Nat Friedman Leads $15M in Superintelligence Insurance Company | AIUC

Rune Kvist, CEO & Founder of the Artificial Intelligence Underwriting Company (AIUC), joins Sourcery to break down how his team is building the confidence infrastructure for AI adoption — and why every AI agent will soon need to be certified and insured. AIUC just came out of stealth with a $15M seed round led by Nat Friedman (NFDG), with participation from Emergence, Terrain, Ben Mann (Anthropic co-founder), former CISOs of Google Cloud & MongoDB, and other top AI operators. Rune was the first product & GTM hire at Anthropic, and now he’s building the “confidence infrastructure” for the AGI era — combining standards (AIUC-1), audits, and insurance to de-risk AI deployments. Key Takeaways: • CEO Rune Kvist: Anthropic’s first product & GTM hire • AIUC is building insurance, standards, and audits for AI agents • Enterprises won’t adopt AI without trust & accountability • AI agent failures — like hallucinations or leaks — are inevitable • AIUC-1 is a fast-updating standard to certify agents (SOC 2 for AI) • Every AI agent will need certification and insurance Molly on X: https://x.com/MollySOShea Rune on X: https://x.com/RuneKvist Visit Artificial Intelligence Underwriting Company: https://aiuc.com Brought to you by: • Brex—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, and travel. As a Sourcery subscriber you get: 75,000 points after spending $3,000 on Brex card(s), white-glove onboarding, $5,000 in AWS credits, $2,500 in OpenAI credits, & access to $180k+ in SaaS discounts. On top of $500 toward Brex travel, $300 in cashback, plus exclusive perks (like billboards..) Visit: brex.com/sourcery • Turing—Turing delivers top-tier talent, data, and tools to help AI labs improve model performance—and enables enterprises to turn those models into powerful, production-ready systems. Visit: turing.com/sourcery • Kalshi—The largest prediction market and the only legal platform in the US where people can trade directly on the outcomes of future events: kalshi.com/sourcery • Fourthwall—The #1 way to sell merch online — Fourthwall is the easiest way to launch a fully branded merch store—used by big brands like MKBHD, Acquired, & even the Smithsonian. 100+ products. No upfront cost. Visit Fourthwall to start today: https://fourthwall.com/ Follow Sourcery for the latest updates! https://www.sourcery.vc/ (00:00) Launching AIUC: $15M Seed Led by Nat Friedman (01:49) What AIUC Does: Insurance, Standards, and Audits for AI Agents (04:20) History Repeats: How Insurance Enabled Innovation (Franklin, UL, Cars) (08:14) The AI Divide: OpenAI vs Anthropic vs SSI (10:39) Confidence Infrastructure: Helping Enterprises Trust AI (14:13) How AIUC Tests Agents & Updates Standards Frequently (17:03) Underwriting AI: Modeling Risk When Agents Go Rogue (21:40) From AGI to Superintelligence: What Comes Next? (28:36) Why AI Regulation Won’t Be Federal This Year (34:36) What Is a Kill Switch — And Do We Need One? (39:28): One Dominant Standard for AI Insurance (45:41) What Enterprise AI Will Look Like in 3–5 Years

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  No one really today knows how these AI agents fail. You might have seen OpenAI launched their ChatGPT agent. And Sam Altman on stage puts out a big caveat, like, don't use it for anything important. We don't yet know how this works. Certificates and insurance is a way to build confidence for companies to adopt AI. What it does is we've built a standard, AIUC1, that outlines what are all the things that can go wrong with an AI agent? And for each of those risks, what are the things an AI company should do to mitigate that risk? We are coming out of stealth, having raised a $14 million seed round from Nat Friedman, Emergens and Terrain.

• 0:32Molly O'Shea?

  Your investors are super ingrained into this base, especially Nat Friedman. I mean, he's now a part of the superintelligence team at Meta. What kind of advisement are you getting from them? Because you're an underwriting company that does insinuate you are a financial institution. How do you put dollars against this? Could you break down what is a kill switch in AI?

• 0:51Speaker 00

  It might be what's called a loss of control scenario, where basically the AI agent becomes so agentic that it starts to take action that we no longer want. So if kill switch is like...

• 1:11Molly O'Shea?

  Runa, welcome to Sorcery.

• 1:13Speaker 00

  Thank you. Thanks for having me.

• 1:14Molly O'Shea?

  I think we have some big news today.

• 1:17Speaker 00

  We do.

• 1:17Molly O'Shea?

  Can you share this big news?

• 1:19Speaker 00

  We are coming out of stealth, having raised a $14 million seed round from Nat Friedman, Emergens and Terrain.

• 1:25Molly O'Shea?

  Wow, this is big. How big was the round? What was the process like?

• 1:29Speaker 00

  It's $14 million.

• 1:32Speaker 00

  It was a very quick process. We got an inbound term sheet preempted. And that prompted us to look up. I knew I wanted to work with Nat. Got introduced to him. A week later, we had a term sheet and we got it done.

• 1:47Molly O'Shea?

  That's simple.

• 1:48Speaker 00

  Yep, pretty much.

• 1:49Molly O'Shea?

  Okay, given that, I think we should, we should understand more about how we got here, how it became that simple.

• 1:55Speaker 00

  Yep.

• 1:55Molly O'Shea?

  So what exactly is your background? I know you're very impressive.

• 1:59Speaker 00

  Most relevant to this conversation, I, I started working in AI when I joined Anthropic back in early 2022 as the first person to work on product and go to market. This is before ChatGPT. Is there that moment in time where it just become obvious that something like radical was happening here, but the world just did not know yet? And I joined Anthropic to help think through like, where is the value gonna accrue? How does Anthropic sit into that? How are businesses and individuals gonna interact with this AI?

• 2:28Molly O'Shea?

  So now you're working on the artificial intelligence underwriting company.

• 2:33Speaker 00

  That's right, bit of a mouthful.

• 2:35Molly O'Shea?

  Bit of a mouthful, very intense.

• 2:38Speaker 00

  Super fucking intense, yes. Yeah, we ensure and certify AI agents.

• 2:44Molly O'Shea?

  So what does that mean?

• 2:45Speaker 00

  Basically, no one really today knows how these AI agents fail. You might have seen actually yesterday, OpenAI launched their ChatGPT agent. And Sam Altman on the stage puts out a big caveat, like, don't use it for anything important. We don't yet know how this works. It's kind of like, if you're ordering a burrito and you're not that hungry, go ahead and use this, this is an awesome tool. But when you start putting in this in like meaningful context, if you're around a hospital or a bank or any kind of big business with real customers, no one really knows whether they can trust any particular agent. And certificates and insurance is a way to build confidence for companies to adopt AI. What it does is, we've built a standard, AIUC one, that outlines what are all the things that can go wrong with an AI agent?

• 3:29Speaker 00

  And for each of those risks, what are the things an AI company should do to mitigate that risk? And spelling that out in enough detail, across hallucinations, jailbreaks, all these terms that people have heard about, that you can have an independent third party go in and audit a company and test that agent to see how likely it is to fail. And if you do well, you pass the certificate. The insurance comes on top of that. If you meet the certificate, you may want to go a step further in taking risk off the table from your customers and insure them in case it fails. So if it hallucinates a refund policy, if it spews Nazi propaganda, if it leaks your customers' data, if it discriminates against the people you're hiring, the enterprise is gonna be on the hook.

• 4:13Speaker 00

  And so everyone's asking, who's gonna pay? And that's where insurance can step in and help, help pay the bill. How did you come to this model? Yeah, so it started with a, like a deep trip into the rabbit hole and like, throughout history, how have markets solved these collective action problems? And the most interesting example starts back in seventeen fifty-two. Ben Franklin, all-American hero, and he's living in Philadelphia at the time. Philadelphia's population was growing ten X over that century. And the houses were just getting closer and closer and they were just burning down frequently. So the whole city would burn down. And Ben Franklin wanted to solve that problem. So he started an insurance company, the first fire insurance company. And all of a sudden,

• 4:59Speaker 00

  he was on the hook financially when houses burnt down. So he had a direct financial incentive to go and reduce fire risk. So they asked themselves, how are we gonna make sure that all the houses are built in a way that makes them less likely to burn down? They built the first building codes, saying like, oh, how far should houses be apart? Can we make sure to not build houses under trees that are likely to catch lightning? And then they wanted to make sure that people obviously met these standards. So they built the playbook, that probably wasn't the term he used, but the playbook for fire inspections, really the audit mechanism. So this triangle between insurance, standards and audits was something that he stumbled into. We call it the incentive flywheel. And that then shows up again and again throughout history.

• 5:41Speaker 00

  So when the light bulb came out, starting like around the year nineteen hundred, light bulbs also led to the houses burning down. The insurers again were like, we don't wanna pay for this. So if you look at any of these light bulbs, they will have a little UL certificate, the underwriter's laboratory, funded by the insurers that like specified, how do you need to test light bulbs and toasters to make sure they don't burn down? Later on, cars killed a lot of people as they were getting really like everyone needed their own car post World War II. And it was the insurers that were taking the charge on looking at the data for what's causing accidents. Figuring out what are the interventions that we need?

• 6:23Speaker 00

  What is the kind of car crash testing we need to incentivize the car manufacturers to build safer cars? And a lot of their research is what then eventually became embedded into laws, like seatbelt laws, airbag adoption. Before that was law, that was often incentivized by the insurers, like you get cheaper insurance if you have a car with an airbag. So that model of insurance, building the standards and running the audits to both build confidence in technology and incentivize security is really battle tested. And that's what we think is missing when it comes to AI.

• 6:57Molly O'Shea?

  I think it would be good to take a step back. AI has evolved so fast, like we really weren't talking about agents and like a tangible aspect until maybe a couple of months ago, like now there's real applications and usability with it. So I'd love to get your perspective of the evolution of AI and AI safety. Some companies baked it in from the start, some just progressed and were all gas, no brakes.

• 7:25Speaker 00

  Yep.

• 7:25Molly O'Shea?

  So what was that evolution like?

• 7:28Speaker 00

  Yeah, I think when you started all the way back in like GPT two, it's kind of obvious what was coming, like already at GPT two, you could start to see what GPT three, four, the agents, what are they going to look like? And there are kind of two schools, one was like, there's no risk today. GPT two was like literally could not spell his own name. So like there's, there's no risk, therefore all gas, no brakes. And then there's another school that's like, we can obviously see this is going to be a really big deal. In short order, they're going to be able to like help people hack. They're going to be able to help create bioweapons, they're going to be able to do all these kinds of things. And we need to think about that in advance.

• 8:09Speaker 00

  And that debate kind of played out for a long time without really any evidence either way, because it was just too early as the models were just getting better and better. And now we're getting to this point where. Everyone agrees pretty much that this is getting pretty serious. So you have companies like Anthropic have always been in the camp like, hey, this is really risky. Companies like OpenAI have gone a little bit back and forth on what they think. And with their release yesterday, they're saying like, wow, now the risk levels are really high. They are actively sharing that with the world that they're now like enhancing the security levels. So I think we thankfully haven't had the big incidents yet. But everyone's agreeing that now they're sufficiently good that they can do real damage. When the models are not good, they're not going to do any damage.

• 8:51Speaker 00

  But as they get really good, we're going to delegate like real work to them, real task, real critical systems. And no one has sufficiently good answers to say, oh, there's no risk here.

• 9:01Molly O'Shea?

  Right. So there's companies like, I call it super safe, super intelligence. I don't know why.

• 9:09Speaker 00

  Extra double.

• 9:09Molly O'Shea?

  I just add, I just add more supers to it.

• 9:12Speaker 00

  Yes.

• 9:12Molly O'Shea?

  There's companies like that. Ilya takes it super seriously.

• 9:15Speaker 00

  Yep.

• 9:16Molly O'Shea?

  Again, super. And then with Anthropic, maybe we could start with Anthropic and then go to SSI. They have different levels of safety. So what do those levels mean? And could you break those down?

• 9:29Speaker 00

  Yeah. So they call them security levels, security level one, two, three. Really, it's a process of saying when models get sufficiently good and therefore sufficiently dangerous, we need to match that with the levels of security we're implementing. What are the level of tests we're going to run? What is the level of cybersecurity protection we need to have in place? What are the kind of policies that match each of these security levels? And so we're slowly climbing through the temperatures now. And Anthropic, you were talking about just before we got on here about whether security level three is going to be a thing. And I think the short answer is, yeah, it's just a question of time. And is it going to be this side of Christmas or after? I don't know yet, but it's really like companies ways of. Matching proportionally the risk with the mitigations.

• 10:13Molly O'Shea?

  Hmm. And then what about super safe, super intelligence?

• 10:17Speaker 00

  They've spoken much less publicly about it and also they don't have a, their whole strategy is one shot to safe, super intelligence. So they haven't put out a product yet. And so therefore a bunch of the risks that come when you put the product out in the world, they don't have to deal with. There's still some risk when you're building it, as you're building it in-house. But these things are much more relevant to Anthropic and OpenAI where you have thousands, millions of people using this every day.

• 10:39Molly O'Shea?

  We're assuming some companies have that. We also assume a lot of companies don't have that. One of the largest companies is telling people caution, watch out, don't put your sensitive data in it. That's where you come in. So could you explain a little bit further on AI UC, right?

• 10:59Speaker 00

  Got it.

• 10:59Molly O'Shea?

  Use the acronym.

• 11:00Speaker 00

  There you go. What we do?

• 11:01Molly O'Shea?

  Yeah, yeah, like.

• 11:02Speaker 00

  Yeah.

• 11:03Molly O'Shea?

  Where do you come into play and how do you start integrating with partners?

• 11:05Speaker 00

  Yeah. So the starting point for all of this is if you're a business looking to adopt AI, you face a hard choice. On the one hand, if you're a bit nervous, you face that your competitors are going to basically make you irrelevant by moving ahead. Or you can lean into this new technology wave as everyone's saying they're doing. And then you risk making headlines. And so we talked about some of the examples before of like hallucinated refund policies, brand disasters happening, data leakages, et cetera. The question they're all trying to answer is like, can we trust this AI agent? And where we come in is creating the confidence infrastructure between the AI companies and the people that are adopting this.

• 11:45Speaker 00

  So we work with the AI companies to earn the confidence of their customers by saying, hey, these are all the things you guys need to pay attention to. Helping them meet that standard, these best practices for what businesses really need to have confidence in it. And then as an extra layer of protection, saying if something goes wrong, we will pick up the bill. So if you work backwards from that conversation between an AI company and an enterprise, it's really like, how can you equip their companies to say the most confidence building things in a sales, in a sales meeting? And typically, the problem they have is that they all want to say how responsible they are. But enterprises look at them and be like, we obviously can't trust you. When it comes to financial products, you obviously have third-party auditors.

• 12:29Speaker 00

  Even in cybersecurity today, if you get a pen test, you can't do that yourself. No one's going to trust you to grade your own homework. So you need a third-party that can basically say whether you're up to scratch. That's the role we play, where the independent third-party that can kind of broker the confidence between them.

• 12:44Molly O'Shea?

  You mentioned your SOC 2 for AI agents. For people who aren't familiar with SOC 2, can you break that down a bit?

• 12:51Speaker 00

  Yeah. So SOC 2 is a security standard that says, if you're selling a SaaS product, what are the risks that you need to look out for? Things like privacy and security. And what are the things that every company must meet to get SOC 2 certified? And SOC 2 certified is basically like a gold star you can put on your shirt when you go and you meet your customers and say, hey, we meet all these best practices that we know that you'll need to be able to adopt this. Because of course, enterprises have real security requirements, and SOC 2 is just a way of summarizing that into one kind of trusted badge. There's a question about like, how trustworthy it is, but the purpose is really to broker trust between software companies and their buyers. But SOC 2 doesn't speak to all these new concerns that are coming up with AI agents.

• 13:34Speaker 00

  It doesn't say anything about, what are you doing with my training data? What are you doing with my data as it comes to your training models? How are you thinking about measuring hallucinations? Oh, we read this blog post about jailbreaks. What are you, are you guys protected against that? What mitigations do you have in place? Do you have a plan when your AI agent inevitably fucks up? All of these kind of new questions SOC 2 is silent on. And so we've seen a lot of demand, both in the AI companies, but also from the enterprises being like, who can create this like, language for what are the risks that really matter? And actually, like, down to the level of a codebase, what do you need to do to mitigate that risk?

• 14:13Molly O'Shea?

  How do you go down to the codebase for this? Because things are moving so fast. Like, how do you model this out?

• 14:20Speaker 00

  Core of the standard is we actually just have to test how the AI agents perform in real life settings. So if you're worried about, if you want to know whether an AI agent can get jailbroken, the best way to figure out is to try and jailbreak it. And the field is moving really fast. So what's really important is that you build this connection between the latest research papers and what the standard does and the kind of testing that's required. So where some of the old standards get updated every five, 10 years, we're going to update our standard, AIUC1, every three months to make sure that as new kind of threat vectors come out, as new capabilities come out, that the testing that's being done reflects what's possible.

• 15:01Molly O'Shea?

  Because you're an underwriting company that does insinuate you're a financial institution.

• 15:06Speaker 00

  Right.

• 15:07Molly O'Shea?

  How do you put dollars against this? When you're underwriting, like, what are you underwriting? And how do you actually cost that out? People are probably looking at AI, of course, as a trillion dollar industry. And so if these companies are growing so fast and are, you know, like $10 billion, now they're hundreds of billions of dollars. They're approaching a trillion dollars in value. How do you underwrite this?

• 15:30Speaker 00

  Yes. The first thing is, what are the risks that you want to cover? And so if we're backwards from the problem of we're trying to build confidence infrastructure between AI companies and businesses, you start from what are the things, what are the risks that are keeping the businesses up at night and are holding up adoption? And those are the things that we talked about before. Hallucinations, jailbreaks, are you going to mess up the refunds? Do you, are you going to infringe IP, et cetera, et cetera? For each of those risks, you have to figure out, what is the, insurers think about this in terms of what is the frequency? How often does it fuck up? And what's the severity? Like, how bad is it when that happens? The core of underwriting these systems is testing, really getting a robust sense of any particular AI agent, how likely is it to fail?

• 16:13Speaker 00

  And you can only do that by, like, just getting it to fail thousands and thousands of times over. And then you want to connect that with. Some of these things are actually not new. Like, so what is the cost of a brand disaster? Well, brand disasters are not new. Maybe AI-generated brand disasters are new, but we have tons of data on like, what does it actually cost? And so connecting these two up is the core to underwriting it. And then frankly, there's going to be some trial and error.

• 16:40Speaker 00

  This field is moving. There are gonna be mess ups that no one will have predicted. So there's also a way of like, how do you structure the risk so that you don't lose the skin on your nose when that happens? Back to the amounts of, you say, like, trillions of dollars. We obviously don't have a, it's not a $14 trillion seed round we've raised.

• 16:57Molly O'Shea?

  Is there, this is also insinuating there's a, there's an AI reinsurance company.

• 17:02Speaker 00

  Yes.

• 17:03Molly O'Shea?

  So.

• 17:03Speaker 00

  Coming up next, next podcast.

• 17:07Speaker 00

  In the first instance, we work with existing insurers.

• 17:11Molly O'Shea?

  Okay.

• 17:11Speaker 00

  So when we help companies get insured, ultimately, the guarantee of the money comes from established insurers, some of the oldest ones in the, in the industry, that have the balance sheets to provide this. What these insurance companies are missing is, frankly, they don't know how AI works. They don't really understand the risk and they don't know how to run these tests. So they bring the balance sheets and some of the rigor in how to think about the risk. And we bring the technical capabilities to saying like, what do we think the risk is here? What is the kind of data? What is the kind of monitoring that was required here? And when you put that together, that's what unlocks this industry.

• 17:47Molly O'Shea?

  You've mentioned a couple scenarios, a couple use cases. I mean, there, there was the whole Gemini situation early on with their image generation product. There was the Nazi thing with Grok. There was, there's like all these instances for that. But I'm not sure many people have talked about the enterprise level of this and what kind of data is being affected at the enterprise. So could you go into that a little bit further?

• 18:10Speaker 00

  Yeah, they're actually kind of related. I think what those examples are showing is that it's actually really kind of hard to build a powerful model that is never going to say something outrageous. And so if you're an enterprise and you're using, you're building, you're deploying a customer support chatbot, for example, built on top of those models, someone is going to be able to get your customer service chatbot to say something outrageous. And so that's something that keeps them up at night. You'll have seen. Lots of people have questions about like, oh, is OpenAI training on my data?

• 18:50Speaker 00

  Great. Enterprises have teams dedicated to privacy that all they're doing is checking what is happening with our customers' data. And so they have the same questions like, what is actually hap- are you training on our data? And data leakage is a big problem. You've seen some of these chatbots leaking other customers' data. So all of these kind of, you might think of them as kind of mundane concerns, but they just really matter for businesses. Like, a bank and a hospital makes really strong promises to their customers, and it has to flow through the entire chain. So when they, before they can work within a company, they need to know that those promises are going to be met. That's the beginning of that answer.

• 19:28Molly O'Shea?

  I'm curious what the state of adoption is in enterprise, if you have any sort of, like, inside look onto that. I've, I've heard rumors about, you know, the AI legal space, and there might be huge, high-flying companies that rhyme with Schmarvy, but, you know, not many actual lawyers are, are using it in-house. So like, implementation inside is, is low, but adoption is really spread across. How deep is AI, like, actually implemented within the enterprise? And how long do you think that's going to take?

• 19:59Speaker 00

  Yeah. So we're transitioning from this phase of, everyone for a couple of years have known that they must say to the world that they're using AI for everything. Every board would have said, like, we have an AI mandate, we're going to adopt or die. And so that means everyone talking a big game about how they're using AI. In practice, there have been a lot of proof of concepts, sandbox, enterprise deployments, internal deployments, kind of in low-stakes scenarios. And that's enough that you can say you're using AI. But there's still a gap for this to, like, when you look at what is the number of lines of code that are being contributed to JP Morgan's codebase that's generated by AI? As of very recently, that's extremely low. It's now starting to happen. We're starting to make, like, a way into, like, real use cases, both hospitals adopting this, banks

• 20:42Speaker 00

  adopting this, and businesses adopting this. But it feels like it's really in the last six months that you're moving from toy projects to kind of real adoption. But still, that's just the frontier companies that are-

• 20:56Speaker 00

  Like when you look at the long tail of businesses, they're still like, still lots of the world runs on paper, right? Like, there's still a long way to go.

• 21:06Molly O'Shea?

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• 22:08Molly O'Shea?

  As we think about superintelligence, superintelligence has become like the next word. I feel like we've already skipped over AGI. AGI is out, we're over it. We were tired of hearing about it. Now we've skipped to superintelligence.

• 22:23Speaker 00

  And now the word, we also don't know exactly what it means, but it sounds really important.

• 22:28Molly O'Shea?

  Sounds really important. So what do you think about superintelligence?

• 22:32Speaker 00

  Let's start from what it is. I think AGI was kind of like, can we build a system that's as smart as a human across all of the domains? And everyone was like, that's the milestone of the future. When that happens, everything's going to change overnight. And now we're kind of in a situation where we have systems that, they still fail in a number of dumb ways, but it's starting to like, they're really gosh, they're really smart, they're really good at coding, they're really good at science, they're really like, getting pretty good at common sense. They're not very good at robotics, they're not very good at a number of things. But in a lot of ways, when you talk to something like O3, From OpenAI, you're like, this one is just a lot smarter than me.

• 23:13Speaker 00

  And so in some ways, it's like, we have AGI now, and yet the world hasn't completely changed. So then what's next? And superintelligence is like this idea of like, well, if you plot very roughly AI in a curve like over time, how is the IQ going up? We've now like hit human level for a lot of use cases, but it's not going to stop here. Now you can use AI to build AI. Great, this is just going to keep going up. And so superintelligence is just the idea that you'll have something that is like radically smarter than us, where we look like what monkeys look like to us. And so far, it's a little bit hard to pin down reasons why that's not going to happen and why that's not going to happen in like within a decade.

• 23:57Speaker 00

  And that seems like the most important fact.

• 23:59Molly O'Shea?

  You were an early employee at Anthropic. I'm sure you're still friends with people there and you're very connected to community. Are there like actual concerns on the inside about the rapid adoption? Or I guess the rapid advancement of these models? I mean, clearly you're building the company in here, but like, I'm curious about the other externalities around it, whether it's like jobs or yeah, I guess let's start with jobs.

• 24:24Speaker 00

  Yes, short answer. I think everyone who's building these models is in like a kind of a dual state where everyone's like, wow, this is just so exciting. We're just making new breakthroughs. This is going to advance science. This is kind of like, yeah, this is going to allow us to cure cancer short order. Like who does not want that? And the field is just moving so fast. And so it's just like an electric space. And at the same time, everyone's also like, wow, this is just deeply on charter territory, both at the model level, but also at the societal level. And just no one has good answers for what's going to happen to employment. Everyone is just doing their best at guessing, really. And so those are, those are real concerns. And I think what a lot of people are kind of responding to that is like, everyone had those concerns when like,

• 25:08Speaker 00

  back when the industrial revolution was going to happen. It's like, what are going to happen to all the jobs? The computers came out, isn't that going to eradicate all the jobs? And it turns out it didn't. The question is, is this time different? I think there's really good reasons to think that this time is really different. And in particular, because when you look at the marketing claims of all the AI and companies. Their their value proposition is we're gonna do the work for you. And so everyone's like, well, so what's gonna be left?

• 25:34Molly O'Shea?

  Yeah.

• 25:35Speaker 00

  And so I think that's just a very real risk. Anthropic have made some pretty bold statements as to what they think is gonna happen to white collar jobs. I think they're like, I can't remember the details, but like by the end of next year, you're gonna you're gonna have models that can automate something like fifty percent of all white collar jobs. And that's like a terrifying prospect. You're now starting to see Congress waking up and using terms like AGI. They haven't they haven't made it to superintelligence yet, but that seems to be about next month.

• 26:00Molly O'Shea?

  Yeah.

• 26:00Speaker 00

  Uh, thinking about like, this is gonna just rip the social contract. And that is not a problem that the model companies are gonna solve. Um, yeah.

• 26:11Molly O'Shea?

  I was actually just at the Raze AI conference in Paris.

• 26:15Speaker 00

  Yep.

• 26:15Molly O'Shea?

  It was amazing, like everyone was there. I don't know if you were there, but maybe you were.

• 26:19Speaker 00

  No.

• 26:20Molly O'Shea?

  Uh, there was no sign of superintelligence talk there either. And so I was like, kind of joking, it's funny about your joke about Congress, but.

• 26:27Speaker 00

  Yes.

• 26:28Molly O'Shea?

  I was joking, I tweeted like, degrowth is real. We're not even talking about superintelligence in Europe, they're way behind.

• 26:34Speaker 00

  Absolutely. I mean, I grew up in Denmark. This is, yeah, they live in a, most of them live in a different world. You'll see a lot of commentary based on like people who tried their first version of G P T four. And they're just confident that this thing cannot do math.

• 26:52Molly O'Shea?

  Yeah.

• 26:52Speaker 00

  And it takes about fifteen seconds on a mobile phone to be like, no, it can it can really do math. And at the same time, I also have a lot of respect for, like, if you build products with these models every day. It's also hard to believe that AGI is gonna happen by end of twenty twenty six. The superintelligence, like, it's it's actually still kind of hard to get some of these things to work in practice as reliable as you need it to for this to like, I don't know. Uh, schedule doctor's appointments with the level of precision, like life and death scenario. And so I think it's like, actually really useful to embody both of those perspectives, both like, wow, the real world is really freaking complicated. And the last mile really matters and like the models can't do it on their own yet.

• 27:35Speaker 00

  So there's a lot of work to be done and hold in mind like, oh, two years ago.

• 27:41Speaker 00

  Thinking about whether we could like get the precision that's required to base life and death decisions on it wasn't even a question. It was terrible. I couldn't write marketing copy. That was really fucking awesome with marketing copy. And it's a real question about whether it can run tasks that life and death depends upon. And that's just insanely fast. And so you need to hold both of those perspectives in at once.

• 28:03Speaker 02

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• 28:39Molly O'Shea?

  One of the craziest things that has happened recently that gives it more tangibility against like, oh, no, this is really moving super fast is Google's launch of V03. Because now we're seeing what deep fakes like actually look like. Like, these videos are, they're hilarious. Some of them are just so scary. Yes. Then I think, what was that show? I think Mountain Head came out. And so like, they were kind of like modeling the premise on the story, like, this big tech CEO put out this like deep fake generator for this social media platform. And then all of a sudden, like, war breaks out, like terrorism, like all this kind of stuff, like rips the earth and shatters

• 29:21Molly O'Shea?

  it. And so I think like, once we kind of saw, and we are, you know, we're creatures where until something bad happens, we'll really react to it. We're not as proactive as some people would like. I mean, there are definitely some more cautious people out there. But I think like that instance made it much more tangible. I think like the Grok 4 release made it more tangible because that was like a huge leap in performance. Yep. From your perspective, do you think? Talking back to the government layer of this, do you think AI regulation will become federal law this year? CalShe has a market on this and it's like, I think the last time I checked it was like 25%.

• 30:05Molly O'Shea?

  Like, there's a 25% chance right now on their markets.

• 30:07Speaker 00

  Yes. To answer your question directly, I think there's not going to be federal law this year. But it's worth noting that there's about a thousand state bills in the US, so not federal but state level, that regulates AI. And or is proposed to regulate AI in some level. So the public consciousness has really caught on to AI. Because actually, deepfakes are just quite visceral. Like, it doesn't feel that futuristic. Like, you can just imagine that happening to your daughter or your cousin. And that is happening, right? And I'm starting to see people lose their minds to AI. And so the regulatory machinery is actually working.

• 30:51Speaker 00

  I'm just not sure it's going to happen at the federal level. That's working really fast. And there's right now a debate between the all gas, no brakes, which is basically relying on the company, the AI company's voluntary commitments, saying like, we're going to be really responsible. And then the top-down legislation. Which of these is right? We think both of them have a space, but actually, the challenge is because AI is moving so fast, the chance that the regulators are going to get it right is just really quite small. Take EU AI Act, starting with like a regulatory first approach. They started drafting that in 2021. It's a long time ago in AI world.

• 31:35Speaker 00

  And just as it's about to be implemented, there's now concerns about like, oh, is it actually fit for purpose? Do we need to like redraft it? And so the pace is really slow. And we're proposing a third way, somewhere in between, for markets to come in and regulate. And that's throughout history.

• 31:53Speaker 00

  When you look back at like early fire insurance, when you look at when the light bulb came out and lots of the houses were burning down, when you look at early cars were really quite dangerous, often the market stepped in before regulation to try and figure out like, how do we actually prevent this? That was often led by the insurers, because the important thing about insurers is they pay the bill when bad stuff happens. And so they are directly incentivized to figure out what are the standards that are required for people to build safe cars or build houses that are not going to burn down? And how do we enforce that before we give people insurance policies? We're proposing that the market has a big role to play because it's going to be able

• 32:37Speaker 00

  to adapt a lot faster and it's going to enable faster adoption rather than slower adoption. So the EA Act is definitely not created to like, speed up European AI adoption. It's focused really on downside risk. And we think in a world where the US is competing on a global scene with China, you really needed to both be fast and secure. We think the market has a lot to offer there.

• 33:02Molly O'Shea?

  Yeah, I think it's, I think it's clear the US is not gonna hold back on this one, especially because it's, we're, we're on a global stage here.

• 33:10Speaker 00

  Yep.

• 33:11Molly O'Shea?

  To your point, it really comes down to like, this is the new race.

• 33:15Speaker 00

  Yep.

• 33:15Molly O'Shea?

  The AI race is the next one.

• 33:17Speaker 00

  Yep.

• 33:17Molly O'Shea?

  I'm actually attending, this might come out on the same day, but winning the AI race, it's, it's being put on by the All In Summit.

• 33:26Speaker 00

  Yep.

• 33:26Molly O'Shea?

  Well, the All In Podcast and the Hill and Valley Group. So that, that is a main question, and I think it's like coming together and making sure that we can win this and that we have the right tools to do it. And so you're, you're proposing and producing a middle ground, a middle layer to make sure, okay, we are going to take this step forward, we're going to go really hard, but like, we need the insurance to make sure if something slips up, someone's accountable.

• 33:53Speaker 00

  Yeah, because if all you want is to move really fast, you still have to look out for the kind of disasters that can set the industry back. So take a look at nuclear. Nuclear is set back by at least a generation by, of course, both Chernobyl, but also the Three Mile Island in 1979 in the US, where you give the public and politicians a reason to shut down, because if you're not doing it responsibly, you shouldn't be trusted to do it.

• 34:21Speaker 00

  So we think investments in security is like required for progress to be fast. And if you want progress to be fast, you need the businesses to adopt it fast. That's where all the revenue comes from. And you need to earn their confidence. So that confidence infrastructure is required. And so if you're all brakes, no gas, you should want businesses to be able to move forward with confidence. If you're a doomer, you should also want well incentivized market players to try and put a number on what is this risk? And drive adoption of these best practices that reduces the risk.

• 34:52Molly O'Shea?

  The alternative is also a kill switch.

• 34:54Speaker 00

  Yes.

• 34:55Molly O'Shea?

  Right?

• 34:55Speaker 00

  Yes.

• 34:56Molly O'Shea?

  Kalshi has a market on this. So I think Kalshi's market, last time I checked, it was like five to eight percent. I think it's likely that we'll have a kill switch.

• 35:04Speaker 00

  By end of year or?

• 35:05Molly O'Shea?

  I'm not sure what the range is. It might have been, I'm not sure. We can check. But could you break down what is a kill switch in AI?

• 35:14Speaker 00

  Yeah. So a kill switch is like, does anyone, anywhere have a button? That they can press if something really bad happens, that shuts off AI. And this is really interesting when one of the concerns that's coming up now that AI is getting really good and getting really agentic. Is that there might be what's called a loss of control scenario where basically the AI agent becomes so agentic. That it starts to take action that we no longer want. It might copy itself onto service that we can no longer control. And all of a sudden, humanity stands with a big disaster on our hands.

• 35:53Speaker 00

  And so a kill switch is like, can we, in all deployment scenarios, retain a button, that big red button we can press to make this go away? And maybe an analogy is like, in any given building, you're going to run a fire, what's it called? A fire drill where everyone exits the building. You cannot run a company where people can't exit the building pretty fast if something really bad happens. And that's the same thing here.

• 36:17Speaker 00

  Whether that's gonna happen by any of you, I don't think so, but it's

• 36:22Speaker 00

  it's growing in awareness, even in this administration, you see David Sacks articulating, like, these kind of catastrophic scenarios are not outside of the realm of possibility, and you need to plan for it. You actually also hear Chinese government taking, like, using the term kill switch pretty seriously and thinking like, you cannot develop AI without knowing what the kill switch is. Technically, it's pretty hard to get right, such that you in all deployment scenarios have this kill switch. But that's the kind of thing that would let us have the confidence to move a little bit faster if we knew that there was a big red button, if things were going wrong.

• 36:58Molly O'Shea?

  For national security, it would also make sense to have a kill switch, because if we have adversaries that deploy their AI, whether it's through robotics or through cyber attacks, you would want to know how to shut off their systems.

• 37:12Speaker 00

  Yeah, unfortunately, I think when people are thinking about kill switches, they, no one is planning to build a kill switch that, the US would not allow China to push out kill switch. And so I think it's only gonna be for your own AI. It's really focused on, like, is our AI going rogue? But when you're thinking about a military scenario, unfortunately, the kill switch may not quite help us. Yeah.

• 37:38Molly O'Shea?

  I'm coming from a defense tech, defense tech manufacturing conference. It's top of mind. I'm like, looking at all of these one-to-many drone deterrence systems, like Epirus. I'm like, how do we have that against other countries if it's deployed? Before, we were talking about real-life scenarios, and maybe this is an extreme, but you want to, you mentioned the Terminator. I was talking about iRobot. So could you explain that a bit?

• 38:10Speaker 00

  Yeah, the, the kind of scenario that is keeping a bunch of people up at night is kind of taking the current AI development to its perhaps logical conclusion and being like, well, they sure seem to be becoming more agentic. They seem to be able to take tasks, be able to do tasks effectively that are longer and longer and might take a human a day to do or a month to do. And while all these science fiction Movies depict is a scenario where at some point the interest of the AI starts to diverge from humanity and then we're in real trouble. And today, that remains, thankfully, mostly science fiction.

• 38:55Speaker 00

  And it's a little bit up to your like general attitude on risk, whether you think we're seeing all the signs that this is like literally gonna happen within the next decade, or whether AI is mostly just mimicking the fact that it's red eye robot. So if you ask it, what's your plan to like take over humanity? It will, it has a plan because it's read, it's read all the science fiction. But actually, that doesn't correspond to having a plan or having any intention to do it. It's, it's just the parrot of the training data. And jury's still out.

• 39:28Molly O'Shea?

  Jury's still out. Who knows what's gonna happen? As we kind of wrap up the conversation on safety in particular, do you think that all of these forces will converge into one dominant standard for AI safety? Or do you think it'll be fragmented?

• 39:46Speaker 00

  I think it's gonna merge into one dominant standard. So what you often see is when standards serve the purpose of creating confidence, whether that's in financial markets with like something like Moody's or Ico, or even in security where you have things like SOC two, often one or two emerge that everyone agrees that that is the thing we're looking to. I think that's gonna happen here. I think it's gonna happen both at the application layer. It's gonna happen at the model layer. It's gonna happen at the data center layer specifying what are these, these security levels that are meaningful. And then I think frankly, this is also a geopolitical question of is global AI gonna be built on American standards?

• 40:30Speaker 00

  Or on Chinese standards or something else? You, you see both. You see China investing a lot in adding people to the International Standards Organization, ISO, to start to influence standards. You see private companies like Huawei.

• 40:49Speaker 00

  Really taking the lead on things like 5G. And of course, a lot of the world is running on 5G. And so I know a lot of people in the White House also thinking about is AI going to be built based on American standards? Or is it going to be Chinese standards? Is it going to be European standards with the UAI Act? And so I suspect that that's also going to be a bit of a winner take all dynamic. And I suspect there's going to be a lot of interest in making sure that's AI is built on American standards.

• 41:14Molly O'Shea?

  I think we're winning, so.

• 41:17Speaker 00

  Well, I think America is winning on AI as it stands, but the lead is really shrinking. And when it comes to standards, for better or for worse, the debate is happening right now between companies is like, what do we think about the EU AI Act? And the US doesn't have a standard that can quote unquote compete or complement it. And so from that perspective, it's not obvious that the US is winning now. I think US has a lot of interest and I think the US has all the things to to make that happen. But at the moment, it's not obvious that that's happening. And at the moment, it's not, it's less obvious today than it was a year ago that

• 41:57Speaker 00

  at the model layer that the US is going to be way ahead.

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• 42:43Molly O'Shea?

  Your investors are super ingrained into this base, especially Nat Friedman. I mean, he's now a part of the super intelligence team at Meta. What kind of advisement are you getting from them? How close are you? And like, what is the relationship between you and your investors?

• 43:00Speaker 00

  Yeah, we're really close. They're just like Nat is just the, I would say, the preeminent AI investor.

• 43:09Speaker 00

  Both made a lot of great investments, but also, as you just mentioned, is very close to the metal as to how this is actually being built. Both the small questions of how to build AI, but also some of the big questions. Nat himself spent a lot of time on some of the risk questions that we were dealing with when he was leading GitHub. They built Go Pilot, which is like the first real AI product that worked. And he led the efforts that led to Microsoft basically ensuring all of their customers against IP risk to say, oh, they're really worried about IP. We're just gonna step in and if they get sued, we're gonna cover their ass.

• 43:47Molly O'Shea?

  Yeah.

• 43:48Speaker 00

  So he spent a lot of time in this particular problem set. And so it's just immensely valuable. Obviously, we've only been working with him for a couple of months now. And but he's really N of one.

• 43:58Molly O'Shea?

  And then in terms of your team, this is a new problem. How did you assemble a team for this and who's a part of your team?

• 44:05Speaker 00

  Yeah, so the key thing to get right is you need to both have a foot in the insurance world because you need to play with their balance sheets. You need them on board and in the AI world. So the founding team has both a former McKinsey partner in insurance, who then went on to be the COO of an AI audit organization that does pre deployment testing, for example, of OpenAI's models. And then our CTO is a former Thiel fellow who built an AI company and then an underwriting company. And so the thing is like that marriage between the technical and the financial understanding, that's really critical to pull this off.

• 44:43Molly O'Shea?

  How big is the team so far?

• 44:44Speaker 00

  We're just the four of us now.

• 44:46Molly O'Shea?

  Just four?

• 44:46Speaker 00

  Yeah.

• 44:47Molly O'Shea?

  What are you, are you gonna be hiring with this round? What's the, what's the plan for scaling?

• 44:52Speaker 00

  Yeah, we're gonna be hiring. Though I think there's a lot of wisdom in the startup community now where it used to be that big teams were really prestigious, now small teams are really prestigious. I think there's a lot of wisdom in that. So we're gonna be hiring, we're gonna be hiring both on engineering and insurance. We're also gonna be doing it pretty slowly enough, slowly enough that it's gonna be really painful.

• 45:15Molly O'Shea?

  Okay.

• 45:17Speaker 00

  Building a company like this that has many different legs, you just have to be really close to all, all parts of the business to really succeed.

• 45:23Molly O'Shea?

  Are you gonna have any AI agents?

• 45:25Speaker 00

  Yes.

• 45:27Molly O'Shea?

  On the team?

• 45:28Speaker 00

  We already do.

• 45:30Molly O'Shea?

  What are their names?

• 45:33Speaker 00

  Try to not name them because then you get to attach them. It's really hard to turn Bob off at night.

• 45:38Molly O'Shea?

  Hard.

• 45:41Molly O'Shea?

  As we look out over the next couple of years, maybe three to five years, although things are moving really fast.

• 45:47Speaker 00

  Also, that's a couple of years after superintelligence?

• 45:49Molly O'Shea?

  That's a couple of years after superintelligence.

• 45:52Speaker 00

  After the super, superintelligence.

• 45:53Molly O'Shea?

  Super duper intelligence, okay? What does this whole world look like? Is every AI agent gonna have AI UC? Like, how is this gonna work in enterprises?

• 46:04Speaker 00

  Yeah, I think every agent is going to need to be certified. And every AI agent is gonna need to carry some kind of insurance. And like a simple analogy is like, if you're driving on the road today, you obviously must have insurance. And the reason for that is if you hit me, I need to make sure that you can, that I have recourse. And as the agents start to do real, meaningful workloads, the question is gonna be like, who's responsible and who's gonna pay when they mess up? And we think insurance is gonna be the answer. So we think every agent is gonna have some kind of insurance attached to it. Yeah.

• 46:41Molly O'Shea?

  What's been the biggest challenge for you starting this company?

• 46:43Speaker 00

  Building a new standard is really kind of a chicken and an egg problem. It's like, the whole purpose is to build confidence. But at the very beginning, everyone's like, where's the confidence gonna come from? You guys are brand new. And so finding a way to build partnerships, we build a big coalition around the standard with both some of the top professional services firm that enterprises work with today. Academics at Stanford and MIT and lots of folks in the AI world. Bringing them together for also have a consortium where trusted people are saying, this is the path forward. Getting that ball rolling, getting the first 10 of those was really hard. The 11th becomes a lot easier.

• 47:28Speaker 00

  And I think the other point is, for this to really work, you need to have the insurers on board. And the...

• 47:38Speaker 00

  Average insurer is spending less time on Twitter than you are. Spending less time engaged in what's coming and come from a perspective of like, what could go wrong and what do we know here? And so finding, striking that right balance between like inviting them into the future, while also just taking it seriously. The reason why people trust insurers is that they're really, really rigorous. And so having to meet that has been also just like an interesting challenge.

• 48:03Molly O'Shea?

  Well, what was the process like getting insurers involved? How did you find them that would be interested in this?

• 48:09Speaker 00

  Yeah, so one of the founding team members, Rajiv, used to serve insurers at McKinsey, where he was in their insurance practice. So that immediately meant we started from a perspective of having connections, knowing their language. But it's interesting to move into the insurance world. The way they build new partnerships like this, where they're getting into like new waters, is so different than in Silicon Valley. Silicon Valley is all about like, what's your product?

• 48:35Molly O'Shea?

  Yeah.

• 48:36Speaker 00

  Whereas if you go and hang out at Lloyds of London, the world's oldest insurer in London, they take you to the pub and they look you deeply into the eyes for a couple of hours. And they ask questions like, how are you planning to raise your kids? They're really just trying to understand, can I trust you? Of course, I need to know what your product is, but it's much more of a personal trust relationship. It feels much more like a one big handshake and much less like which features are you building? And when are they coming out? So that's also been just interesting and fun.

• 49:09Molly O'Shea?

  We can't gloss over the fact that you were the first product and go to market hire at Anthropic. So how are you thinking about go to market with this company?

• 49:21Speaker 00

  Yeah, what we think is really important is the people who have the most burning version of this problem are some of these hot AI companies that have raised hundreds of millions of dollars. They want to grow extremely fast and they want to win the big logos. They all want to move into the Fortune 500. And if you're a two or three or four, five year old company trying to sell to those companies, you have a trust problem. They're willing to do almost anything to earn the confidence of their customers, anything they can do to take the, take risk off the table for them. So that's where we're, we're starting to work with AI agent unicorns like Ada, like cognition, solving their problems. And I think it's going to be the right way to establish this market.

• 50:06Speaker 00

  And then eventually it's going to be the enterprise themselves. Once Walmart has like 50 AI agents deployed, they're not going to want each individual provider to ensure them. They're going to want to have one big coverage policy. So that's, that's broadly how we're going to market, like helping some of the most interesting AI agent companies earn the confidence of their customers.

• 50:25Molly O'Shea?

  Do you think that trend of just spending a ton of money on go to market is going to continue? Like, how does that evolve as things advance?

• 50:31Speaker 00

  Yeah, I think we're in a super cycle. I think we are. It is still, it is wild to think this, but it's still early days. And, um, I think people are still losing their minds over their salaries that Meta is willing to pay. But it's a direct consequence of taking this worldview really seriously, that in 10 years, everything looks unrecognizable. And that whoever is on top now may not be on top in a year's time. That is what's predating this enormous amounts of investment, both into the technology, but also into go to market. The question is really like, you can buy more compute, but it's hard to buy your customers' confidence. You really need someone else to say that you're trustworthy.

• 51:15Speaker 00

  Like, no matter how many billboards you put up saying like, we're really the good guys. We're really responsible. In practice, it just turns out that in most industries, having third parties go and look at that is going to be more effective. And so hopefully we can find ways for them to like, actually earn the confidence, not just get a stamp on it, but actually do the things that means that they deserve the confidence of their customers.

• 51:36Molly O'Shea?

  Yeah, it's going to be fun.

• 51:38Speaker 00

  Ah, we're in for a wild ride.

• 51:39Molly O'Shea?

  Chaotic.

• 51:40Speaker 00

  Chaotic. Yeah, I think chaos is like, I think there's a lot of good stuff coming out of AI, but chaos is really like the key term. And yeah.

• 51:50Molly O'Shea?

  As we think about chaos, you're the founder of the company. One of our sponsors is Brex and they're all about spending smarter, moving faster, all based on performance.

• 52:01Speaker 00

  We are a Brex customer.

• 52:02Molly O'Shea?

  You are?

• 52:03Speaker 00

  Yes.

• 52:03Molly O'Shea?

  Do you have a metal card?

• 52:04Speaker 00

  I don't actually.

• 52:05Molly O'Shea?

  We're gonna get you a metal card right now.

• 52:07Speaker 00

  Yeah, and I honestly like have a feature request. How can you become a Brex customer and not get shoved a metal card in your face?

• 52:16Speaker 00

  I'll file it, file it.

• 52:18Molly O'Shea?

  We need them ASAP. Okay.

• 52:20Speaker 00

  Yes.

• 52:21Molly O'Shea?

  What do you like about Brex? How'd you get Brex?

• 52:23Speaker 00

  Um, well, Brex is just really freaking easy to set up. And what I like is that they're a technology company. I feel like they see me and so far it's been smooth.

• 52:33Molly O'Shea?

  And Anthropic is a customer.

• 52:34Speaker 00

  Yep.

• 52:35Molly O'Shea?

  Well-integrated. Okay, so on the question of performance, at least for you and the company. So how do you think about measuring metrics for milestones? What are you looking to achieve? What like, what is the next kind of evolution for you? What's coming?

• 52:50Speaker 00

  Yeah, the next milestone for us is really getting to a point where businesses that are buying AI, who today are like, oh, feel like in analysis paralysis between like moving fast and making headlines or getting left behind, that they know that they can come to us and use the AIUC one standard and push that upon all the AI companies as a way to gain confidence. So the kind of metric I'm interested in tracking is how many times is an enterprise asking their AI vendors to get certified? That over the next six months, that's gonna be the number one metric we're measuring.

• 53:28Molly O'Shea?

  Very specific.

• 53:29Speaker 00

  Very specific.

• 53:30Molly O'Shea?

  That's good. Okay.

• 53:31Speaker 00

  Yeah, I think that's it.

• 53:32Molly O'Shea?

  That's it?

• 53:32Speaker 00

  This is really fun.

• 53:33Molly O'Shea?

  This was amazing. Well, thank you so much.

• 53:35Speaker 00

  Thanks for having me.

• 53:36Molly O'Shea?

  Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.vc, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today and don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.