Cracks in NVIDIA's Dominance: As AI Shifts From Training to Inference, the Chip War Pulls TSMC to Its Core

Generative AI is expanding the AI chip market fast, and NVIDIA still leads—but cracks are showing. Google's in-house TPU and a wave of inference-focused startups are rising, and the battleground is shifting from AI training to inference. The core of this contest runs through a name Taiwanese readers know well: TSMC. Whoever designs the winning AI chip—NVIDIA, Google, or a challenger—almost all of them rely on TSMC's most advanced process to build it. The key distinction is training versus inference: training 'teaches' a model and demands raw power, NVIDIA's stronghold; inference is the everyday use of a trained model, where cost and efficiency rule. As AI moves from development to mass deployment, inference volume dwarfs training, and the question becomes 'cheaper and more efficient,' not just 'most powerful'—eroding NVIDIA's moat. History warns that 'one player always wins' rarely holds; Intel's CPU dominance gave way as computing paradigms shifted. For Taiwan, the point is not betting on whether NVIDIA falls, but recognizing that whoever wins, advanced manufacturing flows to TSMC—Taiwan's strongest card, as long as it keeps its process lead. Watch inference market share, cloud players' in-house chips, and TSMC's advanced-node progress.