How AI Energy Use Stacks Up

100 text queries per day × 365 days

12.4 kWh/year

Estimated annual electricity consumption for those queries

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That's a useful foundation for your argument. Someone using text AI quite heavily may consume less electricity for their AI requests over a year than many household appliances consume in a few days.

But the distinction between the impact of one user's queries and the infrastructure required to provide the service becomes essential as we broaden the analysis.

2. Comparing everyday activities

The following comparisons use electricity consumed, with gasoline consumption identified separately. That's important because a kilowatt-hour of electricity and a gallon of gasoline have different carbon footprints.

Activity	Approximate energy use	Comparison
One ChatGPT text query	0.34 Wh	Baseline
100 ChatGPT queries	34 Wh	Less than two full smartphone charges
Fully charging a smartphone	19 Wh	About 56 text queries
Streaming one hour of video	77 Wh	About 226 text queries
Running a 100 W television for four hours	400 Wh	About 1,176 text queries
Running a 1,500 W space heater for one hour	1,500 Wh	About 4,412 text queries
Average US household per day	29,600 Wh	About 87,000 text queries

Sources: OpenAI's reported query estimate, EPA smartphone charging methodology, IEA streaming research, and EIA household electricity data. Television and heater figures are calculated examples based on stated wattages. Streaming research is from 2020, so current devices and networks may differ.

Here's where the broader comparisons become interesting.

Driving a gasoline car

EPA estimates approximately 393 grams of greenhouse gases per mile for an average gasoline passenger vehicle, including tailpipe CO₂, methane and nitrous oxide.

Using Google's reported 0.03 grams CO₂e per median Gemini text prompt, one mile of gasoline driving produces approximately the same operational/lifecycle-combined emissions reported for 13,100 such prompts. The comparison isn't perfectly equivalent because the AI figure includes a different set of lifecycle boundaries, and the vehicle figure excludes manufacturing and fuel supply impacts.

Nevertheless, the difference in magnitude is enormous.

A gasoline lawn mower illustrates a similar issue. As an illustrative assumption, burning 0.25 gallon of gasoline during an hour of mowing produces about 2.2 kg of CO₂ from combustion alone, before other pollutants or fuel-production emissions. That's using EPA's 8.89 kg of CO₂ per gallon.

Amazon deliveries

A package has an environmental footprint from production, packaging, warehousing, transportation and final delivery. No single emissions value represents every package.

Amazon reported that consolidating Prime deliveries in the US during 2024 avoided approximately 335,000 metric tons of CO₂e and 494 million boxes. That illustrates the potential scale of reducing deliveries, although the avoided emissions cannot be attributed exclusively to each box.

A useful distinction here: society generally accepts that deliveries, driving and lawn maintenance generate emissions in exchange for convenience or economic value. We typically debate their efficiency and regulation rather than expecting people to reject the activities categorically.

3. Text, images and video are very different AI workloads

This is arguably the strongest technical distinction in your argument.

The 2026 ML.ENERGY work found video generations consuming roughly 26 kilojoules to 1.16 megajoules in its tested configurations. That converts to approximately 7–322 Wh for a generated video, depending substantially on model, resolution and frame count.

The difference between writing a paragraph and rendering a video could be hundreds or thousands of times in electricity consumption.

The same argument applies to model design. A large reasoning system might expend substantial computation exploring possible solutions, while a small, task-specific model can complete a classification using relatively little electricity. Research published in Nature illustrated an approximately 16,000-fold difference between an efficient question-answering model and a particularly energy-intensive image model.

Therefore, a categorical objection to AI loses some of the distinctions that matter most for environmental policy.

Your local LLM example

An LLM installed on your laptop can process language without contacting a remote server. That shifts the immediate inference electricity consumption to the device you already own.

For illustration, running a laptop at an additional 30 watts for one hour uses 30 Wh, equivalent in electricity to approximately 88 of the reported average ChatGPT queries.

But local inference is not automatically more efficient. It depends on model size, processing speed, device utilization and whether a cloud server can process many users' requests efficiently. A local model still carries the environmental cost of producing its hardware and training its model.

Research into local language models increasingly measures intelligence per watt, rather than simply equating the largest model with the most useful model.

That is a more productive environmental conversation: How much useful work do we obtain from the energy we consume?

4. The data-center industry is where the numbers become serious

This is the other half of the picture. Individual text prompts may use little electricity, but the infrastructure industry is expanding very rapidly.

In 2024, data centers accounted for about 1.5% of global electricity consumption. The IEA projects more than a doubling by 2030, with AI the most important driver of that growth.

And in the United States, the numbers are considerably more striking.

This is a substantial industrial expansion. It is reasonable to question the infrastructure, the economics, the public subsidies, and whether capacity construction is outpacing demonstrable demand.

But one important problem with using these figures to condemn AI generally is that data centers do much more than run generative AI.

They support cloud applications, banking, internet services, storage, enterprise software, streaming infrastructure, advertising, search engines, e-commerce and a considerable amount of conventional corporate computing.

AI is increasingly integrated into these industries, so the boundaries are blurring. Nevertheless, the entire 415 TWh global data-center figure cannot be attributed to ChatGPT or generative AI.

There is also a distinction between training and inference. Training frontier models requires large clusters and concentrated computing investments. Inference occurs whenever trained models are used, across potentially billions of requests. Both contribute, and their relative shares change as usage expands.

Who consumes all this computing?

A useful accounting framework would separate:

Sector or activity	What generates the demand
Individual consumers	Text conversations, search, personal creativity, images and video
Corporate productivity	Coding assistants, document processing, analytics, customer support
Advertising and entertainment	AI images, video, personalization and content production
Industrial and scientific applications	Engineering, research, simulation, prediction and optimization
AI developers	Training, experimentation, model evaluation and serving infrastructure
Non-AI digital services	Traditional cloud computing, storage, commerce and communications

We do not yet have sufficiently standardized, publicly disclosed electricity consumption by these categories to assign defensible percentages to each. And many categories overlap.

That absence of transparent reporting is itself a legitimate target of criticism. If the argument is about environmental responsibility, we should know how much electricity companies consume, what they produce with it, and the environmental consequences.

5. What happens to communities where data centers are built?

This is where I think the anti-AI argument becomes considerably stronger.

The objections concern physical infrastructure and who bears the consequences of private decisions.

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Local news video reports showing the kinds of data-center noise concerns that residents are raising. The clips are illustrations of the issue, not measurements of environmental impact.

The concerns include water supplies, continuous cooling equipment noise, backup generators, diesel exhaust, increased transmission infrastructure, land development and the possibility that residents will subsidize grid upgrades for large corporate customers.

A 2025 peer-reviewed study examining Northern Virginia documented the tensions between data-center expansion and local residents, emphasizing infrastructure burdens, socioeconomic impacts and whose interests receive priority.

Water consumption makes the distinction particularly clear.

US data centers consumed approximately 17.4 billion gallons of water on-site in 2023, according to Lawrence Berkeley National Laboratory. That's direct consumption, much of it associated with cooling, rather than the total water required to produce their electricity.

But location matters more than the national total for many communities. A data center requiring large volumes of water in a water-stressed watershed can create a serious problem, even if its total contribution to national water consumption is relatively small.

The design also matters. Some systems use evaporative cooling; others use air cooling or closed-loop systems with different energy-water tradeoffs. A 2026 UC Berkeley report emphasized that local water effects vary with the cooling technology, water source, climate and local hydrology.

The electricity bill question

The financial issue may be even more revealing.

Electric utilities often finance infrastructure investments over decades and recover costs through electricity rates. If a utility expands its grid to serve enormous anticipated data-center demand, who carries the financial risk if that demand does not materialize?

A Harvard Belfer Center analysis published in April 2026 identified the growing conflict over whether residential customers would subsidize expensive infrastructure for large industrial electricity customers.

That gets directly to your concern about corporate malfeasance.

A company can negotiate a private development agreement. Residents may then face the consequences in their neighborhoods, their water systems and potentially their electricity bills.

And the people carrying those costs might have little meaningful role in approving the project.

That is a governance problem, whether the facility serves AI, cloud storage, cryptocurrency mining or some other industrial purpose.

6. Where the moral argument gets tangled

I see at least three different objections being bundled into the anti-AI position.

First, there is the environmental objection. AI consumes energy, water and materials, and expanding its use could accelerate environmental damage. This deserves a quantitative response involving the scale of consumption, environmental consequences and benefits received.

Second, there is the labor and cultural objection. AI threatens livelihoods, professional identity, creative ownership and the perceived value of human work. This is especially relevant to LinkedIn, where professional identity and economic security are prominent concerns. An argument about data-center energy may carry some of this deeper anxiety, although we shouldn't assume everyone raising environmental concerns has another motivation.

Third, there is the corporate power objection. Powerful companies are developing technologies, building infrastructure, accumulating market influence and changing economic relationships with insufficient public involvement.

This third objection may be the most interesting in terms of your broader thinking about economic systems.

The same people who might benefit enormously from inexpensive AI-assisted work may be disadvantaged when corporations use AI to consolidate control, displace workers without sharing productivity gains, or impose infrastructure costs on communities.

AI could make an independent professional vastly more productive. The same technology could enable a corporation to eliminate that professional's job, capture the gains and further concentrate decision-making.

The technology does not determine which economic arrangement prevails.

7. What would a more rational environmental policy look like?

The natural response is to measure and regulate the activities according to their effects, rather than categorically condemning the technology.

I'd frame it around five requirements:

1. Transparent measurement. Publish comparable energy, water and emissions data for text, images, video, training and other computational workloads.
2. Full-cost accounting. Make companies account for electricity infrastructure, water use, emissions, equipment production and neighborhood impacts.
3. Local authority. Give affected communities a meaningful role in approving facilities and establishing enforceable operating conditions.
4. Efficiency incentives. Reward useful output per unit of energy and encourage smaller models, efficient hardware, cleaner electricity and appropriate workloads.
5. Shared economic benefit. Ensure workers and communities can participate in the value being created, rather than absorbing costs while ownership captures the returns.

That last point is especially relevant to Co-capitalism: the opportunity is to engineer systems in which the individual benefit and the collective benefit reinforce each other.

Environmental protection can be built into the profitability model. A facility that reduces local water consumption, pays its full infrastructure costs, and delivers valuable services is economically different from a facility that depends on shifting its costs to the public.

8. Putting the whole issue in perspective

I would be careful about making the argument that AI's individual environmental impact is so small that the environmental objection becomes irrelevant. At billions of uses, even tiny individual demands accumulate. And when computing becomes cheaper, businesses may deploy far more of it, potentially offsetting efficiency gains.

But the reverse argument is equally weak: using AI to produce a paragraph doesn't make someone environmentally irresponsible simply because a major corporation is constructing a gigawatt-scale computing campus.

The data support a much more differentiated position.

The environmental case against ordinary text AI use is relatively weak compared with many accepted consumer activities. The case for scrutinizing the growth of AI infrastructure, resource consumption, and corporate accountability is considerably stronger.

And the benefits deserve equal consideration. AI can also improve electricity-grid management, industrial efficiency, research and productivity. The IEA identifies meaningful potential energy-sector benefits, though those gains are not guaranteed and shouldn't simply be deducted from projected consumption.

I think there is a compelling argument to make about the moral high ground you describe. Environmental responsibility is being applied selectively when AI becomes a test of someone's ethics while far larger everyday consumption choices go largely unexamined.

But there's an even more important question underneath it:

Why are we arguing about whether individuals should use AI when we could be deciding together how AI should be developed, operated, paid for and used to create value?

That puts the argument back where you have been placing so many of these questions: in the relationships among individual choice, corporate authority, public accountability and shared prosperity.

The technology is already offering extraordinary new capabilities. What remains open is who controls the systems, who profits from their use, and whether those benefiting from the technology are also accountable for its costs.

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Annual energy for 100 requests per day

13.3 kWh

Calculation based on estimated Llama 3.1 405B accelerator electricity and reported average ChatGPT inference electricity. Not a measured full lifecycle estimate for either service.

AI data-center electricity per US household, annually

225–375 kWh

Midpoint: approximately 300 kWh per household per year

Equivalent to a continuous 26–43 watt load