Overview
- Nvidia's software platform is the central asset shaping its lead, with tools and libraries that let developers run AI code efficiently on the company's hardware.
- CUDA, the company’s compute toolkit, has been built into research, university courses, and corporate stacks over roughly two decades, creating large sunk costs for developers.
- Those long-term investments produced strong network effects that raise switching costs so high that faster or cheaper rival chips alone will likely fail to lure most users.
- Nvidia GPUs power major AI systems such as large language models and self-driving research, which reinforces demand for the company’s integrated hardware‑plus‑tooling stack.
- For investors this reframes Nvidia as a platform play like Apple, meaning competitors must offer compatible tooling and migration paths, not only better silicon, to pose a real threat.