I mean, did anyone expect anything different? The frontier providers that Copilot depends on switched to token-based billing in a desperate and ludicrous attempt to somehow turn a profit on AI, so of course Microsoft is gonna jack up their prices too.
That's not even remotely an "atheist's worst nightmare." A deity exists and it's horrifying? That's a theist's worst nightmare, and the basis for several arguments for atheism.
If the guy has any actual desire to do good in the world, the best thing he could do now is drop out. Given his stated politics, I figured the tattoo was from when he was young and stupid, like he said, but this shit... People don't outgrow this shit.
He needs to go.
That only works if they can keep everyone involved quiet. If he's actually deceased, that fact is going to leak.
The only question swirling in my mind is how long they'll Weekend at Bernie's him before admitting the truth.
There was a lot of news about the Muttsee Dam solar project a few years ago, so it's a real thing and a good thing. But it's hardly current news. It's been operational since 2022.
It wasn't. But that doesn't mean we shouldn't strive to make it be the best version of itself it could be, and fight tooth and nail against attempts to make it worse.
Very serious. Your personal amount of usage means nothing at all in this conversation. It is entirely about tokens per watt. The amount of energy the memory operations involve scale incredibly well when people are accessing the same object in memory simultaneously. Last I looked it was around a 10x difference for the same models efficiency.
Hold up. Are you talking about caching? Because if you are... yeah. That has nothing to do with the model and everything to do with the service layer around the model. The same service layers can be - and have been - implemented in tools like Lemonade Server, llama.cpp, Ollama, etc.
And I really do want to know your sources.
Mine say GPT 5.5 is probably using quite a lot more than 0.34 Wh per query (0.34 Wh is what Sam Altman claimed for the then-current version of GPT in June of 2025, but he hasn't released numbers since then and no one has done an independent analysis). With Claude, an independent estimate from last year pegged Sonnet at 0.8 Wh for a short prompt, 2.8 Wh for a medium one, and 5.5 Wh for a long one. Current numbers are, again, almost certainly much higher. And just for fun, there's DeepSeek (which I've never used and never would use), with the reasoning-tuned DeepSeek-R1 hitting a whopping 29 Wh for a complex query.
Meanwhile, small, open models are probably in the 0.07 - 0.2 range, depending on the model, the hardware it's running on, and the nature of the query. Of course, there are much weightier open models too, with ones like Llama 3.1 405B using about 9 Wh for a medium-length prompt. On the other hand... who is going to run that on their local machine?
Look... If I'm wrong, and using local models the way I do - sparingly and infrequently - really does consume more electricity than using Claude Code, I want to know. I have no problem whatsoever with eschewing AI models entirely, since I despise all of them. But given how tight-lipped OpenAI and Anthropic are about energy consumption per average prompt, and what independent analyses have estimated, I am highly skeptical that they are acting as some sort of paragons of environmental stewardship.
You’re probably burning more energy turning it off and on again. It doesn’t really use any noticeable power sitting idle.
I am absolutely not burning more energy than a frontier model by doing things like putting my laptop to sleep or shutting down unused services when I want to conserve battery power.
Anyway, a direct comparison would be pretty difficult because your model is probably tens of billions of parameters, not over a trillion.
True.
Energy consumption per output token will probably be a bit higher for the frontier models but something that people have found is that higher quality models often need fewer tokens to achieve the same goal.
That's actually not true. In fact it's much the opposite. Frontier models churn through tokens at a much higher rate, because of their higher complexity and higher number of parameters. Research is still new on this, but having a frontier model analyze your code files versus a small, local model for the same task seems to be enormously wasteful. If you must use a frontier model for something, have it do that work after receiving the output from an agent using a small model to read and summarize your code.
Plus how many times do you re-prompt your local model vs Claude Fable or Opus for example to get the desired result?
...Almost never? I'm not a fan of letting AI do much of ANY of my coding, because it will inevitably bloat my codebase with garbage regardless of which model I use. So I severely restrict my model usage to simple, clearly-defined, narrow-scoped tasks that can save me a bit of time, and that's it. With guardrails and discipline like that, I barely ever have the need to re-prompt.
Are you serious?
- Local model: Spends most of its time turned off. Only active when I want it to be active, and only for a little while. Dedicated solely to generating the small amounts of code I use it for. Does nothing else. Costs $0 per token, and electricity costs are negligible.
- Frontier model: Always on, running on millions of GPUs. Would be burning down the planet even if hardly anyone was using it. Incredibly wasteful, being used for trivial tasks and convincing people that their horrible ideas are visionary every day. Misspelling "strawberry" for the masses. Trained specifically to be addictive. Can easily cost a software developer who is addicted to AI thousands of dollars a month, with the recent price increases.
I'd love to see some data to back up the assertion that frontier models are somehow cheaper and more efficient than running a model locally.
No, there really isn't. The frontier models are created through massive plagiarism. They're designed to be addictive to use. They consume massive amounts of resources to feed you slop. They are inherently unethical. We're burning the planet down to keep them running, and we don't even have a demonstrable financial ROI to show for it.
Stop using them. If your employer makes you use them, maliciously comply by wasting tokens until the financial pain is too great for them to bear and they stop. If you yourself are addicted, switch to small, local, open-source, open-weight models you can run yourself. You won't burn the world down running a small model on your own computer.
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