WEBVTT 00:00:01.200 --> 00:00:05.837 has generated $250 [music] billion with a B in market value for Microsoft. Scott 00:00:05.920 --> 00:00:07.597 Nadella, chairman and CEO of Microsoft. 00:00:07.680 --> 00:00:08.957 >> Since you've been the CEO, three and a 00:00:09.040 --> 00:00:11.037 half years, the [music] stock is up 00:00:11.120 --> 00:00:14.717 about uh I guess it's about 120%. I'm 00:00:14.800 --> 00:00:18.917 good for my 80 billion. I am going to spend $80 billion building out Azure. 00:00:19.000 --> 00:00:22.317 [music] Maybe after the industrial revolution, this is the biggest thing. 00:00:22.400 --> 00:00:24.397 That's our goal with our frontier model. 00:00:24.480 --> 00:00:26.317 Our model should be the [music] best 00:00:26.400 --> 00:00:31.437 model that they can use as a base. We create technology so that others can 00:00:31.520 --> 00:00:33.277 create more technology. That's who we 00:00:33.360 --> 00:00:35.197 are. [music] We're tool maker. 00:00:35.280 --> 00:00:38.957 >> Please welcome Satia Nadella. 00:00:39.040 --> 00:00:42.120 >> All right. 00:00:43.360 --> 00:00:48.237 >> Hi guy. Good to see you coming out. 00:00:48.320 --> 00:00:52.717 >> Good to see you. >> Good morning guys. 00:00:52.800 --> 00:00:55.837 >> How are you? >> Good. >> Thanks for joining us. 00:00:55.920 --> 00:01:00.177 >> Crazy weekend, but here we are. Do we need to paste the frontier? 00:01:00.260 --> 00:01:02.280 [laughter] 00:01:03.120 --> 00:01:06.077 >> So, let's start with the common sense 00:01:06.160 --> 00:01:10.397 part first, which is we should do what 00:01:10.480 --> 00:01:13.757 it takes to build stuff that serves 00:01:13.840 --> 00:01:17.437 humanity first and is in human control. 00:01:17.520 --> 00:01:20.317 You know, it's kind of crazy that we have to start with that level of common 00:01:20.400 --> 00:01:22.957 sense, but I think it's a good place. 00:01:23.040 --> 00:01:26.557 Then when I think about pacing whatever 00:01:26.640 --> 00:01:29.597 the first thing that at least I believe 00:01:29.680 --> 00:01:32.077 is the broad diffusion of this 00:01:32.160 --> 00:01:34.717 technology is the most critical thing 00:01:34.800 --> 00:01:37.677 because the benefits of this tech 00:01:37.760 --> 00:01:41.197 showing up everywhere is really what's 00:01:41.280 --> 00:01:45.037 all about right so at the end of the day if you sort of say serving humanity let 00:01:45.120 --> 00:01:47.677 it actually reach humanity in ways that 00:01:47.760 --> 00:01:52.237 it serves humanity and that means you got to have choice you have to have 00:01:52.320 --> 00:01:55.757 compet competition. You have to have all kinds of business models whether they're 00:01:55.840 --> 00:01:57.357 open weights, close weights, what have 00:01:57.440 --> 00:02:00.957 you. Then the other aspect I think that 00:02:01.040 --> 00:02:03.197 is not talked about when we talk about 00:02:03.280 --> 00:02:06.797 control is actually the control that for 00:02:06.880 --> 00:02:09.917 example customers have, enterprises or 00:02:10.000 --> 00:02:12.397 businesses have around this technology 00:02:12.480 --> 00:02:14.637 because sometimes this is so opaque, 00:02:14.720 --> 00:02:17.437 right? I want my privacy. I want to be 00:02:17.520 --> 00:02:19.677 able to embed my knowledge in a set of 00:02:19.760 --> 00:02:22.797 weights I control. I want to see all of 00:02:22.880 --> 00:02:25.837 the coot uh that's being generated. I 00:02:25.920 --> 00:02:30.717 want to use it to do fine-tuning of my own models. My IP shouldn't leak. So, 00:02:30.800 --> 00:02:35.197 there's an entire body of things that nobody's talking about as much, which is 00:02:35.280 --> 00:02:37.437 my I really want to make sure that this 00:02:37.520 --> 00:02:41.677 tech is in my control. Then we get to uh 00:02:41.760 --> 00:02:45.837 what is I think a real issue of safety 00:02:45.920 --> 00:02:49.037 and we should take it seriously which is 00:02:49.120 --> 00:02:51.837 we should take all the time we want uh 00:02:51.920 --> 00:02:56.957 to test things. In fact I love this idea of having third party testers. Oh wow. 00:02:57.040 --> 00:03:00.957 You know I you know I grew up in a company that's always done testing. Uh 00:03:01.040 --> 00:03:05.037 so it's novel that we should say wow they're having embedded third party 00:03:05.120 --> 00:03:07.917 testers. Why not? It's a great idea. In 00:03:08.000 --> 00:03:09.677 fact, the only thing I would say is we 00:03:09.760 --> 00:03:12.317 should avoid like these, you know, cozy 00:03:12.400 --> 00:03:16.717 arrangements of who's testing what, who has access to what, and it should be 00:03:16.800 --> 00:03:20.077 broad. >> Were you were you surprised though when 00:03:20.160 --> 00:03:23.037 both the essay landed and then it seemed 00:03:23.120 --> 00:03:25.437 like there was a circling of the wagons 00:03:25.520 --> 00:03:27.117 amongst the frontier companies? I 00:03:27.200 --> 00:03:30.717 >> I I think that it comes my my suspicion 00:03:30.800 --> 00:03:35.597 is it comes genuinely from this place where when you start seeing in fact it's 00:03:35.680 --> 00:03:40.317 fascinating, right? We are when you start seeing reward hacking um and 00:03:40.400 --> 00:03:42.237 what's happening in these environments 00:03:42.320 --> 00:03:45.277 right with these agent swarms there is 00:03:45.360 --> 00:03:50.157 the mundane there is some DevOps error where somebody misconfigured 00:03:50.240 --> 00:03:52.957 a container [laughter] >> right right or these API keys 00:03:53.040 --> 00:03:57.277 >> or an API keys or yeah exactly there's no monitoring uh there's internet access 00:03:57.360 --> 00:03:59.437 there's sort of classic I would call it 00:03:59.520 --> 00:04:02.157 basic devops and then there is real 00:04:02.240 --> 00:04:04.717 novel new stuff right which is what is 00:04:04.800 --> 00:04:09.357 this uh reward board hacking uh that you know with these persistent agents and so 00:04:09.440 --> 00:04:13.517 on and that's a place where I'll admit that the science is not there it's I 00:04:13.600 --> 00:04:17.757 thought Yakob's post which is a good one which he said he called it we're growing 00:04:17.840 --> 00:04:19.837 intelligence not building intelligence 00:04:19.920 --> 00:04:22.797 so it's an experimental science and so 00:04:22.880 --> 00:04:27.757 the more experimental sciences uh then you really need to make sure you're 00:04:27.840 --> 00:04:32.077 doing those experiments in controlled environments if anything the place where 00:04:32.160 --> 00:04:34.077 I would love is taking even the hugging 00:04:34.160 --> 00:04:39.277 face incident in other places more transparency on what would it take in 00:04:39.360 --> 00:04:43.277 fact one of the fascinating things right now is the insider risk I mean think 00:04:43.360 --> 00:04:46.397 about it right if you're sitting in an enterprise this is all test time compute 00:04:46.480 --> 00:04:49.277 by the way right so it's not like oh it's going to only happen when in some 00:04:49.360 --> 00:04:52.237 training run it can happen for a very 00:04:52.320 --> 00:04:55.517 mundane task uh that I give one of these 00:04:55.600 --> 00:05:00.077 frontier models inside an enterprise uh where I say you know I don't I was you 00:05:00.160 --> 00:05:01.757 know telling David this suppose I say 00:05:01.840 --> 00:05:04.157 hey go optimize my working capital it 00:05:04.240 --> 00:05:08.877 may fake my books uh right [laughter] because this is like a new type of 00:05:08.960 --> 00:05:12.557 insider risk >> and so what is the way to do that I 00:05:12.640 --> 00:05:16.957 would say oh go build a maybe a causal model like a semantic model that 00:05:17.040 --> 00:05:18.877 actually checks and verifies so I think 00:05:18.960 --> 00:05:21.997 there's a lot of product building um I I 00:05:22.080 --> 00:05:26.797 would say making things more robust which is classic engineering that we 00:05:26.880 --> 00:05:30.317 should be talking a lot more about transparently versus saying hey this is 00:05:30.400 --> 00:05:33.757 so mystical that you know we can't figure this out. Do you do you buy this 00:05:33.840 --> 00:05:35.197 argument that it's mystical? 00:05:35.280 --> 00:05:38.797 >> I I mean I I buy the argument that we do 00:05:38.880 --> 00:05:43.757 not understand the latent space. Uh right other than I thought you know as 00:05:43.840 --> 00:05:47.597 you said like do we understand the brain? We don't. We do functional MRIs 00:05:47.680 --> 00:05:51.917 and do neuroscience and we're trying to figure this out continuously getting a 00:05:52.000 --> 00:05:54.077 little better understanding. So I do 00:05:54.160 --> 00:05:56.237 think that in that sense we don't 00:05:56.320 --> 00:05:59.037 exactly uh have a complete un that's why 00:05:59.120 --> 00:06:01.917 by the way I also I don't believe in new 00:06:02.000 --> 00:06:06.397 release right so that's why I think making sure that the coots are in 00:06:06.480 --> 00:06:11.357 language that we can all understand in fact they're transparent so that when 00:06:11.440 --> 00:06:15.277 when I go back to an enterprise that's using all these models and if you have 00:06:15.360 --> 00:06:18.637 the full coot uh then you can >> chain of thought 00:06:18.720 --> 00:06:22.157 >> chain of thought and so then you can really go look at it deeply in fact you 00:06:22.240 --> 00:06:26.477 can have multiple models uh and you can look at the coot across those I think 00:06:26.560 --> 00:06:28.797 these are all things that I think will become very important 00:06:28.880 --> 00:06:30.557 >> satia you've worked with you've worked 00:06:30.640 --> 00:06:33.437 with technologists for decades 00:06:33.520 --> 00:06:35.997 uh and when you see as a leader of one 00:06:36.080 --> 00:06:39.597 company Microsoft which has very crisp 00:06:39.680 --> 00:06:42.717 communications with the public uh and 00:06:42.800 --> 00:06:44.637 you see what's happening with Daario and 00:06:44.720 --> 00:06:47.277 his team people coming out saying 10% 00:06:47.360 --> 00:06:50.157 chance we all die uh what do you think 00:06:50.240 --> 00:06:55.117 is going through those technologists minds. Do you believe they actually 00:06:55.200 --> 00:06:58.797 believe that this is going to kill humanity or are they going through some 00:06:58.880 --> 00:07:03.117 psychosis or are they seeing something working on those frontier models that is 00:07:03.200 --> 00:07:05.997 terrorizing them? You're not a psychologist, but you have worked with 00:07:06.080 --> 00:07:09.997 technologists for a long time. Handicap what's going on in these organizations 00:07:10.080 --> 00:07:12.797 that's all the making people feel the 00:07:12.880 --> 00:07:15.437 need to resign and say we're all going 00:07:15.520 --> 00:07:19.997 to die. >> Yeah. you know, it's it's hard for me to 00:07:20.080 --> 00:07:21.917 speak to what's happening in any of 00:07:22.000 --> 00:07:25.037 these places, but let let's just say uh 00:07:25.120 --> 00:07:29.117 how we I grew up even inside of Microsoft, you know, for example, you 00:07:29.200 --> 00:07:32.957 know, one of the biggest things you learn as an early sort of engineering 00:07:33.040 --> 00:07:35.997 lead is how to deal with a showstopper bug. 00:07:36.080 --> 00:07:38.717 >> Yeah. >> Right. I mean, that's kind of like 101, 00:07:38.800 --> 00:07:40.397 right? Which is why you're faced, you're 00:07:40.480 --> 00:07:43.677 like, you know, you have a bug. Um what 00:07:43.760 --> 00:07:47.277 do you do? do you stop uh and fix or you 00:07:47.360 --> 00:07:50.397 defer or you go in and say hey this is 00:07:50.480 --> 00:07:52.317 such an edge case that's kind of the 00:07:52.400 --> 00:07:57.997 judgment so I do think and as the stakes go up you want to like transaction 00:07:58.080 --> 00:08:01.517 processing I remember working on databases right you know wow like you 00:08:01.600 --> 00:08:04.077 know you got to take very seriously any 00:08:04.160 --> 00:08:07.117 bug uh where if the transaction is going 00:08:07.200 --> 00:08:09.597 to get lost right data loss is a thing 00:08:09.680 --> 00:08:13.037 that you stop the thing for so I feel a 00:08:13.120 --> 00:08:15.197 little bit culture culturally in the AI 00:08:15.280 --> 00:08:17.517 industry rediscovering maybe because 00:08:17.600 --> 00:08:20.077 when you see and it's possible that they 00:08:20.160 --> 00:08:22.957 see stuff which are showstoppers before 00:08:23.040 --> 00:08:25.757 the rest and if you see a showstopper 00:08:25.840 --> 00:08:29.037 stop the show um right [laughter] to fix 00:08:29.120 --> 00:08:32.317 the bugs yeah when you saw the the 00:08:32.400 --> 00:08:35.437 hugging face run and it was super 00:08:35.520 --> 00:08:39.757 performative Dwaresh did his whole post civilizations 00:08:39.840 --> 00:08:44.317 what do you think what what's your take on that testing they ran because they 00:08:44.400 --> 00:08:48.477 could have run a test where they had 3,000 agents defend a bunch of websites. 00:08:48.560 --> 00:08:53.357 Instead, they instructed them to hack websites and you know the hiding of 00:08:53.440 --> 00:08:56.317 information all this anthropomorphicizing 00:08:56.400 --> 00:08:58.797 whatever of the agents. I mean the way 00:08:58.880 --> 00:09:00.637 at least I understand it was it was 00:09:00.720 --> 00:09:03.677 actually you know basically trying to uh 00:09:03.760 --> 00:09:08.717 do an eval uh for cyber gym and um as I 00:09:08.800 --> 00:09:12.157 understand it given that eval it sort of 00:09:12.240 --> 00:09:14.317 figured out a way to say let's just say 00:09:14.400 --> 00:09:17.117 reward hack uh and that's what led it to 00:09:17.200 --> 00:09:19.917 hugging phase in fact it speaks to I 00:09:20.000 --> 00:09:24.477 think what's the pre you know clear issue right now which is you can have 00:09:24.560 --> 00:09:28.317 these things if they're are longunning persistent agents 00:09:28.400 --> 00:09:30.557 become essentially like new insider 00:09:30.640 --> 00:09:35.677 risks. Uh and so that I would start from the very basics of saying okay what is 00:09:35.760 --> 00:09:38.877 containment look like. So for example like one of the things that I think is 00:09:38.960 --> 00:09:41.197 going to be really an issue and a thing 00:09:41.280 --> 00:09:44.557 that needs great solutions is true 00:09:44.640 --> 00:09:48.477 aggressive monitoring of agent activity. 00:09:48.560 --> 00:09:52.317 Uh that's behavioral >> evidence >> evidence and so everything has got to be 00:09:52.400 --> 00:09:56.237 auditable. uh and then every object it access, right? If it goes and gets a 00:09:56.320 --> 00:09:59.197 secret, oh, it's going to go chain a couple of things, you should be able to 00:09:59.280 --> 00:10:01.597 see it when it's starting to chain a 00:10:01.680 --> 00:10:03.917 couple of uh vulnerabilities uh to go 00:10:04.000 --> 00:10:06.637 hack. And so I think that these are the 00:10:06.720 --> 00:10:09.597 ways um that you really have to sort of 00:10:09.680 --> 00:10:12.317 deal with these situations versus saying 00:10:12.400 --> 00:10:15.597 um in in fact I think the core of my 00:10:15.680 --> 00:10:19.677 take is we will have to get the 00:10:19.760 --> 00:10:23.517 engineering process around building out 00:10:23.600 --> 00:10:27.117 this experimental science to be more robust. 00:10:27.200 --> 00:10:29.357 >> Yeah. >> Thanks. 00:10:29.440 --> 00:10:31.597 >> So I think I think that's a great point. 00:10:31.680 --> 00:10:36.157 I love how you uh differentiated in the HuggingFace uh episode between the 00:10:36.240 --> 00:10:39.997 mundane things they got wrong like the misconfigured sandbox and HuggingFace 00:10:40.080 --> 00:10:43.837 had credentials just sitting in a public repository and there was no monitoring 00:10:43.920 --> 00:10:47.437 and then you have the genuinely novel behavior, the swarms of agents, the 00:10:47.520 --> 00:10:51.357 reward hacking. That's the stuff that has everyone freaked out. I agree that, 00:10:51.440 --> 00:10:55.837 you know, we have to now figure out how to fix the bugs or, you know, fix the 00:10:55.920 --> 00:10:59.197 deeper problem that's coming from that reward hacking. What what do you think 00:10:59.280 --> 00:11:02.957 that means for and and and and I think to their credit I think what the 00:11:03.040 --> 00:11:05.677 Frontier Labs are saying is we are now 00:11:05.760 --> 00:11:08.877 going to slow down the pace of let's say 00:11:08.960 --> 00:11:13.037 raw power and shift towards reliability and predictability and you know what 00:11:13.120 --> 00:11:17.117 they call alignment which I think is good business practice I guess what do 00:11:17.200 --> 00:11:21.037 you think that means for what we see in terms of new products for the next year 00:11:21.120 --> 00:11:25.677 or two does it mean we just kind of improve what we already have or do we 00:11:25.760 --> 00:11:28.397 see new capabilities what do you think this going to mean A great question, 00:11:28.480 --> 00:11:30.877 David. I I do think there's already a 00:11:30.960 --> 00:11:33.197 massive model overhang, right? I mean, 00:11:33.280 --> 00:11:35.677 um capability overhang in the sense of 00:11:35.760 --> 00:11:39.277 the models are very good except the 00:11:39.360 --> 00:11:44.477 broad diffusion uh requires a lot of things, right? even requires uh 00:11:44.560 --> 00:11:48.477 essentially if you're compressing workflows and changing workflows to 00:11:48.560 --> 00:11:53.197 happen differently u the amount of change management that needs to happen 00:11:53.280 --> 00:11:55.037 in order to even incorporate these 00:11:55.120 --> 00:11:57.917 systems is sort of what's taking time so 00:11:58.000 --> 00:12:01.037 to some degree I would say the and also 00:12:01.120 --> 00:12:04.957 uh the the ability to create these new form factors right I mean if you think 00:12:05.040 --> 00:12:09.757 about coding agents and coding agents became really usable when you discovered 00:12:09.840 --> 00:12:13.997 that you could have an agent loop with a file system uh and that was the 00:12:14.080 --> 00:12:18.317 breakthrough that just made coding agents work. Um and I think now maybe 00:12:18.400 --> 00:12:20.797 with KUA right so which is with Astra 00:12:20.880 --> 00:12:23.757 with KUA uh could be a way for us to 00:12:23.840 --> 00:12:26.717 even do computer use or we just use long 00:12:26.800 --> 00:12:28.957 trajectory tasks that can get completely 00:12:29.040 --> 00:12:31.357 automated. So I think these type of 00:12:31.440 --> 00:12:34.397 product innovations where the model plus 00:12:34.480 --> 00:12:37.997 the harness allow us to do things that 00:12:38.080 --> 00:12:40.317 then lead to broad adoption. Right? I 00:12:40.400 --> 00:12:42.237 even go back to the chat GPT moment for 00:12:42.320 --> 00:12:45.677 me, right? Which was it was that RHF at 00:12:45.760 --> 00:12:49.917 the very end that made a chat conversation possible. Mhm. 00:12:50.000 --> 00:12:52.157 >> Uh and so I think that yes, so there's 00:12:52.240 --> 00:12:55.837 some science, there is some form factor 00:12:55.920 --> 00:13:00.717 that then leads to broad diffusion and we now need to find the next level of 00:13:00.800 --> 00:13:05.117 these things that are doing real work in the real enterprise. Um and in that 00:13:05.200 --> 00:13:09.117 context by the way the other thing is it's going to be a multimodel world 00:13:09.200 --> 00:13:12.237 right so at this point just out of resilience right I mean think about 00:13:12.320 --> 00:13:16.397 right every enterprise now comes to me and says hey this model does refusals 00:13:16.480 --> 00:13:20.637 here this model I want weights here I don't and so the people are going to 00:13:20.720 --> 00:13:23.117 want multiple models so one of the other 00:13:23.200 --> 00:13:25.117 things that we have to get right is some 00:13:25.200 --> 00:13:27.997 standards of interop right like even KV 00:13:28.080 --> 00:13:32.477 cache like why the heck can't I use multiple model families and have KV 00:13:32.560 --> 00:13:36.557 cache reuse uh right we've had document standards 00:13:36.640 --> 00:13:39.917 you and I lived through it right but we've sort of you know you kind of have 00:13:40.000 --> 00:13:43.837 things that are interoperable in the real world everywhere else so I think 00:13:43.920 --> 00:13:47.997 this industry also has to wake up and say hey in fact if I were talking about 00:13:48.080 --> 00:13:50.317 the most important pressing things is 00:13:50.400 --> 00:13:53.197 how do I have more standards on uh 00:13:53.280 --> 00:13:57.837 interoperability how do I have a harness that is external to a model so that my 00:13:57.920 --> 00:14:01.277 memory is not tied to one model I mean this is the first time you're going to 00:14:01.360 --> 00:14:04.477 have a technology where your use of it 00:14:04.560 --> 00:14:07.117 and the exhaust in the data could not be 00:14:07.200 --> 00:14:11.677 yours. Uh I mean that you know like it's like if I g sold you a database and said 00:14:11.760 --> 00:14:15.597 hey the data you put into your database is not yours and it's mine. It goes away 00:14:15.680 --> 00:14:18.957 if I took away the license. How would you feel about it? So therefore I think 00:14:19.040 --> 00:14:21.437 we have some serious issues like that to deal with. 00:14:21.520 --> 00:14:22.557 >> I think that's a good segue. 00:14:22.640 --> 00:14:27.437 >> Sorry. Let me just ask one question to connect the um economic incentive 00:14:27.520 --> 00:14:29.517 argument on what's going on. The 00:14:29.600 --> 00:14:33.037 argument is the Frontier Labs are facing 00:14:33.120 --> 00:14:37.437 token compression. 50 bucks for OpenAI's 00:14:37.520 --> 00:14:41.037 kind of million token output versus I 00:14:41.120 --> 00:14:45.117 think someone estimated Deep Seeks new is like can go as low as 15 cents for a 00:14:45.200 --> 00:14:46.637 million tokens of output. Let's call it 00:14:46.720 --> 00:14:50.797 60 cents. 99% cost reduction. 00:14:50.880 --> 00:14:53.437 If that is the the big kind of economic 00:14:53.520 --> 00:14:58.237 crux of what the frontier labs are facing, why would most tokens be paying 00:14:58.320 --> 00:15:02.317 50 bucks? Most enterprises pay 50 bucks when they could pay 60 cents for most of 00:15:02.400 --> 00:15:06.237 their tasks. Doesn't that also beg the question, are they in the wrong business 00:15:06.320 --> 00:15:09.917 model? And I I asked this for you as the CEO of Microsoft, what's the right 00:15:10.000 --> 00:15:13.917 business model? Do you want to be making the frontier model? Do you want to be 00:15:14.000 --> 00:15:17.997 running the compute and charging for rent on your compute? Or do you want to 00:15:18.080 --> 00:15:20.877 be in the application layer? I know you talk about this a lot, but I just love 00:15:20.960 --> 00:15:25.037 your perspective from where we sit today and how this all kind of >> um 00:15:25.120 --> 00:15:28.397 >> Yeah, I think the the fundamental thing that I think we're observing is good 00:15:28.480 --> 00:15:32.477 old-fashioned competition, right? I mean, for me, if I look back at it, we 00:15:32.560 --> 00:15:36.797 were we had like some real great closed source assets, Windows. What was the 00:15:36.880 --> 00:15:38.637 check against it? It was of course the 00:15:38.720 --> 00:15:41.197 Mac, but Linux 00:15:41.280 --> 00:15:45.677 >> uh we had a great closed source product called SQL Server. What was the check 00:15:45.760 --> 00:15:50.237 against it? there was always a substitute called Postgress or MySQL. So 00:15:50.320 --> 00:15:53.517 I think that's what's happening a little bit of it is there's real competition 00:15:53.600 --> 00:15:55.997 between closed source and the open- 00:15:56.080 --> 00:16:01.197 source check is real. Um, and that's good quite frankly uh because without it 00:16:01.280 --> 00:16:04.637 I don't think we're going to have a broad frontier ecosystem or broad 00:16:04.720 --> 00:16:06.637 diffusion because otherwise we'll just 00:16:06.720 --> 00:16:08.557 we'll be back to some uh you know 00:16:08.640 --> 00:16:11.757 mainframe uh locket that's just not uh a 00:16:11.840 --> 00:16:15.197 thing to your point about if anything 00:16:15.280 --> 00:16:17.757 given that we will now hopefully 00:16:17.840 --> 00:16:21.277 continue to have a much richer choice in 00:16:21.360 --> 00:16:23.917 every layer. Right. So to me hopefully 00:16:24.000 --> 00:16:26.077 we can start building these AI because 00:16:26.160 --> 00:16:29.597 today the royalty of an AI product all 00:16:29.680 --> 00:16:32.637 going to just the model layer doesn't 00:16:32.720 --> 00:16:36.637 make sense if you really want to build a product company right it just cannot be 00:16:36.720 --> 00:16:39.517 in fact if anything like that's the same thing right which is if you take the 00:16:39.600 --> 00:16:44.397 database if there was no open-source check on closed source uh the prices 00:16:44.480 --> 00:16:47.677 wouldn't have been at a place where people could have built the app tier 00:16:47.760 --> 00:16:51.917 successfully and the with a margin and so I think the apps are going to become 00:16:52.000 --> 00:16:53.997 you know much more viable economically 00:16:54.080 --> 00:16:56.957 which is great for the ecosystem. uh 00:16:57.040 --> 00:17:01.117 there are going to be all these other layers of middleware call it right which 00:17:01.200 --> 00:17:03.677 is hey what's my memory system what's my 00:17:03.760 --> 00:17:08.157 harness and orchestration layer so there's going to be a very rich tools 00:17:08.240 --> 00:17:12.557 ecosystem there the model companies will do fine uh in fact you know the paro 00:17:12.640 --> 00:17:15.117 they can manage the token pricing based 00:17:15.200 --> 00:17:19.837 on their model family if anything I want them to work on even the KV you know 00:17:19.920 --> 00:17:23.997 these these standards >> such that we can use multiple model f in 00:17:24.080 --> 00:17:28.477 fact it's better for them in fact I worked on Windows interrupt with Unix 00:17:28.560 --> 00:17:31.117 first. >> In fact, it was counterintuitive, right? 00:17:31.200 --> 00:17:34.957 We used to think, oh my god, this interrupt means we'll be less used 00:17:35.040 --> 00:17:39.597 except we were more used. >> In fact, we became weirdly enough 00:17:39.680 --> 00:17:44.077 because there were so many variants of Unix at that time that Windows interrupt 00:17:44.160 --> 00:17:46.237 made Unix better and Windows better. And 00:17:46.320 --> 00:17:48.637 in fact, we were able to penetrate the 00:17:48.720 --> 00:17:51.277 enterprise primarily because we did that 00:17:51.360 --> 00:17:53.997 interrupt work. And so that's at least 00:17:54.080 --> 00:17:55.997 how I think about it. Satya one of these 00:17:56.080 --> 00:17:58.557 we're in this interesting moment where 00:17:58.640 --> 00:18:01.037 on the one hand you have these experts 00:18:01.120 --> 00:18:04.717 asking for regulation asking for 00:18:04.800 --> 00:18:07.517 oversight governance it typically always 00:18:07.600 --> 00:18:11.117 leads to some restriction of freedom 00:18:11.200 --> 00:18:14.637 and general society 00:18:14.720 --> 00:18:18.797 are put in a position where now we have to opine on whether this is right or 00:18:18.880 --> 00:18:21.517 wrong but then on the other side most 00:18:21.600 --> 00:18:23.997 people's lived experience 00:18:24.080 --> 00:18:26.397 is not this magical productivity boost 00:18:26.480 --> 00:18:29.437 of AI. At best, it's integrating our 00:18:29.520 --> 00:18:31.597 Apple Eyewatch data to tell us why we're 00:18:31.680 --> 00:18:33.677 sleeping less. That's like functionally 00:18:33.760 --> 00:18:36.557 the bar for most people. Or why is my 00:18:36.640 --> 00:18:41.837 kid an into chat GPT? Uh so can 00:18:41.920 --> 00:18:45.597 you just help us bridge this? I mean, you see so many enterprise applications. 00:18:45.680 --> 00:18:49.757 Where's the magic? Like where is the where are the gains in profits? Where 00:18:49.840 --> 00:18:52.077 are the huge upside breakthroughs that 00:18:52.160 --> 00:18:57.997 AI is creating that will somehow make all of this tension understandable for 00:18:58.080 --> 00:18:59.917 everybody? >> Yeah, it's a great it's a great point. I 00:19:00.000 --> 00:19:03.357 mean, I think this is the real question 00:19:03.440 --> 00:19:06.157 which is how do we truly see this in the 00:19:06.240 --> 00:19:10.637 productivity stats? How do we really see it in the GDP growth? That's broadbased. 00:19:10.720 --> 00:19:12.637 It's not just supplier 00:19:12.720 --> 00:19:15.357 >> or supply side. Um I mean the the one 00:19:15.440 --> 00:19:18.317 example that I I love and I get back to 00:19:18.400 --> 00:19:19.997 in fact healthcare is a good one right 00:19:20.080 --> 00:19:23.757 if you think about um health care and 00:19:23.840 --> 00:19:26.317 even the simple doctor patient 00:19:26.400 --> 00:19:27.997 interaction in our case we have this 00:19:28.080 --> 00:19:30.957 thing called DAX copilot um that's the 00:19:31.040 --> 00:19:33.517 place which is the most tangible example 00:19:33.600 --> 00:19:38.077 I can always point to when a doctor can spend more time with the patient caring 00:19:38.160 --> 00:19:40.237 for them versus just the entry into an 00:19:40.320 --> 00:19:43.037 EMR system that's a good productivity 00:19:43.120 --> 00:19:46.797 gain If it can triage uh the inbox for 00:19:46.880 --> 00:19:48.637 the doctor so that they can be more 00:19:48.720 --> 00:19:52.557 responsive uh that's helpful for uh uh 00:19:52.640 --> 00:19:57.197 for the patient and the care system the administrator in fact keying like the 00:19:57.280 --> 00:19:58.717 insure like because it's the 00:19:58.800 --> 00:20:02.157 triangulation of the pay patient and the 00:20:02.240 --> 00:20:05.117 health system. Yeah. Uh that's of all in 00:20:05.200 --> 00:20:07.197 fact most of healthcare is sort of all 00:20:07.280 --> 00:20:10.397 workflow cost. Uh so taming of that 00:20:10.480 --> 00:20:14.397 workflow complexity that's a helpful thing. But do you see that in Microsoft 00:20:14.480 --> 00:20:15.677 with the people that you're helping? 00:20:15.760 --> 00:20:19.437 >> Yeah, absolutely. We see that and and by the way even in in simple co-pilot 00:20:19.520 --> 00:20:24.317 cases, right, which is if you look at the amount most people think about jobs 00:20:24.400 --> 00:20:27.357 which I think there is going to be displacement there is but the bottom 00:20:27.440 --> 00:20:29.437 line is what are the new jobs that get 00:20:29.520 --> 00:20:32.477 created uh is going to be one of the key 00:20:32.560 --> 00:20:35.117 aspects of it. But also a lot of 00:20:35.200 --> 00:20:40.397 knowledge work unfortunately is drudgery right who you know I get up in the 00:20:40.480 --> 00:20:41.997 morning and I think about like man all I 00:20:42.080 --> 00:20:44.397 do is email triage right you know 00:20:44.480 --> 00:20:47.997 >> uh what if uh even just these workflows 00:20:48.080 --> 00:20:52.077 that are taking away time from things that you could be spending time on 00:20:52.160 --> 00:20:54.477 >> okay well you're bring you're bringing up this great point if you go all the 00:20:54.560 --> 00:20:57.757 way back to like the turn of the century the industrial revolution when we had a 00:20:57.840 --> 00:21:01.517 7-day work week you know a lot of people forget why did we introduce the weekends 00:21:01.600 --> 00:21:05.197 it was to sort of manage the tension between different uh religious groups 00:21:05.280 --> 00:21:06.717 that had to work in the same factory. 00:21:06.800 --> 00:21:11.757 And then when you look at long run GDP outside of some exogenous events, it 00:21:11.840 --> 00:21:15.117 sort of is, you know, between two and 400 basis points. 00:21:15.200 --> 00:21:18.637 >> And so what happens is as productivity boosts come in, 00:21:18.720 --> 00:21:23.277 >> human work steps back and you kind of accomplish the same amount of work. 00:21:23.360 --> 00:21:27.117 >> Do you think that that happens here? Is that is there a risk that we have a 00:21:27.200 --> 00:21:29.277 three-day work week and we're just still 00:21:29.360 --> 00:21:34.477 growing at two and a half%. Yeah, that's a great qu or will we find new things 00:21:34.560 --> 00:21:38.717 and this is where the excitement at least I have for what the real impact of 00:21:38.800 --> 00:21:40.957 AI would be is instead of just thinking 00:21:41.040 --> 00:21:44.237 about hey it has helped me augment some 00:21:44.320 --> 00:21:46.957 workflow or simplify something that's 00:21:47.040 --> 00:21:49.277 happening today is it inventing new 00:21:49.360 --> 00:21:52.077 things uh is it speeding up drug 00:21:52.160 --> 00:21:56.397 discovery um is it taking the u I don't 00:21:56.480 --> 00:21:59.917 know let's again go back to my example of okay the working capital management 00:22:00.000 --> 00:22:02.797 of a small business has become so much 00:22:02.880 --> 00:22:05.917 more efficient uh that suddenly it's no 00:22:06.000 --> 00:22:10.397 longer just oh I have an ERP or a QuickBooks like thing but I truly am 00:22:10.480 --> 00:22:12.797 making decisions based on the ability to 00:22:12.880 --> 00:22:15.677 introspect my invoices my emails and 00:22:15.760 --> 00:22:17.677 what have you and some somehow optimize 00:22:17.760 --> 00:22:20.477 my working capital that's productivity 00:22:20.560 --> 00:22:24.157 that didn't exist and so I do hope that 00:22:24.240 --> 00:22:29.517 we will start seeing GDP growth which we did see in the industrial era um during 00:22:29.600 --> 00:22:31.117 the first phase of it. >> Yeah. 00:22:31.200 --> 00:22:33.997 >> Right. So, so that I think is what is 00:22:34.080 --> 00:22:35.757 needed, right? Which is in order for all 00:22:35.840 --> 00:22:38.717 of this to play out quite frankly, we do 00:22:38.800 --> 00:22:42.877 need to see at least 7 8% GDP growth 00:22:42.960 --> 00:22:45.757 that is real and that's broad-based. 00:22:45.840 --> 00:22:48.877 >> What's the what business is Microsoft in 00:22:48.960 --> 00:22:51.837 in relation to AI? Obviously, Azure has 00:22:51.920 --> 00:22:53.757 been crushing it. you're turning away 00:22:53.840 --> 00:22:56.797 customers uh and you're doing $175 00:22:56.880 --> 00:22:59.197 billion in capex buildout, but your 00:22:59.280 --> 00:23:02.397 capex is far below what Meta is doing, 00:23:02.480 --> 00:23:06.797 far below what Google's doing. They're doing secondary raises and raising debt, 00:23:06.880 --> 00:23:10.397 350 billion. The Frontier Labs are spending 500 billion. You were so early 00:23:10.480 --> 00:23:12.957 to the party with the precient open AI 00:23:13.040 --> 00:23:16.317 investment, but then co-pilot didn't 00:23:16.400 --> 00:23:19.997 exactly land. I don't think it didn't get great reviews. You don't have a 00:23:20.080 --> 00:23:21.677 frontier model. What's the business? 00:23:21.760 --> 00:23:24.877 Please come back. [laughter] >> No, but what's the business here? What's 00:23:24.960 --> 00:23:29.037 the >> Do you need to have a frontier model? 00:23:29.120 --> 00:23:31.757 >> Did Did we tell you there was one journalist on the panel? [laughter] 00:23:31.840 --> 00:23:35.277 >> No, no, no. It's I mean I mean it sincerely because I'm just curious. 00:23:35.360 --> 00:23:39.517 You're a great strategist. We know that about you. Microsoft missed the mobile 00:23:39.600 --> 00:23:42.477 revolution. >> Is Microsoft going to miss the AI 00:23:42.560 --> 00:23:44.957 revolution? You don't have a frontier model? Because I always found it 00:23:45.040 --> 00:23:48.157 perplexing that you didn't. And what's the strategy there in all seriousness? 00:23:48.240 --> 00:23:51.357 Like do you think open source is going to win? you should have that play. 00:23:51.440 --> 00:23:55.357 >> Yeah. So, let me walk you uh through the sort of where we are and what we're up 00:23:55.440 --> 00:23:59.037 to on each of these. By the way, on the capex side and the buildout side, we 00:23:59.120 --> 00:24:01.757 started early. So we if you sort of 00:24:01.840 --> 00:24:05.597 cumulatively look um it's a good I'm not 00:24:05.680 --> 00:24:09.197 sort of saying you know right right now speaking about a lot of capex is not a 00:24:09.280 --> 00:24:12.877 feature it's a bug but that [laughter] said but if you really go actually add 00:24:12.960 --> 00:24:17.117 up the math uh given when we started because we started multiple years before 00:24:17.200 --> 00:24:20.397 people woke up to even actually needing to build and so that's kind of one 00:24:20.480 --> 00:24:24.557 aspect of it. The other aspect of it is we are calibrating our capex in such a 00:24:24.640 --> 00:24:28.637 way that we don't we don't want to build for one or two customers right so we 00:24:28.720 --> 00:24:32.397 want to build for the long tail right because that's I think most important 00:24:32.480 --> 00:24:36.157 and that's I mean that if you're a hyperscaler you're not a supplier to two 00:24:36.240 --> 00:24:40.637 model companies that's not a business uh you have to sort of basically build a 00:24:40.720 --> 00:24:45.917 system that is great for lots of third parties uh and our own one in that 00:24:46.000 --> 00:24:49.517 context we're pretty thrilled with the progress we're making uh with even 00:24:49.600 --> 00:24:53.117 copilot if you sort of look at the subscriber numbers we gave which is this 00:24:53.200 --> 00:24:56.237 is goes back in fact to Chamat's fundamental point which is these are 00:24:56.320 --> 00:25:00.877 real enterprises using it for real workflows u and the fact that we now 00:25:00.960 --> 00:25:05.597 have 30 plus million not over forum remember the total knowledge worker base 00:25:05.680 --> 00:25:09.037 right where most people talk about 3 billion people 4 billion people on the 00:25:09.120 --> 00:25:11.357 internet the entire office 365 or 00:25:11.440 --> 00:25:14.237 Microsoft 365 is the the the sort of the 00:25:14.320 --> 00:25:18.717 standard when it comes to knowledge work there's 450 million that's including all 00:25:18.800 --> 00:25:20.157 students in the world >> oh wow 00:25:20.240 --> 00:25:22.797 >> right So when we talk like the market 00:25:22.880 --> 00:25:26.077 quote unquote as defined is maybe 300 uh 00:25:26.160 --> 00:25:29.517 250 even of real enterprise users and of 00:25:29.600 --> 00:25:31.837 that we've got the penetration of close 00:25:31.920 --> 00:25:33.597 to 30 million on that and it's growing 00:25:33.680 --> 00:25:36.957 and so on. The aspect on the model side 00:25:37.040 --> 00:25:41.197 is we're thrilled about obviously our investment in open AAI the access we 00:25:41.280 --> 00:25:44.957 have to their IP which we have for a long time we're going to use that but we 00:25:45.040 --> 00:25:46.797 are well on our way building our MAI 00:25:46.880 --> 00:25:49.277 models right if you look at it we have a 00:25:49.360 --> 00:25:54.397 flash cyber model that you know with our harness orchestrating other models 00:25:54.480 --> 00:25:55.917 outperforms 00:25:56.000 --> 00:25:59.277 um on cyber gym even a mythos uh same 00:25:59.360 --> 00:26:03.917 thing we're seeing in coding same thing we're seeing in uh knowledge work right 00:26:04.000 --> 00:26:08.557 So our goal is to basically hill climb from the bottom by the way uh not 00:26:08.640 --> 00:26:10.797 distilling anything. So from the very 00:26:10.880 --> 00:26:13.997 bottom using our RLES our data uh and 00:26:14.080 --> 00:26:17.917 then also have a differentiated position with enterprises going back to 00:26:18.000 --> 00:26:21.837 addressing some of the things that they want which is hey can I have the weights 00:26:21.920 --> 00:26:27.197 can I have the weights that I can then add to my knowledge uh these are the 00:26:27.280 --> 00:26:30.477 things that we will do with our foundation. Your best advice I think to 00:26:30.560 --> 00:26:35.197 enterprises is AI sovereignty is important. Putting your data into a 00:26:35.280 --> 00:26:38.717 frontier model probably not a good idea and then you're going to be that harness 00:26:38.800 --> 00:26:40.797 for them to to help them. So my 00:26:40.880 --> 00:26:43.677 implement my advice is more like use all 00:26:43.760 --> 00:26:46.317 but be independent of all. So for 00:26:46.400 --> 00:26:49.757 example my asset test is you should 00:26:49.840 --> 00:26:51.757 always eval 00:26:51.840 --> 00:26:53.757 that matter to you right. So what's the 00:26:53.840 --> 00:26:56.957 outcome you want? you should go run that 00:26:57.040 --> 00:27:01.677 outcome through all the models. Then here's the test I would do. I would pull 00:27:01.760 --> 00:27:03.837 out a model and see whether I can retain 00:27:03.920 --> 00:27:06.717 the eval. If I can't, that means you 00:27:06.800 --> 00:27:11.037 really are dependent on something that may or may not be yours. 00:27:11.120 --> 00:27:14.237 >> Right. >> Right. That's so so my fundamental 00:27:14.320 --> 00:27:18.637 enterprise architecture would say you should have a model system that 00:27:18.720 --> 00:27:21.197 fundamentally allows you to be able to 00:27:21.280 --> 00:27:23.837 continuously hill climb on your own on 00:27:23.920 --> 00:27:25.917 eval 00:27:26.000 --> 00:27:29.837 uh while using all models closed open u 00:27:29.920 --> 00:27:33.917 if you want you can even fine-tune any of these models but you can even 00:27:34.000 --> 00:27:36.637 substitute models >> s just to build on Jason's question you 00:27:36.720 --> 00:27:40.957 had this um incredible moment I think we put it here where you said you know 00:27:41.040 --> 00:27:42.877 we're good for our 80 billion. But just 00:27:42.960 --> 00:27:45.597 to expand the question, um there's 00:27:45.680 --> 00:27:48.237 effectively this sort of bank of AI that 00:27:48.320 --> 00:27:52.957 has emerged and there's this financing mechanism that just is so important to 00:27:53.040 --> 00:27:57.117 the entire ecosystem and now broadly to the entire economy. But you've been very 00:27:57.200 --> 00:28:00.877 disciplined. You have an enormous balance sheet. You're also an investment 00:28:00.960 --> 00:28:05.037 grade issuer. So you could do what Jensen did, but you've taken a very 00:28:05.120 --> 00:28:08.637 different capital allocation approach, much larger bets, very concentrated, and 00:28:08.720 --> 00:28:12.317 you've kind of stayed into your own ecosystem. just talk us through your 00:28:12.400 --> 00:28:14.237 mindset as a capital allocator at 00:28:14.320 --> 00:28:16.317 Microsoft and that balance sheet. Yeah. 00:28:16.400 --> 00:28:20.397 So the way I'm sort of looking at our 00:28:20.480 --> 00:28:21.997 book of business whether it's the hypers 00:28:22.080 --> 00:28:26.077 scale our model or our app tier and the 00:28:26.160 --> 00:28:31.037 shape of the demand um and then what's the way to build out for it. And so if 00:28:31.120 --> 00:28:34.077 you think about these assets right there are two classes of it. There are the 00:28:34.160 --> 00:28:37.677 long lead um long duration assets like 00:28:37.760 --> 00:28:40.317 the the land power cold shell let's call 00:28:40.400 --> 00:28:43.597 it. Then there is the kit. the kit is 00:28:43.680 --> 00:28:46.957 the short-term uh asset uh that you can 00:28:47.040 --> 00:28:51.517 much more you know uh be demand driven in other words right I have to forecast 00:28:51.600 --> 00:28:54.477 let's say two years three year out demand and then and then also 00:28:54.560 --> 00:28:57.757 >> the kit means the racks the chips >> the racks the chips and what have you 00:28:57.840 --> 00:28:59.597 and that's 60% of the cost or what have 00:28:59.680 --> 00:29:01.597 you right so therefore so what we do is 00:29:01.680 --> 00:29:05.837 we go build as much um we lease we even 00:29:05.920 --> 00:29:07.837 rent now right now we're even renting 00:29:07.920 --> 00:29:09.837 quite a bit because we kind of were 00:29:09.920 --> 00:29:13.677 short on supply uh But the overall goal 00:29:13.760 --> 00:29:17.117 is to build more lease some and then if 00:29:17.200 --> 00:29:19.277 really need to surge we will even rent 00:29:19.360 --> 00:29:22.317 that's kind of on the on the on the uh 00:29:22.400 --> 00:29:25.917 assets and then the chips themselves we 00:29:26.000 --> 00:29:28.477 will try to be first of all make sure 00:29:28.560 --> 00:29:31.197 that we're matching demand and as I said 00:29:31.280 --> 00:29:35.837 my goal is not to have just two customers three customers uh it's great 00:29:35.920 --> 00:29:39.197 to have openi being one of our largest customers it's great that they're 00:29:39.280 --> 00:29:41.357 growing uh but we need more uh is the 00:29:41.440 --> 00:29:45.197 kit over earning right now and do do we 00:29:45.280 --> 00:29:48.157 need is the is the industry pushing for 00:29:48.240 --> 00:29:51.517 diversification more silicon more memory 00:29:51.600 --> 00:29:52.717 more vendors 00:29:52.800 --> 00:29:56.157 >> yeah what's happening is the workloads 00:29:56.240 --> 00:29:59.597 that are now at scale uh they obviously 00:29:59.680 --> 00:30:03.517 grew up from what GPUs were there but 00:30:03.600 --> 00:30:06.877 now the the shape is so well understood 00:30:06.960 --> 00:30:11.677 uh that you're able to optimize for a very different world right So you can 00:30:11.760 --> 00:30:13.597 sort of start building 00:30:13.680 --> 00:30:15.597 >> um and saying well you know there are 00:30:15.680 --> 00:30:18.957 these multiple phases in um an inference 00:30:19.040 --> 00:30:23.677 or a training phase so why not build silicon that's optimized for these uh 00:30:23.760 --> 00:30:27.197 and that's just going to lead to a systems architecture that I think is 00:30:27.280 --> 00:30:31.677 going to by definition have a lot more uh diversity uh I mean I know you have 00:30:31.760 --> 00:30:35.997 Jensen coming he himself if you look at his own architecture is changing quite 00:30:36.080 --> 00:30:39.437 drastically >> quite drastically >> um and so I think that there is going to 00:30:39.520 --> 00:30:43.997 be a lot more choice even there in that layer. So ours we have Jensen stuff 00:30:44.080 --> 00:30:48.477 which is I think our primary thing. We have our own uh OpenAI is building their 00:30:48.560 --> 00:30:50.157 chip so that's also going to be there. 00:30:50.240 --> 00:30:55.117 AMD is in there. So we I I my thing is to run whether it's the OpenAI models, 00:30:55.200 --> 00:30:58.237 the anthropic models or our own models on a heterogeneous kit. 00:30:58.320 --> 00:31:00.557 >> Sax I want to let you get in here before we run out of time. 00:31:00.640 --> 00:31:02.477 >> Yeah. So you know we've heard now from 00:31:02.560 --> 00:31:05.117 the the various frontier lab leaders Sam 00:31:05.200 --> 00:31:07.997 Dario Elon Demis that we need to 00:31:08.080 --> 00:31:10.237 prioritize alignment like we're talking 00:31:10.320 --> 00:31:13.197 predictability reliability robustness uh 00:31:13.280 --> 00:31:16.157 as opposed to maybe just say raw raw 00:31:16.240 --> 00:31:18.157 power. Do you think the Chinese labs 00:31:18.240 --> 00:31:20.237 will follow suit? 00:31:20.320 --> 00:31:23.437 I think that that's the dialogue um that 00:31:23.520 --> 00:31:25.277 is I think should be prioritized right 00:31:25.360 --> 00:31:28.237 so because at some level my own premise 00:31:28.320 --> 00:31:31.357 would be that 00:31:31.440 --> 00:31:36.237 that China should also deeply care uh 00:31:36.320 --> 00:31:38.957 about the same safety concerns if the 00:31:39.040 --> 00:31:43.917 United States uh cares about them right why should it be different for them it's 00:31:44.000 --> 00:31:47.197 not like they won't have the same hacking problem 00:31:47.280 --> 00:31:50.477 >> uh it's not as if uh they don't want to 00:31:50.560 --> 00:31:55.277 make sure that their citizens um are benefiting from AI just like we will 00:31:55.360 --> 00:31:57.437 want our citizens to benefit from AI. So 00:31:57.520 --> 00:32:00.477 I think that there's a possibility of 00:32:00.560 --> 00:32:02.957 international norms around it. If we 00:32:03.040 --> 00:32:07.597 really are concrete about what's the risk, why is this risk so idiosyncratic 00:32:07.680 --> 00:32:12.637 that the only people who are worried about it is the Americans. Uh it doesn't 00:32:12.720 --> 00:32:15.917 make sense, right? It's not like a thing that is sort of said, "Oh, I'm going to 00:32:16.000 --> 00:32:19.997 only show up in the United States. I'm going to be something. If it is going to 00:32:20.080 --> 00:32:22.877 go wrong, it's going to go wrong everywhere at the same time." So I think 00:32:22.960 --> 00:32:27.117 the Chinese should care. I mean they're they are a superpower. 00:32:27.200 --> 00:32:30.317 >> Well that's you use the word idiosyncratic and I think that is the 00:32:30.400 --> 00:32:32.877 right word is I don't think we know yet 00:32:32.960 --> 00:32:37.677 is this um you know conversation we're having in the US over the past week. Is 00:32:37.760 --> 00:32:39.757 it idiosyncratic to us because we have 00:32:39.840 --> 00:32:45.197 you know the strong I guess you could say doomer type uh school of thought or 00:32:45.280 --> 00:32:47.917 is it something that the rest of the world will basically feel as well? 00:32:48.000 --> 00:32:50.237 >> It's a great question >> and if they do then presumably they'd 00:32:50.320 --> 00:32:54.717 want to act on it as well. Yeah, I I just feel my my take there is that we 00:32:54.800 --> 00:32:56.317 are ahead 00:32:56.400 --> 00:32:59.997 >> and we are who we are which is we argue 00:33:00.080 --> 00:33:03.117 we sort of we compete uh we are more 00:33:03.200 --> 00:33:07.117 transparent which is all by the way virtues as far as I'm concerned so 00:33:07.200 --> 00:33:11.117 therefore the fact that this debate is happening here the world will be better 00:33:11.200 --> 00:33:13.277 off for it right so to some degree us 00:33:13.360 --> 00:33:15.917 setting if anything I would love a US 00:33:16.000 --> 00:33:19.997 set us to lead in the norms that allow 00:33:20.080 --> 00:33:22.717 us to defuse use this technology broadly 00:33:22.800 --> 00:33:27.677 and create safety standards uh that work for the world including China. But 00:33:27.760 --> 00:33:31.197 >> what do you think we should be doing that we're not doing and what are you 00:33:31.280 --> 00:33:35.757 doing at Microsoft to change the narrative the populist 00:33:35.840 --> 00:33:39.277 sentiment that we have to shut down super intelligence stop building data 00:33:39.360 --> 00:33:40.397 centers 00:33:40.480 --> 00:33:44.557 >> etc. So, so to me I think this is I am 00:33:44.640 --> 00:33:47.197 squarely focused on one of the to 00:33:47.280 --> 00:33:50.317 answering Chamat's question from earlier 00:33:50.400 --> 00:33:53.037 which is whom is it benefiting and give 00:33:53.120 --> 00:33:55.517 me concrete stories right uh we talked 00:33:55.600 --> 00:33:58.077 about the productivity benefits a bit uh 00:33:58.160 --> 00:34:00.557 whether it's in healthcare or in general 00:34:00.640 --> 00:34:05.517 knowledge work coding but I'll give you another example right I was looking at 00:34:05.600 --> 00:34:07.517 data centers because after all we didn't 00:34:07.600 --> 00:34:10.557 talk much uh today on that but there's a 00:34:10.640 --> 00:34:12.557 challenge on how does one earn 00:34:12.640 --> 00:34:15.597 permission uh to open a data center in a 00:34:15.680 --> 00:34:18.797 region. In fact, we just have some of 00:34:18.880 --> 00:34:21.837 the best longitudinal data now for a 00:34:21.920 --> 00:34:26.157 data center we built out in Quinsey, Washington, uh for 20 years, close to, 00:34:26.240 --> 00:34:28.477 you know, 2008 is when we started it. 00:34:28.560 --> 00:34:31.277 And when I look at that data and what it 00:34:31.360 --> 00:34:33.117 has meant for that community, right, 00:34:33.200 --> 00:34:35.837 where uh the tax revenues have gone up 00:34:35.920 --> 00:34:39.997 12 times, uh the paidin taxes have gone 00:34:40.080 --> 00:34:44.637 down by a third. Um the growth is higher 00:34:44.720 --> 00:34:47.357 than Seattle in Quinsey. This is a rural 00:34:47.440 --> 00:34:50.717 town. Uh they have a new school, a new 00:34:50.800 --> 00:34:54.717 hospital, a new town center, a new aquatic center. Wow. 00:34:54.800 --> 00:34:57.997 >> Uh we have two and most people say, "Oh, there not that many jobs." In fact, 00:34:58.080 --> 00:35:03.037 there have been 1,200 construction jobs in that region all through that 20-year 00:35:03.120 --> 00:35:06.317 period, right? Because it's not like you just build it and leave. You 00:35:06.400 --> 00:35:08.957 continuously refurbishing, building, expanding. 00:35:09.040 --> 00:35:10.477 >> And how big, how big is that data center? 00:35:10.560 --> 00:35:14.477 >> Uh I think it's now going to be at least 4 or 500 megawatt 00:35:14.560 --> 00:35:16.717 >> and it sort of will keep expanding. 00:35:16.800 --> 00:35:21.037 >> Um and so so these are uh so that's a 00:35:21.120 --> 00:35:25.437 real like that community. So earning it like just not saying hey these are all 00:35:25.520 --> 00:35:28.397 the benefits but seeing it >> but how do you get people to tell that 00:35:28.480 --> 00:35:30.957 story because that's what's missing today is those stories aren't being 00:35:31.040 --> 00:35:34.797 organically told and if a Microsoft executive gets on stage and says don't 00:35:34.880 --> 00:35:36.717 worry it's good for the community. 00:35:36.800 --> 00:35:40.157 >> Yeah. No I don't think Yeah. So I think storytelling is one thing. The other one 00:35:40.240 --> 00:35:42.957 is I think we just need more people 00:35:43.040 --> 00:35:47.597 outside of the tech industry to say yeah because if you go to Quinsey Washington 00:35:47.680 --> 00:35:49.757 they will tell you thank god for this 00:35:49.840 --> 00:35:51.677 data center. It's part of like you know. 00:35:51.760 --> 00:35:54.957 So to me that's like when it's tangible 00:35:55.040 --> 00:35:58.717 uh like that uh because that's the only way to earn permission because at some 00:35:58.800 --> 00:36:01.117 level the skepticism of any of us in the 00:36:01.200 --> 00:36:04.077 tech industry just saying things uh is 00:36:04.160 --> 00:36:06.557 so high that I think we have to now do 00:36:06.640 --> 00:36:09.597 the hard yards of actually doing things 00:36:09.680 --> 00:36:13.677 in the world uh which allow people to say okay I now believe you. 00:36:13.760 --> 00:36:16.397 >> It's a new muscle. >> It's a new muscle. It's a new muscle. 00:36:16.480 --> 00:36:18.877 >> So I think you're a good spokesperson to 00:36:18.960 --> 00:36:20.797 flex that muscle. I hope you do it more. 00:36:20.880 --> 00:36:22.397 Thank you for being with us. >> Thank you so much. 00:36:22.480 --> 00:36:26.470 >> We appreciate you. [music] 00:36:29.440 --> 00:36:33.760 >> Thank you, sir. Appreciate your time.