The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:15:34 · Harry Stebbings, Satya Nadella
[00:15:34] S1: Yeah. 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? [00:15:45] S0: Yeah. 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 AgenTeq 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 NEMET could be one vertically integrated model and app tier and all the way to the silicon and system. It's unlikely to me. I thought at some point that it might be the case that there will be 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 horizontal specialization with multiple winners in each layer and then the ability for people to compose.
00:15:45 · Satya Nadella, Harry Stebbings
[00:15:45] S0: Yeah. 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 AgenTeq 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 NEMET could be one vertically integrated model and app tier and all the way to the silicon and system. It's unlikely to me. I thought at some point that it might be the case that there will be 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 horizontal specialization with multiple winners in each layer and then the ability for people to compose. [00:17:02] S1: The thing I didn't see is the depth of each layer. Like, what I know now that I wish I'd known twelve months ago, it's just like the infinite demand for inference. [00:17:09] S0: Yeah. That's a great point.
00:17:09 · Satya Nadella, Harry Stebbings
[00:17:09] S0: Yeah. That's a great point. [00:17:10] S1: Yeah. And I wish I'd known it twelve months ago. I would've done some very different investing. Speaking of that, we've had, like, Mustafa Silliman saying training runs of a 100,000,000,000 are coming, and only a handful of labs can pay for that. A $100,000,000,000 training runs. Do we have the energy infrastructure and power to enable this scale across the stack, but especially when we Yeah.
00:17:32 · Satya Nadella
[00:17:32] S0: 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 and D, right? Because when somebody says training run, it's just a percentage of R and D, right? Companies that are heavy in R and D spend what? 15, 20% on R and D. And so therefore, yeah, at some point, if you have enough revenue, you can spend enough on 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're long lead items. 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 this come online? What will be the demand supply? No one knows. 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 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.
00:18:55 · Harry Stebbings, Satya Nadella
[00:18:55] S1: In terms of the inflation of GDP growth rate, the world has kinda stabilized 2% a year for a very large amount of time. Does AI have the ability to to seriously inflect that in a short amount of time? [00:19:06] S0: I think so, and I somewhat hope so because that is the point. Let's take even as a percentage of any of our GDPs, the amount of money we spend in our healthcare. 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. 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? That means the IT backlogs were huge, none of us even recognized it. [00:20:03] S1: Exactly. I was taught that you had to be the top one or two in a market to win in venture. We're in, you know, factory, which is fifth, and it's winning. Fifth. Yeah. This breaks all rules of venture. The question that I have, again, slightly naughty, not on schedule, but it's just like, will we see the tipping in terms of capability advancement in alternative functions, finance, sales, marketing, that we've seen in coding? Because coding's been the mother lode.
00:20:03 · Harry Stebbings, Satya Nadella
[00:20:03] S1: Exactly. I was taught that you had to be the top one or two in a market to win in venture. We're in, you know, factory, which is fifth, and it's winning. Fifth. Yeah. This breaks all rules of venture. The question that I have, again, slightly naughty, not on schedule, but it's just like, will we see the tipping in terms of capability advancement in alternative functions, finance, sales, marketing, that we've seen in coding? Because coding's been the mother lode. [00:20:26] S0: I think that that's a good point. It it... People talk a lot about, you know, 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, sort of in contact centers, in the supply chain systems, in these marketingcash to order systems, I think it's possible, and we're seeing it. Is the demand as elastic as coding? I think science may be as elastic, right, or engineering in general. Like, by the way, if I 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. [00:21:20] S1: But even just like optimization of maintenance reports, like you said with Rolls Royce, which I would never consider.