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- Hacker News
- 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.
by danielbln - I see aviation sentiment raised from time to time. In aviation failure is catastrophic, and systems you operate do not change on the fly (pun intended).
You can probably drill SREs that way but you are only teaching them how to _react_ efficiently, not how to _fix_ unique unicorn root causes of these failures. Aviation analogy would be an airplane engineer that tries to drill himself for in-flight engine failures AND engine debugging / engine redesign both at the same time.
It never made sense in software engineering, and never will. Minute spent on drills is a minute better spent on reworking codebase to reduce changes of future incidents. This post is very SRE centric.
- I've seen a variation of this where random engineers are pulled into production incident calls and engineers are not expected to be familiar with the system.
They were asked to "just use AI" and understand the component, triage the issue, build a fix etc. The engineer was forced to choose between accepting a potentially mediocre fix AI has suggested or risk being coming across as an incompetent resource who doesn't know how to leverage AI.
You can guess what the engineer chose. The fix wasn't bad but it was suboptimal for some edge cases. We had to later revise it. Have enough of these situations, engineers will eventually definitely give up understanding the system in detail.
by devsda - Code too.
I work with programmers and it's not uncommon that they can remember with shocking detail about code they've written in the past.
Someone might mention an issue that has cropped up and they'll stare off into space for a few moments and actually remember where that issue stems from in the code, because they remember writing it like 8 months ago.
This skill will be lost when AI is generating all code, we'll be stuck in a perpetual loop of having AI keep track of the state of the code in order for AI to extend and maintain it.
by INTPenis - The more code writes autonomously, the less intuition the human owners have about that code. Loss of intuition is a seed of technical debt that grows with time. Over a long enough horizon, it can make looking at your own codebase feel like the first day on the job (sometimes at a company you started).
Luckily, there are ways to mitigate this and essentially translate those human intuition of how the codebase “should” be into guardrails for the agents. But without that, your setting your sails in a stochastic sea where each wave looks nothing like the last.
by jtfrench - I find the use of AI like quicksand.
The more I use it, the more I have to rely on it to make changes/ fix things in the same system. In the end, I come out feeling empty; no intuitive knowledge of the system "I" built or fixed.
Code review is important but it does not replace the mental model I am able to build when I do all the steps of software development manually without AI.
by krtkush - Author has a good head on their shoulders, but few if any companies are going to spend time on incident simulations for their SREs.
Why not? Because even pre-AI, very few companies spend time practicing restoring their backups, or disaster recovery, or picking infrequently-used runbooks to practice, or seeing whether they can easily rotate secrets without downtime, or trying to redploy the system onto another vendor's cloud/platform, or, or, or... It is the least-sexy operations work that exists. No executive cares about this. Ops organizations push for flashy work, same as everybody else: new infrastructure for new projects, cool chatbots, new flashy dashboards, make charts go up and to the right, etc.
Airline pilots go through disaster simulation training because the government mandates that training. If it wasn't a condition of holding a pilot's license, no company would pay for it.
Want SREs to spend time training for disasters? Take a step back. Support professional licensure. Make it a condition of holding a license. You won't get industry-wide professional behavior until you professionalize the work. It won't happen without licensing because every corner cut that is not immediately visible to consumers translates to additional profit, and increasing competition eventually requires these corners to be cut in order to keep up with competition and stay in business. Forcing all players to submit to licensing requires all players to pay these costs and thus forbids them from cutting them to become more competitive.
by solatic - A natural evolution of engineers losing touch with the customers and users.
I'm noticing some of the concern play out regarding AI weakening the capabilities of software people.
I gave the team an exact solution on a silver platter and they still failed to identify how to go about it after 3 days slamming it into Claude. The resolution is literally 1 line of code that could be arrived at in about 30 minutes of patient, old school troubleshooting.
I think what's happening is the AI system draws poorly aligned and led engineers into this ego inflation feedback loop where they are completely detached from reality because these tools can simulate a better one.
by bob1029