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

All right. Thanks, everybody, for joining us. We have the pleasure of having Ita and Liz from Arista with us. I will try to actually open it up to see if anyone has a question. But as a lot of people know, I tend to ask a lot, but thanks for joining us, ladies. I really appreciate you guys making the trip out. And Ita, congrats again on the early retirement announcement. Looking forward to working with you in the next couple of quarters. But before retiring, we're going to have to torture you a lot on this little topic called AI that everybody wants to know about.

As we think about AI sizing here. A lot of people look at your supply chain, Broadcom talks about $200 million last -- for this year, going to $800 million for their AI chips. Obviously, there's a lag on the supply chain thing. But as we start thinking out over the next year or 2, how should we think about that lag in the supply chain from your angle with those AI chips as well as is there a way to think about how big AI could be in the next year to 3 kind of whatever time horizon you want to have us think about?

Yes. So it's pretty hard to bridge kind of from the Broadcom numbers. I mean, obviously, all of those chips don't come to us. If you think back to our Analyst Day last November, which is kind of pre-ChatGPT, et cetera, we were talking about AI then, right? So the 7,800 product sets that we have, is a good AI vehicle and is deployed in some AI use cases today, not kind of the post-GPT back-end discussions that we've been having more recently, but definitely, there's some AI traffic that's traversing some of that equipment today. The problem is we don't really know what's an AI use case and what's not, right? Because that chassis has been deployed in DCI, it's deployed in spine switch situations as well, right? So that's 1 factor as it's not as simple as how do you count it. I don't know how Broadcom is counting it, honestly, in terms of where those go. So firstly, they will come to us. Secondly, we've got lots of use cases that consume those chips. And then thirdly, you have the supply chain and you have the fact that you're still living in a 52-week lead time world on silicon for now. So it's kind of -- you're definitely going to be dislocated, right? So I think it's hard for us to tie back to those numbers. I understand why everybody wants to do that, but it's not completely something that we can quantitatively thread through to their numbers. Yes. The sizing of the market, I mean, the industry folks are trying to do that. I think the 650 Group size of that $2 billion to $3 billion in 2024, growing pretty aggressively beyond that. Again, I'm not sure that anybody really knows exactly kind of what that's going to be. We think about it as AI is going to underpin kind of the investments and innovation around the network for the next 3, 5 years, which is great, right? And we're fully engaged in that. But in terms of being able to slot in and say this is exactly what's going to happen. That's not clear yet, right? I think we still live in a world where we've talked for a long time about 2024 being a kind of digestion used at cloud here. I think that's still our view that underpins the comments we made on the last call. And I don't know if there's anything about AI right now that changes that dynamic. We think it absolutely can be a driver for the future, but it's going to take some time to get together.

Yes. So we frequently talk about networking, especially high-end networking, where you guys play speeds needs to be thrived, right? Does AI actually accelerate this 800-gig road map, understanding we're just talking about 400 gig real material deployment today or is that 400-gig system actually good enough to be able to handle essentially the workloads that AI -- the AI-based workloads?

I mean certainly, 400-gig can be deployed into those use cases that is being deployed in those use cases today, right? But obviously, you want the fastest speeds that you can have, right? If you're going to generate more and more data, you want to want more and more speeds. So everybody is going to want 800 gig to go faster, right? But that in and of itself is not going to make it go faster, right? There's still a set of work that has to be done. I mean you've finally had silicon delivered. So we're working kind of on the systems now and there's customer qualifications, et cetera. So it does take time to get there. There's definitely emphasis on moving those days as fast as you can, but still from experience, it just takes time to actually get to a quality product that you can qualify in chip and volume.

Yes. Is there a way to think about the capacity for -- that's sort of required for the average CPU-based application versus the average GPU application today, let alone down the road? And do we have to worry at all about substitution effect within your revenue base of CPU versus GPU kind of -- or CPU versus AI workload potentially?

I don't know, if you want to take the first part of that question.

I think from that capacity standpoint, if you look at CPU connections today, they're connect at 10, 25, maybe a 100 gig, some of these larger customers. All those CPUs don't necessarily talk at the exact same time either, right? So there can be some undersubscription or oversubscription built into the network. But when you talk about GPU, these things are connecting at 200, 400 today, and they want to go as quick as possible. They all have to speak at the same time. So from a network capacity standpoint, much more demands on the network from a GPU processing standpoint, right? So when you're building on an architecture for an AI cluster, you're looking at a non-blocking architecture, which means that all nodes can talk at the same time at full capacity or maybe even like undersubscribed, right, so that you can have the capacity in case of a failure. So I think that they're definitely driving much more demands on the network, even just from a GPU standpoint.

So I think the #1 question I've got in this quarter on you guys is, well, why won't this all be InfiniBand based when you talk about AI workloads? So what advantage does Arista's Ethernet have over NVIDIAs and InfiniBand, especially when you include some of the other pieces of the NVIDIA portfolio overall that can help the capacity and scalability of what they've got?

Yes. I think that really comes down to scaling these AI clusters, right? So in the past, historically, maybe InfiniBand had some technology advantages. It got -- it scaled to a 40 gig faster. It had RDMA. If you look now, we have RDMA over converged Ethernet we have for the last 10 years has been deployed in many of these high-performance compute clusters. If you look at like the sheer technology rate, we're at 400 gig going to 800 gig, [ 1.60 ] is coming down the road, too. So I think technology-wise, Ethernet has taken what it wants from the InfiniBand world. And then when you talk about scale, when you think about like the scalable technology, Ethernet is what's deployed today. It's multi-sourced, which is very important to these large cloud providers. It's multi-tenant, which is very important if you're going to take these GPU clusters and carve them up and service multiple customers. It's open standard space. It's the same operational model that they have today. So I think that there's a lot of benefits as these AI clusters scale out. Then it goes back to how big is it really going to be right? And I think that goes back to what you believe AI is going to be as pervasive as everybody wants, like everybody is going to want their data center to be AI-enabled.

So you guys have a huge advantage here. Thesis early on was we're going to focus in on the cloud and cloud-based workloads. And where you're seeing a lot of -- essentially, the picks and shovels deployment today is with those hyperscalers, including 2 of your largest customers. And big question that I think is on everyone's mind is does that networking mix actually change when you think about that AI-related CapEx versus the traditional kind of CPU-related CapEx. So how should we be thinking about that mix shift of percentage going towards switches and networking gear in sort of this AI world?

Yes. I mean that's a question that we get all the time as well, right? And I think you don't have a lot of quantitative -- usually, you get this data from white papers, et cetera, they are published by customers over time after they've had some experience, and they've deployed some stuff in their network. We haven't really seen a ton of that yet. Again, the industry analysts are starting to talk about maybe your -- we always talked about networking being kind of high single digits, 10% of the overall data center spend, and they believe maybe with AI, it's maybe 15%, right? Those numbers are strong out there. But again, I don't know that you've had real validation yet of a real-life kind of large footprint, where that's been measured on a consistent basis, so that's kind of where the industry guys are putting in.

Got it. And as we think about the cloud and hyperscaler potential refresh, even some of your large enterprises, right, 2017, 2018, that was a good couple of years for you guys, given the 100 gig kind of cycle is really ramping then the hyperscalers have kind of moved towards 5, 6-year depreciation cycle, depending on what you want to talk about. But why wouldn't we also need to have that refresh of those systems on top of potentially some of the AI spending that you could get. So what's the balance going to be? Or what's the feedback on the balance between what they need to refresh versus what they kind of need to invest? And how does that impact you?

Yes. I mean we can't forget, they grew triple digits last year, right? There's been a lot of spending that's kind of already happened both last -- and they'll be big contributors again this year, right? And then after that, I think it just comes back to where are their priority is going to be, right? And that's going to be driven by how much new investments do they need to make? How much of that is AI-driven -- versus refreshing their existing? I mean, I think Microsoft -- on Microsoft's call, I mean they were clear that they're going to end up having to kind of make sure they invest to support Azure in the meantime, while they're kind of building out this new AI footprint, right? So I think you're going to see some mix of that, exactly how -- what it is. We'll have to wait and see. But they've been making big investments over the last year, this year as well into kind of their existing footprint.

Got it. And circling back on AI. We frequently hear about you guys externally selling. But on the internal side of things that you guys are thinking about your own kind of cost, are there areas of that would be leveraging AI that you are currently testing or planning or kind of helping out on the -- in internal perspective?

Yes. I mean the biggest place that it would move the needle would be around all those software development resources, maybe a little bit on the hardware side, where you're using kind of design, chip design, testings that type of stuff, right? And I think -- so [ Ken ] is looking to see kind of what's truly valuable in terms of deploying it in that area because that's where most of our like large spend items are. So that will be the focus, I think, to begin with if there's something that can be done kind of to make the software team more efficient. We're always constrained in terms of the talent that we're able to hire and find at the level that he wants to have, and if there's something that can make that more efficient. There's definitely a lot of things on the list to go work on. That's probably the most important place, but I don't think he hasn't kind of concluded how he wants to think about that or how we would deploy that. Yes.

Yes. And I think 1 of the surprising things including for myself, admittedly, this year is actually the resilience of your enterprise installed base, let alone capturing new customers. So I guess what's enabling you guys to actually gain share within the enterprise specifically? And then I got a follow-up from around campus for that.

Yes. I mean it's a couple of things, right? I think there's -- there is a refresh in the enterprise. We've always talked about kind of the digitization, transformation, whatever you want to call it, but we are actually seeing enterprises more and more reliant on their IT infrastructure in a real way that like it impacts their business if it doesn't work, right? And that causes them to want to make an investment and then we have an opportunity to go and show kind of just the differentiation that we bring and how much more reliable and how much easier it is to manage an Arista footprint because of the single image across both campus and data center and because of the visibility and all the other things that we can provide, right? So I think that's -- there's a pull from an enterprise perspective. And then we're more ready as well to go play in that market. Our CloudVision is way more featured -- fully featured than it was before. It's doing a very nice job of managing everything across an enterprise footprint, and customers are really seeing value in that. right? I think it's the combination. The need is there, and then we're just more ready. We've got kind of campus data center routing. We're able to kind of address it from a sales better from a sales perspective, we're continuing to add salespeople to that. So I think it's the right time for us to kind of be more successful and be more targeted in terms of those accounts.

Yes. So back in May, we were chatting and you actually said campus was starting to become like half of the spear into the organization. So I believe at that time. So what is actually enabling you guys to get in there as there's just this question as to why does Arista have the right to win in that campus environment? And do you feel you're still on pace for that, I believe it was $750 million goal that you outlined at the last Analyst Day?

Yes. I think, look, the reason why we went in campus is the same reason why we went in the data center, right? Again, campuses have become more complex. Campuses are -- even if it's an office footprint, there's hybrid work, there's all the different ways that people want to access the network, et cetera. So that's way more complex, but it's not just offices anymore. It's retail, it's hospital and colleges, again, the complexity -- the level of complexity has just gone up, and that's driving this need for a more simple approach to the network. So that it can be managed efficiently. And that's the complexity doesn't break it, right? So I think that's a big driver of it. It's the same -- it all comes back to the same thing of this high quality and then simplistic in a good way in terms of being able to manage the network.

Yes. So obviously, AI has been a big topic, but now as we start to turn the page towards the follow-up here, the question we're going to get is, what does 2024 look like for a lot of companies? You're not going to guide here, I don't think. But I think you'd start a little bit of what happened at Piper this week. But walk us through the puts and takes as you think about it -- that we should be thinking about for calendar '24. You had mentioned a digestion period potentially, what else should we be thinking about?

Yes. I think, look, we'll do more of this, obviously, in November at the Analyst Day. Jayshree did -- she's long had probably, since the Analyst Day last year had this goal to be able to -- can you grow at least 10% in a year when cloud is muted or cloud is kind of in that down part of the cycles, right? And are more muted part of the cycle. So we talked about this a little bit on the call right at the end, but I mean that is a goal that we're kind of starting to feel better about. And obviously, we'll talk more about kind of the different ways that you can get there in November at the Analyst Day. But 1 assumption, obviously, as cloud is more muted and then you're relying more on enterprise and some of the other cloud parts of the business to drive the growth, right? But again, whatever 1 we pick, it's never going to be the perfect answer for what actually happens, right? Whatever we -- so we need to have multiple different ways to get there. And so, we'll come back and kind of address those a little bit more at the Analyst Day. But that's -- I think that's something that's firming up in our minds that that's possible even if you assume that cloud is muted.

Is there anything to think about for more so the purchase commitments or inventory turns? Like where do you actually see inventory turns as kind of a normalized state for you? Or where does purchase commitments on an normalized environment actually go to CNS the right way you pointed us to is think about it as that inventory together?

Yes. So the -- just commitments that are coming down nicely, right? And they should, right? Because we want to step back out of the contract manufacturer supply chain now that everything is returning to normal, right? So we had to -- in order to kind of make it all work, we had to kind of step in and backstop, if you like, purchase commitments on pretty much the entire BOM, which we never normally do, right? So now we're kind of coming back out of that, and we're -- we'll be left with the key components that we have always buffered that we buy directly. That's what you're seeing in that raw materials line on the balance sheet. A big chunk of that is silicon and the size of that is going to be very much linked to what the lead times are for those parts, right? And right now, that's still at 52 weeks. Does it get better? We'll have to see, right? But that will drive that number. So when you kind of just start the purchase commitments are coming down, they need to keep -- they need to continue to come down. We're very focused on doing that, making sure we're taking advantage of every lead time reduction that we can get. On the other side, you have raw materials that's going to be driven by those lead times. And then the finished goods days of inventory is still not back to where it was kind of pre-COVID, number is bigger, but obviously, the business is much bigger, too. So as things get better, we'll probably add a little bit more there just to get back to kind of something days numbers and then really it will be about the raw materials and where are those lead times end up.

Got it. There's about 5 minutes left. If anyone has a question, please feel free to raise your hand. I'll call on you. Otherwise, I can keep going.

Yes. I have one. How hard is it for you guys to get chips these days compared to the suppliers. They're all very tight that you guys can fit on. But so [indiscernible] time rate is it? Maybe you could give some color on that?

Yes. I think the -- part of that key kind of components, the key chips, it's always been -- it's a longer lead time, like currently 52 weeks, but there was predictability in terms of what you were going to get and when you were going to get it. And I think that's still the case, right? So if we've given our visibility, then -- and they make commitments, they've been executing to those commitments pretty well. The problem children were like the small parts that nobody really expected, and we hadn't taken a real action to try to resolve early on because we didn't understand that, that was going to end up being kind of the golden screw, but that is much better. I mean that's really coming back to normal.

Yes, maybe my first one. So obviously, GPU there clear focuses [indiscernible] of the build-out and typically, you see that deployed up front where [indiscernible] is there a way to think about the timeline from GPU deployment to why you start needing to really accelerate the network and purchases for -- how to think about the difference between GPU is deployed, when you see the lag between start to benefit from the exposure?

Yes. I mean, simplistically, you'd say, once you deploy those GPUs, you're going to want to make sure you've got enough networking around it because that investment is so significant that relative to the network that you're going to want the network to be there and not to be a bottleneck in any way, right? I think what's happening right now in terms of those first deployments that we're seeing, I mean a lot -- at least a good chunk of those are going in with InfiniBand because that's kind of the bundle that you're buying. So again, that's why I think it does take some time before we start to see AI be a big driver. Jayshree has talked about trials with customers now around infrastructures pilots next year. And then that you start to kind of ramp to volume beyond that.

Maybe just keeping on that AI theme and probably more of you for Liz, because I think most forget that you're an engineer at heart. Obviously, you guys are picks and shovels of AI here and actually the systems behind the thing. But -- how should we think about potentially other AI-based products coming out of Arista or leveraging AI in your products?

Yes. I mean I think that if you look at our Ava, which is part of the Awake acquisition, right? So this is our network detection and response product, we actually had some AI and ML capabilities that we utilize in Ava to detect anomalies within the network. So it utilizes all of the EOS data, it's state that we have from the architecture and it's able to kind of process through what is normal activity, what is abnormal activity, what needs to be flat, what needs to be remedied. And so we're offering that today as kind of in Ava. I think that we'll continue to work on those types of product lines and see what else that we can do kind of with the data and the raw state data that EOS captures.

Yes. Anything to call out in terms of sales cycles for this year, could they actually start to get better in the back half of the year as we lap some of those tougher sales cycles picking up at all or anything to think about as we think of going into '24?

In terms of seasonality or...

Just in terms of sweating assets for longer at customers and those deals that you expected maybe to close are getting pushed out potentially? Or is that starting to slow down, and we're actually seeing those deals, especially on the non-AI side, non-hyperscaler side actually closed?

Yes. I don't know that we've seen any huge difference there. I mean, obviously, the lead time change is a big deal from a customer perspective because instead of having to make decisions for 12 months from now or even longer, you can now kind of starting to get back to maybe like a 6-month lead time so you can make decisions in a closer horizon, right? So I think that's definitely a factor. It's going to allow customers to -- some customers hesitate to make something that decide something for 12 months from now once those lead times get shorter. I think yes, we'll lose some visibility. But from a customer perspective, I think it's really important, right? It gives them a lot more flexibility, whether that's the enterprise or the cloud, right? I mean, to have to wait 12 months to get a switch and once you have a need that's just too long. So it really is good to start to see those lead times come down just from a pure business perspective.

And we're coming up on time here, and it's kind of a bitter sweet here a little bit for me. But obviously, you've announced that you're going to be stepping down retiring here, and congrats again. I guess why retire now? And given that you're involved in the CFO search, what is Arista looking for out of the next CFO?

Mean why, honestly, because I can, right? I mean, it's something I've been thinking about a little bit more. I spent a lot of time in Ireland. I've got a lot of family in Ireland and stuff. So it will just make that whole thing a lot easier. What are we looking for? I mean, again, you've met the team, you know the team, right? It's a very smart kind of -- it's a culture, where everybody is humble and focused on the business and finding somebody that fits that, that can kind of help the team and book in the teams going forward. I think that's the key, right? So culture is a big part of the search for sure.

Awesome. Well, thank you very much for making the trip out and congrats again on your retirement and looking forward to the next big thing out of Arista.

Okay. Thank you very much. Thanks.

Thanks, everyone.