How NVIDIA almost went bankrupt: The big CUDA bet | Jensen Huang and Lex Fridman

Jensen Huang explains NVIDIA's strategic evolution from an accelerator company to what he calls an 'accelerated computing' company, navigating the fundamental tension between specialization and general-purpose computing. The company's journey began with programmable pixel shaders, then added FP32 compatibility to enable broader computing applications, eventually leading to CUDA development. The most critical decision was putting CUDA on consumer GeForce GPUs despite the enormous cost. This decision increased GPU costs by 50% while the company operated on 35% gross margins, consuming all profits and dropping market cap from $6-8 billion to $1.5 billion. However, this created the install base necessary for developer adoption, as Huang argues that install base - not technical elegance - defines architectural success, citing x86's dominance despite criticism versus failed RISC architectures. The strategy involved distributing CUDA to millions of GeForce users, including researchers and students who were often gamers, creating a platform foundation for the deep learning revolution. Huang also describes his leadership approach of gradually shaping belief systems within the company, board, and industry through continuous communication rather than sudden announcements. He spends years laying conceptual groundwork so that when major decisions are announced, they seem obvious to stakeholders. This method extends beyond internal management to industry partnerships, as NVIDIA operates as a platform company that designs integrated solutions but opens them for integration into other companies' products and services.