Arista Networks (ANET) Q2 2026 earnings call transcript

Mr. Rudolph Arajo, Arista's Head of Investor Advocacy, you may begin. Thank you, Regina. Good afternoon, everyone, and thank you for joining us. With me on today's call, I'd like to issue a law, Arista Network's Chairperson and Chief Executive Officer and Chantal Bright-Up Arista's Chief Financial Officer. This afternoon, Arista Network issued a press release announcing its fiscal second quarter results for the period ending June 30, 2026. If you want a copy of this release, you can find it on our website. During the course of this conference, callers and efforts management will make forward-looking statements including those relating to our financial outlook for the third 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 levels and management, lead times, purchase commitments, components, supply and product invasion, which are subject to the risks and uncertainties that we discuss in detail in our document's file or the FCC, specifically in our most recent form 10Q and form 10K, and which could cause actual results to defer materially from those anticipated by these statements.

These forward-looking statements apply as of today, and we should not rely on them representing our views in the future. We undertake no obligation to update these statements up at this call. This analysis of our Q2 results and our guidance for Q3-26 is based on non-gap and excludes stock-based competition expense, intangible asset amortization, gains losses on strategic investments and income tax effect of these non-gap exclusions, including the recognition of direct access tax benefits associated with stock-based awards. A full reconciliation of our selected gap to non-gap results is provided in our earnings release.

With that, I would turn the call over to Jay Shree.

Thank you, Rudy, and welcome, everyone, to our second quarter, 2026 earnings call. Arista is experiencing significant demand to achieve our first $3 billion quarter in revenue. To put this in perspective, just five years ago, our entire year was $2.9 billion in 2021. We have also entered the prestigious Fortune 500 list in 26, and in addition to that, we are now included in the Russell 50. Our AI fabrics momentum with e-mailing switches now exceeds 100 cumulative customers from the initial four to five customers I spoke of in 2024.

Arista has developed innovative features to enable smart system upgrade, SSU, deep analytics, load balancing at scale for AI training workloads. Our latest member is the 7060XE7 for 100-terabit capacity and 1.6-terabit throughput, as well as the first liquid cooling options highlighting our continued scale-out leadership. Over a decade ago, we pioneered the use of leaf and spine topologies in the data center and cloud networks. We are now building upon that with our lossless and high-performance AI fabrics. The maximum possible scale for an AI network generally depends on two things, the number of tiers in the network and the number of ports per device, often known as radix. Increasing the tiers and ports is expensive and power hungry.

Our customers deploy and often choose the risk of flagship 7800 AI spine to achieve that high scale without adding additional tiers. Scale across is an important application. The scarcity of compute capacity, physical space and gigawatts of power mandates that the AI infrastructure must be designed thoughtfully. The Arista 7800 platform continues to be the flagship spine for distributed scale across applications, providing traffic isolation, contextual routing and security. The scale across switching and routing can is forecasted to be roughly $15 to $20 billion in 2030 and Arista is well poised in this segment. Our scale across AI innovations deliver programmable and deterministic routing, SRV6, multi-plane forwarding, multi-tenancy and traffic engineering as well as low-brass balancing across the regions.

We are capable of providing near-instantaneous recovery in the event of transient congestion, packet loss or physical failure of AI clusters independent of their geographical location. This scale across use case is expected to be $3.6 billion in 2026. While compute and AI accelerators supporting billions of parameters often grabbed the headline, we did introduce a suite of EOS extensible operating system innovations to enable a robust AI network capable of diverse models and accelerators.

At this point, I'd like to invite Ken Duda, our president and chief technology officer to highlight some of these AI innovations.

Thanks, Jay Shree. I have never witnessed the combination of rapid innovation and scale deployment that we are seeing in AI networks. I'd like to call your attention to three innovations, SSU, MRC and SRV6.

First, SSU. SSU is Arista's smart system upgrade, the ability to upgrade switch software without any disruption. Frequent upgrades are a hard reality today, especially as AI both uncovers security vulnerabilities and creates tools to exploit them. While many competing systems require a full reboot to address these issues leading to expensive and disruptive downtime, Arista's EOS handles these upgrades seamlessly. We ensure our customers stay secure without sacrificing even a single minute of valuable XPU cycles.

Second, to maximize XPU utilization, you need MRC or multi-path reliable connection. See, in first generation AI networks, every packet on an XPU to XPU flow has to take the same path. That means if two flows hash to the same link, they both run at half speed. MRC enables senders to spray a single flow across many paths through the fabric, where receivers reassemble any data that arrives out of order, eliminating the performance hit from fabric hash collisions. But how is the sender supposed to control which paths the flow will use? And that's where the third innovation comes in. SRV6 or segment routing, it's not new, but using it to load dollars in AI fabric, that's the game changer.

The sender tags each packet with a stack of SRV6 segment IDs, dictating the exact path the packet will take. The system then uses real-time congestion signaling to dynamically shift packets away from hotspots. Because Arista EOS provides a single, unified operating system, we support this SRV6 intelligence all the way from the scale-out fabric to the long-distance scale across routing. It gives our customers the combination of high quality, hot performance, and operational simplicity that Arista is known for.

Thank you, Ken. Undoubtedly, the Arista EOS architecture shines with its inherent state-sharing programmability and single binary. And all of this, as you just pointed out, is foundational for reliable accelerator operation and optimal compute utilization. Arista's multi-part and multi-plane monitoring with explicit SRV6 probes ensures that reliable performance for accelerator communication. We have collaborated with leading customers to build that AI fabric with that operational excellence. Well, as you know, during the last six months, have been quite vocal and candid in sharing our supply chain challenges and concerns, not only affecting us, but affecting the entire industry. While the industry-wide supply tightness and rising component costs persist, Arista has taken individual and aggressive proactive steps.

Arista is making solid progress here in addressing our tight supply chain. After all, we have a lot of experience that comes from many years. During the lawsuit crisis, we had to build a manufacturing site in the United States in 2016, during COVID challenges in 2021. And here we are back again in 2026. Our manufacturing rigor is based on a three-pronged approach. The number one is the people and leadership. Recently, we've hired a terrific global operations executive, Eugenia Corralis, with over 35 years of engineering, networking, and manufacturing expertise.

And she has built an outstanding suite of leaders for new product engineering, contract manufacturing, global supply chain, direct fulfillment, and logistics. We are also partnering closely with our key suppliers to meet our prioritized forecast. And I'm so thankful to them for their continued cooperation. And finally, Arista is leaning in with our increased multi-year purchase commitments, now almost tripling from a year ago at 3.6 billion to approximately 9.7 billion by the end of Q226. To describe that relentless execution, I would like to invite Todd Nightingale, our chief operating officer and president. Over to you, Todd.

Thanks, Dishi. Arista has spent the last six months improving our supply chain, growing product demand, and we're seeing significant improvements. We've secured multi-year agreements with leading vendors of strategic components, qualified new suppliers in key areas to limit risk, and built out supply chains for next-gen AI technologies. Our capacity has been increased in both manufacturing and distribution, and we've negotiated better component delivery terms to drive up both factory efficiency and capital deployment. Relationships with our strategic silicon vendors continue to be strong with really excellent collaboration in both supply chain and technical engagements.

Our memory supply has been secured for 2026, and we have extended visibility well into 2027 across DDR4, DDR5, and NAND memory. And importantly, we've increased our resiliency through optionality and expanded vendor qualification. For PCBs and optics, we're now able to build the capacity of a 12-month window and strengthen our engagement and commitments from key suppliers. We've improved our lead times and inventory management of thousands of component skus, improving sub-component pipelining and multi-sourcing, and providing increased flexibility with reduced inventory risk. In a new area, we've now established a liquid cooling supply chain capable of driving and delivering the next generation of AI infrastructure. This includes cold plate, quick disconnect, and tubing vendors with capacity agreements for cutting-edge new AI technology.

And to match our capacity with customer demand, we've increased both our manufacturing and distribution capacity. We have now three contract manufacturers and three distribution facilities providing geographic diversity in the U. S. , in Asia, and in Mexico. By focusing on vendor stability and diversity, risk mitigation, and predictable delivery terms, the innovation for new AI products and capacity across our factories. We are making significant improvements and in significant capacity increases across our supply chain.

Thank you, Tod. My God, it's so gratifying to hear the great strides and progress that you have made in such a short time. Great job by you and the team. Given our improving stance and supply chain, we are excited to increase our guidance for the third time this year to 12.6 billion revenue in 2026. We are now projecting 40% annual growth, which is an incremental $2.1 billion over our analytical goal of $10.5 billion and an incremental $1.1 billion over our recent projections of $11.5 billion in May of 2026. Our renewed enthusiasm in fulfilling demand in the second half of 26 is expected to apply across all our product sectors in a widespread manner, including the back-end AI fabrics, the core data center front-end, and campus and routing adjacencies.

And with that exciting guidance, I'd like to turn it over to none other than our Chief Financial Officer Chantel for more financial specifics.

Thank you, Jay Sri. It's great to see the supply chain ecosystem gaining traction to meet our customer's demand. Let's review how that is translating into our financial performance and outlook. To start off, total revenues in Q2 were just over $3 billion of 37.7% year over year and above our guidance of $2.8 billion. This significant growth was driven by our AI and enterprise customers. Congratulations to the employees on our first $3 billion quarter.

International revenues for the quarter came in at $697.8 million or 23% of total revenue up from 15.5% last quarter. This quarter of the quarter increase was primarily influenced by strong organic growth across our international regions, combined with a shift in the geographic mix of sales to our large global customers. The overall gross margin in Q2 was 63.4% down from 65.6% in the prior year, driven by end customer mix, and up from 62.4% in the prior quarter, benefiting from both tariff refunds and customer mix. Operating expenses for the quarter were $411 million or 13.5% of revenue, up slightly from the last quarter, 396.8 million due to an additional investment in liquid cooling, high rate X switching, and AI optimizing software.

Our R&D spending came in strong at $278.1 million or 9.2% of revenue, up slightly from the last quarter at $271.5 million. A risk that continues to demonstrate its commitment and focus on networking innovation. Sales and marketing expense was $109.8 million or 3.6% of revenue down slightly from 3.8% of revenue last quarter, representative of the highly efficient or risk to go to market methodology. Our G&A cost came in at $23.1 million or 0.8% of revenue up slightly from 21.8 million last quarter, reflecting our strong base cost productivity within a pure played networking business model.

Our operating income for the quarter was $1.5 billion or 49.9% of revenue, an incredible financial outcome for the company. Other income and expense for the quarter was a favorable $120.3 million and our effective tax rate was 20.3%. Overall, this resulted in net income for the quarter of $1.3 billion or 42.9% of revenue. Diluted earnings per share for the quarter was $1.2 based on $1.276 billion diluted shares, representing a significant 39.7% increase from 73 cents in the prior year.

Now turning to the balance sheet. Cash equivalent and marketable securities ended the quarter at approximately $13.3 billion up from $12.4 billion at the end of Q1. In the quarter, we did not repurchase our common stock. At the $1.5 billion repurchase program approved in May 2025, $817.9 million remain available for repurchase and future quarters. The actual timing and amount of future repurchases will be depended on market and business conditions, stock price and other turning to operating cash performance for the second quarter. We generated approximately $1.1 billion of cash from operations. This was driven by a robust earnings performance coupled with an increase in deferred revenue. DSOs came in at 68 days up from 64 days in Q1 due to the timing of customer shipments and invoicing.

Our inventory turns remained at 1.7 for the quarter. We ended the quarter with $2.5 billion in inventory up from 2.4 billion last quarter. Inventory level fluctuations are expected to continue as we work through the balancing of component timing and availability. This could result in quarters of elevated inventory balances affecting the timing of cash flow from operations ahead of the deployments. Our purchase commitments at the end of the quarter were $9.7 billion up from $8.9 billion at the end of Q1. As mentioned in prior quarters, this expected activity mostly represents purchases for chips related to new products and AI billion up from $6.2 billion in the prior quarter. The majority of the deferred revenue balance is product related.

Our product deferred revenue increased approximately $600 million sequentially 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 are 57 days up from 54 days in Q1 reflecting the timing of inventory receipts and payments.

Capital expenditures for the quarter were $29.7 million. Our construction work to build expanded facilities in Santa Clara remains on track and we expect construction to be completed by the end of fiscal 2026. These exceptional Q2 results combined with the ability of the ecosystem to deliver what is required are foundational to underpin our financial outlook for the company.

Reflecting our strong momentum, we are raising our 2026 fiscal year outlook to 40 percent revenue growth, equating to approximately $12.6 billion. Within this guide, our 2026 campus revenue goal is at least $1.25 billion and our AI fabric's goal is at least $3.5 billion. For gross margin, we are maintaining the range for the fiscal year 62 to 64 percent inclusive of mixed and anticipated supply chain cost increases per memory in silicon. We have increased our fiscal 2026 operating margin target now at a range of 48 to 49 percent while maintaining an expected tax rate of 21.5 percent.

Now more specifically, our guidance for the third quarter is as follows. revenues of approximately 3.3 billion gross margin of approximately 63 percent operating margin between 48 and 49 percent. Diluted earnings per share between $1.06 and $1.08 with approximately $1.279 billion diluted shares. Our effective tax rate is expected to be approximately 21.5 percent.

In closing, the aristotem is energized. We are well positioned for this AI super cycle and for ethernet networking overall. This is our intro combination of our innovation, our culture, and our focus. The opportunity ahead of us is tremendous and we are ready to capture it. Now back to you Rudy for Q&A.

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

So Jay Shiree, you took up the calendar year 26 revenue guidance substantially from last quarter. But if I remember correctly, you kept the AI target unchanged and only touched the campus. And you kind of explained kind of the thought process there. Were you seeing from other customers and other verticals and other use cases that sort of ultimately result in sort of that algorithm that you just laid out going into the balance of calendar 26? Thanks. Yeah, no, I, if you had to ask me whether our AI number or campus number will go up, I think Chantel, Todd, Ken, and I absolutely believe it will. The question is not whether it will go up. The question is what is that number.

And that I would like to reserve that 1.1 billion question to, well, it depends on how we ship. You know, if we ship more front-end AI or if we ship more Wi-Fi or wired or etherlink switches or routing. So I'd like to give our customers the priority and Todd's team the flexibility to ship what we can. And that's why we're not holding ourselves to a number.

Absolutely, Michael. So, listen, I don't want you to believe that suddenly we wave the magic bond and all our problems have gone away. The industry is going to have a two-year problem. I don't think we get out of it as an industry to 2028. But Erista is taking individually and specifically steps in the first half of this year that we believe will have results in the back half of this year. Over to you, Todd.

Yeah, on the Silicon side, you know, both how we work through all the supply chain and how we've improved delivery on those components, but also the technical engagements for new platforms that are super exciting to get into the field. Both have been going very well. As far as memory, or I would just say memory, PCB, optics, etc. You know, we've been able to work pretty hard at adding, you know, optionality, qualifying new vendors very quickly through our engineering teams, and that helps us bring, you know, multiple paths to delivery possible through the supply chain. We've also worked very hard, especially in the last six months, to improve our terms, and that means having shorter lead times and being more nimble.

So, if our demand shifts from one speed to another, we're carrying less risk. We're able to be more nimble and that's been incredibly successful, especially in the last six months.

Hey, George. First of all, I think it depends on the use case. Let me take the scale up use case, which we are less prevalent in. I think there's very much a philosophy there on copper if you can, optics if you must. So, I think you're going to see a lot of copper in that two meter, three meter distance, well within a rack, that type of thing, and the importance of pluggable optics. But, in some cases, there is a number of instances of proprietary implementations of traditional co-packaged optics that's been floating around. Arista is not a fan of five different proprietary implementations. We're going to have, and we're going to commit, and Andy and the team have been working hard at this to really solve one, which is an open CPO.

And we don't think open CPO is going to happen overnight. But the idea here is to use socketed optical engines, pigtail fibers, and allow these modules to be fully pre-tested. And whether they're soldered on the board or nearby, the idea is to have a truly open interface that can operate with multiple vendors and multiple switch configurations. And sometimes these open CPOs are often called NPO too, nearby optics, right? So, with big fans of that, it's very early stages. It probably comes into examples and trials next here. And probably from our perspective and the industry perspective with all the supply chain shortages, the majority of the world will still remain pluggable optics and copper. But there will be some amount of co-packaged optics in 2028 and 2029.

So, that's how we kind of see the world.

You're sneaking in more than one question there, but thank you for reading the press release. And I think, you know, the mythos and the vulnerabilities is very front and center. And first, I just want to acknowledge Ken's architectural design of EOS and how we have the lowest vulnerabilities, and we're absolutely committed to building that rock solid foundation. But to make AI secure isn't just our responsibility. It really comes across the board. We need to work with the leading model providers. We need to build a robust infrastructure and we need to work with the best security vendors. So, we thought nothing better than getting the best of breed network model and security providers to do this together, because vulnerabilities are a fact of life.

But how we proactively deal with them with the right technology, with the right systems, with the right process is exactly what that webinar. I think it's September 8th, tune in for that, because I sure won't do justice to that in this call, but it's very important. In terms of our own approach to models, you know, every day it's in the news as a China model, whether it's a deep seek or whatever. But from our standpoint, the network must be robust no matter what the model. There's going to be a lot of enterprise AI agents. There's going to be a lot of diversity of models. It's going to be training at some point, but it's going to move to inference.

Earth is just taking a lot of care to make sure that we build that multi-model network that can handle all of the traffic and all of the different priorities and skills. So, undoubtedly, as you can understand, some of the work that Ken and the team did on MRC sometimes requires us to do work on the network. It sometimes requires us to deal with the packet spraying and how the end host behaves. Can you want to say a few words on that with MRC?

Yeah, I mean, look, we're extremely excited about the group and diversity in the industry. The more diversity there is among models and model makers between AI infrastructure providers, the better for us. And we've been a best-of-breed player from the very beginning, and we continue that tradition as we expand into AI networks. And MRC is one example of that, strongly supporting the industry's growth and enabling a best-of-breed solutions through open and interoperable standards.

Oh, I can't escape that question. No care. And thank you for the wishes. I'll bask in that glory for two seconds. But look, we're going to increase the number by a billion or more. And undoubtedly, we remain very committed to our two longest partners, Microsoft and Meta. And I fully expect there to be one, maybe two, 10% customers. I'll leave it at that just because we want all the flexibility on what's in the product sectors and who are the 10% customers as we ship. So I want to give talk the ability to ship lots and lots of products.

Thank you.

So let me kind of parse it as much as I can. So first, we live in NVIDIA World, and I think we all can safely say it's a high percentage of the GPUs we connect to. So there's really two use cases there. One is where NVIDIA provides the full vertical stack, and usually that's an envy link, so there's very little participation from Arista or anybody else in scale up, and generally, Arista does better there in the scale out of scale across domain. The second is the non-in-video accelerators, and you've heard me talk about our enthusiasm with the MI series from AMB. We're excited about the Google TPUs, you know, increasingly we see that as a formidable training processor, and we're very excited about the range of inference accelerators as well, and we have a number of partners working with, you can imagine, many of our customers are building their own in-house.

So now parsing your question a little bit, in that sector where it's non-in-video, Arista will be excited and will be working more closely, both in the scale up and scale out, in some cases to build custom racks with their custom processors, so that we can better tune our network with the behavior for their inference or training engines. In the interior cases, it's going to take a bit longer. I feel a little bit like, you know, two or three years ago when we were talking about Infiniban, and now we don't mention Infiniban, but it took two or three years to move to Ethernet, so it will take time to go from a proprietary scale up that's been around a long time with envy link to these other alternatives, even if it's, even if Ethernet's really good, but I expect us to do much better there.

Great. On the 1.6, I'll go back. History is always a great lesson for what will happen. If I look at the 800T, we were in trials in 23, 24, and the real ramp came to us in 25 and 26. It takes about six months, maybe even a year sometimes, and in this case, I think it will definitely take a half a year to get to production with the advent of liquid cooling and all the trials in the second half of this year. In terms of customers, it will be single-digit, but very large customers, and I think the real production will still be in 2027.

And you were asking Chantal a question on refresh. Over to you, Chantal.

Yeah, thank you. Thank you. I think, James, nice to hear from you. I think from the, you know, the piece that is in campus and that is in the AI, that in between goal that you're referring to, we are pretty excited. It will come down to that 1.1 billion raised, how that gets divided out, but where are we excited? We're super excited by the new local acquisition. We're very excited about what we're seeing on international growth. We're excited on the land and expand that we're seeing going both from campus to data center and data center to campus. So there's a lot there to be, you know, working through, and we'll see how much we get to through the end of the year.

Yeah, I was hoping Cal would be here and we could tell him we were excited about the front-end data center and the enterprise is doing really well, but Cal, this one's for you, this answer from Chantal.

So they improved about 100 sequentially despite some higher costs. So, A, maybe Chantal, can you help us think about the tariff refund benefit in the quarter? And then just sequentially was the list more mixed related, or was there any sort of pricing relief that's flowing through to offset some of the higher costs? Thank you. Yeah, it's a great question. I would say, you know, of the kind of the, I think versus guide is probably the best way to do it versus guide, you'll see there's probably 20 to 30 dips of tariff refund and the remainder more of a mixed equation. So I would put it at 20 to 30 for the quarter versus the guys.

Probably in the range of maybe 30 to 40 dips, if you look versus, you know, prior year, but it's in that kind of range and the rest would be custom mixed oriented.

I wouldn't put anything in there yet due to a price increase in that equation. No, we, as we said before, the price increases will really help or affect us only towards the end of the year and more like next year, because we're still going through a lot of back loss.

Oh, okay. Actually, that's a great question, Simon, because in a nutshell, the answer is this is our core or risk to product line we're talking about. Campus and AI are the newcomers to it. So if you take out the campus and the AI, which we believe will grow, then one of the things that our new software analyst tells us and questioning me on is why is your core data center front end so flat? And so I'd like to first make the observation that it isn't going to be flat, but we couldn't answer that question in Q1. We needed to go through the quarters to understand how the demand which translated to shift ends. So we fully expect some growth in the front end and core data center and the enterprise team.

And I just want to give a shout out to Chris Smith and Ashman, the campus team, and I'll give a shout out to Kumar. They've been on record ground and they've been growing tremendously well. So I expect great contributions from the enterprise into that incremental 1.1 billion number in addition to AI. And likewise, the routing routing is an adjacency both for edge routing use cases, but also some of the DCI and scale across. This is an incredibly large dam and getting larger. So we expect that 1.1 to consist of all our product sectors.

And we'll know exactly what it is once we ship it.

Yeah, that's a good question, Jeff. We definitely see the why scale across such a big thing. I was talking to one of the industry luminaries and he goes, well, this will be temporary because once people get capacity of compute and power, they'll go back and the answer is no, they won't because we're going to be in a constant state of scarcity for power, space, and compute. Getting all those free to lock in, whether you're a cloud titan, an AI titan, or a neo-cloud is going to be very, very difficult.

So currently our scale of cars is dominated by the cloud and AI titans, but I see no reason why it wouldn't apply to and we're already seeing it apply to several neo-clouds. So I would say less than the enterprise and definitely more than the neo-clouds and titans.

Thank you. Yeah, and one, the market research is suggesting this, I'm going to work backwards to answer your question. The market research is suggesting that it's going to be about a 15 billion dollar if you add some of the optics, maybe 20 billion dollars in 2030. So I would say in 2026 as we capture 1.2, this market is still maybe 3 to 4 billion, but quickly going to 15 in the next four years.

Yeah, good question, Lita. We continue to see the importance of blue box because of, I would say, three things, and then I'll tell you the counter view on why, you know, a white box may still be useful. The three things they value greatly with the EOS is operational excellence. They don't have to put a lot of staff, mostly these neoclabs don't have staff, and even if they're paying a little more for the capex, it more than makes up for the operating costs that they would incur. So that's a huge piece. The second is the AI features themselves. You heard can talk about a few of them. There's a tremendous depth and breadth to EOS that they can't recreate in a white box or they can figure out how it is.

And the third is reliability and vulnerability, which is becoming top and center. You can't put these things in the middle and have them blow up. So and, you know, our combination of both EOS and NetDI, a diagnostics layer, which plays in both the hardware and software, has been very compelling. So we continue to see that while it's interesting to talk about these open NOSs, that more and more customers want either EOS itself in its entirety or a hybrid combination often of an open NOS and EOS. Now that doesn't mean it's everywhere. If somebody wants to just go put in commodity hardware and get the ultra low price, such a market exists in every large market.

And it's not, it's there in campus, it's there in data center, and it'll be there in AI too. So the raw customers who will just prefer to go with the white box because it's ultra cheap or that have large amounts of staff like the Titans often do, and they can manage it and deal with it and they're built with it. But having said that, I think in this current scarcity of supply chain and people and needing to deploy AI fast, Earth is really winning out with the system-wide approach on EOS and good hardware. Okay, Regina, we have time for one last question.

Yeah. Hi, Adrian. Hope you're doing well. I think with the, you know, we still have the same kind of two quarters of visibility that we refer to. But what we would say is given where we are at this time of year, you know, J-Treen are guiding based on what we're confident we can get the supply for. And if the supply was to release a little more, there is an opportunity to do better in the year, so we'll have to wait and see.

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