Four-House Cross Research
AI / Data Centre Power Demand
GS SUSTAIN
JPM AM
MS Research
SemiAnalysis
GS: Apr'24 / Oct'25 / Feb'26 / May'26 (Commodities) · JPM: GTA 1Q26 + 2Q26 · MS: "Flexible Power" Mar'26 · SemiAnalysis: "US Grid Constraints" 2026
Key Takeaways
Six messages for the Investment Committee from 7 reports across three houses
1
Demand has converged — and GS May'26 just put a hard near-term anchor on itGS + JPM + MSGS May'26 ✨
GS +220%, JPM +228%, MS 320 GW build-out — three independent teams arrive at near-identical conclusions on the 2030 picture. GS Commodities May'26 adds the operational anchor: US DC power demand goes from 31 GW (2025) to 66 GW (2027) — more than doubles in 24 months. US DC capacity reaches 94.7 GW by end-2027. DC share of US peak summer power: 4.1% → 8.5%. This is equivalent to adding a new country the size of Japan to global electricity in 24 months. US power demand CAGR of 3.2-3.8% is the highest since the 1990s.
2
Supply constraint is real but unevenly distributed — and only 50-60% of planned capacity actually landsCRITICALJPM 2Q26 ✨GS May'26 ✨
Raw generation capacity (Bottleneck 1) is solvable — JPM 2Q26 confirms 221.2 GW of US grid additions planned over 2026-28 with batteries doubling (+115%) and gas additions halving (-52%). But the new GS Commodities May'26 realisation analysis is the missing piece: historically only 72% of DCs scheduled within 4 quarters come online on time; the haircut tightens to 60% next year, 50% over two years. Raw schedule reaches 133.3 GW US DC capacity by Dec'27 but GS forecast caps at 94.7 GW (38.6 GW / 29% haircut). The true binding constraint remains grid interconnection & T&D labour — GS quantifies a 78,000 skilled worker gap requiring 3-4 years of training. The slippage is structural, not cyclical.
3
Inference load flexibility changes everythingMS NEW
As AI inference rises to 45-50% of DC workload, power demand shifts from stable/predictable (training) to volatile/spiky (inference). The system challenge shifts from "can we supply enough power" to "can we supply power flexibly enough." This creates a new investment category: energy storage systems (ESS) for millisecond-response peak shaving and frequency regulation.
4
Sector rotation follows a clear sequence — ESS is next, and JPM 2Q26 just validated itMS ROTATIONJPM 2Q26 ✨
MS maps the AI power investment rotation: Nuclear (2023-24, +4.8x) → Gas Generators (2024-25, +3.2x) → Backup Generators (2025, +2.1x) → Fuel Cells (2025-26, +4.5x) → ESS (next). Each wave lasted 6-12 months. The JPM 2Q26 data confirms the rotation is already happening in the supply mix: 26% of new 2026-28 US capacity is batteries, up from 12% in 1Q26. Direct read-through to TSLA Megapack, FLNC, CATL, BE BTM. Understanding where you are in this rotation is more important than the demand headline number.
5
GS warns we are still in the "Appraisal" phase — but transition signals are building
Using their AI Innovation Cycle framework (Shale Oil analogy), GS places AI infrastructure in the Appraisal/Hopes & Dreams phase — the best window for infrastructure equities. But reinvestment rates at 87% and CROCI declining from 31% toward the 24% historical low are early warning signals. Three transition triggers to monitor: financial inflexibility (not triggered), return erosion (deteriorating), product oversupply (not triggered).
6
SemiAnalysis: the grid can't deliver firm capacity fast enough — so >50% of new DCs go Behind-The-Meter by 2028SemiAnalysis ✨
A fourth, more bearish-on-grid lens. Tracking 40,000 generation assets, SemiAnalysis finds only ~15 GW of net-new firm (ELCC-accredited) capacity added per year — versus US DC demand rising +21→+84 GW (2026-30). Nameplate solar+BESS add 20+ GW/yr each but their ELCC contribution is minimal and declining (Duck Curve + duration saturation). Grid headroom goes RED by 2027 (PJM 2027/28 BRA cleared 6.6 GW short). Result: BTM powers >50% of new US DC by 2028, equipment TAM crosses 50 GW/yr by 2029. Contrarian read-through: 2026 = peak turbine orders for GEV/Siemens/MHI; winners rotate to Bloom/INNIO/Wärtsilä. New ERCOT theme: Batch Zero hybrid structures (NMA/BYOG/WLPUN/PCLR) + NRG 5.4 GW BYOG play.
7
Four houses, four dimensions, one complete frameworkSYNTHESIS
Goldman Sachs SUSTAIN tells you WHERE in the cycle (6P constraints, Innovation Cycle phase, BTM framework). GS Commodities May'26 ✨ adds WHEN it lands: 31→66 GW in 24 months with a 50-60% realisation haircut, plus regional reliability tiering (PJM/MISO/BPA at risk). J.P. Morgan tells you HOW BIG the gap is (219 GW global DC capacity by 2030, 221.2 GW US grid additions, >2,600 GW queue, 9-18 GW shortfall). Morgan Stanley tells you WHERE money goes next (inference flexibility, ESS rotation, Na-ion cost curve). SemiAnalysis ✨ tells you HOW power gets delivered — the ELCC firm-capacity drought forces >50% of new DC behind the meter by 2028. All four dimensions converge on: flexibility-led capacity build, ESS + fuel cells as the next investable waves, regional concentration (TX / GA / VA = winners; PJM / MISO / BPA = stress beneficiaries), and a structural BTM shift that puts 2026-peak risk on gas-turbine OEMs.
Goldman Sachs
WHERE in the cycle + WHEN it lands
6P constraint framework · AI Innovation Cycle / Shale analogy · BTM 14 GW · Vera Rubin server data · CO₂ social cost $145-170B · AI drug discovery value $83-412B · Green Reliability Premium · 87% reinvestment rate · 32→82 Buy-rated stocks · May'26 (Commodities) ✨ US DC 31→66 GW by 2027 · 94.7 GW capacity end-2027 · 8.5% peak summer share · 60%/50% realisation haircut · Regional reliability tiering (PJM/MISO/BPA at risk)
J.P. Morgan
HOW BIG the gap
2Q26 ✨ 82→219 GW global DC capacity (CAGR 21.7%) · AI workload 54%→71% of DC · US DC: 220→600 TWh (12% of US power) · 221.2 GW US 2026-28 additions (Batteries 26%, Gas 13%, Solar 49%) · 35.1 GW retirements · DC power mix: Tech 45% / Cooling 38% / Power conv 11% / Network 5% · >2,600 GW interconnection queue
Morgan Stanley
WHERE money goes next
Training vs inference load profiles · ESS 321 GWh (bull: 590) · Sector rotation map · 9-18 GW US shortfall (Exhibit 12) · Na-ion cost RMB 0.32→0.21/Wh · BTM ESS IRR 23%/36% · System rebalancing to flexibility · CATL, Tesla, LGES, Fluence, BYD
SemiAnalysis ✨
HOW power gets DELIVERED
BTM >50% of new US DC by 2028 · BTM equipment TAM 50+ GW/yr by 2029 · US DC demand +21→+84 GW (2026-30) · Only ~15 GW net-new ELCC/yr · Grid headroom RED by 2027 (PJM 6.6 GW short) · "No gas until 2028" (<10 GW/yr) · 2026 = PEAK turbine orders (GEV/Siemens/MHI) · Winners: Bloom/INNIO/Wärtsilä · ERCOT Batch Zero (NMA/BYOG/WLPUN/PCLR) · NRG 5.4 GW BYOG (~$2.5bn EBITDA)
Detailed Headline Forecast Comparison
All data points across six publications for line-by-line comparison. GS May'26 and JPM 2Q26 are the latest releases. Metric column & header stay fixed while scrolling.
| Metric | GS Apr'24 | GS Oct'25 | GS Feb'26 | GS May'26 ✨ | JPM 1Q26 | JPM 2Q26 ✨ | MS Mar'26 | Signal |
|---|---|---|---|---|---|---|---|---|
| DC Power Growth 2030 vs 2023 | +160% | +175% | +220% | US +113% to 2027 | +228% | +220% | 320 GW | CONVERGED |
| Global DC Demand 2030E (TWh) | ~1,068 | 1,131 | 1,316 | N/A (US-only) | ~1,350 | ~1,310 | N/A | tightening |
| Global DC Capacity 2030E (GW) ✨ | N/A | N/A | N/A | N/A (US-only) | N/A | 219 GW | N/A | NEW |
| Global DC Capacity 2025 base (GW) ✨ | N/A | N/A | N/A | N/A | N/A | 82 GW | N/A | CAGR 21.7% |
| US Power CAGR to 2030 | 2.4% | 2.6% | 3.2% | near-term sharp | ~3.1% | ~3.2% | 3.8% | MS highest |
| AI Share of DC 2030E | ~20% | ~39% | ~50% | N/A | ~65% | ~71% | 45-50% | JPM ↑↑ |
| DC % of US Power 2030E | 8% | 11% | ~14% | 8.5% (2027 peak) | 13.5% | ~12% | N/A | CONVERGED |
| Hyperscaler CapEx+R&D 2026E | N/A | N/A | >$1T | N/A | $664B+ | N/A | N/A | Unprecedented |
| US 2026-28 Total Additions (GW) ✨ | N/A | N/A | N/A | ~62 DC-only* | 222 | 221.2 | N/A | refined |
| US Batteries Additions 26-28 (GW) ✨ | N/A | N/A | N/A | N/A | 26.7 | 57.5 (26%) | N/A | ↑115% ✓ESS |
| US Nat Gas Additions 26-28 (GW) ✨ | N/A | N/A | 42 peaker+12 CCGT | N/A | 60 | 28.8 (13%) | 15-20 | ↓52% ⚠ |
| US DC Demand 2025-28 (GW) | N/A | N/A | N/A | 31→66 by 2027 | N/A | N/A | 74 GW | MS unique |
| Net Shortfall 2025-28 (GW) | N/A | N/A | N/A | 3 high-risk regions | N/A | N/A | 9-18 GW | MS unique |
| DC ESS Deploy 2030E (GWh) | N/A | N/A | N/A | N/A | N/A | N/A | 321 (bull:590) | MS unique |
| BTM Solutions (GW) | N/A | N/A | 14 GW | N/A | N/A | N/A | BE 5-8 GW | GS+MS |
| CO₂ Increase (mn tons) | 215-220 | 215-220 | 285-290 | N/A | N/A | N/A | N/A | ↑35% |
| Innovation Cycle / Next Rotation | N/A | N/A | Appraisal | N/A | N/A | N/A | ESS next | Complementary |
| Na-Ion Cost (RMB/Wh) | N/A | N/A | N/A | N/A | N/A | N/A | 0.32→0.21 | MS unique |
| Reinvestment Rate 2026E | N/A | N/A | 87% | N/A | N/A | N/A | N/A | GS warning |
| US DC Demand 2025 (GW) ✨ | N/A | N/A | N/A | 31 GW | N/A | ~25 GW | N/A | GS NEW |
| US DC Demand 2026E (GW) ✨ | N/A | N/A | N/A | 41 GW | N/A | ~33 GW | N/A | GS NEW |
| US DC Demand 2027E (GW) ✨ | N/A | N/A | N/A | 66 GW | N/A | ~41 GW | N/A | GS NEW |
| US DC Capacity end-2027 (GW) ✨ | N/A | N/A | N/A | 94.7 GW | N/A | N/A | N/A | vs 133.3 raw |
| DC % of US Peak Summer 2027 ✨ | N/A | N/A | N/A | 8.5% | N/A | N/A | N/A | 2x in 2yrs |
| Realisation Rate (Adjusted) ✨ | N/A | N/A | N/A | 60% / 50% | N/A | N/A | N/A | vs 72% hist |
*Note on US 2026-28 Total Additions: GS May'26's ~62 GW is DC-specific capacity, JPM's 221.2 GW is total US grid additions (all sectors). Not directly comparable. GS realisation-adjusted DC additions: 2026 ~13.7 GW + 2027 ~18 GW (50% of 36.3 raw schedule) + 2028 estimate. GS Feb'26 figure "42 peaker + 12 CCGT" refers to gas-fired additions specifically.
Global DC Power Demand Trajectory (TWh)
GS revised 3x: +160% → +175% → +220%. Now within 3% of JPM's +228%. Equivalent to adding a Top 10 power consuming country.
Forecast Evolution: 23 Months of Upward Revisions
Apr 2024 (GS)+160%(1,068 TWh)
Initial framework. Introduced Jevons Paradox / 3-constraint model (budget, demand, no constraint). AI = ~20% of DC. ChatGPT = 6-10x power per query vs Google search. 32 Buy-rated stocks.
Oct 2025 (GS)+175%(1,131 TWh)
AI share doubled to 39%. Introduced 6P constraint framework. Added labour analysis (78K T&D gap). $790B grid capex. Green Reliability Premium $40/MWh. 82 GW capacity needed.
Feb 2026 (GS)+220%(1,316 TWh)
Major revision. >$300B hyperscaler capex upward revision. 87% reinvestment rate. Vera Rubin integrated. 105 GW (incl 14 GW BTM). CO₂ up to 285-290 mn tons. US CAGR 3.2%. AI Innovation Cycle in Appraisal phase.
1Q 2026 (JPM)+228%(~1,350 TWh)
Independent validation. US DC: 168→620 TWh. AI = 65% of DC capacity. Hyperscaler capex $664B+. Binding constraint: baseload mismatch and interconnection queue. 222 GW total US additions.
2Q 2026 (JPM, Apr 30)219 GW(global DC 2030)
NEW DATA: Global DC capacity 82→219 GW (2025-30, CAGR 21.7%). AI workload share of DC: 54%→71% (2025→30). US DC consumption: ~220→~600 TWh, reaching 12% of total US power. US 2026-28 build: 221.2 GW additions (revised mix: Solar 49% / Batteries 26% / Gas 13% / Wind 11%) vs 35.1 GW retirements. Material shift: Batteries doubled vs prior, gas additions cut in half — validates MS ESS rotation thesis.
Mar 2026 (MS)320 GW
New dimension: inference flexibility. ESS as next rotation. 74 GW US demand with 9-18 GW shortfall. DC ESS: 321 GWh by 2030. Sodium-ion cost disruption. BTM ESS IRR 23%/36%.
May 2026 (GS Commodities) ✨+113%(US DC 31→66 GW by 2027)
NEW DATA — Near-term operational view: US DC power demand 31 GW (2025) → 41 GW (2026) → 66 GW (2027) — more than doubles in 24 months. US DC capacity reaches 94.7 GW end-2027 at 70% utilisation. Realisation haircut: historical 72% on-time → only 60% next year → 50% over 2 years materialise (raw schedule reaches 133.3 GW Dec'27, GS adjusted 94.7 GW = 38.6 GW haircut). DC % of US peak summer power: 4.1% (2025) → 5.3% (2026) → 8.5% (2027). Regional tiering (NEW): Mid-Atlantic / Mid-Continent / Northwest = elevated reliability risk; TX / GA = marginal tightening; TN / NE / FL = critically tight already. Authors: Wei, Struyven, Dart.
GS Commodities May'26 ✨ US DC Capacity Trajectory — Raw Schedule vs Risk-Adjusted
Monthly data (May'26 → Dec'27) read directly from GS chart. Headline: 31 → 41 → 66 GW demand (capacity 44 → 59 → 94.7 GW end-Dec'27 at 70% utilisation, matches article exactly)
2025 Demand
31 GW
2026E Demand
41 GW
2027E Demand
66 GW
2027 / 2025
+113%
This is GS Commodities Research's most operational forecast to date — bottom-up from Aterio facility-level data (locations, permitting, construction status, satellite imagery). Both lines start identically at 47.1 GW (May'26) and diverge progressively: raw schedule reaches 133.3 GW by Dec'27 while GS risk-adjusted forecast caps at 94.7 GW. Capacity utilisation assumption: 70%. The Dec'27 GS forecast of 94.7 GW × 0.70 = 66.3 GW demand validates the article's "66 GW in 2027" headline to within rounding.
Realisation Haircut — Why the GS Line Diverges from Raw Schedule
YoY additions scheduled: 13.6 GW (2026), 36.3 GW (2027) vs realised: 6.4 GW (2024), 8.5 GW (2025). Historically only 72% of DCs scheduled for activation in the next 4 quarters actually come online on time. GS applies a tighter haircut: ~60% of next-year capacity, ~50% over two years. Net result by Dec'27: raw 133.3 GW vs adjusted 94.7 GW = 38.6 GW haircut (29% of raw). Drivers of slippage: developers submitting in multiple regions and choosing only the best site, supply chain, labour shortages, 18-24 month build cycle post-permit.
GS May'26 ✨ DC Share of US Peak Summer Power
Most direct measure of grid stress: how much of the summer peak DCs eat. 4.1% → 8.5% in 2 years.
This is the cleanest single metric for grid stress. Peak summer demand is when reliability margins are thinnest — adding DCs that are essentially 24/7 baseload to an already stressed peak window creates outsized grid strain. The 8.5% by 2027 is more than double the 2025 baseline in just 24 months. No comparable load category has ever scaled this fast against the US summer peak.
⚠️ Reliability Read-Through
If DC share grows to 8.5% of peak by 2027, the marginal reserve margin in the most stressed regions (PJM = Mid-Atlantic, MISO = Mid-Continent, BPA = Northwest) compresses materially. GS flags these three regions as having planned generation additions insufficient to absorb the incoming DC load — implying potential brownout risk or forced load-shedding during summer peaks. Direct beneficiaries: backup power (Generac), BTM fuel cells (BE), demand response platforms, and ESS for peak shaving (TSLA Megapack, FLNC).
GS May'26 ✨ Regional Power Reliability Tiering
2027 annual DC additions in each of Mid-Atlantic / Texas / Mid-Continent are individually scheduled to exceed the entire nation's total 2025 additions.
| Region | RTO / Grid | Reliability Risk | Driver |
|---|---|---|---|
| Mid-Atlantic | PJM (VA, MD, PA) | 🔴 ELEVATED | Largest DC cluster (Loudoun). Planned generation lags incoming demand. |
| Mid-Continent | MISO (IL, IN, MI, IA) | 🔴 ELEVATED | Coal retirements + new DC siting. Planned gen insufficient. |
| Northwest | BPA / WECC (WA, OR) | 🔴 ELEVATED | Cheap hydro draws DCs, but limited new generation. May refuse some projects. |
| Texas | ERCOT | 🟡 MARGINAL | Heavy DC additions but solid generation pipeline (gas + solar + storage). |
| Georgia | Southern Co. / SERC | 🟡 MARGINAL | Vogtle nuclear + new gas. Major DC growth absorbed. |
| Tennessee | TVA | 🟣 CRITICALLY TIGHT | Constrained DC additions — already at capacity ceiling. |
| New England | ISO-NE | 🟣 CRITICALLY TIGHT | Pipeline + gas constraints. Won't see major DC builds. |
| Florida | FRCC | 🟣 CRITICALLY TIGHT | Constrained — DC growth bottlenecked by generation. |
GS May'26 ✨ Investment Implications by Region
Where the regional tiering points capital in 2026-27
🔴 Elevated Risk Regions (PJM / MISO / BPA): Backup power becomes mandatory — Generac portable+stationary, fuel cells (BE BloomEnergy, FCEL), and demand response. ESS for peak shaving (TSLA Megapack, FLNC) sees pricing power. Co-location at owned generation becomes critical (ETR, CEG, VST own-and-supply model).
🟡 Marginal Tightening (TX / GA): These remain the highest-growth DC markets in absolute GW terms. Hyperscalers will pivot toward TX in particular — ERCOT's faster interconnection process is a major edge. Read-through to solar+storage developers focused on ERCOT (NextEra residential is less relevant; utility-scale developers are the play). Nuclear in GA (Vogtle units 3&4) is a unique national asset.
🟣 Critically Tight (TN / NE / FL): These regions are excluded from the next wave of DC capex. Watch for spillover demand to neighbouring regions (TN → MISO/PJM border zones, NE → upstate NY ISO).
Cross-House Read
GS May'26 regional tiering complements MS Mar'26's ESS rotation thesis — the elevated-risk regions are precisely where ESS economics will pay first (peak shaving + frequency regulation at scale). The JPM 2Q26 mix shift (batteries +115%, gas -52%) is the macro confirmation. The three houses now converge on: flexibility-led capacity in stressed regions = the highest IRR sub-segment in AI power.
JPM 2Q26 ✨ Global Data Centre Capacity (GW) — AI vs Non-AI
82 GW (2025) → 219 GW (2030) — CAGR 21.7%. AI workload share rising from 54% → 71%.
2025 Base
82 GW
2030 Forecast
219 GW
AI Workload 2030
~155 GW
CAGR 5yr
+21.7%
JPM's 2Q26 release (April 30, 2026) introduces a GW-denominated capacity forecast that complements the existing TWh trajectory. The 2.67x build-out by 2030 is consistent with GS Feb'26 (105 GW US + 72 GW RoW = 177 GW DC-specific) and MS (320 GW total power including non-DC). AI workload rises from ~54% of capacity in 2025 to ~71% by 2030 — at the high end of all houses, exceeding GS Feb'26's 50% and MS's 45-50%.
Source: JPM AM Guide to Alternatives, 2Q26 (Apr 30, 2026), p.39 — citing IEEE Communications and McKinsey & Company forecasts. Implication for IC: A 137 GW capacity build over 5 years requires ~$4.1tn cumulative capex at $30bn/GW DC capex (excl. compute), tracking the GS hyperscaler reinvestment rate of 87%.
JPM 2Q26 ✨ Data Centre Power Consumption Breakdown
Where the GW actually goes — informs which equipment vendors capture the spend
| Component | % Power | 2030 GW Equivalent | Key Beneficiaries (our mapping) |
|---|---|---|---|
| Tech equipment (GPU/CPU/Memory) | 45% | ~99 GW | NVDA, AMD, MU, SNDK, TSM |
| Cooling | 38% | ~83 GW | Vertiv (VRT), liquid cooling players |
| Power conversion & regulation | 11% | ~24 GW | Eaton, Schneider, Vertiv (VRT) |
| Networking (optics, switches) | 5% | ~11 GW | COHR, LITE, ALAB, CRDO, GLW |
| Lighting | 1% | ~2 GW | (de minimis) |
Key Investment Insight
Cooling at 38% of DC power is the surprise number. This is up from ~25-30% historically because liquid cooling at high-density AI racks (e.g. Vera Rubin NVL8 at 24kW/server) requires far more pumping power than air cooling. Read-through: Vertiv (VRT) and other thermal management vendors capture both cooling (38%) and power conversion (11%) = ~49% of DC power spend — a structurally larger wallet share than the headline narrative implies.
JPM 2Q26 ✨ U.S. DC Power Consumption + % of Total US Power
McKinsey-sourced forecast: U.S. DC consumption 220 → 600 TWh by 2030, reaching ~12% of total US power
Critical anchor for the entire "AI power" investment thesis: data centres rise from ~5% of US power demand in 2025 to ~12% by 2030. This is the single largest concentrated source of incremental US power demand growth — the entire residential sector for comparison contributes only ~0.6pp to the 3.2% CAGR per GS. The 2.7x growth in 5 years is the structural tailwind behind every name in the AI power supply chain — generators (GEV), fuel cells (BE), ESS (TSLA/FLNC), and optical networking (COHR/LITE/ALAB).
Cross-house reconciliation: JPM 2Q26's 12% (~600 TWh) sits between GS Feb'26 (~14% = ~790 TWh) and the prior consensus. The difference is largely how aggressive each house is on AI inference scaling — JPM 2Q26 implies more efficient AI compute per query than GS Feb'26.
JPM 2Q26 ✨ U.S. 2026-28 Generation Mix — Major Revision
Total 221.2 GW additions (vs 1Q26 estimate of 222 GW). But the mix has shifted dramatically.
| Source | JPM 1Q26 | JPM 2Q26 | Change | % |
|---|---|---|---|---|
| Solar | 111.1 GW | ~108.4 GW | -2.4% | 49% |
| Batteries / ESS | 26.7 GW | ~57.5 GW | +115% | 26% |
| Natural Gas | 60 GW | ~28.8 GW | -52% | 13% |
| Wind | 22.2 GW | ~24.3 GW | +9% | 11% |
| Other | 2.2 GW | ~4.4 GW | +100% | 2% |
| TOTAL ADDITIONS | 222.2 GW | 221.2 GW | -0.5% | 100% |
| Total Retirements (Coal 69% / Gas 30%) | 34 GW | 35.1 GW | +3.2% | — |
| NET ADDITIONS | 188 GW | 186.1 GW | -1.0% | — |
⚡ Material Mix Shift — Validates MS ESS Rotation Thesis
Batteries doubled (+115%, 26.7 → 57.5 GW) while gas additions halved (-52%, 60 → 28.8 GW). This is exactly the rotation MS flagged in March: as inference workload makes DC power demand spiky and unpredictable, the marginal capacity addition shifts from baseload gas to flexible ESS for peak shaving and frequency regulation. The 2026-28 grid is being built more for flexibility than for raw capacity. Direct read-through to ESS platforms (TSLA Megapack, FLNC, CATL) and BTM fuel-cell flexibility providers (BE).
AI Share of DC Power (%) — All Houses
JPM 2Q26 most bullish at 71% (revised up from 1Q26's 65%). GS Feb'26 + MS converge at ~50%. Gap widened.
The AI share divergence is the single biggest remaining disagreement across houses. JPM 2Q26 revised up to 71% (from 65% in 1Q26) implies even faster inference scaling and broader AI adoption. MS aligns with GS at 45-50%, framing inference as the dominant workload driver by 2030.
US Power Demand CAGR — Five Estimates
Levels not seen since the 1990s. MS highest at 3.8% (incl non-DC drivers).
GS Feb'26 breakdown of 3.2% CAGR: Front-of-meter DC = 1.5pp, BTM DC = 0.5pp, residential = 0.6pp, commercial (ex-DC) = 0.4pp, industrial = 0.5pp, transport = 0.2pp, other = -0.4pp. Data centres alone contribute 2.0pp.
GS: Hyperscaler Investment Surge
CapEx + R&D exceeds $1T in 2026E. Reinvestment rate 87% of OCF.
| Metric | Prior Est. | Feb'26 Rev. | Change |
|---|---|---|---|
| 2026-27 CapEx+R&D | ~$700B | >$1,000B | +$300B |
| Reinvestment Rate '26 | 79% | 87% | +8pp |
| Net Debt/EBITDA | ~0.3x | ~0.3x | Stable |
| CROCI 2027E | ~31% | 28.7% | -2.3pp ⚠ |
| Hyperscaler EBITDA '27E | $972B | $1,079B | +$107B |
Over the last two months, GS analyst forecasts for 2026-27 hyperscaler CapEx + R&D rose by over $300 billion. Balance sheets remain healthy at 0.3x ND/EBITDA. However, CROCI declining from 31% toward the 24% historical low-end is an early warning signal for AI Innovation Cycle phase transition.
GS: AI Innovation Cycle — Shale Analogy
Currently in Appraisal / Hopes & Dreams Phase — best window for infrastructure equities
ExplorationComplete
Public/private companies pursue unlocking new opportunities. Higher risk. (GPU development, early LLM research)
Appraisal / Hopes & DreamsCURRENT ← We are here
Street most bullish. Multiple expansion for infrastructure. Thematic investment dominates stock-picking. Power supply chain +196pp since 2025.
Execution / EfficiencyApproaching — monitor signals
Stock-picking replaces thematic buying. Focus shifts to corporate returns, balance sheets, market share. Multiples compress for laggards.
Technology Extension / LegacyFuture
Potential second leg via optimisation or wider applications. Mature technologies.
Three Phase Transition Signals:
1. Financial inflexibility — reinvestment rate at 87%, but balance sheets healthy (0.3x). NOT triggered.
2. Corporate return erosion — CROCI declining from 31% toward 24% low. DETERIORATING.
3. Product oversupply — compute/token demand still voracious. NOT triggered.
2. Corporate return erosion — CROCI declining from 31% toward 24% low. DETERIORATING.
3. Product oversupply — compute/token demand still voracious. NOT triggered.
MS: Training vs Inference Load Profiles
Core MS insight: as inference share rises, power demand characteristics fundamentally change
Training
Hyperscalers, academia, startups
Months-long continuous GPU runs
Stable, predictable load curve
Location-flexible (remote OK)
Baseload power sufficient
Workload: batch processing
Power demand: STABLE & PLANNABLE
Inference
Software, auto-drive, search, ads
Real-time user-triggered requests
Spiky loads with sudden large ramps
Latency-sensitive → must be urban
Millisecond-response flexibility needed
Workload: streaming + real-time
Power demand: VOLATILE & SPIKY
Inference rising to 45-50% of DC load by 2030 means power systems must shift from "can we supply enough" → "can we supply flexibly enough"
GS: Jevons Paradox & Emissions Impact
Budget constraint operating — efficiency gains absorbed by rising demand
Jevons Paradox: Three Scenarios
Budget Constrained ← CURRENT
Same budget, 2x compute speed, 1/5 servers. Max power +20%. Efficiency gains drive increased demand at same spend.
Demand Constrained
AI solutions well-defined. Efficiency → 1/10 servers, -50% money, -40% power. Would signal Execution Phase.
No Constraint
Same servers, 4x budget, 9x compute, 5x power. Pure scale-up. Unlikely but highest demand scenario.
Emissions Impact (GS Feb'26)
DC CO₂ emissions revised to 285-290 million tons (2030 vs 2023), up 35% from 215-220 mn. Driven by higher demand + BTM simple-cycle nat gas + softer renewables PPAs. Social cost PV: $145-170 billion at $190/ton. Partially offset by AI drug discovery value ($83-412B).
US Data Centre Capacity Build-Out
GS: US DC capacity 32 GW (2025) → 95 GW (2030). RoW: 42 GW → 72 GW.
| Metric | 2023 | 2025E | 2027E | 2030E | CAGR |
|---|---|---|---|---|---|
| Global DC Power (TWh) | 411 | 607 | 870 | 1,316 | +26% |
| US DC Power (TWh) | ~165 | ~305 | ~520 | ~790 | +25% |
| US DC Capacity (GW) | ~25 | 32 | 55 | 95 | +21% |
| RoW DC Capacity (GW) | ~30 | 42 | 55 | 72 | +13% |
| DC as % of US Power | ~4% | ~5.5% | ~9% | ~14% | — |
Of the 905 TWh growth by 2030, GS sees ~60% in the US (up from ~50% previously). Hyperscale + cloud workloads growing at 14% CAGR; AI workloads at 98% CAGR over 2023-26.
AI Drug Discovery Value (GS Healthcare)
First quantification of AI's "Pervasiveness" benefit
| Metric | Without AI | With AI | Delta |
|---|---|---|---|
| Drug discovery success rate | 6.4% | 10.3% | +370 bps |
| Additional discoveries/yr | — | +28 drugs | +28 |
| Pre-clinical + testing time | 13 years | ~10 years | -3 years |
10-Year Pipeline PV Uplift:
Discount Rate 21%
$83B
Discount Rate 12%
$236B
Discount Rate 8%
$412B
AI boosting drug discovery success rates by 370bps and cutting development timelines by 3 years. First concrete quantification answering "what are the goods AI is delivering?"
MS: US DC Power Shortfall 2025-28 (Exhibit 12)
Total demand 74 GW. After all solutions: 9-18 GW net shortfall remains.
| Solution | Low | Mid | High | Probability |
|---|---|---|---|---|
| Nat Gas Turbines | 15 GW | 18 GW | 20 GW | 90% |
| Bloom Energy Fuel Cells | 5 GW | 7 GW | 8 GW | 90% |
| Nuclear Co-location | 5 GW | 10 GW | 15 GW | 75% |
| Bitcoin Site Conversions | 10 GW | 13 GW | 15 GW | 90% |
Net Shortfall After All Solutions:9 GW (mid) to 18 GW (low)
MS: "We believe the most likely outcome skews towards the low end of our range" — i.e. closer to 18 GW shortfall
Three Bottleneck Layers (Cross-House Framework)
Disaggregated supply constraint analysis from our research
1. Raw Generation Capacity
OVERSTATED as constraintJPM: 222 GW gross US additions 2026-28 (net 188 GW after 34 GW retirements). GS Feb'26: 105 GW DC-specific capacity including 14 GW BTM. Green Reliability Premium $40/MWh = only 3.4% of hyperscaler 2027E EBITDA ($1,079B). Hyperscalers will pay — this is solvable with capital and time.
Sources: GS + JPM
2. Grid Interconnection & T&D Labour
UNDERSTATED — true binding constraintGS: 78,000 skilled T&D worker gap requiring 3-4 year apprenticeship training. Current: ~45K apprentices/yr, need ~65K from 2027. JPM: >2,600 GW in interconnection queue, 5+ year wait. Transmission: 7-10 year lead time. Transformer lead times 128-144 weeks (2.8 years) at 4-6x cost. This is a human capital problem money cannot solve quickly.
Sources: GS + JPM + IOU Article
3. Load Flexibility (Inference)
NEW DIMENSION — MS uniqueAs inference rises to 45-50% of DC workload, power demand shifts from stable baseload (training) to volatile, spiky, unpredictable load curves. System needs millisecond-response peak shaving and frequency regulation. ESS provides this — not replacing generation, but complementing it. DC ESS deployment: 321 GWh by 2030 (bull case: 590 GWh).
Sources: MS
GS Feb'26: DC-Specific Capacity Additions (105 GW)
Up from 82 GW (Oct'25) and 72 GW (Apr'24). Includes 14 GW BTM (new).
Gas dominates near/medium-term: peakers (40%) + CCGT (12%) = 52% gas total. BTM (13%) is entirely new in Feb'26 — reflects hyperscaler onsite simple-cycle nat gas to bypass 5+ year grid queues. US DC nat gas demand projected >7 Bcf/d by 2030. Grid capex: >$600B in 2026-2030.
JPM 2Q26: Total US Grid Additions 2026-28 (221.2 GW gross)
2Q26 revision (Apr 30): Net 186.1 GW after 35.1 GW retirements. Material mix shift toward batteries.
Material 2Q26 revision: Solar still dominates at 49% (108 GW) but at ~25% capacity factor = only ~237 TWh effective generation over 3 years vs nameplate of ~950 TWh. Batteries now 26% (57.5 GW) — doubled from 1Q26's 26.7 GW. Natural gas cut in half to 13% (28.8 GW) from 60 GW. Retirements: 35.1 GW (69% coal, 30% gas). The grid is being rebuilt for flexibility, not baseload.
GS: Power Generation Timeline
Renewables + BTM GasNear Term
Key constraint: IRA safe harbour, land/supply
Solar, battery storage, simple-cycle nat gas. BTM = 14 GW of onsite generation bypassing grid. Hyperscalers deploy in months vs years for grid connection.
Nat Gas CCGT2029
Key constraint: Turbine availability
Combined cycle more efficient than peakers. GE Vernova, Siemens Energy key suppliers. Turbine lead times 3-4 years. >7 Bcf/d US DC nat gas demand by 2030.
Nuclear2030-35+
Key constraint: Permitting, build, uranium
Large-scale + SMR. Meta signed 2,600 MW with Vistra (20yr PPA). 50 GW nuclear needed to fully offset DC emissions. Capacity factor 90%+ vs solar 25%.
NVIDIA Server Evolution — Power vs Compute
Efficiency +650% over 4 gens, but absolute power per server +269%
| Generation | Max Power | Compute | Intensity (kW/pF) | vs A100 |
|---|---|---|---|---|
| DGX A100 | 6.5 kW | 5 pF | 1.30 | Baseline |
| DGX H100 | 10.2 kW | 32 pF | 0.32 | -75% |
| DGX B200 | 14.3 kW | 72 pF | 0.20 | -85% |
| NVL8 (Rubin) | 24 kW | 140 pF | 0.17 | -87% |
GS 6P Constraint Framework (Feb'26) + Cross-House Overlay
Six constraints governing the pace and shape of AI data centre power buildout
Pervasiveness
MediumGS: Inference power intensity rising. AI drug discovery success rates +370bps (6.4%→10.3%). Still in Appraisal phase — not yet demand-constrained.
MS: Inference reaches 45-50% of DC load by 2030. Drives fundamental shift from capacity to flexibility requirements.
Productivity
MediumGS: Vera Rubin NVL8: 0.17 kW/pFLOPS (vs A100: 1.30). +650% efficiency over 4 gens. But max power per server +269%. Pent-up demand absorbs gains.
MS: Efficiency gains per unit confirmed, but offset by higher absolute power per inference server. Jevons Paradox operating.
Parts
HighGS: 105 GW total (incl 14 BTM). Nat gas >7 Bcf/d by 2030. Turbine availability key constraint for CCGT.
MS: Bloom Energy fuel cells: 5-8 GW at 90% probability. Transformer lead times 128-144 weeks at 4-6x cost. Brownfield sites with grid connections = premium assets.
People
CriticalGS: 78,000 T&D skilled labour gap. 3-4 year apprenticeship training. Current: ~45K/yr, need ~65K from 2027. Wage inflation may help supply.
MS: Confirms labour as most severe bottleneck. Drives accelerated BTM adoption (less T&D workers needed). Grid automation and contractor premium persist.
Price
LowGS: Green Reliability Premium $40/MWh = 3.4% of hyperscaler 2027E EBITDA ($1,079B). CROCI impact: -0.8pp. Not a meaningful constraint.
MS: Solar+ESS LCOE $74-100/MWh approaching CCGT $67/MWh. Na-ion at RMB 0.21/Wh further reduces ESS costs. BTM ESS: 23% unlevered IRR.
Policy
High ↑GS: Rising public concerns about DC impact on electricity affordability. Push for ring-fencing costs. IRA sunset modest near-term impact.
MS: White House Ratepayer Protection Pledge (Mar'26) = BYOP era. PJM BTM rules may increase co-located DC fees. Texas SB-6 'kill switch' bill.
MS: Global DC ESS Deployment Forecast (GWh/yr)
From ~15 GWh (2025) to 321 GWh (2030). Bull case: 590 GWh. US = 53% share. DC ESS CAGR ~85% vs utility-scale ~45%.
DC-specific ESS is a fraction of total utility-scale ESS (total market reaching 1,200+ GWh by 2030 per Rystad). But growth rate is faster because inference volatility requires onsite ms-response capability that utility-scale grid storage cannot provide due to transmission latency.
MS: Power System Rebalancing (Exhibit 19)
Traditional generation's share declining as ESS/flexibility rises to ~22% by 2030. Structural shift.
MS frames this not as ESS replacing generation, but as the marginal investment dollar shifting toward flexibility. Traditional baseload remains essential (gas CCGT, nuclear), but "demand shorts" — the gap between what generation provides and what inference loads need in real-time — are absorbed via ESS. By 2030, ~22% of system contribution from flexibility/storage.
MS: Sector Rotation Sequence (Exhibit 4)
AI Power basket accreted >$1.5T market value since Jan 2023. Capital rotated sequentially. ESS is next.
Each rotation wave lasted 6-12 months before the next segment takes leadership. Nuclear peaked in 2024 (Constellation, Cameco). Gas generators peaked 2024-25 (GE Vernova +196pp). Fuel cells are the current wave (Bloom Energy 10x in 12 months). MS identifies ESS as the next wave — the question is timing, not direction.
ESS Use Cases for AI Data Centres
ESS in DCs is not about providing baseload — it's about managing what baseload cannot handle
Peak Shaving
Absorb sudden inference load spikes (100s of MW in seconds) that would otherwise trigger demand charges or destabilise local grid. Saves $2-5/MWh in demand charge avoidance.
Frequency Regulation
AI GPU loads create high-frequency power quality noise. BESS provides millisecond-level voltage and frequency stabilisation, preventing damage to $2M+ per rack GPU hardware.
Time-Shifting / Arbitrage
Charge during low-cost overnight hours, discharge during peak windows. BTM ESS unlevered IRR 23%, levered 36% — purely from peak/off-peak price differential. Self-funding economics.
Backup / UPS Replacement
Replace diesel UPS with battery. Faster response (<10ms vs >10s for diesel), lower emissions, lower maintenance. Critical for 99.999% uptime requirement.
Renewable Integration
Buffer intermittent solar/wind to provide firm power. Addresses fundamental mismatch: 50% of new US generation is solar at 25% capacity factor, but DCs need 24/7 supply.
Grid Deferral
ESS defers ~10% of infrastructure capex. Net deferral value ~$1.15M per $10M investment delayed 5 years. Reduces upfront grid upgrade burden for both utilities and hyperscalers.
MS: ESS Levelised Cost of Energy (LCOE)
Solar+ESS approaching CCGT parity in US. Already cheaper than coal in China.
| Configuration | 2025 | 2027E | 2030E | Benchmark | Status |
|---|---|---|---|---|---|
| US Solar+ESS (20%,3H) | $90 | $82 | $74 | CCGT $67 | Approaching |
| US Solar+ESS (35%,4H) | $95 | $87 | $78 | CCGT $67 | Converging |
| US Standalone ESS (20%,3H) | $140 | $120 | $100 | Peaker $93 | 2030 parity |
| China Solar+ESS | $48 | $46 | $44 | Coal $50 | Already cheaper |
| China Standalone ESS | $65 | $55 | $45 | Gas $70 | Already cheaper |
BTM ESS Returns (DC-specific, peak/off-peak arbitrage only)
US Unlevered IRR
23%
US Levered (50%) IRR
36%
China Unlevered IRR
13%
China Levered IRR
22%
Sodium-Ion: The Next Cost Curve Disruption
CATL commercialised. At 100 GWh scale: >30% cheaper than LFP. Changes ESS economics.
| Parameter | LFP (Baseline) | Na-Ion Current | Na-Ion at Scale | Advantage |
|---|---|---|---|---|
| Cell cost (RMB/Wh) | 0.35-0.40 | 0.32 | 0.21 | -34% vs current |
| Energy density (Wh/kg) | 160-180 | 160 | 180 | Approaching LFP |
| Low-temp performance | Degraded | Superior | Superior | Cold climate key |
| Cycle life / decay | Good | Slower | Slower | Lower replace cost |
| Low SOC full power | No | Yes | Yes | Better grid response |
| Key material | Lithium | Sodium | Sodium | No supply constraint |
At RMB 0.21/Wh (from 0.32), sodium-ion undercuts LFP by 30%+ at 100 GWh scale. CATL already in commercial production. Key DC advantages: better low-temperature charging, slower degradation, full power at low SOC. Combined with ESS LCOE approaching gas parity, Na-ion could accelerate crossover by 1-2 years.
MS High-Conviction ESS Beneficiaries: CATL · Tesla · LGES · Fluence · BYD
MS: ESS Drives Lithium Demand
New ESS installations could push lithium market into deficit — unless Na-ion scales faster
| Year | ESS Install (GWh) | ESS Shipments | Li Demand (kt LCE) | Bull Case Li |
|---|---|---|---|---|
| 2024 | ~200 | ~300 | ~200 | ~200 |
| 2026E | ~500 | ~550 | ~380 | ~420 |
| 2028E | ~950 | ~1,100 | ~700 | ~850 |
| 2030E | ~1,800 | ~2,200 | ~1,100 | ~1,400 |
If ESS shipments follow the bull case curve, lithium demand from ESS alone reaches 1,400 kt LCE by 2030 — potentially pushing the market into deficit, depending on supply response and sodium-ion substitution pace.
ESS Investment Vehicles — Tiered Risk/Reward
Three tiers from lowest to highest risk
Tier 1: Scaled Platforms (Lowest Risk)
Tesla (TSLA) · BYD · CATL
Megapack is the dominant utility-scale ESS platform. Tesla also has Autobidder AI software + UK Ofgem retail licence = full vertical integration. CATL is the Na-ion leader. These companies have proven manufacturing scale, positive margins, and diversified revenue. ESS = upside optionality.
Tier 2: Pure-Play ESS (Medium Risk)
Fluence (FLNC) · LGES
Fluence: GS Buy-rated, MS high-conviction. Leading software-defined ESS platform. But dropped 55% ($33→$15) in one month. LGES: Korean battery giant with strong ESS credentials but less direct DC exposure.
Tier 3: Emerging / Pre-Profit (Highest Risk)
EOSE · FCEL · Plug Power
EOSE: zinc-based ESS with interesting chemistry but -126% gross margin, $969.6M net loss, securities investigations. Technology thesis is right but unit economics unproven at scale.
SemiAnalysis ✨ — The Behind-The-Meter Thesis
A fourth research lens, distinct from GS / JPM / MS. The core call: the grid structurally cannot keep up, forcing the marginal buyer behind the meter.
Central forecast: Behind-The-Meter (BTM) will power well over half of new US data centres in 2028+, and the Total Addressable Market for DC BTM equipment crosses 50 GW/year by 2029. This is a fundamentally different framing from the other three houses — where GS/JPM/MS debate how much power is needed and which generation fills it, SemiAnalysis argues the delivery mechanism itself shifts off-grid because the grid's firm-capacity additions are structurally capped.
US DC Demand 2026
+21 GW
US DC Demand 2030
+84 GW
Net-new ELCC/yr
~15 GW
BTM TAM 2029
50+ GW/yr
The Three-Step Argument
1. Grid supply is structurally constrained — barely 15 GW of net-new firm (ELCC-accredited) capacity added per year, rising toward 20 GW+ by end of decade. 2. That constraint pushes the marginal buyer behind the meter — onsite generation energises by 2027-28 vs grid timelines slipping toward 2030. 3. The shift reshuffles winners and losers across equipment OEMs and IPPs — fuel cells / RICE win, big-3 gas turbine OEMs face a 2026 order peak.
The Core Model — Demand vs Grid Supply vs Headroom
SemiAnalysis Energy Model: three building blocks. The gap between DC demand and net-new firm capacity is what BTM must fill.
US DC gross power demand rises from +21 GW (2026) to +84 GW (2030) (cumulative incremental). Against this, the grid adds only ~15 GW net-new ELCC/year — and that firm capacity must also serve non-DC load growth (industrial, fabs, electrification). The shortfall between DC demand growth and available firm grid capacity is the BTM addressable market.
Why "ELCC" Not "Nameplate" Is the Right Denominator
ELCC = Effective Load Carrying Capability — the "true" firm capacity value a grid operator can actually rely on. Solar + BESS each add 20+ GW nameplate/year, but their ELCC contribution is minimal and declining. Solar's value falls as the Duck Curve saturates (all panels generate at the same hours); 4hr BESS de-risks <4hr events, then incremental 4hr BESS adds little as that risk nullifies and the pain shifts to >4hr events. Nameplate badly overstates what renewables add to firm capacity — the nameplate-to-ELCC gap is exactly what caps how much new load a market can host.
US Grid Headroom Goes RED by 2027
Headroom = accredited supply − peak demand − required reserves. Already near zero; turns negative 2027.
SemiAnalysis models headroom subregion-by-subregion. A market goes "red" when its reserve margin falls below the required target — at which point there is no spare accredited capacity to host an incremental large load without eroding reliability. Across a growing set of subregions, that threshold is crossed by 2027.
Live Example — PJM 2027/28 Base Residual Auction
PJM's 2027/28 BRA cleared roughly 134,478 MW of unforced capacity (UCAP), yielding a 14.4% reserve margin against a 20% target — a physical deficit of about 6,517 MW UCAP. This is not PJM-specific: NERC's 2025 Long-Term Reliability Assessment flags 13 of 23 North American assessment areas as facing resource-adequacy shortfalls over the next decade. The UCAP view (which nets out forced-outage risk and ELCC discounts) turns red first — materially tighter than the headline ICAP/nameplate view.
"No Gas Until 2028" — The Firm Capacity Drought
<10 GW gas/year added in 2026 & 2027. A stack of bottlenecks, not one.
| Bottleneck | Detail |
|---|---|
| Queue conversion | PJM: ~57 GW cleared studies, but since 2020 ~24 GW with executed agreements (incl. 13.5 GW gas) terminated pre-COD. Permitting = 29% of milestone changes (Jan'23-Jan'26). |
| Technology mix | Bulk of ordered gas is CCGT + combustion turbines. CCGTs most efficient but slowest build — 4-6 years planning-to-COD in some ISOs. |
| Supply chain | Gas turbine + generator step-up transformer lead times each stretched to 3-4 years vs ~18-month historical norm. Total gas-plant development now ≥4 years even optimistically. |
| Project-specific | Equipment cost inflation, community pushback, labour availability, financing close risk. |
Faster technologies — Fuel Cells and RICE engines — are being aggressively secured directly by DC operators for onsite generation, precisely because they bypass the CCGT timeline. This is the mechanical driver of the BTM shift.
Why BTM Wins: Speed & Certainty of Timeline
The buyer's decision, driven by AI Labs (OpenAI, Anthropic) for whom compute is the lifeblood.
Speed: Onsite generation energises in a fraction of grid-interconnection time — requested BTM in-service dates cluster around 2027-28, against grid timelines routinely slipping toward 2030.
Certainty: The schedule sits in the buyer's hands, not the utility's. Utility timelines are notoriously unreliable — they revise down promised load with little to no penalty. One developer was told a 500 MW 2027 ramp could only be delivered in 2029 due to long-lead equipment.
Economics flipped by relaxed uptime: AI labs/hyperscalers have relaxed redundancy requirements — Meta's self-built AI DCs (e.g. Prometheus) target just two nines and forgo backup gensets entirely. This removes the historical cost barrier to BTM (redundancy was the main driver of BTM cost overrun). Power as % of total TCO is near-insignificant for an AI lab, so any power secured is "worth billions."
Buyer Collateral Burden (NEW risk signal)
Securing grid-connected power now often requires developers to post substantial letters of credit, security deposits, or take-or-pay commitments to fund the generation built to serve their load. Switch closed a multi-billion-dollar performance LC facility in 2026 for exactly these obligations — yet utilities often face no symmetric penalties for late delivery.
ERCOT "Batch Zero" — The Hybrid Structures Bridging BTM & Grid
NPRR1325 & PGRR145 (ERCOT board June 1, 2026; effective July 11). Two questions: how you source power, and how you connect/meter.
Sourcing Power
| Structure | What it does |
|---|---|
| NMA (Net-Metering Arrangement) | Sources from existing generation (operating before Sep 1, 2025 — SB6 trigger). Co-locates with new load, nets behind single meter. Under PUCT review (Project 39169) + 120-day transmission security assessment. |
| BYOG (Bring Your Own Generation) | Sources from new generation. Sits outside SB6 net-metering review. Three parallel tracks: Batch Study (withdrawal limit), Generation Interconnection (export limit), Transmission Planning (upgrades). |
Connecting & Metering
| Structure | What it does |
|---|---|
| PUN (Private Use Network) | Entire campus (load + co-located gen) behind a single interconnection point. ERCOT meters only net exposure. The long-established base case. |
| WLPUN (Withdrawal-Limited PUN) | Connect more MW than transmission alone supports, in exchange for an enforced grid-withdrawal cap (e.g. 1,000 MW load that never pulls >100 MW). Not faster interconnection — a way to energise more load sooner within existing limits. |
| PCLR (Provisional Controllable Load Resource) | Dispatchable flexible load, no on-site gen needed. Connects at full size but ERCOT can dispatch it down during constraints. The explicit bridge-to-firm path. |
~2,885 MW of Announced ERCOT Co-Location Deals (NMA bucket)
Crusoe — Goodnight Campus: 525.5 MW (265.5 + 260) serving ~1 GW IT campus; TCEQ filing for up to 933 MW gross gas, ~665 MW likely 19× GE Vernova LM2500 (~35 MW each). · AWS — Comanche Peak: 1,200 MW adjacent to Vistra's nuclear plant; 20-yr PPA ramping to full by 2032. · CyrusOne — Thad Hill: 400 MW (190 + 210) adjacent to Calpine's plant. · CyrusOne / Constellation — Freestone: 760 MW potential (380 contracted + 380 option).
Winners & Losers — The Contrarian Call
SemiAnalysis first flagged Bloom in Dec 2024 as the biggest BTM beneficiary.
| Player | Category | SemiAnalysis View |
|---|---|---|
| Bloom Energy (BE) | Fuel cells | 🟢 BIGGEST WINNER — flagged Dec'24. Highest BTM exposure. |
| INNIO, Wärtsilä, Bergen | RICE / recip engines | 🟢 WINNERS — fast-deploy onsite gen for 2028 buyers. |
| GE Vernova (GEV) | Gas turbines | 🔴 2026 ORDER PEAK risk — highly grid-exposed. |
| Siemens Energy, MHI | Gas turbines | 🔴 LOSERS — grid-connected buildout exposure. |
| CEG / Vistra / TLN | IPPs | 🔴 NEGATIVE — as grid demand eases (relative) & BTM surges. |
⚠️ The Peak Turbine Orders Call
SemiAnalysis sees 2026 as a potential PEAK for turbine orders for the big-3 OEMs (GEV / Siemens / MHI). With BTM more favourable and timelines targeting 2028, utility turbine orders for 2030+ capacity are unlikely to rise further. Most buyers focus on 2028 — and that flows to Bloom, INNIO, Wärtsilä, Bergen. The surge in "contracted load" drove massive orders, but growing skepticism on utilities' ability to serve on time + financing challenges = recipe for a 2026 order peak. This is a direct watch-item for any gas-turbine OEM exposure — the order-peak thesis argues for caution on 2030+ capacity bookings.
NRG — A Potential 5.4 GW ERCOT BYOG Play
The interesting IPP exception on the BYOG side.
NRG is well positioned for ERCOT's new BYOG/WLPUN framework — it has gas turbines available to pair with co-located load, making it a natural supplier of the on-site generation these structures are built around. On its Q4 FY25 call, management pointed to a contracted large-load opportunity implying roughly $2.5bn of incremental EBITDA — built on blocks >1 GW under 10-20 year contracts with investment-grade counterparties — with first power potentially online by late 2029.
Geography of the 5.4 GW (Larry Coben, Q4 FY25, Feb 24 2026)
"Our focus in PJM, at least initially, will be the 1 GW of uprates — it's faster and quicker to market. The demand is there for Texas... we would focus in PJM on the 1 GW of uprates and probably the other 5.4 outside of PJM." Deal structure: blocks >1 GW, 10-20 year contracts, investment-rated counterparties, first power late 2029, then ~1 GW/year after.
Context: Against the recent 20-year Microsoft–Chevron agreement in West Texas (~2.67 GW Project Kilby), SemiAnalysis sees no reason NRG couldn't land a comparable long-dated, hyperscaler-anchored gas deal. Management still frames front-of-the-meter generation as the primary near-term focus.
How SemiAnalysis Reframes the Cross-House Picture
Where this fits against GS / JPM / MS
| Dimension | GS | JPM | MS | SemiAnalysis ✨ |
|---|---|---|---|---|
| Core question | WHERE in cycle + WHEN | HOW BIG the gap | WHERE money rotates | HOW power gets DELIVERED (grid vs BTM) |
| Firm capacity metric | Nameplate + realisation % | Nameplate GW | Flexible vs baseload | ELCC-accredited (~15 GW/yr) |
| Headroom view | Regional reliability tiers | >2,600 GW queue | 9-18 GW shortfall | Goes RED by 2027 (UCAP) |
| Gas turbine view | 42 peaker + 12 CCGT | 28.8 GW (13%) | 15-20 GW | <10 GW/yr; 2026 ORDER PEAK |
| Fuel cell / BTM | BTM 14 GW; fuel cells help | N/A | BE BTM IRR 23%/36% | BTM >50% of new DC by 2028; TAM 50+ GW/yr by 2029 |
The Synthesis
SemiAnalysis is the most operationally granular and most bearish on the grid of the four houses — it tracks 40,000 generation assets and 40,000+ construction datapoints with satellite imagery. Its ELCC framework explains why the GS realisation haircut and MS shortfall happen: nameplate additions are real but firm (accredited) capacity barely grows. The BTM thesis is the logical endpoint — if the grid can't deliver firm capacity fast enough, the largest buyers self-supply. For the AI-power portfolio this sharpens three things: (1) reinforces the fuel-cell/BE structural window beyond what GS/MS modelled; (2) introduces explicit 2026-peak risk for gas-turbine OEMs (GEV, Siemens Energy, MHI) that the other houses don't flag; (3) elevates ERCOT-specific BYOG/co-location structures (NRG) as a distinct investable theme.