The material chokepoint most models don't see

A transformer core moves past a solid iron block. If it were, eddy currents circulating in the metal during AC cycles would dissipate the transformer's rated power as heat within minutes. Cores are built from thin laminations of silicon-alloyed iron in which the crystal grains have been oriented along the direction of magnetic flux. This "grain orientation" is achieved through a specific rolling, annealing and coating process that took decades to develop and is dominated globally by roughly five producers. The resulting material (grain-oriented electrical steel, or GOES) has an order-of-magnitude lower core loss than random-orientation steel, which is what makes practical 100-MVA-and-larger transformers possible at 99%+ efficiency.

GOES production is capital-intensive and slow to scale. A greenfield GOES line takes 3-5 years to design, permit and commission, and roughly 12-24 months to reach qualified output at commercial defect densities. Since the shale-gas and renewables buildout began in the early 2010s, GOES has cycled between periodic shortage and glut. Since 2022 the balance has been squarely on the shortage side, driven by simultaneous demand from grid expansion, renewables integration, EV charging infrastructure and now hyperscale data centres.

The three grades that matter

GOES splits into three commercial grades: conventional (CGO), high-permeability (HGO or Hi-B), and domain-refinedEach represents a step up in magnetic performance and a corresponding step up in price. Modern high-efficiency transformer designs use HGO or domain-refined material for the core; the very highest-tier designs (railway traction, HVDC valve transformers, hyperscale substation LPT) use domain-refined material with laser-scribing to further reduce core loss. Nippon Steel and JFE dominate the domain-refined grade; Cogent and POSCO offer competitive alternatives at slight cost disadvantage.

The AI-relevant point is that the hyperscale substation transformer lands squarely in the segment where domain-refined GOES matters most. It is the segment where the supply base is most concentrated, where lead times have expanded most, and where price has moved most. This is the "materials layer" chokepoint of the interconnect stack, and it plays the same role that Kuraray's cathode carbon plays in the capacitor essay or Japanese silicon nitride plays in the wide-bandgap essay: small revenue, operating leverage, extreme concentration.

What is the 200-tonne transformer bottleneck?

The large power transformer is the piece of equipment that steps utility voltage down to something a data centre can use. A hyperscale campus typically has multiple 50-150 MVA units in its main substation, plus smaller distribution transformers throughout. A single LPT weighs 100-400+ tonnes, ships by rail or specialised truck, and is oil-filled, so site delivery and installation is a construction project in itself. Each unit is essentially bespoke: designed to the utility's voltage class, the site's short-circuit rating, the loading profile and the environmental constraints. There is no mass-market SKU.

The market structure and where the concentration is placed

Three companies (Hitachi Energy, Siemens Energy, GE Vernova) hold the majority of the global market for large power transformers over 100 MVA. Hitachi Energy inherited ABB's power grids business in a 2020 acquisition and has become the largest single player, with reported multi-year backlogs. Siemens Energy carved out from Siemens AG in 2020 and is the closest Western competitor. GE Vernova spun out of General Electric in 2024 as the dedicated energy business. Mitsubishi Electric and Hyundai Electric round out the top five with strong Asian franchises. Below them, a long tail of regional and specialist producers (WEG, Prolec, SGB-SMIT, Delta Star, Virginia Transformer) serve smaller units and regional markets.

US production capacity is thin and expanding slowly. Prolec-GE, Virginia Transformer, Delta Star, Pennsylvania Transformer and a handful of smaller manufacturers make up the domestic base, which was estimated in the DOE's 2023 supply-chain review at roughly 20% of US LPT demand at typical utility replacement rates. That share is now unable to keep pace with hyperscale-driven demand. Multiple capacity expansions have been announced (Prolec in Monterrey, Hitachi in South Boston Virginia, HHI in Alabama), but a new LPT plant takes 3-5 years to build and qualify. None of the announcements will substantially relieve US supply before 2028.

The lead time story, in detail

Pre-pandemic, an LPT for a US utility was a 40-60 week engagement. Order placed, engineering complete in ~10 weeks, GOES procured and processed into laminations, core stacked, windings wound, tank assembled, oil-filled, factory tested, shipped. In 2026 that same engagement is quoted at 120-150 weeks by most Western OEMs, with some hyperscale-scale units pushing 200 weeks. Three factors drove the expansion, in roughly the order that counts.

First, demand doubledUtility grid modernisation (regulated capex), renewables interconnection (each wind or solar project needs a step-up transformer), EV charging build-out, industrial reshoring, and now AI data centres have all been ordering LPTs simultaneously, at rates the manufacturing base was never sized for. Global LPT demand has been reported to have doubled between 2020 and 2025.

Second, the supply base did not expand in stepTwo decades of low-single-digit utility spending growth had left OEMs with lean production footprints. Adding a new manufacturing bay costs $100M+ and takes 3-5 years. Announced capacity expansions have been substantial (Hitachi Energy alone has announced roughly €4.5B of grid-equipment capex through 2027) but the ramp curves lag demand by 2-3 years minimum.

Third, materials tightenedGOES supply, described above, is the immediate constraint at the material layer. Transformer oil (mineral or ester), copper wire, insulation paper (kraft pressboard), and specialty bushings have all seen extended lead times through 2024-25. Each is a smaller constraint than GOES but the compounding effect holds.

The equipment that routes and protects

Switchgear is the equipment that connects and disconnects circuits under load, isolates faults, and protects downstream equipment from short circuits. A hyperscale campus has switchgear at every voltage class: MV switchgear (typically 5-38 kV) in the substation and at row-level distribution, LV switchgear (below 1 kV) at the panel-board level. Different physics govern each: MV switchgear must interrupt current at kV levels without sustaining an arc, which is what drives the choice of insulation medium.

The SF6 phase-out

Sulphur hexafluoride (SF6) has been the standard insulation gas for high-voltage switchgear since the 1970s. It has excellent dielectric strength (per unit volume, roughly 3× that of air), good arc-quenching properties, and long service life. It is also the most potent greenhouse gas ever regulated, with a GWP roughly 23,500× that of CO₂ and an atmospheric lifetime of 3,200 years. Under the EU F-gas Regulation revised in 2024, SF6 will be prohibited in new MV switchgear from 2028-2030 depending on class, with HV following. US EPA and Californian rules are moving in the same direction.

The industry response has been alternative insulation gases (Hitachi Energy's EconiQ using C4-fluoronitrile, Siemens' Blue GIS using clean air, GE's g3 using C5-fluoroketone) and vacuum interrupters for medium-voltage classes. The alternatives are more expensive, some bring residual environmental concerns (fluoroketones degrade to PFAS in the atmosphere), and each requires qualification against long-established SF6 designs. Data centre operators specifying substations in 2026 face a real design choice between staying with SF6 for immediate delivery and paying a premium for clean-gas alternatives that align with corporate sustainability commitments.

Kilometres of XLPE per campus

A hyperscale data centre campus typically comprises 5-15 buildings on a site of a few hundred hectares, connected by underground medium-voltage cable runs. The main substation feeds distribution transformers scattered near the buildings, and the distribution transformers feed the racks. Total MV cable per site can run into the tens of kilometres. Every kilometre is engineered for the voltage class (typically 15-35 kV), fault-current rating, ampacity and burial depth.

The dominant cable technology is cross-linked polyethylene (XLPE) insulated cable with a copper or aluminium conductor. XLPE is thermoset (cross-linked during curing so it does not melt at operating temperature), which lets it handle higher continuous current than earlier PVC-insulated designs. Copper conductors are used where ampacity per cross-section matters most; aluminium is used where cost dominates and larger cross-sections are acceptable.

Global MV cable capacity concentrates in three European majors (Prysmian, Nexans, NKT), two Asian franchises (Sumitomo Electric, LS Cable & System), and a rising Chinese cohort (Hengtong, ZTT, Far East Cable). Similar to LPT, capacity expansion is slow: a new XLPE extrusion line takes 2-3 years to qualify. Prysmian and Nexans have both announced major capex programmes for MV and HV cable capacity through 2027, driven by offshore wind, HVDC interconnectors, and data centre demand.

The commodity underneath everything

Copper is the material that binds the interconnect stack together. Every transformer winding, every busbar, every MV cable, every rack-scale power distribution unit uses copper as the conductor of choice. A hyperscale AI data centre uses roughly 30-50 tonnes of copper per MW of IT load, according to widely cited industry estimates from 2024-25, which is 3-5× the copper intensity of a legacy enterprise data centre. The multiplier comes from higher rack currents (800VDC at ~750A per rack), denser distribution, more redundancy, and higher-ampacity bus systems.

Global copper mine production is roughly 22 Mtpa (2024). Adding EV, renewables, grid modernisation and AI data centre demand together, most credible forecasts point to a sustained deficit through the 2030s absent significant new mine investment. Mine development takes 10-20 years from discovery to first production, and the most attractive undeveloped ore bodies (Chile, Peru, DRC, Mongolia) are increasingly complicated by permitting, community consent and geopolitical risk. Copper is unlikely to run out (recycling handles roughly a third of current supply), but the price signal from the deficit is likely to be substantial, and it will show up in every layer of the interconnect stack.

The generators that used to be an afterthought

Every data centre has backup generation. It used to be a routine specification: rated for eight to twelve hours of runtime on onboard diesel, sized for full load, arranged in an N+1 or 2N redundancy configuration, exercised monthly, and forgotten about the rest of the time. In 2026, backup generation is a strategic variable. Diesel gensets have lead times running past two years for the largest units. Natural-gas gensets, favoured for lower emissions and lower fuel storage requirements, run longer. Behind-the-meter gas turbines (below) increasingly displace both.

The three largest genset manufacturers globally are Caterpillar, Cummins and Rolls-Royce mtu in the diesel/gas engine class. Mitsubishi Heavy Industries and Kohler round out the top five. All five have publicly discussed extended lead times through 2024-26 driven by simultaneous demand from data centres, industrial resilience upgrades, and hospital/critical-infrastructure spending. Caterpillar reported record backlogs on its Solar Turbines gas-turbine business in 2025; Cummins similarly on its Cummins Power Generation segment.

How are hyperscalers bypassing the grid, one campus at a time?

The behind-the-meter (BTM) power story is the biggest strategic development in the interconnect stack of the past two years. When grid interconnect timelines run four to seven years and hyperscalers want capacity in eighteen months, the arithmetic no longer works. The response has been to develop on-site generation at scale, sized to be the primary or majority power source; backup framing understates the role. Three technology classes are being deployed.

Gas turbines

The most mature option. GE Vernova's aeroderivative LM6000 (46-52 MW), LM2500 family (LM2500+ ~28 MW, LM2500+G4 ~33-34 MW), and stationary 7HA-class (~350 MW); Siemens' SGT-A65 and larger frames; Mitsubishi Heavy's H-class. Aeroderivatives can start in minutes and load-follow, which matches data centre demand well. Combined-cycle configurations (adding a steam turbine that runs off exhaust heat) push efficiency to 60%+. Multiple hyperscale campuses in Texas, Georgia and Virginia have announced or built dedicated gas-turbine plants sized at hundreds of MW.

Fuel cells

Bloom Energy has become the most visible BTM fuel-cell vendor for data centres, with multi-hundred-MW deployments announced with hyperscalers including Equinix and various colocation operators. Bloom's solid-oxide fuel cells run on natural gas (or hydrogen where available), operate at high efficiency (~60% electrical, higher with cogeneration), and can be sited compactly on the data centre campus. FuelCell Energy uses molten-carbonate technology in a similar deployment model. Fuel cells have significant advantages (no combustion emissions, quiet, modular) and one significant disadvantage (installed cost per kW is higher than gas turbines at scale).

Small modular reactors (SMR)

The most-discussed and least-deployed category. As of 2026, no commercial SMR is operating in the United States. Multiple pilot projects are in development: Amazon's investment in and PPA with X-Energy (four-unit HTGR site in Washington), Google's MOU with Kairos Power for a fluoride-salt-cooled unit, Meta's exploration of nuclear PPAs, and various utility-scale SMR programmes (NuScale in Idaho with UAMPS, formerly, since restructured; GE Hitachi BWRX-300 at OPG in Ontario; Rolls-Royce SMR programme in the UK). First commercial SMR power to a hyperscale customer is unlikely before 2029-31 at the earliest. But the announcements matter now because they signal the direction of travel: hyperscale customers are willing to commit to novel generation technologies if it decouples them from grid timelines.

Sizing the interconnect market

The published range

Global electrical grid equipment markets are large and well-documented. Total global T&D equipment revenue is roughly $150-200B annually. Large power transformers are approximately $30-40B of that. MV switchgear runs $40-55B. HV/MV cables $50-70B (including submarine cables, which are outside the data centre scope). Gensets and standby generation $20-25B. Fuel cells for stationary power ~$3-5B. Wrapping around all of it, global copper markets are roughly $250-300B in mine output value at 2024-25 prices.

The AI data centre slice of these markets is smaller but growing fast. Combining rack-level content per MW with hyperscale build rates yields a bottom-up estimate.

Bottom-up TAM for AI data centre interconnect

Rack-level and campus-level interconnect content per MW of IT load, indicative:

• Large power transformer (50-150 MVA class): ~$30-45k per MW at OEM prices, plus installation.
• MV switchgear (substation + distribution): ~$25-35k per MW.
• MV cables (site-scale copper/XLPE): ~$20-30k per MW depending on campus layout.
• Backup generation (gensets sized for full IT load at N+1): ~$40-60k per MW installed.
• Behind-the-meter primary generation (where deployed): $800-1,500k per MW installed for gas turbine, $2,000-3,500k per MW for fuel cell, $6,000-12,000k per MW for SMR (illustrative future).
• Copper and misc conductor: ~$25-35k per MW at 2025 prices.

Excluding the behind-the-meter primary generation category (where deployment stays absent), interconnect content averages roughly $150-200k per MW of IT load for a hyperscale campus. Including primary-generation BTM (where applicable), the figure can climb toward $1M+ per MW.

Applying that content per MW to the ~7-9 GW of AI IT-load additions modelled in Part II (and reused in Part III) gives a 2026 TAM of roughly $1.2-1.7B in narrow interconnect equipment, or $12-18B including construction, engineering and servicesIncluding BTM primary generation lifts the ceiling substantially: by 2030, if 20-30% of new AI capacity is BTM-primary (not grid-primary), the total could exceed $60B. Full workings in Annex §A1.

Profit pools

The profit-pool inversion that Parts II and III described (smaller components hold better margin) plays out somewhat differently in interconnect. The categorisation is more about scarcity rent than about qualification barriers.

• GOES (~25-35%): materials-industry margins, protected by concentrated supply and long expansion lead times. The best-defended layer.
• Large power transformers (~15-22%): heavy-industrial margins, currently benefiting from scarcity pricing in the current cycle. Sustainable at elevated levels as long as backlogs persist.
• MV switchgear (~18-25%): better than transformers because product diversity is higher and switching-mechanism IP holds. Digital and clean-gas products deliver higher margins than baseline.
• MV cables (~10-15%): commodity-plus economics; margin comes from scale and specialty products (submarine, HVDC).
• Copper (fluctuating with commodity): mining margins vary widely by ore body and cycle; smelter margins compressed while fabrication margins land in the low-teens.
• Backup gensets (~15-20%): healthy through the current lead-time cycle, would compress in a normal market.
• Fuel cells (~5-15% and improving): Bloom Energy and peers still scaling into profitability; margin trajectory positive but modest.
• SMR (loss-making): pre-commercial; all margin discussion is speculative until units are operating.

The consolidation is done, the operations phase is beginning

The strategic map of interconnect equipment consolidated over 2018-2024 through a series of large transactions. The big pieces have moved. What remains is bolt-on M&A, capacity expansion capex, and operational execution.

What is still on the board

• Regional US LPT manufacturers (Delta Star, Virginia Transformer, Pennsylvania Transformer) as consolidation targets for larger OEMs looking to expand domestic capacity. Small revenue, high strategic value under CHIPS Act and DOE Loan Programs Office funding.
• SMR developers as investment targets from hyperscalers and industrial groups. Post-restructuring NuScale, private Kairos and Oklo, Rolls-Royce SMR: equity valuations vary widely by regulatory progress.
• Cable manufacturers outside the top five (Southwire in the US, regional European specialists) as consolidation targets for Prysmian/Nexans/NKT. Data centre demand has raised the strategic value of MV cable capacity.
• Fuel cell and specialty gas turbine developers as venture and strategic capital continues to flow into BTM power.

What could change the structure of this market

Trade-offs, and the argument against

The permitting and policy layer

Grid interconnect spans beyond an equipment problem. Federal, state and local permitting timelines for new transmission lines, substations and generation facilities have expanded over the past two decades. NEPA reviews, state siting boards, community consent, environmental impact assessments, transmission cost allocation between ratepayers and load: each is a genuine process with legitimate constituencies, and the cumulative timeline for a major new interconnection can consume years. The Inflation Reduction Act and various federal permitting reform proposals have made incremental changes, but nothing fundamental has shifted. Interconnect is now as much a policy problem as an equipment problem, and the equipment problem is severe enough on its own.

The commodity risk

Copper prices could correct if global demand normalises faster than expected, if mining supply comes online faster, or if AI capex slows materially. GOES supply could ease if Chinese exports increase (currently limited by domestic demand and provenance rules). Interconnect vendors have benefited enormously from scarcity pricing over the past three years; some of that will normalise as capacity expansions land in 2027-28.

Who wins, who is pressured

• Winners: the three global LPT majors (Hitachi Energy, Siemens Energy, GE Vernova) through the current cycle, with margins holding as long as backlogs persist; MV cable majors (Prysmian, Nexans, NKT) similarly; the top switchgear vendors as SF6 alternatives expand ASPs; Bloom Energy and gas turbine OEMs on BTM demand; GOES producers on durable material scarcity; integrated grid-to-chip platforms (Eaton, Schneider, Vertiv) on the bundling thesis.
• Under pressure: hyperscalers whose expansion depends on grid interconnect timelines they cannot control (some Meta, Microsoft, Amazon deployments have visibly slipped); utility-scale customers competing with hyperscalers for the same LPTs and switchgear; smaller regional LPT builders whose capacity is being locked up by hyperscale contracts and priced out for utility replacement customers.
• The dominant risk: a slowdown in AI capex would relieve the equipment scarcity, normalise lead times, and compress the margins that OEMs have been enjoying. The equipment vendors are levered to hyperscaler capex in the same way that Parts II and III described the semiconductor and cooling vendors being levered.

Ten leading indicators to watch

1. LPT backlog-to-revenue ratios at Hitachi Energy, Siemens Energy and GE Vernova. The sector's health in one number.
2. GOES capacity announcements from Japanese and Korean producers. The materials layer's leading indicator.
3. US ISO interconnect queue timelines (PJM, ERCOT, MISO, CAISO). When these compress, the BTM strategy's economic case softens.
4. Announced hyperscale BTM primary-generation deals. The pace of the workaround.
5. SMR commercial operating date announcements. Especially X-Energy, Kairos, GE Hitachi BWRX-300, NuScale.
6. Federal permitting reform legislation. Would compress interconnect timelines if it lands.
7. Bloom Energy quarterly bookings. The BTM fuel-cell leading indicator.
8. Copper spot price and LME inventories. The commodity temperature check.
9. Any regulatory ruling on Amazon. Talen or similar co-located nuclear deals. Sets precedent for BTM nuclear siting.
10. Announced hyperscaler capex slowdowns or accelerations. The demand-side signal.

Closing

The interconnect stack is the layer of AI infrastructure most exposed to physical grid constraints and policy timelines, and the layer where the industry response has been to route around the constraint through behind-the-meter power. That response is now visible in every hyperscaler's capacity plan. Gas turbines, fuel cells, and eventually SMR will take on some fraction of AI power demand that the grid cannot serve on the timeline hyperscalers need. The underlying equipment layers (LPTs, switchgear, cables, gensets) remain in shortage, and the materials layer beneath them (GOES, copper) is structurally tight. For operators, the message is that interconnect capacity is the constraint to plan around, and the planning horizon is now measured in years. For investors, the interconnect equipment vendors have been re-rating over the past three years and the multi-year backlogs suggest that re-rating has further to run.

This is Part IV of a six-part series. Across the earlier three parts (capacitor stack, wide-bandgap semiconductors, thermal management) and this one, the through-line has been consistent: the physical infrastructure of AI compute has become supply-constrained across every layer at once, in ways that most compute-side commentary underweights, and the profit pools are migrating in each layer toward the components where qualification, materials chokepoints or lead-time scarcity create durable pricing power. Interconnect is the layer where those dynamics land most heavily on the calendar. The next several years of AI infrastructure growth will be paced by grid connections, transformer factories and generator lead times.

Annex: how every derived number was built

Every figure in this piece is either published (traceable to a named source), derived (calculated from published inputs), or estimated (my judgement, labelled). This annex covers the latter two.

§A1. TAM model for AI data centre interconnect

Status: derived. Full workings below.

Content per MW inputs. LPT (50-150 MVA class): ~$30-45k per MW of IT load at OEM prices, based on published utility procurement data and hyperscale-scale unit pricing through 2025. Add ~30-50% for installation, testing and commissioning. MV switchgear: ~$25-35k per MW based on Schneider Electric, Eaton and ABB product-line pricing scaled to campus loading. MV cables: ~$20-30k per MW based on Prysmian/Nexans quotations for XLPE MV cable systems, scaled to typical campus layout of 5-10 km MV runs per 100 MW. Gensets (N+1 diesel at full load): ~$40-60k per MW installed based on Caterpillar/Cummins list pricing plus integration and fuel systems. Copper and misc: ~$25-35k per MW at 2025 copper spot around $9,500/tonne, at 30 tonnes per MW.

Total interconnect content per MW. Sum of the above: ~$140-210k per MW of IT load, midpoint ~$175k. Author's build; individual line items are estimates.

Volumes. AI IT-load additions per Part II model: ~7-9 GW in 2026 rising to ~14-18 GW by 2030. Independent cross-check against Kuo's VR200 shipment estimates and JLL/McKinsey capacity forecasts.

Narrow TAM (equipment only). 8 GW × $175k/MW = ~$1.4B of narrow interconnect equipment in 2026 per author's bottom-up model. Extending to 2030 at 15 GW × ~$180k/MW (nominal content stability) = ~$2.7B by 2030.

Wide TAM (including EPC, engineering, services). Applies a 8-10× multiplier to the narrow TAM for engineering, construction, installation and commissioning services. Author's assumption; based on utility grid capex ratios where equipment is typically 15-20% of total substation project cost. Gives ~$12-18B for 2026, ~$20-28B by 2030 excluding BTM.

Including BTM primary generation. Assumes BTM primary share of new AI capacity rises from ~5% in 2026 (~0.5 GW at ~$1.5M/MW gas turbine average) to ~25% by 2030 (~4 GW × ~$1.5M/MW blended average across gas, fuel cell, early SMR). Adds estimated ~$0.7B in 2026 rising to ~$6B by 2030 per author's model. Combined wide TAM including BTM: ~$13-19B for 2026 rising to ~$26-34B by 2030. Bull case with heavier SMR deployment and higher grid-constraint pressure could reach $45-65B by 2030. Sensitivities are dominated by (1) AI capacity growth rate, (2) BTM share assumption, (3) SMR commercial operating date.

§A2. GOES supply and share

Status: estimated. Global GOES production stays inconsistently published. Author's estimates: Nippon Steel + JFE ~30-40% combined (dominant in domain-refined grade); POSCO ~10-15%; Cogent (Tata) and ArcelorMittal ~15-20% combined; NLMK/DK ~5-10% (subject to sanction-related uncertainty); Chinese producers (Baosteel, TISCO, WISCO) ~25-30% combined (largely consumed domestically). Add small production from AK Steel (Cleveland-Cliffs) in the US, and Indian producers (SAIL). Figures directional only; consult Metal Bulletin and SteelOrbis for current data if precision is needed.

§A3. LPT lead time trajectory

Status: published across multiple sources, with ranges. Pre-pandemic US LPT lead times of 40-60 weeks are documented in DOE 2014 and 2017 supply-chain studies. Post-pandemic expansion to 120-150 weeks is documented in DOE 2022 and 2023 studies, plus EPRI, T&D World and utility engineering-firm surveys through 2025. Specific hyperscale-class units have been quoted at 200+ weeks by industry contacts through 2025-26; not independently verified.

§A4. Interconnect queue timelines

Status: published. Lawrence Berkeley National Laboratory's annual "Queued Up" report tracks US ISO interconnect queues. As of the 2024 edition (published 2025 with 2024 data), typical queue times for large generation projects run 3-5 years in most ISOs, with large-load interconnections (data centres) reporting similar or longer waits in PJM, ERCOT and MISO. Individual data centre operators have publicly cited 4-7 year timelines for specific sites.

§A5. Copper intensity per MW

Status: estimated ranges, cited in industry press. BHP, Wood Mackenzie, S&P Global Commodity Insights and IEA have each published estimates in the 30-65 tonnes per MW range for data centres, with wider variation for AI-optimised hyperscale (higher end). The 3-5× multiplier vs legacy enterprise is a widely cited industry rule of thumb; specific studies vary. Global copper demand estimates and deficit forecasts through 2030 are from the same source cohort.

§A6. Behind-the-meter deal tracker

Status: compiled from press releases and trade press through mid-2026. Major announced or contracted deals include: Amazon. Talen (Susquehanna, ~960 MW capacity rights), Microsoft. Constellation (Three Mile Island Unit 1 restart, ~835 MW targeting 2028), Meta. Constellation (Illinois nuclear PPA, ~1,100 MW), Amazon. X-Energy (Washington HTGR site, 4 units × 320 MW each), Google. Kairos (fluoride-salt SMR MOU, ~500 MW), plus numerous smaller gas-turbine and Bloom Energy deals. Deal sizes and timelines subject to regulatory approval and construction risk; figures are announced capacity.

§A7. Other published figures used

• Hitachi Energy backlog: reported multi-year highs through 2025; specific backlog values from company disclosures.
• Siemens Energy grid capex programme: €4.5B+ announced through 2027, various company disclosures.
• Eaton acquisitions: Fibrebond $1.4B, Ultra PCS $1.55B, Boyd Thermal $9.5B, others undisclosed; Eaton press releases and Reuters.
• SF6 GWP: ~23,500 relative to CO₂, atmospheric lifetime 3,200 years; IPCC AR6 (2021).
• Bloom Energy hyperscale deployments: multiple announcements through 2024-26; Bloom Energy press releases and trade press.
• Copper spot price: ~$9,500/tonne baseline 2025, ~$10,500-11,500/tonne range through 2026; LME.

The complete series

This is Part IV of the AI Power Chain series. The six parts cover the physical infrastructure of AI compute, layer by layer, from the medium-voltage utility bus to the 0.8V transistor gate. Each part has a technical companion that walks through the underlying physics.

For readers who want the physics behind this piece (transformer efficiency, GOES manufacturing, SF6 alternatives, XLPE cables, aeroderivative turbines, SOFC fuel cells, four SMR families), see the technical companion: The Interconnect Stack: Technical Companion.

The Capacitor Stack (Part I) covers hybrid supercapacitors, EDLCs, MLCCs, silicon capacitors and Al electrolytics. Technical companion: The Capacitor Stack: Technical Companion.

The Wide-Bandgap Stack (Part II) covers SiC and GaN power semiconductors after the substrate glut. Technical companion: The Wide-Bandgap Stack: Technical Companion.

The Thermal Stack (Part III) covers data centre liquid cooling from die to dry cooler. Technical companion: The Thermal Stack: Technical Companion.