20 deals logged. $105.56 billion in disclosed one-time capital commitments (power purchase agreements and the Google–SpaceX monthly lease isn’t counted as one-time capital).

Where it concentrated: Texas and the US grid markets drew the most activity; India saw the single largest multi-year commitment ($30bn, AirTrunk); France had two campuses on former power-plant sites. Two deals bypassed the public grid entirely — built behind-the-meter, next to their own power.

The lead: a platform that finances AI chips like real estate

In June, Broadcom, Apollo, and Blackstone announced a $35 billion platform to finance more than 20 gigawatts of custom AI computing through 2028; the first money funds an expansion for Anthropic (Apollo). It buys Broadcom XPUs — custom AI chips built to one customer’s exact design (unlike a GPU, which anyone can buy off the shelf) and makes that computing power available to frontier labs, named as Anthropic and OpenAI. The notable part is who’s funding it: Apollo and Blackstone through their credit and insurance arms, not a traditional venture fund.

How is works? Long-dated capital, the money behind insurance policies and credit funds pays for the chips up front. The labs commit to pay for the computing power over several years, and those commitments are what the financing rests on. The chip stops being something a lab buys and writes off and becomes an asset a pool of capital owns and gets paid back on over time, closer to how a building or a fleet of aircraft gets funded.

Why it’s worth noticing: for two years the constraint on AI has been physical; power, grid, buildings. This points to a financial constraint: how you fund chips that lose value every year. And the capital being tapped isn’t venture money; it’s insurance and credit.

The open question is whether AI chips make good collateral. A building holds value for decades; a top-end AI chip can be outclassed in a few years. So the structure leans less on the resale value of the hardware and more on the labs’ commitment to keep paying.

Intelligence layer

Verse raised $54M in June 2026, strengthening investment in growth-stage software startups building in this layer — using AI to find grid capacity, forecast demand, or manage on-site power. These are venture-scale rounds ($5–70M). Verse’s $54M in June follows GridCARE’s $64M in May, Emerald AI’s $25M in March, and Amperon’s Series B in January.

Verse’s Dispatch Intelligence manages a data center’s on-site solar and batteries and schedules when the site draws from the grid, allowing it to operate sooner rather than waiting in the interconnection queue (the multi-year line to connect a large load to the grid).

Software-scale startups at the AI/energy intersection are small relative to the physical infrastructure deals — generation and data-center capacity, funded in the hundreds of millions to billions (in June, roughly $1.3bn to $35bn). Both address the same constraint — the multi-year wait to connect a data center to the grid.

The more useful detail is the investor set. Verse’s round was led by Bessemer, with GV and NVIDIA participating. NVIDIA is also an integration partner: it is incorporating Verse’s software into its DSX AI Factory reference design for data-center builders.

The implication is straightforward. NVIDIA is treating the grid connection, not chip supply, as a key constraint, and is addressing it in the software layer as well as through hardware. Tracking which strategic investors recur across these rounds is a way to see where the constraint — and the value — is moving.

Quick explainers

• XPU = a custom AI chip built for one customer’s specific work (a GPU, by contrast, is a general chip anyone can buy).

• Gigawatt (GW) = a unit of power; one gigawatt is roughly enough to power a mid-sized city. “20GW of compute” means data centers drawing about that much electricity.

• Behind-the-meter (⚡) = a data center wired straight to its own power plant, skipping the public grid.

• PPA (power purchase agreement) = a long-term deal to buy electricity from one supplier.

• CMBS = a bond backed by commercial property — here, income from a data center.

• Grid markets (ERCOT = Texas; PJM = Mid-Atlantic; MISO = Midwest; SPP = central plains; TVA = Tennessee Valley; RTE = France) = the regional operators that run the power grid.

Sources

All 20 June deals, in log order:

1. Broadcom + Apollo + Blackstone — Apollo

2. KKR / Helix Digital Infrastructure — Data Center Dynamics

3. Ardian + Verne — Ardian

4. DayOne Data Centers — PR Newswire

5. Related Digital + Blackstone (Stargate “The Barn”) — Oracle

6. Meta + Reliance (India DC) — Meta

7. Meta + CleanMax / Fourth Partner (renewables) — Meta

8. Crusoe + Bergen Engines — Crusoe

9. Hitachi Energy / Canduct — Hitachi Energy

10. Google + SpaceX/xAI (compute lease) — Data Center Dynamics

11. Csquare IPO — The Deal

12. AirTrunk (India) — Forbes

13. Google + Intersect (Meitner, BTM) — POWER Magazine

14. Microsoft + Chevron (Project Kilby, BTM) — TechCrunch

15. Google (Chesterfield, VA) — VPM

16. Digital Realty + Blackstone (NoVa) — Yahoo Finance

17. Google (Bridgeport, AL) — AL Daily News

18. Blackstone / QTS (CMBS) — CoStar

19. EDF + SoftBank + Eclairion (France) — Data Center Dynamics

20. Meta + Crusoe (compute capacity) — Data Center Dynamics

The claim

For forty years, a data center was an IT building that happened to use electricity. That era is ending. A modern AI data center is a power-and-cooling building that happens to contain computers. The software didn’t force that flip. The heat did. Here’s the proof.

The proof

1. The racks got ten times denser. A rack is the cabinet that servers sit in — a metal frame about the size of a large fridge. For decades, a full rack drew 10 to 20 kilowatts. A kilowatt is simply how fast something uses electricity; a typical American home averages a little over one kilowatt across a day. So an old server rack used about as much power as ten to twenty homes.

Nvidia’s current AI rack — the GB200 NVL72 — draws about 120 kilowatts, and low-130s under full load, according to HPE. Same floor footprint as the old cabinet, roughly eight to ten times the power. That is a hundred homes’ worth of electricity flowing into one fridge-sized cabinet. (Nvidia; HPE, 2024–26.)

2. All of that power becomes heat, and air can’t carry it anymore. There is a rule of physics no computer escapes: nearly every watt of electricity that goes in comes back out as heat. For forty years, fans and moving air handled the job, because 10–20 kilowatts per rack is within what air can carry. At 120–140 kilowatts in the same space, it isn’t. Air physically cannot move heat off the chips fast enough. So cold liquid is now piped directly against the chip. Nvidia and the Open Compute Project treat liquid cooling at AI density as a requirement, not an upgrade (Nov 2024).

And once liquid enters, the building changes. A single AI rack weighs about 1.36 tonnes — roughly 3,000 pounds — so floors, doors, and plumbing are re-engineered around it. Berkeley Lab describes a data center as four physical layers: the servers that compute, the storage that remembers, the network that connects, and the infrastructure that powers and cools the other three. In an AI facility, that fourth layer is now the main event. It decides whether the building can host AI at all.

3. The electricity data shows the flip already happened. US data centers used about 176 terawatt-hours in 2023 — 4.4% of all US electricity — up from 58 TWh in 2014 (DOE / Berkeley Lab, Dec 2024). Look at the timing: consumption was flat at roughly 60 TWh through 2016, then turned upward from 2017 — exactly when GPU-accelerated servers entered the fleet. What is known: use tripled in nine years, and the turn matches a change in hardware. What it suggests: this growth is structural. It tracks server architecture, not the economy.

What it means

The limit on AI moved. The chip is no longer the scarce thing — you can order AI hardware and install it within months. The scarce thing is a location that can deliver enormous amounts of power and absorb enormous amounts of heat, at the same spot, at the same time.

In the US, a new power project now waits a median of about five years from grid-connection request to operation (Berkeley Lab, Queued Up: 2025). To be precise: that five years measures power plants joining the grid, not a data center’s own hookup — but if new chips need new power, they are on that clock. That is why “where” now rivals “how much.” The same rack can power up in months in one region and wait years in another. So when you hear “AI needs more compute,” translate it: AI needs more power, in a place that can also take the heat away.

The counterargument

“Efficiency will save us — it did before.” Partly right, and worth taking seriously. From 2010 to about 2016, computing grew enormously while data center electricity stayed nearly flat, because efficiency gains and the shift to hyperscale facilities absorbed the growth. Two honest replies.

First, that flat era ended for a physical reason, not a lack of effort. Efficiency per computation is still improving — but AI raised the power packed into each rack by roughly ten times, and better efficiency now shows up as more computing per building, not less power drawn by it.

Second, keep the scale honest: globally, data centers still use only about 1.5% of the world’s electricity (IEA, Apr 2025). The global total is not the problem. Concentration is. The US carries 45% of that load, clustered in a handful of grid regions, and the strain is local — this substation, this county, this watershed. US data centers directly consumed about 17 billion gallons of water in 2023 (Berkeley Lab). The fact: a small share of world electricity. What it suggests: national averages will keep saying everything is fine while individual grid regions run out of room.

The chip used to decide what a data center computes. Heat now decides what a data center is. So the more interesting map of the next decade isn’t where the best models get trained — it’s which places can hand a building a hundred-plus megawatts and take the heat back. Which places would you put on that map?

Sources

IEA, Energy & AI (Apr 2025) · DOE / Lawrence Berkeley National Lab, 2024 United States Data Center Energy Usage Report (Dec 2024) · LBNL, Queued Up: 2025 Edition (emp.lbl.gov) · Nvidia GB200 NVL72 documentation (developer.nvidia.com) · HPE (2024–26) · Berkeley Lab via Pew Research (water, 2024–25).

The Numbers

Start with one chip. The chips that do AI math are called GPUs graphics processing units. According to the Congressional Research Service, an advanced data-center GPU is rated to draw between 350 and 700 watts. For comparison, that is about what a microwave oven pulls while it is running. One chip, one microwave.

Now stack them. Chips sit inside servers, and servers get bolted into tall metal cabinets called racks. Lawrence Berkeley National Laboratory, the US government lab that tracks data center energy reported in its 2025 update that a rack of ordinary servers draws about 3 to 5 kilowatts. A rack built for AI can draw up to 100 kilowatts. Same size cabinet, twenty to thirty times the power. NVIDIA’s current flagship AI rack packs 72 GPUs into one cabinet and draws roughly 120 kilowatts, according to NVIDIA’s own technical documentation. That is more heat than fans can push out of a metal box, which is why these racks are cooled with liquid pumped past the chips, the way a car engine is.

Now zoom out to the building. The International Energy Agency (IEA) breaks down where a modern data center’s electricity actually goes: about 60% is used by the servers themselves. Cooling takes anywhere from about 7% in the most efficient large facilities to more than 30% in older, smaller ones. This is the part we miss. Every watt that goes into a chip comes back out as heat and removing that heat costs electricity too. You pay once for the computing, then you pay again to un-heat the room.

Stack the buildings, and the numbers start showing up in national statistics. Berkeley Lab found that AI servers in the US used about 2 terawatt-hours of electricity in 2017 and about 40 terawatt-hours by 2023 — twenty times more in six years. (One terawatt-hour is roughly enough electricity to power a mid-sized city for a year.) The IEA projects that electricity used by AI-style servers will keep growing about 30% per year through 2030, compared with about 9% for ordinary servers. And the facilities themselves keep scaling: the IEA estimates a typical AI-focused data center already uses about as much electricity as 100,000 households, while the largest ones under construction will use twenty times that.

What This Means

Here is where we move from fact to inference, what the numbers suggest, not what they prove. If the power draw is set by the hardware itself, then AI’s electricity demand is structural. You cannot fix it with tidier software alone, because the multiplication happens in physical objects: chip times rack, rack times building, building times campus. Each layer inherits the layer below it.

There is a second, quieter implication. The IEA notes that an AI data center is about ten times more capital-intensive than an aluminum smelter — the classic example of a power-hungry factory. When the machines are that expensive, letting them sit idle burns money. So, operators run them as close to around-the-clock as they can. That may make AI load not just large, but unusually steady and inflexible: it does not switch off at night, and it is costly to power down when the grid is tight.

Big, concentrated, and always-on, that specific combination, more than the raw size, is what utilities and grid operators are now working through.

The Honest Pushback

The strongest objection is efficiency. Every chip generation does more math per watt than the one before. Software is improving too, newer models can often do the same work with less computing. The IEA has run this scenario: if efficiency improves faster than expected, global data-center electricity demand in 2035 comes in about 20% below its base projection. And that cooling range: 7% to 30% shows how much room the buildings themselves still have to improve. This is a real, fair objection, and it is why every serious forecast is a range, not a single number.

But notice what has happened at the chip level. Even as each generation got more efficient per calculation, the power rating per chip went up, not down. Designers spent the efficiency gains on more speed instead of less power. And in past computing booms, making computing cheaper mostly made people buy much more of it.

So, the fair conclusion is that efficiency will likely decide how much AI we get per watt, not how many watts get used.

If the draw is set at the chip, the thing worth watching is not the next benchmark score. It’s the power rating on the next generation of chips. If that number climbs again, the electricity question climbs with it — rack by rack, building by building. So far, the draw isn’t an accident of young technology. It’s the design.

Sources

1. GPU power draw: 350–700 W per chip — Congressional Research Service, Data Centers and Their Energy Consumption (Primary, Tier 1 gov), updated May 2026 — congress.gov

2. Rack density: ~3–5 kW conventional vs. up to ~100 kW AI racks — LBNL / DOE, US Data Center Energy Usage Report: 2025 Update (Primary, Tier 1 gov), Jun 2026 — eta.lbl.gov

3. NVIDIA GB200 NVL72: 72 GPUs, ~120 kW per rack, liquid-cooled — NVIDIA technical documentation (Primary, corporate), 2025 — docs.nvidia.com

4. Servers ≈60% of data-center electricity; cooling ~7% (hyperscale) to >30% (enterprise) — IEA, Energy and AI (Primary, Tier 1), Apr 2025 — iea.org

5. US AI (accelerated) server electricity: 2 TWh (2017) → 40 TWh (2023) — LBNL / DOE, 2024 US Data Center Energy Usage Report (Primary, Tier 1 gov), Dec 2024 — report PDF

6. Accelerated servers growing ~30%/yr vs. ~9%/yr conventional, to 2030 — IEA, Energy and AI (Primary, Tier 1), Apr 2025 — iea.org

7. Typical AI data center ≈100,000 households; largest under construction ≈20× that — IEA, Energy and AI (Primary, Tier 1), Apr 2025 — iea.org

8. AI data center ~10× more capital-intensive than an aluminum smelter — IEA, Energy and AI (Primary, Tier 1), Apr 2025 — iea.org

9. High Efficiency Case: 2035 data-center demand ~20% below Base Case — IEA, Energy and AI (Primary, Tier 1), Apr 2025 — iea.org

AI IS MORE THAN SOFTWARE

Here’s the fact: by 2030, the IEA projects US data centers will consume more electricity than the country’s aluminum, steel, cement, and chemical production — combined.

In plain terms: the limit on how much electricity is available may show up before any limit on how smart AI can get. By 2030, the International Energy Agency (IEA) projects that data centers in the United States will use more electricity than the country’s aluminum, steel, cement, and chemical industries combined. That is not a software number. It is a heavy-industry number, attached to a technology that almost nobody thought of as a big power user a few years ago.

Behind every AI response is a data center.
And behind every data center is electricity.

The Numbers

Start with how big this already is. In 2023, data centers used about 4.4% of all the electricity in the US. That is according to a December 2024 report from the US Department of Energy and one of its national labs. Their own estimate says that share could grow to somewhere between 6.7% and 12% by 2028. That is a wide range, and it is wide on purpose: even the people building these models are not sure exactly how fast construction and power hookups will keep pace with demand. In plain numbers, that means data center electricity use could roughly double or triple in five years, from about 176 terawatt-hours a year to somewhere between 325 and 580 terawatt-hours. (One terawatt-hour is roughly enough electricity to power a mid-sized city for a year.)

This is not only a US pattern. The IEA estimates that by 2030, the world’s data centers combined will use roughly as much electricity as Japan’s entire economy uses today, all of it, for data centers alone. In the US, data centers are expected to account for close to half of all new electricity demand between now and 2030. And in 2025 alone, electricity demand from data centers grew about 17% worldwide, while total electricity demand grew only about 3%. Data centers are growing several times faster than the power grid around them.

The spending backs this up. Five of the largest tech companies spent more than $400 billion in 2025 building this infrastructure, and that number is expected to climb roughly 75% higher in 2026, according to IEA reporting. The money is moving fast. The electricity needed to power what that money buys is not moving nearly as fast. It is worth noting that these numbers come from two separate sources, the US government’s own energy lab and the International Energy Agency worldwide, and they point in the same direction independently, which is one reason to take the trend seriously even with the uncertainty built into it.

What This Means

Here is where we move from fact to inference, what the numbers suggest, not what they prove outright. A company can announce and fund a new data center in a matter of weeks. A new power line or power plant typically takes years to get approved and built, no matter how much money is behind it. If that gap holds, the real limit on how fast AI gets built may end up being how fast a region can add electricity, not how good the chips or the models are. That would hand real power to whoever controls land, the process for connecting to the power grid, and approval speed: utility companies, grid operators, and places that already have spare electricity to sell. It also means AI may end up getting built wherever the power already exists, not necessarily wherever the talent or the money wants it to be.

The Honest Pushback

Here is the strongest case against all of this: efficiency. Every year, computer chips do more work per unit of electricity. Cooling systems improve. Companies get better at shifting computing jobs to wherever power is cheapest or most available. If efficiency keeps improving faster than demand grows, this power ceiling could turn out to be smaller than it looks today, the same way some past predictions about computers using runaway amounts of electricity did not fully come true. This is a real, fair objection. It is also exactly why the Department of Energy’s own estimate is a wide range, 6.7% to 12%, instead of one confident number. That range is the industry itself admitting it is not sure how this plays out.

That said, history is not fully on the side of “efficiency will fix it.” In past computing booms, efficiency gains rarely shrank total energy use. At best they kept pace with growth, and in some cases, they made computing so cheap that people simply used far more of it. So, the fair conclusion is that efficiency is more likely to fuel demand than to cap it.

If the real limit is shifting from chips to electricity, the thing worth watching changes too. Not the next model release. The next power line that gets approved or delayed. The next utility that says how much new electricity it can deliver, and by when.