Same here. I'm "happy" that I'm old "enough" to be able to wrap up my career in a few years time and likely be able to get out of this mess before this "agentic AI slop" becomes the expected workflow.
On my personal project I do sometimes chat with ChatGPT and it works as a rubber duck. I explain, put my thoughts into words and typically I already solve my problem when I'm thinking it through while expressing it in words. But I must also admit that ChatGPT is very good at producing prose and I often use it for recommending names of abstractions/concepts, modules, functions, enums etc. So there's some value there.
But when it comes to code I want to understand everything that goes into my project. So in the end of the day I'm always going to be the "bottle neck", whether I think through the problem myself and write the code or I review and try to understand the AI generated code slop.
It seems to me that using the AI slop generation workflow is a great fit for the industry though, more quantity rather quality and continuous churn. Make it cheaper to replace code so that the replacement can be replaced a week later with another vibe-coded slop. Quality might drop, bugs might proliferate but who cares?
And to be fair, code itself has no value, it's ephemeral, data and its transformations are what matter. Maybe at some point we can just throw out the code and just use the chatbots to transform the data directly!
This is pretty much how I use LLMs as well. These interactions have convinced me that while the LLMs are very convincing with persuasive arguments, they are wrong often on things I am good at; so much so that I would have a hard time opening PRs for code edited by them without reading it carefully. Gell-man amnesia and all that seems appropriate here even though that anthropomorphizes LLMs to an uncomfortable extent. At some point in the future I can see them becoming very good at recognizing my intent and also reasoning correctly. Not there yet.
On my personal project I do sometimes chat with ChatGPT and it works as a rubber duck. I explain, put my thoughts into words and typically I already solve my problem when I'm thinking it through while expressing it in words. But I must also admit that ChatGPT is very good at producing prose and I often use it for recommending names of abstractions/concepts, modules, functions, enums etc. So there's some value there.
But when it comes to code I want to understand everything that goes into my project. So in the end of the day I'm always going to be the "bottle neck", whether I think through the problem myself and write the code or I review and try to understand the AI generated code slop.
It seems to me that using the AI slop generation workflow is a great fit for the industry though, more quantity rather quality and continuous churn. Make it cheaper to replace code so that the replacement can be replaced a week later with another vibe-coded slop. Quality might drop, bugs might proliferate but who cares?
And to be fair, code itself has no value, it's ephemeral, data and its transformations are what matter. Maybe at some point we can just throw out the code and just use the chatbots to transform the data directly!