On Reddit, from r/SaaS, u/curiosity_catt wrote "The AI slop refactor wave is coming and I haven't felt this excited about consulting rates since 2010." It's an interesting piece, but what was amusing about it was that only the source of the refactor was different: we've been here before.
Multiple times.
The opening paragraph, edited for presentation, looks like this:
Twelve years in and I'm starting to see something familiar. Around 2010, I made decent money fixing what offshore contractor work left behind. Founders thought they were getting the same product for a quarter of the price, then 18 months later they were paying someone like me to make it actually work.
The author is saying the AI vibe coding experience is yielding the same pattern, and it may very well be: users think the AI promises working code, and it delivers "working code" because the users don't know what they don't know, and thus the "solution" looks like the first half of a tutorial the user didn't bother to read.
That's a very fertile market: someone does something badly, and has to pay someone else to fix it, even if they use the same tools that created the problems in the first place.
It is the new offshoring, even down to the provenance of the AIs; the most commonly-suggested on-premises models are from other countries, with those countries' bias baked in in some surprising (and not-so-surprising) ways.
The offshoring cycle, though, wasn't "from 2010" - there was one in the prior decade, too. Awards were handed out for cost optimization as development work was sent to cheap consultants offshore, to be followed by pink slips as productivity went into the red. Sure, expenditures went from dollars to cents, but return on those cents went flat.
As has been said before: coding isn't the same as typing, and a lot of investors and managers seem to struggle with that idea. AI can type, marvelously well at times, and that can translate into code, but the actual expertise - the thing that was lost in offshoring and the thing the AI can't really infer except as a sort of cultural haze - the actual expertise is found in people, and they're your people, not interchangeable cogs.
People care about systems making sense; the AIs don't. People can factor in experience from having been there and done that; the AIs have no "there" and "that" is built from a million casual StackExchange posts that discuss a tight focus on one aspect of real problems.
There's a real danger, too, in that the users who create the problem of overdependency on AIs see AI as "the solution" - okay, fine, the AI wrote slop THIS time, all that's needed is to say "don't forget security, whatever that means" or "someone said I should use HATEOAS, can you implement that or something" - which kicks the can down the road another few yards, hopefully just enough that the next iteration of the model can repair the problems just introduced.
That Reddit post is amusing, and prescient, and at the same time, forgetting the history of the industry it belongs in.
The question for me is not "is this accurate" but "when will the bean counters learn that money saved today isn't the same as making money tomorrow?"