Nvidia opened GPUs to programmers and CUDA became its AI moat

Before CUDA, GPUs were mainly graphics accelerators. In November 2006, Nvidia announced CUDA so developers could run nongraphics workloads on graphics chips, using GPUs for parallel computation instead of only pixels.

That software decision changed Nvidia's business architecture. Rather than sell chips as interchangeable components, Nvidia built a developer platform around libraries, tooling and backward compatibility.

The payoff became visible as AI and accelerated computing scaled. Nvidia's FY2026 filing described CUDA as the foundational development platform across its GPUs, while revenue reached $215.9 billion, up 65% year over year.

Nvidia reported FY2026 revenue of $215.9B, up 65%, with CUDA described as foundational to its computing platform.

CUDA made the GPU programmable enough for scientists, engineers and later AI researchers to reuse the same hardware family.

The proprietary software stack raised switching costs that raw chip benchmarks alone could not create.

Investing before the AI wave let Nvidia meet demand with an ecosystem, not just a component catalog.

Hardware leverage compounds when developers get a stable software layer that makes each new chip easier to adopt.

CUDA became the connective tissue across Nvidia's data-center, AI, scientific computing and graphics businesses. Competitors could build accelerators, but developers had already built workflows around Nvidia's software stack.