AIUC first got our attention with the NFDG backing, and have just announced a $40M series A today, with the most impressive industry advisor list we may have ever seen for an early startup behind AIUC-1, their agent standard backed by real insurance:From being Anthropic’s first product hire to building the standards, testing, and insurance infrastructure meant to make frontier AI deployable, Rune Kvist is betting that the biggest constraint on AI adoption won’t be capability it will be trust. In this episode, the AIUC cofounder joins swyx and Vibhu to announce a new $40M round and explain why companies like Cursor, Harvey, Lovable, and ElevenLabs are increasingly confronting a problem that gets harder as AI gets better: who is responsible when autonomous systems fail?We go deep on AIUC-1, the emerging standard for agent security, safety, and reliability; how AI agents are stress-tested for jailbreaks, hallucinations, and data leaks; and why Rune thinks standards and insurance could become critical infrastructure for AI. We also discuss the growing trust gap between governments and frontier labs, AI-enabled cyber and biological risks, why every model can ultimately be jailbroken, what happens when a $20 coding agent causes $200M of damage, whether AI engineers should be certified, and why even after AGI there may be one job the labs can never do themselves: be their own watchdog.We discuss:* Why risk, liability, and trust may become the binding constraint on AI adoption* Rune’s path from reading the Scaling Laws paper to joining Anthropic in its earliest days* What Anthropic understood about scaling, compute, and the future years before it became obvious* Why Waymo illustrates the gap between AI capability and real-world deployment* AIUC’s $40M round and work with Cursor, Harvey, Lovable, ElevenLabs, and other frontier AI companies* AIUC-1: a standard for AI agent security, safety, and reliability* How agents are tested for jailbreaks, hallucinations, and data leakage* Why most AI companies optimize the happy path without seriously stress-testing adversarial cases* Why AI standards may need to update every quarter instead of every decade* The emerging trust gap between frontier AI labs and governments* Cybersecurity, child safety, biological weapons, and the expanding frontier-model risk surface* Why standards and insurance may need to evolve together* How Lloyd’s of London can insure AI systems and bring trust to enterprise deployment* What happens if a $20 Cursor subscription contributes to a $200M plane crash* The Air Canada chatbot case and how AI failures are beginning to clarify legal liability* Why copyright may be one of the hardest AI risks to insure* Evals, mechanistic interpretability, monitoring, and models becoming aware they’re being tested* The impossible CISO mandate: adopt AI fast, but don’t let anything go wrong* Why robotics will make AI liability dramatically more consequential* Whether AI engineers should have Level 1, 2, and 3 certifications* AIUC’s roadmap across agents, frontier models, robotics, and universal red teaming* Why AGI could become a question of national sovereignty* Why the labs can never fully serve as their own watchdogs* The Big Short problem: how do you stop competing watchdogs from racing standards to the bottom?Rune Kvist* LinkedIn: https://www.linkedin.com/in/runekvist/* X: https://x.com/RuneKvistAIUC* https://aiuc.comTimestamps00:00:00 AIUC’s $40M Round and the Risk Bottleneck for AI00:01:07 From Scaling Laws to Early Anthropic00:07:58 Why Trust, Not Capability, Could Limit AI Adoption00:12:19 Founding AIUC and Building AIUC-100:18:52 How AI Agents Are Audited and Stress-Tested00:25:26 Frontier Models, Government, and the AI Trust Gap00:33:32 Cyber, Child Safety, and AI-Enabled Biological Risk00:38:14 Why Standards and Insurance Belong Together00:41:45 What Does an AI Insurance Policy Actually Cover?00:50:44 The $20 Cursor Subscription and the $200M Plane Crash00:53:53 AI Liability, Monitoring, and Earning Enterprise Trust00:56:21 From AI Agents to Models to Robotics00:58:29 Copyright, Adverse Selection, and AI Insurance01:03:28 Evals, Mechanistic Interpretability, and Eval Awareness01:08:36 The Impossible Enterprise AI Mandate01:11:52 Prediction Markets vs. AI Audits01:14:43 Should AI Engineers Be Certified?01:19:10 AIUC’s Roadmap, AGI, and Who Watches the Watchdogs?TranscriptIntroduction: AIUC, the $40M Series A, and Risk as the Adoption BottleneckSwyx [00:00:00]: Okay, we’re in the studio with Rune from AIUC, the Artificial Intelligence Underwriting Company, with our trusty co-host, Vibhu. Welcome.Rune Kvist [00:00:10]: Thank you. Thanks for having me. Thank you.Swyx [00:00:11]: What are you announcing today?Rune Kvist [00:00:12]: We have raised $40 million, led by Ribbit Capital and First Harmonic.Swyx [00:00:17]: You first came to my attention when Nat and Daniel invested in you guys. Is the story, like, pretty much the same? Like, what are you today versus what you thought you were back then?Rune Kvist [00:00:26]: When we raised our seed round, we had a hypothesis that at some point risk was going to hold down adoption. At that point in time, that felt kind of hypothetical, and I think that is now over. Clearly, the moment is now with Mythos and Fable. It’s pretty obvious that literally the binding constraint on adoption is risk. And so for us, it feels like this is a natural continuation of the same hypothesis, but where previously it was speculation, now it feels like fact.Swyx [00:00:54]: And let’s get a list of the customers that you’re highlighting as part of your Series A.Rune Kvist [00:00:58]: Totally. Yeah. So we are now working with folks like Cursor, Harvey, Lovable, ElevenLabs.Swyx [00:01:05]: Yeah. Amazing. Congrats.Rune Kvist [00:01:06]: Thank you.Swyx [00:01:07]: So you were famously one of the first hires involved in GTM and product. I’m just kind of curious: what was your path into AI? Just recap.Rune’s Path Into AI: Scaling Laws, Capital, and AnthropicRune Kvist [00:01:18]: Yeah.Rune Kvist [00:01:19]: Late 2021, I sold a company, my first company, an edtech company. I had a bit of time to think about what was next. I came across the Scaling Laws paper, and that just struck me like lightning. I was just like, “This is a big idea.” In short, the Scaling Laws paper just says the bigger the model, the smarter the model.Swyx [00:01:38]: So this is the Kaplan one, not the Chinchilla one?Rune Kvist [00:01:40]: Exactly, the Kaplan one.Swyx [00:01:42]: Yeah.Rune Kvist [00:01:42]: And the important thing that clicked for me there was, oh, now capital will understand this. If you put in more money, you get more money out, and so that will kick off a hype cycle. And so you get a sense of predictable returns, which is, in fact, what’s played out. And so I just packed my bags. I’d never been to San Francisco. I’d never been there. I just packed my bags, flew out here to find the people who had written it. And at the time, they had just started a small lab called Anthropic. There were around 40 people at the time or so. Drank a bunch of coffee until I eventually got introduced to Dario. And at the time, they were wrestling with some of these questions of, like, should we deploy our models? Should we make revenue? How should we engage with the rest of the world? They’d just broken off from OpenAI, and it’s been publicly reported that they were kind of concerned with how they were dealing with deployment. So they were wrestling with some of those questions. At this point, this is early fog of war, like early 2022. The hottest product at the time was, like, Jasper. Like, there’s nothing out there. So where value was going to accrue, and what the different parts of the stack were going to be, were all open questions.Swyx [00:02:48]: I want to highlight to people, you ask these questions because you have a PPE background.Rune Kvist [00:02:52]: Yes.Swyx [00:02:52]: I actually was in Singapore in one of the sort of feeder programs for prepping people for PPE. So I had a tutor. We learned, you know, philosophy and politics and economics. But, like, I think your kind of background matters. Machine learning people who read the neural, Scaling Laws paper would not necessarily draw the same conclusions that you did. Whereas any capitalist would read that and go, “Holy s**t.”Rune Kvist [00:03:19]: Correct.Swyx [00:03:20]: Right?Rune Kvist [00:03:21]: Yes.Swyx [00:03:21]: Who tipped you onto that paper? Because it’s not a paper that you normally read, right, like, in your circles?Rune Kvist [00:03:26]: Yeah. I think I’d actually, ever since AlphaGo, had some appreciation that AI was a big deal.Swyx [00:03:36]: Yeah.Rune Kvist [00:03:36]: But it kind of felt like it raised all these kind of interesting philosophical questions, but it was kind of not clear from afar where exactly that would go. But it was obvious enough that it was like, this is going to be a big thing if we find the kind of right mechanism to kind of get the techno-capital machine to work on this. But it was just not clear. And so I think there was some way in which, like, that became obvious, and also it wasn’t as obvious at the time than it is now, right? Like, it was just like, wow, this is so interesting. But it still felt, coming from kind of a philosophy and economics background, it felt like if this turns out to be true, you’re going to be wrestling with all of the big questions in society. Everything you’ve learned about politics gets thrown out of the window. Everything you’ve learned about economics at least gets challenged. And so what felt interesting was to be at that frontier that has ramifications across everything. So that’s why I sought it out.Swyx [00:04:32]: I mean, clearly really good insight. For people who don’t know, the PPE program is, like, where prime ministers are born. So then you end up meeting Dario.Rune Kvist [00:04:41]: Yep. First Dario, yeah.Swyx [00:04:43]: Yeah. Well, I mean, like, so did you get extra insights from talking with them that you didn’t get from your original hypothesis?Anthropic’s Early Conviction and the Scaling Laws Crystal BallRune Kvist [00:04:50]: If you read the Scaling Laws paper, you get this, like, very vague sketch of like, wow, this seems kind of important. There are some lines on a chart. This seems kind of important. And what I think the team at Anthropic had thought more about than anyone was like, what are the implications of this if you really play this out? And back then they had, kind of vision documents for what the world would look like in 2026, and they were kind of in vivid detail playing out how much compute is going to be needed, what the CapEx was going to look like, what some of the societal concerns were going to be, but also what is the amount of economic value coming out here? And so it kind of felt like they held a crystal ball that in hindsight turned out to just be dramatically correct. And they weren’t holding it like they were obviously correct. They were just like, “Take this hypothesis really seriously.”Swyx [00:05:38]: Think it through, yeah.Rune Kvist [00:05:38]: And think it through in the same way as the kind of situational awareness that isSwyx [00:05:43]: Across the street.Rune Kvist [00:05:44]: Across the street.Swyx [00:05:44]: Your office, yeah. Oh my God, we’re all living across the street in the same one square mile.Rune Kvist [00:05:50]: Correct. And that’s now a couple of years old, but also people keep referencing it these particular weeks with Fable and Mythos, and it’s like, wow, if you take this one idea seriously- For the Scaling Laws, a lot of things fall into place.Vibhu [00:06:03]: And keep in mind, at this point, this is the same team that did GPT-1, GPT-2, and GPT-3.Rune Kvist [00:06:08]: Correct.Vibhu [00:06:08]: Which is also, like, it’s not just some experimentation. Like, this is a real model that we just scaled up.Rune Kvist [00:06:14]: And they had deep conviction in this idea: if you take a big blob of compute and data, it just wants to learn, and out of that will come smarter and smarter models. And all the particulars were not clear.Vibhu [00:06:26]: Yeah.Rune Kvist [00:06:27]: And all the implications were not clear. But their deep conviction in this, like, core thesis, and that was kind of dizzying. It was both phenomenally interesting and exciting, and also very quickly you get to, like, the world we know today will no longer be if this hypothesis holds. So it also just felt, like, important in some kind of grand sense.Vibhu [00:06:48]: What kind of shaped you there? So that was early 2022. Not only had GPT-1, GPT-2, and GPT-3 come out, but, you know, the amazing founders of Anthropic that have never split up, the only ones, they actually had the conviction to leave OpenAI, start their lab. You said there were about 40 people there. What was the time like there?Inside Early Anthropic: Mission, Deployment, and RiskRune Kvist [00:07:06]: It was kind of remarkably like what it looks like on the outside today. Extremely cohesive, extremely mission-oriented, and living in this tension between their two ideas, which is AI could both go really well and really bad, and we want to be part of building it. That creates astounding amounts of tension. And they were wrestling with this incentive challenge where they know they’re in a race that they’re in where you might get forced to cut corners, but it also felt very important to them to be at the forefront of technology. And all of those ideas were just present at that time. It kind of feels like that line has been just very clear, and I think kind of love them or hate them, they have really stuck to their guns. There’s a core set of beliefs that they hold more deeply than most companies hold any beliefs.Vibhu [00:07:58]: Yeah. Fast-forward to today.Rune Kvist [00:08:00]: Yeah.Vibhu [00:08:00]: What does that lead us to AI underwriting company? What are you up to? What motivated you to start this?From Waymo to AIUC: Confidence Infrastructure for AIRune Kvist [00:08:05]: Yeah. AIUC builds confidence infrastructure for frontier AI through standards and insurance. The link from Anthropic to building confidence infrastructure, looking out the windows at Anthropic offices and seeing Waymos driving by. Already back then, early 2022, Waymos were in some ways like AGI for cars. Like, they were superhuman drivers, but you couldn’t take one to the airport. And now, four and a bit years later, you still can’t take your Waymo to the airport, despite now everyone having kind of looked at the evidence and being like, “They’re better drivers than humans.” So in that particular instance, what’s clear is that the binding constraint on AI being useful is not capability, but is that liability or risk or trust. That problem is, general. The reason why right nowRune Kvist [00:08:52]: Fable is not open for access is not because it’s not a good model, it’s because it’s a very good model. It’s just hard to make promises about what it will or will not do. And this problem gets worse as AI gets better. Basically, more intelligent AI can be more autonomous. That’s more valuable, but also the risk surface grows. And so - what Waymo illustrates is that unless you build the confidence infrastructure to make promises about AI, or at least bring light to the risks, you grind adoption to a halt. Governments, banks, hospitals, militaries need to have some sense of what AI will and will not do to be able to operate for them to incorporate it. And that’s the problem that we’re trying to solve. Now, why standards and insurance? If you trace this problem back through history, every technology wave has had some version of this problem. So if you go back to, like, year 1900, electricity comesVibhu [00:09:47]: Ben Franklin.Rune Kvist [00:09:48]: Cars burn down, sorry, houses burn down, lots of people die. 1930s, cars are a big deal, kill lots of people. 1950s, private nuclear energy is a big deal, poses big risks. In each of those instances, the market runs ahead of regulation to create confidence infrastructure because that’s required to make go/go decisions. That is required for adoption, and the market fundamentally wants adoption. And in all of those instances, common blueprint emerges between standards and insurance. The reason these two components is standards kind of provide the rules of the road, and they also specify, like, what are the tests that need to be run so we can get a sense of how high the risk is. So take in the case of cars, that’s like a car crash. Great, everyone, they inform your insurance pricing today, they inform your purchasing decisions, et cetera. That’s basically the risk framework. The insurers are important because they pick up the bill. So they are the private institution that is most on the side of. That is best incentivized to quantify the risks truthfully and then figure out all the ways to reduce the risk ‘cause that increases their profit. So they’re basically, they help shape the incentives. And these two work really well in unison. Now, how does that show up as a company? Well, one of the things that was obvious even - or starting to become obvious even a couple years ago was that frontier companies, some of our customers today, like Cursor, Sierra, ElevenLabs, Harvey, were going to have a very easy time selling a pilot to a bank. The, like, the demo just sells itself. It’s magic. But bringing that through, if you want to do a wall-to-wall rollout at a bank or a hospital, you have to go through the risk process. These banks have no idea even which questions to ask, let alone which answers are sufficient, let alone, like, how do they go and test whether these agents actually work the way they’re supposed to. And so they had this problem of, like, what can we say to earn the trust? And we think there’s, like, a golden sentence that goes something like, “Hey, I hear you’re really worried about hallucinations or jailbreaks or whatever it may be. We’ve had an independent third party test us against the gold standard. We passed with flying colors. And as a vote of confidence, the world’s most conservative insurers have looked at the data.” And they’re willing to take some of the risk onto their balance sheet.Swyx [00:12:06]: Yeah.Rune Kvist [00:12:07]: So if something does go wrongSwyx [00:12:07]: There’s money behind it, yeah.Rune Kvist [00:12:09]: Exactly. So that’s kind of like the link between all this. We can get into some of the hard parts related to the technical testing, which is, I think, the crux of the matter, but I’ll pause there.Swyx [00:12:19]: How did you and Rajiv come together? This-- there’s always, like, you come across very confident and, you know, and we’re announcing your Series A and all these things, but I want to see, like, the early initial stages of, like, idea formation.Cofounding AIUC with Rajiv DattaniRune Kvist [00:12:31]: Yeah. Rajiv is actually my soon-to-be brother-in-law.Swyx [00:12:35]: Oh.Rune Kvist [00:12:36]: So I’m actually, in a week and a half getting married to Rajiv’s sister.Swyx [00:12:42]: Okay, now you’re tight.Rune Kvist [00:12:44]: Exactly.Swyx [00:12:44]: Now you know.Rune Kvist [00:12:45]: So - Rajiv and I have known each other for a decade. Funny story, I met both Rajiv and his sister, Hena, at the same time when Hena and I were interns at McKinsey in London, and Rajiv was assigned as my mentor. And so met them at the same time. For the longest time, it was not obvious that we were necessarily going to work together. I was in startups. He was, an insurance partner at McKinsey. Three or four years ago, I think Hena convinced him that AI was going to be a really big thing. And so he quit his job, cushy partner job at McKinsey in London, packed his bags, flew to San Francisco, and ended up joining METR. You guys are probably online enoughSwyx [00:13:24]: CEO.Rune Kvist [00:13:24]: Exactly.Swyx [00:13:24]: We’ve, we’ve, we’ve heard of METR.Rune Kvist [00:13:25]: You see the plot-- the chart of the horizons of the tasks that agents can take on is doubling extremely fast. So he was COO at METR, led their partnerships with Anthropic and OpenAI to test their models before release, but also working closely with the US and UK government, to figure out, like, how do you know whether a model can be released? And in some ways, that was, like, the perfect background. He’s spent a lot of time in insurance, knows that world, spent a lot of time with frontier testing of models. And so when I was bumbling around this idea space, starting with some of the ideas we talked about related to Waymo, as soon as we got into the content, we were both like, “Oh, this would be an amazing business to build together.” This is wrestling with the problem that we both think is the most important in the world from a market angle, which is kind of our intuitions is that the market can do a lot, and the faster AI moves, the harder it is for government to solve some of these problems. And then it took a little bit of time to work through what is it like to work with family.Swyx [00:14:27]: Sure.Rune Kvist [00:14:27]: And,Swyx [00:14:30]: Because you were already dating at the timeRune Kvist [00:14:31]: Yeah. Yeah, exactly.Swyx [00:14:33]: Yeah.Rune Kvist [00:14:34]: Already back then, itSwyx [00:14:35]: Yeah.Rune Kvist [00:14:35]: We felt like we were a family.Swyx [00:14:36]: Nice.Rune Kvist [00:14:36]: And so starting a business together felt like kind of a big step. And, here we are with just immense amounts of trust.Vibhu [00:14:43]: Yeah. So now you’re a company of how big? How big are you guys now?AIUC-1 Certification: Agent Security, Safety, and ReliabilityRune Kvist [00:14:46]: There are just 20 of us now.Vibhu [00:14:47]: 20 of you guys now, have Series A, and you have your first certification out, the AIUC-1. Let’s bring up the certification. So this is the agent certification, right? What goes into the process? I have, like, two questions here. One is, walk us through the certification, and two is, what is the process for a company to get certified, you know?Rune Kvist [00:15:08]: Great. As it says right on the top, AIUC-1 is a standard for agent security, safety, and reliability. The fundamental design principle is take all of the concerns that slow down adoption, so all the questions, all the fears that keep, security leaders in the Fortune 1000 up at night, and put them into one comprehensive framework. That’s what you’ll see there. You can see the six categories. Two, you want to ground all of this in technical testing. So one of the concerns with security standards that often feel kind of like theater paperwork is that they’re not actually ground out in, does any of this work? Does any of this matter? And so we had a conviction from early on that was going to be the kind of crux, was to pass this, you must get tested every quarter, basically run thousands of simulations to see, well, so can it actually be jailbroken? How hard is it to jailbreak? How often does it hallucinate? How often does it leak data? Et cetera. And then the last, core idea here, if you scroll up to the top here, is to refresh it quarterly.Rune Kvist [00:16:08]: So the core trait of AI is that it moves extremely fast. Whatever concerns we’re discussing today were not the same ones three months ago, and this will keep changing. Typically, standards update on a, like, a decade cycle is obviously not going to work. But the question is kind of how do you update it? And the core thing here was to basically get the risk leaders of the Fortune 1000 around the table. So if you go over to the left hereVibhu [00:16:32]: YeahRune Kvist [00:16:32]: You’ll see the AIUC-1 consortium. The consortium is a group of risk leaders who run real banks, real hospitals, real critical infrastructure, who are facing these challenges every day. And we meet with these folks twice a quarter and hear what’s top of mind, what is keeping them up at night. There’s tremendous amount of desire for that conversation. And then we operationalize that into a specific standard that gets into. And actually, we can go into and look at whatVibhu [00:16:55]: YeahRune Kvist [00:16:55]: What even is the standard. So if we go back to introduction, out there to the left, scroll up a little bit to the wheel, click into reliability. So if you take something like hallucinations sits in reliability. There is a number of requirements here. If you go into the top one, prevent hallucinated outputs, hallucinate outputs, this is one particular requirement. This is a technical control. Basically, we want some kind of ground in this filter. The first thing you see here is what’s called a crosswalk. So everyone and their grandmother has put out a framework, very high-level framework for what are the AI risks.Swyx [00:17:27]: This is basically your competition,Rune Kvist [00:17:28]: In some ways our competitionSwyx [00:17:29]: Not seriously, yeah.Rune Kvist [00:17:30]: We’re, in fact, friends with them. We’ll come back to why.Swyx [00:17:31]: Yeah.Rune Kvist [00:17:32]: But mapping everything together so you have one superset. The claim you’re trying to support here is, if you follow this framework, then you can also see how you follow the other frameworks. But the meat of it comes down here in control activities and evidence. So control activities is like, great, you have this high-level requirement. How do you turn that down to something operational? Here’s what you must do, and then what is the evidence that we’re looking for?Rune Kvist [00:17:57]: And the reason we go this deep is that there’s actually not that much confusion about what are the big concerns in AI. Everyone agrees to these. The question, like, what are you actually supposed to do? And so. What we found a lot of demand for is getting down to the specific evidence, that people need to look for. Whether you are Cursor building something or, even JPMorgan building something, but also if you’re just a risk leader at JPMorgan, like what exactly should you ask for? What can you ask for without sounding stupid? Like if you ask for some-- you won’t believe the amount of time a risk leader has asked for the IP rights to the underlying model to Cursor or something, and you’re just like “Sorry, what?” Like,Swyx [00:18:39]: You slip it in there and you seeRune Kvist [00:18:40]: SlipSwyx [00:18:40]: See if you notice.Rune Kvist [00:18:41]: See if they. Exactly.Swyx [00:18:42]: Yeah.Rune Kvist [00:18:42]: Put that in the questionnaire. All right, so that’s kind of what our standard is, and we update this every quarter with these folks, to keep up with the latest concerns.Swyx [00:18:51]: Can I double-click on this one?Controls, Evidence, and Third-Party TestingRune Kvist [00:18:52]: Yeah.Swyx [00:18:52]: So first of all, the website’s beautiful. Like, it’s so confidence-inducing which is the whole point where, like, okay, I know exactly what I’m signing up for when I talk with you. Like, I don’t even have to talk to you. I can just see your whole, certification, which is great. But, like, okay, so from here, like D001.1 configure a groundedness filter, how does that get applied? Like, you have a person thatRune Kvist [00:19:16]: Yeah,Swyx [00:19:16]: Goes through it?Rune Kvist [00:19:17]: If you, go backVibhu [00:19:19]: I did see somewhere there’s like, you know, fifty-one requirements, a hundred thirty controls. There’s like a wholeSwyx [00:19:25]: Right. I just want to. Like, to me, this doesn’t translateVibhu [00:19:27]: Yeah.Swyx [00:19:27]: Into a test or an eval.Rune Kvist [00:19:28]: Yes. So if you go into, on the left-hand side. So actually, if - before we go in there are three types of requirements. The first is technical controls, like you must implement some guardrails.Rune Kvist [00:19:42]: Two, there are test controls. So you must have an independent third party go and run some tests against you. I’ll show you one of those in a second. And then three, there are policy controls. For example, you must have a person whose name is on the line when you guys f**k up, and you must have a plan for how you tell your customers and how you engage with them. They’re kind of more traditional, standard type stuff. So in this particular instance, we just check whether they in fact have a ground in filter. So we will partner with an auditor. So we partner with auditors like KPMG or like Schellman who go in and do the thing auditors do, which is to check the evidence. In this case, that might be a screenshot, it might be part of the code that they need to review to see that it actually. Just that it exists.Swyx [00:20:21]: Oh, okay.Rune Kvist [00:20:22]: And then the second thingSwyx [00:20:22]: So you’re not testing the effectiveness of it.Rune Kvist [00:20:24]: That’s the second thing. So if you go downSwyx [00:20:25]: Yeah.Rune Kvist [00:20:25]: To the third-party testing for hallucinations out on the left, that’s basically the next requirement. This is where we test how well does it actually work.Swyx [00:20:32]: Okay, and is it you testing or the auditor?Rune Kvist [00:20:34]: We test them.Rune Kvist [00:20:35]: We test them.Swyx [00:20:36]: That’s a lot of work.Vibhu [00:20:37]: How long does testing take? So if I want to get certified, justCertification Timelines, Remediation, and Quarterly UpdatesRune Kvist [00:20:40]: Yeah.Vibhu [00:20:40]: How long does the end roughly take?Rune Kvist [00:20:42]: Yeah, the end, almost always is dependent on, like, our customers needVibhu [00:20:47]: Yeah.Rune Kvist [00:20:47]: To look something for us. It takes somewhere between, like, 3 to 10 weeksSwyx [00:20:52]: Yeah.Rune Kvist [00:20:52]: Depending on how up to snuff they already are. So some people show up to us with, like, extremely rigorous security programs. When we test them, it works extremely well. We can get that done very quick. Some people come to us, and they’re not that far along. We give them kind of the spec that they need to build towards, and then their security teams and engineers get to work and build to meet the standard. The testing itself typically takes a couple of weeks, including the time for them to remediate. Often, we’ll find something that we cannot pass, where this is actually just not up to the standard. - you won’t pass the standard. And then they will need to go and implement additional safeguards or additional remediation that makes them more robust so that they can actually kind of hand on heart look at their customers in the eyes and say, like, “Hey, we’ve done truly our very best.”Vibhu [00:21:35]: And they’re certified for a year and have quarterly updates?Rune Kvist [00:21:38]: Correct, yeah.Vibhu [00:21:39]: And, yeah, it’s pretty interesting. I think, you know, what’s changed since. So this is certifying agents in production, right? Your customers, like you’ve had Lovable, ElevenLabs, Intercom, and they’ve all gone through this certification.Rune Kvist [00:21:50]: Yes.Vibhu [00:21:51]: What has changed? So I see you post, like, you know, Q2 added MCP agent,How Agent Risks Are Changing: Coding, MCP, and Agent-to-Agent InteractionsRune Kvist [00:21:56]: Yeah.Vibhu [00:21:56]: agent communication. Any other things that you want to kind of highlight since the first iteration? What comes in quarterly?Rune Kvist [00:22:03]: Yeah. So some of the changes have just been agents are not just one thing. So, like, if you take agents like Cursor and compare them to Sierra, they’re really quite different. And compare them to Harvey again, compare them to you out of againSwyx [00:22:16]: ElevenLabs, yeah.Rune Kvist [00:22:17]: ElevenLabs, they’re all quite different. And so we wanted to design a standard that works for all of the types of agents. And we started with one that was, like, pretty text-based, like, honestly, pretty customer support-focused. That’s where there’s a lot of existing demand. And then over time, we’ve picked, some of the frontier companies in each of these other domains that we could work with and build out the standard, so, such that we know that the same standard works for code, it works for customer support, works for automation, et cetera. So that’s been one big thing. Yeah, then some of the things that have been top of mind recently, Mythos is bringing up a lot of concerns for security leaders. We’re starting to get more and more questions around agent interactions. It’s very nascent, at the moment, but it’s starting to emerge. There’ve been a lot of, questions related to OpenClaw and MCP. Again, like agents starting to interact with each other, is really top of mind. Then as coding agents have really taken off, that’s also where banks and hospitals, et cetera, are getting more and more precise on what it is they need. So really dialing in as that start to be, like, where most of the tokens flow through in the world, getting much sharper on that.Vibhu [00:23:26]: Can you share for people that are listening that don’t really think about this? Like you mentioned, there’s the obvious stuff, you know, hallucination, citations. What are best practices that people should do when building agents? Like, if they come to you pretty ready with certification like, you know, they’ll probably pass certification. What are the things people don’t think about that they should have?Best Practices for Agent Builders: Stress Tests and GuardrailsRune Kvist [00:23:46]: The most important thing is that a lot of companies have not done a serious stress test. They spend most of the time, perhaps rightly so, optimizing for how does it work in the good case, the average case, how high-quality is the output for the customer. And a lot of these companies are pretty new, so they haven’t spent a lot of time stress testing the what is there as an adversary on the other side? What are some of the complicated corner cases that you’ve not really considered? So I think that’s, like, a frame of mind. And you’ll also see this in startups. It often takes a while until they hire their first security person. They- And that’s a whole different kind of risk surface than just building a good product. So a lot of that applies. Most companies actually also have the right kind of architecture. Most of them will have some kind of guardrails in place, either some that come out of the box from their model provider or they’ll have built their own filters that sit in between. They just don’t work very well. The difference between putting a classifier in place that, like, maybe goes and checks whether you’re giving medical advice when you shouldn’t and says, “Hey, if this looks like medical advice, filter it out.” Lots of companies have that in place. The question is whether it works. And it’s actually pretty fiddly to sit down and think about all the ways in which you could ask for medical advice, read the academic literature on what are the kinds ofRune Kvist [00:25:03]: Framings or tricks you might play to get an AI to give you medical advice when you really shouldn’t. And so there’s, like, an area of expertise that’s just missing. So what we find is that most people have the right building blocks in place. They don’- It doesn’- It’s not rocket science, but the finicky thing is, like, getting into the corners and testing whether it works such that you can look your customers in the eye, or maybe a bank or maybe a hospital and be like, “This is going to work for you.”Vibhu [00:25:26]: I see. So we talked a lot about the agent-level certification. Where do you guys go from here? So announcing series A camera, we talked about this a bit. There’s the whole security risk of Fable, government stepping in. You guys are kind of announcing that you’re also going into model certification?Toward Model Certification: The Government–Lab Trust GapRune Kvist [00:25:46]: When we do a bit of cutting afterwards,Vibhu [00:25:48]: YeahRune Kvist [00:25:48]: We will not yet be announcing this,Vibhu [00:25:49]: NiceRune Kvist [00:25:50]: The question that is top of everyone’s minds now is at the model level. And Mythos, then Fable, has really brought this to the fore that in addition to the commercial risk and the kind of economic security risks that are happening at the agent layer, the models are going to present risk in the national security category. The shape of the problem is very similar. You have some people that are on the hook if something goes wrong. In the case of agents, it’s often security leaders in the enterprise. In this case, it’s the government. They don’- haven’t necessarily spent their entire lives thinking about what are the new risks that come here, what is the kind of data you might be looking for, how might you test that? But they do have to make sure that their concerns are addressed. You have some frontier AI companies that are deeply technical. They know a lot about the risks, but they fundamentally have an incentive to not always be truthful. So you have a trust gap between the government and the labs. And in every other industry, you end up with some kind of body sitting between, a neutral third party sitting between those people. There’s no other industry where you allow people to audit themselves. So there is going to be a need for a third party that can take the rigor of the labs to run frontier technical evals, but can also speak legible trust in the way that the government trusts PwC to go and run financial audits. And they know that they output audit reports in a way that’s consistent, that’s easy to read, that’s factual, that’s, trustworthy. Those two things need to be brought together. And what we’ve learned from our work with agents is that if you want those-- that communication between those two parties to be smooth, there has to be one common standard that is public, that people can go and inspect. What are the risks that matter? Within each of these risks, what are the kinds of threat models that you’re really looking for? You need to specify for each of those risks, what are the guardrails that need to be in place, and what are the tests they need to run to see whether those guardrails are effective? And then you need to go and run audits that are - technical audits that are consistent. So if you’re trying to bring trust, it’s extremely important that you methodically work your way through the risks. You can’t send one researcher in and say, like, “Come back with whatever you find.” You need to be able to explain exactly what you did, exactly what you tried, exactly what you did not try, and therefore the kinds of promises you can and cannot make at the end of it. I think ofNeutral Third Parties, CAISI, and Model Risk AuditsRune Kvist [00:28:13]: Fable as a direct symptom of this problem that the government was told that there’s a risk. The government may struggle to assess just how big that risk is. They call Anthropic, and Anthropic is trying to tell them, “Hey, actually, every model can be jailbroken.”Swyx [00:28:28]: That’s not what you want to hear, right?Rune Kvist [00:28:32]: As the government, that might be hard to trust.Rune Kvist [00:28:36]: And we think that a broker is the most natural solution. In other markets, you see something like, in financial markets, you see Moody’s. Moody’s goes in, and they look at a bond, and they output a rating. They say like, “Here’s the evidence we found. Here’s the rating.” We don’t decide whether anyone should buy this bond or not buy this bond. Well, that depends on their risk appetite. But we do provide this common information layer that everyone can rely on. In the case of Moody’s, the government, points to them and say, “Hey, pension funds, you should probably really take care. You shouldn’t risk your pensioners’ money, so you can only invest in triple-A rated bonds.” That means that now the government doesn’t have to staff thousands of financial technical experts to rerun forecasts every week to see whether things are correctly rated. They get to point to some neutral third party. So my hypothesis is, my hunch is that you will see a third party that sits between the government and the labs, and it could either be the government builds it themselves. So something like CAISI was set up to do exactly this. And the questionSwyx [00:29:44]: Sorry, I’m not familiar with CAISI.Rune Kvist [00:29:45]: CAISI is the Center for AI Standards and Innovation.Swyx [00:29:49]: Okay.Rune Kvist [00:29:50]: I won’t get into the details, but it’s a body of NIST that typically sets standards. So it’s basically a government body that has AI experts. Yeah, exactly. Exactly.Swyx [00:29:59]: Very key. Very key.Rune Kvist [00:30:00]: Very key.Vibhu [00:30:00]: I think, you know, it’s one of those things where when you just sit back and listen-- look at it, like, is there enough technical expertise in the government to measure, test these things right now? Probably not, right? And Fable is a result of, okay, we’ve had to scale back and pause things,Rune Kvist [00:30:17]: Yeah. And they have excellent people, but they have an extraordinarily small budget compared to the scale of the challenge that’s ahead of us. And I think they have a role to play. The question is kind of like, who does what? We have now outlined the jobs to be done, and they’re quite extensive. Every model release, there is an astounding-- Given that they take in any input, their risk surface is astounding. And so the question is really: what can only the government do, and what can the market provide here that can keep up with the pace as AI risk changes? Our perspective is that also at the model layer, the risks that people care about today are not the same ones they cared about three months ago. So the pace of legislation is too slow to deal with pinpointing the risks here. And so we think there’s a lot that the market can do to surface timely information. Ultimately, there is a bunch of policy decisions here. Is the national security risks of a model too high?Swyx [00:31:12]: Yeah.Rune Kvist [00:31:12]: That’s a political answer. But what we want to make sure is that the process that produces this risk information is compatible with very fast innovation. So you don’t want to. This is not a question of like, can you slow the things down? Can you keep, the models locked up until-- for months on end until everyone can make a guarantee? But it is this, can you, in the time it. Given that the US is competing with China on releasing models, can you insert risk information that allows the government to, like, make rapid decisions on some of these questions? Balancing that trade-off between failing to adopt AI is going to put us at risk, but also reckless adoption is going to put us at risk. And that’s a very kind of fine balance that they’re going to need, like, a lot of high-quality intelligence to make.Chinese Models, Data Flows, and National Security ConcernsSwyx [00:31:55]: Just a side mention, because you mentioned Chinese models, any specific concerns that you’re hearing from your CISOs about that? ‘cause I guess it’s free, but.Rune Kvist [00:32:05]: CISOs have a bunch of concerns around data flows in general that they’re really concerned about. So there’s a lot of questions like, if these models are Chinese, where does that, where does that data go? I think a lot of this can be addressed, but they come up often.Swyx [00:32:18]: I mean, they understand they’re running on American GPUs.Rune Kvist [00:32:21]: Some of them, some of them understand that they’re running on American GPUs.Swyx [00:32:23]: They’re not, like, phoning home every time you, like, call home.Rune Kvist [00:32:26]: No. A year ago, there was not a lot of understanding of this. I actually think, you’re seeing the security leaders becoming kind of AI literate at a blistering pace, and you’re actually also seeing my Twitter timeline that’s very pilled and my LinkedIn feed that used to not at all be pilled kind of converge. They’re both talking about Fable.Swyx [00:32:45]: Right. Yeah, that’s true.Rune Kvist [00:32:46]: They are both talking about whether you can prevent models from being jailbroken these days.Swyx [00:32:51]: Yeah.Rune Kvist [00:32:52]: Like national security national security risks are now the conversation that is actually emerging. Other than that, I think you mostly see a kind of general picture: there are no concerns with any particular model or any particular model output, but there is a general nervousness of having critical infrastructure run on models that are not produced in America by Americans where the American government has control.Swyx [00:33:14]: But it doesn’t necessarily show up in your framework that directly, or it might, I don’t know.Rune Kvist [00:33:18]: There’s a bit of stuff in there actually on the, like, the provenance of the models and disclosing that. But I think there’s a bunch of use cases where running a Chinese open-source model is just the best solution.Swyx [00:33:27]: Yeah.Rune Kvist [00:33:27]: And a concern is slightly more macro here, which is not best addressed at any particular certification level.Vibhu [00:33:32]: Is there anything interesting that you see at the. You know, if you’re trying to fill that middle gap, that mediation gap, any interesting stuff that you guys forecast would be required other than, you know, what the average person might expect?Cyber, Child Safety, Bio Risk, and Expert CoordinationRune Kvist [00:33:47]: There’s a bunch of interesting questions about what are the risks that matter here. So right now, the risk of the day is cyber, because it’s very real, very tangible. And some of the risks that are also emerging as pretty real and pretty tangible are things like child safety is becoming both extremely important, but also politically important. And then there are some of the risks that are coming down the pipeline that today feel kind of speculative, but people who spend a lot of time with the models see them coming down is things like, risks that relate to biology.Rune Kvist [00:34:18]: And specifically whether models will help adversaries produce biological weapons and making that extremely cheap, extremely accessible, producing-- making the chance of another COVID or worse pandemic. COVID was not engineered to be bad, as if you were trying to do that. So I think those are some of the risks that are coming down the pipeline. I think one other thing to just note is that agents are kind of deliberately narrow. So, like, when a frontier agent company puts a chatbot that interacts with customers, they’ve really tried to narrow the topics it’s interested in talking about. Such that if you ask it, like, “What do you think of the president?” it will just decline, which means that the kind of risk area is somewhat smaller. For models, it is infinite. And so there’s not a single expert out there who can competently evaluate the risks of cyberattacks and fifteen-year-olds having month-long conversations with a chatbot and seeing whether it will in fact recommend suicide or something horrendous like that, and can evaluate the risks that terrorists can use AI to produce bioweapons. The risk surface is just too big. And so the central challenge actually becomes how do you get those subject matter experts to work within a one coherent framework that outputs one coherent report and rating that the world can go and inspect? ‘Cause that global perspective is central, but there’s not a single organization today that could produce that.Swyx [00:35:47]: And you would be the presumptive one when you put out your model standards.Rune Kvist [00:35:51]: We think there can be one company that can, with a consortium of experts, build one coherent standard. I think we’ve shown that across all of the enterprise risks today. We think it could be one company that could, with a consortium, specify the audit rules, basically like the inputs and outputs that all these technical experts need. What access do they need? How should they treat infosec- info security? They can look at whether the eval- evals are well-produced without necessarily being able to say, “Hey, is this a threat or not a threat?” But overall, evaluating whether the evals are good, well-constructed, that set of audit rules that basically becomes the interface for all these experts, we think one clearinghouse could put together. To be clear. When I say one company, I think of it as one company coordinating lots of this in the same way that when we saw our consortium, it’s not like we say we have all the answers on agent security. What we say is we are taking on the role of eliciting all of the concerns and being the secretary that puts it together and runs a tight house such that the standard updates lockstep every quarter, and that the audit reports that come out, in this case, 100-page audit reports, uniform and crisp and clear all to the level of detail that is required for executives that need to make a clear go/go decision. So that’s kind of the role that we think we might play.OWASP, Frameworks, and the Operational Audit LayerSwyx [00:37:11]: I think in many ways you’re performing the role that OWASP used to do there, and you said, like, you know, competition and partners.Rune Kvist [00:37:18]: Yeah.Swyx [00:37:19]: Can you go more into, like, how they partner?Rune Kvist [00:37:20]: Yeah. So first of all, OWASP is basically an open source community of security practitioners that are coming together to build frameworks for addressing the latest security concerns. We think they are phenomenal at creating frameworks. We’- In fact, we’- First of all, we’re partners with them, so we have a joint article. Two, we’ve learned a lot from them. We think they’re a tremendous source of intelligence. What OWASP does not do is building the machine that runs third-party audits such that a company like Cursor or a company like JPMorgan could get a third party to go and review them against this and say, “Hey, you’ve passed the standard, and here is the report that you can use to build trust and preempt your partners’ or customers’ questions.” So they fundamentally try to do something different. You - They are part of the information gathering and intelligence gathering and creating clarity, but the operational layer of turning this into promises is not the business they try to be in.Swyx [00:38:14]: The standard is emerging and is doing very well. Was it necessary to then also do underwriting? Obviously it’s in the name, so please remember you thought about it first. I feel like if you just have enough consensus, you don’t actually need the money angle, but it does help.Vibhu [00:38:30]: I did want to also note, you guys are a profit company too, right? It’s not profit where there’s a whole business side to it as well?Why For-Profit Standards and Insurers MatterRune Kvist [00:38:39]: Yeah. Yeah, so I’m just getting crazySwyx [00:38:41]: I think about the money part.Rune Kvist [00:38:42]: Yeah. Yeah, let’s get into the money part. Let’s start from actually your question, profit versus profit. In the security space today, cybersecurity, most of the standards are produced by nonprofits. I think that’s an issue.Rune Kvist [00:39:00]: The question you have to ask yourself is, how do you create good incentives for these standards to be good and keep up?Rune Kvist [00:39:09]: Nonprofits tend to not have these adverse profit incentives where they, hollow out their standard and create a race to the bottom, but they’re also not at all responsive by default to the communities that they serve. There’s no process-- They don’t have customers that they serve where they go and ask, “What do you want? What do you want? What do you want?” And when you look at the overall satisfaction with the security standards today, people tend to just not like them very much. You do see in other domains, that profit standards can serve the world quite well. So there are examples, like we talked about Moody’s before. It’s not without flaws, but, it is absolutely critical societal infrastructure that gets run at an astounding scale today. Your credit score, it’s FICO. It’s also a profit business. And when you go back even further in history, some of the crash testing standards came out of insurance companies.Rune Kvist [00:40:06]: The insurance companies together founded the Insurance Institute for Highway Safety because they were very interested in, like, how can we use standards to drive down mortality and save money? Go back, prior-- Our name actually pays homage to the Underwriters Laboratories, UL, which, was started right around when electricity came out. Houses started burning down. Insurers, again, were paying the bill, and they were maybe also good people, but their profit incentive was, let’s prevent houses from burning down. Let’s test all the electrical products, the light bulbs. All the light bulbs in here are probably tested, the toasters, et cetera. And they set up, an entity to create those standards. Today, UL has a profit entity and a profit entity. What they’ve recognized, they spun - They started profit. They spun out a profit because what they recognized was like, hey, actually to serve customers well, you need a profit entity. The lesson here is one of the ways that the market can align incentives so you’re both responsive to customersRune Kvist [00:41:07]: And not hollowing out your standard over time is to align it with insurers because they fundamentally have good incentives. And so if you’re a profit standard that works closely with insurers, you get the feedback loop in such that you’re really tuned into your customers, but also have their interest at heart. So that’s the model that we - the kind of inspirational model that we’ve learned a lot from, and that’s also where the name comes from. In some ways, the term underwriting can both be associated with insurance, but it’s also a broad term for, like, making decisions.Rune Kvist [00:41:40]: If you underwrite a decision, you’re fundamentally kind of taking ownership for the consequences of it.AI Insurance Contracts, Lloyd’s of London, and ElevenLabsSwyx [00:41:45]: Yeah, I mean, what does an insurance contract look like for AI?Rune Kvist [00:41:49]: Yeah. Most of the demand comes today for insurance contracts is, sitting between people who’ve built AI and people who are buying AI.Swyx [00:41:56]: Yes.Rune Kvist [00:41:57]: And what you want—the reason why people want insurers involved, both for the traditional reasons, hey, if something goes wrong, we want to be compensated, but it’s in particular because insurers can bring trust to the equation. Because insurers will pay for the damages, if they’re willing to write an insurance policy, that is them saying, “Hey, we think there is risk here, but that is manageable.” And that is kind of a. Their incentive aligns with the enterprises adopting it, so that’s a really a good signal to the market. In the same way, actually, one of the things that Waymo tried to get their first permit to even operate in San Francisco was to get a lot of insurers to stack up a huge insurance policy. In the case if something went wrong, not because Google can’t pay, but because it was very valuable to have a third party go and look at that dataRune Kvist [00:42:47]: That are trusted by governments, trusted by enterprises as conservative people and say, “Hey, we’ve looked at it. We’re actually willing to take some of this on our balance sheet.” So that’s, that’s kind of the reason why people are interested in it. What it looks like is, in some ways like every other insurance contract. You specify what are the perils you want to cover, how much do you want to cover them, like up to what limits, and what does it cost to cover that. And in the case of, if we take a really concrete example, ElevenLabs, bought a first of its kind AI agent insurance policy. They work with some of the biggest, enterprises that work with governments. They’re really interested in going above and beyond and making promises to their customers. So they wrote a policy that covers just some of the core concerns that their customers have been asking about. And, the crucial thing was really to get Lloyd’s of London, the world’s oldest insurer, one of our partners, to look at this data and be that third party alongside us to say, “Hey, we think there’s something here that’s worth underwriting.” and that’s actually what it looks like. And so they will show that contract to their customers, and they can see how much they’re covered for. They can see what exactly it covers, and that will also probably change next year. They will want to write an insurance policy that might cover more.Swyx [00:44:04]: When you say Lloyd’s, is it reinsurance, or are they sharing somehow at the same level orRune Kvist [00:44:11]: Yeah. So typically, the way, new companies get into insurance is that they partner with insurers such that the insurers take the majority or all of the financial risks. Fundamentally, if insurance is useful, because it brings trust, you have to be able to pay the bill. Lloyd’s of London is 400 years old. They’ve never not paid a claim. They’re extremely trusted. What Lloyd’s of London struggle to do on their own is to figure out which of the risks are real, what should we be looking for, what are the kinds of technical controls, and running the tests. So they use AIUC-1 as kind of the underwriting framework, and we produce a bunch of eval results that then directly feed in to inform the pricing. So this means that ElevenLabs customers know that payment will be there. They don’t have to look to our series A and see, like, do we think they have enough cash on the balance sheet? They will look at Lloyd’s.Swyx [00:45:05]: Yeah.Rune Kvist [00:45:05]: Yeah.Swyx [00:45:05]: And Lloyd’s, like, famously very creative. I think I remember some headline like, they insured Jennifer Lopez’s, butt or something.Rune Kvist [00:45:13]: Correct.Swyx [00:45:13]: Right?Rune Kvist [00:45:13]: And I think, was it, David Beckham’s right foot?Swyx [00:45:16]: So, yeah. Right?Rune Kvist [00:45:17]: And stuff like this.Swyx [00:45:18]: So, like, clearly not a large data set.Rune Kvist [00:45:22]: Exactly. It’s actually a remarkable institution that’s both kind of has some of the truly school virtues of having been around for a long time. They, like, really. They really operate like a trusted entity, and they have appetite to figure out the future. And I think there’s a lot of recognition that both there is, like, tremendous amount of risk in AI that is poorly understood today, so getting into this business carries real risks. But also this is where lots of the risk exposure will happen in the future. This is the one market where risk is truly growing. This is the one market that will also take out some of the existing markets. Take, like, auto insurance. When there are no human drivers, how’s that market going to look? Well, it’s clearly going to change. How are you going to assessSwyx [00:46:08]: You want to insure Waymo?Rune Kvist [00:46:10]: I. All I’ll say is the principles for how you insure Waymo are very similar to how you insure other kinds of AI.Swyx [00:46:15]: Right.Rune Kvist [00:46:15]: So again, crash testing, that’s what we do for customer share at Lovable. That will also need to happen for Waymo, which is not how you do it for human drivers. So there’s this growing awareness that the world is changing very fast, and the only way to learn how to underwrite AI is to write some policies. You may incur some losses and think of that as R&D expense, really. But the question for them is, like, who are the trustedtechnical partners they can get into this business with that can help them navigate and make sure they don’t make, kind of foolish mistakes? But also who is willing to hear the wisdom that they have? They’ve done this before. They’ve seen it was. They were there when cyber came out. So there are lots of ways in which AI feels completely new, but there’s also lots of ways in which risks look the same. And so there’s actually a tremendous amount of wisdom sitting in some folks that may have gray hair, but really have, like, a keen sense of, how to quantify risk.Swyx [00:47:08]: Yeah. And the number is. So it’s basically like I want fifty million dollars worth of coverage against these perils, and Lloyd’s will give you a quote on it, and then you have, like, a small markup or something, and then you turn it around and do that? Is that as simple as it is?Risk Capital, Premiums, and Working with InsurersRune Kvist [00:47:23]: You basically share some of that premium.Swyx [00:47:25]: Yeah.Rune Kvist [00:47:25]: X percent goes to the people who do the pricing of it.Swyx [00:47:28]: You’re. It’s kind of like a. It’s kind of like a merchant bank for insurance type of thing.Rune Kvist [00:47:33]: Exactly. You basically split the fee, and you can think of the insurance supply chain as, like, there’s bringing the capital, there is doing the pricing, and there is doing the distribution. And typically, you will pay out some X percent of premium here, Y percent of premium here, and the rest of it will go here.Swyx [00:47:46]: Does all the insurance world work like this, or is there some point at which, like. So if right now you have equity capitalRune Kvist [00:47:51]: Yeah.Swyx [00:47:52]: At some point, maybe you start raising, debt or whatever, and then you have enough of a bank account and enough history, let’s say you’ve been in operation for ten yearsRune Kvist [00:48:00]: Correct.Swyx [00:48:00]: That you don’t need Lloyd’s anymore?Rune Kvist [00:48:02]: That’s totally an option. And I could see some worlds where that makes sense, specifically if there are risks that we feel high confidence that we’d want to insure where the incumbent insurers are too slow to find appetiteSwyx [00:48:13]: Okay.Rune Kvist [00:48:13]: Or simply struggle to evaluate it such that they don’t want to do it. But by and large, in general, you do not want to compete with insurers on, bringing risk capital to the game for two reasons. One is that’s fundamentally a cost of capital game. They have extremely low cost of capital. Startups have high cost of capital, by and large. And two, you want to hedge your bets, and it’s very helpful then to also have a portfolio of home insurance, of car insurance. And we’re not about to become a car insurer nor a home insurer.Rune Kvist [00:48:43]: So they have some natural advantages, which makes it much more likely that we’ll partner.Swyx [00:48:48]: Yeah.Rune Kvist [00:48:48]: And they bring that, the capital at scale, and we bring the technical expertise.Swyx [00:48:51]: You’re, you’re going to work with them for a long time.Vibhu [00:48:52]: How are the discussions with the insurers as well? So basically, they’re going off of your certification, right? They’re trusting the diligence on you that your certification is valid, you tested the right things, and they’re backing the money that, you know, you have the right testing in place. So any interesting takeaways from working with insurers?Rune Kvist [00:49:12]: I think the maybe the first thing is they feed into the standard as well. So if there are things that they feel like they need that they’re not seeing, we are also taking that as input into the standard, because fundamentally we think a good standard is one that creates a really healthy promise ecosystem, and we think insurers are a critical part of that. And again, they are the most well-incentivized to. They see all the lost data across every. Any particular CISO knows their particular concerns. Insurers see the concerns across the entire portfolio and often have direct access to, like, what exactly happened, who was at fault, et cetera, as they do part of their forensics. So they’re actually, like, a great source of intelligence on this. One of the big takeaways from cyber insurance, which is a market that didn’t work that well, was that the insurance and the technical expertise was not married up. What our conviction is that standards have to precede insurance. Fundamentally, what everyone first and foremost want, whether you’re a CISO at JPMorgan or a CISO at Cursor or an underwriter at Lloyd’s of London syndicate, is you want to not have an incidentRune Kvist [00:50:19]: In the first place. You want to know that the risk is well-managed, and only then does insurance start to make sense. So we’ll see the standard ecosystem basically run ahead of the insurance. And the reason why we. You asked us kind of why I also do insurance, this is kind of proving what we think a whole promise confidence infrastructure ecosystem needs to look like, and we think it’s very compelling to bring that to life, even if we think the standard is kind of the core linchpin that unlocks the rest.Claims, Liability, Air Canada, and Duty of CareSwyx [00:50:44]: There’s been no claims yet, right?Rune Kvist [00:50:45]: Nope.Swyx [00:50:46]: This is one of those things where, you know, if people haven’t really worked through what it means to cover things.Rune Kvist [00:50:52]: Yeah.Swyx [00:50:52]: So for example, I pay Cursor $20 a month.Rune Kvist [00:50:55]: Yep.Swyx [00:50:56]: And I write a vibe code something that makes, a plane crash, causing $200 million worth of damage.Rune Kvist [00:51:02]: Yes.Swyx [00:51:02]: Do I claim $20 or do I claim two hundred million?Rune Kvist [00:51:07]: Yeah. So these are all great questions.Rune Kvist [00:51:10]: And fortunately, kind of all of insurance and legal history kind of helps answer some of those questions. I think the first thing is people have limits on their policy. So if you want to claim $200 million, you have to. Someone has to have paid a lot for that insurance policy upfront to have $200 million of coverage. And ultimately, the way this works is that, you start from a lot of uncertainty. This is not just an insurance, but also, like, can you use. Can Anthropic use books on the internet to train up? Well, they can go and look at precedent, they can But ultimately, this- these things get settled in court, and you hammer it out over time. So you start from this, like, place of ambiguity, which is both why insurance can be hard to do early on, but it’s also why people want insurance, because that ambiguity slows down adoption.Swyx [00:51:57]: Yeah.Rune Kvist [00:51:57]: That also sits at the heads of the,Swyx [00:51:59]: Yeah. In some ways, actually, the first incident will help to, establish a lot of this.Rune Kvist [00:52:05]: Exactly. And there have been a number of incidents out there that have just not been covered by insurance.Swyx [00:52:09]: Yes.Rune Kvist [00:52:09]: Take the now old, example from Air Canada, whereSwyx [00:52:14]: I was going to bring that upRune Kvist [00:52:15]: Chatbot hallucinated a refund policy, and the question was, Air Canada in that case were like, “Hey, we have nothing to do with this. This chatbot messed up, but, like, sorry.” And the courts were like, “No, if you put your chatbots to interact with your customers, they make legally binding promises on your behalf.” That is now precedent for everything in the future where you will. If someone were to deploy a chatbot like that again, they should not expect to be able to just pawn off and say, “Sorry, my chatbot lied. It’s nothing to do with me. I bought it from OpenAI.” No, if you’re putting this in front of your customers, you are taking responsibility for it. And so every court case, whether insurance is involved or not, clarifies liability, and liability is kind of the foundation for insurance. There’s another reason why standards and insurance come together. Liability for. I’ll go on a little tangent hereSwyx [00:53:06]: PleaseRune Kvist [00:53:06]: Get into the weeds of it.Swyx [00:53:06]: Please.Rune Kvist [00:53:07]: Liability, often one of the core concepts is whether someone was negligent. Should they have seen this? Should they have prevented this? And the question is: how do you judge that? Well, you basically judge whether they’ve met their duty of care. What does that mean in practice? Well, often they look to standards. So if there’s a standard that is broadly adopted that says you must have a groundedness filter or you must have a jailbreak filter, it becomes way harder to claim ignorance that these things existed. And so setting standards help clarify liability. Coins-- courts will often point to standards and being like, “Well, this seems like best practice to do.” It’s there for everyone to see. So there’s another way in which, like, standards are kind of civilization infrastructure that insurance can then build on, which promises can then build on.Swyx [00:53:53]: I totally get that. We don’t have to get certified to write these, to, you know, make these, like, bots and all these.Rune Kvist [00:54:00]: Correct.Swyx [00:54:00]: But, like, basically, whenever we get. Go for the audit, I think people, like, start to shape up and all this stuff. I wonder if, like, that means that you don’t also then become, like, the approving authority for me to ship to production. You know, like, yes, you check once per quarter. I want to ship once a day.Shipping to Production: Ongoing Testing and TrustRune Kvist [00:54:19]: Yeah.Swyx [00:54:19]: And I don’t know when one of my things breaks, like one of your certifications or not.Rune Kvist [00:54:24]: So there’s a couple things. There’s a couple of requirements in there that relate to how do you yourself, where you have to tell your customersSwyx [00:54:33]: It’s like an ongoing monitoring.Rune Kvist [00:54:34]: How are you yourself testing before you make at least major releases? We don’t go and audit people every day, but at least there is now a trail where if you do a major mess up, then your customer may come and ask you, “Hey, you promised me that you were going to run these evals yourself.” And for lots of them, most of the. PRs that people merge will not fundamentally alter the product experience, but some of them will. Thank you.Swyx [00:54:57]: And sometimes you don’t know.Rune Kvist [00:54:58]: And sometimes you don’t know. There are inherent risks that everyone knows that when they buy software, there can be bugs, and this is just part of it. But if you’re selling to mom-and-pop shops, they may not care. They’re just like, “Well, I want to use your tool, so I’m just going to willing-- be willing to take that risk on.” If you’re selling to a big bank, they might be like, “Sorry, we’re making promises to our customers. If you can’t make a promise to us that we can pass on, we don’t want to work with you.” Then it’s up to you to say, “Do I care for my agent to get used as critical infrastructure in this mission? If so, at least I can make promises about what processes I run, and then we can go and test it every quarter to be like, well, does it seem like, it’s still, that it still meets the standard.” So from my perspective, it’s kind of a way to. Big companies by default kind of have some amount of trust when they ship AI.Rune Kvist [00:55:49]: If you’re a young company, if you’re just starting out, by default you have no trust. And there are very few places where you can go and get trust. So one of the things that most of our customers did before they started working with us is that they would make their own security blog posts. That’s great. But also, who’s going to trust you saying, “We’re so secure”?Rune Kvist [00:56:05]: Like, anyone can write that. But it’s very hard. Where do you go and get that trust?Vibhu [00:56:08]: Yeah.Rune Kvist [00:56:08]: And so I think making the standards more legible makes it easier for smaller companies to prove that they’re doing what they ought to be doing, because the default assumption is that it’s the Wild West.Vibhu [00:56:21]: Is there a roadmap you have of, like. There’s a lot of work to be done here, right?Rune Kvist [00:56:25]: Yep.Vibhu [00:56:25]: This is the first one.Rune Kvist [00:56:26]: Yeah.Vibhu [00:56:26]: Anything on the roadmap of what you see is next, what’s coming, what’s, what’s missing?The Roadmap: Agents, Models, Robotics, and World ModelsRune Kvist [00:56:32]: I think when we zoom out, AIUC-1 deals with agents. Next up, we will deal with models. Next up from that, we will deal with robotics, of which, in some ways, Waymo is the first robot. But the exact same problem is going to be someone’s going to develop a robot, someone’s going to need some promises, they’re going to struggle to make the promises. And - You see this playing out when, like, if you think Fable concerns are bad, like, see when Waymo hits a dog. And that’s if people lose their mind. Imagine when first robot knocks off a toddler off a kitchen table.Swyx [00:57:03]: Yeah.Rune Kvist [00:57:03]: You’re going to see some real strict liability.Vibhu [00:57:07]: I mean, you could see it, right? Like, Cruise got fullyRune Kvist [00:57:10]: Destroyed.Vibhu [00:57:10]: All permits are gone, yeah. Yeah.Rune Kvist [00:57:12]: Correct. So physical AI, the level of stringency just goes up and up. So that’s kind of like the big picture. Agents, models, robotics. I think within agents, the current set of agents are well-covered by this. But as the technology progresses, as agents get longer horizons, new types of failure modes will emerge. And so it’s mostly of can you make sure the standard keeps up when they appear? And you also start to see new modalities. Like today, world models are mostly a kind of a research question. There’s no one who’s really using it. But that will also bring in just new kinds of ways to create value, but also more risk surface that no one knows how to grapple with today. You’ll start to see true agent interactions that are not mediated by humans. There’s going to be a bunch of interesting questions. You’re basically going to need a new legal system. How do they build trust amongst each other? How. One of the core things when humans trade with each other is that you know that you have recourse. You can sue them. How do you make sure that there is a persistent balance sheet behind any agent such that if you trade with it and it screws you know you can get your money back? Those are some of the questions we’re going to have to deal with. And the technical testingRune Kvist [00:58:24]: Of multi-agent systems is also going to be interesting and complex.Swyx [00:58:29]: Very fun. Are there any perils that are uninsurable right now that people wish that you would?Copyright Risk, Adverse Selection, and Information AsymmetryRune Kvist [00:58:35]: Yeah. One of the places where there’s a bunch of appetite for insurance and not a lot - a lot of demand, but not a lot of supply, is when it comes to copyright.Swyx [00:58:46]: Oof.Rune Kvist [00:58:47]: In some ways, copyright is kind of mundane. It’s always been an issue. There’s a couple of reasons for this. The first is people who have trained on copyrighted materials almost always know that they’ve done that.Rune Kvist [00:58:59]: So if you want to buy insurance for it probably signals that you might be a high-risk customer. The people who are most interested in getting insurance for copyright infringementSwyx [00:59:09]: Okay. YeahRune Kvist [00:59:09]: Are the people who are most likely to have copyrightedSwyx [00:59:10]: Yeah. It’s like a, it’s like a lemon problem.Rune Kvist [00:59:13]: Exactly.Vibhu [00:59:13]: I actually think there’s another side to it too, right? Like, if you’re building on something. So say I’m using an open model.Rune Kvist [00:59:19]: Yeah.Vibhu [00:59:19]: I don’t know what it’s trained on, right?Rune Kvist [00:59:21]: Yes.Vibhu [00:59:21]: And how far down that chain does copyright go?Rune Kvist [00:59:24]: Yes.Vibhu [00:59:24]: Am I liable to take down my product because company X trained on copyright?Swyx [00:59:29]: But there’s safety in numbers. If everyone’s doing it, then you.Rune Kvist [00:59:33]: Correct.Vibhu [00:59:33]: I mean, I would say until, you know, Fable is rolled back from everyone that used it, right?Rune Kvist [00:59:38]: Yeah. I think it’s a hard question. I don’t know the answer to it.Vibhu [00:59:39]: It is.Rune Kvist [00:59:39]: But I think your intuition is, your intuition is right in kind of like, what is the kind of duty of care?Rune Kvist [00:59:47]: And people don’t today think of it as customary that you go and you, like, dissect the open model’s training data and you check everything. In fact, lots of people use them. It’s seen as kind of generally acceptable to not check for this. And therefore, like, we’re not going to hold you to specificVibhu [01:00:02]: I mean, we also really can’t, right? We don’Rune Kvist [01:00:04]: Exactly.Vibhu [01:00:04]: We don’t know the training data.Rune Kvist [01:00:05]: So you can then ban it, but I think no court is going to get a copyright question and be like, “This actually needs to get banned.”Swyx [01:00:09]: Unless you hire Nicholas Carlini and he can extract it for you.Rune Kvist [01:00:12]: Exactly. Though he’s in short supply.Swyx [01:00:15]: Yeah. He’- You only have so many Carlinis, but,Rune Kvist [01:00:17]: Exactly.Swyx [01:00:18]: Yeah, go ahead.Rune Kvist [01:00:19]: So I think this is also fair that, in the case of labs, there’s a lot of interest for this. But the thing that makes lab want it is what makes this insurer suspicious of it, and so you have a lemon’s problem.Swyx [01:00:30]: Yeah. Is there, like, a theory of insurance where adverse selection dominates the risk-sharing aspect of insurance? Like, where does this. Like, teach us insurance.Rune Kvist [01:00:40]: A lot of insurance does come back to, like, practical versions of microeconomics 101.Swyx [01:00:45]: Yeah. It’s very. It’s like, it’s like this is whyVibhu [01:00:47]: High-risk adverse.Swyx [01:00:48]: You need to pool health insurance, because if you make it too hyper-specific, then only people who are guaranteed to get the disease will sign up for your insurance.Rune Kvist [01:00:56]: Exactly.Swyx [01:00:56]: Same thing.Rune Kvist [01:00:57]: The core problem is one of information asymmetry. People buying insurance know something about their risk that insurers do not know. And so the question is actually. And this comes back to the same problem is, if you rely. You can break a lot of these information asymmetries if there is. Some kind of testing that reveals the underlying true risk. And so if you were able to, in the case you mentioned, have good diagnosis of whether someone has it or what the probability is that someone has it, that the insurers trust, then they might be willing to insure it. But if they don’t, if there’s no kind of common information, then - only the patient will knowVibhu [01:01:32]: Yeah.Rune Kvist [01:01:32]: That’s what breaks it down. So the question is, again, how do you create credible signaling between players?Rune Kvist [01:01:39]: This is also the whole reason why Moody’s exists. Moody’s just does credible signaling. That’s also why Moody’s could never-- Moody’s has to be independent. If Moody’s was owned by JPMorgan, then JPMorgan cannot use it as a signaling mechanism. So a lot of the basics of standards and certification are just communication devices. It’s just a trust gap. And, that’s where you have to think about what are the incentives of the messenger. And one and another way you can break a lot of this is through transparency. If you are transparent in how you operate, you just cannot mess with others nearly as easily. You make it much more costly, and that increases trust. This is one of the reasons why there’s a change log here.Rune Kvist [01:02:16]: Every little changeSwyx [01:02:18]: YeahRune Kvist [01:02:18]: You can go back and find, and it means that if we were to make the standard worseSwyx [01:02:24]: Oh, wow, that’s a lot of changes in one update.Rune Kvist [01:02:27]: Yeah.Swyx [01:02:27]: Okay.Rune Kvist [01:02:28]: And a lot of this is just as things get clearer, you can see a lot of clarifications, you can see some revisions. As things get hammered out, you want to change this. But if you make it all public, you make it much harder to mess with people, or at least you become found out very easily.Rune Kvist [01:02:42]: And so this is a way of reducing the information asymmetries by just making more of the information public.Vibhu [01:02:50]: I like how you do know when future versions are coming.Swyx [01:02:52]: Yeah.Vibhu [01:02:53]: So I guess it’s quarterly.Swyx [01:02:53]: I mean, they justRune Kvist [01:02:54]: It’s quarterly.Vibhu [01:02:54]: Yeah.Swyx [01:02:54]: It’s kind of quarterly.Rune Kvist [01:02:55]: Yeah.Swyx [01:02:55]: Not that surprising.Rune Kvist [01:02:58]: Yeah, but this is also a promise. Like, if we now don’t deliver on July 15, basicallySwyx [01:03:03]: I mean, you can just batch it up, and then whatever you got, you just ship it.Rune Kvist [01:03:05]: You just batch it up.Swyx [01:03:05]: Yeah. That’s not that hard.Rune Kvist [01:03:06]: But it’s kind of like we deposit some amount of trust every time we meet this commitment.Vibhu [01:03:12]: Yeah.Rune Kvist [01:03:12]: And in the startup land, it feels easy to ship a new version of a standard once a quarter. In the enterprises who are used to this, like, decade-long cycle, we often get met with, like, incredulity. Like, there’s just no way. And then you show them the change log.Swyx [01:03:28]: One thing I wanted to also, like, try to really think about is, you know, you said something about how if you have tests for the thing, then you can insure it.Rune Kvist [01:03:35]: Yes.Swyx [01:03:36]: Right? And so really what your standard is, what AIUC is, is establishing a framework for the audits to happen so that you can at least test, like, all these, like, baseline standards of care have been met, and therefore people can insure against standard risks that everyone has. I wonder if, like, there needs to be develo-- you need to develop other tests. We’ve covered mech interp in the past. Any interest in that, or are there other kinds of tests that we’re not thinking about?mech interp, Eval Awareness, and MonitoringRune Kvist [01:04:02]: Yeah, I think mech interp is a big one. A lot of interest in that. I think everyone would agree that there’s, like, promising scientific potential.Rune Kvist [01:04:15]: We’re still a while, a little bit away at least, from this being, like, commercially available on demand such that there’s, like, now a selection of vendors you can go to.Swyx [01:04:26]: Goodfire would say that it is commercially available.Rune Kvist [01:04:28]: Exactly.Swyx [01:04:29]: And it justRune Kvist [01:04:30]: We would agree with them. We think that the work that they’re doing is tremendous.Swyx [01:04:33]: Yeah.Rune Kvist [01:04:33]: We’re not quite at a point where we could literally require it. But it’s the kind of thing where you can imagine relatively soon you could put in an optional control for if people use mech interp as a way to reduce risk, you at least get credit for it. We can’t require it because it’s going to be hard to require everyone to become Goodfire customers.Swyx [01:04:49]: What good does credit do me? This is - this is a pass-fail, right? Do I care about credit?Rune Kvist [01:04:54]: It’s a pass-fail, but it’s also a 100-page audit reportSwyx [01:04:57]: HuhRune Kvist [01:04:57]: That you’d be surprised at how much security leaders actually sit down and digest this stuff.Swyx [01:05:02]: Okay.Rune Kvist [01:05:02]: And I promise you that if someone is using mech interp today they will have a slide on it because they’ll try and get credit for it.Swyx [01:05:11]: It is cool. It’s fancy, yeah.Rune Kvist [01:05:12]: But it’s just easier if you have a third party saying, “Yep, they have mech interp, and actually.”Swyx [01:05:16]: Just to spell it out for people who have been following our mech interp podcastRune Kvist [01:05:21]: Yeah.Swyx [01:05:21]: It is literally like, oh, you’re using, you know, OSS. It is activating these three dangerous things. We monitor for it, and we log it out in whatever tool of choice. Gray Swan has, like, Signal or whatever, and that’s it. That’s the mech interp-based activation, signal. Okay.Rune Kvist [01:05:38]: Yeah. So I think mech interp is interesting, and I think if that promise truly comes to fruition, you can make stronger promises than you can with evals. And so I think that’s very compelling. Another thing that I think will become increasingly important is just kind of good school monitoring, and slightly after the fact. One of the things you’re seeing with eval, some of the challenges that are emerging is that the agents are starting to become aware that they’re being evaluated.Swyx [01:06:04]: Yeah, eval awareness.Vibhu [01:06:05]: Yep.Rune Kvist [01:06:05]: Exactly, which is a problem. It means that they basically, if they know they’re being watched, they won’t do the thing that they think they get punished for. And by default, unless you know how to kind of reduce eval awareness, you should trust evals less. And one of the kind of truest things, monitoring, like, is the source of truth. Did you in fact give medical advice, and how quickly do you know? How often - have you done that in the past? How fast do you respond? How often do you detect it? How fast do you detect this? So I think that is also a paradigm. It’s slightly more intrusive. You actually will look at some customer data, but I think will become more prevalent over time.Swyx [01:06:45]: People talk about this like we should not write about eval awareness because it’s going to leak into the data set and then be. Like, we should just. Like, we should, like, never talk about it, only meet in person and, like, talk offline unrecorded. Like.Rune Kvist [01:06:57]: Did you guys see the Anthropic research where. I think this was literally Anthropic did that test.Swyx [01:07:04]: What?Rune Kvist [01:07:05]: It took. I can’t remember the details here, but they, ran some studies on misalignment, and then they took out the training data- That related to LessWrong discussing misalignment, and they ran the same test again and the failure rate went down.Rune Kvist [01:07:20]: So it, in fact, was some evidence pointing towards it had learned the - either the ability or the propensity to do that.Swyx [01:07:28]: Yeah, I mean, so there’s the hyperstition effect, and then there’s, like, the Luigi/Waluigi effect.Rune Kvist [01:07:31]: Correct.Swyx [01:07:32]: Which is like you are. The more you try to train for it, you create the opposite.Rune Kvist [01:07:36]: Yes, there you go. That’s exactly it.Swyx [01:07:38]: In some ways, I think the very success with Anthropic is a result of hyperstition, like the fact that you wanted this thing to exist in the world, and now it does. But, like, then it also creates the opposite as well.Rune Kvist [01:07:48]: Yes.Swyx [01:07:49]: Like, I think people who are maybe newer to this space don’t remember Waluigi, but, like, I do think it’s very important for understanding that when you train for a thing, you also train the opposite of the thing ‘cause it’s just a big flip.Rune Kvist [01:08:02]: Yes.Rune Kvist [01:08:03]: Yes.Vibhu [01:08:04]: I think, you know, just going back to where we were at, like, there’s a lot more than just mech interp that there’s value in just having added, right? So your version of how fast can you measure stuff? Do you have logging? Do you have evals? You know, do you see other parts of the stack, like the inference providers that you use, the services? Okay, am I using Chinese model on their home API? Am I using through certified vendor here? Am I hosting myself? What am I doing on the inference engine side? There’s just, like, so many levels of stuff that gives, you know, information that you can standardize out, right?Managed Agents, Enterprise Controls, and Generative MediaRune Kvist [01:08:36]: Yeah. And you also see increasingly, in addition to just the basic chatbots, you’re increasingly seeing big companies adopting agent platforms where they’re building on top of Google’s Agent Studio, et cetera that comes with a bunch of, likeVibhu [01:08:52]: Managed agents.Swyx [01:08:53]: Managed agents.Vibhu [01:08:53]: It’s everywhere now.Swyx [01:08:54]: Everyone has managed agents.Rune Kvist [01:08:55]: Exactly.Vibhu [01:08:56]: And there’s even levels. You can host your own managed agents, OpenAI’s Agent SDK, or hosted by Anthropic, or Google does both.Rune Kvist [01:09:03]: Correct. And then these are just ways to kind of strengthen the security guarantees you can make. And in some ways, it’s kind of bread and butter enterprise security. They. Like, they love to host things on their own premises because it gives them really a sense of control. And I think you’ll, you’ll see, just like you do in every other enterprise market, if you really sell to the enterprise, you start to compete on some of these security features. And this is also happening in AI, unsurprisingly. And I think you are seeing some amount of enterprises wanting. Enterprises are really grappling with the thing that makes agents useful is that they’re stochastic, and the thing that makes them really hard to adopt is that they’re stochastic, and these are in tension.Rune Kvist [01:09:46]: Leaders come out on different sides of that table, in part depending on how much the CEO is trying to get the stock price to go up by saying they’re AI native and that we must be willing to take the risks. We see, we actually see phenomenal tension in the heads of the CISOs of the Fortune 1000, where on the one hand you have the CEO saying, “We must adopt, otherwise we’re becoming irrelevant, and if we f**k up, you’re fired.”Swyx [01:10:08]: Oof.Rune Kvist [01:10:08]: And that’s kind of like the core emotional tension that we see showing up again and again. And one of the core problems that we solve for them is to take that abstract emotional concern and turn it into a framework, in some ways just providing clarity to that concern.Vibhu [01:10:23]: So anything in here. So something I think we kind of skipped over. We talked a lot about agent language models, skipped over world models.Rune Kvist [01:10:31]: Yeah.Vibhu [01:10:31]: You guys have voice, which is interesting with ElevenLabs.Rune Kvist [01:10:34]: Yeah.Vibhu [01:10:34]: How about generative media? So, you know, generating images, videos, that’s a category that actually has a lot of usage. Is there anything in your current policy? Is it separate policy? How do you see that space?Rune Kvist [01:10:46]: Yeah.Vibhu [01:10:46]: It’s like we did talk a bit about copyright,Swyx [01:10:49]: Music.Vibhu [01:10:50]: Yeah, music as well.Rune Kvist [01:10:51]: Yeah. I think a lot of the concerns that come up there either relate to, copyright or there’s a lot related to, let’s call it broadly safety. So, like, this could be not safe for work or just very graphic materials, are kind of some of the core things. We have done some work on this. There’s a little bit in the standard as well that deals explicitly with that. Video, we have not done a lot in yet. And I think for proper production, that has still. Especially proper production without a human in the loop, that’s still got some ways to go. It’s obvious that it’s coming, but it’s very rare that it’s like shot deploy a video to the internet. But eventually that will also happen.Vibhu [01:11:33]: We see, like, you know, Luma has Luma agent where it’s still pretty human in the loop.Rune Kvist [01:11:37]: Yeah.Vibhu [01:11:37]: So it’s not justRune Kvist [01:11:37]: And that just makes complete sense as the technology matures, and over time, it will become so good that people will not want to slow things down by having a human in the loop. And then, the need to make promises will grow.Swyx [01:11:52]: Why not just have prediction markets on everything?Prediction Markets vs. AuditsSwyx [01:11:55]: Right? It’s very EA adjacent.Rune Kvist [01:11:56]: Yes. The core thing is that the people. Prediction markets rely on public information. There is not a lot of public information. It’s just insiders trading on each side.Swyx [01:12:06]: Yeah.Rune Kvist [01:12:09]: That’s illegal.Vibhu [01:12:10]: There’s leaked information.Rune Kvist [01:12:12]: There is leaked information. The core challenge is that often you have private sensitive information, and you need to convey confidence and trust around that. And you can, of course, for some claims, like can any model be jailbroken, you could rely on public evidence ‘cause there would be lots of people being like, “Well, there’s tons of studies, and actually they all can, so that resolves fine.” I think that’s good. For, hey, this new unreleased Methus model, how capable is it actually?Rune Kvist [01:12:43]: Prediction markets have not a lot to say because actually just no one knows. And so I think that’s the core place where some of this breaks down, is that actually lots of the world’s information that guides some of these high-level decision is private and often also just not known.Vibhu [01:12:56]: I think the thing with prediction markets that people like is it’s not, it’s not answering the broad question. It’s a specific, right? So will a model do this by this date, or is a model capable to do this by then, right?Rune Kvist [01:13:07]: Yes.Vibhu [01:13:08]: That’s a little distinction there.Rune Kvist [01:13:10]: Yeah. And often the most interesting question, if you are, say, the head of security at a bank. The question you’re really trying to answer is, will this product, this agent, do this bad thing that maybe primarily I care about, specifically in the setting that I care about? And the question is like, what’s the closest-- That information may not exist anywhere. So prediction markets aggregate existing information. This information may not exist, and you want some very specific and you’re willing to pay for it. That’s kind of where a third-party audit comes in. We also don’t really use prediction markets to figure out whether, public companies have committed fraud in their books. You use audits. You probably could, but the information’s just not that available. And if so, it would be like just trading on vibes. Actually it would have been really interesting to see whether prediction markets two thousand and one were predicted Enron going bankrupt and they kind ofSwyx [01:14:02]: Yeah.Rune Kvist [01:14:02]: Could you have told-- could you have sensed from like the craziness of the CEO or some other traits that they were more likely to cook their books than others?Swyx [01:14:10]: Or enough insiders leak it then thatRune Kvist [01:14:12]: That could also be right.Swyx [01:14:13]: Right. Which is like, I mean, this-- that’s the sort of the ideal dream of prediction markets. You have liquid markets and everything.Rune Kvist [01:14:20]: Yeah.Swyx [01:14:20]: And then you can compose your exact set of risks to offset.Rune Kvist [01:14:24]: Yes.Swyx [01:14:25]: Right?Rune Kvist [01:14:25]: Yes. Yeah. And I think, like, prediction markets will bring lots of new information to it. So the thing is mostly not like which one is it, and more like what are the types of questions that prediction markets are really goodSwyx [01:14:37]: Yeah.Rune Kvist [01:14:37]: And what are the ones where the information doesn’t even exist for insiders such that no one can in fact trade on it and it needs to get generated.AI Engineer Certification and TrainingSwyx [01:14:43]: Okay, one self-serving question and then one open-ended one, on like the future of AIUC. Self-serving question would be, so you have your standard, right?Rune Kvist [01:14:52]: Yes.Swyx [01:14:52]: I run, you know, a large AI engineer conference. Like, there’s been a lot of talk about us certifying AI engineers.Rune Kvist [01:14:58]: Yep.Swyx [01:14:59]: Training programs, level one, level two, level three. I was a CFA myself, so I know what-- that’s what the finance industry does.Rune Kvist [01:15:04]: Yes.Swyx [01:15:05]: Would it help if I had AI engineer level one, level two, level three, and then it would-- they would, like, work with these guys? I don’t know.Rune Kvist [01:15:12]: If you think of the highest level objective as, like, accelerating secure deployment of agents, then that would totally help. Because one of the things that happens often now is that folks build agents, they bring it to the decision-maker, and the decision-maker surfaces a bunch of security considerations that they had not thought of, and now it’s not built to spec. Now you have to go and - like, add these filters, et cetera. So if you shifted that left, like if everyone knew what the spec they were building to, if everyone knew the grading schemeSwyx [01:15:41]: Yeah.Rune Kvist [01:15:42]: That would be awesome if they were already trained. So by defaultSwyx [01:15:44]: But you’re the grading scheme, right?Rune Kvist [01:15:45]: Say again.Swyx [01:15:45]: I don’t get to set the grading. You guys, you set the grading scheme.Rune Kvist [01:15:47]: We set the grading scheme. And I think what’s, valuable is, like, if you can turn those intoSwyx [01:15:52]: Training programs.Rune Kvist [01:15:53]: Training programsSwyx [01:15:54]: Yeah.Rune Kvist [01:15:54]: Such that peopleSwyx [01:15:54]: Which you’re, you’re not doing.Rune Kvist [01:15:55]: We’re not doing that.Swyx [01:15:56]: Yeah.Rune Kvist [01:15:56]: I think there’s value in doing it.Vibhu [01:15:57]: There are others doing. I mean, not to interrupt, but you knowSwyx [01:16:00]: Yeah.Vibhu [01:16:00]: OpenAI has theirSwyx [01:16:02]: Anthropic also has like a CCTA thing.Vibhu [01:16:04]: Yeah. You know, they want hundred thousand deployed certified consultants, right?Rune Kvist [01:16:09]: I really think it’s good for. We will accelerate adoption if we have more people who know how to build secure agents, and we are not working on the side of training people at the moment. I think it’s, like, very aligned with our mission. We only have so much, attention.Swyx [01:16:24]: I’ll tell you why I haven’t done it.Rune Kvist [01:16:26]: Yeah.Swyx [01:16:26]: It’s not like I haven’t thought about it before.Rune Kvist [01:16:28]: Yes.Swyx [01:16:28]: It’s just being prescriptiveRune Kvist [01:16:30]: Right.Swyx [01:16:31]: About like, well, this is what you should know, therefore, like, the stuff that I didn’t include is what you don’t need to know.Rune Kvist [01:16:35]: Yes.Swyx [01:16:36]: And I’m like, “That sucks.” Like.Rune Kvist [01:16:37]: Yes. Yeah.Vibhu [01:16:39]: But I think it’s like, you know, the very interesting defensible thing you guys do is your opinionated 100-page report of here’s what matters, right? Here’s the, like, prescriptive definition of the requirements you need to be certified, so.Rune Kvist [01:16:55]: Yeah, and I think that’s a choice. I think basically that’s a, that’s a choice, and I think that serves some audiences very well, where if you’re trying to deploy this into a bank or a hospital, et cetera, clarity of the - those boundaries is extremely valuable.Rune Kvist [01:17:09]: There’s lots of other settings where being much more experimental, much more trying it out is just the better fit. And so to me, this makes a ton of sense. Also, you’d have to rewrite your curricula every freaking three months.Swyx [01:17:21]: It’s fine. I do that. Like, it’s okay. But yeah, no, for me, it’s actually - like, genuinely, like, the consequences of getting it wrong and, like, affecting somebody’s career is a big responsibility.Rune Kvist [01:17:35]: Yeah. Like, I think that’s exactly right. And I think a lot of our work actually goes like, we don’t want to carry. We also don’t think of ourselves as able to carry the, kind of the true north of what’s, like, secure or not secure, but we can coordinate the forum where you listed all of that.Swyx [01:17:52]: Yeah. Your consortium is fantastic.Vibhu [01:17:54]: Do you think this can be crowdsourced in a way? Like, for your example, for what is AI engineer certification, right? This is a pretty big podcast. There’s a lot of takes that people can have and, you know, discussions that can.Swyx [01:18:05]: And people reasonably disagree. So who am I to say, like, that’s a correct question, that’s a wrong question?Rune Kvist [01:18:09]: Yeah.Swyx [01:18:09]: Right? So, like, I don’t know.Vibhu [01:18:10]: We’ll have an exit.Rune Kvist [01:18:13]: Yeah.Vibhu [01:18:14]: Vent your frustration to someone that’s listening, you know?Rune Kvist [01:18:16]: Exactly. And I think there’s also you. Or it matters a lot what the promise is. So if the promise is, “Hey, if you’ve taken my course, you will not f**k up,” you can’t make that promise, clearly. You could make a promise of like, “Here’s the. Some important things that everyone should at least know,” and then you have to fill out the rest there. At least the promise changes. Of course, there’s some subtlety in how do you communicate this such that people really get it. But I think it’s important to dial in, and we have a section in our center on, like, what is the promise and what is the promise not, because it’s impossible to guarantee that nothing will go wrong. If you need a guarantee that nothing will go wrong, you cannot work with frontier AI, but you can make some claims.Swyx [01:18:56]: Yeah, for sure. Cool. Wanted to end with open-ended, where is AIUC going? I think you talked about model stuff, robotic stuff. And just open-ended, like, where, you know, what is in the future for you guys?AIUC’s Future, Hiring, and Universal Red TeamingRune Kvist [01:19:10]: Very near term, we’ve now started to work with some of the frontier companies in each of the categories that are taking off, and we’ll, we’ll continue that work to make sure that we cover all of the use cases that are really taking off. We see a lot of interest once the first one in the market moves. Lots of people want to follow them. And we think basically AIUC-1 will get to a point where all of the Fortune 1000 will organize their risk processes around the standard.Swyx [01:19:38]: And you have 50%?Rune Kvist [01:19:39]: No, we do not have 50% today.Swyx [01:19:41]: Oh.Rune Kvist [01:19:41]: I think there is some world where probably by end of year, we might have representation in our consortium for 50% of the Fortune 1000.Swyx [01:19:48]: I see. Got it.Rune Kvist [01:19:49]: So that’s on the agent layer. And then we think, yeah, the model layer, it’s going to be. It just brings. Are now surfacing the concerns that are most likely to slow down adoption of AI. And then, yeah, we think robotics comes after that.Swyx [01:20:03]: What are you hiring for? What’s hard to hire for?Rune Kvist [01:20:05]: We are hiring, across the board, across market and numbers of technical staff. The people who do really well on our technical team are folks who are really excited about kind of being truly full stack. So let’s say when we started working with Cursor, we’d never done coding, tools before. So taking the standard and extending it, fleshing out what does frontier evals look like for long horizon coding agents, and taking that problem all the way from, like, working with Cursor and other folks in this space down to, like, fleshing out and shaping a new version of the standard. So that’s like a truly a full-stack, entrepreneurial technical people do extremely well at AIUC. The hard part is building one universal red-teamer that works across from Harvey to Cursor and everywhere in between that both has one consistent methodology, one consistent taxonomy of what are the risks and the attacks, and making. We think that’s fundamentally the best way to make consistent promises. JPMorgan is buying both. They want to have one framework, one consistent way that this comes out, and the mechanics of making that happen, you get to deal with a lot of the complexity of the real world. I think we have good answers in a bunch of that, but there are some pretty hard engineering problems in executing that.Swyx [01:21:18]: Can I push a little bit? Like, must you have one? Why not just be like, “Okay, look, forty percent of our use cases are coding agents, so we will specialize in coding agents,” and that’s the, that’s the one of them.Rune Kvist [01:21:28]: Yes.Swyx [01:21:29]: And then, okay, thirty percent is like RAG.Rune Kvist [01:21:31]: Yes.Swyx [01:21:31]: Just do RAG.Rune Kvist [01:21:32]: Yes. I think there’s some wisdom in that question.Swyx [01:21:36]: Yeah.Rune Kvist [01:21:37]: It depends on. What we found that there’s a lot of value on is being able to. If the decision-maker on the buying side, let’s say you’re the head of risk at a bank and your biggest risk is not in coding or in customer support or whatever the top two biggest use cases, but it’s somewhere else, you want to still make sure that framework has something to say about it to the burning question you have. Otherwise, you’ll not earn that trust. Now, it’s true that a lot of the burning questions follow where there’s a lot of adoption. And so great, so do we. So we do today do not cover every single edge, but we have a framework that we can add all of these within. We have one global taxonomy of risks and attacks that keeps adapting.Rune Kvist [01:22:20]: As, like, every time a new incident occurs that has never been seen before, great, let’s go and update the taxonomy so we bake that in. So I think we have one coherent universal approach. It doesn’t mean that we spend equal amounts of time on code and insert niche use case. We do spend time where people care. We think it’s very valuable to have one language.Swyx [01:22:43]: Yeah. That makes sense. That’s, that’s a, that’s an important choice. We were going to end actually, but I thought of one final ending closing question, which is, take this however you want, right? Let’s say one and a half years from now, OpenAI’s secret panel of five experts declares that we have reached AGI.AGI, Watchdogs, and the Need for Independent OversightSwyx [01:23:00]: Do you expect your business to change?Rune Kvist [01:23:03]: No. I think there is some important way. I think the last businesses to exist beyond the labsSwyx [01:23:10]: Will be underwriting.Rune Kvist [01:23:12]: Well, there is one, there’s one job that the labs can never do for themselves, which is to be their own watchdog.Swyx [01:23:19]: There you go.Rune Kvist [01:23:21]: So I think kind of to the extent that you believe this frame of, like, you’ll see hyper-concentration, like the labs will kill all the startupsSwyx [01:23:29]: YeahRune Kvist [01:23:29]: Which, we can go into the pros and cons.Swyx [01:23:32]: I feel like the labs actually care a lot about this, right? There was the whole superposition, what do we do when we have models smarter than us and then a tier above, right, models smarter than them training them.Rune Kvist [01:23:41]: YesSwyx [01:23:41]: The labs actually think about this a lot.Rune Kvist [01:23:42]: They think a lot about. I think the there are some of the smartest people on these topics work at the labs. So the problem is not whether they care. The problem is that they will all be stuck in a race where they might have incentive to cut corners, and they might have incentive to withhold information from the government, et cetera. And so one kind of feels like eternal truth is that you need an independent third party to go and inspect that data and share information, in this case, say, with the government. It’s more of an incentive problem than an interest problem. I think they’re fundamentally all trying to make this go well.Swyx [01:24:14]: What I’m not hearing is, like, AGI, whatever that label means to you, to me, to them, doesn’t fundamentally have, like, a qualitative shiftRune Kvist [01:24:23]: CorrectSwyx [01:24:23]: In, likeRune Kvist [01:24:24]: CorrectSwyx [01:24:24]: You still have to evaluate the models.Rune Kvist [01:24:26]: And I think the one thing that would make this a qualitative shift is, there’s. For some definitions of AGI, it will just get nationalized. It’ll be a threat to sovereignty.Swyx [01:24:34]: Yes.Rune Kvist [01:24:34]: And then at that point, it kind of maybe every company is the government is every company. I struggle to think about that world. But at that point, you’ve kindSwyx [01:24:42]: We. I don’t think we’ll move fast enough.Rune Kvist [01:24:44]: Right.Swyx [01:24:44]: You know, like, we’re not, we’re not set to do that.Rune Kvist [01:24:47]: Yeah.Swyx [01:24:48]: But I have discussed this a lot on the podcast.Rune Kvist [01:24:51]: Yeah.Swyx [01:24:52]: I mean, you know, as far as the watchdog concern, I will also mention that because I have my finance background, I often think about the scene in The Big Short where they talk to, like, Moody’s, but also Standard & Poor’s. And then the lady at Moody’s is like, “Well, if I don’t give you a triple A rating, you’re just going to go down to Standard & Poor’s.”Rune Kvist [01:25:09]: Yes.Swyx [01:25:09]: So actually the watchdog is a natural monopoly because if you have race dynamics in watchdogs, then the watchdogs will compete each other to the lowest possible standard.Closing: Insurers, Incentives, and Trust InfrastructureRune Kvist [01:25:18]: Correct.Rune Kvist [01:25:20]: And so I think what one of the things, one of the reasons why we’re very excited about having insurers be around this table is that insurers are the only ones that do not have this dynamic because they pay the bill. If they keep lowering the pricesSwyx [01:25:32]: Yeah, you willRune Kvist [01:25:33]: They also pay the bill.Swyx [01:25:33]: You won’t find the market clearing.Rune Kvist [01:25:35]: And this is not true for Moody’s where, they don’t directly pay the bill if they make recommendations that are off. So we think that balancing factor is pretty important. And I think it also highlights that there’s, like, no system that’s perfect. You need scrutiny of Moody’s, you need scrutiny of the watchdogs, for sure.Swyx [01:25:52]: Beautiful. Thank you so much for indulging. This is a beautiful conversation covering everything. Congrats on your success so far.Rune Kvist [01:25:59]: Thanks for having me.Swyx [01:26:00]: Yeah. Awesome.Rune Kvist [01:26:00]: Appreciate it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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