Someone wrote an anti-data-center op-ed last week.

They researched it with an AI assistant.

They drafted it with an AI assistant.

The assistant answered from a rack in a building exactly like the one the op-ed condemns.

Every query drew power off a grid the piece argues the industry is straining.

None of that proves the op-ed is wrong.

It does suggest we’re asking the wrong question.

The dominant data center story is an outrage story: the developer is greedy; the neighbor is a NIMBY; the booster is a shill for the tech bros; the opponent is virtue-signaling.

Each of these is a claim about the believer—about motive, sincerity, and status.

None of them can tell you whether a specific facility is a fair deal for the people who live next to it.

A hypocrite can be right.

A saint can be wrong.

The character of the person holding the opinion isn’t what matters.

Instead of asking whether someone is sincere—or whether they’re reading the right newspapers (or Substacks)—ask a different question:

Is the arrangement itself fair?

Who bears the costs?

Who captures the benefits?

Does the price paid match the burden imposed?

That question needs a lens.

If someone tells you data centers are either saving the economy or destroying the grid, don’t argue. Ask them to score the deal.

This issue lays one out, scores a real facility with it, and shows why much of today’s data-center debate is arguing about the one thing the scorecard tells you to ignore.

Once you start separating the arrangement from the outrage, you’ll notice the same mistake in almost every infrastructure debate.

If you invest in, site, finance, or regulate this infrastructure, the character debate is a distraction you can’t afford.

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This is where the article stops describing the debate and starts scoring it.

Below, I apply the framework to a real data-center tariff, compare two nearly identical projects that receive opposite fairness scores, and show why Ohio and Texas are quietly moving toward the same underlying model.

You’ll also get:

• the three fairness axes,

• the but-for gate,

• the seven distributive tests,

• a side-by-side comparison of two seemingly identical projects that receive opposite fairness scores, and

• two real regulatory case studies showing how PJM and ERCOT are already converging on this approach.

The goal isn’t another opinion.

It’s a scorecard you can apply to the next project that lands on your desk.

Once you’ve scored a real project, it’s surprisingly difficult to go back to arguing about motives.

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The campus was finished.

The financing had closed.

The customer was ready.

Power had been contracted.

The building still couldn’t turn on.

Not because the money disappeared.

Not because permits were delayed.

Not because demand weakened.

One machine hadn’t arrived.

That surprised me.

For the past two years, most of the conversation around AI infrastructure has focused on capital spending, hyperscaler demand, and billion-dollar campus announcements.

Those numbers are easy to measure.

They’re also becoming less useful for answering the question investors really care about:

When does a project begin producing value?

That question sent me down a surprising rabbit-hole.

It led to a machine most investors never think about, because it has been around for over 100 years.

Relative to the campuses it serves, it’s inexpensive.

It receives almost no attention outside the utility industry.

Yet a missing transformer can leave a fully funded AI campus waiting years for electricity.

That isn’t a procurement detail.

It’s becoming an investment question.

Last week I argued that capital no longer guarantees a project will receive power. That edition was about regulation in PJM and the ability of a California utility to provide the electricity.

This week I wanted to understand some other pieces of the connectivity puzzle.

The answer turned out to be much smaller — and much more consequential — than I expected.

AI Grid Report exists for readers who want to understand the physical constraints behind the AI buildout: power markets, transmission, interconnection, utility planning, and the infrastructure bottlenecks that determine where capital becomes operating capacity.

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Hundreds of millions of dollars were committed.

The site was selected.

The permits were approved.

Customers were ready.

The project still couldn’t turn on.

Not because financing disappeared.

Not because demand dried up.

Because the electricity never arrived.

If you invest in AI infrastructure, utilities, energy, or data centers, this isn’t an isolated case. It’s an early indication of how the economics of AI infrastructure are beginning to change.

For the past two years, investors have understandably focused on hyperscaler capital spending, GPU demand, and multi-billion-dollar campus announcements. Those numbers are easy to measure and easy to compare.

The harder question is whether those projects will receive power when they need it.

That question is increasingly important because every additional month waiting for electricity changes construction schedules, financing costs, projected returns, and competitive positioning. Building a data center and energizing one are no longer the same challenge.

This week’s research begins with a project in Santa Clara that appeared to have every advantage.

The land was secured.

The financing was complete.

The permits were approved.

A power agreement had already been signed.

Yet the project remained dark.

What happened there reveals something much larger than a single delayed development.

It exposes a structural constraint that is beginning to shape where AI infrastructure can be built.

After tracing the Santa Clara case, I found the same pattern appearing across multiple U.S. power markets. The bottleneck isn’t disappearing. It’s moving into parts of the system that receive very little attention but increasingly determine whether a project becomes an operating asset or an expensive stranded investment.

In this week’s report, we examine:

• Why more and more projects fail after the press release rather than before it.

• Why interconnection queues are only the first stage of a much longer timeline.

• Why even locating beside an existing nuclear plant doesn’t guarantee access to power.

• Why the same data center can become operational in one market while remaining stalled for years in another.

Most coverage of AI infrastructure follows the announcements.

AI Grid Report follows the physical system that determines whether those announcements ever become reality.

Every week I publish original research on power markets, transmission, interconnection, utility planning, and the infrastructure constraints that shape capital allocation across the U.S. energy system.

If your work depends on understanding where AI investment can actually become operating infrastructure—not simply where it is announced—I think you’ll find value in becoming a subscriber.

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For paid subscribers: the full report begins below, including the complete Santa Clara case study, supporting figures, and the analytical framework I now use to evaluate whether an AI project is likely to receive power on schedule.

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Instead of writing more, I thought I’d share what was going through my mind after spending the week researching this issue.

One conclusion kept coming back to me.

The market may be asking the wrong question.

In this short video, I explain why I think the next AI boomtown won’t necessarily be the place a…

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The most widely quoted number in China’s AI buildout may also be the least useful.

Over the past few weeks, one number has dominated the AI infrastructure conversation.

$295 billion.

Beijing’s proposed ¥2 trillion national AI infrastructure plan has been compared repeatedly with the roughly $725 billion that U.S. technology companies expect to invest this year.

The conclusion often sounds obvious.

America is spending more.

China is catching up.

The race can be measured in dollars.

That interpretation is appealing because it is simple.

It is also incomplete.

The comparison mixes one year of private U.S. investment with a five-year Chinese government program. It ignores purchasing-power differences that dramatically change what each dollar buys. Most importantly, it measures money committed rather than useful computing power delivered.

Those are not the same thing.

For investors, that distinction matters.

Capital markets tend to reward headline spending long before they measure whether that spending produces usable infrastructure. AI is no different. A billion dollars invested in servers, substations, cooling systems, or transmission does not necessarily create the same amount of computing capacity—or create it on the same timeline.

The question investors should be asking is not:

Who is spending more?

It is:

Who is converting capital into useful compute more effectively?

That turns out to be a much harder question to answer.

It also leads to a very different view of the AI race.

In this research supplement, we break China’s AI buildout into the individual layers that determine outcomes—from construction costs and electrical infrastructure to chips, deployment speed, and the time required to bring new compute online.

Rather than comparing spending alone, we introduce a framework that separates two different competitive advantages:

• The cost of useful compute

• The speed at which that compute reaches production

Those two measures tell a very different story from the headlines.

If you’re already familiar with AI Grid Report, you’ll recognize the second concept immediately. Speed-to-power has become one of the defining themes of our research because deployment speed increasingly determines investment outcomes across AI infrastructure. This report extends that thinking beyond the U.S. grid to compare the broader economics of AI buildouts in the United States and China.

If you’re new here, welcome.

Every week, AI Grid Report examines the intersection of AI infrastructure, power markets, transmission, utilities, semiconductors, and capital allocation. Our goal is simple: identify the physical constraints shaping the AI economy before they become consensus.

Subscribers receive original research, proprietary frameworks, and in-depth analysis designed for investors, developers, operators, and energy professionals.

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Every investor is looking at Northern Virginia.

That may be exactly why it’s worth looking somewhere else.

For years, Northern Virginia has been the center of the data-center universe. Loudoun County’s Data Center Alley now contains one of the largest concentrations of digital infrastructure on the planet. Billions of dollars have poured into the region, and the AI buildout has only accelerated that trend.

Success, however, creates its own problems.

Power is harder to secure than it was a few years ago. Grid upgrades take time. New demand continues to arrive faster than infrastructure can be expanded. The market that attracted the first wave of AI investment is becoming a more difficult place to support the next one.

That observation led me to a simple question:

Where does the next boomtown emerge?

Not the next place to announce a project.

The next place capable of supporting one.

The distinction matters because the beneficiaries extend far beyond the companies building data centers. Utilities, generators, transmission owners, equipment suppliers, infrastructure developers, and local communities all participate when a region becomes a destination for large-scale investment.

Hundreds of billions of dollars are expected to flow into AI infrastructure over the coming decade. Some proposed campuses already require more electricity than many cities consume today.

The obvious assumption is that capital will continue flowing to the same places.

The grid data suggests the story may be more complicated.

Over the past several months, I’ve been tracking interconnection queues, transmission plans, utility filings, large-load requests, generation development pipelines, and regional infrastructure constraints across the major U.S. power markets.

A pattern began to emerge.

The regions attracting the most attention are not always the regions with the clearest path forward.

Some highly publicized markets are running into increasingly visible bottlenecks. Others rarely appear in AI headlines despite possessing many of the ingredients developers care about most: available power, development flexibility, existing infrastructure, and room to grow.

The next AI boomtown may already be taking shape.

Most investors simply are not paying attention to it yet.

This week’s research explores where that opportunity may emerge and what the underlying grid data is signaling.

Among the findings:

• PJM continues to dominate the conversation but faces mounting infrastructure challenges.

• MISO is attracting far more investment activity than many investors realize.

• Several regions possess stronger power-delivery characteristics than their reputations suggest.

• Some of the most interesting opportunities may emerge far from the markets receiving the most attention today.

For investors, the goal is not simply identifying where capital is flowing.

The goal is identifying where it may flow next.

If you’re new to AI Grid Report, this is exactly the type of research we publish every week.

Subscribers receive original analysis on AI infrastructure, power markets, grid constraints, transmission planning, utility strategy, and the capital-allocation implications shaping the future of the U.S. energy system.

Our goal is simple: identify the trends that matter before they become consensus.

Whether you’re an investor, developer, operator, utility executive, consultant, or infrastructure professional, AI Grid Report is designed to help you understand where power is moving, where capital is following, and where opportunities may emerge next.

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The full report examines the regional data, infrastructure constraints, transmission realities, capital flows, and power-market dynamics shaping the next generation of AI infrastructure development.

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Several readers asked how we arrived at the ranking in Issue #11, The Most Overlooked Winner in the AI Infrastructure Race.

The main article focused on a question that is becoming increasingly important as AI infrastructure development accelerates:

Which power markets appear best positioned to support large-scale AI and data-center growth over the next decade?

Rather than focusing exclusively on electricity prices, tax incentives, or headline announcements, the analysis evaluated the structural characteristics that influence whether proposed demand can realistically reach energized operation.

The conclusion surprised many readers.

Several regions that dominate industry discussion did not rank at the top of the framework. Meanwhile, one region that rarely appears in conversations about AI infrastructure emerged as the strongest overall candidate.

That result generated a number of questions:

• How were the regions evaluated?

• Why did certain markets score higher than others?

• What role did queue composition, demand pressure, transmission requirements, and infrastructure readiness play in the final ranking?

• Why did historical performance matter less than current queue structure?

This research supplement provides the supporting analysis behind the conclusions presented in Issue #11.

Inside, you’ll find:

• The complete regional evaluation framework
• Queue composition analysis for each finalist region
• Demand-pressure calculations and supporting data
• Transmission and infrastructure considerations
• Historical performance comparisons
• Supporting charts and source documentation
• Additional analysis that did not fit into the main publication

If you have not yet read Issue #11, I recommend starting there first:

Issue #11 — The Most Overlooked Winner in the AI Infrastructure Race

The full research supplement is available below for paid subscribers.

If you’re already a subscriber, thank you for supporting this work.

AI Grid Report exists to help investors, developers, operators, utilities, and energy-market participants understand how AI infrastructure, power markets, grid constraints, transmission planning, and energy policy are reshaping capital allocation across the United States.

Each week, we analyze the developments that may matter before they become consensus.

The goal is simple:

Identify where the energy system is changing, what those changes mean for capital, and where opportunities may emerge as a result.

If that appeals to you, we hope you will join our community.

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Premium subscribers can download the full Research Supplement #11 below, including the methodology, supporting data, regional analysis, and source documentation behind this week’s ranking.

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Everyone assumes the AI infrastructure race will be won by the same handful of regions.

ERCOT.

PJM.

Maybe MISO.

Those are the regions attracting the headlines, the investment dollars, and the bulk of developer attention.

But attention and opportunity are rarely the same thing.

Over the past several weeks I’ve been comparing the smaller U.S. grid regions through a simple lens:

If you needed a large amount of power for a new AI project, which grid would give you the shortest credible path to energization?

Figure 1 — The Three Contenders

On paper, the outcome looks obvious.

One has enormous renewable resources.

Another sits next to one of the largest concentrations of demand in North America.

The third is smaller, quieter, and rarely appears on anyone’s shortlist.

If you ranked them by reputation alone, you’d probably put that third contender last.

The data says otherwise.

New to The AI Grid Report?

The AI Grid Report helps investors, developers, bankers, and energy executives understand the forces shaping AI infrastructure, power markets, and capital allocation.

Each week we combine proprietary research, grid analysis, and market intelligence to answer a simple question:

Where is the next infrastructure opportunity emerging—and what are most people missing?

If you’re interested in the intersection of AI, energy, and investment, subscribe below to receive future issues.

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Paid subscribers can continue below for the full analysis, including:

• The complete comparison of SPP, NYISO, and ISO-NE

• The demand-pressure framework driving the rankings

• The full Speed-to-Power analysis

• Our conclusion on which overlooked grid offers the strongest path to new AI infrastructure

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Following this week’s AI Grid Report issue 10 on the queue behind the queue, we continued refining the framework, incorporating additional sources, feedback, and new data. The result is the a research supplement about the AIGR Speed-to-Power Index.

What the Index Measures

The AIGR Speed-to-Power Index measures a question that traditional interconnection data often misses:

How quickly can a large-load project move from announced demand to energized operation?

Most public discussion focuses on queue size.

We focus on how quickly projects move from demand to energized operation.

The index evaluates the physical and procedural factors that determine whether a project can move from concept to power on a realistic timeline.

Among other factors, the framework considers:

• Large-load queue magnitude

• Post-approval energization delays

• Transmission build requirements

• Substation and infrastructure readiness

• Regional pathways for co-located generation and alternative interconnection structures

The goal is not to estimate total demand but rather to identify where demand is most likely to reach power first.

Why Speed-to-Power Matters

For much of the last decade, investors could assume that approved demand would eventually become operating demand.

That assumption is becoming less reliable.

AI infrastructure is forcing investors to confront a question that rarely mattered at this scale before:

How much time sits between demand and delivered power?

A signed agreement no longer guarantees a predictable timeline to energization.

The gap between announced capacity and energized capacity is beginning to influence project economics directly.

Projects that can reach power sooner may command a premium over projects with larger headline demand but weaker execution certainty.

In that environment, speed-to-power becomes more than an operational metric.

It becomes a capital allocation metric.

Three Findings That Stood Out

1. Queue Size Is Becoming a Poor Proxy for Deliverable Power

Several regions continue to report exceptionally large demand pipelines.

Far fewer projects have demonstrated a clear path from approval to energized operation.

The difference between those two numbers may become one of the most important metrics investors track over the next several years.

2. The Delay Often Begins After Approval

Interconnection approval remains important.

But in several regions, transmission upgrades, substation capacity, equipment availability, and energization timelines now represent a second waiting period that can stretch for years.

Approval can arrive years before usable power.

3. Alternative Paths Are Becoming Strategically Important

Grid operators are increasingly evaluating frameworks that allow developers to pair generation with load, accept flexible service arrangements, or pursue alternative energization structures.

The regions creating workable pathways around traditional bottlenecks may attract projects that cannot afford to wait for conventional timelines.

The full Speed-to-Power Index, methodology, regional scoring framework, and supporting analysis are available below for paid subscribers.

If you’re already a subscriber, thank you for supporting this work.

AI Grid Report exists to help investors, operators, and developers understand how AI infrastructure, power markets, grid constraints, and energy policy are reshaping capital allocation across the United States.

Each week, we track developments across ERCOT, PJM, MISO, utilities, transmission planning, tax-credit markets, and large-load growth to identify the trends that may matter before they become consensus.

If you’re not yet a subscriber, I invite you to join us.

Subscribers receive weekly analysis, proprietary research, original frameworks, and occasional research supplements like the Speed-to-Power Index.

The goal is simple: help readers understand where the energy system is changing, what it means for capital, and where opportunities may emerge as a result.

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While the market is focused on how much power is waiting for approval, the real investment race begins after approval.

Most discussions around AI infrastructure center on generation interconnection: how much power is waiting in the queue, where projects sit in line, and how quickly new supply can reach the grid.

Across ERCOT, PJM, and MISO, another backlog is taking shape behind the public one.

The market still talks about AI like a software story.

The grid is turning it into a power story.

And that may determine who captures value first.

Welcome to AI Grid Report.

We spend an unusual amount of time reading Independent System Operator (ISO) filings, grid queue reports, and transmission planning documents so you don’t have to.

Then we connect those dots to AI infrastructure and where capital is being deployed.

Paid subscribers receive the full analysis—and my personal view on where the edge is forming before it becomes obvious.

If you’re already here: thank you.

If you’re new: you picked a good week.

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A month ago I wrote the first recap. The premise was that capital is pouring into AI infrastructure on the assumption that power, transmission, and interconnection are solvable inputs. They aren’t: they are the constraint, and the constraint is neither fully capitalized nor properly priced.

The second month tested that argument against the regions themselves. Four issues plus bonus content, working outward from a single auction print toward a national map. The capacity auction that printed at the cap for the second time, with FERC extending the ceiling rather than letting the market discover what an uncapped print would look like. A Midwest retirement schedule that erases roughly a third of average demand on a timetable the replacement queue can’t meet. A pan-regional review showing nine grids making nine different bets on how to firm the same renewable buildout. A political backlash that is no longer stopping the build but is increasingly choosing where it survives. And, as bonus content, a discussion of how the transferable tax credit market has quietly become the policy tool deciding which physical systems are financed.

What follows is the second month, condensed. The full archive is at www.theaigridreport.com.

ISSUE 06 · The Market That Can’t Settle

PJM’s December 2025 capacity auction settled at $333.44 per megawatt-day. That was the cap. It was also the second consecutive PJM auction to print at the cap, after the July auction settled at $329.17. Together the two auctions secured 134,479 MW for $16.4 billion in annual payments — at a procured reserve margin of 14.8%, the lowest in PJM’s history. The cap was supposed to be temporary. On April 28, FERC extended it through the 2029/30 delivery year. The market still doesn’t know what an uncapped print would look like.

The mechanics underneath the print are the more interesting story. Roughly 5,500 MW of fossil capacity is scheduled to retire — Brandon Shores, H.A. Wagner, Conemaugh, Keystone — but none of it is leaving. Brandon Shores and Wagner are operating to May 2029 under FERC-approved RMR agreements; Keystone and Conemaugh were held open through 2032 under a Pennsylvania consent decree. PJM connected 2,117 MW of new generation across all of 2025, almost entirely solar, while forecast load growth of roughly 5,250 MW arrives over the same window, primarily in Northern Virginia. The capacity market is procuring the gap the system cannot fill, and FERC has set the price at which that procurement may occur.

The PJM-versus-ERCOT comparison anchors the piece. Same demand, same generation technologies, different process. ERCOT’s connect-and-manage interconnection, energy-only market, and single-state regulator move the queue at near-zero withdrawal. PJM filters two-thirds of projects out before they reach completion. PJM can’t copy ERCOT’s structure, but it can copy ERCOT’s cadence — and until it does, the cap is the auction, and the auction is a political instrument with FERC’s overseeing it.

ISSUE 07 · MISO Has a Retirement Problem

MISO’s headline number is a posted retirement schedule, not an auction price. Roughly 25 GW of operating generation is scheduled to retire — about 37% of MISO’s average demand, with no other major ISO close. SPP sits at roughly half that exposure; PJM is in the mid-teens; ERCOT remains below 3%. Of the 25 GW, roughly 14 GW is subbituminous coal and 8 GW is natural gas. The dispatchable, fuel-secure capacity that operates through extended weather events and low-renewable-output stress is the part of the fleet leaving the system.

Michigan is the version of the problem visible at a single regulator. DTE Energy has disclosed 8.4 GW of data-center load — Oracle (1.4 GW, approved), Google (1.0 GW, decision due September 2026), ~2 GW in advanced discussions, and 3–4 GW earlier-stage. Over the same window, ~4.6 GW of Michigan coal is scheduled to retire: Monroe (3.3 GW, 2028 and 2032) and J H Campbell (1.3 GW, operating under its fourth federal 202(c) emergency order). The same balance sheet is selling the retiring capacity and buying the new load, and the regulator can watch the arithmetic in a single docket. Campbell is the precedent the rest of the 2028 fleet now has to underwrite to — a coal unit nominally retired a year ago that is still operating, with roughly $200 million in operational losses being contested before FERC.

The queue does not solve this on a one-for-one basis. MISO has ~230 GW active in queue, and it looks like an order-of-magnitude replacement for the retirements until you apply withdrawal rates and accreditation. Coal operates at 45–55% capacity factor; solar at ~25%. Once the cascade compresses the nameplate into accredited firm capacity, the apparent 9:1 replacement ratio falls to roughly 1.6:1. The retirement schedule prints either way. The replacement supply does not.

ISSUE 08 · What America Is Trying to Build

The interconnection queue is more than a backlog. Every project in it has committed capital and accepted years of transmission-upgrade exposure before delivering a megawatt. The composition is therefore a map of what developers believe is worth building — and it now reveals a national power system converging around one consensus and diverging around one unresolved question.

The consensus is the disappearance of coal. Across more than 1 TW of active queue volume in the seven major RTOs, roughly 0.5 GW of new coal appears. The existing fleet still operates, and in PJM still settles in the capacity auction; no one is willing to spend years queuing new coal through interconnection. Solar plus storage has become the national default, with roughly 956 GW of solar and 890 GW of batteries sitting in active queues at end-2024. A decade ago the storage figure was close to zero. The queue is now a renewable-plus-battery expansion in aggregate, regardless of the regional politics surrounding it.

The divergence is the firming layer. PJM still leans on gas and a long-dated nuclear bet — including a roughly 10 GW nuclear cluster concentrated in restarts and uprates rather than greenfield. ERCOT is attempting to firm renewable growth primarily through storage, with 176 GW of batteries in queue, the largest in the country, alongside a 233 GW large-load queue that is more than 70% data centers. MISO has shifted back toward gas as coal retires. CAISO, NYISO, and ISO New England show almost no new gas at all and are implicitly assuming either slower hyperscaler arrival, lower reliability tiers, or longer-than-planned operation of the existing thermal fleet. None of those assumptions is obviously wrong, and none is obviously safe. Same continent, different bets.

ISSUE 09 · Data Center Opposition Is Deciding Who Wins

Local rejections of US data-center projects totaled 49 across all of 2025. The first five months of 2026 produced 89. Q1 alone saw 20 cancellations representing more than $41.7 billion in shelved investment. The slope is the story: the rate of refusal is now rising faster than the rate of construction, even as the pipeline of roughly 2,788 facilities implies a two-thirds expansion off the installed base.

Both sides of the argument have real numbers. Virginia’s data-center industry supports roughly 74,000 jobs and more than $9 billion in annual GDP contribution; the state’s sales-tax exemption returns 48 cents per dollar foregone, well above the 17-cent average for other Virginia incentives. JLARC (Joint Legislative Audit and Review Commission) still classified the benefit as moderate and concluded the exemption does not fully pay for itself. The 833% year-over-year jump in PJM’s capacity-auction print, and the $11.24 monthly residential bill increase Virginia’s SCC approved for 2026, are the kitchen-table version of the same fight. The percentage of Virginia residents comfortable with a new local data center fell from 69% in 2023 to 35% in 2026. Support for state tax breaks fell from 61% to 37%. A 34-point swing in three years is what gives legislators cover to move.

The resistance is selecting the composition of the buildout rather than halting it. MISO’s data-center footprint has grown at 43% per year since 2020, driven by Virginia saturation and developers routing toward jurisdictions where opposition has not yet organized. The political-risk filter is converging on the same answer as the power-economics filter from Issue 07: the projects most exposed to community resistance are the same ones weakest on supply-side fundamentals. The version of the buildout that survives is the version whose power is sited where no one is close enough to object.

BONUS CONTENT · Transferable Tax Credits Are Quietly Reshaping Infrastructure Capital

The transferable tax credit market reached $42 billion in 2025, up 27% from $32 billion in 2024. With tax equity and preferred equity included, total tax credit monetization hit $63 billion. The market grew through OBBBA reconciliation, not despite it. Eligibility tightened, the qualifying pool narrowed, and the buyer-side market remained intact. The §6418 transferability provision that opened the market in 2022 is still the policy tool deciding which physical systems are financed.

The framing matters more than the mechanism. A transferable tax credit is technically a financial instrument, but economically it is beginning to function as a directional signal for capital allocation into energy, manufacturing, grid infrastructure, and industrial capacity. A $5 million tax liability becomes $4.5–4.6 million in credits funding a real project with a placed-in-service date. The project exists before the capital arrives, which makes the accountability structurally different from an ESG score the rating agency can revise after the fact. For family offices and long-duration capital pools that can underwrite illiquid, politically shaped, operationally complex infrastructure cycles, the structure fits unusually well. The question has moved from how to minimize the tax bill to what physical system the tax bill should fund.

The Through-Line

Five pieces, one argument extended: the constraint is moving, and it is moving in a direction the headline numbers don’t capture.

PJM’s capacity auction shows the political ceiling rather than the physical scarcity price. MISO’s retirement schedule happens whether the queue connects or not. The pan-regional queue shows what developers believe — and what they no longer believe — about which firming technology will pay. The rejection database shows which jurisdictions have organized against the build and which have not yet. The transferable tax credit market shows which projects are financed at all. Each of these is a price the market is paying for a problem it has not solved, and each of them surfaces somewhere other than where the original constraint sat.

A month ago I wrote that the grid constraint had shifted. The second month sharpened the claim: every mechanism the system uses to manage the constraint — auction caps, RMR contracts, emergency orders, queue filters, siting moratoria, tax credit eligibility — relocates the cost rather than resolving it. The open question is the same as last month, with two more data points behind it. How long can the market continue talking about the headline before it is forced to price the outcome?

Go Deeper on Each Issue

– PJM printed at the cap for the second time, and FERC extended the ceiling rather than letting the price discover itself →

– MISO’s retirement schedule is 25 GW, and the queue replacing it is composed of different generation types →

– Nine regions, nine queues, one disappearance and one emerging firming-layer disagreement →

– The rejection rate is now rising faster than the construction rate, and it is choosing where the build survives →

– Transferable tax credits have evolved beyond accounting exercises into the financing layer for the physical buildout →

What Comes Next

The third month moves underneath the queue and into the wires. Transmission planning, the FERC co-location order, and the question of whether behind-the-meter generation is a way around the grid or a new layer of it. The Constraint Index continues to update weekly. The full archive, the index, and the paid deep dives are at theaigridreport.com.

The Fight That Everyone Wants to Have

Local rejections of US data-center projects totaled 49 across all of 2025.

The first five months of 2026 produced 89. Twenty projects were canceled in Q1 alone — more than $41.7 billion in planned investment shelved.

The political revolt against data centers is no longer a Northern Virginia curiosity.

It is a national pattern with a measurable slope, and the slope is the story: the rate of refusal is now rising faster than the rate of construction.

That one fact changes how you read every headline about AI infrastructure spending.

US data-center capacity has grown at 24% annually since 2020. The average facility coming online in 2025 was 80 MW — three times the size of five years ago. The latest campuses are sizing to 1 GW and beyond. Virginia alone hosts 665 facilities. Its data centers consumed roughly 40% of the state’s electricity in 2024. Northern Virginia’s “Data Center Alley” accounts for an estimated 14% of all data centers worldwide.

That concentration is the mechanism behind the backlash now taking shape.

This is the constraint this issue examines — not whether the buildout happens, but which version of it survives contact with the communities it lands in.

Below the paywall:

• who is right on jobs and water — and why both sides can cite real numbers

• why electricity-rate fights become political fast

• what the financing structure says about long-duration AI capex

• and why the projects facing the least political resistance may end up shaping the build

The resistance is not stopping the build.

It is increasingly deciding what gets built — and where.

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What the queue is, and why it matters

An interconnection queue is the waiting list for new power plants trying to connect to the grid.

But it is more than a backlog.

Every project in the queue has already committed capital, entered a multi-year approval process, and accepted the possibility of transmission-upgrade costs before it can deliver power. In practice, the queue is a map of what developers believe is worth building — and where they believe future electricity demand will justify the wait.

That matters because the queue increasingly reveals a very different picture of America’s future grid than most public policy debates do.

Across the country, developers are attempting to build massive amounts of:

• solar

• battery storage

• gas generation

• transmission-connected AI infrastructure

while coal has almost completely disappeared from new development plans.

The U.S. power system is fragmented across regional grid operators. Seven major regions — including PJM, ERCOT, MISO, CAISO, SPP, NYISO, and ISO New England — publish detailed project-level queue data. Together, along with the non-ISO Southeast and Western utility regions, they represent nearly all US electricity generation.

The scale is enormous.

Roughly 1,400 GW of generation and 890 GW of storage — 2,290 GW combined — sit in active US interconnection queues as of end-2024, per Lawrence Berkeley National Laboratory (LBNL — a US Department of Energy national lab whose Electricity Markets and Policy group publishes the annual Queued Up interconnection report). Solar represents the largest share of proposed capacity, followed by battery storage. Gas remains meaningful but regionally concentrated. Wind is increasingly uneven by geography. Coal is almost absent.

The queue does not guarantee these projects will ultimately be built.

But it does reveal something extremely important: what the market believes the future grid will need.

What the composition implies

Coal is not underrepresented in the queue; it’s almost absent.

The existing coal fleet still operates. Some plants continue clearing in PJM’s capacity auctions, while others remain online under reliability agreements that delay retirement. They’re reliable; they’re necessary, but they’re politically toxic. Developers are no longer attempting meaningful new coal capacity in any major U.S. interconnection queue.

What replaces it is where the regions diverge.

ERCOT is building solar and storage in roughly equal proportions. PJM shows a broader mix of gas, solar, storage, and a small but unusual cluster of nuclear projects. MISO’s newer queue cycles tilt more heavily toward gas than its earlier renewable-heavy cycles. CAISO, NYISO, and ISO New England show almost no new gas development at all.

The same country.

The same physics.

The same Inflation Reduction Act.

Different bets.

Why the queue matters

The queue is not a forecast of what will ultimately be built.

Withdrawal rates remain high across most regions, and projects reaching commercial operation often spend years moving through the interconnection process. The queue reflects what developers are attempting — not what will necessarily survive.

But that still makes it one of the most important forward indicators in the power market.

Every project in the queue has already passed an economic screen. Capital has already been committed. Developers have already decided the opportunity is worth years of waiting, study costs, and potential transmission-upgrade exposure.

That is a very different signal from:

• a press release

• a policy target

• a utility planning document

• a political speech

The queue is capital already moving toward the grid.

What this issue covers

This week’s premium section will explain:

• why some regions are still building dispatchable supply while others are not,

• why storage exploded across the queues, and

• what the interconnection data may already be revealing about the future shape of the US grid.

Continue Reading

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Most wealthy investors think about taxes as something minimized quietly in the background.

That framing understates what’s happening. The transferable tax credit market reached $42 billion in 2025, up 27% from $32 billion in 2024. When tax equity and preferred equity are included, total tax credit monetization hit $63 billion. The market grew through OBBBA (Trump Administration signature tax legislation passed in 2025) reconciliation, not despite it.

But there is a more strategic question in this conversation:

If you are going to write the check anyway, what exactly do you want your capital funding?

A ballroom? Or a power plant?

That question sounds rhetorical until you look at how transferable tax credits are working inside the real economy.

What’s changing is not the desire to reduce taxes. That has always existed.

What’s changing is the realization that the tax code is the policy tool that influences which physical systems are built.

The §6418 transferability provision in the 2022 IRA opened a market that didn’t exist before. The transferability mechanism survived the 2025 OBBBA reconciliation. Eligibility rules tightened — meaning the pool of qualifying projects narrowed — but the buyer-side market remained intact. Some credits were eliminated; some had their eligibility windows shortened; some of the eligibility criteria, especially around foreign content, were tightened. The window that exists today is real, but it is not the same window that existed in 2024.

A transferable tax credit is usually described as a financial instrument. Technically, that is true. Economically, however, it is beginning to function more like a directional signal for capital allocation into energy, manufacturing, grid infrastructure, and industrial capacity.

The U.S. is entering a period where physical infrastructure constraints are dominating economic outcomes in a way the financial markets are just beginning to price.

The constraints appear in transformer shortages, interconnection delays, transmission bottlenecks, power availability, natural gas infrastructure, and generation lead times that can’t be resolved on software timelines.

Data center expansion timelines are accelerating faster than transmission and grid delivery capacity. That reality is changing how capital evaluates long-duration opportunity.

Capital Is Becoming More Intentional

Family offices, private investors, and long-duration pools of capital are no longer thinking purely in terms of quarterly returns.

Many are asking a different question:

“What systems do we want our capital to support?”

The U.S has entered a period where enormous amounts of physical infrastructure need to be rebuilt, expanded, or modernized simultaneously.

The AI buildout alone is exposing how underbuilt the grid really is.

Data centers require massive injections of power. Power at that scale depends on transmission, transformers, substations, permitting, land access, and infrastructure investment cycles measured in years rather than quarters.

The Grid Is Quietly Repricing Everything

This is particularly visible inside energy infrastructure. Unlike the software cycles that dominated the last decade, the outcomes here are physical and measurable — whether a transmission corridor is built, a generation project reaches commercial operation, or grid capacity expands has direct consequences for industrial growth, electricity pricing, and regional competitiveness.

Stocks like Eaton, Vertiv, and Schneider have traditionally been seen as downstream of the AI buildout. Not anymore — they are the gating layer, and the market has noticed. All three have broken out of long-term ranges to the upside, which confirms the thesis but does not, on its own, signal current entry points. The structural story and the entry timing are separate questions.

The market spent the last decade believing software was the bottleneck.

The bottleneck is becoming physical infrastructure. The result is that electricity itself is beginning to reprice.

It means the real opportunity is not simply “AI.”

It’s the underlying physical systems required to support it.

Transferable tax credits are directing capital toward those systems.

If electricity becomes constrained by transmission and deliverability rather than generation alone, how should infrastructure itself be valued relative to the applications sitting on top of it?

Family Offices May Be Uniquely Positioned

Large institutions and public markets often struggle with long-duration infrastructure cycles because the timelines rarely align cleanly with quarterly expectations. Family offices, however, can operate with substantially longer investment horizons.

The structure fits family offices unusually well. The infrastructure cycle is long-duration, illiquid, politically shaped, and operationally complex — conditions many quarterly-managed pools of capital struggle to underwrite.

That dynamic makes the current environment particularly interesting because the underlying infrastructure cycle will not resolve quickly.

Grid expansion doesn’t occur on software timelines.

Transmission projects can require seven to ten years to complete, transformer manufacturing capacity can’t expand overnight, and AI-driven power demand is rising inexorably.

That mismatch creates long-duration investment opportunities tied to hard assets, constrained systems, and real economic necessity.

The Psychology Is Changing Too

Historically, taxes were viewed primarily as something minimized quietly in the background through accounting strategies, portfolio structuring, and conventional tax planning.

Some investors are now beginning to ask a different question:

“Is there a more focused, intentional way to allocate tax dollars while still complying with tax policy?”

Transferable tax credits stop feeling like accounting exercises and begin functioning more like participation in a national industrial buildout tied to energy infrastructure, manufacturing capacity, grid resilience, and domestic economic expansion.

Investment structures carry clearer accountability than the metrics that have dominated philanthropic and ESG-scoring frameworks. A transferable tax credit settles against a placed-in-service date, a real project, and a real tax liability. An ESG score reflects a methodology the rating agency can revise after the fact.

The opportunity is no longer viewed exclusively through the lens of tax reduction or financial optimization, but through participation in the construction of systems that are strategically important to the economy itself.

That change in mindset may ultimately become one of the defining characteristics of this cycle.

Why This Matters Beyond Taxes

The market still tends to describe transferable tax credits as niche financial instruments.

What is emerging is a system in which tax policy, infrastructure constraints, industrial strategy, energy demand, and capital allocation are becoming intertwined.

The immediate question is whether this cycle produces attractive returns. The credits themselves are purchased at a discount — that’s the market incentive— and that discount is tax-free—that’s the policy tool. So, in practical terms: a $5 million tax liability becomes $4.5–4.6 million in credits directly funding a real infrastructure project with a placed-in-service date. The project exists before the capital arrives. The accountability is built in from the start.

That discount alone may not move the needle for family office allocators—though it has created a powerful enough incentive to have established a $42 billion market in two years, mostly for corporations— but pair it with the idea that taxes can be redirected toward causes and infrastructure projects family offices genuinely want to support, and the equation starts to look very different. The larger question is whether it changes how sophisticated investors think about participation in the construction of physical systems. “My taxes fund national parks” is no longer an aspirational joke at a cocktail party. “My taxes fund the energy buildout (and not a ballroom)” is a legitimate claim.

If infrastructure becomes the limiting factor behind AI, manufacturing, and industrial growth, what happens to the value of the systems that make all three possible?

The AI Grid Report

AI Grid Report is an independent research publication focused on the intersection of energy infrastructure, AI-driven electricity demand, grid constraints, industrial policy, and long-duration capital allocation. Recent editions have covered ERCOT, PJM, MISO, interconnection bottlenecks, transmission constraints, and the physical infrastructure dynamics increasingly shaping economic outcomes across the US energy system.

If these themes interest you, you can subscribe to AI Grid Report here →

Subscribe now

About Dakota Ridge Capital

Dakota Ridge Capital works with family offices, private investors, and strategic capital navigating the energy and infrastructure buildout — including direct access to transferable tax credit opportunities tied to real projects already in development.

If you are evaluating how your capital allocation intersects with the infrastructure cycle, the conversation is worth having before the next reconciliation window closes.

Request a private briefing → https://www.dakotaridgecapital.com/contact-us

Where We Are in the Walk

Six issues in, the AI Grid Report (TAGR) has covered the methodology, the macro tariff and Gulf threads, and the first two regional deep dives.

→ Issue 04 — ERCOT. The Lone Star grid clears its interconnection queue at near-zero withdrawal. Connect-and-manage interconnection, no centralized capacity market, one regulator. Texas doesn’t have PJM’s auction problem; it doesn’t have auctions.

→ Issue 06 — PJM. The capacity auction cleared at the ceiling. Twice. PJM’s queue doesn’t clear; ERCOT’s does. Same demand. Same generation. Different process.

This week, we’re looking at MISO (Midcontinent System Operator)— the largest physical footprint on the US grid by territory, stretching from Manitoba to northern Louisiana.

MISO’s headline number is not an auction price.

It is a retirement schedule.

The Number

~25 GW of MISO operating generation is on a planned retirement schedule. That is roughly 37% of MISO’s average demand.

No other major ISO is close.

SPP is next at roughly half the exposure. PJM sits in the mid-teens. ERCOT — despite being the fastest-growing load center in the country — remains below 3%.

MISO has its own retirement problem.

What’s Leaving

The 25 GW is concentrated.

Subbituminous coal accounts for roughly 14 GW. Natural gas adds another ~8 GW.

Nuclear is notably absent from the near-term retirement schedule. Xcel’s Monticello plant — the one obvious candidate, previously scheduled to retire in 2030 — received NRC (Nuclear Regulatory Commission) approval in February 2026 to operate through 2050. Prairie Island is in line for similar treatment. MISO’s nuclear fleet has been materially de-risked at the same time the dispatchable coal and gas fleet is leaving.

The issue is not simply the volume of retirements.

It is the type of generation leaving the system.

Much of what’s retiring is dispatchable, fuel-secure capacity that can operate through extended weather events, peak demand periods, and low renewable output conditions (i.e., no sun, no wind).

That distinction becomes critical once replacement timing starts slipping.

What We Are Diving Into This Week

ERCOT cleared its queue. PJM cleared at the ceiling. MISO has not had to clear either.

What MISO has is firm capacity leaving the system on a posted schedule, while much of the replacement remains trapped in the queue rather than connected to the grid.

In this week’s premium article, we’ll show which plants are retiring, which utilities own them, what the replacement queue actually contains, and why replacing coal megawatts with renewable megawatts is not a one-for-one exchange.

We’ll also cover the news thread that may have quietly turned MISO’s retirement schedule from a posted plan into an active planning problem — and why the fourth federal emergency order on a single Michigan coal unit may be the precedent the rest of the 2028 fleet now has to underwrite against.

Keep Reading ->

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PJM cleared at the cap. Again.

The system keeps finding the ceiling. It hasn’t found the solution.

On December 17, 2025, PJM’s capacity auction cleared at $333.44 per megawatt-day.

That is the cap.

It is also the second consecutive PJM auction to clear at the cap. The previous auction, held in July 2025, cleared at $329.17. Both auctions hit the price ceiling that FERC set in late 2024 to keep residential bills from a sudden jump.

Together, those two auctions secured 134,479 MW of capacity for $16.4 billion of annual payments — at a procured reserve margin of 14.8%, the lowest in PJM’s history.

That number is now the most-watched price in US electricity.

It is also a price the system has never actually been allowed to discover.

It is also, increasingly, the price that determines whether AI infrastructure is built on schedule.

The Cap Was Extended — Again

The cap was supposed to be a temporary instrument. It is now in its fourth consecutive year.

On April 28, FERC approved PJM’s request to extend the price collar for two more delivery years — 2028/29 and 2029/30. The extension keeps the cap at roughly $325/MW-day and the floor at $175/MW-day, the same parameters that capped the last two auctions. Without it, PJM had estimated the next auction’s price cap would have defaulted to about $550/MW-day.

When PJM holds its next Base Residual Auction this summer, the cap will still be in place. The auction after that — in December — will be capped too.

PJM’s Base Residual Auction (BRA) procures capacity three years ahead of delivery. The December 2025 auction cleared capacity for the June 2027–May 2028 delivery year. The summer auction clears for June 2028–May 2029. The December auction clears for June 2029–May 2030. All three are now capped.

The forward gap exists so generators can build new supply for a known revenue stream. Except the forward gap is now shorter than the build cycle for new gas generation, which is the structural problem the rest of this issue describes — and which is why FERC extended the cap. New supply cannot arrive in time, so the cap stays on to protect ratepayers from the price the auction would otherwise produce.

The market still does not know what an uncapped clear looks like. It is now four years since it last did. The two capped auctions were already records.

What would the market actually clear at without the cap?
$400? $500? Higher?
There is no consensus — only suppressed price discovery.

The auction is clearing at the ceiling. The price hasn’t been allowed to move.

Same Demand. Same Generation. Different Process.

On the supply side, firm capacity is exiting faster than it is being replaced (or, at least, trying to exit). On the demand side, ~5,250 MW of new load arrives every year inside PJM’s forecast — almost all of it is data centers.

The clearing price is the cost of that gap — the dollar value, expressed per MW per day, of every megawatt of firm capacity PJM needs but doesn’t have. When supply is short, the price rises until enough supply (or enough demand response) is willing to commit. When the gap is structural — firm supply exiting faster than replacement — the price keeps rising until something stops it. This time, FERC stopped it with a cap.

In ERCOT, the same supply technologies, the same data center developers, and the same hyperscale tenants run through a queue that clears fast.

In PJM, two-thirds of projects in the queue never reach completion.

The queue is full. Most of it never meets its intended load.

That is the entire difference between a $333 / MW-day capacity clear and a market—ERCOT— that doesn’t pay for capacity at all.

If PJM and ERCOT are running the same technologies, why does one system convert and the other stall?
Is it structure, governance, or execution?

In This Week’s Edition

This week breaks down:

• what is leaving PJM (~5,500 MW of fossil capacity scheduled to retire — Brandon Shores, H.A. Wagner, Conemaugh, Keystone) — and why scheduled retirements are not real retirements

• what is replacing it (2.1 GW in 2025, almost entirely solar)

• how Northern Virginia became the place where the December auction price was decided

• what FirstEnergy’s Energize365 program — a $36B transmission build-out by PJM’s largest transmission owner — tells us about the supercycle hiding inside the auction print

• what ERCOT does differently — and which parts of it PJM can copy

The full Constraint Index shows which PJM zones are most exposed to the next auction, and which signals to watch going into the summer auction.

Continue reading:

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A month ago I launched The AI Grid Report. The premise: capital is pouring into AI infrastructure on the assumption that power, transmission, and interconnection are solvable inputs. They are not. They are the constraint — and the constraint is not priced.

Over five issues plus bonus content, I have walked through what that looks like in practice. The grid that cannot absorb what capital wants to build. An oil shock that rewires the electrification story through the wires rather than the headlines. A methodology for scoring where the grid breaks. A transformer bottleneck that puts America’s AI timeline in China’s hands. A single-name wipeout that showed what happens when the macro is right and the execution inverts the order of operations. A Texas paradox where everything connects — and that is the problem. And, most recently, a review of the sorry tale of projects failing to make it through the interconnection queue, and wasting billions in sunk costs.

What follows is a guided tour of the first month: the sharpest argument from each issue, condensed. If any of it lands, the full archive is available at theaigridreport.com

ISSUE 01 · AI Needs Power — The Grid Can’t Keep Up

The launch issue opened with a number that should have reframed the AI infrastructure debate: in PJM, the largest U.S. power market: 67.4 percent of projects that enter the interconnection queue eventually withdraw, and in CAISO, the figure is 75.6 percent. Two out of three projects in the mid-Atlantic, and three out of four in California, never reach commercial operation. The system is not failing; it is filtering—and it is filtering on patience and balance sheet depth, not on project quality.

The scale underneath those withdrawal rates is what gives them weight. Roughly 2,300 gigawatts of generating capacity currently sits in U.S. interconnection queues—nearly double the 1,200 GW installed across the entire country, spanning over 25,000 projects with median wait times of three to seven years. A hyperscale AI training campus needs 500 megawatts to 1 gigawatt of continuous power on a two-to-three-year construction timeline, while a transmission line takes seven to twelve years. That gap is not a temporary mismatch; it is how the two systems operate.

The issue ended with the preliminary view of the AI Grid Constraint Index—a heatmap scoring nine U.S. grid regions across five constraint dimensions, with PJM and CAISO at the top for overall constraint, and ERCOT showing a zero percent withdrawal rate and a story we would have to come back to. The point of the index is not to describe the grid, but to measure where it breaks.That distinction matters, because most models still treat the system as if it processes projects rather than eliminates them.

ISSUE 02 · The Grid Can’t Keep Up With Oil’s Shock

The Strait of Hormuz disruption gave us a natural experiment in how electrification demand interacts with infrastructure that can’t respond at the same speed: Brent crude moved above 100 dollars, and a chokepoint responsible for roughly 20 percent of global oil flows was open only to vessels with Iranian escort. The first-order effect was the obvious one—substitution toward electric alternatives accelerates when fuel prices spike. The second-order effect was the more interesting one: WTI and Brent inverted, because the replacement for lost Middle East supply was not prompt North Sea production but U.S. exportable crude, making WTI the marginal scalable barrel pulled from both directions.

The point of the issue was not the oil price, but that electrification does not remove constraints—it relocates them from oil to electricity. Capital is concentrating in precisely the regions least able to absorb it: PJM accounts for over 40 percent of committed U.S. data center capital while sitting firmly in the constrained zone; CAISO carries 14 billion dollars in committed capital against the worst interconnection profile in the country; and ERCOT, still buildable, is accelerating toward the same wall.

The analytical frame shift is the part that matters. The consensus view treats the energy transition as a demand story, where capital follows visible growth; the constraint view treats it as an infrastructure reality, where capital selection depends on where the system can support that demand. The mismatch between the two—demand compounding in months, infrastructure responding in years—is the systematic mispricing, and it is also the edge.

BONUS ISSUE· Behind the Score

This was the methodology issue—the one that explains how the Constraint Index is built, because if the scores are going to influence capital allocation, anyone using them should know exactly how they are calculated. The index combines interconnection queue data with federal energy data to produce five constraint scores per region: queue depth, wait time, withdrawal rate, demand pressure, and capacity margin. Each score isolates a different failure mode of the grid, and together they form a constraint profile rather than a snapshot.

The counterintuitive finding is that a large interconnection queue is often the clearest signal that a system is constrained, not that it is growing. Queue size measures competition for access—not conversion. In several U.S. power markets, the volume of generation waiting in queues is multiple times larger than current peak demand—SPP at 6.0x, CAISO at 5.5x, and PJM at 4.0x. The biggest queues are frequently the worst places to build, because most of that pipeline will never connect, with inflows consistently exceeding completions and the backlog compounding rather than stabilizing.

The thresholds are calibrated so that current U.S. conditions span the full one-to-five range, with room for scores to move as constraints tighten or ease. A score of four is severe because the system is approaching failure, and a score of five is critical because the constraint is already binding. The purpose of the scale is not classification but early detection—tracking how close each region is to tipping into the next level of constraint before the market reprices.

ISSUE 03· America Can’t Build AI Without China’s Permission

This is the issue that surprised me most to write. The AI buildout does not have a chip problem or a capital problem—it has a transformer problem. Thirty to fifty percent of U.S. data centers planned for 2026 will be delayed or canceled, according to Sightline Climate, and of the 12 GW expected online this year, only 5 GW is under construction. Power transformer lead times have stretched to 128 weeks, with generator step-up transformers at 144 weeks, while the data center deployment cycle runs under 78 weeks. The math doesn’t work.

The U.S. imports roughly 80 percent of its power transformer supply. Chinese transformer imports to the U.S. rose from 1,500 units in 2022 to over 8,000 through October 2025—a five-fold increase in three years—and then surged another 182 percent in the first two months of 2026. A decade of reshoring has not moved the needle. The country racing to dominate artificial intelligence cannot manufacture the electrical hardware to plug it in, while the policy response—tariffs on steel and copper—raises the cost of the equipment it cannot produce in sufficient volume.

The deeper story is demand convergence. Six sectors are now competing for the same transformers and switchgear simultaneously: AI data centers, clean energy deployments, aging infrastructure replacement, extreme weather rebuilds, manufacturing reshoring, and electrification. Total demand is approaching 455 GW-equivalent against a supply chain sized for a fraction of that. The transformer bottleneck is not one industry’s problem waiting to be solved; it is the collision point where five other structural demand drivers arrive at the same factory floor—and one country makes most of what comes off it.

BONUS ISSUE· Fermi — When the Model Breaks at the Company Level

Fermi was supposed to be the vertically integrated answer to the hyperscaler power problem: a private 11-to-17 gigawatt campus in the Texas Panhandle: gas and solar by 2026, nuclear by 2032, eighteen million square feet of data center shell, Pantex next door, a 99-year Texas Tech lease, a Korean EPC partner, Siemens turbine orders, and Executive Order branding—everything except the one thing that had to come first: a signed anchor tenant. Three SEC filings in three days told the rest of the story, with the co-founder CEO out on April 17, CFO Miles Everson resigning “without Good Reason” on the 19th, and being parked on the board via family-trust designation, then Fermi2.0 unveiled on April 20 with Marius Haas as Executive Chairman and Jeffrey Stein—a restructuring-situations director—on the board. Shares fell ~19% on the day and ~75% from the October IPO.

The macro thesis was correct: interconnection queues do outrun GPU generations, and hyperscalers will pay a premium for grid-independent gigawatt-scale power. The single-name execution inverted the order of operations the model required, as Fermi drew equipment-level debt against an unbuilt customer base, priced four AP1000 reactors with a 2031-to-2038 delivery window as near-term cash flow, and upsized nameplate from 11 GW to 17 GW without a single binding offtake agreement to carry one. The incoming chairman is a BayPine and ex-Dell transactions operator, and the new director’s last five board seats include Rite Aid, Westmoreland Coal, and Dynegy—this is not a build-the-grid dream team, but a sell-it-or-restructure-it team.

The lesson? Offtake is the gating input, not GW nameplate. A one-gigawatt position with a signed hyperscaler contract is worth materially more than a seventeen-gigawatt position without one, and powered land with a short path to energization is the scarce asset. The integration premium Fermi tried to capture end-to-end was never the premium the market was offering, and selling into that scarcity is often better than attempting to own it. The bundle was the bug.

ISSUE 04 · Everything Connects in Texas. That’s the Problem

ERCOT has a zero percent withdrawal rate, and every project that enters the interconnection queue eventually gets through, with no mass cancellations, no multi-year attrition, and timelines shorter than any other major U.S. grid—on paper, the most efficient interconnection process in the country. The paradox is that ERCOT also carries the highest demand pressure ratio in the country, at 5.4x, with nearly six times more capacity attempting to connect than the system currently supports. A queue that clears and a grid under the most stress cannot both be true —unless the constraint has moved somewhere else.

It has: ERCOT trades friction for speed, while PJM filters projects at the queue and ERCOT lets them through, absorbing the consequences on the operating grid. Connection is not delivery; when the transmission path to load centers is saturated, connected projects hit curtailment, congestion, and negative nodal prices, meaning a wind farm can clear the queue, connect to the grid, and still have nowhere for its power to go. Speed of process does not eliminate the constraint—it relocates it from the application stage to the electrons that can’t reach the customer willing to pay for them. ERCOT’s CEO confirmed the volume problem in mid-April, noting that the grid is on track to nearly triple in size by 2030, and that the ISO is shifting from serial to batch interconnection studies to keep up.

For AI infrastructure, the tradeoff is clear enough: lower power costs, faster interconnection, and favorable regulatory treatment, but no guaranteed reserve during stress events, exposure to an energy-only market, and reliance on self-sufficiency. The fastest system is not necessarily the safest. Capital is still pricing connection as completion, which it is not—connection is cheap, delivery is uncertain, and cost is variable. The constraint has not disappeared in Texas; it has moved from the queue to the grid itself. And that’s a better place to be.

ISSUE 05 · Withdrawal Rates

The launch issue opened with the headline number — 67.4 percent withdrawal in PJM, 75.6 percent in CAISO. This issue went underneath it, across all seven tracked queues, and translated the statistic into the language capital allocators actually use. Across PJM, CAISO, NYISO, ISONE, MISO, SPP, and ERCOT, 14,856 projects have withdrawn — approximately 2,093 gigawatts of proposed generation, more than twice the entire installed capacity of the United States, walked away. No storm. No policy reversal. No single cause.Projects simply did not make it out of the queue. The headline numbers suggest abundance; the withdrawal numbers tell a different story; most models still treat the headline as real.

The spread is the analytical anchor. ERCOT clears at 0 percent; NYISO loses 81.2 percent. That spread is not random and it is not a supply problem — it is a grade on process design. Study timelines, fee structures, cost allocation rules, and speculation filters separate the systems that convert queue entries into operating generation from the systems that don’t. Regions with longer studies and ambiguous cost allocation lose three out of four projects. Cluster-study mechanics make withdrawal contagious — when one project leaves a cluster, costs reallocate to the survivors and some of those become uneconomic and withdraw too. Withdrawal begets withdrawal. And the filter is not neutral: in MISO 95 percent of withdrawn projects are clean, in SPP 89 percent, in CAISO 80 percent. Gas plants are more likely to survive their study windows. The queue is a clean-energy tax that nobody voted for.

The framing this suggests is private equity. Treat each queue entry as a deal moving through a PE shop, and the US interconnection system has a 67 percent deal-break rate after DD in its largest market. No PE fund could survive those metrics. LPs would stop re-upping, stated deployment capacity would be discounted, the strategy would be wound down. Headcount would be reduced. The capacity illusion follows directly: PJM’s ~254 GW active queue, haircut by the 67 percent rate, is closer to ~83 GW of expected installed capacity. CAISO’s ~89 GW becomes ~22 GW. The queue is three to four times larger than the actual pipeline, and a PPA signed for a project in that queue is not a power contract — it is an option on a power contract.

The Through-Line

Five issues, one argument: Small change, big impact.

Capital is modeling AI infrastructure as if the grid is a neutral input that will scale to meet demand, but the grid is not neutral, and it will not scale on that timeline. Interconnection queues filter rather than process; transformers arrive on multi-year lead times into a demand curve that accelerates in months, and fast systems relocate constraints rather than eliminate them. Single-name execution fails when sequencing is inverted. The mismatch between where capital is flowing and where the grid can deliver is the systematic mispricing—and it is the edge. The open question is how long the market can continue pricing the headline before it is forced to price the outcome.

Go deeper on each issue

This piece pulls together the first five issues plus bonus content. Each one stands on its own if you want to go deeper.

– AI doesn’t scale where power isn’t available—and most timelines assume that constraint doesn’t exist→ https://www.theaigridreport.com/p/ai-needs-powerthe-grid-cant-keep

– Energy demand is no longer tracking GDP, which means traditional signals are starting to break down→ https://www.theaigridreport.com/p/the-grid-cant-keep-up-with-oils-shock

– The U.S. can design the system, but still depends on external supply chains to actually build it→ https://www.theaigridreport.com/p/america-cant-build-ai-without-chinas

– What happens when the macro is right but execution fails (and capital learns the hard way) → https://www.theaigridreport.com/p/fermi-inc-frmi-how-a-good-macro-call

– The bottleneck didn’t disappear—in ERCOT, it moved from interconnection to delivery, where it’s harder to see and harder to price → https://www.theaigridreport.com/p/everything-connects-in-texas-thats

– How to measure where the grid breaks (the Constraint Index methodology) → https://www.theaigridreport.com/p/behind-the-score-how-we-measure-grid

– The queue looks like capacity, but most of it never becomes supply—and the withdrawal rate is where that illusion breaks → https://www.theaigridreport.com/p/the-withdrawal-rate

What Comes Next

That is the first month, and the Constraint Index will continue to update weekly. The regional deep dives continue—PJM next, followed by the withdrawal rate as a cross-market metric, and then the transmission layer underneath all of it. The full archive, the weekly index, and the paid deep dives are available at theaigridreport.com.

Across the seven US interconnection queues we track, 14,856 projects have withdrawn.

That is approximately 2,093 GW of proposed generation — more than twice the entire installed capacity of the United States — walked away.

No storm. No policy reversal. No single cause.

Projects simply did not make it out of the queue.

The headline queue numbers suggest abundance.

The withdrawal numbers tell a different story.

Most models still treat the headline number as real.

From Texas to Everywhere Else

Last week, we looked at Texas.

ERCOT clears its queue. 0% withdrawal rate. Every project that enters gets through.

That is the exception. Everywhere else, the queue behaves more like a sieve.

→ CAISO: 75.6% withdrawn

→ NYISO: 81.2% withdrawn

→ ISONE: 75.3% withdrawn

→ PJM: 67.4% withdrawn

→ SPP: 64.0% withdrawn

→ MISO: 54.5% withdrawn

The median project in six of seven tracked regions never becomes a project.

The Spread Is The Story

0% in Texas and 81% in New York. That spread is not random.

It is a grade on process design — study timelines, fee structures, cost allocation rules, speculation filters.

The regions with the longest studies and the most ambiguous cost-allocation rules have the highest withdrawal rates.

The regions with faster processes and clearer rules have lower ones.

Withdrawal is not a supply problem; it is a process outcome. Most commentary still treats it as a supply problem.

What Withdrawal Measures

A withdrawal is not a cancelled project.

It is a completed study, an interconnection agreement negotiation, consultant fees paid, sometimes years of developer time, and then the developer walks away.

The costs are already sunk.

The grid study is already done.

The planners already slotted the project into their load-flow models.

When a project is withdrawn, all that work is unwound.

It is the most expensive form of queue churn.

The Open Question

If two-thirds of the projects in PJM’s queue never get built, why is the queue still growing?

The answer is structural—and uncomfortable.

Developers are not filing because they expect to build.

They are filing to preserve optionality — a queue slot, a study result, a position in the stack — while they watch the market.

The queue has become an options market on future interconnection rights.

Most of those options expire worthless.

The open question is whether the market should allow that to continue.

In This Week’s Edition

This week’s edition breaks down:

• why the spread from 0% to 81% is a grade on process design, not supply

• which generation types disproportionately die in the queue — and what that says about the energy transition

• how headline queue GW overstates the actual pipeline by a factor of three

• what a withdrawal-adjusted queue looks like for AI infrastructure planning

The full Constraint Index shows which regions have functional pipelines — and which ones are running on paper volume.

Continue reading

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The Paradox

Texas has a 0% withdrawal rate.

Every project in the interconnection queue gets through.

→ No mass cancellations.
→ No multi-year attrition.
→ No system breakdown at the application stage.

On paper, it’s the most efficient grid in the United States.

But the queue is only one part of the system.

The grid behind it is a different question.

From Hardware Constraints to System Constraints

Last week, we outlined the emerging hardware constraint — transformers, substations, and long-lead equipment.

This week, the focus shifts.

Texas doesn’t fail at the hardware layer. It doesn’t fail at the queue.

It fails somewhere else.

Two Systems, Two Outcomes

The contrast with PJM is stark.

PJM:

• 9,251 active projects

• 67.4% withdrawal rate

• ~3.5-year average wait time

ERCOT:

• 1,832 active projects

• 0% withdrawal rate

• ~710-day average timeline

Two systems. Two completely different outcomes.

At first glance, the conclusion seems obvious:

PJM is constrained. ERCOT works.

But the data doesn’t fully support that.

The Signal Beneath the Surface

Demand pressure tells a different story. Demand pressure is measured as the ratio of generation ready to deliver power to peak load (which is a close proxy for the grid’s capacity to satisfy that load).

PJM demand pressure:

• ~3.0x

ERCOT demand pressure:

• 5.4x — the highest in the country

Nearly six times more capacity is attempting to connect than the system currently supports.

Where the Model Starts to Break

So:

• every project gets through

• timelines are shorter

• the queue appears functional

And yet:

• the system is under the highest pressure in the country

Those two things should not coexist.

The Open Question:

In This Week’s Edition

Most analysis stops at the queue.

That’s not where the problem is.

This week’s edition breaks down:

• why 0% withdrawal does not mean low risk

• where ERCOT is already showing signs of saturation

• how the constraint moves from the queue to the operating grid

• what the reliability tradeoff actually means for AI infrastructure in Texas

The full Constraint Index shows exactly where this pressure is building—and where it’s about to break.

Continue reading →

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Fermi Inc. (FRMI) Had the Right Macro and the Wrong Everything Else

Three SEC filings in three days. Co-founder CEO out. CFO resigned “without Good Reason” and was placed on the board via family-trust designation. A restructuring specialist director was brought in. “Fermi 2.0” announced. Shares down 19% on the day, 75% from October’s IPO.

The obvious read is that an AI-power story cratered. The more accurate read is that the AI-power thesis was right and the single-name execution was wrong — and the distance between those two things is where the money is.

Fermi was supposed to be the vertically integrated answer to the hyperscaler power problem:

• A private 11-to-17 GW campus in the Texas Panhandle.

• Gas and solar in 2026, nuclear by 2032

• 18 million square feet of data center shell

• Pantex (the place where every nuclear warhead in the America stockpile is built, refurbished, and taken apart) next door

• A 99-year Texas Tech lease

• A Korean EPC partner

• Turbine orders with Siemens

• Executive Order branding.

Everything except the one thing that had to come first: a signed tenant.

This week’s filings aren’t a strategic evolution. Rather, they’re a founder removal dressed in a press release.

The incoming chairman is a BayPine / ex-Dell transactions operator.

The new director’s last five board seats include Rite Aid, Westmoreland Coal, and Dynegy.

This isn’t a build-the-grid dream team; it’s a sell-it-or-restructure-it team.

The full post-mortem — original thesis, where it broke, five management failures ranked by materiality, the read on “Fermi 2.0,” and what it all means for how we underwrite AI power going forward — is below for paid subscribers.

Physics over narrative, constraints not forecasts. Offtake is the gating input, not nameplate GW.

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