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.
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.
So your really making the claim that there’s over a 900x demand for AI in the last year?
I’m calling bullshit.
No.
?
When you lock onto something really hard in a discussion like this it becomes very obvious you’re faltering and dont really have much to contribute.
Ypu claimed up above a 900x increase in token efficiency, and claim that demand is outpacing efficiencies.
So yes you are.
I’m calling bullshit on demand outpacing efficiencies in general.
Based on what rationale?
You made the claim it is.
Its based on every company I know pulling back on token spend because it keeps getting more expensive.
Based on the fact the the growth they anticipate is everyone will use it everywhere. And thats not going to be true any time soon.
Based on the fact queries cost more and more tokens on the new models.
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.