OpenAI embraces the 'platform paradox' by selling API access to startups that compete directly with its own apps like ChatGPT. The strategy is to foster a broad ecosystem, believing that enabling competitors is necessary to avoid losing the platform race entirely.

Elon Musk's newly approved trillion-dollar pay package is less about the money and more about securing 25% voting control of Tesla. He views Tesla's future not in cars but in humanoid robots, and he sought this control to direct the development of this potentially world-changing technology.

Insiders in top robotics labs are witnessing fundamental breakthroughs. These “signs of life,” while rudimentary now, are clear precursors to a rapid transition from research to widely adopted products, much like AI before ChatGPT’s public release.

The rapid progress of many LLMs was possible because they could leverage the same massive public dataset: the internet. In robotics, no such public corpus of robot interaction data exists. This “data void” means progress is tied to a company's ability to generate its own proprietary data.

The current trend toward closed, proprietary AI systems is a misguided and ultimately ineffective strategy. Ideas and talent circulate regardless of corporate walls. True, defensible innovation is fostered by openness and the rapid exchange of research, not by secrecy.

The robotics field has a scalable recipe for AI-driven manipulation (like GPT), but hasn't yet scaled it into a polished, mass-market consumer product (like ChatGPT). The current phase focuses on scaling data and refining systems, not just fundamental algorithm discovery, to bridge this gap.

The AI industry is not a winner-take-all market. Instead, it's a dynamic "leapfrogging" race where competitors like OpenAI, Google, and Anthropic constantly surpass each other with new models. This prevents a single monopoly and encourages specialization, with different models excelling in areas like coding or current events.

The choice between open and closed-source AI is not just technical but strategic. For startups, feeding proprietary data to a closed-source provider like OpenAI, which competes across many verticals, creates long-term risk. Open-source models offer "strategic autonomy" and prevent dependency on a potential future rival.

Initially, even OpenAI believed a single, ultimate 'model to rule them all' would emerge. This thinking has completely changed to favor a proliferation of specialized models, creating a healthier, less winner-take-all ecosystem where different models serve different needs.