Nvidia: The AI Stack Is Changing

The market is likely overpricing the near-term threat to Nvidia’s moat and underpricing the longer-run shift in bargaining power. Software substitution in AI is a multi-year adoption problem: enterprises rarely rip out an incumbent compiler/runtime stack until there is a clear cost/performance edge and a migration toolchain, so the first-order revenue impact on NVDA should be limited for the next 2-4 quarters. The more important second-order effect is that customers now have a credible path to multi-sourcing, which can compress future pricing power and reduce the mix contribution from the highest-margin layers.

The tighter bottleneck is moving down the stack to HBM and power delivery. If Rubin production is being cut because memory supply is binding, the earnings transfer goes to memory suppliers and potentially to packaging/thermal and grid equipment names, while GPU vendors face volume constraints but not necessarily margin collapse. That favors SK Hynix and peers over a pure short-NVDA thesis; the real loser is likely hyperscaler capex timing, as delayed rack deployments push revenue recognition and cash conversion into 2026 rather than 2025.

Contrarian view: consensus is too focused on “CUDA risk” as a headline narrative and not enough on the fact that AI economics remain compute- and electricity-limited. A durable rise in data-center power demand creates a second-order winner set in utility interconnect, transformers, and cooling, but the market will need order-book confirmation before rerating those names. Falsifiers: a visible enterprise migration off CUDA within 1-2 earnings cycles, or, conversely, a step-up in NVDA guidance that proves HBM scarcity is temporary and the platform remains sticky.