Broadcom and the Rise of Custom AI Silicon

In the age of artificial intelligence, the spotlight tends to favor the brightest names—those whose GPUs power the large language models that now shape our economic and technological destiny. But while Nvidia justifiably holds the marquee, it is Broadcom (AVGO)—engineering quietly behind the curtain—that has begun to redraw the boundaries of AI infrastructure. Not with fanfare, but with a blueprint. Not by dominating the stage, but by shaping it.

The emergence of custom AI silicon is not a side story in the AI revolution—it is the underlying rewiring of the compute stack itself. And in this deeper rewiring, Broadcom has positioned itself as the unseen architect—supplying the ASIC engines, the connective tissue, and the system logic that hyperscalers are increasingly assembling into proprietary intelligence machines.

Why Hyperscalers Are Moving Beyond General-Purpose Chips

The great unbundling of AI compute has begun. Where once there was only the GPU, today there is a new ecosystem of purpose-built silicon—custom ASICs engineered not to generalize but to optimize. Google’s TPU, Amazon’s Trainium, Microsoft’s Maia, and Meta’s Artemis each serve a precise function, tightly bound to their respective software stacks and inference workflows.

Inference—the day-to-day workhorse of AI—is now the battleground. It must be efficient, low-latency, and endlessly scalable. For this, hyperscalers increasingly seek to control their own silicon destiny. But designing and manufacturing such chips at scale is no small feat. This is where Broadcom enters—not as a rival to Nvidia, but as a foundational partner to the hyperscaler era.

According to Table 1, Nvidia remains the undisputed titan of AI training, holding over 90% share. But in inference, the landscape is beginning to open, with ASICs already accounting for 20% and rising. This is not a retreat from Nvidia, but a parallel expansion—GPUs and ASICs, each in their domain. And Broadcom is powering the growth of that domain.