20VC: Microsoft CTO on Where Value Accrues in an AI World | Why Scaling Laws are BS | An Evaluation of Deepseek and How We Underestimate the Chinese | The Future of Software Development and The Future of Agents with Kevin Scott

Kevin Scott is the CTO of Microsoft, where he leads the company’s AI and technology strategy at global scale and played a pivotal role in Microsoft’s partnership with OpenAI. Prior to Microsoft, Kevin spent six years at Linkedin as SVP of Engineering. Kevin has also enjoyed advisory positions with Pinterest, Box, Code.org and more. In Today’s Episode We Discuss: 04:10 Where is Enduring Value in a World of AI 10:53 Why Scaling Laws are BS 12:26 What is the Bottleneck Today: Data, Compute or Algorithms 15:38: In 10 Years Time: What % of Data Usage will be Synthetic 20:04 How Will AI Agents Evolve Over the Next Five Years 23:34: Deepseek Evalution: Do We Underestimate China 28:34 The Future of Software Development 31:53 The Thing That Most Excites Me in AI is Tech Debt 35:01 Leadership Lessons from Satya Nadella 41:13 Quickfire Round

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

• 0:00Speaker 00

  This is the best time to be alive if you have an entrepreneurial spirit. I can very clearly see what we're doing now and like what we're doing next. And I don't see the limit to the scaling laws. Don't believe in this like one agent for everything sort of theory. I think you'll have a lot of agents and the reason I think you're going to have a lot of agents is because your product managers are probably going to have to be domain experts. The agents definitely, they will definitely be less transactional, less session oriented going forward.

• 0:32Harry Stebbings?

  You are listening to 20 VC with me, Harry Stebbings. Now, I think the show today for me just really emphasizes the power of the internet. I started 20 VC with nothing from a bedroom in London as a 17 year old. And today I sit down with the CTO of Microsoft, one of the world's most valuable companies and leaders in AI. So I'm thrilled to welcome Kevin Scott, the man responsible for AI and technology at Microsoft, where he played a pivotal role in Microsoft's partnership with OpenAI. This is an incredible discussion on scaling laws, the future of inference, data, compute, where we go from here. It's time to get the notebooks out and this was such a joy to do. But before we dive in today, turning your back of a napkin idea into a billion dollar startup

• 1:16Harry Stebbings?

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• 2:00Harry Stebbings?

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• 2:44Harry Stebbings?

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• 3:27Harry Stebbings?

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• 4:10Harry Stebbings?

  Kevin, I am so excited for this. I was just telling you, I was listening to you and Shrap on my run. I don't think I've ever run quite as fast, which clearly means the conversation was brilliant.

• 4:19Speaker 00

  Either brilliant or awful in the you trying to end your run so you can be done with it.

• 4:25Harry Stebbings?

  I've never done a 10K so fast. I wanted to start with a super easy question. My job as a venture investor is to try and determine where value lies in different given moments. And I look at the world today, and for the first time in quite a long time, Kevin, I don't know.

• 4:43Harry Stebbings?

  And my question to you is, in this next generation of AI, where does value lie sustainably, do you think?

• 4:51Speaker 00

  Yeah, I don't know. I think the thing that you just described, which is like, all of a sudden, things have gotten a little less clear than they have been, is exactly the thing that happens at the beginning of every big technological paradigm shift and every new cycle that's driven by it. So it was super confusing in the early days of the internet, and I think it was super confusing in the early days of mobile, where everybody, you know, had these ideas about what was going to be valuable. And very few of those ideas were actually the durable ones that proved all the way through.

• 5:26Harry Stebbings?

  In those moments of transition where there is this confusion, what have you learnt as the right action to do? Is it to be active, to iterate and learn, but you'll make so many mistakes that you regret? Or should you sit on your hands and watch others make those mistakes?

• 5:41Speaker 00

  Oh, God, no. Like, you definitely shouldn't do the latter. This is the best time to be alive if you have an entrepreneurial spirit. The thing I think that you have to do in these moments is not forget the things that you've learned from the past moments about what works. And it's not like, okay, well, like, it's do this specific thing, but it's like how you go about doing that exploration that you just described, which is, you know, product matters. I've been saying this for the past couple of years, that models aren't products, because everybody like was just so fascinated by the infrastructure itself. And like, this is also Characteristic of the beginning of these cycles is you have technical people who get just swept up in the

• 6:26Speaker 00

  technical bits and they kind of forget that the only thing that really matters is making good product. And like, that's where we're at right now. Like, you have to make good product and, you know, you have to have ideas and have conviction and then you have to go get stuff done really fast so that you can see whether you're full of crap or not about the conviction that you have. And you have very few patterns at the beginning of a cycle to go snap to. Like, you're not looking at someone else's success and saying, okay, well, like, I'm gonna do that, but just a little bit better. Like, you're trying to figure out something completely new. And the only way to figure that out is like, you gotta launch stuff and gather data and iterate and

• 7:06Speaker 00

  you be super, super brutal with your own self about what you're seeing. Like, you can't love your idea so much that you overlook what it is you're seeing about the data and the feedback that you're getting.

• 7:21Harry Stebbings?

  You said their models aren't products. You know, I just had Andrew from Cerebrus on the show. And I asked him this question. I said, if we think about compute or kind of hardware, and then we think about models and we think about apps, where does the value lie? And naturally, he said, compute. But when you think about that kind of three-pronged tier of value, and you said their models aren't products. If they're not products, does that mean they're not valuable?

• 7:45Speaker 00

  No, no, they're super valuable, but they're only valuable to the extent that you can connect them to things that users need via product. So like, in the limit, I think product is the most important thing. If you build good models and you build good infrastructure around models and you have good, efficient compute and like, you have all of these other things, you're going to get lots of ability to monetize all of those things because as people build those products, like, they will need to consume your platform and your infrastructure and like, all of that's good. But Yeah, I mean, like, most of the value has to be in the products. Like, you know, we don't build infrastructure just for the sake of infrastructure. We build infrastructure so people can make product.

• 8:25Harry Stebbings?

  This is a leading question. Then again, if you think about, and I think you might know, but if you think about those products, who benefits most? Is it startups who are able to integrate new technologies very easily from the bottoms up, starting from nothing? Or is it Microsoft integrating AI into incredible distribution already? Google doing the same? Who benefits most in that respect?

• 8:48Speaker 00

  Yeah, I don't, I don't know. Again, if you look to past cycles, like, you've got a pretty good mix of where value gets created across startups and new ventures and existing enterprises. And so I think everybody's kind of doing the same thing. Like, you're trying to discover the new. If you're a big company like Microsoft with a long tradition and a bunch of successful things already in the market, like, the thing that you are trying to do is figure out what of the things that you already know super well and like which of the customers that you're already serving super well, can you do for them with this new set of capabilities that you can provide?

• 9:30Speaker 00

  Yeah, hopefully, you know, like, I run, among other things, Microsoft Research. And so, like, I also have a charter of like, you know, hey, can we go try to shine flashlights in places that no one else has shown them before and like, try to discover some like, super disruptive, brand new things. But like, that's kind of the job of the startup ecosystem as well. And I also am an angel investor and like, I advise startups and I've worked at startups. And, you know, so I, I think it's just really important that you've got lots of people hunting for those interesting new things. And like, I have super high conviction on that in the AI platform transition that we're going through right now, because it's impossible for any entity.

• 10:12Speaker 00

  Like Microsoft or any other big company to have enough imagination and enough perspective to know what every interesting thing is. So having just this vibrant ecosystem, lots and lots of people sort of exploring where value exists, I think is incredibly exciting and necessary. And I also think that there has never been a moment where the tools, the infrastructure and the platforms are as cheap and accessible and available and easy to use as they are right now. So it's just super easy to like pick stuff up and just go get cracking.

• 10:52Harry Stebbings?

  I was listening to your show, as I mentioned earlier, and you push back on the idea of reaching scaling laws and this kind of asymptote of efficiency or effectiveness. When many people suggest that we are hitting scaling laws soon, for us, why do you think we're not? And that's a ridiculous statement.

• 11:10Speaker 00

  I can very clearly see what we're doing now and like what we're doing next. And I don't see the limit to the scaling laws. Like if you're just sort of thinking about the raw capability of the models and like how well you can condition them to reason over increasingly complicated things, like there at some point will be maybe a limit. I intuitively feel like there must be. There are some people who don't believe that there is a limit, that the limit that human beings have on intelligence is like you've got so many neurons like packed into your skull and like you've got about a 20 watt power envelope and like that's the limit. Some people believe that, you know, if you're, you have AIs, that there is no such limit and like things, you know, will continue to scale into like, you know, weird territory.

• 11:54Speaker 00

  I don't really like that. I don't necessarily believe. I believe we will get to some point where we'll hit a scaling asymptote and, you know, like there'll just be diminishing marginal returns and like it's so expensive that we will decide it's not worth spending that next dollar to like make this thing one unit smarter because we haven't, you know, figured out how that translates into something that's useful for the people who are using the tool. I think that point will come. I don't, I just don't see it yet. It's not in the viewfinder right now.

• 12:25Harry Stebbings?

  When we think about like the three core elements that make up kind of efficiency in this respect, it's kind of data computing algorithms. When we drill into data, what are your biggest observations on data efficiency? The importance of quality of data versus quantity, synthetic versus human? How do you think about that today?

• 12:45Speaker 00

  The mix of synthetic data is going up. High quality data is becoming much more useful, especially in the post-training parts of the model production pipeline than low quality data. So like I think we're clearly at the point now where if you have the right infrastructure and you have super high quality data and super high quality expert human feedback, you can amplify that into the right set of tokens for training bigger and bigger models. And like that stuff is way more value than just, you know, sort of the undifferentiated tokens that are like floating around on the web.

• 13:22Harry Stebbings?

  What questions do we not know about data and its usage that we would like to know and would be most helpful to know?

• 13:29Speaker 00

  There's sort of a super interesting thing right now that we don't have around assessment. So it's very hard to know quantitatively what the incremental value of a token of data is to the quality of a model generated that uses it in its training. So in other words, like if you're sort of asking like, okay, well, I think my data is super valuable and like if this gets used in a model, it's going to make the model better. Most of those assertions that people make are unfounded in any kind of science. And like the measurements we do have show that there's a pretty big disconnect around

• 14:09Speaker 00

  what some people think valuable data is versus like how valuable it actually is to producing capabilities in models that are legitimately useful. And most of what's legitimately useful is like models, people who think of models as repositories of factual information and they're treating them like the world's worst and most expensive databases, like it's not super useful. Like we've got search indices and like databases and those things. Are like plenty good enough for retrieving information. Like what you want models to be able to do is to be able to reason over information. So like if you give them access to information, like how well are they able to reason over a set of information to go do something that's useful for you?

• 14:53Speaker 00

  And so you just need different tokens for training a model to make them good at reasoning than you do at making them, you know, recallers of facts.

• 15:03Harry Stebbings?

  It's so funny you said about reasoning that because it just made me think about kind of inference. I get really annoyed by the word inference. I wish we'd just like delete it and just call it usage. It's usage, there's training and there's usage. My question to you is you've been very clear about the transition of emphasis importance from training that which we had over the last few years to inference. What are we not talking or seeing in inference that we need to spend or think more about?

• 15:28Speaker 00

  You know, I think the thing that most people miss, although the DeepSeek R1 launch, you know, a few weeks back clued everybody into it is that we just have an incredible track record over the past handful of years. And it's many years now of just repeated year over year, mind boggling progress in optimizing the performance of models. So that the performance of inference is just better and better and better. Over time, the models have gotten bigger and the API calls have gotten cheaper. And like a little bit of that is because you get 2X benefit price performance from hardware every generation. Like if you're lucky, but you get a much bigger improvement price performance wise from all of the things that you're doing and the software stack.

• 16:13Speaker 00

  Again, there, there's just a ton of work happening there. You know, the DeepSeek R1 stuff, which, which was good work is, you know, the way you should think about it is it's like a point on a line of price performance improvement that maybe was invisible to everyone else, but like not invisible to the people who are like, you know, neck deep in optimizing these systems and it's not the last point, like it just marches on.

• 16:39Harry Stebbings?

  What was the internal sentiment towards that when it came out?

• 16:42Speaker 00

  I was surprised at what the public reaction was.

• 16:46Harry Stebbings?

  Why?

• 16:46Speaker 00

  Oh, because we've had models more interesting than Deep Sea Car one that like we chose not to even launch them. I was surprised at how interesting people thought that it was. They did good work. It was good, solid, technical work and like it was super cool. They chose to, you know, release this thing and make it open source-ish, you know, and it's like really interesting like seeing how the public reacted to what they did.

• 17:12Harry Stebbings?

  Is there anything that you learned from how the public reacted in the release that you take with you to your releases?

• 17:18Speaker 00

  Even when you have, you've made it as easy and cheap as humanly possible for folks to go do something, they still have super strong preferences about the how. And so like we're, we're paying very close attention to that. You know, we have to give people more, more how than we have been doing it because like developers want lots and lots of choice.

• 17:40Harry Stebbings?

  What did you believe that you now no longer believe? Or what have you changed your mind on in the last 12 to 24 months?

• 17:46Speaker 00

  When I was a graduate student, I was like a complete open source zealot, you know, and as I've gotten older, like I, I sort of have become a lot more pragmatic and it's like, okay, well, like it, it's probably more important for me to make a set of pragmatic decisions about how it is I'm going to go build these things rather than singly optimizing for my curiosity.

• 18:09Harry Stebbings?

  When you look forward to the next three to five years, three to 10 years, how do you think about the pervasiveness of open versus closed and which will be more dominant than the other?

• 18:19Speaker 00

  I don't know. Like I think there's gonna be lots of both. Let's just like forget about AI, which is sort of the controversial thing at the moment. It's like a thing where, you know, in industry structure hasn't settled yet and like we don't know exactly what it's gonna be. But you just sort of pick your previous things like search, for instance, like there's a whole bunch of open source, uh, search engine projects out there and people who want to do search to like have a search feature in their application or who want to go build a search engine themselves have lots of options. Like they can go grab something, open source is a starting point. They can stand up a product, like they can go. They can go load their data into something like Azure Cognitive Search,

• 19:03Speaker 00

  which is a search as a service platform, like Google has one, Amazon has one, like they're, they're readily available. And then you still have search engines like Bing and Google that are out there. They all exist. All of the economics in search go to like somebody who stood up a gigantic infrastructure and who's sort of running like a whole search business like with its own feedback loop. Yeah. And so I think, you know, we're probably going to have similar sorts of things happening here for the infrastructure layer. You're going to have lots of open source infrastructure products and people are going to use them in lots of different ways. But like, you're also going

• 19:44Speaker 00

  to have a lot of people who don't want to have to go stand up their own infrastructure from scratch or to like take an open source project and go build it, you know, out where it's lacking for the things that they need it to do. It's good to live in a universe where you have both of those things.

• 20:04Harry Stebbings?

  You mentioned earlier the centrality of products. Taking that into account with the current conversation here, how do we think about whether chat is the right UI for the next kind of paradigm of this product realm? OpenAI and ChatGPT has made it the default. To what extent do you think it is the right default and how we will see that change?

• 20:24Speaker 00

  Yeah, I don't know. I like, I think it's a, you know, reasonable step in the right direction. Like the thing I've been saying for a few years now is like, I think one of the most interesting things happening with AI is we've had one paradigm for using computing devices for effectively 200 years since Ada Lovelace wrote the first program. So if you want a computing device to go do something for you, you had to be a programmer yourself, which is a pretty high barrier to entry for a lot of people, or you have to rely on the fact that a programmer has anticipated some need that you might have and like packaged up like a piece of software into an application that then you were able to run.

• 21:09Speaker 00

  And those are the two ways you can get a computing device to do something for you. Until now, the thing that changes with AI is it can understand a thing that you want your computing device to go do for you, and it can figure out a way to go make that thing happen. And you don't have to be a programmer. And you know, it's, it's kind of a profound change because like it basically means, and like, I don't think this is next year, but it's probably not going to be 10 years. This whole notion that you have teams of people whose job is to go anticipate a bunch of very granular user needs in some narrow space.

• 21:52Speaker 00

  And then they're going to go write a bunch of code and then figure out how to hang that code onto some user experience and they hope that they've done a good enough job and they've anticipated the needs in the right way and they've designed the user interface in the right way. And you know, they've gotten all of the code right and you know, they just sort of grind away on figuring out what that feedback loop is. That's going to change. Like you just aren't going to need as much of that anymore. What you're going to need instead, some kind of agent actuating those capabilities on your behalf, rather than you having to, you know, do this weird impedance matching that we've got right now between like how a user has a set of expectations and how, uh, you know, a product team has imagined what those expectations are.

• 22:37Harry Stebbings?

  Is there a role in engineering or product teams that we have today, which you're like in 20 years' time, people will look at and go, what? You had secretaries who typed out, you know, voice recorded notes from a doctor? What?

• 22:51Speaker 00

  The role of engineers are, you're still going to have to have people who build capability infrastructure. So make this thing happen in the real world, like provide access to like this, you know, weirdly situated repository of information, like, you know, it's like just a bunch of. Capability things that people will need to build, but like the user interface that surfaces those capabilities will probably be agents. And you know, product managers like all don't believe in this like one agent for everything sort of theory. I think you'll have a lot of agents. And the reason I think you're going to have a lot of agents is because your product managers are probably going to have to be domain experts, like people who sort of deeply

• 23:32Speaker 00

  understand something like medicine or drug discovery or early round venture investing, or you know, like, you know, just sort of pick your thing. They will have to deeply understand the idiosyncrasies of that. And they will have to help set up the feedback loops that help agents that are like assisting people doing those tasks, like better and better do their job. It'll be a combination of the product manager and the users of the agents, teaching the agents how to be better and better at the things that you're trying to get them to assist you with.

• 24:07Harry Stebbings?

  I often think that we overestimate adoption in a year or the short term and underestimate in the long term. When I look at the hype around agents, I share the excitement, but I question the immediate adoption or the expectation that some of the world's largest companies will be using agents in the next year or three years even. To what extent do you think I'm right? Or to what extent do you think actually this wave is different, given the distribution of someone like Microsoft?

• 24:36Speaker 00

  Usage always follows utility. So like you make useful things, like they get used a lot. Clearly with software development agents, like we're getting a lot of adoption right now. Yeah, we've gone very quickly from, you know, developers being skeptical about these tools to like, you will get this from my, you know, cold dying fingers. Like I think of this as like one of the most essential tools in my toolkit and I'm, I will never give it up. And you know, the agents are, you know, becoming more and more powerful.

• 25:07Harry Stebbings?

  Can I ask you, to what extent is there lock in there? When I look at them and when I speak to, to people about them, you're right, there's user love, but everyone says, oh, but there's no lock in. I'd happily switch to the next person tomorrow. To what extent does that mean it's valuable?

• 25:21Speaker 00

  There's no lock-in in search. Like you can send your next query to a different search engine than the one you're using right now, and yet you don't. It is our job, building these agents to like grind and grind and grind and like go every day, try to make the agent better and better and better and to do more and more and more of value for our users. If you do that and you do it well, like they will continue to choose you.

• 25:46Harry Stebbings?

  Can I ask, when you think about a five-year time horizon, what will the interaction model like between humans and agents?

• 25:53Speaker 00

  I think the thing that's missing right now with our agents is like they are conspicuously missing memory, which makes them awfully transactional. And even in the places where agents have memory, it's like a pretty limited form of memory. And so like, I think one of the things is going to happen, because I know lots of people are working on it right now, is memory is going to get a lot better over the next year or so, which means that as you're using an agent, like and it remembers more and more about your past interactions with it, it will be able to conform itself more and more to your preferences and it will be able to do things that we do very naturally, which is like you solve a problem once and you record

• 26:37Speaker 00

  the solution to a problem and then you don't go and solve it from first principles over and over and over again. Yeah. So memory even gives you the ability with these agents to have some kind of abstraction and compositionality where, you know, you can just sort of build up like more and more powerful ways of doing things inside of the agent over time, because it's, you know, remembering the past things that it's done and learned. The agents definitely, I mean, for sure, like this is going to be true. Like they will definitely be less transactional, less session oriented going forward. I hope we get more asynchronous things happening over the next 12 months, which mean like right now, you know, it's very interactive.

• 27:18Speaker 00

  Like you go to your agent and like you send a prompt in and it goes and does something immediately and like gives you the response back. And it's like, yep, I've done it. And so, you know, I think there's going to be more over the next year. Of you sort of dispatching your agent to go do something and it like goes and works while you are not paying attention to it. Because the thing you want with agents, by the way, like this is, you know, you, we should just never lose the plot on where we're going. So, you know, the first generation of agents are good at five second tasks. And then the generation after that, we're good at five minute tasks. And, you know, what we're going towards are things that you can delegate,

• 28:00Speaker 00

  increasingly complicated tasks and increasingly, you know, beefy work to over time. The same way that you would a coworker. In order to do that, I mean, so this is how I kind of think about the future. Like, that's what everybody's going to want, and that's where the capabilities are headed. And so, you know, how do you think about how to build product around where the future is almost certainly going to be? And like, what do you need to go augment these systems with to allow them to do more of this thing that is like what ultimately I think we, we want?

• 28:34Harry Stebbings?

  We mentioned that, you know, software development being one of the most widely adopted usage mechanisms that we're seeing today. When we look forward five years, what percent of net new code do you think will be AI created versus human created?

• 28:48Speaker 00

  What time horizon? You say five years?

• 28:49Harry Stebbings?

  Five years.

• 28:50Speaker 00

  Ninety-five percent is going to be AI generated. I think very little is going to be line by line is going to be human written code. Now, that doesn't mean that the AI is doing the software engineering job. And so I think the, the more important and interesting part of authorship is still going to be entirely human.

• 29:12Harry Stebbings?

  What does authorship mean in a world where you're not the input master?

• 29:17Speaker 00

  Well, so like, it's just, it's just raising the level of abstraction. So like, when you, you're designing an iPhone application and X code, like you don't write all of the code, like you sort of drag a whole bunch of, you know, user experience elements around on the screen. Like, you know, the, the system is just emitting a crap ton of boilerplate code for you. This is like the same trend, like we're raising the level of abstraction. Abstraction, like we are changing the interface that the programmers use to communicate to the machine that here's a problem that needs to be solved. One of the things is true, like the extraordinarily good programmers right now, even when they're using tools that are at a very high level of abstraction,

• 29:59Speaker 00

  they they understand like all the way down. So like if something's broken, like you can go into the machine code, like you can go look at the boilerplate that your, you know, your dev environment is generating, you muck around and figure out what's going on. Like the same will almost certainly be true when you've got mostly AI-generated code that, you know, the very best programmers are going to be able to say, okay, well, you know, the thing admitted this, but like, you know, something's off. Like, let me go splock down into the lower levels of abstraction.

• 30:27Harry Stebbings?

  Is everyone a programmer in a world where you have a bolt or a lovable, which allows you to create simple websites, but websites?

• 30:35Speaker 00

  Yeah.

• 30:35Harry Stebbings?

  With pure prompts?

• 30:37Speaker 00

  Yeah, I think so. It also doesn't mean everybody is solving the same sorts of programming challenges. So, so again, you know, like if you think about this as sort of raising everyone's level. So it makes everybody a programmer in that like you no longer, you no longer have to go get someone to make a website for you. But if you are trying to solve the word, the world's hardest computational problems, like you still, I think you're going to need computer scientists. And like they are going to use these tools like insanely well to go solve problems that were just harder than they could solve before.

• 31:15Harry Stebbings?

  Will the structure of engineering teams be fundamentally different in the future?

• 31:19Speaker 00

  Yeah, I think so. But, you know, maybe not in the ways that people think. I'm guessing that that it will get easier for small teams to go do big things. Like the reason that's important is I think small teams are just faster than big teams are. You can do a lot with like 10 really great, super motivated engineers, uh, you know, with really powerful tools.

• 31:45Harry Stebbings?

  What would you most like to do, but because of scale, decision-making, whatever it is, you're not able to do within Microsoft?

• 31:53Speaker 00

  Scale is usually tough for two reasons in a technology company. But it does mean that sometimes you are slower than you would like to be. And sometimes slow is necessary, but like sometimes slow is like a side effect of big. Where have you been slow where you would like to be fast? There are things that can't go faster than they go because like laws of physics are attached to them. Like we, we have, uh, we have been running a thousand miles an hour building infrastructure over the past, uh, you know, two and a half years since G P T four. And like we, we are literally going as fast as is possible to go.

• 32:36Speaker 00

  And it's still like, you know, you just sort of wish you could change, you know, the rate at which concrete can be poured and like, you know, power grids can be augmented. And, you know, all of this sort of stuff, like I wish it could go a little bit faster. But what I would love to be able to do in an ideal world at Microsoft and everywhere else is like, I, I don't want there to be any space between an engineer's ambition for, uh, what they want to do. And like a good idea they want to try and their ability to go try it. And so like a lot of our internal use of A I right now is to try to figure out like, how to go enable that for all of our people at Microsoft. And like, you know, there's another thing too, like if you've ever managed, uh,

• 33:20Speaker 00

  any size engineering team, like one of the nastiest problems that you have, that's very zero sum traditionally is like accumulation of tech debt. At some point, you're going to be confronted with a painful trade off. It's like, I, I gotta get this thing out, which means I can't quite get the technical bits of it in exactly the state that I want them to be. And so like, I'm going to launch now and I'll go fix this thing later. And the minute that you've done that, you have minted technical debt. And technical debt is just sort of like financial debt. It carries interest and you have to pay the interest payments. And if you don't pay the tech debt down plus interest, like you will be in trouble at some point because it will just sort of accumulate to this.

• 34:03Speaker 00

  Large extent and then things start failing in your infrastructure. And so like, one of the things I am absolutely most excited about with AI is like, I think we can turn this very zero sum problem of tech debt accumulation into something non-zero sum, like where you don't have to make those trades the same way that you have in the past. And like, there's a big research initiative, uh, we've started at Microsoft Research about a year ago, where like, the whole mission of the lab is like, eliminate tech debt at scale using these new AI tools. It's just super exciting stuff. Again, I've been leading engineering teams for 20 years now and like, tech debt is like, just my mortal enemy.

• 34:45Harry Stebbings?

  What have you learned from doing that program now for the last year?

• 34:48Speaker 00

  That the AI tools are more capable than people think they are. I think honestly, right now, there's a bigger gap than there was even two years ago between what the most capable frontier models can do and what they're being used for.

• 35:01Harry Stebbings?

  I would love to move into a quick fire, if that's okay. So let's start with, um, which competitor do you most respect? Google, Anthropic or Meta and why?

• 35:10Speaker 00

  If I gotta pick one, maybe Anthropic. Dario's doing a good job.

• 35:14Harry Stebbings?

  What was the best advice you've ever received?

• 35:16Speaker 00

  Yeah, I had a mentor one time who, uh, told me that you can sort of imagine an individual or a team's competencies on a histogram where the bucket all the way on the left is idiot and, uh, the bucket all the way on the right is genius and like, middle bucket's mediocre or average. And their assertion was that you could take everything that you do and like, everything that you're trying to do and assign it to one of those buckets. And that the mistake that people make is with great effort, you can take something and move it up one, maybe two buckets to the right on the histogram. And that the mistake that people always make is they focus on trying to improve

• 36:01Speaker 00

  at the things that they're worst at. And if you believe this theory, like the best you're ever gonna do if you're an idiot at something is to get mediocre at it. And all of the time that you spend trying to get to mediocre, you are not spending doing the things that you're a genius or very good at. I think that's very good advice because like the the thing about everything that's worth doing is you probably have to do it with a team and it is super easy to construct a team where you compliment people.

• 36:31Harry Stebbings?

  What are you bad at that you've consciously decided not to get mediocre at then?

• 36:36Speaker 00

  Oh dude, I'm bad at so many things. Like I'm super impatient with bureaucratic things. Like I hate budgets and facilities and like all of the mechanical parts. So like being a engineering leader, I just like bureaucratic things just bug. And like I could probably be a very mediocre bureaucrat if I wanted to be just terrible at it.

• 36:58Harry Stebbings?

  Satya is one of the most incredible leaders of our generation. What have been your biggest lessons from working so closely with Satya and seeing him operate?

• 37:06Speaker 00

  You know, I think his just core leadership principle is that you have to simultaneously for people, create energy and you have to produce clarity. So like you, you really do have to make sure and like he's very good at this. Like he's, you know, his job's hard, but he is always trying to make sure that the energy of conversations is positive and that, you know, that people walk out of reviews and conversations and like anything that we're doing where they are carrying energy with them that's going to help them go do the hard thing ahead of us. And like you also have to at the same time, like you

• 37:48Speaker 00

  can't just like go produce a bunch of energy and rah rah rah and like not at the same time clarify for folks like what the most important things are.

• 37:58Harry Stebbings?

  I love that. Create energy, produce clarity. We mentioned DeepSeek earlier. Do we underestimate China's ability in AI?

• 38:05Speaker 00

  I don't think I have. We should really, really, really respect the capability of Chinese.

• 38:13Speaker 00

  Entrepreneurs, scientists and engineers, they are very good. Like, we shouldn't, you know, if you are underestimating it, like, you shouldn't. You know, I think maybe some people did, like, that's another interesting thing about that deep-seak reaction is like how surprised everyone seemed to be, like, oh, my God, like, this is coming from China, like, that shouldn't have been surprising.

• 38:33Harry Stebbings?

  What's the crazy AI prediction that most people would call science fiction that you believe to be true?

• 38:39Speaker 00

  It is already the case that I think the frontier models are probably better health diagnosticians than your average GP is. It's a good thing to sort of realize and act on as quickly as possible because we have a whole world of people who have inadequate access to high-quality healthcare, including my own family in rural central Virginia, where, you know, it's just not, not good. And so, yeah, they're, they're just sort of a bunch of these things like this where, yeah, the models are already really good and you've got, basically need the whole world to wake up to the fact that they're good so that we can go deploy this stuff and like,

• 39:19Speaker 00

  deploy it because the thing that we really care about is the good of the public, not trying to, you know, sustain some status quo.

• 39:28Harry Stebbings?

  Kevin, a lot of people ask you a lot of questions, team members, journalists. What question are you not often asked that you think is an important question that you should be asked?

• 39:39Speaker 00

  I don't know, are we going fast enough?

• 39:41Harry Stebbings?

  Do you think we're going fast enough?

• 39:42Speaker 00

  No.

• 39:43Harry Stebbings?

  Is it possible to go much faster?

• 39:45Speaker 00

  Yeah, I think so.

• 39:46Harry Stebbings?

  How could we go faster?

• 39:48Speaker 00

  I think in a bunch of different ways, like, we could, the thing that I would want in my ideal world is we really invest super heavily in education. I would love to see every child feel as if These new tools that we're building right now are for them, accessible to them, expressly built for them to go accomplish the things that they think are most important. Like, I want billions of human beings off taking all of this creative energy that we all have and doing like the most amazing thing with the best tools that they possibly

• 40:31Speaker 00

  have. I don't want anybody feeling constrained by anything. And then like, I would love to make sure that, you know, across the public and private sector that we are creating every incentive that we possibly can to go deploy these tools to like produce good, whether it's, you know, you've got healthcare and climate change and education and, you know, and, and, and like pick your thing where we don't think we've got enough of. Like what everybody's thought ought to be is like, if I had a piece of technology that could create abundance in this thing where we currently think there's scarcity, like let us go invest in that.

• 41:13Harry Stebbings?

  Kevin, listen, I've so enjoyed talking to you. I so appreciate your tolerance with the wide range of questions, the future pontifications, and you've been fantastic. So thank you so much. Thank you for keeping me company on my runs. And this has been awesome.

• 41:29Speaker 00

  You're very welcome. Thank you for having me.

• 41:32Harry Stebbings?

  I mean, that's just why you have to love the internet. You know, I started 20 VC from a bedroom in London with no money. And there I get to sit down, hang out and ask any question to the CTO of Microsoft. What an incredible opportunity. Kevin was fantastic on the show there. If you want to watch the episode, you can find it on YouTube by searching for 20 VC. But before we leave you today, turning your back of a napkin idea into a billion dollar startup requires countless hours of collaboration and teamwork. It can be really difficult to build a team that's aligned on everything from values to workflow. But that's exactly what Coda was made to do. Coda is an all-in-one collaborative workspace that started as a napkin sketch. Now, just five years since launching in

• 42:15Harry Stebbings?

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• 42:53Harry Stebbings?

  To try it for yourself, go to coda.io/20vc today and get six free months of the team plan for startups. That's coda.io/20vc to get started for free and get six free months of the team plan. And while Coda keeps the engine running smoothly, Shopify puts the pedal to the metal when it's time to sell. I spend my time looking into successful businesses of today and tomorrow, and often, there's a business that's behind the business helping drive success. For millions, that is Shopify. Shopify is home to the number one checkout on the planet, boosting conversions by up to an astonishing 50%, meaning way less carts going abandoned and way more sales going into the business. Winner. So if you're into growing your business,

• 43:38Harry Stebbings?

  your commerce platform better be ready to sell wherever your customers are. Go to Shopify.com/20vc to start your $1 per month trial today. That's Shopify.com/20vc in letters. And while Shopify helps you drive sales, don't forget what really keeps those customers coming back: trust. Trust isn't just earned, though. It's demanded. That's why over 9,000 companies, including Atlassian, Cora, and Factory, rely on Vanta to automate their security compliance. So Vanta helps businesses achieve certifications like SOC 2 and ISO 27001, turning months of tedious work into this beautifully fast and straightforward process. Their platform automates compliance across over 35 frameworks.

• 44:22Harry Stebbings?

  It centralizes workflows, and it proactively manages Risk, all while saving you time with automation and AI. So whether you're just starting or scaling your security program, Vanta connects you with auditors and experts to get audit ready quickly and build trust with your customers. Get $1,000 off your first year by visiting vanta.com/20vc. That's V A N T A dot com forward slash 20 vc. As always, I so appreciate all your support. Now stay tuned for an incredible new format of the show. On Thursday, we will be doing the daily deal, where I sit down with Jason Lampkin of Sasta, and we go through IPOs, M and As, fundraises, the big financings of the last week and analyze each one in turn. It's going to be a lot of fun.