The AI chip market is having a moment that feels uncomfortably familiar. Demand is surging, valuations are climbing, and Wall Street is revising forecasts upward faster than chip fabs can spin up production lines. The problem, according to analysts at Morgan Stanley and Goldman Sachs, is that the math stops working somewhere between the PowerPoint deck and the factory floor.
Both firms have raised concerns that AI chip revenue forecasts for 2026 and 2027 have outpaced what realistic capacity and demand trajectories can actually support.
How we got here
The current frenzy traces directly back to the large language model breakthroughs of 2022 and 2023. ChatGPT arrived, cloud providers started placing GPU orders at scale, and NVIDIA’s H100 and A100 GPU series, built on the CUDA software ecosystem, became the de facto standard for training and running large AI models. NVIDIA’s data center segment became the primary engine of growth across the entire AI hardware category.
Corporate guidance followed the hype upward. Consecutive quarters brought upward revisions, and market valuations for leading AI hardware companies got rebuilt around an assumption of sustained annual growth exceeding 30%. For semiconductors, a sector with a well-documented history of boom-and-bust cycles, it is the kind of number that makes veteran analysts reach for antacids.
