WEBVTT 00:00:10.040 --> 00:00:13.017 Welcome. Today, we have Satya Nadella. 00:00:13.100 --> 00:00:15.637 Thank you so much for coming. Absolutely. 00:00:15.720 --> 00:00:19.097 It's my pleasure. And, realizing it's a finals week. 00:00:19.180 --> 00:00:22.877 So it's a different time of the day whatnot, 00:00:22.960 --> 00:00:27.557 but I appreciate the students that you all made it here. 00:00:27.640 --> 00:00:30.957 I was thinking this morning how it's kind of fitting 00:00:31.040 --> 00:00:34.817 that you're the last person that we're having to this class. 00:00:34.900 --> 00:00:37.417 And I say that because if I think back, 00:00:37.500 --> 00:00:42.357 your bet of putting a billion dollars into OpenAI in 2019 00:00:42.440 --> 00:00:47.357 feels like that really set the stage for this Cambrian 00:00:47.440 --> 00:00:49.337 explosion, if you will, around AI. 00:00:49.420 --> 00:00:52.797 So I'm curious to just kick it off like, 00:00:52.880 --> 00:00:56.177 what was the thought process to make that bet? 00:00:56.260 --> 00:00:59.727 Yeah. I mean, I think it is fascinating to look back 00:00:59.810 --> 00:01:04.307 now, six, seven, eight years in some sense. 00:01:04.390 --> 00:01:11.390 I think the thing that I feel at least 00:01:11.750 --> 00:01:14.987 got me convinced that this was the right thing 00:01:15.070 --> 00:01:20.007 to go try at that time is quite frankly, 00:01:20.090 --> 00:01:22.387 what I describe as a prepared mind, which 00:01:22.470 --> 00:01:28.347 Microsoft's obsession has always been in natural language. 00:01:28.430 --> 00:01:32.627 And of course, we were mostly focused 00:01:32.710 --> 00:01:36.467 on trying to get to natural language 00:01:36.550 --> 00:01:41.547 with some machine learning, some NLP, 00:01:41.630 --> 00:01:46.087 but fundamentally, if you had asked us even in 2017, 2018, 00:01:46.170 --> 00:01:52.467 it would be some combination of some symbolic logic plus machine learning. 00:01:52.550 --> 00:01:56.027 So we were perhaps at that stage not the truest 00:01:56.110 --> 00:02:02.587 believers that deep learning can even get you NLP breakthroughs. 00:02:02.670 --> 00:02:06.027 But that was something we wanted to have happen. 00:02:06.110 --> 00:02:10.487 So I would take shots. In fact, most people talk about just the OpenAI bet. 00:02:10.570 --> 00:02:15.627 But we bought a bunch of companies, have invested in a whole lot of others, 00:02:15.710 --> 00:02:18.427 because the fundamental thing we were conditioned to do 00:02:18.510 --> 00:02:24.247 was anyone who had an ambitious angle on natural language 00:02:24.330 --> 00:02:27.247 irrespective of what sort of lineage they came from, 00:02:27.330 --> 00:02:32.427 we would always take it, whether it was organically inside the company or outside. 00:02:32.510 --> 00:02:37.867 And that is when Sam-- and in fact, we were one happy family at that time. 00:02:37.950 --> 00:02:40.487 So the idea was there. That's what I'm saying. 00:02:40.570 --> 00:02:43.267 You really did kick off so many things. 00:02:43.350 --> 00:02:45.087 Everyone was in the same place. 00:02:45.170 --> 00:02:50.387 And so to some degree, the scaling laws paper came out 00:02:50.470 --> 00:02:53.987 and their ambition on pushing the transformer with more 00:02:54.070 --> 00:02:59.677 compute and data was an appealing thing for us to take a shot at. 00:02:59.760 --> 00:03:01.457 And of course, what's happened. 00:03:01.540 --> 00:03:04.057 It's pretty stunning that-- the fact that the capability 00:03:04.140 --> 00:03:09.657 graph has just stayed at that scaling law Pareto 00:03:09.740 --> 00:03:11.817 is just pretty amazing. 00:03:11.900 --> 00:03:16.857 I mean, as someone who worked at Microsoft, I guess 20 years ago, 00:03:16.940 --> 00:03:18.997 you've changed the culture like so much. 00:03:19.080 --> 00:03:23.897 And I think it's striking to me that when you took that bet, I mean, was there 00:03:23.980 --> 00:03:27.517 an uprising within Microsoft saying, hey, we can do this ourselves? 00:03:27.600 --> 00:03:31.997 Yeah, I mean, I think Microsoft over the years, 00:03:32.080 --> 00:03:35.857 I always say that at the end of the day, the core bet has to be the organic bet 00:03:35.940 --> 00:03:40.017 and what one does inside. And then there are partnerships, there's 00:03:40.100 --> 00:03:44.397 M&A. And I think any company like-- in some sense when 00:03:44.480 --> 00:03:48.577 you grow up in Microsoft, you learn that you can create a lot of enterprise value 00:03:48.660 --> 00:03:51.557 by building, by partnering. 00:03:51.640 --> 00:03:55.607 If I look back, the PC revolution 00:03:55.690 --> 00:03:59.407 wouldn't have been possible, but for what people describe 00:03:59.490 --> 00:04:02.767 as the Gates-Grove model, which is Intel, Microsoft coming 00:04:02.850 --> 00:04:08.407 together to create essentially what was a PC ecosystem. 00:04:08.490 --> 00:04:13.167 I worked on SQL Server, and so what we did with SAP to build our database business 00:04:13.250 --> 00:04:15.627 and for them to build the ERP application. 00:04:15.710 --> 00:04:18.587 So we are conditioned quite frankly, 00:04:18.670 --> 00:04:25.387 for these type of ecosystem partnerships as well as organically build. 00:04:25.470 --> 00:04:28.707 And so I would say there was not an uprising in some sense. 00:04:28.790 --> 00:04:30.227 They would have always been like, 00:04:30.310 --> 00:04:33.807 hey, whenever you allocate your scarce resource of whether it's 00:04:33.890 --> 00:04:37.127 capital or more-- in this case, it's more than capital, 00:04:37.210 --> 00:04:42.947 the biggest decision was about compute concentration on a particular effort. 00:04:43.030 --> 00:04:45.587 I mean, that was more the big bet. 00:04:45.670 --> 00:04:51.387 And that's why we made it because this was the group that wanted to go drive it. 00:04:51.470 --> 00:04:54.327 And we benefited obviously immensely from it. 00:04:54.410 --> 00:04:58.127 And now one of the reasons why he's in town is that there was a big developer 00:04:58.210 --> 00:04:59.647 conference called Build. 00:04:59.730 --> 00:05:03.307 And yesterday you announced this frontier intelligence ecosystem, 00:05:03.390 --> 00:05:05.267 which kind of right in line what you're saying. 00:05:05.350 --> 00:05:08.087 And you had a bunch of other pretty huge announcements. 00:05:08.170 --> 00:05:09.827 And I'd love to talk about some of those. 00:05:09.910 --> 00:05:12.237 So you launched seven new models. 00:05:12.320 --> 00:05:14.567 And I thought what was really interesting about at least 00:05:14.650 --> 00:05:19.547 Mustafa's description of how you did those models is that all the data is very clean. 00:05:19.630 --> 00:05:23.907 There's a lot of focus on, let's say, not breaching any copyright things. 00:05:23.990 --> 00:05:26.927 I'm just interested to hear from you why seven? 00:05:27.010 --> 00:05:28.307 What was the thought behind that? 00:05:28.390 --> 00:05:31.087 Obviously you want your own models, which makes a sense. 00:05:31.170 --> 00:05:34.827 Yeah, I think if you step back-- in fact, 00:05:34.910 --> 00:05:38.467 I think you call this class the frontiers class. 00:05:38.550 --> 00:05:45.247 And so I think one of the challenges of this conceptualizing, how does 00:05:45.330 --> 00:05:48.607 anyone, any individual entrepreneur or developer 00:05:48.690 --> 00:05:51.517 company, participate at the frontier? 00:05:51.600 --> 00:05:53.077 There are frontier models, but how 00:05:53.160 --> 00:06:00.160 does one have real agency to add value, derive value, and protect 00:06:00.320 --> 00:06:02.077 value? Because that's the question. 00:06:02.160 --> 00:06:06.077 If you have a model that basically learns from data, 00:06:06.160 --> 00:06:08.117 what's the future of the firm even? 00:06:08.200 --> 00:06:11.277 Which is, the firm today is about tacit knowledge 00:06:11.360 --> 00:06:14.077 inside the company that comes about 00:06:14.160 --> 00:06:17.117 because of its operations and human capital. 00:06:17.200 --> 00:06:19.237 And in a world where there is going 00:06:19.320 --> 00:06:25.777 to be tokens and humans collaborating together, what's the future of the firm? 00:06:25.860 --> 00:06:28.217 So there are some substantially big questions. 00:06:28.300 --> 00:06:30.957 And so our vision for this is simple, 00:06:31.040 --> 00:06:35.157 which is we believe a frontier ecosystem is 00:06:35.240 --> 00:06:40.317 one where every company can actually operate 00:06:40.400 --> 00:06:45.537 at the frontier with their own IP compounding over time, 00:06:45.620 --> 00:06:48.867 not just the human capital, but even that token capital. 00:06:48.950 --> 00:06:52.427 So that is the motivation we have. 00:06:52.510 --> 00:06:57.847 So for example, when the models we built-- I'll come back to the lineage. 00:06:57.930 --> 00:07:01.587 There's a nice technical report that I would encourage folks 00:07:01.670 --> 00:07:03.867 to go read, because I think it's probably 00:07:03.950 --> 00:07:07.987 one of the most transparent, good, detailed document 00:07:08.070 --> 00:07:12.027 on the entire pipeline that's been written lately 00:07:12.110 --> 00:07:16.307 from a model of this size, and I think you'll learn a lot from it. 00:07:16.390 --> 00:07:20.227 But the purpose of both, let's take out thinking in coding models, was to be 00:07:20.310 --> 00:07:24.387 able to do this in such a way that we can license it along 00:07:24.470 --> 00:07:28.867 with the weights and really allow every company 00:07:28.950 --> 00:07:30.687 to build their own hill-climbing machine. 00:07:30.770 --> 00:07:35.847 So we ourselves climbed our hill using, as you said, 00:07:35.930 --> 00:07:38.947 very clean lineage of data, making sure 00:07:39.030 --> 00:07:43.347 we were not adding a bunch of synth data in the mix. 00:07:43.430 --> 00:07:45.827 So all that was very much true so 00:07:45.910 --> 00:07:51.867 that we could truly have a model where reasoning emerged. 00:07:51.950 --> 00:07:57.187 And so we now have a fantastic, good, efficient model, 00:07:57.270 --> 00:08:02.827 but inside of a hill-climbing machine that any company sets up, it can go 00:08:02.910 --> 00:08:07.627 learn using the traces of that company and those tasks. 00:08:07.710 --> 00:08:10.427 So our goal is every company starts 00:08:10.510 --> 00:08:17.247 thinking strategically about what's the RLE environment that they set up? 00:08:17.330 --> 00:08:20.827 What is the private evals that they have? 00:08:20.910 --> 00:08:25.407 How do they then welcome any model into that gym, 00:08:25.490 --> 00:08:31.907 so to speak, and then allow them to retain the IP and not leak value? 00:08:31.990 --> 00:08:35.807 So to me, that's what I think every company will need to start doing. 00:08:35.890 --> 00:08:40.587 Because if you're just a consumer of a foundation model, 00:08:40.670 --> 00:08:46.877 then I'm not sure how you can retain enterprise value, let alone create. 00:08:46.960 --> 00:08:51.677 So the only way I see this ecosystem quite frankly 00:08:51.760 --> 00:08:56.697 being non-zero sum or positive sum where lots of participants 00:08:56.780 --> 00:09:00.337 can all be at the frontier, is they're 00:09:00.420 --> 00:09:03.917 able to take frontier models, take open-weight models, 00:09:04.000 --> 00:09:08.857 take a model like ours which is a licensed IP, and then 00:09:08.940 --> 00:09:13.557 hill climb on their own environment, and then build out their own IP. 00:09:13.640 --> 00:09:15.117 So that's the core premise. 00:09:15.200 --> 00:09:19.437 And we unpacked that in a lot of detail and all the tooling around it. 00:09:19.520 --> 00:09:21.017 For example, one of the coolest things 00:09:21.100 --> 00:09:25.577 is if you're a Microsoft 365 customer, we can bootstrap even. 00:09:25.660 --> 00:09:27.417 Because after all, what is Microsoft 365? 00:09:27.500 --> 00:09:30.937 Today you use it to run your business. 00:09:31.020 --> 00:09:35.417 People communicate with other people related to a business process. 00:09:35.500 --> 00:09:40.457 So you can imagine we can bootstrap the RLE. 00:09:40.540 --> 00:09:44.477 In fact, we can even generate the evals for, let's say, 00:09:44.560 --> 00:09:48.907 an HR onboarding process based on the observation of what you're doing. 00:09:48.990 --> 00:09:52.937 That's unique to the company. And first of all, it's their data. 00:09:53.020 --> 00:09:56.257 Think of we built a multi-tenant SaaS application. 00:09:56.340 --> 00:10:00.337 We now can turn that into a multi-tenant hill-climbing 00:10:00.420 --> 00:10:04.457 service where the data and the environment and the models 00:10:04.540 --> 00:10:07.937 and the traces and the outcomes are owned by the company. 00:10:08.020 --> 00:10:11.547 Do you think that most companies, though, have the right talent to be able to build 00:10:11.630 --> 00:10:13.557 those hill-climbing machines? Yeah, it's a great one. 00:10:13.640 --> 00:10:15.777 So that's why I think this is the easy button on it. 00:10:15.860 --> 00:10:18.237 So we are now not saying you need to build. 00:10:18.320 --> 00:10:24.497 So you have the hill-climbing machine that has been instantiated for you. 00:10:24.580 --> 00:10:27.377 All you need is a bit of strategic discipline 00:10:27.460 --> 00:10:30.677 in making sure that these models, the harness, 00:10:30.760 --> 00:10:36.777 the context, the evals are all artifacts and constructs that you understand 00:10:36.860 --> 00:10:40.417 and you manage them as assets. 00:10:40.500 --> 00:10:45.467 Just like how you have historically done where you cared about privacy, 00:10:45.550 --> 00:10:49.207 you cared about confidentiality, you cared about security, 00:10:49.290 --> 00:10:53.527 I think in a world where AI comes into your company, 00:10:53.610 --> 00:10:57.207 these things will become as important architectural and 00:10:57.290 --> 00:11:00.927 strategic considerations. And one of the other products you announced 00:11:01.010 --> 00:11:04.147 was Scout around enterprise cloud. 00:11:04.230 --> 00:11:07.687 I'm kind of interested to hear the vision thinking behind that. 00:11:07.770 --> 00:11:10.327 One of the things that we're very excited about 00:11:10.410 --> 00:11:15.927 is when I look at Copilot and its evolution, it started with chat. 00:11:16.010 --> 00:11:18.927 And chat became very powerful, especially with reasoning 00:11:19.010 --> 00:11:24.727 models, because you could not only just essentially 00:11:24.810 --> 00:11:30.227 use it more like search, but you could use it as a thinking assistant essentially. 00:11:30.310 --> 00:11:34.907 And so that became powerful. Then Cowork was the next form factor. 00:11:34.990 --> 00:11:38.967 And Cowork is pretty neat as a way to delegate tasks. 00:11:39.050 --> 00:11:44.517 It's a multi-step reasoning tool-calling agentic loop, 00:11:44.600 --> 00:11:50.497 and so therefore you're able to do a short TAS assignment. 00:11:50.580 --> 00:11:54.157 It's very much like what we were doing with GitHub Copilot, let's 00:11:54.240 --> 00:11:56.697 say, even two years ago when the agent loop started coming. 00:11:56.780 --> 00:11:58.917 So we're now doing it for knowledge work. 00:11:59.000 --> 00:12:04.817 But now with Scout, you essentially have the third form factor, which is autopilot. 00:12:04.900 --> 00:12:11.517 So now you have the long-running agent where it continuously is operating. 00:12:11.600 --> 00:12:14.417 It's monitoring. It's got a heartbeat. 00:12:14.500 --> 00:12:21.317 It's dreaming. All the things that you expect from a Claw you can now have. 00:12:21.400 --> 00:12:24.697 And you can create it. You can have one with your identity. 00:12:24.780 --> 00:12:27.977 So essentially, if I have an Entra ID, 00:12:28.060 --> 00:12:31.257 I can give Scout my Entra ID as a delegated ID, 00:12:31.340 --> 00:12:36.717 and it's essentially my digital twin that's working on my behalf 00:12:36.800 --> 00:12:41.397 continuously. But not just that, we can also allow 00:12:41.480 --> 00:12:47.197 you to mint more autopilots. And those things can have their own identity 00:12:47.280 --> 00:12:51.257 and their own sandboxes. And so it's a pretty complete system. 00:12:51.340 --> 00:12:55.397 So I think of it as an enterprise OpenClaw 00:12:55.480 --> 00:12:59.377 and a UI that fits in nicely with the rest of the Copilot system. 00:12:59.460 --> 00:13:01.657 And it makes sense because you have those identities, 00:13:01.740 --> 00:13:04.157 you can address the security question. 00:13:04.240 --> 00:13:07.037 I mean, obviously, I don't know how many of you have set up 00:13:07.120 --> 00:13:12.257 OpenClaw, but I struggled-- YOLO and get all my credentials because I'm like, 00:13:12.340 --> 00:13:16.777 I don't trust it. Yeah, we even announced. 00:13:16.860 --> 00:13:19.877 In fact, Peter was on stage with us at Build 00:13:19.960 --> 00:13:21.797 as well, because one of the other things 00:13:21.880 --> 00:13:24.157 is we were even working with the OpenClaw Foundation 00:13:24.240 --> 00:13:28.377 to make sure that it can be run securely. 00:13:28.460 --> 00:13:31.797 We will have, in fact, on Windows an out-of-the-box 00:13:31.880 --> 00:13:38.547 experience where you can install OpenClaw and have it secured 00:13:38.630 --> 00:13:45.387 or contained in what is this new, essentially a container called MXC, 00:13:45.470 --> 00:13:48.907 which is essentially a way to sandbox the environment. 00:13:48.990 --> 00:13:51.307 And so I think containment is key. 00:13:51.390 --> 00:13:54.107 Because after all, you now have these long-running agents 00:13:54.190 --> 00:13:57.427 that are able to generate code and execute code. 00:13:57.510 --> 00:14:01.207 And so therefore it will become very important to govern the execution. 00:14:01.290 --> 00:14:06.047 And so we have a container that then you can set policy and isolation boundaries. 00:14:06.130 --> 00:14:09.967 It can be process level isolation, session level isolation. 00:14:10.050 --> 00:14:12.467 You can even have a VM boundary if you wanted. 00:14:12.550 --> 00:14:16.347 For me for example, if anything I wanted to ever run even, 00:14:16.430 --> 00:14:19.427 I'll just run it on Windows 365, which is my cloud instance. 00:14:19.510 --> 00:14:25.087 So a complete cloud instance that's fully isolated for long-running agents. 00:14:25.170 --> 00:14:30.627 So I think we are all going to learn how to work with many agents. 00:14:30.710 --> 00:14:35.677 And we're also going to learn how to isolate the environments for these agents, 00:14:35.760 --> 00:14:39.207 just like how back in the day, we thought about processes. 00:14:39.290 --> 00:14:41.537 We're going to think about the process boundary, session 00:14:41.620 --> 00:14:45.697 boundaries, and container boundaries for agents. 00:14:45.780 --> 00:14:48.537 One of the other things that you also announced 00:14:48.620 --> 00:14:55.457 was around bringing, we'll say, AI to consumers. 00:14:55.540 --> 00:14:59.777 And I'm kind of curious what does that mean. 00:14:59.860 --> 00:15:03.897 I mean, there's a lot of big announcements with NVIDIA. 00:15:03.980 --> 00:15:05.977 Yeah, I know there are a couple of things on that. 00:15:06.060 --> 00:15:12.777 One is we're very excited about this concept of unmetered intelligence. 00:15:12.860 --> 00:15:18.857 So if you think about it, every PC historically the install base 00:15:18.940 --> 00:15:22.717 had a lot of GPUs. If you count the number of PCs with GPUs, 00:15:22.800 --> 00:15:26.007 it's pretty substantial-- the dGPU install base. 00:15:26.090 --> 00:15:28.177 So one of the things that we are trying to make sure 00:15:28.260 --> 00:15:32.437 is that in a world where these models are there, 00:15:32.520 --> 00:15:34.877 there is applications that are being written, 00:15:34.960 --> 00:15:37.217 the tokens are in short supply, we 00:15:37.300 --> 00:15:42.057 want to tap into essentially the edge compute silicon. 00:15:42.140 --> 00:15:45.537 And in that context obviously NVIDIA 00:15:45.620 --> 00:15:48.957 announced a new SoC which we are very excited about their RTX. 00:15:49.040 --> 00:15:50.617 So we have a Surface Laptop which 00:15:50.700 --> 00:15:54.397 is going to come out in the fall, which is built on it. 00:15:54.480 --> 00:15:58.697 In fact, all our OEMs will have fantastic designs for it. 00:15:58.780 --> 00:16:01.077 We also announced a dev box. I mean, think about it. 00:16:01.160 --> 00:16:03.877 It's going to have a petaflop of AI compute. 00:16:03.960 --> 00:16:05.777 It's going to have 20 CPU cores, 128 00:16:05.860 --> 00:16:12.497 gigabytes of unified memory for both the CPU and the AI compute. 00:16:12.580 --> 00:16:19.057 And it's going to run something like a trillion parameter model locally. 00:16:19.140 --> 00:16:25.237 And by the way, and we also worked with Jensen to get Windows working on a GB300. 00:16:25.320 --> 00:16:27.357 So we even have a DGX workstation. 00:16:27.440 --> 00:16:32.247 So I think of it as a data center desktop. 00:16:32.330 --> 00:16:35.407 And so I think that there's going to be real demand for all of this 00:16:35.490 --> 00:16:38.487 because people will want-- especially when you install 00:16:38.570 --> 00:16:41.467 something like Scout or Claw or what have you, 00:16:41.550 --> 00:16:46.887 and I want it to just keep working 24 by 7 00:16:46.970 --> 00:16:49.407 and I don't want to get billed for it, the best way 00:16:49.490 --> 00:16:53.167 to do that is to run it on your laptop or on your desktop. 00:16:53.250 --> 00:16:58.607 So we are very excited about just even the rebirth of the existing PC form 00:16:58.690 --> 00:17:03.447 factor with this new unbelievable functionality 00:17:03.530 --> 00:17:07.207 brought forth because of both the silicon innovation and the model capabilities 00:17:07.290 --> 00:17:12.187 that now we can have locally. So that was a lot of what we talked about. 00:17:12.270 --> 00:17:14.847 But the other thing that we also said 00:17:14.930 --> 00:17:19.207 is just as there's new functionality in the old form 00:17:19.290 --> 00:17:22.647 factors, I think there's a real opportunity 00:17:22.730 --> 00:17:26.247 to create new form factors for the agent era. 00:17:26.330 --> 00:17:28.567 So that's where Project Solara comes in. 00:17:28.650 --> 00:17:34.137 And our goal there is to say, we showed two reference designs, 00:17:34.220 --> 00:17:40.917 one is a badge and the other one was a desk companion, if you will. 00:17:41.000 --> 00:17:42.797 But the badge is pretty interesting. 00:17:42.880 --> 00:17:47.857 So you can imagine an agent that has a fingerprint reader 00:17:47.940 --> 00:17:51.237 and a badge that has a fingerprint reader as well 00:17:51.320 --> 00:17:57.557 as a camera, and has enough onboard compute-- it's a MediaTek processor-- 00:17:57.640 --> 00:18:00.717 to be able to wake up something like Copilot. 00:18:00.800 --> 00:18:04.797 And I can literally get notified. 00:18:04.880 --> 00:18:08.277 Like I can even give it, say, a coding task or whatever, 00:18:08.360 --> 00:18:12.197 I can dictate to it, it will take the input 00:18:12.280 --> 00:18:16.877 and then go execute it in the cloud, notify me back. 00:18:16.960 --> 00:18:22.757 You can imagine in healthcare if I was a nurse, I was moving station to station, I 00:18:22.840 --> 00:18:26.857 could use that to badge in the data versus the phone. 00:18:26.940 --> 00:18:29.837 Like right now we're conditioned either we're entering in the PC 00:18:29.920 --> 00:18:34.677 or we are using the phone. In an agent era where you really have ambient intelligence 00:18:34.760 --> 00:18:38.517 and ubiquitous computing, you can imagine these form factors 00:18:38.600 --> 00:18:43.077 now that are just endpoints for long-running agents 00:18:43.160 --> 00:18:49.797 that wake up, notify, and help you get both output input that's 00:18:49.880 --> 00:18:51.177 right there in the real world. 00:18:51.260 --> 00:18:55.177 And so we're very excited about bringing even a platform for it. 00:18:55.260 --> 00:18:58.477 So we will build some. But the goal here is also to have even by the way 00:18:58.560 --> 00:19:02.857 new platform rules. So Windows has always been-- it's 00:19:02.940 --> 00:19:06.997 fascinating that we are the only open platform out there. 00:19:07.080 --> 00:19:09.417 You can go through our App Store or not. 00:19:09.500 --> 00:19:12.017 You can install anything on Windows. 00:19:12.100 --> 00:19:15.877 It's always had that ethos of being not something that only 00:19:15.960 --> 00:19:18.477 Microsoft-- you don't need to call Microsoft to build 00:19:18.560 --> 00:19:20.897 applications for Windows. How about that? 00:19:20.980 --> 00:19:25.237 So that's the openness we want even in this new agent platform 00:19:25.320 --> 00:19:28.347 so that we don't have the carryover of these platform 00:19:28.430 --> 00:19:32.467 rules that were written for the previous era. 00:19:32.550 --> 00:19:34.087 I'm going to switch gears a little bit. 00:19:34.170 --> 00:19:38.247 So here we're at Stanford University, probably 00:19:38.330 --> 00:19:41.747 the center of the world in terms of AI-pilled people. 00:19:41.830 --> 00:19:47.707 And when you get outside of the Bay Area, Seattle, 00:19:47.790 --> 00:19:50.927 people are looking at AI and saying, like, what's good for me? 00:19:51.010 --> 00:19:53.067 And I think there was a prior speaker that 00:19:53.150 --> 00:19:55.207 used the metaphor, which I found quite powerful, 00:19:55.290 --> 00:20:01.227 which was as electricity came about, we didn't sell electricity, we sold light. 00:20:01.310 --> 00:20:03.367 And what do you think is that equivalent for AI? 00:20:03.450 --> 00:20:06.587 Because right now there's not a lot of good messaging around AI 00:20:06.670 --> 00:20:08.627 of how it's going to benefit people. 00:20:08.710 --> 00:20:10.987 Yeah, I think that's right in the sense 00:20:11.070 --> 00:20:16.987 that we have perhaps gone too into-- the bubble 00:20:17.070 --> 00:20:20.427 that I guess we all live in is more about 00:20:20.510 --> 00:20:25.537 hyping the tech and the tech progress for its sake. 00:20:25.620 --> 00:20:28.177 We live in it and it's great to be impressed by it 00:20:28.260 --> 00:20:30.397 and push the frontiers of it and what have you. 00:20:30.480 --> 00:20:35.577 But at the end of the day, the world will evaluate us in what was the value we 00:20:35.660 --> 00:20:39.217 created for the world, one community at a time. 00:20:39.300 --> 00:20:42.717 I mean, it should always be the case. 00:20:42.800 --> 00:20:48.137 And so unless I can see the true benefits of this technology 00:20:48.220 --> 00:20:52.897 be broad spread. We talked about healthcare. 00:20:52.980 --> 00:20:55.597 When we suddenly start seeing AI in healthcare 00:20:55.680 --> 00:21:02.317 change the cost equation, the care one can get on, not in an abstract sense, 00:21:02.400 --> 00:21:04.877 but when it happens to someone in our community, 00:21:04.960 --> 00:21:09.197 in our family when-- or even take economic opportunity. 00:21:09.280 --> 00:21:14.697 Talking about this as something where it takes away jobs, 00:21:14.780 --> 00:21:21.077 it's clear that any technology that's disruptive will have real displacement. 00:21:21.160 --> 00:21:22.577 But at the same time, there is going 00:21:22.660 --> 00:21:26.437 to be new economic activity where humans will have agency, 00:21:26.520 --> 00:21:29.177 which will have wages, which in fact, if you think about it, 00:21:29.260 --> 00:21:32.497 if what is current intelligence gets commoditized, 00:21:32.580 --> 00:21:35.677 humans are the one species that are most adaptive, 00:21:35.760 --> 00:21:41.857 and the sense of creating new value on top of what's the new commodity. 00:21:41.940 --> 00:21:46.577 And that has to not be abstract, but it has to be real. 00:21:46.660 --> 00:21:51.957 And it will happen. But until that transition happens, to your point, 00:21:52.040 --> 00:21:54.157 as we go from electricity to light, 00:21:54.240 --> 00:21:57.297 and the light is not seen only by the AGI-pilled people 00:21:57.380 --> 00:22:01.697 in the zip code, but it's seen by the world as something 00:22:01.780 --> 00:22:06.337 that they can thrive in. And even my point about that frontier ecosystem, 00:22:06.420 --> 00:22:09.597 when every company is not sitting there thinking that, 00:22:09.680 --> 00:22:13.577 oh my God, if I let any one of these frontier models 00:22:13.660 --> 00:22:17.057 into my organization, it's just going to run over 00:22:17.140 --> 00:22:23.967 all the IP I've created. Why would they welcome that? 00:22:24.050 --> 00:22:25.907 By definition they should not. 00:22:25.990 --> 00:22:31.547 And so I think that's why as entrepreneurs, as students, 00:22:31.630 --> 00:22:35.927 and as incumbents, we have to shape this to an ecosystem which 00:22:36.010 --> 00:22:38.067 is positive-sum by definition. 00:22:38.150 --> 00:22:40.927 If we are not and it's about a few firms that 00:22:41.010 --> 00:22:43.587 have all the returns and everybody else 00:22:43.670 --> 00:22:50.167 it's all in bad shape, you will absolutely 00:22:50.250 --> 00:22:53.347 lose social permission or we will lose social permission. 00:22:53.430 --> 00:22:56.647 OK. I'm going to switch over to questions. 00:22:56.730 --> 00:23:01.127 So I'm generally curious about your custom silicon program 00:23:01.210 --> 00:23:05.247 and how you've learned from other hyperscalers like Google 00:23:05.330 --> 00:23:09.647 and Amazon, who have made some progress there. 00:23:09.730 --> 00:23:13.727 It seems like at the hardware side, their chips 00:23:13.810 --> 00:23:16.887 that they offer are actually pretty bifurcated. 00:23:16.970 --> 00:23:19.087 They have training chips and inference chips 00:23:19.170 --> 00:23:23.527 versus in AMD you've kept it unified too. 00:23:23.610 --> 00:23:25.967 On the networking side, we had unembodied 00:23:26.050 --> 00:23:30.227 class talk about the optical mouse system that they built. 00:23:30.310 --> 00:23:33.047 That was really interesting. And at the software side, it seems 00:23:33.130 --> 00:23:37.167 like they built their own versions of CUDA 00:23:37.250 --> 00:23:40.607 with Neuron and XLA, respectively, 00:23:40.690 --> 00:23:45.727 whereas you guys are building on something based off of Triton for mining. 00:23:45.810 --> 00:23:49.207 So I'm curious given these differing design decisions 00:23:49.290 --> 00:23:51.527 that you don't agree, what have you learned 00:23:51.610 --> 00:23:53.947 and where are you taking your own customers? 00:23:54.030 --> 00:23:59.367 Yeah, I think the key thing is to recognize 00:23:59.450 --> 00:24:00.967 what are the new workloads. 00:24:01.050 --> 00:24:06.487 Whenever you think about any new system, 00:24:06.570 --> 00:24:10.547 you want to be motivated by what's the new software or what's the new workload. 00:24:10.630 --> 00:24:15.397 And the good news here is that there are these three dominant new workloads. 00:24:15.480 --> 00:24:17.977 There's the training workload, there's the inference workload, 00:24:18.060 --> 00:24:22.757 and now we can say there is the long-running agent that uses 00:24:22.840 --> 00:24:26.417 inference and regular compute. 00:24:26.500 --> 00:24:28.897 So if you said that's what you have, 00:24:28.980 --> 00:24:33.157 then you can start from a first principles looking at it. 00:24:33.240 --> 00:24:35.457 And these are interesting type of workloads. 00:24:35.540 --> 00:24:38.697 They're not like the previous scale-out workloads. 00:24:38.780 --> 00:24:45.780 These are synchronous data parallel workloads where 00:24:46.100 --> 00:24:50.477 to a means, I guess point, which is you got to even think about the scale-up part. 00:24:50.560 --> 00:24:54.947 Some of the tricks that worked for us for scale-out in the past won't work. 00:24:55.030 --> 00:24:58.557 So therefore you now need to even innovate on the scale-up and the scale-out 00:24:58.640 --> 00:25:05.640 to really keep things coherent and the MFUs on a training run 00:25:06.160 --> 00:25:09.477 are maximized and so on. So therefore, the way we come out of this 00:25:09.560 --> 00:25:13.477 and say, OK, even just yesterday we announced there's 00:25:13.560 --> 00:25:17.187 Maia 200 that's essentially being codesigned 00:25:17.270 --> 00:25:23.567 with our own models, plus the OpenAI models, because we have that IP. 00:25:23.650 --> 00:25:29.707 And right now, in fact, Maia 200 is running GPT 5.5 in multiple data centers 00:25:29.790 --> 00:25:33.527 powering Copilot, and giving us total TCO advantage. 00:25:33.610 --> 00:25:35.867 So that's a great way to round trip 00:25:35.950 --> 00:25:39.267 for an inference workload what's the advantage of that is. 00:25:39.350 --> 00:25:44.667 We not only did that but we also built 00:25:44.750 --> 00:25:50.427 Cobalt which is our ARM processor for compute. 00:25:50.510 --> 00:25:52.507 And we're benchmarking it to improve 00:25:52.590 --> 00:25:57.047 both for latency performance when it comes to, for example, 00:25:57.130 --> 00:25:58.867 the agentic loop because the place where 00:25:58.950 --> 00:26:02.627 you need great cores are for these agent loops. 00:26:02.710 --> 00:26:08.087 So we're using all the GitHub Copilot traces to optimize our ARM processor even. 00:26:08.170 --> 00:26:11.987 And bringing all this together with even the networking stack. 00:26:12.070 --> 00:26:16.087 And so our approach would be to not-- and at the same time, 00:26:16.170 --> 00:26:19.667 we love to have the GPUs because they're general purpose to your point, which 00:26:19.750 --> 00:26:21.367 is, in fact, we're using GPUs. 00:26:21.450 --> 00:26:27.907 In fact, we're using the old GPUs in our fleet to accelerate our data warehouse. 00:26:27.990 --> 00:26:34.287 So Fabric is seeing 7x plus performance gains because of GPU acceleration. 00:26:34.370 --> 00:26:38.567 So we think of our fleet as a heterogeneous fleet, 00:26:38.650 --> 00:26:42.067 where we will use software to get the maximum benefit out 00:26:42.150 --> 00:26:45.867 of it and do smart workload placement. 00:26:45.950 --> 00:26:49.547 At the same time, optimize for the high-volume workloads 00:26:49.630 --> 00:26:53.887 like inference and training and agent loop with our own ground-up system. 00:26:53.970 --> 00:26:55.307 And there's lots of design points. 00:26:55.390 --> 00:26:57.447 Most people get fixated on the AI accelerator. 00:26:57.530 --> 00:26:59.267 But the AI accelerator is one, the CPU 00:26:59.350 --> 00:27:03.967 is one, the network accelerator, the storage accelerator, 00:27:04.050 --> 00:27:06.427 the AI WAN is another one. 00:27:06.510 --> 00:27:10.987 You want to be able to really do multi-data center hops even. 00:27:11.070 --> 00:27:17.097 So, lots of stuff. It's a great time, by the way, to be in computer architecture. 00:27:17.180 --> 00:27:22.777 I think when I started in the industries when the Patterson book first came out 00:27:22.860 --> 00:27:28.657 and that was the RISK versus CISC debate, I feel like we're back at a time like this 00:27:28.740 --> 00:27:33.777 where you can really rethink from the physical design 00:27:33.860 --> 00:27:38.247 of a data center to-- by the way, the electrons. 00:27:38.330 --> 00:27:40.497 I mean, one of the places where I'm very excited about 00:27:40.580 --> 00:27:43.657 is the efficiency with which we can bring the electrons all 00:27:43.740 --> 00:27:48.337 the way to the CPU so that the tokens are that much more 00:27:48.420 --> 00:27:50.757 efficient without all the losses in between. 00:27:50.840 --> 00:27:54.177 So I think there's just a tremendous design [INAUDIBLE]. 00:27:54.260 --> 00:27:56.257 And another announcement you had yesterday 00:27:56.340 --> 00:27:58.997 was around quantum, which is kind of adjacent to what you talked about. 00:27:59.080 --> 00:28:01.367 So I'm kind of curious what was the announcement and what 00:28:01.450 --> 00:28:03.597 was the recent advances there? 00:28:03.680 --> 00:28:07.577 Yeah, so look, this quantum, we have 00:28:07.660 --> 00:28:10.747 been on at this for now the last 20 plus years, 00:28:10.830 --> 00:28:14.707 and it's really exciting to see the progress. 00:28:14.790 --> 00:28:21.007 I'll just say one of the things is, even independent of the quantum program, 00:28:21.090 --> 00:28:25.407 even with what we were able to achieve in the last couple of years, with even 00:28:25.490 --> 00:28:28.507 the natural atom-based quantum computers with our stack-- 00:28:28.590 --> 00:28:30.107 we worked with partners on it. 00:28:30.190 --> 00:28:35.867 We're able to generate now these very good traces, which those traces-- 00:28:35.950 --> 00:28:39.287 basically, if you think about what's the purpose of a quantum computer, 00:28:39.370 --> 00:28:42.167 a quantum computer can simulate nature. 00:28:42.250 --> 00:28:45.787 I mean, since nature is quantum. 00:28:45.870 --> 00:28:49.767 And so instead of relying on DFTs or what have you, 00:28:49.850 --> 00:28:56.247 you can now have a lot better fidelity of say, chemistry 00:28:56.330 --> 00:28:59.947 or molecular dynamics or what have you. 00:29:00.030 --> 00:29:04.307 And those traces then can be taken back and you can train a model. 00:29:04.390 --> 00:29:09.167 In fact, we are doing that with our material science models, 00:29:09.250 --> 00:29:11.947 where you can take the traces from even a whatever, 00:29:12.030 --> 00:29:17.207 an early stage quantum computer, to improve the data on which you 00:29:17.290 --> 00:29:22.327 train a model for something like material science or chemistry. 00:29:22.410 --> 00:29:27.667 Now, our quantum program itself is, as I said, there's a software side to it, 00:29:27.750 --> 00:29:31.427 which we will put on ion trap machines, which we're putting with partners. 00:29:31.510 --> 00:29:34.607 We're putting it on a photonics-based machine. 00:29:34.690 --> 00:29:36.427 We're also putting it on natural atoms. 00:29:36.510 --> 00:29:40.967 We have a partnership with in Denmark called QuNorth, where we will even 00:29:41.050 --> 00:29:45.807 have a quantum computer powered by Atom Computing with our stack 00:29:45.890 --> 00:29:49.127 within the year and so on. So that's one side of it. 00:29:49.210 --> 00:29:52.367 The second side is ultimately in order 00:29:52.450 --> 00:29:57.367 to build a quantum computer at scale, at utility-scale, 00:29:57.450 --> 00:30:00.727 you need fault tolerance. 00:30:00.810 --> 00:30:04.687 Our bet on that has been that we-- 00:30:04.770 --> 00:30:11.770 there was a theoretical physicist who theorized 00:30:12.000 --> 00:30:19.000 essentially a state of matter called a Majorana in the 1930s. 00:30:20.000 --> 00:30:24.517 And so one of the things that we felt that that was the state of 00:30:24.600 --> 00:30:29.677 matter that we needed to essentially fabricate and make real. 00:30:29.760 --> 00:30:35.037 So we launched our first QPU, which was Majorana 1 a year ago, 00:30:35.120 --> 00:30:38.197 which essentially proved out the fundamental physics 00:30:38.280 --> 00:30:44.197 breakthrough, that you can actually have this and then instantiate it. 00:30:44.280 --> 00:30:47.077 And now we've got Majorana 2, which 00:30:47.160 --> 00:30:52.637 allows this to be built at industrial scale. 00:30:52.720 --> 00:30:57.997 And so there's a lot of detail in terms of how long these qubits can be stable for. 00:30:58.080 --> 00:31:00.517 And by the way, one of the other things 00:31:00.600 --> 00:31:03.837 is we have perfected the digital control of this quantum computer 00:31:03.920 --> 00:31:06.067 because that's going to be super important. 00:31:06.150 --> 00:31:09.867 So overall, we feel that the quantum program at Microsoft's 00:31:09.950 --> 00:31:11.527 progressing on two dimensions. 00:31:11.610 --> 00:31:16.507 One is in the near-term with even what are the quantum computers that I think 00:31:16.590 --> 00:31:20.977 are most easy to fabricate and build today with these things like natural atoms. 00:31:21.060 --> 00:31:22.347 And then in the long-run, we want 00:31:22.430 --> 00:31:28.787 to build out what is needed in order for true quantum computers 00:31:28.870 --> 00:31:32.167 to act like utility-scale quantum computers. 00:31:32.250 --> 00:31:35.627 On that ladder, if you had a guess at timeline? 00:31:35.710 --> 00:31:40.067 I'm the third CEO at Microsoft to keep 00:31:40.150 --> 00:31:42.667 going on the quantum journey. 00:31:42.750 --> 00:31:47.247 I would say that what I'm now a lot more bullish is it may not-- 00:31:47.330 --> 00:31:50.447 it's kind of like the previous discussion. 00:31:50.530 --> 00:31:54.267 I think of quantum as the new accelerator. 00:31:54.350 --> 00:31:57.767 And remember, by the way, quantum is not going to replace classical. 00:31:57.850 --> 00:32:00.547 Quantum is not going to be great at storage and memory and so on. 00:32:00.630 --> 00:32:02.127 It's going to be great at computation. 00:32:02.210 --> 00:32:06.647 And so you have to marry classical plus quantum in order to do things. 00:32:06.730 --> 00:32:10.987 And so therefore, I think of this as maybe a lot more staged even. 00:32:11.070 --> 00:32:13.467 So if you have 100 logical qubits with good error 00:32:13.550 --> 00:32:19.787 correction, we can start using it to generate synth data for science models. 00:32:19.870 --> 00:32:22.267 Like that'll be a pretty important milestone that 00:32:22.350 --> 00:32:25.467 may be even more achievable in the short-run. 00:32:25.550 --> 00:32:30.467 So we'll see. We I think made the claim even yesterday 00:32:30.550 --> 00:32:32.707 that by the end of the decade, we 00:32:32.790 --> 00:32:35.707 believe we will be able to build a quantum computer that starts 00:32:35.790 --> 00:32:37.857 solving some real challenges. Real problems? 00:32:37.940 --> 00:32:39.810 Yeah. Amazing. Next question. 00:32:46.590 --> 00:32:50.907 Thanks so much for coming. I spent nine years at Microsoft. 00:32:50.990 --> 00:32:57.187 I joined a year after you became a CEO as a market employee. 00:32:57.270 --> 00:33:03.617 So I went through cloud transformation and then I go into AI fascination. 00:33:03.700 --> 00:33:08.477 And I think my program was such an amazing experience for me. 00:33:08.560 --> 00:33:11.417 And it shaped the younger person. 00:33:11.500 --> 00:33:14.417 How do you think it contributed and is it 00:33:14.500 --> 00:33:18.177 still contributing to the culture and success of mine? 00:33:18.260 --> 00:33:20.677 Yeah, I mean, first of all, we're very, very thrilled about, 00:33:20.760 --> 00:33:25.037 obviously students coming in and joining and having essentially the Mac program. 00:33:25.120 --> 00:33:26.897 There are a couple of programs like that at Microsoft we 00:33:26.980 --> 00:33:31.857 created where people can even rotate through various functions. 00:33:31.940 --> 00:33:37.717 At the end of the day, any company for it to be at the frontier, so to speak, 00:33:37.800 --> 00:33:41.477 has to be able to get people coming with fresh ideas, 00:33:41.560 --> 00:33:43.837 fresh energy, and reshaping it. 00:33:43.920 --> 00:33:47.857 I always say to anyone joining Microsoft, of course you want to come in and learn 00:33:47.940 --> 00:33:52.697 about how Microsoft works, but we also want Microsoft to learn from you. 00:33:52.780 --> 00:33:55.897 And more importantly, for you to have the agency to reshape 00:33:55.980 --> 00:33:59.967 what is Microsoft's culture. It's not a static thing. 00:34:00.050 --> 00:34:04.107 It's an organic thing that gets shaped by the behaviors, 00:34:04.190 --> 00:34:07.627 the decisions of people at the company. 00:34:07.710 --> 00:34:12.047 And so we always would welcome students coming in, 00:34:12.130 --> 00:34:15.327 building their career at Microsoft. 00:34:15.410 --> 00:34:18.947 One of the things as a 50-year-old company, I mean, 00:34:19.030 --> 00:34:22.767 Mike's an alum at Microsoft and still engaged with us. 00:34:22.850 --> 00:34:27.467 We have people who've come, had a tour of duty, gone out, come back. 00:34:27.550 --> 00:34:32.287 And so at this point, the way to think 00:34:32.370 --> 00:34:36.687 about it is the uniqueness of the Microsoft 00:34:36.770 --> 00:34:40.747 is our core DNA has remained. 00:34:40.830 --> 00:34:46.907 We are a developer tools platform, knowledge worker tools company. 00:34:46.990 --> 00:34:49.767 That's kind of what we've done for 50 years. 00:34:49.850 --> 00:34:55.567 But the interesting thing about us is that we have been able to reinterpret 00:34:55.650 --> 00:35:01.567 that with every new platform. In fact, I joined the company back in the '90s when 00:35:01.650 --> 00:35:04.967 my existential competition was Novell. 00:35:05.050 --> 00:35:10.127 And now, it's some foundation lab I'd not even heard of five years ago. 00:35:10.210 --> 00:35:12.847 But that is the thing that I think 00:35:12.930 --> 00:35:16.587 keeps us vibrant, which is our existential challenge, 00:35:16.670 --> 00:35:23.107 or what we need to compete with is new and fresh versus it's the same old. 00:35:23.190 --> 00:35:25.607 And I think that that's an attractive part. 00:35:25.690 --> 00:35:28.207 When you come to Microsoft, you will 00:35:28.290 --> 00:35:32.887 be able to go at that mission of being able to empower people 00:35:32.970 --> 00:35:38.167 and organizations all over the planet, which means a lot to us. 00:35:38.250 --> 00:35:43.607 But to do so, recognizing that we as a company 00:35:43.690 --> 00:35:46.967 can bring a lot to that mission. 00:35:47.050 --> 00:35:49.607 I have a follow-up question. So one of the many attributes I've 00:35:49.690 --> 00:35:52.927 admired about you is you have a growth mindset, 00:35:53.010 --> 00:35:55.597 and you really look at your leadership team and drive it. 00:35:55.680 --> 00:35:58.477 How have you instilled that across the company? 00:35:58.560 --> 00:36:00.717 Because you clearly have. You just pointed that out, that you've 00:36:00.800 --> 00:36:03.257 been able to deal with these platform transformations. 00:36:03.340 --> 00:36:10.197 Yeah, I mean, at some level, I think it's not something you instill per se, 00:36:10.280 --> 00:36:14.877 Mike, you invoke what is innate in all of us, I think. 00:36:14.960 --> 00:36:20.637 I mean, you have to do it more out of practice. 00:36:20.720 --> 00:36:23.957 Mostly what I have to exhibit more 00:36:24.040 --> 00:36:29.617 than anything else is my ability to confront my fixed mindset. 00:36:29.700 --> 00:36:33.417 Because at the end of the day, all of us, it's easy to talk about growth mindset, 00:36:33.500 --> 00:36:36.777 but it's very difficult to exercise it individually. 00:36:36.860 --> 00:36:38.357 As somebody said to me, which I always 00:36:38.440 --> 00:36:45.440 liked as a sort of a nice quip, is everybody likes change, 00:36:45.860 --> 00:36:48.577 except they want the other person to change, not themselves. 00:36:48.660 --> 00:36:53.257 And that, I think, is the challenge of growth mindset. 00:36:53.340 --> 00:36:55.617 So it's not about talking about growth mindset. 00:36:55.700 --> 00:36:59.037 It's about having the courage to confront one's own fixed mindset. 00:36:59.120 --> 00:37:01.957 So it can become corporate dogma to your point. 00:37:02.040 --> 00:37:06.037 So one of the keys, the reason why it's worked at Microsoft 00:37:06.120 --> 00:37:10.277 is we never made it like, oh-- --some mandate. 00:37:10.360 --> 00:37:15.697 It started with you. And also it's not like trademarked to Microsoft. 00:37:15.780 --> 00:37:20.957 I mean, if you exercise growth mindset or you confront a fixed mindset, you'll 00:37:21.040 --> 00:37:23.257 be a better human being first. 00:37:23.340 --> 00:37:25.997 You'll be a better colleague, a better friend, a better 00:37:26.080 --> 00:37:29.137 neighbor, a better parent, a better student, everything. 00:37:29.220 --> 00:37:32.337 So you're not even doing this for Microsoft's sake. 00:37:32.420 --> 00:37:34.017 You're doing this for yourself. 00:37:34.100 --> 00:37:38.877 And I think giving that oxygen, leaving that at that, as opposed 00:37:38.960 --> 00:37:42.697 to some new corporate thing, has been very, very helpful. 00:37:42.780 --> 00:37:48.037 So I'm an advocate of it, not just at Microsoft, anywhere. 00:37:48.120 --> 00:37:51.987 And more importantly, it's that practice of-- 00:37:52.070 --> 00:37:55.547 there are two things that I feel that were pretty influential 00:37:55.630 --> 00:38:01.127 for me, which I learned through my wife's readings, quite frankly. 00:38:01.210 --> 00:38:04.747 One was this thing called nonviolent communications, which 00:38:04.830 --> 00:38:09.567 is also another form of developing a sense of empathy, 00:38:09.650 --> 00:38:12.287 understanding where the other person is coming from, 00:38:12.370 --> 00:38:16.067 not having your amygdala always triggered and what have you. 00:38:16.150 --> 00:38:20.307 So that's one which I think is it's a great read if you've not read it. 00:38:20.390 --> 00:38:24.487 And then, of course, Carol Dweck's work on growth mindset. 00:38:24.570 --> 00:38:26.527 These are two things that are I think, 00:38:26.610 --> 00:38:30.227 relevant for children and students and child psychology. 00:38:30.310 --> 00:38:33.307 But I think they apply to corporate cultures 00:38:33.390 --> 00:38:35.787 because I think one of the fascinating things 00:38:35.870 --> 00:38:40.127 is what Herbert Simon described as the bounded rationality. 00:38:40.210 --> 00:38:45.690 I think humans are great, but we have this unfortunate-- 00:38:50.700 --> 00:38:55.897 we don't see what's in our interest all the time. 00:38:55.980 --> 00:39:01.217 We get hijacked often without being 00:39:01.300 --> 00:39:06.297 able to do the simple calculus to what 00:39:06.380 --> 00:39:10.137 does it mean to be at the frontier of our own behavior. 00:39:10.220 --> 00:39:14.117 And I think that these are nice practices that gets us and pushes us. 00:39:14.200 --> 00:39:18.457 So think of it as your training run that you need. 00:39:18.540 --> 00:39:21.877 That's great guidance. Thank you so much for the answer. 00:39:21.960 --> 00:39:26.557 And I had my dissertation 10 minutes ago, but I really wanted to meet you. 00:39:26.640 --> 00:39:28.077 Thank you so much. All right. Good luck. 00:39:28.160 --> 00:39:33.997 We realize it's finals week. Next question. 00:39:34.080 --> 00:39:35.677 Yeah. Thanks so much for coming. 00:39:35.760 --> 00:39:39.497 I was wondering, how did you become such a good public speaker and what-- 00:39:39.580 --> 00:39:42.880 [LAUGHTER] 00:39:44.780 --> 00:39:51.637 I don't know, man. I mean, I'm glad you think I'm a good public speaker. 00:39:51.720 --> 00:39:57.937 Let's leave it at that. I mean, look, I think, like anything else, 00:39:58.020 --> 00:40:01.497 it's not that I think about public speaking as a key thing 00:40:01.580 --> 00:40:03.817 that I'm trying to develop or what have you. 00:40:03.900 --> 00:40:10.900 But the lucky part that I find myself is in particular, even 00:40:12.020 --> 00:40:18.097 I think one of the things is when I became CEO, you had to talk about things, perhaps 00:40:18.180 --> 00:40:21.037 that you didn't get the opportunity to talk about previously. 00:40:21.120 --> 00:40:23.137 Maybe that's a better way to characterize it. 00:40:23.220 --> 00:40:26.057 But the good news is, it's not as if I was not thinking 00:40:26.140 --> 00:40:28.817 about those things previously. 00:40:28.900 --> 00:40:32.937 And I reflected on it. Is that why is it that I was thinking about those things 00:40:33.020 --> 00:40:38.377 previously? And I think that comes out of just natural interest, 00:40:38.460 --> 00:40:41.477 for example, thinking about technology, 00:40:41.560 --> 00:40:45.457 but its impact, what does it mean? 00:40:45.540 --> 00:40:48.347 I have many pet passions. 00:40:48.430 --> 00:40:53.727 Like what does any technological progress like AI 00:40:53.810 --> 00:40:59.367 mean to the Global South? What does it mean to even what has been a dream-- 00:40:59.450 --> 00:41:01.627 and I grew up as a son of a development economist, 00:41:01.710 --> 00:41:04.447 so he instilled in me that, hey, this convergence growth 00:41:04.530 --> 00:41:07.107 is going to happen and it's going to be great and so on. 00:41:07.190 --> 00:41:09.247 And so I'm obsessed about it. 00:41:09.330 --> 00:41:13.367 And so as long as I think you have these passions that 00:41:13.450 --> 00:41:19.927 allow you to think broadly, and then for you to be able to talk freely about it, 00:41:20.010 --> 00:41:26.887 is the other one. And so I'm not particularly an expert on public speaking, 00:41:26.970 --> 00:41:33.507 but I think the more any of us can have broad interests that we can articulate. 00:41:33.590 --> 00:41:35.607 And in today's day and age, the media 00:41:35.690 --> 00:41:40.907 allows us to be able to have our own outlets. 00:41:40.990 --> 00:41:44.767 And so I think that this is a great time to both build that interest and then 00:41:44.850 --> 00:41:47.257 to be able to have different medium, whether it's speaking, 00:41:47.340 --> 00:41:50.317 whether it's writing, whether it's podcasts, what have you. 00:41:50.400 --> 00:41:51.877 There are a variety of ways I think 00:41:51.960 --> 00:41:56.420 we can express, reach, debate, which I think is fantastic. 00:41:59.820 --> 00:42:03.477 Thank you for coming. I was wondering if your undergraduate self was 00:42:03.560 --> 00:42:06.797 sitting in the audience right now, what advice would you give him? 00:42:06.880 --> 00:42:08.417 And knowing what you know now, I guess, 00:42:08.500 --> 00:42:14.317 and what would you tell him to put his energy into and to maybe avoid? 00:42:14.400 --> 00:42:19.217 Yeah, it's a great question. I mean, it's such a privilege in some sense. 00:42:19.300 --> 00:42:20.240 I wish I could. 00:42:23.000 --> 00:42:24.697 Because everything is in front of you. 00:42:24.780 --> 00:42:29.157 You're risk on 100% of the time. 00:42:29.240 --> 00:42:33.277 Maybe that's what it is, which is-- 00:42:33.360 --> 00:42:37.257 let me just say two interesting things. 00:42:37.340 --> 00:42:41.097 Yesterday on Hacker News, I came across, I forget, one of the CS classes here. 00:42:41.180 --> 00:42:46.797 It had the guidelines on how to use coding agents, which I thought was well done. 00:42:46.880 --> 00:42:53.477 They had the do's and don'ts. And so the fascinating thing I find 00:42:53.560 --> 00:43:00.560 right now is the ability to learn new things 00:43:01.000 --> 00:43:03.837 has become so much easier. 00:43:03.920 --> 00:43:09.117 Because you have this very accessible, personalized tutor 00:43:09.200 --> 00:43:14.357 that's deep that you can go and work with. 00:43:14.440 --> 00:43:20.997 And so I would say more so than any assignment anxiety 00:43:21.080 --> 00:43:27.557 or I don't grade anxiety or what have you, you can have-- 00:43:27.640 --> 00:43:32.857 one of the terms one of my colleagues uses-- real cognitive coverage. 00:43:32.940 --> 00:43:37.237 Like test coverage, you can now have 00:43:37.320 --> 00:43:42.587 cognitive coverage that really follow you through your curiosity. 00:43:42.670 --> 00:43:48.107 If I were back as an undergrad, I would be trying to-- it's kind of what I 00:43:48.190 --> 00:43:49.687 do with GitHub sessions today. 00:43:49.770 --> 00:43:54.847 GitHub app, which is what are all the coding agents and what are all they doing? 00:43:54.930 --> 00:43:56.407 So that's what I would do. 00:43:56.490 --> 00:44:03.490 I would be sitting with, what are the 10, 100 agents 00:44:03.710 --> 00:44:09.607 and me at Stanford learning? But I need to have cognitive coverage. 00:44:09.690 --> 00:44:11.447 It's not I've offloaded to the 100. 00:44:11.530 --> 00:44:17.087 The key is, what am I instructing them? 00:44:17.170 --> 00:44:19.227 And then when they get something done, 00:44:19.310 --> 00:44:24.427 can I understand what they did in order to have learned? 00:44:24.510 --> 00:44:27.947 It's kind of like it's 100 classes. 00:44:28.030 --> 00:44:30.747 It's so fascinating. And that I think, is what I think will happen. 00:44:30.830 --> 00:44:37.747 One of these days somebody's going to break a new pedagogy that goes with-- 00:44:37.830 --> 00:44:39.637 the tools, for example, are evolving. 00:44:39.720 --> 00:44:41.717 Like think about what happened in developer tools. 00:44:41.800 --> 00:44:46.657 We went from saying, hey, we have 100 CLIs to now 00:44:46.740 --> 00:44:50.377 we need a thing that to manage our CLI complexity, which 00:44:50.460 --> 00:44:53.537 is the new AD, which is like for example, the GitHub app is 00:44:53.620 --> 00:44:55.457 fantastic in that context because it's 00:44:55.540 --> 00:44:58.657 like the new inbox for managing my sessions. 00:44:58.740 --> 00:45:02.857 What's the moral equivalent of that, that allows a student 00:45:02.940 --> 00:45:07.057 to navigate through their learning experience 00:45:07.140 --> 00:45:14.140 and be max curious, but really getting deeper, faster on things 00:45:14.580 --> 00:45:17.217 that you're trying to cover? 00:45:17.300 --> 00:45:20.997 And I think that that's what I would do and not have anxiety. 00:45:21.080 --> 00:45:24.637 Because you can always push a button to get an assignment done. 00:45:24.720 --> 00:45:28.477 So that's no longer the case. And the grades may or may not matter. 00:45:28.560 --> 00:45:34.907 And so therefore there's a lot of relitigation on the things we valued. 00:45:34.990 --> 00:45:38.457 That's a really good answer. Next question. 00:45:38.540 --> 00:45:41.017 Hey, thank you so much for coming. 00:45:41.100 --> 00:45:44.877 So, like, whenever we're interacting with a computer, 00:45:44.960 --> 00:45:49.977 pretty much we always interact with a GUI interface. 00:45:50.060 --> 00:45:55.297 But agents, they just so happen to be good at coding, 00:45:55.380 --> 00:46:00.077 happen to be not that good at interacting with GUI interfaces. 00:46:00.160 --> 00:46:03.097 So for example, if I want to design a poster 00:46:03.180 --> 00:46:06.497 and I want to be able to do it, it is easier for me 00:46:06.580 --> 00:46:09.457 to get it to generate my HTML/CSS code 00:46:09.540 --> 00:46:14.617 rather than to use a GUI design product. 00:46:14.700 --> 00:46:20.337 In that case, what do you think importation 00:46:20.420 --> 00:46:27.420 of [INAUDIBLE] for BUI interfaces versus a seal. 00:46:27.750 --> 00:46:30.547 Yeah, I mean, I think you're bringing up a couple of different things. 00:46:30.630 --> 00:46:34.737 One is essentially codegen is powerful, 00:46:34.820 --> 00:46:39.287 and therefore HTML and other web UI, 00:46:39.370 --> 00:46:45.887 as an artifact creation process, I think is going to really proliferate. 00:46:45.970 --> 00:46:49.187 So basically, we've gone from-- we in fact, 00:46:49.270 --> 00:46:51.927 always Bill used to have this thing where, what's 00:46:52.010 --> 00:46:55.987 the difference between building an app, writing a document, 00:46:56.070 --> 00:47:01.147 or creating a website? At this point, there's none. 00:47:01.230 --> 00:47:04.767 You can just basically do all three by using code. 00:47:04.850 --> 00:47:11.167 So that's one side of it. But I think that the direct manipulation is the challenge. 00:47:11.250 --> 00:47:14.527 I think in the intermediate time frame, what's going to happen is you're going 00:47:14.610 --> 00:47:16.167 to have an intermediate format. 00:47:16.250 --> 00:47:20.547 So you're going to do the HTML and then you can convert into Excel, PowerPoint, 00:47:20.630 --> 00:47:23.187 PowerPoint into intermediate format and then have agents. 00:47:23.270 --> 00:47:25.447 So I think that that's what you see in Copilot 00:47:25.530 --> 00:47:29.007 and elsewhere when you think about artifact creation. 00:47:29.090 --> 00:47:32.367 But the ultimate thing is, can you truly 00:47:32.450 --> 00:47:37.937 teach even the agent on the model, the Canvas, 00:47:38.020 --> 00:47:40.557 and the direct manipulation of the Canvas, 00:47:40.640 --> 00:47:47.557 which has to be done fundamentally by teaching it the semantics of that Canvas? 00:47:47.640 --> 00:47:50.067 And so it has to be exposed to whether it's through APIs 00:47:50.150 --> 00:47:54.557 or whether it is through a protocol or what have you. 00:47:54.640 --> 00:47:57.057 And so therefore, I think you will see innovation like that. 00:47:57.140 --> 00:47:59.297 But it is true that direct manipulation-- by the way, 00:47:59.380 --> 00:48:00.757 you talked about, one of the other things that 00:48:00.840 --> 00:48:02.717 struck me is one of the nice little features 00:48:02.800 --> 00:48:07.437 we added to GitHub yesterday was a thing called Canvas, 00:48:07.520 --> 00:48:12.777 and the reason was not because the agents need UI, 00:48:12.860 --> 00:48:15.037 but we need UI, because it's now become 00:48:15.120 --> 00:48:19.077 too dense to just keep tracking my CLI session 00:48:19.160 --> 00:48:23.077 or the chat session because, first of all, it's linear 00:48:23.160 --> 00:48:26.937 and it's painful you're trying to scroll through it. 00:48:27.020 --> 00:48:29.237 And so one of the things that we said 00:48:29.320 --> 00:48:31.997 is now we can-- for example, I can have a Kanban board 00:48:32.080 --> 00:48:37.277 as a visualization, which both the agent and I are working on. 00:48:37.360 --> 00:48:42.117 So I think that this idea of generated UI becoming 00:48:42.200 --> 00:48:45.997 the new way for human agent interaction 00:48:46.080 --> 00:48:51.237 might be one of the coolest things that will happen across all product lines. 00:48:51.320 --> 00:48:56.157 Next question. Hi, Santhya. Thank you so much for coming. 00:48:56.240 --> 00:48:58.437 If you were at our age like college freshman 00:48:58.520 --> 00:49:00.217 and you had the world at your fingertips, 00:49:00.300 --> 00:49:04.357 what's the problem that you would encourage us in [INAUDIBLE]? 00:49:04.440 --> 00:49:09.417 Oh man, I was that, but I don't know. 00:49:09.500 --> 00:49:15.357 I mean, it's always interesting to look back and say, 00:49:15.440 --> 00:49:18.377 what would I pick? I don't know. 00:49:18.460 --> 00:49:24.197 I mean, in an interesting world like ours right now, 00:49:24.280 --> 00:49:26.677 I think you have to go and say, what 00:49:26.760 --> 00:49:31.547 is the thing that you have inherent interests in 00:49:31.630 --> 00:49:36.807 and the world will value? I think always whenever people are making choices, 00:49:36.890 --> 00:49:39.367 I think they have two things they're trying to intersect. 00:49:39.450 --> 00:49:45.067 They're trying to intersect something that they believe they have a real passion for. 00:49:45.150 --> 00:49:50.907 But they're also doing, quite frankly, the calculus on, what does the world value? 00:49:50.990 --> 00:49:53.407 They always have some destination in mind. 00:49:53.490 --> 00:49:55.727 I want that career, I want that job, 00:49:55.810 --> 00:49:58.807 I want to start that company or what have you. 00:49:58.890 --> 00:50:00.667 And so I would focus on that. 00:50:00.750 --> 00:50:05.527 Answering those two questions is what I think will lead. 00:50:05.610 --> 00:50:08.647 And in my case, I would probably go step back and look at that. 00:50:08.730 --> 00:50:11.787 It may, may be even outside. 00:50:11.870 --> 00:50:16.387 One of the things if I go back to the computer industry-- 00:50:16.470 --> 00:50:21.427 I was an electrical engineer and I then drifted into software. 00:50:21.510 --> 00:50:27.547 But if I went back now, I may go back into hardware, 00:50:27.630 --> 00:50:33.967 just because there is just such an unbelievable time in-- 00:50:34.050 --> 00:50:38.187 There are a lot of things that I would love to go deep on understanding what 00:50:38.270 --> 00:50:43.647 the optical side of networking would look like, some of the system design. 00:50:43.730 --> 00:50:47.487 So I think that's how I think things will pan out. 00:50:47.570 --> 00:50:50.427 People will pick the thing that they're good 00:50:50.510 --> 00:50:54.047 at, then they see the trajectory of what they're good at and say, 00:50:54.130 --> 00:50:57.187 wow, I'm going to bet on myself to get good at this 00:50:57.270 --> 00:51:03.147 and start something and/or policy. 00:51:03.230 --> 00:51:08.227 And people talk about safety engineering, but I was thinking, wow, there are so 00:51:08.310 --> 00:51:11.787 many aspects of what does it mean 00:51:11.870 --> 00:51:17.867 to have safety around AI that require people to think through deeply. 00:51:17.950 --> 00:51:20.307 And so anyway, so there are lots of choices out there. 00:51:20.390 --> 00:51:23.227 Along those lines, have you guys hired like philosophers 00:51:23.310 --> 00:51:26.087 to help you with the AI of guidance? 00:51:26.170 --> 00:51:28.557 I think Mustafa is a philosopher dropout or something. 00:51:28.640 --> 00:51:30.160 So he's-- Sung. 00:51:31.460 --> 00:51:36.377 So we have a quasi wannabe philosopher. 00:51:36.460 --> 00:51:41.017 But he thinks clearly from that. 00:51:41.100 --> 00:51:42.737 I mean, he's been, obviously, since being 00:51:42.820 --> 00:51:47.937 a founder of DeepMind to now, he's always thought about it, 00:51:48.020 --> 00:51:51.497 and we have always had folks in MSR 00:51:51.580 --> 00:51:55.697 who are brought real, deep, multidisciplinary approach 00:51:55.780 --> 00:52:02.577 to it, whether it's the economist, the moral philosophers, the sociologists. 00:52:02.660 --> 00:52:05.857 And I think we'll always have that. 00:52:05.940 --> 00:52:08.817 Next question. Hi, Satya. 00:52:08.900 --> 00:52:11.997 I was curious, what do you think about space data centers. 00:52:12.080 --> 00:52:16.437 Because I've had a lot of startup CEOs that come here and talk and just try 00:52:16.520 --> 00:52:18.477 to convince me that we should be looking at that. 00:52:18.560 --> 00:52:21.297 And the research I'm doing is showing that Elon is probably 00:52:21.380 --> 00:52:24.137 the only one that's being able to do it profitably just 00:52:24.220 --> 00:52:25.957 from the large costs per kilo. 00:52:26.040 --> 00:52:27.287 I was just curious where you're. 00:52:27.370 --> 00:52:34.370 I'm not an expert on any of that in both the supply side of it 00:52:34.690 --> 00:52:39.447 and the economic side of it, but it's plausible, at least, 00:52:39.530 --> 00:52:42.967 what I've read and what I've talked to people who are experts in it. 00:52:43.050 --> 00:52:49.887 Seems like it makes sense. The question really is you now need 00:52:49.970 --> 00:52:54.127 to not only solve both, how do you get there, 00:52:54.210 --> 00:52:58.447 but how do you build the stack that operates there 00:52:58.530 --> 00:53:02.807 and then solve all the practical issues of RMA and others. 00:53:02.890 --> 00:53:06.967 Because therefore, I think there's a whole supply chain. 00:53:07.050 --> 00:53:13.087 When you think about the data center, most people-- it's a complex project. 00:53:13.170 --> 00:53:18.527 We're built on the shoulders of unbelievable engineering depth. 00:53:18.610 --> 00:53:21.607 Starting from civil engineering, on 00:53:21.690 --> 00:53:24.407 and to electrical engineering, to mechanical engineering, 00:53:24.490 --> 00:53:31.207 to then ultimately have it meet the needs of computing. 00:53:31.290 --> 00:53:34.727 And so that level of sophistication 00:53:34.810 --> 00:53:39.687 for this new payload in space has to get built, and it could get built. 00:53:39.770 --> 00:53:42.817 And as far as I'm concerned, from a Microsoft standpoint, I would love to. 00:53:42.900 --> 00:53:49.747 I mean, I think we have a few instances where we have already had some Azure SKUs. 00:53:49.830 --> 00:53:54.167 We had a program where we even put Azure SKUs in space 00:53:54.250 --> 00:53:57.447 and what have you, but they were more like edge. 00:53:57.530 --> 00:54:01.087 And so to the degree to which if somebody says to me 00:54:01.170 --> 00:54:06.507 that there's gigawatts available or megawatts available, 00:54:06.590 --> 00:54:12.247 I'm happy to plug myself in. Next question. 00:54:12.330 --> 00:54:19.167 Yeah. So it seems that Meta is now pulling back from building frontier open models. 00:54:19.250 --> 00:54:21.687 And I understand that Google [INAUDIBLE] 00:54:21.770 --> 00:54:26.717 doing some work with open models or flash and [INAUDIBLE], 00:54:26.800 --> 00:54:31.717 but they tend to be pretty small for writing like one [INAUDIBLE]. 00:54:31.800 --> 00:54:36.957 Given how you personally embrace open source, the biggest example being Linux. 00:54:37.040 --> 00:54:39.957 I'm curious, do you see Microsoft and AI, 00:54:40.040 --> 00:54:44.517 OpenAI building frontier open models like at the parameter 00:54:44.600 --> 00:54:50.177 [INAUDIBLE]. Yeah, I think the thing that we're focused on, 00:54:50.260 --> 00:54:52.777 we definitely will always have open rate models. 00:54:52.860 --> 00:54:56.297 And to your point, they will be more for what we will ship. 00:54:56.380 --> 00:54:59.337 In fact, we launched two even yesterday, 00:54:59.420 --> 00:55:04.897 both an instruct model and a plan model for local agent loop and what have you. 00:55:04.980 --> 00:55:07.997 So they are derivatives of what we have done with Phi Silica 00:55:08.080 --> 00:55:11.697 before called Ion Instruct and Ion Plan. 00:55:11.780 --> 00:55:14.997 And they'll run on Windows and they'll be open rate. 00:55:15.080 --> 00:55:18.597 The thing that we have focused on with the MAI 00:55:18.680 --> 00:55:22.727 lineage of models is, again, think of them as licensed, 00:55:22.810 --> 00:55:25.347 but we are going to license them pretty broadly. 00:55:25.430 --> 00:55:26.927 So for example, you can go to base 10. 00:55:27.010 --> 00:55:29.547 You can go to fireworks and you can then fine tune 00:55:29.630 --> 00:55:32.187 even using their inference stack. 00:55:32.270 --> 00:55:37.487 And so on. But the reason why we're doing that is because we want quite frankly, 00:55:37.570 --> 00:55:40.027 every company, whether it's a SaaS company, an AI 00:55:40.110 --> 00:55:42.107 native or an enterprise company to have 00:55:42.190 --> 00:55:48.217 their own model that they can then post-train, they can RL, 00:55:48.300 --> 00:55:51.307 and what have you. So therefore, that'll be our goal 00:55:51.390 --> 00:55:57.907 is to build an ecosystem around the MAI lineage of models. 00:55:57.990 --> 00:56:02.707 And the reason why we want to make sure they're still licensed 00:56:02.790 --> 00:56:09.027 is because at this point, there's going to be real need for inspection, safety, 00:56:09.110 --> 00:56:12.897 so there's the accounts at which we are. 00:56:12.980 --> 00:56:14.687 And even if you look at the Chinese models, 00:56:14.770 --> 00:56:17.247 they're also quickly becoming closed source and so on. 00:56:17.330 --> 00:56:22.687 So I think there will be. And I know Jensen's working on some open rate models, 00:56:22.770 --> 00:56:24.847 so we're definitely supporters of it. 00:56:24.930 --> 00:56:29.227 But we want to make sure that we are all leading. 00:56:29.310 --> 00:56:31.787 The ultimate goal here is to have everyone 00:56:31.870 --> 00:56:36.667 have real agency in being able to take some model 00:56:36.750 --> 00:56:41.027 and to be able to then add to it and then protect it 00:56:41.110 --> 00:56:47.347 from having it go back. So I think that they are [INAUDIBLE]. 00:56:47.430 --> 00:56:49.047 It won't be open. It will be licensed. 00:56:49.130 --> 00:56:52.307 What does that? So we'll license the weights. 00:56:52.390 --> 00:56:56.067 OK. And then how does, if someone's using it on fireworks 00:56:56.150 --> 00:57:00.527 or together or something like that, how does that help y'all at Microsoft? 00:57:00.610 --> 00:57:04.307 It will be licensed. And so therefore, we will have an economic model 00:57:04.390 --> 00:57:07.367 in all of this. Well, I think we're out of time. 00:57:07.450 --> 00:57:09.367 But thank you so much for coming. 00:57:09.450 --> 00:57:13.260 Thank you so much. Fantastic and--