Chapter 2.3 — Power & Data Centers

Three years ago the scarce input in AI was the GPU. By mid-2026 it is the electron, and the unglamorous gear that moves it. Global data-center electricity is on track to roughly double by 2030, US grid-connection queues stretch past eight years, and the transformers and gas turbines needed to energize a site now carry multi-year waits. The trade has migrated from silicon to the grid, and the companies that can deliver a megawatt on a schedule now capture the rent. This is also where the two superpowers diverge most sharply: America is power-constrained, and China is not.

Every chapter so far has described things that consume electricity. This one is about whether the electricity exists. A modern AI data center is less a building full of computers than a substation with servers attached: a single frontier campus now draws as much power as a mid-sized city, and the constraint on building more of them has shifted decisively from the chips inside to the power outside. Jensen Huang and Sam Altman both say it plainly now, the binding limit on AI is electricity, not compute. Understanding this layer means understanding two things in sequence: how big the demand has become, and why the supply cannot keep up.

The demand wall

The numbers are stark and they come from sober sources. The International Energy Agency puts global data-center electricity use at roughly 415 TWh in 2024, rising to about 945 TWh by 2030, close to 3% of all electricity on earth and roughly a 2.3-fold increase in six years.¹ In the United States, the Department of Energy's Berkeley Lab found data centers consumed about 4.4% of national electricity in 2023 and projected 6.7% to 12% by 2028.² Nearly half of all US electricity-demand growth this decade traces to data centers.

Demand of that size runs straight into a grid that cannot connect it fast enough. Berkeley Lab's 2025 survey found roughly 2,060 GW of generation and storage waiting in US interconnection queues, about twice the entire existing US generating fleet, with median waits around five years nationally and more than eight in PJM, the mid-Atlantic grid. The price signal is unmistakable: PJM's capacity auction cleared at $329.17 per megawatt-day for 2026/27, against $28.92 two years earlier, roughly a tenfold jump, and data centers accounted for about 40% of the resulting $6.3B cost. Morgan Stanley estimates US data-center demand reaches about 74 GW by 2028 against a power shortfall near 49 GW, and Gartner projects that around 40% of AI data centers will be power-constrained by 2027. There is a skeptics' case, worth holding: interconnection queues are inflated by duplicate and speculative "phantom" requests, and more than 750 GW of requests were withdrawn in 2025, so some of the demand is not real. But the price and the withdrawals point the same way: the scarcity is real, and it is priced.

Power Infrastructure and Key Suppliers

The build divides into those who generate the power, those who make the gear that moves it, those betting on new nuclear, and those who build the sites. The roster worth recognizing:

The bridge: gas and nuclear restarts

Solving a power shortage takes years, so the industry has split its response into a near-term bridge and a long-term endgame; distinguishing the two is essential for underwriting this layer.

The bridge is natural gas and nuclear restarts. Behind-the-meter gas, generation built on-site to bypass the grid queue, can be deployed in roughly eighteen months, and around 101 GW of it has been announced, though only about 2 GW is actually operating so far. The catch has moved to the turbine. GE Vernova's gas-turbine backlog reached 116 GW in mid-2026, targeting 125 GW by year-end; Siemens Energy is carrying a record order book above €130B; and Mitsubishi Power is sold out through 2028, so new reservations now book four to five years out. The interim gap is being filled with reciprocating engines from Cummins and trucked mobile turbines. Nuclear restarts are the other real near-term lever: Constellation is restarting the former Three Mile Island Unit 1 (its Crane Clean Energy Center) on a twenty-year Microsoft contract, fast-tracked to 2027 and backed by a $1B federal loan, and Holtec brought the Palisades plant in Michigan back online at the end of 2025, the first-ever US reactor restart.

The endgame: small modular reactors, and the timeline gap

The endgame is small modular reactors, and here timing discipline matters because the market can price a 2030s technology as if it solves the 2027 crunch. The marquee deals are real but distant: Amazon with X-energy targeting 5 GW by 2039, Google with Kairos aiming at 500 MW by 2035, Meta committing to 1.2 GW from Oklo. Oklo itself broke ground at Idaho National Laboratory in 2025 and guides first power to late 2027 at the earliest, has no revenue and no reactor-design approval, and its market value has swung between roughly $5B and $13B on sentiment alone. When Truist initiated coverage of Oklo, NuScale, and Nano Nuclear in 2026, it rated all three Hold and told investors they "want proof." The timing distinction is straightforward: restarts and gas can add megawatts this decade; most new nuclear belongs to the next.

Cooling: the wall inside the rack

Between the power source and the chips sits cooling, which has quietly become mandatory rather than optional. Nvidia's GB200 and GB300 rack-scale systems use direct-to-chip liquid cooling, with public rack-power estimates clustering around roughly 120–150 kW depending on configuration and measurement boundary. Nvidia had not published a comparable Vera Rubin rack-power specification at the cutoff, so precise next-generation figures remain estimates rather than product facts. Vertiv is the scaled incumbent, and its reference architecture claims large energy, footprint, and space savings; a field of specialists sits around it, including CoolIT, Boyd, LiquidStack, and Submer. Direct-to-chip has won the current generation while immersion cooling remains a niche waiting for the density that may eventually force it, and the coolant-distribution units that feed these systems are themselves a near-term supply constraint. The normalized package-to-rack-to-100-MW comparison is in §2.13.

The gigawatt campus

The demand shows up physically as a new class of building: the multi-gigawatt campus. OpenAI and Oracle's Stargate flagship in Abilene, Texas is targeting roughly 450,000 Nvidia GB200 GPUs and about 1.2 GW, part of a program now running toward 7 GW and more than $400B on the way to a $500B, 10 GW headline. Meta's Hyperion campus in Louisiana scaled to 5 GW and more than $50B, drawing scrutiny over its water use, while its Prometheus site targets going online before the end of 2026. xAI's Colossus 2 in Memphis is heading toward roughly 1 GW, about 40% of the city's daily load, powered in part by dozens of portable gas turbines that ran without permits, the poster child for the permitting and environmental-justice risk these campuses create. The bull case, from UBS and others, is that queues and permitting physically cap overbuild, so what looks like excess behaves like a rolling upgrade; the bear case, from Man Group, is that power built for 2024–25 demand could strand by 2027–28. Both are testable against site cancellations, of which there have already been a few.

The bottleneck is the boring gear

If demand is the story's villain, the electrical supply chain is its choke point, and it is the least glamorous and most investable part of the whole atlas. A gigawatt campus needs transformers, switchgear, and high-voltage equipment, and all of it is now on allocation.

Large power transformers run 128 to 144 weeks, with the biggest high-voltage units quoted up to four years, against roughly a year before the boom; high-voltage breakers run about 125 weeks. The order books show the demand in real time: GE Vernova's electrification backlog reached about $76B against $38B of prior-year sales, a book-to-bill near 2.5; Eaton's data-center orders rose about 240% year on year; and nVent lifted its 2026 organic-growth guidance to 21–23%. The market has noticed, which is the risk.

Through 23–24 July 2026, approximate total returns were 58% for GE Vernova, 88% for Vertiv, and 28% for Eaton. The fundamentals are genuine and order-backed, but the crowding is genuine too, so the live edge is now in monitoring lead-times and book-to-bill for the inflection rather than in the thesis itself. One signal to respect: in May 2026 the North American reliability regulator issued a rare Level 3 alert after more than 1,000 MW of data-center load tripped off the grid in seconds, a reminder that the physics of connecting these loads is not yet solved.

America is power-constrained; China is not

This is the layer where the two systems diverge most, and it is a durable Chinese advantage rather than a temporary one. Everything above describes the American problem: a grid that struggles to add capacity, a permitting process measured in years, and a gas-and-nuclear bridge running into its own supply limits. China has close to the opposite situation. It carries well over twice the installed generating capacity of the United States and adds more new capacity in a single year than many countries operate in total, across coal, hydro, nuclear, wind, and solar. When a Chinese province needs a new substation or transmission line for a compute cluster, the state builds it on a timescale American permitting cannot match.

China has also turned this into policy. Its "east-data, west-compute" program (东数西算) deliberately routes data centers to the western interior, where hydro, wind, and coal power are cheap and abundant, and runs the fiber back to the eastern population centers. The result is that power, the thing throttling the American build, is close to a non-issue for the Chinese build. China's binding constraints are the ones in other chapters, the advanced chips and the memory it cannot yet make at scale; power is not among them. For an investor this means the power trade is an American trade, and the Chinese expression of the AI build is in compute and localization, not in megawatts.

The megawatts that can actually arrive

The central question is whether power scarcity caps the AI build or merely reshapes it. Watch data-center site cancellations and renegotiations, the clearest real-time tell. Watch whether the roughly 750 GW of interconnection requests withdrawn in 2025 becomes a trend, which would signal that some of the demand was speculative. Watch the next PJM capacity auction for another record or a mean-reversion. Watch the transformer and gas-turbine lead-times, the single best monthly gauge of the whole cluster. And watch for regulatory intervention on cost allocation and large-load ride-through, which the reliability alert makes a question of when, not if.

Owning the bottleneck

The power and electrical complex offers order-backed exposure to a physical deployment constraint, but the securities are not substitutes for one another. Constellation (CEG), Vistra (VST), and Talen (TLN) combine generation with contract, commodity and regulatory risk. Eaton (ETN), Vertiv (VRT), GE Vernova (GEV), Quanta Services (PWR), nVent (NVT), Powell (POWL), and Siemens Energy (ENR.DE) supply electrical and grid equipment; Cummins (CMI) and gas midstream names add fuel-cycle exposure. Many already carry premium valuations, so lead times, backlog quality, book-to-bill and customer concentration matter more than the narrative alone. Pre-revenue small-modular-reactor names such as Oklo (OKLO) and NuScale (SMR) are long-dated technology and regulatory options, not solutions to the current power shortage.

The paradox of being long the problem

The trade breaks on a single event above all others: a hyperscaler cutting or pausing its 2027 capital-spending guidance, which would leave peak-priced turbine and transformer orders stranded and de-rate the entire electrical complex hard and fast. Short of that, an acceleration in interconnection-queue withdrawals would signal that the demand was partly speculative, and a genuine step-change in energy efficiency per token, of the kind a second efficiency shock in the models layer could produce, would soften the demand curve. And here is the paradox at the heart of owning this layer: faster grid and permitting reform, or a breakthrough in transformer and turbine manufacturing capacity, would relieve the scarcity that is the entire investment case. To be long the power bottleneck is to be long the problem staying unsolved.

Chapter 2.3 endnotes

Sources

Linked evidence for this chapter's figures and load-bearing claims: ^{3 4 5 6}