So when I see broad stroke "LLMs are useless" opinions, my immediate reaction is "you're holding it wrong" or (being more blunt, forgive me) "you don't know what you're doing".
Even more bluntly: I don't think it's even "you are using it wrong", but rather, it's "you haven't really even tried, so you are just talking about something you don't have a freaking clue about". 99% of AI critics have not tried, or have tried so lightly it doesn't even count as trying (and hence, something that they would do wrong or right), or have tried many years ago.
There are good tell-tale signs to distinguish people like this, e.g. the classic "LLM is just a statistical parrot" argument. Reasoning LLMs became mainstream more than 3 years ago and do well in any reasoning benchmark, and all you need to do is to give
any modern-day LLM, even some free online model like ChatGPT/Deepseek (Claude Opus in Claude Code is obviously even better), any programming task which requires reasoning - and watch it do exactly that: solve a problem which never exists in the training data, by following a step-by-step thinking process (which Anthropic nowadays unfortunately hides from the sight; in that regard, Deepseek might be a better first introduction as you can enable the thinking visibility), disproving the "just repeats the training data" theory.
LLMs are "statistical parrots"
and generic reasoning machines
and huge collections of nearly all human knowledge, and the combination of these three traits make them do useful work.
Sometimes they'll be wrong, you correct them, and they'll learn and adjust from that. Often better than people would.
Combination of two factors: the model itself has been trained with thousands of times more input data than any human being has seen. It averages out strong wrong biases some people have. (The flipside is that it might not been pushy enough when the user indeed is seriously wrong and a "strong" opinion happens to be correct.) - then the user prompt has quite significant weight, so it's easy to push the probability horizon where you want - i.e., correct the wrong idea.
Because the training input tends to be quite balanced, user prompt can easily push it to a certain direction; this is the "learning" and "adjusting" you mention. Both a blessing and a curse. If the user is wrong, user can easily manipulate the LLM to agree.
Reasoning depth of the LLM actually helps here. When you are disagreeing with the LLM, it IS a good idea to tell
your reasoning of why, because that stays in context, and it then takes it into account every time it reasons further about the issue.
You just saying "no, not this, but that" gets some weight because the machine is trained to follow user's orders. But telling also
why makes it reach the same conclusion, and just like with human beings, means it will reason around this limitation and find the correct solution, instead of keeping finding the solution it thinks is right, only to dismiss it because user said it's forbidden (without any explanation why), creating a loop.
Those who don't want to let the AI think and want it to be a stupid machine only, with very peculiar processes they want to micromanage, will be most disappointed. Those who create long lists of "donts" in CLAUDE.md and get a stroke every time it accidentally breaks one of the rules, will be disappointed. Give it some real work and real responsibility and you will actually see how much it can do.
I have an excellent (almost to the level of hand-tuned assembly) compiler for an obsolete processor that was written almost entirely by an LLM. That processor, yes, was very popular during its day, and still has its adherents, but it's a niche of a niche of the internet, and "its day" was actually prior to the internet; yet Claude could tell me that Read-Modify-Write was in significant use on C64 machines, but almost never used on Atari machines, so this optimisation could work here and that one was better there. The amount of internet real-estate dedicated to that is miniscule. You don't need that much source material for the data to be represented in the weights.
And this is one of the core strengths. It's almost unbelievable how much these machines
know, and how correct their information usually is. They deserve all the amazement.
I think (and this may be an unpopular opinion I guess) there is a fair amount of copium going on when people decry the abilities of LLMs.
Well said. But it's psychology. The cycle of admitting being wrong is highly personal. Many of us were rightfully skeptical, and to be fair, say 4-5 years ago when ChatGPT was the fanciest thing and mostly hallucinated total bullcrap to no end, and in context of programming, was only able to output variations of popular example programs, it
was hard to see how this would ever become revolutionary for programming, like some were saying. But they were right; maybe some of them saw something critics didn't, maybe some were just optimists and were right by mere luck.
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