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

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.

You are listening to 20VC with me, Harry Stebbings. Now, I think the show today for me just really emphasizes the power of the internet. I started 20VC 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 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 5 years since launching in beta, Coda has helped 50,000 teams all over the world get on the same page. Now, at 20VC, we've used Coda to bring structure to our content planning and episode prep, and it's made a huge difference. Instead of bouncing between different tools, we can keep everything from guest research to scheduling and notes all in one place, which saves us so much time. With Coda, you get the flexibility of docs, the structure of spreadsheets, and the power of applications, all built for enterprise And it's got the intelligence of AI, which makes it even more awesome. If you're a startup team looking to increase alignment and agility, Coda can help you move from planning to execution in record time. To try it for yourself, go to coda.io/20vc today and get 6 free months of the Team plan for startups. That's coda.io/20vc to get started for free and get 6 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, your commerce platform better be ready to sell wherever your customers are. Go to shopify.com/20vec to start your $1 a 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, Core, 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. Now Vanta automates compliance across over 35 frameworks, 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 vanta.com/20vc.

You have now arrived at your destination. Kevin, I am so excited for this. I was just telling you, I was listening to you and Shrepp on my run. I don't think I've ever run quite as fast, which clearly means the conversation was brilliant.

Either brilliant or awful, uh, in that you were trying to end your run so you could be done with it.

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

Yeah, I don't know. I think the thing that you just described, which is 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.

In those moments of transition where there is this confusion, what have you learned is 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?

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 was just so fascinated by the infrastructure itself. And this is also a characteristic at the beginning of these cycles is you have technical people who get just swept up in the technical bits and they kind of forget that the only thing that really matters is making good product. And that's where we're at right now. You have to make good product and you have to have ideas and have conviction and then you have to go get stuff done really fast so that you can see what 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. You're not looking at someone else's success and saying, "Okay, well, I'm going to do that, but just a little bit better." You're trying to figure out something completely new. And the only way to figure that out is you got to launch stuff and gather data and iterate and be super, super brutal with your own self about what you're seeing. 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.

You said that models aren't products. I just had Andrew from Cerebras on the show, and I asked him this question. I said, if we think about compute or 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 that models aren't products. If they're not products, does that mean they're not valuable?

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 to consume your platform and your infrastructure and all of that's good. But yeah, I mean, most of the value has to be in the products. We don't build infrastructure just for the sake of infrastructure. We build infrastructure so people can make product.

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?

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? 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 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 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, you know, 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.

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. First, why do you think we're not? And that's a ridiculous statement.

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's some people who don't believe that there's 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 have AIs that there is no such limit and like things, you know, will continue to scale into like, you know, weird territory. 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, they're just 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.

When we think about like the three core elements that make up kind of efficiency in this respect, it's kind of data, compute, and 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?

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 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 that stuff is way more value than just the undifferentiated tokens that are floating around on the web.

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

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, 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 what some people think valuable data is versus like how valuable it actually is to producing capabilities and models that are legitimately useful. And most of what's legitimately useful is like models. People who think of models as repositories of actual 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. Uh, 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? And so you just need different tokens for training a model to make them good at reasoning than you do at making them recallers of facts.

It's so funny you said about reasoning there because it just made me think about inference. I get really annoyed by the word inference. I wish we'd just 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?

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 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 in the software stack. Again, there's just a ton of work happening there. You know, the Deepseek R1 stuff, 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.

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

I was surprised at what the public reaction was. Why? Oh, because we've had models more interesting than Deepseek R1 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.

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

Even when 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 we're paying very close attention to that. We have to give people more how than we have been doing it because developers want lots and lots of choice.

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? Months.

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'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.

When you look forward to the next 3 to 5 years, 3 to 10 years. How do you think about the pervasiveness of open versus closed, and which will be more dominant than the other?

I don't know, like, I think there's going to 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 going to be. But you just sort of pick your previous, uh, things like search, for instance. Like, there's a whole bunch of open source, uh, search engines 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 as a starting point, they can stand up a product, like they can go load their data into something like Azure Cognitive Search, which is a search-as-a-service platform. Like Google has one, Amazon has one, like 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 somebody who stood up a gigantic infrastructure and who's sort of running a whole search business with its own feedback loop. And so I think 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 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 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.

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?

Yeah, I don't know. I think it's a reasonable step in the right direction. The thing I've been saying for a few years now is 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 have 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 packaged up a piece of software into an application that you were able to run. 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 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, 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 they've gotten all of the code right. And they just sort of grind away on figuring out what that feedback loop is, that's going to change. 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 do this weird impedance matching that we've got right now between how a user has a set of expectations and how a product team has imagine what those expectations are.

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?

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 is your product managers are probably going to have to be domain experts, like people who sort of deeply understand something like medicine or drug discovery or early round venture investing or 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 assisting people doing those tasks 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.

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 3 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?

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 will never give it up. And, you know, the agents are becoming more and more powerful.

Can I ask you, to what extent is there lock-in there?

When I look at them and when I speak 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?

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.

Can I ask, when you think about a 5-year time horizon, what will the interaction model look like between humans and agents?

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 that's 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 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 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 that you can just sort of build up more and more powerful ways of doing things inside of the agent over time because it's remembering the past things that it's done and learned. The agents definitely, I mean for sure, this is gonna be true. 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 means, right now it's very interactive. You go to your agent and you 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, we should just never lose the plot on where we're going. So, you know, the first generation of agents are good at 5-second tasks, and then the generation after that were good at 5-minute tasks. And, you know, what we're going towards are things that you can delegate increasingly complicated tasks and increasingly, you know, beefy work to over time, the same way that you would to 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 want?

We mentioned that software development being one of the most widely adopted usage mechanisms that we're seeing today. When we look forward 5 years, what percent of net new code do you think will be AI created versus human created?

What time horizon? You say 5 years?

5 years.

95% 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.

Like, what does authorship mean in a world where you're not the input master?

Well, so look, it's just raising the level of abstraction. So like when you're designing an iPhone application in Xcode, like you don't write all of the code. Like you sort of drag a whole bunch of user experience elements around on the screen. Like, you know, 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. Like we are changing the interface that the programmers use to to communicate to the machine that here's a problem that needs to be solved. One of the things that's true, like the extraordinarily good programmers right now, even when they're using tools that are at a very high level of abstraction, 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 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 emitted this, but like, you know, something's off. Like, let me go spelunk down into the lower levels of abstraction.

Is everyone a programmer in a world where you have a Bolt or a Lovable which allows you to create simple websites, but websites? Yeah, with pure prompts.

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

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

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

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

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, uh, 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 laws of physics are attached to them. We have been running 1,000 miles an hour building infrastructure over the past 2.5 years since GPT-4, and we are literally going as fast as is possible to go, and it's still you just sort of wish you could change the rate at which concrete can be poured and power grids can be augmented and all of this sort of stuff. 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 I don't want there to be any space between an engineer's ambition for what they want to do, like a good idea they want to try, and their ability to go try it. And so a lot of our internal use of AI right now is to try to figure out how to go enable that for all of our people at Microsoft. And there's another thing too, if you've ever managed any size engineering team, one of the nastiest problems that you have that's very zero-sum traditionally is accumulation of tech debt. At some point, you're going to be confronted with a painful trade-off. It's like, "I got to 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 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 you don't pay the tech debt down plus interest, you will be in trouble at some point because it will just accumulate to this large extent and then things start failing in your infrastructure. And so one of the things I am absolutely most excited about with AI is I think we can turn this very zero-sum problem of tech debt accumulation into something non-zero-sum 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 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.

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

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 2 years ago between what the most capable frontier models can do and what they're being used for.

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

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

What's the best advice you've ever received?

Yeah, I had a mentor one time who 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 the bucket all the way on the right is genius and middle bucket's mediocre or average. Their assertion was that you could take everything that you do and 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, 2 buckets to the right on the histogram, and that the mistake that people always make is they focus on trying to improve at the things that they're worst at. And if you believe this theory, the best you're ever going to 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 I think that's very good advice because 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 complement people.

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

Oh dude, I'm bad at so many things. I'm super impatient with bureaucratic things. I hate budgets and facilities and all of the mechanical parts. So 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.

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?

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 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 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 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.

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

I don't think I have. We should really, really, really respect the capability of Chinese entrepreneurs, scientists, and engineers. They are very good. Uh, like, we shouldn't— you know, if you are underestimating it, uh, like, you, you shouldn't. You know, I think maybe some people did. Like, that's another interesting thing about that Deepseek 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.

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

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 it's just not good. And so yeah, there are just sort of a bunch of these things like this where 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 deploy it because the thing that we really care about is the good of the public, trying to sustain some status quo.

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?

I don't know. Are we going fast enough?

Do you think we're going fast enough?

No.

Is it possible to go much faster?

Yeah, I think so.

How could we go faster?

I think in a bunch of different ways. 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. I want billions of human beings off taking all of this creative energy that we all have and doing the most amazing thing with the best tools that they possibly have. I don't want anybody feeling constrained by anything. And then I would love to make sure that across the public and private sector that we are creating every incentive that we possibly can to go deploy these tools to produce good whether it's healthcare and climate change and education and, and, and, like pick your thing where we don't think we've got enough of. 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, let us go invest in that.

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.

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

I mean, that's just where you have to love the internet. I started 20VC 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.

Soft.

What an incredible opportunity.

Kevin was fantastic on the show there.

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