Qualcomm CEO Cristiano Amon: Future Of AI Devices, AI Fashion, Blending Reality and Computing

26:30Big Technology Podcast Host:They haven't done that yet. What is holding these companies back? Is it a hardware problem, a AI problem? Where is the bottleneck? I think it's a combination of things. But I am more optimistic, I think, than you described. I think we're starting to see, I think, the beginning of some real experiences. I think you have to get the maturity. I think, first of all, the AI models need to get more mature. I think they need to get more capable. I think you had a lot of changes. Even within AI, you started to see a mix of experts. You started to see chain of thought reasoning. So you have different things specialize in specific tasks.

27:17I think we're just the beginning of physical AI, which is really important for you to have context. So I think this is going to happen. The other part of it is compute. you need to have a lot of high performance computing, this is where we come into the picture because you cannot do everything on the cloud because of also latency it is not going to be useful for you if I go back to when you asked me to describe the experience if you and I are walking together in the street and I'm going to say, hey who's this person and you say, this is so and so the answer, you can't say hold on, let me think and let's keep walking i've been thinking about it and then the person went by you missed the point and i think you're gonna have to have certain things you need to do on the device it needs to be fast like all companies right now uh voice to text they're starting to do locally because you can't uh you you don't tolerate any delay so and we're gonna get there yeah we were just talking earlier in

28:19Big Technology Podcast Host:in the room here about uh potentially being on the ski ski hill and having the glasses point you down the hill that suits your skill set. But if you have to wait like two minutes, you might be a bunny hill skier and down the black diamond. So you really want to be able to work fast. And you're going to break your glasses. Yeah, your glasses will be the first casualty. All right. We're here with Cristiano Oman, the CEO of Qualcomm, here at Qualcomm Space at Davos. We're going to be doing four conversations through the week here and thrilled to be here. On the other side of this break, we're going to talk about AI PCs, AI data center, the constraints on the AI build out and robotics.

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30:53Big Technology Podcast Host:Go to shopify.com slash big tech. That's shopify.com slash big tech. And we're back here on Big Technology Podcast, special edition here at We're here broadcasting, talking together on a Monday, going live across our channels on Tuesday. And let's keep going here about how AI will transform devices. The AI PC is a subject that has been interesting to me. A lot of noise about how if you have AI baked into your computer, then you'll be able to be more productive. and it can really transform the way that you work. That's the marketing. In reality, that rollout, that promise has been slow to meet the reality.

31:43Big Technology Podcast Host:This is from the head of product at Dell speaking to The Verge. He says, what we've learned over the course of the year, especially from a consumer's perspective, is they're not buying based on AI. And in fact, I think AI probably confuses them more than it helps them understand a specific outcome. Obviously, Qualcomm has a stake in the success of AI PCs. What is happening today and where is it going? It's a great topic of conversation. Look, first of all, as we enter the PC space, I would argue that a lot of what's driving the sale of Snapdragon PowerPC is the fact that we deliver multi-day battery life, a lot of performance in a very exciting thin and light form factor, right?

32:30So we just built a better PC. On the consumer side, I would agree with that, that you don't see yet a lot of agents. And, you know, I know people want to see this right away. I wish it was seen right away. I don't necessarily disagree with that on the consumer front because Microsoft just launched agents for Windows. It just launched. So I think people are going to use it more and more as you're starting to rely on agents, and I think you're going to see things that are going to be running on your device. But I think that's not the story for AIPC. The story is a little bit different. What we're seeing happening with AIPC and the fact that we actually have the ability to run significant high-performance inference on a laptop, We're seeing it's something else.

33:26What we're seeing is right now you have many, many, many applications and services on your PC that are doing a lot of cloud computation. And if you could rely on the computing that is available on the PC, not only is it going to be faster, but it has a completely different economics. I'll give an example. If you're a SaaS company, and all the SaaS companies right now are being threatened by AI, if you're a SaaS company and you say, I'm going to have an agent within my application, and every time I have this data I'm going to run in, and you're paying for a computer in the cloud, your economics change dramatically if you actually use the computer into the device.

34:16I'll give you like a practical example. There's many things now. You just have a button. You see that on the Microsoft Copilot. You see that across a number of different applications. Summarize this. Like you have a bunch of data. You have several pages of a document. Summarize this. You can go all the way to the clouds and have a cost of cloud compute to run the model. Or you can run that model that summarizes on your text in the computer. That's free because it's the computer that you already have. So we're starting to see a lot of interest for enterprises or even applications to start running a portion of the application on the AI engine on the device.

35:05And that's starting right now.

35:06Big Technology Podcast Host:So the reason to buy AI PC hardware as opposed to like, let's say, letting cloud code take over your computer, mostly it's cost. I think you see, well, I'll just give you one example. There's more. Like gaming, for example. A lot of the gaming engines right now are thinking about using AI on the PC. For example, you can have on an RPG game, you have a dialogue with a character. Like a model, you have a dialogue. The gameplay changes. There's an example of cost. There's an example of a new use case. There's an example of agent. I think the answer to your question is, first of all, why should you buy a Snapdragon PowerPC?

35:51Because by definition, even if you're not using AI, it's going to be a faster, multi-day battery life, and it's going to feel like your phone. You can use your laptop all day without – you go places, don't take the charger with you. The second part of it, why should you buy an AI PC as a consumer? As a consumer, I think over time, you're going to see more and more apps having an AI front end, and they're going to leverage the capabilities on the PC, but it's going to be transparent to you. On the enterprise, I think the economics are going to change, because a lot of the ISVs and SaaS applications are going to require the onboard computing, And I think that's going to make a difference.

36:39Big Technology Podcast Host:Very interesting. So that'll be a requirement from software companies. So Qualcomm has also gotten into the data center world and you're building data centers. So obviously you have the chips in the devices like we talked about, but now you're working on building data centers for AI inference. So let's talk a little bit about, well, actually, why don't you first give us a little bit about why this is a move that Qualcomm is making? Yes, and it was, look, we always believe that what's going to happen with AI in a data center, you started to see all this build out for training. But eventually, and now it's well understood, when we start developing our solutions, that's what we thought.

37:27Eventually, inference is going to take over training. Because just think about that for a second. If you're a company spending billions of dollars building a data center for training, you expect to get a return on that investment. So when you start putting AI into production, you're doing inference. And we always believe that when you go to inference, there's going to be a lot of competition between the different AI players. So then I think the total cost of ownership matters. How much power you consume matters. And the architecture matters. So first answer to your question is, we realize when the data centers start to transition to inference, we have an opportunity to leverage our assets to build a very power-efficient inference solution for the data center, scaling the technology that we develop for the edge.

38:18Big Technology Podcast Host:Because the power is efficient in the phone, and power is such a bottleneck in AI, you can use that advantage and put it in a data center. That's the logic. If you just look at today, you have this very aggressive ramp of growth of AI, and you don't have the same ramp on energy. You know there's a gap between available energy and AI. So I think energy is going to be a scarce resource. Also, to operate an inference data center, that's one of the biggest items in operating expenses. and then I think people wanted to have a different architecture, which is the second part of my answer. The second part of my answer is we believe that the data center is going through another process of disaggregation, and let me explain what I mean by that.

39:09One of the key things that happen in the mobile industry, if you look at your smartphone today, your smartphone, it's a very difficult engineering challenge from a semiconductor standpoint. Because I have to pack a lot of computing in your smartphone. It has to fit in your pocket. It cannot get hot. You're going to touch your face. It cannot get hot. I cannot have fans. I cannot do liquid cooling on the smartphone. And your battery has to last all day. Otherwise, it's not useful. It's worthless. Yes. So in order to do that, we had to perfect the disaggregation of the compute, for lack of a better way to describe it.

39:52I'll give an example. In the PC, everything was CPU-centric. So if you're going to do a decode of music or you do decode of a video, you go and load up the CPU. You can't do that on the phone. It burns too much power. So you create a dedicated hardware just for music decode, a dedicated hardware, just to do JPEG encode when it takes a picture, a dedicated hardware for you to do video decode. And everything is disaggregated. And you do that because you want it to maximize the use of the available energy in the battery for you. And this all exists in the phone? Exists in the phone. It's the most we call heterogeneous compute.

40:32If you look of a Snapdragon today, it has several engines for different things. We don't run everything on a CPU or even, for that fact, on the GPU. Data centers go into that, and we're starting to see disaggregation. There's an architecture that they use for preview. There's an architecture they use for decode. So we're building what we believe is post-GPU. When you started to do inference and you needed dedicated engines, we're building that. I actually believe that the NVIDIA acquisition of Croc validates that you have different engines for different things. And I think that's what we're doing.

41:10And I think that's our focus on data center.

41:13Big Technology Podcast Host:Okay, let's talk about robotics. Are you buying the hype on humanoid robots? I will, like this whole conversation with you, I've been doing comparisons, and I'm going to do a comparison with Automotive to kind of outline our strategy. But let me give you the answer first. I buy the opportunity in a humanoid robot. However, the opportunity is going to manifest itself different, and some of those things are going to take time. For example, to get straight to your question, a robot that is going to be with you in your house and it's going to do everything you ask the robot to do, it's going to take time to train that.

41:53It's very difficult.

41:54Big Technology Podcast Host:Right now, it's teleoperated. It's difficult. Every house is not going to be the same. Every task is not going to be the same. It's going to be a lot of training required. Having said that, a robot that can do certain tasks and do that task over and over, that's actually not a hard problem to solve. So with that, I'm going to give you my comparison metaphor. When we start building platforms for automotive, and we're very proud of our automotive business right now, we also got into a stack for autonomous driving. When you think about autonomous driving, when you think about a robotaxi, like a level five, no steering wheel, you go to the back seat and you take a nap.

42:41That requires a lot of training because you can get to zero to 95 percent. But for you to get to 99.999 percent of the corner case, you have to do a lot of training. However, if you do assisted driving with the human still responsible to pick up the steering wheel and something happens, then you have the ability to put this in every car from level two to plus, two plus plus to level three and then all the way to level four. So that's a massive market opportunity. And that's what we're doing right now. You can bring some form of assisted driving to every single model. I feel the same way about robotics.

43:19If you do a humanoid robot or a humanoid arm and you do anything that it can leverage the world that's being designed for us and you train the robot on a particular task, that I think we're very, it's already happening. And I believe the opportunity from a business standpoint is massive. That's why we're really focused on industrial robots, because you can train a robot, for example. Your task is going to go to the supermarket at night and put the stuff back on the shelf. That's a self-contained problem. You're not training a robot to do everything. I think the robot that will do everything is going to take a little bit of time until we get there.

44:01Big Technology Podcast Host:There was a half marathon in China of humanoid robots, and the highlights looked really funny. Robots pulling on their face at the starting line and robots taking their whole team, holding onto ropes and like flinging them into the side of the course. And people went pretty fast and pretty far with that. The power the robot had as it sort of crashed out of the course. But some of those robots finished pretty, pretty fast. I won't say they beat my half marathon time, which eventually they will. But they were respectable in their finish. And that included time for battery changes. And the argument has been that in China, China is so close to the production process.

44:46Big Technology Podcast Host:Think about their cars, right? There's an electric car boom because they've been building things with batteries and electronics for so long. Demis Asabes, the CEO of Google DeepMind, recently said that China is only a couple months behind the state-of-the-art Western models. But it seems like they're ahead on robotics. Do you agree with that argument? Look, there are many things I think that China is remarkable, I think, what they're doing. I think everybody talks about the China speed. We know that, I think, from having a number of different partners in China using our technology from not only cars, but also phones, now robots, and industrial.

45:31And I think there is some merit in the argument that you're closer to a very large industrial base and you can prototype fast, you can build things fast, you can fail fast. And I think those things are helpful in developing the technology. But the technology is going to be required for robotics. It's very, very broad, right? You go from advanced semiconductors. I think that's one area that the China companies are partnered with companies like Qualcomm and others. You're going to have a lot of ecosystem, I think, that is going to be important for training, a lot of software. But yes, this is fascinating.

46:14Everybody is on a race, and things are moving fast.

46:20Big Technology Podcast Host:lastly i want to talk about industrial ai which is something that i think as far as the ai conversation gets probably the least ink but it's some of the most interesting stuff that's happening today i mean even here at the space we have a robot that we're looking at that was built in just a couple of weeks with a 50 qualcomm chip and moving pretty well talk a little bit about the applications of AI in the industrial space and maybe why you think people aren't paying so much attention to it? It's just it's not sexy enough for like the headlines? You know, I'll say it's probably there's so much attention on data center right now that it probably takes all of the air, I think, in the conversation with data center.

47:09I'll probably even resonate. Just the fact that we said we're building something for the data center, it got a lot of attention. But the reality is the industrial opportunity for AI is massive. It's massive because you can put AI processing on pretty much everything, and you find that every single industry, every single vertical has a massive number of use cases. It's true in retail, It's true in warehousing. It's true in healthcare. It's true in manufacturing, in energy. And we're actually seeing incredible amount of demand, especially because if you actually have ability to process in real time things that come from physical AI, motors, machines, you know, all of those things you can put sensor.

48:01But just to give you an example, we don't get too fancy with different machines. Just in computer vision alone, a camera, you can put a camera on a manufacturing line and you train the model just to see if what's coming in the conveyor belt against the template is what you expected. You do quality control with just a camera. You put the camera, for example, into looking at a shelf of a supermarket, you now can have the ability to check inventory real time. You can actually sell online what's in the store with a real time, I think, management inventory. You can put the same camera on a smart CD and you're reading license plates.

48:48And I think it's a massive, massive opportunity. Some of the many meetings are actually where I'm having here at Davos with industrial companies. They're super interested in industrial AI. And I think that's actually happening right now.

49:03Big Technology Podcast Host:Okay, five minutes left. Two questions for you. One of the reasons why I'm so happy to be speaking with you is because, in a sense, you can see the future, right? Because when something is going to be mass produced, you're the first call that's being made from, let's say, someone building an AI wearable. You're working closely with Meta, so you have a pretty good understanding of the demand that they're anticipating because they need your chips to be able to build things out. thinking about the AI build out and maybe also the AI device build out and looking into the crystal ball that you have of what the future looks like are things going to continue a pace can they possibly keep moving as fast as they have been look I feel that the and I think that question is really directed I think what's probably happening on the data center because on the device side, which is the beginning, I think we're seeing a big trajectory, like, for example, glasses continue to increase quarter over quarter.

50:08But I think the broader question is to the speed on the data center. And here's my answer. If we go back to the year 2000, when the dot-com crashed, right? I have that correction on the dot-com. Go back to year 2000. And you think about what we thought back then, what the internet would be. I will tell you that today, 25 years later, 26 years now, is exactly way bigger than people thought it would be. So whatever they thought is in 2000, the internet will be exactly way bigger right now.

50:43Big Technology Podcast Host:And you can still buy pet food on this one. Yes. However, it didn't happen all in 2000. It happened. So I think what's going to happen is AI right now, in the long run, is going to be bigger than people think. It's probably under hype for the long run. Now, how fast this is going to get deployed and how pervasive, and we'll see. Could we continue to build at the space? It's possible. Could the slowdown? It's also possible. Well, we're excited about it, and I think it's finally, and this is more for Qualcomm, finally people just woke up that the edge opportunity is massive. And I think all of this air that was all about data center, some of it started going to the, paying attention to the edge right now.

51:38And I think we're just the beginning of that curve.

51:41Big Technology Podcast Host:Okay. Finally, I got to ask you a Davos question. Go ahead. We're here at Davos. We have the slopes behind us. This is real for those wondering. You know, the corporations have been through this really interesting journey. There's been moments where they've been into what's called stakeholder capitalism, where they think about the group of people beyond the shareholder. And I think we're kind of in a moment now where there's more of a naked pursuit of the bottom line. I'm not speaking about Qualcomm. I'm just saying broadly, it seems like corporations are much more, they've sort of put away this illusion that they care about much else than the bottom line.

52:24Big Technology Podcast Host:And I wonder if, you know, we're here right outside the World Economic Forum. There's 48 conversations that will happen in this event that will be about AI. people will be talking about how AI will be able to cure cancer or get our best chance at curing cancer and empower the disempowered. And so I'm curious, like from your perspective, do you think AI is going to be the new altruism or the new corporate altruism? And is that a good or a bad thing? It's a complicated question. Look, I think it's a technology, is it too? I think it's going to, like computers did it, and it will continue to do, I think will help accelerate many things.

53:10It will help, you know, accelerate, for example, drug discovery as an example. It will help, you know, many things will increase productivity. As I said before, it's probably going to democratize education. It's going to change how we think about education. This is something that keeps changing. It's going to be a tool. I don't think it's going to be like this change, this society kind of thing. I'll tell you how I'll give you a very personal answer. When I, and this is going to be terrible because it's going to show my age. But when I got out of college, right, it's just the beginning of the Internet.

53:53still I remember going to my first job and and there was like a fax machine and you got to go to the fax machine and you get the faxes that you got overnight and put the other faxes in there and you have somebody still typing you know intercompany memos like we don't talk about this anymore I think when the internet arrived and email arrived it was a revolution and it was I think the AI is going to be that kind of revolution, almost like computers. But it's going to be like us doing things with a computer, just more. That's how I feel about it.

54:32Big Technology Podcast Host:All right. Well, it's been amazing following this space because every time I think I'm caught up, there's something new. And I think that you're going to be right at the center of it with all the devices that are going to come out. And, you know, maybe when OpenAI does release this family of devices, we can talk again about the state of the competition. By the way, we have a great live audience with us. Guys, make some noise so people can hear.

55:02Big Technology Podcast Host:To Cristiano and the Qualcomm team, thank you for having me here at your space at Davos. And very excited to be engaging in a number of really great conversations about the state of AI. I'm sure that our audience by the end of them will have a really good understanding of where things are going. And this was a great way to kick it off. So, Cristiano, thank you so much for coming on the show. No, thank you. Thank you. I really had fun having this conversation with you. Thank you. You too. All right, everybody. Thanks for listening. And we'll see you next time on Big Technology Podcast. Thank you.

55:32Thank you. Thank you very much.

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