Fired for Using Too Much AI… Then Hired to Lead With It

Too Fast to Keep Up

Over a year ago, I was let go from my role as a senior developer.

On paper, my performance was solid — I was shipping on time, writing secure, scalable code, and automating repetitive processes to save everyone time. My pull requests were tight, my velocity was steady, and I was often ahead of sprint deadlines.

So why was I fired?

The reason I was given came down to “AI usage.” Specifically, leadership claimed that my approach to using AI tools was incompatible with the team’s practices. That might sound like a polite way of saying I broke things. I didn’t.

What actually happened is this: I used AI to raise the bar.

And the team wasn’t ready.

When Productivity Becomes a Problem

Let’s get specific.

What I did:

Automated PR reviews: I used AI tools to generate intelligent feedback on pull requests, catching syntax issues, performance bottlenecks, and common security problems.
Flagged vulnerabilities: My AI-assisted reviews surfaced dozens of latent vulnerabilities in the codebase — many of which had been previously missed or ignored.
Streamlined workflows: I applied AI to triage bugs, enrich documentation, and scaffold new modules based on existing patterns.

The result? I was delivering clean, consistent code at a pace the team couldn’t match. But instead of leaning into it, they pushed back.

What I thought would be seen as a force multiplier was perceived as… noise.

“The AI is creating too much extra work for the team,” I was told.

They weren’t wrong. My workflow did surface more issues — ones that would have slipped through otherwise. But instead of treating this as a net positive, the team saw it as a disruption. QA had to work harder. Other devs had to revise their work more often. Managers had to answer questions about priorities and process.

That cultural resistance grew quietly but steadily. And when financial troubles hit — as they eventually did — the company needed to downsize. I was a perfect candidate: highly visible, using “experimental tools,” and easy to pin as “not aligned.”

In hindsight, they didn’t fire me for using AI. They fired me because I exposed how far behind they already were.

From AI Pariah to AI Architect

Today, I work with a team of developers at a company that doesn’t just tolerate AI — it builds around it.

It’s not a trendy experiment or a side project. It’s part of the infrastructure.

We’ve embedded AI directly into our workflows to solve real bottlenecks, not just chase headlines.

Here’s how we’re using it:

1. Claude for Code Writing

Our senior engineers (myself included) use Claude as a real-time coding assistant — not just for writing snippets, but for designing entire modules, scaffolding features, and maintaining internal consistency.

It’s especially useful for:

Translating legacy patterns into modern architectures
Writing test scaffolds and documentation
Exploring edge cases based on historical bug data

It’s collaborative, not hands-off. Think of it as pair programming with an assistant who knows the entire codebase and never gets tired.

2. AI-Triage: Smart Prioritization

Instead of manually reviewing and sorting incoming issues, we use an AI pipeline that analyzes:

Stack traces
Related commits
Known code dependencies
Severity indicators

It then automatically classifies, labels, and routes issues — which used to take hours of PM or dev time — in minutes.

3. AI-Enrich: Making Work Items Useful

When someone logs a vague or minimal work item (“API returns 500 error”), AI-Enrich adds:

Contextual code references
Similar incidents
Likely causes based on surrounding logic
Impact assessment

This turns low-context tickets into actionable tasks, reducing rework and clarification cycles dramatically.

4. AI-Quality: Helping QA Focus

This tool evaluates commit diffs and suggests test paths, edge cases, and potentially impacted features — helping QA prioritize without needing full handoffs.

Before: Testing was reactive, driven by incomplete notes. Now: It’s focused, fast, and predictive.

What changed?

At this company, AI is not treated like a shortcut. It’s a scaling tool — for quality, context, and speed. That mindset shift is everything.

What I’ve Learned: AI Doesn’t Replace Developers. It Exposes Weak Systems.

A common fear is that AI will make developers obsolete.

That fear misses the point.

AI doesn’t replace great developers, it amplifies them. It removes tedium, accelerates pattern matching, and enhances reasoning over large codebases.

What it does threaten is legacy systems, both technical and cultural, that depend on inefficiency to survive.

In my old role, AI showed that our code wasn’t as secure as we thought. That our PR process was too manual. That our documentation gaps were slowing things down. But instead of fixing the problems, leadership tried to get rid of the mirror.

That’s the real danger: companies confusing innovation with instability, and defending the status quo at the cost of actual progress.

“In an infinite universe, some teams still believe the answer to better code is more meetings.” — Douglas Adams

Advice for Developers: Don’t Wait for Permission

If you’re a developer already using AI tools — keep going.

You don’t need to be in a company that gets it right now. But you do need to build the skills and understanding that will define what the next decade of development looks like.

Start by:

Treating AI as a collaborator, not a crutch
Finding ways to integrate it into your daily tools
Measuring how it improves your work, not just how it makes things faster

Advice for Engineering Leaders: Stop Slowing Down Your Best People

Your best engineers are already using AI, whether you approve it or not.

You can try to control that, or you can design systems that benefit from it.

Here’s what that looks like:

Update your performance metrics to reflect speed, safety, and collaboration, not just ticket count.
Invest in workflows that make AI-enhanced code more visible and auditable.
Incentivize experimentation. Don’t punish it.

The future isn’t AI vs devs.

It’s devs who understand AI and teams built around them, versus everyone else still trying to schedule another sync.

Final Thoughts: Fired for Being Too Early Was Still the Right Move

I won’t pretend it didn’t sting to get fired. But looking back, I’m glad it happened.

It pushed me to find a team that shares the same belief I do: That the job of a developer is changing and we’re supposed to evolve with it.

Not everyone is ready. But the right companies? They’re already building for it.

If you’re a developer trying to push forward, don’t slow down. If you’re a leader trying to retain talent, don’t resist change.

AI is no longer an experiment.

It’s infrastructure.

First published December 29, 2025 on 42 Insights.