Deep|NVDA: Rethinking NVIDIA's Moat in the AI Stack

NVIDIA — The Bellwether of the AI Era

NVIDIA's hardware leadership is virtually undisputed. In many respects, the company is not only one of the most successful enterprises of the AI era but also one of the principal infrastructure providers enabling this generational shift in compute — a position that we think deserves full respect.

Over the past several years, the certainty of NVIDIA's roadmap execution has been rare: from Blackwell to Vera Rubin, and onward to Rubin Ultra and Feynman, the cadence has held essentially at one generation per year. At the same time, NVIDIA has not confined itself to the GPU. It has continued to extend at the system level — whether by folding inference capability (such as the LPU architecture) into the platform, pushing co-packaged optics to pave the way for hyperscale clusters, or reshaping the boundary between storage and GPU memory through directions like Storage-Next. At heart, these moves reinforce its "AI factory"-grade system capability. Combined with deep entrenchment in advanced packaging capacity, NVIDIA has erected exceptionally strong moats along both the hardware and the supply-chain dimensions.

However, focusing solely on hardware leadership would still understate NVIDIA's core competitiveness. What we think deserves closer attention is its profit structure: Data Center already accounts for the overwhelming majority of revenue, the chip-level gross margin for the GPU is roughly 84%, and overall GAAP gross margin reached 75% (FY2026 Q4). In the semiconductor industry, this is almost an outlier — closer to the profitability profile of a software company than that of a conventional hardware vendor.