Carbon Capture for Natural Gas-Fired Power Generation: An Opportunity for Hyperscalers

Key Takeaways

• Speed to operation is the value proposition. While grid interconnection can take 3-5 years in constrained regions, hydrogen fuel cell systems can be deployed in a fraction of that time—a large advantage for operators racing to meet data center capacity demands.
• Hydrogen is moving beyond backup. Fuel cells could supply 6-15% of incremental data center power, functioning as primary or near-primary power rather than emergency standby.
• Higher fuel costs can be rational. When the alternative is a multi-year wait for grid power and the development loss that comes with it, paying a premium for hydrogen is better than falling behind.

The Grid Can’t Keep Up With Data Centers

The data center infrastructure buildout is moving at a pace the US power grid was never designed to accommodate. Cleanview tracks about 63 GW of operating US data center capacity and another 392 GW in planning, roughly six times what exists today. Developers in high-demand corridors—Northern Virginia, Central Texas, Phoenix—are finding that the grid can't deliver new capacity fast enough.

Interconnection queues now stretch 3-5 years in many regions, and that’s before accounting for permitting, transmission upgrades, and substation buildouts. For operators that need power online faster than the grid can deliver it, this timeline gap is a fundamental constraint on the business.

This mismatch between AI deployment, generator bottlenecks, and grid timelines is why hydrogen is entering the conversation as a viable power option for a growing subset of data center deployments, alongside natural gas with carbon capture, geothermal, and long-duration energy storage.

Hydrogen Fuel Cells: From Standby to Primary Power

For most of its history in data centers, hydrogen has been discussed as a cleaner replacement for diesel backup generators. That framing undersells the opportunity. 

The shift happening now is more fundamental: hydrogen fuel cells are being evaluated as primary or near-primary power sources for behind-the-meter and facilities that can’t wait for grid connection. Notably, many modern fuel cell systems—including solid oxide fuel cells—can run on natural gas today and transition to hydrogen as clean supply becomes available. Bloom Energy's 2024 deal with American Electric Power to provide up to 1 GW of fuel cells for data center power illustrates this approach: the systems will initially run on natural gas, with the capability to switch to 100% hydrogen in the future.

Furthermore, fuel cell systems are modular, can be deployed incrementally as load grows, and provide continuous power output. This is a promising path, and one that will scale only as fast as the fuel cell supply chain can keep up. Unlike solar or wind, fuel cells aren’t weather-dependent. Running on hydrogen, they produce zero direct emissions at the point of use. Running on natural gas, they still emit CO₂, but their higher efficiency gives them a lower carbon intensity than a comparable gas turbine.

Goldman Sachs estimates that fuel cells could supply 6-15% of incremental data center power (the estimate doesn't specify fuel mix, though most near-term deployments would run primarily on natural gas). This is a meaningful share of new capacity, driven largely by operators who face years-long waits for both grid connections and new gas turbines. Bloom Energy, whose fuel cell products serve data center applications, has been a notable outlier in an otherwise struggling hydrogen sector, with its stock rising sharply on strong revenue performance and growing investor enthusiasm around data center power demand.

Speed to power depends on the fuel. Solid oxide fuel cells run on pipeline natural gas today, so operators do not wait for a hydrogen supply chain. Bloom states it can deliver onsite power for an entire data center in as little as 90 days, with the ability to add modules as load grows under certain circumstances. In all but a few select locations, hydrogen comes later when the same systems switch fuels as clean supply scales. Projects that need hydrogen from day one face additional challenges. Limited hydrogen availability and supporting infrastructure across much of the US can undermine the potential speed advantage of hydrogen-dependent projects. Nevertheless, locations with suitable hydrogen supply and supporting infrastructure may offer viable near-term opportunities.

The Hydrogen Premium: When Paying More Makes Sense

Hydrogen-generated power is more expensive than other forms of electricity generation in most markets. The logistics penalty (including compression, transport, and storage) can push hydrogen from $4/kg at the production site to $8-16/kg delivered, making the economics look unfavorable on a simple $/MWh comparison. In practical terms, hydrogen at $4-6/kg translates to roughly $120-180/MWh on an energy-content basis (LHV). At a fuel cell electrical efficiency of around 50%—typical for solid oxide systems—the effective cost of electricity rises to approximately $240-360/MWh for co-located production, compared to $40-60/MWh for natural gas fuel cells, $51-129/MWh for gas combined cycle, and $144-276/MWh for gas peakers. Operators can improve hydrogen economics by co-locating with production, avoiding the logistics penalty that can double or quadruple delivered costs.

While expensive, this cost comparison misses the central point. The relevant question is not whether hydrogen is cheaper than grid power, but whether it is better than having no access to power at all.

For an operator with signed contracts and a 100 MW facility sitting idle during a multi-year interconnection queue, the opportunity cost is substantial. At average US wholesale colocation rates (~$196/kW/month - a record high), a facility of that size represents roughly $235 million per year in potential revenue—meaning even a single year of delay can outweigh years of hydrogen fuel premiums. 

The Inflation Reduction Act’s 45V Production Tax Credit further compresses the gap, offering up to $3/kg for clean hydrogen production—but projects must begin construction by December 31, 2027, to qualify. That deadline adds urgency to what’s already a time-sensitive calculus.

Reframing the Cost Debate for Speed-to-Power

The debate about whether hydrogen is cost-competitive with grid power misses the strategic challenge facing data center operators today: the fastest path to reliable, clean power. Hydrogen can play a consequential role in that timeline in some markets.

Assessing that requires modeling the full picture: grid interconnection timelines, hydrogen supply chain costs at a specific location, the 45V credit’s impact on project economics, and how hydrogen compares to batteries and other bridging technologies for a data center’s particular load profile.

Relae’s advisory team helps data center operators and technology companies evaluate these tradeoffs with rigorous, independent analysis. If speed to operation is a factor in your energy planning, let’s talk.

Frequently Asked Questions

How much faster is hydrogen fuel cell deployment than waiting for grid interconnection?
Grid interconnection in constrained regions can take 3-5 years once permitting, transmission upgrades, and substation buildouts are factored in. Fuel cell systems, especially those that start on natural gas and then transition to hydrogen, can be deployed in a fraction of that time. They are modular and can be installed and scaled incrementally as a facility’s load grows, though the pace depends on equipment and fuel supply.

If hydrogen costs more than grid power, why would an operator choose it?
For an operator with a 100 MW facility sitting idle during a multi-year interconnection queue, the lost revenue—roughly $235 million a year at average US wholesale colocation rates—can outweigh years of hydrogen’s fuel premium. That makes the higher cost a rational tradeoff against a multi-year wait for grid power, not just an expensive alternative to it.

Do hydrogen fuel cell systems have to run on pure hydrogen from day one?
No. Many modern fuel cell systems, including solid oxide fuel cells, can run on natural gas today and transition to hydrogen as clean supply becomes available. Bloom Energy’s up to 1 GW agreement with American Electric Power follows this model: the systems start on natural gas, with the capability to switch to 100% hydrogen as the hydrogen supply chain matures.

Key Takeaways

• The opportunity: Clean, firm power is a strategic priority for large electricity buyers. Natural gas-fired generation equipped with carbon capture and storage (CCS) is emerging as a key tool in meeting this demand. The existing gas-fired power fleet in the US should be assessed to identify plants well-positioned for carbon capture retrofits that would benefit grid decarbonization. 
• The challenge: The climate benefits of CCS-equipped natural gas plants depend entirely on how often they actually run. Adding carbon capture technology increases the cost to operate the equipment. These higher running costs can make the plant less competitive in auctions where the grid operator picks the cheapest power first. Without mechanisms to keep these plants running continuously, they may be outbid by cheaper, higher-polluting plants, causing grid-wide emissions to stay the same or even increase. 
• The solution: Hyperscalers and other large energy buyers are creating a robust market for clean, firm power. By paying a "clean, firm premium" through long-term offtake agreements, these buyers can offset the higher operational costs of CCS, ensuring these plants are continuously utilized. This corporate leadership not only maximizes the grid-wide climate impact of each retrofit but also provides an important hedge against policy volatility, securing the investment case for clean innovation even when the future of subsidies like the 45Q tax credit is uncertain.

We Need Clean, Firm Power Now

The market signals for clean, firm power are clear. Meta’s nuclear energy projects and Microsoft’s Crane Clean Energy Center demonstrate growing interest in reliable, low-carbon electricity to support the rapid expansion of AI. Similar commitments by Google and Meta to advanced geothermal power also illustrate this trend. 

One of the near-term options to meet this demand is natural gas with carbon capture and storage (CCS). As explored by Relae (formerly Carbon Direct), retrofitting existing gas facilities offers a path to reliable baseload power with low direct emissions, leveraging existing infrastructure to bypass the years-long delays typical of new grid interconnections. 

Recent initiatives from Google and Calpine are already working to prove this concept at scale. This type of corporate leadership is driving the market; over the last decade, voluntary corporate procurement led to more than 40% of new clean energy capacity in the US. Further, recent procurement decisions illustrate that these players are willing to pay a “clean, firm premium” to secure round-the-clock, low-emissions sources of power.

Why Systems-Level Analysis Matters for CCS

While news of corporate procurements often makes headlines, recent analysis finds the number of supply contracts for natural gas power with CCS may outpace the number of secured offtake agreements. Without a power purchase agreement (PPA) to ensure competitive operation, or strong policy support, a generator may need to operate as a “merchant plant” in power markets, competing solely on cost.

A power plant’s ultimate climate impact is determined primarily by how it is positioned in the market, not just its facility-level technology. 

How Power Markets Determine Which Plants Run

Understanding the potential of CCS to deliver clean, firm power and grid-wide decarbonization requires looking beyond the technology performance at a single facility. A retrofitted plant does not operate in isolation; its impact depends on how it interacts with the broader power market’s merit order.

The merit order is the ranking system in competitive power markets where the grid operator dispatches the cheapest offers first. Since carbon capture units are energy-intensive, the retrofitted natural gas plant incurs higher operating costs. This cost increase can inadvertently price the lower-emitting plant out of the market. Without mechanisms to ensure continuous utilization, the CCS plant is potentially outbid by cheaper, more carbon-intensive resources. This creates a risk of increased overall grid emissions.

To illustrate this dynamic, we’re sharing the results of our detailed grid modeling analyses of the Electric Reliability Council of Texas (ERCOT), which serves most of Texas, and the Southwest Power Pool (SPP), which covers parts of 14 states across the central US. Our analysis highlights the value of corporate “clean, firm premiums” in achieving maximum climate benefit and mitigating policy risk present in government subsidy support. 

This type of systems-level grid modeling is necessary in understanding how facility-level reductions translate into real climate benefits. Support to incentivize continuous operation, such as corporate offtake agreements or the 45Q tax credit, is key to ensuring that retrofitting a gas power plant with CCS reduces overall grid emissions. 

Offtake Agreements and Policy Support as Solutions

Power offtake from CCS retrofitted gas plants can meaningfully reduce system-level emissions. By directly matching electricity demand with the supply of power, large energy buyers – the offtakers – ensure the power plant is effectively utilized. This type of arrangement helps ensure any changes to reduce emissions intensity at the facility level translate into broader emissions reductions on the grid.

For these offtakers, the decision to pay a premium for clean power is driven by the goal of additionality – ensuring their procurement has a measurable, additional emissions reduction impact. Beyond physical energy, buyers secure Energy Attribute Certificates (EACs) for CCS, which serve as the verified proof of low-carbon generation required to satisfy corporate zero-emissions targets. As seen in the recent Google and Calpine agreement, these certificates allow buyers to claim the specific climate benefit of the CCS retrofit, justifying a premium over standard wholesale market rates to secure firm, clean delivery.

In the absence of offtake agreements, policy frameworks like the 45Q tax credit (up to $85 per ton of CO₂ sequestered) serve a similar function by offsetting production costs.

However, access to this credit is not a guarantee and carries operational hurdles. To unlock the full credit value, facilities must meet stringent prevailing wage and apprenticeship requirements. Furthermore, the credit is limited to a 12-year window once the facility is placed in service, and requires construction to commence by 2033.

Beyond these eligibility requirements, the long-term outlook for 45Q involves inherent uncertainty. Recent regulatory shifts, including potential changes to the Greenhouse Gas Reporting Program (GHGRP), pose risks to the verification mechanisms required to substantiate captured tons. 

Corporate offtake agreements offer a crucial private-sector complement to this landscape; they provide a stable revenue model independent of policy cycles, ensuring the investment case remains robust over the full life of the asset.

Understanding the Merit Order in Power Markets

Most US power plants operate in competitive deregulated markets, where grid operators dispatch generators based on their marginal cost of production – the cost of generating one additional unit of electricity. The operator ranks these offers from lowest to highest price, creating the "merit order.”

In these auctions, the cheapest resources (typically renewables and base load) are dispatched first. Progressively more expensive units (gas and peaking plants) are called upon until demand is met. The price of the final, most expensive unit required sets the market-clearing price received by all generators in that period. 

The Figure below shows an example generation merit order in the ERCOT energy market.

Case Study: How Support Structures Influence Dispatch

The merit order figure illustrates a hypothetical scenario for a natural gas generator, showing how its market position changes based on technical and policy variables:

• Pre-Retrofit (Stage A): The plant operates with standard marginal costs, sitting competitively in the middle of the supply stack.
• Post-Retrofit (Stage B): Retrofitting with CCS introduces higher operating costs due to the energy-intensive nature of carbon capture. Without external support, the plant’s marginal cost increases (A to B), making it less competitive. The retrofitted plant may be utilized less while cheaper units are dispatched to meet demand.
• Post-Retrofit + policy or offtake support (Stage C): Financial support, whether through the 45Q tax credit (approx. $33/MWh¹) or a corporate offtake agreement, can effectively offset the plant’s higher operational costs (B to C). This effect restores the plant’s competitiveness, ensuring it dispatches consistently.

Testing This With Grid Modeling

At Relae, we apply state-of-the-art grid analysis tools to answer these and more complex analytical questions related to the future energy system. Our custom modeling framework has been used to simulate clean power strategies, assess data center demand response programs, and understand how procurement decisions today impact the future energy system.

While the theoretical impact of a CCS retrofit, a PPA agreement, and the 45Q tax credit on a plant’s dispatch is clear, it’s important to put the theory to the test by modeling their effects on system-wide emissions.

Our Modeling Approach

Because each grid region has distinct power plants and load requirements, they must be modeled separately. For this analysis, we chose to model the ERCOT and SPP power markets to determine the region-specific, grid-wide emissions impact of hypothetical CCS retrofits of natural gas power plants. 

As part of this modeling, we:

• Deployed detailed hourly simulation: We used our custom PyPSA-USA grid model to produce a set of hourly simulations of the ERCOT and SPP electricity markets.²
• Identified suitable retrofits: We identified suitable combined cycle gas power plants for a CCS retrofit in each of the markets, based on key commercial and operational criteria, including size, age, generation profile, and proximity to CO₂ transport/storage.
• Modeled plant and energy assumptions: To reflect the retrofit, we adjusted generator cost and energy use for the identified plants (up to 1.4 GW capacity), fitting all combustion turbines with capture and requiring each plant to consume 20% more fuel per unit of electricity produced to power CCS.³
• Carried out comparative scenario analysis: We simulated several scenarios, including (1) pre-retrofit, business-as-usual, (2) post-retrofit, with and without a PPA, and (3) post-retrofit, with and without the 45Q tax credit, to isolate the impact of different procurement agreements and policy landscapes on grid-wide emissions. 

What Our Analysis Reveals

Results of this analysis reveal how CCS deployment in the power grid interacts with market economics and the role mechanisms that drive high utilization of CCS retrofit plants can have in ensuring system-wide emissions reductions:

CCS With a Firm Offtake Agreement Can Significantly Reduce Grid-Wide Emissions

Pairing a retrofitted plant with a dedicated offtaker can drive meaningful emissions reductions in both ERCOT and SPP compared to business-as-usual (-0.8% to -1.7% CO₂ in ERCOT; -5.2% to -7.3% CO₂ in SPP). Under these arrangements, system-wide emissions fall because the PPA acts as an operational anchor, ensuring the retrofitted plant maintains high utilization rates despite its higher running costs. Ensuring the plant stays utilized prevents the grid from reverting to more carbon-intensive generation to fill the gap.

Our analysis finds the value of the operational “clean, firm premium” for natural gas with CCS power is up to $60 per MWh. This value varies by hour, region and scenario but results generally align with our previous estimate of a $30 per MWh value associated with this type of generation. Other estimates put this value between $19 and $72 per MWh.

CCS Without an Offtake Agreement Can Reduce Emissions, But Is More Reliant on Policy Support

Without a dedicated offtake agreement or policy support, retrofitting natural gas plants with CCS runs the risk of a small increase in grid emissions (+0.7% CO₂ in ERCOT; -0.0% CO₂ in SPP). System-wide emissions are higher because other power plants displace the plants with carbon capture. The higher operational costs of CCS mean the CCS plants have a less competitive place in the merit order and run for fewer hours in the year.

The story changes with the application of 45Q, and grid-wide emissions are lower for both ERCOT and SPP (-1.7% CO₂ in ERCOT; -3.4% CO₂ in SPP). Access to the 45Q tax credit improves each CCS plant’s position in the merit order, meaning that it runs for more hours and successfully displaces higher-emitting generation with clean, firm power.

The Path Forward for Clean, Firm Power

Our analysis illustrates that in competitive power markets, the overall carbon emissions impact of natural gas generation with CCS cannot be measured solely at the power plant level. While clean, firm power remains a strategic priority for large electricity buyers, and CCS is a key tool to meet this demand, the overall climate value of a successful retrofit is linked to the availability of offtake agreements and the plant’s position in the merit order. 

A systems-level perspective captures what facility-level analysis misses: how market dynamics determine the true climate impact of decarbonization investments. Support mechanisms for the continuous operation of low-carbon power plants, like PPAs and the 45Q tax credit, are important tools that ensure clean, firm power reaches the grid, effectively bridging the competitiveness gap.

Frequently Asked Questions 

How can companies ensure CCS retrofits actually reduce grid-wide emissions? 

By securing the plant’s dispatch through a long-term offtake agreement, or by utilizing a policy incentive like 45Q. Relae’s modeling found that offtake agreements have a substantial impact on the emissions reduction potential of CCS retrofits. 

Why would the dispatch decisions of one power plant affect others? 

Power plants dispatch according to marginal cost, and grid stability requires that total supply remain constant at any given moment. So, if one large plant suddenly dispatches less (say, because its operating costs have increased), other potentially dirtier plants may ramp up to fill the gap, increasing total system emissions.

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

• AI's carbon footprint has two distinct parts: embodied emissions from building data centers and operational emissions from running them, both accelerating as global data center electricity use is set to double by 2030, and AI-focused use to triple.
• Managing that footprint will require deliberately steering technology architecture, power sourcing, and materials choices, instead of leaving them to react to demand after the fact.
• Eight concrete strategies, from smarter chip design to firm clean power and carbon removal, can cut AI's footprint today, without waiting on new regulation.
• US data centers used 4% of the USA's total electricity in 2024, and are projected to use as much as 15% by 2030.

Introduction

The rapid growth of artificial intelligence (AI), particularly large-language models (LLM) and generative AI, has taken many by surprise. This surge has led to escalating electricity demands at data centers and raised concerns about the strain on the power grid. It has also sparked the construction of new, larger data centers, resulting in growing embodied emissions tied to building and maintaining AI physical infrastructure.

Managing the risks of increased greenhouse gas (GHG) emissions from AI requires investment, expertise, and new approaches to building and operating many aspects of AI operation and supply chains. The immediate task is to understand these risks, gather the necessary information, and to avoid poor outcomes by proactively managing construction, operation, and emissions associated with the growth in AI. In parallel to that work, it's important to recognize that AI can itself be a real force to reduce emissions incrementally and dramatically across a wide range of sectors.

What Is the Carbon Footprint of AI?

The carbon footprint of AI consists of two main parts: "embodied" emissions that come from manufacturing IT equipment and constructing data centers, and "operational" emissions that come from electricity consumed by servers, memory and networking equipment as they perform AI-related calculations. Both of these aspects of emissions are growing as more data centers are built and existing data centers increase their share of power-hungry AI applications like generative LLM searches, AI agents, and AI image generation.

Understanding Electricity Demand for Data Centers

Today, the electricity demand from AI-specific applications is estimated to be less than 1% of global electricity use. To understand this number, it helps to start with the electricity consumed by the 12,000+ data centers worldwide, which was about 1.5% of global electricity consumption in 2024. (This excludes another 0.4% from cryptocurrency mining.) However, most of the computation at these data centers is not AI; instead, it's more conventional applications like e-commerce, video streaming, social media, and online gaming.

The amount of AI-based computation at data centers is hard to determine, but AI-dedicated accelerated servers consumed about one third of overall data center electricity in 2025, or roughly 0.5% of global electricity. Notably, this is projected to grow at 30% annually, much faster than conventional (non-AI) data center electricity use. However, that electricity use results in a relatively small share of greenhouse gas emissions: about 0.5% of global fuel combustion emissions, with AI data centers representing only a small portion of that value.

Still, the demand for AI applications is rapidly growing, and this is likely to drive up the electricity used by data centers and the associated greenhouse gas emissions. The most important implications of this trend are in the US, which hosts about half the world's data centers. Currently, data centers use about 4% of US electricity, but projections for the future range from a low of 9.5% to a high of 15.3% in 2030.

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How Electricity Sources Impact Data Center Emissions

A large increase in electricity use doesn't necessarily result in a similarly large increase in greenhouse gas emissions. Currently, a significant portion of the electricity powering data centers comes from zero-carbon sources such as wind and solar. This is partly because of large, corporate power-purchase agreements (PPAs) signed by leading data center operators, particularly Amazon, Meta and Google. 

US technology companies have been buying renewable energy for years. Global corporate clean energy procurement hit a record 62 GW in 2024, then fell to 55.9 GW in 2025, the first annual decline in nearly a decade, as elevated power prices and policy uncertainty made even large buyers more selective. Meta, Amazon, Google, and Microsoft still accounted for roughly 49% of global clean energy procurement in 2025, with Meta and Amazon alone securing 20.4 GW combined, including 4.7 GW of nuclear power.

The use of low-carbon power means that the net emissions from these data centers is smaller than the electricity consumption numbers might suggest. Of course, a crucial consideration is whether this low-carbon power is truly "additional," meaning that it is being added to the grid and not simply taken away from other uses. Data center operators are also expanding beyond their traditional wind and solar PPAs by exploring novel approaches to try to meet this standard, including geothermal projects in the US and Taiwan.

However, the projected electricity demand from AI applications at data centers will be difficult to meet entirely with low-carbon power, at least in the near term. Despite installing over 43.2 GW of wind, solar and battery projects in the US in 2025, these generators face a long wait for interconnection approval in many parts of the country. Geothermal and hydro power, which offer steady ("baseload") low-carbon electricity, remain constrained in the near term. And the interest in scaling up nuclear power, from restarting full-scale reactors to novel small modular reactors (SMRs), faces significant regulatory, cost, and supply chain hurdles.

One important source of low-carbon electricity that has not received enough attention is natural gas-fired power equipped with carbon capture and storage (CCS). This technology has the potential to significantly reduce emissions from existing power plants and enable new projects to achieve near-zero emissions.

The Role of Embodied Emissions in Data Center Construction

Embodied emissions include all emissions associated with the extraction, production, transportation, construction, and disposal of materials used in construction.

The embodied emissions from constructing data centers are substantial, and include concrete, steel, and IT hardware. Scope 3 GHG emissions for data centers—which include embodied emissions—range from approximately one-third to two-thirds of overall lifetime emissions. At Microsoft, Scope 3 emissions made up about 86% of the company's total FY2025 footprint and grew roughly 12% year over year, with capital goods driving most of that increase. In FY2024, capital goods alone accounted for about 41% of Microsoft's Scope 3 emissions, and purchased goods and services (including IT hardware) accounted for another 34%. In response, Microsoft has started using wood in some data center construction to reduce this impact. While using wood offers a partial solution, it cannot fully offset the emissions of even a single facility, and wood supply chains remain limited.

Major data center operators are working hard to address this challenge, including emphasizing the need for standardized emissions measurements and disclosures for key building materials. Ultimately, achieving deeper decarbonization will require further action to address both operational and embodied emissions.

Eight Strategies to Reduce the Carbon Footprint of AI

1. Adapt Technology Architecture

Efficiency is the foundational strategy in any clean energy approach. As such, chipmakers are developing ways to cut energy use from the outset, such as incorporating more memory directly onto computer chips or hard-wiring basic calculations. These innovations have already reduced energy consumption in new computer chips substantially, in some cases a 96% improvement. Examples of this include NVIDIA's Blackwell platform and the company's newer Rubin platform, launched in 2026, continues that trajectory. Likewise, servers are being designed with new architectures that minimize internal data transfers, delivering additional efficiencies. Even more efficiency gains may be possible with emerging technologies like photonic computing.

2. Optimize Training Geography

There are also significant opportunities to manage AI's energy use through time and space optimization. For example, a large portion of the energy consumption for LLMs occurs during the training phase, prior to a model's deployment for inference. Because these training tasks are not location-dependent, they can be carried out in regions with abundant, low-cost, low-carbon electricity, as part of broader efforts to dynamically move computing tasks to reduce emissions, known as carbon-aware computing. Additionally, server requests for generative AI tasks, like ChatGPT searches, can potentially be routed through systems powered by low-carbon electricity. Although this may add only a few milliseconds of latency, it could substantially reduce emissions from computing operations.

3. Select Appropriately-Sized Models

Not all generative AI tasks, like ChatGPT queries, are equal in terms of energy demand. Leading AI companies are increasingly focusing on using smaller, more efficient AI models to perform these tasks, achieving nearly equivalent quality for far less energy consumption. A notable recent test of that idea came in January 2025, when China's DeepSeek released a model with competitive performance that was trained using less powerful chips and far fewer computing hours than its established rivals. Similarly, many AI applications, such as digital twinning and satellite-based pattern recognition, consume far less electricity than generative LLMs, because of their specialized, relatively efficient models. This can even save energy compared to non-AI approaches: for example, some of the most advanced AI-driven weather prediction models require far less energy than traditional weather simulations, running on a laptop rather than a supercomputer.

4. Address Fugitive Methane Emissions

As data center operators increasingly plan on using natural gas for new electricity supply, reducing upstream emissions from gas production and transmission will be crucial. In the U.S., the Environmental Protection Agency (EPA) 2024 Methane Rule was designed to cut these non-carbon dioxide greenhouse gas emissions by approximately 80%. However, Congress repealed the rule's methane fee in 2025 and barred the EPA from collecting it until 2034. The EPA has since extended compliance deadlines and loosened flare and vent-gas requirements, with litigation over those changes still ongoing. Meanwhile, tools from companies like Kayrros and organizations like Carbon Mapper help detect methane leaks and attribute them to specific operators. The best actors in the industry emit minimal methane, less than 0.5% of what is produced. This standard is achievable for nearly all gas producers.

5. Use Carbon Capture on Power Plants

For both new and existing natural gas-fired power plants, carbon capture and storage technology offers the potential for generating firm, low-carbon power. While many plants currently in operation continue to emit unchecked, this doesn't have to be the case: their emissions can be captured and securely stored geologically. Hyperscalers and project developers should pursue new investments and business models for CCS to reduce existing emissions by 95% or more. For new generation projects, options like NetPower, Arbor, and CES will soon enable emissions abatement of 100%, or even more if combined with biopower to deliver carbon dioxide removal as well. Achieving this will require the development of carbon dioxide pipelines, barges, and storage facilities, which face their own challenges, such as permitting and community approval, that must be addressed directly.

6. Add More Zero-Carbon Power to the Grid

Roughly 8,200 solar, wind, and battery projects in the U.S. are seeking grid interconnection. By the end of 2025, the interconnection queue held roughly 2,060 GW of proposed generation and storage across thousands of projects, and its composition shifted meaningfully. Solar, wind, and storage volumes in the queue all declined year over year (although remained at high absolute levels) while natural gas capacity in the queue grew by 86%. Our blog post, The $5.5 Billion-Dollar Case for Enabling Data Center Load Flexibility, covers one way hyperscalers are working around the wait rather than simply enduring it. These delays need to be addressed, and permitting reform remains an unresolved, live debate. The Manchin-Barrasso bill, which once looked likely to pass, was tabled in December 2024 and never became law. As of 2026, no comprehensive federal permitting law has replaced it. One potential innovation is to use AI to accelerate the development of power flow models and streamline the paperwork required to complete the regulatory process.

7. Invest in Low-Carbon Building Materials

While wood is a promising low-carbon building material, we'll also need glass, concrete, steel, aluminum, and computer chips with minimal embodied carbon emissions. Hyperscalers currently face significant challenges accessing low-carbon versions of these materials, which will eventually be produced using low-carbon hydrogen, carbon capture and storage, and low-carbon electricity. However, these systems require significant investment, workforce development, and permitting to be built. Without these advancements, the embodied emissions from data centers will increase rapidly and significantly in the US, Europe, and globally.

8. Increase Carbon Dioxide Removals

It's already clear that AI applications at data centers will generate emissions from electricity use and embodied carbon that cannot be avoided in the near term. Estimates of current greenhouse gas emissions exceed 300 million tons per year and are likely to grow this decade. These emissions should be measured using full life-cycle analysis and then offset through high-quality carbon removal projects, preferably those with high durability.

To effectively reduce the environmental impact of AI, all eight strategies discussed must prioritize the communities most affected: frontline communities near new infrastructure, consumers facing price increases, and tribal authorities with limited legal protections. Our own research on community opposition to AI data centers found that transparency, not cost or environmental impact alone, is the dominant driver of pushback across 46 stalled or blocked projects. We explore this concern further in our blog, Who Pays for the AI? The Hidden Costs of Rising Data Center Demand, including how ratepayers, not just data center operators, often absorb the cost of new grid infrastructure. Planning should begin by understanding the needs of these communities, ensuring that efforts focus on minimizing harm while maximizing benefits. Equity and justice must be embedded in every stage of planning, production, and permitting across all strategies.

AI's Power Demand Is Indicative of Broader Electricity Demand

AI is just one part of a broader trend of rapidly growing electricity demands, including from electric vehicles, heat pumps, industrial electrification, green hydrogen, and various e-fuels. The challenges AI presents to hyperscalers, communities, regulators, and investors serve as a preview of the complex, far-reaching impacts emerging in other sectors. The same questions keep recurring. Who secures reliable, affordable power fast enough? Who ends up carrying the cost and emissions burden of getting there the wrong way?

Managing AI's power demand will require building the technology architecture, clean firm power supply, and materials strategy to meet that demand deliberately, rather than reactively. AI's carbon footprint underscores the critical need for expertise in clean electricity, grid management, decarbonization, and carbon removal—expertise that will become increasingly vital as more companies realize the complexity and cost of the journey ahead.

Fortunately, AI itself can be part of the solution. With applications in grid management, material science, and advanced manufacturing, AI has the potential to play a powerful role in the climate response.

Read the full 2025 report: ICEF Sustainable Data Centers.

Frequently Asked Questions

How much electricity do AI data centers actually use? 

AI-specific computation likely accounts for around 0.04% of global electricity use today, but data centers overall (most of it non-AI computation) used about 1.5% of global electricity in 2024. In the US, which hosts roughly half the world's data centers, Lawrence Berkeley National Laboratory puts current usage at 4% of US electricity, projected to reach 9.5-15.3% by 2030 as AI-specific demand grows.

Will more efficient AI models like DeepSeek reduce data center energy demand? 

Not necessarily. DeepSeek's 2025 debut showed that competitive models can be trained with less powerful chips and fewer computing hours, but whether that translates into lower total energy demand is contested. Historically, efficiency gains in computing have tended to get absorbed by increased usage rather than reducing total consumption, so the honest answer is that it depends on whether demand growth outpaces the efficiency gained.

What is being done about the embodied emissions from building AI data centers?

‍Embodied emissions, from concrete, steel, and IT hardware, can account for one-third to two-thirds of a data center's lifetime emissions. Strategies include using lower-carbon materials like wood where feasible, developing low-carbon concrete, steel, and chips, and standardizing emissions disclosures for building materials so operators can compare and choose lower-footprint options.

Key Takeaways

• Agentic inference has changed the economics of AI. Tokens are becoming units of work and the economic driver is now the work produced, not token generation. Per-token costs are falling and the willingness to pay for work produced is rising; these two trends compound. This tailwind enhances AI economics and has spillover impacts on all layers of the AI stack.
• The AI infrastructure question has shifted from whether demand will show up to whether the physical stack can scale quickly enough. That stack includes power generation, grid capacity, interconnection, compute, memory, networking, cooling, siting, and community acceptance.
• Carbon Direct Capital and Relae (formerly Carbon Direct Inc.) have a differentiated view because the two entities work across both sides of the constraint: Relae advises hyperscalers and energy buyers on power and grid bottlenecks, while Carbon Direct Capital invests in the technologies that relieve those bottlenecks.
• Carbon Direct Capital sees better risk-adjusted returns investing in the physical foundations of AI, including clean firm power, energy system efficiency, data center efficiency, and inference-optimized compute, rather than chasing late-stage AI application valuations.

A Better Question Than "Is AI a Bubble?"

The most important development in AI economics is agentic AI turning tokens into work, a shift that reframes the bubble debate which dominated investor conversations, sell-side notes, and Chief Information Officer surveys through early 2026. Hyperscalers spent approximately US$380 billion on capital expenditure (capex) in 2025 and have guided to approximately US$720 billion of capex in 2026.¹ Carbon Direct Capital and Relae have worked together to build project-level models for both training and inference facilities to demystify the numbers and understand financial and technical sensitivities. The core finding was that the assets could be bankable using standard assumptions and that the binding constraints were physical, not financial. That conclusion has been reinforced in recent months by new developments.

Concretely, AI is moving from single prompts and answers to multi-step workflows that plan, reason, call tools, verify outputs, and keep state. This shift to inference is the structural successor to training in the initial AI capex cycle; it changes power requirements, time to power, and compute architectures all at once. Goldman Sachs estimates that agentic AI could drive a 24-fold increase, relative to a 2026 baseline, to roughly 120 quadrillion tokens per month globally by 2030 as per-token costs continue to fall. SemiAnalysis makes the same point from another angle: the value of frontier tokens has risen as agentic workflows become useful, while hardware and software improvements have reduced the cost of producing each token.

This does not mean every AI company is attractive, every data center project works, or every valuation is justified. It means the easy bubble framing is missing the more investable question. If token demand is compounding and the unit value of work produced is rising, the scarce resource is not abstract enthusiasm. It is the physical infrastructure required to turn that demand into work produced.

The Data Center Model Still Matters, But the Box is not a Black Box

Our internal modeling for an illustrative 167-megawatt inference data center using Nvidia Blackwell graphics processing unit (GPU) servers suggests the potential for high-teens percent equity returns under a defined set of assumptions.² We built a bottom-up underwriting, beginning with the number of users served per inference data center, assuming approximately how many tokens they will demand daily, and translating that token demand into compute needed based on industry-standard quantization and utilization rates. We then inferred the number of GPUs and servers needed to achieve the desired compute, which ultimately drove the total invested capital and power demand based on assumed thermal power designs and power usage effectiveness (PUE). On the revenue side, we used GPU-as-a-service rental rates as one proxy for the market value of compute capacity. A hyperscaler would not rent scarce compute externally if it had higher-value internal demand for that same capacity. As we will detail below, GPU rental prices have been steadily increasing on the back of inflecting inference demand.

This model is not the entire argument; it is the starting point. An important lesson is that power cost alone does not break data center economics. Electricity is slightly over 10% of total costs in our model: a 50% increase in power price reduces equity-level returns by less than 2%. Access to power, speed of interconnection, and equipment availability matter more. In other words, the economics of the model facility are workable, but only if the facility can be built and powered on the timeline customers need.

That is where most AI commentary remains too superficial. It treats the data center as a black box: capex goes in, tokens come out. That misses the bottlenecks inside and around the box. AI racks are moving far beyond traditional cloud power density. Cooling is shifting from air to liquid and two-phase systems. Networking and high-bandwidth memory become binding constraints in inference architectures. Grid interconnection queue wait times stretch to years. Communities can and do block projects. The technical, physical, and political constraints are increasingly the drivers of potential returns.

Inference Makes the Constraint Structural

While training is episodic, inference is recurring. A training run can be delayed, accelerated, or redesigned. Inference happens every time a user asks a question, a developer runs an agent, a business automates a workflow, or an application calls a model in the background. Agentic inference multiplies that load because one user action can become many model calls, validation loops, and memory reads; industry benchmarks show that agentic systems consume 5–30 times more tokens than a standard chat interaction.

Inference demand is also resilient in both directions. If efficiency gains lower the cost per token, more workflows become economic and total token consumption rises - the classic Jevons Paradox. However, token prices do not necessarily need to fall for inference spend to grow. As the economic unit shifts from tokens generated to work produced, customers may pay more per token when an agent delivers work produced that is worth more than the inference cost. Regardless of token price, tokens must all route through the same physical bottlenecks and we are seeing an increase in inference demand.

The architecture of inference is also changing. Some workloads will prioritize low-latency answers. Others, especially agentic work without a human waiting on every token, will prioritize memory, state, context, and cost per completed task. That means the AI infrastructure stack will become more heterogeneous, not less: XPUs (specialized AI accelerator chips), custom silicon, photonics, memory hierarchies, and edge or regional deployment models will all matter. The pricing data shows demand for more AI infrastructure overall: on-demand GPU rental capacity is effectively sold out across all chip generations in early 2026, with one-year Hopper H100 contract pricing rising 15–20% month-on-month through March 2026 and Blackwell B200 rental rates up 23% in March alone. When rental rates rise into a wave of new chip supply, supply is not catching up to demand.

Power Is Not One Constraint, It Is Several

Saying "AI is power constrained" is true, but not specific enough. The real problem has several layers. First, data centers need more electricity than many local grids can deliver on hyperscalers' timelines. Crucially, some grids can supply sufficient power but not continuously for 8,760 hours per year, conflicting with traditional assumptions about service reliability and leading to novel strategies around flexibility and intermittent self-supply. Second, the grid must be able to absorb large, fast-moving computational loads without creating reliability risks. Third, customers need energy procurement strategies that satisfy cost, reliability, climate, and public-acceptance requirements. Fourth, projects must get built in real communities, through real interconnection processes and real permitting fights. Power is not simply a commodity to purchase. It is an infrastructure development problem.

This is where Relae is directly relevant. Relae has assembled a team of scientific, engineering, and market experts to support a paying power and energy advisory practice serving hyperscalers, energy buyers, and power producers. Its work answers the questions customers are asking before the market prices them: how to get more capacity out of existing physical grid infrastructure; how to assess the costs and value of load flexibility through advanced modeling capabilities; how to make clean firm generation bankable; how to reduce data center energy intensity; how to validate "bring your own power" and "bring your own compute" structures; and how to build projects that communities will accept. 

In the last twelve months alone, Relae has supported hyperscalers on bankability assessments for next-generation geothermal, scoped load-flexibility programs for multi-hundred-megawatt, single-customer sites, and modeled the carbon and reliability profile of "bring your own power" configurations against grid-tied baselines. Carbon Direct Capital leverages our network of technical experts at Relae, including power engineers, geologists, and electrochemists, to conduct credible technical diligence and to gain insights into early stage market trends and emerging preferences.

What Carbon Direct Capital Is Investing Behind

Our investment focus follows the bottlenecks. On the power side, we are investing in technologies that can deliver reliable power on AI timelines. Sage Geosystems is a next-generation geothermal platform with hyperscaler buy-in; Carbon Direct Capital co-led its US$97 million Series B with Ormat Technologies. We could not have made this investment without the deep expertise of the Relae research team which analyzed Sage's technical results to date to help underwrite future project feasibility. ION Clean Energy is a company that retrofits carbon capture technology onto natural gas combined cycle plants to create "blue electrons"; Relae is in active dialogue with multiple large power users on this topic. Carbon Direct Capital is also actively evaluating the enabling picks and shovels around geothermal, nuclear, fuel cells, and more.

On the data center efficiency side, we are investing in technologies that reduce the amount of power required for a unit of AI work. While it is encouraging to see incremental annual gains in chip efficiency, these are scaling far more slowly than compute demand, driving the need for more innovative technological solutions. As one example, a team at Relae helped us understand the fundamental energy consumption requirements of a standard complementary metal-oxide-semiconductor (CMOS) chip, and the potential of all-optical computing as an alternative. This led to Carbon Direct Capital investing in Neurophos, a photonic compute company targeting step-function gains in energy efficiency per chip that are beyond those achievable by existing GPUs. Carbon Direct Capital joined the company's US$110 million Series A alongside Gates Frontier, Microsoft's M12, Aramco Ventures, Bosch Ventures, and others. More broadly, we are studying other layers of the data center technology stack including networking, memory, cooling, and inference-optimized architectures because the next phase of AI infrastructure will not be solved by simply buying more of yesterday's hardware.

The Bear Case Deserves to Be Taken Seriously

There are real risks to the AI boom: Hyperscaler free cash flow can compress if capex grows faster than revenue. Model efficiency gains can reduce the amount of compute required for a given task. Training demand may be more episodic than the market assumes. Local opposition can slow or cancel data center and power projects. Some new data center capacity could become expensive cloud infrastructure competing on price if AI revenue disappoints.

Those risks are why Carbon Direct Capital frames this as an investment in constraints, not in AI enthusiasm. If efficiency improves, inference use cases expand and the bottleneck shifts to deployment, memory, power, and cost per unit of work produced. If training demand slows, inference and enterprise agents still require recurring capacity. If local grids cannot absorb load, technologies that unlock power, reduce energy intensity, or improve flexibility become more valuable. If some AI applications or model developers fail, the upstream physical bottlenecks remain for the rest.

The Investment Conclusion

The AI infrastructure opportunity sits at the intersection of frontier technology risk, project-finance economics, and energy-system engineering. Underwriting this opportunity well requires addressing all three at once; Carbon Direct Capital is built to do just that. The technical team at Relae has a pulse on emerging stakeholder preferences and scientific breakthroughs, understands novel technologies deeply, and is highly experienced in conducting detailed technical diligence to ensure that projects are viable and scalable. Carbon Direct Capital combines these market and technical insights with our commercial underwriting to facilitate new investments. We are not picking AI winners. We are not picking pure energy assets. We are investing in the companies and technologies that have to exist for AI to sustainably scale.

Frequently Asked Questions

Is the AI capex boom a bubble? While valuations vary, token demand and physical infrastructure needs are real and compounding. The correct question to ask is not whether AI is a bubble, but what the binding constraints are. Our modeling shows that constraints are physical, not financial. 

What are the real constraints on AI infrastructure growth right now? AI infrastructure growth is constrained by power availability, grid capacity, and interconnection speed, not capital availability.

Why does inference matter more than training for long-term AI power demand? Inference is recurring and grows with AI usage, it is not episodic like training runs.

What is Carbon Direct Capital investing in, and why? We are investing in clean firm power, energy system efficiency, data center efficiency, and inference-optimized compute—the physical bottlenecks rather than application-layer valuations.

Disclaimer

Carbon Direct Capital Management LLC is an investment adviser registered with the US Securities and Exchange Commission (SEC). Registration as an investment adviser does not imply any particular level of skill or training. Additional information about Carbon Direct Capital Management LLC, including our Form ADV Part 2A Brochure, is available on the SEC's website at adviserinfo.sec.gov.

This content is provided for informational purposes only and should not be construed as or relied upon as investment, legal, tax, or other advice. You should consult your own advisers regarding legal, business, tax, and other matters related to any investment. Any projections, estimates, forecasts, targets, prospects, or opinions expressed are subject to change without notice and may differ from opinions expressed by other employees of Carbon Direct Capital Management LLC, its affiliates, investors, portfolio companies and other individuals, groups or entities. Certain information contained herein may have been obtained from third-party sources believed to be reliable; however, Carbon Direct Capital Management LLC makes no representations about the accuracy or completeness of any such information or its appropriateness for any given situation. Any investments or portfolio companies mentioned are not representative of all investments made by funds managed by Carbon Direct Capital Management LLC, and there can be no assurance that any investment will be profitable or that future investments will have similar characteristics or results. Past performance is not indicative of future results. The content speaks only as of the date indicated. This content does not constitute an offer to sell or a solicitation of an offer to buy any security. Any such offering will be made only pursuant to formal offering documents.

Key Takeaways

• Voice your opinion: The Greenhouse Gas (GHG) Protocol is updating its scope 2 guidance with final standards expected in 2027, which may require hourly and regional matching of renewable energy certificates (RECs), potentially changing how companies claim their electricity-related emission reductions. 
• Act now to secure renewable energy contracts: Companies should move forward with their scope 2 climate commitments today. The GHG Protocol is expected to grandfather in contracts entered into under existing rules.
• Beyond the megawatt hour (MWh): High-impact forward REC contracts measure impact beyond the current annual MWh match requirement, maximizing near-term carbon abatement and social impact for every dollar invested.

Why 2027 Rule Changes Matter for 2030 Targets

Companies racing to meet 2030 climate targets face converging pressures: surging electricity demand, constrained renewable energy supply, and scope 2 accounting rules that could undergo significant changes by 2027.

In a recent webinar, power market experts from Relae (formerly Carbon Direct) and Ever.green explored these changes. Patti Smith, former Electricity Decarbonization Lead at Relae; Julia Millot, Senior Power Decarbonization Manager at Relae; and Liz Pearce, Chief Revenue Officer at Ever.green, unpacked what's changing and how companies can respond.

The stakes are high. Based on GHG Protocol Scope 2 Public Consultation materials, companies may need to match RECs to electricity consumption on an hourly and locational basis as early as 2028. However, we expect the GHG Protocol to grandfather forward REC contracts signed before new rules take effect, enabling companies to continue advancing toward 2030 targets amid rule uncertainty.

Big Changes to the Power Grid

The US power grid is entering sustained demand growth for the first time in decades. "Over the next five years, data centers alone are going to put [the equivalent] of four New York Cities onto the grid," Smith explains, citing forecasts that project around 200 terawatt hours of new data center load through 2030 (i.e. cumulative energy consumption). That demand growth also shows up in near-term grid planning. NERC's January 2026 Long-Term Reliability Assessment forecasts North American summer peak demand rising by 224 gigawatts—a 24% increase—over the next decade, with new data centers cited as the primary driver. These figures highlight that peak capacity and total energy consumption are directly impacted by the data center boom.

Meanwhile, new renewable projects face headwinds. Smith points to interconnection queue delays: "Solar and battery projects are taking three to five years from initial request to operation." At the same time, clean energy tax credits, which were driving wind and solar expansion, have been curtailed. New restrictions on foreign supply chain materials, which are critical to renewable project development, are further hampering the development of new clean electricity projects.

The result: Power demand is rising while new renewable electricity supply is getting throttled. 

The Messy Reality of Electricity Emissions Accounting

Quantifying the emissions from an individual power plant is straightforward. Allocating those emissions to the companies that consume power is far more complicated. 

Grid-supplied electricity comes from many generators that shift constantly, sometimes even second to second. Companies can’t directly measure emissions from a grid-connected load because the generators serving it continuously change. 

Without direct measurement, companies need rules to estimate the emissions they are responsible for. The GHG Protocol’s Scope 2 Guidance provides that framework, establishing how companies estimate electricity-related emissions and how to reduce them through renewable energy purchases.

How Companies Currently Claim Renewable Energy

For the past decade, companies have used renewable energy purchases to achieve their scope 2 emission reduction goals. The most widely used mechanism is the REC, each representing clean energy attributes for one MWh of renewable electricity generated and added to the grid. Currently, when a company buys RECs equal to its annual electricity consumption, it can claim 100% renewable electricity. Under current rules, companies can use purchased renewable energy from anywhere in North America and apply it to any load in North America at any time during the year. 

Importantly, emissions from different power grids vary widely across North America depending on time of day, time of year, and the power grid makeup.

This flexibility allows companies to match a REC from a clean grid against electricity consumption from a dirtier one, creating a potential mismatch between emissions claimed and actual emissions avoided. This gap has drawn scrutiny, contributing to the motivations for the scope 2 rules rewrite.

What's Changing in GHG Protocol Scope 2 Accounting?

On October 19, 2025, after years of consultation, the GHG Protocol released two separate proposals for public consultation: 

1. Scope 2 changes: Moving away from annual REC matching to an ‘hourly and regional’ REC matching requirement.

2. New consequential methodology: A new approach to estimating emissions caused by a company’s consumption and avoided by its renewable energy contracts. 

The hourly matching proposal (24/7): Companies would match RECs to consumption hour by hour within the same grid region, rather than annually across any North American grid. 

"A REC generated on a Texas wind farm would not be able to be used for electricity consumed in New York," Millot explains.

The consequential approach: This proposes a carbon matching methodology, which estimates emissions caused by a load and estimates the emissions a renewable project displaces. 

"Projects in the Carolinas are avoiding 0.6 or 0.7 tons of CO₂ per megawatt hour, whereas a California project is probably closer to 0.2 or 0.3," Smith explains. 

Projects in the Carolinas deliver more than double the climate impact per REC under the consequential rules. In this methodology, the load and generator do not need to be located in the same region.

While the proposed rules and new methodologies work through the public consultation process, it will be important for companies to start to anticipate the potential impacts on their climate goals and strategies. 

Timeline for Scope 2 Accounting Changes

Both the Scope 2 and Consequential Electricity-Sector Emissions consultations closed January 31, 2026, after GHG Protocol extended the original deadline. The GHG Protocol is analyzing feedback with a second consultation and final standards expected by 2027, though the exact timeline is still being finalized.

Companies are encouraged to participate in the public consultation. The GHG Protocol is asking for comments on critical questions, such as: 

• Should proposed rules apply to energy consumers of all sizes? 
• Which geographical boundaries should be used for locational matching? 
• Should existing contracts be grandfathered in? 

The public consultation period is an opportunity to shape the standards that will govern electricity-related emission accounting for years to come.

Why Act Now Instead of Waiting

With final rules still in development, companies with scope 2 emission reduction goals or science-based targets face a decision: Wait for clarity or act now.

Several factors favor early action:

• Inclusion of legacy contracts. "There are a lot of indications from the committees that existing long-term contracts will be grandfathered in," Pearce notes. The draft considers a legacy clause that would allow organizations to apply pre-existing contractual agreements, even if they don’t comply with new rules. 
• Throttled renewable project development. Interconnection delays for new renewable energy projects, elimination of clean energy tax credits by 2028, and limitations on foreign materials needed to develop renewable energy project components mean that new REC supply may be harder to access in future years.
• Renewable project development timelines. "There's generally a lag, sometimes six to 18 months" between contract signing and project operation, Pearce explains. That means even if you sign today, the RECs won’t be generated for up to 18 months from the signing date.
• High-impact opportunity. Through careful project selection, renewable energy investment can go beyond the annual energy match requirement and incorporate additional impactful metrics, such as higher avoided emissions and positive social impacts.

Renewable Energy Buying Options for Companies

Previously, companies have been able to buy renewable energy through the following three paths; however, they all come with their own tradeoffs.

Traditional REC Buying Options

1. REC spot markets make up most corporate renewable procurement. However, they mainly come from existing projects rather than financing new development, which is critical to expanding renewable energy supply to meet rising decarbonization needs.
2. Virtual power purchase agreements (VPPAs) are highly impactful but require large power loads and the ability to manage long-term financial risks. Unavailable to most companies.
3. Utility green tariffs have limited availability throughout the US (depending on the utility(s) that serve your load) and vary in quality. 

Alternative REC Procurement Approach

For companies that want to go beyond the REC spot market and are not large enough to pursue a VPPA, there’s an alternative procurement option available: a high-impact forward REC contract. These multi-year contracts commit to purchasing RECs from specific new projects before they're built, providing the upfront revenue certainty developers need to secure financing at a fraction of the scale and complexity of a VPPA.

Comparing Renewable Energy Procurement Options

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The Path Forward

Despite rapidly increasing grid demand, renewable project headwinds, and changing accounting rules, companies can still meet 2030 scope 2 goals. 

What companies should do now:

• Watch for the next round of GHG Protocol consultation on Scope 2 revisions
• Evaluate forward REC contracts to lock in terms before rule changes
• Prioritize high-impact RECs that deliver measurable climate and social benefits