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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/RuneKvist AIUC https://aiuc.com Timestamps 00:00:00 AIUC’s $40M Round and the Risk Bottleneck for AI 00:01:07 From Scaling Laws to Early Anthropic 00:07:58 Why Trust, Not Capability, Could Limit AI Adoption 00:12:19 Founding AIUC and Building AIUC-1 00:18:52 How AI Agents Are Audited and Stress-Tested 00:25:26 Frontier Models, Government, and the AI Trust Gap 00:33:32 Cyber, Child Safety, and AI-Enabled Biological Risk 00:38:14 Why Standards and Insurance Belong Together 00:41:45 What Does an AI Insurance Policy Actually Cover? 00:50:44 The $20 Cursor Subscription and the $200M Plane Crash 00:53:53 AI Liability, Monitoring, and Earning Enterprise Trust 00:56:21 From AI Agents to Models to Robotics 00:58:29 Copyright, Adverse Selection, and AI Insurance 01:03:28 Evals, Mechanistic Interpretability, and Eval Awareness 01:08:36 The Impossible Enterprise AI Mandate 01:11:52 Prediction Markets vs. AI Audits 01:14:43 Should AI Engineers Be Certified? 01:19:10 AIUC’s Roadmap, AGI, and Who Watches the Watchdogs? Transcript Introduction: AIUC, the $40M Series A, and Risk as the Adoption Bottleneck Swyx [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 Anthropic Rune 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 shit.” 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 Ball Rune 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 is Swyx [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. [truncated for AI cost control]