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

Good afternoon, everyone. Thanks for joining us here at the UBS Tech Conference. I'm David Vogt. I'm the hardware and networking analyst. And we're excited to have with us Arista Networks, Anshul Sadana, Chief Operating Officer. Before we get started, Anshul, let me just read a quick disclaimer from UBS. For important disclosures related to UBS or any company that we talk about today, please visit our website at www.ubs.com/disclosures. So if you have any problems, you can email me later. And with that out of the way, Anshul, thank you for joining us.

Thank you, David.

And I'm sure you don't need to read any disclosures. I think we're good.

I think we're good. We're good.

Perfect .

All this factors apply.

So besides raising guidance and taking targets up, we won't get into that. Okay. So I think we had fiscal year earlier, we had other companies here earlier. Maybe just to level set where we are today with Arista? I know you just had an Analyst Day fairly recently, where you set out targets for fiscal '24, preliminary targets or framework and long-term guide. But I think there are some investors who are a little bit unclear on how we got here. I've talked to a couple of people over the last couple of weeks. So I think the shift from Arista basically architecturally taking share in the hyperscalers over the last couple of years, caught a lot of companies by surprise. Maybe we could start there and talk about kind of what you do differently from a solution software-based architecture and then how does that lead us to where we are today? I know we're going to talk about AI, but I want to kind of level set and set the table first.

Absolutely. I didn't expect any AI questions anyway. But over the last 15 years at Arista, we've grown in data center networking, especially, I'll come to Campus as well. But we started out with building what we believe was the best solution for the whole world for data center networks. We call it cloud networking. It included a change in design. We went from a classic 3-tier access aggregation core, which was the de facto to a leaf spine design, which is a more of a distributed scale-out architecture lends really well to cloud computing. But no one in the industry wanted to do that. And to do that, you have to build very high-speed products. We're the first in the market with 10 gig with 40 gig with 100 gig and pushing the envelope, not just as consumers of merchant silicon. But as drivers of merchant silicon, we work with our partners like Broadcom or Intel and drive their road map and tell them what we need for on behalf of our customers. We couple that with a beautiful system design that is by far, I would say, the most efficient in many ways, whether it's signal integrity, which is how we are getting to linear drive optics, or power efficiency lower power matters to everyone, higher quality. And then running a software stack that is very unique and differentiated from all the legacy stacks out there, including the way we keep all of our states in our database, inside our software and memory. And as a result, it's small bugs, whether it's a memory leak or a small crash of an agent doesn't bring down your network, just how small process restart, the system just continues to forward [ packets ] as if nothing happened. And initially, our competition [indiscernible] like, hey, this is a new kid on the block and this is not going to succeed. But the cloud titans, as we call them, not only embraced it, they partnered with us and we avoid on that architecture for civil generations to a point today where we do a lot of co-development with our biggest customers. It's a very unique situation. Typically, you have a vendor customer relationship. We don't have that. We have an engineering partner, customer relationship and quite often, we are telling the customer what their road map should be, not getting some RFP and getting surprised by it and so on. And we've out-executed our competition clearly in all of these areas and built on that. That was on the cloud side. We took the same approach to the enterprise. But the enterprise needs a little bit more help on the stack, especially with respect to deployment and automation. That's where we built our software suite called CloudVision, which runs on EOS, which is our operating system on the switches. CloudVision runs independently to manage and automate your entire network. And now CloudVision can run both on-prem or as a managed service in the cloud. As a result, we can cater to many, many different types of solutions that is allowed to expand in different verticals, different geos, different parts of the network, including now campus. That's really what the story has been for us for the last 15 years or so.

Great. So that's a great place to start. So maybe we could start with the Titan. So obviously, Titans have been a critical part of the business. I think in 2022, it's disclosed it was like 43% of revenue, this year, it's probably around 40% of revenue. So you've grown exceptionally strong with those partners. How do you think about -- you mentioned coengineering and sharing the road map and helping them kind of understand what they need to go forward. How has that relationship evolved today and since you mentioned AI with regards to their AI road maps. Like how are you involved in what Microsoft is doing and Meta and others within that vertical in terms of thinking about the next couple of years or even 5 years for that matter?

Yes, we are in a very privileged position in partnering with these customers. I was in a meeting recently with one of our Titan customers, along with Andy Bechtolsheim, our Founder and Chairman. And after the meeting, we were talking about it, and quite often, we'd like to talk about what the future could be like. And we are in one of these meetings where we felt, we defined the future. That's what the world will be doing 5 years from now. That's how clusters will be built, that's how power will be delivered. That's how the fiber plant will be structured. You're talking about 2027 architecture. And we do that quite often. Now after that meeting, the customers view was that this was the best meeting they've had in the last 12 months. Now this is a networking team. And they've been circling some really tough questions on what happens in the future as you get to 200 gig [ 30 ] as the cluster size increases, how do you change connectivity? What about the latency? What about different cables out there in the skew lens, skewing of data between the cable lens and so on, all the way to automation, monitoring, security, deep buffers versus shallow buffers, lower latency, helping the application stack get there faster in using the GPUs a lot more efficiently. We're able to do that with pretty much all of our customers -- all of the hyperscaler Titans. And as a result, we have this trust with the customer. Very open relationship. We understand that they want to be multi-vendor. There's no purpose to go lock them in because then once you do that, they work really hard to unlock themselves and go somewhere else a few years later. And we are enjoying this growth with the Titans so far than I think for many years to come.

Does that road map visibility or that co-engineering visibility change with AI versus maybe traditional legacy workloads where, again, you had strong product vision EOS CloudVision, merchant silicon help drive sort of the direction. But given the complexity and whether it's power consumption, whether it's structuring the nodes, has that visibility changed with AI in terms of maybe not order -- not order visibility, but road map visibility. What do I mean by that so you have a better sense today what the next 5 years looks like. And if we had this conversation 5 years ago, what the subsequent 5 years would look like?

And I think to some extent, what's happening is the focus on the future is a lot greater given the investment and the criticality of DCI clusters to the business. So customers are engaging. In the past, it used to be roughly a 3-year road map vision. Now it's becoming 5 years, not necessarily because we know the future that easily. But because the physical build-out, a 100-megawatt building with liquid cooling is far more complex today to think about versus going from a 10-megawatt building to a 30-megawatt building 8 years ago. So just the nature of the problem and the complexity is making our customers think harder and make us think harder as well. And as I mentioned earlier, a lot of these discussions result in us shaping the road map for our suppliers as well, which is critical. And we've been in this position for many years, but now I feel that the pace of innovation has actually picked up. There's so much happening in AI, it changes so quickly that on one hand, you're thinking about a 5-year plan. On the other hand, you're not sure whether the next 6 months are going to work out as you thought or not.

Got it. So maybe just to clarify on how you think about AI for Arista, and we were having this conversation earlier in and Cisco has, I think, a slightly different view of their AI business. Their view is if it's silicon, if it's optics, if they upgrade the DCI because there's more data traffic going because of an AI workload that in their mind is sort of AI. But I think you and Jayshree and the rest of the team have a much more strict, stringent definition. Can you kind of walk through how you're defining it is just the back-end part of the network that's AI today? And just how does that expand for you over time?

And David, I believe this is very much in context of the $750 million goal we gave us.

Correct, right within your goal.

For 2025. Now look, we participate with every major cloud customer, Tier 2 customer out there. So if there's a large AI build-out going on somewhere in the United States, there's a good chance we already involved with that customer in one way or the other. If you start counting everything as AI, there's nothing else left. So of course, 100% of our power revenue is AI, if you can count it that way. But quite often, when we ship a product, whether it's a Top of Rack or a deep buffer 7800 spine, it's not clear to us when we ship the product, is this going to get deployed as an AI cluster or as a backbone or as a DCI network or the Tier 2 spine or a LAN use case. In some cases, we can find out by talking to the customer, but it's not easy to account for it in the system. So the $750 million goal, that's fairly back-end cluster networking for AI. We try our way best to calculate it or track it as best as we can. I think about 2025, we feel really good about that number and tracking it over the long term, is it going to be easy to track. I don't know. We'll find out -- our generations of product change. So for the next 2 years or so, 3 years, it seemed like the right thing to do. We also want to set the right expectation because where we are with the journey in AI with Ethernet and where 800 gig especially is we are right at the cusp of our product transition and a speed transition for our customers. And this time, the speed transition is not coming from DCI or compute or storage is coming from AI. And we know that part of the market really wants to switch to 800 gig as quickly as early as possible, but it is a little bit easier to track as well. But our numbers are purely back-end networking which is our switches with any relevant software, but no optics, nothing else added on top.

Right. And presumably, right now, what you're shipping for AI-related is all training related? Or is there a sense that there is inference use cases that maybe show up in revenue in late '25. Just how do we think about kind of maybe bifurcating the market in terms of training versus inference, and what your customers are using equipment for?

So today, most of our AI deployments are with the large cloud Titans. The large cloud Titans haven't yet reached a point where they have discrete fading clusters versus inference clusters, while some of them are just talking about or just starting to do a little bit of that. Most of the large clusters today based on the jobs they want to run can be used for training or inference, but there are times where they take a very large cluster of 4,000, 8,000 16,000 GPUs and they've run it for training on 1 model for 3 to 4 weeks. They can use the same cluster for inference and the job scheduler will automatically just create mini clusters of 256 GPUs running training for a few hours and so on. But these are not discrete build-out so far. Does that happen in the future? There's a lot of talk about it, maybe in 2, 3 years. I'm not sure how quickly that will happen, especially with the Titans.

Got it. So does that mean economically, that's a different sort of business model for you in the sense that maybe there's an opportunity to put more of your switches and equipment closer to the edge of the network outside of the hyperscalers as training becomes less of the total mix and inference becomes a bigger part of the overall mix. And you could perform inference in smaller clusters further away from the data center, more closer to the edge of the network. Does that broaden the market opportunity for you from a "AI" perspective?

[indiscernible] had a very strong assumption in there. I want to call it out, that inference will happen at the edge. And I think that question is still to be answered. I just honestly don't know the answer. It could happen in the cloud. It could happen on the edge of the cloud or it can happen on the edge of the enterprise as well. A lot of this also comes on to licensing of training models and who owns the data and issues related to data privacy. There's certain industries like health care and medical where just because of laws, it may be hard to just put all the data in the cloud. There are many other industries where it may be easy. I think the cloud will be more efficient added than trying to do it on a discrete 2 Rack, 4 Rack cluster on the enterprise edge. But having said that, I think, number one every non-NVIDIA GPU that I'm aware of, including the ones some of our customers are bidding on their own, their accelerators or what competition is about to present to the market, it's pretty much all Ethernet. And many of them are talking up on how fine NVIDIA is doing training, but all of these other processes will be good at inference. If that works out, that's really good for Arista too. Because wherever they are, they need ethernet switches. Inference also needs networking, and we have a really good shot at that.

So kind of come back to that assumption that you just called out. So a lot of companies are talking about bespoke models that are unique to their own data sets, where maybe they don't want to keep them in the public cloud for governance reasons, privacy reasons, and they want to have maybe that inference closer to the end customer or what the end use case. So it doesn't sound like you're convinced that's a longer-term sort of driver of AI, either use cases and/or spend? Do you think companies -- health care companies or other companies that have privacy focused data sets are going to continue to work within the large Titan or hyperscaler community at this point?

Well, I'm not doubting at all that inference is a massive use case coming to us. It's going to happen. AI is going to turn every industry upside down. And question is, why would the cloud-led go off inference? They can do bundling, they can do smart. They can do discrete build-outs, the cloud customers have done build-outs for different governments of the world where it's a private buildout just for that one entity, no one else has access to it. And why can't they repeat some of these models for other use cases as well or improve their edge too. There was a battle between certain service providers and certain cloud companies in marketing speech on edge computing a few years ago and some ASPs have come and said, "Come to us because we can offer you 1 millisecond roundtrip time to any 5G base station. And one cloud company was at a conference, I wouldn't name them, but they're very popular. They said come to us. We can give you 700 metro pops all around the world with 1 millisecond round trip time. 5 years later, I think we know who won. So I think a lot will change, which is why this whole model that training will be done by a few companies, you license the model, go to on-prem, run your inference engine there is in a static world. The world will change faster. There will be more competition. There'll be more services offered by the cloud companies. There will be more services offered by start-ups in the enterprise trying to succeed. And I don't see the future...

Because we hear often from enterprise customers data storage [indiscernible] USPs or a pretty considerable consideration. So being hold in a trapped for a lack of a better phrase within a hyperscaler to get your data out, to put it back to train it to inference, it's pretty expensive. So obviously, enterprise doesn't have sort of the unlimited budget that the hyperscaler. So that's why there is some thought that maybe you could be a little bit more cost-centric if you are focused on smaller clusters, more bespoke models at the edge of networks.

I can come down to the enterprise stack being really savvy. So operator is being really savvy. If they can truly take advantage of that, it will work, right? It's not that I'm convinced that cloud will win. I'm just not sure which direction it will go. Because if the issue is data and now it's too expensive, cloud will just reduce those costs, those prices and then what. So there will be competition, I will just keep on evolving, how this matter.

So when you think about sort of the use cases for AI, how do you -- how are you thinking about how it affects sort of legacy workloads and demand for whether it's -- I don't know if you want to define it as a legacy switch. That's not a centric, which I know it's pretty difficult to control that line in the sand, what's not AI, what is AI. But is there any way to think about what the workload spend on legacy applications look like versus AI? Is there -- is this completely additive? Is there a portion of the spend that's somewhat cannibalistic in your mind? And how do we think about where the priorities are so clearly, it's AI-centric today. But do we get to an equilibrium where it's a little bit more balanced in terms of capital allocation priorities?

Founder and Chairman, Andy in one of our customer meetings just 2 years ago, I told our customers. This is what people used to do with legacy 100-gig, but for 400-gig, this is what we are shipping. I do tell him Andy customers are still buying it don't call it legacy. The same comment here, we call it classic compute. Not to disrespect interline AMD, they're innovating as well on the X86 side. But the recent 3 quarters worth of -- or 4 quarters worth of trend has truly changed the CapEx model. And customers spending every penny they have on buying GPUs and connecting them and powering them. They don't have any CapEx to us left for the risk. But can we maintain the status quo for the long term? I don't think so. A couple of reasons. #1 CPUs for classic workloads with VMs and so on are going to be far cheaper than buying expensive GPUs. GPUs are great for matrix calculations or mathematical functions, but not for everything else that running standard application for. Enterprises will keep moving to the cloud. Cloud companies often build ahead competing against each other. But at some point, they run out of capacity if they're only spending on GPUs. So somewhere they'll come back. They don't want to lose all the business either. But enterprise is also spending more on AI to have less dollars to move to the cloud right now. I think over time, that will smoothen out just a little bit, not as harsh as it's been. But the classic cluster of compute storage, Top of Rack, spine. Right now, there is less investment going on there and a lot more in AI. Net-net, I think [indiscernible] whichever side wins will do well. I don't think it changes any material outcome for us. Maybe AI is actually more dollars just given the bandwidth intensity that's needed, and it's good for us. But even if customers came back, it will be okay.

Yes. I mean I think we look at companies that are in a position that has a much stronger foothold with the hyperscalers like yourself than some of the legacy network companies that kind of missed some of the...

Calling them legacy is okay.

Sure. I'll call them legacy. But obviously, there's a reinvigoration effectively, right? And there's a lot of discussion that the largest broadly defined networking company has wins with 3 of the 4 hyperscalers. And I think you said publicly at your Analyst Day, obviously, you guys welcome the competition and you'd expect to remain sort of competitively successful. Do you think there's other entrants like how does White box play into this AI strategy? Obviously, they were a big player in the prior cycle, given the complexity, how does that play into what hyperscalers or even enterprise is doing within AI today.

So we touched on this a little bit on the Analyst Day as well. Companies that everyone associates the most with white boxes also happened to be our largest customer. They were just using white boxes, they were in big customers. We partner with them very, very well. And the last decade or so, the industry has largely been on status quo. Amazon and Google start building their own switches 15, 20 years ago for various reasons, long discussion. We can have that later. But when Meta had to make that decision around 2013, 2015, they decided, let's do build because they want the learning as well, but also buy from a good partner and we partnered really well with them, done multiple generations of products that are codeveloped with them to the same spec. And I think they've found a really good match over there. The cadence of networking products has roughly been one new generation every 3 to 4 years for the last 15 years. Now with AI, the world is moving faster and with 100 gig [ 30 ] and 200 gigs [ 30 ] coming soon, and the chip and the power, the signal integrity, the linear drive optics, the software stack, the tuning of load balancing and condition control, RDMA, UEC specs being added on top, things are actually getting far more complex very quickly. In the next 24 months, there'll be more products introduced into the market than what has been introduced in the previous 4 years. And as you very well know, from all the layoff news, the cloud companies are increasing their head count right now. So there are also limited resources and it's an opportunity cost. So they invest in bidding more of their own or they partner with someone and investor resources, maybe in an AI application that will give them a lot more revenue or security for public cloud and so on. So not only have we found a balance but we had to place where the cloud companies want to depend more on us, not less. So at the same time, they do have some [ religion ] on this topic. I don't expect white boxes to go away at all completely. I think the market will mostly maintain status quo. If anything, it was swing just a little bit in favor of companies like us that are good at developing with these companies rather than the other way around. And I think we just stay there.

Got it. So can we just maybe move down a step and touch on Tier 2 cloud, right? We always talk about the hyperscalers. There's been some in your definition, some resegmentation of hyperscaler as I think Oracle OCI has been sort of called out based on their server count. What are your 2 players doing today? And what's the opportunity look like for you there with regards to their investment in AI? And is the landscape any different with competitors, whether it's large networking companies a white box. Because we hear about Microsoft CapEx continuing to go up Meta maybe not so much. But just maybe help us understand how you would define what's happening within the Tier 2 cloud ecosystem?

So Oracle used to be in our Tier 2 cloud segment, but as you said, based on number of servers and the size they're at now, right to upgrade them to the cloud Titan category. The other Tier 2 clouds are mostly serving their own space. It's software hosted company, and they cater to millions of enterprise customers that come to their cloud for their software services or the software stack as a SaaS. And we do really well in those as well. A lot of the Tier 2 cloud is also evolving to offer AI services, especially because sometimes these days, even Tier 1 cloud has no capacity to take on other customers. Some of the cloud companies are signing a step back and EC2 came to the market, they could rent a computer by the hour. Today, not every cloud is letting you rent a GPU by the hour. Their opportunity cost is just too high. You have to sign a multiyear contract, if you want a GPU cluster and just use it for multiple geos. The Tier 2 cloud is finding an opportunity in that ecosystem saying, "Hey, you know what, there's a open space here. Let me offer my services too. And on top of that, some of the AI start-ups that are offering their own cloud services are building on their own as well. And so we're finding a very good match and opportunity there. But just to set expectations, that's a smaller segment than the Titans. Titans are way bigger -- but we do well in the space.

Do they have enough capacity or availability from GPUs to really meet that spillover demand or that excess demand right? So if I think about what NVIDIA shipping, I would imagine the top 5 or 6 companies account for 80%, 85%, 90% of GPU capacity today. So I'm just trying to get a sense of how you're seeing that play out.

Yes. So some of these companies also are either their own processors or non-NVIDIA GPUs and offer other services that they can within that spec. I think that's actually doing okay for us as well. But just like the previous comments on Tier 2 from a few years ago. Tier 2 cloud is just like cloud Titans, its smaller. They're typically ex Google, ex Microsoft, ex Facebook people in these companies. They already have been customers, they like working with us, they like automation, they don't like a legacy stack, they do exactly the way a bigger company does just in a smaller scale. And we do fairly well. I think that will continue to stay strongly as well.

Got it. So with the time that we have left, I wanted to maybe just touch on enterprise is it's been a key driver of the business the last couple of years. You've taken your software, your hardware stack and just kind of replicated the success in the hyperscaler community within enterprise is taking a lot of share. How do you define sort of the opportunity today? I mean you've been growing by 20s, 30% in the enterprise. The market doesn't grow anywhere close to that. So we get pushed back from a lot of investors saying, "Look, you pick the low-hanging fruit where people know the Arista, EOS, CloudVision, they know the hardware. How do we think about maybe across the cycle, what the enterprise looks like for you, putting aside Campus for a second.

When we're just getting started, 1 of our competitors was Force10 and Force10 never attack the big customers. They went to small HPC shops. They went to Universities, they went to customers I never heard of before they even approach a Fortune 500 customer. That is what I call low hanging fruit. What we've done is the opposite. We've gone after the hardest, toughest customers first, one that over from competition. These sales cycles have taken 5 to 10 years. Now the next round is actually a bit easier, but these customers are not as big either. So it's a longer tail of enterprise. But we're seeing customers come to us saying, Arista, we've not only heard good things about you. We have fed up of legacy stack we have. It's causing outages, we have subscription related challenges. We just want to come over. We are winning over there. So I think enterprise will just continue growing. We're gaining share. We're nowhere as penetrated as we are, let's say, in the Titan a long way to go. But that's on the data center side, but also growing in enterprise campus. Enterprise campus, we're getting started from very small numbers and our CloudVision, EOS, our switches, our WiFi fit really well for what these customers needs as well. But these customers have a slow rollout, typically 7 years to refresh and so on. There will be a long tail, but just keeps on growing. That's why we feel pretty good about our enterprise space. Remember, Data center networking plus campus networking added together is a $50 billion TAM. This year makes doing just over $5.5 billion in revenue. They have a long way to go.

No, I get it, but I'm like I look at campus and what other companies have tried to do versus Cisco. And yes, Cisco is a shared donor over time. But to get more than 2%, 3%, 4% market share has proven to be very difficult for competitors over decades. So obviously, you've been very successful from 0 to your target 750, which you reaffirmed a couple of weeks ago, is it -- do you need to invest more in channel, whether it's -- I know you're not going to be like Cisco, but where do you need to get to from a channel perspective to really have this business be like a multibillion dollar business.

The Global 2000, Fortune 500, maybe on Fortune 1000 customers, we can address with a direct sales force. The fulfillment is through the channel, but we address and sell through our direct sales force. For the rest of the market, the mid-market, we absolutely have more depend on the channel as well. Winning more with the channel internationally. And even in the U.S., I would say the smaller regional partners have become really good channel partners for us. The bigger channel partners often are dependent on the rebate dollars and so the bigger companies that will generate enough pull from the market from customers before they will pivot. I think they're starting to get there. We feel good about our opportunity there, too.

So I will -- in the limited time that we have left, let me just ask you, is there anything we didn't cover that you think maybe it's misunderstood by the market or the Street at this point, I think your story has been pretty well discussed the last couple of months on AI, sort of the winter here, at least what the market is indicating. But I just want to give you an opportunity to maybe touch on anything that maybe is not fully understood at this point.

No, I think we've covered it all between the earnings call, the Analyst Day and our discussion today.

Got it. Great. So I think we'll just end it there. Thank you, Anshul. Thank you, everyone, and have a great day.

Right. Thanks so much.