Agreed. But I think it's worth distinguishing between two aspects of this decrease in reliability: one is the increasing use of #LLMs to write shoddy code that would previously have been written better by humans, but the other is the use of LLMs to find longstanding bugs in code written years ago by humans. The former is a real decrease in reliability; the other is illusory.

I agree with the broad brush strokes, but companies are not monolithic. For example, Management at my employer is completely AI-pilled, but my immediate team is much more sceptical about it, perhaps because we understand the technicalities better than Management does, and perhaps because it's hard to be enthusiastic about something that threatens wholesale job losses.

So there's tension within the organisation: pressure to adopt AI, good reasons not to, and a wide range of opinions represented.

What I see is a whole lot of faith-based planning that resembles the "innovation fairy" claptrap of years before. Companies are charging into the world while caving out experienced and trained people to save money in the belief, hope, prayer that the AI fairy will be there to catch them and build foundations under the causeway they are on.

There is no evidence or past performance to suggest the AI fairy will come through, and every indication that it won't

The real question: when an AI-driven system causes harm, where does accountability live?

Failures get attributed to the AI, successes claimed by humans, liability dissolves into complexity.

We don't need humans to understand the system. We need governance architecture. Without that, MTTR is just failing faster and cleaner.

We're living through a time of forgotten history, which is kind-of funny-but-not-really in computer science when the main crowd having learned these lessons is still around and - quite ironically - now often in middle to upper management.

Conversely, there is entire cohorts of folks who are in depths of severe #Ai anxiety.

There is no point having a conversation with them because they are entirely irrational.

I worry.

I wonder how long it will take for the rest of the world to a) realize what's going on, b) arrive at the objectively valid conclusion and c) act on it and see it through.

Recently past experience (COVID-19) shows that being a tall order.

Anyway, I wonder when, if so, people who actively rejected contact with LLMs will be in extremely high demand, being knowledge bearers in teaching and technical lead positions.

Especially during the first review, I often see stuff in the code that I really don't like, either from the way it is coded or from an efficiency standpoint or useless complexity.

And concerning the "full test coverage": Just Friday I fixed a bug that one of our human testers found. Before he tested it, I already had some gut feeling that there could be a problem in the code, so I told the tester about it. And my gut feeling was right. So after I fixed the bug, several automatically created tests failed ...

And concerning the tests: I also had situations where the LLM did some coding that was bad, so that already existing tests failed. And what did the LLM: It hadn't fixed the code but changed the test ...

At least in our department we always require human reviews, so I hope that the quality won't suffer.