Posted by sylvainkalache 3 hours ago
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.
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.
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.
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.
But that's also likely to change with AI assistance. Even an "average" system is better than none. So now teams will have the capacity to bring that in to their systems. Backups / recovery drills that are actually tested (either because they're implementing testing or because the AI screws something up and they need to recover). Either way, it'll be included. Same for security ops. And devops.
I still strongly believe that AI assistance is a catalyst / accelerator, and that the "floor" will rise in most domains. So a small team that only had bandwidth to deal with the happy path previously, will now be able to start incorporating processes and procedures that were historically only done at corporate level. And that's a good thing. Even if it won't look like that in the beginning. But we'll get there, eventually.
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.
Firms aren’t just selling products, they are selling reliability and taking on liability.
Recently discussed something about economy/investing with a friend while at work at a slaughterhouse. I really didn't want him to get scammed buying crypto. So, used ChatGPT to find some sources in Somali, a 3 videos with short description why it's worth watching. Intro into investing, intro about cryptocurrencies and about buying them. Had the text shortened down to 3 pretty short paragraphs, not more than twice this post.
He's a smart guy, but only went to primary Qur'an school. Doesn't read or such, mostly consumes internet in form of video/media. He couldn't read those 3 paragraphs, it was too long. Or rather, it wasn't just 3 paragraphs, it was a lot to read.
Maybe we're already dividing into murlocs and the surface dwellers?
Audit trails, logs, and tight data governance where things can only be accessed with proper roles is the only possible solution.
If an RPA team ever gets direct access to a production database in your company, look for a new job.
For anyone else wondering what RPA means. Never heard that abbreviation before.
> For anyone else wondering
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...
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.
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.
Coding via LLM is not similar to using an abstraction. Imagine a car. The controls like steering wheel, the pedals, the gear levers. Those are abstractions.
But using LLMs are like driving using a remote control that has probabilistic behavior. You just loss what it feels to be in a car and you fail to improve as a driver because of the erratic remote control.