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I suppose I should quote a part, since no one else will read the dreaded wAlL Of tEXt.
Is this short enough for you?
AI helps people find more vulnerabilities in existing code. To address those vulnerabilities, people use AI to generate patches. The resulting pull requests are then “reviewed” by AI. That is, the more AI is in the loop, the less we understand the code base. The mystical “human in the loop” often is nothing more than a rubber stamp.
So what happens when things go bump? Complex systems fail in complex ways, and debugging code is an order of magnitude harder than writing code. Debugging somebody else’s code is harder still. Trying to debug large, complex, distributed systems consisting of components that are effectively opaque to your entire organization is going to be impossible.
since no one else will read the dreaded wAlL Of tEXt.
It’s okay I can just have an AI summarise it for me.
You know what’s harder than debugging? Maintenance, especially of someone else’s code, keeping up to date with security exploits, revamping UX etc. which is why github is full of write only (as in never read, never changed) slop projects. That’s a critical part of a software project with any lifespan.
And then there’s integrating new functionality.
Without a solid architecture (which AI sucks at), strict code readability standards (ditto, but improving) and nuanced reviewing of potential additions to the codebase (ditto) what you get is a spagettified mess. It ends up costing more to maintain and improve than just starting fresh.
The promise of the AI bros was that by the time you needed to maintain or extend, you could just get the next version of Claude or whatever to do it, as it’s capabilities would have improved. That was a lie, and LLMs have hit a logarithmic wall where throwing more compute and bigger training sets at it produces diminishing returns. No AGI for you (unless some genius finds a new architecture). Frameworks / harnesses are still improving, but there’s only so far that can go.
The amount of technical debt that has been racked up by the last two years of AI coding is staggering to contemplate, and the industry will be dealing the fallout for years. Companies will collapse, and god help you if you’ve used it liberally in a codebase as large as an OS - looking at you Microslop.
As someone said a long time ago (way before the LLM craze) “bad developers love new projects, they can write terrible code, take praise and zero responsibility, then toss it to the maintenance (actual) team who has to deal with it”.
(Context: in at least 2 companies I worked at, it was a common practice to have a “development team”, usually with knowledge of latest tools and frameworks, but very little knowledge of processes or industry, and an “application team” that maintains the software long-term, and deals with the processes and users directly)
The other day I went to talk to some subject matter expert because I wanted an experienced human’s knowledge and perspective on my problem. They literally just read me Reddit posts and Gemini search results to answer my questions. Even turning their laptop and being like “see?”
What a waste of everyone’s time.
God forbid we think for ourselves and have real conversations.
I’m also feeling burnt out and I barely use it. The reliance is real. But the novelty is gone.
I saw some headline talking about an upcoming interview with the world’s first AI actor. But…why?
Someone has taught their dog better and better tricks. It’s still a dog!
More like somebody taught their calculator to spell OBOE ShOES. It’s still a calculator.
