This article is all hypothesis and no evidence.

Yes, it's clear that if AI agents handle routine issues then only the most complex issues will be handled by humans. In no way does it follow that humans will be less prepared to handle those incidents than they are now; by definition, this class of problems have no rote solutions. Each issue of this type already requires deep system knowledge to remediate, and still will.

What we are seeing here is an instance of the more general trend where experts are still required to operate complex systems, but AI is destroying the career path that creates those experts. That's what we should be worrying about, not that people are going to be spending less time rebooting boxes. This isn't something you can fix with a "simulator."

When someone else -whether AI agent or a human- solves the recurring minor problems for you, those problems become non-issue, get swept under the rug, just to accumulate more dust.

One day, those may become bigger as they are forgotten, causing havoc. The standard root-cause-analysis depending on systems having certain retention period, which may be expired at that time.

It is important to get real hold of one's systems from end-to-end aspect, which holds true for both AI and human operators...

Meta: The blinking cursor of the "logo" of the blog being sticky in the top left corner makes it impossible for me to read the text. It constantly fires interrupts at me.

Depending on what your goals as the author are, you may or may not want that.

Being able to scroll it out of view might be enough to achieve the aesthetics goal, and the goal of people actually listening to you.

We have been running Agents on infrastructure and letting to create resources, scale up and down, security scans etc.

I agree with premise of thr blog. The question i have been asking internal does knowing your system really matter if you can recreate it in minutes.

We recently had a situation, where in with our internal platform and claude we recreated everything in minutes.

Management in the end cares about the outcome and not how the meat is made.

Isn't there anywhere to "go" from here? In the last decades, introducing new high level abstractions on top of existing paradigms naturally had everyone move up the ladder and work at the next higher level, why should this be different these days? Do we think AI will reach the top of the abstraction ceiling, so there's no where to go from here?

You’d hope these AI incident responders have very constrained production tools to fix things. You’d hope the humans remain familiar with those tools and they are incredibly well documented.

The article does not get the point. What aircraft companies did was separate training and work, and SRE/IT typically does not.

An AI can handle routine incidents and then present learning cases from that routine work for training, because the skill in SRE is not the mechanical log grepping, grafana dashboard browsing etc but forming the hypothesis. AI incident reports can create training cases that are a much better training for hypothesis forming and testing than the work itself can.

1980's .. compilers handle assembly, engineers loos touch with their code ...

Ironies of Automation is front and center in this article which is awesome. Many of the conversations on AI automation are describing or rediscovering the insights the paper covered.