There’s long been a disconnect between concerns about the massive impact of AI data centres on electricity demand and claims by Sam Altman and others that the impact is really modest. Ed Zitron recently posted a summary of OpenAI’s 2025 accounts which helps to clarify things a bit.

In short, if you look at actual electricity demand needed for current AI use, it’s small. And that doesn’t change if demand grows at high but plausible rates. On the other hand, if you look at what is needed to justify the current valuations of AI and its competitors, the implied growth is staggering.

Starting with demand, the OpenAI accounts break out current revenue (what people are paying to use ChatGPT etc) and the cost of revenue (the costs of the data centres used to answer queries)

The cost of revenue is about $8 billion (rounded for simplicity). A conveniently scaled measure of electricity output is a Terawatt hours (TWh). Roughly equal to the annual output of a typical 1GW coal-fired or nuclear power station operating 24/7 is about 10 TW

If 25 per cent of that is electricity, and electricity costs 10 cents/kWh, the implied production electricity use is:

(0.25× 8 * 10^9) / (0.10* 10^9) =20 TWh

or the output of two large power plants running full time (I’ve previously derived the same estimates from reported cost per token)

That is about 0.5 per cent of U.S. electricity demand. Doubling this to cover the broader U.S. generative-AI industry gives a convenient round estimate of 1 per cent of U.S. electricity demand in 2025.

The US Energy Information Authority estimates demand growth of 15 per cent year, which implies doubling over five years

So the U.S. AI sector would rise from about 1 per cent to about 2 per cent of U.S. electricity demand by 2030.

It’s important to remember that this is growth in electricity demand. If AI becomes more efficient in converting electricity into tokens and tokens into useful answers, say doubling every two years, the number of answers would grow tenfold.

A new load equal to 1 per cent of current US demand would be quite manageable in aggregate, requiring the output of perhaps four of five 1GW power stations. The problems would be of the kind we are already seeing – data centers clustered into a few locations and pricing structures where the costs are shifted onto existing consumers. These are fixable problems.

The problem is that this is nowhere enough to make OpenAI and others sustainably profitable, let alone to justify the market valuations we are seeing. It’s harder to analyse these valuations than to measure current demand, but I’ll give it a go.

Assuming a price earnings ratio of 30, a trillion dollar valuation requires profits of around $30 billion a year. Assuming that profit margins can be maintained at 30 per cent, that requires revenue of $100 billion a year, and costs of $70 billion a year, around 10 times the current level for OpenAI alone. Taking account of Anthropic and the AI-boosted valuations of Meta, Alphabet (Google) and Space X, it would be more like 50 times . And, indeed, these numbers are conservative compared to the projections were seeing.

That gives growth equal to 50 per cent of current US demand, which is utterly impossible. Even with data centers spread across the developed world the growth in demand would be unfeasible and any attempt to deliver it would be catastrophic.

I’m going to call BS on this. There is simply no way that demand can grow enough.

OpenAI already has 900 million weekly active users, so there’s not much room for growth on the extensive margin. As for growth in revenue per users, I’m going to appeal to a combination of introspection and casual observation.

As regards introspection, I’m a heavy user mainly for enhanced search and testing ideas. I’m using OpenAi’s $20/month plan, as well as playing with other options like Mistral and Copilot. If OpenAI doubled their price, I’d switch to one of the others. If they all raised prices to (say) $100/month, I’d buy a high-powered computer with Nvidia chips and switch to local hosting.

Most people I know are not massively online, and use the free version of ChatGPT. They are unlikely to pay for it. A lot will be satisfied with Google’s (awful) Gemini which is “free”, that is, funded by advertising. Google already dominates the advertising market, so there’s no potential for growth there.

That leaves business uses. The short-lived fad for “tokenmaxxing” suggested the possibility of huge growth in demand. However, the disappointing results have brought that back to ground. Both at the firm level and at the aggregate economy level, there’s no indication that AI is delivering the productivity gains required to justify massive new expenditure.

Still, this yawning gap helps to explain the ferocity of the debate over AI and LLMs. I think stoclk market valuations are crazy, and don’t treat them as reflecting reality. I follow the EIA and expect that the hyperscaling boom will go the way of tokenmaxxing. On this view, electricity use for LLMs will remain modest.

Those most hostile to AI assume that the hyperscalers will deliver on their promises at least as far as making massive investments in data centres. That gives you the disaster scenario.

On the other hand, many critics point to the huge volume of useless slop produced by AI. These two criticisms contradict each other. There’s a partial reconciliation with Sturgeon’s Law (90 per cent of science fiction is crud. But 90 per cent of everything is crud).

Both viewpoints lead to the conclusion that we should not have rapid growth in data centres. The disaster scenario suggests a need for urgent action. The sceptical version suggests that building power stations to support data centres will rapidly prove non-viable, leaving a legacy of stranded assets, mostly gas-fired power plants that will be an obstacle to a clean energy transition

For the moment, the hype about AI and data centers has not produced a comparable boom in power plant construction. Indeed some projects have been canceled or delayed. Others like the Trump-branded Fermi project appear to be nothing more than vaporware.

From a range of social, economic and environmental viewpoints, the best thing to do regarding AI would be to abandon massive expenditure on hyperscaling and focus on learning how to use and manage AI to yield the best ratio of benefits to cost.