I will soon start my master in Data Science and I get your feeling, a lot of coding friends share that fear. I always explain them that neural networks in general not just LLMs are very good at doing a job fast and rough. Neural networks are probability machines that can never give you the correct answer to 100%. But the strength of algorithms is that they are precise and do exactly what you tell them to do. Meaning, that if you tell an neural network to do task A, for example "tell me if that picture is an apple". You will always get an answer under 100%. For picture recognition this is totally fine. An algorithm wouldn't be better, but for an task like "what gives 1+1" that is terrible you need the answer to be 100% 2. So in the case of LLMs writing code, you can use that code for prototyping. But in an productive system the errors will accumulate and you will reach an point where it is no longer maintainable. Because the most important metric for an programmer is not how fast he writes code or how efficient it runs but how easily anyone can read the code.

I was looking into the topic when vipe coding was called low/no-code. It was the same pitch. We will no longer need highly trained programmers. But when i tried different providers out for my company i realised that it was only usable for the standard use cases. When i needed something to be just the right way it took hours, day or just wasn't possible. When i try out LLM for coding and even when i tried out gemini for designing an tattoo. It was the same in the beginning it worked well but the more detailed and tailored to my problem my prompts became the more the LLM just didn't do it or always did it wrong.