Decoded: it's next to useless, it's damaged pace, and it isn't value for money. It's boiling the ocean.

Implicitly they've woken up to the value proposition which was latent in their tech hires: detailed knowledge of their code and systems. They've just tossed that away, and even worse they've smooged it into unfathomable information systems which probably share aspects of it with their competitors.

AI destroyed their value.

CTOs / CEOs have demonstrated how completely useless they are pretty much across the board over the last few years. Groupthink and bandwagons, zero innovation or use of brain.

> AI makes it cheap to write code. That is not the same as it being cheap to ship it, or to maintain it. One participant put it cleanly: cognitive debt is the new technical debt.

It being expensive to ship or maintain software still sounds like technical debt, no?

For cognitive debt, I'd expect something like context switching and reviewing large amounts of code being exhausting.

What CTOs did not notice yet is that cheap code exposes inefficiencies elsewhere.

Migrating Spring Boot apps from 3.x t 4.x is now easy given all the tooling available.

But the administrative load can't be reduced by faster code delivery and that's the new bottleneck.

Considering all the possible levels of abstraction software can represent, I'm imagining it as a fractal. The worst case - a mistake can be introduced by generative AI at any of the abstraction levels at any moment. Meaning, at worst case the whole thing has to be in the head of at least one person to validate the result against. The moment a project is growing beyond a single person capacity to hold it in one head possibility of plausibly looking error introduced at any abstraction level is added to the usual multi-engineer coordination costs.

Copyright issues don't seem to be addressed by any large language model providers.

If an LLM is trained on GPL code then that code has become an intrinsic part of the model (because if it hasn't then what was the value of training on it). So shouldn't that model now also be licensed GPL?

And how do I know the LLM output is not reproducing substantial chunks of GPL'd code, making my code GPL?

> Two years ago, the mandate was simple: spend on AI, no questions asked. That era is over.

2 years is an era now?