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Qualcomm CEO Cristiano Amon: Future Of AI Devices, AI Fashion, Blending Reality and Computing
00:55:17
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- [00:00]
A
What does the AI device of the future look like? Let's ask the CEO. Building the chips that will power it. That's coming up with Cristiano Amman right after this. This episode is brought to you by Qualcomm. Qualcomm is bringing intelligent computing everywhere. At every technological inflection point. Qualcomm has been a trusted partner helping the world tackle its most important challenges. Qualcomm's leading edge AI, high performance, low power computing and unrivaled connectivity solutions have the power to build new ecosystems, transform industries and improve the way we all experience the world. Can AI's most valuable use be in the industrial setting? I've been thinking about this question more and more after visiting IFS's Industrial X Unleashed event in New York City and getting a chance to speak with IFS CEO Mark Muffett. To give a clear example, Muffett told me that IFS is sending Boston Dynamics spot robots out for inspection for bringing that data back to the IFS nerve center, which then, with the assistance of large language models, can assign the right technician to examine areas that need attending. It's a fascinating frontier of the technology and I'm thankful to my partners at IFS for opening my eyes to it. To learn more, go to ifs.com that's ifs.com welcome to Big Technology Podcast, a show for cool headed and nuanced conversation of the tech world and beyond. We are here at Davos at the Qualcomm space and we have a great show for you today. We're going to talk about the future of the AI device. We're going to talk about what an AI PC is and whether anybody's going to want it. We're going to talk about the data center, build out robotics and industrial AI. And here to do it with us is the perfect guest, Qualcomm CEO Cristiano Aman. Cristiano, great to see you.
- [01:46]
B
Great to see you too. Very happy having this conversation with you.
- [01:49]
A
Definitely it is a perfect time for us to have this conversation because talk of an AI device is going from three theoretical to concrete and Qualcomm might be at the center of it. So let me give for our audience, if you're new to Qualcomm, a little bit of a introduction to the company. $170 billion company, so it's very big. It's the designer of the Snapdragon Snapdragon chip which is in mobile phones, notably high end androids, also PCs, autos and increasingly wearables. There's also The Dragon Wing chip, which we're going to talk about, which is in industrial use cases like robotics. And you just got into AI data center building servers for AI inference. So a chip designer really at the center of the AI story, whether it comes to wearables or in the data center.
- [02:38]
B
I like that. Okay, very good. I think that's a great introduction of Qualcomm. Maybe I'll just add one thing to it. I think, you know, Qualcomm is a very unique semiconductor company. I think especially in, in today's environment when connectivity is important, computing is important, AI processing is important. And one of the few companies that had all of it in, in under a single roof. And we're probably one of the few semiconductor companies that go from 5 watts to your earbud now to 500 watts when you think about a data center. And it's an exciting time for the company, also exciting time for technology since AI is going into everything.
- [03:19]
A
And designing the chip for the smartphone has put you in a very interesting position because as we all start to imagine what an AI device is going to look like, obviously when it comes to AI, the compute underneath is really important and you're in position to do it. And recently you've talked about how your belief is that the market opportunity for an AI device and we're going to get into what the form factor is going to look like. But the market opportunity is 10 billion devices, which would make it bigger than the smartphone market. How do you get to that number?
- [03:51]
B
So it's interesting and I think for you to, for you to get at that number, it's actually important to see how the smartphone had evolved over different generations. And I think you have a couple of things. You have the evolution of phones, you have the evolution of compute, and then how AI changes that going forward. And maybe I will, I'll take us a little bit into that journey just to talk about it. One of the biggest change, I won't go all the way back to 2G, but one of the biggest change that happened in the phone industry, when we develop broadband into cellular and we said we can have broadband speeds, we realized that on the other side of the broadband, you need a computer. So your phone need to become a computer and you need to develop a computer that will fit in the palm of your hand. And that's, that's the smartphone. That's the smartphone that changes computer forever because it's our inseparable device we carry with us all the time. And it is doing. It's been at the center of our digital life. Now as you keep advancing I think in smartphones right now we are in the billion. Every single year is 1.2 billion phones are purchased. It's the number one consumer electronics and everybody has one. When you start thinking about what's happening with AI and especially as computers using AI now understand us, then you're starting to go into not only the computer that you carry but also the computer that you wear. Especially because if agents are going to be useful for you, they're going to be with you all the time. And then you started to go from carrying a phone to also having a glass or a ring or a bracelet, a watch and all of those things. But they changed the nature of what they of wearable used to be wearable was when you talk about wearables and technology was designed to just extend your phone functionality. Like for example, yes you have a smartwatch will tell you the time but also give you now your sensors back to the phone and give it notifications from the phone to you. But that's all going to change. It's all about connecting to a model, connecting to an agent. As those things change and we all going to start wearing those things, then you start to think about big numbers. You know if, if, if you have everybody has end up getting watch a ring on, on a glass that's not connected to an agent then you're talking about order of magnitude as big as the phone. And I think that's exciting. That's how we think about the future of the, of the mobile industry.
- [06:32]
A
But here's the question. The question is why does it need to be wearable? I was speaking with Sam Altman right before the end of the year and now OpenAI is going to build a family of devices but the rumor had been that it's going to be a smartphone sized device, no screen and it just listens to you and then it will push you notifications about your life. And I was like well why can't it just be an app on the phone? Why does it have to be a wearable?
- [06:58]
B
Okay, it doesn't have to look we're working with them. Unfortunately I cannot tell you what it is. What are the Cristiano you will see and it's going to be exciting. But, but here's we need to be thinking about this a little bit different, right? Wearable is one of, one of the things it's going to be more so I'll start first answering your question. This whole category of personal AI devices is humans already decided what they're going to wear a long time ago. I don't think you and I are going to be wearing a big helmet. I think we can wear glasses, we can wear jewelry. So humans kind of decide what they're going to wear. And you can put, you can, you can make, you know, that's our job to make electronics very dense. And a lot of computing power in small form factors come from our phone DNA. And you can put electronics in all of this. Plus connectivity. Connect to an agent is going to be very useful. But you could have something in your desk. It could have, you know, something in, you know, next to your, to your bed. You can, you can connect to agents in different devices. And I think what we'll see, everything will become, will become smart in one way. Because, see, the biggest fundamental thing is now the computers understand what we see, what we say, what we write. And that changes a little bit the human computer interface and with that changes the whole definition of what the computer is. So wearable is the most logical thing to us because we're thinking about mobility and things you're going to carry with you, but you could have things in your desk. See, the way to think about this is let's think about devices that get caught in the transition of technology. For example, you have a laptop right in front of you. I can bet you right now, and I see it's Qualcomm powered, I can bet you that the laptop has the ability for you to touch the screen, but you probably don't touch that often. You use the keyboard. That's what it was designed for. The user interface was designed for this. You touch your phone. Now the phone, when you pull your phone out of your pocket, you're going to be touching, going to apps. It's not very natural for you to point in the phone like this to try to record images. Glasses are your head moves, camera move of you. Maybe you can talk to the phone. Maybe the phone is here and you talk to it before you pick it up. So therefore there's going to be other things that can be in your desk that you're going to talk to. So we don't know how those things are going to pan out. But I think going back to your question, wearables is logical that wearable is going to be things that we'd be wearing and carrying around.
- [09:52]
A
But help us flesh out a little bit about what this experience will be like. Yes, I mean, obviously we're not, we're not there yet.
- [09:58]
B
No.
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A
And we've had many stops and starts. Google Glass was an example. People were wearing computing on their heads a long time ago. Now it seems like the technology is actually getting there to the point where maybe it will be useful, maybe it can make sense of our context. So Cristiano, when you think about, all right, I'm going to put chips and glasses and maybe some other different formats and people will use them and have X experience, what is that experience?
- [10:25]
B
Yes, let's talk about the experience and I'm going to break this conversation. I'll talk about the experience and I'm going to talk about the technology that goes, you know, behind it. So think about how glasses are performing today. You know, you have, for example, the meta Ray Ban glasses. I think there's going to be other glasses coming within the Google ecosystem this year. And what are the glasses doing today? Like, you have cameras, so you see what you see, can understand the image, can annotate the image. You have a microphone, it has a speaker, may or may not have a display. You know, you have use cases even without the display. Like you have the meta Ray ban glasses, what the experience look like, you're going to be, first of all, for those things to be, to get scale, they have to have very low friction and the experience has to be useful, otherwise it's like a gimmick, you're not going to use it. So the experience can be like this. I am, I'm talking to you. And then let's say I see somebody in the audience and I, and I just said, who is this person? And the glass will tell me. I don't know, let me check. I check on the web. Is this person here? Is this, is this person name? I said, oh, okay, yeah, you know, you met her before. There was an email that was sent to you from, from this person. He has to be something like you have, like you have your friend with you all the time, you walking on the street. And I said, what is this? This is what is. Or even something like you go into your day and your agent is going to come to you and say, you know, I noticed that right now you seem to be free. Can I talk about your agenda? There's a conflict we need to resolve. Those are examples of how this experience going to be. It's going to be this agent that it has ability to understand your context, understand what is around you, around the, what you see, what you say and react in real time. And what is interesting is we're not there yet, but you see the beginnings of the change. And I like to do parallels. So I'M going to go tell you the parallel with the smartphone. When the first time the smartphone arrived, like when you, like when you saw the iPhone, you saw the Android, maybe, I don't know, I may get this number wrong, but maybe like there was 10 apps and you say, okay, those are the 10 new apps. You couldn't at the time imagine that you're going to have probably hundreds of thousands of apps. And if you probably look in your phone right now, you know you have a ton of apps. So your phone got better over time because all of a sudden a new app became available in the app store. And I think that's how it's going to be with those agents. Eventually the agent gets integrated with some other service and you started to see it. For example, we have a customer of us in India that is doing smart glasses. They integrated with the digital payment system. So now you can look at a QR code and say, pay this and it will pay. And so you go from translate this, explain this to me, pay this, you can get a bill. And you say, I got this bill. Please pay this bill. Get out of my checkings account, notify me when it's done, and you may take a picture and email to me because I want to keep a copy of it. Those are going to be how you're going to interact with those computers. And that's what the experience is going to be look like.
- [13:52]
A
Is there a world where we get too close to computers, where think about sometimes that free time is really nice. And now the agent's being like, aha, you know, he has a moment. I'm going to go and help him resolve a conflict or I'll help him understand who this person is as opposed to them, you know, him going up and asking who the, you know, have we met before? Does there eventually come a point where humanity and computers come too close together?
- [14:22]
B
That's a good question. And I think, I don't know the answer to the question, but I think, like everything it's going to be for you to decide. Look, it's, you know, there are some of us, not all of us, sometimes just put your phone down and it's going to be like that. You're just going to have to decide when it's time to disconnect. But I feel it's going to be a little bit different because now we are going to. It's going to be easier to work with computers and the computers are going to be easier to work with us. And I'm going to use this question that you asked me to tell something funny. I wasn't in CES and I was having a conversation with a customer, Qualcomm about this. Exactly. This thing about the smart glasses and the camera and the fact that now the camera see what you see and can annotate the image. And then somebody said, what if sometimes there are things that you want to forget? And then the answer was, well, you made. But the AI won't forget. But those are going to be interesting things. Like with technology, I think how humans are going to use it and how those are going to be developed, we're going to see it.
- [15:43]
A
The natural extension of this conversation is as AI becomes more powerful and humanity comes closer to AI, there's going to be people that are going to want to say, let's just bring us together. Elon Musk has talked about how the reason for building neuralink, his brain computer interface company is he said eventually AI is going to get more powerful than humans and we better merge with them or they're going to destroy us. So I want to just ask you, would you merge with AI?
- [16:13]
B
No, but look in the, in the conversation that we just had, we're talking very consumer centric. When you said about, you know, too much technology, but it's easier to also understand when you move from the consumer to the enterprise. If you actually think about the fact that if you have the ability to learn everything in real time, like we're actually seeing some use cases right now, especially for industrial. When you have somebody that is as an operator of an equipment or of a refiner or everything, and then all of a sudden you have this agent with you that you get to a particular equipment and you say, how do I operate this? And it will say, here's how you're going to operate it. You do this, you do that. So the ability for you to have access to knowledge in real time, I think there's incredibly incredible opportunity to actually democratize knowledge and learning, I think. So that's another thing about the connection between AI in terms of augmenting human capabilities. We can say that because we saw that with phones. With phones is how many nations got access to the Internet and became, you know, access to digital through the phone. You know, there wasn't to a computer. And I think it was. It's incredibly empowering to have people to be connected and ability Internet. I think maybe that's going to be the same thing with those personal AI devices.
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A
Okay, I'm going to move off this in a second, but I asked if you'd Merge with AI and you said no very quickly.
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B
Yes.
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A
Why the reflexive no?
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B
Because look, it's, it's different. I think, you know, I think it's fun. I think people like to have those stories about science fiction. I have a very clear belief. I think there's humans, there's humanity. AI is our creation. It's trained on the stuff that we do. I think if you look at a lot of those models, so it's really a tool design to augment, but it won't take away our humanity.
- [18:23]
A
Okay, very quickly on form factor. You've mentioned glasses a number of times. You didn't mention earbuds. And you know, when you think about the way that this competition is shaping up, you have different companies making different bets on different form factors. Especially when you look at the tech giants. Big technology as we like to cover here on the big technology podcast, you have meta making a big bet on AI powered glasses. Google, as you mentioned, I think we're going to see a very big bet from them. Google Glass Part 2. Maybe they'll have a new name, Apple, it might be 2027 until we see a pair of glasses for them. Maybe their big bet is going to be the AirPods and how AI already is delivered in the AirPods with things like Translate and I mean serious inside there, but still has some work to do and maybe they'll do it with their Google partnership. Why, why do you think glasses over earbuds?
- [19:21]
B
Look, I won't say one over the other. We have the benefit, I think of being, I would assume the majority of the companies that are actually building personal AI devices. We have, I think the benefit of working with them. So we have a pretty broad visibility. Like I give an example, there are some companies right now they're designing an earbud over camera.
- [19:44]
A
An earbud with a camera.
- [19:45]
B
With a camera. Because if you put it in your ear and you have a camera, it can see in front of you. So it can provide some context in addition to having just a speaker and a microphone. I think it go back to the coaster conversation. What are the things that humans are going to wear and wear most of the time? Glasses. I am a believer that glasses is the most natural and maybe because I wear glasses since I was 13, so you know, it doesn't, I'm used to them. But when you turn your head, you know your camera goes with you. It's close to your eyes. You should thinking about this. I should have thought about that when you asked the question about wearables because that's the most simple way to answer that question. If the AI understands what we see, what we say, well, here is going to be closer to our senses. And glasses, it captures everything. It's closer to your mouth, closer to your ear. But earbud is. It's the same thing. It's just missing the vision. And that's why some people are putting a camera on an earbud. But if you just have an earbud connected to an IP address, you can connect to an agent and you can have a conversation with the agent.
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A
What about pin?
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B
Same thing. It's another way to put a camera on it. There's pendants, the jewelry. So we'll see. But I think you're going to see people experimenting with form factors. I think glasses is likely going to be the primary way that those device is going to be built.
- [21:17]
A
So let's say glasses is the winner. Do you think that style matters? Let me give you a binary here. I have the more stylish glasses with the worst assistant or the less stylish glasses with an amazing assistant, which wins.
- [21:33]
B
This is a great question. This is a great question because we're going to see another thing happening in the industry, which is when you start thinking about wearables, then you're going to have the mix of fashion and technology. And I actually think I'm going to make a prediction here. I don't want to be offensive to any other company, but I think that's where horizontal model is going to win versus vertical model. And the reason I'm saying that is because it's very unlikely that everybody on earth is going to use the same exact glasses. People want different form factors. They want different colors. It is different, is the same, especially things that you wear. As a result, I think you're going to have different brands. There are going to be. It will be a little bit of an interesting dynamic because is that. Is that a ray ban or, you know, is a ray ban that you're wearing or it's a meta. If it is a ray ban made by that consumer electronics company, is the some electronics brand or is ray ban? We'll see. But I think you're going to have the combination of fashion and technology and. And there's going to be choices, different brands for different people from different age groups and et cetera. So I think that we're going to see a lot of diversity. Very unlike the phone space when, you know, most people will carry a similar phone. I think that's going to be different.
- [23:04]
A
I'm going to Answer my own question. I'll take the better assistant and the ugly glasses over the nice glasses and the bad assistant.
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B
Yeah, the best thing is maybe the, the, the most nice, maybe the most successful glass is going to pair with the best assistant eventually.
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A
You would think we get there.
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B
Yeah, I think so.
- [23:24]
A
Handicap the AI device race for us. We have many companies that are running at this, we have Meta that's been making this multi year Metaverse bet, which has really transformed into the smart glasses bet. We have Google, which all indications are, I mean if you look at their recent Thinking Game documentary, they're just like pointing their phone at things and saying, what is this? It's like you need glasses. You have OpenAI, you're working with OpenAI on this project. Family of devices that are going to be in a bunch of different places. And Apple obviously has to be considered a power player here as well. Who wins?
- [24:03]
B
Look, I'll answer this question by going into the beginning of the Internet, right? So Orkut wasn't the social media that won, ended up being Facebook and then later Instagram. I think MapQuest wasn't the main map. Eventually it was Google Maps. So it's early to call. I think you see all those companies, I think they have big ecosystem, they're investing on their ecosystem. We'll see what happens. However, I'm going to try to give you a little bit of an answer. I think I have this view and this is maybe a longer conversation that we're going to have time for. But I think at the end of the day, the winner of the Edge is going to be the winner of the AI race. The reason I say that is because especially for, for everything that is personal, the Edge has real context. You know, you can Edge, meaning your.
- [25:05]
A
Phone, your device, the devices that you.
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B
Use as where the humans are, right? The humans don't knock on the data center and say, give me some AI they're experiencing to some other devices over there. And what happened is if you look how models got trained, models got trained on the information available on the Internet. But when you fast forward to a model, that is when you add physical AI, understanding our world, understanding your context, understanding you, that's going to be a lot more useful for you than a generic model that got trained on data available on the Internet. So whoever had access to that data is in a very, very strong position. So it's companies that have presence in all of those different devices already. I think they have an advantage. I will not bet against them.
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A
All right, but then let me take This a level deeper than with you because we have seen those companies, I'll just name them Amazon, Apple, Google, Meta. They've all tried to build this contextually aware personal assistant. We've heard presentations about Alexa and Apple intelligence and all the different buddies you can have in the meta properties. Google obviously with Gemini. But even though they have all this data, we still don't really have an assistant that's capable of doing what they've promised. I mean Apple might be the most notable and promising this contextually aware assistant that will help you figure out when your flight is and tell you all right, time to get to the airport. They haven't done that yet. What is holding these companies back? Is it a hardware problem, a AI problem? Where is the bottleneck?
- [26:45]
B
I think it's a combination of things, but I am more optimistic, I think than I think then you describe. I think we're starting to see, I think the beginning of some real experiences I think you had. 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 mix of experts, you started to see chain of thought reasoning. So you have different things specialized in specific tasks. I 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 compute. And 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 came and say, hold on, let me think it, 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 going to have to have certain things you need to do on the device. It needs to be fast. Like all companies right now, voice to text, they're starting to do locally because you can't, you don't tolerate any delay. So. And we're going to get there.
- [28:23]
A
We were just talking earlier in the room here about 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 know, you might be a bunny hill skier and down the black diamond. So you really want to be able to, to work fast.
- [28:42]
B
And you're going to break your glasses.
- [28:43]
A
Yeah, your glasses will be the first casualty.
- [28:46]
B
Yes.
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A
All right. We're here with Cristiano Aman, 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 if we have time. We'll be back right after this.
- [29:06]
B
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A
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- [31:48]
B
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. So we just build a better PC. On the consumer side, I would agree with that. That you don't see yet a lot of agents and, and 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 it's going to, people are going to use it more and more as you 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 see happening with AIPC and the fact that we actually have the ability to run significant high performance inference on a laptop, we're seeing is something else. What we're seeing is right now you have many, 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 are 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 computer in the cloud, your economics change dramatically if you actually use the computer into the device. I'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 on 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 cloud 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 are 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. And that's starting right now.
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A
So the reason to buy AI PC hardware as opposed to like let's say letting Claude code take over your computer, mostly it's cost I think you see.
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B
Well, I just gave 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 are example of new use cases, example of agent. I think the answer to your question is first of all, why should you buy a Snapdragon power PC? Because 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, you can use your laptop all day without you can 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.
- [36:19]
A
Very interesting. So that'll be a requirement from software companies. So Qualcomm is 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.
- [36:44]
B
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 put. Eventually inference is going to take over training because just think about that for a second. If your 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 inferen 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, leverage our assets to build a very power efficient inference solution for the data center. Scaling the technology that we develop for the edge.
- [37:58]
A
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.
- [38:08]
B
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, already there's a gas between the 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 to another process of disaggregation. And let explain what I mean by that. One 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. I'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 JPEG encode when I take a picture, dedicated hardware for you to do video decode. And everything is aggregated. And I think and you do that because you wanted to maximize the use of the available energy in the battery for you.
- [40:07]
A
And this all exists in the phone.
- [40:08]
B
Exists in the phone. It's the most we call heterogeneous compute. If 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 prefilled. There's an architecture they use for decode. So we're building what we believe is post gpu when you started to do inferencing. You need the dedicated engines. We're building that. I actually believe that the Nvidia acquisition of CROC validates that you diff different engines for different things. And I think that's what we're doing and I think that's our focus on data center.
- [40:53]
A
Okay, let's talk about robotics. Are you buying the hype on humanoid robots?
- [40:59]
B
I will like, like this whole conversation with you. I've been, 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, I buy the opportunity to humanoid robot. However, the opportunity is going to manifest itself different and some of those things going to take time. For example, to get straight to your question, a robot that is going to be with you in her house and it's going to do everything you asked a robot to do. It's going to take a time to train that. It's very difficult teleoperated. It's difficult. Every house not going to be the same. Every task is not going to be the same same. It's going to be a lot of training required. Having said that, a robot that can, 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 in auto, when we start, you know, 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 Robo taxi, like a level five, no steering wheel, you go to the back seat and you take a nap. That requires a lot of Training because you can get to 0 to 95%, but for you to get to 99.999% 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 to 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. If you do a humanoid robot or humanoid arm and you do anything that it can leverage the world that's been designed for us, and you train the robot on a particular task that I think 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 gonna 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 gonna take a little bit of time until we get there.
- [43:41]
A
There was a half marathon in China of humanoid robots and the highlights looked really funny. Robots falling on their face at the starting line and robots taking their whole team holding onto ropes and like flinging them into the the side of the course. And people went pretty fast and pretty far with the power the robot had as it sort of crashed out of the course. But some of those robots finished 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. Think about their cars, right? They have this electric car boom because they've been building things with batteries and electronics for so long. Demis Hasabis, 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?
- [44:47]
B
Look, there's. 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. And I think there is some merit in the argument that you're closer to a very large industrial base and you can, 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 can be required for robotics is very, very broad. You go from advanced semiconductors. I think that's one area that the China companies are partner with companies like Qualcomm and others. You're going to have a, a lot of ecosystem. I think that is going to be important for training a lot of software. But yes, this is fascinating. Everybody is on a race and things are moving fast.
- [46:00]
A
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 is 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 it's 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.
- [46:34]
B
Like the headlines, you know, that's. 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 Data Center, I'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, you know, AI processing on pretty much everything. And you, 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 model demand, especially because if you actually have ability to process in real time things that come from physical AI, motors, machines, all of those things, you can put sensor. But just to give 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, you know, is what you expected. You do quality control with easy, you know, with just a camera. You put the camera, for example, into looking at a shelf of a, 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 small smart city and you're reading license plates. And I think it's a massive, massive opportunity. Some of the many meetings actually where I'm having here at Davos is with industrial companies. They're super interested in industrial AI and I think that's actually happening right now.
- [48:43]
A
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. 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 buildout 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 apace? Can they possibly keep moving as fast as they have been?
- [49:28]
B
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 personal, on the device side, we're just at the beginning. I think we're seeing a big trajectory, like for example, glasses continue to increase quarter over quarter. But I think the broader question as to the speed on the data center, and here's my answer. If we go back to the year 2000 when the dot com crash, right. You 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.
- [50:23]
A
And you can still buy pet food on this one.
- [50:25]
B
Yeah. 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 right 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 this slow down is 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. And I think we're just the beginning of that curve.
- [51:21]
A
Okay, finally I got to ask you a Davos question.
- [51:23]
B
Go ahead.
- [51:24]
A
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. And I wonder if 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 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?
- [52:32]
B
Thing? It's a complicated question. Look, I, I think it's a, it's a. Technology is a tool. I think it's going like, like computers did it and, and we'll continue to do. I think will, will help accelerate. Many things will help, you know, accelerate, for example, drug discovery as an example. It will, 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 keep 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 how. I'll give you a very personal answer when I. And this is, 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 still. 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 computer, just more. That's how I feel about it.
- [54:12]
A
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. With all the devices that are gonna come out. And 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 it. To Cristiano and the Qualcomm team, thank you for having me here at your space at Davos and very excited to. 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.
- [55:01]
B
No, thank you. Thank you. I really had fun having this conversation with you. Thank you.
- [55:04]
A
You, too. All right, everybody, thanks for listening, and we'll see you next time on Big Technology Podcast. Thank you.
- [55:13]
B
Thank you much.