OpenAI Is Building Its Own AI Chip With Broadcom. Should Nvidia Investors Be Worried?
🚀 OpenAI and Broadcom launched Jalapeño, a custom AI inference chip, as part of a plan to deploy 10 gigawatts of accelerators between late 2026 and 2029.
💰 Broadcom's AI semiconductor revenue surged 143% year-over-year to $10.8 billion in fiscal Q2 2026, with over $30 billion in orders booked that quarter.
📈 Broadcom reaffirmed expectations for AI chip revenue to exceed $100 billion in fiscal 2027, representing a doubling of current annual projections.
🤖 Nvidia's data center revenue reached $75.2 billion in fiscal Q1 2027, demonstrating that it still sells more hardware annually than Broadcom expects from its custom chip business over a full year.
⚙️ Jalapeño is an application-specific inference chip lacking CUDA support, making it less flexible than Nvidia's general-purpose processors for training and broad developer use.
📉 Custom silicon adoption by deep-pocketed buyers like Google and Meta could gradually pressure Nvidia's pricing power and its high gross margins of approximately 75%.
📊 Nvidia trades at about 22 times forward earnings, suggesting the market has already priced in some erosion of its dominance compared to Broadcom's low-30s P/E ratio.
🛡️ Analysts conclude that Nvidia's lead in performance and software scale remains enormous, meaning customer-designed chips will chip away at the edges rather than close the moat.
- Broadcom's AI semiconductor revenue jumped 143% year-over-year to $10.8 billion in fiscal Q2 2026, driven by insatiable demand for custom accelerators and networking.
- The company booked more than $30 billion of AI orders during the fiscal second quarter of 2026 alone, indicating a robust pipeline for future growth.
- Broadcom projects its AI chip revenue will exceed $100 billion in fiscal 2027, signaling sustained high-growth momentum in its custom silicon segment.
- Nvidia's massive scale allows it to sell more AI hardware in a single quarter ($75.2 billion) than Broadcom expects to generate from its entire AI chip business for the year.
- Custom inference chips are less flexible than Nvidia's general-purpose processors and do not support CUDA, potentially limiting their ability to compete for training workloads or developer loyalty.