I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming. The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.
As this article points out, an LLM can spit out chunks of regurgitated code that it scraped from the internet, but that does not make the LLM a programmer. The resulting output is an attempt to find an existing pattern in the database which fits with what the user has asked for, but it is not a product of actually understanding the use case for the code. It is just statistical correlation.
So, sure, an LLM can be set up to generate output related to X task. If you can collect and clean data that can be used to train the kind of output you want, it should be able to produce an approximate facsimile of the results you want. Is that valuable for your use case? Maybe.
We’re still just talking about what is essentially a complex search function. The statistical model returns results from its database that correlate most closely to your input. That does not mean it returns the right answer. If there is no good correlation, it will still return a result.
As long as you understand that the result you get is just a correlation based on your input and may or may not be relevant to your specific problem, and you are not fooled into believing that the LLM actually understands what you’re asking and produced a result by “thinking” about it, then you might be able to use an LLM as an effective tool - to search a large collection of information for something that is relevant(ish) to what you’re asking for.
The real mistake has been broad misunderstanding of what LLMs actually do, and trying to use them as general-purpose problem solving tools (or worse, as accurate and reliable sources of information).
Language models are not databases and they are not markov bots (similar function but work directly using statistical word association maps). The big difference is that those things are algorithms someone wrote and can fully comprehend what they do, but machine learning models are large algorithms built by another algorithm processing training data. There is much more uncertainty about what is going on under the hood.
There is also great uncertainty about what concepts like understanding or thinking might mean in computer science terms. The main thing we can really know is that ultimately a human mind is a computer, which means that understanding and thinking have some yet unknown mathematical representation, and therefore a comparison can be made. We should eventually be able to quantify whether or to what extent a given algorithm thinks. But you said in another comment that you don’t believe minds can be represented mathematically; this should mean that such comparisons would be apples to oranges, but you’re making them anyway for some reason, and implying they have predictive power for the limitations of LLMs.
Certainly they do have limitations, at least individually and possibly as a technology. There are things given models are bad at, there are things they initially seem to be able to do well as humans but fail in different ways that suggest over-reliance on pattern matching. But these have been determined empirically through testing. The idea that they are “just statistical models” and this knowledge can be used to say what is impossible for them from philosophical first principles keeps getting repeated but has never worked in practice. The reality is that no one knows enough to say for sure where the line is.