0:01has generated $250 billion with a B in market value for Microsoft. Scott Nadella, chairman and CEO of Microsoft.
0:07Since you've been the CEO, three and a half years, the stock is up about uh I guess it's about 120%. I'm good for my 80 billion. I am going to spend $80 billion building out Azure. Maybe after the industrial revolution, this is the biggest thing. That's our goal with our frontier model. Our model should be the best model that they can use as a base.
0:28We create technology so that others can create more technology. That's who we are. We're tool maker.
0:35Please welcome Satia Nadella.
0:43Hi guy. Good to see you coming out.
0:54Thanks for joining us.
0:55Crazy weekend, but here we are. Do we need to paste the frontier?
1:03So, let's start with the common sense part first, which is we should do what it takes to build stuff that serves humanity first and is in human control. You know, it's kind of crazy that we have to start with that level of common sense, but I think it's a good place.
1:23Then when I think about pacing whatever the first thing that at least I believe is the broad diffusion of this technology is the most critical thing because the benefits of this tech showing up everywhere is really what's all about right so at the end of the day if you sort of say serving humanity let it actually reach humanity in ways that it serves humanity and that means you got to have choice you have to have
1:52compet competition. You have to have all kinds of business models whether they're open weights, close weights, what have you. Then the other aspect I think that is not talked about when we talk about control is actually the control that for example customers have, enterprises or businesses have around this technology because sometimes this is so opaque, right? I want my privacy. I want to be able to embed my knowledge in a set of
2:19weights I control. I want to see all of the coot uh that's being generated. I want to use it to do fine-tuning of my own models. My IP shouldn't leak. So, there's an entire body of things that nobody's talking about as much, which is my I really want to make sure that this tech is in my control. Then we get to uh what is I think a real issue of safety and we should take it seriously which is
2:49we should take all the time we want uh to test things. In fact I love this idea of having third party testers. Oh wow.
2:57You know I you know I grew up in a company that's always done testing. Uh so it's novel that we should say wow they're having embedded third party testers. Why not? It's a great idea. In fact, the only thing I would say is we should avoid like these, you know, cozy arrangements of who's testing what, who has access to what, and it should be broad.
3:17Were you were you surprised though when both the essay landed and then it seemed like there was a circling of the wagons amongst the frontier companies? I I I think that it comes my my suspicion is it comes genuinely from this place where when you start seeing in fact it's fascinating, right? We are when you start seeing reward hacking um and what's happening in these environments right with these agent swarms there is
3:45the mundane there is some DevOps error where somebody misconfigured a container right right or these API keys or an API keys or yeah exactly there's no monitoring uh there's internet access there's sort of classic I would call it basic devops and then there is real novel new stuff right which is what is this uh reward board hacking uh that you know with these persistent agents and so on and that's a place where I'll admit that the science is not there it's I
4:13thought Yakob's post which is a good one which he said he called it we're growing intelligence not building intelligence so it's an experimental science and so the more experimental sciences uh then you really need to make sure you're doing those experiments in controlled environments if anything the place where I would love is taking even the hugging face incident in other places more transparency on what would it take in fact one of the fascinating things right now is the insider risk I mean think
4:43about it right if you're sitting in an enterprise this is all test time compute by the way right so it's not like oh it's going to only happen when in some training run it can happen for a very mundane task uh that I give one of these frontier models inside an enterprise uh where I say you know I don't I was you know telling David this suppose I say hey go optimize my working capital it may fake my books uh right because this is like a new type of insider risk and so what is the way to do that I
5:12would say oh go build a maybe a causal model like a semantic model that actually checks and verifies so I think there's a lot of product building um I I would say making things more robust which is classic engineering that we should be talking a lot more about transparently versus saying hey this is so mystical that you know we can't figure this out. Do you do you buy this argument that it's mystical?
5:35I I mean I I buy the argument that we do not understand the latent space. Uh right other than I thought you know as you said like do we understand the brain? We don't. We do functional MRIs and do neuroscience and we're trying to figure this out continuously getting a little better understanding. So I do think that in that sense we don't exactly uh have a complete un that's why by the way I also I don't believe in new release right so that's why I think
6:03making sure that the coots are in language that we can all understand in fact they're transparent so that when when I go back to an enterprise that's using all these models and if you have the full coot uh then you can chain of thought and so then you can really go look at it deeply in fact you can have multiple models uh and you can look at the coot across those I think these are all things that I think will become very important satia you've worked with you've worked with technologists for decades
6:33uh and when you see as a leader of one company Microsoft which has very crisp communications with the public uh and you see what's happening with Daario and his team people coming out saying 10% chance we all die uh what do you think is going through those technologists minds. Do you believe they actually believe that this is going to kill humanity or are they going through some psychosis or are they seeing something working on those frontier models that is
7:03terrorizing them? You're not a psychologist, but you have worked with technologists for a long time. Handicap what's going on in these organizations that's all the making people feel the need to resign and say we're all going to die.
7:17Yeah. you know, it's it's hard for me to speak to what's happening in any of these places, but let let's just say uh how we I grew up even inside of Microsoft, you know, for example, you know, one of the biggest things you learn as an early sort of engineering lead is how to deal with a showstopper bug.
7:36Right. I mean, that's kind of like 101, right? Which is why you're faced, you're like, you know, you have a bug. Um what do you do? do you stop uh and fix or you defer or you go in and say hey this is such an edge case that's kind of the judgment so I do think and as the stakes go up you want to like transaction processing I remember working on databases right you know wow like you know you got to take very seriously any
8:04bug uh where if the transaction is going to get lost right data loss is a thing that you stop the thing for so I feel a little bit culture culturally in the AI industry rediscovering maybe because when you see and it's possible that they see stuff which are showstoppers before the rest and if you see a showstopper stop the show um right to fix the bugs yeah when you saw the the hugging face
8:33run and it was super performative Dwaresh did his whole post civilizations what do you think what what's your take on that testing they ran because they could have run a test where they had 3,000 agents defend a bunch of websites.
8:48Instead, they instructed them to hack websites and you know the hiding of information all this anthropomorphicizing whatever of the agents. I mean the way at least I understand it was it was actually you know basically trying to uh do an eval uh for cyber gym and um as I understand it given that eval it sort of figured out a way to say let's just say reward hack uh and that's what led it to
9:17hugging phase in fact it speaks to I think what's the pre you know clear issue right now which is you can have these things if they're are longunning persistent agents become essentially like new insider risks. Uh and so that I would start from the very basics of saying okay what is containment look like. So for example like one of the things that I think is going to be really an issue and a thing that needs great solutions is true
9:44aggressive monitoring of agent activity.
9:48Uh that's behavioral evidence and so everything has got to be auditable. uh and then every object it access, right? If it goes and gets a secret, oh, it's going to go chain a couple of things, you should be able to see it when it's starting to chain a couple of uh vulnerabilities uh to go hack. And so I think that these are the ways um that you really have to sort of deal with these situations versus saying um in in fact I think the core of my
10:15take is we will have to get the engineering process around building out this experimental science to be more robust.
10:29So I think I think that's a great point.
10:31I love how you uh differentiated in the HuggingFace uh episode between the mundane things they got wrong like the misconfigured sandbox and HuggingFace had credentials just sitting in a public repository and there was no monitoring and then you have the genuinely novel behavior, the swarms of agents, the reward hacking. That's the stuff that has everyone freaked out. I agree that, you know, we have to now figure out how to fix the bugs or, you know, fix the deeper problem that's coming from that reward hacking. What what do you think
10:59that means for and and and and I think to their credit I think what the Frontier Labs are saying is we are now going to slow down the pace of let's say raw power and shift towards reliability and predictability and you know what they call alignment which I think is good business practice I guess what do you think that means for what we see in terms of new products for the next year or two does it mean we just kind of improve what we already have or do we see new capabilities what do you think this going to mean A great question,
11:28David. I I do think there's already a massive model overhang, right? I mean, um capability overhang in the sense of the models are very good except the broad diffusion uh requires a lot of things, right? even requires uh essentially if you're compressing workflows and changing workflows to happen differently u the amount of change management that needs to happen in order to even incorporate these systems is sort of what's taking time so
11:58to some degree I would say the and also uh the the ability to create these new form factors right I mean if you think about coding agents and coding agents became really usable when you discovered that you could have an agent loop with a file system uh and that was the breakthrough that just made coding agents work. Um and I think now maybe with KUA right so which is with Astra with KUA uh could be a way for us to even do computer use or we just use long
12:26trajectory tasks that can get completely automated. So I think these type of product innovations where the model plus the harness allow us to do things that then lead to broad adoption. Right? I even go back to the chat GPT moment for me, right? Which was it was that RHF at the very end that made a chat conversation possible. Mhm.
12:50Uh and so I think that yes, so there's some science, there is some form factor that then leads to broad diffusion and we now need to find the next level of these things that are doing real work in the real enterprise. Um and in that context by the way the other thing is it's going to be a multimodel world right so at this point just out of resilience right I mean think about right every enterprise now comes to me and says hey this model does refusals here this model I want weights here I
13:18don't and so the people are going to want multiple models so one of the other things that we have to get right is some standards of interop right like even KV cache like why the heck can't I use multiple model families and have KV cache reuse uh right we've had document standards you and I lived through it right but we've sort of you know you kind of have things that are interoperable in the real world everywhere else so I think this industry also has to wake up and say hey in fact if I were talking about
13:48the most important pressing things is how do I have more standards on uh interoperability how do I have a harness that is external to a model so that my memory is not tied to one model I mean this is the first time you're going to have a technology where your use of it and the exhaust in the data could not be yours. Uh I mean that you know like it's like if I g sold you a database and said hey the data you put into your database is not yours and it's mine. It goes away if I took away the license. How would
14:17you feel about it? So therefore I think we have some serious issues like that to deal with.
14:21I think that's a good segue.
14:22Sorry. Let me just ask one question to connect the um economic incentive argument on what's going on. The argument is the Frontier Labs are facing token compression. 50 bucks for OpenAI's kind of million token output versus I think someone estimated Deep Seeks new is like can go as low as 15 cents for a million tokens of output. Let's call it 60 cents. 99% cost reduction.
14:50If that is the the big kind of economic crux of what the frontier labs are facing, why would most tokens be paying 50 bucks? Most enterprises pay 50 bucks when they could pay 60 cents for most of their tasks. Doesn't that also beg the question, are they in the wrong business model? And I I asked this for you as the CEO of Microsoft, what's the right business model? Do you want to be making the frontier model? Do you want to be running the compute and charging for rent on your compute? Or do you want to be in the application layer? I know you
15:19talk about this a lot, but I just love your perspective from where we sit today and how this all kind of um Yeah, I think the the fundamental thing that I think we're observing is good old-fashioned competition, right? I mean, for me, if I look back at it, we were we had like some real great closed source assets, Windows. What was the check against it? It was of course the Mac, but Linux uh we had a great closed source product called SQL Server. What was the check against it? there was always a
15:47substitute called Postgress or MySQL. So I think that's what's happening a little bit of it is there's real competition between closed source and the open- source check is real. Um, and that's good quite frankly uh because without it I don't think we're going to have a broad frontier ecosystem or broad diffusion because otherwise we'll just we'll be back to some uh you know mainframe uh locket that's just not uh a thing to your point about if anything
16:15given that we will now hopefully continue to have a much richer choice in every layer. Right. So to me hopefully we can start building these AI because today the royalty of an AI product all going to just the model layer doesn't make sense if you really want to build a product company right it just cannot be in fact if anything like that's the same thing right which is if you take the database if there was no open-source check on closed source uh the prices
16:44wouldn't have been at a place where people could have built the app tier successfully and the with a margin and so I think the apps are going to become you know much more viable economically which is great for the ecosystem. uh there are going to be all these other layers of middleware call it right which is hey what's my memory system what's my harness and orchestration layer so there's going to be a very rich tools ecosystem there the model companies will do fine uh in fact you know the paro
17:12they can manage the token pricing based on their model family if anything I want them to work on even the KV you know these these standards such that we can use multiple model f in fact it's better for them in fact I worked on Windows interrupt with Unix first.
17:29In fact, it was counterintuitive, right?
17:31We used to think, oh my god, this interrupt means we'll be less used except we were more used.
17:37In fact, we became weirdly enough because there were so many variants of Unix at that time that Windows interrupt made Unix better and Windows better. And in fact, we were able to penetrate the enterprise primarily because we did that interrupt work. And so that's at least how I think about it. Satya one of these we're in this interesting moment where on the one hand you have these experts asking for regulation asking for
18:04oversight governance it typically always leads to some restriction of freedom and general society are put in a position where now we have to opine on whether this is right or wrong but then on the other side most people's lived experience is not this magical productivity boost of AI. At best, it's integrating our Apple Eyewatch data to tell us why we're sleeping less. That's like functionally
18:33the bar for most people. Or why is my kid an into chat GPT? Uh so can you just help us bridge this? I mean, you see so many enterprise applications.
18:45Where's the magic? Like where is the where are the gains in profits? Where are the huge upside breakthroughs that AI is creating that will somehow make all of this tension understandable for everybody?
18:58Yeah, it's a great it's a great point. I mean, I think this is the real question which is how do we truly see this in the productivity stats? How do we really see it in the GDP growth? That's broadbased.
19:10It's not just supplier or supply side. Um I mean the the one example that I I love and I get back to in fact healthcare is a good one right if you think about um health care and even the simple doctor patient interaction in our case we have this thing called DAX copilot um that's the place which is the most tangible example I can always point to when a doctor can spend more time with the patient caring for them versus just the entry into an
19:40EMR system that's a good productivity gain If it can triage uh the inbox for the doctor so that they can be more responsive uh that's helpful for uh uh for the patient and the care system the administrator in fact keying like the insure like because it's the triangulation of the pay patient and the health system. Yeah. Uh that's of all in fact most of healthcare is sort of all
20:07workflow cost. Uh so taming of that workflow complexity that's a helpful thing. But do you see that in Microsoft with the people that you're helping?
20:15Yeah, absolutely. We see that and and by the way even in in simple co-pilot cases, right, which is if you look at the amount most people think about jobs which I think there is going to be displacement there is but the bottom line is what are the new jobs that get created uh is going to be one of the key aspects of it. But also a lot of knowledge work unfortunately is drudgery right who you know I get up in the morning and I think about like man all I do is email triage right you know
20:44uh what if uh even just these workflows that are taking away time from things that you could be spending time on okay well you're bring you're bringing up this great point if you go all the way back to like the turn of the century the industrial revolution when we had a 7-day work week you know a lot of people forget why did we introduce the weekends it was to sort of manage the tension between different uh religious groups that had to work in the same factory.
21:06And then when you look at long run GDP outside of some exogenous events, it sort of is, you know, between two and 400 basis points.
21:15And so what happens is as productivity boosts come in, human work steps back and you kind of accomplish the same amount of work.
21:23Do you think that that happens here? Is that is there a risk that we have a three-day work week and we're just still growing at two and a half%. Yeah, that's a great qu or will we find new things and this is where the excitement at least I have for what the real impact of AI would be is instead of just thinking about hey it has helped me augment some workflow or simplify something that's happening today is it inventing new things uh is it speeding up drug
21:52discovery um is it taking the u I don't know let's again go back to my example of okay the working capital management of a small business has become so much more efficient uh that suddenly it's no longer just oh I have an ERP or a QuickBooks like thing but I truly am making decisions based on the ability to introspect my invoices my emails and what have you and some somehow optimize my working capital that's productivity
22:20that didn't exist and so I do hope that we will start seeing GDP growth which we did see in the industrial era um during the first phase of it.
22:31Right. So, so that I think is what is needed, right? Which is in order for all of this to play out quite frankly, we do need to see at least 7 8% GDP growth that is real and that's broad-based.
22:45What's the what business is Microsoft in in relation to AI? Obviously, Azure has been crushing it. you're turning away customers uh and you're doing $175 billion in capex buildout, but your capex is far below what Meta is doing, far below what Google's doing. They're doing secondary raises and raising debt, 350 billion. The Frontier Labs are spending 500 billion. You were so early to the party with the precient open AI
23:13investment, but then co-pilot didn't exactly land. I don't think it didn't get great reviews. You don't have a frontier model. What's the business?
23:23No, but what's the business here? What's the Do you need to have a frontier model?
23:29Did Did we tell you there was one journalist on the panel?
23:31No, no, no. It's I mean I mean it sincerely because I'm just curious. You're a great strategist. We know that about you. Microsoft missed the mobile revolution.
23:40Is Microsoft going to miss the AI revolution? You don't have a frontier model? Because I always found it perplexing that you didn't. And what's the strategy there in all seriousness?
23:48Like do you think open source is going to win? you should have that play.
23:51Yeah. So, let me walk you uh through the sort of where we are and what we're up to on each of these. By the way, on the capex side and the buildout side, we started early. So we if you sort of cumulatively look um it's a good I'm not sort of saying you know right right now speaking about a lot of capex is not a feature it's a bug but that said but if you really go actually add up the math uh given when we started because we started multiple years before people woke up to even actually needing to build and so that's kind of one aspect
24:20of it. The other aspect of it is we are calibrating our capex in such a way that we don't we don't want to build for one or two customers right so we want to build for the long tail right because that's I think most important and that's I mean that if you're a hyperscaler you're not a supplier to two model companies that's not a business uh you have to sort of basically build a system that is great for lots of third parties uh and our own one in that context we're pretty thrilled with the progress we're making uh with even copilot if you sort
24:50of look at the subscriber numbers we gave which is this is goes back in fact to Chamat's fundamental point which is these are real enterprises using it for real workflows u and the fact that we now have 30 plus million not over forum remember the total knowledge worker base right where most people talk about 3 billion people 4 billion people on the internet the entire office 365 or Microsoft 365 is the the the sort of the standard when it comes to knowledge work there's 450 million that's including all students in the world oh wow
25:20right So when we talk like the market quote unquote as defined is maybe 300 uh 250 even of real enterprise users and of that we've got the penetration of close to 30 million on that and it's growing and so on. The aspect on the model side is we're thrilled about obviously our investment in open AAI the access we have to their IP which we have for a long time we're going to use that but we are well on our way building our MAI models right if you look at it we have a
25:49flash cyber model that you know with our harness orchestrating other models outperforms um on cyber gym even a mythos uh same thing we're seeing in coding same thing we're seeing in uh knowledge work right So our goal is to basically hill climb from the bottom by the way uh not distilling anything. So from the very bottom using our RLES our data uh and then also have a differentiated position with enterprises going back to
26:18addressing some of the things that they want which is hey can I have the weights can I have the weights that I can then add to my knowledge uh these are the things that we will do with our foundation. Your best advice I think to enterprises is AI sovereignty is important. Putting your data into a frontier model probably not a good idea and then you're going to be that harness for them to to help them. So my implement my advice is more like use all but be independent of all. So for
26:46example my asset test is you should always eval that matter to you right. So what's the outcome you want? you should go run that outcome through all the models. Then here's the test I would do. I would pull out a model and see whether I can retain the eval. If I can't, that means you really are dependent on something that may or may not be yours.
27:11Right. That's so so my fundamental enterprise architecture would say you should have a model system that fundamentally allows you to be able to continuously hill climb on your own on eval uh while using all models closed open u if you want you can even fine-tune any of these models but you can even substitute models s just to build on Jason's question you had this um incredible moment I think we put it here where you said you know
27:41we're good for our 80 billion. But just to expand the question, um there's effectively this sort of bank of AI that has emerged and there's this financing mechanism that just is so important to the entire ecosystem and now broadly to the entire economy. But you've been very disciplined. You have an enormous balance sheet. You're also an investment grade issuer. So you could do what Jensen did, but you've taken a very different capital allocation approach, much larger bets, very concentrated, and you've kind of stayed into your own
28:10ecosystem. just talk us through your mindset as a capital allocator at Microsoft and that balance sheet. Yeah.
28:16So the way I'm sort of looking at our book of business whether it's the hypers scale our model or our app tier and the shape of the demand um and then what's the way to build out for it. And so if you think about these assets right there are two classes of it. There are the long lead um long duration assets like the the land power cold shell let's call it. Then there is the kit. the kit is
28:43the short-term uh asset uh that you can much more you know uh be demand driven in other words right I have to forecast let's say two years three year out demand and then and then also the kit means the racks the chips the racks the chips and what have you and that's 60% of the cost or what have you right so therefore so what we do is we go build as much um we lease we even rent now right now we're even renting quite a bit because we kind of were
29:09short on supply uh But the overall goal is to build more lease some and then if really need to surge we will even rent that's kind of on the on the on the uh assets and then the chips themselves we will try to be first of all make sure that we're matching demand and as I said my goal is not to have just two customers three customers uh it's great to have openi being one of our largest customers it's great that they're
29:39growing uh but we need more uh is the kit over earning right now and do do we need is the is the industry pushing for diversification more silicon more memory more vendors yeah what's happening is the workloads that are now at scale uh they obviously grew up from what GPUs were there but now the the shape is so well understood
30:06uh that you're able to optimize for a very different world right So you can sort of start building um and saying well you know there are these multiple phases in um an inference or a training phase so why not build silicon that's optimized for these uh and that's just going to lead to a systems architecture that I think is going to by definition have a lot more uh diversity uh I mean I know you have Jensen coming he himself if you look at his own architecture is changing quite drastically
30:36quite drastically um and so I think that there is going to be a lot more choice even there in that layer. So ours we have Jensen stuff which is I think our primary thing. We have our own uh OpenAI is building their chip so that's also going to be there.
30:50AMD is in there. So we I I my thing is to run whether it's the OpenAI models, the anthropic models or our own models on a heterogeneous kit.
30:58Sax I want to let you get in here before we run out of time.
31:00Yeah. So you know we've heard now from the the various frontier lab leaders Sam Dario Elon Demis that we need to prioritize alignment like we're talking predictability reliability robustness uh as opposed to maybe just say raw raw power. Do you think the Chinese labs will follow suit?
31:20I think that that's the dialogue um that is I think should be prioritized right so because at some level my own premise would be that that China should also deeply care uh about the same safety concerns if the United States uh cares about them right why should it be different for them it's not like they won't have the same hacking problem
31:47uh it's not as if uh they don't want to make sure that their citizens um are benefiting from AI just like we will want our citizens to benefit from AI. So I think that there's a possibility of international norms around it. If we really are concrete about what's the risk, why is this risk so idiosyncratic that the only people who are worried about it is the Americans. Uh it doesn't make sense, right? It's not like a thing that is sort of said, "Oh, I'm going to
32:16only show up in the United States. I'm going to be something. If it is going to go wrong, it's going to go wrong everywhere at the same time." So I think the Chinese should care. I mean they're they are a superpower.
32:27Well that's you use the word idiosyncratic and I think that is the right word is I don't think we know yet is this um you know conversation we're having in the US over the past week. Is it idiosyncratic to us because we have you know the strong I guess you could say doomer type uh school of thought or is it something that the rest of the world will basically feel as well?
32:48It's a great question and if they do then presumably they'd want to act on it as well. Yeah, I I just feel my my take there is that we are ahead and we are who we are which is we argue we sort of we compete uh we are more transparent which is all by the way virtues as far as I'm concerned so therefore the fact that this debate is happening here the world will be better off for it right so to some degree us setting if anything I would love a US
33:16set us to lead in the norms that allow us to defuse use this technology broadly and create safety standards uh that work for the world including China. But what do you think we should be doing that we're not doing and what are you doing at Microsoft to change the narrative the populist sentiment that we have to shut down super intelligence stop building data centers etc. So, so to me I think this is I am
33:44squarely focused on one of the to answering Chamat's question from earlier which is whom is it benefiting and give me concrete stories right uh we talked about the productivity benefits a bit uh whether it's in healthcare or in general knowledge work coding but I'll give you another example right I was looking at data centers because after all we didn't talk much uh today on that but there's a challenge on how does one earn
34:12permission uh to open a data center in a region. In fact, we just have some of the best longitudinal data now for a data center we built out in Quinsey, Washington, uh for 20 years, close to, you know, 2008 is when we started it.
34:28And when I look at that data and what it has meant for that community, right, where uh the tax revenues have gone up 12 times, uh the paidin taxes have gone down by a third. Um the growth is higher than Seattle in Quinsey. This is a rural town. Uh they have a new school, a new hospital, a new town center, a new aquatic center. Wow.
34:54Uh we have two and most people say, "Oh, there not that many jobs." In fact, there have been 1,200 construction jobs in that region all through that 20-year period, right? Because it's not like you just build it and leave. You continuously refurbishing, building, expanding.
35:09And how big, how big is that data center?
35:10Uh I think it's now going to be at least 4 or 500 megawatt and it sort of will keep expanding.
35:16Um and so so these are uh so that's a real like that community. So earning it like just not saying hey these are all the benefits but seeing it but how do you get people to tell that story because that's what's missing today is those stories aren't being organically told and if a Microsoft executive gets on stage and says don't worry it's good for the community.
35:36Yeah. No I don't think Yeah. So I think storytelling is one thing. The other one is I think we just need more people outside of the tech industry to say yeah because if you go to Quinsey Washington they will tell you thank god for this data center. It's part of like you know.
35:51So to me that's like when it's tangible uh like that uh because that's the only way to earn permission because at some level the skepticism of any of us in the tech industry just saying things uh is so high that I think we have to now do the hard yards of actually doing things in the world uh which allow people to say okay I now believe you.
36:13It's a new muscle. It's a new muscle.
36:16So I think you're a good spokesperson to flex that muscle. I hope you do it more. Thank you for being with us.
36:29Thank you, sir. Appreciate your time.
