AI季度收入首次覆盖基础设施折旧

AI revenue has crossed its first serious accounting test: $25B in quarterly sales now exceeds $21B in estimated chip and data-center depreciation. i.e. AI infrastructure is starting to pay for itself before power, labor, financing, and leases are counted. --- Chart from Bloomberg bloomberg. com/news/articles/2026-06-25/ai-demand-begins-to-justify-massive-cost-of-data-center-buildout  > **引用原帖 Rohan Paul (@rohanpaul_ai):** > This is a brilliant report. The State of the AI Economy by @exponentialview > - $110B real AI revenue over 12 months, after removing double-counting. so $1 spent on Claude is counted once, even if part of it later flows to Amazon or another infrastructure provider. > - $175B current annualized run rate, showing fast acceleration. Measured by end-customer spend, not supply-chain pass-through revenue. Excludes China, internal AI savings, ad uplift, consulting, and systems integration. > - Growth running roughly 3x faster than mobile or internet adoption waves. > - The pace of revenue formation has sharply accelerated. New $1B revenue now arrives in under 2 days, versus 180 days in 2023. > - Enterprise AI has moved beyond pilots, but deep company-wide rollout is still early. > - AI earnings-call mentions reached 31% of tracked S&P 500 firms. > - Only 20% of tracked firms made quantified AI impact claims. > - Hyperscaler AI revenue roughly covers AI infrastructure depreciation for now. GPU economics depend heavily on 6-year compute life assumptions. > Other AI infrastructure gets modeled over 14 years. > - Token price cuts do not automatically reduce revenue. > - Every 10% token price cut drives 12-18% more token usage. > - AI demand looks price elastic, meaning cheaper AI expands usage faster than prices fall. > - Power availability and data-center costs remain major limits on future scaling. > https://x.com/rohanpaul_ai/status/2070288396644491317