Jensen Huang's core strategy is to be a market creator, not a competitor. He actively avoids "red ocean" battles for existing market share, focusing instead on developing entirely new technologies and applications, like parallel processing for gaming and then AI, which established entirely new industries.

Major AI labs plan and purchase GPUs on multi-year timelines. This means NVIDIA's current stellar earnings reports reflect long-term capital commitments, not necessarily current consumer usage, potentially masking a slowdown in services like ChatGPT.

While NVIDIA's CUDA software provides a powerful lock-in for AI training, its advantage is much weaker in the rapidly growing inference market. New platforms are demonstrating that developers can and will adopt alternative software stacks for deployment, challenging the notion of an insurmountable software moat.

Despite powering the AI revolution, Jensen Huang's strategy of selling GPUs to everyone, rather than hoarding them to build a dominant AGI model himself, suggests he doesn't believe in a winner-take-all AGI future. True believers would keep the key resource for themselves.

Google training its top model, Gemini 3 Pro, on its own TPUs demonstrates a viable alternative to NVIDIA's chips. However, because Google does not sell its TPUs, NVIDIA remains the only seller for every other company, effectively maintaining monopoly pricing power over the rest of the market.

Nvidia will likely only revive its ambitions to compete with AWS if its massive hardware profit margins are threatened by competitors like AMD or hyperscalers building their own chips. Only then would Nvidia move up the stack to capture value through an "inference as a service" business model, moving beyond hardware sales.

While Nvidia dominates the AI training chip market, this only represents about 1% of the total compute workload. The other 99% is inference. Nvidia's risk is that competitors and customers' in-house chips will create cheaper, more efficient inference solutions, bifurcating the market and eroding its monopoly.

Facing bankruptcy in the 90s, NVIDIA couldn't afford to build a physical prototype for its make-or-break NV3 chip. The team relied entirely on simulation, a high-risk strategy that paid off and saved the company, ironically foreshadowing its future dominance in creating hardware for complex simulations.

NVIDIA's primary business risk isn't competition, but extreme customer concentration. Its top 4-5 customers represent ~80% of revenue. Each has a multi-billion dollar incentive to develop their own chips to reclaim NVIDIA's high gross margins, a threat most businesses don't face.