This is the first paragraph of one of my posts from earlier in the year, “AI is Giving Me Gas: The Collision of Tech Hype and the Carbon Budget.” It may be bad form to start a Substack post citing another Substack post, but clearly these two posts are related. The sub-title of the above referenced post” “We are scraping the bottom of the 1.5°C carbon budget. Big Oil’s response? Build 252 gigawatts of new gas power to feed the AI boom.”

I also wrote about this back in September of last year, in a post titled “New Gas Generator Plants and the Plan to Flood the (Electricity Demand Growth) Zone.” In this post there’s a link to an AI analysis I did in a report called “The Future of U.S. Natural Gas Power Generation: Projections, Accuracy, and the Confluence of Limiting Factors to 2030.”

The electricity demand growth tied to AI and data centers has Big Oil salivating, with plans—dreams?—of 100-plus new gas-fired generation plants in place by 2030. Not that the supply chain and turbine manufacturing capacity can deliver, but the explosion in small diesel or natural gas generators nonetheless seems a happy enough ending, boding well for Big Oil sales.

This doesn’t bode well for the rest of us, unfortunately. In the news of late is Elon Musk’s xAI company, which has used a large fleet of mobile, trailer-mounted gas turbines (rather than diesel generators) to power its “Colossus” AI data center in Memphis, Tennessee. These turbines are deployed as a temporary, “quick and dirty” solution to bypass power grid constraints while constructing the facility, which houses Nvidia H100 GPUs for training the Grok AI model.

To get the data center operational in just 122 days, xAI used mobile turbines (approximately 35 to 62, depending on the report and timeline). Each turbine is capable of providing 2.5 MW of power, with reports indicating a total capacity exceeding 35 MW to over 100 MW. There’s been pushback from local residents and environmental group. It turns out that neither noise pollution or emissions of nitrogen oxides and formaldehyde are being welcomed, and in January 2026, the EPA ruled that xAI violated the law by operating dozens of these, at times, unpermitted, gas generators. I’m guessing any fines actually levied against xAI will be just part of the cost of doing business. The broader AI data center industry is facing a shortage of power, with many companies increasingly using on-site, fossil-fuel-based generators to bridge the gap.

By the way, if you want to dive deeper into the issue of power strategies and developments for AI data centers, check out Distilled, on Substack. Distilled is written by Michael Thomas and he’s undertaken as series of articles on the issue of AI data centers and approaches being pursued for power, including data centers building their own power plants.

Wet Dreams and Sloppy Seconds

The accuracy of future demand predictions itself is highly questionable, considering the wide-ranging numbers and, among other issues, the double, triple, or greater duplicate counting of generation sources among data center hyperscalers. These eager corporations reach out to more than one potential generation source to cover their bets. Accurate forecasting seems hindered by some combination of wishful thinking and double counting. Here’s an AI summary of this issue:

The “duplication issue” (often called “phantom load” or “speculative queuing”) refers to the practice where data center developers submit multiple applications for electrical grid interconnection for the same single project. 

Because securing power is now the primary bottleneck for AI and hyperscale facilities, developers “spam” the queue to hedge their bets. They might file requests for the same 500 MW project in three different states (or three different sites within the same utility territory) to see which one gets approved first. Once one is approved, the others are withdrawn, but in the meantime, they clog the study queue and artificially inflate demand forecasts. 

Grid operators warn that these “phantom” requests make it impossible to accurately plan for new power plants, as the requested demand on paper is often 5x to 10x higher than what will actually be built. 

Estimate Ranges of Duplication

Industry data suggests that the vast majority of current interconnection requests are duplicate or speculative. 

• • Overall “Phantom” Rate: Experts estimate that 80% to 90% of the data center capacity currently in US interconnection queues will never be built.
  • Realization Rate: Utilities often project that only 10% to 20% of the requested data center load in their pipelines will actually materialize.
  • Speculative Ratios (Firm vs. Requested):
    • AEP (American Electric Power):Reported 24 GW of firm commitments but has requests for 190 GW of additional load—a ratio of nearly 8:1 (speculative to firm).
    • Oncor (Texas Utility):Reported a queue of 186 GW of data center requests. For context, the utility’s entire current peak demand for all customers is only ~50 GW, suggesting the queue is inflated by nearly 400% of the grid’s total existing capacity.
    • PSE&G (New Jersey):Reported a 9.4 GW large load pipeline but expects only 10–20% of those inquiries to result in actual projects.
    • ERCOT (Texas Grid):Has received requests for over 220 GW of new load by 2030 (mostly data centers), which is more than double the state’s all-time peak demand record. 

Why This Is a Problem

1. 1. Planning Paralysis: Utilities cannot distinguish real projects from “zombie” projects. If they build transmission lines for all 190 GW (in AEP’s case), they would bankrupt ratepayers. If they wait to see which are real, they risk being too slow for the 24 GW that is real.
   2. Queue Backlogs: The “phantom” requests force grid engineers to perform complex impact studies for projects that don’t exist, delaying the connection of viable power plants and real factories by years.
   3. Artificial Scarcity: The illusion of zero capacity drives up power prices and panic-buying of land, further fueling the cycle of speculative multiple-filing. 

Why is this a Problem?

You might also ask, from the perspective of Big Oil, “Why is this an opportunity?”

Big Oil loves the high estimates of power demand and the expanded market for their products, and not just more volume, but more over the next several decades, just when we need to reduce carbon emissions, not raise them. Big Oil is in a frenzy to keep their business going for decades more, despite the counter need for this industry to decline.

There’s temptation, too, for the utilities who contract or build new generation capacity. While solar/wind/batteries can meet new energy needs (and be quicker and cheaper), power utilities remain drawn to action that follows business-as-usual thinking, and for many utilities, especially without governmental and regulatory guidance, that means more power plants.

The opportunity is now for Big Oil to future-proof the industry.

Does the Trump Administration strike you as leaning on governance and regulation to push for clean energy?

I don’t think so, but then maybe I’m as wrong as all those wild estimates about power requests to feed the AI industry.

The post Fossil Fuel Demand Growth Uber Alles first appeared on David Guenette.

I’ve been saying that AI’s projected electricity demand is celebrated by fossil fuel companies because this growth in electricity demand provides an anchor for Big Oil to keep selling natural gas for decades to come.

What me worry?

Sure I worry. A much clearer and devasting understanding about the “carbon budget” mankind faces has recently been recalculated and the general gist is that we have a smaller amount of carbon we can dump into the atmosphere before we push beyond 1.5C.

Or, as AI Summary puts it:

The 2025 Global Carbon Budget reports that the remaining budget to limit global warming to 1.5°C is “virtually exhausted”. Fossil fuel emissions continue to rise, projected to reach a record 38.1 billion tonnes in 2025, driven by, for example, high demand. While emissions from land-use change have declined, total global emissions remain at record highs. 

Key Findings on Recent Carbon Budget Changes 

• • 5°C Budget Exhaustion: Scientists warn that at the current rate of emissions, the budget for limiting warming to 1.5°C could be exhausted in approximately three years, with only about 130 billion tonnes of CO2 left to emit.
  • Rising Emissions (2024-2025): Fossil fuel emissions have continued to grow, with 2024 seeing a 0.8% increase. The 2025 projection is a 1.1% increase in fossil fuel CO2 emissions.
  • Total Emissions Flat: Despite rising fossil fuel emissions, the total CO2 emissions (including land-use changes) for 2025 are projected to remain relatively flat compared to 2024, due to a decrease in emissions from deforestation.
  • Weakened Sinks: The Global Carbon Project (GCP) notes that climate change has weakened natural land and ocean sinks, accounting for 8% of the rise in atmospheric CO2 concentration since 1960.
  • The “Carbon Clock”: The Potsdam Institute for Climate Impact Research continues to track the rapid shrinking of the remaining carbon budget for both 1.5°C and 2°C, highlighting the urgency of the situation.

The 2025 Global Carbon Budget, often highlighted by sources like Carbon Brief and Euronews, indicates that while some nations are transitioning to cleaner energy, the overall global trajectory is not yet declining fast enough to meet international climate targets. 

Want to worry more? Many scientists are now projecting that we are already on the path to or actually at 1.5C, with higher temperature increases in the average global temperature moving faster than previously considered.

“US leads record global surge in gas-fired power driven by AI demands, with big costs for the climate,” written by Oliver Milman and published in The Guardian on January 29, 2026, puts the issue of overspending our carbon budget squarely on gas generation and the new and planned gas generation planned to address the electricity-hungry AI data centers. The main projected culprit for the voracious spending down of said carbon budget largely rests with the U.S.

The article reports that “Much of this new capacity will be devoted to the vast electricity needs of AI, with a third of the 252 gigawatts of gas power in development set to be situated on site at datacenters.” I wrote about this back in September of last year, in a post titled “New Gas Generator Plants and the Plan to Flood the (Electricity Demand Growth) Zone.” In this post there’s a link to an AI analysis I did in a report called “The Future of U.S. Natural Gas Power Generation: Projections, Accuracy, and the Confluence of Limiting Factors to 2030.” Here’s that report’s Executive Summary:

Executive Summary

1.1 Overview of Projections and Core Findings

An analysis of U.S. energy market trends and projections indicates a notable ambition for future natural gas power generation. A key projection from the firm Enverus suggests the United States is on a trajectory to construct 80 new natural gas power plants by 2030, which would add an estimated 46 gigawatts (GW) of new capacity. This figure is a focal point for assessing the future of the nation’s energy infrastructure. However, a comprehensive review of the current market and regulatory landscape reveals that this aggressive projection is highly speculative. It is a needs-based assessment rather than a realistic forecast of what can be built, as its feasibility is called into question by a complex and multi-faceted set of constraints.

Looks like Big Oil has been busy selling the idea of new gas generation for AI and other growing electricity demand. Forty-six GW has blossomed to 252 GW, although the first number is U.S. back in 2025, and the second number is worldwide in 2026.

You have to admire the ambition of the fossil fuel industry. The amount of additional carbon emissions these new plants will spew is staggering… and staggeringly dangerous. Here’s a quote from The Guardian article:

The gas projects in development in the US will, if all completed, cause 12.1bn tonnes in carbon dioxide emissions over their lifetimes, which is double the current annual emissions coming from all sources in the US. Worldwide, the planned gas boom will cause 53.2bn tonnes of emissions over projects’ lifetimes if fulfilled, pushing the planet towards even worse heatwaves, droughts, floods and other climate impacts.

Of course, as I discovered in my analysis, the desire to build new gas plants is tempered by ability, especially in terms of supply chains, where turbines from the two main manufacturers of gas generators—GE Vernova and Siemens Energy—are mightily backlogged. Building out manufacturing capacity for such complex machines is no fast undertaking. Unfortunately, a recent development has entered the market, as described in the AI Summary of the search prompt “Using jet turbines for new gas generator plants and capacity”:

Using jet turbines, specifically “aeroderivative” gas turbines derived from aircraft engines, has emerged as a critical, fast-deployable solution for new, high-demand gas generator plants, particularly for data centers and AI-driven power needs. Companies like ProEnergy are retrofitting used military and commercial jet engines (e.g., GE CF6-80C2) into 48-megawatt power generators to provide rapid, “behind-the-meter” electricity. 

This approach addresses severe bottlenecks in securing new, large-scale utility turbines, which often face 3- to 7-year wait times. 

There’s a lot more to find with this prompt, but nothing to keep you calm if you worry about carbon emissions.

Here’s what it all means: All efforts to restrict and reduce carbon emissions will be more than offset by all these goddam new gas generators if they come to be. Offset and then some.

A Brief Interruption About My AI Overlords

I use AI as an effective productivity tool. My purview for climate change is a wide one. I write climate fiction that seeks to be based on realistic and accurate information about the causes and consequences of climate change, which means I’ve been hard at work understanding the science (enough to tell the difference between truth and bullshit). I give talks about climate change, and I have little interest in providing information that is wrong, so I make an ongoing effort to get things right and up to date. I find myself researching climate policy, political realities, and economic benefits of the clean energy transition. I’m active within the climate fiction world, reading widely in the genre and critiquing the various approaches to it. I’ve been at all of this for many years.

The world of climate change is extremely wide and multi-faceted. Keeping up on the latest findings of science and technology and policy proposals and economic and political realities is gained by triangulated a diverse and wide range of information resources. I’ve found that AI can be useful as a research agent, where, in response to a thoughtful prompt, AI ranges far and wide across the Web to collect and then collate and then analyze the relevant sources and then synthesize these findings into well-produced reports.

I have high confidence in these reports, and I haven’t found hallucinations to be a problem, but that’s because I know the subject well enough and broadly enough to cast a critical eye on sources and am able to review AI’s findings. What I can’t so easily do—although, of course, I’ve done this all too often and with all too much effort—is to search the Web high and low for the information, explanation, and opinion I seek.

For me, AI is a useful tool. It is not my buddy, nor do I spend much time generating funny images. For me, AI’s current capabilities are impressive.

This is not to say that the present hysteria about AI—the very hysteria that supports Big Oil’s play to flood the world with greenhouse gas-generating electricity—is sensible. My take is that there is wild financing being accumulated mainly in the hope of the debt accumulators being first to market. I believe that AI’s market will actually take a long time to develop as a deeply useful and pervasive embedded tool—evolving human culture is still a slow undertaking. I believe that there’s likely to be something on the order of a crash or bubble because of the disconnect between all that money and all that non-market.

I also believe that the rush for adding huge amounts of electricity generation is hysterical, albeit not in a funny way. I believe that whatever additional capacity may be required can be more cost-effective and more quickly produced with clean energy and by bringing digital management to the grids that already contain huge amounts of spare capacity that currently cannot be managed well.

And now back to our regularly scheduled programming.

Big Oil is the Enemy

I guess I shouldn’t be surprised that the fossil fuel industry doesn’t care about carbon emissions. If Big Oil did care, Big Oil would have a lot to care about.

There’s no rational explanation for the fossil fuel industry not knowing that carbon emissions must come down, not go up. Denial doesn’t cut it, not with the now long-held and clear scientific consensus on global warming and all the resulting data collection. Denial doesn’t cut it, not with the growing evidence in the form of extreme weather and not with the majority of individuals’ personal experiences. It is as if a competent adult would argue that he didn’t know that someone could be killed if he pointed a loaded gun at that person and pulled the trigger. The “Go figure” argument is as absurd for Big Oil as it is for our proverbial idiot.

Speaking of idiots, in America, Trump is the gaslighter-in-chief in regard to climate and the contributory role of fossil fuels, just as he is on so many other topics, including, of late, the ridiculous and entirely and patently demonstrable falsehoods around the ICE/CBP killings and related inciting behaviors. That’s why the connection between fighting for the American democracy and the climate fight are one in the same. The old order of Big Oil, along with a rogue’s collection of other “Bigs,” has placed its bet on a rising fascist state, damn the consequences.

I’m placing a different bet.

The post AI is Giving Me Gas first appeared on David Guenette.