Comment by amarcheschi

I anticipate a control issue, where agents can produce code faster than people can analyze and beside applications with small visible surfaces, nobody will be able to check what is going on

I saw people with trouble manipulating boolean tables of 3 variables in their head trying to generate complete web applications, it will work for linear duties (input -> processing -> storage) but I highly doubt they will be able to understand anything with 2nd order effects

I'm honestly slightly appalled by what we might miss by not reading the docs and just letting Ai code. I'm attending a course where we have to analyze medical datasets using up to ~200gb of ram. Calculations can take some time. A simple skim through the library (or even asking the chatbot) can tell you that one of the longest call can be approximated and it takes about 1/3rd of the time it takes with another solver. And yet, none of my colleagues thought about either looking the docs or asking the chatbot. Because it was working. And of course the chatbot was using the solver that was "standard" but that you probably don't need to use for prototyping.

Again. We had some parts of one of 3 datasets split in ~40 files, and we had to manipulate and save them before doing anything else. A colleague asked chatgpt to write the code to do it and it was single threaded, and not feasible. I hopped up on htop and upon seeing it was using only one core, I suggested her to ask chatgpt to make the conversion run on different files in different threads, and we basically went from absolutely slow to quite fast. But that supposed that the person using the code knows what's going on, why, and what is not going on. And when it is possible to do something different. Using it without asking yourself more about the context is a terrible use imho, but it's absolutely the direction that I see we're headed towards and I'm not a fan of it

I wouldn't really pay attention to what CEOs say, it's their job to sell things and drum up investment.

No one really knows exactly how AI will play out. There's a lot of motivated reasoning going on, both from hypesters and cynics.

That statement seems extremely hyperbolic. Just today I tried automating some very pedestrian code for a system that wasn't particularly well-documented and ChatGPT 4o hallucinated the entire API. It was deeply frustrating and wasted more of my time than it would have taken to just slog through the documentation.

I won't deny that LLMs can be useful--I still use them--but in my experience an LLM's success rate in writing working code is somewhere around 50%. That leads to a productivity boost that, while not negative, isn't anywhere near the wild numbers that are bandied about.

This quote to me feels more along the "no computer will ever need more than 640kb of RAM" lines in terms of historical accuracy. Like whoops, nope.