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Generative AI Is an Engineering Disaster. A shockingly inefficient trillion-dollar project.
(www.theatlantic.com)
this post was submitted on 15 Jul 2026
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The interesting bits are when it derives the likely hood of something being correct and does more passes, or splits the data apart in the first pass and opens up new context processes with specialized instructions to handle it. The code stuff goes full on ororborus on some models, writes out the code on one pass, checks for issues on another pass, runs the code looking for errors on a third pass and goes back to step 1 if it fails.
They're getting a lot out of it for it primarily just being a weighted token generator wrapped in an orchestrator.
Particularly software development with very good and very quick tests allow rerolling and that is very appealing in those scenarios. Problems being that very good tests are rarer than people like to think and sometimes it just gets stuck in a loop. At work the other week someone set it at the task of fixing a bunch of build warnings that had accumulated over the years. It succeeded after burning through tokens to take 30 tries at it. It's solution after all that hard thinking? It put // @ts-nocheck at the top of every file and called it a day.
But superficially, someone handed it a chore and didn't have to think about it, and if no one looked deeper, then it was able to get to the desired behavior simply by rerunning the given task over and over without human intervention until it worked. Which is also broadly relatable as there's a lot of humans in the industry acting broadly the same, but I've always been frustrated by those folks anyway.