are misaligned?

Joe Weisenthal: I'm going to let Joe ask the question about why you're both working at a nonprofit instead of one of the labs later, but one question I do have is when I think of Meter, you guys always come up in the context of these time horizon charts, and I I don't mean this as an insult or anything, but I hardly ever hear anyone talk about the actual like safety aspect of your mission. Why do you think that is?

Chris Painter: Yeah, so I think there's some distinction between our motive for assessing time horizons and the kind of how it gets used then by the rest of the world or kind of like what the origin of the rest of the world's interest in it. For Meter, I think the the reason that we work on things like the time horizon charts is because if we're trying to establish the stakes for talking about, could AI systems go rogue or one day could they like try to take over and subvert human control? Three years ago, if you went back to around when Meter started about four-ish years ago, and if you it was started by Beth Barnes, Paul Christiano, and this was kind of the initial motive, is if you went back then and you said, why don't I think that AI systems are going to go rogue and like take over or overthrow humanity today? The kind of most intuitive, you know, you can come up with a lot of abstract reasons, debates about the goals AI systems might or might not eventually have, but the kind of most damning in the moment reason is the AI system just can't do much, right? It doesn't make sense to talk about a question-answer system that like can't even reliably answer programming questions saying like, is it going to hack my systems or like backdoor me in some way? It just doesn't make any sense to talk about that.

Joe Weisenthal: going to write you a poem that you asked for. [laughter] Right. Or it won't even At the time, they couldn't do anything by themselves. And so, if you're like kind of being able to subvert human control depends on agency. And so, we wanted to come up with a measure that kind of tracks agency over time to kind of say, when would this argument no longer apply? When are AI systems now able to kind of do long complex enough actions by themselves that the argument kind of the goalposts almost move somewhere else to like, well, we would catch the AIs or the AIs don't want to subvert human control. And so, I agree that there is a distinction between like how I think partially the exercise of trying to come up with these measures throws off things that are very like grounded and intuitive measures of AI progress that might be more intuitive than just benchmarks, right? So, if you a lot of people are in the game of making just benchmarks where you say like, here's my harm bench or something, the AI gets 70%. That's much less of a kind of grounded or long-lasting metric. Like, it's hard to say what that means or how that generalizes. But the idea with time horizon is like, maybe it's more intuitive, and I think that helps both for safety and for like business understanding.

Joe Weisenthal: So, let's talk about what this chart I you go the the main chart here of meter.org right on the front page, it's this time horizon chart, and it shows Claude Opus 4.6 as of February 2026 able to complete a task length in 11 hours and 59 minutes with a 50% success rate. I have to admit, the first time I saw this chart or versions of this chart, what I assumed, and I suspect others assumed, is that it was able to go off and work on a task for 11 hours and 59 minutes and then come back with an answers. But apparently it's not that. Why don't you walk us through like, what's what's really being measured here? By the way, the previous high was all was GPT 5.3 Codex. That was 5 hours and 50 minutes. So, I guess part of the reason this chart has blew people's minds cuz literally that's basically a double. But why don't you talk to us about what's really being measured here?

Joel Becker: Yeah, so so fundamentally, you know, in simple terms, we are plotting the difficulty of tasks the AIs are able to complete over time. And you know, the particular way that we measure the difficulty of tasks is in how long it takes humans to complete those same tasks that we're asking the AIs to do. So, in this case, you know, we're talking about for Opus 4.6, something like tasks that take humans 12 hours to do, we predict that it will succeed at those tasks around 50% of the time. And yeah, you know, it turns out that that when you plot using this particular difficulty measure how performant AIs are relative to how long it takes humans to complete these tasks, we see an exponential increase in capabilities for AIs. And what that ends up meaning is that you keep on having these doublings of of capabilities every, let's say, four months it seems on recent trends, where you know, the next model is not merely going to have necessarily, you know, an hour longer time horizon, but but perhaps be having some multiple of the time horizon of the of the previous model that's come out.

Joe Weisenthal: So, then explain how that number, that 12 hours, is established. So, there is some engineering task, and you say, okay, this is a task that would require 12 hours. But humans have all different types of talent, capabilities. How do you establish that, okay, this is was a 12-hour task this was a 6-hour task this is whatever it is.

Joel Becker: Yeah so so the simple answer is literally we get humans to sit down and complete the tasks that we give to AIs under as close to identical conditions as possible. So first we we come up with the tasks and that's you know that's a whole kind of kettle of fish we can we can talk about exactly how we do that and then using essentially the the same tools that we're about to give the AIs we take talented humans you know not people who have seen this particular type of task before but people who have relevant expertise. So if it's a software engineering task you know they they have software engineering expertise machine learning task they have machine learning expertise and then we time them. We see we see how long it takes for them to complete those tasks successfully and then roughly we call the difficulty of the task as measured in human times complete as the average time it took these humans to complete the task. Then we'll run the AIs on this same set of tasks typically today for the very easiest tasks that they're more or less always going to succeed. There's some mid-range of tasks where you know perhaps they succeed 50% of the time or perhaps for some tasks in that range they succeed 0% of the time and for others 100% of the time and so they're getting 50% on average let's say and then for the much harder tasks perhaps they're getting closer to 0% and then [snorts] the point at which we predict you know in the middle of all of these 0% and 100% by task the point at which we predict that they'd have a 50% chance of succeeding that is either a 50% chance of succeeding on some task or 50% of the tasks of that difficulty that we think they would succeed on that's what we're going to call the time horizon of these models.

Chris Painter: I think one thing also that could be good to explain here is the task distribution. I mean this is not this is not all activities that humans do we are specifically here interested in or the like there's some question in what tasks are you know like Joe mentioned we're having people come into our office do the task to get a sense of how long it takes. We're not having them come in and like you know paint paintings or write novels or you know we're we're focused here specifically on things that are in the distribution of work that a um engineer at a like we like to think of it as like a frontier AI lab the tasks that they might be doing. So this is things like software engineering it's fine-tuning AI models it is like software machine learning that kind of task.

Tracy Alloway: Wait can I just ask why did you decide to focus on engineering because you could have widened it out to you know if we're talking about AI being capable of you know taking over the world there are all sorts of substantive tasks that would fall under that category so why just do engineering?

Chris Painter: Yeah I think that for one thing maybe other people on the team are maybe Joe has thoughts about this but I think my particular motive in being interested in the time horizon on software tasks is that first of all it's the thing that the industry is very like already even before we started working on this is very focused on so it's one of the capabilities that you should expect to come along for the ride earliest it's the thing that like a lot of optimization pressure is being exerted on and then I think that it is kind of the like thing that you would expect as an early warning kind of sign of this AI R&D automation. So to some extent Meter thinks of itself as trying to build you know science that or advanced science that can say when are we getting to the point that AI systems could improve themselves or speed up the pace of AI development when will AI research kind of feed on itself and the kind of core capability for that might be software engineering and machine learning research ability. There are other skills that could be relevant to taking over the world um I think other people have done time horizons on like cyber security since yeah.

Joe Weisenthal: But I suppose it is true like the basilisk isn't going to paint its way into like power or something like that. Okay the other might deceive you you might be very convincing or cunning in some way and hand over the keys.

Chris Painter: I might say a few a few mental models you know we don't have perfect evidence of this whatsoever but my rough sense sort of colloquially or you know my prior before evidence comes in is that if we did study tasks on these very different distributions you know not machine learning not software engineering I'm not sure about painting exactly but you know perhaps other kinds of task distributions that we could enumerate that basically we would see this similarly shaped exponential progress over time where every I'm not sure exactly but let's say you know 4 months 6 months something like that the level of capabilities as measured in time horizon would be doubling at something like that pace maybe from a much lower level. So you know one example that we do have better evidence of is that the AIs today are much less performant at you know anything that requires vision capabilities seeing what's on a screen clicking around at a at a computer but they're getting you know tremendously better at that at that sort of thing over time.

Joel Becker: I just do mention quickly we did actually do a very kind of brief investigation of this on other task distributions that's on our website somewhere like cross-domain time horizons I think we looked at data from the Teslas shared on self-driving I'm forgetting the other there's like OS world maybe some of these are like somewhat similar still kind of in the distribution of software tasks but trying to get further afield into things like vision.

Joe Weisenthal: How big is the sample size on the humans who are actually doing work? And also is it getting harder getting like human engineers into the room to compete with like Claude Opus 4.6 versus say if I was a mediocre engineer and I'm not I'm a non-existent engineer but if I was a mediocre one I would like maybe I would feel good about going up against like GPT-3 or something and maybe I would feel a lot worse about myself going up against like Claude.

Joel Becker: Yeah you know on these tasks I'm in I'm in a pretty similar position myself to you. So we have approximately three although it varies quite a lot across tasks human baselines per task so you know typically we're averaging over something like three. I think the the final numbers it's my impression that they're not going to be so sensitive to the particular baselines that we choose.

Chris Painter: Aren't the longer tasks weak more weakly baselined?

Joel Becker: Yeah so so indeed I think it will get a lot harder to baseline these tasks as the length of tasks the AIs are able to successfully complete gets longer and longer. You know you might think at some point the length of task that they can complete is longer than the doubling time. You know in in 4 months time they're going to be able to complete tasks of more than 4 months and then it's you know kind of becomes perhaps close to impossible to get these 4 months long baselines. Of course we're not at that point yet but you know definitely it has become more difficult to get these baselines as as time has gone on. At the moment not impossible but but very challenging.

Tracy Alloway: Joe these are the future jobs for displaced engineers right? It's competing against the codes for benchmark purposes.

Joe Weisenthal: benchmark of the year We found the jobs. So we mentioned at the beginning the most viral chart in AI is this chart that you have on the front of your website your website defaults to this and it shows you know this doubling. So if if we actually like go back to November let's say November 2025 Gemini 3 Pro 3 hours and 44 minutes Claude Opus 4.6 12 hours. That's at the 50% success benchmark. If we go to the 80% benchmark which the website doesn't default to the improve the pace of improvement looks a little less impressive to me. So okay now it's like it does not have the same gap pretty clearly. Now 80% is still not 100% and I know that this is your Meter's goal is about like you know human safety and all this stuff but when we think about people look at this and they use it as a stand-in for how performant are these models even 80% you know certainly for like any business application. I understand you're not like serving business here per se but probably businesses care about this. Even 80% may not be very good enough and it does not look as crazy when you look at the 80% chart as it does at the 50% chart. Why the focus on the 50% chart and given like why not look at the chart that just does not look as impressive?

Joel Becker: Yeah maybe two central things to say. One to my eyes the 80% chart basically does look as impressive or the doubling time is about the same. This is cope on my part. It [laughter] was it's the same it's the same increase but at a an offset of It's it's the same pace of progress you know it's something like five times smaller than the than the 50% than the 50% number but you know that only takes you two doublings and if each doubling takes around 4 months that means that in in 8 months time you're going to have the same 80% success rate roughly as as you do 50% success rate today. That's one thing to say. Maybe a second thing to say is you know remember at the beginning I said essentially what we're doing is plotting the difficulty of tasks that these AIs can complete over time just with this particular measure that ends up showing this clean exponential trend and we've picked a particular number as our difficulty number and you know that that is this 50% reliability threshold. We could have picked a different one. I think there are reasons for picking the 50% one in particular it's the one that's statistically we're we're better able to to measure for some for some technical reasons it's the one that shows up in previous literature that there there are some couple of a couple of other reasons why why we can go for 50% rather than 80%.

Chris Painter: Maybe a final thing to say is that this 50% number is sort of equivocating between these tasks it's able to complete 50% of the time and 50% of the tasks it's able to complete 100% of the time and 50% it's able to complete 0% of the time. And actually I think the situation is it's somewhere in between but it's a little bit closer to the latter where there are some tasks that it's completing with near perfect reliability and some tasks in that range that it's completing with very low reliability. And for downstream economic applications or for applications inside of these major AI companies or something you know you might think that that's more favorable in some sense that there are some of these tasks where we're getting 100% reliability even even for very challenging tasks.

Joel Becker: I think two other things may maybe it could be useful to just explain when you you said that there are technical reasons why it's easiest to measure at 50%. One like it is just the case that it is 50% is the point at which it is like least sensitive to like the distribution is kind of thickest right? I mean correct me if this is wrong, but my I mean, there are like to resolve something like 95% you would need way more samples because then you need to have some that are like you need way more samples to be able to resolve that level of precision.

Chris Painter: I think that there are some caveats to that picture, but but let's say even more extreme. You know, let's say that we cared about, you know, 99%. In that case, if we had 1% label noise, quotes and quotes, if you know, if sometimes we were accidentally grading some of the failing tasks passing, some of the passing tasks as as failing, then we just never be able to estimate that reliably, right? Okay. Um and at a 50% this comes a little bit closer to washing out. And then 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 horizon, the length of task that it takes a human to do the task, the 50% 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. And I just find that intuitive, yeah.

Tracy Alloway: How much interest you got on these charts from potential investors 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.

Chris Painter: I would say you know, we don't get an enormous amount of inbound from investment firms. I mean, 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, you know, 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, would 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 of 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 or something like that. But we don't, you know, it's not where we put our time. We're we're focused on informing the public. The public includes some investors.

Tracy Alloway: 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 policy maker 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?

Joel Becker: 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, but I think there's a long way to go and and in important sense I think we're behind on this task. We're measuring some underlying technical trend and some point I do think that's that implies greater 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.

Chris Painter: 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 Claude Code is it's very easy to imagine like you're sitting there it'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 could come out of the loop.

Joe Weisenthal: What happens today, as I'm sure this has been tried, like if you go to like Chat GPT and you say, here's a here you have Claude Code access, go build something. And the AI is, what actually happens today when AI is working with AI?

Chris Painter: Yeah, my my sense is that at some point, you know, at a further away point than would have been true some some time ago, the AIs will more or less fall on their faces. That's that, you know, there are some things they're not so capable of today.

Joe Weisenthal: Like collaborative hallucinations will they just like, you know, just like devolve into terrible

Chris Painter: Yeah, I think all sorts of ways this this can go. You know, at some point they're going to need to rely on external resources and and and today they're 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, you know, as 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 Claude Code or a Codex or or one of these other agentic AI tools and then they handle the the software engineering component. And possibly there's still still some 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 AIs having those ideas and and to be moved to a greater level of abstraction. But if we were purely relying today on on these fully autonomous capabilities, you know, could you manage research department, any any department of your choice inside of a major AI company, you know, my my guess is probably not. Mhm.

Tracy Alloway: Actually, on this note, this reminds me something I wanted to ask. So when you look at the domain-specific time horizon charts, so the ones that show like, I think you call them task suites or something like that. Like I guess productivity by specific job and you see these different lines. So sometimes you see like almost horizontal lines and sometimes you see squiggly or steeper lines. What is actually happening there? Like how are we supposed to interpret that? Like is this a measurement problem or is it saying something very fundamental about like what AI can and can't do under current conditions?

Chris Painter: The thing that I think would be good for Joel to explain is that I think that 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.

Joel Becker: Yeah, maybe one thing to say on that chart showing the time horizon on on on these different task distributions relative to my guesses ahead of time, you know, 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 that we published, although, you know, imperfectly so.

Chris Painter: On this difficulty translating what we might call raw AI capabilities in some sense, you know, capabilities on benchmarks or something to 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. You know, not not hugely overestimating. I I think we do see that people are getting real utility out of these modern agentic AI tools, but overestimating to some extent.

Joel Becker: 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 in the benchmark world. There's some notion of code quality if you're if you're working in software engineering, but but for other tasks there's there's um

Joe Weisenthal: Beautiful code, elegant code, people always talk about.

Joel Becker: Yeah, yeah, yeah. For for other tasks there's going to be If Anna Wintour was coding, this is what it would look like. [laughter]

Chris Painter: 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 they involve working in much larger code bases or or sort of more open-ended problems, maybe with something even adversarial going on. In the uh 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 and you need to 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. I I don't want to overstate that. You know, it's not it's not an enormous effect, but you know, that's that's one thing that gets in the way of these productivity increases.

Joel Becker: You know, and then I do think there's 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 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 in your head. And so this verification step, quotes and quotes, would take less time. You know, I I don't expect these frictions to be sort of so fundamental in some sense or or I imagine they they go up levels of abstraction. I think not only is the underlying technical progress real, but I think the the productivity improvements are also going to show up increasingly, but yeah, there are all these frictions.

Joe Weisenthal: Tracy alluded to this question when she asked about VCs and investor interest. So people see these charts and regardless of what Meta'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. And they sort of go back and forth and like the 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 I'm very strange industry, right? Like the only thing that I can think of is cigarettes where 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. So, 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.

Chris Painter: 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 a ways. I'd 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 and 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? It's to build it. And I think that there's obviously you could have questions about how sincere that is for 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 maybe.

Joe Weisenthal: 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. So, to your Oppenheimer point, it's like very strange.

Chris Painter: And I think one thing to emphasize is, you know, while while it could be that there's a mix of motivations now, there're definitely many people, I think in the Bay Area, who sincerely believe that the the technology is headed to some place that will be very difficult for a human where it will be very difficult for humanity to stay kind of in the driver's seat or like stay in control in 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. So, 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 principles. Famously, the leaders of the labs really do not get along. They're not friends. It's not easy for them to kind of sort out the safety thing among themselves. And then even if all of the US AI 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 a lot of people then have who are in the industry have the 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 AI development to be in globally. You 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 the 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 or, you know, control the technology via some kind of like public action or government.

Tracy Alloway: Can I just ask very quickly since you brought up China and I don't want to forget to ask this question, but Kwenn 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?

Chris Painter: I think one thing to say is that the capabilities are are lagging behind. We think that they're they're lagging behind. I'm not sure that

Tracy Alloway: So, they're still relevant, they just like don't make it onto the chart or

Chris Painter: Um so, we we do try to prioritize just because Meter has has limited resources, staff time in particular. The the models that we that we anticipate being on the frontier and in general, the Chinese models have been something like, you know, 9 to 12 months, let's say, behind the US models. And I think the gap by time horizon is 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 like that. The Chinese models are stronger according to benchmark scores than they would be on, you know, truly held out problems in in some sense. Problems problems like on the on the Meter task.

Joe Weisenthal: the benchmark? Is that what that means

Chris Painter: or I I'm not sure technically, you know, exactly how that shakes out, but but something spiritually spiritually close to that. I'm not sure that's true for for all Chinese models. I'm sure it's true for for lots of models outside of China. But that I I think that's at least one possibility.

Tracy Alloway: I'm very curious, when you talk to external actors in all of this, and I'm going to group them into, I guess, policy makers, investors, and the labs themselves, like who are you interacting the most with at the moment?

Chris Painter: 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, they're kind of like people, you know, who are maybe like you too, right? People who are kind of like People listening to this podcast. People with kind of who have to make important decisions that will be informed by the the pace of AI progress or like the 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. There's some sense in which like the companies in the industry spends more time thinking today about frontier capabilities assessment than the government does.

Joe Weisenthal: Yeah. 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 Meter to be spending more time talking to governments.

Tracy Alloway: Yeah, that's kind of what I was getting at cuz our sense is in 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. But 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. I seems like they mostly care about, you know, our data centers and like where they're located and stuff like that. It would be nice if we had policy makers 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 the sort of like capitalist dynamic, right? There's competition and it's it's like you have a lot of people that are really worried about oh, what if the other guys get to ASI or AGI first or what if the Chinese, etc. How 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 in tension with the safety element?

Chris Painter: Yeah, I maybe yeah, people on our team would have different views on this. I personally don't feel that 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 I think that like I think that the element of this that feels like it could be in tension 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. So, 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 out 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 or yeah, like it at least gives you a kind of financial obligation to continue scaling at least compute.

Joel Becker: 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, yeah, people make these commitments that they wouldn't if they were trying to like slow or you know maximize social resilience of the technology. Yeah, I'm not sure how these things shake out, but I 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 if they're better complying better complying with your will in some sense. And so, you have capitalist incentives, standard capitalist incentives to to invest in in in that kind of research. Maybe that doesn't cover, you know, the broad suite of of safety research that that seems important. So, it it certainly doesn't rule out capabilities progress as as being an important axis on on which on which you do want to scale, but you know, I think I think there are some some forces in each direction.

Tracy Alloway: Since you mentioned compute just then, can you talk a little bit more about I guess the relationship between like the time horizon improvements and the cost of compute at the moment and like what you've actually seen and how that impacts it?

Chris Painter: Yeah, so so one extraordinary fact from from my perspective, I'm not sure how to how to fit these facts together, but something like the R&D spends 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, you 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 into AI progress. I expect that to 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 that capabilities progress, this exponential capabilities progress, might slow down at some point 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 basically already baked in, right? Like the the data centers have already been built, you know, plans for data centers even beyond 2027, 2028 presumably, you know, coming coming to fruition, coming 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 maybe you need to think about, you know, other arguments for how capabilities might slow, but that's roughly how I think about it.

Joe Weisenthal: There's a very good or interesting critical substack post called against the meter graph by someone named Nathan Whitkin who brings up one 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? It seems like you know, maybe this will be the last job of humans is just sitting doing benchmarks. 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 and ooh, 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 [sighs and gasps] don't know. This like this does not It seems like a conflict of interest to me.

Joel Becker: Yeah, so the short answer is, you know, in general we are incentivizing these people to to complete the task as soon as possible. Um 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.

Joe Weisenthal: a bonus if they do it faster than

Joel Becker: Yeah, yeah, approximately there's a there's a bonus if they complete it faster faster than anyone else. You know, another thing to say is I think it just is true that our baselining methodology or or the ways in which we compare to humans in some ways leaves a lot to be desired. That's 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 maybe that would be the comparison that we're making. And indeed, we'd be doing all of 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 I do think time horizon still represents progress over over what's come before in in the science of measuring AI capabilities, but you know, in some ways I'm sympathetic to a lot 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, you know, shortest baseline time that we end up observing or the longest time, you know, it's actually not going to make that much difference to the final measurements. You know, of course, we do think these people are talented software engineers or cybersecurity people or or so on depending on the task, but you know, perhaps we could have found even more talented people. They would have completed it in half the time and so, you know, 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 that wouldn't change the doubling time. It would mean you'd get to the same level after another 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 of slowing in in the recent past and and I think in in the near future as well. You know, in fact, it shows some signs of speeding up.

Joe Weisenthal: Well, I was going to ask about this because I think recently the statistic that you would always hear was like a doubling every 7 months, something like that. How fast do you see it going in the near future?

Joel Becker: Yeah, so I was a doubling over every 7 months person. There was there was controversy in our team about 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 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-4O, since the 2024 models onwards, you'd see something closer to this sort sort of like four or five months trends. And some people believed in that and you know, some people like me have the intuition that well, we have so few data points. We should we should really be estimating over this larger number of data points and the larger number of data points says every six or seven months. There are a couple of things that have changed my mind and and made me realize my colleagues were right since since then. One is that for the models that have that have come out since, you know, what's what trends has has better predicted how performant those models would be and it's very clear that the answer to that is the the four month doubling time and not this seven month doubling time. You know, there's some some possibility that could speed up again. We we've seen it we've seen it speed up once. I think there are some reasons in principle why you why you might expect it to speed up again. I think there are some caveats about this, you know, these are these are maybe some some takes that my my colleagues would agree with and so, you know, maybe maybe you should discard that or you you know, you should think that they're going to convince me in the way that they did with the with the four month versus seven month doubling times.

Chris Painter: I have some suspicion that the tasks that Meta is measuring performance on are, you know, in some sense, more and more 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. And 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 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 and and automatically. I think that progress is real. I think that progress generalizes to some extent to other types of tasks. I think I think we're seeing, you know, remarkable progress in these more messy tasks for example.

Tracy Alloway: I've one last question, which is like how big's your team, funding, and like also how many people at Meta 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 set and now I want to work on something and 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 and said Miles Brundage, someone who has like a little think tank, he's talked about this. What's like how many people are like rich already and they're like, okay, now I want to work for something that's public facing.

Joel Becker: Yeah, so Meta right now is about 30 people, although we're growing and hoping to grow fast. We are hiring, I should say, meta.org/careers. And yeah, 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.

Joe Weisenthal: Right, yeah, there's no no IPO or anything for Meta, but we do try to pay competitively on cash compensation, right?

Joel Becker: That's an area where we feel we can like somewhat compete with labs and it's true that I think a a lot of our team is just motivated by trying to kind of do something different, like 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 Meta 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 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 making about AI.

Joe Weisenthal: not ex-AI, but ex as in former AI lab staff who were maybe there was a tender at some point and now they work at Meta?

Joel Becker: Yeah, we do have some of 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 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 are excited in 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.

Chris Painter: Chris is very polite. I think that's I think that's wonderful.

Joel Becker: I'm tempted to be a little bit a little bit more aggressive. In this conversation, I think we have spoken through Meter'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 you know, coming coming decades, maybe maybe even coming centuries. And we've also spoken about some of the ways in which Meter work is is not might 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? You know, maybe maybe a couple of reasons. I think clearly the central reason is that we are bottlenecked on on technical talent, on incredibly capable people to to to come work on these questions. I was on a Meter 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 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 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 that are going to shake the world for years to come, Meter's Meter's a great place to go.

Chris Painter: Well, 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's 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 cuz you don't have you're not building the thing yourself. In practice or that's the story that people always tell is you have to build, you know, 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, at this point has been that AI labs are like pretty game to play ball on that. Um and the thing that what 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.

Joe Weisenthal: Interesting. John, Chris, thank you so much for coming on Odd Lots. Absolutely fascinating conversation and I appreciate you taking your time. Great to have you in studio.

Chris Painter: Yeah, thank you so much.

Joel Becker: Thank you so much for having us.

Tracy Alloway: That was a really interesting conversation. And it to that we were starting from the end sort of the idea of like, okay, here are some really important questions. Like let's just set everything aside.

Joe Weisenthal: And there's 30 people working on them. And there's, you know, and like how many people want to do it? And it's like, okay, we try to match cash, comp, etc. Like that seems like kind of a tricky issue. If 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. Um 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 Seek when that new version came out, that was like this huge thing where everyone started to panic and then to not see it even like land on the time horizon chart is kind of interesting.

Tracy Alloway: I guess it's I mean I guess I buy the reasoning from their perspective that the only interesting question from Meter's 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, okay, we know that Deep Seek and Quan 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 really exciting.

Joe Weisenthal: know. Like that's why my first question was like you're here for AI safety purposes, but everyone seems to get excited about the line go up chart, right? Like there's a disconnect.

Tracy Alloway: Like I say when an industry basically says it's worried by itself, Yeah.

Joe Weisenthal: pay attention.

Tracy Alloway: It's really strange. And this gets back to, you know, a very 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 interpretation is like oh, they're saying this cuz they want to get investors and so forth and they need all this money. But look, it was also true that OpenAI and Anthropic, but OpenAI a little more, were like founded with these very exotic corporate structures like a private company owned by nonprofit, etc. Which they presumably did because they took pretty seriously the fact that this technology and science was like very strange and not just like

Joe Weisenthal: it's not just enterprise software.

Tracy Alloway: Right. Like they were self-limiting in a way. One other interesting thing too that uh this idea is like, okay, like first of all, what's the difference between 7 months and 4 months time doubling? Not much. You know, it's like these people are like, oh, I think 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 2 years. And someone else is like, no, no, I think it's going to be 3 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, etc. So perhaps part of the story is just this intense focus now on the software engineering side as what these labs are working on and sort of like all these other side quests are not as important. So maybe we will see even more rapid progress on some of these technical benchmarks because clearly from the labs' 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?

Joe Weisenthal: Let's leave it there. Okay, this has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me at TracyAlloway. And I'm Joe Wiesenthal. You can follow me at The Stalwart. Follow our guest Chris Painter. He's at ChrisPainterYep. And Joe Becker, he's at Joe_BKR. Follow our producers Carmen Rodriguez at CarmenArmand, Dash Bennett at DashBot, Kell Brooks at KellBrooks, and Kevin Lozano at KevinLloydLozano. And for more Odd Lots content, go to bloomberg.com/odd lots for the daily newsletter and all of our episodes. And you can chat about all these topics 24/7 in our Discord, discord.gg/odd lots. And if you enjoy Odd Lots, 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.