If capability increase continues as it has, then an incident that cannot be resolved by AI will stump humans no matter the practice.

I like the plane example from the article,but I think in reality it will be like code. 1.5 years ago engineers would routinely say that they still write code by hand here or there to keep their skills sharp, and that's just not something you hear much if at all.

If an SRE is faced with a situation an AI can't solve, then said SRE will use the AI systems to triage further, point it to different places and so on.

This works for SREs with pre-AI experience and intuition, possibly less so with new recruits coming in post-AI. I don't know what the solution to this is, maybe practice drills is it, but I have a hunch the entire field will be subsumed, same as many other engineering fields.

There is only so much need for taste and judgement, before even that has been incorporated into the models.

It’s tough. The models can at this point very quickly identify issues in a Kubernetes cluster, for example. This because these systems give you a TON of observability, and it can easily see all the different moving parts.

That doesn’t mean the proposed solution is always right, but it is absolutely landing on the root issue faster than most humans would be able to, even pre-AI. Just because it can remember and run through a bunch of commands more quickly that I can.

There are lots of incidents where the symptom doesn’t always clearly point to the issue, so having something that can fairly exhaustively check a lot of different things very quickly is pretty useful!

But I at least partly agree, I think the more complicated and obtuse a system is, the harder it is for AI to do this. But we’ve invested time in making systems much more straightforward to understand and operate from one place (Kubernetes in general), and AI thrives on that.

And yeah, it means your debugging skills wane a bit because, yeah, if the bot can diagnose the issue in 5 minutes, it’d be irresponsible to not use it.

And I’m not really happy about it, and personally I’ve always been able to figure out a tricky bug given enough time. I don’t want to loose that skill. But everyone is under a lot of time pressure these days.

Like the fact that software "engineering" is mostly nothing like real engineering (and it’s further regressing now due to LLM coding!), the general lack of drilling is again one of the things that make software-related stuff look really naive and amateurish from the perspective of those dealing with the real world. Imagine if the military, police, fire service, and so on did not drill and rehearse incident response?

If you are in a situation where you dont know what happened when something goes wrong, the business incentives will not accept “its too complex” as an answer.

Firms aren’t just selling products, they are selling reliability and taking on liability.

> If capability increase continues as it has, then an incident that cannot be resolved by AI will stump humans no matter the practice.

I disagree with this. Whatever the AI produces must be embodied in some kind of information system. The moment the output is on disk, it's fish in a barrel for any competent operator.

I've worked in environments that are beyond the pale with regard to complexity. It will take AI another 10 years to product something as complicated and coherent as a semiconductor manufacturing operating system, which is clearly feasible for humans to manage today.

Nah, LLM models are already the new compilers. A commodity only engineers know how to use (in the context of software engineering in production environments)