Arista Networks, Inc.

Welcome to the first quarter 2026 ERISA Network's Financial Results Earnings Conference Call. During the call, all participants will be in the listen-only mode. After the presentation, we will conduct a question-and-answer session. Instructions will be provided at that time. If you need to reach an operator at any time during the conference, please press the star key followed by zero. As a reminder, this conference is being recorded. and will be available for replay from the Investor Relations section on the Arista website following this call. Mr. Rudolf Araujo, Arista's Head of Investor Advocacy, you may begin.

Thank you, Regina. Good afternoon, everyone, and thank you for joining us. With me in today's call are Jayshree Ulal, Arista Network's Chairperson and Chief Executive Officer, and Chantel Bright, Arista's Chief Financial Officer. This afternoon, Rista Networks issued a press release announcing its fiscal first quarter results for the period ending March 31, 2026. If you want a copy of this release, you can find it on our website. During the course of this conference call, Arista Networks Management will make forward-looking statements, including those relating to our financial outlook for the second quarter of the 2026 fiscal year, longer-term business model, and financial outlooks for 2026 and beyond, our total addressable market and strategy for addressing these market opportunities, including AI, inventory management, lead times, and product innovation, which are subject to the risks and uncertainties that we discuss in detail in our documents filed with the SEC. specifically the most recent Form 10Q and Form 10K, and which could cause actual results to differ materially from those anticipated by these statements. These forward-looking statements apply as of today, and you should not rely on them as representing our views in the future. We undertake no obligation to update these statements after this call. This analysis of our Q1 results and our guidance for Q2 2026 is based on non-GAAP and includes stock-based competition expense, intangible asset amortization, gains, losses, and strategic investments, and income tax effect of these non-GAAP exclusions, including the recognition of direct access tax benefits associated with stock-based awards. A full reconciliation of our selected GAAP to non-GAAP results is provided in our earnings release. With that, I would turn the call over to Jayshree.

Thank you, Rudy, and welcome everyone to our first quarter 2026 earnings call. Arisca has experienced significant velocity in all our sectors in Q1 and are now commanding the number one market share in high-speed switching in the greater than 10 gigabit Ethernet category. With that, we have overtaken many incumbent vendors according to major market analysts for 2025. Our cloud and AI networking strategy for diverse AI accelerators continues to gain traction. Unlike typical workloads, AI workflow patterns can be long-lived elephant flows or short-lived and simply not predictable. This implies careful attention to performance, where a flow can cause burstiness for a long duration of milliseconds. The intensity of a flow can determine the line rate throughput. The shifting traffic patterns to massive flows synchronized to all-in-all or all-reduce or bursts of collective communication are all important for AI training and inference applications. I would like to take a moment to review our three AI fabric use cases. In scale-up mode, we have familiar technologies such as NVLink and PCIe that have enabled vertical scaling of single compute modes or racks. The advent of eSun, Ethernet for scale-up networking specifications, allows for increasing or decreasing computing power in a flexible manner with Ethernet to automatically adapt to workflow demands. Scale-up will be a new entry for Rista in 2027 and beyond, where we will be working closely with our customers to build AI racks with very fast interconnects for co-packaged copper, CPC, or open co-packaged optics, CTO, as well as supporting collectives and memory acceleration. Scale-out or horizontal scaling involves adding more machines to a least fine fabric, moving workloads across multiple servers or nodes, or even connecting other elements like storage or CPUs. As you scale up or out with massive data sets, bottlenecks can be resolved with collectives and protocol acceleration at L2, L3, cluster load balancing, all at wire rate. The system must deliver consistent performance without degradation as more nodes participate. Arista is a shining example here, with greater than 100 cumulative customers to date in 800 gigabit Ethernet deployments, and we expect the addition of 1.6 terabytes in 2027 at production scale. Scale across drives across the cloud in AI, as the AI accelerators in a location may need to be distributed to achieve the appropriate bandwidth capacity with the optimal power. As workloads become more complex and more distributed, the bisectional bandwidth must scale smoothly to avoid bottlenecks and preserve performance. This demands sophisticated traffic engineering, deep routing, encryption properties, and integrated optics based on Arista EOS staff and using Arista's flagship 7800 R3 or R4 series. The 7800 has established itself in this category as the premier scale across choice. You can see with Arista's accelerated networking strategy and these three types of AI fabrics, these are critical to deployment of diverse accelerators and frontier models. Traditional static network topologies with hotspot jitter that slows down job completion time or increases time to first token for inference are all not the way to go. Arista's EtherLink portfolio addresses both the synchronous flows for massive training and the low latency for concurrent swarms of real-time inference in this era of trillions of tokens, terabits of performance, and terawatts of power. In 2024, you may recall, we discussed four Ethernet-based AI training deployments, and of course, since then, we've expanded and exploded to countless others. This fourth customer from the group has officially moved from InfiniBand to Ethernet at production scale over the last two years. The high-speed Ethernet AI vSpine with flexible air or liquid-cooled infrastructure overcomes the physical constraints of power and space for AI workloads. It results in a low-latency distributed AI supercomputer fabric across global regions. What is clear to me and us is our networking prowess with data, control, and management, and multi-planar orchestration is not only central to our AI switching performance, but also important for high-speed optics transmission. At the recent Optical Cyber Conference, Arista unveiled its Extended Plugable Optics, XPO form factor, designed specifically for optics innovations at high speed. Now endorsed by greater than 100 vendors, salient features include record-breaking throughput, delivering 12.8 terabits per pluggable module, unprecedented rack density, achieving 204.8 terabits per OCP rack unit, integrated cold plate capable of cooling up to 400 watts power per module, and the universality and flexibility across a range of pluggable optics, copper, as well as linear halftime or retimed interfaces. A special kudos to Andy Beckleshine, Arista's chief architect, for driving from OSFP 10 years ago to this next-generation XPO, bringing structural improvements in power, footprint, and cost reductions. Our enterprise business experienced strong results in Q1 2026, both in data center and campus. Our VeloCloud acquisition is also integrating well into our branch and campus strategy, bringing more distributed enterprise use cases and a new channel motion with managed service providers, MSPs. To share some recent wins, let us hear now from Todd Nightingale and Ken Duda, our co-presidents, to delineate our Arista 2.0 centers of data strategy. Over to you.

Thanks, Deidre. Arista is diversifying its business with new customer acquisitions covering a broad set of use cases, all unified by Arista's EOS stack and its ability to modernize enterprise infrastructure operating models. Our first highlighted win is a NeoCloud AI network. The customer was constrained by an incumbent white-box architecture that simply could not keep pace with the massive scale-out requirements of AI. Risa was selected as a commercially proven and reliable scale-out architecture with unmatched stability of EOS and the ability to connect AMD MI Series XPUs. Risa's AI deep and spying EtherLink products were deployed at 800 gigabits, to provide the incredible performance modern AI networks require. The AI fabric was tuned using Arista's cluster load balancing to scale out to thousands of XTUs, minimizing hotspots and congestion. On the software side, the customer leveraged AVD, Arista's validated design framework, to automate network provisioning, which both reduces the total cost of ownership, but also provides an easy path to reliable network deployment at scale, where without AVD automation, a small mistake can cost precious days of debugging time. This was a strategic NeoCloud win with large potential for upside growth in an area where we are seeing enormous opportunity and velocity in both NeoCloud and Sovereign Cloud customers. Our next win is in the service provider sector, with a leading regional fiber-to-the-home provider serving hundreds of thousands of subscribers. As subscriber bandwidth demands have surged, this customer realized their legacy routing architecture was too rigid, too brittle, and too costly to scale. They needed a solution which would modernize their next-generation backbone and internet-peering edge. Arista won this upgrade by proving an automation-first approach with a modern operating model driving operational savings and increased subscriber reliability. On the hardware side, we deployed popular 7280 routing platforms using EOS's FLX capabilities, which unlock deep buffering, a rich control plane software stack, and full internet route scale. On the software side, Arista's AVD framework again automates router provisioning to reduce the time it takes to turn off services while also reducing errors. Here we saw great results from our technology partnership with Palo Alto Networks, ensuring the routing edge integrated securely and seamlessly with our overarching security architecture. And here at ERISA's core value proposition of lower operating costs and greater reliability, drove a competitive win. Now I'll hand it off to Todd.

Thanks, Don. Our third win is in the insurance services sector. Following a year of strategic collaboration, the customer wanted to modernize their infrastructure with a streamlined, automated foundation capable of delivering granular, real-time insights to secure and monitor critical applications. Here, observability was truly the key. Arista secured this comprehensive win after executing a flawless proof of concept, proving our architecture significantly exceeded operational standards. To achieve deep network observability, the customer deployed our R3 series for filter and delivery roles on our monitoring fabric, DMS. Additionally, they deployed campus switches to radically simplify out-of-band management. Leveraging rich telemetry capabilities of EOS, the customer unlocked advanced features like VXLAN header stripping and transitioned to a fully automated declarative operational model. Our final win is within the manufacturing sector where we're seeing amazing momentum. Here we have a customer operating more than 100 factory sites globally, servicing consumer, healthcare, aerospace, defense, and AI infrastructure customers. This was a true mission-critical use case, and their legacy campus network had become the bottleneck for achieving real 24x7 production. Shifting traffic patterns, manual provisioning, and importantly, a lack of visibility and forensics into microbursts and drops were keeping them from achieving their goals. Arista won an extensive bake-off against two established vendors, both of whom proposed campus design that could not match what Arista delivered, a universal leaf spine campus based on open standards running a single EOS binary across campus, data center, and WAN. The cognitive campus solution leveraged 100-gig campus spine, high-powered PoE leaves, and Arista Wi-Fi 7. Cloud Vision drove provisioning, configuration, and lifecycle end-to-end with consistent tooling across the network infrastructure. Here, it really was ERISA's modern operating model that drove differentiation in the engagement. Hit list production upgrades, latency analyzer for microburst visibility, and true packet drop forensics. The teams were able to significantly reduce production-impacting maintenance windows and expose events that had previously caused line interruptions. In all four of these examples, Arista's support team stood out to customers for its best-in-class service, well-known for troubleshooting issues with customers long after Arista gear is no longer suspected to be a fault. Arista's modern operating model also played a key role, especially the AVD tooling that Ken mentioned for architecture, validation, and deployment. We're excited about the momentum across the entire enterprise business and especially the diversification that it brings to Arista. Thanks, Nishu.

Thank you, Todd. Thank you, Ken. It was so fantastic to hear of happy customer outcomes. We had another fitting example of that at our Innovate 2026 event here in the headquarter facility held in March. The energy and enthusiasm of our greater than 250 customers who attended was truly infectious and inspiring. I want to especially give a shout-out to Ashwin Kohli and Divya Wagner's team, who have already improved our outstanding net promoter score from 87 to 89 ratings, translating to a 94% customer approval. This really exemplifies the lowest security vulnerabilities in the tech industry. It enhances our ability to better cope with the many risks that AI is creating. As I look ahead at the year, our Arista 2.0 momentum continues to march on and resonate. Our demand is actually the best I have ever seen in my ERISTA tenure. The supply, however, is a slightly different and opposite tale. We are experiencing industry-wide shortages across the board, be it wafers, silicon chips, CTUs, optics, and, of course, memory that I referred to last quarter, coupled with elevated cost to procure these. Clearly, our demand is outstripping our supply this year. While we hope the supply chain will ease in the next year or two, the ERISCA operations team has been diligently engaging with our vendors in strengthening supply agreements and engaging in multi-year purchase commitments. We anticipate gross margin pressure due to mix and trade-offs we are making to pay more to assure supply continuity to our customers. Nevertheless, it gives us confidence to increase our forecasted growth slightly to 27.7%, aiming now for $11.5 billion for 2026. We also increased our AI target now to $3.5 billion this year, thereby more than doubling our AI sales annually. And with that good news, over to you, Chantel, for the financial details.

Thank you, Jayshree. I continue to be impressed by our company's ability to deliver such a breadth and depth of networking innovation. It is a core test that underpins our strong financial return to shareholders. Q1 to detail our most recent financial outcomes. To start off, total revenues in Q1 were $2.71 billion, up 35.1% year-over-year, and above our guidance of $2.6 billion. Growth was seen across the customer sectors, led by our AI and specialty providers customers within the quarter. International revenues for the quarter came in at $418.9 million, or 15.5% of total revenue, down from 21.2% last quarter. This quarter-over-quarter decrease was primarily influenced by America's base sales to our large global customers. The overall gross margining came in with 62.4% within the guidance range of 62 to 63%, and down from 63.4% in the prior quarter. This quarter-over-quarter decrease is due to the lower mix of sales to our enterprise customers in the quarter. Operating expenses for the quarter were $396.8 million, or 14.6% of revenue, down slightly from last quarter at $397.1 million. Our R&D spending came in strong at $271.5 million, or 10% of revenue, despite a slight sequential decrease due to the timing of new product introduction costs. Arista continues to demonstrate its commitment and focus on networking innovation. Sales and marketing expense was $103.5 million, or 3.8% of revenue, down from 4% last quarter, representative of the highly efficient Arista Gold Market methodology. Our G&A cost came in at $21.8 million, or 0.8% of revenue, down from $26.3 million last quarter, reflecting our strong base cost productivity within the PurePlan networking business model. Our operating income for the quarter was $1.29 billion, or 47.8% of revenue. Let me pause here to thank the greater Arista team for all of their efforts and resulting excellent execution in a dynamic environment. Other income and expense for the quarter was a favorable $110.8 million, and our effective tax rate was 21.1%. Overall, this resulted in net income for the quarter of $1.11 billion, or 40.9% of revenue. Our diluted share count was 1.27 billion shares, resulting in a diluted earnings per share for the quarter of 87 cents, up 31.8% from the prior year. Now, turning to the balance sheet. Cash, cash equivalents, and marketable securities ended the quarter at approximately $12.35 billion. In the quarter, we did not repurchase our common stock. Of the $1.5 billion repurchase program approved in May 2025, $817.9 million remain available for repurchase in future quarters. The actual timing and amount of future repurchases will be dependent on market and business conditions, stock price, and other factors. Now, turning to operating cash performance for the quarter, we generated approximately $1.69 billion of cash from operations in the period, the strongest in the history of ERISA. This was driven by a robust earnings performance coupled with an increase in deferred revenue. ESOs came in at 64 days, down from 70 days in Q4, due to the linearity of shipments within the quarter. Our inventory turns improved slightly, landing at 1.7 versus 1.5 in the prior quarter. We ended the quarter with $2.38 billion in inventory, up from $2.25 billion last quarter. This marginal increase is a calculated investment in the mix of raw materials to fulfill our growing demand. Our purchase commitments at the end of the quarter were $8.9 billion, up from $6.8 billion at the end of Q4. As mentioned in prior quarters, this expected activity mostly represents purchases for chips related to new products and AI deployments. We will continue to have some variability in future quarters as a reflection of the combination of demand for our new products, component variability, and the lead times from our key suppliers. This could also result in quarters of elevated inventory balances ahead of the deployments. Our total deferred revenue balance was $6.2 billion, up from $5.37 billion in the prior quarter. The majority of the deferred revenue balance was product-related. Our product deferred revenue increased approximately $643 million versus last quarter. We remain in a period of ramping our new products, winning new customers, and expanding new use cases, including AI. These trends have resulted in increased customer-specific acceptance clauses and an increase in the volatility of our product deferred revenue balances. As mentioned in prior quarters, the deferred balance can move significantly on a quarterly basis, independent of underlying business drivers. Accounts payable days were 54 days down from 66 days in Q4, reflecting the timing of inventory receipts and payments. Capital expenditures for the quarter were $54.5 million. We continued the construction work to build expanded facilities in Santa Clara. In Q1, we incurred approximately $40 million in CapEx related to this program, and estimate it will reach $180 million in 2026. These Q1 results have provided a strong start to our fiscal year 2026. As Jayshree mentioned, we are now pleased to raise our 2026 fiscal year outlook to 27.7% revenue growth, delivering approximately $11.5 billion. We maintain our 2026 campus revenue goal of $1.25 billion and raise our AI Fabrics goal from $3.25 to $3.5 billion. I would like to take this opportunity to remind the audience that the timing and outcome of customer projects with acceptance terms can create quarterly and sequential dynamics that do not follow prior year trends. For gross margin, we reiterate the range for the fiscal year of 62% to 64%, inclusive of mixed and anticipated supply chain cost increases for memory and silicon. Given this challenging supply backdrop, I am proud of our sourcing team's execution, which strongly contributes to the gross margin outlook holding in our guidance range. we feel confident that we can source the necessary supply to meet our customers' needs. Our operating margin outlook remains at approximately 46% for the fiscal year, with the tax rate expected at 21.5%. On the cash front, we will continue to work to optimize our working capital investments with some expected variability in inventory and cash flow from operations due to the timing of component receipts on purchase commitments. More specifically now, our guidance for the second quarter is as follows. Now with the added quarterly metric of diluted earnings per share. Revenues of approximately $2.8 billion, gross margin between 62% and 63%, operating margin between 46% and 47%, and diluted earnings per share of approximately $0.88 with approximately 1.27 billion diluted shares. Our effective tax rate is expected to be approximately 21.5%. In closing, we are optimistic about the fiscal year ahead. The industry has many times demonstrated the pattern of landing on Ethernet as the winning technology, and that is where Arista shines best. We appreciate our customers' choice of working with us to achieve their business outcomes. Now, Rudy, back to you for Q&A.

Thank you, Chantal. We will now move to the Q&A portion of the Arista earnings call. To allow for greater participation, I'd like to request that everyone please limit themselves to one question. Your line will be placed on mute after your question. Thank you for your understanding. Regina, please take it away.

We will now begin the Q&A portion of the Arista earnings call. To ask a question during this time, simply press star and then the number one on your telephone keypad. If you'd like to withdraw your question, press star and the number one again. Please pick up your handset before asking questions to ensure optimal sound quality. Our first question will come from the line of Simon Leopold with Raymond James. Please go ahead.

Great. Thank you very much for taking the questions. I wanted to explore your commentary around the scale across opportunity in particular, and I guess what I'm trying to get a better sense of is how much revenue, if any, did that contribute last year, and how material is that to the $3.5 billion forecast you're getting this year, and how should that trend longer term? Thank you.

Sure, Simon. I think last year on scale across, we were just beginning. So I think they were small numbers. And majority of the numbers were really scale out. That's sort of our heritage and that's where we excel. If I were to anticipate how it would be this year, again, scale up is virtually zero and non-existent because it really only comes to play after the ESOM spec. So consider that more a 27, 28 kind of number. So I think the number will be really shared between scale across and scale out. I don't know if I can say it's 50-50 or 70-30 or 60-40, but scale across will definitely contribute at least a third of our AI number.

Our next question will come from the line of George Notter with Wolf Research. Please go ahead.

Hi, guys. Thanks very much. Maybe just continuing the discussion on scale up. You know, we are starting to see rack design wins. One of your competitors in the ODM space, I think, has got a couple of designs that they've announced at least. And I know you're kind of pointing towards ESON as being kind of a key component. catalyst in generating business there. But can you talk a little bit about where you are in terms of designs with customers, progress, anything you can tell us there would be great. In fact, I think a few quarters ago you said you had five to seven scale-up rack designs that you were at least working on. Maybe you can update that. Thanks a lot.

Yeah, that's correct, George. I think there is no doubt in our minds that we will have a number of racks, a number of scale-up use cases in 2027. Maybe some of them will be in early trials, but the majority of them are looking at really starting with 1.60, and 1.60 trips will really happen in 2027. There may be a few, a handful of them that try some experimental stuff at 800 gates, but we continue to see at least five to seven rack opportunities. Some of them are multiple racks with the same customer. We're actively designing with them. There's a huge amount of liquid cooling. you know, designs with very dense cabling options, acceleration of collectives and memory features we have to work on for low latency. So I definitely feel we're in active engineering phase with Ken and Hugh's teams this year. But unlike the ODMs, I think we're held to a higher bar, and we have to just make sure that this thing is production-worthy and specification-adhering to eSIMs. So I would say today's scale-up is mostly limited to NVLink from NVIDIA and maybe some PCI switching, but majority of the Ethernet scale-up will only really happen in 27 and 28.

Our next question will come from the line of Antoine Gabin with Newstreet Research. Please go ahead.

Hi, thank you very much for taking my question. So with supply outstripping demand, I'm wondering, you know, how much does your current supply allow you to grow this year and next? If you have data, you know, top line growth guide of 28% growth, a good reflection of how much supply you've secured for this year. And what could that number look like next year, you know, based on how much supply you think you can get as of today?

Antoine, I think the supply chain problem, and Todd, maybe you can add to this, is not a one- or two-quarter phenomenon. We now think it's a one- or two-year phenomenon. You know, at first we thought it was memory. Now it's all the wafer fabrication facilities. Every chip is challenged, and you can see how Chantal has leaned in with the purchase commitment for multiple years. So while we will continue to improve it, this is a reflection of, not just demand, but how much we can ship this year. And as we continue to ship this year, we can give you better visibility on next year. But I can just tell you, we see multi-year demand, and we are going to do everything, including hurt our gross margins, to supply to that demand this year and next year, because we believe that we certainly don't want to keep GPUs idle and AI infrastructures underutilized because Avista didn't supply the network. So can the number get better this year? I think this reflects our best attempt at a good number. We started out at 20%, 25% growth. Yeah, so we started out at 20%, we're at 25%, now we're at 27.7%. Could we improve to the tail end of the year? We'll see. But the amount of decommits we're seeing doesn't feel good. So we think a lot of this will continue into next year and keep us constrained for the next couple of years.

Our next question will come from the line of Aaron Rakers with Wells Fargo. Please go ahead.

Yeah, thanks for taking the question. You know, Jayshree, last quarter you had alluded to kind of engagements with other hyperscale CloudTitan customers. I think you also pointed to maybe having one or two new 10% customers this year. I'm curious of, you know, where we stand today. Any updated thoughts on adding one or two new customers at 10% plus? And, you know, maybe qualitatively just talk about your engagements you're having beyond your two big CloudTitans across the hyperscale vertical. Thank you.

Yeah, absolutely. First of all, two big ones. We never take them for granted. Microsoft and Meta, they're our all-time favorites. They've been on 10% and greater customers for over a decade, and the partnership could never be stronger, and it continues to get better, both in cloud and in AI. In terms of the new entrants, we still expect at least one, maybe two, And maybe I should caveat this by saying certainly in demand we see one or two. We shall see, Todd, how we do on shipments to see if we can achieve greater than 10%. The two of them have very interesting characteristics. They exhibit what I would call the three use cases I just alluded to, scale up, scale out, and scale across, where we really have a fabric notion of creating, you know, so far we've been working with them a lot on the front end, and now we get to complement that on the back end, definitely for scale out and scale across, and maybe even a little bit of scale up in some of these use cases. The other thing we're seeing with a lot of these use cases is the lack of power in sites and the ability and demand to distribute and get a more multi-tenant scale across is very high in these two use cases. A third common thread we're seeing across of them, much as we all talk about ODM and white boxes, they deeply appreciate EOS and the features and the reliability and the observability and the Just the fact that we have a robust, highly scalable Layer 2, Layer 3 stack commands a lot of superior advantages. So I believe the diversity of these cloud titans is largely due to the fact that we have great hardware and software combined. Ken, you want to say a few words on that?

It's just been an incredible journey to look through this and see the level of infrastructure that we're getting and how well positioned Our hardware and software roadmaps are to address these ever-evolving, more advanced use cases. It's just a blast to get to work on this stuff.

That's always fun when your job is a blast. So, Ben, I still see one, maybe two 10% customers. And, Todd, hopefully we can ship it.

Oh, sorry, Erin. And our next question will come from the line of Ben Reitzes with Milius Research. Please go ahead.

Oh, there you go, Jayshree. Here I am. So I wanted to ask around the constraints. Are you able to say what the number was in the quarter and what it's taking away in terms of the $2.8 billion guide? Is it safe to say things would have been $100 million or $200 million higher for both? And then if you don't mind, just... if you can touch on why the gross margin should go back up to 63, you know, what is it that you guys are doing that gives us confidence that it can actually expand a tad from here?

Yeah, I think that maybe I'll just, I think I haven't, I think that, I don't think the commentary about the demand as stripping the supply is a key one, key two. I think we're talking about looking ahead to three, two, four into next year. So, I don't think there's something outside of what we've got or what we've delivered in the first half. I think in the sense of the margin. So the margin is a mix of things, right? And I think that all the team members are executing in full force. I think the supply chain is doing everything they can on ensuring that we have the best supply at the best price. And so we've incorporated that. I think that the mix of customers, the only chance for mix expansion or margin expansion would be due to mix. And so I think that's the opportunity as we look to see what we can deliver in the second half then. I think that would be the opportunity.

The teams are also doing everything they can to make sure we control our costs, especially on the manufacturing side. That includes bringing on secondary providers, calling new components, et cetera, to make our supply chain more resilient and more cost-effective and normal.

And one thing to clarify also on gross margins, we view this as a partnership with our customers. So while we did consider and have raised prices a little bit, unlike our competitors, we haven't done two price increases. We haven't done major price increases. And the price increases really come into play. once our backlog starts to reduce, right? So you won't see the impact of that. So our gross margins are a strong factor of costs going up and are still eating a lot of the costs and, you know, giving our customers the benefit and promise of the pricing we said we would give to them.

Our next question will come from the line of Michael Ng with Goldman Sachs. Please go ahead.

Hey, good afternoon. Thanks for the question. I was just wondering if you could talk about whether or not Arista is seeing networking-attached opportunities for customers that are using TPU or TPU-like architectures, and then anything you'd comment about as it relates to growing NeoCloud traction. Is that something that you think may be a little bit underappreciated by the analyst community? Thank you very much.

Yeah, Michael, you're absolutely right. I'll take your second question first. It's easy to talk about the titans because the numbers are so ginormous, right? But the neoclouds are a very important sector because they don't always have the staff to do everything they want to do. And they really lean on Arista's design expertise, EOS expertise. You know, network design configurations, we can provide them. You know, a family of 22 products we have in AI. So, yes, I would agree with you. It's an underappreciated, and the new cloud is very strong this quarter, if I recall, Chantel, for us in the specialty and cloud providers. What was the other question? You had a 1A, 1B. The TPU.

Oh, yeah, the TPU.

So, in general, we are seeing diverse accelerators. Last time, I spoke about the AMD accelerators. This time, I will definitely give a nod to the TPUs because in particularly scale across use cases, we're seeing multi-tenants connecting to different AI accelerators, including TPUs as well. So, I think... The diversity of accelerators is creating tremendous multi-accelerator opportunity and multi-protocol features that we can provide for them in our network.

Our next question will come from the line of Sean O'Loughlin with TD Cowan. Please go ahead.

Great. Thanks. You know, congrats on the results, and thanks for letting me join in on the fun here. Jayshree, I wanted to get your thoughts on, you know, we've been talking a lot about agentic AI and the demands that it's placing on maybe some of the more general purpose infrastructure that has been maybe in the background over the last couple of years. You've talked in the past about a two-to-one pressure, you know, on front-end networking created by back-end. First, I guess, is that still the correct way to think about it? And second, you know, as agentic workflows become more common, is there any additional demand for From your perspective, having a single image EOS platform on the front and the back end, or is the front and back end still pretty siloed?

Yeah, well, first of all, Sean, welcome to your first call. It will be fun. Join the fun. So agentic AI, it's kind of a buzzword, but let me sort of break it into how the biggest killer application we see in agentic AI right now is still training. And, indeed, it's going to move to more distributed inference, and we'd also like to see agentic AI move into a lot of enterprise use cases, all of which you're seeing, by the way, but I would say large, medium, small. The largest killer agentic AI application is training. The medium is enterprise and the small is, medium is entrance and the small is obviously enterprise. In terms of backend versus frontend, we are now seeing way more backend activity, particularly with our large AI titans and cloud titans, because there is just so much scale they need to prepare for the billions of parameters and tokens. And this is where a lot of, so much so that I think the frontend, they might come back and refresh, but they're almost ignoring right now in favor of the backend. Having said that, though, by virtue of the backend deployments, I don't know if we anymore see a two-to-one to the front end, but we at least see a one-to-one. And the one-to-one can be wide area, CPU, and storage. Those are probably the three common use cases. Not all the customers are up and lifting everything and doing all three, although we've had cases where some of them did an upgrade of the front end before they went into the back end. But usually they will have to come back to that because the minute you put that kind of performance pressure and scale on the back end, you almost have to do something in the front end. But at the moment, I would say it's more one-to-one. And at the moment, I'd also say the scale across in the back end has become a bigger use case than we imagined this time last year.

As you mentioned here, it's just how good it feels to have the same set of products and the same common operating system management suite and operating model across the front end and back end. This lowers costs for the customer, simplifies their design process to get that leverage, and we're one of the few vendors who can do that.

I think only.

Yeah, I think so.

I think only. Yes, absolutely. Good point, Ken.

Our next question will come from the line of Amita Marshall with Morgan Stanley. Please go ahead.

Great, thanks. Appreciate the question. Maybe just a question on XPO monetization or just how it helps you kind of continue to being share with customers or just mind share with customers by being so front-footed with the technology. Thanks.

Yeah, thank you, Medha. I think, as you know, we're not a classic optics vendor. But almost always, whenever we're selling our switches, it has to connect to something. And usually it's some form of copper or optics. So, and these innovations with OSFP, I remember this super well, where everybody was saying, oh, no, no, we can just use QSFP. It has proven to be, you know, not only a contribution for Richter, but really for the industry-wide. And that's still how we see it with XPO as well. you know, while the industry has been talking a lot about co-packaged optics, these are still science experiments and they're very proprietary with individual vendors doing their own thing. We embrace open CPO a few years from now, but we think XPO has a 10-year run, especially at 1.60 and 3.0 where you need liquid cooling and you need that kind of capacity. So, you know, all those scale-up racks we're talking about wouldn't be possible without XPO or CPC or any one of those technologies. So we see this as the last decade was greatly influenced by OSFP. The next decade will be greatly influenced by XPO and XPO. Remember 99% of the optical market today that we connect to is all pluggable optics. So this is a very crucial invention and innovation, not just for Arista, but the industry at large.

I think this is a great example of how Arista enables an ecosystem. And then we profit as that ecosystem grows. And what XPO unlocks is a standard interoperable multi-vendor way to get to four times the network density and liquid cooling, which is absolutely critical for these AI use cases. Without that, there's a huge bottleneck at the front panel, and none of the actual rack space is required. You get through OSFPs. So we're really enabling the future growth of our industry this way, which we benefit, and others benefit as well.

Yeah. It's stunning to me. I remember when I first talked to Andy and Vijay, they said, oh, we think we'll get about 20 signatures, and then it was 40, and now it's north of 100. So it tells me the whole consortium is coming together for things like Ethernet, IP, and standards and standardization of optics.

Our next question will come from the line of Tal Liani with Bank of America. Please go ahead.

Hi, guys. Can you hear me?

Yes, Tal.

We can hear you. Yes.

Hello. I promised myself to be nice today, so I have a good question for you.

I promise to be nice, too.

Deferred revenues. Deferred revenues doubled in the last year, and it went up. If I combine short-term and long-term, it went up $826 million. It went up significantly in the last four quarters. What needs to happen? What are the conditions to recognize deferred revenues? Meaning, What needs to happen for deferred revenues to be recognized over the next few quarters? Is it about data center going live and traffic goes into data centers? Or what are the sources for the deferred revenue increase?

Thanks. Right, right. Charles, so I really do like you, so I'm going to be nice to you, not because I have to, but because I like to. So I think if you remember 10 years ago, Charles, we had a similar phenomenon where in the cloud, the whole vSpine design was brand new. Nobody really knew how to build it or monetize it, and they were building some of the world's largest networks for Azure, et cetera, right? And we had new products. They had new designs. They had done traditionally the access aggregation core and were now moving to the slapback topology. And we had some fairly lengthy qualification cycles. So I would say there's a customer aspect to it and a product aspect to it. is they need to have the space, they need to have the facilities, they need to have their, in this case, GPUs now. Back in then, it used to be CPUs. They've got to have their rack and stack. And many cases, by the way, we're running into examples where it's literally they need to manually install the cables, and that takes several months, right? Thousands of people have to do that. So there's certainly a customer acceptance piece of it, which starts with being ready. There's also a new product. Many of these new products in the Arista Etherlink family, particularly for the AI, are brand new. Brand new chips, brand new software. The familiarity with it, particularly in the back end for scale out and scale across, is new to them. So there's a level of testing and level of making sure it works with the rest of their ecosystem, including the front end, that is super important. And Arista bears a huge responsibility to that as well. So all this to tell you that the length of time to qualify this, which used to be two to four quarters, has extended more like six to even eight quarters. It's gotten much longer. Chantel, you want to add something?

Yeah, the only other thing I'd add, thank you, Jayshree, is that we do recognize some of it every quarter. So it's not like it's one balance that says aging and parental. We recognize things every quarter. Things come in and things are recognized to the P&L. So I just want to make sure you understand about that.

It's not piling. Some things go in and some things come out. Yeah. Does that make sense, Tal? Tal, you're on mute?

No, no. Oh, he does.

Okay. All right.

I have a question. Our next question will come from the line of Amit Daryanani with Evercore.

Please go ahead. Thanks for taking my question. You know, I guess, Jershi, you folks have kind of positioned XPO as the next OSFP, and I'd love to kind of understand that. XPO ramps from, you know, the OFC demos to potentially deployments in 27. You know, how do you see changing the optics architecture within AI clusters and then maybe specifically for Arista? Does that change the growth profile or your content or AI rack or cluster as you go forward? Thank you.

Yeah. Thank you, Amit. I think you should look at XPO as... a partner to OSFP. So at 400 gig and 800 gig, you'll be fine with OSFP. And as you go to higher speeds in 27, 28, or even beyond, OSFP will run out of steam, and this will be the new connector of choice. So the migration to higher speeds equals the migration to XPO, particularly for scale-out and scale-across. Within a rack of scale-up, there's still a number of choices. I think within short distances of two to three meters, you're still going to see a lot of co-packaged copper. And I think XPO in terms of density will be another alternative. But I don't rule out open CPO as well over there. They're really looking to maximize their density in a minimum amount of space. So I think XPO will be particularly prevalent in scale-out and scale-across and will be one of the choices in scale-out.

Our next question comes from the line at Ryan Koontz with Needham. Please go ahead.

Hi, this is Jeff Hobson on for Ryan. I appreciate the question. On the scale across, it seems like that would be a really good fit for all Arista's capabilities. And I know you mentioned it would maybe be around a third of revenue this year. But is this something where scale across could even be larger than scale out over the next couple of years? Thanks.

Hi, Ryan, or rather Jeff. I think the answer to that would lie on how well we do with both and what form factors are used for both. Majority of the scale across today is a very premier, valuable, heavy-duty routing platform, the 7800. So if we do lots of that, it could get well beyond the 30%. But some of them may do it with fixed boxes, too, or fixed switches and choose to add a lot of cables. in which case it wouldn't go well about that. So we don't know what we don't know. But I would agree with you that ScalarCross is by far the most significant and differentiated opportunity that really highlights a risk of prowess in both platforms and software.

Our next question comes from the line of Sonic Chatterjee with J.P. Morgan. Please go ahead.

Hi, thanks for taking my question. Just maybe slightly related to the last question here. Just trying to think about, you said though, most of the cloud revenue new term is going to be scale out and scale across as we wait for scale up to ramp. How are you thinking about your market share when it comes to scale out versus scale across? in the early days of Scale Across, what are you seeing in terms of market share? And are you seeing customer decisions being led in Scale Across by sort of the incumbent in Scale Out? Or is it a different decision altogether in terms of how they're designing vendors for Scale Across?

Thank you. Good question, Sunil. You're making me think. So I would say... If it's a greenfield deployment, then they tend to think of it together, because they're not only building the sites, but they're thinking of the interconnects across them, and therefore market share is generally strong in both. In some cases where Arista has not been a historical participant within the data center, we now have an opportunity to offer the scale across multi-tenant in a non-greenfield situation and let's say in a brownfield, where now they've got disparate data centers or AI clusters that we now have to bring in. And so once again, I think Arista's a really fitting example to be in scale across for both those use cases, but has the additional opportunity in a brand new data center to be in all use cases, if that makes sense. So it's giving us a chance to participate with different types of accelerators and different types of models because people aren't getting the power and they're having to distribute the data centers. And as a result of distribution, you need more traffic engineering, routing, multi-tenancy. So I would say scale across is the common denominator in all our use cases and scale up and scale out, maybe nice options and brand new

Our next question comes from the line of Carl Ackerman with BNP Paribas. Please go ahead.

Yes, thank you. Jayshree, you are doing more networking design today more than ever. Does that change your ability to monetize your services to capture more of the work of the other value that you're adding to these applications? And I guess as you address that, given the large mix of services revenue within Deferred, Could services revenue accelerate faster and represent perhaps 25% or 30% of sales going forward?

Thank you. I don't think so, Carl. I think we're a product company, and the majority of our customers our revenue generation and interest in Arista as a company for all the designs we're doing comes from our product heritage and it's not like we charge for services in fact we work closely with our partners also we will you know recommend network designs we will support services and certainly things like we are the gold standard for worldwide support but I don't expect services as a function of our revenue to go up I continue to be see ourselves as a product led company

Our next question comes from the line of Matt Nickman with Truist. Please go ahead.

Hey, thanks so much for taking the question. I just want to go back to gross margin. So I know we were sort of in that 62-ish range. They dipped about 170 bps year-on-year. And I want to dig into whether it was primarily mix-related or, you know, maybe if you can quantify how significant the memory and cost-related impacts were, if there's any color you can provide. Thanks.

Yeah, I think it's a great question. I would say the majority, if you look at – even if you look at prior quarter or prior year, the majority of the difference is mix of the customers. And just to clarify, you know, our larger customers, you know, have a lower gross margin accretion, and so that mix is the primary driver. And then the secondary, although not as significant, would be things – depending on the quarter, depending how deferred is moving tariffs or the memory cost or the silicon cost, depending on the quarter. So, secondary driver, but the primary driver is mixed with the customer segments.

Our next question comes from the line of David Boat with UBS. Please go ahead.

Thanks. Hi, this is Andrew for David. From a high level with $2.4 billion almost of inventory and almost two years in COGS of purchase commitments, how should we think about the supply constraints and where that inventory and purchase commitments are not satisfactory to meet demand? Where are the holes in your inventory?

I wouldn't say we have holes in our inventory, but we have surging demand, especially on the newest platforms, which, of course, is driving our need to for the most modern silicon from our providers, and it's driving need for an expanded amount of memory, even more than we were expecting before the year began. So that's driving us to be a buyer in the market. Luckily, we've got pretty good spending power. We're a very reliable partner in these scenarios, and so we partner closely with these vendors. there's no doubt that, like, the newest platforms that we're delivering, especially in the AI space, is driving needs of ours in the high end of our portfolio.

Yeah, and just to add to that, David, the real hole is lead times. We are experiencing such significant racial fab shortages that we're not getting the chips in time. You know, so more than a hole, I would just say, you know, our purchase commitments are multi-years because we're having to deal with forecasts that are out multiple years so that we get them in time because the lead time of these trips is so long. So I think that's the biggest hole, lead time.

Yeah, we are experiencing 52-week lead times pretty reliably with reservation needs beyond that, and our customers certainly cannot wait that long.

Our next question comes from the line of James Fish with Piper Sandler.

Please go ahead. Hey, guys. So maybe for you, The guide raises primarily all on AI. Are you guys prioritizing these shipments or what's given the hesitancy around sort of the non-AI, non-campus at this point and leaving that roughly flat still? And, Jayshree, just for you, just as we think about the mix here on gross margin, what are you guys seeing in terms of blue box adoption now? And are you seeing any sort of net pull-in of demand? Just given, you know, you have a lot of smart customers here, and they're very much aware of the supply chain constraints. Thanks, guys.

Yeah, thank you. Thanks, James. I'll start with mine first in the sense of the order of your question. So I don't think we're saying because we're raising the revenue and contributing to AI that we're not excited about all the other customer segments. I think you heard both Jayshree and I talk about we're very happy with how the year started, what we're seeing above all three customer segments. We're very happy what we're seeing in enterprise, which I wouldn't say is quite AI yet. So let's hope that is the non-AI bucket that you referred to. So we'll wait and see. We're in Q1, reporting Q1. We'll see how the year goes. But we're very confident across all three that we're seeing strong demand. So I think I would leave it in the sense of let's see where we get to in our future quarter guides.

Yeah. And I would agree with that. Just to remind everybody, we've raised now from 10.5 or whatever we just said last September to 11.5 billion. And yes, a high degree of that is AI, but we have aggressive commitments on the campus to go to a 1.25 billion quarter and continue to service and grow our data center and cloud just as well. So all three are growing, but certainly AI is taking the news headline. Regarding Blue Box adoption, One of the customer use cases you actually heard about was moved from, that you heard from Ken, moved from white box to blue box. And their goal right now is their desire to move to blue boxes. It works, number one. It scales, two. It actually does the job for us with AMV accelerators, number three. And down the road, they may use open operating systems, but they were very pleased with the diagnostics capability, the platform SDK where, you know, we literally rewrite every piece of software and bit-fiddle all the Broadcom chip transistors together. very, very well, and the EOS features. Down the road, they may use some open losses as well, but that would be a really good example of a blue box that has EOS today and may go down to other losses. And we continue to see that, particularly in the neoclouds. We've always seen a bit of that in the cloud and AI crisis because they know how to work with open losses. So we've had that hybrid strategy always, but we're certainly seeing more of that in the neoclouds now.

Regina, we have time for one last question.

Our final question will come from the line of Ben Bolin with Cleveland Research. Please go ahead.

Good afternoon, everyone. Thank you for taking the question. Jay, you referenced inference a little bit earlier, said it's kind of a smaller use case right now. I'm interested in your thoughts on where you think enterprise is in terms of their ability to consume inference and create agents, and then how that develops over time and where you think the front-end networks and edge networks are today in their ability to support those use cases. Basically, just do we get the sustained investment period because what you're seeing now bleeds and becomes much more significant in enterprise, and how long-lasting that might be.

Yeah, no, Ben, I tend to agree with your thesis that while today we are in a training fever that a more distributed AI, generative AI paradigm with instances, which means you don't always need the GPU. You're going to have high-end CPUs, and you're going to have a smaller set of parameters and tokens to manage, and you're going to have specific agentic AI use cases and applications. We're seeing very, very early trials and stages, nothing super big yet, but we are seeing, I mean, they're not in the hundreds of thousands of GPUs that you see on the AI titans, but We're frequently seeing our customers in certain high-tech sectors want to deploy clusters that are thousands, few thousands, definitely not 10,000, but in the hundreds to thousands. And they tend to be exactly as you said, not training, but more inference-based, more agentic AI edge inference-based as well. So I think we'll see more of that. This is... This is the calm before the storm, if you will. And as AI gets more distributed, I think it doesn't need GPUs alone. It's going to need more high-performance compute. And many of them seem to feel to us like high-performance compute HPC use cases that are sort of getting revived for AI. So I agree with your thesis, Ben. I think it's going to take a couple of years to fully happen.

This concludes Arista Network's first quarter 2026 earnings call. We have a presentation posted that provides additional information on our results, which you can access on the investor section of our website. Thank you for joining us today and for your interest in Arista.

Thank you for joining, ladies and gentlemen. This concludes today's call. You may now disconnect.