Why Qualcomm is betting on AI data centers

So, Cristiano, you run Qualcomm, and your technology powers many Android phones,

met a smart glasses, a bunch of coming wearables, and a growing number of cars. And I want to get into all of that.

But I want to start with something else, which is you also have a very interesting view of the U.S. and China.

Qualcomm has a very strong Snapdragon brand in China.

That's something I've actually learned a lot being here at the summit that you guys are having, is just the rabid fan base for Snapdragon in China.

And I think that gives you a unique window into the two markets.

And I want to start with how people in China are feeling about AI relative to the United States.

The sentiment, when you look at the sentiment and how it pulls on AI around the world, it's very different based on geographies.

Maybe could you contrast the two a little bit? If I do a

compare and contrast, the broad adoption of AI into the devices and in consumers, I see that happening in China very, very fast.

In the United States, I see the development of frontier models in enterprise adoption AI. Those are kind of dominating the conversation.

That's the difference in conversation between both. And when you talk to people in China about AI, does it feel like they're imagining maybe a different future than people in America?

The sentiment in America, when you look at the polling, it's incredibly negative.

Look, every new technology has – it will bring uncertainty, right? So, you know, it's interesting.

You know, if you go back in time and you see different technology transitions, some of the concerns.

Obviously, what happened in the United States, given my perspective, is AI has been an important productivity tool for a number of sectors.

For example, a company like Qualcomm, I was even asked today in a different conversation, said, well, you guys are doing a lot.

You're transforming the company. You're doing a lot of things in parallel. How can you allocate capital to do all of those things in parallel?

And if you actually look, we have been relatively expanding the company with kind of a flat scale of operating expenses.

A great part of this is productivity gains using AI to the development process.

So you have clearly those aspects of AI for productivity games, and they are obviously concerns about is that going to impact jobs?

Are companies going to leverage their own AI and they're not going to need as much manpower?

So in that conversation, it's probably universal. I don't think that's unique to the United States.

You do have this conversation about data centers. Data centers is massive. You look at those massive deployments.

you need more and more compute. It's a wall, I think, of the demand for compute.

And you obviously have concerns about, is this going to increase my electricity bill?

So those conversations, I think, are natural. The latter, I don't see it in China.

I see more of it's like a feature, it's an evolution.

The two countries are looking at this a little bit different. I think, for example, China has been really focused on physical AI and everything on the edge.

There's a bigger focus on China right now.

But I believe in general, because of the uncertainty associated with AI, you're going to have a lot of questions.

And to be frank, we're in the early days of AI. Early, very early. I like to do this metaphor.

It's almost like in the early days of the internet, if I could have to say, okay, we already know what's going to happen.

AltaVista is going to win the search, and MapQuest is going to win the map, and Orkut is going to win the social media. No.

I think we still have a lot of things that needs to happen on AI, and some of those questions will be answered.

This episode is brought to you by Mercury, AI-native banking that's loved by more than 300,000 entrepreneurs, including me.

Visit mercury.com to learn more. Mercury is a fintech, not a bank. Check the show notes for details.

This episode is also brought to you by Jira Byadlassian, where teams and agents get the context, coordination and control to move work forward.

Try it free at Jira dot com. That's J.I.R.A. dot com.

Thanks also to Granola, the AI notepad for people in back to back meetings. It works everywhere you do and lets you focus on what matters.

Try it at Granola dot A.I. slash sources and use the code sources for three months off.

Right now in the U.S., a lot of people are talking about this idea of pacing, pacing the frontier of AI development.

You're seeing things like the hugging face breach, the models becoming more capable, cyber risks increasing.

And I'm curious where you stand on this because you, the company and you, you've been in Washington.

You're obviously a voice in the policy space in the U.S. on this. When you're seeing this debate, what do you make of it right now?

So I'll provide my opinion and I will, two comments. I think first, I'll give you my opinion for free.