Arista Networks, Inc. (ANET) Earnings Call Transcript & Summary

Perfect. So why don't we go ahead and get started. I'm Aaron Rakers, the IT hardware analyst here at Wells Fargo. Pleased to have Martin Hull, who is the Vice President and General Manager of the Cloud and AI Platforms business at Arista; as well as Brendan Gibbs, the Area Vice President of Product Line Management at Arista. I think you joined not too long ago?

Yes, I just joined earlier this year in March.

Perfect. So we're going to talk a little bit about this thing called AI and networking, and we're going to see where that goes from there. But first of all, thank you for joining us. I think where I want to start is at the high level because I get this question all the time. How does -- how do we think about the intensity of networking in AI, especially as we go from what used to be, it's not that long ago, 10,000 GPU clusters to 100,000-plus GPU clusters. How does that -- how do you guys define the network intensity maybe relative to what traditional networking look like? -- feel free to go wherever you want with that.

Sure. So I'll take that first. I mean, what a surprise you started with AI, right? The investor community is hyper focused on AI, which I think is completely appropriate. We have committed to a $750 million for AI back-end revenue fiscal year '25. In the most recent earnings, Jayshree and Chantelle reiterated that, but they also communicated that there's an incremental pull-through of the front-end network, which is an additional $750 million. So the AI back-end network target for us is $750 million for fiscal year '25, which is calendar year '25. So the network intensity of that AI network on the back end is there's been a shift in terms of how people will build traditional data center networks at the front end, where you'd have a degree of oversubscription. The total amount of networking in a normal data center isn't the sum of all the parts. You don't expect everything to be working at full speed all the time. You pivot to the AI back-end network, and it's the opposite. The expectation is that the GPUs are going to be running at 100% throughput all at the same time and constantly for hours, days or weeks. So the network has to be architected with that paradigm. I need more networking than the sum of the parts. I want to put in additional capacity so that I can deal with device, link, optic, failure and not cause any outage or performance impact to the compute, that GPU cluster that's performing these high-performance jobs. So the network intensity of the network paradigm shift has happened to the AI back-end networks. When you think about the ratios, though, the ROI -- sorry, the total spend on a GPU node is an order of magnitude higher than the spend on a traditional compute-only node. So the ratio of how much networking from a dollar perspective, I would say, has gone down because the GPU cluster cost has gone up so high. So we have more networking in the back end, but as a ratio of the spend, it's gone down. So I think it's still in that mid- to low single digits. And the other aspect on the AI back end, we see where this goes. The other aspect on the AI back-end network is interconnect, optics, cables. One of the large spend items is those optics as a CapEx and OpEx aspect to them. So anything we can do that makes the compute as close as possible -- sorry, makes the network as close as possible to the compute means I can use lower cost optics or cables that gets the CapEx down. If they're consuming less power, it gets the OpEx down. If I make them more reliable, it brings the reliability, the uptime and the quality up. So those are aspects that we've been looking at in terms of how do we make the AI back-end networks perform better. And this is where we bring our value proposition about engineering and architecture and design and multi-standard interoperability. So that's how I think about the network intensity. It's very intense. It's not oversubscribed. It's undersubscribed. And that's why we are just as excited about AI back-end networks as it seems all of you are.

Aaron, if I could just jump in, I mean, you were talking about the relative spend on a GPU or server compute node relative to network. I think one of the things that's so exciting for me and for Arista overall is because the majority of spend and majority of power is on GPUs, which then means that the relative value of the network is commensurate, where best-of-breed can really help, like a well-tuned high-performance network can unleash the capabilities of GPUs, even though it's a much smaller fraction of the spend and a much smaller fraction of the power. And as one of the global leaders in not only AI, but also overall Ethernet, that really speaks well to the Arista opportunity for helping these customers get the most out of their massive spend on GPUs, having high quality. The way I like to think about it is networks are essentially like the nervous system of an AI cluster. And one of the things -- of course, then each GPU would then be like a little neuron. Networks are required and Arista builds the network, the best of networks. We have a technology advantage, we believe, and a leadership capability with our best-of-breed software that then helps differentiate and make that network perform better. So it fits very well with what you said, Martin, in terms of the intensity of server versus GPU, but a best-of-breed network then makes the compute even better.

So I can't help but to ask this because I'm a numbers guy and I look at spreadsheets. I mean, mid- to low single digit, first of all, that is just the network switch spend component. That's not the optics and the cabling and everything, correct?

Yes. When we talk about our 2025 AI number, it's the switch infrastructure, not the optics.

And that mid- to low single digit is comparative to what historically?

I think Jayshree and I, we've talked maybe in the past, low teen, mid-teen. What? So I think it's in the high single digits. And I think just because the cost of the GPUs is an order of magnitude higher for the GPU clusters, that's why it's moved a little bit. And it's always difficult to try and figure exactly where it is. We've got a portfolio of products from shallow buffer fixed systems to deep buffer modular. The ASP of a port is a variable. So when you calculate that ratio compared to the compute spend, it's going to move a little bit.

Yes. That's perfect. So back at your event in mid-2024, you had [indiscernible] get on stage. We talked about the scale-out cluster deployments growing exponentially, right, like 10,000 going. I think he showed a slide at 60,000 GPU clusters, 80,000 GPU clusters. Now we're talking 100,000. Where does this go in your mind? Like the Radix scaling continues. We're going to see that obviously continue to advantage maybe Ethernet in these architectures. But simple question, where do we go from here?

So there's a couple of aspects to that one, right? And I think that the networking technology can continue to keep pace with these customers' ambitions for a few years. Then you look at other limits, space and power. There's a lot of discussion in the industry about how do we get access to more power, how do we get real estate, how do we get buildings. And so I think what will become a limiting factor on some of the size of these clusters is going to be power availability and physical space. Once you start to do that, I build large clusters, but I build multiple large clusters and then interconnect them. So we create pods, islands that are then interconnected. My jobs need to stretch across them, they can and I can segment. And then we're going to have synchronous and asynchronous parallel technologies running in these areas. And so how big does it come? There are some people out there, leaders of these companies talking about very, very large numbers, hundreds of thousands of GPUs. It's not necessarily clear if they are all a single cluster or it's a total aggregate number of GPUs and there are some clusters and they have an interconnect. We can build out and we've built out front-end networks, and we measure our Tier 1 customers having more than 1 million servers. That's a very small set of customers. If you start talking about having 1 million GPUs, again, that's going to be a very small set of customers. We're going to have a lot of expertise in how to build both front-end and back-end networks. But it's not unrealistic that you could have customers with 1 million GPUs. We've already seen public hundreds of thousands, but they're not necessarily all in one cluster.

But also, you have to think about like what is the GPU in this context. I mean, like what is it connecting at? Because you could go like Martin said, 32,000 to 100,000, to 200,000. If they're all at 100 gig, 400 gig, you could grow out scale as like a scale-out model. You can also think about this as each GPU continues to improve in terms of performance. Maybe it goes from 400 gig to 800 gig to 1.6 TB and growth in that perspective. We're going to be ready regardless. So all of our systems that we're selling now are 800 gig ready now. So today, that really means 2x 400. So you're having stamped out GPUs where each GPU is connected at 400 gig. Each one of our ports does 2x400. Eventually, GPUs are going to be capable of processing 800 gig. And we're already ready, but the technology will progress. So just like Martin said, you could scale out hundreds of thousands, multiple hundreds of thousands or you could scale in terms of performance per GPU or both.

Yes. That's fascinating. I mean it feels to me like one of the other elements of this might be is what I think, Martin, you were alluding to a little bit is like it -- maybe it's not singular clusters that we think about, but how do then we go and connect disparate clusters. So that's not in the -- would you argue that's not in the $750 million, like if you start connecting over data center interconnect or geographically dispersed clusters, that's another element of this?

So if it's connecting multiple back-end networks together with an interconnect, it will only be a very small percentage of that, but that would be included in that back-end networking number. If you transition to the front end and you're using your existing data center interconnect, how do we spot that anyway? And that's one of the things we've said as we go forward, we can talk about the 2025 number, but there will come a point in time, whether it's '26 or '27, where AI-data center, data center-AI, can we really spot the difference because the products that we sell are the same. So it's only when we're tightly engage with the customer, we understand the relationship of what they're using these systems for, so that we could identify this is an AI. But even today, if you say about the data center, how much is leaf, how much is spine? -- same products. But there will come a time when the AI -- and I don't know you guys ever stop talking about it. There'll come a time when it's harder and harder for us to identify pure AI as against AI adjacent.

But connecting multiple clusters is already a thing now because Martin hit on one of the key pragmatic realities of space and power. I mean we can think abstractly of 100,000 GPUs means exports and stuff, but you're talking about hundreds and hundreds of thousands of cables and optics and the amount of sheer space that requires is pretty staggering in power measured in the gigawatts. So you pragmatically have to connect things at longer distances just because you can't physically cram all the boxes and the cables and the optics in one physical space.

That makes sense. Maybe I should have started here, but I mean, it seems like it's played itself out or at least I think the investor mindset understands where we're going. But the delineation between Ethernet and InfiniBand for those that haven't seen it, you guys did a recent webcast and talked a lot about this. But where are we at today in that transition point? Why is that transition point happening? Does InfiniBand go away? Like your views on that architectural debate?

Yes. So as a technology company, we are 100% committed to Ethernet. And so that $750 million number is an Ethernet number. There's no InfiniBand in there. If we look at the landscape today, there's a single vendor of InfiniBand. There's multiple vendors for Ethernet. You can get into the commercial aspect of this one and say that these large customers and their supply chain and sourcing teams want diversity of supply chain, right? We also saw how that worked out 5 years ago. If you didn't have diversity of supply chain, you were left with some problems. So supply chain diversity, technical innovation across a multi-vendor standards-based community gets you to a better outcome. We've seen this over the last 50 years since Ethernet first became technology. So Ethernet will win out over time, and then we start to debate how much, how fast, when, who, where. There's some other aspects to it, and that is that there's an Ultra Ethernet consortium, which Brendan can speak to in terms of making some enhancements to Ethernet. Ethernet is a great technology for AI today, but that's not to say you can't improve on something that's really good. So there will be some improvements to Ethernet coming in the next year or so. I think it is a couple of quarters away. Then we come down to commercials. The cost per gig or the cost per bit on Ethernet tends to be lower. Part of that is due to this multi-sourcing and the ecosystem that innovates in that technology space. So InfiniBand today is deployed in a lot of AI clusters. We've also seen a lot of the very large customers come out publicly and say that they are committed to Ethernet. A part of that is because they're building their own accelerators, their own GPU, XPUs whether it's -- we can roll the names off, they've come out and said they're building their own in-house accelerators. None of those have got an InfiniBand adapter on them. They're all Ethernet-based. So over time, as these customers roll out their own homegrown technology, take control of their own destiny, it's inevitable that Ethernet wins out over InfiniBand. And I say, when and how is something we can constantly debate, but I think it's inevitable that Ethernet wins out.

Yes. That was the conclusion we came to as well. One of the things that -- I do cover NVIDIA as well, and we'll get to Spectrum and talk a little bit about the competitive landscape there. But what -- so RoCEv2 has been out for a while, right, RDMA over converged Ethernet. NVIDIA talks about SHARP, like what they can do in-network compute around things like congestion control, protocol reduction, et cetera. Does Ethernet have that capability? -- just kind of segue into the Ultra Ethernet consortium and what things need to be done that takes that next step on Ethernet?

So I'll quickly talk about the ROCE side of it, and then I'll pass over to Brendan to talk about UAC and the enhancements that are coming. RoCEv2 is not a new technology. RoCEv2, I mean I've been at Arista for 13 years. I remember talking about RoCEv2 at least 10 years ago. And you say, will this work? Do your product support it? This is 10 years ago. So RoCEv2 is not a new technology. It's been embedded in our products for at least a decade. So that's not a new enhancement. It's been there. So whether we did not tell, people have been using RoCE just for RDMA or for early AI type clusters. So then it's great. Can you do better than the great? Or what's happening in the Ultra Ethernet Consortium, Brendan? Sorry, I'm asking questions for you.

Perfect. Well, I mean, first, I want to kind of recognize that everything we're doing today from an Arista Ethernet for AI, all the $1.5 billion that we projected for 2025 AI revenue, none of that requires Ultra Ethernet. So what we're talking about is the fact that Ethernet today is well suited for purpose with RoCEv2. It just works. That said, it's complicated. So what we're seeing with the UEC, Ultra Ethernet Consortium, they're inventing something called Ultra Ethernet Transport, which is UET. It's more of a good-to-great scenario. So we're not reliant upon it, but we can anticipate improvements in the future. What the consortium is really trying to address is the complexity and cost associated with Ethernet. And keep in mind that Ethernet is already way lower cost than InfiniBand. What we're trying to do is further improve it from there. So the reality is you can build large-scale networks with Ethernet today, but there's a complexity associated with that because now you have to do complex load balancing, complex congestion management and the tuning of these parameters to assure a lossless transport so you don't drop packets is complicated. So what we're trying to do with UEC is -- well, first of all, I think we're expecting the first specification, which should be [indiscernible] probably in the Q1 time frame. Don't hold us to that, but that's kind of what the consortium is looking at. There's about over 100 member companies. So this is a significant initiative with lots of different vendors, Arista as being one of the initial founding members. What we're really anticipating is that Ultra Ethernet is going to bring a few things. Number one, it's going to bring higher scale at lower cost. It will also bring higher reliability with more modern congestion management, and it will also bring integrated security. So I'll start with that latter point. There's really no integrated security whatsoever in InfiniBand. What Ultra Ethernet is going to do is going to bring native encryption to the entire AI workflow. It will just be part and parcel of the flow of any sort of UET compliant capability. And what this is going to do is especially help multi-tenancy. So you think about any cloud provider offering GPU as a service, they're going to have multiple tenants with their own AI workloads, being able to have as part of the design of the transport, separated group encryption so that one tenant's workload, it remains encrypted and completely separate from another tenant's workload, even though they're on the same network is going to be part of the advantage that Ultra Ethernet will have not only over today's Ethernet, but of course, over InfiniBand. I think also what we're going to have is the ability to have, like I said, more modern congestion management. One of the reasons that we build, as Martin was saying before, with the answer to your intensity question, we build lossless networks, essentially no oversubscription line rate performance is because there's a large tax to be paid if you drop a packet because AI is fundamentally a collective solution, meaning you're only as strong or as fast as your weakest link. If you start dropping packets, you now need to have the entire workload, the entire collective pause, and there's something in the AI called go back end, go back to a prior checkpoint state. That slows down the entire workload and you might have heard of job completion time or JCP. That slows down the entire AI workload. What Ultra Ethernet is aiming to do is essentially eliminate that kind of tax of dropped packets. Of course, we still don't want to drop packets, but sometimes things happen, whether it's because of a failure or because of congestion. So Ultra Ethernet will deliver mechanisms to offer very rapid acceleration. So you get to wire speed quicker and also much faster recovery from any sort of failures. And then the first point that I raised kind of like I said, going backwards is more scalable and cheaper. These are all related. And so part of the way that Ultra Ethernet will achieve that kind of fast scale up and fast scale-down capability is by moving to what they call packet spring on the individual NICs. This should make the NICs even cheaper because part of the way -- and this answers your question about SHARP and that sort of thing. Part of the way that some vendors offer their solution is they've got what's called a DPU, not a traditional NIC, but basically a higher performance like a SmartNIC with a DPU on there, such as from a Pensando, AMD or NVIDIA's BlueField-3. These tend to be higher cost, higher power type of solutions because they push some of the intelligence right down to the NIC. Ultra Ethernet will obviate the need for that. It will allow the ability to offer packet spraying, which gives you a very high entropy load balancing over a network, pushed down to the NIC. So lower cost NICs that gives you much higher scale of your network overall. So again, this is a good-to-great scenario. Arista is leading in AI and Ethernet today. Ethernet is really going to be the choice for future AI. Ultra Ethernet will just make that even better, more secure, cheaper, more scalable.

You gave me like a lot to unpack there in a future report. So I'm not going to do it today. But I'm going to keep this simple high level. Version 1 standard release 1Q, what does that mean from a timing of product? When does it show up in products?

Well, one of the smart things that they did that will make it a complex answer is that a lot of these things are optional because it's meant to allow today's products to be compliant. So everything that we're shipping today is Ultra Ethernet compliant. There are features that will push on the NICs. So now you're going to be required to say when will the Ultra Ethernet compliant NICs start to ship? I don't know. The spec, like I said, is expected in Q1. A lot of the NIC vendors like Broadcom, like AMD, NVIDIA, they're all members of UEC. So it's up to them. As far as features on the network side, we'll have to see once the spec is finalized, but...

With software upgrades to existing products.

Exactly right. So some of the control plane signaling of some of these features, it will be a software upgrade to existing products. So you're not reliant on new hardware or ripping out and replacing everything, a software incremental upgrade so people can retrofit existing networks into UAC and as I said optional. So if you don't have it, it doesn't turn on. And one other point, just to finish off what Brendan was saying, we said there's over 100 companies in the Ultra Ethernet consortium. It's end customers, systems vendors and silicon vendors, making this a true consortium of the players who are most interested in the future of Ethernet.

Yes. That's perfect. I want to go down the path of the competitive landscape in the time we have left. So maybe the first question would be is getting to the competitive landscape. There's all kinds of estimates out there about how large this market is, right? I know 650 groups oftentimes referenced, and I think their TAM estimate has been like $20 billion. I think it was massively raised here mid-part of this year. You're $750 million, right, you have like a 40% market share in high-performance networking. $750 million doesn't seem like that is commensurate with that like level of market share, it is just $20 billion overstay. I'm just trying to...

Yes, I think you've got to dig into a lot of the details. So I would say it's $750 million back end, but also $750 million front end. So I would say $1.5 billion for us. when you look at the $20 billion, that subdivides to a lot of pieces. A big part is NIC. A big part also surprisingly is optics and cables, and then there's the networking portion of that. Networking portion is maybe $6 billion-ish in 2025, of course, growing from there out of that $20 billion that $650 million specifically said. So we're forecasting $1.5 billion next year in terms of revenue. You can do math. Right now, what I would say, though, is the thing that's exciting about what $650 million group and other analysts are seeing for kind of the market share is -- the market TAM rather is 2 things; number one is we're seeing a lot of growth projected from 2025 to 2028, and all of that is coming from Ethernet. InfiniBand is forecasted to be flat to down. So you asked the question earlier, is InfiniBand going to go away? No, of course, customers have it. They're going to want to continue to infill that. But the growth is all coming from Ethernet, which really speaks well to the opportunity ahead of us. Like Martin said, we're completely Ethernet-based, and we're a leader in global Ethernet for data center and for AI. So that's great for us. Second thing I would note is if you look at not only AI, but overall spending for data center, it's an and. So you're still seeing consistent growth in traditional Ethernet-based data center plus and the extra spend for AI Ethernet. So it becomes multiple vectors for continued growth, both of which we're going to continue to compete vigorously for. So I see multiple avenues for potential opportunity for growth for us for that.

So this question popped up in my head as you were talking there. a lot of this growth, I think, is widely anticipated. Do you think that we should -- or have we fully appreciated maybe the refresh cycle of these because a lot of this has been net new greenfield deployments. How do you guys think about -- because that's a big part of Arista's traditional business, right? You go through these server refreshes in the past in the core business. Should we not think that these refresh cycles would kick in on these big GPU cluster opportunities 3 years, 5 years from now, I don't know.

Well, I think that there's a couple of things I would say for that is, number one, Martin said, eventually, AI, data center, it's all going to be the same sort of thing. So if you think about the long tail of enterprise, which are just getting started thinking about AI. They're not in large-scale deployments. For a lot of them, it's going to be a refresh of their data center. They're going to refresh the front end for inference or other sort of things, and it's going to look like a refresh of the data center. So as the #1 market share holder for global Ethernet, that refresh, even if it's driven by AI, it's still going to be opportunity for us.

Yes. So it's a blurring of the line, what's traditional and what is modern.

Yes. It definitely blurs. And I think also -- you hit the nail on the head also on another advantage of why Ethernet is going to win because as customers look to upgrade their back end, 100 gig to 400 to 800, Hopper to Blackwell to whatever the next is, you're going to have an opportunity to upgrade the network and that network can then go from the back end to get reused. So if you have a 400-gig switch, put that in your front end, upgrade the back end. So that's a refresh cycle for us, but it's an opportunity for the customers to have consistent operations, consistent platform to help them with their TCO of the network. You don't get any of that with InfiniBand. That's another reason customers are betting on Ethernet, and that's another reason why we're going to benefit from that.

That makes a ton of sense. Does that also allow like I think I asked this question on the last earnings call that like Arista wins on back end, just pulls in the front end or vice versa. Like if you're -- like wouldn't there be in theory, like a goodness to have both Arista front end and back end? Isn't there a better together architectural view on that?

Well, clearly, we will certainly support that idea. So you're right, and that is one of the -- it's like the front end is pulling in the back end at the moment. If you're using Arista in the front end of your data center today and you're looking for a partner for the AI back end, then it's clearly you're going to at least call on us. Incrementally, over time, once you've got that AI back end and if we secure that with Arista technology, that incrementally helps us. So these things are complementary, and we're never going to sort of push back on using the same vendor in both places. But you do have that supply chain diversity, right? So there's choices. One of the benefits of Ethernet is that choice. So we have to be earnest and attentive to our customers to making sure we understand their short and medium and long-term plans. We continue to do that, right? And then you talk about what's the upside. That's why we communicated that $750 million for the front end. That is an identifiable pull-through from the AI back end, characterizing it as this is the upgrade pull-through cycle. And Jayshree on the earnings call referred to, it depends on how the front-end network is today, whether it's an all-new deployment. So there's a lot of pull-through. Whether it's an upgrade or whether it's just a retrofit of certain parts. So we'll see that spectrum. So between 30%, 100% or 200% pull-through, we kind of put the midpoint on that one and say, if it's $750 million on the back end, it's about $750 million on the front end. It does get harder and harder to characterize it. We'll be able to do that in large deployments. But if it's an enterprise customer that Brendan referred to, who's deploying a new enterprise application, that's an AI-driven application. If they have a conversation with us about building a new data center, is that AI-driven? Or is it just an enterprise application driver? So the lines definitely get blurred there. In a few years' time, as we go down that path, I think we're going to continue to see these rollouts and these upgrade cycles, but what's the trigger is going to be harder to tell.

Perfect. So I'd be remiss if I didn't ask you guys like the competitive landscape. I mean you guys have executed superbly on the competitive landscape in high-performance traditional networking for many years. This AI dynamic has, obviously, NVIDIA wants their part of the equation with Spectrum-X. How do you guys or what have you seen competitively from that regard? And maybe I'll throw it out there in the 5 minutes we got left, other names, too, like Scheduled Fabric or Silicon One. How do you characterize the competitive landscape?

So we are internally grateful to NVIDIA for helping stimulate this AI market with the GPUs. But when we talk about networking, we've got our networking technology and NVIDIA has ours -- has theirs. And obviously, we will compete on that AI back end. If you look at the existing high-speed networking market today, we characterize being 100 gig or higher, we have the #1 market share. Cisco has #2 market share. So clearly, they're a strong competitor there, and we continue to do what we can to gain success. And hopefully, they don't do the same things. So I'd say that NVIDIA and Cisco are 2 of those large competitors out there. Given their history on the compute side, then you can't discount HPE, you can't discount Dell because they've got that ability to attach compute to networking and they have a networking portfolio as well. If you go outside the U.S., then you have some of the smaller Chinese vendors out there as well. So that's how I'd kind of name in there. But Cisco has #2 market share at the moment on the AI back-end network, and NVIDIA is clearly a strong presence there as well.

Yes. I just -- I'm going to put this out there because I hear him talk about this all the time. I think Jensen brought it up on the last. He talked about like Spectrum-X providing 1.6x the performance benefit of traditional Ethernet. And I think the key to that is traditional non-optimized Ethernet versus what your EtherLink product. So it's not -- that's not a competitive comparison, right? -- it's kind of...

It's always interesting to have somebody say that their product is better than something else without publishing all the test results to show how they got there. We continue to work with our customers on designing, deploying and tuning the Ethernet networks that they have. And there's many customers out there now saying that, a, they're using Ethernet; b, they're using Arista Ethernet. So clearly, they're getting the results that they like, and we don't necessarily have to go around it and point out failings of other companies.

Yes. That's perfect. So look, we've got a couple of minutes left. I'm just going to -- I mean, high level and the engagements you guys have, the discussions you have with customers, how do you characterize the durability of demand in this environment? I mean there's these vectors of like power availability recently over the last few weeks, have we hit a plateau as far as scaling of large language model performance. But how would you characterize what you see from a demand durability perspective?

The first thing I would say from a demand perspective, and I'll pass to Martin, is the customers are starting to see ROI. I mean you can look at multiple different hyperscalers who have taken the most aggressive quickest leaps into this, and they're starting to see the business ROI. That's critical, of course, because no one is going to keep spending billions of dollars on this if they're not seeing the business benefits. So we're starting to see -- Google has said publicly 20% of their coding can be done with AI. We've seen Meta who has said they've had more successful algorithms from AI from kind of the former cookie-based model. You're seeing multiple different scenarios. You guys can all see these public reports. So the customers themselves are seeing the benefits from making the investments, which gives them seemingly the impetus to continue investing, which gives us then in turn, hope and anticipation for continued success because AI doesn't seem like it's going to go away. We start to see AI getting more and more entrenched into longer tail of maybe enterprises. Even though it's earlier days, you can start to see key use cases arising, which is going to then drive more durability into their needs like from financial services such as fraud detection, life sciences, maybe for accelerating drug trials or finding key interactions they might not have otherwise found. So these are key tangible business benefits that then speaks to durability of AI as a really sustainable type of technology investment. And so of course, we have invested significantly ourselves at Arista into having the most broad technology portfolio, the broadest product offering with the highest quality software. So the more AI gets consumed, the more AI continues to last, more we think we're going to benefit.

So then you come to the technology side of it. So we announced, as you referred to, midyear, we announced our 800-gig EtherLink portfolio. That's now starting to get shipped in production from our manufacturing, John McCool referred to that in the last earnings call. So as we go into calendar year 2025, for the AI, it's going to be an 800-gig cycle. The industry is already talking about what comes after 800 gig. And the answer is 1.6 terabit seems fairly easy. We just keep doubling numbers. That's predicated on 200 gig SerDes. So there's an upgrade of the underlying technology on the silicon that goes on to the systems that goes into the optics to get us to 1.6 terabit without doubling the cost, and that's the reason people do this. And the industry is already talking about what's after 1.6 terabit. Well, we can all do math, as Brendan referred to earlier, 3.2 terabits. So as you go from 400 gig to 800 gig to 1.6 to 3.2, we believe we can keep up with the growth of the AI clusters, the language models and the pace of evolution on the XPUs or the accelerators, whether those are commercial or homegrown accelerators. So as customers start to build out higher performance clusters, larger clusters, then the networking of that, we think can keep up you go from 7-nanometer to 5-nanometer to 3-nanometer. I don't think the networking is going to go below 3 nanometers in the next few years, even though the CPUs and the GPUs are already moving to 2 nanometers. So networking normally stays roughly 1 process node behind where the compute is. So we've got a path to follow the compute process nodes. We've got a path to go from 100-gig SerDes to 200-gig SerDes to go from 800 gig to 1.6 to 3.2. So we can see that pathway. Is technology going to get in the way of the AI clusters? I don't think so. Are the AI clusters going to continue to get deployed if there's a tangible business ROI for the hyperscalers or for the enterprises? Absolutely.

Yes. Martin and Brendan, thank you so much.

Thank you. Thank you.

Appreciate it.