After Google published the Transformer paper in 2017, which laid the foundation for large models, core authors gradually left to found or join companies like OpenAI, Character.ai, Cohere, and Adept, creating a talent outflow path that Altman has publicly stated would have made it difficult for OpenAI to exist today if Google had fully committed early on. The allocation of resources is driven by the protection of the search advertising cash flow worth hundreds of billions, with cautious internal reviews and productization leading to delays in releasing R&D results, motivated by the desire to avoid disrupting existing business models, while also repurchasing key talent through high-value licensing deals to strengthen Gemini. This is similar to Microsoft's early conservative cases in search or mobile, or IBM's path dependency during the PC era; currently, the industry is transitioning from laboratory advantages to commercial execution capabilities, shifting from paper dominance to competition in deployment and speed. Essentially, this represents a transfer of pricing power: core talent and architecture are shifting from monopolies within large companies to a decentralized entrepreneurial ecosystem, where the mechanism is that when protecting existing cash flow takes precedence over aggressive innovation, technological advantages are diluted by external speed. The cost of protecting cash flow is often missing out on paradigm shifts, and talent outflow is the most expensive symptom of corporate malaise, with speed being more decisive than resources in determining the outcome of AI competition.