Welcome to Broadcom Inc.'s first quarter fiscal year 2026 financial results conference call. At this time, for opening remarks and introductions, I would like to turn the call over to Ji Yoo, Head of Investor Relations of Broadcom Inc. Thank you, operator. Good afternoon, everyone. Joining me on today's call are Hock Tan, President and CEO; Kirsten Spears, Chief Financial Officer; Charlie Kawwas, President, Semiconductor Solutions Group; and Ram Velaga, President, Infrastructure Software Group. Broadcom distributed a press release and financial tables after the market closed, describing our financial performance for the first quarter fiscal year 2026. If you did not receive a copy, you may obtain the information from the investor section of Broadcom's website at broadcom.com. This conference call is being webcast live, and an audio replay of the call can be accessed for one year through the investors section of Broadcom's website. During the prepared comments, Hock and Kirsten will be providing details of our first quarter fiscal year 2026 results, guidance for our second quarter of fiscal year 2026, as well as commentary regarding the business environment. We'll take questions after the end of our prepared comments. Please refer to our press release today and our recent filings with the SEC for information on the specific risk factors that could cause our actual results to differ materially from the forward-looking statements made on this call. In addition to U.S. GAAP reporting, Broadcom reports certain financial measures on a non-GAAP basis. A reconciliation between GAAP and non-GAAP measures is included in the table attached to today's press release. Comments made during today's call will primarily refer to our non-GAAP financial results. I will now turn the call over to Hock. Thank you, Ji. Thank you everyone for joining us today. In our fiscal Q1 2026, total revenue reached a record $19.3 billion, and that's up 29% year-on-year and exceeding our guidance on the back of better than expected growth in AI semiconductors. This top-line strength translated into exceptional profitability with Q1 consolidated adjusted EBITDA hitting a record $13.1 billion, which is 68% of revenue. These figures demonstrate that our scale continues to drive significant operating leverage. We expect this momentum to accelerate as our custom AI XPUs hit their next phase of deployment among our five customers. Looking ahead to next quarter, Q2 2026, we're guiding for consolidated revenue of approximately $22 billion, which represents 47% year-on-year growth. Let me now give you more color on our Semiconductor business. In Q1, revenue was a record $12.5 billion, as year-on-year growth accelerated to 52%. This robust growth was driven by AI semiconductor revenue, which grew 106% year-on-year to $8.4 billion, way above our outlook. In Q2, this momentum accelerates. We expect semiconductor revenue to be $14.8 billion, up 76% year-on-year. Driving this is AI revenue growth, which will accelerate very sharply to 140% year-on-year to $10.7 billion. Our custom accelerator business grew 140% year-on-year in Q1. This momentum continues in Q2. The ramp of custom AI accelerators across all our five customers is progressing very well. For Google, we continue our trajectory of growth in 2026 with strong demand for the seventh generation Ironwood TPU. In 2027 and beyond, we expect to see even stronger demand from next generations of TPU. For Anthropic, we are off to a very good start in 2026 for 1 GW of TPU compute. For 2027, this demand is expected to surge in excess of 3 GW of compute. Our XPU franchise, I should add, extends beyond TPUs. Contrary to recent analyst reports, Meta's custom accelerator MTIA roadmap is alive and well. We're shipping now. In fact, for the next generation XPUs, we will scale to multiple gigawatts in 2027 and beyond. Rounding off for customers four and five, we see strong shipments this year and which we expect to more than double in 2027. We also now have a sixth customer. We expect OpenAI deploying in volume their first generation XPU in 2027 at over 1 GW of compute capacity. Let me take a second to emphasize our collaboration with these six customers to develop AI XPUs is deep, strategic, and multi-year. We bring to the partnerships, each of them unmatched technology in service, silicon design, process technology, advanced packaging, and networking to enable each of these customers to achieve optimal performance for their differentiated LLM workloads. We have the track record to deliver these XPUs at high volumes at an accelerated time to market with very high yields. Beyond technology, we provide multi-year supply agreements as our customers scale up deployment of their compute infrastructure. Our ability to assure supply in these times of constrained capacity in leading-edge wafers, in High-Bandwidth Memory, and substrates ensures the durability of our partnerships, and we have fully secured capacity of these components for 2026 through 2028. Consistent now with the strong outlook for our XPUs, demand for AI networking is accelerating. Q1 AI networking revenue grew 60% year-on-year and represented 1/3 of total AI revenue. In Q2, we project AI networking to accelerate a lot more and grow to 40% of total AI revenue. We are clearly gaining share in networking. Let me explain. In scale out, our first to market Tomahawk 6 switch at 100 Tbps, as well as our 200 G SerDes are capturing demand from hyperscalers, whether they use XPUs or GPUs this year. This lead will extend in 2027 with our next generation Tomahawk 7 featuring double the performance. Meanwhile, in scale up, as cluster sizes and our customers expand, we are uniquely positioned to enable these customers to stay on Direct Attach Copper through our 200 G SerDes. We next step up to 400G SerDes in 2028, our XPU customers will likely continue to stay on Direct Attach Copper. This is a huge advantage as the alternative of going to optical. It's more expensive and requires significantly more power. Reflecting the foregoing factors, our visibility in 2027 has dramatically improved. Today, in fact, we have line of sight to achieve AI revenue from chips, just chips in excess of $100 billion in 2027. We have also secured the supply chain required to achieve this. Turning to non-AI semiconductors. Q1 revenue of $4.1 billion was flat year-over-year in line with guidance. Enterprise networking, broadband, service storage revenues were up year-over-year, offset by a seasonal decline in wireless. In Q2, we forecast non-AI semiconductor revenue to be approximately $4.1 billion, up 4% from a year ago. Let me now talk about our Infrastructure Software segment. Q1 Infrastructure Software revenue of $6.8 billion was in line with our guidance, up 1% year-on-year. For Q2, we forecast Infrastructure Software revenue to be approximately $7.2 billion, up 9% year-on-year. VMware revenue grew 13% year-on-year. Bookings continued to be strong, and total contract value booked in Q1 exceeded $9.2 billion, sustaining an ARR annual, which is annual recurring revenue growth of 19% year-on-year. Let me reinforce that this growth in our Infrastructure Software business reflects our focus and investments in foundational infrastructure. Our Infrastructure Software is not disrupted by AI. In fact, VMware Cloud Foundation, VCF, is the essential software layer in data centers integrating CPUs, GPUs, storage, and networking into a common high-performance private cloud environment. As the permanent abstraction layer between AI software and physical chips, silicon, VCF cannot be disintermediated or replaced. It allows enterprises, in fact, to scale complex generative AI workloads effectively with agility that hardware alone cannot provide. We are confident that the growth in generative and agentic AI will create the need for more VMware, not less. In summary, let me put it all together for Q2 2026, we expect consolidated revenue growth to accelerate to 47% year-on-year and reach approximately $22 billion. We expect adjusted EBITDA to be approximately 68% of revenue. With that, let me turn the call over to Kirsten. Thank you, Hock. Let me now provide additional detail on our Q1 financial performance. Consolidated revenue was a record $19.3 billion for the quarter, up 29% from a year ago. Gross margin was 77% of revenue in the quarter. Consolidated operating expenses were $2 billion, of which $1.5 billion was R&D. Q1 operating income was a record $12.8 billion, up 31% from a year ago. Operating margin increased 50 basis points year-over-year to 66.4% on favorable operating leverage. Adjusted EBITDA of $13.1 billion or 68% of revenue was above our guidance of 67%. Let's go into detail for our two segments. Starting with semiconductors. Revenue for our Semiconductor Solutions segment was a record $12.5 billion, with growth accelerating to 52% year-on-year, driven by AI. Semiconductor revenue represented 65% of total revenue in the quarter. Gross margin for our Semiconductor Solutions segment was up 30 basis points year-on-year to approximately 68%. Operating expenses of $1.1 billion reflected increased investment in R&D for leading-edge AI semiconductors and represented 8% of revenue. Semiconductor operating margin of 60% was up 260 basis points year-on-year, reflecting strong operating leverage. Moving on to Infrastructure Software. Revenue for Infrastructure Software of $6.8 billion was up 1% year-on-year and represented 35% of revenue. Gross margin for Infrastructure Software was 93% in the quarter, and operating expenses were $979 million in the quarter. Q1 software operating margin was up 190 basis points year-on-year to 78%. Moving on to cash flow. Free cash flow in the quarter was $8 billion and represented 41% of revenue. We spent $250 million on capital expenditures. We ended the first quarter with inventory of $3 billion as we continue to secure components to support strong AI demand. Our days of inventory on hand were 68 days in Q1 compared to 58 days in Q4 in anticipation of accelerating AI semiconductor growth. Turning to capital allocation. In Q1, we paid stockholders $3.1 billion of cash dividends based on a quarterly common stock cash dividend of $0.65 per share. During the quarter, we repurchased $7.8 billion or approximately 23 million shares of common stock. In total, in Q1, we returned $10.9 billion to shareholders through dividends and share repurchases. In Q2, we expect the non-GAAP diluted share count to be approximately 4.94 billion shares, excluding the impact of potential share repurchases. We ended the first quarter with $14.2 billion of cash. Today, we are announcing our board of directors has authorized an additional $10 billion for our share repurchase program effective through the end of calendar year 2026. Moving on to guidance. Our guidance for Q2 is for consolidated revenue of $22 billion, up 47% year-on-year. We forecast semiconductor revenue of approximately $14.8 billion, up 76% year-on-year. Within this, we expect Q2 AI semiconductor revenue of $10.7 billion, up approximately 140% year-on-year. We expect infrastructure software revenue of approximately $7.2 billion, up 9% year-on-year. For your modeling purposes, we expect consolidated gross margin to be flat sequentially at 77%. We expect Q2 adjusted EBITDA to be approximately 68%. We expect the non-GAAP tax rate for Q2 in fiscal year 2026 to be approximately 16.5% due to the impact of the global minimum tax and the geographic mix of income compared to that of fiscal year 2025. That concludes my prepared remarks. Operator, please open up the call for questions. Thank you. To ask a question, you will need to press star one one on your telephone. To withdraw your question, press star one one again. Due to time restraints, we ask that you please limit yourself to one question. Please stand by while we compile the Q&A roster. Our first question will come from the line of Blayne Curtis with Jefferies. Your line is open. Good afternoon, and thanks for taking my question. It's just a clarification, then the question. Just clarification, Hock, on the greater than $100 billion. I think you said AI chips. I just want to make sure you're clarifying the difference between the ASICs and networking and didn't know how rack revenue fits in there. The question, you know, I think the biggest overhang on the group here is that, you know, you grew roughly double in the quarter AI. I think that's what, you know, kind of cloud CapEx is growing this year. I'm just kind of curious your perspective, you know, I think given the outlook that you have for 2027, you should be a share gainer. I'm just kind of curious your perspective in terms of the pessimism that investors kind of think of that the hyperscalers need to get a return on investment in this year or next year, or if not, the year after. I'm just kind of curious your perspective, how you factor that into your outlook. Well, what we see, what we have seen over the last few months and continue to see even more is. It's really not so much talking about hyperscalers. Our customers, Blayne, is limited to those few players out there, and some of them are hyperscalers, some of them are not hyperscalers, but they all have one thing in common, which is to create LLMs, productize it, and generate platforms. Be it for enterprise consumption in code assistance of agentic AI, or be it for consumer subscription that we know about. Whatever it is that few prospects and many of whom are customers now, who are creating this, whether it's generative AI, agentic AI, but creating a platform. That's our customer. With respect to each of those guys, we are seeing very stronger and stronger demand for compute capacity. For training, which is something they do need constantly, but what is very, very interesting and surprising to us is very much for inference in order to productize the LLMs, their latest LLMs they create and monetize it. That inference is driving a substantial amount of compute capacity, which is great for us because these players, these five customers, six customers of ours, are on the path to creating their own custom accelerators, and beyond that, their own design architecture of networking clusters of those custom accelerators. I think we're going to see demand keeps picking up as we've heard announcements in the past six months. Now, to clarify your first part, Blayne, when I say we forecast, we have a line of sight that our revenue in 2027 will be significantly in excess of $100 billion, I'm focusing on the fact that these are pretty much all based on chips. Whether they are XPUs, whether they are switch chips, DSPs, these are silicon content we're talking about. Thanks so much. One moment for our next question. That will come from the line of Harlan Sur with JP Morgan. Your line is open. Yeah, good afternoon. Thank you for taking my question, and congratulations to the team on the strong results. Hock, you know, there's been a lot of noise around CSPs and hyperscalers embarking on their own internal XPU, TPU design efforts, right? We call it COT or customer-owned tooling. This is not a new dynamic with ASICs, right? I think the Broadcom team has been through this COT competitive dynamic before over the 30 years, right? That you've been a leader in the ASIC industry, very few of these COT initiatives have ever been successful. Now on AI, some of these COT initiatives are coming to the market now, but it looks like they're at least 2x less performant than your current generation solutions, 2x less complex in terms of chip design complexity, packaging complexity, IP. Maybe just a quick two-part question. Hock, one for you is, given your visibility into next year, do you see these COT Science projects taking any meaningful TPU, XPU share from Broadcom. Maybe the second quick question for either you or Charlie is, given that Broadcom's TPU, XPU programs from a performance complexity IP perspective are 12 to 18 months ahead of any of these COT programs, how does the Broadcom team widen this gap further? Well, that's a great question. You know, it fits into that. I purposely took the time in my opening remarks to say that when any of our, any, I guess, hyperscaler or LLM developer tries to create, become self-sufficient entirely in creating what you call a customer-owned tooling or COT model, they face tremendous challenges. One is technology, which is as a technology as it relates to creating the silicon chips, and particularly in XPUs, that they need to do the computing and that, and that they, that's needed to optimize and run, train, and inference on the workloads they produce or their LLM. It's, it's the, it's that technology we talked about comes from, comes in from different dimensions. You need the best silicon design team around. You need cutting-edge, really cutting-edge SerDes, very advanced packaging. Now for, and most, and just as much you need to understand how to network clusters of them together. We've been doing this for 20 years, more than 20 years in silicon. In this particular space today, in generative AI, if you're trying to as an LLM player to do your own chip, you cannot afford to have a chip that is just good enough. You need the best chips that is around because you're competing against other LLM players, and most of all, you're also competing against NVIDIA, who is by no means letting down their guard. They are producing better and better chips if with every passing generation. You have to, as an LLM trying to establish your platform in the world, have to create chips that are better than if not competitive with not just NVIDIA, but all the other LLM platform players that you're competing against. For that, you really need, I believe, and we see that firsthand, a partner in silicon with the best technology, IP, and execution around. Very modestly, I would say we are by far way out there, and we will not see competition in COT for many years to come. It will come eventually, but we're still a long way off because the race which we see continues. One thing I add in there that is particularly unique to us, when you create a silicon, you really have to get it up and running in high volume in production very quickly, time to market. We are very, very experienced in doing that. Anybody can design a chip in a lab that works well. Can you produce 100,000 of those chips quickly at yields that you can afford? We don't see too many players in the world that can do that. Charlie? I think you covered it very well, Hock. Thank you, Hock. Thank you, Charlie. One moment for our next question. That will come from the line of Ross Seymore with Deutsche Bank. Your line is open. Hi. Thanks for letting me ask a question. Hock, in your script, you leaned a little bit more into the networking differentiation than you have in the past. I guess kind of a short-term and a longer-term question. The short term is, what's driving that up to 40% of the AI revenues? The longer-term question is that percentage mix in that $100 billion+, is that changing now? What sort of leadership do you expect to maintain in that business, whether it's scale out or scale up? Is your leadership position there helping on your XPU side as you can optimize across both the compute and the networking sides? Well, let's address the first part of that fairly complex question first, Ross. Yes, in networking, especially the, you know, with the new generation of GPUs, XPUs that are coming out there, we're running at 200 G SerDes out there in terms of bandwidth. The Tomahawk 6 that we introduced over six months ago, in fact, over, closer to nine months ago, we're the only one out there. Our customers and the hyperscalers wants to run with the best networking with the most bandwidth out there for their clusters. We are seeing huge demand for this only 100 Tbps switch out there. That's driving a lot of the demand. Couple that with running bandwidth on scaling out Optical transceivers at 1.6 Tb. We are, again, the only player out there doing DSP at 1.6 Tb. That combination is driving, I would say, the growth of our networking components even faster than our XPUs are growing, which is already pretty remarkable. That's what you're seeing. At some point, I would think these things will settle down, though we're not slowing down the pace because as I said, next year in 2027, we'll launch next generation Tomahawk 7, two extra performance, and we'll probably be the, by far, the first out there, and then we'll continue to sustain that momentum. At the end of the day, to answer your question, yeah, expect as a composition of our total AI revenue in any quarter that we'll be ranging between probably 33%-40% AI networking components. Great. Thanks, Hock. Thanks. One moment for our next question. That will come from the line of CJ Muse with Cantor Fitzgerald. Your line is open. Yeah, good afternoon. Thank you for taking the question. I'm curious, you know, how are you thinking about the move to disaggregate prefill and decode from the GPU ecosystem and the impact to customer silicon demand? Are you seeing any potential changes in sort of the relative mix between GPUs and customer silicon? I'm not sure I fully understand your question, CJ. CJ, could you clarify what you mean disaggregate? Sure. You know, pushing off, workloads to CPX for prefill and, working off of Groq for decode, and, you know, having that disaggregated kind of world, and does that put, you know, any pressure in terms of, the demand for custom versus going with, you know, a full GPU stack? Okay. I get what you mean. That word disaggregation kind of threw me off. In a way, what you're really saying is, how is the architecture of AI accelerator, be it GPU or XPU evolving as workloads starts to evolve? That's what we are seeing very much in particular. The one-size-fits-all with general purpose GPU gets you only that far. It can still keep going on because you can still run different workloads, like you run mixture of experts, even though you want to run mixture of experts with sparse cores to be very effective, you hear the term. In a GPU, you're designed for dense matrix multiplication. You do it with software kernels, but it's not as effective as you'd hard-code it in silicon and make those XPUs purposely designed to be much more performing for Mixture of Experts workloads, say. The same applies for inference. What that drives down to is you start to see designs of XPUs become much more customized for particular workloads of particular LLM customers of ours. The design starts to depart from what is the traditional standard GPU design. Why, as we always indicated before, XPUs will eventually be more the choice simply because it will allow flexibility in making designs that work with particular workloads, one for training even, and one for inference. As you say, one perhaps will be better at prefilling and one to be better at post-training or reinforcement learning or test-time scaling. You can tweak your TPUs towards the XPU, sorry, Freudian slip, to a particular kind of workload LLM that you want. We're seeing that. We're seeing that roadmap in all our five customers. One moment for our next question. That will come from the line of Timothy Arcuri with UBS. Your line is open. Thanks a lot. I had just a question on sort of the puts and takes on gross margin as you begin to ship these racks. I mean, obviously, it's gonna pull the blended margin down, but I'm wondering if there's any guardrails you can give us on this. It seems like the racks are maybe 45%, 50% gross margin. I guess, should we think about that pulling gross margin down like 500 basis points roughly as these racks begin to ship? I guess, you know, part of that, Hock, is there some, like, floor to the gross margin, you know, below which you wouldn't be willing to do, you know, more racks? Thanks. I hate to tell you that you must be a bit hallucinating. Our gross margin is solidly at the number Kirsten reports. We will not be affected by the gross margin and by more and more AI products going out. We have gotten our yields, we've gotten our cost to the point where the model we have in AI will be fairly consistent with the models we have in the rest of the Semiconductor business. I would agree with that. I think on further study, relative to even comments that I did make last quarter, the impact relative to our overall mix is actually not gonna be substantial at all, so I wouldn't worry about it. Oh, okay. Thank you so much. One moment for our next question. That will come from the line of Stacy Rasgon with Bernstein. Your line is open. Hi, guys. Thanks for taking my question. I don't know if this is for Hock or Kirsten, but I wanted to dig in a little more to this substantially more than $100 billion next year. I'm trying to just count up the gigawatts. I counted, I don't know, eight or nine. You have three from Anthropic, one from OpenAI, that's four. You said Meta was multiple, at least two. That gets me to six. Google, I figure, should be bigger than Meta, like at least three. You know, that's nine, you got a few others. I thought that your content per gigawatt was sort of, you know, call it in a $20 billion per gigawatt range. I guess what I'm asking is my math around the gigawatts you plan to ship in 2027 correct? How do I think about your content per gigawatt as that ships? Maybe it will be quote-unquote substantially more than $100 billion. Stacy, you have a very interesting perspective, and I gotta admire you for that. You're right. You can look at it at gigawatts, which is the right way to look at it instead of dollars, 'cause that's how we sell our chips to. You have to realize we depending on our LLM customer, our six customers now. Sorry, not five, six. Six. The dollars per gigawatt chip dollars varies, sometimes quite dramatically. It does vary. You're right. It's not far from the dollars you're talking about. If you look at it by gigawatt in 2027, we are seeing it getting close to 10 GW. Got it. That's very helpful. Thank you. Sure. Our next question that will come from the line of Ben Reitzes with Melius Research. Your line is open. Hey, thanks. Hock, great to be speaking with you. Wanted to ask you about your commentary about supply visibility on those four major components through 2028. You know, A, how'd you do it? This is probably the. You know, you're the first one to kinda go out through the 2028 timeframe. Secondly, after this astounding growth in 2027 for your AI business, do you have enough visibility to grow quite a bit in 2028, based on the supply that you see and that kinda commentary? Thanks a lot. The best answer is, yeah, you're right. We anticipate this sharp accelerated growth. Nobody could anticipate the rate of growth it's showing, but we kinda anticipate a large part of it, or I guess, over the longer than six months. We were early in being able to lock up T-glass. The infamous T-glass you all heard about. We were very early. We've locked up substrates. We have worked on our good partners on the rest of the stuff we talked about. The answer to your question is, it's somewhat anticipation early and the fact that we have very good partners out there in these key components. What else can I say except that, yes. Charlie, you wanna add anything? Yeah, just maybe a couple of quick ones. I think you covered that piece really well. I think, Ben, the other piece that's really important, as Hock said, we build custom silicon for six customers. We have very deep strategic multi-year engagement with them. They share with us, because of this custom capability, exactly what they anticipate at least over the next two to three years, sometimes four years. Because of that's exactly why we went and secured all the elements Hock talked about. When we secure this, it requires investments with these partners, sometimes developing not just more capacity, but the right technology and capacity for that. We have to go secure it for multiple years. You're right. We're probably the first one to secure that up to 2028 or beyond. Can you grow in 2028 with what you see in supply? Sorry to sneak that in. Yes. Thank you. Thank you. Our next question that will come from the line of Vivek Arya with Bank of America Securities. Your line is open. Thanks for taking my question. Hock, I just wanted to first clarify the Anthropic project you're doing, the $20 billion or so for a gigawatt this year. How much of that is chips and how much of that is kind of racks? I just wanted to understand when you say $100 billion in chips, is it a distinction between chips versus your rack scale projects? Just that project is supposed to triple next year. My question is, you know, your AI business is transitioning from kinda one large customer that was, you know, where you had kind of exclusive partnership to now multiple customers who are using multiple suppliers. How do you get the visibility and the confidence about, you know, how your share will progress? At these multiple customers, because it's, you know, it's a very kind of fragmented engagement that they have across a whole range of cloud service providers and so on. What are you doing to ensure that, you know, you have solid visibility, and, you know, the right market share at this fragmented set of customers who are using multiple suppliers? Vivek, you have to understand one thing about first, as Charlie correctly put down very nicely. We only have very few customers, to be precise, six. For the volume we are driving, the revenue we're driving, we only have just six. Prior to that, even less recently. Number two, also have to understand with the dollar each of them spend and the criticality of the nature of what they're embarking on, and that's why I threw out this term. Meta has MTIA. That's their custom accelerator program. To them, as to every one of my customers in this space, it's a strategic play. It's not optionality. To them, long-term, short-term, medium-term is strategic, extremely strategic. They don't stop, they are very clear, each of them, on where they want to position this custom silicon within their trajectory of their LLM development and the trajectory of how they develop inference for productizing those LLM. That part, we have very clear visibility. Anything else on GPU, using Neocloud, using cloud business, these are all transactional and optionality. You point out very correctly. It seems very confusing. Trust me, not for us, nor those customers we have. They're very strategic, they're very targeted, and they know exactly what they're building up and how much capacity they want to build up each year. The only thing they think about is can they do it faster. Otherwise, it's very strategic and targeted on a projected roadmap. Anything else you see in the mix is pure, I call it, opportunistic for these guys, the optionality. It's very clear. On the clarification, Hock, Anthropic racks versus chips? Thank you. I'd rather not answer that, but we're okay. As Kirsten said, we're good on our dollars and margin. Thank you. Thank you. Thank you. Our next question that will come from the line of Tom O'Malley with Barclays. Your line is open. Hey, guys. Thanks for taking my questions. I have one for Hock and one for Charlie. Hock, I know you're very specific, in particular, about what you put in the preamble, and you noted that customers are staying at Direct Attach Copper through 400 G SerDes. Is there any reason you're pointing that out in particular, especially as a leading pioneer in CPO? On Charlie's side, as you're adding more customers here, I would imagine customers that design ASICs with you are gonna use scale-up Ethernet. Maybe talk about scale-up protocols and how you see Ethernet developing there as well. Thank you. Okay. No, I'm just highlighting the fact that on networking, our technology is really very, very uniquely positioning us to help our customers. More than our customers, even customers using general purpose GPUs, not just XPUs. Which is that, you know, if you are running a trying to create LLMs and running, creating your own AI data centers and designing it, architecting it, you truly want larger and larger domains or clusters for. You really want to connect XPUs to XPUs directly where you can. The best way to do that is use Direct Attach Copper. That's the lowest latency, lowest power, and lowest cost. You want to keep doing that, especially in scale-up, as long as possible. In scaling out, we're past that. We use optical. That's fine. I'm talking about scaling up in a rack, in a cluster domain. You really want to use Direct Attach Copper as long as you can. We are still based on our technology that Broadcom has with on, especially on connecting XPU to XPU or even GPU to GPU. We can do it with copper, we can push the envelope from 100G to 200G to even to 400G. We have SerDes now running 400G that can drive distance on a rack to run copper. All I'm trying to say is you don't need to go run into some bright, shiny objects called CPO, even as we are the lead in CPOs. CPOs will come in its time, not this year, maybe not next year, but in its time. Charlie? Yeah. No. Well, well said, Hock. On the question of Ethernet, with the debut of the cloud Ethernet became the de facto standard in every cloud for the last two decades. If you look at, the debut of the, backend networks, as Hock articulated, there was two years ago a big fight about what protocol should be used to achieve the latency, the scale necessary on scale-out. The industry at the time, 24 months ago, was not clear. We were clear. We were very clear actually about what the answer should be. Again, because of the deep engagements with our partners, they made it very clear to all of us and the industry, GPU or XPU, that Ethernet is the scale-out of choice. Check mark. Today, everyone is talking about scaling out with Ethernet. When it comes to scale up, yes, exactly like what happened three years, four years ago, on scale up now, what's the right answer for this? What we're hearing consistently and what we're seeing is the right answer is Ethernet. As you know, last year, we've announced with multiple hyperscalers and many of our peers in the semiconductor industry that Ethernet scale-up is the right choice. That's what we believe will happen. Time will tell, but a lot of the XPU designs we're doing, we're being asked to scale up through Ethernet, and we're happy to enable that. Thank you both. Thank you. Our next question that will come from the line of Jim Schneider with Goldman Sachs. Your line is open. Good afternoon, and thanks for taking my question. Hock, it was helpful to hear you discuss the progress of your other full custom XPU engagements outside of TPUs. As we look into next year, is it fair to assume that those are mostly targeting inference applications or not? Could you maybe qualitatively speak to either the performance or cost advantages relative to GPUs that is giving those customers the ability to forecast in such a large scale? Thank you. Thanks. It's, you know, most of our customers begin with inference, simply because that tends to be, you know, that tends to be the easiest path to start on. Not necessarily from anything else than the fact that, you know, when you do inference, it's less compute. The question is, do you need this general purpose, massive dense matrix multiplication GPUs when you can do it more efficiently, effectively with customs inference, silicon XPUs that do the job better or just as well, much cheaper cost, lower power. That's what we find these customers starting with. They are now in training, and many of our XPUs are used both in training as well as inference. By the way, they are interchangeable, just a GPU can be used not just for training, which they are perhaps more perfectly suited to, but they can be used for inference. What we're seeing is our XPUs are used for both, and we are seeing that going on. We're also seeing very rapidly more for those customers who are much more matured in the progression I talked about in their journey towards complete XPU, that they will start to develop two chips each year simultaneously, one for training, one for inference to be specialized. Why? Because what we're seeing very clearly for these players, LLM players, is you do the training to achieve a higher level of intelligence, smarts for your LLM. Great. You get yourself a great LLM state-of-the-art or more. You've got to productize it, which means inference. You can't then decide at that time you got your model going as the best, because if you decide then to do your inference productization, it'll take you a year at least to productize. At which time, somebody else is gonna create an LLM better than yours. There's a leap of faith here that when you do training to create the next level of super intelligence in your LLM, you have to be investing simultaneously in inference, both in terms of the chip and the capacity. Our visibility is really coming out better and better as we find those six customers get more matured in their progression towards better and better LLMs. Yeah, that is the trend we are seeing. It's not happening to all our six customers yet, but we are seeing a majority of them headed in that way right now. Thank you. One moment for our next question. That will come from the line of Joshua Buchalter with TD Cowen. Your line is open. Hey, guys. Thanks for taking my question, and congrats on the results. Appreciate all the details on the expectations for deployments at specific customers. I was hoping you could just maybe reflect on how visibility has changed over the last one to two quarters that gave you the confidence to give us more details. On a specific one, you mentioned greater than 1 GW for OpenAI in 2027. With that deal being for 10 GW through 2029, that implies a pretty sharp inflection. I guess in 2028. Is that the right way to think about it? Was that sort of always the plan? Thank you. Yes. Well, yeah, this as you all seen and you all know, in this generative AI race that we are in now, and I shouldn't use the word race, let's call it progression among the few players we see here. I mean, it's a competition. Each is trying to create an LLM better than the other and more tailored for specific purpose, be they enterprise, be they consumer, be they search. Each one is trying to create it more and more. All of that requires not just training, which is important to keep improving your LLM models, but inference for productization and monetization of your LLMs. We are and probably call it the fact that we've been engaged with some of them now for more than a couple years. We're getting better and better visibility as they have more and more confidence that the XPUs they are working on with us is achieving what they're getting at. As they get the sense that the XPUs they are working on with the, with the software, with the algorithm they needed, that they are having more confidence that this XPU silicon is what they need. They work, and it gets better and better. As it get better, we get more visibility, as Charlie puts up perfectly. At the end of the day, we only have six guys to work on. These six guys are all, as I said, very look at XPUs and AI in a very strategic manner. They don't think one generation at a time. They think multiple generation, multiple years. In spite of all the hubris, noise out there on what's available, they think very long-term on how they deploy the XPUs they develop with us, how they deploy in achieving better and better LLMs that they want to create, and more than that, how they deploy in monetizing. We are part of their strategic roadmap. We are not in just optionality of, "Oh, shall I use a GPU? Shall I use it in the cloud because I need to train for six months?" No, this is more than that. The investment these guys are making are long-term, and it's great to be part of that long-term roadmap as opposed to a transactional roadmap. The noise, as I answered an earlier question, is there's a lot of noise that mix up short-term transactions with what is long-term strategic positioning of our business and our product. To sum it all, I think our business in XPUs is a strategic, sustainable play for all the six customers we have today. Thank you. Thank you. That is all the time we have for Q&A today. I would now like to turn the call back over to Ji Yoo for any closing remarks. Thank you, Cherie. Broadcom currently plans to report its earnings for the second quarter of fiscal year 2026 after the close of market on Wednesday, June 3rd, 2026. A public webcast of Broadcom's earnings conference call will follow at 2:00 P.M. Pacific. That will conclude our earnings call today. Thank you all for joining. Cherie, you may end the call. This concludes today's program. Thank You all for participating. You may now disconnect.