AI can code -Can it copilot?

I was recently asked to talk about the future of AI in programming within our company. Naturally, tools like GitHub Copilot, Replit’s Ghostwriter, and Lovable came up—they’re fundamentally changing the way we write and manage code. It’s incredible to see AI not just assisting with syntax or autocompletions, but actually generating meaningful code.

That got me thinking: Programming languages are just languages. AI has already proven itself with human languages—translating, summarizing, and even mimicking nuanced writing styles. Now, it’s doing the same with code. But the key difference? Programming is inherently structured and reason-forward.

AI is excellent at code generation when the task is well-defined. It understands patterns from millions of Git commits, so when given a specific change—akin to a precise Git commit—it produces impressive results. But ask it to take a loosely framed idea and turn it into a structured, scalable system, and it stumbles. The takeaway? AI is only as good as the prompt you give it.

This shift doesn’t diminish the role of engineers—it redefines it. AI accelerates execution, but the architecture, abstraction decisions, and long-term thinking remain human-led. Developers now need to evolve from writing code to structuring problems in a way that AI can execute efficiently—breaking work into logical increments, ensuring clarity in direction, and orchestrating AI like a highly efficient but narrow executor.

I ended my talk with something that felt surreal to say out loud:

“Don’t let artificial learning out-learn you.”

We’re at an inflection point where AI is actively coding, but that doesn’t mean it’s designing or thinking. The best technical leaders will be the ones who understand how to integrate AI into developer workflows—maximizing its strengths while ensuring the long-term scalability and maintainability of what it produces.

It’s an exciting time. Let’s stay ahead.