Custom ASIC Is No Longer One Market
Scope ownership, attach leverage, and the separation of profit pools in AI infrastructure
Coming out of discussions at Google Cloud Next last week, we expect custom AI silicon to move back toward the center of the AI infrastructure debate. The spend trajectory across hyperscalers, dedicated infrastructure operators, and frontier model labs continues to support our view that custom silicon remains a key component of the buildout. The next phase of the discussion should focus on how that spend is captured, which layers of scope carry durable economics, and where strategic control shifts as customers become more sophisticated.
The public conversation still tends to group custom ASIC exposure into a single market narrative at a point when the underlying market is becoming more layered. Custom ASIC now covers programs with meaningfully different economics, margin structures, durability, and control points. That shorthand was useful when the category mostly referred to a compute die. The category now increasingly describes full systems spanning compute, memory, networking, I/O, packaging, and integration.
That shift changes how we frame the opportunity. Vendor exposure should be evaluated by scope quality: which layers of the program are owned, how scarce those layers are, how much execution risk they remove, and whether the role can support durable earnings quality as hyperscalers retain more architectural control internally.
Across the cohort, the businesses being grouped together are doing very different work: some vendors are selling broad execution ownership across compute, packaging, and networking, others are selling attach, others are selling I/O and modular implementation, and others are monetizing physical design, foundry adjacency, and packaging coordination. Hyperscalers will continue to outsource what is scarce, risky, or time-sensitive, while continuing to push to reclaim the layers where internal ownership lowers cost or improves control, and the variable investors should be tracking is which vendor owns which layer of the stack, how durable that scope is, what attach travels alongside it, and where insourcing pressure is most likely to land first.
Why this matters now
Google’s most recent TPU roadmap is the most timely evidence for this view. By separating the eighth-generation TPU family into training-oriented and inference-oriented chips, Google is signaling that workload classes are now diverging at the silicon level. We expect that divergence to deepen as training systems continue to optimize around scale, synchronization, memory bandwidth, and reliability, while inference systems optimize around latency, utilization, cost per token, and deployment flexibility.
That divergence should also change supplier allocation. The full partner structure across these systems is not completely disclosed, but supply-chain checks point to a more layered model in which different external partners participate in different parts of the stack while Google retains significant internal architectural ownership. The same direction of travel is visible across AWS, Microsoft, Meta, OpenAI, and Anthropic. Each customer appears to be making its own decision about where internal architecture matters most and where external execution, IP, or capacity can create leverage.
The framework
The full report separates the cohort into five distinct business models, each with its own profit pool and its own relationship to insourcing pressure. Premium full-scope orchestration carries the broadest scope and the strongest margin tier alongside the most concentrated insourcing pressure across the next two to three generations. Flexible compute plus attach can be margin dilutive on standalone compute and is decided on whether attach travels alongside the compute award. Hybrid I/O and modular custom silicon represents a different price point and is the most important business-model experiment in the group. Back-end implementation and turnkey execution carries lower margin and higher beta to ramps, while foundry-adjacent packaging and enablement carries scarcity exposure tied to advanced packaging tightness, with revenue conversion that is more lumpy than headline ASIC framings imply.
Applied across Broadcom, Marvell, MediaTek, Alchip, and GUC, the framework produces five very different exposures. The margin spread alone runs from blended margins in the teens at the back end of the stack to mid-50s at the premium full-scope orchestration tier. That gap is too wide to treat custom ASIC exposure as economically equivalent across the vendor base. Custom silicon is continuing to be important part of the AI infrastructure buildout, but the next phase of the debate will be decided by scope quality rather than socket wins: who owns scarce IP, who reduces execution risk, and who can attach higher-quality content to the compute program itself.
A Broadcom dollar, a Marvell dollar, a MediaTek dollar, an Alchip dollar, and a GUC dollar do not carry the same margin structure, durability, or strategic risk. Treating them as comparable assets confuses exposure with exposure quality. The vendors that retain scarce IP, reduce execution risk, or attach higher-quality content to custom compute should have a better chance of holding their economics through the buildout. The vendors with thinner positions will need volume, repeatability, or scarcity to support valuation durability. That distinction is the core of the full report.
What full subscribers receive
The full institutional report, including the framework, company sections, scorecards, margin work, and watchlist, written in our voice and structured for buy-side use.
The custom silicon stack map with a layer-by-layer view of where economic value, insourcing risk, and strategic leverage actually sit.
The five-model business framework covering premium full-scope orchestration, flexible compute plus attach, hybrid I/O and modular silicon, back-end implementation, and foundry-adjacent enablement.
Full company sections for Broadcom, Marvell, MediaTek, Alchip, and GUC, including stack ownership, revenue pool exposure, and the central underwriting question for each name.
The comparative framework table covering primary role, scope ownership, main revenue pool, margin quality, insourcing risk, attach leverage, customer concentration, and what investors are underwriting.
Per-company scorecards summarizing strengths, weaknesses, opportunities, threats, and margin tier.
A dedicated margin debate section that traces gross margin tiers across the cohort and explains why hyperscalers will continue to pressure pricing.
The Google case study applied to the broader market transition rather than as a single-customer note.
A risks section covering what would weaken and what would strengthen the framework.
The full What We Are Watching section across the cohort and per name, updated through subsequent notes.