Broadcom's AI semiconductor revenue reached $16.7 billion in its fiscal third quarter of 2026, a 221% jump from a year earlier, with custom chip orders from Google, Meta, OpenAI, and Anthropic. The company also raised its longer-term AI revenue forecasts sharply, building a custom-chip business that makes the AI hardware picture more complicated than it used to be.
Broadcom posted $16.7 billion in AI semiconductor revenue for its fiscal third quarter of 2026, up 221% from the same period a year earlier. Nvidia still leads the AI chip conversation, but Broadcom is building a business that complicates that picture.
Custom chips versus general-purpose GPUs
The distinction between the two companies starts with what they sell. Nvidia makes general-purpose graphics processing units that can run almost any AI workload out of the box, while Broadcom makes custom application-specific integrated circuits, or ASICs, designed from scratch around a specific customer's needs.
Those custom accelerators accounted for 73% of Broadcom's Q3 AI revenue, and the client list includes major names in AI development. Google has partnered with Broadcom across multiple generations of its Tensor Processing Units, and Meta's MTIA chip family runs through the same pipeline. OpenAI is developing its so-called Jalapeño chips with Broadcom's help, and Anthropic has joined the roster as well. Broadcom claims a roughly 70-80% market share in custom AI accelerator design.
Forecasts climb sharply
Management revised its fiscal 2026 AI revenue guidance upward to approximately $58 billion, representing roughly 186% growth year-over-year. From there, the projections accelerate: fiscal 2027 guidance targets around $115 billion, up from a prior estimate of more than $100 billion, and fiscal 2028 carries a forecast of approximately $230 billion. The total AI backlog, based on current demand and active projects, exceeds $73 billion.
Broadcom ties that projection to what it describes as multi-gigawatt deployments, meaning data centers so large they require their own power infrastructure.
Where each company fits in AI hardware
Nvidia dominates the training of large AI models, where raw compute flexibility matters most. Broadcom, however, thrives in inference at scale, where a company like Google knows precisely what computation it needs to perform billions of times per day and can justify building a chip optimized for exactly that task.
The two companies also overlap in AI networking silicon, where Broadcom has a long-established position in switching and connectivity chips that move data between processors inside large clusters. Designing a custom chip is a multi-year collaboration, and Google has worked with Broadcom on TPUs across multiple chip generations, which means switching costs are high and the revenue is sticky in a way that commodity hardware revenue rarely is.
Source: Crypto Briefing
Trading involves risk.
Risk Disclaimer
