The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:00:00 · Satya Nadella
[00:00:00] S0: Dario and Ilya and Sam and Greg and Elon were all, I guess, one happy family. They were really the believers in scaling laws, and that's what I believed in back in 2018, 2019. I think the biggest change now I have is I used to always think that alignment will just fall out. I no longer believe that.
00:00:20 · Harry Stebbings
[00:00:20] S1: This is 20 VC with me, Harry Stebbings. Now this show, honestly, is quite an emotional one for me. I started 20 VC from my bedroom in London. I was 18 years old. I had no money. I had no contacts in my phone book. And today, I have the chance to sit down with Satya Nadella, CEO of Microsoft, one of the greatest companies of the last three or four decades. It was twelve years in the making with this show. We've done 3,000 shows to build up to sitting in that room with Satya. There really is no overnight success. Work hard. Be kind. Never give up. And I will just say on Satya, the warmth that he was able to imbue in this interview in such a short space of time was almost unlike anything I've seen before. The clarity of his arguments and his ideas, his ability to go very deep very quickly. It was almost unlike anything I've ever seen before. It was a show I'll always remember, and I really hope you enjoy it. But before we dive into the show today, base 44 is one of those products I used, and I just thought straight away, man, I wish I'd had this when I started 20 VC with $50 and no contacts in venture. I wanted to see what it could do, so I built a guest research hub for the show. Upcoming guests, company notes, previous interviews, sponsored contacts, follow ups, all in one amazing location. I described it in plain English. A few minutes later, I had the product open in front of me. Database, login, back end, and hosting already handled and just all in one. It made it so easy, and that's what I loved. I didn't write a long spec. I didn't stitch five different tools together. I built the thing, and I started using it. For any founder listening, that's the point. The faster you turn an idea into something real, the faster you launch. Apps, dashboards, internal systems, AI agents, whatever you need to build, Base forty four gets you there. Go to base44.com/20vc. That's base44.com/20vc. While base forty four helps you build faster, LARADIN helps you measure AI impact. Every company now runs two workforces, humans and AI agents. Laredon is the first unified dashboard across both workforces that measures their work and optimizes their impact. Laredon answers four questions. Who's using AI? Which tools? What is it costing? And was it actually being produced for every team across every vendor?
00:02:42 · Harry Stebbings, Satya Nadella
AI ROI stops being a guess and becomes a number that you can consistently and confidently measure. Gainsight, Vertiv, SurveyMonkey, and many other enterprise organizations run their AI programs on Larridin. Thousands of AI tools and millions of dollars of AI spend are measured. You bet on AI. Larridin proves that it's working. Visit larridin.com to learn more. That's larridin.com. You have now arrived at your destination. Satya, thank you so much for doing this with us. I would say, you know, thank you for coming to the studio, but you're actually hosting us here. So really appreciate you taking the time. [00:03:20] S0: No. Like, Thank you so much, for having me on the show, Harry. It's been a long time coming, but I'm finally glad we were able to do it. Now you wrote [00:03:25] S1: a seminal piece, ten days ago or so, and you said that we should assume a model is compromised. And I just wanted to expand on that. Does that mean that we can't or shouldn't rely on the disclosures of the model provider companies?
00:03:25 · Harry Stebbings, Satya Nadella
[00:03:25] S1: a seminal piece, ten days ago or so, and you said that we should assume a model is compromised. And I just wanted to expand on that. Does that mean that we can't or shouldn't rely on the disclosures of the model provider companies? [00:03:39] S0: Yeah. I think the challenge, the way I see it right now, is the models are becoming more and more capable. Right? Scaling laws are working. Except one of the assumptions I had was that you'll have alignment just fall out of scaling, and it doesn't seem to be. And there's more science challenges there that need to be tackled, and mechanistic understanding is also elusive. And so given that, I think the best posture to have, if you want to continue to use these sophisticated models, is to treat these models in a trust architecture. I mean, after all, zero trust is not something new. Right? We've had zero trust architectural approaches. So the claim, at least I'm making, is let's create an engineered zero trust environment that can contain any malfunctioning agent before it does any harm. Right? So that's the point. Because if you really wanna have agents that are more capable and yet less aligned, then you need to have a trust system that you can rely on. [00:04:35] S1: Can I ask, what does that trust system look like? Who's the architecture of it? Who's the arbiter of it?
00:04:35 · Harry Stebbings, Satya Nadella
[00:04:35] S1: Can I ask, what does that trust system look like? Who's the architecture of it? Who's the arbiter of it? [00:04:40] S0: It's a great point. And so that's why I kind of look at it and say, You leave AI safety outside of this, right? Because that alignment, for example, at least my take, is that it really mostly has to show up in training, right? In fact, maybe even in pretraining. I don't think you can even RL your way to alignment. You have to truly get alignment to be something that truly gets embedded in the weights right at the very beginning of it. And so that's the model itself. Hopefully, at some point, we'll get aligned. If you leave AI safety, then the trust architecture is going to be just, you know, what a company like Microsoft does or all the cyber folks do. So what we have done, for example, is we built these two things, right, agent three sixty five and perception, right? What does it do? It gives every agent an identity. It then logs all of its activity, all its tool calls, in fact, every model call and the COT unit. In fact, that's where I'm also asking that all the COT better be in English and not in new release. And then you can check using multiple models. It better be tamper proof. You can't have the agent go modify its own logs. And then on that, you set policy and then you behaviorally monitor the activity of the agents so they're confirmed to the policy. So that's the architecture. [00:05:53] S1: We don't have that architecture today. Is that just symbolic of where we are in the production?
00:05:53 · Harry Stebbings, Satya Nadella
[00:05:53] S1: We don't have that architecture today. Is that just symbolic of where we are in the production? [00:05:58] S0: But our architecture exists. So there are two... I think people conflate what I'll call an engineered security, zero trust architectural system for containment and AI safety. Because I think people say, AI safety is hard and elusive and not yet achieved, so therefore there must be nothing we can do on security. And I'm not saying that these two things are all got to come together at the same time. In fact, I would just say, Hey, we have to deal with the situation as it exists, so let's build a trust system outside, in fact, of the model, and then just be super aggressive in setting policy and monitoring for policy deviation. On the monitoring side, if we think [00:06:37] S1: about chain of thought, how reliable is that?