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Monday, December 29, 2025

Ironically, Home Owner Solar Power Damages Universal Service

A shift of consumer electrical service charges from “usage” to “a connection fee,” as happened in the telecom industry, will upset traditional thinking about how to support an effective and yet affordable electrical grid under changing usage patterns. 


Some will argue that such a switch is harmful to lower-income customers. Others will argue higher electricity costs are equally harmful to lower-income customers. As always, there are trade-offs. 


Assume a traditional utility rate that might include charges for fixed and variable costs (generation and customer usage) of:

  • Fixed costs: $100/month (embedded)

  • Energy rate: $0.20/kWh

  • Average bill (500 kWh): $100


But self generation even by consumer customers changes the business model, to say nothing of large business customer self generation. 


If, for example, a home owner installs a solar system, the grid-delivered electrical usage bill could fall to $20 to $30 a month. But the common costs of maintaining the grid do not change. Eventually, that causes a capital recovery problem. 


So assume pricing changes to include a heftier “access to the grid” fee, with lower usage fees, possibly including a grid access fee of perhaps $60 per month; usage of $20 per month at $0.08/kWh. 


The customer still pays $80 per month to remain connected to the grid and the utility does not go out of business. 


The telecom industry had to make this explicit shift over the last couple of decades as usage of fixed networks dropped dramatically, replaced by use of mobile networks. At the same time, demand for the core “voice service” changed as internet access became the anchor product on the fixed network. 


So today the fixed network is supported more by fixed “access” charges than “usage.” And even at that, policymakers argue that fixed cost recovery mechanisms are insufficient. 


Still, the advantages are that explicit revenue mechanisms to cover shared and fixed costs are available, even as more customers remove themselves from the fixed network. 


Subsidies are targeted and transparent and cross-subsidies are policy choices, not accidents. Even on the replacement mobile networks, the widespread use of flat fee access, with “unlimited” data usage, national voice calls and text messages, show the reliance on “access to the network” pricing, rather than “minutes of use” or “bytes consumed” usage models. 


The implications for the electricity network, as more customers move to self generation, is rather obvious. The sunk costs of the grid must be paid for, irrespective of individual customer usage. 


“Access to the network” becomes the “product,” rather than usage.


What’s really happening is a decoupling of value from volume, something that also happens in other infrastructure contexts. 


The grid’s value is optionality and insurance, but it’s priced like a commodity pipeline. Distributed generation exposes that mismatch.


As for the argument that access fees hurt low-income customers, consider today’s situation, where solar power benefits homeowners with the means to self generate; living in sunnier climates. 


Renters, those without capital or physical means to generate their own electricity and people living in less-sunny climes are disadvantaged. 


Access fees help ameliorate such problems, while still protecting an electrical utility’s ability to build and maintain universal access networks under conditions where “best customers” are creating their own substitutes. 


Access fees, rather than usage, now dominate telecom service fees. People actually pay for ability to use the networks, not the amount of usage of those networks, which once was the case. 


If electrical energy networks must have a “universal service” character, then we also have models for ensuring such access for lower-income customers. We use subsidies.


Electricity Business Can Learn from Telecom Evolution

Oddly enough, local electricity generation by businesses and homeowners exposes a key problem for electricity supplier economics. Traditional pricing assumes energy consumption is equal to grid usage. 


But distributed generation breaks that assumption. Essentially, customers remove themselves, at least partially, from the system, but retain the optionality of using the grid for reliability, backup, and peak load balancing. 


But fixed costs stay embedded in the price of per-kiloWatt hour charges, so rates will rise as sales fall. At the same time, new demand driven by high-performance computing and associated data centers increases the need for new investments in transmission infrastructure as well as generation, increasing the fixed costs. 


The basic problem is a combination of high fixed costs; low marginal costs per additional kWh and the impact on ability to cover fixed costs when demand is reduced by local generation. 


Since fixed costs do not decline proportionally with local generation, all remaining sales must cover more fixed cost per kWh consumed. 


This pushes per-kWh rates upward for customers who still rely heavily on the grid. 


But the network still must be designed for peak load, sized to serve customers when solar output drops (night, winter, clouds). So self-generation reduces energy delivered, not the need for the grid.


Share of Customers with On-Site Generation

Utility Retail Sales (as % of original)

Fixed Cost Recovery per kWh

Average Retail Rate Impact for Non-Solar Customers

0% (baseline)

100%

$0.10/kWh

Baseline

10%

93%

$0.108/kWh

+8%

25%

82%

$0.122/kWh

+22%

40%

68%

$0.147/kWh

+47%

60%

52%

$0.192/kWh

+92%


What’s really happening is a decoupling of value from volume, something that also happens in other infrastructure contexts. 


The grid’s value is optionality and insurance, but it’s priced like a commodity pipeline. Distributed generation exposes that mismatch.


So what might be done to fix this problem? Fixed monthly connection charges are one way of “socializing” grid costs. Time-of-use pricing and demand charges also can help. But as with mobile and fixed telecom networks, “access” to the network might be more important than usage charges. 


So reframing the product might be conceptually necessary. The “product” electrical utilities sell is reliability, capacity, and load balancing, not just energy. 


Energy is a commodity that is part of the service, but grid access becomes the actual “product.” 


Beyond all that, perhaps more explicitly cross subsidies are needed, as once was the case for communications services, where business user profits subsidized consumer usage. Perhaps business customers and self-generators must subsidize customers unable (for financial or physical reasons) to participate in self generation. 


Until pricing reflects capacity and availability, not just kWh, rising self-generation will continue to raise rates for those most dependent on the grid.


This reminds me very much of how economics of the “telecom” business changed with competition. 


Both electrical grids and telecom networks have the same core traits:

  • Extremely high fixed costs

  • Very low marginal cost per additional unit of usage

  • Peak demand, not average usage, drives capital investment

  • Universal-service expectations layered on top of commercial economics


Historically, both industries solved this with implicit cross-subsidies. But widespread technology changes and deregulation changed the telecom business model. 


Traditionally, high prices for business customers (especially long distance calling) provided the profits that allowed affordable service for consumers. 


This worked as long as high-margin users couldn’t easily bypass the network and suppliers had pricing power. 


Self generation in the electricity business has the same dynamics. When high-value customers (commercial, industrial, affluent residential) can self-generate, electricity providers lose the profits that allow them to serve mass-market customers reliant on the grid with affordable rates.


The cross-subsidy that once flowed invisibly is exposed. The analogy with telecom after deregulation, mobile substitution for fixed voice, embrace of internet protocol and reliance on internet access as a core service for the fixed network illustrate the issues. 


Dimension

Traditional Telecom Access

Electric Grid (Emerging)

Core asset

Nationwide access network

Transmission & distribution grid

Cost structure

High fixed / low marginal

High fixed / low marginal

What drives capex

Peak simultaneous usage

Peak demand & reliability

Primary pricing unit

Minutes / lines

kWh

Implicit subsidy source

Business & long-distance margins

High-usage / high-income customers

Subsidy recipient

Residential & rural users

Low-income & non-solar customers

Bypass mechanism

VoIP, wireless, OTT apps

Rooftop solar, storage, microgrids

Resulting problem

Access prices no longer cover costs

Volumetric rates no longer recover fixed costs

Regulatory response

Access charges, USF fees

Grid access charges, demand charges (emerging)

Political constraint

Universal service obligation

Universal service + decarbonization goals


The problems are similar. Neither industry can simultaneously have volume-based pricing; high fixed costs; widespread abandonment of the core network and stable rates for mass-market customers. 


The telecom industry adapted by shifting its revenue model. Today,  customers do not primarily pay for minutes or megabytes anymore. They pay for “access to the network.” Think of it like Wi-Fi access. One pays to be connected, not for usage (bytes consumed or time connected or bandwidth provided). 


The analogy is a mobile phone service plan offered at a flat fee per month that includes “unlimited” data usage; “unlimited” national calling and text message. 


The customer pays for the ability to use the network, not consumption in a strict sense. 


Today’s electrical energy service problem is that self generation reduced kWh sales while fixed costs remain. As rates rise to cover fixed costs borne by fewer customers, there is more incentive to defect. 


So an access-fee model more effectively recovers shared fixed costs. So self generation no longer erodes fixed cost recovery. And the grid stays healthy.


Thursday, November 27, 2025

AI Factories Tend to Rely on Lots of Natural Gas

The hyperscale high-performance “computing as a service” providers (AWS, Azure, Google, Meta) mostly purchase renewable energy and report high market-based renewable energy percentages, and also use local power grid suppliers that rely on a high share of natural-gas generation (especially in Texas and Northern Virginia). 


Company / AI factory

Company claim (market-based renewables)

Major data-center regions (grid natural-gas share)

Degree of access to natural gas (High / Medium / Low)

Sources

Amazon / AWS

100% of electricity consumed matched with renewable energy (2023–2024, market-based). (Amazon Sustainability)

Heavy footprint in Northern Virginia (PJM/VA — VA generation >50% gas) and Texas (ERCOT — large gas share). US grids overall ~40% gas. (U.S. Energy Information Administration)

High

AWS has major capacity in gas-heavy US grids (Northern VA, TX). Although AWS reports “100% matched” renewables market-wide, the local grid supply for many AI clusters is still gas-dominated, so AWS has strong practical access to gas power for fast capacity scaling. (Amazon Sustainability)

Microsoft / Azure (incl. Azure OpenAI / OpenAI workloads on Azure)

Microsoft reports procuring enough renewables to match 100% of global electricity consumption (market-based). (Microsoft CDN)

Large Azure presence in Northern Virginia, Texas, and other US regions (many gas-heavy grids). US/TX/VA grid gas shares high (see EIA). (U.S. Energy Information Administration)

High

Microsoft powers OpenAI workloads on Azure; Azure’s major data-center regions overlap with gas-heavy grids (so operational access to gas is high despite market-based renewable matching). (The Official Microsoft Blog)

Meta (Facebook / Meta AI data centers)

Meta reports matching much (or all) owned/operated data-center electricity with renewables in its accounting (market-based) while expanding new local projects. (Meta Sustainability)

Big builds in Texas (Coleman County and other TX sites) and Northern Virginia; TX and VA grids have large natural-gas shares. (pv magazine USA)

High

Meta is rapidly expanding AI capacity in TX/VA — both regions with heavy natural-gas generation — so operational access to gas generation is high even as Meta signs renewables locally/contractually. (pv magazine USA)

Google / Google Cloud

Google reports having matched 100% of annual electricity consumption with renewables for years (market-based) and publishes regional hourly carbon-free percentages. (blog.google)

Google’s footprint includes Midlothian, TX (gas-heavy) but also WA/OR (hydro), and other lower-gas grids — a more geographically mixed footprint. (RTO Insider)

Medium

Because Google’s data centers are more geographically diverse (some gas-heavy, some hydro/low-gas), its practical access to gas is medium overall. It also reports regional CFE (carbon-free energy) metrics to show hourly variation. (blog.google)

Oracle Cloud

Oracle claims high renewable coverage in some disclosures (Oracle reports strong renewable procurement claims). (Oracle)

Oracle’s cloud footprint is smaller than hyperscalers and more concentrated in commercial colocation markets (regional mixes vary). Many U.S. colo grids include substantial gas. (Oracle Blogs)

Medium–Low

Oracle’s absolute compute footprint is smaller and it emphasizes renewable procurement; depending on region, local grid gas exposure varies — overall less direct exposure than the largest hyperscalers. (Oracle)

AI firms that rent cloud capacity (OpenAI, Anthropic, Stability, etc.)

OpenAI: uses Azure (Microsoft) for most workloads; Anthropic & others use mixtures of Google Cloud / Azure / AWS (multi-cloud). (The Official Microsoft Blog)

Their practical gas access ≈ the cloud provider(s) they run on. If on Azure/AWS/Google in TX/VA, access is High/Medium as above. (The Official Microsoft Blog)

Varies (follows provider)

These AI model operators rarely own global data centers; they rely on hyperscalers. So their degree of access to gas ≈ the hosting cloud’s regional grid exposure. OpenAI on Azure → High by the table above. Anthropic’s deals (Google/Azure) mean varied exposure. (The Official Microsoft Blog)


The “neo cloud” providers tend to have medium to high levels of access to natural gas for power, though the emphasis on “renewable” sources might not be as high a priority. 


Sites like CoreWeave’s Project Horizon and Galaxy/Helios (West Texas) are virtually designed around proximity to natural-gas infrastructure. For frontier-scale AI (multi-hundreds of megawatts to gigawatt scale), this gives them very-high access to gas-powered electricity.


TeraWulf’s Lake Mariner (NY) is a contrasting model: using mostly low-carbon grid supply (hydro/nuclear/clean energy) that is better for carbon-intensity, with lower reliance on gas.


Hut 8 has a mixed strategy: some sites (Canada) on cleaner grids, some (Texas, Panhandle) on gas-heavy or mixed grids, giving it a balanced, diversified exposure depending on where its compute load is run.


Riot Platforms (Rockdale / Corsicana) are among the most gas-exposed of publicly traded neo-cloud specialist compute providers. 


Company

Major sites / where compute is (or is planned to be) sited

Dominant local power sources / relevant company energy plans

Degree of access to natural gas power (High / Medium / Low)

Why / notes & primary sources

CoreWeave

Rapid expansion into West Texas / Permian projects (Project Horizon / Poolside JV), plus European builds (UK, Spain) and colo deals.

West Texas projects explicitly target locations with access to low-cost natural gas (Permian/Delaware basin) while some EU/UK sites emphasize renewable-backed supply. Net: strong access to local natural gas where it matters for large-scale AI campuses.

High

CoreWeave is anchoring large West Texas campuses that are being designed around low-cost gas-rich markets (Permian Basin/Project Horizon) while also deploying renewables-backed facilities in Europe. This gives CoreWeave high practical access to gas power for large-scale, fast-growing AI capacity. (Barron's)

Hut 8 Corp.

Mixed footprint: multiple Canadian colo/HPC sites (Vancouver, Kelowna, Mississauga/Vaughan), mining campuses (Alberta, Medicine Hat), and a growing U.S. development pipeline (planned US sites, Louisiana/Baton Rouge acquisitions announced).

Canada: strong hydro in some provinces (Quebec/BC/ON) → low gas; Alberta sites and some U.S. development pipeline → higher fossil/gas exposure. Company runs both renewables/hydro-backed sites and gas-exposed mining/power projects.

Medium

Hut 8’s compute estate is geographically mixed: many Canadian colo/HPC sites sit on low-carbon hydro grids, but Hut 8 also owns/operates power-first mining/data campuses in Alberta/other U.S. projects that expose it to gas/thermal generation. Net = medium. (Hut 8 HPC)

TeraWulf (TeraWulf Inc.)

Lake Mariner campus (Somerset/Buffalo, NY) — large hydro/low-carbon facility; Nautilus in Pennsylvania; plus announced / planned large campus development in Abernathy, Texas (joint venture with Fluidstack / Google backing).

Lake Mariner: predominantly hydro / low-carbon (NY grid + hydropower). Abernathy (TX) project would sit in gas-heavy Texas grids. Company messaging emphasizes “sustainably powered” HPC but also expansion into Texas.

Medium

TeraWulf’s core existing operations (Lake Mariner) are strongly low-carbon/hydro-aligned → low local gas exposure today. But an explicit expansion into Abernathy, Texas (large planned capacity) points to future higher gas exposure at those sites. Overall practical access = Medium (mixed existing low-gas + planned gas-region capacity). (Data Center Map)

MARA / Marathon Digital (MARA Holdings)

Large portfolio: Garden City, Granbury, McCamey (TX), multiple West Texas / Delaware Basin projects, plus international sites. Company explicitly pursuing integrated power + data center builds in West Texas (MPLX partnership).

Mix today: some wind/hydro-adjacent sites (Garden City adjacent to wind), BUT public plans to build gas-fired generation facilities in West Texas (MPLX partnership) — initial ~400 MW with potential to scale to 1.5 GW using natural gas from Delaware Basin.

High (increasing)

Marathon/MARA has both renewables-adjacent assets (e.g., Garden City wind) and explicit, recent plans/partnerships to build gas-fired generation co-located with data centers in West Texas (MPLX deal). That makes MARA’s practical access to natural gas high and rising as gas-gen projects come online. (Mara)

Riot Platforms (Riot)

Large Texas footprint (Rockdale, Corsicana) plus Kentucky facility — Rockdale is one of North America’s largest mining campuses.

Texas grids (ERCOT/North Texas) have very high shares of natural-gas generation at times; Riot’s large Texas facilities operate in gas-dominated grids and have been criticized for high fossil generation intensity. Riot’s filings list Texas facilities as core.

High

Riot’s major capacity (Rockdale, Corsicana) sits in Texas, a grid and market with large natural-gas generation share — giving Riot high practical access to gas-fired electricity for large scale compute/mining workloads. (Riot Platforms)


Of course, companies operating multiple sites will have some sites using more or less natural gas, depending on what other sources are available (hydro, for example). 


Company

Site

City

State

Natural Gas Access

Source

CoreWeave

Project Horizon (Longfellow Ranch)

Pecos County

TX

High

https://poolside.ai/blog/announcing-project-horizon; https://datacenterdynamics.com/en/news/ai-startup-poolside-teams-up-with-coreweave-on-2gw-data-center-in-texas/

CoreWeave

Helios (Galaxy) - Dickens County

Afton/Dickens County

TX

High

https://investor.galaxy.com/news/; https://www.datacentermap.com/usa/texas/dickens/helios-data-center/

CoreWeave

Livingston / NJ operations (representative)

Livingston

NJ

Medium

https://www.coreweave.com/news/coreweave-announces-partnership-with-foundation-model-company-poolside-to-deliver-ai-cloud-services

TeraWulf

Lake Mariner

Barker (Lake Mariner)

NY

Low/Medium

https://www.terawulf.com/lake-mariner-mining/; https://www.gem.wiki/Lake_Mariner_facility

TeraWulf

Nautilus (Pennsylvania)

Pittsburgh area (Nautilus)

PA

Medium

https://investors.terawulf.com/news-events/press-releases/detail/83/terawulf-announces-july-2024-production-and-operations

Hut 8

King Mountain / McCamey (JV)

McCamey

TX

Medium

https://hut8.com/2025/02/04/hut-8-operations-update-for-january-2025/; https://www.nasdaq.com/press-release/hut-8-operations-update-february-2025-2025-03-06

Hut 8

Vega / Texas-Panhandle (planned)

Vega

TX

Medium

https://hut8.com/news-insights/press-releases/hut-8-announces-plans-to-develop-four-new-sites

Marathon (MARA)

Garden City

Garden City

TX

Medium/High

https://baxtel.com/data-center/marathon-digital-garden-city-tx; https://www.mara.com/posts/mara-announces-25-megawatt-micro-data-center-project-powered-by-excess-natural-gas-from-oilfields

Marathon (MARA)

McCamey / West Texas projects

McCamey

TX

High

https://www.mara.com/posts/mara-announces-25-megawatt-micro-data-center-project-powered-by-excess-natural-gas-from-oilfields; https://ir.mplx.com/CorporateProfile/press-releases/news-release/2025/MPLX-and-MARA-Announce-Collaboration-on-Integrated-Power-Generation-and-Data-Center-Campuses-in-West-Texas

Riot Platforms

Rockdale

Rockdale

TX

High

https://www.riotplatforms.com/bitcoin-mining/rockdale/

Riot Platforms

Corsicana

Corsicana

TX

High

https://www.riotplatforms.com/bitcoin-mining/corsicana/; https://www.mapquest.com/us/texas/riot-platforms-inc-721940928

Core Scientific

Dalton (GA)

Dalton

GA

Medium

https://www.datacentermap.com/c/core-scientific/

Core Scientific

Grand Forks (ND)

Grand Forks

ND

Low/Medium

https://www.datacentermap.com/c/core-scientific/

Core Scientific

Muskogee (OK)

Muskogee

OK

Medium/High

https://www.datacentermap.com/c/core-scientific/

Compute North (historical)

Big Spring (TX)

Big Spring

TX

High

https://dgtlinfra.com/compute-north-chapter-11-bankruptcy-filing/; historical filings

Compute North

North Sioux City (SD)

North Sioux City

SD

Medium

https://dgtlinfra.com/compute-north-chapter-11-bankruptcy-filing/

Compute North

Kearney (NE)

Kearney

NE

Medium

https://dgtlinfra.com/compute-north-chapter-11-bankruptcy-filing/

TerraWulf (Beowulf/TeraWulf)

Lake Mariner (alternate coord)

Barker/Buffalo area

NY

Low/Medium

https://www.datacentermap.com/usa/new-york/buffalo/lake-mariner-data/; https://www.gem.wiki/Lake_Mariner_facility

TeraWulf

Nautilus (PA)

Pittsburgh area

PA

Medium

https://investors.terawulf.com/news-events/press-releases/detail/83/terawulf-announces-july-2024-production-and-operations

Greenidge

Dresden (Finger Lakes)

Dresden

NY

Low/Medium

https://www.greenidge.com/operations/

CoreWeave

Midlothian / Dallas area (representative)

Midlothian

TX

High

news coverage of cloud builds in Midlothian/Dallas area

Marathon (MARA)

Granbury (Wolf Hollow / Granbury)

Granbury

TX

High

Compute North and Marathon filings; company press releases

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