Broadcom, ASICs, And More AI Winners

Broadcom, We've Seen This Movie Before

Broadcom has substantially lagged AI semi peers such as Marvell and AMD year-to-date, driven by fears around the company’s market share for Google’s upcoming TPUs. However, we’ve seen this movie before. Up to a few months ago, this is the exact reason why Marvell was trading at a discount—the company was ‘losing the Trainium business to Alchip’ and also ‘a shitco’.

In reality, Marvell has a comprehensive portfolio of advanced data center tech—ranging from leading edge semis and IP to optics. For example, Marvell recently closed a broad custom-silicon agreement with Google covering TPU-adjacent semis (XPU-attach). The consensus view was long Broadcom - short Marvell last year, but the reverse trade turned out to be the smart move.

Also with Broadcom, we think the market is now missing the bigger picture here. Once you start looking past the exact market share Broadcom will have in upcoming TPUs, the long term attraction is that Broadcom is the key ASIC partner for the best frontier labs—with Anthropic and OpenAI—as well as other key hyperscalers looking to compete in frontier AI—such as Google and Meta.

Our thesis is that as both Anthropic and OpenAI continue to produce stellar revenue growth in the coming years, we will gradually see more announcements around their ASIC capacity. This will help Broadcom’s multiple as the market gets increased visibility that Broadcom will indeed be a long term growth story.

However, also in the Google TPU business, JP Morgan sees continuous growth in the coming five years after meeting company management last month:

“While it was widely known that AVGO’s revenues would scale each year under its 5-year contract with Google, management incrementally confirmed that it expects to retain a majority volume share through the course of the agreement and that this was a key reason AVGO entered into the partnership. In our view, this confirms that AVGO is anchored as the primary supplier across the 5-year term, which should ease widespread investor concerns around longer-term share-loss.”

However, the real long term bull case is Broadcom’s partnerships with the leading frontier labs. JP Morgan explains:

“Management indicated that true compute demand (ex-China) is concentrated among roughly five frontier model builders that account for the vast majority of demand. While hyperscaler infrastructure can appear GPU-skewed at the aggregate level, management suggested that, when honing in specifically on leading frontier model builders, the mix could be roughly 50/50 between XPUs and GPUs next year (in line with our view that the shift from training-dominated workloads to inference monetization is pulling forward custom accelerator/XPU adoption).The team confirmed that customers are attempting to accelerate next- generation ASIC XPU tape-out schedules and are pursuing a wider variety of chip designs. This reflects the rapid evolution of inference workloads from one-shot inference to reasoning and agentic workloads, as well as customer focus on lowering cost per token (which in our view increases the strategic importance of workload-optimized custom silicon).Management discussed OpenAI’s first-generation XPU custom accelerator, Jalapeno, co-designed by Broadcom, and emphasized that the complexity of developing these cutting-edge XPUs is often underestimated. These products “push the envelope” across multiple design and technology dimensions, including bandwidth, SerDes, power and packaging. Broadcom expects to tape out the follow-on/next-gen OAI chip very soon.Management pushed back on concerns that hyperscaler customer-owned tooling (COT) efforts will materially displace Broadcom’s XPU/TPU share. The key point was that modern XPUs are no longer simple single-die chips; they increasingly require multi-die configurations, advanced packaging, high-speed SerDes and deep productization expertise. Management referenced 6-die and 8-die configurations as examples of the complexity involved and suggested that internal hyperscaler efforts are likely to face significant technical hurdles.On rising component costs, including memory, the team is aiming to keep ASIC margins intact “one way or another,” emphasizing that technology leadership is the key source of pricing power. Management also pushed back on the idea of customers consigning HBM, noting that HBM is an integral part of the XPU design and must be tightly connected to the core logic die for the product to perform properly.”

Broadcom’s head of semis showed just how massive these next-gen XPU packages are going to get:

This is Charlie Kawwas, Broadcom’s head of semis, explaining:

“This is an XPU that was built and shipped last year. As you can tell, it is made of different chiplets. Some are memory-based, some are networking-based, and some are compute. What we are building and shipping this year is this chip. As you can see, it got bigger. You can see from last year to this year, you are now getting three times more memory and the compute that is in the middle is almost three times stronger.What ended up happening is we said for next year, we actually have to build this. So now, you have even more memory. In two years, it is almost an order of magnitude (10x). When you look at the compute, in the earlier version you had one large block, and here you have four of them. And then, this is what we are working on for 2028. I want you to understand two things. One is the speed at which this technology is moving, and hence the innovation that it needs to go through, is extremely rapid. Two, this is how we build these chips—these are actually modular. This is why people come to us, because we actually take these platforms and pre-build them. These little chiplets that go in the chip, we pre-build them, make sure they are production-ready, work with partners—like Samsung and others—and create these memory blocks. This memory integration is one of the reasons why the prices are going higher, because we are putting so much more in them. So, you will have 16 times what we had two years ago, 16 times in a single chip. This is ‘The Beast’—yes, actually, that is what I call it at work. We are building four of these for the largest four LLM players. It is modular.Think of the 2025 chip as being about 1 million units a year. Think of the 2026 chip as being about 2 million units a year. But remember, the 2026 chip is three times the capacity of the 2025 chip, so that is a six-fold increase in compute capacity in one year. Then you go to the 2027 chip, which is probably 4 million to 5 million units, and this is nine times the capacity of the 2025 chip. Now you are at 40 to 50 times the scale of the first one. And then The Beast in 2028 is three times that again.In the next couple of years and beyond—Yes, there is more than Jalapeno. There are definitely more spicy chips and a lot more XPUs that will be coming out—both from the large labs and a lot of startups. What I see is an insatiable demand in gigawatts. For example, we have announced for one of the top five customers that this year we are putting in about 1.5 gigawatts with one of the chips I showed you. Next year, we are going to put in over 5 gigawatts, which we have already contracted with that same customer. So, we are going to go from 1.5 gigawatts to 6.5 gigawatts total. The year after that, I am pretty sure it will be over 10 gigawatts incremental. If you look at the next three years for each of these lab players, they are going to go from 1 to 2 gigawatts this year, to 5 gigawatts, to over 10 gigawatts incremental. Over that three-year journey, they will go from 2 to 5 to 10 gigawatts, which is 17 gigawatts total, starting from just 1 gigawatt today.This means there is a massive challenge in terms of the amount of power that is needed, the amount of silicon wafers that are needed, and the amount of memory that is needed. It is actually going to pose a challenge for the rest of the non-AI businesses, which we also do at Broadcom. Remember, I have 16 franchises. Five of them are in AI, and 11 are in non-AI. AI is not just going to live in data centers—AI has to get to the edge, and these other businesses have to actually enable this edge AI. Just like the internet started from data centers and moved all the way to smart devices today. The same thing has to happen with AI.”

On The Beast, Charlie said that “we are building four of these for the largest four LLM players.” This implies to us that they are also working on an ASIC for Anthropic, and that Anthropic won’t purely rely on TPUs for their ASIC strategy. This makes sense, as you don’t want to source your hardware from a key rival long term.

From Charlie’s comments, it is clear that both 2027 and 2028 will again see strong sequential growth. Based on a recent MOU with Samsung, JP Morgan works out that also 2029-2030 should again be growth years for Broadcom:

“On Saturday, Samsung (covered by Jay Kwon) announced it had signed a memorandum of understanding (MOU) with Broadcom to expand their strategic collaboration across memory and advanced foundry technologies, supporting the next generation of AI infrastructure. We believe that Samsung is and will continue to remain Broadcom’s main supplier of high bandwidth memory (HBM DRAM), and we estimate that around 75-85% of all of Broadcom’s HBM DRAM requirements going forward will be supplied by Samsung. The companies expect the collaboration to result in MORE than $200B of Samsung memory/ foundry products (we estimate Broadcom’s Samsung purchase mix as 90-95% HBM, 5-10% foundry wafers) over the next 5 years through 2030, and based on our calculations, we estimate that this implies > $1T of cumulative Broadcom AI revenues (ASICs + Networking) and around $40-50B of cumulative Samsung foundry-based chip sales (we believe that Broadcom will utilize Samsung for datacenter networking, broadband, and telco service provider chips). Our estimate of >$1T in cumulative Broadcom AI revenues (2026-2030) implies a ~60%+ CAGR on Broadcom’s forward AI revenue through 2030 (>20% growth per year even after 2028) — a significant growth profile, but not a surprise given the team’s #1 market share position in AI ASIC XPUs and #1 market share position in switching/routing silicon and its Tier-1 line-up of ASIC XPU customers (Google, Meta, OpenAI, Anthropic, Asia Customer #5, Asia Customer #6, Softbank/ARM, Apple, and SambaNova).”

XPUs will continue to take share in the AI accelerator market, especially in the scenario where frontier labs such as Anthropic and OpenAI remain dominant in terms of AI revenue market share. Conversely, the more workloads move to open-source models, the better it will be for Nvidia.

When there are a large variety of models and model providers, it makes sense to optimize all of these for a key platform which is available on all clouds, i.e. Nvidia CUDA. Conversely, when there are a few frontier labs which are printing cash due to their dominant positioning in terms of AI revenue market share, this gives these labs the resources to optimize their AI infrastructure with their own XPUs. Names such as Broadcom and Marvell will see large tailwinds from this.

Note that the two scenarios are not “either-or”—both scenarios are playing out today and they will continue to play out. More simple or pre-defined background processes for which open-source and cheaper models are ‘good enough’, will continue to be deployed on these types of models. At the same time, the most advanced models will continue to win the most advanced workloads, such as writing large code bases or doing advanced engineering work.

One reason that we have the $200 per month Claude plan is that Claude can do anything very well—so, you’re writing a large code base, but Claude also understands how to translate your app into 20-30 other languages, knows how to architect the back-end system in the Google Cloud, knows how to optimize the app for marketing, etc. For example, when we tried to translate our app with GLM 5.2, ‘the Claude killer’ according to social media, it crashed. Apparently it can’t do translation. Engineers don’t like to lose hours with models that crash or can only handle a narrow subset of tasks—they like to iterate quickly and build solutions. So, even if we got GLM 5.2 for free, we still wouldn’t use it and just pay the $200 for Claude. It’s a real bargain given its capabilities.

It seems likely to us that for advanced workloads, such as engineering, companies will just continue to pay up for the most advanced models. You don’t want to work with an inferior model on a problem for 1-2 days, and then figure out it isn’t able to do it. It makes a lot more sense to purchase the best models and then get the best solutions in an instant. Engineers cost far more than tokens, and so in our view, companies will pay up for the best models to maximize the productivity of their engineers. This is where the real value is created. Not by purchasing cheaper tokens in the cloud.

We regularly see highly confident claims on social media that the “model layer will commoditize”. However, this is not how things end up in tech. Typically, there are 1-2 big winners when it comes to software. Let’s just quickly go through the winners from the last decades to illustrate. Client OS—Windows. Text editing and spreadsheets—Microsoft Office. Server OS—Linux. Browser—Chrome. Mobile OS—Apple and Android. Programming languages—Python and Javascript. Cloud—AWS and Azure. Search—Google. Code repository—Github. And you can go on like this.

In our view, when you look at revenue market shares in the LLM market 3-5 years from now, this will likely be a market where two players are fairly dominant and capture 60-70% of the market or so. Then, there will be two or more players which capture another 25% or so. Open-source and cheaper models will only capture a small revenue share of the market, given that these models are indeed commoditized. While these will process large volumes of tokens, not much money will be made in this part of the market as these will be extremely cheap tokens.

This is good news for Broadcom as these dominant AI players will allocate resources to scale their ASIC solutions. The only caveat is that—when we look at Charlie’s comments above—it’s also clear that by the end of 2028, a huge amount of AI capacity will have been brought online. The key question is whether we’ll still be in a shortage then, or whether we’ll actually have overcapacity. In case we get overcapacity in AI at some stage, this will lead to a cyclical correction in data center capex for a few years—until AI demand grows again into the available capacity. And then we’ll start a new semi upcycle again.

However, when we look at Broadcom’s valuation, we see the risk-reward as very attractive here. The company is exposed to one of the most attractive areas in AI as the key ASIC partner for the leading AI labs, while trading at a very non-aggressive valuation of 23x forward EPS:

And a valuation of 14x 2028 EPS:

Next, we’ll review two more names which we like here, and we’ll make a similar analysis for both of these like we did above.