stackskipton makes a good point about authority. SRE works at Google because SREs can block launches and demand fixes. Without that organizational power, you're just an on-call engineer who also writes tooling.
The article's premise (AI makes code cheap, so operations becomes the differentiator) has some truth to it. But I'd frame it differently: the bottleneck was never really "writing code." It was understanding what to build and keeping it running. AI helps with one of those. Maybe.
As an SRE I can tell you AI can't do everything. I have done a little software development, even AI can't do everything. What we are likely to see is operational engineering become the consolidated role between the two. Knows enough about software development and knows enough about site reliability... blamo operational engineer.
I knew what an SRE was and found the article somewhat interesting with a slightly novel (throwaway), more realistic take, on the "why need Salesforce when you can vibe your own Salesforce convo."
But not defining what an SRE is feels like a glaring, almost suffocating, omission.
As someone who works in Ops role (SRE/DevOps/Sysadmin), SREs are something that only works at Google mainly because for Devs to do SRE, they need ability to reject or demand code fixes which means you need someone being a prompt engineer who needs to understand the code and now they back to being developer.
As for more dedicated to Ops side, it's garbage in, garbage out. I've already had too many outages caused by AI Slop being fed into production, calling all Developers = SRE won't change the fact that AI can't program now without massive experienced people controlling it.
Most devs can't do SRE, in fact the best devs I've met know they can't do SRE (and vice versa). If I may get a bit philosophical, SRE must be conservative by nature and I feel that devs are often innovative by nature. Another argument is that they simply focus on different problems. One sets up an IDE and clicks play, has some ephemeral devcontainer environment that "just works", and the hard part is to craft the software. The other has the software ready and sometimes very few instructions on how to run it, + your typical production issues, security, scaling, etc. The brain of each gets wired differently over time to solve those very different issues effectively.
What? Maybe OPs future. SWE is just going to replace QA and maybe architects if the industry adopts AI more, but there's a lot of hold outs. There's plenty of projects out there that are 'boring' and will not bother.
AI will not get much better than what we have today, and what we have today is not enough to totally transform software engineering. It is a little easier to be a software engineer now, but that’s it. You can still fuck everything up.
It only matters if any of those can promise reliability and either put their own money where their mouth is or convince (and actually get them to pay up) a bigger player to insure them.
Ultimately hardware, software, QA, etc is all about delivering a system that produces certain outputs for certain inputs, with certain penalties if it doesn’t. If you can, great, if you can’t, good luck. Whether you achieve the “can” with human development or LLM is of little concern as long as you can pay out the penalties of “can’t”.
Operational excellence will always be needed but part of that is writing good code. If the slop machine has made bad decisions it could be more efficient to rewrite using human expertise and deploy that.
My take (I'm an SRE) is that SRE should work pre-emptively to provide reproducible prod-like environments so that QA can test DEV code closer to real-life conditions. Most prod platforms I've seen are nowhere near that level of automation, which makes it really hard to detect or even reproduce production issues.
And no, as an SRE I won't read DEV code, but I can help my team test it.
The article's premise (AI makes code cheap, so operations becomes the differentiator) has some truth to it. But I'd frame it differently: the bottleneck was never really "writing code." It was understanding what to build and keeping it running. AI helps with one of those. Maybe.
But not defining what an SRE is feels like a glaring, almost suffocating, omission.
As for more dedicated to Ops side, it's garbage in, garbage out. I've already had too many outages caused by AI Slop being fed into production, calling all Developers = SRE won't change the fact that AI can't program now without massive experienced people controlling it.
AI will not get much better than what we have today, and what we have today is not enough to totally transform software engineering. It is a little easier to be a software engineer now, but that’s it. You can still fuck everything up.
Wow, where did this come from?
From what just comes to my mind based on recent research, I'd expect at least the following this or next year:
* Continuous learning via an architectural change like Titans or TTT-E2E.
* Advancement in World Models (many labs focusing on them now)
* Longer-running agentic systems, with Gas Town being a recent proof of concept.
* Advances in computer and browser usage - tons of money being poured into this, and RL with self-play is straightforward
* AI integration into robotics, especially when coupled with world models
Ultimately hardware, software, QA, etc is all about delivering a system that produces certain outputs for certain inputs, with certain penalties if it doesn’t. If you can, great, if you can’t, good luck. Whether you achieve the “can” with human development or LLM is of little concern as long as you can pay out the penalties of “can’t”.
And no, as an SRE I won't read DEV code, but I can help my team test it.