Should NVIDIA limit CUDA to graphics-adjacent and short-horizon support, sustain it as a staged external developer platform, or commit immediately to an ungated company-wide computing-platform expansion?
Sustain CUDA as a staged external platform because a public C-oriented toolkit, compatible architecture, and funding capacity justify option-preserving learning, while prior art, absent cohorts and economics, fixed costs, competition, and outsourced supply require hard gates.
Confidence
Moderate
What happened
NVIDIA continued funding and broadening CUDA from a GPU programming model into toolkits, libraries, developer distribution, and integrated accelerated-computing systems; the public record does not establish that management used the exact staged gates proposed in Part A.
Part A correctly framed CUDA as a staged platform investment built on prior GPGPU work, separated company claims from verified economics, and required developer, workload, contribution, investment, portability, and supply gates. Later platform layering and scale are directionally consistent with continued investment, but the public record does not reveal whether those proposed gates were used or quantify their causal return.
Approve a staged developer-platform program with frozen cohort and cost definitions; expand only after active-developer retention, production-workload retention, workload contribution, investment, portability, reliability, and supply gates clear.
Staging converts platform intent into cohort and workload evidence while preserving the option to pause weak layers, cap fixed commitments, and distinguish ecosystem effects from hardware performance, supply, and external demand.