LIVE: Q2 2026 Broadcom Earnings Conference Call

Thank you, Ji. Well, in Q2, revenue was a record $15 billion as we grew 79% year over year. Driving this growth was AI semiconductor revenue at a record $10.8 billion, up 143%. Oops, sorry. Well, let me restart. Thank you, Ji. Thank you everyone for joining today. In our fiscal Q2 2026, total revenue reached a record $22.2 billion, up 48% year over year, above our guidance on strength in AI semiconductors. Q2 operating margin was a record 67% and adjusted EBITDA was a record 69% of revenue, which was above our guidance. Even as our revenue scales up massively driven by AI, our operating and EBITDA margins remain strong and stable. Turning to semiconductors, Q2 revenue was a record $15 billion as I said before, as we grew 79% year over year. Driving this growth was AI semiconductor revenue at a record $10.8 billion, up 143% year over year and above our outlook. Networking represented almost 40% of our Q2 AI revenue. Demand for XPUs and networking is simply insatiable. During the quarter, bookings for AI semiconductors were over $30 billion against the $10.8 billion shipped in the second half of 2026. We expect AI semiconductor revenue to double from the first half we shipped this year. Consistent with this trend, in Q3 we expect AI semiconductor revenue to accelerate to $16 billion, up over 200% year over year. For the full year 2026, we expect to achieve AI semiconductor revenue of $56 billion, up approximately 180% from fiscal 2025. Now, we expect this momentum to continue into fiscal year 2027 and reiterate our AI semiconductor revenue guidance to be in excess of $100 billion. We expect AI semiconductor revenue growth to continue in fiscal 2028 based on the following initiatives we have with our six core customers. As you are aware, with Google we announced in April that we entered into a long-term agreement to develop and supply multiple generations of TPUs and AI networking. Our relationship continues to be strategic and very substantial as we continue to deliver vastly superior technology and execution compared to other alternatives. This ability to provide differentiated value to Google ensures that our business will sustain and grow for the foreseeable future. For Anthropic, as you know, for 2026 we are providing access to Broadcom TPU-based compute of over 1 gigawatt. In April, we entered into an agreement to enable Anthropic to access another 5 gigawatts of next-generation TPU-based compute beginning in 2027. For OpenAI, we have delivered silicon and we are on track for production late 2026. We have a contractual commitment to deploy 1.3 gigawatts in 2027 as part of the larger 10 gigawatt by 2029 agreement we announced last year. For Meta, in April we announced a partnership to deliver multiple generations of MTIA XPUs, and under this agreement we expect to deploy 3 gigawatts through the end of 2028. The initial order for 1 gigawatt, which includes XPUs and our networking, has been received and will start delivery in the second half of 2027. For our other two customers, we expect shipments to begin late 2026 and accelerate into 2027. To date, we have received purchase orders totaling $6 billion. While we have significant IP and execution leadership in XPUs, networking is key to building scalable XPU and GPU clusters. And here in networking, we have at least one generation of technology and product leadership for scale up within racks. We enable direct attach copper based on our industry-leading 200G and 400G SerDes, driving co-packaged copper with Ethernet and PCI Express switches for scale out between racks. We have been shipping the industry's only 100 terabit Ethernet switch, the Tomahawk 6, for over a year. We will now be taping out our next generation 200 terabit switch this quarter, and in CPOs, which is co-packaged optics, 1.6 terabit DSPs, CW and EML lasers. We are the de facto standard in the industry to extend AI clusters across data centers. We remain the industry leader with our Jericho 3 and Jericho 4 fabric solutions, enabling the world's largest deployments at multiple hyperscalers. Our strategic vision is to bring together Broadcom's leading technology and investor partners with the strongest balance sheets to deliver at scale sufficient compute capacity at the lowest cost and power for the leading AI frontier labs, including Anthropic and OpenAI. To deliver this vision, we are creating the AI XPV platform with Apollo and Blackstone and other leading investors to deploy more than 20 gigawatts of compute capacity through 2028. The first tranche of this platform, valued at $35 billion, is in fact currently being launched by Apollo. Now turning to non-AI semiconductors, Q2 revenue of $4.2 billion was up 6% year over year. Bookings during the same period exceeded $6 billion, which is a clear indication we are on the path towards a full cyclical recovery. Broadband, server storage, and enterprise networking together were up, partially offset by seasonal decline in wireless. Consistent with this trend, in Q3 we forecast non-AI semiconductor revenue to be approximately $4.5 billion, up 12% from a year ago. In summary, we expect Q3 semiconductor revenue to be $20.5 billion, up 124% year over year. Let me turn to infrastructure software segment. Q2 software revenue of $7.2 billion was up 9% year over year, in line with our guidance. Bookings continue to be strong as we sustain ARR growth of 17% year over year. For Q3, we forecast software revenue to be approximately $8.9 billion, up 31% year over year. We just released VMware Cloud Foundation 9.1, focused on improving infrastructure efficiency, security, and support for enterprise AI inferencing workloads, with strong server demand globally. The deployment of VCF 9.1 for on-prem cloud computing is extremely strong, driving robust revenue growth. This release adds heterogeneous compute support across GPUs and CPU architectures including AMD, Intel, and Nvidia platforms, enabling enterprise cloud customers to run AI, Kubernetes, and traditional virtualized workloads on a common private cloud environment. So to sum it up for Q3 2026, we expect our consolidated revenue to grow to $29.4 billion, up 84% year over year. We expect operating margin to be stable at approximately 67% of revenue and adjusted EBITDA to be at approximately 68% of revenue. And with that, let me turn the call over to Kirsten.

That's a very complicated set of number questions to begin with. Let's start with 26. If you do the math, basically 2x the first half. We shipped in total AI revenue something in the range of $19 billion if you want to be precise. So if you do what I indicate and 2x that in the second half, you get to pretty much in the range of what we're talking about, which is around $56 billion, Harlan. So that set of numbers still ties up very well. Now your bigger question on the second, which you went into very detailed analysis of, is yes, we keep the momentum going. As we expect to see in 2027, what we will see in 2027 is continued growth of the level we're talking about. And if you drive on that basis of what we're seeing here, almost 2x what 2026 will be, I think you will easily see that 2027 will exceed very easily $100 billion in 2027, which is pretty much what we indicated last quarter and we are continuing to say that it will be over $100 billion in 2027. So in that sense, if anything else, it might be based on what we're doing, very much on track if not stronger, but we're not trying to guide you every quarter what 27 would be like. So we basically say it continues to be in excess of $100 billion in 27, but it is on the same trajectory as we are seeing in the back half of 26.

Well, you know, it's a very, very strong agreement, and it basically reflects the strength of the partnership we have, simply because of the products we do, the multi-generational products and the intellectual property we deploy into this whole program. To answer your question specifically, it's a commitment that is very substantial in dollars, very, very substantial amount of dollars. Now, we also accept the fact that while we like to win every design in that program, we also accept the fact that given the rate of growth of consumption and development and consumption of AI compute even by our partner Google, we fully expect that there'll be some diversity of sources for them, but our commitment from them is a very substantial dollar amount.

I mean, Ross, as Kirsten said in her remarks, structurally the semiconductor margins remain very stable and very solid. It's the mix, particularly the mix between software and non-AI to the very high, rapidly growing AI semiconductor, that is just diluting gross margin.

No, racks. No, racks is only chip business only. We only chips.

Sure. Well, I think the accelerating part, if you talk about power, realize one thing is the dollars per gigawatt, the content dollars of racks per gigawatt, is not accelerating that much because you are creating chips that each individual chip is driving higher and higher power. So you're driving less chips, though the ASP of each chip is going up in price. So dollars per gigawatt, billions of dollars per gigawatt, is relatively stable, but the number of gigawatts will accelerate as I think some of my remarks indicate. And that's what we are saying: the amount of gigawatts required, compute capacity as measured by number of gigawatts, is growing very fast, and we're seeing that. Particularly to the point where for even two of our customers, which is Anthropic and OpenAI, for which we're creating this platform to enable them to run sufficient compute power, we're talking about capacity as measured by gigawatt power that are way ahead of what we expected say 6 months ago. And that's just these guys. We have not talked about the consumption beyond the XPV platform. We have announced here from our other customers, which is Google's own internal workloads, Meta's workloads, and any other customer and the other two customers that we have. So fold that in and you're talking about gigawatts in totality. If you ask about 27 or 28, that will continue to grow. We expect in fact 28 to be a substantial growth from what we are forecasting in 27.

Getting supply is not just about dropping money, though that does work. No, we are very comfortable that we have been able to secure supply of the types you mentioned about for needs 26, 27, working on 28 and 29 right now.

Customers have been coming to us incrementally over the last few months. Expect that to continue, and by and large, yes.

Good question. For 27, we indicated about 10 gigawatt shipment in 27. That's still very much intact. We are planning to ship 10 gigawatts in 27, and nothing has changed. Back half loaded to that extent, yes, which really provides an interesting trajectory into 28 with this back half trajectory. So 28 we expect a lot more gigawatts.

That's a hell of a great question, Jim. It's just very difficult to answer because there are quite a few moving parts there. One of which is, as more and more of our customers turn to XPUs, obviously XPUs use a lot of our networking components across the board. So that's great for us and drives increase in consumption. But it also means that we have been able to sell networking to non-XPU footprints. So that part of it will dilute the growth rate. And this 40% I consider as a situation where stars are aligned, where we are shipping a lot of networking to non-XPU while the growth of XPU is allowing us to grow this networking business to our XPUs, and we get to 40%. But I see that as probably as high as that percentage of total AI revenue would go. Not the first time I indicated that the more expected percentage as a share of total AI revenue for networking would be closer to around 30%.

Can you repeat that question, especially at the front end? I didn't quite get what you're saying here. I don't want to answer the wrong way.

Oh, I have to correct you on that. The deal with Anthropic that we basically disclosed in our 8K recently, the deal we did with Anthropic is we use our TPU chips that we developed to provide the compute capacity to Anthropic. We want that it wasn't backstop in that sense. We were the ones providing the chips to Anthropic. We were the ones providing the compute capacity to Anthropic.

Well, no, I don't think so. I think our business model is actually very straightforward, which is we are developing XPUs, custom AI accelerators, for use by our customers who are pretty much all LLM developers, whether it's for training or inference. We are also creating a portfolio of critical components to enable these XPUs and even GPUs to be clustered and form better performance. And that continues to be the model we do, which is we provide chips, technology in the form of chips, whether they be AI compute accelerators we call XPUs or networking chips that cluster them together, be it switches, PCI Express connectors, DSPs, lasers, NICs, and routers. And that's very much still the model we employ in semiconductors. And as you can see, our financial model and the programs we go through would drive towards a chip business model through the technologies we provide. What we are doing to enable some of these LLM players to be able to get access to the volume of compute capacity, the large gigawatts of compute capacity they need to scale up their models, is we are, as I announced here today, creating in partnership with guys with the best balance sheets around a vehicle to basically have these chips funded for these LLM players who otherwise might have difficulty getting access to our technology, which provides them with the lowest power and the lowest cost.

Well, we're not seeing it. If anything else, as I reported, the high volume of core count of CPUs selling together with GPUs is driving some accelerated growth of our VMware business. And as you can see in Q3, we're seeing an accelerated growth and we expect that to continue for the next multiple quarters as this demand picks up. Long term, given the kind of products we do in infrastructure software, very close to literally the hardware, the tech, as basically the hypervisor where our products are, we do not expect to see any impact on software products.

Well, that's a very interesting thought, and you may be very well right that enterprise consuming AI is still relatively at an early stage of the game. But having said that, what we're also seeing is a lot of what the enterprise is consuming on tokens, they're buying a lot of these tokens from the platforms, the product API platforms. They pull from the platforms of these same names of customers we talked about, which is Anthropic, OpenAI, Gemini. These are the large guys they're pulling it from, and that's where I think a high, substantially most of this token consumption is tied to those LLMs. And these LLM guys, as they productize frontier models, whether it be Opus 4.7, ChatGPT 5.5, or Gemini 3.5, it comes back to the same end demand on compute capacity we provide to all these guys. And so even a growth of enterprise demand that we're now starting to see as enterprise starts to consume AI tokens for their own workloads, for their own productivity, as consumers do, they're buying from the same source, these same few guys. And that's what's driving this very large, I call it insatiable, growth in compute capacity that we are experiencing, and we see that continuing to happen now through 2027 and what we're seeing now through 2028 as well. So this is getting to be quite a sustainable and steepening trajectory of demand.

Well, I guess the answer to that is it's possible, but the reality of the whole issue is this: the compute capacity, most of AI generated, are provided in the form of SaaS models. APIs are pulled from the clouds, whether they're from Bedrock, Vertex, Azure, or first party. It is still provided in the cloud. So most of all that demand at the end of the day, in terms of compute capacity which is what we're doing, comes from those few large frontier model developers and the products they generate to supply to consumer and enterprises globally. The source of demand comes from those frontier model labs who are developing the products which consumer and enterprise like you and us and our companies are consuming. And what we're doing is providing that capacity to that demand source, as opposed to going to a company or bank and trying to provide them XPUs and then they having to try to build it, run it, create a software stack to then write application and run it themselves. I'm sure there are a few enterprises doing that now, but there are not many. It's early stage in that whole game. Right now, the most of the demand is coming from the frontier model players who are creating products, things as I said like code assistants, engineering verticals, which are really coming from the same source of guys who are doing those few guys doing the frontier models. It's not really coming from 100,000 companies directly trying to buy XPUs or for that matter GPUs. It's not.

Well, there's a huge demand of compute. See, a lot of our large, these six customers now realize that lead time to get compute, you need lead time. You need to be thoughtful. And that's not just asking for wafers to get the chips or memory to ensure that HBMs are available or DRAMs are available. They're also talking about, hey, I got to have the power, the power shell. So all this is planning ahead. And what we are seeing, the bookings that are coming, is not for immediate delivery. Some are hoping to have, but the reality they all accept is they need to align quite a few other things in place before they can deliver. But they are placing their orders early and they are placing their orders now, and they are placing their orders in fairly huge demand, which basically gives us a lot more visibility than we normally otherwise would have in semiconductors. Our visibility runs all the way to 2028 right now. Three months ago, I can tell you visibility ran pretty much to 27. Today it runs to 28. And that's a big part of the reason why we are creating this XPV platform as really the platform to plan to build up and put in place such capacity for those frontier model customers of ours who are seeing, as you guys are seeing in some of the financials they're telling you, and in the experiences you have, which is driving huge amount of consumption of tokens from those compute capacity we are giving them. We have the benefit now of a lot of lead time, and we're planning that. And it's not because of shortage of our components. There's also the other element that need to be put in place, which particularly relates to power and connection into infrastructure globally, or through America at least, that enables inference to be distributed to consumers and enterprises throughout the country. So we're just getting a lot of lead time.

Our revenue, our content per gigawatt will increase. Put it simply, our content from the fact that our compute chip, our XPU, will go up in price very dramatically, particularly when you not only put SRAMs into it, sparse cost, you start putting a lot, you start putting embedding CPU cores into the same XPUs and making those chips basically multi-die with lots of HBM. So the trajectory of content increases.