Satya Nadella on the AI Doomer Slowdown, Microsoft's Master Plan & Who Wins AI

That's in the Dell Achievement in CO of Microsoft. And you've been the CO three and a half years.

The stock is up about, I guess it's about 120 percent.

I'm good for my 80 billion. I'm going to spend 80 billion dollars building out Azure. Maybe after the Industrial Revolution, this is the biggest thing. That's our goal without frontier model. Our model should be the best model that they can use as a base.

We create technology so that others can create more technology. That's who we are with toolmaker. Please welcome Sartier Nadela. All right. My guy, good to see you.

Good morning, guys. How are you? Good. Thanks for joining us. Crazy weekend, but here we are.

Do we need to paste the frontier? So 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.

“Then when I think about pacing whatever, the first thing that I 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. So at the end of the day, if you sort of say serving humanity, let it actually reach humanity. And in ways that it serves humanity. And that means you got to have choice. You have to have competition. You have to have all kinds of business models, whether they're open, wage, close, wage, 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 this is so opaque, right, I want my privacy. I want to be able to embed my knowledge in a set of weights I control. I want to see all of the COT that's being generated.

I want to use it to do fine tuning of my own models. My IP shunt leak. So there's an entire body of things that nobody's talking about as much, which is, I really want to make sure that this tech is in my control.

“Then we get to, what is I think a real issue of safety?”

And we should take it seriously, which is, we should take all the time we want to test things. In fact, I love this idea of having third body testers.

They're not a wow, but you know, I grew up in a company that's always done testing.

So it's novel that we should say, wow, they're having embedded third body testers. Why not? It's a great idea. In fact, the only thing I would say is, we should avoid like this cosy arrangement. So who's testing what, who has access to what and it should be broad? Were you surprised though then when both the essay landed and then it seemed like there was a circling of the wagons amongst the frontier come. I think that it comes, 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 saying reward hacking and what's happening in these environments, right? With these agents swarms. There is the mundane. There is some DevOps error where somebody misconfigured a container, right? Right. Right. Two API keys on an API key. So yeah, exactly. There's no monitoring. There's internet access. There's sort of classic call at basic DevOps. And then there is real novel news stuff, right, which is what is this reward hacking 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 at our job post, which is a good one, which is how to call that we're growing intelligence, not building intelligence. It's an experimental science. And so the more experimental science is, 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 about it, right?”

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 that I give one of these frontier models inside an enterprise, where I say, you know, I don't know, you know, telling David this.

And so what is the way to do that? I would say, oh, go build a maybe a causal model, like a semantic model that actually checks and verify.

So I think there's a lot of product buildings. I would say making things more robust, which is classic engineering, that we should be talking a lot more about transparently was saying, hey, this is so mystical that, you know, we can't figure this out. Do you buy this argument that it's mystical? I mean, I buy the argument that we do not understand the latent space, 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 have a complete understanding. That's why, by the way, I also, I don't believe in new release, right?”

So that's why I think making sure that the COTs 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 COT, then you can change a fund. So then you can really go look at it deeply. In fact, you can have multiple models, and you can look at the COT across those. I think these are all things that I think will become very important. It's not that you've worked with, you've worked with technologies for decades. And when you see as a leader of one company, Microsoft, which has very crisp communications with the public, and you see what's happening with Dario and his team, people coming out saying 10% chance we all die.

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 terrorizing them? You're not a psychologist, but you have worked with technologists for a long time. 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. Yeah, it's hard for me to speak to what's happening in any of these places.

But let's just say how we, I grew up even inside of Microsoft, for example, one of the biggest things you learn as an early sort of engineering lead is how to deal with a short stop or bug. That's kind of like 101, which is why on your face, you have a bug. What do you do? Do you stop 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 be like transaction processing, I remember working on databases, right? You know, wow, you know, you got to take very seriously any bug, 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 culturally in the AI industry read discovering maybe because when you see and it's possible that they see stuff which are so stoppers before the rest. And if you see a show stopper, stop the show, right? So fix the bugs. When you saw the the hugging face run and it was super performative to our cash, it is whole post civilizations, what do you think? What what what your take on that test they ran because they could have run a test where they had 3,000 agents defend a bunch of websites.

Instead, they constructed them to hack websites and you know, the hiding of information while this anthropomorphicizing whatever of the agents.

I mean, the way at least I understand it was it was actually, you know, basically trying to do an evil for cyber gym.

And as I understand it, given that evil, it sort of figured out the way to say let's just say reward hack. And that's what led it to hugging face.

“In fact, it speaks to, I think, what's the clear issue right now, which is you can have these things if they're long running persistent agents.”

Become essentially like new inside a risk. 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 reads great solutions is true aggressive monitoring of agent activity that's behavioral evidence evidence. And so everything is got to be auditable 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 vulnerabilities to go hack.

And in fact, I think the core of my take is we will have to get the engineering process around building out this experimental science to be more robust.

Yeah. So I think I think it's a great point. How you differentiated in the hugging phase episode between the mundane things they got wrong, like the misconfigured sandbox and the hugging phase credentials, just sitting in a public repository. And there's 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 problems coming from that reward hacking. I think that means for, 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, but 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 is going to be? Great question. I do think there's already a massive model overhang, right? Capability overhang in the sense of the models are very good. Except the broad diffusion requires a lot of things, right?

It even requires, essentially if you're compressing workflows and changing workflows to happen differently, the amount of change management that needs to happen in order to even incorporate these systems is sort of what's taking time. So to some degree, I would say, and also the ability to create these new form factors, right? I mean, if you think about coding agents and coding agents became really usable and you discovered that you could have an agent loop with a file system.

And that was the breakthrough that just made coding agents work.

And I think now maybe with Kua, right? So which is with Astro with Kua could be a way for us to even do computer use or just use long trajectory tasks that can completely automate it.

“So I think these type of product innovations where the model plus the harness allow us to do things that then lead to broader adoption, right?”

Are you going to go back to the chat GPT moment for me, right? Because it was that RLHF at the very end that made a chat conversation possible. 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. And in that context, by the way, the other thing is it's going to be a multi-model work, right?

So at this point, just not a 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 don'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 interrupt, right? Like even KV cash, like why the heck can't I use multiple model families and have KV cash reuse, right? We've had documents, standards, you know, I live through it, you kind of have things that are interoperable in the real world, everywhere else.

“So I think this industrial says to wake up and say, hey, in fact, if I were talking about the most important pressing things is, how do I have more standards on interoperability?”

How do I have a harness that is external to a model so that my memory is not tied to one model? Because the first time you're going to have a technology where you're use of it and the exhaust and the data could not be yours. I mean, if I gave 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 do you feel about it? So therefore, I think we have some serious issues like that to deal with. I think that's a good segue.

I think that's one question to connect the economic incentive argument on what's going on. The argument is the frontier labs are facing token compression.

Fifty bucks for open AI'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. If that is 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.

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 charging for rent on your compute?

Or do you want to be in the application layer? I know you talk about this a lot, but I just hope you're perspective from where we sit today and how this all kind of. I think the fundamental thing that I think we're observing is good old fashion competition, right? I mean for me if I look back at it, we were we had like some real great close source assets windows. What was the check against it? Of course the math, but Linux.

We had a great close source product called SQL Server. What was the check against it? There was always a substitute called Postgres or my SQL.

“So I think that's what's happening. A little bit of it is there's real competition between close source and the open source check is real.”

And that's good, quite frankly, because without it, I don't think we're going to have a broad frontier ecosystem or broad diffusion, because otherwise we'll just maybe back to some mainframe lock it. That's just not a thing. To your point about if anything given 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?

This 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 close source, the prices wouldn't have been at a place where people could have built the app tier successfully and with a margin.

“And so I think the apps are going to become much more viable economically, which is great for the ecosystem.”

There is 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. In fact, you know, the period of they can manage the token pricing based on their model family. If anything, I want them to work on even the case, you know, these standards such that we use multiple model of them. In fact, it's better for them. In fact, I worked on Windows interrupt with Unix first.

In fact, it is a counterintuitive, right? We used to think, oh my god, this interrupt means we will be less used, except we will more use. In fact, we became a weirdly enough wind 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.”

So at the end of these, we're in this interesting moment where, on the one hand, you have these experts asking for regulation asking for oversight governance, it typically always leads to some restriction of freedom. And general society are put in a position where now we have to apply 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, I watched data to tell us why we're sleeping less. That's like functionally the bar for most people or why is my kid an asshole into chatGPT.

So can you just help us bridge this? I mean, you see so many enterprise applications. Where's the magic? Like where's the where 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? Yeah, 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 broad based? It's not just supplier or supply side.

The one example that I love and I get back to, in fact, healthcare is a good one. If you think about healthcare and even the simple doctor patient interaction. In our case, we have this thing called DAXCO pilot.

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 EMR system.

That's a good productivity gain. If it can triage the inbox for the doctor so that they can be more responsive. That's helpful for the patient and the care system. The administering fact king, like because it's the triangulation of the pair patient and the health system. That's all. In fact, most of healthcare is sort of all workflow cost.

But do you see that in micro enough with the people that you're helping? Yeah, absolutely. We see that. And by the way, even 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, but the bottom line is what are the new jobs that get created is going to be one of the key aspects of it.

But also a lot of knowledge work. Unfortunately, it's drungery.

“I get up in the morning and I think about like, man, all I do is email triage, right? What if even just these workflows that are taking away time from things that you could be spending time on?”

Okay, well, 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 seven 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 religious groups that had to work in the same factory. And then when you look at long run GDP outside of some exogenous events, it's sort of is, you know, between two and 400 basis points. And so what happens is this productivity boosts come in, human works steps back and you kind of accomplish the same amount of work.

Do you think that that happens here? Is there a risk that we have with three day work week and we're just still growing at two and a half percent? Yeah, that's a great question. 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 how it helps me augment some workflow or simplify something that's happening today, is it inventing new things? Is it speeding up drug discovery? Is it taking the, 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.

Yeah, that suddenly it's no longer just, or I have any RP or a quick books like thing, but I'm truly making decisions based on the ability to introspect my invoices, my emails and what have you and some, some hop to my working capital. That's productivity that didn't exist. And so I do hope that we will start seeing GDP growth, which we did see in the industrial era during the first phase. Right, 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 seven, eight percent GDP growth that is real and that's broad based. So what business is Microsoft in in relation to AI? Obviously, Azure has been crushing it.

You're turning away customers, and you're doing $175 billion in CapEx billed out, which your CapEx is far below what met is doing, far below what Google is doing there.

Doing secondary raises and raising debt, $350 billion in the front tier labs or spending $500 billion. You were so early to the party with the pression open AI investment. 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 here? Do you need to have a frontier model?”

Did we tell you there was one journalist on the panel? No, no, no. I mean, it's necessarily because I'm just curious you're a great strategist. We know that about you. Microsoft missed the mobile revolution.

Is Microsoft going to miss the AI revolution? If you don't have a frontier model, because I always found a perplexing that you didn't.

It's the strategy there in all seriousness. Do you think open source is going to win? You should have that play? Yeah, so let me walk through the sort of way beyond what we're up to on each of these. By the way, on the CapEx side and the build-out side, we started early. So if you sort of humanitively look, 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's not said.

But if you really go actually add up the math, 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 of it. The other aspect of it is we are calibrating our CapEx in such a way that 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, if you're a high-prescaler, you're not a supplier to model companies. That's not a business. You have to sort of basically build a system that is great for lots of third parties and our own one P. In that context, we're pretty thrilled with the progress we're making with even Copilot. If you sort of look at the subscriber numbers we gave, which is, this goes back in fact to Jamaz's fundamental point, which is these are real enterprises using it for real workflows. And the fact that we now have 30 plus million, not over 400, remember, the total knowledge work base, right?

Most people talk about 3 billion people, 4 billion people on the internet.

This 450 million, that's including all students in the world, right?

So when we talk, like the market, Kotankod as defined, is maybe 350 even of real enterprise users. And of that, we 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 AI, the access we have to there 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, they have a flash cyber model that, you know, with our harness orchestrating other models outperforms on cyber gym even a mythos.

Same thing we're seeing in coding, same thing we're seeing in knowledge work, right? So our goal is to basically hill climb from the bottom, by the way, not distilling anything.

So from the very bottom, using our our release, our data, and then also have a differentiated position with enterprises going back to addressing 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? These are the things that we will do with our phones. Your best advice, I think, to enter prizes 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.

“So my advice is more like use all, but be independent of all. So for example, my asset test is you should always eval max eval's 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 out 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, right? That's, so my fundamental enterprise architecture would say, you should have a model system that fundamentally allows you to be able to continuously hilt time on your own on eval's that are yours. While using all models closed open, if you want, you can even fine tune any of these models, but you can even substitute model.

So if there's just a build on Jason's question, you had this incredible moment I think we put it here where you said, you know, we're good for our 80 billion, but just to expand the question, there's effectively this sort of bank of AI that is 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 ecosystem, just talk us through your mindset as the capital allocator at Microsoft and that balance sheet.

“Yeah, so the way I'm sort of looking at our book of business, whether it's the hyperscale, our model, or our app tier, and the shape of the demand, and then what's the way to build out for it?”

And so if you think about these assets, there are two classes of it. There are the long, long duration assets, like the land, power, cold shell, let's call it. Then there's the kit. The kit is the short term asset that you can much more, you know, be demand driven, in other words, right? I have to forecast, let's say two years, three year out demand, and then all it means the racks, the chips, the racks, the chips and what have you, and that's 60% of the cost of what have you, right? So therefore, so what we do is we go build as much, we lease, we even rent right now, we're even renting quite a bit, because we kind of are short and supply.

But the overall goal is to build more, lease some, and then if we really need to surge, we will even rent. That's kind of on the 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.

It's great to have open AI being one of our largest customers. It's great that they're growing. But we need more. Is the kit over earning right now?

“And do we need is the industry pushing for diversification, more silicon, more memory, more vendors?”

Yeah, what's happening is the workloads that are now at scale. There obviously grew up from what GPUs were there, but now the shape is so well understood that you're able to optimize for a very different world, right?

So why not build silicon that's optimized for these?

“And that's just going to lead to a system's architecture that I think is going to buy definition, have a lot more diversity.”

I mean, I know you have Jensen coming. He himself, if you look at his own architecture, is changing quite drastically. 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. Open AI is building their chip, so that's also going to be there, AMD's in there. So my thing is to run, whether it's the open AI models, the anthropic models are our own models on a heterogeneous kit.

Sacks, I want to let you get it right now. Yeah, we run at a time.

Yeah, so, you know, we've heard now from the various frontier lab leaders, Sam, Dario, Elon, Demis, that we need to prioritize alignment like we're taking,

flexibility, reliability, robustness, as opposed to maybe just say wall, wall power. Do you think the Chinese labs will follow suit?

“I think that that's the dialogue that is, I think, should be prioritized, right?”

Because it's somewhat my own premise would be that the China should also deeply care about the same safety concerns, if the United States cares about them, right? Why should it be different for them? It's not like they won't have the same hacking problem. It's not as if they don't want to make sure that their citizens are benefiting from AI just like,

we will warrant 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? It doesn't make sense, right? It's not like a thing that is sort of said, "Oh, I'm going to only 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, there are a superpower.

“Well, that's the use word idiosyncratic, and I think that is the right word is.”

I don't think we know yet it is this conversation we're having in the US over the past week. Is it idiosyncratic to us? Because we have the strong, I guess you could say, "Dumer Type School of Thought," or is it something that the rest of the world will feel as well? It's a great question.

I do, then presumably they'd want to act on it as well. Yeah, I just feel my take, there is that we are ahead. And we are who we are, which is we argue, we sort of we compete, we are more transparent, which is all by the way, we're choose as far as I'm concerned. So therefore the fact that this debate is happening here, the world will be better off for it.

So to some degree, us setting, if anything, I would love a US set, US to lead in the norms that allow us to diffuse this technology broadly and create safety standards 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 and to change the narrative, the populist sentiment that we have to shut down superintelligence, stop building data centers. Yeah, so to me, this is I am squarely focused on one of the answering Jamaat's question from earlier,

which is who is it benefiting and give me concrete stories, right? We talked about the productivity benefits a bit, 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 today on that, but there's a real challenge on how does one earn permission 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 Quincy Washington for 20 years, close to 2008 is when we started it.

And when I look at that data and what it is meant for that community, right, where the tax revenues have gone up 12 times.

The paid in taxes have gone down by a third.

The growth is higher than Seattle in Quincy, this is a rural town. They have a new school, a new hospital, a new town center, a new aquatic center. We have to, most people say, oh, they're not that many jobs. In fact, there have been 1200 construction jobs in that region all through that 20 year period. Because it's not like you've just built it and lived. You continuously re-furbishing building, expanding. And how big is that data center?

I think it's now going to be at least 400 megawatts, and it's sort of will keep expanding.

But how do you get people to tell that story? Because that's what's missing today.

“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.”

Yeah, 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.

If you go to Quincy Washington, they will tell you, thank God for this data center. It's part of it.

“Like, you know, so to me, that's like when it's tangible, like that, 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 is so high that I think we have to now do the hard yards of actually doing things in the world, which allow people to say, okay, I now believe you.

It's a new muscle, it's a new muscle, it's a new muscle.

“So I think you're a good spokesperson to flex that muscle. I hope you do it more. Thank you for being with us.”

Thank you so much. We appreciate you. Thank you, sir. Appreciate your time.