Satya Nadella: Microsoft's AI Bets, Hyperscaling, Quantum Computing Breakthroughs

**Satya Nadella** (0:00)
What are the tools that we can put in the hands of people that will give them that sense of empowerment? That's what I would love to work on. I'm not into this anthropomorphizing AI at all. I come at it as it's a tool. There is going to be a job called a software engineer. It's going to be different. But I look at it, you are really taking a software engineer and saying, you're now a software architect.

**SPEAKER_2** (0:29)
It's my pleasure to welcome the Chairman and CEO of Microsoft, Satya Nadella.

**Satya Nadella** (0:40)
Thank you.

**SPEAKER_2** (0:49)
This is the home crowd.

**Satya Nadella** (0:50)
All right. Man, San Francisco, you should move to Seattle.

**SPEAKER_2** (0:57)
I started my career in Seattle. There you go. It's a very fantastic place.

**Satya Nadella** (1:00)
Anyone who's successful starts at Microsoft.

**SPEAKER_2** (1:02)
That's right. So, Satya, you've emphasized before that AI is going to shape all that we do. What does this look like in practice? At Microsoft, how does this actually drive your strategy and particularly thinking about how AI will influence ideas beyond the immediate incredible products suite, like the broader economy?

**Satya Nadella** (1:29)
At Microsoft, I feel we are a platform company, a product company, and a partner company. So I think of those three dimensions. And I've kind of in my 35 years, I've lived through client-client server, web internet, mobile cloud. This is the fourth. So that's just at least how I pattern match. So the first thing that I think about is the platform opportunity. When I sort of look at all the folks here, the interesting thing is the compounding effects of all these platforms, right? So this AI piece, the reason why I think the rate of diffusion is so fast, so well, you know, and so wide is because it builds on the previous generation. I think about like, if the cloud was not there, we wouldn't have been able to build the AI supercomputers, which then led to the models, which then led to the products, right? So that compounding effect is the interesting thing to me. So that's why you always sort of take the previous platform and build the next platform, and you want to be able to get that right. And then you got to build the next generation products on top of it, right? With each one of these platforms shift, there's a new workload, right? I mean, when I first remember looking at the large scale training job, I mean, it's kind of a very different workload to what we built, for example, the cloud with, right? It's a data parallel synchronous workload, which is so different than, let's say a Hadoop job or what have you.
And so the platform itself then completely gets re-change, you know, completely re-litigated and changed. So to me, that's, I think, the exciting thing on the platform side, it's golden age of system software. Quite frankly, you know, today, if I had to think about anybody who's building at the infrastructure layer, not just the hyperscalers, but even the startups, I think that's a tremendous opportunity. Obviously there's a tremendous opportunity in the model side and then the products on top of it. So yeah, we think of these and then ultimately, what's it for? It's for one thing and one thing alone, which is to drive ultimately economic growth and GDP growth. So if I had to ask me, my benchmark for AI is, is it creating surplus in the world around us, one community, one country, one industry, one company at a time?

**SPEAKER_2** (3:56)
I mean, it seems like the app level, you know, you guys have built, you know, sort of the defining apps at the app layer for so many decades. It feels like we're at this weird lumpy moment where, you know, maybe the models have popped up and we're sort of astonished by what's happening. But then, you know, sort of the compute and the apps need to actually catch up. And, you know, the hope here is actually the people in this room will be the people who build those apps.

**Satya Nadella** (4:23)
Yeah, it's a good question, right? One of the questions is, is the model like SQL? Or is it the SaaS app itself and the model, right? I mean, I think the place where, where does the model end and where does the product begin? Because if you sort of say model with some scaffolding and tool calling in some infinite loop is the product, if that is what it is, then I think that that's where it gets a little confusing. But that's like saying a bunch of SQL business logic is with SQL is what is an app. So I think it's still possible for anyone to build an app tier on top of a model. And you have to sort of abstract yourself and say, yeah, the model is just like SQL was to me. And so I think that, I mean, I always dreamt of a moment when AI slash machine learning will have a SQL moment. Because if you think about it, we never had a stable platform layer in the past, because everything was vertically built and integrated. For the first time in this model layer now, we have something like a SQL engine that we can then use to build pretty sophisticated products. And these techniques also, right? I mean, just the inference time compute plus tool calling is giving us, I think, a pretty robust harness to be able to build pretty sophisticated products.