I agree with both you and the GP. Yes, coding is being totally revolutionized by AI, and we don't really know where the ceiling will be (though I'm skeptical we'll reach true AGI any time soon), but I believe there still an essential element of understanding how computer systems work that is required to leverage AI in a sustainable way.

There is some combination of curiosity of inner workings and precision of thought that has always been essential in becoming a successful engineer. In my very first CS 101 class I remember the professor alluding to two hurdles (pointers and recursion) which a significant portion of the class would not be able to surpass and they would change majors. Throughout the subsequent decades I saw this pattern again and again with junior engineers, bootcamp grads, etc. There are some people no matter how hard they work, they can't grok abstraction and unlock a general understanding of computing possibility.

With AI you don't need to know syntax anymore, but to write the write prompts to maintain a system and (crucially) the integrity of its data over time, you still need this understanding. I'm not sure how the AI-native generation of software engineers will develop this without writing code hands-on, but I am confident they will figure it out because I believe it to be an innate, often pedantic, thirst for understanding that some people have and some don't. This is the essential quality to succeed in software both in the past and in the future. Although vibe coding lowers the barrier to entry dramatically, there is a brick wall looming just beyond the toy app/prototype phase for anyone without a technical mindset.

Speaking as someone who has been both a SRE/DevOps from all levels from IC to Global Head of a team:

- I 100% believe this is happening and is probably going to be the case in the next 6 months. I've seen Claude and Grok debug issues when they only had half of the relevant evidence (e.g. Given A and B, it's most likely X). It can even debug complex issues between systems using logs, metrics etc. In other words, everything a human would do (and sometimes better).

- The situation described is actually not that different from being a SRE manager. e.g. as you get more senior, you aren't doing the investigations yourself. It's usually your direct reports that are actually looking at the logs etc. You may occasionally get involved for more complex issues or big outages but the direct reports are doing a lot of the heavy lifting.

- All of the above being said, I can imagine errors so weird/complex etc that the LLMs either can't figure it out, don't have the MCP or skill to resolve it or there is some giant technology issue that breaks a lot of stuff. Facebook engineers using angle grinders to get into the data center due to DNS issues comes to mind for the last one.

Which probably means we are all going to start to be more like airline pilots:

- highly trained in debugging AND managing fleets of LLMs

- managing autonomous systems

- around "just in case" the LLMs fall over

P.S. I've been very well paid over the years and being a SRE is how I feed my family. I do worry, like many, about how all of this is going to affect that. Sobering stuff.

-

Now run that loop 1000 times.

What does the code /system look like.

It is going to be more like evolution (fit to environment) than engineering (fit to purpose).

It will be fascinating to watch nonetheless.

> Unless you are only ever a single engineer, your career is filled with "I need to debug code I didn't write".

That's the vast majority of my job and I've yet to find a way to have LLMs not be almost but not entirely useless at helping me with it.

(also, it's filled with that even when you are a single engineer)

It's nice that AI can fix bugs fast, but it's better to not even have bugs in the first place. By using someone else's battle tested code (like a framework) you can at least avoid the bugs they've already encountered and fixed.

> Unless you are only ever a single engineer, your career is filled with "I need to debug code I didn't write".

True, but there's usually at least one person who knows that particular part of the system that you need to touch, and if there isn't, you'll spend a lot of time fixing that bug and become that person.

The bet you're describing is that the AI will be the expert, and if it can be that, why couldn't it also be the expert at understanding the users' needs so that no one is needed anywhere in the loop?

What I don't understand about a vision where AI is able to replace humans at some (complicated) part of the entire industrial stack is why does it stop at a particular point? What makes us think that it can replace programmers and architects - jobs that require a rather sophisticated combination of inductive and deductive reasoning - but not the PMs, managers, and even the users?

Steve Yegge recently wrote about an exponential growth in AI capabilities. But every exponential growth has to plateau at some point, and the problem with exponential growth is that if your prediction about when that plateau happens is off by a little, the value at that point could be different from your prediction by a lot (in either direction). That means that it's very hard to predict where we'll "end up" (i.e. where the plateau will be). The prediction that AI will be able to automate nearly all of the technical aspects of programming yet little beyond them seems as unlikely to me as any arbitrary point. It's at least as likely that we'll end up well below or well above that point.

That doesn't change the fact that the submission is basically repeating the LISP curse. Best case scenario: you end up with a one-off framework and only you know how it works. The post you're replying to points out why this is a bad idea.

It doesn't matter if you don't use 90% of a framework as the submission bemoans. When everyone uses an identical API, but in different situations, you find lots of different problems that way. Your framework, and its users become a sort of BORG. When one of the framework users discovers a problem, it's fixed and propagated out before it can even be a problem for the rest of the BORG.

That's not true in your LISP curse, one off custom bespoke framework. You will repeat all the problems that all the other custom bespoke frameworks encountered. When they fixed their problem, they didn't fix it for you. You will find those problems over and over again. This is why free software dominates over proprietary software. The biggest problem in software is not writing the software, it's maintaining it. Free software shares the maintenance burden, so everyone can benefit. You bear the whole maintenance burden with your custom, one off vibe coded solutions.

I think back on the ten+ years I spent doing SRE consulting and the thing is, finding the problems and identifying solutions — the technical part of the work — was such a small part of the actual work. So often I would go to work with a client and discover that they often already knew the problem, they just didn’t believe it - my job was often about the psychology of the organization more than the technical knowledge. So you might say “Great, so the agent will automatically fix the problem that the organization previous misidentified.” That sounds great right up until it starts dreaming… it’s not to say there aren’t places for these agents, but I suspect ultimately it will be like any other technology we use where it becomes part of the toolkit, not the whole.

>I would argue that it's going to be the opposite. At re:Invent, one of the popular sessions was in creating a trio of SRE agents, one of which did nothing but read logs and report errors, one of which did analysis of the errors and triaged and proposed fixes, and one to do the work and submit PRs to your repo.

If you manage a code base this way at your company, sooner or later you will face a wall. What happens when the AI can't fix an important bug or is unable to add a very important feature? now you are stuck with a big fat dirty pile of code that no human can figure out because it wasn't coded by human and was never designed to be understood by a human in the first place.

Automatically solving software application bugs is one thing, recovering stateful business process disasters and data corruption is entirely another thing.

Customer A is in an totally unknown database state due to a vibe-coded bug. Great, the bug is fixed now, but you're still f-ed.

„understand how these systems actually function" isn't incompatible with "I didn't write most of this code“

Except now you have code you didn‘t write and patches you din‘t write either. Your „colleague“ also has no long term memory.

Believe the re:Invent session is this one but correct me if I'm wrong: https://www.youtube.com/watch?v=rMPe622eGY0

> one to do the work and submit PRs to your repo

Have we not seen loads of examples of terrible AI generated RPs every week on this site?

Was that session published online somewhere? I’d love to watch that.

An AI thing demoed well? Well, colour me shocked.

Amazon, which employs many thousands of SREs (or, well, pseudo-SREs; AIUI it's not quite the conventional SRE role), is presumably just doing so for charitable purposes, if they are so easy to replace with magic robots.