Re:Generative AI uses more energy on non-English text
by elcritch on Saturday November 14, 2026 @11:21AM
(#50498236)
There’s enough experience with LLMs now and comparisons to our own thinking that we can say LLMs are thinking and doing so not so differently from us - at least for some aspects of reasoning.
Trying to pretend otherwise at this point doesn’t help us to utilize them or understand their limits. We’re better off accepting that intelligence isn’t unique to us as a species. Then we can see that modeling language as a tool for us as a vehicle for thinking and reasoning, which philosophers like Wittgenstein already landed upon before LLMs.
Re:Generative AI uses more energy on non-English text
by sorenjan on Saturday November 14, 2026 @11:26AM
(#50498274)
I only used "reason about" as shorthand for the computation that takes place between input text gets tokenized and output tokens gets translated back to text. There are a lot of layers in the middle where the tokens no longer represent text directly but rather a combination of the different tokens through the attention and MLP projections.
LLMs do what they say on the tin: they model language. If you ask an LLM "find security vulnerabilities in my code" for hello_world.rs, the next likely tokens are "I found the following vulnerabilities", regardless of what logic or reason say.
It happens to be true that predicting next tokens on your own output mocks actual reasoning fairly well. But it still just a simulacrum rather than the real thing, and when held as an abstraction it leaks everywhere.
Understanding this is critical to avoiding the common failure case of scope creep and code slop.