Intel Corporation (INTC) Earnings Call Transcript & Summary

[Presentation] Please welcome, Pat Gelsinger.

Hey, so excited be back at the developer community and the developer conference, and cool to get together and talk about new products and to share a vision of the future together and excited to unlock these massive opportunities created by generational shift in AI. Industries racing toward it, we're bringing AI and making it accessible at scale but also at the client and edge as well. We have exciting achievements to share with you today, advancements in Moore's Law, the underpinning of everything we do. And based on the choice and trust of our open ecosystems that together, we can do almost anything and enable this continuous innovation. And like the intro video was making a little bit of fun of me, we've got a lot to cover today. So let's dive in immediately with the demo of what that video was all about. And to help me with this, please join me in welcoming Rich Felton, the Director of Sports Science and COO at ai.io. Rich, come on up here.

Thank you, Pat. Thanks for having me.

So Rich, the CEO gig is not going to go on forever. So I am looking at what's the next career opportunities here, so...

We're definitely going to take a look. But what we do, for the audience, is we democratize sport and specifically give players or anyone in the world, even yourself, the chance to get access or opportunities to trial for professional sports teams, scholarships, et cetera. And we do all that through a mobile phone using AI and computer vision. And we've been working with Intel since 2021. And what we've built together is a platform, kind of end-to-end, that allows these players to download an app from anywhere in the world.

Anywhere?

Anywhere in the world. You can see some videos up there actually that show that. We've had some great success stories. We've had players even this year sign for Premier League clubs, play in the Premier League, all the way through to players in India, who actually downloaded our app from a shared community phone, so in a remote village down at the...

Never have the opportunity?

Never had the opportunity. And fast forward, they're now on 5-year, fully paid residency programs for all their education, all their football. And that happens through that power of AI through our mobile platform. What it means for the clubs and the organizations is we just massively expanded their reach globally, so they can reach more people. But there's a sustainability story to it as well. So now they get that data upfront, it's all analyzed data. So with that data upfront, it means they can be more targeted with where they travel to and not waste carbon emissions where they don't need to and bring to their doorstep.

Yes, and using our technologies end-to-end, as the slide shows, this is really impressive, Rich.

Yes. And to the point of technologies, the critical thing for both player and club is speed. So we use Xeon-based services on the AWS cloud. We've got -- there's lots of wins around that around cost optimizing those AI workloads, which is -- can be really expensive. It's a video, it's tracking, it's data. But for the clubs, they're autonomously finding players with complex datasets. So for those clubs, we've now had situations, where like for Chelsea, good team.

Not bad.

From the point of the data or what you see on the screen with the tracking from that data being flagged that a player is good enough, they sign players within 2 weeks. That's normally about an 18-month process, huge cost savings for doing that.

That's incredible.

Yes, for players, I think it's -- or anyone here actually who will turn it into a profession, download the app and you can do this.

So I wanted to get a sense for how well am I doing for my second career, right? I went through these tests and so as you saw in the opening video. So what's it look like, Rich? Can you show me?

Should we take a look?

Sure.

Let's go on to the computer. So obviously, you downloaded the app outside Intel HQ there. We gave you a bunch of drills to do, filmed those, everything gets tracked, all the data comes back. It's all benchmarked in relation to some things. We've benchmarked you in relation to the Major League Soccer. So that's what we're going to look at, the video of yourself. So if we click on Pat here, there we go.

There we go.

So this report now -- for everyone here, unless you're a sports scientist or a coach, these metrics probably don't mean too much. They're just numbers. Yes, we can click on here and we can look at what it was. But what we actually do with our benchmarking and part of making things easier for the player and for the coach and the scout, to make this really simple, just turn the benchmarks off for a moment. So we turn anything to a simplified...

So these are my scores here.

These are your scores that we've made into uniform 1 to 10. Because it's easier for everyone to consume. A player can see, "If I'm a 10 on something, I know I'm brilliant." Same for the scout, "It's a 10, I'm interested." So the blue here is your physical and technical scores with the ball and running around.

Okay, I grew up playing soccer, so this is...

Well, the orange scores are your cognitive scores. So thankfully, cognitive scores are off the charts. And what I can actually do, if I show you the Major League Soccer benchmark for your position, I think we've got you as an attacker or as a striker. But these gray lines are the average for Major League Soccer.

It doesn't look good.

Well, you can see physical and technical, maybe as expected. You're not at Messi level, right? But cognitive, again unbelievable, through the roof.

Okay, so maybe I'm in the right job.

Yes, maybe so. And I think the -- okay, so possibly, you're not going to have that career tomorrow. But those scores, those cognitive scores were flagged as interesting. It's a great technical acumen. So one of the teams, the local team, still wanted to give you something to show that...

So thank you to the Earthquakes locally. So what do you think, everybody? Is this pretty impressive? And with Intel as the collaborator, right, using our technologies everywhere, we're excited with just the way that you are democratizing sport.

We're excited with you. And we're going to other verticals. There's lots of things we can do across other industries as well. We track -- think about our mobile phone, health industries, there's lots of areas we can go into. So anyone in the audience that's interested, developers, a QR code there to scan. You can learn more about our story with Intel, how we work together and actually what we're doing. Then if anyone is interested, reach out to us and let's collaborate more with everybody else.

Excellent. Thank you so much, Rich.

Thanks for having me. Appreciate it.

Okay, soccer CEO, I guess, we got the answer, so -- but this idea today of all the things that silicon is touching. And literally, we now have a $547 billion silicon industry. But even more impressive is that it's powering this global technology economy that's now measured at $8 trillion. And I would ask you, what aspect of your life is not getting more digital, right? Everything, sports, as we just saw, but our entertainment, our social experience, our health, right, work, medical. Everything is becoming more digital. It's the foundational aspect of all economy and human experience. And now with the superpower of AI, we're seeing this increasing autonomy and agency, machines acting on our behalf. And we're creating this foundation for the next generation of opportunities and experiences that enable a better future for every person on Earth, right? And with that, I welcome you to the Siliconomy. And as these advances in semiconductors enable new levels of human achievement, this need for compute and capabilities is exponentially increasing. In Moore's Law, in a nutshell, as we're increasing transistors, compute and capabilities, we're decreasing at an exponential rate size, cost and power. This is the magic of silicon. And all of this, the most plentiful material on Earth, God has given us this unique gift called silicon. And today, we're seeing that just infiltrate organizations, where over the last several years, you've seen this 4x increase in managed devices. That's expected to triple again for more than 15x growth. We're just seeing everything become a computer, more plentiful, powerful, affordable processing. And these computers are now becoming part of your thermostat, your picture frames, everything is becoming smart. And AI is representing a generational shift in how computing is used and giving rise to the Siliconomy. But inside of that, a simple rule, developers rule. You run the global economy, right, not politicians, not CEOs. Developers are the ones running this global economy. And you're powered by Moore's Law. And it's your creative passion. This is insatiable drive to innovate, combined with Moore's Law, the fuel, and those coming together enable the Siliconomy. Every one of us is part of it, where we're seeing this evolving economy enabled by this magic, we're replacing industries like oil that define geopolitics for 5 decades. It's now silicon, the Siliconomy, and the technology supply chains that it enables. For developers, this is massive social and business opportunities to push the boundaries, creating solutions that the world will change and be enabling the improvement of every soul on Earth. And it requires a range of different capabilities, next-generation CPUs, NPUs, GPUs, chiplets, new interconnects, specialized accelerators. And our commitment to you is give you the coolest hardware and software ASAP. And we will do that, and we'll do that with the Intel Developer Cloud. And just to show it off, here's my Python code of the day. And it's syntactically and semantically correct, right? So if you want to join the Siliconomy, right, and you want open, trusted, secure capabilities that are the latest, coolest stuff, you go to the Intel Dev Cloud. And last year, we announced at this event, we announced the beta of the Intel Developer Cloud, the path to the latest-gen architectures test, evaluation to give you, the developer, to put you in the driver's seat. And today, we're thrilled to announce the general availability of the Intel Developer Cloud. From the big stuff, Gaudi2 for large language model training, to broad deployment with fourth-gen Xeons to our CPU Max Series for bandwidth and HPC workloads running small systems as well, the latest, coolest stuff available on the Intel Developer Cloud, enabling large-scale, small-scale training and inference solutions available to all. And with that, we're enabling these next generation of capabilities along with our software platforms, our oneAPI toolkit, our OpenVINO toolkit, many of our developer tools. We have three different tiers of service: a freemium open service, a commercial premium and then finally an enterprise-grade service. Also, as I said, this isn't what's available today, it's also what's available tomorrow. So we're putting preproduction hardware in place, beta so that we enable you to take advantage of this sooner and earlier in your developer cycle. So that by the time the hardware is available in volume, hey, you've already been working on it for months, quarters and even years. And this includes modern interfaces and workflows to help you optimize your end-to-end solutions, easy to use, easy to install. And this easy path to access and use Intel-optimized AI, software or hardware platforms, deliver these from small to large, enabling workloads but doing it from your PC and having easy access to this cloud. So it lowers your cost and friction to be able to do it. And just because you're my best friends and you're here today, we're making -- was that fun, you're my best -- you're not my best friends, okay. But just because you're our best friends and you're here today, we're giving you free access, a week of free access to the Intel Developer Cloud. This is part of the package that you got coming in. And I hope you all find a friction-free, expedited way to get to the cloud. Now any time we're together, let's have a little bit of fun. So we designed it as part of our conference today to have a little bit of a Shark Tank-like event. And we've been working, we call it the Intel Ignite community that we work with to take advantage of our technologies, platforms but enable and unleash their passions to leverage Intel but really turn them on in new and powerful ways. And you're going to get to see on the show floor a number of these different companies that we're working with in this way. But we narrowed down to three of them. And they represent artificial intelligence, next-generation systems, platform and edge to cloud, to space. And we're going to share their ideas at the -- throughout the keynote this morning on the show floor. But at the end of the morning, we're going to take these three, and we're going to pick our first winner. So what I'd like to do now is give you the first peak of one of these three. And that is Deep Bender. Co-founders are clearly geeks of geek, geekdom, and they identified a specific video rendering problem with bandwidth and size that they set out to solve using breakthroughs in AI. Let's see the Deep Render update. [Presentation]

What do you think, candidate number one? Okay, we've got two more. And then we'll see which one is our first winner this year. This field of AI is just incredible. But it's also old. It's 50 years old, since the beginnings of AI. And the first 40 years, as I like to say, how much happened in AI? Nothing, multiple winters, nothing happened. In fact, when I was architecting the 80486, one of the things we said is, "We're going to make the 486 a great AI chip." That was in the '80s. What happened? Nothing, right? Then massive quantities of data, right, breakthroughs in computer science and algorithms and big enough compute. And all of a sudden, the last 10 years of AI have been just incredible. And it's been redefining itself with new algorithms and new insights emerging. And with that, we are participating. And our Intel Gaudi processor has been demonstrating performance capabilities rivaling the market leader. And even the largest, most challenging AI and generative AI problems are being delivered and executed on our Gaudi processor. And we're not only rivaling the market leader but at much better TCO. And today, I'm excited to announce we've secured another major partnership on our journey in this area. And that's with Stability AI, one of the algorithmic and market leaders in this space. And with them, we're excited to build the largest AI supercomputer built in Europe, we believe, running all entirely on Xeons and 4,000 Intel Gaudi2s. Stability AI is an anchor customer, and just an enormous build-out that we're undertaking with them to be one of the top 15 AI supercomputers in the world. And we're quite excited about the partnership. But we also have key OEM relationships as well. And partners like Dell are now partnering with us to deliver Gaudi for cloud customers, for enterprise customers and really fill out that AI portfolio, beginning with Xeon for general-purpose CPUs to Gaudi AI accelerators for the largest inference and training environments, this complete AI continuum of capabilities enabling applications that are AI-enabled to on-prem inferencing and complete high-end training systems with Dell, the entire range of solutions. And coming back to the Meteor Lake -- coming back to the machine learning inferencing challenges and some of the performance results we're now demonstrating with Xeon. And Xeon comes with AMX, the AI enhancements as part of the architecture. And we're now producing some of the best results -- no, the best results for the CPU for AI workloads and demonstrating workloads like ChatGPT-J, being able to do 100 words summarizations of news articles based on 1,000 to 1,500 source word content, being able to summarize those offline about 2 graphs per second and online, 1 graph, so being able to bring real-time capabilities on standard Xeon platforms. We've also submitted the first MLPerf results with Xeon Max Series, up to 64 gig of high-bandwidth memory on the CPU package as well and being able to achieve 99.9% accuracies on GPT-J. And all of these, just showing the momentum our CPU and accelerator line is having in the marketplace. And so let's hear from another one of our partners on this journey. Let's hear from Alibaba and how they're using Xeon for their AI workloads. [Presentation]

Thank you, Joe. And we're really excited, partnerships like Stability AI with Dell computers and Alibaba. And we're just working to bring AI capabilities into every platform, every product that we build for our highest end, right, all the way down to our client offerings. And at the high end, Gaudi2, right, delivering impressive results today, Xeon, impressive results today. But the road map is strong. In fact, we've already gotten Gaudi silicon just out of fab and now in packaging. And this will be followed in '25 with our Falcon Shores product, so '23, Gaudi2; '24, Gaudi3; '25, Falcon Shores, where we bring together Gaudi with our GPU capabilities into a single platform. Simply put, our road map is extremely robust. And we are executing aggressively to bring this together. And of course, that execution is based on Moore's Law. And as Gordon said in his eponymous law that nothing can go on forever, no physical quantity can change exponentially forever. But it can be delayed. And he was often amazed by just the creativity of how we just continue to find workarounds to barriers. And we at Intel, we see ourselves as the stewards of Moore's Law and this relentless pursuit of computing and efficiency at scale. And we will not rest. We are committed to continuing this pursuit. And as I like to say, until every element to the periodic table is exhausted, we are not done. And we're committed to Moore's Law so that you can develop with confidence on our platforms. So let's take a look at our second Ignite nomination that we have this year. And this one was just a little bit mind-blowing for me, right, as seeing unlocking the power of wet science by using computational biology and bringing those together. It's a revolutionary young startup, two long-time best friends and being able to do radical breakthroughs in cost, time and numbers associated with it and just the potential to fundamentally disrupt medical, pharma, food and environmental industries. So let's see from Scala right now. [Presentation]

And AI fundamentally restructuring science in so many domains, unleashing new application, new experiences and productivity and creativity. But we also believe it ushers in the next era of the PC, the AI PC generation. And as we discuss, we're participating, we're contributing, we're fueling this high end with Gaudi and Xeon and Max and getting these biggest machines on Earth for training and refinement. And my friends, Sam Altman and Kevin Scott and Jensen, all of them, the work they're doing at the high end is really cool. And as we quickly move to trillion parameter models and beyond, this is amazing and breaking new science. But I'll tell you what's even cooler is if we put it in the hands of every human on Earth, making it useful with nimble models that can run on your PC that enable these models to be literally everywhere, offering personal, private, secure AI capabilities that infuses every aspect of our daily lives at work, at play, at sport, with gamers and our personal assistants, creators, church group, Zoom calls, fantasy football. I might even be able to beat my kids with that. And these trained models being able to run on any PC, analyzing incoming data in my personal environments as well, prioritizing me, assisting me and urgently tailored to my experience. When we think about the AI PC generation, I'd like you to harken back to original Wi-Fi and the Wi-Fi specs. And I was involved in helping to create Wi-Fi. We emerged in 1997, and we sort of went through like 5, 6 fallow years. And then in 2003, Intel launched the first-gen Centrino platform. And it started to drive Wi-Fi at scale, and it gave rise to access points and hardware and comms and new applications. And it initiated a virtual application cycle at the time. We believe the same occurs with AI, where we infuse it into everything with open and secure environments. We see the AI PC as a sea change moment in tech innovation. Andy Grove called the PC, the ultimate Darwinian device, just recreating itself. And we've always gone through this question of what's the killer app? And my simple answer to that is you, right? You're going to be the ones creating these next-generation applications and use cases. And with each passing day, we're seeing more apps to open up and an idea sparking another. And this AI PC performance and capability is the perfect experience at your fingertips. And with that, let's take a look at how my AI PC becomes my new superpower. And let's invite Craig onstage to show us a couple of these examples. So Craig?

Howdy, Pat.

You're looking rather dapper today, Craig.

Well, thank you. I was about to say the same thing. Thanks for having me. I figured we go ahead and take a brand-new look at a couple of the new AI PC applications that are out there. And I know we've been all been looking for the killer application. But here's the deal. We are in the middle of our major acceleration cycle, just like you said, LLMs, OpenVINO toolkits for everyone. And if you don't have the killer app today, wait 5 minutes for your AI applications, it's just right around the corner. So let me show you a couple of quick examples of exactly what that's going to look like here. So really quickly, I'm going to go ahead and take a look at this machine. It's one of our brand-new platforms for the AI PC, and pretty great. And we're running on this one, Audacity, which has a plug-in for Riffusion, so Audacity being an open source music production and Riffusion being an AI music generator. So I figure we go ahead and kick this off. Hey, Pat, what kind of music are you into?

Well, I sort of like lots of genres, I'm really quite open. But there's this -- from my home county, really close to the town where I was born, there was this other singer, you might have heard from her, really close, she was is born there, Taylor Swift. Have you heard of her?

Taylor Swift.

She's the other famous person from my home.

I think a few of us have heard about that. So you guys have all heard it here for the first time, Pat is Swiftie. But that's okay, Pat. I think we're all Swifies, just some of us don't like to admit it. But let's go ahead and we're going to actually create a song now in the style of Taylor Swift, would love to have her come along, but she was busy.

Yes, I didn't invite her today, so sorry.

Anyway, we're going we're going to generate our song here really quick, and we'll let that guy cook. Now let's move on to the next one over here. And so what we're showing up on this machine is GIMP, which is an image manipulation program, open source for everyone and has a plug-in for OpenVINO, of course, which has Stable Diffusion. So let's do text to an image. And what we thought was pretty amazing is that currently, your wife, Linda, is in Africa doing that amazing school that you built out there and all of the philanthropy work. So we would love to do something in the theme of Africa, but put a little fun spin on it if we could for this one. So we'll go ahead and generate that here. And we'll let this guy cook. But let's go ahead and find out the payoffs, where both...

Let's hear Taylor or sort of her.

So not, in the style of You're going to get us sued, Pat. Okay, here we go. And let's go ahead and play this back. [Presentation]

It's not bad. I'm not going to lie, that's pretty good. However, not a single...

Just generated right here, right?

Just generated and locally. So here's the deal. We're going to see a ton of hybrid models. But on your AI PC, you're actually going to be able to actually run some of these locally as well, which is a great way to keep all your IP secure. But let's move on over to our other system here. And we've generated our picture. Is this the giraffe and a cowboy hat that you were looking for, Pat?

Linda loves giraffes. That's her favorite plains animal there. So she's going to love this one.

Then I think we absolutely nailed it. So these are the type of applications that we're really going to be looking for, for this next generation here.

But one more thing, Craig. That's like the ugliest-looking machine I have seen in a long time. And I've seen some ugly ones. So what are you doing there?

Well, what, this guy with the test mode and the blue thing?

Yes.

Just don't worry about this guy. The beauty is on the inside, Pat. This is actually the world's first showing of our Lunar Lake system. I know, right? I bet you didn't see that coming. So this is -- instead of doing the first boot like we normally do, Pat is always pushing us to do really the -- all the way to the envelope with our demonstrations here. So we said, "What's better than the next-gen showoff? We'll just do the next, next-gen showoff." So here we are, Lunar Lake, stable and up and running, not just first booting but running the AI applications that are going to be powering us for the next generation of these AI PCs, Pat. It's going to be great.

Okay, thank you, Craig.

Thank you so much, Pat.

Well, a little bit of unexpected demoware there. And as we bring this age of the AI PC, we are thrilled by the momentum that we're seeing. And we're going to bring millions of AI-enabled PCs, ramping to tens of millions to hundreds of millions to enable tens of billions of tops of capabilities. And we're working with the industry and our OEM partners to make these sustainable, energy-efficient platforms. And the journey begins with our upcoming, new Intel Core Ultra processor launch, formally, Meteor Lake. I'll probably use it up one or twice in the keynote. But now it's the Intel Core Ultra. And we're excited because this brings a whole set of new capabilities, the NPU capabilities, and launching December 14. And we're excited to have this in volume with numerous customers and partners. But this journey of the AI PC is a big deal. You might even say that we might need some co-pilots for the journey. And in fact, our friends at Microsoft co-piloting this journey with us and the Windows 11 PC with Core Ultra, even more powerful and personal with Windows Copilot. And Microsoft is planning to release broadly soon -- no, really soon, their Copilot capabilities that are going to enable this ability to rewrite, to summarize, explain content across a range of questions and use cases right there on your AI PC, taking complex to simple, all done on your AI platform. And Meteor Lake, or the Intel Core Ultra, is a tour de force. And as you look at all the things on this slide, this is the first client that's manufactured on Intel 4 process technologies. So on our 5 nodes and 4-year journey, 7 and 4, check and check. And this is the first platform that we're using EUV, the most advanced lithographic capabilities. It's also our first triplet to be used using Foveros, our advanced 3D packaging technology. It's our first to bring CPU, GPU and NPU onto a single platform with energy and power efficiency, and we're going to deliver it at scale and to do that as power-efficient and going to be delivered into the price points that enable high-volume deployment. Our NPU will enable AI developers to take advantage of the standard software and framework for AI development and hugely expand the applications for edge deployments. But don't just take my word for it. Let's hear from some of our ecosystem partners and the upcoming Intel Core launch. So please join me in welcoming the COO at Acer, Jerry Kao, to the stage and some of the amazing work that they're doing. Jerry?

Hi, Pat. Wow, it's a big pleasure here to be with you.

We've had so many good things with Acer over the years.

And we're so excited for Intel Core Ultra. There are a lot of prospects, a lot of things, but especially with the MPU. Because MPU will bring AI to PC. So maybe you don't know, but Acer have been working with Intel for a while to bring the Core Ultra to laptop.

Show us what you've got.

Do you want to take a look? Okay. It's a sneak peek of our upcoming laptop with Intel Core Ultra in an [indiscernible]. And of course, it's [ Xeon line ] and also slim and powerful notebook with all the [ Xeon line ] features, longer battery life and very lightweight. In addition to that, the most beautiful Intel Core Ultra inside means from CPU, GPU and NPU means an AI PC. First time we saw AI PC in the world and not waiting for that kind of a buzz, Lunar Lake, many years later -- no, not many years but just a few -- this one, it's coming. Talking about AI PC. AI for a lot of people, it's just hardware, but that's wrong. Because AI, the most important thing is the software to unlock the power of the hardware. So for AI portion, actually Acer has been working with Intel to develop a suite of applications to end users to enjoy the AI starting from a laptop computer. In addition to that, by also working with Intel for OpenVINO tools, we also created AI libraries, so that end user -- or I should say, developers who create AI applications based on libraries. And talking a lot, it's showtime.

Okay, let's see it.

Okay. Let's try to make a backgrounder for this laptop. Craig is going to run a demo. And what we're seeing is the exact, identical laptop here. We're going to use the picture of the ballerina here and to add some stable image. Originally, people think about using yours or my picture here, but my team mentioned to me not to do that. Okay, what we are going to do now is using Stable Diffusion and also with a plug-in for GIMP, which [indiscernible] OpenVINO to harness the CPU and GPU and MPU on a platform to create an astronaut with the same pose as the ballerina. And then we'll update the scale to the high definition because we're going to use it as a wallpaper. Okay, Craig, are you ready? Please press the button.

Okay, this has been generated. Now we're going to go ahead and minimize. Let's go ahead and press that button.

I think, ladies and gentlemen, professional people like you guys here, you should know how it would normally take to run Stable Diffusion on a traditional [ Xeon line ] notebook. But today with Intel Core Ultra, it's finished.

Yes. I just love Stable Diffusion, some of the capabilities that it's going to unleash. We really see it as one of the game changers.

That's true. Yes, and in addition to that, Acer is also working with Intel for more AI effects. For example, we had so-called Acer [ Parallax View ], which will add motion to the picture, like what the astronaut is going to do.

So we're seeing it move real quick. And we even do that based on camera angles as well.

Acer, the [ Parallax View ], can also use the notebook camera to track people's face to change your perspective of the image. So you're going to have a 3D look and feel.

It's just incredible, Jerry. And we're so excited to do the launch with you later in the year, just game-changing platform. So thank you so much for joining.

Thank you very much.

Yes. I get to keep this one, right?

No, no, no, it's mine. [indiscernible] December 14, I'll give to you. Thank you very much, guys.

Thank you to Jerry at Acer. But this is just the beginning. OEMs, ISVs, all of these capabilities. And we want the AI PC to realize your visions and dreams. And we're working on the future generations of processor, including Arrow Lake and the first demonstrations of Lunar Lake. And I'm particularly excited that hot out of fab, right, Arrow Lake is on our Intel 20A process technology. This wafer is still a little bit warm, straight out of fab, but the first demonstrations of our 20A process technology with further improvements in performance, power, area, working as expected. And we're excited to show these capabilities to you today. So first-ever demonstration of Lunar Lake, first silicon arriving and healthy on Arrow Lake. And what follows that for our 2025 offering is Panther Lake. And design is well underway. In fact, it's so well underway that we'll be sending it to fab in Q1 of '24, where we'll have the first fab of Panther Lake underway as well. And that's on Intel 18A, the finish line of our 5 nodes in 4 years. And as we see these innovations continuing to push us forward, what is the most important unit of optimization for us as a technology industry? Simply performance per unit and of energy. Every segment of our markets, whether it's data center, it's edge, it's telco, right, it's PC, cloud, AI, they're all power-constrained. So we have to optimize our energies toward how we deliver sustainability and performance in our next generation of Intel products. And literally, we're developing the technology across our product lines to reduce energy consumption. And this is clearly the case for our client products, but it's also in our server products. And Xeon processors, we are now well underway. The execution machine is back at Intel, and we're seeing predictable, stable cadence of new products to meet the data center needs. And over the next several months, we've got exciting stuff coming out. And we're going to be introducing better performance, efficiency and TCO across the product line. And next up is Emerald Rapids. And with Meteor Lake and Emerald Rapids, these are the last products on Intel 7. And with our Core Ultra launch, we'll be releasing Emerald on December 14. And the new Xeons bring huge power performance per watt improvements, increased number of cores, faster memory technologies but in the same power envelope as today's Gen 4 Xeons but providing up to 40% more performance on key workloads like AI in that same socket and that same power envelope. And because of that compatibility at the software and at the socket level, we expect to see a rapid move by OEMs and software developers to take advantage of it. And fifth-gen Xeon comes with the same and further refined versions of our accelerators, AI capabilities for these next-gen workloads, fifth-gen Xeon coming December 14. But 2024 for the Xeon road map, really good. And with that, I'm quite excited, we view many of the products that we have underway. And the next-gen server platform gives us the opportunity to bring both E-core and P-core, efficient cores and performance codes. And these are named Sierra Forest and Granite Rapids. And both of these are progressing on or ahead of schedule. And this gives simplicity and flexibility for system designers, designing in one platform to be able to bring both of these products into the marketplace. The package has the same I/O die that delivers compatibility at the software and hardware level for things like PCIe Gen5 and CXL 2.0, the Sapphire Rapids product, right? And this is on the Intel 3 process technology, provides 2.5x the rack density and 2.4x the power performance over fourth-gen Xeon. And this specifically is unique and beneficial for cloud-scale workload kind of capability. So really excited about this work. And Granite Rapids is a more balanced machine for peak performance and AI capabilities as well as major improvements, 2 to 3x better than Gen 4 Xeon for the broad data center workloads. And we're already well underway on the next version, Clearwater Forest, an 18A version, right, of the E-core product, to arrive in 2025. The road map is healthy. We are executing well. But we kept a little secret. On the Xeon engineering team, they're always a little bit creative bunch and always have a few things up their sleeve, "No, we can't do that, boss." "Yes, you can." "No, we can't." And then they surprise me when they can. And when we showed off Sierra Forest earlier in the year, we showed 144 cores per piece of silicon. What we didn't tell you was that we had 2 die per package. So we have a whopping 288 cores on one Sierra Forest product line with 12 channels of memory and further improvements for cloud-optimized environments, huge gains for cloud-scale customers. And I remember when we produced the first 4-core products. 288 cores? Wow. I must be getting old or something. This is really incredible gains. 2024 is shaping up to be a really, really good year for the CPU and our Xeon customers. But we couldn't talk about data center computing and this proliferation of capabilities without touching on security. And as we think about the criticality of security at the data center, at the edge, the common denominator underneath it is protecting my acts and my data, this underlayment of the superpowers. And technology is neutral, right, neither good nor bad. We can use it to create great things. We can also use it to power cyber threats. And we have to protect our data at rest, in-flight and in use. And the seamless integration of technology into our lives is opening up more attack surfaces and vectors than we've ever seen before. And with that, we've been building in increasing capabilities into our silicon platforms to enable secure, confidential computing. And simply put, security starts with the silicon, security starts with Intel. I began this work almost 25 years ago in my career. You might say, "Wow, man, this is hard work." And we've just been building out more and more silicon-based capabilities. And tomorrow, Greg in his keynote will announce a number of new capabilities and services, specifically in the area of security based on Intel. And this to us is super exciting. I encourage you all to be here tomorrow. Don't miss Greg's keynote as we unveil that. We've talked about the superpowers. This idea of these five technology superpowers just invading everything. Compute, everything becomes a computer. Connectivity, everyone and everything is connected. Infrastructure, the unlimited scale of the cloud, combined with the unlimited reach of the intelligent edge simultaneously addressing latency and higher bandwidth. And AI, this intelligence everywhere, being able to take this data and compute and algorithmic breakthrough, software writing software at scale. But the last of these, sensing. And breakthroughs in low-cost high-resolution sensors, I believe, are just opening up other pathways to bring technology into our everyday lives, bringing more data and capabilities. And we're seeing advances in automation processing, robotics, giving machines human-like capabilities, where even our disabilities become digitally enhanced strengths or superpowers. One of my favorite sounds is hearing my granddaughter call me papa, Papa Pat. And if it were not for my hearing aids, and I have a family that almost every one of them, right, has lost their hearing, I might not be able to hear that in the future. And I believe in this area of superpowers of sensing, we have so much more to do. And with that, I'm super excited to find a soulmate on this journey, Dan Siroker, the founder of Rewind AI. Please join me in welcome Dan to the stage. Thank you, Dan. So tell us your story a little bit.

So I started to lose my hearing in my 20s. And when I turned 30, I tried a hearing aid. And it was magical to lose a sense and gain it back again, feels like gaining a superpower. And ever since that moment, I've been on a hunt for ways that technology can augment human capabilities and give us superpowers. And that's what led me to REWIND. So what's REWIND doing? So REWIND is a personalized AI powered by everything you've seen, said or heard. The way it works is it captures your screen and your audio, it compresses it, encrypts it, transcribes it and stores it all locally on your PC. And then best of all, you can ask any question of anything you've seen, said or heard.

Well, that's super cool. And earlier, we talked about this age of the AIPC beginning now in this ability to capture everything you see, hear and be able to transcribe, analyze, but I mean talking about it can you show me?

Sure. I have a machine let's go. All right. Here's REWIND. And to show you how it works, I'm going to pull up the time line. This is what many of us were probably doing last night looking around and seeing different sessions we might want to attend here at the conference. I can just rewind back and forth in time like a DVR. But the real power comes from asking questions. So here, I'm going to ask REWIND when and where is the session on chatbots. And what REWIND is going to do, it's going to go back through my memories, things that I've captured on my machine, and we'll find that exact moment. Here, it says it's a session on chatbox entitled Demystifying Generative AI show up to its tomorrow at 12:45 p.m. It will be in Club House B, and here's a little summary of the actual setting.

Hey, that's super cool.

And so this is a great example of going back to a specific moment in your past, but maybe instead, you wanted to actually do some work for you. So one of our investors is Sam Altman, I'm just going to ask -- I'm just going to ask REWIND to do my job, which is write me an e-mail to Sam Altman asking him to catch up.

I was talking to Sam last week. So this is good to see. So let's see what we come up with.

Yes. So here, it's going through all of my experiences by prior conversations. It mentions that we raised $33 million from top-tier investors, including him and gives me a little template, I can just copy and paste, send them an e-mail. I don't have to actually think about it.

And this is leveraging core and OpenVINO, right?

Yes. So the -- what I showed you here today was using GPT-4. But what's even better is if we could do this entirely locally. So for the first time ever, I'm going to show you a demo of a personalized AI powered entirely locally on your AIPC using OpenVINO. So let me switch our mode here from GPT to OpenVINO.

Okay. So now everything is going to run local?

Yes. And actually, to prove it to you, you know what I'm going to do, I'm going to actually turn off our Wi-Fi so hopefully, this will work. I'm going to turn off the WiFi.

We're going to break it.

This machine is entirely off of the network. And I'm going to ask a very simple question, what is Pat's favorite sound?

So now we're running locally, right, using OpenVINO Ultra -- Core Ultra.

Exactly. Yes. And so it's going to take the data that's from your -- excuse me, over time, it's going to take data that's from the machine. And here, you can see it knows that your favorite sound is your granddaughters voice calling you Papa.

Okay, very good. So this to me is killer app domain. I am so excited about these capabilities and the ability to run them all locally. This is my data locally on my machine, right? I don't worry about any of the privacy or other things associated with, but selectively leveraging the cloud, what we need to.

That's right. Exactly. And I'll just show you one last example. This is an example of using summarization where I'm going to ask you to summarize this keynote and just for fun, we'll just say use emojis. And we'll see what comes up with. And here, it's -- we spend this up a little bit, but you'll see it's saying this incredible keynote by Pat over here. Future of technology is looking great. Here, there's a starstruck emoji, so the model likes you and wait there's more. We're sharing details of the client performance road maps, et cetera. So this really shows you the power of an AIPC, leveraging the data of everything you've seen in setter heard and truly giving you superpowers.

This is so good. Thank you so much for joining us, Dan. Super excited for REWIND. As you saw with REWIND leveraging Core Ultra and OpenVINO and transforming lives and improving accessibility, but until recently, PCs couldn't connect to hearing aids like mine, because the traditional Bluetooth simply use too much power. Well, recently, Bluetooth low-energy audio that we've worked on with Microsoft is first coming and available since earlier this year and part of the Core Ultra platform. And we've been collaborating with Starkey labs and these hearing aids are right like the ones that I'm wearing here to create a POC for how AI can improve the hearing aid experience. So we're going to join a call here with Arnaud on my Samsung Galaxy book. So Arnaud, how are you?

Hello, Pat, I trust your Keynote is going well.

Yes. I trust you're keeping track of all the great things I have to say, Arnaud.

Yes. I'm watching from backstage. So now your PC is consecutively aware and will automatically switch your hearing aids between ambient aware and focus mode by adjusting the some amplification. You can stand your flow without missing any important information.

Okay? So when I can go up here and I can switch from focus mode, it's ambient mode and now I can hear the other things going on and focus mode. It's just you and I, Arnaud?

Yes, Pat. We won't amplify the background noise, so you can concentrate on our conversation, but we'll make sure you don't miss any important interruption such if they arise.

Well, okay. Maybe the grandkids here at the door. Hopefully, my wife gets it. So we'll dismiss that there. So stay focused just on you and I, but the PC is still detecting.

Your PC recognized the delivery and alerted you, but you choose to ignore it and the PC decided to keep the hearing aids in focus mode.

Okay. Well. Okay. I think I'm going to go talk to who's ever here, just a second, Arnaud. Diane, what's up. Why are you interrupting my demo?

Do I need a reason? It's always a good time to talk to me. Thank you for taking interruption. Okay. So yes, thanks for taking the interruption. Actually, I think I might have wasted my time waiving, because I think your computer actually heard the sound of me saying, excuse me, and it even told you which direction that sound is coming from. So I used to waste a lot of energy. But I'm still glad you came over and I think you've got a really great system over there. It sounds like with the head tracking AI, it actually detected when you left the conference call, and so your hearing aids were automatically switched to ambient mode, so we could chat. And then when you go back, it's going to automatically switch your hearing aids back to focus mode, so you can be focused again on talking to Arnaud. So pretty cool technology there. Tracking AI is actually running on the NPU and the Intel Core Ultra. And another thing we have running on the NPU is an AI summarization technology. So it's actually -- as you're talking to me, it's over there, is really summarizing...

Arnaud is still talking.

Arnaud is still talking and it's summarizing, it's condensing it for you. And so you can just go back there and you'll be able to jump in really quick. You'll know exactly what transpired, why you're gone. Very nice, very sleek and....

Okay. Can I go back now or...

Okay. You can go back.

[Foreign Language]

So Arnaud. I'm back here now, right? And Diane was helping and we were chatting, but you were talking in French when I was gone?

Yes. [Foreign Language] I was explaining the demo in French.

So now not only am I getting real-time summarization of what I missed when I went out of Zoom, right, it brings me back to focus mode when I'm back to the PC, and it also did translation from French in real time as well. What do you think? Is this the AIPC generation? And if that wasn't good enough, right, obviously, we have other sensing deficiencies. In the future, I want my glasses to become AR-enhanced glasses as well. And this is the next generation, we call Visor from Immersed. They're working on to have these enhanced by PC capabilities as well to make them smaller and lighter and eventually as small as my glasses and light as them are today becoming my sensing devices for vision, hearing and every other aspect of human existence. So thank you very much for seeing this glimpse into what sensing of the future will be like and how the AIPC will enable that. Now as we continue our technological breadth, our third ignite submission is actually a little bit more galactic. And what Antaris is working on is how can they bring space's satellite technology ecosystem to make it open and broadly available and how they're using AI and ML to onboard satellite software. So let's see our third submission, Antaris. [Presentation]

So what do you think? Canada #3. And just a little bit, we'll tell you who number #1 is for today. You've heard us throughout our conversation about OpenVINO and how we're using it at the edge and how we're using it with Jerry and Acer and Dan from REWIND and Starkey labs. OpenVINO as Intel's AI inferencing and deployment runtime platform that we spoke about last year. We've now released OpenVINO 2023.1, providing broader applications of support, more natural language processing, computer vision, generative AI, bringing us closer to this vision of any model, any hardware, anywhere. We're now already deploying optimized integrations of capabilities like Llama 2 models from OS and clouds client, its a client. We've seen 90-plus percent increase in OpenVINO downloads in the last year, and we're seeing this being the platform to enable and deploy AI inferencing at the edge. Good news, we're well on our way to write once and bring AI everywhere. And we're working now to broaden the ecosystem and industry. And Edge is diverse applications, but also heterogeneous architectures, and we're seeing a broader set of different platforms. And while many of those are x86 and Intel based, the ARM relationship is expanding nicely with Intel, but also many of those hedges are based on ARM as well. So we're quite excited today to announce that ARMs is supporting the OpenVINO platform. Earlier this year, Qualcomm said they're going to show Llama 2 on their AI implementations of smartphones and PC starting in 2024. I say, 2024, you just saw it running already from client -- from edge to client to cloud, OpenVINO on ARM and x86. And as you look at this picture, right, you see that, boy, the data center offerings transform how companies operate the PC operating offerings transform how people work, but delivering edge transforms how everything operates. And we're going to see this increasing, I'll say, hybrid operation where big training and model creation, but we need to deploy nimble models running on client and edge and they may be tethered to and updating and retraining. But the constant interaction is at the edge. And this is what we call hybrid AI. And this allows you to experience the models that truly deploy broadly at the edge. For the developer community and partners, you now have the tools for success at the edge. And with our hybrid AI SDK that we'll be releasing very early next year, this is building you the capability so that low-code and no-code environments can take advantage of hybrid AI. And while Gen AI is in the spotlight today, this is just a little piece of what we're going to be able to do in the future. And we're changing the status quo with powerful capabilities with new text analysis, language, visual recognition, chatbot interactions and to broadly deploy we need edge affordable hardware. We have to keep enhancing the hardware capabilities for performance and accessibility, and we're making great strides in this optimized runtime environment, enabling new and more capable hardware at the edge, like our Core Ultra and enabling the edge applications without modification to take the latest advancements and benefits of CPU and GPU and NPU, that's what OpenVINO does. For the past 15 years, the dominant developer model has been cloud native. And this has enabled the separation of hardware from business logic and application innovation. And we believe that the next decade or 2 of development isn't cloud native, it's edge native. And that's going to be driven what I like to call the 3 laws of edge and AI computing, the loss of physics, latency. I can't go to the cloud. I need it locally. Economics, the cost of cloud and cost of bandwidth. And finally, the laws of the land, data sovereignty. And we believe these 3 drive the edge and AI era and the next phase of application is edge-native app development. But to do that broadly, we need a lot of this plumbing, this hard work of making the edge accessible, remotable, updatable and this is what Project Strata does. It brings us edge native software platform with services and support, and we're bringing together an ecosystem of Intel and third-party apps to enable this edge environment. And with that, to be able to solve many of these problems and solving things like Zero-touch management, security patching and updates in real time, all of that hard stuff in the platform so that app developers can take advantage of the latest hardware, latest security. And through that Project Strata is enabling this ability to onboard, orchestrate and observe and operate, and we'll be launching this in early 2024. And so as we're continuing the cycle of innovation, we in the tech industry, what do you think we're a pretty cool folks, don't you think? My grand kids don't think I'm very cool. They say I need cooler clothes that fit better, right? And for that, I never really care too much about that. I mean a geeky T-shirt, and I'm a happy guy. But we also are seeing AI intervene in fashion and new capabilities as well. And with that, please join me inviting Meera Bhatia, as the COO of Fabletics and how we combine AI and modern fashion capabilities. Meera? Thank you, Meera. So nice to have you with us today.

Nice to be here. Thanks for having me.

So tell us a little bit more about what we're doing and what we're showing off here.

Yes. So I am here to help you find the right fit for your clothes, so your grandkids can think that you look more fashionable. We are going to be using the Fit:match Concierge solution. As you know, Pat, we're an investor in Fit:match, and we invested because we believe in the team, but we believe in the problem that they're trying to solve for retailers everywhere.

So how does it work? Tell us about it.

So our partner, Fit:match with them, we are revolutionizing the retail industry by solving the universal fit problem. So how many times have you gone into a store and you see something on a hanger and you think that's going to look great on me. But then you get it into the fitting room and it just doesn't fit your body shape, right? It's kind of demoralizing. It's frustrating for everyone. And for a retail business like Fabletics, that means miss revenue, additional cost for returns, wasted production, that's bad for the planet. We don't want to do that, right? So with the Fit:match Concierge solution, we are trying to put it into the adage of it just look better on the hanger.

Well, I think we can all sort of relate to that experience. I hate going to stores partially for that reason. So can you give us a bit of a demonstration here. And what we're seeing is that the environment is powered by Intel's RealSense cameras using our LiDAR technologies. It creates a digital twin, right? It's running on our Core CPUs and using the latest PyTorch AI, all of that running on OpenVINO like we were just talking about. So tell us more.

All right. So basically, a customer can walk into a dress room that's equipped with the Fit:match technology, they complete a full body scan that remains 100% private. And then you get 3D avatar and then we'll match that with curated selection of clothing that's going to suit your shape. So it's kind of like a personal shopper and all with the health of Intel architecture and software. And amazing, the entire process just takes seconds. So it's super streamlined, super quick and easy.

Well, yesterday, we have the booth out here, and I went and I got to test it as well. And you can see my avatar up there. And as I got fitted and didn't know my avatar would quite look so good as this. And I think we're going to tell my wife we have some shopping opportunities.

All right. Well, let's take a look. Let's check out your avatar here. There you are. Looking good. We continue to get you from all angles. That's you. So love it. All right. But then we're going to check out and see some matches for you. So what we have here, we have a selection of curated shirts. It's just designed to fit you. We believe these are going to fit you. I know we squashed your soccer career so -- but I mean, how about better fitting pants, like that doesn't hurt, right?

Look like a soccer player.

Yes, exactly. We want you to look good working out. That's our monitor, right? So we selected all of these fits just for you.

Well, hey, that's super good. And looking at the matches is great as well. And the way that you're transforming the experience is really powerful and mirror the work that we're showing, and we encourage everybody to come to the show floor and see what it's like.

Great. Well, we have -- we've seen great outcomes at Fit:match. I know our partners have seen great outcomes, lower returns, higher conversion, all the things we want to see to optimize our revenue as a retailer. So pretty excited about these results.

Well, thank you so much. I'm looking forward to my new gear.

Can't wait to see you in it. Thank you.

So we have lots going on here at innovation and lots going on in the developer world. But as we said, now is the time. So we have the access to space through Antaris. What do you think? How many of you think they're the best? Okay. Come on, wake up a little bit here. Come on, yes. Okay. Right. Okay. How about Deep Render, right, these advancements and compression. Well, how about Scala. So Greg Lavender, myself, Lama Nachman, we spent time going through all 3 of these, talking to the founding teams, discussing with each one of them. And simply put, I'll say they're all amazing. I just love this passion, the ecosystem of innovation that we get to participate in, unleashing the energy for each one of them. But now it's my pleasure to announce this year's Ignite award winner. This year's winner is Deep Render. So please join me in welcoming to the stage [ Cree ] from the Deep Render. So I'm super excited to give you this award. But you may not know here, but much, much earlier in my career, I worked on video conferencing. We are working on key codecs and compression and H.264. So I really empathize with the problem that you're solving and all the challenges that it is. And so it's really important to our industry. Video is the dominant use of bits, but really important to me as well. So with that, it's my pleasure to give you this year's award. Ignite Winner of the year. Thank you.

Thank you, Pat. Much appreciate it.

So can you give us a look at your demo?

Yes. Now before we jump into the demo, let's speak a bit about the setup. This laptop here comes with an Intel Core Ultra processor. It has an NPU and is one of the AI PCs we talked about before. Deep Render will run at 5x better AI compression technology on the NPU in an efficient, low power and very good compression locally on this device. And Pat, you might not know this, but this is a very significant moment for me. Deep Render spent 5 years researching and developing better compression technology. And with the Intel Core Ultra, we can now bring this technology to hundreds of millions of devices and users. What are 5x better compression technology mean for users? It means 5x faster Internet for everyone and who does not like faster Internet. Yes, let's jump in.

So show us.

Yes. Let's jump in. So here is Deep Render's demo application. And if we click on this button, we can showcase it on a number of videos. Let's go with the people video. Here in this video, we can see 2 videos playing both at the exact same file size and bit rate. On the left side, compressed with the Deep Render AI technology and on the right side compressed with old school traditional compression.

Well, I didn't realize we look so bad today. Yes, that's really impressive results. What do you think?

Thank you, Pat. And yes, to show that even clearer, if we pause and look at the difference in quality, we can use the slider and have here Deep Render compressed frame and now a traditional compressed frame. And yes, there's a clear difference in visual quality, highlighting 5x better compression performance.

I think you did it. This is incredible and the thing that excites me about this is it's a whole new domain and you think there's a lot more to be gained from this AI approach to compression as well.

Oh, yes, AI based compression is a breakthrough innovation. The paradigm shift away from traditional compression towards an AI-only solution and we are only beginning.

Thank you so much, [ Cree ]. So good to have you here. And again, congratulations for being this year award winner. But these innovations truly are powered by the magic of Moore's Law. And when we laid out our 5 node and 4-year journey, it was like, wow, that's a bold, aggressive agenda to go make that happen. Well, we're making it happen. Our Intel 7 products with Meteor Lake and [ Emerald Graphic ] done, Intel 4 with our Meteor Lake product done and successfully ramping today in Oregon, and we're just transferring it to our second manufacturing facility in Ireland. Intel 3 as you saw here, we're demonstrating with Granite Rapids and Sierra Forest ready for first products going forward. We showed you the first Intel 20A, the what's next the breakthroughs in RibbonFET and the power via 20A manufacturing ready for next year on track. But the grand daddy is finishing the race with 18A, the fifth node on our road map. And this is now we're almost finished with the 0.9 PDK key, key milestone when we finalize the design rules and unleash this for our internal designs and you heard me talk about the first 2 designs of Panther Lake and Clearwater Forest for our server and clients in '25. And when you look at the resulting diagrams, and I've been looking at transistor sem diagrams, scanning electron micro diagrams for about 40 years and 18A and the RibbonFET transistor it's a Picasso. It's a work of art. It is elegant. It is beautiful. And if you're not a geeky kind of guy like me, trust me, this is like man, the science behind this is just incredible. And this is one of our test chips and as I said, we'll be bringing our first product wafers in the fab very shortly. And customers like Ericsson are utilizing that. Our work with ARM is progressing very well that we announced back in May of this year. And we'll be sending our first Intel product designs into fab in the first quarter of the year. But we're not stopping there. We're continuing on Intel next where we're making further improvements on gate all around the next generation of malware well ahead on power via we're already well underway on the next version of power via as well. We're already engaging in the next generation of lithography. And before the end of this year, we'll have the first high-NA machine, and that's the generation beyond EUV and we'll be docking that machine in Oregon. So Ann and my Christmas present is the first high-NA machines coming to Oregon this year. Happy holidays to all of us because we are powering the what's next of innovation. And we're working hard to derisk it. And clearly, this is hard work. This is invention right, for it. And our first test chips, and we're doing modular design work. Part of the reason we're so far ahead on PowerVia compare to the industry as we derisk that by running that on a FinFET node with PowerVia to show that we could deliver PowerVia and gate all around in 20A and 18A. And all of this is stay tuned. We are so excited about the work, the momentum and the progress that we're making. But the wafers are cool. But this packaging stuff, wow, this is getting incredibly exciting as well. And 25 years ago, Intel drove the creation of organic packages. And when you see any of these chips this is what we're talking about. This is organic package layer. But that's all changing now, right? Yesterday, we announced that the next generation, and just like we did 25 years ago, is now moving to glass substrates. And this is a glass panel, this gets sliced up for those substrates. Here's an example of a glass wafer that we've now -- isn't that cool looking? Yes, right? You can see through. So just incredible breakthroughs in the next generation of packaging and these packaging improves density, it improves power. It's more thermally favorable, glass to be the substrate of the future. And we're excited to be leading the industry and bringing this to the market as well but it isn't creating prototypes, it's creating volume baby. And with that, Intel is playing this critical role in rebalancing the supply chain and making extraordinary investments capital and working with understanding market conditions, our customers, the chips program office funding. We expect to invest over $100 billion in capital over the next 5 years between our Oregon, Arizona, New Mexico, Ohio facility. And we now have our proposals in the chips program office hands as we're committed to produce the world's most advanced chips in the U.S. at scale. But we're also working with the industry to enabling this next generation of the industry's evolution and the chiplet generation. Intel led the PCIe disruption in the 1990s as we went from rack to system with standard interconnects. Well, we're working with the ecosystem for the next generation. And we launched from this stage, right, last year, the UCIe consortium, and we joined forces with the ecosystem to create this open chiplet ecosystem and with that effort now, we have over 120 members now, and you see a number of demonstrations on the show floor creating this open ecosystem and enabling chipsets designs. And while we're still very early in this progression, this is the who's who of silicon is participating and I'm happy to show you the first test chips from this work and our foundry systems partnering with the UCIe IP that we're working with Synopsys on Intel 3, combined with our advanced EMIB packaging and a TSMC, Synopsys and Intel Foundry, Pipe Creek, the first test chip for UCIe and the beginning of the chiplet era. But it isn't just test chips. What we're finding is as people move into this AI area, there's extreme interest in advanced packaging and these large systems for large language model and using them at scale, bringing memory and packaging capabilities together. We're creating these solutions for customers in the next generation of the 3D silicon era. And this is exciting, and this is just an area that we're continuing to push the envelope for the capabilities that we are going to enable our customers' ecosystem and industry to provide. But of course, we have many other areas of innovation and research as well. And one of those is neuromorphic computing and neuromorphic is algorithmic breakthroughs and the capability that we're enabling with neuromorphic computing, right, as seen next slide. And we're seeing that our Intel Labs is showing the neuromorphic capabilities and being able to solve optimization problems and AI scheduling problems, financial allocations. All of these need a different type of architecture and algorithms and neuromorphic computing is showing unique capabilities in this area with dramatic improvements of power and performance over other conventional architectures. And we're finding a lot of momentum. And last year, we announced our stackable compact system, Kapoho Point, and we've shared our Intel neuromorphic research community where we now have 200-plus groups are participating in it. And this technology, right, is being adopted for accelerating control planning and different types of workloads. Intel is working with a wide range of collaborators to enable this computing capability. And of course, maybe the granddaddy of them all is what happens when we move past digital. And Quantum presents a new era of the harnessing of some of the physical effects. We believe this will enable new chemistry problems, financial optimizations, climate change, travel, all of these and most importantly, maybe security. And it promises to make huge advancements, but probably post 2030 when we see quantum supremacy being able to be achieved. And our approach is different. Intel's qubits differ from the other approaches in the industry, because we are using silicon and simply put -- do it in silicon. We're the only company working on silicon qubits and using the same process and materials that we're already using, tweaking them a little bit to create leading-edge qubits and simply put if we get this working, we can do it at scale. And the most advanced research chip for research is what we call Tunnel Falls. And Tunnel Falls, we just released it. here's a wafer of Tunnel Falls, a 12-qubit device on a 300-millimeter wafer. And while Arrow Lake is hot out of fab, this one is rather cold. We need to operate at that below 1 Kelvin. And for that, we're taking our most advanced EUV technology, our most advanced CMOS fabrication lines and figuring out how to run them at cryogenic temperatures, each of these wafers is providing 24,000 quantum dot devices, and it's a small chip 50 by 50-millimeter square, and over 1 million times smaller than the alternate approaches. And our focus is gaining insights from Tunnel Falls and enabling the research labs and universities with the most advanced capabilities. Not just in the hardware, but also releasing the Quantum SDK, a software stack that we're abling to operate and learn how to with research universities, a silicon-qubit approach and being able to create the programmability, performance and scalability for enabling quantum computing in the future. Last year, we had the first ever Intel Lifetime Achievement Award. And we gave that award to Linus Torvalds, and I was so thrilled to have Linus here to give him that recognition. And we got such good response from that last year. We said let's do it again this year. And with that, I'm excited today to announce the 2023 recipient of the Intel Lifetime Achievement Award for their most worthy technical achievements that they've been bringing forward. And with that, join me in welcoming Fei-Fei Li, recognizing her extensive achievements in the field of AI. And Fei-Fei Li is one of those individuals, She's been dedicated to STEM work dedicated to the responsible use of AI, dedicated to the core advancements of AI in the field and through so many and how to make it truly AI for all and because of this fearless pursuit, she's been recognized by many across the industry, and this among so much more Fei-Fei Li, the most deserving recipient of this year's Lifetime Achievement Award. So in just a moment, we have a great industry luminary session coming up where you're going to hear a lot more from Fei-Fei and Lama Nachman, our fellow in the area of AI and labs and responsible AI will be interviewing her. So Fei-Fei, why don't you just take a seat over there and very quickly, we'll get into that. But fundamentally, what a great time we've had today. And this just summarizes the things that we've talked about. The focus of Intel bringing AI everywhere, making it truly accessible to all volume from client edge network to cloud, delivering the largest systems, but fundamentally making it capable for all. And with that, it truly is a thrill to be with you again to enable you the power of developers and now Lama, Fei-Fei Li. This is a great innovation. Thank you all so very much.

Thank you for standing by, and welcome to Intel Innovation and investor Q&A sessions. [Operator Instructions] As a reminder, today's program is being recorded. And now I'd like to introduce your host for today's program, Mr. John Pitzer, Corporate Vice President, Investor Relations. Please go ahead, sir.

Yes. Thanks, Jonathan. I'd like to welcome everyone live in the room and also everyone joining us virtually on the web. We've got about 90 minutes broken up between 3 different sections of 30 minutes each with Pat Gelsinger, our Chief Executive Officer; Greg Lavender, senior Vice President and Chief Technical Officer; and then David Zinsner, our Executive Vice President and Chief Financial Officer. This is your opportunity to ask our executives questions. Before we begin, just to stay compliant, please note that today's discussion may contain forward-looking statements that are subject to various risks and uncertainties and may reference non-GAAP financial measures. Please refer to Intel's most recent earnings release and annual report in Form 10-K and other filings with the SEC for more information on the risk factors that could cause actual results to differ materially and information on our non-GAAP financial measures, including reconciliations where appropriate to the corresponding GAAP financial measures. With that, Pat, you just wrapped up a 90-minute keynote this morning. I'm sure everyone in the room was there live, but I know that it might make some sense to give some open remarks on kind of the key takeaways from your keynote this morning before we start with Q&A.

Sure, sure. This somewhat summarizes on the slide. I'll start in the middle, right? Obviously, as we talked about, the AI PC generation, as we are kicking off and Meteor Lake now the Core Ultra, here we think really ushers in AI as a major new use case for the PC. And that, to us, is a big deal, right? Like I said, Centrino-like moment. Hopefully, through the keynote, how we're infusing AI capabilities across our platforms, as well came through very clearly on our server platforms, high-end training, client, but also the edge and what we're doing to bring through to our edge platforms as well. And as I said, over the last 1.5 decade, 2 decades, it's been all about cloud native application development. I think the next there is edge native application development. Speaking of app development, we announced the GA of DevCloud. Today, this AZ on-ramp to Intel technologies. One of the things that's always been somewhat frustrating to me is we come up with a major new hardware innovation, and then we have to get it through the cloud vendor through the OEM into the market, into the hands of ISVs, et cetera. How do we just circumvent that in a much more aggressive way, that's the Intel DevCloud. And for that, that will have the latest and greatest of Intel like Gaudi2s, Xeon MAXs, Meteor Lakes, Lunar Lakes, we'll get them on there as soon as they are getting to the beta stage but and then also being able to enable them for scale. So large language model training today, but I can do it on my PC, go to the DevCloud and be up and running when those platforms very quickly. So that half of the page, what we're doing for Xeons and Gaudis and DevCloud and developers. The other half of the page, very much about enabling the advancements in the future in the hard piece of it, 5 nodes in 4 years, right? Obviously, Meteor Lake is now check on Intel 4. So we say 2 of the 5 are done. We showed you updates on Granite and Sierra Forest. Sierra Forest is 288 cores, which is sort of like, as I said, when I didn't hail them at 4 cores, it was sort of like, wow, right now is there are like 288 cores. It's a bit mind-blowing, the system density that, that enables. But we showed the first Intel 20A with Arrow Lake wafers just out of fab and powering on healthy. We showed you the first Lunar Lake and 18A soon coming to fruition with the 0.9 PDK, but then also ushering in what I like to call the chiplet era, right, where racks became systems, systems became advanced package chips. And with that, the UCIe, the traction that it's gaining chiplets, we showed the first UCIe, and you go to the showcase floor and see our first test chip on UCIe, but this idea of 3-dimensional silicon and really bringing that together and particularly, we're finding huge attraction to those technologies for the most advanced AI chips. That's our own chips, but increasingly, all the -- those in the industry taking advantage of 3D silicon construction for that also rolled out the next-generation past organic packages with glass, which glass and silicon, how much better thermal coefficient. And if they have better thermal coefficients and bonding capabilities, you're going to be able to create much denser packaging, right, and the ability to build optics directly into the package is like way cool, right? And to me, it's just -- it's going to bring the pica-joule per bit capabilities and the maximum terabits into a package. And again, for areas like AI PC and AI for large systems, I think this is going to be quite differentiating. And we're seeing tremendous interest for our packaging technologies from foundry customers. And of course, we wouldn't be a developer conference with some geeky things to the future and showing off our Quantum program and some of the areas specifically of silicon in -- being able to use silicon at cryogenic temperatures and be able to generate silicon qubits at scale, I really think, there's a variety of quantum programs in the industry. I think ours is the only one that will be scalable, manufacturable and that will result in a quantum supremacy. Of course, other things, we're making progress on our foundry. Our first 18A are prepay, Arizona, we've just accelerated the build-out. We love Arizona, and building quickly. We have all of our proposals into The CHIPS Program Office. We just recently from a capital capability just in the IMS partial sale, our mask operation and TSMC is an investor is being partnering with us. So super excited about that. The ARM relationship progressing nicely. We participated in the ARM IPO. And the next picture was, if you go to the next slide, just the huge footprint of the manufacturing projects. And for that, the 4 expansions in the U.S., Oregon, Arizona, New Mexico and Ohio, we'll be having the inauguration of Ireland in the very near future, or Intel -- our second Intel 4 facility that we'll have. And then, of course, the Germany and Polish projects which we hope was a nail biter vote at the EU Commission. You might have seen that about a month ago, it was 5.87 to 10, right? It was really close. They weren't quite sure. So we were huge support by the EU Commission on those projects. And with that, bringing the only geographically diverse leading-edge manufacturing capability and what we believe is the most important area of humanity and economics going forward, silicon. As everything is going digital, we will be that provider of silicon at scale with leading-edge capabilities, right, in a geographically diverse and capable way. So with that...

I think there might be one more slide. Hard to get a sense virtually about the scale in which we do things. But this was, I think, a good picture of what we're doing down in Arizona.

Yes, it was 1.5 years ago where we did the groundbreaking on this facility, and look at this today. And these have the largest trusses that have ever been installed on any building ever, the largest cranes that have been used to lift those 60-ton trusses into place. Each fab is over 6 football fields large of open span facilities. These things are just awesome, right? So -- and I know some of you have come and visited them, but to just think about the massiveness of these engineering projects of building the buildings to build the smallest things that have ever been built. Biggest buildings ever done for the smallest things that I've ever been built. It's really pretty fabulous. If you're not a little bit, I'll say, inspired about that magic, right, then you probably shouldn't be in the semiconductor industry.

Great summary, Pat. Logistically, we're going to try to toggle between questions in the room and questions on the web. We're going to start in the room. I think we've got a mic going around. So if you just raise your hand, please just state your name and your affiliation when you get the mic. So why don't we start with Ben upfront.

Pat, It's Ben Reitzes with Melius Research. Great presentation today. I have two questions. Could you provide a little more color on the relationship with TSMC? They just invested with you 10% of IMS. You do some work with them for some of your products there. You're actually increasing your packaging initiatives as well. And we know that they have some issues there that might need solving. So just putting a bow around that, what's the -- how should we be thinking about it as investors as it stands today?

Yes. And when you think about the TSMC Intel relationship, you have one of the most complex relationships that you could imagine, right? I'm a customer of theirs, right? And when we show off Meteor Lake, okay, we have multiple pieces of TSMC silicon that goes into that. So we're a customer. We're also a collaborator, and you saw that and work today. The UCIe initiative, the test chip is using TSMC silicon and Intel silicon with Synopsys doing the bridging of the UCIe IP. So we're collaborators with them. They're also a customer of mine, right? And when we did the IMS initiative, why did they invest in that? Well, one of the largest customers of the mask-making tool as well. So I'm a supplier to them. And of course, we're going to compete for some foundry businesses. So there's 4 dimensions to the TSMC relationship in that regard. And it's a super important relationship for both companies. I chat regularly with C. C. Wei, Mark Liu, Board members. Every time I'm in Asia, I meet with them, vice versa. It's complex relationship. But I think both of us see that if we work together well, that's the right thing for the industry and for our mutual customers as these -- as we move to the chiplet era, we must work well together because more and more of the solutions that our customers will want is a collaboration of our technologies coming together. Sometimes it might be their packages, sometimes it might be mine. Sometimes it will be their wafer, sometimes mine. But most of the time, it will be both, right, as customers will be taking advantage of that. So super important relationship. And when I spent a lot of personal time on and we're doing pretty well.

Do you have a quick follow-up?

Yes. Thanks, John. It was a really neat example, changing gears completely to PCs. You've talked about this Centrino-moment, and you really need a demo with REWIND AI, where they record basically everything they see and do on your PC and then you can ask it questions, and it just -- that was an aha moment like why you need to have some local processing capabilities for privacy, et cetera. Do you just mind kind of -- like are there more apps? Do you -- like what other apps are you excited about that drive this upgrade cycle? And how big do you think it will be in PCs?

And if you -- let's remind ourselves of the Centrino moment, if you would, because the Centrino moment only took about 2.5 years to materialize in the market. right. From 2003 when we launched Centrino until laptop sales, what -- so I think of this as the beginning of that moment, right, with the AI PC. And it is going to take a while for application to emerge. As I indicated in the keynote, we have a copilot on this journey with Microsoft, and you'll see announcements from them in the very near future. So the software infrastructure needs to come to play. Many of these developer tools are just coming together. The REWIND demonstration, literally, it got working about 5 days ago, right? So this is -- very much these things are just starting to come together. I think this is a killer app. I don't think it's the only killer app in that regard. Adobe is just bringing their capabilities as another ISV in the creator space. Obviously, Zoom and Teams how they start to do this for real time. And I like the video demo real-time transcription, summarization and translation, right, for hearing aid applications. So literally, I'm expecting the future. I'm sitting a meeting. You're speaking Japanese, and I'm hearing in English in real time, in addition to translation and summarization services. To me, that's the collaboration of the future. The tower of babel comes through an end with the AI PC right, that we're able to bring. So I think that will be a whole another area. So I see creators, I see, right, this collaboration, personal productivity as another domain, but it really will be what's the killer app, hey, it may not have been invented yet, right? And that's part of what the volume deployment, the Darwinian PC, as Andy Grove would call it. When you start shipping these things in hundreds of millions of units, creative things happen, right? And I think that's what I think of when we think of the Centrino moment for the AI PC that we're unleashing creative energies. I think REWIND is a great example of one of those killer apps. To me, this is so powerful, right? And within minutes, I have it running on one of my PCs now. It's sort of like, okay, this is like f****** cool, right, just it is.

Perfect. And thank you, Jonathan, can we take our first question on the web?

Certainly. And our first question from the web comes from the line Timothy Arcuri from UBS.

Pat, I had two as well. So my first question is on glass. You've always been really far ahead in terms of packaging. And you've been working on this for a long time. And it's going to allow you to put a larger die and put more die on a single substrate. So we all focus on the front end, but can you just double click on this and -- maybe talk about how far ahead you are in terms of packaging and maybe what this is going to enable you to do and when we can see products in the marketplace that we'll have glass substrates.

Good. So glass is -- we've been working on this area for a solid decade plus. So it's been a component research, and we've been working on it for a number of years. There's a lot of work to enable a new substrate and packaging technology, new equipment. We brought you out one of those panels on stage. The production panels were probably even bigger than that. We go to production versions of this. So it's new equipment to handle those, right? It's also the science then of how we're going to carve them up, put them into packages, there's just a ton of industry enabling work that needs to occur. We mentioned this idea of optics directly in the package. We have some breakthroughs in the area of literally building wave guides directly into the glass package. You have better thermal connectivity, so you're able to get much higher bump densities as well as you put these with lower or lower pitches. So you're able to get higher bandwidth throughputs between substrate and silicon die. So this real idea of the 3D silicon we think a key piece of that. It has somewhat better thermal characteristics so we'll get better conduction. So all of those things make this a pretty compelling technology if we get it to work. So in this area, we think we're several years ahead of others. There are a couple of other start-up companies in this area, but we do think it's an area of significant advantage for us, and one that will be bringing to production volumes in the second half of this decade.

Tim, do you have a quick follow-up?

I do. I do, John. Pat, my second question is on 18A. I think you used the words almost finished, and you used the words finalizing design rules. So can you just talk about this in the context of getting external customers to make a big foundry commitment? I know you've talked about a customer yet to be announced, that's already made a commitment. But is the process finalized enough right now for any customer who would be considering the process to have enough information to make a big commitment. And as part of that, do you think that a customer would make a big commitment if it's still part of one organization.

Yes. So sort of teasing that apart a little bit. There's a lot in that question, at the 0.9 PDK which is what we said we're almost finished with. At that point, part of the 0.9 PDK says that you've met your quality, reliability, performance metrics in the industry. And you've adequately solved all of the yield issues so that you get to stabilize the design rules because ultimately, what people design and care about is what's the metal pitches? How much -- where are the transistors lie? How can I design with this. And that's bundled up in the PDK characteristics, the essentially the spice models, if you would, of the transistors, but then the design rules of how you can actually lay out and use it. That's all summarized into the 0.9 PDK. And as we're getting close to releasing that, that's sort of the starting gun for people to be able to design. And we've been giving a number of the test chips and I held up the 18A wafer on stage. We've been giving the preliminary versions of that to customers. And like we mentioned, ARM is very excited about the results that they're getting since we announced that partnership, and I think it was May of this year when we announced that. So all of that's going very well. But really the starting gun happens when we released the PDK, this is that we're getting very close to doing that. And that's the point where I can say we're done with the intervention of 18A now it's just the productization of that. So we're getting very close key milestone for us in the industry. And that really is the point, then that customers will start making commitments against. Now as I also said in the keynote today, our first major designs are completing. So we've been doing the preliminary design just like the external foundry customers. And we'll be sending our first two major designs, Clearwater Forest and Panther Lake are the ones that we spoke about in the keynote today, a major server design and a major client design in the first part of next year, we'll be sending those into fab. And the other proof point I gave was 20A which essentially 20A is a preliminary version of 18A, right? Very similar RibbonFET and PowerVia, we now have Arrow Lake out of Fab showed the wafer today, powered all, looking very healthy as well. So we're giving good solid proof points against that. We do expect to get whale customers as we've called them for foundry. Those are well underway. As I mentioned a few weeks ago, at the Deutsche Bank conference that we now have a prepay, right? As I'd say, if you're willing to put cash on my balance sheet to accelerate our factory build-out and secure supply chains that's a meaningful commitment. So we're very excited about that. But we do hope to make other announcements of customers that are committing to 18A and as I said, very, very good progress and really satisfied. And as I said, the transistor itself, it's a work of art. I am really excited about this technology. And I think customers will find the power performance area and what they're able to do with it quite compelling.

Thanks, Tim. Let's next question in the room.

Brian Hopkins, Forrester Research. And looking at your presentation today, it really seems like that Intel has changed its strategy from at least a perception a few years ago being very insular, very [ SaaS-focused ] very much. This is RIP because you guys were the giant in the industry for so many years. Now we see TSMC partnerships. We see the ARM rollout partnerships, we see chiplets. Can you talk a little bit more about kind of the evolution and thinking and how Intel wins and this kind of multi-process or multi-architecture world we are removing in?

Yes. And there's aspects to our strategy that I would argue haven't changed. Who created PCIe? We did. Who created USB? We did, right? So we've been driving this industry standard aspect for many, many years, open-source technologies and the software domain. So that was always sort of deep in our ethos. But we were also very biased on two things. That's x86, what's the question? And right, our factories were for our products, right? So I'll say, fundamentally, you've seen those two fundamental things change, right? This idea of a deep view of an open, trusted choice ecosystem always part of the Intel culture, but now we realize them to some degree, hey, would I like an x86-only world? Well, of course, I would because I have a unique position in that technology, but that isn't the case anymore, right? And we're going to be embracing a multi-architecture future. And with that, that means GPUs, that means support for ARM, really being much more open. And as I said, we're rebuilding the company. And in that, we're really creating two companies inside of one. A proper foundry that's servicing our internal and our external customers. And for that, even though it's one Intel, right, we're creating clean, simple demarcation that's accountable with firewalls for external customers and addressing a little bit more of Tim's question as well in that, that they can say, okay, my designs are protected and secure, but I also get to work with one Intel. Maybe I'm designing some things are on the foundry. And I'm using some of Intel's chiplets as I compose my solution for the future. So there's clear benefit to both aspects of this going forward. And if that one diagram that I showed of the 3D AI chip. And if you look at that picture, they say, wow, that makes a lot of sense, right? You're going to have base die, you're going to have compute die, you're going to have IO die, you're going to have EMIB bridging right? You're going to have advanced packaging. You're going to have different memory technologies that are composed into an advanced solution like that. Am I going to build all of that silicon? Absolutely not. Am I going to build a lot of it? Hey, if we execute our vision, yes, I'm going to build a lot of it, but doing things like UCIe, I'm enabling an industry, right, to take advantage of this continued compression of design that Moore's Law and now 3D silicon enables. So to me, those are the two fundamental things that are changing, right? They move to a multi-architecture world. Hey, I'm going to be the king of x86 forever and ever, but the x86 will not be king of all markets. And right, we are opening our foundry and fab capabilities to enable a unique supply chain of technologies into the industry, and that's the Intel of the future.

Thanks for the question, Brian. Let's stay in the room, Toshi, I think you might have had questions on -- mics right behind you.

Pat, it's Toshiya Hari from Goldman Sachs. I guess I wanted to ask about your accelerator strategy. You've talked extensively about Gaudi which is gaining a lot of customer traction. If you can sort of double click on that and talk about the areas in which you're seeing traction? I think the MLPerf is also really good. Is it more training? Is it inferences it both? And then maybe Falcon Shores, your early thoughts on that going forward?

Yes. Yes. So coming back and I laid it out in the keynote, but Gaudi2 shipping in volume today. Gaudi3, we're just getting first wafers out, right? And we're now in the package assembly phase of Gaudi3, but that will be a 24-volume product. And then Falcon Shores is the 25-volume product. And Falcon Shores brings two things together, sort of the continuation of the Gaudi architecture but it also brings in some of the MAX capabilities that are more programmable EU. So I'll say it's EU, right, and accelerator, right, capable. So it's a more programmable version as we bring both our HPC and our AI capabilities together in '25 with Falcon Shores. So the road map is super simple. '23 is Gaudi2, '24 Gaudi3 and '25 is Falcon Shores. Use cases for it are, I'll just call it, AI use cases. And with that, a lot of it is inference, right, as people move past the model creation, but there's also a lot of training going on. So I'd say it's clearly -- it's the data center solutions, and I sort of view that falling into sort of 3 different categories, the big high-end training machines, the large volume inferencing machines and then the enterprise AI deployments, which could be a combination of AI, mostly retraining, right, taking foundational models and customizing them on local right data or the inferencing or use of that in it. Now if you think about that third bucket, there's a lot of Xeon in that, that's where a number of cases. It's just the easiest to add a little bit of AI to my already running Xeon application and then the AMX capabilities make us super great, right? It's sort of like I'm able to accelerate the portion of the workload? Do I go rewrite the whole application to move it to a GPU architecture? Heavens no. That is hard to work of decades of software or do I add a set of libraries and specifically for different AI portions. That's what will happen, right? People will add and it sort of as I like to fall, it's the law of momentum, right? Software developers will continue developing. They don't do hard work if they don't have to, and Amdahl's law, right? If the AI portion is a small portion of the workload, then AMX on Xeon is a perfect answer, accelerate the small portion because most of it won't get advantage because, right, it isn't benefiting from any AI acceleration. So that's sort of how we see the different application domains. And then the other thing, of course, that we covered today is getting AI to the client and to the edge, which is a different market. But in the accelerator market, we have two strong offerings, a very differentiated Xeon product line with AI enhancements and we're a lot better than the alternative. Not a little bit better, a lot better and with Emerald Rapids coming later this year, that gets to be even a bigger advantage over anybody in the industry. And now we're the only company really showing up with competitive high-end training and inference scores versus the market leader.

Toshi, do you have a quick follow-up?

Yes, John. Just on the tech road map, obviously, you're doing 5 nodes in 4 years, which is incredible. You've gotten Next and Next Plus on the road map that you showed today. Should we expect you guys to go back to sort of a tick-talk, sort of pre 5 nodes in 4 years cadence? Or do you just kind of stay accelerated, if you will?

Well, what I'll say is we wanted to get to a certain level of completion on 5 nodes in 4 years before we quite lay out the specific cadence and timing and capabilities of next and next, next. So I'm looking forward to a meeting that we might have some time in 2024 when I give you a lot more clarity on that question. But we felt like laying out a lot of thoughts on Next, the Next Plus before we finish this audacious 5 nodes in 4 years. It was just appropriate for us to get a little bit further along on finishing what we laid out so far. So we'll have a great conversation on exactly that question next year.

Thanks, Toshiya. Unfortunately, we don't have a digital twin of Pat yet. I think that's innovation in 2024. And he's got a pretty tight schedule. So we've run out of time for this session, but I want to thank Pat...

Good. Can we do one last. I want to do, well, I liked these folks. Okay, one last question. Oh, okay Dwight. Okay, buddy, how are you doing? So it better be a good question since I made you -- made a...

I can't guarantee that. Dwight Blazin and Davis selected advisers? It's -- Intel's vision, especially in and around the intelligent edge is very PC-centric naturally. In most of the world, the edge of the network is largely going to not be a PC. It's going to be a handset. It's going to be a handset that is running on a rival architecture ARM. Can you talk a little bit about how your ability to be a major player in the future of AI might be constrained simply by the fact that your primary vehicle to be a participant in the edge is potentially not dominating in terms of the numbers at the edge?

Okay. So first thing, I love this question because it allows me to give you 3 different answers, right? And I like all 3 of my answers. The first one is, I'll say, x86 at the edge, right? I mean, next time you're at McDonald's, Chipotle, et cetera, jump over the counter and look what's under the counter. Now you might get incarcerated. So right -- but right, hey, these are all -- I mean its retailers, it's right food, it's supply chain, it's equipment, manufacturing, et cetera. That's the intelligent edge. Now a lot of it will be ARM-based. What do I do today? I announced OpenVINO with ARM, right? So we're clearly embracive of a multi-architecture edge, right? And my silicon and proof points associated with it, hey, we got to show our architectures better. But clearly, we're demonstrating that we are embracing a multi-architecture future of CPUs, GPUs, accelerators and third-party architectures as well. And the relationship that we announced earlier in the year with ARM. Maybe I'm foundry for many of those use cases where I'm not the architecture. So maybe I don't get the product IP margin, but maybe I get the foundry and packaging IP margin. And let's make one more leap from that. This is my third point to make on this, which I really like as well. Who is going to be the foundry for all of the other big cloud AI accelerators? I think I got a decent shot of capturing a lot of that as well. I mean Google is doing the TPU, right? They're ever going to replace that with a Gaudi, right? Amazon is doing Inferentia, right? And Trainium, right? Are they going to replace that with a Gaudi? Maybe, probably not. Can I capture the margin associated with the packaging and the foundry of those? I think I got a decent shot of winning that as well. So we have 2 bites of the apple here, right, in terms of, hey, we are going to make our products great. We're putting AI into every one of them. We're going to push them the client, the edge, the PC, right, for it. We're going to compete for the high end of inference and training at scale with Xeon and Gaudis and Falcon Shores as we them build it out. And I believe I'm highly differentiated be the foundry of scale and the on-ramp or finding with many of the foundry customers today is leveraging those packaging technologies as the fast ramp to starting to use Intel foundry services. Differentiated technology, right, 2 bites of the apple of the AI margin pool, which we think will be very significant over the course of the decade. So I love your question, Dwight. Thank you very much. And thank you all.

Thanks, Pat.

We'll make a quick transition to Greg Lavender. Greg, do you want to join me up front?

Sure. Thanks John.

Appreciate it, Greg. Greg joined Intel when Pat rejoined. Prior to being at Intel, he was the Chief Technical Officer at VMware. Pat likes to say he has more software engineers today at Intel than he did when he was CEO of VMware. Greg is actually giving his keynote speech tomorrow, but we thought that we'd give him a few minutes to make some prepared comments before we break out in the Q&A. And with that, Greg, I'll turn it over to you on your slides.

Well, thanks for -- first off, for letting me out of my fear cage in the engineering bells of the company. I come and talk to you about all the cool stuff we're doing. So I just want to sort of share a couple of slides I've sort of given this talk, I think, about a year ago or so with an Investor Day conference we had. And people always ask me like, okay, Intel has tried to do software before, but they didn't succeed. But we got 19,000 software engineers doing software every day. Most of you, you don't see. It's all in the foundational layers of the products, right, in firmware, bios, memory reference code, which is what we use to make sure DDR5 and DDR6 work at high speed. And we've got to deliver extreme quality with that software and the hardware. In fact, I have all the power management software that gets all the power distribution control, that's how we're able to get like what Pat said, with Emerald Rapids significant power performance per watt improvement. That's a combination of silicon process technology and then the software that we use to manage the power budget as we distribute that across the die and across the SoC. But it's really about moving up the stack and -- because that's where all the software monetization is happening. We talk a lot about open source and we're major contributors into the open-source ecosystem, developer productivity is incredible, right, using things like PyTorch or TensorFlow or now Jackson XLA from Google. We're contributing into all these ecosystems ourselves with our software engineers. But we're starting to put together Software as a Service and Intel Developer Cloud is the proving ground for where we deploy that first on all of our latest hardware in our latest silicon even before that silicon is in the OEM or ODM channel or in the CSPs, right? I could put it Intel Developer Cloud first and get to developers want it, testing it, trying it out, benchmarking and a lot of them are startups. And if you were at the Pat's keynote, you saw 3 of those start-ups, they all did that work. All their work was done in Intel Developer Cloud, right, as they were building out their capabilities, training at scale and doing what they were doing their AI models, et cetera, and then deploying, as you saw on our new Meteor Lake CPUs. So the software is really there to kind of like Pat said, software-defined silicon enhanced. I was going to say software-defined silicon accelerated because we're in the accelerated days of computing here. So whether it's a Gaudi2, whether it's our MAX GPU, whether it's our MAX CPU with high-bandwidth memory. It's all about getting the operating systems enabled, getting all the compilers enabled, getting all the whole software ecosystem enabled, and that's what we do in the open-source space. But as we actually get up to that higher level, maybe the next slide of the stack -- is there a clicker for that.

Like you want to...

Click -- go advance the slide. Yes. So I sort of what you going to get is -- I sort of talking about that stuff at the bottom, I call it the foundational software layer, right, touch all the silicon, I call it market enabling. It allows us to bring competitive products to market, like I just said. This open ecosystem stuff. It's again, it's relatively free. You've got to spend some time as a developer using it. I mean, OpenVINO is a good example. And then that differentiates our product portfolio, like, for example, with OpenVINO deployed at the edge and the client, we can deploy it in the cloud, you can even do training with it, not just inference. But when you get up into the top layer of the stack, and we give Amazon all the credit for basically taking a bunch of open-source software delivering services, they do make a fortunate on, right? Because they deliver those Kubernetes as a Service or whatever Kafka as a Service, they got their whole model there. And so -- well, that's not a proprietary business model. We can all do that. So what we've been announcing before, you've heard of Project Amber. Project Amber is now GA, that's we official tomorrow. Trust as a Serve. We call it Intel Trust Authority. And is the attestation service. The Intel Trust Authority brand is essentially a portfolio name. We have other technologies that will be coming into that portfolio throughout next year. And essentially, it leverages all of the security hardware enforcement we have for trusted execution environment, Security Enclaves if you call them that, to basically do what I call is a security for AI. That is you can take your AI models and you're like at the edge, they get stolen. You can run them in the trusted execution environment. We have 2 technologies in our current shipping products like Ice Lake. It's software guard extensions. It's trust domain extensions in Sapphire Rapids fourth generation and Emerald Rapids fifth generation. So we've now got this deployed. Again, just to preannounce a little bit. I mean, Google has already announced this, but basically, Google has now got their TDX trusted execution environment available in GCP. They announced that a couple of weeks ago at Google Next. They'll be joining me on stage for that. And then we've also got it deployed in Azure cloud and again, customer preview, but we've got a pipeline of customers right, paying customers, and I will -- not preview, but I'll give you a 2 key security industry customers come on stage with me to demonstrate how they're using Project Amber, i.e., Intel Trust Authority and our TDX technology with their products to enhance the security, Zero Trust capabilities for the industries, and these are major enterprise security companies. So the whole security industry is going to adopt this technology. The regulated industries like financial services, health care, obviously, U.S. government, military, DoD, very, very interested in this technology. And so we see a high growth potential over the next 2 to 3 years as computation computing gets adopted and becomes more and more mainstream in the industry. And so we have more things we're working on to kind of pull through that platform value because we've made a big investment in those bottom 2 layers, right, which we don't recover from a software revenue, but we now have a business model our CFO, support from our CEO, to go drive that as a business, as we've said before. So that's kind of the big picture about what we're -- what the strategy is. But make no mistake about it, we don't stop for a minute, making our hardware sync. You've heard me say that software is the soul of the machine. That brings it to life. But now we're going to monetize it ourselves as opposed to giving way that value to other people to monetize.

Great, Greg. That was a great summary. Logistically, again, we'll start with questions in the room and toggle to the web. I think we went upfront. Aaron, if you want to bring up the mic.

It's Sean O'Loughlin here on for Matt Ramsay from TD Cowen. A question about software in Gaudi and the accelerator software stack. I think you've seen one of your competitors really struggle to get their software into general availability on the main frameworks such as PyTorch, TensorFlow and sort of a recent achievement of theirs after years of work. How is that going for you guys? Is it important? Or are we focused on the wrong thing in that front?

No. Some may surprise for which has emerged. I mean it was a TensorFlow, PyTorch kind of like 2 horses in the race with -- there's other things. I mean Pat had so on the screen, MXNet, but OpenVino's in there, too. So let's -- it doesn't get as much publicity, but certainly widely, widely adopted, particularly in edge computing, industrial edge, manufacturing edge, surveillance edge, et cetera. So the -- the way I like to think of it is -- so we're actually a major contributor to PyTorch, I'll give you some statistics tomorrow. Like in terms of that. We actually just got earned then weren't given earned, didn't pay for it. They see it on the governing board of the PyTorch Foundation, now that they move from Meta into the Linux Foundation. So we're now sitting on the governing board of that because we're major contributors. If you deconstruct PyTorch, you look at the bottom layer, it's sort of a plug-in architecture. Guess NVIDIA code plugged in there. You got some AMD code plugged in there. You've got our [indiscernible] that's the open standard data C++ plug-in in there. And then there's a Gaudi plug in there as well. So we can take the PyTorch ecosystem at and all the value at the top of the stack and the productivity that comes from putting in Python. And you don't have to get down into the CUDA, into the hip, into the sickle. You don't have to write there. And what's really been the big game changer is Triton, which came out of OpenAI. So that's both syntactic extensions in Python, right, just to give you sort of the ability to express certain matrix multiplication operations and other sort of GPU-accelerated algorithms. And they use the technology, which is open source originally created by Chris Lattner at Google MLIR which is what open XLA uses what Triton uses. And that allows us, and I'll give a demo of it tomorrow -- that allows us to essentially map those PyTorch model training or model inferencing, semantics onto different hardware. I can run on my CPUs, which I do in Xeon, so it optimizes for Xeon. It optimizes for our Max GPUs and our RGP and it maximizes for Gaudi. So essentially, at the lowest layer, it's a hardware abstraction layer. We call it Virtual ISA It's different for a CPU, a GPU and an accelerator. And so we just use that MLIR technology essentially map to the right architecture. Think of it as the assembly code for the device to get the maximum performance. And we just published our performance benchmarks which I think shows the value of that kind of architecture, software architecture and technology. But that's the fun part of being a computer scientist is making that stuff work.

Sean, do you have a quick follow-up?

Does that answer the question?

We'll pull in the room before going to the web. Anybody has a question in the room? Jonathan?

Certainly, just 1 moment. Our first question from the web comes from line of Christopher Rollins.

This is Matt Myers actually on for Chris, I think for me, I think takeaway here was that Sierra Forest gathered itself 288 cores. Previously, you talked about 144 cores. Just kind of to dig into what exactly changed here I know AMD Bergamo has 128 cores and 256 threats. How should we think about you competitively here? And also, what's been the reception and interest for Sierra Forest? And does it cater to other applications now that other processors don't?

Greg, do you want to start and I can add if needed.

Yes. Yes. I think -- so because one about the great things about having your own fab and your own packaging facilities is we can actually push the envelope on things. And so Sierra Forest originally started out with 144 cores. And we realized we could just take 2 of those dies and put them together on some clever packaging right, using our frivolous technology to basically kind of bond them together and then package it all up into an SoC, still beat the thermal and performance and get twice the power. So if you go look at something like an Amazon, a lot of what they run there are called, I think, about 30%, 40% of the workloads in Amazon are called lamda functions. There's like a little Python scripts or other little things that run to do data management on their S3 object store, and even when I was at Citigroup, we used Lamda services a lot. And you just need lots of cores, lots of cheap cores that just execute that code efficiently. And so having that large core count is extremely useful. And again, to prep for data, I mean you have to prep all your data to go train on it. So you're just doing string matching and string processing and data mapping and tagging and stuff like that. Just having lots of cores just so you can push through lots of relatively basic computations. It's not high-performance computing. And you can just get huge amounts of throughput through there for the same power bill that you're paying for something with fewer cores and maybe it's more hot and more powerful. Not hot, more powerful, but that's performance per watt. So it's really about throughput computing, and we've all heard about that before. It's not about spec-in computing, it's throughput computing, just get more work per unit time done through the CPU.

Is there a quick follow-up?

Nope that's great. Thank you so much.

Thank you. And would you like to go to another question from the web?

Please, Jonathan, that would be great.

Certainly, one moment for our next question. And our next from web question comes from the line Stacy Rasgon from [ Bernstein ] Research.

Greg, first, I wanted to ask a little more about the merging of the Guadi and the GPU road maps. What exactly does that mean? What functions or capabilities you're bringing from each of those architectures into the What are you giving up? So just unclear what that looks like when it comes.

Yes. Good question. So let me see I don't want to go too technical, but I need to sort of say a few things, right? So what do I things Gaudi is really good at is tensor operations. So the silicon, the sort of the give is an ASIC. The silicon for the Gaudi processor is highly, highly attuned to executing those math operations at very, very fast speed. In fact, every Gaudi sort of accelerator has 100 gig Ethernet built in, in and out. And we stack 8 of these things in a sort of a server box, we have running in developer cloud, in fact, and building that as fast as I can build them and get them and come up and buy them up. But essentially, you have 8 Gaudi nodes. Each is a 100 gig per in and out independently in that chassis. So we have an 800 gigs of data in and out, and we actually use an Arista class network fabric to interconnect all that. So the big thing about training is, you guys got to pump a lot of data into those accelerators to do those training algorithms. And so they've mastered this essentially I/O subsystem, right? NVIDIA talks about NVLink, they're going to take their proprietary fabric and want the industry to adopt a proprietary fabric. We're just using Ethernet. We're going to do high-speed advanced Ethernet. And we can move that data around. We can put these 8-node units together into I mean I think I read somewhere that 80% of the DGX market is no more than 8 GPUs. So we have an 8-node Gaudi. But we're putting together in we can have sort of you can do 8, you can do 16, you can do 32, you can do 64, you go 128, you can do 256. We're building a 256-node clustered right now. And it's 8x 256 Gaudi's will be available for somebody who wants to do really, really deep training on this with massive amounts of data, massive amounts of bandwidth and massive amounts of speeds. So I'm taking that architecture, the IO architecture, the network architecture, the systolic array that does the advanced tensor operations. And I'm bringing the GPU core -- called it EUs, they're the execution units. Those are the 4,096 execution units you have in a typical GPU. So we bring those execution units into the same package, right, for all this all the GPU goodness we've developed over the decades, right, but next generation on better process technologies, so higher transistor density. We bring all that together into a package. And now you've got -- if you want a program in Sickle or you want to do Triton and compile kernels down to run on a GPU architecture, that's fine, but we use the systolic array to do the accelerated math. So it's really about a hybrid architecture to bring those technologies together, and that's probably all I should say at this point. But let's just say, I think the best of both worlds we have because I want to be able to take my existing GPU software that's running today that a customer might run in into the cloud or they're running on a MAX GPU. And that would just port forward without any change on to the Falcon source. So it's really important for now. I'm pushing as many GPUs out to researchers and students and academics as I can to get them to basically develop on the MAX GPU, and that will just run forward on to the MAX GPU. Because again, we'll use Triton and those kinds of languages to take a virtual isolator to make sure works if you've invested the time to write the software.

Stacy, do you have a follow-up?

I do, I mean, maybe this is a better question for Dave, I don't know. But clearly, you're moving from giving the software a to monetizing it. Can you give us any color or even qualitative, like how big is the software revenue today like -- where did it go like in 3, 4, 5 years? What are targets -- or how should we be thinking about the evolution of that software or revenue stream?

Yes, Stacy, it's a good question. We can try to address it in Dave's session, but we haven't really gone into details about what the software stand-alone business model will look like. Pat referenced in his Q&A that we may or may not have an event next year for our outside owners. I suspect that if we do, this will be a subject that we'll address at that time.

Got it. That's helpful. Thank you so much for letting me ask the question. I appreciate it.

Greg. One question I get, maybe as a follow-up to Stacy's first question. You talked about Max to Falcon Shores that software compatibility One of the questions I get asked off from your odds owners, all the optimization work we're asking customers to do for Gaudi 2, Gaudi 3. When Falcon Shores comes out, is that all leverageable into the Falcon Shore platform?

So the Gaudi is we don't -- it's not directly programmable in the way that a standard GPU would be done with CUDA or with Sickle the data language, which is really just -- that's an ISO standard extension to C++, so it's just standard C++. So -- but essentially, we do have a sort of we call it a C compiler or component of that. And what we do is we want -- if a customer needs some special optimizations, we'll work with them to build their own little sort of think of it as their own kernel that will basically use our TPC compiler, which is the TensorFlow processor compiler to basically compile to that because it's essentially -- it's a VLIW if you remember a very large instruction. We're an architecture. It's not a classic CMD or SMT a GPU architecture that sort of compiles down to that. But what we've done with those customers we carefully assess the portability of that the port forward into the architecture because we've already got that defined. We've got the virtual ISA, which is the sort of abstractionally or above the hardware. So -- but most of what the customers are bringing to us today, they can just simply run it in PyTorch. And we take -- we optimize it, as I said, to run effectively on the Gaudi accelerators without them having to change their code -- we may give them some different tweaks about, okay, well, your study graph and your dynamic graph, we might give them some recommendations on how to make that more general because sometimes people write things quickly and they just sort of optimize for 1 GPU. And we want to sort of generalize and say, "Well, if you generalize, you can still run your other GPU, but you can run a Gaudi and you be able to run it on our future GPU. So we want to kind of make sure customers kind of write the most portable code that they can write to take advantage of what's available in the market.

Jonathan, can we take the next question online?

Certainly. And our next question comes from the line of Vivek Arya from Bank of America Securities.

Thank you for the very informative presentation. Greg, I'm curious, as generative AI becomes the primary workload in the cloud, how much of the heavy lifting will be done on the accelerator? And how much will be done on the CPU, and what implication does that have on Intel? Obviously, the CPU will always be there. But if a lot more of the workload is being done on the accelerator then does that extend replacement cycles of the CPU? What exactly do you see the implications on the CPU in the medium to longer term?

I think Pat sort of tried to address that in his comments because -- so look, I mean, the capital investments you have to make to build these large-scale systems, whether it's in Azure cloud or AWS cloud or any other cloud or even somebody like Core, which is taking on billions and investment to build out capacity. And so other people can afford that, secularly, the enterprise, which is we know the enterprise or the edge computing, Pat talked about that, is very expensive. But once you've trained these, say, 1 trillion parameter models, right, and we can do that with Gaudi's as well. Why don't you get, it's a big capital investment for my CFO to give me to build these things out in Intel Developer cloud. And so once you make that investment, then you want to monetize that investment over and over again with getting as many models trained as you can. And so -- but when it goes to the actual use cases, let's say, we talked about a program we did with Boston Consulting Group at Intel Vision. Essentially, they have a bunch of internal data that's proprietary. They're not going to go share that with anybody. They don't want to put it in the cloud, right? They trained on our Gaudi CPUs for that particular stuff, but they use a ChatGPT train models and they just specialized what they wanted to do. And then they do the inference on the Gaudi's, right? Because it's cheaper, it's better power. It's actually faster if you personal remarks about that -- results about that. So in that sense, there's going to be a lot of what I call secondary models, which are nimble models, or fine-tuning of models that's going to happen with proprietary data that's within the health care industry, the financial industry, what have you. So we think -- it's not at the edge. It's a data center level where everybody has already got all their data. They may not have it all in the public cloud, and they may not want to take it there. But I think what's going to happen is the market basically develops into the giants who can train these expensive things they offer it as a service, customers will pay by the or by the API call. And -- but they'll build their own chat models and things like that to use inside their environment. But I was at Citigroup. Remember, RPA, robotics process automation was going to revolutionize the industry, mostly end up with a bunch of chatbots. So I think -- but they're not smart chat bots. These are going to be with smarter chatbots, right, that you're going to use for IT help desk, customer call centers, et cetera. So this whole market hasn't even begun to explode horizontally. We're still focused on the scale up. And it's the scale up, we're the Xeon and the scale-out where even our ARC GPUs are going to be used at the edge right to accelerate -- to accelerate the inferencing and hopefully using OpenVINO. So I think that's kind of what's happening. We got this sort of big pyramid spike, but the wings haven't unfolded yet to say, here's the rest of the market and how you address it.

Yes. we've talked about AI not as a market as much as a workload that's going to span across the compute continuum, whether that be cloud to network, to enterprise, to client to edge. And in each one of those nodes, there's going to be different silicon and software strategies to drive optimal TCO for customer for workload, and we think we're well positioned to capitalize on that. Do you have a follow-up?

The second question is on this other silicon that I think some of your peers call the DPU or the And the perception is that there are a lot of x86 CPU cycles that are being used to process the overhead, whether it's security or storage or networking. But if they can just take all that overhead, which I think Google said is about 1/3 of the CPU cycles maybe a different number now, and put it on the DPU, what implication does that have on Intel? Because I imagine the DPU uses alternative ISAs and it's a lot more competitive So does that change how much of the CPU cycles I'm using?

Well, so first of all, let me just give you this fact to it. So I mean -- Pat mentioned stability, AI is buying our Gaudi is, like the number was 400, I think he mentioned -- I mean those get put into sort of these 8 node clusters, as I said. So you got a lot of bandwidth coming in and out. So therefore, you got to move a lot of data in and out. Now the Gaudi's don't have an IPU or we call it an IPU, they call it a DPU. But essentially, the whole -- there's going to be a lot of innovation over the next 18 months in fabric technology, and optics and the kind of things you do to move data quickly between because massive amounts a day, they have to move between the CPU where you're preparing it, you're exactly right. There's a lot of preprocessing that happens before you go to the training step. So once you preprocess that data, you've got to write it to storage or you got to take it off a storage and put it into the GPU or send it directly from the CPUs to the GPUs or the AI accelerators. And so that bandwidth requirement is going to drive the systems architecture, which will really, I think, validate the IPU/DPU offload engine, which is what you're talking about is essentially a place to free up the CPU from having to do that transmit and receive processing, right? But it the fabric as well are going to just get to -- we will be doing terabits per second inside the CPUs, right? So you got to go to feed the CPUs as well teraflops in the GPUs. So there's a whole impedance match issue that's going on right now. If you haven't read about the Ultra Ethernet Consortium UEC, go read about the Ultra Ethernet Consortium because we're partnering with Google on that. And I'll just say that we got great IPU technology, and we're going to be participating in this change in the way the fabric and the communication and the optics is done.

I just might add, what you are characterizing as a risk, I think we actually see as an opportunity. In large part, as we bring down the cost per function, which is what all these architectural changes are doing. Our view is that the number of new use cases tend to dwarf any deflationary impact. I think a good example is what happened with virtualization back in 2008, 2009, the concern going into that trend was that you were going to be able to do on 10 CPUs what used to take 40. Effectively, what we were doing was bringing down the cost of compute in a market that wasn't compute saturated. And every time we do that, there's always a corresponding growth in new use cases and workloads that dwarfs any of the deflationary pressures?

And our IPU has an Intel technology, and I called QET mid quick-assist technology, what it actually is compression, decompression, encryption, decryption, okay, in the IPU. So those are typically expensive things instructions to execute. Our CPUs actually do them quite well, but we took that technology and we pushed it down into the IPU. So we can do very fast encryption, decryption in and out of storage and very compression and decompression in and out of storage, and encryption across the fabric, particularly in these trusted execution environments, where you want to trust that the data is protected on the CPU, you want to trust that the data is protected across the network and you want to trust that the data is protected on the GPU or the accelerator, and that's what this whole trusted computing envelope needs to look like. It's not just in a server, we're in a cluster, it's across the fabric to the accelerator devices.

Jonathan, can we have the next question?

I'm not showing any further questions in the phone queue at this time. [Operator Instructions]

Could you go back a couple of slides because there's 1 slide I wanted to talk about. There's 1 had the 3 circles. Give me back to that one. Just kind of -- I started talking about like Trust as a Service, but this d1 right here is sort of the 3 target areas, right? We're not going to be everything to everybody. Basically is we got unique IP in our hardware, in our software and with our Intel Trust Authority to create this virtuous circle, I call it, which is the SaaS pull-through of hardware revenue. So we're selling the hardware, I can come over the top and sell the SaaS but the demand for the SaaS software and the capabilities that provides pulls the hardware. And I do this as Sun Microsystems when I was there basically acquired my start-up company in 2000. And even today, I think Oracle is making a $1 billion run rate business on that technology that they bought that I had created. And so it's essentially this kind of virtuous circle where the hardware needs the software, and the software needs the hardware. So this is sort of the model that we're using. And then we've got AI as a Service, so Intel Developer Cloud. It's where all of our AI technology delivers first, and then we'll work with our OEMs and the enterprise Si's in particular, to help them adopt that software for running on-prem. And then we have our performance optimization, and I'll make an announcement tomorrow about a partnership with Databricks around how we use our grand elite technology to accelerate Databricks by up to 30% and reduce the AWS service cost that you have for running that with no code changes, we just do it all in the basement by accelerating the instructions.

Perfect. With that, we'll make another quick transition, but Greg, I appreciate the time this morning. I think you'll be on stage tomorrow, more at 9:30, so please listen in either live or via the web. And with that, I'll bring Dave up. Thank you, Greg. Appreciate it. Saving the best for last. I think Dave has a couple of introductory slides, and then we'll move right into Q&A.

Also, the team wanted me to remind everyone that they can requeue if they want, even Stacy Raskin's dog can requeue if you want. -- or she I don't know if the she. Okay. So I thought maybe I'd just do a few slides, teed this up and then we'll do this quick and go to Q&A. The top part of this chart is really Pat's vision, and really a lot of what he talked about in his keynote. It's around execution in terms of getting process technology back, in terms of getting products back to where we want them in the market. It's around building out the foundry business. And of course, what Pat talked a lot about today, it's about driving artificial intelligence across all workloads of compute. But underpinning all of that, that I focus a lot on is the financial discipline. And it's really around smart capital, around driving efficiencies operationally, and it's unlocking value where it's kind of trapped within the company. So if you go to the next slide, let me talk a little bit first about Smart Capital. This is something we talked a lot about at Investor Day last year, and there's really 5 kind of elements to Smart Capital. The first is that we build shelves first. And that's kind of obvious because they take 4-year lead times to build. But it's important because it's the smaller amount of CapEx. It's also the expense it's depreciated over the longer period of time. So it tends to have less impact on gross margins. And so you'll always see us want to have white space ahead of demand. And that's where a lot of CapEx that we're spending today is going. In fact, more than half of our CapEx in '23 will actually be on shelves. The second is government incentives. Obviously, the chips act in the U.S., the chips or the chips investment credit in the EU. There's local credits. Of course, there's the investment tax credit as well. So as a key part and enables us to make the investments both in the U.S. and Europe to build out the footprint that Pat talked about earlier. There's customer commitments. That's another key element of our strategy. Pat talked about the prepay that we got this quarter. We also got a prepay associated with our partnership with Tower to build out one of their nodes. And so this will be an important also element of our strategy. Not only does it provide cash as we're making these investments, but also it helps show commitment from the customer perspective in relationship to our foundry business. Financial partners, obviously, also a big component of this. We announced back in August was, I think, last year. The Brookfield partnership, where they're co-investing with us in Arizona. We're spending almost $30 billion in that investment. They're roughly investing about half of that with us. And then we both kind of share in the financial returns that investment. You're likely to see others of these as we go forward, these kind of partnerships to help augment what we're investing on our own. And then lastly, and Pat talked about this relationship we have with TSMC, but we want to continue to have a foundry relationship. We think that's a good smart way -- Capital Smart way of managing the demand in our supply. And so you will continue, I think you'll see us to use foundries in relationship with our own. And this Chiplet architecture that we're moving to, as Pat even mentioned, really enables even to strengthen that foundry partnership. So in all, what that means is we think we can drive the '23 -- actually '22, '23, '24 CapEx intensity to this kind of mid-30s as a percent of revenue, that should kind of settle longer term into more of a 20% to 30% range, call it, mid-20s. And then we expect to see offsets in the range of 20% to 30% of our gross CapEx investment to enable us to get to this CapEx intensity. Next slide. Then on the operational efficiency side, I think we've done a lot here and probably some of it just because it's common increments, probably it's been hard for investors to really see the fatality of it. We've exited 9 businesses since Pat came aboard. We saved about $1.7 billion annually from exiting those businesses. We have a very regimented approach that we've established to look at all of our investments every -- really every half year or so and rationalize where we're putting dollars and where it doesn't make sense for us to be in. We're not shy around exiting those businesses quickly. The second key aspect of that, and we talked about this was at the end of May that we did that thing yes, end of May, we talked about this is our internal foundry model. So this is essentially taking the manuring and technology development group and our foundry business unit and pulling them out and making them a separate business, much like all other foundries out in the marketplace and really starting to hold them accountable to our P&L. And I think you'll see, as we -- we'll segment report this way in Q1 of next year, but you'll start to see some, I think, meaningful improvement as we progress. I can already see, as we're talking to the team over there and they're starting to look at where their margins are, where their spend is as a percent of revenue, where their operating margins are, and they're already looking for, okay, I know what I need to do to improve that to make the P&L look better. This is a powerful part of the story and what drives a lot of the $8 billion to $10 billion of savings we think we'll get by the end of '25. And then lastly, it's just focusing on OpEx. And in addition to the portfolio optimization, there are a lot of things we can do to drive better efficiency. We cut-out about $2 billion. But we'll probably beat that number when '23 is done and dusted in terms of savings reductions, and we think there are more opportunities to drive efficiency within OpEx. In fact, some of it will be through the usage of AI. And so our goal then -- now that we've gotten the number to a more reasonable numbers is to start to grow that at a rate that's quite a bit below what our revenue growth rate is and continue to see OpEx leverage over time. And then the last slide is the value on last slide. This is probably another area where I think gets underappreciated is that this is a key part of our strategy is to look at assets within our business. And if there's an opportunity to unlock the value by doing something different than just running it within our business, we'll do it. And so the Mobileye example is probably the most significant one where we IPO-ed that last year, late last year, we did another offering, this year that the market cap on that business is $30 billion on its own. We've actually generated cash from that, which has helped us invest back in technology and products. We're likely to see some other ones that are similar to that over time. IMS is another example of it. We got a $4.3 billion valuation. That's a Masquerading business. It's particularly well positioned in the EUV space and the valuation was great. We actually think it's going to be a lot better over time. And I think so do are the partners that joined with us, Bain and TSMC. We got some cash infusion from selling a stake in that. And then we all hope that and expect that the valuation will increase significantly as EUV becomes more and more critical in semiconductor manufacturing. And then lastly, it's just a whole host of partnerships that we've done that have moved spending and so forth off of our books on to someone else's who we think can exploit it better. In some cases, some of it's just relationships that we've built like the Tower or ORMC with the government. So there's a whole host of things that we will do over time in this area to better optimize where the investment is being made and unlock value for shareholders. So with that...

Perfect. Yes, a great summary of the financial discipline. Let's open it up to Q&A in the room. We can start with Ben. Yes, please.

Ben Reitzes of Melius, Thanks, Dave and John. Dave, you recently talked about the data center business being down less than expected. At the same time, talking about Gaudi but pipeline being $1 billion and growing into the -- as you're going out throughout the quarter. How much are those comments interrelated at all? And as we kind of look throughout the year and into next, what are the key milestones that in terms of Gaudi and then the data center improvement should we really be focusing on?

Yes, Okay. So let me unpack it a little bit. just in terms of how data center is doing, it will be down quarter-over-quarter. I don't want to give anybody the impression that this thing -- that the business is going to be up this quarter. And a lot of that is actually our FPGA business had a huge backlog, and they're at the point now where they've kind of satisfied a lot of the pent-up demand. And so there was kind of a natural correction we were going to see in the FPGA business, which we have seen. In addition, we did expect some share loss this half of the year. In fact, we thought we would see share loss in the first half of the year. We actually held share better in the data center business than we thought. But we do expect to see a little bit of that in the back half of the year. And then lastly, there was a lot of inventory in the channel in data center, and much like client by the way, but client kind of recognized it earlier and kind of worked that inventory through the system, and we're now kind of seeing the recovery from that data center was a little bit more delayed. And so we have to see the recovery from that. So I think that we'll inventory digestion we're going to see for Q3 for sure and likely for Q4 before we start seeing that turn around. On the Gaudi side, we will see revenue this quarter, but it will be modest. The $1 billion pipeline's a little bit more of a '24 story than it is a '23 story. But we do expect to see revenue this quarter. We think we'll see more revenue next quarter. And then next year, I think we'll have something that is reasonably meaningful for us from a Gaudi perspective. We still have to build the pipeline up though. We talked about having more than $1 billion at the earnings call. I'd tell you right now, as we sit, it's a lot more than it was at that point. Our expectation is by the time we talk about earnings in the end of October, the number will be a lot higher than where it is today. And the whole nature of our pipeline is you build it as much as you possibly can because you understand that the conversion is not 100%. It's going to be some percentage lower than that. But if we can get a big number coming into the year, the expectation is we can translate that into meaningful revenue for Gaudi next year.

Do you have a quick follow-up?

Yes. Quick follow-up is packaging. Just wanted your take on it. We talked about it with Pat a little bit, but it's kind of coming fast, the needs in the industry, some of your advanced techniques, the dynamics at which this could be material revenue. I was just wondering what your thoughts are. Is this how many months away are we from hearing more about it and seeing it in the P&L?

Yes. The good news with packaging is -- whereas the wafer business, it's -- but you're talking to customers and then you're going to see revenue couple of years out. Packaging from the time you're talking to the time you can turn revenue can be like 3 to 6 months. And so we do expect that a lot of the traction that the team is getting right now will translate into revenue relatively quickly. And in fact, even in '24. It's a good business. The margins are quite healthy. We have a good portfolio for advanced packaging and advanced packaging is tight in the industry. And so customers are out there looking for options. And we think we have a lot of offerings there that can get them what they need, the performance they need and the time frame they need, with the supply they need. And so we're really about that. That pipeline actually is building quite rapidly, too. The only difference is that when you win those businesses, you win them in increments of $100 million as opposed to wafers, where you win them in billion-dollar increments. But the real advantage beyond what we think is a solid business with good profitability is, it's a great cross-selling opportunity. We get a lot of customers through packaging. We show what we can do in terms of servicing customers and then we cross-sell them with our wafer capability, and we think it's a great symbiotic relationship.

Toshiya Hari, please.

I have 2 questions. First on gross margins. I know you guys guided to, I think it was 43% for the current quarter. And you sort of talked about December being up as well given some of the tailwinds. As we go into '24, and I know you're not going to share a number, but how should we think about the headwinds and the tailwinds in your business? Because obviously, you've got a lot of nodes, a lot of products ramping in a very short period of time. So how should we think about it?

Well, look, there are tailwinds for sure, and there are headwinds. I mean on the headwind side, we're going to have this underload hangover, I think, all throughout next year, partly because we won't have ramped back up to full production by -- until later in the year and partly because by the time you see cost of inventory goes onto the balance sheet and then through the P&L, it takes some time. So there's an effect there. In addition, we are still driving a total hunk of start-up costs. And this 5 nodes in 4 years is not cheap to do. And we're doing a lot of nodes kind of stacked on top of each other. And so we're in the billions of dollars of higher start-up costs than we traditionally run at because of the 5 nodes in 4 years. So those things are causing some headwinds. And in addition, some of the products that we're releasing, which have better performance and so forth, also come with an incremental cost to them. That said, there are some tailwinds. Obviously, higher revenue always is good for Intel because we're a high fixed cost business and so incremental revenue has good marginal profitability. And so we can drop that to the bottom line and that helps a lot. In addition, the new products, as we move -- migrate our way through process nodes and through products as we do better in terms of the performance there, the expectation is that we'll just do better in the marketplace. And so I think year-over-year, we'll see gross margin expansion. It may not be hundreds and hundreds of basis points next year. It may be more modest for the reasons I talked about from a headwind perspective, but we do expect margin expansion next year. I think as we progress and we start to get past the 5 nodes in 4 years, we'll talk -- left us hanging there with the -- how quickly we'll do the cadence. But I suspect that our start-up costs will modulate. And I suspect that as we get to where we're at leadership, process technology, with leadership products, that's a whole different dynamic that we'll have in terms of margins for those products that we'll really start to see a lot of the margin improvement. And ultimately, what Pat has said, which I subscribe to, as the finance guy is our goal is to get to 60% gross margins. And we think we have good line of sight to do that, and in particular, given this internal foundry model, what we can drive in terms of efficiencies makes us even more confident in our ability to get to the 60% margin.

Toshiya, do you have a follow-up?

I do. Thanks, John. On capital offsets, 20% to 30% of gross CapEx, I think on the earnings call, you said you might be toward the higher end of that or you're increasingly comfortable that you could do toward the higher end? Obviously, since then, you've had a couple of prepayments and things of that sort. So as you look forward into 2024, could you potentially do better than 30%? And how should we think about free cash flow?

Yes. Potentially, it could be higher. We'll have -- the expectation is that we'll have a clear line of sight into what we can expect from EU -- from the U.S. chips. We already know where we're going to hit from an EU chips perspective. We're -- we'll see what comes of the prepays. There might be opportunities to do better on the prepays. We'll have Skip 1 also helping us next year. And then there's a potential for a skip -- for a second skip that might also augment the offset. So there's a potential. There's going to be an interesting dynamic on the foundry side. As we do better in foundry, there's going to be more demand on us -- on the gross CapEx side. And so we'll have to be balancing the demands from customers on the foundry side in addition to our own requirements for wafers with those offsets to see exactly what ultimately we settle out at. But I feel pretty confident that we're certainly in the 20% to 30%, and we are certainly biased towards the high end. From a cash flow perspective, our goal is to get to breakeven in the more near term. So I would count '24 probably in that time frame. And then ultimately, the goal is to get cash flow as a percent of revenue to be 20%. And I think as we kind of progress through next year and looking into the following year, we have an opportunity to start to move our way towards that 20%.

And Toshiya, just to level set the question. What we said in the last earnings call, is '23 plus '24 we expect to offset at the high end of 20% to 30%. That was a change from 90 days earlier where we talked about '23 stand-alone net capital intensity being in the low 30s. And the reason for the change, is there some uncertainty about when offsets it? Is it the back half of this year? Or is it next year? Are the implication being that we could do better on a 1-year basis in '24 than the high end of 20% to 30%. John, I think we take a question online, please?

Certainly. One moment for our next question from online. And our next question comes from the line of Christopher Caso from Wolfe Research.

Question, Dave, is on CapEx. And I recognize you probably don't want to be specific on the CapEx into next year. But if you give us a sense of some of the puts and takes? You talked about that the CapEx this year was half shell, imagine that's going to come down a bit next year and transition over WFE? In addition, is there any CapEx associated with foundry for some of these prepayments next year? And then any changes in CapEx that relate to market conditions, at least in some qualitative terms?

Yes. So good question. Maybe answering your last part first, and then we'll kind of go in there. So the way I look at CapEx, I kind of bucket into 3 pieces. The first piece is the shell capacity. And in probably most cases, I'm sure there's an exception to that, we'll want to make those investments because the lead times for shell space are pretty long. And you never want to be caught not having the shell and then having the demand. That's the worst answer possible. So we'll always try to invest ahead in shell. So I try to keep that one untouched, perhaps adjusting it around the edges, but mostly keeping it untouched. The second piece is the investment we make in Oregon for process technology because we always want to make that investment because that helps us accomplish the 5 nodes in 4 years. So I definitely don't touch that. Then the third is what I would call the capacity. It's the high volume capacity that we put online to meet demand. And that's the one we are always kind of course-correcting based on our kind of longer-term view of wafer demand where we think our share will be of that wafer demand. And then based on that, what we think we'll need in terms of fab footprint -- quick fab footprint to be able to service the demand and then not be over -- not have too much capacity, but also not have too little capacity. And so we are always every month kind of adjusting those things around the edges to make sure that we're aligned with the expectations there. As we look into next year, we still haven't quite got the full CapEx picture done. We'll have that probably by November, and I imagine that when we have our January call, I'll probably be able to give you some pretty good insight into what we're doing. It's obviously is somewhat market-dependent because that third piece, that capacity-oriented aspect of it is something that we will modulate based on expectations. You're probably right, there probably will be a little bit less as a percent of fab kind of footprint, call it, investment and a little bit more on the equipment side, but probably not significant. I think we'll make a pretty significant investment in clean room capacity next year as we did this year, given what -- we were essentially behind in terms of our clean room requirements. And so heavy investment is going to be necessary next year. And you saw the picture of Arizona. We're not done by any imagination. We're going to still work on that. And then the one thing I'd just say is our goal is to be in this mid-30s CapEx intensity, and we said that would be a '22, '23, '24 kind of goal. And so '24 that you can expect that that's generally what our goal is. And of course, we'll have a better sense of what the denominator looks like from a revenue perspective as we get closer to the beginning of next year.

Chris, do you have a quick follow-up?

I do. Thanks, John. Just to dig more on the start-up costs. And again, you're not providing visibility beyond 18A now for what that is. But just in general, as we get towards 18A -- in the past, when there was a [indiscernible] model, you'd have start-up cost ramp on a year and you get them back in the following year with the [indiscernible] model now, those are more constant. But I guess the question is, as we go into next year, is that an incremental increase in start-up costs? Or is it just continuous headwind similar to what you had this year for start-up costs?

Yes, I'd call it more the latter. It's just going to be a significant year for start-up costs. But this year was also a significant year for start-up costs.

So not really incrementally higher then?

Probably not, although we still haven't built a plan for next year completely. So we'll have to look at that. But I would say first order is probably pretty similar.

This is Gunjan Chobani from Bloomberg Intelligence. Today during the keynote, Pat demonstrated a lot of good use cases and applications for the AI PC and talked about how analogous it is to Centrino era? Does it change your long-term assumptions or projections, whether it be for upgrade cycles or consumption versus what you outlined during the PC TAM event earlier this year? Because intuitively, it should accelerate or pull some of those metrics.

Yes. So we are going to run that business with the assumption that it's got a relatively modest growth rate. We already have a strong share there. So I think share changes will not be significant in that space. And that's how we're going to run the business. That's how we're going to invest in it. That's how we're going to think about it. But we recognize there are things that could catalyze this business for sure. One is the Windows Refresh next year. That could be a strong catalyst, which would be an upside to the business. And I think for sure, if the Centrino moment stimulates a lot of demand, which is not unforeseeable. I mean that is a possibility, then that could be a strong upside case for the business.

Do you have a follow-up or -- John, I think can we take another question online, please?

Absolutely. One moment for our next question. And our next question comes from the line of Aaron Rakers from Wells Fargo.

Yes. So I just wanted to take maybe a step back and think about just the higher-level dynamics that's going on. There's a lot of discussion out there around AI demand and traditional compute demand, and you guys have probably been asked this every which way. But I'm curious, Dave, when you look at the data center business, how are you thinking about where we're at as far as kind of inventory correction or digestion in the server CPU market, maybe what inning we're in right now and how you think about the progression of the demand, if that were to kind of play itself out over the next couple of quarters? Anything you can share there, how you're seeing customers engage right now and just what you're seeing from a server CPU perspective?

So that's a good question. And I did mention we had a relatively tepid view of 3Q when we provided guidance. Pat's comments that were above the midpoint in some ways is also a client comment but was, in some ways, instructed by how we were doing on the data center business as we progress through the quarter. It seems to be going better. And I think, quite honestly, part of that is, and Pat talked about it in the keynote is how good Sapphire Rapids is as a product for AI workloads. I think that's helped support that business better than we expected. My view of data center is we're probably -- I don't know whether I could quote an inning. But we probably -- we've got 2 quarters, whatever that is in innings, you can translate, probably to go before we're in a good place for inventory. But I do think we're set up to do -- to see a decent year next year. I mean, so that will kind of work its way through over the back half of the year that then has that cleaned out in a way that client was cleaned out somewhere in the second quarter. And so there's a natural kind of lift you get as inventory is digested. So that -- I think that will help the data center business. Sapphire Rapids followed by Emerald Rapids, followed by Sierra Forest. We may see some revenue in Granite next year. I mean we just got a whole wave of products that I think are just increasingly better and meet customer demands in a way that I think will be very helpful and creates a tailwind effect in terms of demand for our products. That also is going to help. And then as we talked about, as we build up this pipeline of Gaudi through the year, most of that pipeline, if it turns to revenue, will turn to revenue in '24, that will be also a nice tailwind to the business. So we're set up to see -- we'll have this kind of volatility or what have you -- or air pocket in the business that we're seeing right now. I think that goes away by the time we exit '23, and '24 should be a really good year for the data center business.

Aaron, do you have a quick follow-up?

Yes. I guess I do. Thanks, John. Real quickly on the gross margin variable as we look forward. One of the things that I've always -- I've been a little bit confused by is that, you started this year with the benefit of the elongation of the depreciation cycle and the effect of it. And I guess if I'm reading through some of the material, it seems like that benefit actually increases here over the next couple of quarters. Can you just help us appreciate the positive variable of gross margin related to that element of the accounting aspect, that would be helpful.

Yes. It's -- obviously, it's been helpful. And we get the benefit from a depreciation perspective immediately starting in day 1, but partly because this stuff has to flow through inventory and back out. The benefits as it relates to the gross margins, take some time to show up on the income statement. And I don't know, do we quote a number that...

No. What I was going to say, Aaron, is when we gave our guidance for Q3, and we went through the sequential walk, we actually did not talk about depreciable life change in large part because it's not a meaningful part of the explain sequentially from Q2 to Q3. It's really more about less pre-PRQ charges and less underutilization charges. So just keep that in mind. Now we still have depreciable life inventory that will run through the P&L. So I don't want you to say it's not a benefit. It is absolutely a benefit. It just wasn't all that meaningful in the sequential walk. With that, we've ended our session. I want to appreciate everyone's attendance in the room and thank you everyone online as well. We'll see you in about 4 or 5 weeks on earnings.

Sounds good. Thanks.