Intel Corporation (INTC) Earnings Call Transcript & Summary

So I think we've got everyone in here. Welcome back. I'm Tom O'Malley, semiconductor and semi-cap equipment analyst here at Barclays. We're lucky enough to have Sachin Katti who is the SVP and GM of the Network and Edge Group at Intel. Very nice for you to be here. I appreciate it.

Thank you for having me.

I think that when most of you with Intel Network and Edge Group is not prettiest or the fanciest, but I think that it's an area where people could learn a lot more. And so why don't you start by just giving us a little background about yourself? I know you've been CTO at NEX a little over 2 years and GM for almost a year. So we like to hear about more about your background and your experience and kind of how you got to the position you're in today.

Yes. No, happy to. As Tom mentioned, I took the role to run the business back in February this year, a little less than a year, but I've been at Intel for around 2 years. I was [indiscernible] CTO for NEX. And I'll get into NEX in a minute, but my background is a bit, I guess, unusual. So I'm a professor at Stanford. And so I've made the hard switch from academia to running a business of around $8 billion last year. Pat likes to joke whenever he introduces me that he saved me from a lifetime of boredom in academia. I'm not sure I wanted to sign up for so much excitement, but it is exciting. So -- but nevertheless, before that I did a couple of startups. The last startup got acquired by VMware, and that's how I got to know Pat. I came to Intel and met Pat. So NEX, as you mentioned, relatively less well understood as a part of Intel. So it's the third biggest business in Intel. Roughly last year, it was around an $8 billion business. But NEX was formed 2 years ago by putting together 3 existing businesses in Intel. So the first one was around cloud connectivity and Ethernet. So this is the part of the business that builds Ethernet products, sells it into enterprise and telco markets and now IPUs, Infrastructure Processing Units. This is our version of the SmartNIC and the DPU, and that's being sold into cloud. Obviously, Google Cloud was our definitional partner/customer in building the IPO, and now it's rolled into many other cloud customers. So that's one part of the business. The second business, which is a big chunk is telco and enterprise networking. So selling a variety of Intel Xeon-based products to run telco networks in a cloudified manner. So whether it's 5G Core or 5G Radio Access Networks, and enterprise networking, enterprise security, SaaS [ E ] workloads, you name all of the typical enterprise networking and security applications running on top of Intel. So that was the second business that became part of NEX. And a third one, which you probably all are aware of was previously called IoTG, that is the IoT business group. We renamed it to Edge because it's now becoming more of an Edge computing player that's much broader than this IoT application. And so that is a third business. So overall, that's what NEX is, a combination of Ethernet, Xeon, and other products that have been sold into networking and edge computing market. So had been growing double digits over the last 2 years. Last year it was an $8 billion business. This year, we are going through a painful inventory correction, and we'll get into that in a minute. But yes, super excited to be here.

Perfect. Yes. So I think a good way to kind of start is to dive into each of the 3 segments you kind of talked about. So you just go into each and you said it's been growing double digits, clearly, you're in a cyclical correction right now. But traditionally, could you just outline what those 3 verticals grow at? And what's the consistent long-term growth rate that we should be thinking about when we look at those 3 buckets?

Yes. Maybe let me deconstruct. Each of these different kind of market cycles here. So I'll start with telco because that's the one that, as many of you are probably aware, the recent report from the telco vendors themselves is going through a correction. And this is not atypical. So every new generation of cellular standards, there's a lot of investment at the start of that cycle then they go to a trough period and then the next generation comes along, when 6G comes along, for example. COVID actually accelerated that, because of COVID and remote for a lot of mobile infrastructure build-out that happened in 2020 to 2022. And I think both North America and Europe are going to a lull right now in terms of telco operator spend. So that has been growing double digit, let's say, low teens. This year and next year will be -- we're going through that correction still, we're not out of it. I think I will get into some of the announcements that happened the day before yesterday in a little bit. So telco is one segment. The edge, it's too broad to point -- pinpoint any one factor. But in general, the edge and the industrial and [ automotive ] markets are going through this inventory correction later than the PC and the server markets, right? Again, you see the other recent earnings releases from peers of ours, the industrial and automotive-indexed companies are seeing that inventory correction beginning to happen now. So those segments are going through that. But we have seen quarter-over-quarter sequential growth. So in Q3 we did the quarter-over-quarter sequential growth in our Edge business. So we're beginning to see that turn around and recover. That had been growing high-teens before, right? And so we expect that to start normalizing into next year. Ethernet, I think, is indexed to the enterprise and edge markets. Most of our Ethernet business today is in the enterprise segment. So that -- when it was in a supply-constrained environment last year, I think people are buying every Ethernet adapter they could lay their hands on, that flipped this year. So a lot of inventory in the channel that is now being drained through. And that's at the, let's call it, below 100-gigabit Ethernet speed. I'd say the high-end Ethernet, the X factor is AI. Today, AI is driving a lot of networking spend, but it's essentially to 1 vendor today. And I think as Ethernet becomes an alternative for networking fabrics in the cloud for AI that, that is going to be a big tailwind, but that's coming in the future.

Perfect. So I think that's a great launching point to the AI question, which I think is being addressed in almost every fireside here at the conference. But I've heard Intel mention this concept of hybrid AI. Can you explain what that means to Intel? And how do you see that market developing at the edge in the next couple of years?

Yes. I think when you think about AI, right, so obviously, a lot of attention right now in the cloud as new big models are getting trained. But what we are seeing from customers is that as these models mature and models like Llama 2 and others start coming out, these open-source models, customers want to run inference at the edge. And there's a variety of reasons, right? So it could be because the cloud is too expensive. They don't want to ship all this data back and forth. They don't want to pay the API charges that these big models have in the cloud. I mean if you talk to the startup ecosystem in the Valley, we are all here in the Bay Area, I think a big chunk of the gross margin is sitting in just Open AI API cost, right? So there is a large economic incentive to run a lot of this inference locally with these [ elements ] on the edge AI is being driven by that. The second reason, of course, is privacy. A lot of these are now wanting -- the prompts themselves contain a lot of private data, right? And now a lot of enterprises want to fine-tune these models with their own private data, and they want control over how this data is used, how the prompts actually get engineered and what kind of data is getting exposed. So there's a big impetus to running this kind of inference again at the edge, right? So both those factors, costs as well as privacy and data solvency regulations are pushing inference to the edge. And so let me be precise. We expect models to be trained in the cloud. But over time, the majority of inference will happen at the edge, in our opinion. Now that's already been happening with computer vision. We expect that to happen even in the large language models. If you look at Windows Copilot, for example, I think even Microsoft would want a lot of that inference with Copilot, like applications, to happen in your PC. Because they would much rather offload the cost of serving that inference to your devices rather than have to run it off the cloud, which is quite expensive for them. So coming to hybrid AI, just to close the thought quickly. You will run most of the inference at the edge. But occasionally, you need the power of the cloud, right? So in the edge, we expect, for example, we'll be launching Core Ultra next week, that's our flagship laptop processor, that will be launched in New York next week. It's going to have all of the AI capabilities like an NPU and a GPU integrated into the CPU itself. So you no longer need a discrete part. It's all in one package, in one drive, in the [ accessory ]. But this, we are now able to run a 30 billion parameter large language model like Llama 2, locally. So if you want to do the query, inference query on a 30 billion parameter model, you don't have go to the cloud at all. You can actually do it on your PC on an edge computing device, you can run everything locally. We actually demonstrated this at Intel Innovation back in September. So I think this capability is going to become commonplace and with every generation of this silicon, the size of the model that we can run locally will keep increasing. But occasionally, you may need to go, call it, trillion parameter model in the cloud because that's just more capable. And so that's why -- what we mean by hybrid AI. Just like hybrid cloud, you will run a big chunk of enterprise compute on-prem, but you will also have some of your compute sitting in the cloud. I think the same thing is going to begin to happen to AI. But a big chunk of the inference is going happen locally. But occasionally, people want to use the cloud's capabilities.

I think there's a big debate around the percentage of where that compute takes place, whether it's mostly on the edge or mostly in the cloud. One thing that gets brought up pretty frequently in my conversations is, right now, you really have one device that you're carrying around with you. It's the smartphone, right? And you're talking about a potentially a PC processor or maybe new devices entering the market that you guys can penetrate. Can you talk about how you'd be able to penetrate the smartphone market, if that's where AI takes place? Or do you expect other devices to kind of be the tip of the spear when we're talking about AI at the edge?

Yes. I think if you think about the PC, let me start there. PC, is ultimately a productivity device. And AI today is mostly a productivity enhancer. If you think about the kind of things Microsoft and others are pushing towards, the Copilot, they're about how do I make you more productive. So I think the PC is definitely a natural landing point for a lot of these capabilities. When I talk about the edge, I'm talking a lot about the physical infrastructure around us, right? So not your devices that you're carrying, but if you walk into a McDonald's, the drive-through store, when you're ordering drive-through, when you're ordering some food on McDonald's, that's now being AI-enabled rather than a human taking your order, right? Going into a factory, how do you do better kind of [ well ] detection, quality inspection, these kinds of capabilities. Going into a Home Depot, the customer service kiosk instead of having to call a human, being able to engage with the bot right there and answering queries to you, right? So this kind of infrastructure is definitely the kind of stuff that we see a lot of these AI capabilities beginning to penetrate. So I think the phone, of course is it's going to show up there. That where there is a lot of your personal data. But I think the edge when we think about it is much broader than the phone. It's your PC, it's all of this edge infrastructure around us and a lot of enterprise digital transformation and automation beginning to happen through this edge infrastructure.

You just named several examples of whether this could interact with the daily user on a given day. Can you just talk about, when you look at what the edge may bring in terms of TAM to Intel, how do you size that market?

I think, obviously, these numbers are still in flux. Everyone is trying to get a grip on how big these numbers could be. If you look at some of the estimates, the edge markets broadly defined across hardware and software is roughly a $450 billion market. That's across the entire stack. Hardware is one chunk of that. I think the best way to think about what AI is doing is the AI-related spend that we can track at the edge is growing at 20-plus percentage [ pin-point ], right? So that's -- when we look at the split in our edge compute deployments, the AI-related spend is growing at a much faster pace than the common normal edge itself.

Perfect. So when you have a TAM really very large -- what products are you going to use to address that TAM? And then I think one interesting AI-related product under your segment is OpenVINO. Can you just comment on that as well?

Yes. So I think when we think about the edge, so I think maybe one -- taking a step back. AI at the edge is going to be added to your existing applications, right? So if I think about a drive-through store or a retail store, everyone wants to enhance their checkout experience with the AI capabilities. Everyone wants to augment a human when they're taking orders or customer service. So we look at AI as being added on to existing workflows in many of these locations. So when you think about our products, we are taking our core executive franchise. We have built a great ecosystem on that, so there is a large collection of software that's already running on x86, but we are now adding AI [ assimilation ] capabilities to our product, with Core Ultra next week. So we will have an Intel 4 process node, right? A CPU on that, so it'll be our Intel-first node, but we will add an NPU and a GPU to that same SOC. And the same process on the edge. So every Xeon that's going to ship for the edge will have an integrated accelerator, integrated GPU and an NPU into it, right? So that's the product form factor at the hardware level, so it will have Xeon, Core Ultra and Atom-based systems that will have integrated AI capabilities. Now if you're a developer, you look at this then you probably are wondering, how do I take advantage of all of these capabilities in the hardware because it's going to be a lot of diversity depending on how much capacity you want, in terms of the [ hardware ]. So what we are doing with OpenVINO is our software layer is abstracting the complexity of the hardware. You are a developer, you can pick an off-the-shelf model that is trained wherever, trained on NVIDIA, trained on anything else. Pick it up from Hugging Face, and OpenVINO will take care of optimizing and running that model across this collection of hardware at the edge or on the PC. So OpenVINO is a cross-platform software substrate to be able to run inference at the edge and on the PC. So we make developer's life easy, reduce the time to market to be able to infuse AI into your applications.

Very helpful. So we've talked the edge a little bit. I kind of want to pivot more to the networking side. So NVIDIA has been touting their growth in their networking portfolio, clearly, in InfiniBand, NVLink, Spectrum-X, and that's all driven by their AI investments in their core GPU portfolio, right? So how does Intel compete in the AI networking segment when today, so much of the development is centered around their core GPU product. How do you counter that?

Yes. So today, obviously, I think the AI infrastructure is going to be integrated. That's what's happening in the market. But we are making headway. So with the IPU, for example, when Google announced its NVIDIA instances, it's actually the networking is running through our Intel IPU. And so if you look at a Google NVIDIA instance, Google cloud NVIDIA instance, it's actually our IPUs are sitting in there doing a lot of the networking. At OCP, Open Compute Summit just a couple of months ago, Google announced what it had built with us, co-innovated with us something called the Falcon transport that's running on Ethernet. It is a transfer protocol that is built for this hyperscale AI workloads. So what we are now doing is building on that and we're going to start delivering Ethernet as an alternative to InfiniBand and NVLink. So using that transport, using the Ethernet physical layer and providing an open alternative to do InfiniBand NVLink. That's where the ecosystem is going.

Do you think that you need to see a transition in product offering as in you need alternatives to the core compute away from NVIDIA GPU for you to find success? Or do you think that you're outreached in the example that you just kind of named is going to get enough penetration to shift most of the market in your direction? Just talk about how you're finding ways to win when clearly, at least today, there's a pretty strong stranglehold on at least the core processing power.

Yes. So I think with UEC, right, so this is Ultra Ethernet Consortium. The whole industry is looking to build an Ethernet-based alternative for networking for AI regardless of what GPU you're connecting it to. And it has been designed so that it can connect to NVIDIA GPU and Intel GPU or an AMD GPU or even the hyperscaler's own internal AI accelerators. So we expect that networking, like it always has, will standardize and provide much more alternatives for being able to connect to any computer, whether it's GPUs or CPUs. Now I think the question is where -- how quickly does this transition happen? I think Ethernet is one which has a long history of being able to scale, right? In terms of the largest fabrics that are getting built out there in data centers, that's all based on Ethernet. And Ethernet usually has a much larger ecosystem of vendors and technologies at play. So I think the consensus in the industry is that we will start seeing the shift to Ethernet-based fabrics starting next year and accelerating with the 800-gig generation in 2025, so an 800-gig Ethernet becomes standard in many of these data center deployments. So UEC is obviously racing to standardize how we would do it for AI, but I think we are all investing in building Ethernet-based products for this AI build-out. Are we taking questions?

Yes, go ahead.

You mentioned Falcon RT. I mean that's a Google congestion management engine, right? You don't have access to that yet, have you?

It was co-developed with us. It is integrated into the IPU hardware.

Is it opensourced yet? I though Google didn't want to opensource that.

It is going to be open source.

It's going to be, but it's not yet, correct?

So they opened the stack in OCT and then they'll be opensourcing the implementation.

So it's not an Intel exclusive?

No, no. So we will support it, but anyone can implement that hardware spec. I think right now, the IPU is the only hardware that has that spec in it from a networking side and then the software is going to be opensourced.

And then you mentioned next year is Ethernets. Do you expect to be more of a scheduled fabric type of deployment or more like what Google, Amazon, what you guys are kind of looking at in terms of having the congestion management done with the help of the NIC and the R tangents?

More the latter. So we expect that with UEC and with Falcon RT, we can actually go towards a more reliable transport and a congestion-managed approach rather than a scheduled fabric like InfiniBand.

Ok. So zooming back out, can we talk about the networking business and exposure to both the wired and the wireless side, if you would break down the percentages and just where you see those 2 businesses headed in the next year?

Yes. So let me start with wireless, right? And that our exposure there is on 5G. And that roughly, again, as I said, was growing double digits, a big chunk of our business. So in 5G, we have 2 pieces so one is a Xeon-based business, so all of the 5G core and cloud RAN businesses running on standard Xeon. And then we also do custom compute for 5G. So we announced with NEX and customer associates that we're going to build in APA for them, and that's for their next-generation base station infrastructure. But last week and 2 weeks ago, they announced their current generation custom compute for the base station. That is also built on Intel 4. It's actually the first external product on Intel UE node, the Intel 4 node that Ericsson's current base station infrastructure is beginning to ship on. So for 5G, for wireless, I actually have 2 pieces. One is the standard offering, which is the Xeon-based. Anyone can buy through a Dell or an HP or a Lenovo, and build out their infrastructure. And then there's custom silicon,, the likes of which are for semi-custom silicon that we built for NEX and for others that they build their customer infrastructure on top of it. For enterprise, it's all standard, right? So we build Xeon and Xeon D. Xeon D is the edge variant of Xeon that is optimized for networking workloads and our customers build SaaS CDN applications on top of these workloads.

So you mentioned Ericsson, obviously, news out this week about a shift away from suppliers to AT&T, Ericsson mentioned Nokia losing some of that business. Could you size potentially what those opportunities mean for you? And if you can't be specific on the numbers, just talk about what it means to go from a base-station processing unit sitting at the bottom of the tower to some sort of virtualized processing. Where do you -- clearly you have exposure in the box of the bottom of the tower, but what is the trade-off between the box and what the data center would be like that's serving the X number of towers in the area?

Yes. I think -- so what Tom is referring to is the AT&T announcement on Monday that they are picking Ericsson to be their vendor for their LAN portfolio. And specifically, AT&T is going with Open RAN and Cloud RAN. So Ericsson is a big strategic partner for us. And as I said, both their traditional RAN as well as their Cloud RAN portfolio is all on Intel. So all of Ericsson's RAN portfolio basically is on Intel today. And to Tom's specific question, what's happening, Tom, is the box, which used to be built with custom silicon from us, will now get built with standard Xeon silicon from us, right? And now you are now able to run not just the base station software but potentially other edge computing software, too. And you can run it in a cloud-native manner because it's standard Xeon. So the same Kubernetes infrastructure that you're using to manage a cloud, you can now manage a box sitting at the bottom of the tower. So there's tremendous automation and management benefits that AT&T gets because they can bring the same expertise they use to manage their data centers to running at the top, bottom of the tower and such. So in terms of size, the best way to think about it is we give AT&T flexibility. They can deploy to the bottom of the tower, they can deploy it at their central office, where it's a collection of Xeons rather than one. And then, of course, they can have the same consistent infrastructure in their data center, all the way from the data center, all the way to the bottom of the tower next to their antenna, it's just Xeon. And it's the same cloud-native infrastructure that's running end-to-end their entire network from the 5G Core, all the way to the Radio Access Network.

So there's trade-offs with every technical decision, but when you are virtualizing a network, moving away from the towers, latency becomes an issue. If you don't have processing on-site, can you talk about how you combat the fact that you need to move information over longer distances? And does that help your networking business in any way?

So definitely. I think as they aggregate the compute and run standard Xeon from any of these you do obviously are going to hit latency barriers, right? But many of these cell towers are now connected with fiber, so that latency is becoming less of an issue. And once you aggregate -- the benefit is twofold. One is you can run all of that software that used to run at the bottom of the cell tower in one data center. But second, it becomes the foothold for deploying edge cloud. So the same Xeon that's running your radio access network, your base station software can also be repurposed to run an edge computing workloads. So if someone -- if AT&T wants to deploy a CDN or a gaming application or a streaming application, it's the same compute platform. You don't have to build yet another infrastructure. And that's the benefit of a cloud infrastructure. So you're not building purpose-built infrastructure for a specific workload and we use it across all of these. So that's our big bet. So apart from obviously benefiting from the Radio Access Network build-out that AT&T will do, it also makes it easy to deploy other applications there that should drive even more growth at the end.

It sounds like scalability is a big factor. But when you look at scaling, you need market trends to generally be in your favor in some instances. So what you've seen in the 5G market is at least a slowing broadly some of the tower build-outs. Can you talk about the health of that market? Do you expect there to be a second wind of 5G deployments? I mean China was a large deployment very early in this cycle, and you really haven't even seen the U.S. or Europe live up to the expectations that we originally kind of sought after. So where are we in that cycle? Do we need to see more tower upgrades? Are we going to see more of the micro or small cell build-outs that we originally thought?

Yes. I think China, of course, went through its build-out cycle earlier than everyone else. India is going through it right now. So India has been the fastest-growing market on 5G. We have seen that in our business this year. Europe, I think North America build it out earlier, right? So the U.S. built it out in 2020, 2021 time frame. And I think 2024, '25 is when you'll begin to see the mid-cycle upgrade cycles happening, right? So that's what AT&T announced essentially on 1 day, that mid-cycle upgrade. And 6G will start showing up, let's say, after 2027, 2028 timeframe. Europe, I think, has been more challenging. As obviously, Europe itself went through a crisis, and the fierce competition that exists in every country in Europe, unlike the U.S., where there are basically 3 operators, we are -- a nation in Europe probably has 4 or 5 more operators, right? So it's a tiny market, very competitive market. So the operators there have been going through a more challenging time. I think we expect Europe also to go through an upgrade cycle starting next year. Vodafone just announced it a few months ago that they will be opening it up for an upgrade next year. So I expect end of '24 is when you'll begin to see growth back coming into these markets.

We've talked more on the telco side. Can we just pivot briefly into the enterprise side? Again, another market that's going through a correction here. Can you talk about expectations for when that recovery begins? And then more specifically, what products you're bringing to that market?

Yes. So I think the edge market, as I was saying earlier, are going through -- the enterprise markets are going through a correction later than the PC and the [ cellular ] markets. And we are seeing that in industrial, we are seeing that in VTL and health care kind of segments. However, last quarter, we did see sequential growth in the edge segment, right? So quarter-over-quarter, we grew around 6% in those segments. And we are beginning to see reassurance. Like Pat and Dave had mentioned in the Q3 earnings call, similar to the PC market went through a historic inventory correction in the first half of this year, and they began to see improvement in Q3. And so we expect both the edge as well as the PC markets to be in line with the expectations that we have for Q4 as the quarter progresses. I think we're beginning to -- and they will both benefit from the proliferation of AI and the launch of Core Ultra next week. So I think the best way to sum it up is we went through a historic inventory correction, PC, earlier in the first half of this year. The edge is still going through it, but it also showed quarter-over-quarter growth. We have seen improvement already in Q3 on PC, like Pat and Dave talked about in the earnings call. And I think we expect Q4 to be in line with the expectations we sent to The Street.

I just wanted to -- we have about a minute left here. I was curious if anyone had any questions in the audience who didn't get a chance before. All right. I've got one more here. So when you look out at next year, clearly, your 3 verticals are in different phases of recovery. Some still kind of turning downwards, others as you just talked about kind of inflecting a little bit off the bottom. So what one area are you most excited about, you can attack this 2 ways. One can be revenue exposure, where do you see the most growth? Or 2, what do you thing is most exciting from a technological perspective into the next couple of years?

Yes. I think, on 2, I'd say the edge AI inflection point is the big one. So you -- I missed answering one of the questions -- one of the parts of the question from the last one, which is what products, and I think the product that I'm most excited about is Core Ultra next week. It is a PC product, but it is also an edge product. So we take the same processor but modify it for edge applications. It has an integrated GPU, integrated NPU. So it really brings AI to the edge to your PC. And what we are seeing is a lot of excitement around instead of having to buy and deploy a separate discrete GPU or a very expensive and power-hungry GPU, I can actually buy this system that has all of the AI capability I need packed in, and be able to deploy many of these models for inferences on the edge. So I think a lot of excitement in terms of new upgrades that are happening. I get asked the question, how do I see my business getting impacted by edge AI. I think it's a TAM expansion for me. It's many of these things that are either going to augment humans or eventually potentially some of the tasks that we hire humans to do, that essentially is a TAM expansion for our business at the edge, right? So I think that's really where the growth is going to come from. The second one, which will take a bit longer to play out than just next year is going to be the Ethernet replacement for InfiniBand and NVLink, right? That's going to be a big one. Those are decisions, obviously, for big deployments that happen once every 2 years. So I think over '24 and 2025, we do expect that story to start picking up steam, where Ethernet becomes the AI fabric substrate for networking in sort of proprietary technologies like we have today.

Very exciting times. Thank you so much for joining us, Sachin, and have a great rest of the day.

Thank you.