I think it depends on the problem domain. I have to implement a lot of throwaway ideas quickly, and LLMs are really useful there.
For instance, say I wanted to plot a complicated Matplotlib diagram. It takes me 10+ minutes plus many context switches to get the syntax right (I don't use Matplotlib enough to have all the args at the tip of my fingers). Also I don't know everything Matplotlib is able to do -- I haven't read the entire docs. Fortunately LLMs have and they get me to the right ballpark in 10-20 seconds. I usually want to try maybe 10-15 plots before settling on something. LLMs definitely do get me there much faster.
I think if you have a clear idea of what you want to do, and how to do it, then maybe the time savings are not compelling. But if you're in space where you're ideating and groping at an idea, LLMs can significantly cut down the iteration time and even open up new channels of inquiry that you didn't know existed.
They're primarily generative assistants. Using them to implement ideas in production is probably a secondary use.
For instance, say I wanted to plot a complicated Matplotlib diagram. It takes me 10+ minutes plus many context switches to get the syntax right (I don't use Matplotlib enough to have all the args at the tip of my fingers). Also I don't know everything Matplotlib is able to do -- I haven't read the entire docs. Fortunately LLMs have and they get me to the right ballpark in 10-20 seconds. I usually want to try maybe 10-15 plots before settling on something. LLMs definitely do get me there much faster.
I think if you have a clear idea of what you want to do, and how to do it, then maybe the time savings are not compelling. But if you're in space where you're ideating and groping at an idea, LLMs can significantly cut down the iteration time and even open up new channels of inquiry that you didn't know existed.
They're primarily generative assistants. Using them to implement ideas in production is probably a secondary use.