The software stack, built by Ian Buck and John Nickolls, lets Nvidia GPUs outperform rivals including AMD and Intel despite stronger paper specifications.
The report says CUDA's libraries, optimisation tools and deep integration with machine-learning frameworks create lock-in, making Nvidia effectively a software company as much as a chipmaker.
Open alternatives such as OpenCL, AMD's ROCm and Intel's oneAPI have struggled, leaving customers facing Nvidia's high prices while frontier AI labs still lack similarly durable moats.
With AI now writing its own code, is Nvidia's legendary software advantage on the verge of being automated away?
As AI's energy thirst grows, will brain-inspired chips make Nvidia's power-hungry GPU empire obsolete?
CUDA at the Core: How Nvidia Built a $4.5 Trillion AI Empire and What Threatens It in 2026
Overview
In mid-2026, Nvidia stands as a dominant force in artificial intelligence, driven by its strong financial performance and the widespread adoption of its CUDA platform. The company reported outstanding fourth-quarter earnings, which boosted analyst confidence and fueled expectations for continued market growth. This financial strength is largely powered by Nvidia’s data center segment, which generated the majority of its revenue. Significant investments in research and development further reinforce Nvidia’s leadership, enabling ongoing innovation and solidifying its position at the forefront of the AI industry.