Name: Satya Nadella: Microsoft's AI Bets, Hyperscaling, Quantum Computing Breakthroughs
Uploaded: 2025-07-04T21:11:50.000Z
Duration: 40 min 30 s
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What are the tools that we can put in the hands of people that will give them that sense of empowerment?

I'm not into this anthropomorphizing AI at all.

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.

It's my pleasure to welcome the chairman and CEO of Microsoft, Satya Nadella.

Man, San Francisco, you should move to Seattle.

Anyone who's successful starts at Microsoft.

So, Satya, you've emphasized before that AI is going to shape all that we do.

At Microsoft, how does this actually drive your strategy and particularly thinking about how AI will influence ideas beyond the immediate incredible product suite, like the broader economy?

At Microsoft, I feel we are a platform company, a product company, and a partner company.

And I've kind of, in my 35 years, I've lived through client-client server, web internet, mobile cloud.

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 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've 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, you know, completely relitigated 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?

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.

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.

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.

It's kind of wild how much it's the integration piece that is also the app layer now.

It's just, the models sitting on their own are incredibly smart, but right now they feel sort of, there's just giant gulf between that and the data that really matters to you for business.

Yeah, I mean, I think that's a good observation because I think at least my read of the situation is the model is an important piece, the model scaffolding and all this tool calling, so there's a real app server that you kind of need in order to be able to build sophisticated applications.

But the interesting thing is the feedback loop, the data path inside the product that then is used in order to post-train, in order to be able to do the right tool selection, that seems to be the place where product creation is all gonna happen.

AI scaling laws are continuing to hold and the demand for intelligence appears to be potentially infinite.

Yesterday, Elon was mentioning that there will be 99 hyper-intelligent beings to one human, which is kind of a wild prognostication, but seems possible given this, where does the building for the future of AI truly demand for global compute infrastructure?

How do you anticipate these demands evolving as models don't just become larger, but more intelligent and capable of complex multi-agent interactions?

Yeah, I mean, look, if you sort of really step back and say, first, if you sort of go with the compute or intelligence is one of a log of compute, and then you ask the question, how much energy does compute consume?

Let's take in the United States, maybe 2% today, 3% tops.

That's like massive because then the amount of extra energy that needs to be produced in order for AI to use it is pretty high.

I think that's why we all have to sort of keep in mind that if there's one lesson history has taught us is that if you're gonna use energy, you better have social permission to use energy.

So that means you've got to make sure that the output of this AI is socially useful.

In other words, if we really are not creating social surplus, economic surplus, as measured by countries and communities, then we just can't consume energy.

Like everybody's today hot and bothered about, okay, what do I do about energy production?

I think the real question in the next five years is we've got to produce enough products that are creating great value, which I'm very confident of, by the way, in healthcare and education, in productivity.

So there's many, many domains, but that's the real challenge for us as a tech industry is to prove unequivocally that what we have created is showing up in real stats that is not just an AGI or AI benchmark.

I mean, the hope is that this will show up in sort of the real things that you sort of interact with on a daily basis. 100%.

You know, you go use, you get a mortgage loan and instead of three months or two months of waiting around and you don't know if you're going to get approved, or, you know, there's just so many things that are important parts of your life that get drowned in paperwork or bureaucracy that those things could potentially go away. 100%.

So I think, yeah, if you take even, take some of the public services, right?

I mean, if you take any country, you know, it's GDP or take healthcare, like in the United States, what is it, 18, 19% of our cost is healthcare.

And a lot of it, like everybody talks about the magical drug, blah, blah, blah, except all of the cost is in workflow.

And so if you really take something like a simple thing like discharge, the amount, like you take the backend of an EMR system with just an LLM and a prompt, that itself is going to save so much time and money and energy that it would sort of pay for itself.

You spend an incredible amount of GDP on healthcare and rightly so, but every dollar that's spent on clerical work could have been spent towards some sort of treatment that would have saved someone's life.

Or the simple time allocation of a physician away from paperwork to the patient is right there to be had.

What do you see as being the biggest rate limiter for AI deployment today?

See, here's the interesting thing, right?

And this audience is so young and none of my metaphors would sort of work, but nevertheless, if you sort of came in in the early part of, let's say you were a multinational company, pre-PCs, how the heck did we do forecasts?

The way one would do sales forecast was you would send faxes.

People would then take those faxes and send inter-office memos.

And those inter-office memos would be annotated and a forecast would come hopefully before the quarter end.

And then suddenly people said, with email and PCs and Excel, they said, let me just send an Excel spreadsheet in email.

People enter number and you have a forecast.

So what happened was the work, the work artifact and the workflow changed.

When someone says, I'm going to now do my job, but with whatever, 99 agents that I am directing on my behalf, the workflow is not going to be constant, right?

I mean, you now are really going to have to change even the scope of your job is going to change.

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

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