“So… do you think AI is going to take our jobs?”
I’ve been asked this question at meetups, in Slack threads, and at least twice by my family members who don’t fully understand what I do but are pretty sure a robot is about to do it instead. The conversation about AI taking jobs is everywhere right now, and I get why it makes people nervous.
And every time I’m asked if AI will replace our jobs, I give the same answer — yes and no. Yes, AI is going to change how we work. No, it’s not going to pack your things in a cardboard box and walk you to the door. But the real answer is more interesting than either of those – AI isn’t replacing people, it’s reshaping what it means to be valuable. And that’s what I want to talk about.
AI is a Tool. Let’s Start There.
There’s a word that gets lost in most AI conversations, and it’s an important one: tool.
AI is a tool. A powerful one, an evolving one, sometimes a mind-blowing one — but a tool. And tools don’t wake up in the morning and decide to do your job. They need someone behind them. Someone who understands the problem, sets the direction, and knows what “good” looks like when the output lands.
A hammer didn’t replace carpenters. AutoCAD didn’t replace architects. Excel didn’t replace accountants. What these tools did was raise the bar. They made the work faster, expanded what was possible, and, yes, they changed what the job looked like. But the people who understood the craft? They became more valuable, not less.
AI is doing the same thing right now. And if you’re paying attention, you can see it happening in real time. We’ve seen this pattern before.
The Computer Didn’t Replace Workers — It Created Millions of Jobs
Here’s something I think about a lot. In the 80s and 90s, the fear that computers would replace workers was everywhere — on the news, in union halls, in government debates. Office workers, factory employees, and entire industries are bracing for mass unemployment. “The machines are coming for your jobs” wasn’t a think piece — it was a genuine societal panic.
And what actually happened? Computers created entirely new industries that nobody saw coming. Web development, IT support, cybersecurity, data analysis, UX design, digital marketing, cloud engineering — none of these existed before. The technology didn’t shrink the job market. It exploded in directions no one could have predicted.
The irony? You’re almost certainly reading this article on one of those job-killing machines right now. The device that was supposed to end work became the thing that most of us do our work on. The people who understood how to think, solve problems, and adapt? They didn’t just survive the computer revolution — they thrived in it.
That’s what’s happening with AI right now. The tools are changing. The workflows are changing. But the need for people who understand what to build and why? That’s not going anywhere.
When AI Turned Hours Into Minutes
Let me tell you a story. Not too long ago, I built an analytics pipeline for a customer—a serious one spanning multiple services. Data flowed from a MongoDB cluster through DMS into an S3 bucket. A Lambda function picked it up, stripped out PII (personally identifiable information), moved the clean data to a centralized bucket in us-east-1, and Snowpipe ingested it into Snowflake tables. The whole thing was built with Pulumi because that’s what the customer’s infrastructure runs on.
I built it the way I always do — reading docs, leveraging my experience, writing the infrastructure code, testing, and deploying. And it worked. For a few days.
Then something broke between Snowflake and the S3 bucket configuration. Snowpipe wasn’t correctly picking up files. The kind of bug that doesn’t scream at you — it just sits there quietly, not doing its job.
I could have spent another day or two digging through configurations and cross-referencing documentation between AWS and Snowflake. Instead, I connected Claude Code to the MCP servers for both Snowflake and AWS, provided it with context about my setup, and let it investigate.
It found the bug. A configuration mismatch that was genuinely hard to spot — the kind of thing that hides between two systems that each look fine on their own. But it didn’t stop there. It reorganized some of my Snowflake tables to be more readable, and gave me a set of suggestions for managing my SQL files that I’m still using today.
Did AI replace me in that moment? No. I was the one who designed the pipeline, understood the customer’s requirements, chose the architecture, and built it. The AI helped me debug it faster and improve my work. That’s what a great tool does.
That experience made me realize something bigger about where things are heading.
We’re Living in the Ideas Era
Here’s what really excites me — and I don’t think enough people are talking about this.
An application that would have taken a week or a month to build can now be prototyped in a few hours with the right planning and context. Think about what that means. Not just for experienced engineers — for everyone. The barrier between having an idea and seeing it come to life has never been lower.
We’re already seeing it. There’s an entire wave of “vibe coders” out there — people across all technical backgrounds — including experienced engineers — who are building real products using AI-assisted development. Some of them are creating things that seasoned developers wouldn’t have thought of because they come from different backgrounds and perspectives.
And here’s the part that proves my point about AI creating jobs rather than destroying them: vibe coding became so widespread that it created an entirely new discipline. People started realizing that just vibing with AI wasn’t enough — you needed to provide structure, constraints, and clear context to get reliable results. So now there’s a growing field called context engineering, focused on exactly that. A discipline that barely existed a year ago, born directly from the AI revolution.
That’s not a job being taken. That’s a job being invented.
The Real Shift Is About Ideas, Not Implementation
Lowered barriers are just the beginning. The bigger change is about what we value.
This is the part where I’ll be direct: the balance is shifting. Implementation is becoming easier. The mechanical parts of building software — the boilerplate, the configurations, the standard patterns — AI handles those increasingly well.
But you know what AI can’t do? It can’t sit in a meeting with a customer, understand their pain, and envision a solution that doesn’t exist yet. It can’t look at a broken process and feel that something is off before the data proves it. It can’t decide what’s worth building in the first place.
Ideas are becoming the hard part. And that’s actually beautiful.
For the first time, we’re entering an era where your value isn’t measured by how fast you can type or how many services you’ve memorized. It’s measured by how well you think. How clearly can you define a problem? How creatively can you imagine a solution?
People who have been sitting on great ideas but didn’t have the technical skills to execute them? They now have a path. People who are deeply technical but feel stuck doing repetitive implementation work? They can now focus on the interesting parts. That’s not a threat — that’s an unlock.
So, Will AI Take Your Job?
My honest answer: it will take the version of your job that existed five years ago. In DevOps, that means being valued primarily for memorizing YAML configurations, manually managing infrastructure, or being the only person who understood that one legacy pipeline. But this applies to every field — anywhere the job was mostly about repetitive execution, AI is changing the equation.
But it won’t take the version of your job that’s forming right now — the one where you think critically, design solutions, lead teams through ambiguity, and use every tool available to do work that actually matters.
The people who will struggle are the ones who refuse to adapt. Not because AI forced them out, but because they chose to stand still while the ground moved. That’s not an AI problem — that’s a change problem. And we’ve always had those.
The people who will thrive? They’re the ones who are already leaning in. Learning how to work with AI, not against it. Using it to amplify what they’re good at. Building things that didn’t exist last year.
We’re not at the end of something. We’re at the very beginning. And from where I’m sitting, the view looks pretty exciting.
