They should think about what happens when expectations are so high that a single dev must deliver and maintain multiple products. What stops that single dev from leaving and offering the same product on his own.
CTOs noticed it. When product pipeline is empty, because engineers finished all the outstanding tasks, the engineers are awarded with more work: "The new software engineer is a product leader. Someone thinking about what the product is, not just how it works", or, in other words, engineers are going to be tasked with putting more content "the what" into the product pipeline.
Copyright issues don't seem to be addressed by any large language model provider.
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?
Or alternatively. LLM is not human. Non human generated content has no copy right protection. Meaning all generative model output is automatically public domain.
My worry is that AI will make these companies product-obsessed, the public will mistake the output for AGI, and the real engineering underneath will keep getting overlooked.
> 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.
There were some good insights in there. I like the idea of changing the hiring interview process to focus on testing code review ability. I feel like this would have been useful even before AI.
A candidate who can identify tradeoffs present in some code and make insightful comments is a really effective way to test someone's knowledge, intelligence and taste.
It's actually brilliant because it provides the company with a way to actually improve their engineering posture since the company could land on a candidate who is more skilled than the engineers doing the interviewing.
Most leetcode tech interviews are a series of puzzles which most company insiders can solve but they never include problems that the candidate could solve but which the interviewer could not.
Leetcode interviews are horrible because they test a tiny subset of moderately difficult questions under time constraints and ignore a much larger set of problems that are much more complex. There is an incorrect assumption that someone who can solve extremely complex problems can also solve moderately complex problems under time constraints. This is absolutely not the case. It's almost mutually exclusive in fact since people who work on complex problems don't have the time or interest to practice solving simpler problems so they can never solve those fast enough to compete with fresh university grads who have been practicing those for years and don't know anything else.
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.
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.
CTOs were told that the companies shares will be sold off if they can't produce AI results, and that the CEO will be deposed for "not having an AI strategy", so they should kindly shut up and go along with the flow.
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.