WEBVTT 00:00:00.800 --> 00:00:03.677 Especially in technology business, we 00:00:03.760 --> 00:00:06.237 collectively as an industry have to just 00:00:06.320 --> 00:00:08.717 mature and mature at a very fast rate 00:00:08.800 --> 00:00:10.797 because the impact of digital technology 00:00:10.880 --> 00:00:14.317 in our lives and in our society is so 00:00:14.400 --> 00:00:16.877 deep. Now the first thing that we have 00:00:16.960 --> 00:00:19.757 been doing is building the system all 00:00:19.840 --> 00:00:22.957 the way from power to the DC to the rack 00:00:23.040 --> 00:00:29.240 to the accelerators to the network. You have to create magic. 00:00:35.600 --> 00:00:38.477 Welcome to the stage Satia Nadella. 00:00:38.560 --> 00:00:41.597 >> Hey Satya, thank you so much for doing this. I know last minute schedule 00:00:41.680 --> 00:00:44.077 changes. You can be here in person. 00:00:44.160 --> 00:00:45.837 >> No, not at all. It's my pleasure Anel. 00:00:45.920 --> 00:00:50.317 It's great to be with you. >> Yeah. Right before this, we uh had an 00:00:50.400 --> 00:00:54.237 opportunity to hear from Bob Mechaf about buildouts throughout the 80s and 00:00:54.320 --> 00:00:56.637 90s. So you've got a tall order. I know 00:00:56.720 --> 00:00:59.837 you admire Bob as much as I do. 00:00:59.920 --> 00:01:03.997 U so so may maybe one place to start is 00:01:04.080 --> 00:01:07.597 you know there's so much buildout happening now. What do you think will 00:01:07.680 --> 00:01:10.957 make or break the next decade of this infrastructure buildout that's happening 00:01:11.040 --> 00:01:13.277 now? 00:01:13.360 --> 00:01:17.197 >> Yeah. I mean the way I think um analis 00:01:17.280 --> 00:01:20.077 each of these buildouts um of 00:01:20.160 --> 00:01:22.397 infrastructure always comes down to two 00:01:22.480 --> 00:01:25.997 sets of things right. how efficient is 00:01:26.080 --> 00:01:28.397 the buildout for what is the new 00:01:28.480 --> 00:01:33.117 platform uh and then what's the way it gets to 00:01:33.200 --> 00:01:36.557 ubiquity in some sense it's the app server right I've always thought about 00:01:36.640 --> 00:01:39.917 at least from a a platform perspective 00:01:40.000 --> 00:01:41.997 what's the core primitives of the 00:01:42.080 --> 00:01:46.957 platform and then how does the platform have a developer interface so that 00:01:47.040 --> 00:01:50.157 developers can use it right I mean at the end of the day you could say that's 00:01:50.240 --> 00:01:54.157 what operating systems are right so the operating system book at least I read in 00:01:54.240 --> 00:01:57.917 school was that tenant bomb thing which is you abstract the hardware and expose 00:01:58.000 --> 00:02:01.997 an app model and that's a good way to I think think about the infrastructure so 00:02:02.080 --> 00:02:05.757 in that context the first thing is how do you make things efficient this time 00:02:05.840 --> 00:02:07.757 around it's that tokens per dollar per 00:02:07.840 --> 00:02:11.197 watt right so the token factory 00:02:11.280 --> 00:02:13.197 um has to be super efficient so there's 00:02:13.280 --> 00:02:15.197 a lot of work that goes on I think is 00:02:15.280 --> 00:02:18.557 going on is at the systems level uh all 00:02:18.640 --> 00:02:22.077 the way to power uh right uh and so 00:02:22.160 --> 00:02:26.477 that's one side of it. Then on the app server side, it's more like an agent 00:02:26.560 --> 00:02:30.397 factory. So if you have a token factory, so think of it just as throughput's got 00:02:30.480 --> 00:02:33.437 to be most efficient. Uh but then you 00:02:33.520 --> 00:02:36.637 got to harness that to build very 00:02:36.720 --> 00:02:40.477 efficient effective agents and that is 00:02:40.560 --> 00:02:42.557 an entire different ball of wax, right? 00:02:42.640 --> 00:02:45.117 which is you got to have a set of 00:02:45.200 --> 00:02:47.597 application services so that any agent 00:02:47.680 --> 00:02:51.117 that you're building uh delivers on some 00:02:51.200 --> 00:02:53.917 eval metric that matters in the world 00:02:54.000 --> 00:02:56.317 and so those are the two at least I 00:02:56.400 --> 00:02:59.037 think as the infrastructure pieces. 00:02:59.120 --> 00:03:02.557 >> Yeah, I think you have such a engineer point of view of this and I I remember 00:03:02.640 --> 00:03:04.797 you mentioning the sort of complete 00:03:04.880 --> 00:03:06.877 thought from silicon to operating 00:03:06.960 --> 00:03:09.837 systems to applications. um how do you 00:03:09.920 --> 00:03:11.997 think that cascades down to the core 00:03:12.080 --> 00:03:14.397 part of how networks are designed in all 00:03:14.480 --> 00:03:19.277 this buildout and across the WAN and the edge and how do you think about that? 00:03:19.360 --> 00:03:22.637 >> Yeah, I mean it's it's fascinating. I mean obviously what you all are doing uh 00:03:22.720 --> 00:03:26.557 in fact the first time I remember you and I chatted when you said you're going 00:03:26.640 --> 00:03:30.717 to vertically integrate build this network as a service platform. It's 00:03:30.800 --> 00:03:34.477 pretty impressive to sort of say that's I think a little bit of what we are also 00:03:34.560 --> 00:03:37.997 doing right when people say wow there's a lot of capex being spent. I mean the 00:03:38.080 --> 00:03:42.717 reality is the data center is the computer is becoming more real today 00:03:42.800 --> 00:03:44.877 right I mean that's kind of where you 00:03:44.960 --> 00:03:47.677 want to really think about uh uh the 00:03:47.760 --> 00:03:52.237 complete thought is how do you really go deliver on that tokens per dollar per 00:03:52.320 --> 00:03:54.637 watt uh but just take one of the new 00:03:54.720 --> 00:03:57.757 data centers we just brought live uh 00:03:57.840 --> 00:04:00.077 last week or a week before uh called 00:04:00.160 --> 00:04:03.197 fairwater uh I mean interesting enough 00:04:03.280 --> 00:04:07.357 we built it as a double-decka uh data center this is the first time we did 00:04:07.440 --> 00:04:09.597 that uh and we did that because to in 00:04:09.680 --> 00:04:11.757 fact for the network which is can you 00:04:11.840 --> 00:04:14.877 have the 5 million network connections 00:04:14.960 --> 00:04:17.037 uh that are there in that particular 00:04:17.120 --> 00:04:20.637 data center optimized for cable length 00:04:20.720 --> 00:04:22.637 uh right because we wanted the most 00:04:22.720 --> 00:04:25.677 dense GPU environment uh and so in order 00:04:25.760 --> 00:04:27.837 to do that uh you kind of had to even 00:04:27.920 --> 00:04:30.717 think rethink the construction uh if you 00:04:30.800 --> 00:04:33.517 will um and so to me and then not only 00:04:33.600 --> 00:04:37.757 did we do that in one data center but we also built an AI van And I was sort of 00:04:37.840 --> 00:04:41.917 very pleased to see you all working with Lumen and building your own sort of 00:04:42.000 --> 00:04:45.037 connection between what you're doing and what they're doing. In fact, that's kind 00:04:45.120 --> 00:04:49.517 of a similar thing we did. We now have uh we have a Wisconsin Fairwater and 00:04:49.600 --> 00:04:51.997 Atlanta Fairwater and both of them are 00:04:52.080 --> 00:04:54.157 sort oforked through this AI van. So you 00:04:54.240 --> 00:04:56.877 can now run distributed training across 00:04:56.960 --> 00:05:01.437 these. So to me that's I think what you kind of take the workload. I worked for 00:05:01.520 --> 00:05:05.757 this gentleman who at Microsoft uh called Paul Merittz long time ago. He 00:05:05.840 --> 00:05:09.597 used to always teach us as you know systems folks growing up saying look at 00:05:09.680 --> 00:05:13.277 the end of the day uh you really want to fall in love with sort of the what the 00:05:13.360 --> 00:05:17.357 new workload is and then design infrastructure uh and application 00:05:17.440 --> 00:05:20.477 servers. So that's what we're doing. You look at these new data parallel 00:05:20.560 --> 00:05:24.157 workloads which kind of are unlike anything we've seen before a training 00:05:24.240 --> 00:05:26.637 inference data gen data pipelines for 00:05:26.720 --> 00:05:29.357 all these evalu rubrics what have you 00:05:29.440 --> 00:05:32.397 you go build for that and that's where I 00:05:32.480 --> 00:05:34.157 think the infrastructure is being 00:05:34.240 --> 00:05:39.437 rethought end to end yeah is here somewhere in the audience but I I 00:05:39.520 --> 00:05:41.037 watched the interview you did with him 00:05:41.120 --> 00:05:45.037 and the scale of the buildout um how do 00:05:45.120 --> 00:05:49.117 you feel as an engineer like these are masscale projects X how do you think 00:05:49.200 --> 00:05:53.997 about this and you know as an engineer I know like you understand these systems 00:05:54.080 --> 00:05:57.917 are you in awe just as much as the rest of us are to look at the scale of this 00:05:58.000 --> 00:06:00.317 >> yeah I mean I must say we're in awe of 00:06:00.400 --> 00:06:04.397 it but I'm also quite frankly humbled by 00:06:04.480 --> 00:06:09.837 one fact Anel which is uh the amount of 00:06:09.920 --> 00:06:11.757 expertise that is required to pull 00:06:11.840 --> 00:06:15.197 something like this off right um if you 00:06:15.280 --> 00:06:19.277 think about the electrical engineers the mechanical engineers, the construction 00:06:19.360 --> 00:06:23.837 civil engineers, the obviously the computer engineers, all of them coming 00:06:23.920 --> 00:06:27.757 together, composing on a project timeline, making something like this 00:06:27.840 --> 00:06:30.717 happen, delivering on an SLA. Uh so to 00:06:30.800 --> 00:06:34.317 some degree uh it is what I'm in awe of 00:06:34.400 --> 00:06:37.997 and I I also feel um that this is one of 00:06:38.080 --> 00:06:40.477 those places where we've got to get it 00:06:40.560 --> 00:06:43.757 right that not only do we have to build 00:06:43.840 --> 00:06:46.157 stuff that works and works super 00:06:46.240 --> 00:06:50.717 efficiently. Uh but it's also to do what 00:06:50.800 --> 00:06:52.797 uh I think that's the main thing as 00:06:52.880 --> 00:06:55.677 engineers. uh because for the first time 00:06:55.760 --> 00:06:58.477 as I mean when I say uh tokens per 00:06:58.560 --> 00:07:00.957 dollar per watt if you're really going 00:07:01.040 --> 00:07:03.917 to consume a lot of energy uh one thing 00:07:04.000 --> 00:07:08.397 that I think we all in this era have to be more grounded on is the social 00:07:08.480 --> 00:07:12.397 permission uh to go take that type of energy 00:07:12.480 --> 00:07:17.037 convert it into tokens is not going to be about celebrating like some eal that 00:07:17.120 --> 00:07:19.277 you and I can get hyped up about. It has 00:07:19.360 --> 00:07:24.557 to be something that the world actually benefits from. you guys are building out 00:07:24.640 --> 00:07:28.477 the entire stack like you mentioned which is amazing to watch. How do you 00:07:28.560 --> 00:07:32.797 think you maintain that while also thinking about reliability and trust 00:07:32.880 --> 00:07:36.237 because so many companies depend on what you guys are doing and how much you're 00:07:36.320 --> 00:07:40.317 building up? >> I mean that's a you know sort of when I 00:07:40.400 --> 00:07:42.477 think about my you know priorities for 00:07:42.560 --> 00:07:47.437 us quite frankly there are three uh at the company. One is security 00:07:47.520 --> 00:07:51.757 uh because the cyber landscape just becomes even more complex every day. uh 00:07:51.840 --> 00:07:56.157 and then quality and reliability and if you do a good job on those two you get 00:07:56.240 --> 00:08:00.637 sort of permission to even think about AI that's kind of I'm pretty clear that 00:08:00.720 --> 00:08:04.717 that's the sequence the order of operations even for us because uh you 00:08:04.800 --> 00:08:07.357 know if I look at uh Azure today in the 00:08:07.440 --> 00:08:11.997 60 plus regions around the world uh people count on it uh as critical 00:08:12.080 --> 00:08:15.677 infrastructure and so therefore we don't take it for granted and so you have to 00:08:15.760 --> 00:08:20.717 think about uh operating it uh with those qualities and the fundamentals and 00:08:20.800 --> 00:08:22.477 that I think is the key right which is 00:08:22.560 --> 00:08:25.277 these are um at scale nothing is a 00:08:25.360 --> 00:08:27.837 commodity it's sort of very complex and 00:08:27.920 --> 00:08:30.477 so therefore what and in fact if if you 00:08:30.560 --> 00:08:33.837 think about it the primitives have to be 00:08:33.920 --> 00:08:36.157 rethought right for the next 10x 00:08:36.240 --> 00:08:39.277 everything right um uh and so what you 00:08:39.360 --> 00:08:41.917 built for like we added more data center 00:08:42.000 --> 00:08:45.517 capacity in the last 18 months uh than 00:08:45.600 --> 00:08:49.997 perhaps in the first 10 years of Azure right so if you sort of say that's the 00:08:50.080 --> 00:08:53.517 scale uh then some of the core primitives uh 00:08:53.600 --> 00:08:58.157 have to be rebuilt. Uh but then on top of it, there's one other thing that you 00:08:58.240 --> 00:09:02.237 brought up which I think is super important which is operating around the 00:09:02.320 --> 00:09:04.957 world requires us to respect what I 00:09:05.040 --> 00:09:07.597 think of as the legitimate sovereignty 00:09:07.680 --> 00:09:11.997 needs uh people have right because in Europe they have their own data 00:09:12.080 --> 00:09:15.597 protection laws. So therefore, for example, one of the hardest projects we 00:09:15.680 --> 00:09:18.477 did over the last five years is the EU 00:09:18.560 --> 00:09:21.117 data boundary. uh right which is how do 00:09:21.200 --> 00:09:25.837 you make sure that all the data that uh is European remains in Europe right that 00:09:25.920 --> 00:09:29.677 so that's the data plane now we have a control plane that goes with it that is 00:09:29.760 --> 00:09:32.477 resident um and so we've kind of 00:09:32.560 --> 00:09:35.517 continuously built for what I think is a 00:09:35.600 --> 00:09:38.957 distributed infrastructure but with real 00:09:39.040 --> 00:09:41.917 sovereignty boundaries uh and now with 00:09:42.000 --> 00:09:44.637 AI we're even getting to an another new 00:09:44.720 --> 00:09:49.277 sovereignty boundary which is at the end of the day every firm wants to know that 00:09:49.360 --> 00:09:53.277 their data and their unique understanding of data is being parlayed 00:09:53.360 --> 00:09:55.837 into their competitive differentiation 00:09:55.920 --> 00:09:58.157 versus just fodder for a model company. 00:09:58.240 --> 00:10:02.557 And so that I think is one of the more interesting sovereignty debates right 00:10:02.640 --> 00:10:04.477 what's the future of a firm right if the 00:10:04.560 --> 00:10:06.797 firm is all about knowledge you have 00:10:06.880 --> 00:10:10.557 which is tacet inside uh but if a model 00:10:10.640 --> 00:10:13.197 you know can see like you know 200 great 00:10:13.280 --> 00:10:17.917 samples of data and pick it up then that's a problem so therefore uh you 00:10:18.000 --> 00:10:22.557 really need to think about even sovereignty uh at the firm level very 00:10:22.640 --> 00:10:25.357 differently >> I remember you saying like the hardest 00:10:25.440 --> 00:10:29.917 part is not the models but changing how people think and work with the stuff. 00:10:30.000 --> 00:10:33.917 What have you guys seen work best? What are you seeing from your customers and 00:10:34.000 --> 00:10:36.957 how does that work? Because you know, everybody has a different perception of 00:10:37.040 --> 00:10:40.237 how well these things work, how they don't or whether they're creative or 00:10:40.320 --> 00:10:44.557 not. How what have you seen in that change of in how people think and work 00:10:44.640 --> 00:10:46.557 with these models? 00:10:46.640 --> 00:10:50.157 >> Yeah. I mean I think the 00:10:50.240 --> 00:10:54.237 the first thing that is interesting uh that I see when somebody you know 00:10:54.320 --> 00:10:58.557 besides sort of let's say the hit app right whether it is chat GPT or copilot 00:10:58.640 --> 00:11:00.717 that you can deploy use and I mean you 00:11:00.800 --> 00:11:03.757 know if you go get beyond any firstparty 00:11:03.840 --> 00:11:08.797 stuff that we're doing right even GitHub copilot or what have you uh for an 00:11:08.880 --> 00:11:10.317 enterprise 00:11:10.400 --> 00:11:12.877 they need to start with I think a little 00:11:12.960 --> 00:11:16.637 bit of a change in mindset which is um 00:11:16.720 --> 00:11:19.917 we built all the workflows and uh at 00:11:20.000 --> 00:11:25.117 work uh based on a particular regime of digital infrastructure. I mean the 00:11:25.200 --> 00:11:28.717 reality is nothing much has changed since the be you know when I started in 00:11:28.800 --> 00:11:31.597 computing in terms of the workflow right 00:11:31.680 --> 00:11:35.757 I mean we had PCs uh and then talking to 00:11:35.840 --> 00:11:40.397 minis and then PCs started talking to servers and it became client servers 00:11:40.480 --> 00:11:44.557 right that's sort of what uh was the last time people said oh we'll have 00:11:44.640 --> 00:11:46.877 standard issues of office productivity 00:11:46.960 --> 00:11:49.437 we will have email and it'll replace fax 00:11:49.520 --> 00:11:51.917 machines and inter office memos and we 00:11:52.000 --> 00:11:54.557 will now be able to do a forecast in 00:11:54.640 --> 00:11:57.197 Excel, put in an email, send it around, 00:11:57.280 --> 00:11:59.437 people can enter numbers and you know, 00:11:59.520 --> 00:12:02.237 so there it went and oh, we'll buy an 00:12:02.320 --> 00:12:06.477 ERP system, a CRM system. We'll have a system of record. So, we kind of build a 00:12:06.560 --> 00:12:08.237 pretty sophisticated, I'll call a 00:12:08.320 --> 00:12:12.077 digital information back plane and now 00:12:12.160 --> 00:12:15.837 it's time to relitigate the entirety of it, right? because you kind of say, 00:12:15.920 --> 00:12:17.837 okay, this is how work happens today, 00:12:17.920 --> 00:12:21.517 but I need a mindset shift on if a mo a 00:12:21.600 --> 00:12:23.837 I have multiple agents 00:12:23.920 --> 00:12:26.317 working alongside all of the human 00:12:26.400 --> 00:12:29.277 workflow with digital systems. Uh how do 00:12:29.360 --> 00:12:31.917 I change the process uh of something 00:12:32.000 --> 00:12:36.797 like even forecasting? Um and I think that that's the first one. The second 00:12:36.880 --> 00:12:40.637 thing is you need a new tool chain, right? So you kind of need uh just like 00:12:40.720 --> 00:12:42.957 how the tools of the past were or I had 00:12:43.040 --> 00:12:45.197 Word or I had Excel. what are the new 00:12:45.280 --> 00:12:48.397 tools to be able to build agents uh and 00:12:48.480 --> 00:12:52.797 you know deploy agents, monitor agents, steer agents in fact so there's a 00:12:52.880 --> 00:12:54.717 complete new tool chain uh that is 00:12:54.800 --> 00:12:57.917 required um then that that requires us 00:12:58.000 --> 00:13:02.077 to develop skills not like the elite sort of AI scientist skill like it's 00:13:02.160 --> 00:13:04.237 sort of yeah we may have taken a class 00:13:04.320 --> 00:13:07.197 or two of Excel macros uh but we all 00:13:07.280 --> 00:13:09.757 could sort of use Excel though so what's 00:13:09.840 --> 00:13:14.317 I think everybody's going to be building agents like they did a spreadsheet need 00:13:14.400 --> 00:13:18.557 uh and so that's the skill set that needs to be broadly diffused and then 00:13:18.640 --> 00:13:21.037 the last thing is data because if you 00:13:21.120 --> 00:13:25.677 think about it the most interesting thing that we do is to do this context 00:13:25.760 --> 00:13:29.837 engineering which is how how do I make sure that the data meets the 00:13:29.920 --> 00:13:33.597 intelligence that meets the process change I want to affect right it's not 00:13:33.680 --> 00:13:37.917 like data defined in the silos I have because that's kind of like you have a 00:13:38.000 --> 00:13:41.677 very powerful intelligence in a reasoning engine but it's only as good 00:13:41.760 --> 00:13:44.477 as the data you feed it uh in terms of 00:13:44.560 --> 00:13:46.637 what it should reason and its action 00:13:46.720 --> 00:13:49.437 space and so I think these four or five 00:13:49.520 --> 00:13:52.477 things have to fall in place and that's 00:13:52.560 --> 00:13:57.277 the hard change management uh that we have and it's sort of we have 00:13:57.360 --> 00:14:01.517 to compress what hap think about it right all through the 90s and the 2000s 00:14:01.600 --> 00:14:03.677 you could say even in the cloud like 00:14:03.760 --> 00:14:05.597 what did we do mostly we moved the same 00:14:05.680 --> 00:14:09.037 systems to the cloud so quite frankly we 00:14:09.120 --> 00:14:12.877 had a 30-year run which was pretty stable 00:14:12.960 --> 00:14:17.117 in terms of how we thought about work getting done and now we suddenly have to 00:14:17.200 --> 00:14:19.437 take that 30 years and maybe compress it 00:14:19.520 --> 00:14:21.277 you know in the next three four years 00:14:21.360 --> 00:14:24.157 five years into something that yields 00:14:24.240 --> 00:14:28.797 massive productivity gains. >> You all started working on Azure 10 15 00:14:28.880 --> 00:14:33.037 years ago and a lot of the fundamentals that were set that led to being able to 00:14:33.120 --> 00:14:37.197 build these large models. If you knew that this is how much buildout was going 00:14:37.280 --> 00:14:40.157 to happen, is there some things you would change on how you would do it 00:14:40.240 --> 00:14:44.397 differently? Like if you knew, you know, in 2025, this is the mass scale buildout 00:14:44.480 --> 00:14:47.277 that's going to happen to the level of, you know, national projects that hadn't 00:14:47.360 --> 00:14:51.037 been done before. Are there things you would do differently with that benefit 00:14:51.120 --> 00:14:53.117 of hindsight? 00:14:53.200 --> 00:14:54.637 >> Yeah, I mean the so the interesting 00:14:54.720 --> 00:14:58.477 thing is we did have, you know, 00:14:58.560 --> 00:15:00.397 basically the the cloud buildout was 00:15:00.480 --> 00:15:03.917 about making servers, right? because um 00:15:04.000 --> 00:15:08.397 much more you know it was much more elastic than any of us thought right I 00:15:08.480 --> 00:15:12.957 mean virtual machines uh there was insatiable demand for it right people 00:15:13.040 --> 00:15:17.517 wanted to just spin up a lot more compute uh one of the other things I had 00:15:17.600 --> 00:15:19.757 not realized quite frankly is the amount 00:15:19.840 --> 00:15:22.717 of I'll call it uh capital that was 00:15:22.800 --> 00:15:25.997 spent on say storage systems underneath 00:15:26.080 --> 00:15:31.037 virtual machines uh which suddenly now you could deploy a lot more efficiently 00:15:31.120 --> 00:15:34.557 and so that I think created And then of course the new workloads 00:15:34.640 --> 00:15:38.877 like the Hadoops and the you know the the data parallel workloads of that day 00:15:38.960 --> 00:15:41.277 more batch processing stuff that work 00:15:41.360 --> 00:15:44.397 has done at scale this time around I 00:15:44.480 --> 00:15:46.397 think it's kind of a new infrastructure 00:15:46.480 --> 00:15:49.517 right which is I feel this we've not 00:15:49.600 --> 00:15:52.477 seen anything as synchronous data 00:15:52.560 --> 00:15:56.877 parallel stuff like this and so therefore that's why I think even the 00:15:56.960 --> 00:15:58.557 shape of what we're building we've 00:15:58.640 --> 00:16:02.477 adjusted what we were doing to build for 00:16:02.560 --> 00:16:06.717 AI. But now when I look at even the way 00:16:06.800 --> 00:16:09.357 I think Azure will look five years from 00:16:09.440 --> 00:16:11.517 now, it'll look unlike anything we've 00:16:11.600 --> 00:16:14.797 did in the first 20 years of Azure. Um 00:16:14.880 --> 00:16:19.037 and so the you kind of reorient your 00:16:19.120 --> 00:16:24.157 core compute and storage and the network um or even by the way the memory 00:16:24.240 --> 00:16:27.517 interface. I think one of the more interesting things that may even happen 00:16:27.600 --> 00:16:30.557 is what's going to be uh the model 00:16:30.640 --> 00:16:33.117 architecture, the micro architecture in 00:16:33.200 --> 00:16:36.957 the AI accelerator and the memory. Uh 00:16:37.040 --> 00:16:39.437 that's the thing that perhaps is going 00:16:39.520 --> 00:16:43.197 to completely change. Um and so yes, so 00:16:43.280 --> 00:16:46.477 I think my short answer is we have to 00:16:46.560 --> 00:16:47.997 rethink 00:16:48.080 --> 00:16:51.037 uh the core footprint of what one 00:16:51.120 --> 00:16:54.717 considers a cloud. um knowing at one end 00:16:54.800 --> 00:16:58.237 I have to run an SAP workload at 59s uh 00:16:58.320 --> 00:17:00.717 and on the other end I have to run a 00:17:00.800 --> 00:17:03.277 highly efficient 00:17:03.360 --> 00:17:06.397 uh data parallel training job u right 00:17:06.480 --> 00:17:10.877 which gets has the maximum token throughput and so balancing that out in 00:17:10.960 --> 00:17:14.797 the right way I think is going to be one of the fundamental challenges for us 00:17:14.880 --> 00:17:17.837 >> in the last few buildouts one of the interesting things that happened is 00:17:17.920 --> 00:17:20.157 after this buildout starts maybe in the 00:17:20.240 --> 00:17:23.437 80s then in the 90s um things start to 00:17:23.520 --> 00:17:27.357 standardize. Today, if you go look at data centers and other places, every 00:17:27.440 --> 00:17:32.157 company is doing interesting things that they hold true themselves, but others 00:17:32.240 --> 00:17:35.517 might be doing different things than them. When do you think it'll be more 00:17:35.600 --> 00:17:39.517 standard on how to build this type of infrastructure where we have to 00:17:39.600 --> 00:17:43.117 standardize and then maybe follow up to that is what do you think should be 00:17:43.200 --> 00:17:45.197 standardized or is it the way we're 00:17:45.280 --> 00:17:47.117 doing it now where everybody does their 00:17:47.200 --> 00:17:50.237 own proprietary thing is the way to go? 00:17:50.320 --> 00:17:52.637 Yeah, I mean it it's it's a good point 00:17:52.720 --> 00:17:55.117 because I think the specialization will 00:17:55.200 --> 00:17:58.157 come as the supply chain and the value 00:17:58.240 --> 00:18:02.877 chain here sort of really achieves scale right I mean if I look at it um it's 00:18:02.960 --> 00:18:07.117 sort you know what I mean if you take the the the classic argument right which 00:18:07.200 --> 00:18:09.197 is if you have scale you vertically 00:18:09.280 --> 00:18:12.717 integrate it can be very t you know very 00:18:12.800 --> 00:18:16.477 efficient very tight to sort of the idiosyncrasies of sort of how you think 00:18:16.560 --> 00:18:20.477 about whether it's your model or your application scaffolding and its 00:18:20.560 --> 00:18:24.797 requirements. Uh but for us as a platform provider, I mean like at this 00:18:24.880 --> 00:18:27.357 point we have to make sure that any new 00:18:27.440 --> 00:18:30.237 thing that gets written runs on Azure. 00:18:30.320 --> 00:18:34.797 Um and so therefore uh even at the silicon accelerator level we have to be 00:18:34.880 --> 00:18:38.237 heterogeneous, right? I mean at the end of the day the workloads are going to be 00:18:38.320 --> 00:18:40.157 uh heterogeneous. This the system 00:18:40.240 --> 00:18:43.037 architecture underneath those has to 00:18:43.120 --> 00:18:47.277 support right whether it's Nvidia, AMD and our own accelerators or what have 00:18:47.360 --> 00:18:49.677 you. uh and so I think that that's how 00:18:49.760 --> 00:18:51.277 we are going to have to standardize 00:18:51.360 --> 00:18:54.477 which is to basically build out uh what 00:18:54.560 --> 00:18:57.757 is a way to meet the real world needs um 00:18:57.840 --> 00:19:00.557 which by definition is going to be 00:19:00.640 --> 00:19:02.717 heterogeneous and therefore what's the 00:19:02.800 --> 00:19:05.277 set of standards that emerge I think the 00:19:05.360 --> 00:19:08.557 first place is networking uh if I look 00:19:08.640 --> 00:19:13.597 at the scale up and scale out um network 00:19:13.680 --> 00:19:16.477 for these new workloads 00:19:16.560 --> 00:19:19.677 uh all coming together may be the place 00:19:19.760 --> 00:19:21.197 right because I think that you know the 00:19:21.280 --> 00:19:24.157 model has still and the model evolution 00:19:24.240 --> 00:19:27.437 has still a lot more um influence on 00:19:27.520 --> 00:19:31.597 I'll call it the micro architecture of the one chiplet in the AI you know 00:19:31.680 --> 00:19:34.157 accelerator for sure uh and its 00:19:34.240 --> 00:19:35.997 connection to memory so some of that 00:19:36.080 --> 00:19:39.757 stuff may still be more fastm moving but 00:19:39.840 --> 00:19:42.077 everything else quite frankly you can 00:19:42.160 --> 00:19:45.757 standardize um u and so that I think is 00:19:45.840 --> 00:19:50.317 where some of the exciting uh work by the way and it has to go all 00:19:50.400 --> 00:19:53.757 the way to the edge as well and I think that this is an obviously a massive 00:19:53.840 --> 00:19:58.157 thing for you all to participate in and we're excited about sort of being able 00:19:58.240 --> 00:19:59.677 to sort of say at the end of the day 00:19:59.760 --> 00:20:01.597 what's this managed service around 00:20:01.680 --> 00:20:05.437 networking uh that spans uh the entirety 00:20:05.520 --> 00:20:09.917 of the distributed computing fabric and that requires uh what you are doing as 00:20:10.000 --> 00:20:14.957 well >> and there's so many people here in the audience that are CIOS CTO's that are 00:20:15.040 --> 00:20:18.717 figuring out how to think about all these new tools that are coming out. 00:20:18.800 --> 00:20:23.357 What where have you seen the biggest ROI in Microsoft? Um because you guys are 00:20:23.440 --> 00:20:26.797 obviously at the forefront. You have access to everything. How have those 00:20:26.880 --> 00:20:29.517 workflows changed the most? Yeah, I mean 00:20:29.600 --> 00:20:31.437 I I mean one of the fascinating things 00:20:31.520 --> 00:20:34.877 is the best news now for me Anil is when 00:20:34.960 --> 00:20:39.757 I start you know hearing of the ground up use of the tools right whether it's 00:20:39.840 --> 00:20:43.837 copilot whether it's foundry whether it's GitHub copilot people using them 00:20:43.920 --> 00:20:48.557 right so this is not uh you know some top- down mandate of hey because like 00:20:48.640 --> 00:20:52.237 the obvious use cases I think we're all well on our way right to customer 00:20:52.320 --> 00:20:54.877 service or what have you but the thing 00:20:54.960 --> 00:20:57.277 for example on networking I had myself 00:20:57.360 --> 00:20:59.357 like lost track of the number of for 00:20:59.440 --> 00:21:02.877 example fiber operators uh that power uh 00:21:02.960 --> 00:21:05.997 Azure like there are 500 plus uh 00:21:06.080 --> 00:21:10.477 operators that we have contracts with all over the world uh on our fiber 00:21:10.560 --> 00:21:12.397 operations and I mean at the end of the 00:21:12.480 --> 00:21:14.637 day it's a physical asset things happen 00:21:14.720 --> 00:21:17.677 there are fiber cuts there's issues and 00:21:17.760 --> 00:21:21.037 it's fascinating the devops pipeline 00:21:21.120 --> 00:21:25.517 that is there I mean I mean sometimes oh devops seems sophisticated it's not like 00:21:25.600 --> 00:21:27.677 GitHub actions I mean the thing is 00:21:27.760 --> 00:21:31.997 literally emails going on and and so one 00:21:32.080 --> 00:21:34.157 of our engineers what she did was she 00:21:34.240 --> 00:21:37.037 took Foundry built herself a bunch of 00:21:37.120 --> 00:21:41.357 agents. She's given them all names uh because she she had to there was no 00:21:41.440 --> 00:21:43.357 headcount. There was no way even if she 00:21:43.440 --> 00:21:45.917 had headcount she could grow a team to 00:21:46.000 --> 00:21:48.637 manage what was becoming the most manual 00:21:48.720 --> 00:21:51.037 of processes. And so she fundamentally 00:21:51.120 --> 00:21:54.717 identified the entire process of dealing 00:21:54.800 --> 00:21:58.157 with triages around fiber operations and 00:21:58.240 --> 00:22:01.117 maintenance and cuts and what have you. 00:22:01.200 --> 00:22:05.277 And to me that was like a great firsthand example. Same thing you know 00:22:05.360 --> 00:22:09.677 to your point about supply chain and the data you know these massive buildouts of 00:22:09.760 --> 00:22:11.837 our data center. You should see like the 00:22:11.920 --> 00:22:14.637 I thought man maybe somebody in uh you 00:22:14.720 --> 00:22:19.117 know in finance or in legal will figure out how to do contracts. the people who 00:22:19.200 --> 00:22:21.517 needed to really figure out the agentic 00:22:21.600 --> 00:22:26.397 way to go get contracts done uh because I did one data center contract here with 00:22:26.480 --> 00:22:30.317 this one supplier and replicated everywhere and so on. That's a place 00:22:30.400 --> 00:22:35.117 where completely the supply chain operations was completely inverted. I 00:22:35.200 --> 00:22:38.877 mean if you think about supply chain the basic way I I've learned is at the end 00:22:38.960 --> 00:22:42.317 of the quarter you go to your CFO you get beaten by the CFO for all the 00:22:42.400 --> 00:22:45.197 mistakes you made. Uh and the idea now 00:22:45.280 --> 00:22:47.517 is to actually have these agents help 00:22:47.600 --> 00:22:49.677 with the daily beating so to speak and 00:22:49.760 --> 00:22:55.277 so that is uh the fundamental way you can completely transform a business process. 00:22:55.360 --> 00:22:57.837 >> Amazing. Thank you so much for your time Sati. I really appreciate you doing 00:22:57.920 --> 00:23:03.960 this. >> My pleasure Anel. Thank you so much.