Inside the Race to Measure Frontier Intelligence

Key Takeaway

Stop choosing AI tools by reputation or leaderboard rank. Pick one recurring task you do each week—drafting reports, analyzing data, or writing code—define what “good” looks like, then test two models or agents on the same real task. Track output quality, time saved, latency, and total cost. Keep the winner for that workflow, but rerun the comparison as models and tools change. Your own small, private evaluation is more useful than a generic public benchmark.

Episode Overview

A16Z’s Ben Horowitz and Jennifer Lee speak with Val’s founder and CEO, Rayim Krishnath, about why measuring frontier AI is becoming as important as building it. They discuss the limits of public benchmarks, the value of independent and private evaluations, and why enterprises need task-specific evidence to measure AI ROI, cost, and risk. The conversation also explores how evaluations could inform AI safety policy and international coordination.

Full Transcript

Transcript of Inside the Race to Measure Frontier Intelligence from A16Z. Auto-generated from episode audio; may contain minor errors.

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 was 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, 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.

Evaluations has 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 or 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? AI models keep getting better, but the tests we use to measure them can become obsolete almost as quickly.

In this episode, I'm joined by A16Z's Ben Horowitz and Jennifer Lee for a conversation with Val's founder and CEO, Rayim Krishnath, about the increasingly difficult problem of measuring AI. We get into why public benchmarks can give a distorted picture of model capabilities, why independent evaluation matters, and what it takes to test a new model in a few hours before it launches. But this is becoming about much more than model leaderboards. As companies spend more on AI, they need to know which models and agents actually perform best for their own work, and whether that intelligence is worth what they're paying for it.

Rayim also explains why benchmarks need to evolve alongside the models, how Val's is measuring recursive self-improvement, and why evals could eventually become a shared language for AI capabilities, risk, and policy. So I'll start a question from when Val 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 keep us on the frontier and help model labs continue the hill climb, take us back to the inception of Val's and what you see was missing in the market then.

Yeah, I mean, so I had a background doing research, 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're seeing is there are actually many interesting models coming to market, they weren't all coming from open AI, and then also in particular, it was harder than ever to actually ascertain what was newly capable with the new models.

And so in kind of a first principles way, what we realized is that there would need to be some third-party company that solely existed to build really high-quality evaluations and benchmarks to be able to discern what was newly 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 realized by many different parts of the industry. I guess one of the obvious questions is why do you think the labs can't do this by themselves, because they know the best of where the models are hill-climbing on and what is missing capability-wise, why can't they be the benchmarking stores?

Yeah, I mean, internally, they do build a lot of great benchmarks, and that's what drives model progress. But I think there's an issue when 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 Lama 4, that was a bit of a disaster. And interestingly, what we saw is that on our held-out private benchmarks, the model is actually underperforming, but on all of the major public benchmarks where the questions and rubrics are actually open source, 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 higher-quality, higher-signal benchmarks. But I think it speaks to a broader concept, which the labs, I think, understand that they would like to see a rational buying market. They would like to see that when they invest billions of dollars to build a new model, there are actually substantive ways they can point to evidence and say, we're advancing in these ways. And it's not just entirely self-reported to justify that investment.

And so you also see instances of Demis and others in the industry calling for an ecosystem of third-party evaluators. And what's the historical analog that you have in mind here? There are rating agencies, audit firms. What's the right comparable? Yeah, I think there's honestly lessons to learn across the board. And every time a new trillion-dollar industry emerges, 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 be borne out.

We think about ourselves as trying to sit on both sides of the market. So there are mechanisms by which labs need to prove that new models are very capable. But there are also parallels where enterprises need to figure out what adoption strategy is going to mount to the greatest ROI for them. Yeah. 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 all-nighter versus it being automated?

Take us through that. It's honestly been a journey. And I think the real goal at Northstar we think about is we never want to be kind of a lagging indicator or a delay to a model release. And so that means we have to move really, really quickly and extract the most possible signal with the rate limits or capacity that we have. And so early on, what this looked like was my co-founder, Langston and I pulling an all-nighter 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 massively distributed way, running effectively the maximum possible rate limits with every model we get access to. And we also have this internal system called Steve, Steve, the Economic Val's employee. And so that's been a mechanism by which we're able to actually take more of the human work over time and put it into Steve. How do you deal with the kind of issue that it's a little bit of an AI complete problem in that we still aren't really good at evaluating humans or we haven't agreed on it?

There are things like IQ tests, there's EQ, there's 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, of course, models are really good at hacking the benchmark for the proof. And so how do you think about that issue and what's the limit of what you can achieve? And then within that, how are you going about it? I mean, I think the honest answer is that it's forcing a lot of the more fuzzy or distributed forms of evals to be made explicit.

What is really the distinction between an associate and a partner at a law firm? And there isn't a clear test or an eval for that in the human world. And so we have to first establish a lot of that in these different enterprise or real world workflows for us to be able to test models in the same way. And I think long term, that will be actually the biggest bottleneck, our ability to take companies and their evals and make them legible, because that's how we'll figure out what signal we hill climb on and where we actually adopt.

Very interesting. Actually, maybe one question for you, Ben, on just like how the industry has formed before intelligence came through, like we're now measuring something that's very fluid versus before, like when we were talking about enterprise software, there's Gartner rating on like 70 different metrics, like you can sort of stack rank on the quadrant to say this company has these features covered, these features not. But now it's like very jack frontier. That's very hard to measure given industries. What do you see? 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 missing pieces going forward? 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 used to be X are now R, and so forth. And then what's PG 13, all that kind of thing. And there's no, the famous line is why I know when I see it, and I think that this one, I just think it's going to be necessarily fuzzy, but there will develop norms over time.

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 that 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 one, it's hackable, and then it's too narrow. I think when you also look to some historic analogies, there's a lot of lessons that you can take from them as well on what's gone wrong, what we need to avoid.

I think, for instance, at Val's, one very early decision we made was the decision to never sell training data to labs. It's often a place that we're pushed when we start working with a new lab to actually source and sell for them a bunch of training data. Yeah, that's a lucrative business. Yeah. And actually, a lot of that industry has now built these gimmick-style benchmarks as a mechanism to sell their data. And so that's become kind of their go-to-market as well. But 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 supporting the company, you have a mixed incentive structure.

And then it just becomes pay to pass the audit, or in this case, pay to win the benchmark. And that's really not what the market benefits from 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 specific industries. Some of them are more like consumer mental health related. Maybe first just talk through what are the benchmarks that are most popular and most like read upon, and we'd love to dive into one of them as well.

Yeah, well, we've done a lot of work 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 vibe code bench 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 one benchmark we released recently I'm very excited by is our recursive self-improvement index. It's a topic which a lot of big labs have been talking about 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 apples to 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? Yeah, I think in an ideal world what you want to do is actually take a frontier model and have it train the next version of itself and see 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 which mechanisms and behaviors the models are able to do very good research work and build something new and where they're struggling.

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 the early diffusion model days like a cherry pick whichever image shows up the best and the most perfect like benchmarks as well. Like you pick something that's very popular but maybe already saturated and you rank very well 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 thinking.

I think it's a necessity 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. So insofar 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 labor market. There are new forms of labor that's required out of our population.

And so in the same way we should expect our benchmarks to keep up with the new frontier we want models to do. There's another component of retiring benchmarks which I think 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 retake the bar exam and get certified or if you're an architect you have to get your certification or 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 have is our set of laws.

As an 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 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 you think of evaluating the capabilities of not just the models and agents themselves 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 address like broader domains and tasks and so on. So how do you think about the additional parameters 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 and 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. 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 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 ImageNet which have millions of images you're trying to see a basic categorization for. So it's a one-to-one 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 full stack 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 work those evaluated with models. And do you think that it will become kind of a real time kind of mechanism, like so for something like Open Router, which Stripe just bought with Open Router, look to files and say, OK, 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, you know, Open Router 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. And so, you know, our effort in supporting enterprise and building evals has actually supported a lot of them and also adopting routers. 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 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 this customer 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 a small anecdote related to this actually, you know, was meeting with a company and the Fortune 10 and they the way that they've adopted cloud code has been with roughly a hundred dollar a day budget for their engineers.

And so what I was hearing is that this is actually fundamentally changed how work gets done in this company and that there is a rate limit which resets at 4 p.m. And so it's the most productive hours of work are actually now four to six p.m. when the rate limits reset. But then there's this dead period in the afternoon when people go on walks or get a coffee because they just don't have the rate limits. And so I think what's really illustrative there is that you see that there is a misvaluing of intelligence happening at every layer of the stack.

And so by that, I mean, you have engineers who have one hundred dollars worth of usage limits and they don't really know how to apportion 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 one hundred dollars per employee. They've actually recently increased it to three hundred dollars per employee. So almost an employee's worth of salary and tokens for them to use. And this is actually pretty arbitrary because it's hard to quantify what the right usage limit should be.

But then also Anthropic is running on pretty narrow margins to support this. And they have, you know, massive cost 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 and your costs, you actually have to justify the ROI much more keenly than you've seen over the last six months.

And over time, as we were talking about, I think a firm really is just its evals. And so the ability for a company to make its evals legible in order to solve this ROI calculus is going to be the reason why that company wins out over the competitors in the long term. And maybe just double click on that like similar question to why the labs can't do it themselves or requires a third party agency to rate it. I think it's a lot more understandable that, you know, you need this neutrality across the industry.

But for enterprise, they will argue that they know the task the best for their customers. Like what? How does like those come into product value? And maybe you can talk through sort of Bell Smith 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 are somehow still more foundation model labs getting getting constructed. And and each lab is also releasing more models than ever with many more hyper parameter options.

And they exist within a complex set of harnesses and agents. So the option and would even talk about specific intelligence, this new paradigm that's emerging. So 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 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 try and remedy that, we've started to release some products more openly for enterprises to use, the first of which is called Bell Smith. And so Bell Smith is focused on code gen, the area we're seeing to be the highest expanded and 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 Prado optimal or the highest ROI for them to use.

And actually, we use Bell Smith, a lot of Bells, 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 public ripple benchmark? I think today it's still very unclear whether the best open AI 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 have 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. MuseSpark is also very cheap 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 messy middle, it's actually very non-intuitive what's the right fit.

We're actually seeing in a lot of cases Sonnet is more expensive than Opus because it is so token hungry. And so I think if you were to operate based on use Sonnet where you feel like 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. What are some examples? I think coding is a sign for what's to come in every domain. And a lot of the primitives established there are carrying over to other places.

If you have a very good coding agent, chances are you have a model that can also make PowerPoint slides or DCFs in Excel and with a 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 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 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. Maybe just tag along the earlier question. How are you guys using ValSmith internally to evaluate what's the best coding model for Val's? Yeah, I mean, to be honest, this was actually born out of a problem that we saw as well. So I wanted to do a token maxing experiment and I was able to get unlimited access for our team for a month for some of the coding tools.

And so in retrospect, looking back, we had some we had a lot of insurance spending between one to two billion tokens a day. I think Pete Day was one engineer spending six billion. Yeah, it's also crazy because how much does that equal to two dollars? So, OK, and then I went back and did some math and it looked like in that month we spent roughly one point five million dollars worth of tokens. This is free, by the way. No, I don't want to. 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 50-50, it's 10x. And it was interesting to debrief and see the places where people were using agents and this kind of insecurity to use models all the time everywhere. And so what we were faced with is, OK, 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 for what teams and what projects? And so we ran this experiment of looking at the work that was done.

We looked through a lot of the traces. We looked through our GitHub repo and built out the Val Smith tool. And we found some pretty surprising insights. Like, for instance, the Cognition Devon tool is actually very token efficient. And so that's a place we've chosen to adopt more. 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 one and a half million dollars per month.

Very cool. So is the current operating mode that you're using one, I guess, more token efficient Harness Plus model? And then on top of that, people have some more flexibility to use token based for some higher or more challenging tasks? Yeah. So we have access to all the tools. We give everyone access to everything. But we auto issue recommendations for any GitHub issue or ticket for where to begin their session. 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 a second, because we talked about how quickly benchmarks become obsolete in policy. It's even worse in that laws move much slower relative to capabilities. Ben and Mark spend a bunch of time in D.C. and the policy makers to try to close that gap. So given that, who should define the standards here? Is it labs? Is it independent? You guys? Is it customers? Is it government? How should this work from a policy perspective? Yeah. I think the short answer is that everyone should be involved to some extent.

I think 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's been no material grounding to figure out what policy should cover. And so even when you have proposals from labs to have a third party testing company or ecosystem, it isn't actually made explicit what the behavior and maxim by which they work is. And so I view our role, especially early on, is to just be in evidence gathering mode 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 a more sophisticated conversation about policy. How do you think about who does what? Because the government actually did the first evals and is continuing to do evals in terms of, OK, what's at the frontier and needs to be regulated? So they started with 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 to put it in this 30 day wait period or 60 day wait period or whatever it is, and then what should happen in that wait 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? It needs to be put in some special box for a while to make sure it doesn't break into everything. How do you think about how that relationship works? I think there's effectively two countervailing forces that have to be considered. The first is the desire to move very quickly and ensure that the government process isn't slowing down the rate of technological innovation. 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 doesn't lag behind or slow down the pace of development. Do you think in terms of that already and developing your e-mails, 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 burnout now where models that are being tested for one cybersecurity risk are actually reward hacking and figuring out other ways to get around it.

But what we're trying to evaluate is our models aligned with user intent. And so in those places, we're actually finding evidence that models are exhibiting behaviors 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 should government agencies control and do themselves and where they should partner trust private companies to take care of? And where do you see the gap that's happening today? Yeah, so 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 cybersecurity risk and so forth. And so I think what the government needs to do is go, OK, 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, OK, can a 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 ill 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.

Um, you know, and kind of 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 gonna 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 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?

Um, because, you know, every set up in again, like enterprise set up is very different. Like, um, 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 Facehug. Today, we still talk about, you know, what happened with Fable and AWS for like two months. But a lot of times, like, you know, that's not really how the model is being deployed, the environment they're running on is very bespoke.

So how to like, you know, really bridging those thoughts and again, set up 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 guardrails and put it down to the ground so that people can, like, you know, have the confidence using the models. To say more about how exactly the policymakers should work with the evaluator. What information do they need? How should the relationship work so that it's most effective?

Yeah, I think in the first order, there should be a mechanism by which insights and data can be passed directly to relevant people in government. 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 also track through what will be problems in the future. I think things are moving very, very quickly. It's hard to predict 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 up to the people in the legislative branch to decide where they want to see policy. And so it's not really our place to give recommendations like that. But if they see that there is significant risk in, say, mental health for people under the age of 18 or biosecurity 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 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.

I love to probe on another angle just around geopolitical. I often see evals being like a representation of sort of the value of the model, the model developer. You kind of develop this rubric of what's embedded in the model. And, of course, different countries and labs in those countries care about different things. I mean, I'm born and raised in China. I use a lot of Chinese open source models, too. You still cannot let them just go freely and talk about CPC and all history there because what happens in China.

So how do you think about how evals, I guess, and benchmarks play a role in like standardizing or like being treated by different model labs from different places? Yeah, I mean, to be honest, 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 replicate this data ensuring process and train these very large models. 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 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 where we're going to collectively align around the risks. You know, I think there's actually a lot to learn from nuclear here as well. I think Reagan had this line, trust but verify. And so I think we're starting to see signs of trust in that Xi 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 have the right number of nuclear warheads. And in that example, there were also flyovers. So some extent by which a country could audit another country's nuclear stockpile by having flyovers. And so I think similarly, if there is concern about the societal or even existential risk of AI, it will necessitate us constructing the shared language of evaluations to do the verification process. 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? Um, you know, because now you're dealing, you're not dealing with enterprise 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. 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 biosecurity will become even more important over time.

And so there are clear places where there will be mutual interest in aligning around ways to prevent conflict around cyber bio. Um, in my opinion, I think long term what's actually gonna be the most interesting is the recursive self improvement possibility. And that's a place where you could see one country or one company kind of run away with it and produce models that we don't know much about or operating in ways that are unknown to us. Um, 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 development 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 CloseSource Labs calling for joint conversations between governments today. 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 the bio side or like 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 keep up with that? Yeah, I mean, we're hyper focused on building benchmarks that capture the frontier. And so insofar as we see new places for capabilities or risks at the frontier, we want to make that actually well documentable evaluation on VALS.ai. And I think it takes have increasing coverage over time. You know, for instance, I think 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 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 even grid infrastructure for us to be able to say this is what the offense for defensive capability of models is. And so making sure evaluations are reflective of those new cases is really important for what we do at VALS. And we believe that 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. Thanks so much for having me. Thanks so much, Ryan. Thanks, Ben. I was fun. Thank you. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X at A16Z and subscribe to our substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode.

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