Chubby♨️@kimmonismus · XFuturum Group与NVIDIA的报告将AI技术栈分为能源、芯片、基础设施、模型和应用五层。当前主要瓶颈已从芯片转向能源和冷却，美国五大超大规模企业今年基础设施支出预计高达6900亿美元。尽管Blackwell架构推理成本大幅降低，但推理模型和智能体工作流消耗的token量激增，使效率提升被迅速抵消。报告指出，AI基础设施建设正创造大量电工、暖通技工等高薪蓝领岗位，打破了AI仅影响白领的叙事。同时强调，缺乏能源、芯片制造和本土模型的国家无法真正参与AI经济，仅是消费者。
Futurum Group just published a report with NVIDIA that frames AI as a five-layer stack： energy， chips， infrastructure， models， applications， and the data is worth sitting with.
The five largest US hyperscalers are on track to spend up to $690B on infrastructure this year alone， nearly double 2025. Energy and cooling have overtaken silicon as the primary bottleneck.
Inference on Blackwell is roughly 35x cheaper per million tokens than on Hopper， yet aggregate compute demand keeps climbing because reasoning models and agentic workflows consume far more tokens per interaction.
The efficiency gains get absorbed before anyone notices them.