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Electricity is the first input of AI. Gigawatt-scale campuses are signing nuclear offtakes, building gas turbines behind the meter, and waiting years in interconnection queues.

The buildings: shells, substations, chillers and halls engineered around rack densities that have jumped from ~10 kW to 120+ kW per rack.

Accelerators ship inside engineered systems — 8-GPU baseboards, rack-scale NVLink domains, InfiniBand and Ethernet fabrics, direct-to-chip liquid loops.

The chips themselves: leading-edge logic from a handful of foundries, stacked HBM memory, and advanced packaging that has become its own bottleneck.

The layer that turns racks into computers: kernels, compilers, frameworks, schedulers and serving engines — and the moats they create.

What the machines are for: frontier and open-weights models, from GPT and Claude and Gemini to DeepSeek, Llama and Qwen.

Where AI meets users: chat assistants, coding agents, creative tools, scientific systems and robots.