alt Hacker NewsThis is the 2023 take on LLMs. It still gets repeated a lot. But it doesn’t really hold up anymore - it’s more complicated than that. Don’t let some factoid about how they are pretrained on autocomplete-like next token prediction fool you into thinking you understand what is going on in that trillion parameter neural network.
Sure, LLMs do not think like humans and they may not have human-level creativity. Sometimes they hallucinate. But they can absolutely solve new problems that aren’t in their training set, e.g. some rather difficult problems on the last Mathematical Olympiad. They don’t just regurgitate remixes of their training data. If you don’t believe this, you really need to spend more time with the latest SotA models like Opus 4.5 or Gemini 3.
Nontrivial emergent behavior is a thing. It will only get more impressive. That doesn’t make LLMs like humans (and we shouldn’t anthropomorphize them) but they are not “autocomplete on steroids” anymore either.
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I use enterprise LLM provided by work, working on very proprietary codebase on a semi esoteric language. My impression is it is still a very big autocompletion machine.
You still need to hand hold it all the way as it is only capable of regurgitating the tiny amount of code patterns it saw in the public. As opposed to say a Python project.
As someone who still might have a '2023 take on LLMs', even though I use them often at work, where would you recommend I look to learn more about what a '2025 LLM' is, and how they operate differently?
>> Sometimes they hallucinate.
For someone speaking as you knew everything, you appear to know very little. Every LLM completion is a "hallucination", some of them just happen to be factually correct.
> it’s more complicated than that.
No it isn't.
> ...fool you into thinking you understand what is going on in that trillion parameter neural network.
It's just matrix multiplication and logistic regression, nothing more.
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> Don’t let some factoid about how they are pretrained on autocomplete-like next token prediction fool you into thinking you understand what is going on in that trillion parameter neural network.
This is just an appeal to complexity, not a rebuttal to the critique of likening an LLM to a human brain.
> they are not “autocomplete on steroids” anymore either.
Yes, they are. The steroids are just even more powerful. By refining training data quality, increasing parameter size, and increasing context length we can squeeze more utility out of LLMs than ever before, but ultimately, Opus 4.5 is the same thing as GPT2, it's only that coherence lasts a few pages rather than a few sentences.