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Futurum Group and NVIDIA frame AI as a five-layer stack
Brief
Futurum Group and NVIDIA's report frames AI as a five-layer stack—energy, chips, infrastructure, models, applications—and warns the five largest U.S. hyperscalers could spend up to $690B on infrastructure in 2026 (nearly double 2025). Energy and cooling now outpace silicon as the main bottleneck; Blackwell inference is ~35× cheaper than Hopper, even as token-heavy reasoning and agents drive rising compute needs and reshape workforces and national sovereignty.
Why it matters
Futurum Group and NVIDIA frame AI as a five-layer stack: energy, chips, infrastructure, models, and applications.
Key details
- The five largest U.S. hyperscalers are projected to spend up to $690B on infrastructure in 2026 (nearly double 2025); energy and cooling have overtaken silicon as the primary bottleneck.
- Inference on NVIDIA Blackwell is roughly 35× cheaper per million tokens than on Hopper, but aggregate compute demand keeps rising because reasoning models and agentic workflows consume far more tokens; the build-out is creating high demand for electricians, HVAC technicians, steelworkers, and grid engineers (many earning six-figure salaries) and highlighting national sovereignty gaps for countries without energy, fabs, or domestic models.