The AI Rewrite Fantasy: Why Replacing Your IT Staff with AI Is Still a Mirage

Let me tell you a little story about how I wildly underestimated the effort of rewriting a friend’s legacy Angular application. Like many of us in the enterprise IT trenches, I thought, “Hey, AI can help accelerate this.” I mean, we’re swimming in generative AI hype—tools like ChatGPT and Cursor AI promise to be copilots, pair programmers, and even whole dev teams in a box. So I strapped in and gave it a go.

After dozens of back-and-forths, hallucinated imports, and AI-generated code that was 90% correct—but 100% unusable—I realized something painfully familiar: this was less of a technology challenge and more of a system integration and institutional knowledge problem.

Let’s break down what I learned—and why thinking AI can replace your IT staff is a dangerous illusion.

1. Legacy Systems Are Held Together by Institutional Memory

One of the first things I learned during this rewrite was that legacy applications aren’t just old—they’re tribal knowledge repositories. AI tools can read the code, but they don’t understand the business logic that evolved from hallway conversations, undocumented bug fixes, or that one guy named Mike who left in 2017.

These systems weren’t built cleanly and weren’t documented for AI consumption. No LLM will understand why a certain directive exists or what that obscure service is patching unless someone tells it.

2. AI Lacks Contextual Awareness (At Scale)

Sure, ChatGPT can answer “How do I create a reactive form in Angular?” with grace. But it doesn’t know your internal CI/CD process, how your app interfaces with three different backend APIs (two of which are undocumented), and how your app’s design pattern morphed across four different dev teams.

AI is excellent at local tasks but falls apart in distributed complexity. It doesn’t build context over time the way your team does. It can’t go to the whiteboard with your product manager, negotiate scope, or account for “that undocumented exception handling we do for legacy IE11 users in Saskatchewan.”

3. It’s Not Just the Code

AI can help you write code, but rebuilding a legacy app isn’t just code—it’s infrastructure, testing, deployment pipelines, integration with downstream systems, user training, and risk mitigation. AI didn’t help me design a migration path. It didn’t help me figure out how to avoid a breaking change to 500+ users. That’s what seasoned developers, architects, and DevOps teams do.

You don’t replace IT staff—you empower them. You augment them with AI, sure. But let’s not pretend we can fire the team and give ChatGPT a badge.

4. Even Copilots Need Captains

AI tools like Cursor and GitHub Copilot are great pair programmers—when you already know what you’re doing. In my case, I found them helpful for rephrasing code or boilerplate generation, but when it came time to make architectural decisions or cleanly migrate business logic, I needed a developer’s intuition—something AI hasn’t yet replicated.

The fantasy of replacing developers with AI is a bit like thinking GPS can replace pilots. It works until you hit turbulence or need to land in a storm.

Final Thoughts: CTOs, Let’s Get Real

As a CTO, I love disruptive tech. I see the value in AI. But I also know what it takes to modernize enterprise applications. If you think you’ll use AI to replace your dev team cheaply, you’re not planning for innovation—you’re setting yourself up for a costly failure.

The right question isn’t “Can we use AI to replace developers?” It’s “How do we train our developers to use AI effectively without losing the hard-won understanding of our systems?”

Use AI. Embrace AI. But never forget: your people are still the competitive advantage.

Let’s talk. Have you tried using AI for legacy rewrites? Was it a savior or a siren song? Drop your experiences in the comments or hit me up on LinkedIn.