Everyone keeps asking whether AI will kill our industry. I think we’re asking the wrong question.
I keep hearing the same fear lately, just in different words.
Will AI take my job? Will it make what I do irrelevant?
Did I spend a career building a skill a couple prompts can replace?
I understand the fear. I’ve felt this before. When something I had built came to a sudden end. I was sure the story was over.
What I’ve learned, is that it was never really the end. More often, it was the start of something different and eventually something better. I think that’s what’s happening in our industry again.
Have you ever noticed that when something becomes less expensive or easier, we end up using it more?
It’s called Jevons paradox. More efficient steam engines didn’t burn less coal; they burned more, because cheap power found a thousand new uses. In January 2025, Satya Nadella said it plainly about AI:
AI is making software dramatically cheaper to build. So we’ll build more of it, not less. At SREcon this year, Google’s Michelle Brush made the same point from the reliability side: as AI writes more of our code, it multiplies the complexity we have to operate, and humans stay essential in order to tame it.
More apps, more features, more attacks, more frequent updates isn’t a smaller problem, it’s a bigger operational one.
If this feels familiar, it should. We’ve lived multiple versions of it already.
Every wave arrived with the same promise that some jobs were about to vanish. Each time, the world was wrong about what would disappear and at what speed the transition was going to happen.
Fifteen years ago, the wave was cloud. The promise was the same: move to the cloud and you won’t need operations people anymore. Industry analysts even gave it a name, NoOps, and predicted developers would never have to talk to an operations person again.
I was building infrastructure in those years. I remember the talk of the data center being dead and sysadmins going extinct. AWS, GCP, Azure and all the SaaS companies will run everything for you.
Here’s what actually happened.
We didn’t operate less. We operated far more. When standing up a farm of servers became as simple as a credit card and a few clicks.
The work didn’t disappear. It multiplied, and it changed shape. The cloud era didn’t end operations; it created whole new disciplines to handle the new complexity (multi and hybrid clouds, integrating SaaS and partner APIs): DevOps, SRE, platform engineering, DataOps and more specialized function and application ops teams emerged, even teams like FinOps and products were created to wrestle the bills back down.
The people who assumed the work had vanished got buried by it. The people who owned the new complexity built the companies and careers during the past decade.
This is way older than cloud. When ATMs spread in the 1970s, everyone was sure bank tellers were finished. Instead the number of tellers grew, from around 500,000 to nearly 600,000, because cheaper branches let banks open more of them. The machine did not erase the job. It changed it, and there was more of it.
That’s the lesson I’m carrying into this AI wave. Not skepticism. Just History.
Back in 1983, a researcher named Lisanne Bainbridge wrote something that has stuck with me. She called it the ironies of automation.
Her insight: when you automate the easy parts of a job, what’s left for the human is the hard part, the judgment you can’t reduce to a rule. As she put it,
There’s a quieter danger underneath that one. When we stop doing the routine work, the skill fades. You don’t keep an ability by reading about it. You keep it by using it. That’s true at the gym working out and at 3 am when the apps are down and someone has to actually understand it.
Those are the reps that matters now.
This isn’t a prediction. It’s already showing up in the data.
The 2025 DORA research found that AI helps teams ship faster while making delivery less stable. More speed, less safety, unless the discipline around it is strong. A separate 2025 study found that nearly half of the AI-generated code it tested failed basic security checks.
And when automation fails, it doesn’t fail gently. Last year, an AI agent deleted a production database after being told not to, then misreported what it had done.
Even Amazon felt it this year. After several retail outages, including one where an engineer acted on bad advice from an AI agent, the company did something telling: it put more human review back into the loop.
The companies betting the hardest are admitting it. In July 2026, after cutting thousands of jobs to fund a historic bet on AI, Meta’s leadership told employees that agent development hadn’t accelerated the way they expected.
These are not one-offs. There is now a public catalog of them, the AI Incident Database, with more than a thousand recorded cases of AI systems causing real-world harm.
None of this is an argument against AI. I’m actually really excited about it. It’s an argument for being honest about what our job will become.
Our work moves up to the next level.
typing the code and running the commands.
deciding what’s right, checking what the machine produced, building the guardrails that catch failure before customers feel it, and owning the outcome when something breaks.
We have to build up our own personal context, AI enabled and empowered, to be able to see, to ask and to guide where we apply our new superpowers.
The market already sees it. The old, narrow operator roles are shrinking. And from my experience, the engineers who can comprehend the macro-complexities of whole systems have never been more needed. The work isn’t disappearing. It will demand a lot more from us.
I’ve come to believe that obstacles in front of us are just opportunities in disguise. This one is no different.
AI will help the engineers who treat it as leverage and lean into the harder work that it leaves behind. It’ll hurt the ones who refuse to learn to harness it or to use it to outsource their own thinking.
If you’ve spent your career keeping complex things running, working with code you didn’t write, staying level-headed when the inevitable outages come …, then you’ve been training for this exact moment your whole life.
This is not a threat.
That’s a calling.
It’s a great time to be an SRE.
What are you afraid AI will take from you?
What if it’s actually handing you the harder, more valuable work?
And what’s the one rep you’ve been avoiding?
OpsWerks owns 24/7 operations for platform and SRE teams, so your engineers spend their time on the work that moves the roadmap.
Michelle Brush, “Taming the Unpredictable: Reliability in Chaos,” SREcon26 Americas. Link
Lisanne Bainbridge, “Ironies of Automation,” Automatica, 1983. PDF
Satya Nadella on the Jevons paradox, Jan 27, 2025. Link
Forrester coined NoOps (Mike Gualtieri), 2011. Link
Worldwide public cloud end-user spending forecast, Gartner, Nov 2024. Link
DORA / Google, 2025 State of AI-Assisted Software Development. Link
Veracode, 2025 GenAI Code Security Report. Link
Replit AI coding agent deletes a production database, Fortune, Jul 2025. Link
Amazon retail outages and added human review of AI-assisted changes, Fortune, Mar 2026. Link
Meta cuts roughly 8,000 roles to fund AI spending, NPR, May 2026. Link
Mark Zuckerberg on AI agent progress at a Meta town hall, Reuters, July 2026. Link
U.S. Bureau of Labor Statistics, Occupational Outlook Handbook, systems administrator projections. Link
James Bessen, bank tellers and ATMs, IMF Finance and Development, 2015. Link
AI Incident Database, Responsible AI Collaborative. Link