Keep pulling the thread on Jensen Huang.
NVIDIA's strategic shift to "extreme co-design" at the rack and data center scale was driven by the necessity to solve AI problems that no longer fit within a single computer accelerated by one GPU.
The strategic decision to put the CUDA computing platform on every GeForce GPU was an existential risk for NVIDIA, consuming all of the company's profits at the time.
The installed base of a computing platform is its single most important attribute for success, more so than architectural elegance, as demonstrated by the dominance of x86 over more elegant RISC architectures.
Future AI model training will be limited by compute rather than data, as a significant portion of training data will be synthetically generated.
The next major phase of AI development will be driven by "agentic scaling," where a primary AI system spawns numerous sub-agents to form large, collaborative AI teams.
OpenClaw is as significant for agentic AI systems as ChatGPT was for generative AI systems.
Over the past 10 years, NVIDIA has improved computing performance by a factor of one million, far outpacing Moore's Law, which would have yielded a 100x improvement.
The cost of generating an AI token is decreasing by an order of magnitude every year.
Approximately three years ago, Jensen Huang successfully convinced several DRAM industry CEOs to invest in HBM memory production, predicting it would become a mainstream data center memory.
NVIDIA plans to produce approximately 200 Vera Rubin pods per week.
TSMC's primary competitive advantage is its operational excellence in managing the complex, dynamic demands of hundreds of global customers, not just its transistor technology.
In 2013, TSMC founder Morris Chang offered Jensen Huang the position of CEO of TSMC, which he declined.