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As of 2026-05-11, there are zero operational 1+ GW AI campuses; the largest…

2026-05-11 · 00:22 UTC ·@JigarShahDC ·1 min read

Brief

Jigar Shah (@JigarShahDC) lays out an AI infrastructure roadmap: as of 2026-05-11 there are zero operational 1+ GW campuses (largest 300–600 MW; 1+ GW expected 2027–28). He argues 15–20 1–5 GW campuses are needed for frontier training by 2030, 50–100 regional 100–500 MW hubs for inference, while 100 kW telco and residential 7B on-device options have limited roles.

Why it matters

As of 2026-05-11, there are zero operational 1+ GW AI campuses; the largest existing campuses are 300–600 MW, with 1+ GW sites expected in 2027–28.

Key details

  • Jigar Shah asserts 15–20 'giant campuses' of 1–5 GW are required for frontier training by 2030 and are already under construction, and that the other ~600 GW of planned capacity should stop chasing training use cases.
  • For inference, 50–100 regional hubs of 100–500 MW are non-negotiable because model weights are too large; 100 kW telco towers are useful only for voice AI/AR/VR/autonomous vehicle cases, and residential nodes only support on-device 7B models—distributed inference is real at Tier 4 (on-device) and Tier 3 (edge), but frontier training/agentic workloads will remain centralized due to physics and model size.
Source evidence

The full picture:

🏗️ Today: zero operational 1+ GW campuses. Largest are 300–600 MW. Hopefully in 2027–28.

🏭 15–20 giant campuses (1–5 GW) needed for frontier training by 2030. Already under construction. So the other 600GW should stop chasing.

🏢 50–100 regional hubs (100–500 MW) for frontier inference. Also non-negotiable — model weights are simply too large for anything smaller. The are also data centers that have been identified.

📡 100 kW telco towers — real value for voice AI, AR/VR, autonomous vehicles. Not a general solution. Throughput vs user density is structurally broken for frontier workloads.

🏠 Residential nodes — viable only for on-device 7B models. Not part of the frontier inference stack at all.

The distributed inference dream is real. It lives at Tier 4 (on-device) and Tier 3 (latency-critical edge). Frontier training and agentic workloads will remain centralized — not because of ideology, but because physics and model size demand it.