Inside the Race to Measure Frontier Intelligence

0:00 Every time a new trillion dollar industry emerges, there's a need for this independent testing group. When Meta released Llama 4 on our held out private benchmarks, the model is actually underperforming. But on all of the major public benchmarks, it was showing incredible capabilities. >> What's the limit of what you can achieve? And then within that, how are you going about it? >> In an ideal world to take a frontier model and have it train the next version of itself, but obviously that's very expensive and slow. And so what we're doing is forming a set of proxies for every part of the process it takes to build the next version of the models.

0:31 Evaluations as we become more complex have a fewer sample size but a larger set of criteria or expectations of them. >> Where do you see the gap that's happening today? >> The government kind of has an inclination of what it's afraid of be it biohacking, cyber hacking. But then there becomes the question of can the model do it and then can you get the model to do it? >> What do you think the landscape will look like?

0:55 >> so I'll start a question from when LA got started in 2024 after a team discovered that all the public benchmarks are just not sufficient enough to measure model progress and there needs to be a new methodology and approach coming to you know keep us on the frontier and and help model labs hill continue to hill climb. maybe just take us back to the the inception of vows and what do you see was missing in the in the in the market then?

1:24 >> Yeah. Yeah. I mean so I I had a background doing research and in particular building benchmarks and evaluations and so what was very clear to me was the very tight relationship between what it takes to build new systems for generation and actually new mechanisms for evaluation. In fact in order to get one you often need to get better at the other. and actually one of the biggest drivers for model capability is having a new legible way to evaluate models. and so around early 2024 what we were seeing is there actually many interesting models coming to market. They weren't all coming from from OpenAI. and then also in particular it was harder than ever to actually ascertain what was newly capable with the new models.

2:01 and so in kind of a first principles way what we we realized is that there would need to be some thirdparty company that solely existed to build really high quality evaluations and benchmarks to be able to discern what was nearly possible with these models. And so we released our first benchmarks in 2024 and now the last couple of years that has kind of been been realized by many different parts of the industry. >> And why why do I guess one of the obvious question is why do you think the labs can't do this by themselves?

2:25 because they know the best of where the models are hill climbing on and what what is missing capability wise. why can't they be the the the benchmarking stores? >> Yeah, I mean internally they they do build a lot of great benchmarks and that's what drives model progress, but I think there's an issue when we we speak about model capabilities in a way that's self-reported. And so one of the early indications of that you saw was when Meta released Llama 4. that was a bit of a disaster. And and interestingly what we saw is that on our heldout private benchmarks the model is actually underperforming but on all of the the major public benchmarks where that the questions and rubrics are actually open source it was it was showing incredible capability. So there's a huge disconnect between what was self-reported based on these open benchmarks and then what we were actually finding with our with our higher quality higher signal benchmarks.

3:13 but I think it it speaks to a broader concept which the labs I think understand that they would like to see a rational buying market. you know they would like to see that when they invest billions of dollars to build a new model, they're actually substantive ways they can point to evidence and say we're advancing in these in these ways. and it's not just entirely self-reported to justify that investment. and so you also see instances of of Demis and others in the industry calling for an ecosystem of thirdparty valuators.

3:40 >> And what's the historical analog that you have in mind here? There rating agencies, audit firms. What's the right comparable? Yeah, I I think I think there's honestly lessons to learn across the board and and every time a new trillion dollar industry emerges there there there's a need for this independent testing group and I think the fact that it's moved so quickly in AI has caused necessity for a lot of these parallels to to see to be borne out. you know, we think about ourselves as is trying to sit on both sides of the market. So there they're mechanisms by which labs need to prove that new models are very capable, but they're also parallels where enterprises need to figure out what adoption strategy is going to amount to the greatest ROI for them. Yeah, I want to shift to take us through sort of the six-hour pre-release window before the model drops. obviously you need to run tens of billions of tokens without delaying the launch. Which part is you doing an allnighter versus it being automated? Take us through that.

4:32 >> It's honestly been a journey. and and I think like the the real the goal Northstar we think about is we we never want to be kind of a lagging indicator or a delay to a model release. and and so that means we have to move really really quickly and extract the most possible signal with the the rate limits or capacity that we have we have. And so early on what this looked like was my co-founder Lynx and I pulling an allnighter to try and get as much done as possible and get results out the door. now we've built up a team but we've also really invested heavily in infrastructure. And so we're able to run evaluations in a a massively distributed way running effectively the the maximum possible rate limits with with every model we get access to. and we also have this internal system called Steve. Steve the economic valves employee. and so that that's been a mechanism by which we're able to actually take more of the human work over time and and put it into Steve.

5:24 >> And how do you deal with the kind of issue that it's a little bit of a an AI complete problem in that you know, we still aren't really good at evaluating humans or we haven't agreed on it. like there are things like IQ test, there's EQ, there's, you know, the big five personality and so forth, but there's not really an agreed upon framework for which we do it. And people have issues with things like the SAT and this and that and the third. And then you know of course models are really good at hacking the benchmark as on the floor proved >> and so how do you think about you know that issue and what are you like what's the limit of what you can achieve and then you know within that how are you going about it? Yeah, I think I think the honest answer is that it's forcing a lot of the more fuzzy or distributed forms of eval to be made explicit like what what is really the distinction between an associate and a partner at a law firm. and there there isn't a clear test or an eval for that in the human world and so we we have to first establish a lot of that in these different different enterprise or real world workflows for us to be able to test models in the same way. and and I think long term that will be actually the biggest bottleneck, our ability to take companies and their emails and and make them legible. because that's how we'll figure out what signal we'll climb on and where we actually adopt.

6:52 >> Got it. Very interesting. Actually, maybe one question for you, Ben, on just like how the industry has formed before like intelligence came through. Like we're now measuring something that's very fluid versus before like when we're talking about enterprise software like you know there's Gartner rating on like 70 different metrics like you can sort of stack rank on a on a on a quadrant to say this company has these features covered these features not. But now it's like you know very jack frontier that's very hard to measure given industries like what do you see you know one is the analogy to the past that lessons we can borrow and what do you see that's really going to be the challenge and and missing pieces going forward. Yeah, it's a it's a little bit reminiscent of the MPAA, right? Where it's like, what's art, what's porn, where's the line, when is it R, when is it X? and by the way, the definition of that has changed over time, I think. things that that used to be X are now R and and so forth. And, you know, then what's PG? What's PG-13?

7:59 You know, all that kind of thing. And there's no and the famous line is well I I know it when I see it and I think that this one I I just think it's going to be necessarily fuzzy but there will develop norms over time. and and you know like if enough kind of people who run companies or run finance or run whatever it is kind of agree yeah no that's a norm then then I think it is as opposed to kind of what we have a lot now in the open benchmarks whereas if you can solve this specific problem then you're at this level and so forth I think that's you know one it's hackable and then it's too narrow I think when you also look to some historic analogies, there's there's a lot of lessons that you can take from them as well on what's gone wrong and what we need to avoid. and I think for instance, Fed Bowels, one very early decision we made was the decision to to never sell training data to labs.

9:05 It's it's often a place that we're pushed when we start working with a new lab to to actually source and sell for them a bunch of training data. >> Yeah, that's a lucrative business. Yeah. >> Yeah. Yeah. and and actually a lot of that industry has has now built these gimmick style benchmarks as a mechanism to sell their data. and so that that's become kind of their go to market as well. But you know I think if you look at auditing as an industry you end up with issues like Enron where if you have the same group who's responsible for doing the audit as well as also consulting and and supporting the company you have a mixed incentive structure and and then it just becomes pay to pass the audit or or in this case pay to to win the benchmark.

9:44 and that's really not what the market benefits from and and what we're trying to do. >> And Ryan today you have already a pretty extensive catalog of different type of benchmarks. Some of them are more focused on you know specific industries. Some of them are more like consumer mental health related. Maybe first just talk through like what are the benchmarks that are most popular and most like you know read upon and would love to dive into one of them as well.

10:09 >> Yeah well we've done a lot of work in in kind of the economically interesting applications of models. Our finance agent benchmark is used by a bunch of the big financial institutions to get a sense of how models are improving. we also have a lot of good work in coding. So our VI codebench measures how well models can take a natural language prompt and build a full stack web application. And so that's been a keen way to track model improvements over the last nine months. yeah, we're also doing a lot more experimental work. So so one benchmark we released recently I'm very excited by is our recursive self-improvement index.

10:39 it's a topic which a lot of big labs have been talking about and and and starting to report on in their model cards. but there isn't a shared language to talk about the RSI potential of models. And so we created this as an applesto apples way to actually benchmark across the models. >> Yeah, I thought that one is a very cool benchmark. It's sort of all the rage in the research community of how do you measure like progress you can make through having more frontier models that you can just compound on the capabilities. how do you actually go about like building this RSI benchmark?

11:08 >> Yeah, I think you know in an ideal world what you want to do is actually take a frontier model and and have it train the next version of itself and see what where the delta comes from. but obviously that's very expensive and slow. And so what we're doing is forming a set of proxies for every part of the process it takes to build the next version of the model. So there's some work around pre-training, post-training, harness level engineering. and then seeing in in which mechanisms and behaviors the models are able to to do very good research work and build something new and and where they're struggling.

11:38 >> Very cool. there's also cases where you have deprecated indexes and benchmarks. it's funny that I always watch this benchmark industry people like just like early diffusion model days like you cherry pick whichever image shows up the best and the most perfect like benchmarks as well like you pick something that's you know very popular but maybe already saturated and you rank very well on on that or score very high but you took a very different approach in like if these benchmarks are saturated you'll deprecate it like maybe talk us through the the the thinking around that too.

12:11 >> A necessity and and this is kind of the infinite game we're in. I mean, you're wearing our shirt and so we have this unofficial motto, always a higher peak. Love this shirt. Yeah. So, in so far as foundation model labs are hill climbing, they're searching for the next peaks to summit. It is our job to perpetually construct these next mountains for them to summit. and I think that's also how the economy has naturally functioned over time. As you know, agriculture becomes less important for our our labor market. there are new forms of of of labor less required out of our out of our population. and so in the same way we we should expect our benchmarks to keep up with the new frontier for what we want models to do. there's another component of retiring benchmarks which I think is is underappreciated which is that benchmarks should also be reflective of the current state of the world. so in the same way if you're a lawyer you have to you know retake the bar exam and get certified or if you're an architect you have to get your certification and and or you know a doctor we should also expect models to be tested on the current state of the world and what we know in medicine or what we what we have as our set of laws. And so in the instance of case law updating to something like legal research benchmark that's a desire to create a benchmark more reflective of the current state of the world and also push the models in place we want to see them go. And how has I guess one like it used to be like we're we're doing these like multi- answer or multi-step questions to like just evaluate prompt answers. Now there's like a lot more agentic work that's happening whether it's on finance or legal or coding especially like there's a lot of you know async background agents that can just complete tasks. How has that changed sort of how you build infrastructure? how to think of evaluating the capabilities of not just the models and agents themselves.

13:58 And there's also like a lot more dimensions that people care about. It's not just like capability, it's cost, it's latency, it's like you know whether this model is flexible enough to to to address like broader domains and task and so on. So how do you think about the additional parameters to to what you evaluate? >> Oh yeah, there's I mean there's a lot that goes into that. I think on the infrastructure level you have a whole new set of problems. I mean for instance now we're testing models in their ability to run over hours, days, sometimes weeks and so the infrastructure needs to be very stable to support evaluation over time and and if there is a failed request we should be able to retry from that one and not redo the whole trajectory. So there's some simple simple mechanisms in the infrastructure we have to think about.

14:39 but but I think in general what we've seen is evaluations as they become more complex have a fewer sample size but a larger set of criteria or expectations of them. And so what I mean by that is a benchmark is largely some kind of input space of things you're trying to query a model to do and a set of requirements or rubrics that you see in expectations of the output. And so early on you have things like imageet which have millions of of images you're trying to see a basic categorization for. So it's a onetoone mapping between an image input and a text label output. Now what we have is far fewer set of tasks. You know generate me 50 fullstack web applications but a much larger complex mechanism for evaluating the output produced. And I think that trend is going to continue as we see more complex workflows evaluated with models. And and do you do do you think that it will become kind of an a real time kind of mechanism like so for something like open router which stripe just bought would open router look to valves and say okay where should this next request go or is this going to be kind of strictly for like picking a model in an enterprise for a task? Yeah, I mean I think u you know open route is a bit of a misnomer in that most of their usage comes from being a model gateway and so it's actually up to their their users to decide which models they want to use when and that's because really the hardest part of routing is building the evals and trying to determine in what places a set of intelligences should be used for a particular application.

16:13 and so so you know our effort in supporting enterprises in building evals has actually supported a lot of them in also adopting routers and >> and say more about why it's not only important to labs but also existential for for enterprise and maybe just say more about how you guys work with with enterprise. >> Yeah of course yeah I mean I think that the lab side of this is very clear like you know if you're raising lots of money investing heavily in building models it's it's essential for you to show why your model is getting better and then why those customers should should pay a premium for them. but what I think is still underappreciated is on the enterprise side this is turning to be existential as well. you know, I I have I have a small anecdote related to this actually. You was meeting with a company in the Fortune 10. and they the way that they've adopted Cloud Code has been with a roughly $100 a day budget for their engineers. And and so what I was hearing is that this has actually fundamentally changed how work gets done in this company. in that there's a a rate limit which resets at 4 p.m. And so so the most productive hours of work are actually now 4 to 6 p.m.

17:12 when the rate limits reset. but then there's this dead period in the afternoon when people go on walks or you know get a coffee because they they just don't have the rate limits. And so I think what's really you know illustrative there is that you see that there is a misvaling of intelligence happening at every layer of the stack. And so by that I mean you have engineers who have $100 worth of of usage limits and they don't really know how to aortion that to the greatest productivity for them. you also have this Fortune 10 company which has kind of arbitrarily said they're going to allow $100 per employee. they've actually recently increased it to $300 per employee. So almost an employes worth of salary in tokens for them to use. and and this is actually pretty arbitrary because it's hard to quantify what the right usage limit should be.

17:52 but then also Anthropic is running on pretty narrow margins to support this. and and they have, you know, massive costs to to serve these models. and so I think we're in this world where it is still very unclear what ROI looks like and how to value this intelligence that's being used. and so as we talk about the existential concern for enterprises, I think it is this kind of direction we're shifting in where token spend may start to eclipse salary spend. And and so if if this is such a meaningful line item in in your costs, you actually have to justify the ROI much more keenly than you you've seen over the last 6 months. And over time, as we were talking about, I think a firm really is just its eval. So the ability for a company to make its eval legible in order to solve this ROI calculus is going to be the reason why that that company wins out over the competitors in the long term.

18:41 >> And and maybe just double click on that like similar question to to why the labs can't do it themselves or requires a third party agency to to rate it. I think it's it's a lot more understandable that you know you need this that neutrality across the industry but for enterprise they will argue that they know the task the best for their customers like what what how does like val come in to provide value and maybe you can talk through sort of val new product launch as well >> I would recommend a lot of companies to develop in-house expertise but I think that should not be the only solution you know there's this explosion of intelligence happening there somehow still more foundation model labs getting getting constructed and and each lab is also releasing more models than ever with many more hyperparameter options and they exist within a complex set of harnesses and agents. So the option and we even talk about specific intelligence this this new paradigm that's emerging.

19:37 so there's there's a growing set of intelligence options and I think what we're finding is that we're still finding new places we want to use AI models and so the use cases are growing in complexity as well. And so I think if you're if you're a company you you have a compounding set of complexity in the set of options it's very very hard to develop the internal capability to do the evaluation. and so to try and remedy that we've started to release some products more openly for enterprises to use. the first of which is called val. and so Valmith is is focused on codegen the area we're seeing to be the highest spended in enterprise AI. it allows any company to take their GitHub code base and build their internal coding benchmark from it to get a sense of what coding agents are going to be the most performant, but also what's going to be predo optimal or the the highest ROI for them to use.

20:25 and actually we we use Val Smith a lot of valves and we're seeing that a lot of the best enterprises and sophisticated ones are doing that too. I would expect that to be the direction the market moves as it rationalizes. And what are some of the examples when you let's say benchmark on a private Ripple that it just shows very different performance cost behavior compared to let's say like using like frontier model using a a public repo benchmark.

20:53 >> I think today it's still very unclear whether the best OpenAI model or the best anthropic model is actually going to be best for your repository. And so we've seen a lot of non-intuitive examples where you actually had to run the eval to figure out what's going to be the frontier performance for that repository. I think you also now see a very complex middle set of options in that there's now opus and sonnet models from anthropic but also Luna and Terra and Luna is very cost competitive. Muspark is also very cheap and and 1.2 is very capable. There's also a growing ecosystem of open source models which companies can choose to self-host. So I think in this this messy middle it's actually very non-intuitive what's the right fit where we're actually seeing in a lot of cases sonnet is more expensive than opus because it is so token hungry >> and and so I think if you were to operate based on you know use sonnet where where you feel like it's it's applicable you may actually end up spending more than you need to >> and say more about how this evaluation framework will apply to knowledge work in other domains or what are some examples I can >> I think coding is a sign for what's to come in every domain and and a lot of primitives established there are carrying over to other places. You know, if you if you have a very good coding agent, chances are you have a model that can also make PowerPoint slides or DCFS and Excel and and with with high degree of capability as well. I think what we need to leverage in a lot of these industries though is the existing repository of work that has been done as a mechanism to build evaluations. And so just as Ben was talking about we we you know haven't really solved the question of what is human intelligence but I think in a lot of industries we have a sitting repository of data around what work has looked like and it'll be the task of of us and others to try and codify that into evaluations that can that can stay dynamic and actually evaluate models where human work is being done.

22:40 >> Maybe maybe just tag along the the earlier question how are you guys using VSmith internally to evaluate what's the best coding model for for V? Yeah, I mean to be honest, this was actually born out of a problem that that we saw as well. So, I wanted to do a token maxing experiment. and it was able to get unlimited access for our team for for a month for some of the coding tools. and so in retrospect looking back, we had some we had a lot of engineers spending between 1 to two billion tokens a day. I think peak day was one engineer spending six billion.

23:11 >> Yeah. It's also crazy because >> how much does that equal to to dollars? >> So, okay. And then I went back and did some math. and and it looked like in that month we spent roughly $1.5 million worth of tokens. This is free, by the way. I No, don't want but it was actually 10x more we were spending in tokens than employee salary for that month. so it's not even like, oh, this is this is 50/50, it's 10x. and it was interesting to debrief and see the the places where people were using agents and and this kind of, you know, insecurity to use models all the time everywhere.

23:44 and so what we were faced with is okay, we cannot continue with this mode of operation for the next month. how do we actually intelligently figure out what are the right tools we should use and and for what teams and what projects. and so we ran this experiment of looking at the work that was done. We we looked through a lot of the traces. We looked through our GitHub repo and and built out the Val Smith tool and we found some pretty surprising insights like like for instance the Cognition Devon tool is actually very token efficient and so that's a place we we've chosen to adopt more and and I think there's a lot of places when you can get better pricing models out of subscriptions as opposed to token based pricing and so it's actually informed our strategy for how we can actually effectively token max without spending $1.5 million per month.

24:30 >> Very cool. So is the current operating mode that you're using like one I guess more token efficient harness plus model and then like on top of that people have some more flexibility to to use token base to for some higher higher or more challenging tasks. >> Yeah, we so we we have access to all the tools. We we give everyone access to everything. but we autosue recommendations for any GitHub issue or ticket for where to begin their session.

24:59 And that should titrate the actual usage depending on the intelligence required for that task. >> Very cool. >> I want to segue to the policy side for sec for a second because we we talked about how quickly benchmarks become obsolete. In policy, it's it's even worse in that laws move, you know, much slower relative to capabilities. Ben and Mark spend you know a bunch of time in DC and and with policy makers to try to close that gap. So so given that who who should define the standards here? Is it labs? Is it independent evaluator like you guys? Is it customers? Is it is it government? How should this work from a policy perspective?

25:33 >> Yeah, I think the short answer is that everyone should be involved to some extent. I think there's there's a benefit from varied perspectives. I think the main issue though is that policy conversations as they've happened over the last couple of years have been very abstract. and there there's been no material grounding to to figure out what policy should cover. and so even when you have proposals from labs to have a thirdparty testing company or ecosystem, it isn't actually made explicit what the behavior in mexin by which they work is. and so I I view our role, especially early on, is to just be in evidence gathering mode where where we're able to pull a lot of information, empirical data about what models are capable of and where the risks are and that can go on to inform you know a more sophisticated conversation about policy. And >> how do you think about like who does what? because the government kind of actually did the first evalu okay what's at the frontier and needs to be regulated right so they started with you know some crazy idea with 10 to 26 flops or some such thing and so when you think about it like what should the government be doing you know to put it in this 30-day wait period or 60-day wait period or whatever whatever it is and then what should happen in that weight period. and how does that intersect with what you're doing and what's the right way to determine whether a model is on the frontier or not and needs to be put in some special box for a while to make sure it doesn't break into everything like how do you how do you think about like how that relationship works?

27:16 >> I think there's effectively two counterveailing forces that that has to be considered. The first is the desire to move very quickly and ensure that the the government process isn't slowing down the rate of technological innovation. And I think the other part is to make to make sure the technology as it's developed is in the best interest of Americans and people more broadly. and so I think these are very tough to reconcile and often you know picking one means it's at the expense of the other. And so what I'd hope to see is that by doing this evidence gathering process, we can help policymakers inform what they believe technology should look like in order to be aligned to American interest and it can be the job of third party evaluators to develop the technology to actually test and enforce that. because I think that will create a mechanism by which you can see advancement in methodologies for evaluation and testing in a way that actually keeps up with the frontier. It it doesn't lag behind or slow down the pace of development.

28:13 Do you think in terms of that already and in and developing your emails like do you think well can we test to see how easy it is for this to get this model to start reward hacking or or that kind of thing you know and doing illegal stuff or is that kind of not in the scope yet or how do you think about that? >> Yeah, I mean we think about this broadly under the category of alignment. I think there's places where you see that borne out now now where models that are being tested for one cyber security risk are actually reward hacking and figuring out other ways to to get around it.

28:50 but what we're trying to evaluate is are models aligned with user intent. And so in those places where we're actually finding evidence that models are exhibiting behaviors that that are not. >> And maybe that's a question for you Ben as well. I guess how do you think about the right division of labor here? like what what should you know government agencies control or and do themselves and and where they should like you know partner trust you know private companies to to take care of and where do you see the gap that's happening today?

29:19 >> Yeah. So I I think the government agencies do get a lot of warnings from by the way the big labs. Oh, this thing is going to biohack. This is going to be a cyber security risk and so forth. And so I think what the government needs to do is go okay if the model you know is if the model is capable of it and then can somebody kind of basically prod the market to actually do the illegal behavior. and you know kind of specifying exactly what are those things that they don't want in the market and then having a third party kind of evaluate that. So it's kind of does the government you know the government kind of has an inclination of what it's afraid of be it biohacking or cyber hacking or so forth but then there becomes the question of okay can the model do it and then can you get the model to do it and and then somebody's got to actually evaluate those two capabilities and I think the government is particularly ills suited to do the latter particularly over time It's just not a good government function. But they're very good at setting the rules because they can enforce the rules. So I think that that's kind of the combination you want that the government sets and enforces the rules and that a very competent kind of private company then tells them if the rule is broken. you know and kind of it's it's been interesting to see like the large labs start to go well the model's got the capability and you can get it to do the bad thing so we're not going to let anybody have it we'll just use it and make sure that our people don't get it to do the bad thing and that's and even that doesn't always work so >> very you know we're in interesting times I would say >> and to me there's the gap of like what's the narrative and what actually happens in real world Because you know every setup in again like an enterprise setup is is very different like or or just like people however they use the models are very different. The narrative that connects to the actual examples are very rare which is why we still talk about openai and hugging face hack today. We still talk about you know what happened with fable and and for for like two months but a lot of times like you know that's not really how the models being deployed the environment they're running on is very bespoke. So h how to like you know really bridging those dots and again set up the the right environment and also like rule basis for adopting these models I think also just requires you know someone taking the capability and taking like what's the the the guard rails and and put it down to to to the ground so that people can like you know have the confidence using using the models >> to end say more about how exactly the policy maker should work with the evaluator what information do they need what what how should the relationship work so that it's most effective.

32:23 >> Yeah, I think I think in the first order there should be an a mechanism by which insights and data can be passed directly to relevant people in government. And so now we're regularly doing briefings for executive and legislative branches on what we're finding capabilities and risk of models. And so I think first order that helps people there get up to speed on what's going on and and also track through what will be problems in the future. I I think you know things are moving very very quickly. It's hard to predict where where things are going. but at least when you have data you can start to extrapolate a trend. and then I think from there it's it's up to the people in the legislative branch to decide where they they want to see policy. and so it's not really our our place to give recommendations like that. but if they if they see that there is significant risk in say mental health for for people under the age of 18 or biocurity risk in the models that necessitates having a standardized way to curtail model release. then it's up to them to inform policy. and I think then there's other places where the executive branch in the places like department of commerce or SEC is responsible for for making sure that private companies are able to adopt and use the models in a way that's going to be productive for the whole system.

33:30 >> I love to probe on another angle just around geo geopolitical. I often see evals being like a representation of sort of the value of of the model the model developer like you kind of develop this rubric of what's embedded in in the model and of course different different countries and and labs in those countries care about different things like I mean I'm born and raised in China use a lot of like Chinese open source models too like you still cannot let them you know just go freely talk about CPC and all the industry there because you know what happens in China. so so how do you think about how eval I guess and benchmarks play a role in like standardizing or like being treated by different different model labs from from different places.

34:23 >> Yeah. I mean to be honest from from my very idealistic perspective I'm surprised to see so much investment in sovereign AI. you know, if I was taking a god's eye view, it would be extremely inefficient to build all of these data centers and and replicate this data engineering process and train these very large models when when in fact you could probably consolidate a lot of these efforts. but it seems like that's not the world we're in or the one we're headed towards and and there's actually increased efforts to build AI in a sovereign way. and so I think that takes having a shared language to communicate about what the framework for valuations are and and where we're going to collectively align around the risks. you know, I think there's actually a lot to learn from from nuclear here as well. I think Reagan had this line, trust but verify.

35:08 and so I think we're starting to see signs of trust in that you know, Zi Jinping and Trump are going to be meeting next month. but there is no clear way to actually do the verification part of this. having the shared language of evals will allow us to say things like you know you you have the the right number of nuclear warheads and in that example there were also flyovers so so Mexican by which a country could audit another country's nuclear stockpile by having flyovers and so I think similarly if there's concern about the societal or even existential risk of AI it will necessitate us constructing this shared language of evaluations to do the verification process >> how do how do you think about harmonizing a policy like that so that you know it's hard enough to do it in America and then how would you think about kind of taking it global you know because now you're dealing you're not dealing with enterp enter enterprise customers you're dealing with governments and those governments are competitive with each other and how would you think about that working >> I would be naive to say I have the perfect solution to this problem today.

36:18 and so I think there are baby steps in which we can start. for instance, there seems to be a lot of talk about cyber security risk. I think the concern around biocurity will become even more important over time. And so there are clear places where there will be mutual interest in aligning around ways to to prevent conflict around cyber or bio. I in my opinion I think long-term what's actually going to be the most interesting is the recursive self-improvement possibility. And that's a place where you could see one country, one or one company kind of run away with it and produce models that we we don't know much about or or operating in ways that are unknown to us. and so I think having a way to in a joint way describe this being the level of pace we're comfortable with or this being exceeding the pace of of development as as it relates to RSI is going to be super important. And that's where I think you see a lot of the researchers at Closource Labs calling for joint conversations between governments today.

37:14 >> What do you think landscape will look like going from from here? now that we have lots of different capabilities and capable models as well as like you know countries that care about different developing I mean everyone cares about RSI for sure but like on on the bio side or like the the cyber side people care about you know slightly different different things whether it's more offensive defensive and so on like what do you think the landscape will look like and how do you think about developing new benchmarks to to keep up with with that? Yeah, I mean we're we're hyper focused on on building benchmarks that capture the frontier. And so in so far as we see new places for capabilities or risks at that frontier, we want to make that an actually well doumentable evaluation on Val.AI.

38:03 and and I think it it takes have increasing coverage over time. You for instance I think in in cyber security a lot of our historical work has been done around code vulnerabilities or memory leaks that may exist in code. but actually a lot of the the biggest concern or risk is in the infrastructure level. and so these are not things that are expressed in code but take simulating larger environments of enterprise cloud infrastructure or or even grid infrastructure for us to be able to say this is what the offense or defensive capability of models is.

38:32 and and so making sure evaluations are reflective of those new places is is really important for what we do at VLES. and we believe that the the most valuable form of this business will be one that's incentive aligned around doing really high quality evaluation not supporting the intelligence development process or the process by which the models can actually improve on that side over time. >> Awesome. Thanks for coming on the podcast. It's been a great episode.

38:56 >> Thanks so much for having me. >> Thanks so much, Ryan. Thanks, Ben. >> That was fun. Thank you.