The core issues facing the AI industry have shifted from market space to revenue realization, cash flow generation, and profit distribution. AI labs need to accelerate their expansion into the application layer, converting model capabilities into actual revenue and achieving self-financing after covering inference, R&D, personnel, and infrastructure costs. Large cloud service providers like Microsoft, Google, and Amazon, on the other hand, need to convert their AI infrastructure investments into new revenue while maintaining existing business cash flow. UBS warns that restrictions on electricity and semiconductor capacity, approvals, and financing could delay this process and create a negative feedback loop: slower revenue growth leads to financing difficulties, and tighter financing further postpones infrastructure construction and revenue realization. Regarding profit distribution, infrastructure providers currently reap significant revenue from construction, while private capital bears a substantial proportion of the financing for AI needs. Ultimately, whether AI labs can capture sufficient profits from models and applications, and whether cloud service providers can monetize their infrastructure, will determine the ownership of the AI industry's profit pool.
