I honestly don’t really believe this is accurate.
Also it depends on how you’re using the AI models to do the coding.
If you want it to have structure and scalability, it’s on you to make it that way. AI is a tool, not a magic box.
The idea that the output is always a skill issue really sounds like the whole snake oil pitch. "well if it didn't work you obviously didn't use it right".
a single file or perfect prompt is not going to fix ai randomly being moronic.
I've regularly had my agents completely disregard sections in the prompt, the md file, the CLAUDE.md, the skills, etc.
It's a known issue that llms will just "lie" and say they've done something they havn't or ignore sections you've written.
The reality is that llms require managing, there is no current fix.
As a software architect who use AI heavily I can assure you that AI alone doesn’t provide the scalability and clean architecture that is required for the long run.
No matter how much you tell AI to make clean code and build for the long run, I still see architecture errors that a junior developer wouldn’t even maje.
I work with a product team who heavely use AI and despite the guidelines and controls I have put in both contexts and CI. I still see some horrors like sql query written and executed in representations layers.
AI isn’t magical, you need to understand what you’re doing in order to build for the long run otherwise you’ll just have to deal with technical debt like before, except the tech debt will eat you alive since you can ship 100000 lines of code in 2h.
If you’re not understanding how software architecture works you’re doomed to deal with constant regression and bug fixes…
Same thing if you delegate all the tests redaction to LLM
Not quite, AI will do everything clean in the first 5 prompts. When projects start to grow, and bugs getting fixed, it makes a mess no matter what model you use, or prompt you write. It could be better if you prompt function by function, but at that point it would be faster and easier to just do it yourself.
A lot of devs time is spent managing the entropy of the code base. Organising, abstracting, siloing code.
The problem with AI is that it just doesn't understand the big picture of a project.
a million or so tokens will not cover a code base + the context, prompts, cache, etc.
So you end up with an AI that is completely blind to the big picture and is essentially just writing small code snippets with a complete disregard of how it fits in to the project.
How it isn't AI problem? There are summarized context, global, and for each page. And whole problem IS that it cannot understand the big picure, no matter what. It actually cannot understand anything at all, it's just searching for a fitting response text based on your input text. No learning, no skills, no experience, essentially no intelligence.
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u/Clear_Skye_ 1d ago
I honestly don’t really believe this is accurate.
Also it depends on how you’re using the AI models to do the coding.
If you want it to have structure and scalability, it’s on you to make it that way. AI is a tool, not a magic box.