WEBVTT 00:00:00.000 --> 00:00:02.077 All right, everybody. We're thrilled to 00:00:02.160 --> 00:00:04.797 have the one, the only Tata Nadella 00:00:04.880 --> 00:00:08.397 here, the third CEO of Microsoft for a 00:00:08.480 --> 00:00:11.117 uh impromptu fireside chat with David 00:00:11.200 --> 00:00:17.277 Saxs, Arzar of AI and crypto. Satia 00:00:17.360 --> 00:00:21.597 third, CEO of Microsoft, born in India. 00:00:21.680 --> 00:00:24.157 What an incredible story. came here 00:00:24.240 --> 00:00:27.597 right after college and uh you had a 00:00:27.680 --> 00:00:29.997 little round trip to pick up your wife 00:00:30.080 --> 00:00:31.837 in your book to to bring her here. Tell 00:00:31.920 --> 00:00:35.197 everybody briefly uh how that occurred. 00:00:35.280 --> 00:00:38.877 >> Well um uh you know so that's that's a 00:00:38.960 --> 00:00:42.557 great story of uh the uh the labyrinth 00:00:42.640 --> 00:00:45.437 that is the immigration policies of the 00:00:45.520 --> 00:00:49.437 United States I think. Um I my wife and 00:00:49.520 --> 00:00:51.357 I went to college together in India. I 00:00:51.440 --> 00:00:53.677 came here for grad school. we then got 00:00:53.760 --> 00:00:58.477 married. Uh I got my green card um and 00:00:58.560 --> 00:01:00.557 she couldn't come join because we got 00:01:00.640 --> 00:01:04.317 married. So the story goes basically I 00:01:04.400 --> 00:01:07.837 had to give up my green card. So the funny thing is I went to the American 00:01:07.920 --> 00:01:11.197 embassy in Delhi >> and I said where's the line to give up 00:01:11.280 --> 00:01:14.717 my green card and they said there is no such line. Um 00:01:14.800 --> 00:01:18.797 >> that would be a crazy thing to do in the '9s. So it was a strange thing to give 00:01:18.880 --> 00:01:22.957 up your green card, get an H1 so that she could join, but it all worked out. 00:01:23.040 --> 00:01:25.437 So um you know it's a long lost memory 00:01:25.520 --> 00:01:27.837 but it was you know a way to work around 00:01:27.920 --> 00:01:33.997 it. Um I wanted to ask you uh having 00:01:34.080 --> 00:01:37.517 launched a co-pilot first with GitHub 00:01:37.600 --> 00:01:39.277 then having a co-pilot on the desktop. 00:01:39.360 --> 00:01:43.037 You made a very bold move for Microsoft to put that in the Windows product, 00:01:43.120 --> 00:01:47.597 which I use every day, on the desktop, but you did that before it really could 00:01:47.680 --> 00:01:52.077 recognize the file system and interact with applications. Got a little bit of a 00:01:52.160 --> 00:01:56.317 lukewarm reception, but now you've been doubling down, doubling down, and there 00:01:56.400 --> 00:01:58.717 seems to be, in my estimation, three 00:01:58.800 --> 00:02:01.597 modalities for knowledge workers. 00:02:01.680 --> 00:02:04.397 Elon's building at XAI, 00:02:04.480 --> 00:02:08.797 what they're calling a human emulator, if you saw that leak this week. Yeah. 00:02:08.880 --> 00:02:12.157 uh where they're just building employees and just putting them into their chat 00:02:12.240 --> 00:02:16.557 rooms and email. Then you have Claude came out with co-work this week. 00:02:16.640 --> 00:02:19.997 Incredibly powerful. People are kind of losing their minds over it. I've been 00:02:20.080 --> 00:02:21.597 playing with it for the last 40 hours. 00:02:21.680 --> 00:02:26.637 Truly impressive. What's your vision for Microsoft and how 00:02:26.720 --> 00:02:30.077 knowledge workers will actually put this to use because there seems to be a gap 00:02:30.160 --> 00:02:33.997 between you know playing around with chatbt and getting some interesting 00:02:34.080 --> 00:02:36.477 results and getting business results. 00:02:36.560 --> 00:02:39.117 >> Yeah. So I think it one of the most um 00:02:39.200 --> 00:02:43.357 perhaps illustrative examples um of 00:02:43.440 --> 00:02:46.637 trying to understand these various form factors is looking at coding which is 00:02:46.720 --> 00:02:49.837 obviously a form of knowledge work or uh 00:02:49.920 --> 00:02:53.837 probably the best example of knowledge work and if you think about the journey 00:02:53.920 --> 00:02:56.557 coding has been it started with u 00:02:56.640 --> 00:03:00.637 essentially uh uh the next edit suggest 00:03:00.720 --> 00:03:02.557 right that was the first time in fact my 00:03:02.640 --> 00:03:05.597 own belief in this entire uh generation 00:03:05.680 --> 00:03:10.317 of tech really sort of got formulated where I started seeing I think it was G 00:03:10.400 --> 00:03:11.997 you know there's a codeex model back in 00:03:12.080 --> 00:03:14.637 the day it was preGPT35 00:03:14.720 --> 00:03:17.357 uh that's when next edits started 00:03:17.440 --> 00:03:19.677 working with some real accuracy then we 00:03:19.760 --> 00:03:22.957 went to chat then we went to actions and 00:03:23.040 --> 00:03:25.197 now to full autonomous agents and then 00:03:25.280 --> 00:03:28.397 the autonomous agents can be both 00:03:28.480 --> 00:03:32.877 foreground background in the cloud or local right so that's all the form 00:03:32.960 --> 00:03:36.157 factors that exist today when you're coding and interestingly If you look at 00:03:36.240 --> 00:03:40.397 it, you use all of them, right? It's not like there's only one form factor. So 00:03:40.480 --> 00:03:43.197 that's I think probably one of the other lessons. So for example, when I'm in a 00:03:43.280 --> 00:03:47.197 CLI, I can you go a foreground agent, 00:03:47.280 --> 00:03:51.997 background agent, and then just literally go edit in VS Code, right? 00:03:52.080 --> 00:03:53.837 There all happening in parallel. Right? 00:03:53.920 --> 00:03:56.637 So that sort of shows how these form 00:03:56.720 --> 00:03:59.757 factors even composed. So then you bring 00:03:59.840 --> 00:04:02.237 that to knowledge work to your point. We 00:04:02.320 --> 00:04:06.157 started with chat. chat with reasoning 00:04:06.240 --> 00:04:08.237 sort of goes beyond just request 00:04:08.320 --> 00:04:10.157 response because you now have that chain 00:04:10.240 --> 00:04:13.517 of thought uh where you can see it work 00:04:13.600 --> 00:04:15.677 now they're actions right essentially 00:04:15.760 --> 00:04:18.877 either through computer use or through 00:04:18.960 --> 00:04:21.837 uh AP you know basically skills uh and 00:04:21.920 --> 00:04:24.237 agent calls so you can do actions so 00:04:24.320 --> 00:04:26.157 that's kind of the state of the copilot 00:04:26.240 --> 00:04:31.117 today now there is a way to think about 00:04:31.200 --> 00:04:34.477 you know the the theory of the mind evolution Right? Because you need like 00:04:34.560 --> 00:04:38.397 if you remember you know Jobs had the best line I would say for PCs or 00:04:38.480 --> 00:04:41.357 computers was to say if you it's a 00:04:41.440 --> 00:04:45.837 bicycle for the mind. Bill had a line which I liked as well which was it's 00:04:45.920 --> 00:04:48.077 information at your fingertips. We kind 00:04:48.160 --> 00:04:54.397 of need now a new concept metaphor for how we use computers in the AI age. And 00:04:54.480 --> 00:04:56.637 you have one >> and the one I like actually came from 00:04:56.720 --> 00:05:00.077 the CEO of notion which I you know that manager of >> incredible product. 00:05:00.160 --> 00:05:02.477 >> Yeah. You haven't bought it yet? I'm not 00:05:02.560 --> 00:05:06.237 not that uh but the it's both management 00:05:06.320 --> 00:05:10.957 of you know basically a manager of infinite minds. That's a 00:05:11.040 --> 00:05:14.237 nice way to think about it right when you sort of really look at all the 00:05:14.320 --> 00:05:19.277 agents that you are working with you kind of need to understand what I in 00:05:19.360 --> 00:05:22.877 fact the other term I like is we macrodellegate 00:05:22.960 --> 00:05:25.917 and micro steer in fact you kind of need 00:05:26.000 --> 00:05:30.077 that in in in coding you kind of have it right. So you do a macro delegation and 00:05:30.160 --> 00:05:34.717 then I can in parallel give it instructions while it is doing work. So 00:05:34.800 --> 00:05:39.277 that's sort of the state even today of co-pilot or what have you. you bring up 00:05:39.360 --> 00:05:42.557 a little bit of what one of one of the form factors I'm very excited about and 00:05:42.640 --> 00:05:45.037 you'll see us even in the next week even 00:05:45.120 --> 00:05:48.237 uh do things is while I'm sitting in 00:05:48.320 --> 00:05:52.637 GitHub copilot what it's not as if software developers 00:05:52.720 --> 00:05:54.717 sit in isolation right it's not like the 00:05:54.800 --> 00:05:57.517 only thing I work on is my repo I attend 00:05:57.600 --> 00:06:01.357 meetings um I write specs or others have 00:06:01.440 --> 00:06:04.077 written specs that I'm implementing uh I 00:06:04.160 --> 00:06:06.397 need to have my repo be consistent with 00:06:06.480 --> 00:06:08.397 that so that means using either a 00:06:08.480 --> 00:06:11.197 straightforward MCP server or a skill I 00:06:11.280 --> 00:06:13.917 want to be able to call into my work IQ 00:06:14.000 --> 00:06:16.557 which is the co-pilot bring that in 00:06:16.640 --> 00:06:21.677 that's the type of composition uh of knowledge work that'll happen same thing 00:06:21.760 --> 00:06:25.597 with security say you're a security professional you have lots of logs uh 00:06:25.680 --> 00:06:29.837 how do you sort of really analyze them you drop them into a file system then 00:06:29.920 --> 00:06:33.757 write code on top of it create a dashboard what have you those are the 00:06:33.840 --> 00:06:38.077 types of knowledge work that we can enable there I think you bring up One 00:06:38.160 --> 00:06:41.837 more thing which is can you create quote unquote digital employees, digital 00:06:41.920 --> 00:06:45.997 co-workers or what have you and it's all about credentials right so the I today 00:06:46.080 --> 00:06:48.637 you could like you can literally assign >> are you working on that as well 00:06:48.720 --> 00:06:53.357 >> yeah so in fact we introduced something called agent 365 as a way to give 00:06:53.440 --> 00:06:57.997 identities in fact extending the identities we have for humans today uh 00:06:58.080 --> 00:07:02.157 and the endpoint protection we have for their compute devices to agents so 00:07:02.240 --> 00:07:06.397 >> so you might clone me working in the HR department or working in the marketing 00:07:06.480 --> 00:07:09.677 department and have a virtual correct >> version of me inside of office. 00:07:09.760 --> 00:07:12.877 >> That's correct. So, so there are two sort of modalities there. One is you 00:07:12.960 --> 00:07:15.277 give every knowledge worker infinite 00:07:15.360 --> 00:07:18.237 minds. That's kind of one and then you 00:07:18.320 --> 00:07:22.797 create even infinite minds independent of the your identity because the 00:07:22.880 --> 00:07:26.557 identity is one of the key things you got to get right even for it to work 00:07:26.640 --> 00:07:29.757 right. So permissions >> and decision making 00:07:29.840 --> 00:07:31.517 >> permissions decision making and like one 00:07:31.600 --> 00:07:36.637 of the key in things is who did what to whom is sort of the most important query 00:07:36.720 --> 00:07:40.317 in an organization right at the end of the day the organization needs to 00:07:40.400 --> 00:07:43.597 understand what work got done h and 00:07:43.680 --> 00:07:47.677 what's the provenence of that work uh and how do you trace it back right so 00:07:47.760 --> 00:07:49.677 therefore you kind of want either what 00:07:49.760 --> 00:07:52.637 if it's a human with a lot of agents 00:07:52.720 --> 00:07:55.117 then it's really macro delegation micro 00:07:55.200 --> 00:08:00.317 steering by the human whose identity was passed on. So it's delegation versus a 00:08:00.400 --> 00:08:03.117 separate identity >> and that was done by a level of 00:08:03.200 --> 00:08:07.437 management, product management that you've eliminated, that Alphabets 00:08:07.520 --> 00:08:11.517 eliminated. Meta has started to eliminate in their organization four 00:08:11.600 --> 00:08:13.597 years ago. You had the same number of 00:08:13.680 --> 00:08:15.837 employees you have at Microsoft now, but 00:08:15.920 --> 00:08:18.797 you put a $90 billion onto the top line 00:08:18.880 --> 00:08:20.637 of the revenue in that time and you 00:08:20.720 --> 00:08:24.717 doubled your income during that time. So 00:08:24.800 --> 00:08:28.957 how did that happen? Is that automation of those jobs? Is it you were a little 00:08:29.040 --> 00:08:32.957 bit overstaffed? >> Unpack. >> I think it's it's actually you're 00:08:33.040 --> 00:08:36.957 pulling on a very interesting thread which is at some level what's the big 00:08:37.040 --> 00:08:38.877 structural change that needs to happen? 00:08:38.960 --> 00:08:41.197 In fact, I would say this is probably 00:08:41.280 --> 00:08:46.557 the biggest change in knowledge work since PCs. I mean I always you know 00:08:46.640 --> 00:08:50.397 think about like how did work happen precise right? I mean think about a 00:08:50.480 --> 00:08:52.077 multinational company like ours trying 00:08:52.160 --> 00:08:55.037 to do a forecast. uh right faxes went 00:08:55.120 --> 00:08:57.997 around, inter office memos got sent and 00:08:58.080 --> 00:09:02.077 then you kind of created a you know uh a forecast then suddenly you know PCs 00:09:02.160 --> 00:09:05.997 became standard issue uh you put an Excel spreadsheet put some numbers sent 00:09:06.080 --> 00:09:10.077 it in email everybody entered numbers and you had a forecast so the work the 00:09:10.160 --> 00:09:12.717 work artifact and the workflow all 00:09:12.800 --> 00:09:15.117 changed that's what's happening so for 00:09:15.200 --> 00:09:18.077 example I'll give you uh at LinkedIn 00:09:18.160 --> 00:09:20.877 uh we used to have product managers we 00:09:20.960 --> 00:09:25.437 had designers uh we had front-end engineers and then we had CIS backend 00:09:25.520 --> 00:09:29.997 engineers and so on. So what we did is we sort of took those first four roles 00:09:30.080 --> 00:09:32.397 and combined them in fact increased 00:09:32.480 --> 00:09:34.557 scope and said let's they're all full 00:09:34.640 --> 00:09:37.277 stack builders. So I like that because 00:09:37.360 --> 00:09:39.757 that's a structural change that allows 00:09:39.840 --> 00:09:43.117 for us to increase the change both the 00:09:43.200 --> 00:09:46.797 work and the workflow between these functions and I would assume the 00:09:46.880 --> 00:09:50.077 velocity because you don't have four people communicating and that throughput 00:09:50.160 --> 00:09:53.437 of ideas it's just one person and vibe coding. Exactly. And there's a new 00:09:53.520 --> 00:09:57.277 workflow. So what at the same time as you can imagine if to build an AI 00:09:57.360 --> 00:09:59.197 product today, there's a complete new 00:09:59.280 --> 00:10:01.277 workflow, right? It starts with eval 00:10:01.360 --> 00:10:04.957 right. So basically there's this eval to 00:10:05.040 --> 00:10:08.717 science to infrastructure. And so eval 00:10:08.800 --> 00:10:12.237 are done by these full stack builders and what have you and product managers 00:10:12.320 --> 00:10:15.997 in the new form. The infrastructure is built by the systems engineers at the 00:10:16.080 --> 00:10:19.357 back end because they support the science that supports the product. So in 00:10:19.440 --> 00:10:22.397 some sense there's a new loop uh and you 00:10:22.480 --> 00:10:24.797 have to structurally change and so a lot 00:10:24.880 --> 00:10:27.757 of what is happening inside a tech is 00:10:27.840 --> 00:10:32.317 that change uh which is I think going to be pretty massive uh and at the same 00:10:32.400 --> 00:10:36.077 time a company like ours I have to do everything it's not like I can just so 00:10:36.160 --> 00:10:39.757 go live in the future I have to make sure we're doing a fantastic job of 00:10:39.840 --> 00:10:44.637 doing hot patching on Windows is done with quality uh while at the same time 00:10:44.720 --> 00:10:48.397 building the evals that are improving co-pilot quality right and so both of 00:10:48.480 --> 00:10:51.837 those have to be first class. >> I assume this is the most challenging 00:10:51.920 --> 00:10:54.637 moment of your career because Microsoft 00:10:54.720 --> 00:10:58.157 was so dominant duopoly in some spaces. 00:10:58.240 --> 00:11:00.717 Um, but you really weren't up against 00:11:00.800 --> 00:11:05.197 the competition level. You're up against now. I was talking to Elon, you know, 00:11:05.280 --> 00:11:08.797 and he was sort of saying, well, building cars was pretty easy, uh, 00:11:08.880 --> 00:11:13.437 because I was up against the legacy car makers. And now I'm up against 00:11:13.520 --> 00:11:15.757 just look at the set you're up against. 00:11:15.840 --> 00:11:18.237 Yeah, it's it's a pretty intense time. I 00:11:18.320 --> 00:11:20.477 mean, so the way I I I always think is 00:11:20.560 --> 00:11:23.517 it's always helpful uh when you have a 00:11:23.600 --> 00:11:27.997 complete new set of competitors every decade because that keeps you fit. Uh if 00:11:28.080 --> 00:11:29.757 you think about it, I joined uh 00:11:29.840 --> 00:11:32.957 Microsoft in ' 92 when I had Noel as the 00:11:33.040 --> 00:11:36.317 big existential competitor we had. Um 00:11:36.400 --> 00:11:39.837 and here we are in 2026. Uh and you're 00:11:39.920 --> 00:11:43.997 absolutely right. It's a pretty intense time. I'm glad there's the competition. 00:11:44.080 --> 00:11:47.197 uh it's it's quite honestly at the end of the day when I look at it right as a 00:11:47.280 --> 00:11:49.997 percentage of GDP 5 years from now where 00:11:50.080 --> 00:11:53.277 will tech be right uh it will be higher 00:11:53.360 --> 00:11:56.077 so we're blessed to be in this industry 00:11:56.160 --> 00:12:02.157 it's lot of intense competition but it's not so zero sum as some people make it out 00:12:02.240 --> 00:12:05.117 >> it's getting much bigger >> much the TAM and the you know just the 00:12:05.200 --> 00:12:09.757 impact of this tech is going to be so massive um the question then of course 00:12:09.840 --> 00:12:14.397 is what is like I I always go back to what's the brand identity Microsoft has 00:12:14.480 --> 00:12:16.317 brand permission. We have what do 00:12:16.400 --> 00:12:18.957 customers expect from us. It's sometimes 00:12:19.040 --> 00:12:21.997 we kind of overthink somehow that every 00:12:22.080 --> 00:12:26.157 customer wants the same thing from all of the competitors and finding that out. 00:12:26.240 --> 00:12:29.357 Right. It's kind of a different take on the Peter Theal thing which is you got 00:12:29.440 --> 00:12:32.077 to avoid competition by really 00:12:32.160 --> 00:12:35.197 understanding what customers really want 00:12:35.280 --> 00:12:39.197 from you uh versus thinking everybody's a competitor. 00:12:39.280 --> 00:12:41.357 >> David. >> Yeah. So there are a lot of heads of 00:12:41.440 --> 00:12:46.157 state here obviously at Davos as well as CEOs of Fortune 500 companies and I 00:12:46.240 --> 00:12:49.597 think you got asked a question last night at the dinner about how they 00:12:49.680 --> 00:12:51.837 should think about AI and how to be 00:12:51.920 --> 00:12:56.717 successful and I recall they used the word diffusion and I wondering if you 00:12:56.800 --> 00:13:00.157 could expand on those remarks because that really resonated with some of the 00:13:00.240 --> 00:13:03.117 policy work I've been doing. No, absolutely. In fact, uh what you all 00:13:03.200 --> 00:13:05.837 have been doing to make sure in in this 00:13:05.920 --> 00:13:09.117 context of the American tech stack um is 00:13:09.200 --> 00:13:11.997 broadly used around the world and is 00:13:12.080 --> 00:13:14.397 trusted around the world because I think 00:13:14.480 --> 00:13:18.637 uh when I look back David to me um uh at 00:13:18.720 --> 00:13:21.437 the end of the day you create the 00:13:21.520 --> 00:13:27.117 technology but really the benefits 00:13:27.200 --> 00:13:29.997 come only by intense use. In fact, one 00:13:30.080 --> 00:13:34.637 of my favorite studies has always been this work that uh an economist I think 00:13:34.720 --> 00:13:39.597 out of Dartmouth did uh his name is Diego Kman where he studied uh basically 00:13:39.680 --> 00:13:43.597 what happened during the industrial revolution um how did countries get 00:13:43.680 --> 00:13:46.317 ahead. Um and the simple sort of 00:13:46.400 --> 00:13:49.357 takeaway from that was any country uh 00:13:49.440 --> 00:13:52.157 that brought the latest technology into 00:13:52.240 --> 00:13:55.597 their uh country and then did value add 00:13:55.680 --> 00:13:59.517 technology on top of it. Right? So it's like don't reinvent the wheel, bring the 00:13:59.600 --> 00:14:02.077 latest and then build on top of it. 00:14:02.160 --> 00:14:05.677 That's to me what happens, you know, when you have diffusion. So especially 00:14:05.760 --> 00:14:09.917 with general purpose technologies like AI, it needs to spread like right in 00:14:10.000 --> 00:14:13.837 our, you know, in our own country in the United States, we now need we have the 00:14:13.920 --> 00:14:18.317 tech. The question is, is it being used in healthcare? Is it being used um in 00:14:18.400 --> 00:14:22.237 financial services? Is it being used in every sector of the economy by large 00:14:22.320 --> 00:14:23.517 businesses, small business, public 00:14:23.600 --> 00:14:27.357 sector? Um so to me un unless and until 00:14:27.440 --> 00:14:31.277 we see that diffusion and intense use uh 00:14:31.360 --> 00:14:35.837 we're not going to have the success. Um uh and so that's the phase we are in 00:14:35.920 --> 00:14:38.237 it's f you know it's diffusing faster um 00:14:38.320 --> 00:14:40.237 and so some of the work policy work you 00:14:40.320 --> 00:14:43.677 have done um and in general all you know 00:14:43.760 --> 00:14:45.437 the good news here is the technologies 00:14:45.520 --> 00:14:48.557 there the rails around cloud and mobile 00:14:48.640 --> 00:14:52.717 that were laid out make it possible for this thing to spread right it's not you 00:14:52.800 --> 00:14:56.717 know impossible to get the tokens the question is what are the use cases how 00:14:56.800 --> 00:15:00.797 do and how do you manage the change in all of that um you know like one of the 00:15:00.880 --> 00:15:04.797 questions at least in Davos is it's one for the west and the developed nations. 00:15:04.880 --> 00:15:07.197 What about the global south? Um I think 00:15:07.280 --> 00:15:09.197 global south has a huge opportunity too 00:15:09.280 --> 00:15:11.837 quite frankly because to me like let's 00:15:11.920 --> 00:15:15.037 say you know 40% 50% of the GDP of most 00:15:15.120 --> 00:15:17.757 global south countries is public sector. 00:15:17.840 --> 00:15:21.037 So just imagine this tech making a 00:15:21.120 --> 00:15:23.597 difference in how the governments uh 00:15:23.680 --> 00:15:28.077 really parlay the taxpayer money into services for citizens and there's if 00:15:28.160 --> 00:15:32.077 there's efficiency gains that's probably couple of points for GDP growth right 00:15:32.160 --> 00:15:34.957 there and so I'm very optimistic that 00:15:35.040 --> 00:15:37.597 there's going to be a pull uh and that 00:15:37.680 --> 00:15:39.917 we should as the United States given the 00:15:40.000 --> 00:15:43.197 technology stack we have uh in Europe in 00:15:43.280 --> 00:15:47.757 Asia in you know in South America in Africa and everywhere 00:15:47.840 --> 00:15:49.917 get it to be broadly deployed. 00:15:50.000 --> 00:15:51.437 >> You one of the questions I get asked a 00:15:51.520 --> 00:15:55.117 lot about the AI race is how do you know 00:15:55.200 --> 00:15:59.357 if you're winning or how do you know if the United States is ahead of its global 00:15:59.440 --> 00:16:02.877 competitors? And the answer I give is market share. You [clears throat] know, 00:16:02.960 --> 00:16:05.517 if we look around the world in 5 years 00:16:05.600 --> 00:16:08.557 and we see that American companies, 00:16:08.640 --> 00:16:12.557 American technology has say 80% market share, it means we did a good job. If we 00:16:12.640 --> 00:16:16.717 look around the world in 5 years and see that it's say Chinese chips and Chinese 00:16:16.800 --> 00:16:20.957 models that are being used all over the world, well means we probably lost. So 00:16:21.040 --> 00:16:25.757 you know ultimately usage is the proof of the pudding is in the eating of it. I 00:16:25.840 --> 00:16:27.837 mean the in this case the way that you 00:16:27.920 --> 00:16:31.357 know that you're succeeding is through 00:16:31.440 --> 00:16:35.757 market shares through usage. and and I I would agree with that. But David, since 00:16:35.840 --> 00:16:40.237 you even worked at Microsoft for a few years, um you I you know, one of the 00:16:40.320 --> 00:16:44.317 things that I'm very grounded on is always uh that Bill Gates line of a 00:16:44.400 --> 00:16:48.477 platform, right? So, one of the things that I always think about is it's market 00:16:48.560 --> 00:16:52.397 share, but it's also ecosystem effects, right? See, what the United States 00:16:52.480 --> 00:16:55.037 always has done is not just about our 00:16:55.120 --> 00:16:58.717 market share or even um the revenues to 00:16:58.800 --> 00:17:02.557 US companies. In fact, one of the things I learned at Microsoft is whenever I did 00:17:02.640 --> 00:17:04.717 a country visit, the data I would first 00:17:04.800 --> 00:17:07.997 study is in let's say in the UK or in 00:17:08.080 --> 00:17:09.757 Switzerland or what have you is what is 00:17:09.840 --> 00:17:12.637 the total employment created in 00:17:12.720 --> 00:17:15.597 Switzerland uh in our channel that used 00:17:15.680 --> 00:17:20.237 to be like the number one thing uh in our country reports right and the total number 00:17:20.320 --> 00:17:22.957 >> that be like the number of IT workers the number 00:17:23.040 --> 00:17:24.957 >> exactly we used to have office workers 00:17:25.040 --> 00:17:27.757 channel so channel partners we ISVS so 00:17:27.840 --> 00:17:30.957 number of ISVS uh who were there. So we 00:17:31.040 --> 00:17:33.117 used to have a complete marker of how 00:17:33.200 --> 00:17:35.517 did the ecosystem around the platform 00:17:35.600 --> 00:17:38.397 get built one country at a time and that 00:17:38.480 --> 00:17:40.157 is what the United States has always 00:17:40.240 --> 00:17:42.877 done. In fact the US tech stack 00:17:42.960 --> 00:17:46.157 including in China got built because 00:17:46.240 --> 00:17:50.237 others built around our tech stack. The same thing is going to happen. So that's 00:17:50.320 --> 00:17:52.877 why I think the work you're doing around diffusion, >> right, 00:17:52.960 --> 00:17:55.757 >> is about really increasing the size of 00:17:55.840 --> 00:18:01.357 the pie, the trust in the platform so that there is true economic opportunity 00:18:01.440 --> 00:18:03.837 quite frankly. >> Well, you're right and and I remember 00:18:03.920 --> 00:18:07.997 actually you brought back some memories from this is about a decade ago when my 00:18:08.080 --> 00:18:11.437 company Yammer was acquired by Microsoft. We were part of uh the 00:18:11.520 --> 00:18:14.077 SharePoint group and I remember that the 00:18:14.160 --> 00:18:15.917 um the product managers there were very 00:18:16.000 --> 00:18:19.037 proud of the fact that the revenue from 00:18:19.120 --> 00:18:21.597 the SharePoint ecosystem meaning non- 00:18:21.680 --> 00:18:24.637 Microsoft the um the consulting 00:18:24.720 --> 00:18:28.637 community the implementers who would go into companies implement SharePoint I 00:18:28.720 --> 00:18:32.717 think their revenue is something like seven times greater than Microsoft's own 00:18:32.800 --> 00:18:35.117 software revenue and I think >> in aggregate 00:18:35.200 --> 00:18:39.117 >> in aggregate and I think and I think Bill had a line about you're not an 00:18:39.200 --> 00:18:43.677 ecosystem platform until the revenue on top of your platform 00:18:43.760 --> 00:18:48.477 >> is some, you know, factor of your own revenue. And and I and I think that I 00:18:48.560 --> 00:18:52.317 think what's really important about this is when we talk about diffusion and 00:18:52.400 --> 00:18:56.557 obviously want the United States to have this leading position, it doesn't mean 00:18:56.640 --> 00:18:59.757 it's bad for the rest of the world because they're able to build on top of 00:18:59.840 --> 00:19:02.477 those platforms and create even more value. 00:19:02.560 --> 00:19:04.477 >> 100%. In fact, that's sort of the most 00:19:04.560 --> 00:19:06.237 important point, right? So this is not 00:19:06.320 --> 00:19:11.597 that this is not about uh American tech um and America revenues to the United 00:19:11.680 --> 00:19:15.037 States. It's actually creating opportunity using a new platform 00:19:15.120 --> 00:19:17.357 everywhere. And in fact you know the you 00:19:17.440 --> 00:19:19.517 know like I remember I worked on our 00:19:19.600 --> 00:19:22.317 database products uh in the '90s u you 00:19:22.400 --> 00:19:25.677 know with SAP in fact the combination of 00:19:25.760 --> 00:19:29.197 uh SQL Server and R3 were successful on 00:19:29.280 --> 00:19:32.717 both sides. There's a lot talked about Intel and Microsoft, but one of the 00:19:32.800 --> 00:19:36.477 other things that I grew up in which has sort of been foundational in how I look 00:19:36.560 --> 00:19:40.797 at the world is what we did with a European software company that is still 00:19:40.880 --> 00:19:45.197 uh you know a giant. And so that you know who knows what the next big AI app 00:19:45.280 --> 00:19:47.277 will be and where and what will happen. 00:19:47.360 --> 00:19:49.837 But uh I I sort of go in with the 00:19:49.920 --> 00:19:54.637 attitude that there will be tech companies uh maybe even top five tech 00:19:54.720 --> 00:19:56.797 companies that could emerge everywhere 00:19:56.880 --> 00:20:02.077 with even the American tech stack. you have um done some amazing acquisitions 00:20:02.160 --> 00:20:05.197 and you're quite a dealmaker on top of being a technologist. It's probably the 00:20:05.280 --> 00:20:08.157 least reported aspect of your 00:20:08.240 --> 00:20:09.997 spectacular tenure and the massive 00:20:10.080 --> 00:20:12.397 growth you've had. 00:20:12.480 --> 00:20:15.837 But you did a deal with OpenAI and 00:20:15.920 --> 00:20:18.077 probably one of the most savvy 00:20:18.160 --> 00:20:19.917 slashcontroversial dealmakers of all 00:20:20.000 --> 00:20:23.037 time, Sam Alman. That deal was looked at 00:20:23.120 --> 00:20:27.517 as you you you're you're set up to get a 00:20:27.600 --> 00:20:31.517 windfall in cash which you don't need as Microsoft always nice I'm guessing if 00:20:31.600 --> 00:20:34.237 they IPO but did you create potentially 00:20:34.320 --> 00:20:37.197 and this was the criticism of it 00:20:37.280 --> 00:20:40.157 an ultimate competitor to Microsoft and 00:20:40.240 --> 00:20:44.557 how do you think about that and how can Microsoft which missed Steve Balmer's 00:20:44.640 --> 00:20:46.157 biggest regret missing the mobile 00:20:46.240 --> 00:20:50.317 revolution how can you not have a Gemini 00:20:50.400 --> 00:20:53.917 an XAI a claude that is your own or in 00:20:54.000 --> 00:20:57.597 your mind do you have that because you have the source code of open AI? 00:20:57.680 --> 00:21:00.797 >> Yeah, I think that that's right. So when when people say uh where is your 00:21:00.880 --> 00:21:04.637 foundation model I mean at the end of the day we do have the IP but that said 00:21:04.720 --> 00:21:08.797 I think you bring up a couple different things right one is to us the most 00:21:08.880 --> 00:21:10.877 important thing when I look at what is 00:21:10.960 --> 00:21:14.637 Microsoft's uh strategy today one is we 00:21:14.720 --> 00:21:18.397 want to build token factories right so our biggest business today is Azure 00:21:18.480 --> 00:21:20.557 business and the Azure business the TAM 00:21:20.640 --> 00:21:22.877 given what's going to happen is is so 00:21:22.960 --> 00:21:25.437 huge that we now need to be fantastic uh 00:21:25.520 --> 00:21:29.917 at building these token factories and um that's means a heterogeneous fleet of 00:21:30.000 --> 00:21:33.997 infrastructure and that every hyperscaler has always done which is use 00:21:34.080 --> 00:21:36.637 software to make maximum use of it and 00:21:36.720 --> 00:21:41.197 for TCO and utilization. So that's one side of it. Then there's the app server 00:21:41.280 --> 00:21:44.877 business right which is everybody we you talked about like if everyone's going to 00:21:44.960 --> 00:21:49.997 be building agents have infinite minds have these RL gyms have eval what have 00:21:50.080 --> 00:21:54.077 you there's an entire just like every platform has had an app server this one 00:21:54.160 --> 00:21:57.117 has an app server that's what we're doing with foundry and what have you 00:21:57.200 --> 00:22:01.197 right so there's an app server business in that app server one of the things 00:22:01.280 --> 00:22:03.917 that structurally now is pretty clear is 00:22:04.000 --> 00:22:08.717 anyone building any application or any company is going to use not one model 00:22:08.800 --> 00:22:12.557 but all the models Right? Why would I not? Right? Which is in fact I will 00:22:12.640 --> 00:22:17.677 orchestrate for any given task even multiple models. Right? There's this one 00:22:17.760 --> 00:22:21.197 nice thing that we came out in our healthcare practice called the decision 00:22:21.280 --> 00:22:24.477 orchestrator. What it proves is that by 00:22:24.560 --> 00:22:27.517 assigning roles, right? So investigator, 00:22:27.600 --> 00:22:30.317 data analyst, domain expert, just giving 00:22:30.400 --> 00:22:33.437 even prompted roles to models and then 00:22:33.520 --> 00:22:35.917 orchestrating them gets better results 00:22:36.000 --> 00:22:38.397 than any one single frontier model. Am I 00:22:38.480 --> 00:22:43.037 right to read into that then that you're bullish on the open- source models and 00:22:43.120 --> 00:22:47.117 think large language models will largely be commoditized and that's not where the 00:22:47.200 --> 00:22:49.437 value will occur. >> In fact, the way I think about it is 00:22:49.520 --> 00:22:52.477 that just like Apple thinks that too. By the way, 00:22:52.560 --> 00:22:54.317 >> by the way, what the way you think about 00:22:54.400 --> 00:22:57.837 what happened in the database market, right? You know, I used to be like 00:22:57.920 --> 00:23:01.597 everything is just a SQL database until it was not, right? There was I mean, 00:23:01.680 --> 00:23:05.597 think about it. There doc databases, there is no SQL databases. the 00:23:05.680 --> 00:23:09.597 proliferation of database right who would have thought that the database 00:23:09.680 --> 00:23:13.597 market would have such a richness to it >> or that it could ever be open source 00:23:13.680 --> 00:23:16.957 that was that's I mean talk about Postgress or what has happened even with 00:23:17.040 --> 00:23:20.637 which is open but there are even companies that have backed it and so so 00:23:20.720 --> 00:23:24.877 to me that's what's going to happen it to me a model is like the database 00:23:24.960 --> 00:23:28.637 market you know it's it's got it's got differences but I sort of somehow think 00:23:28.720 --> 00:23:32.077 that uh it's not there are definitely going to be frontier models that are 00:23:32.160 --> 00:23:35.757 closed source you know there going to be open source models that are going to be 00:23:35.840 --> 00:23:38.317 uh uh frontier class. In fact, if 00:23:38.400 --> 00:23:40.717 anything, I think in this next year, 00:23:40.800 --> 00:23:43.357 what will be probably a big part of the 00:23:43.440 --> 00:23:46.077 discussion is what's the future of a 00:23:46.160 --> 00:23:50.717 firm? A firm should be able to take the 00:23:50.800 --> 00:23:54.797 tacit knowledge it has and embed it 00:23:54.880 --> 00:23:59.677 inside a weights in a model that they control. Right? So, when somebody asks 00:23:59.760 --> 00:24:03.357 me how many models should be there, I'll say as many models as firms in the 00:24:03.440 --> 00:24:04.797 world. Right? That's sort of the an 00:24:04.880 --> 00:24:07.837 extreme way uh because because to me 00:24:07.920 --> 00:24:10.397 that's how I think this you know this 00:24:10.480 --> 00:24:12.557 knowledge economy becomes an AI economy. 00:24:12.640 --> 00:24:15.037 >> Are you secretly and you can say it here 00:24:15.120 --> 00:24:18.557 since we're on allin working on an LLM 00:24:18.640 --> 00:24:22.237 to exist on the Windows desktop because that you are 00:24:22.320 --> 00:24:23.837 >> you have it like today there's a five 00:24:23.920 --> 00:24:26.397 silica model which is completely 00:24:26.480 --> 00:24:29.757 resident using NPUs and of course using 00:24:29.840 --> 00:24:34.397 uh GPUs. in fact the largest installation um of high power. In fact, 00:24:34.480 --> 00:24:37.357 it's one of the fascinating the workstation is back. I'm one of the most 00:24:37.440 --> 00:24:42.077 if you went to see each great for Microsoft because you you have a nice 00:24:42.160 --> 00:24:44.957 desktop business. >> Absolutely. And so we and in fact we 00:24:45.040 --> 00:24:47.437 think that that form factor especially I 00:24:47.520 --> 00:24:52.477 mean I I always say this which is u you know I started my career on a command 00:24:52.560 --> 00:24:55.037 line. Who knows I may just end it in a command line. 00:24:55.120 --> 00:24:59.277 >> Well you started at Sun which was the original 5 $10,000 workstation. Do you 00:24:59.360 --> 00:25:01.437 see a time where you'll be meeting with 00:25:01.520 --> 00:25:03.757 your customers here and advocating a 10 00:25:03.840 --> 00:25:07.117 $20,000 desktop machine that has an LLM 00:25:07.200 --> 00:25:09.357 and the hardware? You can you can put a 00:25:09.440 --> 00:25:14.717 DGX card and you can have like just a fantastic machine and the models I and 00:25:14.800 --> 00:25:18.877 by the way you know we are one architecture tweak away from even having 00:25:18.960 --> 00:25:22.877 some kind of a distributed model architecture right even ane architecture 00:25:22.960 --> 00:25:27.197 that shows knows how to really distribute itself right that's the type 00:25:27.280 --> 00:25:29.357 of breakthrough that can completely 00:25:29.440 --> 00:25:32.317 change uh what hybrid AI may look like 00:25:32.400 --> 00:25:34.557 but we're absolutely committed and 00:25:34.640 --> 00:25:37.437 focused on making the PC a great place 00:25:37.520 --> 00:25:42.397 for local models uh and local models that then do even a lot of the prompt 00:25:42.480 --> 00:25:45.517 processing and call into the cloud, right? So there's a whole lot of work 00:25:45.600 --> 00:25:48.557 that can happen and that's sort of definitely something that's underway. 00:25:48.640 --> 00:25:50.877 >> Yeah, I think that the cloud co-work has 00:25:50.960 --> 00:25:53.997 kind of shown the power of tapping into the local file drive and be able to use 00:25:54.080 --> 00:25:56.077 that. That that brings up another point. 00:25:56.160 --> 00:25:59.357 You you got me thinking about Yammer and for people who don't know um you know 00:25:59.440 --> 00:26:03.437 Yammer's claim to fame, this is about 15 years ago, was that it pioneered a lot 00:26:03.520 --> 00:26:07.437 of um well it used a lot of consumer growth tactics to attack enterprise 00:26:07.520 --> 00:26:09.357 software. I'm wondering as you think 00:26:09.440 --> 00:26:13.117 about enterprise adoption of AI. How do 00:26:13.200 --> 00:26:16.477 you think it's going to spread over the next year? It feels like we're at sort 00:26:16.560 --> 00:26:21.117 of a a a critical point. Do you think it's going to be top down? Is it going 00:26:21.200 --> 00:26:25.917 to come from the CEO directing a team, giving them a strategic transformation 00:26:26.000 --> 00:26:29.837 project, and they're going to do an RFP, or do you think it's going to spread 00:26:29.920 --> 00:26:32.637 bottom up in the enterprise through AI 00:26:32.720 --> 00:26:37.517 native employees who are adaptable, who are using the tools in their own lives 00:26:37.600 --> 00:26:41.277 and they start to bring these things to work and start accomplishing amazing 00:26:41.360 --> 00:26:43.357 things. >> Yeah. No, I think, you know, like all 00:26:43.440 --> 00:26:47.277 things, David, I think it's both the top down, bottom up, right? uh the that the 00:26:47.360 --> 00:26:49.757 reason I say that top down is if I look 00:26:49.840 --> 00:26:53.277 at the ROI uh of uh applying AI in 00:26:53.360 --> 00:26:56.317 customer service uh or in supply chain 00:26:56.400 --> 00:26:59.677 or in HR self-service those are the easy 00:26:59.760 --> 00:27:03.917 projects where uh IT and CXOs can make 00:27:04.000 --> 00:27:05.917 calls and that's where you'll see the 00:27:06.000 --> 00:27:09.437 first drop of uh real AI adoption but 00:27:09.520 --> 00:27:13.917 the bottom up is what ultimately will happen right I mean with even with the 00:27:14.000 --> 00:27:16.077 PCs in fact if you think back at the 00:27:16.160 --> 00:27:18.797 lawyers brought word in and then finance 00:27:18.880 --> 00:27:23.197 bought Excel in and then email came and then it became standard issue. That's 00:27:23.280 --> 00:27:27.197 what's happening right now. So for example, these agents when I sort of 00:27:27.280 --> 00:27:29.117 talk about everybody's building agents, 00:27:29.200 --> 00:27:33.197 they are figuring out a way to go create 00:27:33.280 --> 00:27:37.437 these things that are changing workflow and removing drudgery in their work. 00:27:37.520 --> 00:27:40.157 Right? That's sort of the beginning of 00:27:40.240 --> 00:27:43.117 what is a bottomup transformation. Um I 00:27:43.200 --> 00:27:47.197 you know you I was in fact the thing that I'm most excited about is this 00:27:47.280 --> 00:27:48.877 bottomup change even at Microsoft for 00:27:48.960 --> 00:27:52.237 example we manage something like 500 odd 00:27:52.320 --> 00:27:56.797 fiber operators around the world in in Azure today and by the way I had not 00:27:56.880 --> 00:28:00.477 myself realized it a lot of it you know it's called DevOps but it's a it's a 00:28:00.560 --> 00:28:04.957 physical asset things get cut and when you sort of say DevOps that means you 00:28:05.040 --> 00:28:08.397 literally are emailing people and saying hey what happened to that fiber cut how 00:28:08.480 --> 00:28:10.557 do we repair it so there's a lot of back 00:28:10.640 --> 00:28:15.597 and forth so this network the the person who runs our global network basically 00:28:15.680 --> 00:28:19.357 has built to your point about these person they're just digital employees 00:28:19.440 --> 00:28:21.997 essentially that are doing all of that 00:28:22.080 --> 00:28:25.277 devops uh and so that's and there's a 00:28:25.360 --> 00:28:29.357 completely bottoms up uh where you see the tools it's kind of like hey I have 00:28:29.440 --> 00:28:31.997 the new way to build agents it's there 00:28:32.080 --> 00:28:34.237 I'm going to use it to create levels of 00:28:34.320 --> 00:28:36.717 automation uh that remove drudgery 00:28:36.800 --> 00:28:39.197 improve efficiency improve quality and 00:28:39.280 --> 00:28:41.757 that ultimately is a skilling thing 00:28:41.840 --> 00:28:46.237 which is sort of the big issue which is u and skilling is not mystical it's just 00:28:46.320 --> 00:28:48.157 by doing right so it's not like I go to 00:28:48.240 --> 00:28:50.397 a class per se it's like the diffusion 00:28:50.480 --> 00:28:55.517 of the tools uh and using the tools and that I think is what's really going to 00:28:55.600 --> 00:28:57.437 be happening >> and and we're in a very interesting 00:28:57.520 --> 00:29:00.957 moment empowering an existing employee 00:29:01.040 --> 00:29:03.917 with these tools is so much easier than 00:29:04.000 --> 00:29:08.077 hiring and mentoring and bringing up the next generation so it feels like we're 00:29:08.160 --> 00:29:13.117 in a little bit of an indigestion moment at Microsoft, do you think who's going 00:29:13.200 --> 00:29:14.797 to have my job 00:29:14.880 --> 00:29:17.677 >> in 30 or 40 years if the company stays 00:29:17.760 --> 00:29:20.957 the same size? Because given your 00:29:21.040 --> 00:29:24.717 technology first approach, there's really no reason to ever add another 00:29:24.800 --> 00:29:26.717 Microsoft employee at the pace this is 00:29:26.800 --> 00:29:31.117 going and you haven't for years. So, how 00:29:31.200 --> 00:29:34.637 you may have swapped some in and out and changed the texture of it. So, how do 00:29:34.720 --> 00:29:38.717 you think about maybe this next generation? What advice would you have 00:29:38.800 --> 00:29:42.077 for these college graduates who maybe don't have an offer from Microsoft right 00:29:42.160 --> 00:29:46.317 now? And you used to spend a lot of time on that building that group, but maybe 00:29:46.400 --> 00:29:49.437 you don't have that luxury now. Do you think about it ever? 00:29:49.520 --> 00:29:52.797 >> I I it's a great question. I you know there's a little bit of a debate what 00:29:52.880 --> 00:29:56.957 happens to early in career and how is college recruiting. I still am a big 00:29:57.040 --> 00:29:58.717 believer in uh college recruiting 00:29:58.800 --> 00:30:02.877 because at the end of the day um this is 00:30:02.960 --> 00:30:05.677 going to change the curve by which 00:30:05.760 --> 00:30:08.317 anyone can pick up proficiency in a 00:30:08.400 --> 00:30:10.317 codebase let's just takes sort of just 00:30:10.400 --> 00:30:14.157 regular CS hiring u what has changed is 00:30:14.240 --> 00:30:16.797 perhaps for someone who comes in new 00:30:16.880 --> 00:30:20.477 into a team and to be able to ramp up 00:30:20.560 --> 00:30:24.637 thanks to all of uh the markdowns the 00:30:24.720 --> 00:30:28.397 skills uh the fact that I can go ask the agent. 00:30:28.480 --> 00:30:33.117 I mean, think about it, right? It's like having an unbelievable mentor who is 00:30:33.200 --> 00:30:35.197 getting you onboarded onto a codebase 00:30:35.280 --> 00:30:36.797 faster. So, in some sense, the 00:30:36.880 --> 00:30:40.237 productivity curve uh of a college hire 00:30:40.320 --> 00:30:45.117 is going to be much steeper than it ever before. So, I think there might be a 00:30:45.200 --> 00:30:47.597 difference. In fact, one of the things we're experimenting with is a different 00:30:47.680 --> 00:30:49.597 type of apprenticeship, right? which is 00:30:49.680 --> 00:30:53.037 you take somebody who's an IC senior dev 00:30:53.120 --> 00:30:56.477 have like a cohort of college uh hires 00:30:56.560 --> 00:31:00.637 working with them because it's a new way of working it's like I remember like all 00:31:00.720 --> 00:31:04.477 you know everybody who joined Microsoft would say go how did you know whatever 00:31:04.560 --> 00:31:07.437 um Cutler implement Malik or what have 00:31:07.520 --> 00:31:09.997 you right he would go try to read uh his 00:31:10.080 --> 00:31:12.397 code to understand uh what great 00:31:12.480 --> 00:31:14.957 craftsmanship looks like nowadays I 00:31:15.040 --> 00:31:17.117 think that great craftsmanship uh comes 00:31:17.200 --> 00:31:20.157 by looking at even how the 10x 100x 00:31:20.240 --> 00:31:24.317 engineers use AI to build great quality 00:31:24.400 --> 00:31:29.277 products. uh and that is what these new college grads will learn and learn 00:31:29.360 --> 00:31:32.957 faster and so that's a beneficial thing for a company like us because at the end 00:31:33.040 --> 00:31:34.637 of the day you know until we saw 00:31:34.720 --> 00:31:37.837 longevity or something we need people to 00:31:37.920 --> 00:31:41.837 come into the workforce be successful at Microsoft so we are very committed but 00:31:41.920 --> 00:31:46.877 we are also making sure that the scopes of the jobs make sense for what the 00:31:46.960 --> 00:31:50.157 aspirations of people are going to be both who are currently in the workforce 00:31:50.240 --> 00:31:52.637 and people who are entering the workforce. 00:31:52.720 --> 00:31:58.570 Okay, on that note, Sache Nadella, >> thank you so much. [applause]