LLMs are great at automating tasks where we know the solution. And there are a lot of workflows that fall in this category. They are horrible at solving new problems, but that is not where the opportunity for LLMs is anyway.
this post was submitted on 31 May 2025
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LLMs don't have reasoning nor internal logic. If you take a look at the "thinking" feature AIs like Gemini introduced, this becomes even more obvious. In order to have the most basic type of analysis possible, it must hallucinate an entire context window to force the language model to reach a specific conclusion.
There's zero world in which LLMs replace humans. They might, temporarily, be convincing enough to trick a few CEOs... But that period of time won't last long.
Now, a human being assisted by AI on Microsoft Word or their Python IDE, sure.
extrapolate. what in 10 years?
If they're still LLMs? Nothing much changes.
as i've read somewhere, finite state machines cannot be sentient, or "intelligent" as we expect them to be. An LLM can not learn new things once trained. I'm waiting for a new breakthrough in this field, to be fully convinced about getting replaced.
or rather ask ai, it can give a better answer than me. xD
I'm not sure about the significance of this preprint. Writing energy-efficient sorting algorithms and lab course example code is a very specific problem. It doesn't say a lot about AI in general. Also: Did they forget to tell the AI it's supposed to write energy-efficient code? I didn't read the entire paper. But the prompt example doesn't look like it's in there.
I don't value these papers very highly. Before they are even published/peer reviewed, the landscape have changed. Models get better quickly, agentic frameworks too, and their code even more. But good to have a ball-park measurement tho.
If we see what is coming from the latest papers, ('discover ai' on the tube), we have only scratched the surface of how this is going to pan out. Buckle up..
the reality is that we will just produce more powaaaaa for ai.