The race to build frontier AI is no longer rate-limited by chips, capital, or talent. It is rate-limited by transformers, gas turbines, and the seven-year average it takes a queued project to actually energize. ERCOT just revised its 2030 data-center demand projection from 29 GW to 77 GW in a single planning cycle. Approval rate on the queue: under 2%. The Stargate, Colossus 2, and Anthropic-Google compute build-outs all sit on the same constraint set.
The queue numbers above carry the historical shadow-ratio caveat: only 15–25% of queued projects energize. Applied uniformly, the "effective" PJM data-center queue is closer to 33–55 GW than 220 GW. But hyperscaler-backed queue applications have a higher conversion rate than the historical average — these are not speculative renewables developers filing to flip permits.
PJM has 220 gigawatts of proposed projects waiting on a grid whose summer peak demand is 154.
Even if every queued data-center project had unlimited capital, three physical-supply constraints would still cap the rate of energization:
1. Heavy-duty gas turbines. GE Vernova, Siemens Energy, and Mitsubishi Heavy Industries collectively produce ~80 large-frame gas turbines per year. 2027 delivery slots are largely sold; 2028 is the first cycle with material availability. Backlog clearance time: 24–36 months from order.
2. Large-power transformers (LPTs). Global LPT manufacturing capacity (Hitachi Energy, Siemens, GE, Hyundai) is tight. 765 kV and 500 kV class transformers carry lead times of 30–60 months. The US imports ~80% of its LPTs; tariff and supply-chain risk is non-trivial.
PJM, ERCOT, and the National Picture
3. Skilled trades. Lineworker, substation technician, and PE workforce contracted during 2007–2019 flat-load period. Re-staffing to 2025-2030 build requirements takes a full apprenticeship cycle (4 years minimum). This is the constraint nobody talks about loudly.
The dossier's load-bearing claim is "Grid energization constrains frontier AI training compute deployment through 2028." Three competing hypotheses; observables; diagnosticity scored 1 (consistent) to 5 (decisive against).
Reading: H2 (bottleneck moves) is the most-consistent hypothesis. H1 (binds) is close behind. H3 (dissolves) is essentially refuted by the LPT and turbine supply-side constraint. The headline forecast favors H2 — the bottleneck does not so much bind as redirect: AI compute deployment continues via BTM and nuclear restart, the public grid queue stays constipated, and the apparent "queue size" overstates the effective constraint on frontier compute.
Three Scenarios For The 2026-2029 Window
The US grid is unique in its degree of state-by-state regulatory fragmentation and queue-based interconnection process. Other major economies have different constraints and different policy levers — and the gaps between them are themselves strategic information.
The dossier's headline claim resolves on quarterly PJM/ERCOT planning reports. But six secondary questions require regulator filings, manufacturer orderbook data, and Federal Register publications the public discourse hasn't yet surfaced.