New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.

While known for its GPUs, NVIDIA's true competitive moat is CUDA, a free software platform that made its hardware accessible for diverse applications like research and AI. This created a powerful network effect and stickiness that competitors struggled to replicate, making NVIDIA more of a software company than observers realize.

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.

For a hyperscaler, the main benefit of designing a custom AI chip isn't necessarily superior performance, but gaining control. It allows them to escape the supply allocations dictated by NVIDIA and chart their own course, even if their chip is slightly less performant or more expensive to deploy.

NVIDIA's commitment to programmable GPUs over fixed-function ASICs (like a "transformer chip") is a strategic bet on rapid AI innovation. Since models are evolving so quickly (e.g., hybrid SSM-transformers), a flexible architecture is necessary to capture future algorithmic breakthroughs.

GPUs were designed for graphics, not AI. It was a "twist of fate" that their massively parallel architecture suited AI workloads. Chips designed from scratch for AI would be much more efficient, opening the door for new startups to build better, more specialized hardware and challenge incumbents.

The massive profits NVIDIA earns from its near-monopoly in AI chips act as the primary incentive for its own competition. Tech giants and automakers are now developing their own chips in response, showing how extreme profitability in tech inevitably funds new rivals.

NVIDIA is moving from its 'one GPU for everything' strategy to a diversified portfolio. By acquiring companies like Grok and developing specialized chips (e.g., CPX for pre-fill), it's hedging against the unpredictable evolution of AI models by covering multiple points on the performance curve.

The competitive threat from custom ASICs is being neutralized as NVIDIA evolves from a GPU company to an "AI factory" provider. It is now building its own specialized chips (e.g., CPX) for niche workloads, turning the ASIC concept into a feature of its own disaggregated platform rather than an external threat.