The Interconnect Stack: Transformers, Switchgear and the Grid Equipment That Gates the AI Buildout
Every gigawatt of AI capex assumes an interconnected grid connection with sufficient MV switchgear, a large power transformer and a set of medium-voltage cables to route the current. In 2026 that assumption no longer holds. LPT lead times have doubled since 2023, GOES supply is concentrated in three producers, US interconnect queues run four years, and hyperscalers have started building their own generation to escape the wait. A layer-by-layer analysis from grain-oriented electrical steel to grid queue reform.
- Grid interconnect is now the binding constraint on AI, not compute, not cooling, not chips. New large-load interconnect timelines in PJM, ERCOT, MISO and CAISO commonly run 4–7 years from application to energisation. Every hyperscaler ordering GW-class capacity in 2026 is planning around a wait that could stretch into the next decade.
- Large power transformer lead times have doubled since 2023. A 138/33 kV or 230/34.5 kV substation-class LPT that took 50–60 weeks pre-pandemic now takes 120–150+ weeks in North America. Global order books at Hitachi Energy, Siemens Energy and GE Vernova have been reported past record highs, with the three sitting on multi-year backlogs.
- GOES (grain-oriented electrical steel) is the underlying materials chokepoint. Roughly five producers globally (JFE Steel, Nippon Steel, POSCO's spinoff, ArcelorMittal / Cogent, NLMK), with China's Baosteel and TISCO adding capacity. GOES is the transformer core material and its manufacturing takes 12–18 months to expand. It is the Kuraray of the interconnect stack.
- Behind-the-meter power has moved from workaround to strategy. Amazon at Susquehanna, Meta at Clinton (Constellation), Microsoft at Three Mile Island, Google's Kairos SMR MOU, plus dozens of gas-turbine and Bloom Energy fuel cell deals. What used to be "on-site generation for redundancy" is now "on-site generation because we cannot wait for the grid".
- Copper demand from AI adds a materials layer stress that most models miss. Hyperscale racks use roughly 3–5× the copper per MW of a legacy enterprise data centre. On top of EV and renewables demand, AI could add 1–2 million tonnes/year of incremental copper consumption by 2030, into a supply base already forecasting deficits.
- The M&A window for interconnect assets has closed. Hitachi bought ABB Power Grids in 2020. GE broke out Vernova in 2024. Cameron International, Emerson process, PGE, Sensata: the big consolidations are done. What remains is bolt-ons and JVs, not platform deals.
- Digital and asset-managed transformers are the emerging profit lever. Adding sensor packages, oil-quality monitoring and thermal telemetry lets OEMs sell a service tail alongside the box. Hitachi Energy, Siemens Energy and Mitsubishi Electric all now have dedicated digital-transformer product lines. Software attach is small revenue today, high-margin, and structurally growing.
- Chinese equipment is qualifying into the AI supply chain for cables and medium-voltage switchgear, less so for large power transformers. TBEA and Baoding Tianwei have made international sales, but hyperscalers' provenance rules and utility qualification cycles are the real barriers.
- Bottom-up TAM. AI data centre interconnect capex sits at roughly $12–18B in 2026 (~$150–200k per MW of IT load, on ~8GW of new build) rising to $45–65B by 2030 as the buildout scales. Transformers, switchgear and cables dominate; behind-the-meter generation is the emerging category.
The series walks a single physical path. It begins at the medium-voltage utility bus at the site fence, steps down through the substation and switchgear, arrives at the datacenter rack where 800V DC is stabilised by the capacitor stack, is converted by silicon-carbide switches to 48V, is distributed across the rack by copper busbars and whips, is stepped down again by multi-phase controllers on the accelerator board to 0.8V, and finally routed through the on-package power delivery network to a transistor gate drawing over 2,000 amperes. Waste heat from every conversion stage is removed by the thermal stack. The whole thing is packaged inside a factory-modular building because there aren't enough electricians to build it stick-frame. Six essays. One 800V → 0.8V staircase.
- Part I. The Capacitor Stack — 800VDC at the rack
- Part II. The Wide-Bandgap Stack — SiC and GaN conversion
- Part III. The Thermal Stack — removing the waste heat
- Part IV. The Interconnect Stack — busbars and whips (you are here)
- Part V. The On-Package Delivery Stack — 48V to 0.8V
- Part VI. The Modular Datacenter Stack — how the building gets built
The mismatch between AI capex velocity and interconnect physics has driven the behind-the-meter generation wave, the acceleration of small modular reactor pilots, hyperscaler direct multi-year procurement contracts with transformer OEMs, and a redrawing of the map of where data centres can actually be built. The utility-equipment supply chain, which had been low-growth and stable for two decades, is now one of the two or three most strategically constrained resources in AI infrastructure.
The interconnect value chain, layer by layer
The capacitor stack divided by timescale. The wide-bandgap stack divided by process step. The thermal stack divided by heat-path position. The interconnect stack divides by voltage class and physical position between utility and rack. Each layer has a different competitive structure, a different concentration profile, and a different lead-time exposure. The market map below names the players at every layer. Two things stand out before reading it: the Japanese and Korean depth in materials and heavy equipment (GOES, LPT, HV cables) and the fact that the same four or five industrial groups (Hitachi Energy, Siemens Energy, Schneider Electric, ABB, Eaton) appear at multiple layers. Interconnect is a heavily integrated business, and it has been that way for decades.
