The difficulty was that Wardenclyffe had never really belonged to the world of engineering. It was a creature of conviction. When the press demanded proof of concept, Tesla offered them instead a kind of gospel, writing that civilization’s highest calling was to harness the earth’s energy and beam it, free of charge, to every corner of the globe. Wardenclyffe was meant to be that dream’s first physical expression. But the useful irony of Tesla’s predicament was that several of his most valuable inventions—high-frequency alternating current and early wireless signaling—were developed as intermediate steps toward a goal whose premise was, even in his own time, understood to be dubious.

A Taller Tower

It’s fitting then that on February 5, 2026, Tesla CEO Elon Musk appeared on the Dwarkesh Podcast and declared that within 3 years, space would be the cheapest place to run AI data centers. Days earlier, SpaceX had filed with the FCC for authorization to launch an unprecedented one million satellites as orbital data centers,1 framing the effort as humanity’s “first step towards becoming a Kardashev II-level civilization–one that can harness the Sun’s full power.” The application came right as SpaceX finalized its acquiring xAI.

Much ink has already been spilled on why this is, to put it mildly, premature. The economics are punishing: although solar energy is more plentiful in space, orbital data centers are projected to cost roughly three times their terrestrial equivalents unless launch prices fall by an order of magnitude, and hardware would degrade rapidly in the unforgiving environment. The filing is silent on these questions, containing no technical specifications, no deployment schedule, and no cost estimates. But Musk has since offered a rough first sketch: a “mini” orbital compute satellite with exceptionally long solar arrays, paired with a comically small radiator, often the limiting component to prevent chips from overheating. Suffice it to say, the presentation did little to resolve the core economic and engineering doubts.

Musk’s ambitions, like Tesla’s, are largely vision-driven. For Musk, SpaceX exists to make humanity multiplanetary, and each venture along the way—Falcon 9, Starlink, and now orbital data centers—seeks to monetize intermediate steps, with each new revenue stream funding the next stage. Orbital data centers are Starship’s Starlink, a use case that could generate the cash and industrial capacity to keep climbing. And the bet may not be wholly indefensible. Terrestrial data center costs are rising fast, with over $160 billion in U.S. projects blocked or delayed by local opposition since 2023, narrowing the gap. And where Tesla had one financier and no fallback, Musk commands the world’s most valuable private company and the only operational super-heavy-lift rocket on Earth.

Our contention is that a well-resourced, ideologically-driven actor spending aggressively into a speculative frontier can generate demand signals, train workforces, and pull supply chains into existence that the market, left to its own tendencies, would never produce. This creates a “crowding-in” effect where significant upfront capital catalyzes additional activity by de-risking a sector, improving expectations, and creating complementary inputs. Even a partial push by Musk or other orbital compute projects like Google’s Project Suncatcher or the Nvidia-backed Starcloud to build orbital compute would set in motion a cascade of intermediate developments that matter regardless of whether a million satellites ever reach orbit.

The Wardenclyffe may never transmit power; what matters is what gets built on the way up.

Reshoring Precision Optics

Although Starlink already uses laser links to route internet traffic across its constellation, synchronizing computation across a distributed data center is far more demanding. This requires multi-terabit throughput, ultra-low latency, and far more links per satellite. The number of optical transceivers required, and the performance demanded of each, would be orders of magnitude beyond what Starlink currently possesses.

The United States cannot currently manufacture these at scale. The most intricate and expensive segment of photonics manufacturing—packaging and assembling individually fabricated optical components into a finished module—has largely been offshored, with Asia holding the majority of the global manufacturing footprint. In 2025, every transceiver in a Google TPU pod was made in China, and roughly 70% of Nvidia’s came from two Chinese companies. Beijing has made photonics a strategic priority and sees it as one avenue to sidestep U.S. chip export controls—that is, by pairing advanced photonics with legacy hardware that isn’t restricted. Optical transceivers also run firmware, so a compromised module can spoof identity, kill links, or enable deeper intrusion. When most transceivers come from a country with a history of embedding backdoors in firmware, that poses a serious security risk.

An orbital data center buildout would force this problem open. SpaceX’s classified government contracts and the dual-use nature of its constellation effectively preclude sourcing critical components from Chinese manufacturers. Given SpaceX’s pattern of vertical integration—roughly 85 percent of components are built in-house—it would almost certainly build domestic optical packaging and testing lines. These effects would propagate through three channels:

Workforce

SpaceX functions as an extraordinarily intense training ground, and people leave. As of January 2026, SpaceX alumni have founded 141 startups that have raised over $10.6 billion, and the crowding-in mechanism is already at work: In February 2026, three former SpaceX engineers who developed optical communications links for “compute-hungry” Starlink satellites raised a $50 million Series A for Mesh Optical, a company explicitly aimed at building the largest optical transceiver manufacturing footprint outside of Asia. An orbital data center program would train optical engineers at multiples of the current Starlink workforce, dramatically increasing the pipeline for exactly this kind of spinout.

Upstream suppliers

Even at 85 percent vertical integration, SpaceX still relies on over 3,000 suppliers, so it would likely purchase specialized inputs and optical test and measurement equipment. When SpaceX creates significant domestic demand for this equipment, suppliers have reason to establish U.S. service centers, training programs, and application engineering teams. That infrastructure, once built, is available to any American company, including new entrants and startups like Mesh, at lower cost and shorter lead times than sourcing from overseas.

First-mover advantage

China’s foothold in transceiver manufacturing was established in part because Huawei pioneered linear-drive pluggable optics, a design simplification that greatly reduces cost and power consumption. The next architectural transition from what are known as pluggable transceivers to co-packaged optics is still in its early stages, and the manufacturing processes are not yet locked in. A domestic buildout that produces next-generation optical architectures could establish American dominance before the market consolidates.

Radiation-Tolerant Compute

Every chip in an orbital data center would operate in a radiation environment that terrestrial hardware was never designed to survive. The SpaceX filing is exclusively for low Earth orbit (LEO), due to the added launch cost, networking challenges, and harsher conditions of higher regimes like geostationary orbit (GEO). Even in LEO, cosmic rays and solar events continuously bombard silicon, causing “bit flips” that corrupt operations and—in extreme cases—frying a whole chip. Any orbital compute platform would need processors that can withstand these effects, through either hardened-by-design chip architectures or novel shielding materials.

The problem is that the domestic industrial base for this kind of hardware is surprisingly thin. In recognition of this fact, Musk recently floated a “Terafab” in Austin to produce these advanced chips for SpaceX and just listed openings for engineers at the proposed facility. Indeed, in 2025, the entire global radiation-hardened electronics market was valued at roughly $2 billion, which is a rounding error in the $600 billion-plus broader semiconductor industry. The Department of War’s Trusted Foundry program, which guarantees access to secure domestic fabrication for sensitive military applications, covered only 2 percent of military chip purchases as of 2021.

The reason is simple: demand. The Department of War tried to stimulate the industry by growing its microelectronics budget to over $1 billion in 2024 in areas where private foundries “lack the incentives or scale to develop solutions independently,” including radiation-hardened chips. Still, the military needs radiation-hardened chips in quantities of hundreds to thousands—simply not enough to sustain the production lines that a healthy industry requires. An orbital data center constellation would totally change the arithmetic. SpaceX would need domestically produced radiation-tolerant processors at a volume that would, for the first time, create a commercial-scale demand signal to build out American industrial capacity. Companies like Cosmic Shielding Corporation, whose Pentagon contracts currently max out at a meager $4 million, would see a qualitatively transformed demand signal, and their expanded capacity would benefit both private and military use cases.

Just weeks from Musk’s declaration, we can already see the infancy of a crowding-in effect. On February 25, Nvidia CEO Jensen Huang seemed skeptical on the economics of orbital data centers. However, less than three weeks later, Nvidia unveiled plans to build the first major purpose-built GPU module for orbital data centers, which would yield 25 times more compute for space-based inference than the H100 that Starcloud first put in orbit just months earlier. Although it’s unlikely that such a major push into a new vertical was planned in just three weeks, Musk’s aggressive socialization of orbital compute created a permission structure to seriously discuss the breakthroughs that will be needed, galvanizing the world’s most important chipmaker to decide the market was real enough to publicly commit.

Expansion of Low-Level Software Controls and Chip Governance

Once launched, orbital chips cannot be inspected, swapped, or physically patched. That constraint would push more of a chip’s operating logic into updatable firmware and low-level control software, making remote updates one of the only ways to improve or constrain device behavior after deployment.

This expansion in the reach of low-level software would matter not only for efficiency but for security and governance. In orbit, remote updates would become one of the few tools left to retune device behavior, refine low-level scheduling, and improve fault recovery without replacing the chip. For national security, the strategic spillover is that the same software-mediated update paths can make chips more governable after deployment, especially in U.S. export-controlled or restricted settings. Low-level software has already been used for compute governance. In 2021, Nvidia announced a new RTX 3060 driver that would hamstring performance by 50 percent if Ethereum cryptocurrency mining was detected (in a plot twist of cat-and-mouse, users later jailbroke this limitation using a leaked beta driver). This episode didn’t demonstrate omnipotent vendor control—that’s a good thing. But it did highlight that AI accelerators are mediated by low-level software layers that can classify workloads, enforce policy, and be revised after sale. Space-based data centers would intensify investment in exactly these capabilities, spawning further feature-rich chips whose operation is mediated by signed firmware, attestation, and policy-controlled updates. On Earth as in orbit, that would give U.S. actors more ways—when necessary for national security—to enforce sanctioned operating modes long after deployment.

Power-Flexible Data Centers

Moving to space would also catalyze a power-flexible regime for data centers, which would address a major challenge at home: helping integrate data centers into the U.S. electric grid. In 30-degree LEO (one of the inclinations requested by SpaceX), satellites circle the planet roughly every 95 minutes, of which 30 are spent in eclipse and thus deprived of solar irradiance. Spacecraft bridge that gap with batteries; that is standard practice today. But powering a compute cluster through every eclipse is daunting: a 1-megawatt facility, for example, would require 500 kilowatt-hours of stored energy to sustain full output through eclipse. Meanwhile, the largest publicly-known battery system in space today is on board the massive ISS and rated at only 360 kilowatt-hours.

This reality would reward a power-flexible model of distributed computing, where a satellite preparing to enter eclipse can throttle down its power, checkpoint workloads for later (temporal shifting), delegate workloads across the constellation (spatial shifting), or combine any of these three, thus avoiding disruption to high-priority, latency-sensitive workloads. The result would be a constellation-scale orchestration. This would feed innovation in terrestrial ventures, which already show promise: in a National Grid trial earlier this month, startup Emerald AI’s software cut the power draw of a 96-GPU Nvidia Blackwell Ultra cluster by more than a third in under 60 seconds without disrupting priority workloads. Orbital data centers would stimulate this market, supercharging the development of intelligent schedulers, distributed computing, and power management.

Hear Us Out. Please.

“Hype” has become a dirty word in technology, and often deservedly so. At its worst, it means fraud and vaporware. But, at its best, it is a refusal to accept that the world as it is exhausts the world as it could be. The most consequential leaps in human capability have never emerged from prudence. They have emerged from the collision of an almost childlike insistence that the frontier is closer than anyone believes with the mature scientific apparatus to actually reach for it. We have fallen into a cultural malaise that treats this kind of ambition as naïveté, a learned cynicism that mistakes the cautious for the wise. America’s quintessential (literal) moonshot—the Apollo program—was pursued on an accelerated timeline that no cost-benefit analysis could justify, driven by a combination of geopolitical urgency and sheer will, and it reshaped the industrial and technological trajectory of a generation.

We are not arguing that Musk’s plan will work. We are arguing that the attempt is worth defending, because the things that must be built along the way are things we need regardless. There is something clarifying about an impossible goal: it reveals, in the striving, capacities that no reasonable objective would ever have called into existence. We should not immediately scoff at our most ambitious builders who, in reaching for something improbable, might deliver what no one else is trying to build.

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What pushed them there? Start with PJM’s latest capacity auction.

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The bottom falls out at PJM

In mid-December, PJM ran its annual capacity auction. This auction is supposed to answer a boring question: three years from now,1 will there be enough firm capacity on the system to serve peak demand?

During weeks like this one, where an extended cold snap has triggered Energy Emergency Alerts around the country, we can see why sufficient capacity on the grid is so important. And according to PJM’s recent report for the 2027/28 delivery year, the region is coming up soberingly short.

For the first time in its history, PJM reported that the whole RTO has failed to meet its reliability requirement; it procured about 6.6 GW less than what was needed to meet PJM’s Installed Reserve Margin—the margin of safety included for reliability. PJM emphasized that clearing short of the reserve margin does not mean for certain that there will be outages, but “maybe” is not exactly a reassuring turn of phrase when it comes to blackouts.

Meanwhile, PJM’s report lists a whopping clearing price of $333.44 per MW-day. That makes it the third straight auction to set a new record price, which has risen by over one thousand percent compared to three years prior.

The situation is under [price] control

What’s more, this number has in fact been significantly suppressed by the fact that a recent FERC-approved settlement imposed a temporary “price collar” (a cap and a floor) on PJM’s last two auctions. That collar was the product of pressure from governors in PJM states, who argued that consumers were headed for an avoidable price spike while PJM struggled to find a lasting fix.

We know what the market-driven price would have been, though, as PJM published a “no cap-and-floor” counterfactual for the same auction. In that scenario, the clearing price would have been roughly $530/MW-day, meaning that the reality of our supply constraint is even worse than the headline.

This constraint should concern ratepayers. If PJM is genuinely short on firm capacity, suppressing the clearing price can also suppress the incentive for new supply to enter (or for existing resources to stick around) at a moment when the system desperately needs more.

Loudest among the leaders arguing for the price cap was Pennsylvania Governor Josh Shapiro, who first pushed the idea in 2024. Shapiro has emerged as something of a national leader (as well as a likely presidential candidate), and others across the region are following his lead.

Indeed, affordable energy has been the biggest issue on the ballot in some states, arguably deciding races like New Jersey’s gubernatorial win for Mikie Sherrill late last year. It’s no surprise then that in response to this crisis, Democrats and Republicans alike might grasp for a bigger lever and take this issue to the White House. In fact, that’s exactly what several governors did last week.

Governors take PJM problems to Washington

On January 16, Governors Josh Shapiro (D-Pa.), Wes Moore (D-Md.), and Glenn Youngkin (R-Va.) convened with the Trump administration to announce an unusually direct intervention into PJM’s market design. Notably, one key stakeholder wasn’t in the room for the announcement: PJM itself.

Why did these governors—including prominent Democrats who have made hay out of pushing back against the Trump administration—go to the White House? Because governors can’t order PJM to change its market rules, since PJM’s tariff lives under federal jurisdiction. All action must run through FERC. For instance, PJM must first make a filing at FERC, or FERC itself must launch a proceeding. A White House intervention is a top-down way to force action.

According to a DOE fact sheet that followed the talks, the White House’s regulatory push has two marquee components.

1. Extend the region’s price collar on capacity.
   This one is pretty straightforward: the plan is to prolong the price cap and floor in effect at PJM.

2. Create a 15-year procurement pathway for new power plants.
   This is more novel. The administration is urging PJM to “accelerate development of reliable power generation by providing 15-year revenue certainty for new power plants.” Traditionally, PJM has held its capacity auction three years ahead of the year being targeted. But even with a multi-year lead, the capacity auction’s timeline is too short to reliably bring major new plants online. Generation companies can easily take over a decade to finance, plan, build, and launch a whole new power plant. The one-time emergency auction that DOE proposes would provide a long-term contracting tool on a 15-year term, creating an additional incentive for new build, atop PJM’s annual rhythm of capacity commitments. In the long run, the goal is to make Mid-Atlantic electricity more “reliable and affordable” by “building more than $15 billion” of new reliable baseload generation.

You compute, you pay in full

How do they propose paying for that new build? Per DOE:

“Require data centers to pay for the new generation built on their behalf—whether they show up and use the power or not.”

It’s a big deal. Here, DOE is not merely calling on data centers to contribute to meeting their electricity demand, which many a hyperscaler press release has reassured the public they already do. Rather, it’s something far more aggressive.

Consider the status quo. Suppose that a region scrambles to procure and finance new capacity because it expects a 5-GW wave of compute campuses. If 3 GW of those campuses are delayed, or if they use only 2 GW of the steel in the ground that’s been laid, households still get the bill for all 5 GW. But by tying data centers to pay “whether they show up…or not,” DOE is forcing seriousness on the part of developers: if you want PJM to treat your computing needs as real enough to justify new plants, you should be on the hook.

The price collar constrains supply

The push that Governor Shapiro led—and DOE has now extended—to put a price cap on capacity is a rational political response. When prices jump, households and small businesses pay. A price control can relieve that pain, buy time, and let political leaders say, credibly, that they took some sort of action to protect ratepayers.

But it’s a band-aid solution, and that palliative quality is precisely the problem. In a capacity market, high prices are the mechanism by which the system rewards more supply. If PJM is telling you it’s short on firm capacity, then suppressing the clearing price suppresses the incentive to build the next marginal megawatt. It makes today’s bill less painful to open but tomorrow’s shortage harder to solve. Thus, there is a tension between DOE’s proposal to (1) extend the price collar but (2) incentivize $15 billion in new reliable baseload power. The sole solution to PJM’s scarcity is more supply, and only the latter intervention can achieve that.

What happens next

Importantly, nothing announced at the White House changes PJM rules immediately. PJM market changes run through FERC’s oversight, PJM’s stakeholder process, and eventual tariff filings. And recall that PJM has plenty on its plate,2 even if it struggles to deliver competent solutions.

After chronic fumbles, PJM has its job laid out pretty clearly, and the market reform itself is once again in PJM’s court. The White House visit put the—biggest—political thumb on the scale. For state leaders, it’s fingers crossed.

After PJM’s process wound down, FERC stepped in with a related but separate decision that caught many by surprise. On December 18, 2025, the Commission issued an order directing PJM to rewrite its tariff treatment of “co-located load,” and it did so with unusual specificity. Today’s edition explains what exactly FERC deemed so unworkable, what FERC is requiring PJM to adopt instead, and whether that solves the region’s broader large-load challenge.

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The CIFP Process in PJM Timed Out Without a Solution

First, a quick recap on the CIFP. The record of comments was split into several camps. Some parties, including Amazon, wanted demand response, but on a strictly opt-in basis, which would reduce peak load without chilling investment in new compute campuses. Others, including Google, Microsoft, and Constellation, argued that PJM should first improve the credibility of its load forecasts, lest PJM base its new policies on exaggerated demand figures. This motif of “forecast first, reform later” had surfaced quite early (starting back in August) alongside broader worries that the system was overreacting to potentially inflated projections, whether due to double counting, poor data, or speculative projects squatting in the interconnection queue.

As a whole, the CIFP-LLA ran as a compressed sprint. Readers may recall that early in the process, one flagship concept was the NCBL (Non-Capacity Backed Load). But over the course of September and October, backlash to the mandatory NCBL reached a fever pitch, and PJM moved away from the construct. By late October and early November, stakeholders were coalescing around packages of smaller, ameliorative ideas instead of a drastic new service model. Multiple competing bundles of reforms circulated, with a mix of ideas being proposed: forecasting improvements, staged energization, interconnection acceleration, and extended-horizon auctions to secure reliable generation over many years. The months-long process culminated in a late-November Members Committee vote.

None of the packages achieved sufficient votes to move ahead. The CIFP had failed to produce a consensus solution by year’s end. Many stakeholders were furious, including governors of PJM states, who criticized the outcome and urged decisive action.

FERC’s December 18th Order

Against that backdrop, FERC’s Dec. 18, 2025 order is particularly relevant, as it forces closure on one specific part of the broader dispute. The Commission declared that PJM’s tariff treatment for co-located loads is “unjust and unreasonable”—a legal finding that triggers mandatory reform. At its core, the Commission argued that the lack of consistent rules for co-location arrangements meant uncertainty for developers, reliability risks for the grid, and undue cost-shifting onto other ratepayers. FERC therefore directs PJM to rewrite the tariff within 60 days and—in a bit of hand-holding—enumerates a menu of four transmission service options that PJM must offer to customers.

The Old Menu: Network vs. Point-to-Point

To understand the change, it helps to review the old menu of options. Suppose that you are a large customer in PJM. Your transmission service offerings fall into two buckets:

• Network Integration Transmission Service (NITS) is the default for load that gets studied and planned as part of the electrical network’s obligations. PJM plans transmission to serve you and coordinates capacity on your behalf. In return, you pay transmission costs on a shared basis, based on your contribution to peak demand.

• Point-to-Point Transmission Service (PTPS) is a reservation for a specific amount of capacity. You’re buying a defined number of megawatts between a Point of Receipt and Point of Delivery. PTPS comes in both firm and non-firm variants, but it’s designed for moving power across the system, not for serving load at a single location.

The existing tariff had several deficiencies that made it ill-suited for co-located arrangements at scale. This created at least three problems. First, PJM’s Behind the Meter Generation (BTMG) netting rules allowed cost-shifting. Some co-location configurations sought treatment to net on-site generation against their load. This means, for instance, that if you had a 200-MW compute campus and 200 MW of generation sitting around on site, you could subtract your on-site generation from your total needs—thus reducing your net withdrawals to virtually zero for billing purposes—while still benefiting from backup service during outages. Second, customers had no way to purchase less than full service even if they wanted it. A campus expecting its co-located generator to provide 90% of its needs had no option to buy firm service other than to buy transmission planning and capacity for 100% of its load. Third, PJM’s planning models had blind spots. If co-located loads showed up as negligible on the network because of netting, system planners couldn’t see the reliability risks. Perpetuating all of this was the longstanding reality that transmission owners across PJM each had their own way of treating co-location arrangements, sowing inconsistency and confusion across the RTO.

The New Menu: Four Ways to Take Service

The recent FERC order remedies these issues by requiring PJM to offer four transmission service options to any customer taking service on behalf of co-located load:

1. Network Integration Transmission Service (but on a “gross demand” basis)

2. Interim Non-Firm Service

3. Firm Contract Demand

4. Non-Firm Contract Demand

Here’s what each provides.

Option 1: Network Integration Transmission Service

This is the traditional path for loads that want full integration into the grid. PJM studies your entire load, incorporates it into planning, and builds transmission to serve it. You pay NITS transmission rates plus capacity charges, and you’re treated as network load in all respects.

The change for co-located loads is that you must take NITS on a gross demand basis. No netting is allowed. If your data center has a 200 MW peak demand and a co-located 180 MW generator, you still pay based on 200 MW, not the 20 MW net withdrawal. This eliminates the cost-shift that troubled so many ratepayers.

Ideal use case: loads that need firm, always-available service and are willing to pay for full network integration.

Option 2: Interim Non-Firm Service

This is a bridge product, available only to customers who are ultimately pursuing NITS but whose network upgrades are not yet complete. It provides interruptible service at NITS rates but without a capacity charge. This means PJM can curtail you during emergencies. Once your upgrades are done, this service terminates, and you move to full NITS.

The interim service addresses a time-to-power problem. Network upgrades can take years. This lets you energize sooner, albeit without firm priority, while the necessary facilities are built.2

Ideal use case: loads that want NITS eventually but need to energize before upgrades finish, and can tolerate curtailment risk in the interim.

Option 3: Firm Contract Demand

This is the option that breaks new ground for PJM. You choose a megawatt cap (your contract demand), and PJM provides firm service up to that level. You have the same curtailment priority as traditional firm transmission customers. PJM plans only to your contract demand, not your gross load. If you exceed your contract demand, you face penalties.

You can combine this with Option 4 (Non-Firm Contract Demand) to meet load above your firm contract level.

The benefit of Firm Contract Demand is cost-effectiveness. A 100 MW co-located load that expects to draw 60 MW from the grid only during extended generator outages can size its firm service to match that expected usage, rather than paying to build a full 100 MW of transmission it will rarely use.

Ideal use case: loads that want firm backup service but only at a level below gross demand, because on-site generation will cover most needs most of the time.

Option 4: Non-Firm Contract Demand

This is interruptible service with no long-term commitment. Reservation periods range from one hour to one month. PJM does not plan upgrades to support this load, and you can be curtailed in emergencies. But the selling point is that you pay no capacity charge.

This option provides flexibility for variable or occasional grid usage without imposing planning obligations on the system.

Ideal use case: loads that want short-term access without committing to long-term service, or as a supplement to Option 3 for usage above the firm contract level.

How the Options Fit Together

As touched on earlier, there is a structural relationship among these offerings. Options 1 and 2 together constitute the pathway to becoming network load; Option 2 is simply an on-ramp that lets you start energizing before upgrades are ready. As the more novel offerings, Options 3 and 4 are alternatives to network load. They let you buy less service; it’s up to your needs and risk tolerance.

All four options require that customers pay regulation and black start charges—on a gross demand basis. This ensures that even loads taking minimal transmission service still contribute to the ancillary services they benefit from. Thus, the December 18th order closes the cost-shifting loophole that long allowed co-located loads to freeride on grid reliability while paying almost nothing.

Not The End of The Story

FERC’s move is not pro-data center or anti-data center. It is a practical cleanup of a tariff that had become too ambiguous to administer fairly or safely. Under the old framework, a co-located campus could make itself negligible on paper but suddenly look to the grid for significant backup. The fault does not rest with data centers per se. Neither should it be pinned on the transmission owners serving those compute campuses. PJM’s old menu was more or less binary, an untenable model for the needs of data centers deploying fast and needing power soon. PJM can meet the challenge with a legible, standardized set of service options so customers can choose what they actually need and PJM can plan, build, and bill accordingly.

This new tariff design is not a resolution to the now-expired CIFP that kicked off this “Standby, or Stand Firm” series, but it is a step to better accommodate the large loads that are causing PJM’s crisis of resource inadequacy in the first place. A cleaner co-location framework does not conjure new generation, accelerate multi-year transmission construction, or provide a crystal ball for load forecasting, despite the many earnest, entertaining, and creative comments submitted to PJM.

Even FERC’s ability to intervene is limited, but it’s been clear and decisive. And last Friday, governors from around PJM descended on the White House to further crack the whip on PJM, calling for an emergency auction and the construction of billions in new power plants. That’s for the next issue.

Here are the rules:

1. Submit your answer via direct message using the button below.

2. Deadline is Monday, Oct. 20, 23:59:59 AoE (Anywhere on Earth).

Here are the categories and prizes:

“Correct Answer”

• Shout-out: Every correct entrant will receive an acknowledgment in our next edition (with your consent).

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Until Next Time

Please share Policy Gradients with that friend or colleague who would find value in it. As always, I respond to each and every message I receive.

Onwards and upwards,

Daniel

In a historic event for our great nation, delegates from the thirteen states convened in the city of Philadelphia, Pennsylvania with one mission: they had a new vision for the future of their union. They wanted new governance, and they wanted it now. The electric bills were too bloody high.

This was the scene two weeks ago as a bipartisan bloc of PJM-state governors came together to take skyrocketing capacity costs and fumbled process into their own hands. All of this is unfolding as PJM’s CEO Manu Asthana prepares to abdicate at year’s end.

But beneath those political headlines, an in-the-weeds fight has been playing out in the PJM Interconnection, one that decides whether AI gets built here, at scale, and on time: what kind of power service data centers receive, how much risk they shoulder, and how quickly they connect.

The Roadmap for This Edition

Today is Part 2 of Standby, or Stand Firm?, a series on AI data centers, flexible load, and PJM’s plan for dealing with it: Non-Capacity-Backed Load (NCBL). In Part 1, I explained PJM’s concept for NCBL, a “standby” lane for large loads that become the first to curtail when the grid is stressed. I also highlighted some flaws of NCBL that triggered the swift backlash from all sides.

That’s where today’s issue picks up. First, I’ll give an update on recent changes to NCBL and noteworthy counter-proposals from stakeholders. After that, today’s edition will serve as a primer dedicated to all things “flexible data center” and “flexible load.” I’ll unpack key terminology, including service class (what you’ve contracted for) versus market mechanisms (how you behave). We’ll also look at how AI data centers actually flex, from the campus level down to the GPU core. To top it off, we’ll finally learn the lore behind the flexible data center: its origins and the key scientific result that’s been all the rage this year.

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What’s New With NCBL?

Mandatory NCBL is no more

They proposed. Hyperscalers said, “No.”

PJM has walked back the mandatory component of its NCBL construct, unveiling changes in its meeting last Wednesday, October 1st. In Part 1, we witnessed that the third rail of the NCBL proposal, the thing that turned so many heads, was the mandatory backstop. The idea of forcing standby status—allocating NCBL whether you opted in or not—was a non-starter for data centers that live and die by uptime and service commitments to their customers. This latest announcement has its jargon, some of which I demystify in the section “Service Classes vs. Market Mechanisms” that follows. For now, I’ll give a quick rundown.

In PJM’s newest slide deck, flexibility takes the form of pre-existing, voluntary tools:

• Demand response (DR): paid, event-based reductions during system stress.

• Price-responsive demand (PRD): a pre-filed, price-indexed reduction plan, modified by PJM to use the energy-market offer price.

PJM also outlines process changes:

• Load Forecasting: add a state-commission review of large-load adjustments before the forecast is finalized; require submitters to disclose duplicative requests.

• Expedited Interconnection: create a 10-month Expedited Interconnection Track for “sponsored generation” (e.g., supported by a state commission) with strict eligibility (e.g., >500 MW) and project responsibility for paying 100% of needed network upgrades.

• Longer-term procurement: open a discussion for alternatives to the region’s traditional reliability backstop, which solicits capacity for a single delivery year at a time; this would allow procurement of capacity for longer periods.

In short, PJM is stepping away from a blunt, mandatory standby class and toward a menu of voluntary flex behaviors. It also floats a variety of process changes, all aimed at improving long-term resource adequacy in the region.

Hyperscalers become generators…of policy

Industry stakeholders, for their part, have mounted an unusually proactive response to the heavy-handed NCBL since its bombshell release in August. In a scarcely seen, coordinated intervention in regional market design, hyperscalers including Amazon, Google, and Microsoft have proposed a complementary package of reforms consisting of three pillars:

Improving Load Forecasting: count only loads with verifiable commitments; reduce counting of duplicates; implement “reality checks” informed by supply chain conditions, expert studies, etc.

Demand-Side Actions: enable limited-hours DR; establish an emergency step to run on-site backup generation before any manual load-shed; offer a purely voluntary curtailment option.

Procurement of Multi-Year Capacity: if the region remains short, run a multi-year capacity procurement to incentivize new supply. Generators bid for contracts lasting 1-7 years, though shorter terms clear first. The price is set at PJM’s standard reference point (the maximum on its capacity demand curve) and gets locked in for the life of each contract. This temporary program sunsets after the 2031/32 delivery year.

PJM vs. Hyperscalers: Where do their latest visions agree?

1. Voluntary vs. mandatory: Agree.
   PJM scrapped the compulsory allocation of NCBL.

2. Load forecasting: Agree.
   PJM’s newly proposed due diligence for load-forecasting (state-level review, duplicate-request screens) is in spirit with hyperscalers’ requests that PJM first ensure estimates of future need are credible.

3. Demand-side mechanics: Specifics TBD; room to agree.
   PJM’s pathway is to use some combination of existing DR and a modified PRD mechanism. Hyperscalers go further, crafting a limited-hours DR product (24–100 hours/year).

4. Emergency playbook: TBD; room to agree.
   PJM has only vaguely flagged a review of manual load-shed allocation, to be conducted at a later date. Hyperscalers ask for a specific rule to dispatch on-site backup generation immediately before manual load-shed. This request may raise environmental/permitting wrinkles.

5. Generator interconnection: TBD; room to agree.
   PJM has proposed a 10-month Expedited Interconnection Track for sponsored generation. The latest coalition deck says nothing on this, but hyperscalers have historically supported generation projects that are colocated or paired with load.

6. Longer-term capacity procurement: TBD; room to agree.
   PJM mentions the idea of longer-term reliability purchases. Hyperscalers lay out a concrete process for procuring multi-year resources beyond PJM’s usual capacity auction, which procures capacity for just one year.

What’s the upshot? Both sides are now gesturing toward voluntary flexibility executed through DR-like mechanisms. What remains is to decide contractual and operational details: explicit curtailment caps, advance notice, published priority order, compensation for curtailment, and an emergency sequence—including whether and when to use backup generation. On some of these fronts, industry stakeholders have offered their version of the specifics.

Furthermore, both parties are looking at future years, with recommendations to reinterrogate potentially inflated load forecasts and to allow contracts for longer-term capacity. The industry slide deck’s recommendations for improving the accuracy of future load forecasts are sensible—and I would not be surprised to see PJM’s official numbers decrement as a result of implementing these actions—but if you ask me, it’s largely wishful thinking on the part of the industry coalition. Even if estimates come down, it’s fanciful to imagine they’ll do so by enough to dig us out of the present situation. We’re fundamentally in a state of resource inadequacy.
Update 10/8/25: The above section was revised for accuracy.

Certain unalienable rights

Hyperscalers gave us a reminder that state governors aren’t the only ones staging an intervention in PJM. The giants of the AI industrial base, who have seldom seen regional grid policy as part of their job, are architecting their own vision for regulating flexible load. In doing so, hyperscalers are drawing a line: the freedom to choose firm over flexible is, for them, a certain unalienable right.

For my part, I don’t think that flexibility ought to be verboten. Rather, it can be a fantastic opportunity. I do think that forced, open-ended flexibility poses a massive risk. If exposure to that risk is unbounded, you can’t plan AI workloads. If you can’t plan, you don’t build. That’s the decision calculus.

Just CHILL: The Response to Large Loads Out West

Let’s take a brief excursion inland. The same story of large loads has transpired across the country, albeit more cool-headedly. Consider the Southwest Power Pool (SPP), whose board approved a High-Impact Large Load (HILL) policy on September 16th, establishing a 90-day study-and-approval path for large loads paired with new or planned generation. This is an excellently generative idea, literally, since it enlists loads to help solve the supply shortage. A companion idea—wryly named Conditional HILL (CHILL)—would provide expedited, non-firm access in exchange for defined curtailment rights. That makes it something of a more mature cousin to NCBL, but CHILL has been deferred to a later tariff revision. PJM is moving in the right direction, but the execution has been decidedly rougher than out west.

Fundamentals of flexibility:
service classes vs. market mechanisms

Many readers will know the buzzwords. Demand response. Demand flexibility. Demand-side management. Flexible data centers. Flexible load. Same thing…right? The ontology of flexible load involves two animals: (1) service classes and (2) market mechanisms.

• Firm service class (a high-priority contract).
  This is the default class. You energize when ready and—short of extraordinary conditions—remain served. You have top curtailment priority; all others curtail before you. No pre-commitment to reduce; any turn-down in power is at your discretion.

• Flexible service class (a lower-priority contract).
  This is a distinct class that has gone by various names in various jurisdictions (e.g., interruptible/curtailable/non-firm service). You opt into a lower-priority lane in exchange for something you value, often earlier energization and sometimes lower charges. The contract has clear terms:

  • caps on total hours (or total energy) curtailed

  • notice windows (e.g., day-ahead advisory, plus same-day call)

  • published priority of each customer in the curtailment order

  • basic verification so that performance is auditable.

  PJM’s proposed Non-Capacity-Backed Load (NCBL) is best understood as an instance of flexible service.

• Market mechanisms (DR, PRD).
  These are behaviors theoretically available to a customer in any class.

  • Demand response (DR): event-based reductions that you perform for compensation when the operator signals so.

  • Price-responsive demand (PRD): a pre-filed, rules-based plan that automatically reduces your load in real time as the market price crosses certain thresholds.

Two clarifications for good measure

1. Participating in DR/PRD does not change your service class. A firm customer can do DR/PRD and remain firm.

2. In a flexible contract, your system operator may use DR/PRD-style tools to implement your obligations, but that does not make DR/PRD the service class.

In short: service class sets your curtailment priority while DR/PRD market mechanisms are agreed ways to behave during special events.

How to Flex Your Data Center

In practice, the tools that sophisticated customers use to execute DR and PRD behaviors aren’t just light switches; their power draw can be tuned smoothly. Could AI factories do the same? As I wrote in Part 1, ventures like Verrus AI and Emerald AI’s orchestration at an Oracle data center in Phoenix show that software can, in real time,

1. modulate the total load of a site

2. delegate jobs among sites (spatial shifting)

3. defer jobs to later (temporal shifting).

Flexible to the core: GPU-level power controls

But I’ve spoken to a few engineers building something neater: fine-grained GPU controls that fit naturally into DR/PRD. Using dynamic voltage and frequency scaling (DVFS) to adjust the clock frequencies of GPU cores across multiple discrete setpoints, even an individual chip can track a price signal or instruction quasi-continuously. Practically, that means you don’t need to halt jobs when the grid tightens; you just run the entire fleet less intensively.

In other words, operating your site at 80% can be achieved in multiple ways:

1. four-fifths of GPUs at full tilt with one-fifth idle

2. the entire fleet running at a moderately reduced clock frequency (e.g., ~90%) that corresponds to an 80% power draw1

3. something in between.

Furthermore, the more levels and axes along which we can adjust computing load (temporal/spatial, campus/rack/chip, etc.), the more sophisticated the solutions available to us, including highly intelligent, reinforcement-learning-based workload management that can conduct a country of data centers in a seamless symphony.

This is a dynamic field evolving every day, with low-hanging fruit to be picked. If you’re a reinforcement learning engineer or an operations researcher, this is one of the most fascinating ways you could help improve the efficiency, reliability, and competitiveness of American AI and the grid. Every innovation can be deployed to AI factories at massive scale.

Flexible Data Centers: The Lore and The Seminal Whitepaper

In February 2025, a team at Duke University’s Nicholas Institute—Tyler Norris, Danny Profeta, Esteban Patiño-Echeverri, and Cowie-Haskell—gave a crisp name to a practical idea: curtailment-enabled headroom, herein abbreviated CEH. It’s presented in their now-landmark paper “Rethinking Load Growth,” and the claim is straightforward: if large loads pre-commit to brief, bounded curtailments in narrow windows when the system is tight, the grid can absorb much more demand without waiting years2 for new build-out. Their first-order national estimate:

• 76 GW for ~0.25% annual curtailment

• 98 GW for ~0.5% annual curtailment

What do “narrow windows” mean? Grid stress is concentrated in a few hours of the year—late-afternoon peaks on the hottest days, frigid morning ramps, the occasional unplanned outage window—when load spikes against the capacity ceiling. CEH supposes that large loads hold back just during those spikes, not across the whole year.

For calibration: 1 year = 8,760 hours.

• 0.25% ≈ 22 hours/year.

• 0.5% ≈ 44 hours/year.

For context, many government and industry studies have forecasted U.S. data-center-driven demand growth by 2030 to be in the neighborhood of 80-100 GW. If this is the case, the implications from Norris et al. are huge. Even a small exercise in CEH could be enough to cover 100% of load growth—zero new generation needed.

PJM, Meet CEH.

How well does CEH scale in PJM? The region now projects roughly 32 GW of additional coincident peak by 2030, with about 30 GW attributed to data centers.

Against that backdrop, Norris et al. estimate that PJM would free up 17.8 GW under the ~0.5% curtailment scenario: that means we could accommodate 56% of incremental load growth if customers coordinate and curtail.

CEH: ~Necessary But Not Sufficient

CEH is a concept with the potential to circumvent America’s resource inadequacy for years to come if we have the right service classes, market mechanisms, and hardware in place. Of course, that’s turning out to be no small “if.”

First-order analysis is a first step

The Duke paper is intentionally first-order: it aggregates load and aggregates supply. In practice, America’s generators and batteries do not sit in one big lump, and neither does load. Delivery is fine-grained, bottlenecked by particular buses and substations. In other words, a surplus three states away doesn’t help if your local breaker is already at its limit. Norris et al. are upfront about this limitation, and they encourage further investigation.

We need hard numbers and hardware

There remain two practical gaps to planting these flexible loads around America:

• Siting intelligence.
  Developers lack visibility into local constraints: substation hosting capacity, feeder headroom, queue interactions at transmission voltage, and more. At the distribution level (low voltage), some maps of hosting capacity exist. What’s missing is a PJM or nationwide analogue at the transmission level (high voltage). Builders need to see substation headroom—posted publicly and updated quarterly, for instance—with standard options and timelines for upgrades. That lets builders land where the grid actually has room.

• Physical equipment.
  At nodes of the grid known to be at capacity, the limiting pieces can be prosaic: power transformers and other high-voltage gear. Wait times for distribution transformers have stretched into the two-year range, and large power transformers are globally supply-constrained. Under these conditions, solving power bottlenecks calls for targeted substation expansion and clear cost-sharing. Notably, that could include Contributions in Aid of Construction (CIAC), which are direct, customer-funded upgrades for work that can be imputed to a specific load.

Curtailment-enabled headroom and the findings of Norris et al. were a watershed result. Flexible power as a ramp to a permanent regime will be all but necessary if we are to maximize the medium-term realization of American data centers, but it certainly won’t be sufficient. All paths forward will require us to move both data and metal.

Next Time

Who thought that grid policy in PJM would be like watching a period drama? After all, not every commodity market has governors descending on Philadelphia to declare the causes which impel them to the separation.

By popular demand—and by the sheer frenzy of activity in PJM—the Standby, or Stand Firm? series will continue with Part 3 on a simple premise:

This will give us a grading rubric for “flexible load done right.” And depending on how fast negotiations over NCBL wrap up, we might be able to give PJM a report card.

1

Update 10/10/25: Supplemental derivation added.

Dynamic switching power for a GPU core scales with capacitance, voltage squared, and frequency. It turns out that voltage and frequency are linked. Reducing frequency allows for a reduction in operating voltage:

The exact relationship (i.e., the curve of V vs. f) is complex and varies by chip, but we can use a realistic estimate to illustrate the principle. Assume a 10% reduction in clock frequency (i.e., new frequency is 90% of old frequency). Realistically, this might allow for a 5% reduction in voltage (i.e., new voltage is 95% of old voltage)​. Plugging these values in, we can calculate the new power level:

To summarize, setting clock frequency (i.e., computing speed) to 90% of its maximum value has the convenient side effect of lowering the transistor voltage required. Empirically, a realistic value for this new voltage would be around 95% of the maximum voltage. The result is a power use of approximately 80% the maximum power of the chip.

AI and data centers absolutely dominate the energy policy discourse these days. Many readers are already familiar with the basics: data centers are poised to represent nearly half of all U.S. load growth from now until 2028, and in pockets of the country where they’re most likely to be deployed (Texas, Virginia, Pennsylvania, etc.), they will represent not just one of the biggest energy issues for policymakers, but one of the biggest issues period.

We’re already seeing the challenges of new data center deployments. AEP Ohio imposed a moratorium on new data-center hookups that lasted more than two years while regulators struggled to address grid costs. Just last week, the city council of College Station, TX unanimously shot down a land sale for a 600-MW data center campus, with residents citing strain on the grid as a key concern. And in a move that sparked a week’s worth of drama on Energy Twitter, the PJM Interconnection has proposed a restrictive new service category for large incoming loads: Non-Capacity-Backed Load (NCBL).

Today is Part 1 of a two-part series. In this installment, we’ll break down what NCBL actually does, what aspects of stakeholder backlash are on the mark, and why time to power is what dominates data center decisions. In Part 2, we’ll lay out the fixes—what “flexible load done right” might look like—and how to get there.

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Flying on Flexible Fare

Suppose you’re flying from DC to Philadelphia—to pay a visit to PJM headquarters in Valley Forge, PA, of course. You can pay $180 for a guaranteed seat. But on that same fare-selection page, the airline makes you an offer: pay the bargain price of $110 for a standby seat instead. A “standby” seat means you board last and—if it’s a popular itinerary—you might get bumped to a later flight entirely.

For fast‑arriving, large loads on the electric grid (a term that’s quickly become a Pavlovian stand‑in for “data centers”), that’s the latest idea from PJM: you can pay one price for the guaranteed seat, or you can avoid the seat-reservation fee if you agree to fly standby and be first to get bumped when the plane is tight.

People do not like it. The Data Center Coalition says the NCBL proposal is too vague to operationalize. Constellation has accused PJM of gross jurisdictional overreach. Cy McGeady of Equinix says the proposal obviates PJM’s entire designed purpose of creating a capacity market in the first place.

So what’s going on? First, some background…

There Are Really Two Electricity Markets

Before we unpack the chaos unfolding at PJM, we should understand the two layers of electric service: energy markets and capacity markets.

Energy (kWh)

This is the way most of us are used to thinking about electricity: it’s a commodity that you consume. Prices swing with fuel, weather, and congestion. At month’s end, your bill reflects how much you used and when. In the energy market, generators are paid for how much they produce.

Capacity (MW-year)

This is like the insurance premium for the worst day of the year. You’re paying to ensure enough megawatts will be there when the system gets tight. In the capacity market, resources (a fancy umbrella term for generators and other power sources, including batteries and storage) are paid simply for being available, not for how much they produce.

Every U.S. ISO (Independent System Operator) and RTO (Regional Transmission Organization) runs energy markets. What differs is the reliability layer on top:

• Centralized capacity markets (PJM, ISO-NE, NYISO).
  These regions have formal auctions that secure future capacity and set a price for it. Consumers in these jurisdictions pay for both energy and capacity (though, as a household customer, you won’t see “capacity” itemized; it’s baked into the supply rate that your provider charges).

• Resource-adequacy programs (MISO, CAISO, SPP).
  These regions don’t run a big capacity auction. Instead, regulators instruct each utility or retail supplier to prove that they’ve lined up enough capacity for a peak day, plus a reserve margin. If one utility company is short, the ISO can procure the deficit (using methods beyond the scope of this discussion) and charge the short party. Essentially, it’s capacity by proof, not by a single market clearing price.

• Energy-only (ERCOT).
  In Texas, there is no capacity market. This is a laissez-faire approach where scarcity pricing is meant to attract investment. The basic idea is that if a shortfall does arise, there will be a natural incentive to build more resources in order to chase the lucrative marginal price during those moments of high demand. The result is more resources on the grid and improved reliability overall.

Belonging to the first of these categories, PJM solicits promises to meet capacity three years ahead through its Base Residual Auction (BRA). The BRA is PJM’s auction to identify generators who will pre-commit to meeting future demand at the lowest cost. The resulting clearing price at auction basically represents the price that customers will pay for carving out a “guaranteed seat” of 1 MW.

The capacity market price signal has exploded

PJM’s capacity auction cleared at $329.17 per MW‑day for 2026/27. It was $269.92 per MW‑day for 2025/26, which was an explosion from just $28.92 per MW‑day for 2024/25 the year prior. That’s a jump by over 800%! In essence, the recent shortfall in generation has meant an enormous premium being paid to those who can step up and guarantee availability.

That said, it’s worth emphasizing that the retail electric prices that residents see are a complicated business, and data centers certainly shouldn’t get all the blame. As frustrated ratepayers feel the pain of rising prices, it’s a picture that deserves more color (more on this in a future edition of Policy Gradients).

What is NCBL? A “Standby” Lane in PJM for Large Loads

Now, we’re ready to understand the capacity debate that’s been happening. Non‑Capacity‑Backed Load (NCBL) is a proposed lane for big new customers (generally ≥ 50 MW) willing to accept lower-priority service under certain rules. Stakeholder discussions are ongoing, and PJM has signaled an intent to file with FERC by year’s end, with operations targeting a delivery year of 2028/29 at the earliest.

• NCBL does not pay a capacity charge. It is subject to pre-emergency curtailment.
  NCBL is excluded from the BRA and associated capacity charges. In return, it can be curtailed under defined pre-emergency conditions, which are conditions of high demand that do not yet rise to the level of an emergency. In these scenarios, electric customers in the NCBL lane are the “standby” passengers who are first to be bumped.

• NCBL curtailment is PJM-instructed, utility-executed, and auditable.
  Before the delivery year in which NCBL takes effect, it is the responsibility of each transmission owner (i.e., utility company) and participating customer (e.g., a data center) to agree on an operating procedure for curtailment: who calls whom, what constitutes minimum notice, ramp-down expectations, steps for restoring power, data reporting, and more. During a curtailment event, PJM instructs the transmission owner to curtail the load that is being served. Afterward, the transmission owner is responsible for sending telemetry data (i.e., before-and-after data from meters and sensors) to PJM for audit and verification that the load truly was curtailed.

• NCBL participants receive offsets for Bring-Your-Own-Generation (BYOG) and Demand Response (DR).
  Bring-Your-Own-Generation (BYOG) is on-site generation that you own or control. Demand Response (DR) means voluntarily lowering your load in response to a grid signal during periods of high demand, typically in exchange for compensation. Customers under NCBL can credit BYOG and new DR to offset an equivalent portion of their flexibility obligation, since both BYOG and DR can reduce the ad hoc load that a customer poses to the grid. For example, if a hyperscaler were to build an on-site gas-fired power plant accredited at 100 MW and to add new DR accredited at 25 MW, their assigned quantity of NCBL could be reduced by up to 125 MW (though the exact math remains to be determined).

• NCBL is transitional.
  PJM envisions NCBL as an interim measure given the grid’s current resource inadequacy.

But here’s the kicker that is making hyperscalers sweat: under the new proposal, PJM first seeks volunteers for NCBL status, but if a delivery year still falls short due to large-load additions, PJM can “allocate NCBL” (read: “force NCBL status upon you”) by area. In other words, you might be designated flexible whether you like it or not. Ouch.

There is nothing inherently wrong with flexibility. In fact, the flexible data center model could unleash a fantastic amount of untapped American power for AI (more on this in Part 2). But the concept recently proposed by PJM falls short of this framework in some crucial ways.

Where NCBL makes sense

NCBL gestures in the right direction, with ingredients for a product that could complement the capacity market rather than undermine it. Here are some aspects of the proposal that accord with a sensible conception of flexible load.

• NCBL is still counted in grid planning.
  Even if a customer elects the NCBL lane, PJM still counts that load in transmission planning. What this means is that although NCBL makes no financial contribution to capacity, the engineering contribution of the load does not go ignored. In the longer term, engineering studies will still take NCBL into account when properly sizing the grid.

• NCBL participants receive offsets for BYOG and DR.
  As described earlier, customers can nominate BYOG resources and new DR to offset their own NCBL obligation. This is a good thing, as it allows important or sensitive loads to take matters into their own hands and invest in upgrades that reduce exposure to curtailment. Plus, this is a way for customers to contribute to grid reliability more broadly and ultimately help solve PJM’s resource inadequacy.

• NCBL is transitional.
  As described earlier, PJM has proposed NCBL as a temporary strategy for dealing with rapid load additions. Say what you will about PJM’s recent performance, but even astute watchers of the grid have been caught off guard by the sheer speed and scale of the AI-driven surge in new load. Accordingly, some sort of transition plan is appropriate as we build toward a grid that can fully accommodate the demand for AI.

Where NCBL has issues

That’s about where the sensible stipulations end. Here are some less attractive effects of NCBL.

• NCBL is removed from the BRA.
  As described earlier, PJM’s Base Residual Auction buys commitments three years ahead to meet future resource needs. The BRA serves at least two vital functions.

  • First, the BRA ensures the physical reliability of the grid. If NCBL is invisible from the auction, total capacity will be underestimated and underbuilt, jeopardizing long-term reliability.

  • Second, the BRA ensures that capacity is priced to reflect the value that consumers actually derive from it. Suppose that NCBL has been dished out across the grid at a quantity determined top-down by PJM. That portion of demand—hidden from the auction by the invisibility cloak PJM is proposing—has been treated as non-existent, which means capacity for that year will be underpriced. For perhaps no consumer class is this more distorting than the incredibly high-value use case of compute for AI. The current proposal also absolves NCBL of fair cost allocation with other grid citizens. Think of it this way. Have you ever had a colleague who shirked chipping in for a shared coffee machine—or furniture or some other group purchase—but went ahead using it anyway? Even if they pay for the coffee cups and pods that they use, they haven’t partaken in a fair cost allocation for the mere availability of the shared resource. There are certain fixed costs that complicate the picture too. It’s not great. Ironically, at a moment when data centers are coming under public fire and facing existential hurdles over cost causation, this move by PJM is more than a little tone-deaf.
    Update 9/18/25: This paragraph revised based on feedback from Samuel Roland.

• NCBL is tapped earlier in the curtailment order.
  In the pre-emergency playbook, NCBL is curtailed before capacity-backed DR. This is a bit surprising. Traditionally, DR customers are ready to ramp down power in response to incentives, and some sophisticated customers are already doing so in real time. But under the new proposal, NCBL is an even lower-priority lane than DR, arguably exposing them to undue risk.

• It’s all fun and voluntary…until the mandatory backstop triggers.
  This feature of NCBL has widened eyes, and it’s not hard to see why. As we’ve explained, in the case of a shortfall, PJM could just dole out NCBL status until the region meets capacity constraints for the year. But the whole point of a standby airfare class or a flexible lane to power is that it’s opt-in. It offers customers the option to voluntarily accept lower priority in exchange for some benefit, such as a lower fee. Stakeholders across the board consider this mandatory backstop to be heavy-handed—and understandably so—since it subjects customers to unsolicited service interruptions, all in order to address a shortage of grid resources that should have been solved on the supply side anyway (by building more generation).

Why Hyperscalers Are Freaking Out

It’s tempting to view NCBL as a “discount deal” that lures data centers with waived capacity fees. After all, in its own slide deck, PJM says that NCBL “could offer significant savings to participants.” But in reality, for hyperscalers, the capacity line item barely moves a go/no-go decision. Time to power does.

On the revenue spreadsheet, the variables that dominate are (1) energization date and (2) how fully a campus can run once it’s live.

While a 100-MW campus might “save” on the order of ~$12M/year by not buying capacity1, that’s pocket change relative to capex, supply-chain commitments, and the cost of letting racks sit dark. If GPUs have already been installed or even ordered, just a couple weeks of downtime eats multiple times those ostensible electricity cost savings2.

As Dean Ball, the lead architect of America’s AI Action Plan, put it to me in a recent conversation,

“Data center operators are accustomed to exceptionally high uptime (often more than 99.99% of a year), but in a world with rapidly growing data center costs and lengthening waits for new data centers to be connected to the grid, time is far more valuable than savings on the marginal cost of electricity.”

(Dean is also the author of Hyperdimensional, where you can find some of the most prescient and original thoughts on rapidly improving artificial intelligence. It’s telling—of the importance of the issue—that Dean chose to make his long-awaited return to Substack with “Out of Thin Air,” which puts a spotlight on time to power. The piece even includes complete draft text for an executive order meant to accelerate interconnection for large flexible loads.)

If we’re serious about building AI in America, we shouldn’t be waiving capacity fees as a measly carrot in return for the ability to cut data centers offline and tell them, “Tough luck!” If anything, hyperscalers might be willing to pay a premium in exchange for accelerated energization. Moreover, these premiums could go toward relieving some of the cost-causation (i.e., expensive upgrades to the grid) that’s partly fallen on the backs of residents in so many states.

Fixing the Flexible Fare

The Non-Capacity-Backed Load (NCBL) category proposed by PJM on August 18th, 2025, which poses an uncontrolled, open-ended curtailment risk and shifts costs onto people minding their own business, is dispositively negative for data center feasibility. It’s just not going to fly. But these problems are fixable. Under an opt-in, rule-based, fairly financed, and growth-incentivizing framework, flexibility need not be a burden. It’s our greatest opportunity.

Up Next

In Part 2, we’ll lay out the policy toolkit to make flexible load a real product—explicit, voluntary, and conducive to resource growth—that solves our real capacity problems.

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