Datacentres drive up big tech's carbon emissions to a third of those of France

GLM 5.2 isn't quite modern Opus tier, as seen in this comparison where Opus 4.5 scores 4/5 on some coding tasks where GLM 5.2 scores 0/5: https://www.tryai.dev/blog/gpt-5.6-build-off-12-models But yes, GLM 5.2 is cheap.

But the real standout on price is DeepSeek V4 Flash, which competes, more or less, with models in between Sonnet and Haiku. From third-party providers, it costs around $0.09/M, $0.18/M out, compared to $3M/in, $15M/out for Sonnet and $1M/in, $5M/out for Haiku. To get the price of DSv4 (Flash and Pro) so low, DeepSeek did a lot of innovative optimization work that will likely show up in other open weight models in the future.

I agree with using smaller models, it's just that the majority of people I know feel like they need the biggest, beefier, behemoth model possible (with the longest thought setting) and consume much more than necessary when a flash or smaller model would be OK. I would also like to be able to use a smaller model, but given ram prices I would have to sell a kidney to buy ram now

> There isn't much evidence at all that inference is "subsidised" (and by whom?)

Well... why else would the major providers now tighten the screws on per-token pricing?

> There isn't much evidence at all that inference is "subsidised" (and by whom?)

None of the big providers are profitable. It’s subsidised by overly enthusiastic VCs.

> In any case, you can go buy a MacBook Pro M5 48GB or an AMD R9700 and run Qwen 3.6 35B-A3B (a very capable model) and the only "subsidy" is you plugging it in

Right, people could. But they won’t, because that’s a bloody expensive computer and they don’t need that to ask ChatGPT. That war is lost already.

Subscription to the big players’ services would need to increase massively for that to happen. And the computational cost is only part of the problem; these models also eat a lot of storage and RAM, which is not exactly getting cheaper.

I don’t use AI so this comes across to me as a bit of a culture shock, but is asking AI to rename a variable across project really something AI users are wasting their tokens on? In emacs I can do that with `S-l r r` or `M-x lsp-rename`.

Using AI to do this seems extremely inefficient and wasteful, not to mention improper and unprofessional, and that is looking past the moral implication of training on stolen code and polluting our climate.

Work without effort and thinking machines were also once fiction.

that could still be cheaper than actually sucking CO2 out of the air --- or not, let the market do it's thing

> I imagine it'd be a lot easier to get a giant datacenter through the queue and connected if said datacenter also generated more than it's own needs

You'd be wrong. Projects get evaluated FIFO, and each has to do a grid impact study. Data centers are buying other generation projects to get a better position in line. This is all the result of bad regulation.

In YOUR state. This is because projects are hopping around and looking for short lines with other considerations. However that doesn't change the fact that its too hard to bring generation onto the grind in the US.