Let’s cut to the chase: no, it can’t.
Not now. Not anytime soon.
In my experience working with engineering teams across industries, I’ve seen just how fast cloud infrastructure is evolving. Automation and AI are certainly becoming more embedded in daily workflows. And yes, they can take over certain tasks, especially the tedious, repetitive ones. But if you think that means engineers are becoming obsolete, you’re missing the plot entirely.
If anything, what AI is doing is revealing how critical skilled engineers still are, and will continue to be.
The Story Behind the Hype
The industry loves a good headline. “AI is coming for your job!” “DevOps without the devs!” It’s easy to get swept up in that energy.
But the real world isn’t a press release.
Here’s a quick example from a client I worked with recently: they proudly demoed a Terraform configuration to spin up their environment. On paper, it looked solid—clean, properly formatted, no syntax issues. But it didn’t work. At all.
Turns out, the code was AI-generated. It looked polished, but it wasn’t functional. It hadn’t been validated. It didn’t reflect the actual infrastructure setup. It was all surface, no substance – a reminder that code generation without understanding is just theater.
And that’s not a one-off. I’ve seen it happen more than once.
Fast Isn’t Always Smart
Let’s be clear: tools like Copilot, ChatGPT, or Claude can be incredibly helpful. They can speed up scaffolding, suggest fixes, and help with repetitive scripting. But their knowledge comes from public data. They don’t know your stack. They don’t know your security policies. They don’t follow your team’s naming conventions or compliance rules.
In fact, I’ve heard stories of AI-generated infrastructure code that includes deprecated syntax or outdated APIs. One engineer told me he had an entire Azure policy fail silently because the AI-generated config used the wrong version schema. It took hours to debug.
That’s the risk. AI gets you there faster, but if it’s the wrong direction, you just crash sooner.
Augmentation > Automation
Where AI is making progress is in deterministic workflows — tools that operate on rules, not guesses. Think of systems that understand Policy-as-Code and can generate merge-ready fixes based on defined organizational standards.
They aren’t trying to be creative. They’re trying to be right.
This kind of AI behaves more like a very efficient junior engineer: it flags issues, prepares clean pull requests, and works quietly in the background. But it doesn’t deploy without you. It still relies on human judgment and review.
And that’s the point. It’s not replacing anyone. It’s removing friction. It’s streamlining the work so engineers can focus on harder, higher-value challenges.
Even Gartner seems to agree. In their 2024 Hype Cycle for Site Reliability Engineering, they flagged “AI Assistants for Infrastructure as Code” as a rising category, specifically because they enhance, not replace, engineering roles.
Some Things Only People Do Well
No AI tool is going to understand why your U.S. West region is under a deployment freeze due to legal concerns. Or that your team is shipping a critical feature by Friday and can’t risk a surprise regression. Or that your lead engineer is OOO and won’t be reviewing that PR until Monday.
It won’t sit through a sprint retro. It won’t handle an outage call at 2 a.m. It won’t balance trade-offs or debate system design with the team.
Even as AI gets better at writing and fixing code, it still doesn’t handle the nuance. DevOps isn’t just scripts and pipelines. It’s conversations. Decisions. Context.
And none of that’s going away.
Looking Ahead: What Might Change
Now, while I don’t believe engineers are being replaced, I do think their role is shifting. We’re seeing the rise of what I’d call “AI-aware engineers.” These are people who guide AI tooling — tuning it, checking its output, defining the rules it should follow.
Expect to see more emphasis on policy-as-code, declarative security, and infrastructure governance baked into CI/CD pipelines.
A CNCF survey recently reported that 67% of platform teams view AI-enabled automation as a top investment. Not to cut headcount but to eliminate manual toil.
This is the new path: let AI handle the routine, while humans tackle the real problems.
Final Thoughts
Trying to replace DevOps engineers with AI misses the point entirely. Automation is valuable, sure. But DevOps is fundamentally about context, coordination, and accountability – things no machine is ready to own.
What we should focus on is how to best pair engineers with the right tools. Let AI take the grunt work. Let people steer the ship.
Because the future isn’t hands-free. It’s hands-on – just with a lot more support behind the scenes
