AI Power Weekly

AI Power News 4/27/2026

Will McKnight — Mon, 27 Apr 2026 17:38:52 GMT

Microsoft Loses Its Exclusive Hold on OpenAI

Microsoft and OpenAI announced Monday they have rewritten their seven-year partnership, ending Microsoft’s exclusive right to sell OpenAI models. OpenAI can now run its products on Amazon, Google, or any other cloud provider, though Azure stays the default unless Microsoft cannot deliver what a workload needs. Microsoft keeps a non-exclusive license to OpenAI intellectual property through 2032 and continues to collect a capped revenue share through 2030. Microsoft shares fell nearly 3% on the announcement. Alphabet and Amazon ticked up.

Every grid forecast and substation buildout for the past three years assumed OpenAI compute would concentrate inside Microsoft’s data center footprint. That assumption just expired. Power planners now have to model OpenAI workloads landing in any of three hyperscale clouds, which means more parallel power deals, not fewer.

Watch which AWS or Google Cloud site brings the first OpenAI workload online, because the hyperscaler that moves first resets the price for every AI power deal that follows. (Bloomberg)

Duke Energy Files the Largest Utility Capital Plan in U.S. History

Duke Energy unveiled a $103 billion five-year capital plan on April 24, the largest spending program ever filed by a regulated U.S. utility. Roughly 60% of the budget funds new generation, including 5 GW of new natural gas in the Carolinas and Indiana and 4.5 GW of new battery storage by 2031. Since November, Duke has signed 1.5 GW in new data center service agreements with customers including Microsoft and Compass, lifting committed data center load to 4.5 GW. Data centers will drive roughly 75% of the economic-development load growth Duke projects through 2030. (Fortune)

Siemens Energy Raises 2026 Outlook on AI Power Demand

Siemens Energy raised its full-year guidance on April 23, citing AI data center demand. The company now expects revenue growth of 14% to 16%, net profit around €4 billion, and free cash flow before tax of roughly €8 billion, nearly double the prior forecast. Second-quarter order intake hit a record €17.75 billion, with Grid Technologies orders up 41% and Gas Services up 32% year over year. Grid Technologies is the unit that builds the transformers, switchgear, and high-voltage equipment that connects new generation to large industrial loads. (Bloomberg)

Kevin O’Leary’s 7.5 GW Wonder Valley Project Nears Approval in Utah

The Stratos Project, a hyperscale data center campus on 40,000 acres in Box Elder County, Utah, moved closer to final approval this week. The buildout proposes 7.5 gigawatts of new power generation, almost twice the entire state’s current electricity consumption of about 4 GW. Kevin O’Leary’s O’Leary Digital is the developer, with West GenCo handling permitting and natural gas access through interstate pipelines. Financing terms, anchor tenants, and the actual gas supply for a project this size remain unannounced. (Salt Lake Tribune)

FERC Punts Large-Load Interconnection Rule to June

The Federal Energy Regulatory Commission confirmed last week it will rule on data center interconnection reform in June, missing the April 30 deadline that Energy Secretary Chris Wright set in February. The proposed rule would let large loads of 75 MW or more file joint interconnection requests alongside new generation, compressing what is now a multi-year sequential process into a single review. State regulators at NARUC have warned the federal proposal encroaches on traditional state authority over retail load and rate design. The two-month delay is short, but it pushes any actual queue impact into late 2026 at earliest. (Utility Dive)

U.S. in Talks with Engie to Cancel Three More Offshore Wind Leases

Engie confirmed on April 26 that it is in talks with the U.S. government to cancel its three remaining offshore wind leases, following TotalEnergies’ lease surrender in March. The discussions stem from the Interior Department’s December 2025 stop-work order on all five offshore wind projects under construction, an order currently blocked by temporary injunctions in five federal courts. Every canceled lease removes potential offshore generation from the East Coast queue, where regional grid operator PJM has flagged a capacity shortfall heading into the early 2030s. (Maritime Executive)

The AI Power Bill Has Reached Voters

A CBS News investigation on April 25 profiled Atlanta resident Carolyn Kayne, whose monthly electricity bill has nearly doubled in two years as Georgia Power filed six rate hikes in three years, citing data center load growth. A Fortune poll published the same week found a majority of U.S. households now connect data center expansion with rising electricity costs. Public sentiment is becoming the new constraint that hyperscalers will spend the rest of 2026 trying to manage. (CBS News)

Have fun this week,

Will

Sources

AI Power News 4/20/2026

Will McKnight — Mon, 20 Apr 2026 19:04:25 GMT

Oracle and Bloom Energy Expand Fuel Cell Deal to 2.8 GW

Oracle agreed Monday to purchase up to 2.8 gigawatts of power from Bloom Energy, the California fuel cell maker, to supply AI data centers across the United States. Fuel cells generate electricity from natural gas through a chemical reaction rather than combustion, producing power on-site without the grid connections and permitting timelines that can stretch for years. An initial 1.2 GW is already contracted and deploying now, with the rest following through next year. Bloom secured $7.65 billion in data center contracts across just 90 days in early 2026.

This deal is not really about power supply. It is evidence that the grid interconnection queue is so broken that building your own power plant on-site is now the faster path. Bloom delivered Oracle’s first fuel cell installation in 55 days, against a 90-day target. That is the benchmark grid utilities now have to compete with. The hyperscalers are quietly becoming their own utilities.

Watch whether Amazon and Microsoft accelerate their own off-grid strategies in response to Oracle’s lead. (Bloomberg)

ERCOT Projects Texas Grid Could Quadruple to 368 GW by 2032

Texas grid operator ERCOT told state lawmakers this week that peak electricity demand, currently at a record 85,508 megawatts, could nearly quadruple to 367,790 megawatts by 2032, driven almost entirely by data center construction requests. ERCOT, the independent system operator that manages the power grid for most of Texas, is responsible for matching electricity supply with demand in real time. CEO Pablo Vegas added a key caveat: these numbers represent every company that has filed to connect to the grid, not every company that will actually build. ERCOT is now replacing its old interconnection review process with a system called Batch Zero, which groups large new requests of 75 MW or more into batches for faster evaluation. The Federal Reserve Bank of Dallas estimates that wholesale power prices in Texas could rise as much as 50% from data center demand growth alone. (Insurance Journal)

EIA Projects US Power Demand Will Hit Records in Both 2026 and 2027

The U.S. Energy Information Administration projected this month that American electricity consumption will climb from a record 4,195 billion kilowatt-hours in 2025 to 4,260 billion kWh in 2026 and 4,388 billion kWh in 2027, driven primarily by AI data centers. If the forecast holds, it would mark the first time since 2007 that U.S. power demand has risen for four consecutive years, and the strongest four-year growth streak since 2000. The Department of Energy separately estimated that data centers accounted for 4.4% of all U.S. electricity use in 2023 and expects that share to reach 12% by 2028. For context: at 12%, data centers alone would consume roughly as much power as every home in the country combined. (EnergyNow)

Judge Orders GE Vernova to Stay in Vineyard Wind Contract

A Massachusetts judge granted Vineyard Wind a preliminary injunction this week, blocking GE Vernova from walking away from its turbine supply and maintenance contract. GE had sent a termination notice in February, citing more than $300 million in unpaid bills. Vineyard Wind is the first utility-scale offshore wind project in the United States and has faced construction setbacks, including a blade failure in 2024. The injunction keeps GE crews on-site through the legal proceedings and prevents a shutdown that would have stalled the project indefinitely. A separate court ruling earlier this month also cleared five major U.S. offshore wind projects to continue construction after the Trump administration missed a final appeal deadline. (Vineyard Gazette)

Eos Energy and TURBINE-X Target AI Data Centers with On-Site Storage

Eos Energy Enterprises and TURBINE-X Energy announced a joint development agreement Tuesday to deploy up to 2 gigawatt-hours of Eos zinc battery storage paired with on-site gas turbines at AI data centers, with first installations planned for 2027. Zinc batteries use more abundant and less expensive materials than lithium-ion cells and are designed for longer discharge cycles, making them suited to the sustained, high-draw loads that AI workloads generate. The pitch is speed: the partnership claims it can deliver hyperscale power capacity in months rather than the years required for traditional grid interconnections. Eos shares rose 12% on the announcement. (Globe Newswire)

Fermi CEO and CFO Exit as Project Matador Loses Momentum

Fermi Inc. lost both its co-founder CEO Toby Neugebauer and CFO Miles Everson in the span of two days this week, sending shares down 22%. Fermi is developing Project Matador, a planned gigawatt-scale AI data center campus in Texas that was positioned as one of the most ambitious buildouts in the country. The departures follow months of friction: a tenant pulled out in December, investors filed a class-action lawsuit, and executives faced pointed questions on the March 30 earnings call about the company’s inability to name a single signed anchor tenant publicly. Fermi announced a pivot to a “Fermi 2.0” strategy, creating an Office of the CEO led by two co-presidents and launching a search for a permanent chief executive. The company also disclosed it no longer expects to hit its original target of 1.1 gigawatts of capacity by end of 2026. (Axios)

Gas Turbine Shortage Is Slowing the AI Power Buildout

The rush to build natural gas power plants for AI data centers has run into a hard physical constraint: there are only three major gas turbine manufacturers in the world, none of which invested to scale during the previous decade of flat power demand. GE Vernova ended 2025 with an 80 GW backlog stretching into 2029. Siemens Energy is carrying a €136 billion order backlog, the largest in its history. Wood Mackenzie estimates turbine prices will rise 195% by end of 2026 compared to 2019 levels. Even if all three manufacturers deliver on their expansion plans, analysts estimate total output will rise only 20% to 25%, nowhere near enough to absorb the current wave of demand. The bottleneck is not just the grid. (Bloomberg)

DOE Directs FERC to Fast-Track Data Center Grid Connections

Energy Secretary Chris Wright directed the Federal Energy Regulatory Commission this month to initiate a fast-track rulemaking that would allow data centers and other large power users to file joint interconnection requests alongside new generation sources. FERC, the federal regulator that oversees interstate electricity transmission, has a deadline of April 30, 2026 to respond to the directive. The proposed rule would allow large load customers to request co-located generation and grid connections simultaneously, cutting study times and reducing the cost of required network upgrades. It would also require those customers to fund the full cost of the grid infrastructure they need, a provision aimed at ensuring other ratepayers are not left with the bill. (Gibson Dunn)

U.S. Utilities Announce $1.4 Trillion in Capital Spending Through 2030

A new analysis of 51 U.S. investor-owned utilities, released this week, found they plan to spend a combined $1.4 trillion on infrastructure through 2030, a 27% jump from last year’s $1.1 trillion projection and roughly double the $700 billion invested across the entire previous decade. Duke Energy has committed $102.2 billion and Southern Company pledged $81.2 billion. The spending covers new power plants, transmission line upgrades, distribution modernization, and grid hardening. The surge reflects utilities trying to keep pace with data center load requests that are arriving faster than steel can be put in the ground. For energy investors, this is the clearest signal yet of where capital is flowing at scale. (Tech Insider)

Have fun this week

Will

Sources

AI Power News 4/13/26

Will McKnight — Mon, 13 Apr 2026 17:57:55 GMT

Meta Funds 10 Gas Plants for Its Louisiana AI Campus

Meta has agreed to finance 10 natural gas power plants totaling 7.5 gigawatts of generation capacity to support its $27 billion Hyperion data center campus in Richland Parish, Louisiana. That figure is more than a 30% increase to Louisiana’s entire current grid capacity, and it does not count the additional 2.5 GW of solar and battery storage Meta also agreed to fund. The deal, struck with utility Entergy Louisiana, also includes roughly 240 miles of new 500-kilovolt transmission lines (the high-voltage highways that carry power long distances across the grid). The original plan, approved just last year, called for three gas plants. Meta tripled it. (Bloomberg)

Meta built a regional power system and handed the operating license to Entergy. At some point the distinction between tech company and energy developer stops meaning anything, and Hyperion is past that point.

Watch whether the Louisiana Public Service Commission approves the gas units on schedule, and whether other states start asking why their utilities do not have the same arrangement.

Microsoft and Nvidia Launch AI for Nuclear Initiative

At CERAWeek in Houston on March 24, Microsoft and Nvidia announced a joint initiative to use AI tools to speed up nuclear power plant construction. The collaboration targets three specific bottlenecks: permitting, engineering design, and plant operations. AI tools will flag documentation errors, unify data across a plant’s full lifecycle, and support digital twins (virtual replicas that let engineers run tests before touching physical hardware). Early results are meaningful: nuclear startup Aalo Atomics cut its permitting timeline by 92% using Microsoft’s AI tools and estimates it saves $80 million per year as a result. (Axios)

Permitting delays are one of the biggest reasons nuclear plants take 10 to 20 years to build in the United States. If AI can compress that meaningfully, it changes the economics of every advanced reactor project in the queue.

Meta Signs 6.6 GW of Nuclear Deals to Power Its AI Supercluster

Meta signed agreements with Vistra, TerraPower, and Oklo to add 6.6 gigawatts of nuclear capacity by 2035, enough to power the Prometheus AI supercluster the company is building. The deals include expanding existing nuclear plant operations, funding advanced reactor development, and backing Oklo, a small modular reactor startup (SMR means a compact, factory-built nuclear reactor designed to be deployed faster and cheaper than a traditional plant). The combined scale makes Meta one of the largest corporate nuclear buyers in US history. (CNBC)

Read alongside the Louisiana gas story, Meta’s energy strategy comes into focus: gas plants to cover the next few years, nuclear contracts locked in for 2035. The company is moving faster on both fronts than most utilities move on either.

TotalEnergies Exits US Offshore Wind, Redirects $1B to Oil and Gas

The Department of the Interior announced on March 23 that TotalEnergies, the French energy major, agreed to surrender its US offshore wind leases. In exchange, the company committed to invest $1 billion, roughly the stated value of those leases, in oil, natural gas, and LNG production inside the United States. (NPR)

Offshore wind, which generates electricity from turbines built in the ocean, has faced a sustained federal effort to slow its development since early 2025. The TotalEnergies agreement is the most significant exit yet. Meanwhile, federal judges this week refused to stop construction on five other wind farms, including Dominion Energy’s Coastal Virginia Offshore Wind project, which began delivering power to the grid on Monday. The two stories running in parallel capture where offshore wind actually stands: federally hostile at the policy level, but too far along at the project level to unwind entirely.

DOE Uses Emergency Powers to Freeze Coal Plant Retirements

The Department of Energy has now issued at least 16 emergency orders since May 2025 directing utilities to keep coal plants running past their planned closure dates, using a rarely invoked authority called Section 202(c) of the Federal Power Act. The most recent orders cover more than 2 GW of coal capacity across the Midwest, Mountain West, and Pacific Northwest. (POWER Magazine)

Earthjustice filed a legal challenge to one Indiana order this month, calling it a federal override of decisions utilities made on their own timelines for sound business reasons. The DOE’s stated justification is grid reliability. The EIA (the federal agency that tracks energy data) now expects retirement delays to continue throughout 2026. For grid planners and utility engineers, the practical problem is real: generation is being kept online without a long-term plan for what replaces it. (EIA)

Xcel Energy Proposes Large-Load Tariff for Data Centers

Xcel Energy filed a proposal with Colorado regulators in early April to create a separate rate tier for customers consuming more than 50 megawatts of power at peak, a threshold that targets data centers specifically. The structure requires a $120,000 upfront deposit plus full payment for load and transmission studies before a project is approved. The goal is to stop residential customers from subsidizing the grid upgrades that large industrial users require. (Colorado Sun)

77 large-load tariffs are now pending or in place across 36 states, up from just 14 approved between 2018 and 2024. State regulators are moving faster than federal policy on this. For utility executives and rate case teams, designing a large-load structure that survives a commission challenge is the immediate problem. The whether is settled.

Crusoe Signs Two Energy Storage Deals for AI Data Center Buildout

AI data center developer Crusoe announced back-to-back storage partnerships this month. The first expands a battery microgrid deployment with Redwood Materials from 4 to 24 Crusoe Spark modular data centers. The second is a deal with Form Energy for up to 12 GWh of multi-day iron-air battery storage, with deliveries beginning in 2027. Multi-day storage is a battery technology that can discharge continuously for 100 hours or more, unlike a standard lithium-ion battery which typically runs for 4 hours. (Data Center Dynamics)

Data centers need power that never stops. Multi-day storage is one of the few technologies that can cover an extended grid outage without a diesel generator. This deal is an early signal that hyperscale operators are starting to treat long-duration storage as infrastructure, not a backup.

Big Tech Accelerates Investment in Next-Generation Nuclear

Microsoft, Google, Amazon, and Meta are all now signing long-term power purchase agreements (PPAs) with nuclear developers before construction begins, giving those developers contracted revenue early enough to attract private financing. That changes the risk math that has historically made nuclear hard to fund privately. The IAEA said this month that nuclear is the only energy source that checks all five boxes AI data centers require: low-carbon, round-the-clock, high power density, grid-stable, and scalable at the speeds the industry needs. (Insurance Journal)

Coastal Virginia Offshore Wind Delivers First Power Despite Federal Headwinds

Dominion Energy confirmed Monday that its Coastal Virginia Offshore Wind project began delivering electricity to the grid, a significant milestone for the largest offshore wind project under active construction in the United States. The Trump administration issued a stop-work order on the project earlier this year. Federal judges declined to enforce it after Dominion showed the project was too far along to halt without serious safety and financial consequences. (NPR)

Dominion has ratepayers and bond markets committed to this project in ways that make reversal genuinely costly. The first power delivery does not end the political risk, but it does make the project harder to stop. Watch for the administration’s next move and whether Dominion faces permit challenges at the federal level as it scales up capacity.

Have fun this week,

Will

Sources

AI Power Weekly News Roundup 3/30

Will McKnight — Mon, 30 Mar 2026 19:37:23 GMT

SoftBank and DOE Announce 10 GW Data Center Campus on Former Ohio Uranium Site

The Department of Energy, SoftBank, and AEP Ohio announced a public-private partnership to build a 10-gigawatt data center campus at the former Portsmouth Gaseous Diffusion Plant in Pike County, Ohio, a Cold War-era uranium enrichment facility now owned by the federal government. SB Energy, SoftBank’s energy arm, will build 9.2 GW of new natural gas generation to power the campus, while SoftBank is investing $4.2 billion with AEP Ohio to build and upgrade transmission lines across southern Ohio.

At full buildout, 10 GW would be more than half the total operating capacity of every existing U.S. data center combined. The project is part of Japan’s $550 billion U.S. investment commitment made during tariff negotiations with the Trump administration.

The Portsmouth site is a genuinely clever move. The federal land is already cleared, the Cold War infrastructure footprint means transmission access exists, and the symbolic resonance of powering AI on a former nuclear weapons site is not lost on this administration. The natural gas-heavy power plan will draw fire from clean energy advocates, but the scale signals the federal government is treating AI infrastructure as a national priority on par with the original Manhattan Project facilities.

Watch for state regulators and environmental groups in Ohio challenging the gas generation buildout, and how this competes with existing PJM interconnection queue projects for equipment and contractors. (DOE)

Trump Administration Pays TotalEnergies $928 Million to Exit Offshore Wind

The Department of the Interior and TotalEnergies signed agreements March 23 to relinquish two offshore wind lease areas, including the Carolina Long Bay lease with roughly 1,300 MW of potential capacity off the North Carolina coast. The government will reimburse TotalEnergies $928 million in lease fees paid in 2022, dollar for dollar, on the condition TotalEnergies invest that amount in U.S. oil, gas, and LNG projects including Rio Grande LNG in Texas. TotalEnergies pledged not to pursue any new offshore wind projects in the United States. This sets a precedent every other offshore wind leaseholder is watching closely, and the loss of 1,300 MW off the Carolinas forces grid planners to rethink Southeast coastal power planning. (Utility Dive)

Seven Hyperscalers Sign White House Ratepayer Protection Pledge

Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI signed the White House’s Ratepayer Protection Pledge in early March, committing to fund all new power generation and grid infrastructure required by their data centers at no cost to residential electricity customers. Signatories must negotiate separate rate structures directly with utilities and pay those rates whether they consume the electricity or not, and must make backup generation available to grid operators during emergencies. This is the first formal, White House-brokered commitment from hyperscalers to bear the full cost of AI’s electricity buildout. It directly changes how utilities like AEP, Duke, and Dominion approach future interconnection negotiations. (White House)

X-energy and Talen Energy Sign SMR Agreement for PJM Market

X-energy and Talen Energy signed a Letter of Intent March 19 to evaluate deploying three or more four-unit XE-100 small modular reactor plants across Pennsylvania and the PJM Interconnection market, the grid serving 65 million people from Illinois to New Jersey. The XE-100 is an 80-megawatt high-temperature gas-cooled reactor, a design that uses helium instead of water as a coolant and can generate heat for industrial processes in addition to electricity. Talen already operates the Susquehanna nuclear station and has direct experience structuring hyperscale power deals. Talen’s stock rose 7.1% on the announcement. (World Nuclear News)

Bipartisan REWIRE Act Would Fast-Track Grid Capacity Upgrades

Senators Dave McCormick (R-PA) and Peter Welch (D-VT) introduced the REWIRE Act in early March, creating a categorical exclusion from full environmental review for grid capacity projects built within existing rights-of-way. Reconductoring, upgrading existing transmission towers with advanced high-capacity wires that can double or triple power flow without new construction, could cut U.S. grid infrastructure costs by $85 billion by 2035 according to industry estimates. The bill also directs FERC to increase financial returns for utilities that deploy advanced conductors, giving them a direct incentive to move faster. Transmission permitting is the single biggest bottleneck between new power generation and the load growth AI is driving. (Senator Welch)

FERC Approves 11 New Cybersecurity Standards for the Power Grid

FERC unanimously approved 11 updated Critical Infrastructure Protection standards March 19, enabling secure use of virtualization technologies across the high-voltage transmission system and tightening baseline cybersecurity requirements for all grid-connected systems. New mandates include updated password protocols for remote access and intrusion detection requirements for lower-impact grid assets that were previously lightly regulated. As AI data centers connect to the grid in ever-larger blocks, a cyberattack on a control system near a major data center cluster is a scenario utilities and operators now model explicitly. The updated standards are the regulatory foundation that lets utilities safely adopt the software-defined grid management tools now in wide deployment. (FERC)

Have fun this week.

- Will

Sources

Top AI Power Weekly News - week of 3/16/26

Will McKnight — Wed, 18 Mar 2026 16:17:21 GMT

Iran Strikes Three AWS Data Centers in UAE and Bahrain

On March 3, Iranian drones struck three Amazon Web Services data centers in the Middle East, hitting two facilities in the United Arab Emirates and one in Bahrain. The attacks caused structural damage, disrupted power delivery, and triggered fire suppression systems that added water damage on top. AWS, or Amazon Web Services, is one of the giant companies that runs cloud computing infrastructure for thousands of businesses worldwide. This was the first confirmed military strike on a hyperscale cloud provider. (Fortune)

Banking apps including ADCB and Emirates NBD went dark. Payments platforms Alaan and Hubpay failed. Ride-hailing service Careem reported outages across the Gulf. Iran’s state media explicitly framed the Bahrain facility as a legitimate military target for “supporting U.S. military and intelligence activities.” AWS told customers to migrate workloads to unaffected regions immediately. (CNBC)

The cloud industry built redundancy for software failures and power outages. It is not clear the same thinking has been applied to drone warfare. Every enterprise with workloads in geopolitically exposed regions now has a harder version of the risk question to answer, and cloud providers do not have a clean answer ready. (Bloomberg)

Watch for whether AWS announces plans to relocate or reinforce Middle East infrastructure, and whether other hyperscalers follow with formal geopolitical risk assessments of their own data center portfolios.

Revolution Wind and Vineyard Wind Reach the Grid in the Same Week

On March 16, two major U.S. offshore wind projects crossed the finish line on the same day. The 704 MW Revolution Wind farm, a joint venture between Ørsted and Skyborn Renewables, began delivering electricity to the New England grid. Connecticut’s energy regulators estimate it will reduce New England’s wholesale power costs by up to $500 million annually once fully operational. The 806 MW Vineyard Wind 1 off Nantucket, Massachusetts, completed installation of its final turbine, becoming the first large-scale offshore wind project ever fully constructed in the United States. (Utility Dive)

The milestones came the same day the U.S. Justice Department declined to appeal the federal injunction blocking the Trump administration’s stop-work order on offshore wind construction. Both projects reached completion despite sustained federal opposition. Together they will power roughly 750,000 homes. (Electrek)

For energy investors, the legal strategy held against an adversarial federal posture. The remaining U.S. offshore wind pipeline is watching closely to see whether that precedent sticks.

Trump Administration Weighs $1 Billion to Kill Offshore Wind Projects

Reports from mid-March indicate the Trump administration is considering offering offshore wind developers up to $1 billion in taxpayer funds to voluntarily abandon their projects. The proposal has no announced mechanism and came the same week Revolution Wind and Vineyard Wind reached the grid. (EDF)

The offer signals the administration may escalate from stop-work orders to financial inducements, a different kind of pressure with direct implications for contract enforceability. Any investor with offshore wind exposure now has to model a scenario where the federal government tries to buy its way out of the energy transition rather than just block it.

Seven Tech Giants Sign White House Ratepayer Protection Pledge

Seven major U.S. tech companies signed a White House commitment on March 4 to “build, bring, or buy” the power generation needed to run their AI data centers rather than drawing from the public grid. Residential electricity bills have risen 36% since 2020, averaging 17.44 cents per kilowatt-hour, and the administration framed the pledge as consumer protection. (The Hill)

The pledge has no enforcement mechanism. Grid rules are set state by state across 50 public utility commissions, and converting a White House announcement into binding regulatory requirements is a different problem entirely. The gap between the commitment and the policy infrastructure to enforce it is large. (Inside Climate News)

Data Center Developers Move to Finance Nuclear Directly

Meta’s deals with Vistra, TerraPower, and Oklo could unlock up to 6.6 GW of nuclear capacity by 2035, combining existing reactor restarts with next-generation designs. AI infrastructure companies are moving past signing nuclear power purchase agreements and toward financing plant construction outright. (CFACT)

The shift reflects the hard reality of nuclear timelines. Power purchase agreements signed today will not deliver electrons for a decade or more. Small modular reactors, compact designs that can be factory-built and deployed faster than conventional nuclear plants, are the main target of this direct financing push. Developers who want firm, always-on power for AI workloads are concluding that if they want it built, they need to help pay for it.

EIA Projects Record 24.3 GW of Battery Storage Additions in 2026

The U.S. Energy Information Administration projects 24.3 GW of new battery energy storage will come online in 2026, part of a record 86 GW of total new generating capacity expected this year. Battery storage, which captures electricity when supply is high and releases it when demand spikes, is now the second-largest category of new capacity behind solar. (EIA)

A 24 GW single-year addition would roughly double cumulative U.S. storage capacity. Utilities are deploying storage at scale to firm up intermittent renewables and absorb the load spikes driven by data center buildout, particularly in ERCOT (Texas) and CAISO (California). For grid reliability, storage at this scale is the piece that makes renewable-powered data centers viable at scale.

Sources

How are the Iran war and the closure of the Strait of Hormuz affecting the energy industry?

Will McKnight — Fri, 13 Mar 2026 17:35:01 GMT

When US and Israeli forces launched Operation Epic Fury on February 28, striking thousands of targets across Iran in the opening days, the strikes hit military sites, nuclear facilities, and Iranian energy infrastructure. Iran retaliated across the region. Twelve days into a war neither side has shown signs of ending quickly, the question for the global economy is what happens to the Strait of Hormuz, the 21-mile-wide channel between Iran and Oman through which roughly 20 million barrels of oil per day transit the global economy.

The answer, so far, is that the strait is effectively shut to most commercial traffic. Insurers, shipowners, and crews are unwilling to take the risk. Tanker transits have collapsed, and the downstream effects are cascading through oil markets, natural gas systems, European electricity grids, Asian economies, and hundreds of billions of dollars in Gulf AI investment simultaneously (Bloomberg).

What has the war hit, and what is the impact?

Both sides have struck energy infrastructure. The initial US and Israeli strikes targeted Iranian oil refineries and export terminals alongside military sites, hitting Iran’s own production capacity in the opening hours. Iran’s retaliatory strikes have spread across the region: drone attacks hit QatarEnergy’s LNG production facilities, forcing Qatar to declare force majeure on its gas exports. Force majeure is a legal term for “we cannot perform our contracts due to events outside our control,” and it means buyers around the world are suddenly without supply they were counting on. Qatar supplies 20 percent of global liquefied natural gas (LNG is natural gas that has been chilled to liquid form for transport by ship), and this shutdown happened with essentially no warning (Al Jazeera).

Strikes also hit Amazon Web Services data centers in the UAE and Bahrain, damaging two facilities and hitting a third with debris. This is believed to be the first known deliberate military strike on commercial hyperscale data centers (Yahoo News).

The pattern across all of this is the same: modern conflict does not stay on the battlefield. The same war taking lives in the region is now degrading energy supply and targeting the physical infrastructure of the global AI industry. That is a new reality, and it matters for how you think about what comes next.

Which energy sectors are bleeding fastest?

Oil markets took the first and hardest hit. Brent crude, the global benchmark, has surged more than 57 percent since the war began, jumping from roughly $70 to over $110 per barrel driven almost entirely by supply fear, not actual shortfall. Rystad Energy puts a four-month Hormuz disruption scenario at $135 per barrel (CNBC). The IEA announced a coordinated release of 400 million barrels from member nations’ emergency reserves, which put a ceiling on the panic for now. US gasoline prices have risen roughly 60 cents per gallon since the war began, reaching a national average of around $3.58 (NPR).

LNG markets are arguably worse. Oil has strategic reserves and diversified shipping lanes. LNG does not have the same slack. European LNG benchmark prices (the TTF hub, Europe’s main gas trading point) climbed from €31.9 to €54.3 per megawatt-hour in less than a week. Asian LNG futures jumped 51 percent. European prices jumped 77 percent (CNBC). These are energy-price moves that normally take months or years.

Electricity grids feel it through gas. In most countries, gas-fired power plants are what the grid calls on last, when demand is highest. The final unit of power on the grid in any given hour sets the price for everyone. When gas prices spike 77 percent, electricity prices follow. In Spain, wholesale electricity prices jumped nearly 700 percent in a single week, from roughly €18 to €137 per megawatt-hour (The Olive Press).

Who is most exposed, and who has cushion?

Asia is the most exposed region by far. South Korea maintains a mandatory nine-day minimum LNG stockpile requirement and has announced emergency fuel price caps, the first since the late 1990s, alongside a 100 trillion won ($68 billion) stabilization fund. The country is highly exposed: any sustained disruption to Gulf LNG supply pushes it toward that threshold fast. Government offices across Southeast Asia are cutting work weeks, limiting travel, and imposing alternating driving days (Al Jazeera). Japan and South Korea are scrambling for spot LNG from Australia, Canada, and the US, but alternative cargoes take weeks to arrange.

India is the most vulnerable major economy. The country imports 90 percent of its crude oil, holds roughly 45 days of strategic reserves, and has 9 million citizens working in Gulf countries affected by the conflict. The combination of oil import costs, remittance disruption, and inflation risk is severe (New Lines Institute).

China has the most buffer. Beijing holds an estimated 1.2 billion barrels of strategic crude reserves, roughly 108 days of imports at current refinery levels. More importantly, oil and gas make up a relatively small share of China’s power mix compared with most other Asian economies. China’s aggressive solar and wind buildout, often criticized as overbuilt, turns out to be strategic insulation (CNBC).

Europe is in an uncomfortable middle ground. The EU has diversified its gas supply significantly since Russia invaded Ukraine in 2022, and storage levels are healthier. But the bloc is re-exposed through Qatar, which became a key LNG supplier precisely because it replaced Russian pipeline gas. EU officials are quick to say 2026 is not 2022. What they mean is supply is more diversified. What they cannot say is that pricing will not hurt (Euronews).

What does this mean for the AI industry’s energy buildout?

The Gulf was in the middle of a historic AI construction boom when the war started. Saudi Arabia, the UAE, and Qatar collectively committed hundreds of billions of dollars to data centers, chips, and AI infrastructure over the coming years. That buildout now has a targeting problem: the AWS strikes in the UAE demonstrated that data centers are viable military targets, something no hyperscaler had factored into its Gulf infrastructure risk calculus (The Information).

Existing projects will likely continue. Walking away from sunk capital is expensive, and the hyperscalers have long-term government relationships in the region. But the next wave of capacity decisions, the facilities that were still in site-selection and negotiation phases, may quietly shift to locations in Europe, Southeast Asia, or North America. The war has introduced a physical security variable that no lease rate or power deal can easily offset (CNBC).

For the US AI buildout specifically, the pain is indirect but real. Electricity accounts for roughly half of a data center’s operating costs. When energy prices spike globally, the economics of expansion tighten. And the conflict is straining semiconductor supply chains: the Middle East supplies critical inputs including helium and bromine used in chip manufacturing, and logistics disruptions are adding supply-side cost pressure to data center operators at exactly the wrong time (CNBC).

This conflict is twelve days old. The Strait of Hormuz, which has never been fully closed in modern history, is effectively closed now. How long it stays that way will determine whether this is a price shock or a structural reshaping of global energy systems.

Stay safe and have a nice weekend,

Will

Sources

AI Power Update March 2nd-9th

Will McKnight — Mon, 09 Mar 2026 14:28:53 GMT

Tech Giants Sign White House Ratepayer Protection Pledge

Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI gathered at the White House on March 4 to sign what the Trump administration called a “Ratepayer Protection Pledge.” Each company committed to build, buy, or bring their own power supply for new AI data centers, rather than drawing from the shared grid and passing those costs along to homes and businesses. The political backdrop matters here: data centers have already added $7.7 billion in transmission costs for ordinary Americans over the past two years, and electricity prices are forecast to rise 6% through 2026 as AI demand outpaces supply.

The problem is the pledge is voluntary, with no enforcement mechanism and no clarity on what actually counts as “building your own power.” No Congressional role was specified. No state regulator role was specified. Trump’s statement at the signing amounted to: “They can build their own power plants as part of their factory, so that no one’s prices will go up.” One energy attorney described the implementation as “complicated...much more complicated than Trump made it sound.”

This is a PR commitment that puts real public pressure on these companies in a way that will be hard to walk back when rate cases come up at state utility commissions. That’s not nothing. But the enforcement gap is wide enough to drive a data center through, and every one of the seven signatories has ongoing grid interconnection requests that this pledge does nothing to change.

Watch: Whether state utility commissions treat this pledge as binding evidence when tech companies next apply to connect new facilities to the grid.

(Inside Climate News)

California Considers Lifting Its 50-Year Nuclear Moratorium

California lawmakers introduced legislation last month that would allow the state to approve advanced nuclear reactor technologies already licensed by the federal government since 2005. California’s moratorium on new nuclear construction has been in place since 1976. The immediate driver is AI: data centers requesting grid connections in the state have already triggered questions about costly grid upgrades, and California’s goal of 90% clean electricity by 2035 leaves very little room for new gas plants. Separately, a push to regulate data center energy use was reduced to a study requirement after Big Tech lobbied against stronger rules.

(Insurance Journal)

INL and NVIDIA Launch Prometheus Project to Halve Nuclear Deployment Timelines

The Idaho National Laboratory and NVIDIA announced a partnership called Prometheus in February, the first project under the Department of Energy’s Genesis Mission, a national initiative to accelerate discovery science and energy innovation. The goal is to use generative AI and digital twins to design, license, manufacture, and operate nuclear reactors. Digital twins are virtual replicas of physical systems that let engineers test scenarios in software before committing to expensive real-world builds. The targets are ambitious: at least 2x faster deployment schedules and more than 50% reduction in operational costs compared to today’s nuclear projects.

(POWER Magazine)

Five States Challenge Midwest Grid’s $22B Transmission Portfolio

Utility regulators from Arkansas, Louisiana, Mississippi, Montana, and North Dakota filed a complaint with FERC, the Federal Energy Regulatory Commission, asking it to unwind a $22 billion set of high-voltage transmission projects approved by MISO, the grid operator covering the Midwest and parts of the South. MISO calculated the portfolio’s benefits at well above its cost. The states’ own consultant put the actual benefits at $4.3 to $7.2 billion, a fraction of MISO’s estimate. FERC has not yet ruled, and the complaint is facing pushback from MISO itself, six other state utility commissions, Ameren, Xcel Energy, and the Data Center Coalition.

(Utility Dive)

FERC to Finalize Data Center Grid Connection Rules by April 30

FERC has set an April 30 deadline to finalize rules governing how large power loads connect to the transmission grid. The rules apply to any facility pulling more than 20 megawatts, which covers virtually every serious data center. The decisions will determine how much security deposits new data centers must put up, how quickly they can advance through the interconnection queue, and how grid costs are allocated when a large load triggers expensive upgrades. Data centers are now the dominant driver of new interconnection requests across every major U.S. grid region.

(CSIS)

AI Transmission Buildout Draws Organized Landowner Pushback

As utilities race to build the high-voltage lines needed to connect new AI data centers, landowners and local communities are pushing back across the country. In the Midwest, the $22 billion MISO transmission package is caught in a monthslong political fight. In other regions, opposition groups are filing complaints against dozens of proposed routes, citing damage to farmland, waterways, and property values. Transmission spending is projected to nearly double to $50 billion per year by 2028, and without a faster permitting process, the buildout that AI infrastructure depends on may be slower than the industry expects.

(AP via U.S. News)

Go forth and have fun this week,

Will

Sources

Using AI Agents in the Power Industry: A Beginner’s Guide

Will McKnight — Thu, 05 Mar 2026 19:08:48 GMT

Most people think building software requires knowing how to code. That was true a year ago. It is not true anymore.

Claude Code is Anthropic’s AI system that writes, runs, and debugs code directly on your computer. You describe what you want in plain English. Claude figures out the code. You get a working tool to do almost any manual task that you’re doing in your day-to-day work.

I have been using it to build agents for my own business, and the results have surprised me in ways I did not expect. Some things worked immediately. Some things took five iterations. This is an honest account of both.

What actually is an agent, and why is it different from chatting with ChatGPT?

A regular AI conversation is like asking a smart friend for advice. You ask a question. They answer. Done.

An agent is different. An agent takes a goal, breaks it down into steps, uses tools to complete those steps, and keeps going until the job is finished. It does not just answer questions. It does things.

Think of it this way: asking Claude “who should I follow up with this week?” gets you advice. An agent actually searches your inbox, finds past contacts, groups them by topic, and hands you a prioritized list. That is the difference. Advice versus action.

Agents can browse the web, read and write files on your computer, pull data from external services, send emails, and chain all of those steps together without you touching anything after the first setup.

What can agents do for people in the energy industry?

The energy industry runs on data, documents, and repetitive processes. That is exactly where agents shine.

A load forecasting agent could pull weather data, historical usage patterns, and grid demand numbers every morning and produce a plain-English summary for your operations team. No spreadsheet wrangling. No manual data pulls.

A regulatory filing monitor is one of the most valuable builds I can imagine for energy companies. FERC (Federal Energy Regulatory Commission) governs wholesale electricity markets, natural gas pipelines, and hydropower licensing. NERC (North American Electric Reliability Corporation) issues reliability standards, violation notices, and enforcement actions. State PUCs handle rate cases and tariff filings. ISO and RTO filings from grid operators like PJM, MISO, CAISO, and ERCOT contain market rule changes that affect dispatch and pricing. EPA rulemakings affect emissions reporting and permitting for new generation. An agent that monitors all five simultaneously, filters by keywords relevant to your business, and surfaces only what matters is worth a significant amount of someone’s time each week.

An RFP comparison agent could scan incoming vendor proposals, extract the key specs and pricing from each document, and produce a comparison table automatically. What used to take a junior analyst two days takes the agent twenty minutes.

None of these require a full-time developer. They require knowing what problem you want to solve, and maybe an hour or two of back-and-forth with an LLM.

What have I built, and what actually worked?

I want to share my actual experience here, because most “here’s what AI can do” posts skip the failures.

SEO and ranking monitor. Watches my website’s search ranking and surfaces content ideas based on keywords I am not ranking for. Drafts content outlines automatically. Took two sessions to get right. Saves several hours every week now.
Pitch deck and website creator. I describe the company, audience, and message. Claude generates the full deck structure, the slide copy, and a matching website mockup with layout and design. This one worked on the first try. UI/UX and design-focused tasks are where Claude is genuinely one-shot capable right now.
Email history scraper. Scraped my entire email history, found contacts I had not followed up with in months, and grouped them by keyword. Clusters like “grid storage” or “data center development” showed up immediately. Runs every Sunday and gets me a prioritized outreach list every Monday morning. Took five iterations to handle all the edge cases.

Complex agents that chain multiple steps together rarely work perfectly on the first build. The email agent worked, but it took four or five conversations with Claude to handle edge cases: duplicate contacts, unusual email formatting, contacts with no clear topic keyword. That is just the reality of software. Even experienced developers spend most of their time debugging, not writing new code. The difference now is that you do not need to understand the code to fix it. You describe what broke and Claude fixes it.

How do you actually build one from scratch?

Here is the exact process I use. It works for complete beginners.

Go to Claude, ChatGPT, or any LLM and describe the problem in plain English. I use Claude personally, but any capable LLM works for this. Do not think about code yet. Write out exactly what you want to happen. “I want something that looks at my inbox every Monday, finds emails from people I have not replied to in 90 days, and makes a list grouped by what we were talking about.” The more specific you are, the better the result.
Ask whether it can build it and what the best approach would be. Paste your description and ask: “Is this something you can help me build as an agent? What would be the best approach?” Claude will tell you what is realistic, what tools it would use, and whether there is a simpler way to get the same result.
Ask for extremely detailed step-by-step instructions. Tell Claude: “Give me every single step, including how to install anything I need, how to set up any accounts or API keys, and how to run the code. Assume I have never done this before.” Claude will write instructions detailed enough that you do not need to figure anything out yourself.
Follow the instructions and keep the conversation open. Things will break. When they do, copy the error message and paste it back to Claude. Say: “This happened when I ran step 4. What do I do?” Claude will diagnose and fix it. You do not need to understand why it broke.
Iterate one feature at a time. After the basic version works, ask Claude to add improvements one at a time. Add one thing, test it, then add the next. Adding five features at once is how you end up with something broken in five different ways at once.

That is the whole process.

When a general-purpose agent is not the right tool

Claude and ChatGPT are excellent at text, research, email, and document workflows. But some tasks in the power industry require models trained specifically on engineering data, domain conventions, or industrial signal patterns that generic LLMs were never built for.

Dirac generates detailed, animated work instructions automatically from CAD files for complex mechanical assemblies. What used to take twelve hours of manual authoring now takes ninety minutes. Anduril selected Dirac as its core manufacturing documentation platform in January 2026 and reported an 87.5% reduction in authoring time.

SparkCognition builds AI trained specifically on SCADA data and sensor patterns from industrial energy equipment: wind turbines, power generation assets, oil and gas facilities. Their predictive maintenance system identifies equipment failures a median of 37 days in advance with over 90% confidence and no false positives. In one deployment on a fleet of new turbines, it identified a manufacturing defect that would have caused catastrophic damage to a $100 million asset months before traditional monitoring would have caught it.

OpenDrawing generates bills of materials automatically from electrical single-line diagrams. Engineers upload a drawing and the system extracts every component and quantity without manual data entry. It is built specifically for the symbol libraries and tagging conventions used in electrical construction drawings, which a general LLM cannot interpret reliably.

AutoGrid, now part of Schneider Electric’s software portfolio, manages distributed energy resources in real time using AI trained specifically on grid signal patterns and DER (distributed energy resource) behavior data. It currently manages over 5,000 MW of distributed energy resources across more than 100 utility and industrial partners. A general-purpose agent can summarize a DER report. AutoGrid is making real-time dispatch decisions at grid scale.

When does it make sense to use a purpose-built platform instead of building your own agent?

When the task requires a model trained on specific domain data: engineering drawings, industrial sensor streams, power market signals, or equipment failure patterns that general LLMs have no reliable grounding in.
When your organization requires on-premises deployment. Cloud-based LLM APIs are not always acceptable in regulated environments, for NERC CIP (Critical Infrastructure Protection) compliance, or where data sovereignty is a hard requirement.
When you need enterprise-wide deployment with SSO, role-based access controls, audit logs, and compliance certifications. Building your own agent gets one person a useful tool. A purpose-built platform gets your whole organization a vetted, supported system.
When accuracy needs to be mission-critical rather than “good enough.” A regulatory filing monitor built on Claude is genuinely useful. A platform certified for NERC compliance tracking is a different category of reliability.
When you need deep integration with existing enterprise systems like SAP, OSIsoft PI historian, or SCADA platforms that require validated data pipelines and purpose-built connectors.

Building your own agents and using purpose-built platforms are not competing approaches. Most energy teams will end up doing both. Start with one problem you deal with every week. Ask Claude if it can help. See what happens.

Good luck this week.

Will

Sources

Claude Code overview

Anthropic AI-Driven BOM Creation from Single Line Diagrams

OpenDrawing

Dirac

Production Planning, Automated

Anduril Selects Dirac to Power AI-Driven Work Instructions | PR Newswire

SparkCognition Industrial AI

SparkCognition suggests improved methods to maximize turbine health

Wind Power Engineering AutoGrid

Distributed Energy Resource Management

Schneider Electric to acquire AutoGrid | PV Magazine USA

FERC: What We Do | Federal Energy Regulatory Commission

About NERC North American Electric Reliability Corporation

The AI Power Update Feb 23 - March 1, 2026

Will McKnight — Mon, 02 Mar 2026 14:57:44 GMT

Trump Announces “Ratepayer Protection Pledge” at State of the Union

At Tuesday’s State of the Union, President Trump unveiled the “Ratepayer Protection Pledge,” a commitment by major technology companies to fund their own electricity needs rather than passing costs onto utility customers. Energy Secretary Chris Wright confirmed that “all the brand-name hyperscalers” have signed on, covering companies including Google, Microsoft, and Amazon. Anthropic separately committed to covering 100% of electricity cost increases its data centers cause for residential customers. Trump told the audience no electricity prices would rise from AI data centers and that “in many cases, energy prices will go down.”

The pledge has no enforcement mechanism. Energy experts quickly called it “meaningless” without binding legal force. What it does signal clearly: the White House understands that AI’s electricity costs are becoming a political liability, and it wants Big Tech on record as the responsible party.

Watch whether Trump follows with an executive order that gives these commitments actual legal weight, or whether legislators at every level decide voluntary pledges are not enough. (Axios)

Amazon Commits $12 Billion to Louisiana Data Centers

Amazon announced a $12 billion investment to build data center campuses across Caddo and Bossier Parishes in northwest Louisiana, creating 540 direct jobs and roughly 1,700 additional positions in the region. The company pledged to cover 100% of costs for new energy infrastructure and grid upgrades needed to serve the facilities, working with utility Southwestern Electric Power Company (SWEPCO), with no costs passed to ratepayers. Amazon also committed $400 million to local water infrastructure to avoid straining municipal supplies. (GeekWire)

Google and Xcel Energy Announce 1.9 GW Clean Energy Deal with World’s Largest Battery

Google and Xcel Energy announced a 1.9 gigawatt clean energy package for a new data center in Pine Island, Minnesota, headlined by a 300 MW, 30 gigawatt-hour iron-air battery from Form Energy. Iron-air batteries store electricity by oxidizing iron and can hold power for up to 100 hours, covering multi-day weather events at a fraction of lithium-ion’s manufacturing cost. The full package adds 1,400 MW of wind and 200 MW of solar, and Google covers the total cost under Minnesota’s large-load customer framework. This is the arrangement regulators and advocates are pushing for everywhere: a tech company funding a massive clean energy buildout without shifting costs to residential customers. (TechCrunch)

Hawley and Blumenthal Introduce Bipartisan GRID Act in Senate

Senators Josh Hawley (R-MO) and Richard Blumenthal (D-CT) introduced the Guaranteeing Rate Insulation from Data Centers Act, the first federal bipartisan bill requiring data centers to fund their own electricity rather than raise residential utility bills. New data centers over 20 megawatts, enough to power roughly 15,000 homes, would be required to source all power outside the public grid, with existing facilities getting a 10-year transition window. The bill also mandates public disclosure of current and projected electricity demand. A populist Republican and a liberal Democrat landed on the same basic answer. (NBC News)

NRC Accepts Holtec SMR Construction Permit Application

The Nuclear Regulatory Commission accepted Holtec International’s application to build two SMR-300 reactors at the Palisades Energy Center in Michigan, each generating approximately 340 MW. This is the first major licensing application for a dual-unit small modular reactor (SMR) plant under the Part 50 framework, with an 18-month NRC technical review ahead. Holtec is simultaneously working to restart the existing 800 MW Palisades reactor, decommissioned in 2022, which would make it the first U.S. nuclear plant brought back from decommissioning status. (Federal Register)

Offshore Wind Projects Resume After Court Victories

Five major offshore wind projects resumed construction after federal courts issued preliminary injunctions blocking the Trump administration’s stop-work orders, including Vineyard Wind 1, Sunrise Wind, Empire Wind, and Coastal Virginia Offshore Wind. Dominion Energy reported $228 million in costs from delays and idle equipment during the BOEM suspension, plus $580 million in losses tied to Trump tariffs on steel and materials for the 2.6 GW Coastal Virginia project. Vineyard Wind 1 is now 95% complete with 60 of 62 turbines installed, targeting commercial operations by December 2026. The Trump administration has filed appeals, and broader legal challenges to the executive order suspending wind energy approvals remain pending. (Clean Technica)

xAI Expands Colossus Cluster to 2 GW

Elon Musk’s xAI confirmed it purchased a third building to expand its Colossus supercomputer complex to approximately 2 GW of compute power near Southaven, Mississippi, representing more than $20 billion in investment in the state. The expansion brings xAI’s total GPU count to approximately 555,000 NVIDIA chips across three facilities, making it the largest AI training cluster in the world by a significant margin. Colossus runs primarily on on-site natural gas turbines, and environmental groups have filed lawsuits claiming xAI operated 35 gas turbines at the original Memphis site despite permits for only 15. (Data Center Dynamics)

FERC Orders PJM to Create New Rules for Data Center Co-Location

FERC ordered PJM Interconnection, the nation’s largest grid operator, to establish new transmission service options for data centers co-located with generating facilities. FERC found that PJM’s existing behind-the-meter rules lack clarity on rates and conditions for these arrangements, and the new services will let data centers limit grid withdrawals while paying only for infrastructure they actually use. This ruling signals what FERC may require across other regions, with operators in MISO, SPP, and other RTOs expected to file similar proposals in 2026. (FERC)

Good week to follow the money and the meter. Make it a great March.

Will

Sources

How is AI advancing nuclear R&D today?

Will McKnight — Thu, 22 Jan 2026 19:47:46 GMT

If you follow nuclear energy through headlines, it feels like we’re living in the future already. SMRs seem like they’re right around the corner, fusion is always announcing another breakthrough, and big tech keeps signing new nuclear power deals for their data centers.

But if you step back and look at the grid, and the reality is that the U.S. has barely added net-new nuclear capacity in decades.

This week I sat down with Kevin Kong of Everstar to understand how companies like his are using AI to quietly change the slow, painful parts of nuclear development to help SMRs, advanced fission, and eventually fusion get deployed to our grid faster.

What are the main ways AI can help?

The biggest misconception about AI in nuclear is that it’s here to “design reactors.”

That’s not where the leverage is.

Nuclear already has solid physics, proven designs, and decades of operational experience. What it doesn’t have is speed. And most of that slowness has nothing to do with engineering itself.

Permitting is the obvious one. Nuclear projects live and die by regulatory approvals, and the work required to get there is enormous. Teams spend months digging through prior filings, regulatory guidance, environmental reviews, and historical precedent just to justify why a specific design or site should be allowed. AI doesn’t replace regulators or human judgment, but it can dramatically reduce the time spent searching, synthesizing, and drafting the first pass of compliant documentation.

“Nothing happens without approvals. You can have sites ready, designs ready, money ready, but it all comes down to getting green lights.” -Kevin Kong

Simulation and engineering workflows are another major area. Nuclear engineers already rely on physics-based models, but running those models and iterating on designs is slow and expensive. AI is increasingly being used as a way to accelerate iteration, explore more design scenarios, and reduce the overhead around setting up and validating each run.

Siting is another challenge where AI is useful. Choosing where to put a reactor requires decades of weather data, seismic risk, soil behavior, water access, environmental protections, local and tribal regulations, and emergency planning. That’s a massive research and synthesis problem. AI is well suited to pull together fragmented information and surface what actually matters for a given site.

That brings us to how AI can also help the industry deal with legacy information. Nuclear operators are constantly bouncing between outdated documents, poorly searchable government portals, and internal systems that were never designed to talk to each other. AI helps translate that mess into something usable, searchable, and consistent, which sounds boring until you realize how much time it saves.

“People are jumping between documents from a file cabinet in room C from the 1970s, government portals with terrible search, Google just to find the right document, and then back into Word redrafting things that have already been rewritten dozens of times.” - Kevin Kong

In short, AI is helping nuclear today by removing friction from everything around the reactor, while simulation and physics-based AI are also helping engineers get closer to nuclear fusion. Firms like NVIDIA working alongside university research labs on the problem (NVIDIA).

How is it being used today?

One of the clearest early use cases is in permitting and licensing, the part of nuclear development that most people outside the industry never think about but that eats years off every project. Nuclear labs and tech giants are already experimenting with this. In late 2025, Idaho National Laboratory announced a collaboration with Microsoft to lean on Azure cloud and AI tools specifically to help streamline the licensing process for new nuclear technologies (INL), speeding up regulatory research and documentation that traditionally takes huge amounts of time.

Permitting isn’t the only place AI is being embedded. On the operational side, utilities and vendors are beginning to use AI-driven scheduling and workflow tools to tighten up the mess of tasks that go into plant upgrades and construction plans. A recent report on collaborations between Aalo Atomics and Microsoft highlighted how generative AI and “AI agents” are being used to organize and optimize workflows around permitting and project planning (Nuclear Newswire) for a demonstration reactor, cutting cost and time by identifying the most impactful tasks to tackle first.

AI is also creeping into simulation and engineering, although it’s less flashy than the fusion headlines you see but arguably just as meaningful. Researchers and labs are feeding advanced algorithms with physics-based models to reduce iteration time and explore design spaces more quickly. These hybrid approaches, where AI augments deterministic models, are part of broader industry efforts to advance reactor design and safety analysis that organizations like the International Atomic Energy Agency have been talking about for years (NUCNET).

Even outside direct plant design or permitting, AI is being adopted in adjacent areas that matter down the road. Legacy reactors and academic facilities are starting to experiment with AI for remote monitoring, cybersecurity, and predictive maintenance, which future commercial designs will depend on for safety and reliability. These efforts don’t make big headlines yet, but they are essential groundwork for any grid-scale deployment.

Where are the gaps?

Despite the progress, there are still major problems AI hasn’t solved yet.

Construction and manufacturing remain huge challenges. The cost gap between building reactors in the U.S. versus countries like China is massive.

“Gigawatt-class reactors in the U.S. cost around ten billion dollars. In China, it’s closer to two and a half.” - Kevin Kong

Regulatory acceptance is another open question. AI can assist with analysis and drafting, but regulators still need to trust the outputs, understand how conclusions were reached, and feel confident that safety isn’t being compromised.

Fusion has its own suite of unsolved problems. AI can help with materials discovery and simulation, but the industry is still far from physics break-even, let alone commercial viability. Many fusion announcements are more about fundraising than deployment, and AI doesn’t change the underlying physics constraints. It can accelerate progress, but it can’t skip steps.

“There’s a big difference between physics break even and commercial break even, and we’re nowhere near commercial today.” - Kevin Kong

There’s also the human factor. Nuclear is a deeply conservative industry for good reason. Introducing new tools, even helpful ones, requires training, cultural buy-in, and clear accountability. AI that isn’t trusted or understood won’t get used, no matter how powerful it is.

Why does this matter?

The reason that the industry is so excited about nuclear is that it can potentially help our world reach a state of energy abundance.

“As energy costs come down, you start unlocking things that were previously too expensive like clean hydrogen, new transportation systems, more compute.” - Kevin Kong

The idea of energy abundance should be exciting to everyone and the main takeaway for me after this conversation was that AI will help make the nuclear industry move at a pace closer to the rest of the modern economy and thus help us reach abundance faster.

Sources

Holiday AI Power News Catchup

Will McKnight — Tue, 06 Jan 2026 15:42:34 GMT

Hope everybody had a great holiday season. We missed some big news in the AI/power industries including developer acquisitions, data center announcements, more $ towards nuclear, and more setbacks for wind. Let’s catch up on some of the top stories

Google bought Intersect, for $4.75b. Intersect is a solar + storage developer projecting to reach almost 11 GW of capacity by 2028. By bringing Intersect in-house, Google hopes to focus on building co-located “energy parks” with power generation and battery storage alongside data centers, easing traditional grid interconnection bottlenecks that can take years to resolve. Story

Elon Musk’s AI company, xAI, has purchased a third building in the Memphis, Tennessee area to expand its Colossus supercomputer complex, aiming to increase computing capacity to nearly 2 GW of power draw and house over 1 million GPUs. The expanded facilities will be near a natural gas power installation that the company is also developing to supply energy to the cluster. Story.

Oracle and Microsoft win Approval for 1.4GW Michigan data center, with power to be supplied by DTE Energy. Utilities are still cashing in on the AI boom, even as some big tech companies opt to install power generation behind the meter. Story.

The U.S. Department of the Interior pauses 7.1 GW of offshore wind projects under construction, citing national security concerns. Developers and State officials are criticizing the move as one that may cause energy insecurity for communities that were relying on these projects. Story.

The House passes the SPEED Act, a permitting reform bill to help projects get through the permitting process and start construction faster. Offshore wind takes another blow as an amendment was included to exclude those projects from the bill. Story.

The U.S. Department of Energy (DOE) announced that three nuclear fuel makers will receive $900m each to enhance domestic uranium enrichment capabilities and reduce reliance on Russian suppliers. The initiative targets expanded production of both low-enriched and high-assay low-enriched uranium for power and advanced reactors. Story.

LLMs can't solve the world's hardest problems. These scientists build new AI models that can.

Will McKnight — Tue, 09 Dec 2025 16:59:24 GMT

AI adoption in the energy sector is accelerating quickly, but large language models (LLMs) like ChatGPT, Google Gemini, and Claude can’t do everything that the industry needs them to do. While LLMs dominate headlines, the hardest engineering problems in the power industry cannot be solved by these generic models trained on readily available internet data. They require a different kind of model; one built on real world data and the laws of physics.

Last week I sat down with Greg Fallon of Geminus AI to talk about how companies like his are training their own AI models to solve scientific problems that LLMs can’t, like grid simulation and optimization.

This week’s newsletter will dive into our conversation and break down why our grid needs AI, where LLMs fall short, and how scientists are building new models to help us tackle some of the biggest problems the energy industry is facing today.

Why does our grid need AI?

Our electric grid is changing fast, driven by issues like:

A rapid surge in electricity demand, driven heavily by AI data centers.
A slow interconnection process that causes multi-year wait times to install new power generation resources.
A wave of inverter-based resources, adding dynamic, hard-to-simulate behavior to the grid.
Aging substations, transformers, and circuits, many of which were never designed for modern loads.

Why can’t LLMs be used for these complex energy problems?

LLMs are powerful, but they are fundamentally the wrong tool for complex engineering problems like physical system modeling. They only learn from text and images that are readily available on the internet, not real-world sensor data or the laws of physics.

There are a few primary limitations that make LLMs unsuitable for engineering tasks:

1. They don’t understand physical cause and effect

LLMs generate text that sounds plausible, not predictions that obey thermodynamics, circuit behavior, or power-flow equations.

“Getting LLMs to do any sort of reliable prediction is really hard, if not impossible”
Greg Fallon, CEO of Geminus AI

2. LLMs don’t have access to clean engineering datasets

Industrial datasets are proprietary and generally not posted on the internet where an LLM would be able to train on them. Even if they got access, the datasets are usually small, noisy, and incomplete, meaning an LLM may not be able to use that data.

LLMs thrive on massive, clean datasets that the energy industry typically doesn’t have.

“There’s a huge hesitancy to share data in the energy industry. There are even laws in a lot of countries that energy data is so precious that it can’t leave the borders”
Greg Fallon, CEO of Geminus AI

3. Physical assets behave differently in the real world

Two identical transformers operating in different environments will not age the same way. Two identical pumps won’t respond the same under stress. These tiny variations become major failure points and LLMs don’t have the ability to generalize these differences.

“Everything is very particular, that 2% difference can be massively impactful.”
Greg Fallon, CEO of Geminus AI

Scientific AI is the method that can actually solve these problems

Unlike LLMs, scientific AI benefits from several built-in moats:

Enterprise data stays private, and widespread sharing is prevented.
Model behavior must reflect asset-specific quirks, which generic models can’t capture.
Physics models remain core to engineering, meaning simulators will always be in the loop.
Accuracy and reliability matter. Hallucinations are unacceptable for grid decisions.

This method is a different class of AI entirely, designed to augment engineering judgment rather than simply analyze and generate text and images.

Instead of relying on language patterns, scientific models combine:

Simulation data already used by engineers
Real-world sensor data
Physics-informed algorithms
Scientific machine-learning techniques that accelerate modeling

This hybrid approach allows Geminus’ models to behave like fast, accurate surrogates for complex engineering simulations.

“Creating foundational models that can be based on scientific algorithms have more repeatable results.”
Greg Fallon, CEO of Geminus AI

The results are surprising

Greg shared a couple of examples of just how fast scientific AI can unlock value:

Aerospace simulator built in just three weeks

A physics simulator used in aerospace required seven days to compute a single case. Training a traditional ML model with this method would take years.

Geminus reduced the requirement to 12 simulation runs and combined them with additional data to build a functional model in weeks.

Simulation of a massive oil field with over 2,000 wells

A brute-force model would have required nine months of compute time.

Geminus built the model in 4–5 hours, enabling operators to optimize settings and increase output by 15% in a single day.

These kinds of speed-ups open the door for real-time decision support; something that is impossible with today’s slow engineering workflows.

The ultimate goal: an ultra-intelligent engineering assistant

During our conversation, Greg described a future where enterprises have a unified intelligence layer to cover every side of the business; a system that can answer engineering questions instantly by pulling from simulations, models, and historical data.

Imagine typing:

“Is it possible to connect a 100 MW generator at this substation?”

Instead of waiting weeks for interconnection studies, the system generates an accurate, physics-informed response in seconds.

This isn’t a generic chatbot. It’s a domain-specific engineering brain built on physics and scientific AI.

And this isn’t just a pipe dream. The industry is getting close to making this reality.

“We can already do really significant pieces right now.”
-Greg Fallon, CEO of Geminus AI

How this applies to the power industry

Scientific AI has several practical applications for utilities today:

1. Speeding up interconnection studies

New generator interconnections often wait 5–7 years for approval.
Faster models can reduce engineering and regulatory bottlenecks.

2. Modeling inverter-based resources

Solar, batteries, and other inverter-based resources introduce fast dynamics that existing tools struggle to predict.
Scientific models can replicate this behavior more accurately.

3. Understanding grid stress and failure risk

Utilities face constant questions about asset life, overload risk, and weather exposure.
Physics-based models provide clearer insight to improve grid resiliency and reduce power outages.

4. Improving efficiency and reducing emissions

More accurate forecasting and optimization can keep renewables online longer and limit unnecessary fossil fuel power generation.

Who else is working in this space?

Geminus is part of a growing ecosystem of companies training their own AI models for the energy industry. Others include:

Ansys & Siemens Digital Industries – simulation-driven machine learning
WattsUp - developing AI models for predictive maintenance on distributed energy resources, starting with EV chargers
OpenDrawing - developing AI models to automate the electrical project cost estimating and takeoff processes

While their approaches differ, the trend is clear: the future of AI in the energy industry needs to be grounded in science.

Why are most AI data centers still powered by fossil fuels?

Will McKnight — Mon, 01 Dec 2025 20:26:37 GMT

While renewable energy has been dominating the headlines over the last few years, it seems that there has been a shift recently, especially ever since the recent boom in AI data center construction projects. Instead of installing large solar fields and battery storage systems, data center developers have been installing natural gas power generation. In this week’s edition of AI Power Weekly, we will dive into the recent surge in data centers, why most of them are getting their power from natural gas, and when we might see a shift to renewables.

Don’t we have cheap clean energy available? Why can’t data centers just run on wind and solar?

It is true that solar and wind are very cheap right now on a $/kW installed basis, but it isn’t the cheapest for 24/7 power.

Data centers require 24/7 power availability because any interruptions could be catastrophic and cause mass interruptions in service, which simply cannot happen. When data centers go down, the software services that rely on them also go down. This can result in everything from your flight being cancelled because their systems went down, to your local coffee shop needing to go cash-only for the day because their payment system stopped working.

Solar and wind aren’t available when the sun isn’t out and the wind isn’t blowing, and the only way to fill that gap with clean power is with large battery banks, and those typically only come with 2- or 4-hours’ worth of storage. In order to provide 24/7 reliable power with only wind and solar, powerplants need to be significantly oversized by 3-5x to provide much more power generation and energy storage than is actually necessary, resulting in incredibly high costs and long construction timelines.

The solution for this is firm energy, a power generation source that can be turned on any time with the flip of a switch. Natural gas or diesel generators are a couple of examples. Firm power generation sources can be used on their own to power entire data centers, or alongside solar/wind installation to bridge the gap when not enough power generation is coming from the renewables.

So, data centers need firm energy. Are there any clean options for that?

They exist, but none of them can be deployed fast enough or cheaply enough to keep up with the current pace of data center construction.

The cleanest source of firm power generation available today is nuclear, but it takes years to deploy new nuclear powerplants, with most new construction not expected to be online until the 2030’s due to regulatory or technical challenges.

Long duration energy storage is available today, but the technology is either experimental, too expensive, or unable to support the scale required to support a 50+MW data center campus.

Hydrogen is also an option that can be used in the same generators that use natural gas (with some upgrades), but the technology is still too young and green hydrogen is also too expensive to produce at scale for it to be competitive pricewise with natural gas. There are currently no green hydrogen production facilities operating in the US that can provide enough fuel to power a large data center.

There are also some new power generation technologies that have been developed in recent years that can use hydrogen, biofuels, and even natural gas to generate firm power without combustion or with little to no emissions. Companies like Emission Free Generators, Petra Power, Bloom Energy, and Mainspring Energy are working on these solutions, but they’re still much too small (the largest generator available is 1MW) to meet the scale required by data center developers today.

When will we see a shift?

A major breakthrough in one of these areas will need to happen before data centers can completely switch away from natural gas

Cleaner firm generation methods need to reach scale
The timeline of nuclear SMR deployments needs to be sped up
Long duration storage needs to become much more affordable
Major tax breaks or incentives need to be put into place to incentivize clean energy generation even when it’s not the most economical option

Sources:

When will nuclear fusion get out of the lab and onto the grid?

Will McKnight — Mon, 20 Oct 2025 12:31:37 GMT

It seems like every week there’s a new article that drops about “a major breakthrough” in nuclear fusion research coming from various research labs around the world, or even private companies breaking ground on fusion powerplants before the technology is even developed. The idea of infinite energy is no doubt exciting, but with all of these news drops it’s tough to figure out exactly where the tech is, and how far we are from seeing it deployed in the real world. Let’s dive in and figure it out together.

Fusion’s promise

Nuclear fusion is super exciting to the energy community because it replicates the same process that powers our sun, fusing hydrogen atoms together to form helium while releasing huge amounts of energy. The fuel for this process comes from isotopes like deuterium, which can be extracted from seawater and other abundant resources of which the earth has enough to power civilization for millions of years, and tritium, which will be produced from within the reactors. That’s why it’s often referred to as an “infinite” energy source.

It also produces no carbon emissions, has minimal risk of nuclear meltdown, and generates almost no long-lived radioactive waste. In theory, fusion could deliver stable baseload power for the whole world, enough to replace coal, natural gas, and fission powerplants without the environmental tradeoffs.

Fusion’s challenges

The problem is that bringing the power of the sun to earth isn’t easy. To get hydrogen atoms to fuse you need them to hit over 100 million degrees Celsius, hotter than the sun’s core, then keep that plasma confined long a long period of time. Scientists can get the initial reaction started, but the problem that they have continuously faced is keeping the plasma reaction going long enough to produce more energy than it required to start the reaction. It has been achieved at the laboratory scale but scaling that up and maintaining the reaction long enough to get meaningful energy production out of it has proven difficult to say the least (Interesting Engineering).

If they’re spending more energy to start the reaction than they get out of it, then it’s not really an “infinite” energy source, is it?

What’s new this year?

AI Fusion Control: Researchers at Google DeepMind and Commonwealth Fusion Systems are teaming up to try to use AI for plasma control (Interesting Engineering).
China’s Artificial Sun: China’s “artificial sun” project, the EAST Tokomak hit 120 million degrees Celsius and sustained the reaction for over 17 minutes. They’re claiming to be on track to be the first country to commercialize fusion (SCMP).
UK Ignition Milestone: Scientists in Britain have announced that they have achieved sustained fusion energy pulses exceeding prior European records (Redditch Standard).
Helion’s Commercial Leap: Helion Energy, in collaboration with Microsoft and the DOE, have broken ground in Washington on what they claim will be the world’s first commercial fusion power plant, targeting an online date of early 2030’s (425Business).
Germany’s Fusion Plant Design: A German company, Gauss Fusion, has presented a design for what could be Europe’s first commercial fusion power plant, targeting an online date of mid 2040’s (World Nuclear News).

So, is all the news hype or legit?

There has been some major progress in recent years, but the real answer is that no one really knows. It’s pretty easy to read between the lines of the news above and see that, while big advancements are being made in fusion, it’s still going to be a long time before we see any commercial deployments. Companies can design and build powerplants all they want, but until a nuclear fusion reaction can be sustained for indefinite periods of time in a laboratory, it will be impossible for anyone to rely on it for baseload power.

Sources

How bad are the health effects of living near a data center?

Will McKnight — Mon, 29 Sep 2025 14:35:20 GMT

Data centers are a necessary piece of our digital lives, powering cloud storage, AI models, social media, and streaming services. But when they move into your neck of the woods, they don’t do it quietly. Residents of some communities have started to come forward about the significant health impacts caused by data centers that they have started to experience. This week’s newsletter will look at those impacts and discuss what developers and municipalities can do to make sure their computing power isn’t harming our communities.

Where has this been reported, and what side effects are they reporting?

The most well-documented case comes from Granbury, Texas. After a large bitcoin mining facility opened, residents began reporting health issues.

Neighbors have complained of severe migraines, vertigo, nausea, panic attacks, and permanent hearing loss. The constant, low-frequency roar of cooling fans has been described as a “dull, inescapable hum” that rattles windows and disturbs sleep. Some said they could even feel vibrations in their walls at night. Noise measurements in the area clocked the sound in at 90–100+ decibels (FOX4 News), much higher than acceptable residential exposure levels. In 2024, a group of residents sued, claiming the facility had caused “irreversible hearing damage, tinnitus, and debilitating vertigo” (Earthjustice). The lawsuit is still ongoing.

The effects mentioned thus far are only the noise-related issues. The list of potential health effects grows if you take into account data centers that are being powered or backed up by natural gas or diesel generators, which can give people respiratory disease and cardiovascular disease caused by their emissions (EPA).

Granbury isn’t the only case. Communities near similar crypto/data center hybrids in Kentucky, New York, and North Carolina (TechPolicy Press) have reported chronic sleep disturbances, stress, and reduced quality of life due to 24/7 noise and vibration.

What can be done to prevent this?

One solution is for municipalities, local air boards, and state or federal regulatory bodies to step in and set maximum decibel levels and make sure that they are enforced. Restricting generator hours/emissions and requiring data centers to prepare health impact assessments before they start construction would also help reduce health risks without relying on developers to take the initiative and do it themselves.

Developers have access to modern technology that can solve all of these problems today, so improving facility design is the most obvious solution that they could begin right now. They can put noisy equipment underground and use acoustic barriers to drastically reduce low-frequency noise and vibration (Future Bridge Americas). They can also use solar and battery storage as the primary source of power generation and only use fossil fuels in emergencies when renewables aren’t producing enough power.

Both of these solutions would drastically increase data center construction costs. Soundproofing a data center is not cheap, and a huge amount of standard 4-hour lithium-ion batteries would be required to keep it powered 24/7, bringing powerplant construction costs way higher than gas turbines. But there is no arguing that those two solutions would improve the quality of life for communities near data centers. If developers want to cut costs by willingly ignoring the health effects that their data centers cause, they should be required to select sites that are far away from any residential areas.

Sources

This tiny island is cashing out with each new .ai website registration

Will McKnight — Tue, 16 Sep 2025 14:36:23 GMT

Anguilla is an island in the Caribbean with an area of only 35 square miles. It has historically been a tourism-driven economy but that has been changing over recent years because it owns the top-level country code .ai (their equivalent of .us) and is in the right place and right time with the rapid boost in AI startups. Every time new .ai domains are registered, purchased, or renewed, that money flows back to the island. This week’s newsletter dives into the history of Anguilla, how much they are making, and what they’re doing with the newfound wealth. And don’t worry power industry folks, we will briefly discuss where they get their power from. I’ve always been fascinated by how these remote islands keep the lights on.

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A quick history of Anguilla

People have been living on Anguilla for at least 3,000 years, as evidenced by petroglyphs and other artifacts found on the island. These indigenous people are believed to be settlers from the South American continent (Wikipedia).

Europeans spotted the island in the mid 1500’s and started building in 1631 when the Dutch West India Company built a fort. It was destroyed by the Spanish just two years later. English settlers arrived about 20 years later and the island remained an English territory except for one brief period in 1666 when France took control. The island was promptly returned back to the British with the Treaty of Breda, and remains a British territory to this day, despite multiple attempts by the French in the 17th and 18th centuries to forcibly regain control.

As was the case in most British territories in the Caribbean, agriculture was the primary export, and slaves from Central and West Africa were forced to work on tobacco and cotton plantations. Once slavery was outlawed in the early 1800’s most plantation owners left the island.

The British tried to give St Kitts & Nevis control of the island, but Anguillans were starkly opposed to that, and it eventually boiled over to a revolution in 1967 where St Kitts police forces were kicked out and Anguilla returned to being a British colony.

When did they get electricity?

The island remained mostly undeveloped until the 1975, when the first island-wide electrical grid buildout began. Prior to that, only a few small electrical networks existed. The Anguilla Electricity Company Limited was incorporated in 1991 as a government-owned utility and is the exclusive supplier of electricity to the island. Their peak load is about 15MW and, like most small islands, most of that power comes from diesel and propane, aside a 1MW solar installation that went live about 10 years ago (ANGLEC).

How much do they make from each new .ai website?

It typically costs about $70/year for a standard .ai registration, and Anguilla keeps 90% of that revenue. It doesn’t seem like much, but if you multiply it by all of the .ai domains that have been purchased over the last few years, it adds up quickly. I personally have registered four different .ai domains while playing around with different ideas, and many other people have been doing the same thing while they think about entering the AI game.

They also occasionally make even more $ by auctioning off valuable domain names. If someone stops paying their registration fees on a domain and their registration expires, that domain ends up back with Anguilla, who can then auction it off to the highest bidder. They just started these auctions in April, 2025 and brought in over $600k in revenue just in the first month (DomainIncite).

In 2024, they made $39m from their newfound domain registration business, more than 20% of the total income of the island, and that number is projected to keep growing over the coming years (BBC). The newfound revenue is a welcome sight to the island’s government, providing some economic stability to it’s tourism-dependent economy. This windfall is helping them to build out critical infrastructure projects and improve healthcare and education.

Sources

How are electric utilities using AI in our transmission system today?

Tue, 02 Sep 2025 15:35:57 GMT

Electric utilities have historically been known for being risk-averse and thus, hesitant to be the first users of new inventions. Before thinking about deploying it, they usually wait for private companies to try it first. This trend might be changing, though, as many utilities are quickly adopting AI solutions that are custom-built for their use cases. This week’s newsletter briefly discusses how our electric utilities are using AI to keep our grid up and our power flowing.

What are the main things that they’re using AI for?

Predictive maintenance is one of the main use cases. Advanced software and drones with AI cameras are being used to keep track of equipment health and help utilities replace circuit breakers, transformers, and power lines before they go down and cause outages.

It’s also being used for demand forecasting and grid optimization. Smart meters, distributed energy resource management software, and advanced SCADA systems use AI to help utilities make real-time decisions on when to sell or procure excess power.

Legacy Outage Management Systems (OMS) have also started using AI models to quickly pinpoint fault locations and dispatch repair crews during outages.

Which utilities are using it, and who are they buying it from?

There are tons of legacy processes that utilities can improve with AI, and many of them have started implementing the technology to improve those processes.

NextERA Energy is using their own AI platform for preventative maintenance, and it’s already cut maintenance costs by 25-30% for their renewable operations (AInvest)
National Grid invested in an AI Company called Avathon and their working together to analyze historical failures and predict future ones to prevent downtime (National Grid).
Pacific Gas and Electric worked with Microsoft and Schneider Electric to build and deploy EcoStruxure DERMS, an AI-powered distributed energy resource management system. It helps utilities to monitor their resources, forecast future need, and control their fleet of power generation systems remotely (Microsoft).
Xcel Energy is using AI to improve its transmission interconnection process, a process that is notorious for slowing down or killing potential projects. This is one of my favorite use cases because I’ve been stuck in the interconnection queue before. They’re working with GridUnity on this (Renewable Energy World).
California Independent System Operator (CAISO) is using AI to automate and optimize their outage management process. It’s still an early-stage pilot program, so there are no results to look at yet. They’re partnering with OATI to test it out (APPA).

What does this mean for you?

Utilities: Come up with a plan to implement AI and keep an eye on these deployments to see how they work out.

EPCs/Contractors: Utilities will need help selecting and implementing AI into their systems. Become knowledgeable on the available options so that they can turn to you for help.

Sources

Where should you get the power for your next data center project?

Tue, 26 Aug 2025 14:17:23 GMT

AI data centers are a 24/7 load and need reliable, dispatchable energy sources that can be turned on with the flip of a switch. When your local utility can’t provide enough power to meet the load of your data center project, you need to creatively engineer the best solution for your location. This week’s newsletter goes into depth on how to engage your local utility, and your options for on-site power generation if they say no.

First, check with your local provider

The easiest option will always be to get the power from your local electric utility. They own the transmission lines, the substations, and usually the power plants too.

After you decide on the location of your project, go find out who the power provider is for that area. Electric utilities in the U.S. are separated into service territories, meaning you usually only have one option.

Once you figure out who the utility is for your area, you’ll need to do some digging on their website to find the industrial/commercial interconnection request form (it can go by a few different names). If there isn’t a section of the website, you’ll need to submit the generic “contact us” form or pick up the phone and give their customer service team a call.

If they can’t meet your needs, you’ll need to generate it yourself

Building your own power plant may sound like quite the daunting task, but it is very doable if you work with the right financing and construction partners. Your best bet is to find an EPC (Engineering, Procurement, and Construction) firm that specializes in power plant construction, and a financial firm that specializes in power plant financing.

EPCs do “turnkey” projects, meaning they can handle everything from technology selection to engineering design to permitting, to equipment procurement and installation. Some can even handle the day-to-day operations of the plant to keep it up and running.

There are a few different financing options for power plants, including PPAs (Power Purchase Agreements) that can involve as little as $0 up front. It is essentially a long-term agreement (usually 15-30 years) to purchase a set amount of electricity at a set rate.

How do I decide what kind of power to use?

Your main options are:

Natural gas (if within close vicinity of a pipeline)
Diesel (but it’s an extremely expensive fuel)
Solar (battery, natural gas, or diesel backup would be required, and it would be very expensive)
Wind (battery, natural gas, or diesel backup would be required, and it would be very expensive)
Nuclear (slowly rolling out - it will be at least 4-5 years before you could get a plant built and permitted)
Hydrogen (Hydrogen production is currently nowhere near where it needs to be to support large-scale power generation deployments)

It is best to rely on your EPC firm to do a feasibility study to review your location and recommend the best options for your power plant. They have software that can look at potential solar production, distance from pipelines, and other factors to estimate costs to build various types of power plants and recommend the most economical and environmentally friendly solutions. Most firms will charge a few thousand dollars for this service, but some may be willing to do some “back of the napkin” budgetary calculations for free if they see potential in the project or if you have a good relationship with them.

What does this mean for you?

Data center developers: Build relationships with EPCs and financing partners early. You want to have partners like this on speed dial for when your project progresses.

Utilities and grid operators: Data center customers move fast and have ways to generate their own power if you can’t keep up with their load

EPCs and contactors: Building relationships with AI companies should be a top priority for you. It is a well-funded and fast-moving industry that prioritizes speed and reliability over cost.

Why does AI consume so much water?

Will McKnight — Tue, 19 Aug 2025 19:07:51 GMT

An AI data center can consume up to 5 million gallons of water per day, as much as a town of 50,000 people (EESI). As facilities scale to meet the surging demand for GPUs (Graphics Processing Units), their water usage has become one of the most pressing and overlooked environmental costs of AI.

This week’s newsletter breaks down how AI data centers use water, why it matters, and what we’re doing to stop the leak.

Aren’t they just computers? Why do data centers need water in the first place?

GPUs running at full load generate enormous amounts of heat. To keep them from failing, data centers must continuously cool down their facilities and equipment. The cheapest, most common solution is water.

Most facilities rely on evaporative cooling. Water is pumped into chillers and cooling towers, where it absorbs heat before evaporating and being dumped back into the local water system at higher temperatures. Only 20% of the water used during the process is recovered, while the other 80% evaporates away into the air. This process causes strain on local water supplies.

Source: EIDA

How much water are we talking?

The numbers are pretty crazy:

The average 100‑megawatt data center can use up to 500,000 gallons of water per day, enough for approximately 6,500 households (SeattlePi)
One Google facility in Iowa consumed 1 billion gallons in 2024, making it the highest-use site in Google’s network (SeattlePi)

Unlike power, which can be added more easily across regions, water is fundamentally local. That makes siting decisions far more complex. In Chile, Arizona, and the Netherlands, new AI data centers have already sparked public backlash over water allocation during drought years, with some data center projects even being scrapped or banned due to concerns about water availability (Fox).

Why it matters

Water has become the second most critical input for AI infrastructure, alongside electricity. And unlike grid power, water rights are often tied up in legal frameworks designed for agriculture and municipalities.

Cities are facing tough choices on whether water should support farms, residents, or data centers.
Regulators are adding water usage reviews to environmental permitting, delaying projects.
Developers now need to consider water availability as much as they already consider land and grid access.

The issue will only get bigger: global data center water consumption is projected to reach more than a trillion gallons per year by 2030 if current growth continues (EESI).

What’s being done?

The industry is testing alternatives, but adoption is slow:

Closed-loop cooling reduces evaporative losses significantly by recycling water (DOE).
Immersion cooling submerges chips in dielectric fluid, cutting water use by over 90% (Intel).
Air-based or water-free cooling is being tested but requires higher upfront costs (Meta).

The challenge is scaling these solutions fast enough to catch up with the rapid pace of data center construction. Most facilities today still depend on traditional evaporative cooling, and many are still being built in regions with high water stress because of cheap land and tax incentives. The fast adoption of these water-saving technologies is crucial as we build out data center capacity.

What does this mean for you?

Cities and utilities: Expect rising conflict over water allocation as data centers compete with agriculture and residents.
Regulators and policymakers: Treat water as critical infrastructure in permitting, just like grid power.
Developers and EPCs: Cooling design will become a differentiator; expect more scrutiny from communities and regulators.
Manufacturers: Huge market opportunity for water-efficient cooling systems and alternative technologies.

Sources

America’s Nuclear Moonshot: Three reactors online by July 4, 2026

Will McKnight — Tue, 12 Aug 2025 18:02:33 GMT

Did you know that the U.S. built the world’s first small nuclear reactor? It was installed on a submarine in the 1950s. But today, as AI’s power needs explode, it’s Russia and China, not the U.S., that are generating real electricity from modern small modular reactors (SMRs). The Department of Energy wants to change this with a pilot program that they just launched to get three SMRs online within the next 11 months, a remarkable feat if you know how slow nuclear projects typically move. This week’s newsletter breaks down what SMRs are, how we lost our lead, and what we’re doing to catch up.

What is an SMR?

A small modular reactor (SMR) is a type of nuclear reactor that uses fission to generate between 50MW and 300 MW of electricity. That is less than a third of the size of typical large reactors, which can often reach 1,000MW.

SMRs are great because they can be built on assembly lines, shipped to sites on 18-wheelers, and paralleled together to get as much power as you need. They’re safer and faster to deploy than traditional nuclear plants.

These reactors are ideal for data centers because they provide dense, reliable, 24/7 power without taking up much space. Unlike wind and solar, SMRs don’t depend on the weather or massive and expensive battery banks. That’s why they’re suddenly on everyone’s radar as AI pushes energy demand to new highs.

Source: Utility Dive

We invented the tech. But we never deployed it

The U.S. built the first small nuclear reactor in 1954 for the USS Nautilus, a nuclear-powered submarine. Over the next two decades, we experimented with grid-connected small reactors like Fermi 1(150MWe) in Michigan and EBR-I(20MWe) in Idaho.

But by the 1980s, the U.S. nuclear industry shifted entirely to massive 1,000+ MW plants. SMRs were set aside, mostly due to cost, regulation, and a preference for economies of scale. No new small reactors were deployed, and the existing ones were eventually shut down.

Source: History Link

Russia and China filled the gap

Russia was the first to put SMR-style reactors on the civilian grid with the Bilibino EGP-6 units in the 1970s. Later, in 2019, they launched Akademik Lomonosov, a floating nuclear barge housing two 35 MWe reactors that now power the remote city of Pevek.

China skipped the legacy phase entirely and went straight to next-gen tech. In 2023, it became the first country to connect a Generation IV SMR to the grid, with its HTR‑PM project: two high-temperature gas reactors that produce 210 MWe(WNN).

Now it’s time for the U.S. to catch up

Earlier this year, there was no expectation that we would see any SMRs installed on American soil before the 2030s. That has changed over the last few months.

On Tuesday, the Department of Energy selected 11 startups, Oklo, Aalo Atomics, Antares Nuclear, Atomic Alchemy, Deep Fission, Last Energy, Natura Resources, Radiant Energy, and Valar Atomics, to participate in a pilot program to try to get three SMRs online by July 4th, 2026 (Barron’s).

In May 2025, President Donald Trump signed an executive order directing the DOE to skip the traditional licensing process used by the Nuclear Regulatory Commission (NRC) for these pilot projects.

It will be extremely difficult for anyone to meet these aggressive timelines, but now that the licensing process is out of the way, it isn’t completely out of the question.

What does this mean for you?

Data center developers: Start evaluating nuclear sites today. If we keep the current pace, SMR projects will be feasible in the U.S. well before 2030.
Utilities and grid operators: Don’t wait for federal funding. Partner with tech companies and SMR vendors now to reduce your risk with this early stage technology.
Power plant EPCs and suppliers: Prepare for a new kind of customer: fast-moving, AI-focused, and willing to pay a premium for reliability.