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Cake day: August 31st, 2026

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  • The original point was that expanding data centres doesn’t disprove a big increase in efficiency per token. Usage, advsnced models/tokens per query and infrastructure can all grow at the same time as the energy required for an individual token falls. And thats an ongoing process.

    Companies you know cutting token spend, you thinking ubiquitous AI adoption is unrealistic, and newer models using more tokens per query are all arguments about future/total demand. None of them actually tell us whether a token is dramatically cheaper or more energy efficient to produce than it was a year ago. Realistically thats a learning curve problem, if someone is throwing a basic question at a model far more complex than it needs they are creating the inefficiency and that needs to be made more obvious/streamlined.

    I’ve not claimed demand had increased 900x. You inferred that from the existence of new data centres, which was the faulty bit of reasoning I was responding to in the first place.








  • You’re just repeating the same point I’ve just addressed, so I’ll explain in more detail.

    Data centres expanding is perfectly compatible with huge efficiency gains per token. If the cost of inference falls, companies can afford to run vastly more inference, serve more users, use larger models and embed AI into more products.

    It’s the same reason more fuel-efficient cars didn’t eliminate demand for roads or petrol. Efficiency lowers the cost of using something, which can increase total consumption.

    So “if software got more efficient, they wouldn’t need more data centres” is basically backwards. The relevant question is whether total AI demand is growing faster than efficiency is improving.