Speaker 1Dario 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 Align will just fall out. I no longer believe that.
Speaker 2This is 20VC with me, Harry Stebbings. Now, this show, honestly, is quite an emotional one for me. I started 20VC 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 12 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, Base44 is one of those products I used, and I just thought straight away, man, I wish I'd had this when I started 20VC 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, sponsor 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, backend, 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. Base44 gets you there. Go to base44.com/20VC, that's base44.com/20VC. While Base44 helps you build faster, Laradin helps you measure AI impact. Every company now runs two workforces, humans and AI agents. Laradin is the first unified dashboard across both workforces that measures their work and optimizes their impact. Laradin answers four questions. Who's using AI, which tools, what is it costing, and what's it actually being produced for every team across every vendor? 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 Laradin. Thousands of AI tools and millions of dollars of AI spend are measured. You bet on AI, Laradin proves that it's working. Visit laradin.com to learn more. That's L-A-R-R-I-D-I-N.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. 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 a seminal piece 10 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?
Speaker 1Yeah, 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, right? And mechanistic understanding. 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're really going to have agents that are more capable and yet less aligned, then you need to have a trust system that you can rely on.
Speaker 2Can I ask, what does that trust system look like? Who's the architecture of it? Who's the arbiter of it?
Speaker 1It's a great point. 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 pre-training. I don't think you can even RLE a way to alignment. You have to truly get alignment. It has to be something that truly gets embedded in the way it's right at the very beginning of it. And so that's the model itself, hopefully at some point we'll get aligned. So if you leave AI safety, then the trust architecture is going to be just 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 365 and Perception, right? What does it do? It gives every agent an identity. It then logs all of its activity, all its tool calls. Every model call and the COT in it. 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, right? 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.
Speaker 2We don't have that architecture today. Is that just symbolic of where we are in the program?
Speaker 1Well, that 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, right? Because I think people say, well, 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.
Speaker 2On the monitoring side, if we think about chain of thought, how reliable is that? It's not, right?
Speaker 1We know that the chain of thought itself can be unfaithful. And in fact, it can be made worse when the model company says, well, it's unfaithful and I'm going to hide it. And so it becomes even more problematic. And that's where I think a little bit of the push for standardization and the CO2 at least for trusted partners being available as is so that it's inspectable, it's auditable and I can check using multiple models and what have you, right? So that's going to be one of the, in fact, that's one of the things that may even drive more open weights, quite frankly, because if you can't trust, it's funny. Close trade models are more powerful, but if they kind of don't recognize the need to be more transparent, especially as their trusted partners use it, then we may have a problem.
Speaker 2So that's one of the things that we need to be thinking about. And that's why I think it's important for us to think about that as well. So that's one of the things that we need to think about.
Speaker 1So I think that there's a part of everyone, because this is not something that somehow an unaligned open weight Chinese model will probably attack even China. So it's not like you can say, oh, the rest think that this will only be used as an attack vector. So setting that aside, I'm pretty pleased with some of the progress even the open weight models in the West are making. And obviously, Jensen's working on it. The other folks are working. They're working on it. We will have even the MAI models that will be not open weight. I think of it more as licensed weights. Think of it that way. I want to turn this into just a simple, straightforward software business as opposed to a network effects intelligence business. So I think there will be different business models. And I ultimately trust in your ability to use those models and control those models is what's going to determine what gets used.
Speaker 2As a venture investor, I have to predict where I'm traveling. I have to predict where my traffic will go today, between closed, open. When you think about the two different dimensions, which is like traffic and dollars, if we look forward, say, 24 months, what percent of traffic goes through open versus closed?
Speaker 1It's a great question. I think it depends on what the frontier looks like, which is I think most things seem to get saturated. And when it gets saturated, open weights or even just cheaper models of even closed weights seems to work. Because especially one of the things is if you can. If you can train on your own traces, then you really don't need the latest model to be able to solve that same problem if the problem has not changed. So if you have an eval that you want to continuously keep moving forward, then you may want to pay. But even there, by the way, I'm very thrilled about this Hydrofusion stuff we now have in GitHub. I just saw this. Yeah, because the cool thing there is it's a learned model. Anyway, we are now going into these agent swarms and multiple sub-agents. So I can call a lot of sub-agents. That even happen to be local models. And so I think my suspicion is that there will always be a market for frontier. There will always be some new things. science may be the next thing that Frontier is being set at. But I think predominantly token usage will be commodity token usage. Does that mean it's open source predominantly? It may be open rates, or as I said, it can also be cheap closed rates, right? Because there'll be lots of price competition, let me put it that way, right? Because if you look at my own usage inside of Copilot, we use a lot of closed source, cheaper models, along with
Speaker 2open rate models. You said about kind of the importance of owning your own intelligence for some of your amazing customers that you have. You were talking about some in the UK. Is that the model environment that we have moving forward, specialized intelligence for companies with their own data?
Speaker 1Yeah. So I think this is, to me, one of the most important things, which is what's the future of a firm in an era of intelligence, right? Because if you go back even to Hayek, his fundamental description of an economy is local intelligence. Tacit knowledge is distributed throughout the economy, right? Without that, you know, there can't be only one firm, and it can't be one intelligence. And so that's sort of the foundational thing. In fact, capitalism works because of that. Price discovery works because of that. A pricing mechanism works because of that. So therefore, I think it'll be important for every firm to be able to accumulate knowledge at increasing scales using AI versus leak. That's why I called it the reverse information paradox, right? Which is Kenneth Arrow kind of described this paradox of information. And where if you're a seller and you disclose the secret without a patent or a trade secret protection or copyright protection, you leak the value. Now, for the first time, the buyer needs the protection because the buyer, even when most people think of it as, oh, my data, it's even my exhaust of using it, right? Thumbs up, thumbs down signal. You're creating value for the model company versus capturing that value for yourself. So I think one of the things that at least I ascribe is that, you know, if you're a seller and you disclose the secret, you disclose the value. And so I think that's one of the things that we need to have for this to be stable, more of that surplus that gets created by use of intelligence to remain at the firm. And by the way, it doesn't need to be only by embedding your knowledge and weight. It can be in context. It can be in your evals, right? You may have a benchmark. In fact, one of the things that I'm very gung-ho about is we all become, every firm will have a private eval benchmark, which is their core IP. And every model comes in, and every model comes in, and every model comes in, and every model comes in, and tries to sort of hill climb on that eval. And that to me is how a firm works.
Speaker 2I love this. Private evals, benchmarks for each company. This is for a rarefied air of companies that would seem your Harvey's or your Lagora's or the kind of very forward-thinking AI companies. Will that disperse to every company in the world you mentioned? Yeah, it happens.
Speaker 1Yeah, it happens, Harry, always, right? Which is, you know, there was a day when I remember in the late 80s and the early 90s that the few companies that had deployed PCs and were doing forecasting, which felt magical. They were able to put it in Excel spreadsheets, send it in email, and avoid the fax machine and inter-office memos. And it felt like, oh, wow, when will this happen? And it took 10 years, maybe, and it spread out throughout. This time around, what is considered, oh, I'm an AI-native company, and I do this. I think, you know, five years from now, it'll be pretty passe, I would imagine. And then that's kind of what, I mean, these tech cycles always start like that, right? Which is even cloud. I remember in the beginning of cloud, it was like the big deal was, oh, who's cloud-native and who's not? And, you know, if you're not cloud-native, and now everybody is, right? Even Lloyd's, you know, which is, what, 500 years old, is cloud-native. And so it happens.
Speaker 2Do we move into an implementation economy then? Because when we think about these customers, as you said, some of you are very big customers, they will need heavy FDE support, much more so than they have done in traditional years, in the last 10 years. Will we have a very heavy implementation economy to transition existing customers into this?
Speaker 1Yeah, this, it's interesting, right? The implementation economy, like systems integrators have always existed, right? You know, we've depended on implementation economy for lots of other technology diffusion. So this one, I think, has different contours, right? Because- What do you think is different about this one? It's just, it's the combination of tech and business, if I could say. But it's not classic management consulting, it's not classic systems integration, and it's not classic sort of technology support. It's sort of, like, for example, think about this, right? You want to start, like, when I look at my FDE engagements, right? Even take the Unilever example I talked about at the keynote today. It's fascinating. They've built an agentic system to say, hey, we want to really have order to cash automated using an agentic system, plus the trade claims. And by the way, they started using a frontier model, and then they substitute for, in fact, I think they're using currently an MAI model for all the trade claims, because it's sort of a lower cost. And it's, by the way, they control it, right? Because they're FDEs, but a lot of who got deployed, and now their IT team itself is, you know, managing it all. But it started with, what's the business? What's the eval? What's the way to conceptualize the agentic architecture? So it's sort of business consulting combined with technical consulting. And I think, yeah, you're right in saying that there will be a new set of skills that will get built in the ecosystem. Today, I think all of us have, quote, unquote, our own FDEs, but FDEs will become like, you know, traditional systems integration will become new FDE.
Speaker 2You've mentioned, obviously, MAI. But when we look at a lot of different players, it's kind of this realization that you need to be full stack, from chips to models to application. Is that the future? You have to have vertical ownership?
Speaker 1Yeah, it all depends on the industry structure, right? You know, I think that I would say today, you know, there'll be, who knows, five, six closed models all competing, maybe five, six, open weight models all competing. And so therefore, at some level, if you're building an application, like the agentic system, you could even have something like what I said, which is a router with even a decision engine built into it. And then you just route to the best model for a given task. So that's a possibility. Of course, there's always the risk that there could be one vertically integrated model and app tier and all the way to the silicon and system. It's unlikely to me, right? I thought at some point that that might be the case that one, if anything, it's going the other way, which is every day you wake up and you say, wow, there's more competition in every layer of the stack, whether it's in silicon, whether it's in systems to manage the silicon, in the model layer, in the app layer. And so I think that's a healthy sort of direction of travel, but you never know, right? Because this is one of those things where it all depends on what becomes the dominant design. But my hope is that this becomes a little bit of a special specialization with multiple winners in each layer and then the ability for people to
Speaker 2compose. The thing I didn't see is the depth of each layer. What I know now that I wish I'd known 12 months ago, it's just like the infinite demand for inference. Yeah. That's a great point. Yeah. And I wish I'd known it 12 months ago, I would have done some very different investing. Speaking of that, we've had like Mustafa Suleiman saying training rounds of 100 billion are coming and only a handful of labs can pay for that. $100 billion training runs. Do we have the energy structure and power to enable this scale across the stack? But especially when we do.
Speaker 1Yeah. I mean, the way I come at it is ultimately it has to come from the world GDP inflecting and then that allowing someone to then have as a percentage of R&D, right? Because when somebody says training run, it's just a percentage of R&D, right? Companies that are heavy R&D spend what? 15, 20% on R&D. And so therefore, yeah, at some point, if you have enough revenue, you can spend training. And then of course, you'll have enough revenue if you're contributing to the world economy growing so that you can get your fair share, so to speak. And then you also bring up the other side, which is the supply side, which is we have power shortages, we have silicon shortages, we have all kinds of things today. They will all. They're long lead items, right? You just can't snap your finger and have power generation or you can't have a new fab. And so now, interestingly enough, given the demand, everyone's building. And so the question is, when will all of online, what will be the demand supply? No one knows, right? Quite frankly, any one of us who are sitting here today will just be speculating. But my feeling is that if you start by thinking of the stable state would be just like today, right? Income statements are suddenly not going to be different, right? For a company, it's going to be, hey, I have some revenue, I have some cost of revenue, and I have to have some profit. And if that is going to be true, then the question is, what is the world GDP growth rate? That's the bigger thing for me.
Speaker 2In terms of the inflation growth rate, the world has kind of stabilized 2% a year for a very large amount of time. Does AI have the ability to seriously inflect that in a short amount of time?
Speaker 1I think so. And I somewhat hope so, because that is the point, right? When I look at it, let's take even as a percentage of any of our GDPs, the amount of money we spend in our healthcare, right? So that's why I look at even the deployment of Copilot in NHS and say, wow, if it can improve patient outcomes and reduce cost in the healthcare, that is by definition helping the GDP of the United Kingdom. And so I think that there are these pockets of the economy which are ripe for adoption of AI and really, in fact, there's the least amount of displacement, the most amount of benefit. And so that I think is where I think we'll have to find the best fit. Software development is another one, right? It's fascinating to see how much the demand is elastic for software development, right, which is that means the IT back Backlogs were huge and none of
Speaker 2us even recognized it. I was taught that you had to be the top one or two in a market to win in venture. We're in factory, which is fifth and it's winning, fifth. This breaks all rules of venture. The question that I have again, slightly naughty, not on schedule, but it's just like, well, we see the tipping in terms of capability advancement in alternative functions, finance, sales, marketing that we've seen in coding because coding has been the mother load.
Speaker 1I think that that's a good point. People talk a lot about, do you have a verifiable reward and it's easy to do these because a lot of the magic has been RL on top of these pre-trained models. That is what has helped coding. Can we crack that in knowledge work seems much harder, but again, all the examples I talked about in contact centers, in the supply chain systems, in these marketing slash or cash to order systems, I think it's possible and we're seeing it. Is the demand as elastic as coding? I think it may be science may be as elastic or engineering in general. By the way, if I now look at even what's happening inside, not just in software development, let's take silicon development. That's another thing that's going through a radical shift. Your ability to tape out much faster, simulate everything in silico is much better. Your first spin can completely come out. But even just like optimization of maintenance reports, like you said, with Rolls Royce, which I would never- It's unbelievable. I mean, it's a thing that they've been dealing with forever. Schedule maintenance, and I had not realized that, wow, that's a big optimization problem that you can go now run.
Speaker 2So I've watched many, many of your shows and we're going to do a quick fire round. And people haven't asked some of these questions, which I've always wanted to know. And I think everyone wants to know, but people haven't asked them. What's the exercise routine given the travel schedule, Satya? Well, I mean- You're in good shape.
Speaker 1Well, I try to be. I don't know whether it's all well hidden, but the one thing I've gotten into the habit, this has been now maybe 25 years. 25 years even on, which is every morning I get up and I used to be able to run. Unfortunately, my knees have gone away. And so now at least I walk with incline. Even this morning I was able to get a half hour in. And so that's what, in spite of any jet lag, wherever I am, I get hit the gym every day.
Speaker 2I too have bad knees, it's my age as well. What have you changed your mind on in the last 12 months?
Speaker 1It might be because after all, I was one of the first, obviously when 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 I think the biggest change now I have is I used to always think that alignment will just fall out. So I think this alignment thing may be a harder, different problem. And so, yeah, that's the one thing I've changed my mind on.
Speaker 2What looks crazy today, but will be incredibly common and accepted in five years?
Speaker 1Yeah. That thing that we just talked about, those evals, right? What is a firm? I think a firm would be
Speaker 2an AI benchmark. Final one for you. There are many things to be excited about over the next 10 years, for you specifically. For me, it's my mother's got MS, I'm so excited for the drug development that could be capable. What are you most excited for?
Speaker 1I mean, that's a great one, I think. One of the things that I've always been, I'd say, been looking forward to is a general purpose technology, even as powerful as this. This is not just another general purpose technology, whatever this is. I hope this spreads, diffuses throughout the world faster than anything else, and throughout all sectors of the economy, unlike anything else. And truly for once, there is a real shift in the frontier of that global growth that happens where it's not about just counting winners and losers, but it's kind of like everyone gets lifted. That's been my dream. In fact, my father was a developer. He was a development economist and it was always about convergence growth. When will the global south get something that just suddenly boosts that? I hope this is it.
Speaker 2That's it. This has been such a joy to do. Thank you so much for taking the time. Absolutely. It's a pleasure, Harry. But before we leave you today, Base44 is one of those products I used and I just thought straight away, man, I wish I'd had this when I started 20VC 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. Sponsor contacts. Follow ups. All in one amazing location. Database. Login. Backend and hosting. Already handled. And just all in one. It made it so easy. The faster you turn an idea into something real, the faster you launch. Absolutely. And I think that's the key. That's the key. Absolutely. Absolutely. Absolutely. Absolutely. something real, the faster you launch. Apps, dashboards, internal systems, AI agents, whatever you need to build, Base44 gets you there. Go to base44.com forward slash 20VC. That's base44.com slash 20VC. Who's using AI? Which tools? What is it costing? And what's it actually being produced for every team across every vendor. Gainsight, Vertiv, SurveyMonkey, and many other enterprise organizations run their AI programs on Laridin. You bet on AI, Laridin proves that it's working. Visit laridin.com to learn more. That's L-A-R-I-D-I-N.com. you
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