Power Is the New Silicon

Why the AI bottleneck moved from the foundry to the substation — and what that means for the next $5.2 trillion of capital

Part 1 of a five-part series on the structural opportunity in modular AI infrastructure

There is a comfortable story about the AI infrastructure shortage that the market has been telling itself for two years. It goes like this: NVIDIA cannot ship H100s, then H200s, then Blackwell, fast enough. Hyperscalers fight for allocation. Neoclouds raise capital, get on the wait list, and as soon as the GPUs arrive, the revenue starts flowing.

That story is wrong now. It was already becoming wrong in 2024. By the time you finish reading this article it will be obvious that the binding constraint on AI compute is no longer the chip — it is the kilowatt-hour, the substation, and the five-year median wait to connect anything material to the North American or Western European grid.

This matters because almost every dollar deployed into “AI infrastructure” right now is being underwritten against the wrong scarcity. If you are a capital partner trying to deploy $15M to $200M into this cycle, the most expensive mistake you can make is to assume that the limiting reagent is silicon. It isn’t. It hasn’t been for a year.

Let me show you the numbers.

The 2,060 GW backlog

The Lawrence Berkeley National Laboratory tracks every project sitting in U.S. transmission interconnection queues. Its most recent dataset, updated through end of 2025, shows 2,060 GW of generation and storage capacity waiting to connect — alongside 408 GW that has already signed an interconnection agreement but still has not reached commercial operation.

For perspective: total installed U.S. generating capacity is roughly 1,280 GW. The waiting room is bigger than the entire grid.

Three numbers from that dataset matter for anyone thinking about AI infrastructure investment:

1. The median time from interconnection request to commercial operations has doubled — from under 2 years for projects built between 2000–2007, to over 4 years for projects built in 2018–2024, with a median of 5 years for projects coming online in 2023.

2. The completion rate is collapsing. Of all projects that entered queues between 2000 and 2018, only 14% of capacity ever reached operation. Roughly 80% of projects withdraw before they connect.

3. Late-stage withdrawals are rising. Even projects with executed interconnection agreements — projects that look operationally certain on paper — are now pulling out in greater numbers, triggering re-studies that further delay everyone behind them in the queue.

When the AP, McKinsey, or Bloomberg writes the next “AI is using too much power” headline, this is the actual constraint they are describing. Not the megawatt-hour. The procedural, political, and physical impossibility of getting new megawatt-hours connected to where the chips already are.

The PJM signal

The clearest market price for “power, now, in the place AI wants it” is the PJM capacity auction. PJM is the regional transmission organization that covers the Mid-Atlantic, including Northern Virginia — the densest data-center market on the planet.

The PJM capacity auction price moved from $28.92 per MW-day for the 2024–2025 delivery year to $329.17 per MW-day for 2026–2027 — a more than 11× increase in two years. The 2026–2027 auction cleared at the FERC-imposed price cap — meaning the true clearing price would have been higher had regulators not intervened.

PJM is telling you, in dollars per megawatt, what every operator in Loudoun County already knows: there is no slack left. The market is rationing what cannot be expanded fast enough.

Texas tells the same story in a different language. ERCOT’s large-load interconnection queue grew from approximately 56 GW to 205 GW between September 2024 and October 2025 — a 73% increase driven primarily by data-center requests. AEP, the utility serving much of that footprint, doubled its contracted large-load pipeline to 56 GW over a similar period.

These are not the demand curves of a market that has solved its supply problem. These are the demand curves of a market that has not yet noticed it is broken.

The McKinsey math

McKinsey’s April 2025 Cost of Compute report puts a number on the gap. In the firm’s base-case scenario, data centers will require $6.7 trillion in cumulative capital expenditure by 2030, of which $5.2 trillion is AI-specific. That base case assumes 125 incremental gigawatts of AI capacity added globally between 2025 and 2030 — bringing total AI data center demand to 156 GW.

In their upside scenario, the figure climbs to $7.9 trillion and 205 incremental GW. In their downside, $3.7 trillion and 78 GW. Even the downside assumes a doubling of current global data center capacity in five years.

Now hold that 125 GW number next to the U.S. interconnection queue’s actual delivery rate. Berkeley Lab’s data implies that the U.S. can realistically deliver 60–75 GW of grid-connected new capacity on current timelines. The rest of the McKinsey forecast — the part that justifies hyperscaler capex, the part that justifies neocloud valuations, the part that justifies the $500 billion Stargate program — assumes one of three things must happen:

• Grid interconnection reform unlocks 20–30 GW of stranded capacity. (Possible, but slow, and dependent on FERC and state regulators.)

• Hyperscalers self-generate behind the meter. (Happening — BCG estimates 30–50% of new capacity will be self-generated by 2030.)

• Operators deploy compute at sites that already have power, on a timeline measured in months instead of years.

That third path is where the modular thesis lives.

Modular as a grid bypass

A traditional hyperscale data center takes 36 to 60 months from greenfield to first commercial revenue. Add the average 5-year wait for grid interconnection (where one is needed beyond what the site can already deliver) and you are looking at the better part of a decade between capital commitment and first dollar earned.

The hardware that capital was committed against will have depreciated through two full GPU generations by then. The H100 that made sense to procure in 2024 is competing with B200 in 2025 and B300 in 2026; the operator who locked in H100 capacity on a 60-month build schedule is selling vintage compute at a discount to operators who deployed Blackwell on a 195-day timeline.

This is the structural insight that animates everything DCXPS does.

A modular AI data center — two reinforced 40-foot ISO containers, 450 kW of IT load, mixed air- and direct-to-chip liquid cooling, populated with 14 NVIDIA B300 servers and 35 H200 servers — can be deployed at a site with existing power capacity in 195 days from contract to first revenue. Phase 1 is 10–12 weeks of strategic planning, site selection, container reservation, and regulatory work. Phase 2 is 15–18 weeks of system integration: mobile DC installation, hardware setup, energy-system integration. Phase 3 is 6–8 weeks of commissioning, GPU server initiation, configuration, and platform connection.

Then 15+ years of operating cash flow.

The math behind this is not exotic. It is what every modular industrial deployment has done since pre-cast concrete: pre-fabricate the complex parts off-site, ship them to where the resource is, assemble in place, commission, operate. The novelty is not the modular concept — it is that, for the first time in the history of computing, the constraint that justifies modular deployment is real, structural, and measurable in dollars per MW-day.

The economics of co-location

Here is the framework that should drive site selection in the modular era, in order of priority:

Tier 1 — Sites with existing, owned grid connection at adequate capacity. Retiring fossil generation sites, decommissioning industrial facilities, large utility properties. Power is on the meter, permits are in place, transmission is sized. Deployment timeline: 90–195 days.

Tier 2 — Sites with co-located generation. Combined heat and power (CHP), biogas, hydro, nuclear, or large renewable installations with surplus capacity. The generator becomes the anchor; the data center becomes the off-taker. Deployment timeline: 6–9 months, contingent on commercial framework.

Tier 3 — Sites with secured but not-yet-built generation. Greenfield renewable projects with executed interconnection agreements and PPAs. Higher risk, longer timeline, but available on negotiated terms. Deployment timeline: 12–18 months.

Tier 4 — Pure greenfield. Don’t bother. This is the hyperscaler trap. Capital is committed against a timeline you do not control and a counterparty (the utility) that has no commercial incentive to move quickly.

Every category-leading neocloud in 2026 — CoreWeave, Crusoe, Nscale, IREN — is now executing some version of Tier 1 or Tier 2. Crusoe built its entire thesis on stranded gas at the wellhead. IREN converted bitcoin-mining sites that already had power on the meter. Nscale anchored at Norwegian hydro. The pattern is not coincidental. It is the only pattern that works in the post-interconnection-queue era.

Why we chose Kladno

DCXPS’s first site is Kladno, in the Czech Republic, 30 kilometers from Prague. The site is co-located with a 200 MW power plant. We have direct on-site interconnect — meaning the kilowatt-hour does not transit transmission infrastructure we do not control. We are within a fully GDPR-compliant jurisdiction, EU-incorporated, and positioned to serve the demand profile that the EU AI Act will define starting in August 2026 (more on that in the third article in this series).

The site supports 9 deployable modular unit positions. Each unit is structured as a ring-fenced Delaware LLC SPV. The fleet, at full deployment, represents $405 million of contracted infrastructure capacity — and roughly $1.37 billion of gross compute revenue over the 6-year operating term, based on Chapek’s current list pricing for B300 and H200 capacity.

The first units come online in October 2026. The bottleneck, for us, is not power. It is not permits. It is not GPUs (allocations are secured). The bottleneck is capital deployment velocity — and that is the negotiation we are having with partners right now.

What to watch

A few signals that will tell you which way this market actually moves over the next 18 months:

1. PJM’s next capacity auction. If the 2027–2028 auction continues at or near the cap, the rationing thesis is confirmed and behind-the-meter / modular economics get materially more attractive.

2. FERC Order 2023 implementation. The cluster-study reforms are well-intentioned but slow. If they begin to materially shorten queue times in 2026, hyperscale greenfield re-enters the calculus. If they don’t, modular continues to take share.

3. The DATA Act. The proposed Senate exemption for off-grid data centers from FERC oversight would dramatically accelerate behind-the-meter economics. Worth tracking quarterly.

4. Hyperscaler self-generation announcements. Every gigawatt of self-generation Microsoft or Meta announces is implicit confirmation that the grid path is closed.

5. Neocloud margin compression. If sustained capacity rationing continues, the operators who locked in power before the squeeze will see margins expand, not compress. Watch CoreWeave’s gross margin trajectory.

Where this leads

The five-part series this article opens will work through, in sequence:

• Article 2 unpacks the unit economics of a $45M modular SPV in forensic detail — the math, the waterfall, the sensitivities, the comparables.

• Article 3 examines why the EU AI Act and the CLOUD Act create a structural opening for EU-sovereign AI infrastructure that US-incorporated operators cannot fully address.

• Article 4 maps the training-to-inference transition that will reshape the compute geography by mid-2027 — and why modular’s 6-year horizon is built for exactly this transition.

• Article 5 frames AI infrastructure as a distinct asset class, with a hardware-backed exposure profile that does not exist in tokenized compute claims or platform equity.

If you are a family office, fund, or strategic capital partner thinking about how to participate in the next stage of AI infrastructure deployment, the questions to bring to the conversation are not “can we get GPUs” or “is the market real.” Those are answered. The questions are: whose power, on what timeline, at what cost of capital, with what exit profile.

We’re happy to walk through ours. The Kladno site has 9 unit positions. The minimum participation is $15M. The structure is a ring-fenced Delaware LLC SPV per unit, with hardware owned by the SPV and DCXPS operating under a 15%/85% EBITDA split, paid second.

For the data room and a structured introduction to the management team, write to investors@dcxps.com.

The factory is being built. The question is whether you own a piece of it.

Jiri Fiala is CEO and co-founder of DCXPS, building Tier 3 modular AI data centers and the Chapek bare-metal GPU cloud platform. Next in this series: “The 195-Day Data Center — A Forensic Walk-Through of $45M of Unit Economics.”

This article does not constitute an offer to sell or solicitation of an offer to buy any security. Any such offer will be made only by means of definitive transaction documents to qualified investors. See risk factors in the DCXPS Confidential Investor Memorandum.