The AI Bubble Debate Misses the Point: The Bottleneck Is Physical

Key Takeaways

• Geothermal shares the clean firm category with only three other technologies: nuclear, natural gas with carbon capture and storage (CCS), and solar or wind paired with long-duration energy storage (LDES).
• Buyers comparing geothermal to solar or unabated natural gas power contracts are comparing different products: one isn’t firm, the other isn’t clean, and both remain cheaper options because they satisfy less of what buyers currently need. 
• Geothermal’s cost premium is real, but so is the value it delivers. Geothermal can reach 90%+ capacity factor, delivering dispatchable clean power around the clock, and it sidesteps the gas turbine manufacturing backlogs and pipeline infrastructure delays slowing other firm power technologies. 
• Geothermal is closer to commercial readiness than most buyers realize. As demand for firm, low-carbon power intensifies, geothermal warrants a serious look alongside nuclear and gas with CCS.

Geothermal is Gaining Traction Despite Its Cost Premium

Hyperscalers, corporate energy buyers, and data center developers face unprecedented pressure to procure clean power. Wind and solar have played a major role in meeting this need, but they are intermittent, and load growth is accelerating and changing. This market pressure is creating bottlenecks that cannot be solved without new grid infrastructure and/or substantial additions of energy storage. Geothermal, which does not suffer from intermittency, has gained tremendous momentum as a challenger to wind and solar, yet it carries one seemingly glaring disadvantage: its cost. 

Yet, major utilities and hyperscalers alike are lining up for geothermal offtake anyway: Southern California Edison and Google have signed separate contracts with Fervo, and Meta has two 150 MW agreements in place with Sage and XGS. These contracts are signaling a growing shift in power markets: not all megawatt-hours are created equal, and the cheapest contracts may no longer contain what buyers truly need. 

Fast, Clean Firm Power: What Buyers Need Today 

In the clean firm power evolution, clean came first. Buyers signed solar and wind power purchase agreements (PPAs) and matched their annual consumption with renewable energy contracts, or offset them with renewable energy certificates (RECs). With lax carbon accounting, solar and wind remain unbeatable on price. But annual emissions tallies conceal something important—a buyer can procure as much renewable electricity as it consumes over a year while still depending on the rest of the grid during the hours when those resources are unavailable. To close that gap, buyers need a source that delivers clean power around the clock, firmly in every hour, on demand. 

Two unforgiving demands have followed since: speed and location. With today’s rapid buildout of new load, a power source needs to come online in time to deliver power when the customer needs it. It is also important to connect near a customer’s location because existing transmission assets are often fully subscribed. Failure on either axis can disqualify a project. However, a contract that can meet all attributes: clean in every hour, firm on demand, quick to deliver, and able to reach the load, might be the best (or only) option available. It is also likely to be considerably more expensive than those annual solar benchmarks that only met the clean condition. 

Four Technologies Make the Clean Firm Power Shortlist

Given this strict set of constraints, what power options should buyers consider? The right set of technologies should aim to solve as many of the aforementioned conditions as possible, and that leaves a fairly tight shortlist: 

1. Solar or Wind paired with Long-Duration Energy Storage (LDES): To achieve the level of reliability that many buyers, including data centers, require, solar or wind would have to be paired with LDES, beyond 4–8 hours, to be able to cover evening hours and remain online during increasingly common extreme weather events. While there are promising LDES technologies on the horizon, most remain expensive and not yet widely commercial. Costs rise significantly compared to a standard solar or wind PPA, and viability varies by location. This will be increasingly competitive as storage costs fall, but not yet a mature firm-power solution at scale.
2. Natural Gas with CCS: With large amounts of unabated natural gas generation coming online, CCS is an attractive option to mitigate a large portion of these emissions while retaining time to power (by building gas first and adding CCS later) and the dispatchability of gas-fired power. On the flip side, CCS is operationally complex, geographically restricted, and - unlike other options here - failure at any point in the CCS chain directly impacts the carbon intensity of power delivered. Gas-fired generation is also reliant on the gas delivery pipeline infrastructure, which may face capacity constraints. Finally, natural gas with CCS is 50% more costly per MWh than unabated firm gas.
3. Nuclear: Nuclear is seeing renewed interest from a wide range of corporate buyers and investors. Conventional nuclear and small modular reactors (SMRs) are attracting funding and offtake deals. While nuclear power holds high potential, long development timelines, high capital costs, and technology risk remain barriers to commercializing the next wave of nuclear technologies. 
4. Geothermal: Geothermal can provide firm, dispatchable, and low-carbon power with capacity factors typically above 90%. Development timelines can be faster than nuclear in regions where resources are already proven, with some projects approaching commercial readiness now. Geothermal technology is still geographically constrained, but enhanced geothermal systems (EGS) are poised to  expand their geographical flexibility by reducing the number of siting variables. 

How to Evaluate the Right Clean Firm Power Technology for Your Project

How do these various options stack up? We compared them across six dimensions:

1. Firmness: how reliably a resource can deliver power on the hour, on demand
2. Carbon: the lifecycle emissions intensity when considering the operational emissions of the generating facility and the broader system carbon impacts required to meet any reliability gaps
3. Time to power: a measure of how long it takes to get from “we need power” to delivery, including interconnection and schedule risk
4. Cost (current): the likely cost of a PPA if signed today
5. Execution risk: a measure of how likely a project’s claims regarding price, timing, and performance will hold
6. Siting flexibility: a measure of how constrained a resource is to a specific geography

This frame is ultimately something the buyer should score themselves; below you will find our current read. Red (unfavorable) represents a weakness that can end an evaluation on its own, green (favorable) means the dimension won’t constrain the decision, and yellow (moderate) sits somewhere in between. 

Comparing the clean firm power options is, perhaps ironically, challenging along the “clean” and “firm” lines. Every serious contender delivers low-carbon power around the clock, because that is the price of admission. Not one technology earns an unfavorable rating in those categories. And that is the catch: every weakness in the comparison table sits somewhere else. The dimensions that ultimately win (or end) these contracts may be the ones that the “clean firm” label can obscure or oversimplify.

A resource that cannot be energized for a decade, or sited near load, or delivered at a price a buyer cannot defend internally, may be infeasible regardless of what else it offers. None of the four clean firm generation technology groupings clear every hurdle today, but they can satisfy different buyer priorities.  

Solar or wind with LDES offers more buyer flexibility, at a price. Enough generation, storage, and other firming can deliver the reliability required, and the buyer sets that target. But the increments get expensive quickly, especially when reliability gaps remain long or unpredictable. Covering more hours means more storage and more generation to fill it, so each step toward firmness parity costs more than the one before it. Firming shortfalls also carry carbon consequences, depending on what gets called to compensate. Those emissions may be absent from GHG accounting ledgers, but that doesn’t stop them from hitting the atmosphere. 

Gas with CCS is challenged by execution and siting because the product depends on a chain: gas generation, capture, transport, and a permitted storage site capable of permanent CO₂ disposal at scale. Each piece can be developed and tested, but low-carbon electricity requires all of them to work together, which can only be demonstrated once all are built. A buyer cannot substitute a link, and cannot observe the whole chain before committing. Clean, in this sense, is conditional, and the delivered power could end up being far more carbon-intensive than advertised.

Nuclear ranks poorly on time, cost, and execution, but full-scale reactors and small modular reactors carry those weaknesses differently. A large reactor arrives in one piece, and its schedule and final cost come into focus as construction advances. By then, years and billions are committed; a buyer cannot learn from a fraction of a reactor before deciding whether to finish it. Small modular reactors are designed to break that capacity into increments a buyer could add as demand grows, which would ease the one-piece problem. But none are operating commercially in the US yet, and the signed deals target first power in the 2030s, so the modular promise is still years out. 

Geothermal’s weaknesses work somewhat differently. Conventional hydrothermal is tightly constrained by location, which buyers can screen before choosing where to procure, or co-locate where possible. Next-generation geothermal, including EGS, could widen that geography considerably. 

For EGS, the largest remaining uncertainty is execution: whether the developers can replicate successful drilling and reservoir performance at commercial scale. Part of that risk belongs to the rock, and no contract can make a reservoir perform. But drilling also produces information before the entire project has been built, and geothermal capacity can often be developed incrementally as needed. A buyer can require resource evidence before committing, tie later commitments to demonstrated performance, or spread procurement across projects rather than depend on a single field. 

Geothermal’s flexibility has limits. A buyer who needs full capacity by a fixed date may have little room to wait for wells to perform before committing to the next phase. And successful wells do not eliminate risk in project expansion or guarantee performance in other locations. Execution risk stays with the resource. What geothermal offers is more opportunity to learn about that risk while a buyer can still do something about it. 

Revisiting the Geothermal Premium

Geothermal contracts, especially EGS, remain expensive. But that premium looks different on this side of the comparison. Clean has a cost, but so does firmness, speed,  reach, and execution. Every clean firm option pays for those attributes somewhere. Solar and wind contracts will remain cheap by comparison, but it’s the wrong comparison: the market signing geothermal contracts is pricing a much larger product. 

That still leaves a hard decision. Geothermal and gas with CCS carry execution risk and siting constraints. Nuclear asks more of the buyer on cost and time. Firmed solar asks more as reliability requirements rise. The best option will change with each buyer, location, and project. Geothermal’s place in that choice is changing quickly. EGS is expanding where projects can be developed, drilling costs are falling, and commercial experience is growing. Those advances are bringing geothermal into consideration for buyers who previously had little reason to look its way. 

And that may be the more revealing aspect of the geothermal market today: buyers are signing those contracts with the alternatives in full view. Their decisions offer an early indication of what clean firm power delivered in the right place and at the right time is worth, and which risks buyers are willing to carry to get it. 

Frequently Asked Questions

Is Geothermal Cheaper Than a Solar PPA? 

No, a geothermal power contract can be significantly more expensive than a solar PPA. But they're not the same product: solar meets annual clean-energy targets, while geothermal delivers firm, dispatchable power around the clock, which is why buyers are increasingly comparing it to nuclear and gas with CCS instead.

Is Geothermal Cheaper Than Solar with LDES? 

Not necessarily on a per-contract basis, but the cost structures behave differently. Solar with LDES gets more expensive with every additional hour of reliability a buyer needs, since covering more hours means adding both more storage and more generation to fill it. Geothermal's premium, by contrast, is priced into the contract upfront as a firm, dispatchable product from day one.

How Does Geothermal's Risk Compare to Gas with CCS? 

Differently, in a way that matters to buyers. Gas with CCS depends on a chain (generation, capture, transport, and storage) that can only be verified once the whole system is built, so a buyer can't test it piece by piece. Geothermal generates performance data incrementally through drilling, letting buyers require resource evidence or tie commitments to demonstrated results before committing further, though this data does not come free.

Which Deploys Faster, Geothermal or Nuclear? 

Geothermal, in regions with already-proven resources. Fervo's Cape Station, for example, broke ground in 2023 and is on track for first power in late 2026 and 500 MW by 2028: a fraction of a typical new-nuclear large reactor timeline.

Key Takeaways

• On May 4, 2026, the North American Electric Reliability Corporation (NERC) issued a rare Level 3 “Essential Actions” Alert in response to repeated events in which 1,000+ megawatts (MW) of computation load dropped off the bulk power system in seconds, leading to major grid stability issues.
• The pattern has since escalated: on July 22, 2026, a transmission fault in Ashburn, Virginia took more than 3 GW of data center load offline in seconds—roughly 3% of PJM demand at the time.
• NERC also published Reliability Guidelines that push the same concerns into long-term planning, explicitly recommending resource adequacy models that capture firm vs. flexible load, behind-the-meter resources, and AI training operating windows.
• For transmission operators and balancing authorities, the releases compel new scrutiny of how computational loads affect stability and resource adequacy. For hyperscalers and other large loads, those assessments now sit on the critical path: if operators cannot show through advanced modeling that they can integrate the new loads, interconnection and buildout plans stall.
• Meeting the bar takes advanced grid modeling at multiple time and spatial scales, from sub-second stability through long-horizon capacity and resource adequacy, to evaluate the role of large load portfolios considering demand response, storage, and co-located generation.

Why Grid Frequency Matters for Large Loads

When we turn on the lights or charge our phones, it’s easy to forget that electricity travels through the power grid as alternating current. Sixty times a second—far faster than our eyes can see—the flow of electricity alternates back and forth along the wires making up both the transmission and distribution parts of the North American grid. 

Power generation equipment and most large industrial loads are designed to work with this 60 Hertz (Hz) alternating flow and must be synchronized precisely to this rhythm to function. Grid synchronization is so important that it can even have geopolitical implications.

For some electrical equipment, getting out of sync with the grid’s frequency can lead to malfunctions or even physical damage and destruction. That’s why grid-connected equipment is protected by circuits that automatically disconnect from the grid (“trip offline”) if the grid frequency begins to deviate by even one percent. For minor equipment, this is easily managed. However, when large amounts of generation or load trip offline quickly, it can lead to rapidly cascading grid blackouts affecting tens of millions of people with costs in the billions.

Grid operators pay extremely careful attention to factors that could cause grid frequency to deviate. The grid’s frequency stays near 60 Hz only when total power generation and consumption (load) are closely balanced. If load suddenly drops below generation, physical rotating generators like gas turbines can begin to speed up, making grid frequency rise. 

This becomes particularly dangerous when large grid-connected loads all trip offline simultaneously because of minor frequency deviations or other factors. If these loads are large enough, they can trigger a cascading sequence of rising frequency and further equipment and generator trips, potentially causing a complete “grid collapse” blackout. The North American grid may be getting closer to this scenario. 

What Triggered NERC’s Highest-Urgency Alert

Data center load drops are now a documented grid stability threat. On May 4, 2026, NERC issued a rare Level 3 “Essential Actions” Alert—its highest-urgency notification—in response to a pattern of customer-initiated load reductions in which 1,000+ MW of computational load (data centers) dropped off the bulk power system (tripped offline) in seconds. These were “customer-initiated” because protection circuits at data centers detected problems with grid-supplied power and automatically disconnected to protect their sensitive computing equipment from electrical damage. 

Paired with a new Reliability Guideline on emerging large loads, the alert highlights the urgent need to better understand the potential for these events to cause grid instability or even blackouts. Together, these two documents reset the bar for the detailed grid modeling and planning needed for any utility, independent system operator (ISO), or hyperscaler with material data-center growth in its footprint.

How the Alert Reshapes Grid Interconnection

For utilities and ISOs, the alert and guideline raise the standard of evidence required to connect computational loads safely to the grid. Modeling assessments now sit on the critical path for large load interconnection decisions, and the same studies will increasingly inform reserve margin, transmission, and dispatch program designs.

For hyperscalers and other large loads, the consequence is direct. Plans that assume firm service without supporting analysis will face longer queues and tougher interconnection conditions. Buildout timelines now depend on whether utilities and ISOs can show, through stability and resource adequacy modeling, that the system can absorb the load and respond safely to its disturbances.

For storage developers, particularly long-duration and fast-responding assets, these events elevate the reliability value of rapid response and load-shifting resources. The same grid modeling improvements that capture flexible load behavior also surface storage's full reliability contribution.

For flexibility platforms, the same modeling work that satisfies NERC's expectations unlocks faster, cheaper interconnection. Demand response, large-load shifting, and co-located dispatch coordination are now both technical and commercial enablers.

A Higher Bar for Power Analysis

These pressures point to a higher bar for power analysis at multiple time and spatial scales, for utilities and the large loads they serve.

At sub-second to second timescales, electromagnetic transient (EMT) models capture fast electrical switching and the uninterruptible power supply behavior that determines whether a data center stays connected during a disturbance (“rides through”). The alert asks for these models to be more detailed, validated against actual equipment, and shared between large loads, transmission owners, and planners.

At seconds-to-minutes, dynamic stability simulation covers system frequency response, voltage recovery, and oscillation behavior after disturbances. NERC now expects annual stability studies and explicit load drop contingencies in planning files.

At hours-to-years, capacity expansion and production cost modeling determine whether the system has enough resources, in the right places, with the right flexibility, to keep up with computational load growth. NERC’s May 2026 Large Loads Reliability Guideline is most explicit at this scale, calling for resource adequacy studies that represent firm and flexible load components, behind-the-meter resources, AI training operating windows, and probabilistic scenarios across many weather, load, and outage combinations on a network-aware footprint. 

Rising to the Challenge

Since the alert was issued, its expectations have begun hardening into rules. Registered entities were required to report to NERC on their progress against the seven Essential Actions by August 3, 2026, and on July 16, 2026, FERC directed NERC to go further: to develop mandatory reliability standards for computational loads and revise its registration criteria, with the first standards and Rules of Procedure changes due December 31, 2026 and a second-phase work plan due March 1, 2027. NERC's Large Loads Action Plan anticipates new "Computational Load Owner" and "Computational Load Operator" registered entity types alongside the first three computational load standards. 

The practical consequence is that the modeling described above is no longer only good planning practice: utilities, ISOs, hyperscalers, and other large loads should expect the data-sharing, study, and commissioning expectations in the alert to return as auditable requirements, and should build the capability before the compliance deadline rather than after it. 

Frequently Asked Questions

What is a NERC Level 3 Alert, and what does it require?

A Level 3 “Essential Actions” Alert is the most urgent of NERC's three alert levels, reserved for risks that need immediate, documented industry response. The May 4, 2026 alert directed registered entities to take seven essential actions on computational load—covering modeling, system studies, commissioning, protection, fault recording, and direct operational communication with large load operators. Written responses were due to NERC by August 3, 2026.

Why do data centers disconnect from the grid during minor disturbances?

Data centers run voltage- and frequency-sensitive computing equipment protected by automatic transfer systems that switch to on-site UPS or backup generation the moment grid power looks abnormal. Those protection settings trip faster, and at smaller deviations, than conventional industrial loads, so a fault lasting milliseconds can move a gigawatt of demand off the system in seconds. Because the shift is customer-initiated, grid operators get no warning and no time to rebalance.

How does the alert change interconnection for hyperscalers and other large loads?

Modeling assessments now sit on the critical path for large load interconnection. A plan that assumes firm service without stability and resource adequacy analysis behind it will face longer queues and tougher interconnection conditions, because the utility or ISO has to be able to show the system can absorb the load and respond safely to its disturbances. In practice, buildout timelines are now tied to someone else's study queue.

What modeling do utilities and large loads need to meet NERC's expectations?

Electromagnetic transient (EMT) models validated against actual equipment for sub-second ride-through behavior; dynamic stability simulation with explicit load-drop contingencies for seconds-to-minutes frequency and voltage response; and capacity expansion and probabilistic resource adequacy modeling that separates firm from flexible load, represents behind-the-meter resources, and reflects AI training operating windows. The paired Reliability Guideline is most explicit about the last of these.

Key Takeaways

• Enhanced geothermal systems (EGS) overcome traditional geothermal energy limitations by engineering subsurface conditions rather than searching for them, enabling widespread deployment of clean firm renewable power.
• Induced seismicity from high-pressure injection has caused major EGS project cancellations, but advanced approaches like Sage Geosystems’ gravity-assisted fracturing mitigate this risk by avoiding overpressures and directing fractures downward away from fault zones.
• Sage’s $97 million Series B financing, co-led by Ormat Technologies and Carbon Direct Capital, will fund the first commercial EGS facility at an existing Ormat plant—accelerating the transition from innovation to grid-scale deployment.
• For hyperscalers racing to power AI infrastructure, EGS offers a credible path to firm, 24/7 low-carbon power at scale.

Geothermal Energy: The Heat (And Pressure) Is On

For decades, geothermal energy has occupied a compelling yet narrow place in the clean energy landscape. It offers what the grid increasingly needs— firm, renewable, low-carbon power—yet has remained constrained by limited siting flexibility, high upfront resource risk, and persistent concerns around induced seismicity. 

Enhanced geothermal systems (EGS) change that equation. Instead of searching for ideal subsurface conditions, EGS engineers them directly. In doing so, EGS rewrites the rules of where geothermal energy can be deployed and how far it can scale, with the potential to transform this historically niche resource into a widely deployable form of clean firm power.

One such solution, Sage Geosystems, uses a pressure-managed EGS approach to extract geothermal energy from engineered subsurface reservoirs, while explicitly addressing the seismicity risks that have constrained earlier projects. 

How EGS Scales Geothermal Energy

Conventional geothermal power relies on a narrow set of subsurface conditions: sufficiently high temperatures, naturally occurring fluid, and enough permeability to circulate fluid through hot rock. In practice, those conditions coexist in only a few places—nearly all US commercial geothermal power generation is concentrated in California, Nevada, and a handful of sites across Utah and Hawaii.

EGS reduces this constraint by engineering permeability and fluid access rather than relying on their natural presence. While fluid access and permeability are harder to find, heat is not: the Earth’s natural geothermal gradient ensures that hot rock exists almost everywhere at sufficient depth. 

By reducing the number of variables that must be discovered rather than designed, EGS expands siting flexibility and lowers the resource risk that has historically constrained geothermal development. The Department of Energy (DOE) estimates this approach could unlock more than 5,500 GW annually of US resource potential, which, when converted to electric power, is roughly comparable to the total installed power capacity of the US today.

One remaining challenge has been induced seismicity. When you inject pressurized water into rock and create fractures, you are adding lubrication to geological systems that have been static for millions of years. If those fractures propagate into existing fault zones, the faults can slip, producing earthquakes. Projects in Basel, Switzerland (2006) and Pohang, South Korea (2017) triggered magnitude 3.4 and 5.4 events, respectively, both leading to project cancellations and regulatory backlash that set the industry back years.

Sage's approach to EGS is designed to address this risk directly. Rather than relying on high-pressure hydraulic stimulation, Sage uses a gravity-assisted fracturing approach that helps avoid the high overpressures that can drive fault slip. Further, its approach biases fracture growth downward and away from shallow, critically stressed fault systems. By understanding causes and conditions, Sage aims to work with the subsurface, not against it. 

This is not a minor technical detail. It is the difference between a technology that can scale with community acceptance and one that faces opposition at every site. For a hyperscaler evaluating geothermal offtake agreements, seismicity risk translates directly into permitting risk, timeline risk, and reputational risk. 

The Clean Firm Power Gap Driving EGS Adoption

To understand why this matters, start with the problem hyperscalers are trying to solve. Solar and wind have scaled dramatically, but they face a structural limitation: they do not generate power when the sun is not shining or the wind is not blowing. Batteries help bridge short gaps, but current technology cannot economically cover multi-day periods of low renewable output. Nuclear provides firm generation, but faces permitting timelines that extend well past 2030.

This creates what might be called the 'clean firm power gap'—the difference between what hyperscalers need (24/7, low-carbon, scalable to gigawatts) and what current markets can supply. A single large AI training cluster can consume more than 100 MW continuously. Meta, Google, and Microsoft are planning data center campuses that will require gigawatts of capacity. The gap between demand and available clean firm power supply is widening, not narrowing.

Geothermal energy aligns closely with this need. Unlike solar or wind, geothermal power plants run continuously, with capacity factors that routinely exceed 90%. And unlike nuclear, geothermal projects can, in principle, be permitted and built on shorter timelines. The challenge has never been performance, rather availability: with the emergence of EGS, geothermal power is expanding where clean firm power can realistically be built, arriving at a moment when the grid’s need for dependable, low-carbon supply has never been greater.

Sage Raises $97 Million to Deploy Geothermal at Ormat Site

Sage Geosystems announced $97 million in Series B financing co-led by Ormat Technologies, the world's largest geothermal operator, and Carbon Direct Capital, a leading energy investing firm. Ormat will host Sage's first commercial facility at an existing Ormat plant.

The investment signals that EGS has become investable to the industry built to scale it. For Ormat, the logic is clear: conventional geothermal is constrained by resource availability. EGS expands the addressable market, but requires the subsurface capabilities that conventional operators don't typically possess by Sage does.

Why the Partnership Structure Works

EGS proposes that the fastest way to scalable power is to eliminate the resource risks that beset conventional geothermal projects. These risks do not simply disappear: they are transferred into subsurface and remain unproven at scale. Conventional operators locate naturally permeable reservoirs. EGS requires creating permeability in crystalline rock and managing induced seismicity risks that don't exist in hydrothermal systems. Sage is actively addressing the seismicity problem that ended projects in Basel and Pohang. Ormat brings everything else: turbines, plant operations, grid expertise, and six decades of operational knowledge.

Building at an existing Ormat site provides another advantage: established subsurface characterization, proven geological stability, and grid infrastructure already in place. For a first commercial deployment, this de-risks demonstration in ways greenfield sites cannot.

Both companies move faster together because the technical capabilities required to make EGS work don't naturally exist within a single organization.

What Hyperscaler Demand Means for the Power Sector

Meta's 150 MW power purchase agreement with Sage—announced in August 2024, with delivery planned for sites east of the Rocky Mountains—adds another dimension to this story. Hyperscalers have concluded that waiting for clean firm power technologies to mature before signing contracts means those technologies may not be available when needed. So they are becoming anchor customers, providing the revenue certainty that enables projects to secure financing.

For geothermal power specifically, this demand signal is transformative. Contracted offtake from creditworthy counterparties changes project economics fundamentally. It lowers the cost of capital, enables debt financing, and de-risks the investment case for additional capacity. The hyperscaler model has already accelerated deployment in solar, wind, and battery storage. Its application to geothermal power may prove similarly catalytic.

The Final Constraint

EGS is not a silver bullet, but it is beginning to look like a credible answer to a growing-problem: how to deliver clean firm power at scale, in more places, and on timelines that match accelerating demand. Advances in subsurface engineering are reducing the resource and seismicity risks that once confined geothermal to a narrow footprint, while partnerships with incumbent operators are showing how those advances can be integrated into existing energy infrastructure. 

At the same time, hyperscalers are reshaping the market by signaling demand early, underwriting first deployments, and pulling technologies forward rather than waiting for them to mature on their own. That combination of technical progress, industrial adoption, and committed buyers is what turns promising concepts into deployable systems. 

Whether EGS ultimately fulfills its potential will depend on repeatable and continued performance under real-world conditions. But the recent alignment of science, incumbents, and demand suggests EGS is moving beyond possibility and into a phase where the final constraint is no longer what the Earth can provide, but what the energy system is prepared to build. 

Frequently Asked Questions

What is an enhanced geothermal system?

An enhanced geothermal system, or EGS, produces geothermal energy by engineering underground conditions needed to circulate fluid through hot rock. Unlike conventional geothermal projects, which depend on naturally occurring heat, fluids, and permeability occurring together, EGS can create or enhance permeability and fluid circulation, greatly expanding the locations where geothermal power may be developed. 

Why is EGS important for data centers and AI infrastructure?
AI and data centers require large amounts of electricity around the clock, creating demand for power sources that are both low-carbon and firmly available. EGS could provide high capacity factor (greater than 90%), 24/7 clean electricity in more locations than conventional geothermal, making it a potentially valuable complement to intermittent renewable resources. 

What is induced seismicity, and how are new EGS technologies addressing it?

Induced seismicity refers to earthquakes caused by changes in underground pressures or stresses caused by human activities. Earlier EGS projects demonstrated that high-pressure fluid injection can activate existing faults and in some cases triggered noticeable earthquakes and intense public backlash. New EGS approaches are being designed to better control reservoir pressure, fracture development, and proximity to faults, reducing seismicity risk while maintaining the fluid circulation needed to extract geothermal energy.

Can EGS be deployed anywhere?

EGS substantially expands geothermal’s geographic potential, but it does not make every location equally suitable. Projects still depend on factors including underground temperature, how deep they need to drill to access that temperature, water availability, seismic risk, and whether the rocks are of type suitable to hold and sustain engineered fracture networks.

Key Takeaways

• Community opposition has blocked, withdrawn, or stalled more than $170 billion in announced AI data center investment across 20 US states since January 2024. The pace is accelerating: 6 cancellations in 2024, 25 in 2025, and more than 20 additional cancellations by May 15, 2026.
• Data center opposition is bipartisan. It spans red and blue counties, every region, and multiple grid operators, with nearly two-thirds of the blocked investment sitting in counties that voted for Donald Trump in 2024. 
• Process and transparency, more than resource concerns alone, drive the fastest and most durable opposition. How a developer runs the engagement process shapes both community sentiment and the project’s ultimate success.

How Many AI Data Center Projects Have Been Cancelled? 

Between January 1, 2024, and May 15, 2026, community opposition blocked, withdrew, or stalled 46 announced AI data center projects across 20 US states, representing more than $170 billion in announced investment. These values are disclosed or derived for 35 of the 46 projects; the remaining 11 carry no public figure.

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The pace of successful opposition has accelerated sharply. Cancellations rose from 6 in 2024 to 25 in 2025. The first five months of 2026 added more than 20 additional cancellations, the fastest stretch on record. 

Virginia leads the state count with 11 blocked projects, followed by Indiana with 7 and Texas with 5. Together, those three states account for roughly half of all cancellations in the dataset. The PJM grid region carries the largest single share of blocked investment at $70 billion across 13 projects, followed by MISO at $37 billion.

Is Opposition to Data Centers Bipartisan?

Yes. The opposition wave crosses party lines on every measure we examined. Republican-leaning counties hosted 28 of the 46 projects (61%), Democratic-leaning counties hosted 16 (35%), and 2 fell within five points.

Weighted by announced investment, about two-thirds of blocked dollars sat in Republican-voting counties. Strong Republican counties (those Trump won by more than 15 points) account for $99 billion across 23 projects. Strong Democratic counties account for $29 billion across 9 projects. The remaining $44 billion spans Lean Republican, Tossup, and Lean Democratic counties.

Why Are Communities Opposing Data Centers?

Communities raise a consistent set of concerns across the country: water demand, electricity rates, air quality where developers propose gas co-generation, rural character, and a lack of transparency in the development process. 

Across the seven cases that we studied in depth, process, and transparency concerns were the most consistently cited factors associated with opposition. Non-disclosure agreements between developers and local officials, ownership structures in which the ultimate end-user was not publicly identified, and closed-door pre-application negotiations produce opposition faster and more durably than any other concern.

The pattern holds across very different communities: a diffuse civic mobilization in rural Georgia, an NGO water coalition in a Texas college town, a conservation coalition anchored by the Southern Environmental Law Center in Southside, Virginia, and an institutional civic organization with legal-expert and celebrity support in northern Virginia. Each produced the same outcome, and each flagged process and transparency as a dominant or top-three concern in the public record.

By the time a project reaches its first public hearing against organized community opposition, the political path of the project is largely set. Late-stage benefits packages consistently fail to reverse that trajectory. Communities read them as concessions, not commitments.

Assess Community Opposition Risk Before You Site

Community opposition is now a structural feature of the AI data center siting environment. The patterns are clear enough to act on now. Relae's Community Impacts team helps developers and capital partners implement responsible development standards through pre-siting community intelligence, calibration of benefits design to specific community contexts, and building the verification scaffolding that turns commitments into outcomes. 

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Frequently Asked Questions 

Which states are banning data centers in the US?

The first statewide moratoriums on data centers have arrived. In July 2026, Governor Hochul signed the country's first statewide moratorium, an executive order pausing state environmental permits for up to one year for new data centers of 50 MW or more. Texas followed weeks later, halting approvals of new data center grid connections until state regulators audit their power, water, tax, and ownership details. This is a snapshot from August 2026, and we will likely see additional changes in the months ahead.

The local picture is more developed. Individual municipalities and counties have adopted moratoria and zoning ordinance amendments that block or restrict data centers within their jurisdictions. The City of Peculiar, Missouri, removed data centers from its light-industrial zoning entirely. Monroe County, Georgia, and Jones County, Georgia, both adopted moratoria after project denials in 2025. Cassville Township, Wisconsin, and San Marcos, Texas adopted zoning and code amendments in 2026. State legislatures in Virginia, Indiana, Texas, and Missouri have taken up data center siting, ratepayer, and permitting legislation, though most bills remain in progress rather than enacted.

How much data center investment has been blocked in the US?

More than $170 billion in announced AI data center investment has been blocked, withdrawn, or stalled by community opposition across 46 projects and 20 US states between January 1, 2024 and May 15, 2026. 

Relae arrived at this figure from data on 35 of the 46 projects; 11 have no publicly disclosed investment value. The pace has accelerated sharply: 6 cancellations in 2024, 25 in 2025, and more than 20 additional cancellations in the first five months of 2026 alone. Virginia leads the state count with 11 projects. The PJM grid region carries the largest single share of blocked capacity at $70 billion across 13 projects.

What causes a data center project to be cancelled by community opposition?

As of August 2026, communities cite a consistent set of concerns across cancelled projects: water demand, grid strain and residential rate impacts, air quality where developers propose gas-fired co-generation, rural character and farmland conversion, and lack of transparency in the development process. 

In the seven cases we studied in depth, process and transparency were together the most consistent driver of opposition. Non-disclosure agreements between developers and local officials, shell LLC ownership structures that conceal the end-user, and closed-door pre-application negotiations produce faster and more durable opposition than any single resource concern. Late-stage benefits packages consistently fail once that transparency-driven frame has formed.

Key Takeaways

• The electricity demand surge is real and accelerating. Just last year, data center load in the US was projected to increase from 25 GW to 120 GW by 2030. Today, Texas’ preliminary long-term load forecast projects over 187 GW of data center load by 2030, more than double today’s total peak demand of approximately 91 GW.
• Flexible loads that respond dynamically to policy signals are becoming a regulatory requirement. Texas Senate Bill 6 (SB6), signed into law in June 2025, is the clearest signal yet. It makes remote curtailment equipment a condition of interconnection for new loads of 75 MW or more, so utilities can disconnect them during declared firm load shed events, and separately creates a voluntary demand response program that those loads can elect to join. 
• Relae’s power system modeling puts a dollar value on what data center load flexibility is worth. Our ERCOT analysis shows that data center demand response can eliminate forced load shedding risk, even at 40 GW of data center buildout – preventing $5.5 billion in annual consumer welfare losses by curtailing an average of 5% of demand for under 1% of operating hours. 
• Flexible load curtailment and compute uptime do not need to be in conflict. In our modeling, demand response operates as a "ghost battery" at the data center's grid node, absorbing grid stress as a physical battery would. That construct has a direct real-world analog: on-site battery storage lets data centers draw from stored energy during grid stress events rather than curtailing workloads. Technologies such as those demonstrated by Emerald AI have shown 25% load curtailment at cluster scale while preserving compute service quality.

Data Center Electricity Demand is Testing Grid Limits

Electricity demand in the United States is growing at its fastest pace in decades. Leading the surge is a rapid buildout of data centers, driven by the expansion of artificial intelligence. ERCOT, the grid serving most of Texas, is projecting up to 187 GW of new data center load by 2030, against a total peak demand today of approximately 91 GW.

The scale of this shift extends across the country. According to NERC's 2025 Long-Term Reliability Assessment (LTRA), summer peak demand across the US bulk power system is forecast to grow by 224 GW over the next 10 years, 69% above the prior year’s 10-year projection of 132 GW, with data centers as the dominant driver. As previously explored by Relae, data center energy capacity in the US is projected to increase from 25 GW to 120 GW by 2030, characterizing it as the first wave of a longer demand surge that electrification of buildings and transport will reinforce.

Policymakers are responding in real time, and regulatory responses like Texas SB6 are already rewriting the rules for how data centers connect to the grid. The pace of change is fast, but the siting, infrastructure, and interconnection decisions made today will have lasting consequences: they will determine whether data centers are grid assets or grid liabilities, and the financial difference between the two is measured in billions.

New Solutions for Data Center Demand Response: Texas SB6 as a Test Case

The rapid nature of this new wave of load growth means traditional approaches to managing the grid may not be sufficient. In the past, lead times on new sources of electricity demand allowed utilities to procure supply-side resources in advance. The scale and immediacy of data center deployment is revealing limitations of this approach. States, utilities, independent system operators (ISOs), and regulators are actively pursuing novel approaches for grid management in response.

Texas has become a focal point of US data center expansion, with SB6 as a leading policy response. The bill, which took immediate effect upon Governor Greg Abbott's signature on June 20, 2025, is the most significant restructuring of large-load interconnection rules in ERCOT's history. It requires large loads over 75 MW to install remote curtailment equipment as a condition of interconnection, so grid operators can disconnect them during declared firm load shed events, and creates a separate voluntary demand response program, procured competitively with at least 24 hours’ notice, that those loads may elect to join. If data centers are adding substantial new load to the grid, this reasoning goes, they should also contribute to grid stability by demonstrating load flexibility—reducing power draw at times of peak demand.

While demand response programs currently exist, incentivizing voluntary curtailment from data centers is challenging. In an AI compute arms race, the value of uninterrupted compute time far exceeds any available curtailment payment, such as via PJM’s capacity market mechanisms. 

Approaches to bridge that gap are coming to fruition: EPRI’s DCFlex program is working with hyperscalers and utilities to develop the technical protocols, measurement standards, and contractual frameworks that would make large-load demand response a routine grid service. Innovators like Emerald AI have demonstrated a 25% power reduction across a 256-GPU cluster over three hours during an Arizona grid stress event, while preserving compute service quality, helping to bridge the valuation asymmetry between energy and compute. 

Hyperscalers are already putting a flexible load commercial strategy into action. In March 2026, Google announced  1 GW in demand response contracts with multiple US utilities, including Entergy Arkansas, Minnesota Power, and DTE Energy.

The conversation has shifted from whether data centers can be flexible to how that flexibility gets structured and deployed. 

Putting a Dollar Value on Data Center Load Flexibility

For developers, investors, and grid operators navigating data center growth, the challenge has been making high-stakes siting and interconnection decisions without a clear picture of what load flexibility is actually worth, what inflexibility costs the system, or how curtailment requirements will reshape the regulatory landscape. 

Prior research has established that flexible data center load can absorb substantial grid stress. For instance, research from Duke University’s Nicholas Institute found that 22 of the largest US balancing authority areas could absorb approximately 98 GW of new flexible load if 0.5% of that load’s annual energy is curtailed, or 76 GW at a stricter 0.25% curtailment level. 

Relae’s analysis goes further by quantifying the economic cost at each increment of flexible load growth, and the precise threshold at which that flexibility stops being optional. We zeroed in on ERCOT, a region with high data center load growth and immediate regulatory stakes, determining the value of implementing flexibility and, conversely, the system risk of failing to do so.

How We Modeled It 

Assessing the impacts of load growth and flexibility solutions requires a systems-level analysis, best achieved via power market modeling. At Relae, we deploy our in-house power system modeling framework to navigate this complexity. 

Our toolkit includes CD-PyPSA-USA, used for this analysis, which is built on the Python for Power System Analysis (PyPSA) platform. This grid model simulates how power networks operate and evolve over time by solving for the least-cost optimization of the entire power system. Critically, our model is customized to explicitly represent complex, real-world dynamics, including data center load flexibility, co-located generation, and various policy constraints.

For this analysis, we simulated ERCOT operations under a range of data center growth scenarios. We modeled loads from 5 GW up to 40 GW in 5 GW increments, pairing each with sufficient on-site gas generation to cover roughly 70% of data center energy needs–a conservative estimate on the approach developers are taking today. 

We ran each scenario under two conditions: no load flexibility (“flex00”, the baseline) and 25% emergency curtailment capability (“flex25”), consistent with solutions exhibited by Emerald AI. This approach allowed us to determine the system's response to step-changes in electricity demand.

What Our Analysis Reveals

Before doing the analysis, we expected to see load flexibility become more important as more data center load is added to the grid, preventing forced load shedding with high lost-load costs. But we didn’t know how large this effect would be, or what amount of new data center load would start to trigger it.

Our analysis found that without flexible load management, forced load shedding first appears at 30 GW, small in scale at first (4.4 GWh over 3 hours) but growing sharply as load increases. At 35 GW, we observe 50 GWh of shedding across 39 hours. At 40 GW, shedding reaches 158 GWh across 81 hours, equivalent to nearly three times ERCOT’s average hourly energy consumption, with an economic cost at VoLL of approximately $5.5 billion.¹

By enabling on-demand data center load flexibility, forced load shedding is eliminated in every scenario we tested. The same 40 GW case instead sees 165 GWh of controlled, short-duration curtailment spread across 86 hours, less than 1% of hours in a year. Further, the average demand response in these hours was less than 5% of the nameplate data center load, with the largest event reaching 14% of data center load. The grid stays balanced, consumers remain connected, and data centers deliver substantial value to the system via flexible loads.

Load Flexibility Is High Value and Presents an Opportunity for Storage

Our modeling puts a dollar figure on what flexible load is worth. At a VoLL of $35,000/MWh, each hour of demand response in the 40 GW scenario delivers approximately $64 million in avoided consumer welfare losses. Over a full year, that adds up to $5.5 billion, achieved through an average of just 5% demand response across the 86 hours of curtailment needed to eliminate all forced load shedding.

That value points directly to an opportunity for storage. In our model, demand response functions as a “ghost battery” at the data center’s grid node, absorbing grid stress exactly as a physical battery would, without any electrons needing to flow. That virtual battery can become a real one. On-site battery storage allows a data center to dispatch stored energy during grid stress events rather than curtailing workloads, maintaining compute continuity while relieving grid pressure.

The implications point in two directions. 

• For the hyperscaler or data center operator, physical storage converts a compliance obligation into an uptime guarantee: the curtailment event becomes a battery discharge, with negligible impact to the compute stack. 
• For the storage developer, co-location with large data center loads represents a high-value deployment opportunity with a clear commercial case. The avoided welfare costs per curtailment hour our model quantifies is the value a well-positioned battery asset, co-located at a data center node, can credibly claim to preserve.

The Data Centers of Tomorrow Need to be Grid Assets

The data center buildout underway is large enough to reshape grid reliability across entire regions, and the regulatory environment is beginning to reflect that scale. Texas SB6 is the most prescriptive example to date: it requires new large loads above 75 MW to install remote curtailment equipment operable during firm load shed events.

Our modeling quantifies what load flexibility is worth across this landscape: data centers with curtailment capability can provide significant value and avoid billions in consumer welfare losses annually. For developers and investors, designing that capability in from the start can convert a compliance requirement into a long-term grid asset.

The federal picture has moved in the same direction since this analysis was published. In May 2026, NERC issued a rare Level 3 Alert on computational loads; in June, FERC ordered six RTOs and ISOs to revise or justify their large-load interconnection rules, and in July, FERC directed NERC to develop mandatory computational-load reliability standards by the end of the year. We covered what that means for grid modeling and interconnection in Inside NERC’s Level 3 Alert on data center loads. Flexibility is no longer only a Texas statutory question; it is becoming part of the federal reliability framework.

Frequently Asked Questions

What is data center load flexibility, and how does it work?

Load flexibility is a data center’s ability to reduce the power it draws from the grid on short notice, during the small number of hours when the system is under stress. In practice, that means shifting or pausing deferrable compute, drawing on on-site batteries or generation, or pre-cooling the facility ahead of a peak. The point is not to consume less overall—it is to move a thin slice of demand out of the hours when the grid can least afford it.

What does Texas SB6 require of large data centers in ERCOT?

Texas Senate Bill 6, signed June 20, 2025, makes remote curtailment equipment a condition of interconnection for new loads of 75 MW or more in ERCOT, so utilities can disconnect them during declared firm load shed events. Separately, it creates a voluntary, competitively procured demand response program those same large loads can elect to join, with at least 24 hours’ notice. The mandatory piece is the disconnection capability; paid participation in demand response is a choice.

What does data center inflexibility cost? How much curtailment avoids it?

Relae’s ERCOT modeling found that without flexibility, forced load shedding first appears at 30 GW of data center load and reaches 158 GWh across 81 hours at 40 GW—roughly $5.5 billion a year in consumer welfare losses at ERCOT’s $35,000/MWh value of lost load. Enabling curtailment eliminated forced shedding in every scenario we tested, at an average of under 5% of data center demand across 86 hours, less than 1% of the year. Each hour of demand response in the 40 GW case is worth about $64 million in avoided losses.

Is data center load flexibility proven today, and how does it compare to on-site batteries?

Data center load flexibility is past proof of concept and into commercial deployment: Emerald AI cut power to a 256-GPU cluster by 25% for three hours during an Arizona grid stress event without degrading compute service quality; EPRI’s DCFlex initiative is building the protocols and contracts, and Google has signed 1 GW of data center demand response with US utilities. 

On-site batteries reach the same result from the other direction—instead of curtailing workloads, the facility discharges stored energy, which is why our modeling treats demand response as a “ghost battery” at the data center’s grid node. For operators who cannot pause compute, storage turns the same compliance obligation into an uptime guarantee.

Key Takeaways

• Accurate load forecasting is needed to distinguish and prioritize real demand, align capital deployment, and reduce delays in bringing new power capacity online. Traditional load forecasting was built for predictable, gradual demand growth, not for the scale, uncertainty, and dynamic behavior of data centers. 
• The system-level fix to data-center load forecasting requires probabilistic, more frequent, category-specific methods paired with mandatory data standards and policy alignment. Together, these give planners visibility into the range of possible futures and the likelihood of each. 
• Without that fix, today's forecasts conflate real demand with speculative submissions, reducing accuracy. Inaccurate forecasting in either direction is expensive: underbuild adds friction to economic development; overbuild risks raising retail rates. Both can erode public trust in planning.
• Behind-the-meter generation (BTM) and load flexibility can help achieve speed-to-power in the near term. Just 1% data-center flexibility could unlock 100 GW—more than the entire US nuclear fleet.

Load Growth Is Increasing, Uncertain, and Concentrated

For two decades, US electricity demand was flat. Utilities, transmission planners, and corporate buyers built their planning models around that reality. Then AI workloads changed it.

AI load growth is large, uncertain, and concentrated in major power markets. While load forecasting projections vary across studies, the trajectory is clear: electricity demand is scaling faster than the bulk power grid was designed to handle. Accurate load forecasting is needed to distinguish and prioritize real demand, align capital deployment, and reduce delays in bringing new power capacity online.

On April 30, 2026, Relae (formerly Carbon Direct) hosted a Trellis Group panel on load forecasting in the AI era. Panelists included Derya Eryilmaz, PhD, Vice President of Power Commercialization at Relae; John Miller, Director of Transmission Policy at the Corporate Energy Buyers Association (CEBA); Daniel Padilla, Strategy and Business Development Lead at Emerald AI; and Sam Hodas, Head of US Government Affairs at National Grid. Jake Mitchell, Director of Climate Tech Innovation at Trellis Group, moderated.

The conversation explored where load forecasts fail, what they cost, how to fix them, and near-term solutions to overcome grid constraints. Here is what the panel found.

What Is Load Forecasting?

Load forecasting is the practice of predicting how much electricity will be consumed across a region, at what times, and under what conditions. It informs the major capital and procurement decisions on the grid: where to build transmission, how much generation to procure, what capacity to bid into wholesale markets, and how corporate buyers secure clean, firm power.

Long-term forecasts inform multi-year decisions about transmission and generation. Short-term operational forecasts inform real-time grid operations and trading. The two often sit in separate workflows, but short-term operational forecasts should feed into long-term system planning to improve accuracy as demand patterns shift.

The Bulk Power Grid Is Under Strain

Large power users face constraints on clean, firm power, transmission capacity, multi-year interconnection queues, and aging infrastructure. The strain is most acute in PJM Interconnection (PJM) and the Electric Reliability Council of Texas (ERCOT), the two US markets expected to see the most significant load growth. Each constraint raises the cost of getting load forecasts wrong.

Hodas from National Grid describes the operational reality on the utility side: aging infrastructure inherited from a different demand era. “We’ve got transmission lines that are 70 to 100 years old in New York and Massachusetts, some of the oldest in the country, still in operation.” Replacing or upgrading that infrastructure requires investment, and ratepayers are already pressed. 

Why Today’s Load Forecasts Fail

Traditional load forecasting was built for predictable, gradual demand growth, not for the scale, uncertainty, and dynamic behavior of data centers. 

Most utilities and Independent System Operators (ISOs) produce load forecasts on annual or biannual cycles. They aggregate submissions from individual customers, run that data through a deterministic single-peak load estimate against a single capacity scenario, and pass the consolidated forecast up to regional planners. Regional Transmission Organizations (RTOs) roll those bottom-up utility forecasts into a regional view. 

This worked when demand was flat and predictable. It no longer works with nonlinear growth driven by data centers. Eryilmaz from Relae identifies key structural limitations. 

Four Structural Limitations to Traditional Forecasting Methods

• Over-stating and double-counting. Data centers bid into multiple regions while shopping for power, inflating regional forecasts and blurring the line between real and hypothetical demand—the speculative-load problem.
• Deterministic models (vs probabilistic models). Most planning runs a single peak load estimate against a single capacity scenario, missing the geographic concentration and uncertainty inherent in integrating large loads into the system.
• Aggregated submissions. Utilities report large loads as a single block of gigawatts, with no resolution into workload type, ramp schedule, or operational shape. Planners reverse-engineer peak-demand assumptions rather than measure them.
• Infrequent cadence. Annual or biannual forecasts cannot catch an 80% queue reduction or a multi-gigawatt addition between cycles.

The Speculative-Load Problem

The core challenge in load forecasting is distinguishing real versus hypothetical load. While data center electricity demand is projected to grow by 13-27% annually through 2028, the majority of the projects in the data center queue may not materialize, inflating regional load forecasts.

American Electric Power's Ohio utility (AEP Ohio) introduced a tariff requiring data centers to put up firm financial commitments before getting in line for grid connection. Its interconnection queue dropped from 30 gigawatts to 5.6 gigawatts. More than 80% of the submitted load was speculative: projects that disappeared once commitment became required.

ERCOT shows the same overstatement problem on a larger scale. Roughly 225 gigawatts of data center demand sits in the ERCOT queue against a historic system peak of 85 gigawatts. Texas Senate Bill 6 introduced similar financial obligations for new loads, but those rules apply only to interconnections after 2025, and the cleanup of speculative demand has not yet materialized.

The speculative-load problem shows up in interconnection times. An average new project in PJM can wait 4 to 5 years to become operational. Some of that delay is a real backlog. The rest comes from the inability to distinguish real submissions from speculative ones.

As Eryilmaz puts it, “Load forecasting is actually the center of all of these problems. It is a tool to help planners make the right investment decisions.”

The Cost of Inaccurate Forecasting

As Miller from CEBA notes, “A single misforecasted project can swing a transmission plan by hundreds of megawatts.” Significant inaccuracies can erode public trust in the planning process in two main ways. Underbuilding adds friction to economic development and can limit corporate access to clean power markets. Conversely, overbuilding risks raising retail rates if capacity remains underutilized. 

The goal is to achieve right-sized infrastructure investment. When planning aligns with actual large load growth, it can be net beneficial to retail rates. By spreading fixed costs across more usage, significant new demand can put downward pressure on the rates via the “denominator effect.” 

On the other hand, forecasting variability can distort capacity procurement and interconnection queue prioritization. When load forecasts spike upward, grid operators like PJM have to scramble to buy additional electricity capacity on short notice. These emergency procurements lock in major dollar commitments on the basis of unstable forecast numbers. 

PJM, Midcontinent Independent System Operator (MISO), and Southwest Power Pool (SPP) have also reshaped their interconnection queues to make room for new large loads, but those queue priorities depend on the same forecasts that are unreliable in the first place. 

“There is no substitute for good backbone regional transmission planning,” Miller says. “Full stop. That is the enabler of all of the load growth that we’re talking about.”

BTM Generation and Load Flexibility: A Near-Term Bridge

Hyperscalers’ need for power is way faster than that of utilities and RTOs. Generation alone cannot scale fast enough to meet this new demand, and hyperscalers need speed-to-power.

As Eryilmaz frames it, behind-the-meter generation and load flexibility are interim solutions to the timing mismatch between data center urgency and the grid's slower build cycles. BTM generation and flexibility work differently:

• BTM is power generated on the data center's side of the utility meter, bypassing grid interconnection entirely. The structure gives operators large, reliable blocks of power without waiting years for grid approval.
• Load flexibility is the demand-side approach. A data center modifies its grid draw in response to grid signals. In practice, that can mean curtailing compute workloads during stress events, pre-cooling facilities ahead of a heat wave, drawing from on-site batteries or generators, or shifting workloads to data centers in less-constrained regions.

The Value of Load Flexibility

Relae’s power system modeling quantifies the dollar value of load flexibility in ERCOT. Load flexibility can eliminate forced load shedding risk, even at 40 gigawatts of data center buildout, preventing $5.5 billion in annual consumer welfare losses by curtailing an average of 5% of demand for under 1% of operating hours.

Padilla from Emerald AI reinforces the scale and value of load flexibility: “With just 1% flexibility, we can unlock 100 gigawatts of data centers across the US. That’s more than the entire US nuclear fleet.”

Silicon Valley Power, a municipal utility, is the first US utility to tie flexibility to interconnection speed: flexible data centers get connected faster. NVIDIA, EPRI, Digital Realty, and PJM are partnering on the Aurora AI Factory, the first purpose-built reference design for flexible AI data centers. 

But standardized policy for load flexibility is lagging. Padilla highlights this challenge: “Today, if a data center wants to be flexible, they have nowhere to point. We need standardized tariffs, interconnection rules, and product definitions for large loads that reward them with upsizing interconnection in response to flexibility.” 

Flexibility Takes Many Forms, but it Isn't Universal

Flexibility means accepting brief, predictable downtime, and some workloads can't tolerate it. Hospital systems and mission-critical enterprise applications need 99.999% uptime, the "five nines" standard. As Padilla puts it: "99.9% uptime, with brief and predictable curtailments, is plenty" for most AI workloads. That distinction determines which data centers can participate in flexibility programs.

Miller points out that compute-level flexibility is not always feasible. BTM batteries and virtual power plants (VPPs) are among the alternatives that can offset what data centers withdraw when the grid is stressed, even at facilities whose compute workloads cannot pause directly.

Better Load Forecasting: The Longer-Term Fix

While BTM generation and load flexibility can help address near-term speed-to-power, the longer-term fix is improving load forecasting methods and the standardization of data provided by the data centers themselves.

Eryilmaz outlines three technical shifts for better load forecasting:

• Embed short-term operational forecasting into long-term planning. Short-term spikes, weather risk, and reserve considerations carry direct implications for multi-year capital decisions. The line between operations and planning breaks down when growth is nonlinear.
• Replace deterministic models with probabilistic methods. Risk metrics like loss of load hours (expected hours per year that demand exceeds supply) and expected unserved energy (total expected energy shortfall) measure both how much capacity the system has and the conditions under which it might fall short. The North American Electric Reliability Corporation (NERC) has suggested both metrics as part of its reliability framework.
• Forecast load by category. Treating all data center load as a single block hides the differences in load profiles, operational schedules, and ramp-up timing that drive system planning.

Policy Alignment

Technical forecasting improvements only scale with policy alignment, and Miller proposes a two-part fix:

On the top-down side, RTOs need authority to take an independent view of utility-submitted forecasts. They should require milestones, such as firm financial commitments and secured financing, before counting a submitted load against the regional forecast. 

On the bottom-up side, state regulators set the rules that govern how individual utilities prepare their forecasts. Large load tariffs play a big role in how utility-level forecasts come together. Federal and state authorities need to row in the same direction. Hodas frames the same alignment from the utility side: “Grid investment unlocks economic growth, but for us to make those investments, we need regulatory certainty.”

Standardizing Large-Load Data

The Federal Energy Regulatory Commission (FERC) has since moved: in June 2026 it issued show cause orders directing six RTOs and ISOs—CAISO, ISO-NE, MISO, NYISO, PJM, and SPP—to revise or justify their large-load interconnection rules, and in July 2026 it directed NERC to develop computational-load reliability standards and registration criteria by the end of the year. Both are useful first steps. But as Eryilmaz argues, voluntary disclosure has not closed the gap.

The industry cannot meaningfully compare ISO forecasts when each utility submits load data in different shapes (e.g., using different methods and data standards) on different schedules. Mandatory submission requirements and published methodologies, applied consistently across utilities, ISOs, and state regulators, are the only path to forecasts whose components are actually comparable.

Getting Load Forecasting Right Starts Now

The system-level fix to improving forecasting is through probabilistic, category-specific methods paired with data standardization and policy support. Together, these account for the scale, uncertainty, and dynamic behavior of data center loads, and give planners visibility into the range of possible futures and the likelihood of each.  

All forecasts will be wrong to some degree, but as Miller puts it, “It's ultimately not about having a perfect prediction. It's about baking in methods to account for uncertainty.” These system-level improvements won't eliminate errors entirely, but they will minimize them, leading to more confident investment decisions and a grid better prepared for what's ahead.

Frequently Asked Questions

What is load forecasting, and why is it harder now with AI data center loads?

Load forecasting predicts how much electricity a region will consume, when, and under what conditions—the basis for where to build transmission, how much generation to procure, and how corporate buyers secure clean, firm power. It was designed for two decades of flat, gradual demand growth. Data center load is none of those things: it is large, geographically concentrated, arrives in gigawatt blocks with no disclosed operating shape, and can be withdrawn as quickly as it appeared.

What is speculative load in an interconnection queue, and how do planners tell it apart from real demand?

Speculative load is capacity requested by projects that may never be built — often the same data center bidding into several regions at once while shopping for power, which counts the same gigawatts more than once. The tested filter is a financial commitment: when AEP Ohio required firm commitments before queue entry, the utility's reported data center pipeline fell from about 30 GW to roughly 5.7 GW. Milestone requirements, independent RTO review of utility submissions, and mandatory data standards are the tools planners have.

Is load flexibility proven and scalable today, or still emerging?

The modeling case for load flexibility is strong; the commercial case is still early. Duke's Nicholas Institute found the 22 largest US balancing authority areas could absorb roughly 76–126 GW of new load if it accepts modest curtailment, and Relae's ERCOT modeling shows demand response eliminating forced load shedding risk at 40 GW of data center buildout, avoiding $5.5 billion in annual consumer welfare losses. What is still missing is the market plumbing—standardized tariffs, interconnection rules, and product definitions—so a data center willing to be flexible has somewhere to sign up.

What should a company look for when evaluating a region's load forecast?

Ask whether the forecast is probabilistic or a single deterministic peak, how often it is refreshed, and whether large loads are broken out by category and operating shape rather than reported as one block of gigawatts. Then ask what milestone or financial commitment a project must clear before its megawatts count toward the forecast. A forecast that cannot answer those three questions cannot tell you how much of the queue ahead of you is real.

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