Satya Nadella: Whether to Trust Frontier Models | Why Companies Need to Own Their Own Intelligence

Can I ask, what does that trust system look like? Who's the architecture of it? Who's the arbiter of it? It's a great point. So that's why I kind of look at it and say, you leave AI safety outside of this, right? Because that alignment, for example, at least my take is that it's really mostly has to show up in training, right? In fact, maybe even in pre-training. I don't think you can even RL your way to alignment. You have to truly get alignment to be something that truly gets embedded in the weights right at the very beginning of it. And so that's the model itself. Hopefully, at some point, we'll get aligned.

So if you leave AI safety, then the trust architecture is going to be just, you know, what a company like Microsoft does or all the cyber folks do. So what we have done, for example, is we build these two things, right? Agent 365 and perception, right? What does it do? It gives every agent an identity. It then logs all of its activity, all its tool calls, in fact, every model call and the COT unit. In fact, that's where I'm also asking that all the COT better be in English and not in new release. And then you can check using multiple models. It better be tamper-proof, right? You can't have the agent go modify its own logs.

And then on that, you set policy, and then you behaviorally monitor the activity of the agents so they're confirmed to the policy. So that's the architecture. We don't have that architecture today. Is that just symbolic of where we are in the production? No, that architecture exists. So there are two, I think people conflate what I'll call an engineered security, zero trust architectural system for containment and AI safety, right? Because I think people say, well, AI safety is hard and elusive and not yet achieved. So therefore, there must be nothing we can do on security. And I'm not saying that these two things are all got to come together at the same time. In fact, I would just say, hey, we have to deal with the situation as it exists. So let's build a trust system outside, in fact, of the model, and then just be super aggressive in setting policy and monitoring for policy deviation. On the monitoring

Satya Nadellaside, if we think about chain of thought, how reliable is that? It's not, right? We know that the chain of thought itself can be unfaithful. And in fact, it can be made worse when the model

company says, well, it's unfaithful, and I'm going to hide it. And so, it becomes even more problematic. And that's where I think a little bit of the push for standardization and the COT, at least for trusted partners being available as is, so that it's inspectable, it's auditable, and I can check using multiple models and what have you, right? So, that's going to be one of the... In fact, that's one of the things that may even drive more open weights, quite frankly, Because if you can't trust, it's funny, closed trade models are more powerful, but if they kind of don't recognize the need to be more transparent, especially as their trusted partners use it, then we may have a problem.

Can I ask you, when we think about that rise of open source, I completely agree with you in terms of closed being more powerful. Open source today is predominantly Chinese models. Is that a concern then when we think about safety being baked into pre-training, when we think about disclosures, as we said there, and then juxtaposed with the majority of open source being Chinese? Yeah, I mean, I think there are two sets of things. I do believe if you sort of step back, somehow AI safety not being there is not a good thing for China. It's not a good thing for the United States. It's not a good thing for the UK.

So I think that there's a part of everyone, because this is not something that... Somehow an unaligned, open-weight Chinese model will probably attack even China. So it's not like you can sell, the rest think that this will only be used as an attack vector. So setting that aside, I'm pretty pleased with some of the progress even the open-weight models in the West are making. And obviously, Jensen's working on it. The other folks are working on it. We will have even the MAI models that will be not open-weight. I think of it more as licensed weights. Think of it that way. I want to turn this into just a simple, straightforward software business as opposed to a network effects intelligence business. So I think there will be different business models. And I ultimately trust in your ability to use those models and control those models is what's going to determine what gets used. As a venture investor, I have to predict where traffic will go today, between closed, open. When you think about the two different dimensions, which is like traffic and dollars. If we look forward, say, 24 months, what percent of traffic goes through open versus closed? It's a great question. I think it depends on

what the frontier looks like, which is I think most things seem to get saturated. And when it gets saturated, open weights or even just cheaper models of even closed weights seems to work. Because especially one of the things is if you can train on your own traces, then you really don't need the latest model to be able to solve that same problem if the problem has not changed, right? So if you have an eval that you want to continuously keep moving forward, then you may want to pay. But even there, by the way, I'm very thrilled about this hydrofusion stuff we now have in GitHub where- I just saw this. Yeah, because the cool thing there is it's a learned model.

Anyway, we are now going into these agent swarms and multiple sub-agents. So I can call a lot of sub-agents that even happen to be local models. And so I think my suspicion is that there will always be a market for Frontier. There will always be some new things. Science may be the next thing that Frontier is being set at. But I think predominantly token usage will be commodity token usage. Does that mean it's open source predominantly? It may be. It may be open weights. Or as I said, it can also be cheap closed weights, right? Because there'll be lots of price competition, let me put it that way, right?

Because if you look at my own usage inside a co-pilot, we use a lot of close source, cheaper models, along with open rate models. You said about kind of the importance of owning your own intelligence for some of your amazing customers that you have. You're talking about some in the UK. Is that the model environment that we have moving forward, specialized intelligence for companies with their own data? Yeah, so I think this is, to me, one of the most important things, which is what's the future of a firm in an era of intelligence, right? because if you go back even to Hayek, his fundamental description of an economy is local intelligence. Tacit knowledge is distributed throughout the economy. Without that, there can't be only one firm and it can't be one intelligence. And so that's sort of the foundational thing. In fact, capitalism works because of that. Price discovery works because of that. Or pricing mechanism works because of that. So therefore, I think it'll be important for every firm to be able to accumulate knowledge at increasing scales using AI versus leak. That's why I called it the reverse information paradox, which is Kenneth Arrow described this paradox of information

where if you're a seller and you disclose the secret without a patent or a trade secret protection or copyright protection, you leak the value. Now, for the first time, the buyer needs the protection because the buyer, even most people think of it as, oh, my data, it's even my exhaust of using it, right? Thumbs up, thumbs down signal. You're creating value for the model company versus capturing that value for yourself. So I think one of the things that at least I ascribe is that we need to have...

Satya NadellaFor this to be stable, more of that surplus that gets created by use of intelligence to remain at the firm. And by the way, it doesn't need to be only by embedding your knowledge and weight. It

can be in context. It can be in your evals, right? You may have a benchmark. In fact, one of the things that I'm very gung-ho about is we all become, every firm will have a private eval benchmark, which is their core IP. And every model comes in and tries to sort of hill climb on that eval. And that to me is how a firm works. I love this. Private evals, benchmarks for each company. This is for a rarefied era of companies that would seem your Harveys or your Lagoras or the kind of very forward thinking AI companies. Will that disperse to every company in the world you mentioned? Yeah, it happens. Yeah, it happens, Harry, always, right? Which is, you know, there was a day when And I remember in the late 80s and the early 90s that the few companies that had deployed PCs and were doing forecasting, which felt magical.

They were able to put it in Excel, spreadsheets, send it in email and avoid the fax machine and inter-office memos. And it felt like, oh, wow, when will this happen? And it took 10 years maybe, and it spread out throughout. This time around, what is considered, oh, I'm an AI native company and I do this. I think, you know, five years from now, it'll be pretty passe, I would imagine. And then that's kind of what, I mean, these tech cycles always start like that, right? Which is even cloud. I remember in the beginning of cloud, it was like the big deal was, oh, who's cloud native and who's not? And, you know, if you're not cloud native, and now everybody is, right?

Even Lloyd's, you know, which is what, 500 years old, is cloud native. And so it happens. Do we move into an implementation economy then? Because when we think about these customers, like, as you said, some of your very big customers, they will need heavy FDE support, much more so than they have done in traditional years in the last 10 years, will we have a very heavy implementation economy to transition existing customers into this? Yeah, it's interesting, right? The implementation economy, like systems integrators have always existed, right? You know, we've depended on implementation economy for lots of other technology diffusion.

So this one, I think, has different contours, right? Because- What do you think is different about this one? It's just, it's the combination of tech and business, if I could say. But it's not classic management consulting, it's not classic systems integration, and it's not classic sort of technology support. It's sort of, like, for example, think about this, right? You want to start, like, when I look at my FDE engagements, right, even take the Unilever example I talked about at the keynote today. It's fascinating. They've built an agentic system to say, hey, we're going to really have order-to-cash automated using an agentic system, plus their trade claims.

And by the way, they started using a frontier model, and then they substitute for, in fact, I think they're using currently an MAI model for all the trade claims because it's sort of a lower cost. And it's, by the way, they control it, right, because they're able to train on their traces. And so we had some FTEs, but a lot of who got deployed, and now their IT team itself is, you know, managing it all. But it started with what's the business? What's the eval? What's the way to conceptualize the agentic architecture? So it's sort of business consulting combined with technical consulting. And I think, yeah, you're right in saying that there will be a new set of skills that will get built in the ecosystem. Today, I think all of us have, quote unquote, our own FDEs, but FDEs will become like, you know, traditional systems integration will become new FDE.

You've mentioned, obviously, MAI. But when we look at a lot of different players, it's kind of this realization that you need to be full stack from chips to models to application. Is that the future? You have to have vertical ownership? Yeah, it all depends on the industry structure, right? You know, I think that I would say today, you know, there'll be, who knows, five, six closed models all competing, maybe five, six, you know, open weight models all competing. And so therefore... At some level, if you're building an application like a Gentic system, you could even have something like what I said, which is a router with even a decision engine built into it, and then you just route to the best model for a given task. So that's a possibility. Of course, there's always the risk that there could be one vertically integrated model and app tier and all the way to the silicon and system. It's unlikely to me, right? I thought at some point that that might be the case, that there will be one. If anything, it's going the other way, which is every day you wake up and you say, wow, there's more competition in every layer of the stack, whether it's in silicon, whether it's in systems to manage the silicon, in the model layer, in the app layer.

And so I think that's a healthy sort of direction of travel, but you never know, right? Because this is one of those things where it all depends on what becomes the dominant design. But my hope is that this becomes a little bit of a horizontal specialization with multiple winners in each layer, and then the ability for people to compose. The thing I didn't see is the depth of each layer. What I know now that I wish I'd known 12 months ago, it's just like the infinite demand for inference. Yeah, that's a great point. And I wish I'd known it 12 months ago, I would have done some very different investing. Speaking of that, we've had like Mustafa Suleyman saying training runs of 100 billion are coming, and only a handful of labs can pay for that. 100 billion dollar training runs. Do we have the energy infrastructure and power to enable this scale across the stack, but especially when we do- Yeah, I mean, the way I come at it is, ultimately, it has to come from the world GDP inflecting, and then that allowing someone to then have as a percentage of R&D, right? Because when somebody says training run, it's just a percentage of R&D, right? Companies that are heavy R&D spend, what, 15, 20% on R&D.

And so therefore, yeah, at some point, if you have enough revenue, you can spend enough on training. And then, of course, you'll have enough revenue if you're contributing to the world economy growing so that you can get your fair share, so to speak. And then you also bring up the other side, which is the supply side, which is we have power shortages. We have silicon shortages. We have, you know, all kinds of things today. They will all, they're long lead items, right? You just can't, you know, snap your finger and have power generation or you can't have a new fab. And so now, interestingly enough, given the demand everyone's building, and so the question is, when will all of this come online?

What will be the demand supply? No one knows, right? Quite frankly, any one of us who are sitting here today will just be speculating. But my feeling is that if you start by thinking of the stable state would be just like today, right? The income statements are suddenly not going to be different, right, for a company. It's going to be, hey, I have some revenue, I have some cost of revenue, and I have to have some profit.

Satya Nadellait. And if that is going to be true, then the question is, what is the world GDP growth rate?

That's the bigger thing for me. In terms of the inflection of GDP growth rate, the world has kind of stabilized 2% a year for a very large amount of time. Does AI have the ability to seriously inflect that in a short amount of time? I think so. And I somewhat hope so, because that is the point, right? When I look at it, let's take even as a percentage of any of our GDPs, the amount of money we spend in our healthcare. So that's why I look at even the deployment of co-pilot in NHS and say, wow, if it can improve patient outcomes and reduce cost in the healthcare, that is by definition helping the GDP of the United Kingdom. And so I think that there are these pockets of the economy which are ripe for adoption of AI and really, in fact, there's the least amount of displacement, the most amount of benefit. And so that, I think, is where I think we'll have to find the best fit. Software development is another one, right? It's fascinating to see how much the demand is elastic for software development, right? That means the IT back.

clogs were huge and none of us even recognized it i was taught that you had to be the top one or two in a market to win in venture we're in you know factory which is fifth and it's winning fifth yeah this breaks all rules of venture a question that i have again slightly naughty not on schedule but it's just like well we see the tipping in terms of capability advancement in alternative functions finance sales marketing that we've seen in coding because coding has been the mother load I think that that's a good point. People talk a lot about, you know, do you have a verifiable reward and, you know, it's easy to do these because a lot of the magic has been RL on top of these pre-trained models. That is what, you know, has helped coding. Can we crack that in knowledge work seems much harder. But again, all the examples I talked about sort of, you know, in contact centers, in the supply chain systems, in these marketing slash or cash to order systems, I think that it's possible and we're seeing it.

Is the demand as elastic as coding? I think it may be science may be as elastic, right? Or engineering in general. Like, by the way, you know, if I look at even what's happening inside, not just in software development, let's take silicon development. That's another thing that's going through a radical shift, right? Your ability to tape out much faster, simulate everything in silico is much better. Your first spin can completely come out. But even just like optimization of maintenance reports, like you said, with Rolls-Royce, which I would never... It's more. It's unbelievable. Like, I mean, it's a thing that they've been dealing with forever, just scheduled maintenance.

And I had not realized that, wow, that's a big optimization problem that you can go now run. So I've watched many, many of your shows, and we're going to do a quick fire round. And people haven't asked some of these questions, which I've always wanted to know, and I think everyone wants to know, but people haven't asked them. What's the exercise routine given the travel schedule, Satya? Well, I mean, you're in good shape. Well, I try to be. I don't know whether it's all well hidden. but you know the one thing i've gotten into the habit you know this has been maybe 25 years even on which is every morning i get up and you know i used to be able to run unfortunately my knees have gone away and so now at least i walk with incline even this morning i was able to get a half hour in and so that's what in spite of any jet lag wherever i am i get hit the gym every day i do have bad knees it's my age as well uh what have you changed your mind on in the last 12 months? It might be, because after all, I was one of the first, obviously, when Dario and Ilya and Sam and Greg and Elon were all, I guess, one happy family, they were really the believers in scaling laws. And that's what I believed in back in 2018, 2019. And I think the biggest change

now I have is I used to always think that alignment will just fall out. I no longer believe that. So So I think this alignment thing may be a harder, different problem. And so, yeah, that's the one thing I've changed my mind on. What looks crazy today, but will be incredibly common and accepted in five years? You know, it might be that thing that we just talked about, this evals, right? What is a firm? I think a firm would be an AI benchmark. Final one for you. There are many things to be excited about over the next 10 years. For you specifically, like for me, it's my mother's got MS. I'm so excited for the drug development that could be capable.

What are you most excited for? I mean, that's a great one, I think. One of the things that I've always been, I would say, been looking forward to is a general purpose technology, even as powerful as this. And people say, oh, this is not just another general purpose technology, whatever this is. I hope this spreads, diffuses throughout the world faster than anything else and throughout all sectors of the economy, unlike anything else. And truly, for once, there is a real shift in the frontier of that global growth that happens, where it's not about just counting winners and losers, but it's kind of like everyone gets lifted.

That's been my dream. In fact, my father was a development economist. It's about convergence growth. When will the Global South get something that just suddenly boosts that? I hope this is it. That's it. This has been such a joy to do. Thank you so much for taking the time. Absolutely. It's a pleasure, Harry.