This research paper examines the structural basis of NVIDIA's dominance in the global AI accelerator market, which stands at 80–92% market share as of 2026. The central argument is that NVIDIA's competitive advantage is not attributable to hardware specifications but to a 20-year software ecosystem called CUDA — a deliberately engineered platform dependency that produces measurable real-world performance advantages over theoretically superior competing hardware. The paper presents empirical benchmark evidence showing NVIDIA's H100 delivering 38.7% more real-world throughput than AMD's MI300X despite the latter's 32.1% theoretical TFLOPS advantage across 52 benchmark tests. It analyses the competitive failures of AMD, Intel, and Google TPU through the lens of software ecosystem depth rather than hardware capability, and identifies the principal structural threat to NVIDIA's dominance: the rapid commoditisation of AI inference workloads, where custom ASICs from Amazon, Google, Meta, and Microsoft offer 50–70% cost reductions and are growing at a 44.6% CAGR. The paper introduces a dual-thesis framework: NVIDIA's training market dominance is assessed as durable within a 2026–2028 horizon; its inference market position is assessed as structurally contested, with one projection placing inference share declining from above 90% to 20–30% by 2028. Four risks are examined — inference market erosion, model architecture shift, open-source abstraction layer development, and TSMC supply chain concentration — with probability, horizon, and impact assessments for each. A dedicated India analysis covers measurable infrastructure commitments (L&T, Yotta, E2E Networks, IndiaAI Mission's $1.2 billion programme), workforce exposure across India's 5 million IT professionals, the structural pressure on India's labour arbitrage IT services model from Agent-as-a-Service economics, and modelled workforce transition estimates of 500,000–800,000 affected roles over five years. The paper concludes with three forward scenarios (Best Case, Base Case, Risk Case) and structured recommendations for four stakeholder groups: Indian IT companies, IT professionals, investors, and policymakers. Research is based on five phases of validated primary and secondary sources including AIMultiple benchmark studies, Bloomberg Intelligence, SemiAnalysis, New Street Research, Xpert.Digital, and NVIDIA's GTC 2026 Keynote. All sources are quality-rated and editorial flags are maintained throughout.