Physics / mechanism
Datacentres are large-scale compute, storage, and networking facilities. Hyperscale operators (AWS, Azure, GCP, Meta, Oracle) dominate, running facilities at 100 MW–1 GW+ per campus. Key parameters: PUE (Power Usage Effectiveness, best-in-class ~1.2), rack density (now routinely 30–60 kW/rack for AI workloads, pushing toward 100+ kW with liquid cooling), bandwidth (400G/800G optical interconnects between switches), and latency (<1 µs intra-rack, ~5 µs cross-fabric). AI inference/training is the dominant growth driver; NVIDIA H100/H200/B200 clusters define the current deployment frontier. Global datacentre power demand is expected to exceed 1,000 TWh annually by 2026.
Competitive landscape
The primary tension is hyperscale vs. sovereign/edge vs. colocation. Hyperscalers commoditise compute; colocation players (Equinix, Digital Realty) sell interconnection density. Edge computing fragments the model but hasn’t displaced core. On the silicon layer: merchant silicon (Broadcom Tomahawk/Jericho) competes with custom ASICs (Google TPU, Microsoft Maia, Amazon Trainium). Cooling: air vs. direct liquid (DLC) vs. immersion—immersion scales better at high rack density but has higher capex and operational friction.
| Layer | Incumbent | Challenger |
|---|---|---|
| Compute | NVIDIA GPU | Custom ASIC / RISC-V accelerator |
| Networking | Broadcom + InfiniBand | Ultra Ethernet Consortium / photonic switching |
| Cooling | CRAC air cooling | Direct liquid / immersion |
Companies using
Connected ideas
Sources
Frontier (open questions)
- To be added.