Broadcom Inc. Q1 FY2025 earnings call

• Semiconductor Business: Q1 semiconductor revenue was $8.2B, with AI revenue at $4.1B up 77% y/y. R&D investments include taping out 2nm AI XPU packaging 3.5D and next-gen Tomahawk 6 switch. Non-AI semiconductors had slow recovery with various segments showing different trends.
• Infrastructure Software: Revenue up 47% y/y driven by transition from perpetual licenses to subscriptions and VCF adoption. Collaboration with NVIDIA on VMware Private AI Foundation with 39 enterprise customers.

In fiscal Q1 2025, total revenue was $14.9 billion. The semiconductor solutions segment had revenue of $8.2 billion, representing 55% of total revenue, up 11% year on year. AI revenue within semiconductors was $4.1 billion, up 77% year on year. Non-AI semiconductors revenue was $4.1 billion, down 9% sequentially. The infrastructure software segment had revenue of $6.7 billion, 45% of total revenue, up 47% year on year. For Q2 2025, consolidated revenue is guided to be approximately $14.9 billion. Semiconductor revenue is expected to be $8.4 billion, up 17% year on year. AI revenue is expected to be $4.4 billion, up 44% year on year. Infrastructure software revenue is expected to be $6.5 billion, up 23% year on year.

• Q2 2025 consolidated revenue guided at approximately $14.9B.
• Semiconductor revenue expected at $8.4B, up 17% y/y.
• AI revenue expected at $4.4B, up 44% y/y.
• Infrastructure software revenue expected at $6.5B, up 23% y/y.
• Adjusted EBITDA expected to be about 66% of revenue.

• Geopolitical tensions and tariffs impacting semiconductor business.
• Uncertainty around AI diffusion rules and their potential impact on shipments.

Q: Hey, guys. Thanks a lot and congrats on the results. Hock, you talked about four more customers coming online. Can you just talk a little bit more about the trend you're seeing, can any of these customers be as big as the current three? And what does this say about the custom silicon trend overall and your optimism and upside to the business long term?

A: Well, very interesting question, Ben. And thanks for your kind wishes. But what we're seeing is and by the way, these four are not customers as we define it. As I've always said, you know, in developing and creating XPUs, you know, we are not really the creator of those XPUs, to be honest. We enable each of those hyperscalers partners we engage with to create that chip and to basically, to create that compute system. Call it that way. And it comprises the model, the software model, working closely with the compute engine, the XPU, and the networking that ties together the clusters of those multiple XPUs as a whole to train those large Frontier models. And so you know, and the fact that we create the hardware it still has to work with the software models and the algorithms of those partners of ours before it becomes fully deployable and scale, which is why we define customers in this case as those where we know they have deployed at scale and we'll receive the production volume to enable it. Right. For that, we only have just the three. The four are, I call it partners, who are trying to create the same thing as the first three and to run their own Frontier models, each of it on there to train their own Frontier models. And as I also said, it doesn't happen overnight. To do the first chip, could take would take typically a year and a half. And that's very accelerated and which we could accelerate given that we essentially have a framework and a methodology that works right now. It works for the three customers. No reason for it to not work for the four. Still need those four partners to create and to develop a software which we don't do to make it work, and to answer your question, there's no reason why these four guys would not create a demand in the range of what we're seeing with the first three guys. But probably later. It's a journey. They started it later. And so they will probably get there later.

Q: Good afternoon, and great job on the strong quarterly results, Hock and team. Great to see the continual momentum in the AI business here in the first half of your fiscal year, and the continued broadening out of your AI ASIC customers. I know, Hock, last earnings, you did call out a strong ramp in the second half of the fiscal year, driven by new three-nanometer AI accelerated programs kinda ramping. You just help us either qualitatively, quantitatively, profile the second half step up relative to what the team just delivered here in the first half? Has the profile changed? Either favorably less favorably versus what you thought, maybe ninety days ago because quite frankly, I mean, a lot has happened since last earnings. Right? You've had the dynamics like deep seek and focus on AI model efficiency. But on the flip side, you've had strong CapEx outlooks by your cloud and hyperscale customers. So any color on the second half AI profile would be helpful.

A: You're asking me to look into the minds of my customers. And I hate to tell you they don't tell me, they don't show me the entire mindset here. But one why are we beating the numbers so far in Q1? Seems to be encouraging in Q2. Pardon me? From improved networking shipments, as I indicate that's to foster those XPUs and AI accelerators even in some cases GPUs together, for the hyperscalers. And that's good. And partly, also, we think there is some pull-ins of shipments and acceleration, call it that way, of shipments yes, in fiscal 2025.

Q: Great. Thank you for taking my question. Congrats on these pretty great results. It you know, it seems from the news headlines about tariffs and about Deepseek that there may be some disruption. Some customers and some other complementary suppliers seem to feel a bit paralyzed perhaps. Have difficulty making tough decisions. Those tend to be you know, really useful times for great companies to sort of emerge as something bigger and better than they were in the past. You've, you know, grown this company in a tremendous way over the last, you know, decade plus. And you're doing great now, especially in this AI area. But I wonder if you're seeing that sort of disruption from these dynamics that we suspect are happening based on, you know, headlines of what we see from other companies. And how aside from adding these customers in AI, sure there's other great stuff going on, but should we expect some bigger changes to come from Broadcom as a result of this?

A: You post a very interesting set of issues and questions. And those are very relevant interesting issues. And don't need issue the only problem we have at this point is I would say it's really toward to no way we're all linked. I mean, there's the threat, the noise of tariffs, especially on chips, that has a material lines in it. Nor do we know how it will be structured. So we don't know. But we do experience and we are leaving it now. Is the disruption on that is paused in a positive way. I should add a very positive disruption in semiconductors, on a generative AI. Generative AI for sure. And we I said that before, so at the risk of repeating, you know, but it's we feel it more than ever. Is really accelerating the development of semiconductor technology. Both process and packaging as well as design. Towards higher and higher performance accelerators and networking functionality. We've seen that innovation that those upgrades occur on a every month. As we face new interesting challenges. And when particularly with XPUs. We're trying within as to optimize to Frontier models of our partners, our customers, as well as our hyperscale partners. And we it's a lot of I mean, it's a it's a privilege almost for us to be to participate in it and try to optimize. And by optimize, I mean, you look at an accelerator. You can look at it for simple terms, high level, to to perform, to want to maybe mention not just on one single mentoring, which is compute capacity, how many teraflops? It's more than that. It's also tied to the fact that this is a distributed computing problem. It's not just the sing the compute capacity of a single XPU or GPU. It's also the network bandwidth. It ties itself to the next adjacent XPU or GPU. So that has an impact. So you're doing that. You'll have the balance with that. Then you decide, are you doing training or you're doing prefilling? Post training. Fine tuning. And again then comes how much memory do you balance against that? And with it, how much latency you can afford, which is memory bandwidth. So you will look at the at least four variables, maybe even five. If you're in clone in memory bandwidth. Not just memory capacity, when you go straight to inference. So we we have all these variables to play with and we try to optimize it. So all this is very, very I mean, it's a great experience for our engineers to push their envelope on how to create all those chips and so that's the biggest disruption we see right now. From sheer trying to create and push the envelope on generative AI. Trying to create the best hardware infrastructure to run it. Beyond that, yeah, there are there are other things too that come into play because with AI, as I indicated, just not just drive hardware for enterprises, it drives the way the architect their data centers. You know, data requirement they keep keeping data private on on under control becomes important. So suddenly, the push of what loads towards public cloud. May take a little pause as large enterprises particularly, have to they have to take direct device that you want to run AI workloads. You're probably thinking very hard about running them on-prem. And suddenly, push yourself to a saying, got to upgrade your own data centers. To do you know, and manage the your own data to run it on-prem. And that's also pushing a trend that with we have been seeing now over the past twelve months. Hence, my comments on VMware Private AI Foundation. This is to especially enterprises pushing direction at quickly recognizing that how where do they run their AI workloads. So those are trends we see today and a lot of it coming out of AI, a lot of it coming out of sensitive rules on sovereignty, in cloud and in data. On as far as you're mentioning tariffs is concerned, I think that's too early for us to figure out where to online. And probably maybe give it another three, six months we probably have a better idea where to go.