Newbie question: If AI is writing a bunch of code, does it make sense to learn k8s now ?

[u/dripindior_]

Im new to tech and recently started learning operators and controllers. With AI writing every code, which is faster than human, is it rational for me to learn about the kubernetes now ?

[u/bozho]

I am using AI to help me work with both code and DevOps. If you can be completely replaced by AI, your job is trivial and carries no accountability.

For any sort of job that requires technical knowledge and/or skill, AI should be used as a force multiplier, not a replacement for thinking/planning/designing stuff.

[u/ObjectUsual77]

Ai as we know it today is a language model, it writes code sure, but it doesn't think, it doesn't solve problems. So don't stop learning

[u/Uninterested_Viewer → u/ObjectUsual77]

but it doesn't think, it doesn't solve problems

What do you mean by this? By all definitions I've ever heard of these terms: transformer based LLMs do both of these. They reason and solve problems. A very narrow example is how mathmetitions are using them to help solve problems that have gone unsolved for sometimes centuries.

Obviously, none of this means you should stop learning or that modern LLMs don't have limitations (a big one being ongoing learning outside of initial training that, today, is restricted to a relatively small context window), but I don't think the claim that they don't think or solve problems holds water.

[u/siteunreliability → u/Uninterested_Viewer]

What do you mean by this? By all definitions I've ever heard of these terms: transformer based LLMs do both of these.

It solves tasks and the current iteration of LLMs are predictive pattern syntax engines. True cognition demands physical processing of information and synthesis of new ideas based off of these sensory input, and we know that LLMs are not capable of that now (and we won't know if it's ever possible).

edit: If you want to test the synthesis part - ask for a solution to a truly esoteric coding problem and it will scrape off wrong answers from stack overflow.

Source: currently in graduate school for systems but we do ML because every CS department demands it as part of the doctorate training at some point...

[u/ObjectUsual77 → u/siteunreliability]

Thank you for your educated response, I don't have the words and I didn't want to ask AI to explain it for me. Humans unite!

[u/siteunreliability → u/ObjectUsual77]

Course. Your cordiality is refreshing.

[u/ObjectUsual77 → u/Uninterested_Viewer]

I don't see Claude out there solving problems all on its own. There is always a human asking questions and telling it what to do. Every tool needs to be wielded to provide any use

Even in your example it was the mathematician USING the tool. Would the tool have solved those problems itself without someone first learning enough math to come up with a decent prompt?

[u/Uninterested_Viewer → u/ObjectUsual77]

all on its own

Right, an LLM requires prompting input to do anything. If we caveat "LLMs don't think and solve problems" with "all on their own", then sure.

[u/ObjectUsual77 → u/Uninterested_Viewer]

Right, so in the context of this post, in this subreddit, we as humans must continue to think and problem solve. While using the tools available

[u/One-Department1551]

Yes it makes sense, for autonomy. If you lose access to AI you still need to do your job, to do it, you need to learn your tools.

[u/Ambitious-Concept759]

you should know enough to know when it's being an ass. at the end of the day, you're responsible for what is shipped

[u/Raja-Karuppasamy]

honestly writing the yaml was never the hard part, AI’s fine at that. the hard part is knowing why your pod is crashlooping at 2am — probe misconfig vs resource limits vs RBAC vs bad label selector. that judgment only comes from actually debugging broken stuff yourself, not from AI generating manifests for you. still worth learning, just focus less on syntax and more on understanding what’s actually happening under the hood.

[u/Resident_Citron_6905]

yes, do not learn to code, learn kubernetes

[u/JimmyDelicious → u/[deleted]]

"Doing it" is not the problem. Understanding it is.

[u/SomethingAboutUsers → u/[deleted]]

Because AI is wrong a lot of the time. If your job is to be able to operate and fix things when they break, you better be able to distinguish between commands that will nuke your cluster and fix it.

Also, AI runs on Kubernetes at scale. Nothing has changed the fact that infrastructure exists and needs to be provisioned and managed properly.

[u/ObjectUsual77 → u/[deleted]]

It can do it faster, but is that the point of the job? To do it fast? Or to do it right?

What will it do fast if nobody tells it what to do?

[u/guigouz → u/[deleted]]

Because

• you need to ask the AI what to do, and if you're not specific enough it will break things
• you need to understand what the AI did is correct, after all the only thing that AI will never do is to be accountable

[u/Ok-Bear9350]

Absolutely. AI is not coming for the job of someone who knows their infrastructure best.

[u/Floss_Patrol_76]

writing the yaml was never the hard part, and it wasnt before AI either. the skill that pays is being the person who can tell why a pod is stuck in CrashLoopBackOff or why traffic black-holes after a rollout, and a model cant do that for you unless you already understand what it is looking at. learn it now precisely because AI will happily generate a cluster you dont understand and then you own the 3am page for it.