"It's Faster to Just Do It Myself" — The Efficiency Paradox

"It's faster to just do it myself." If you've tried using AI for complex work, you've probably said this. And here's the thing. You're not wrong.

Highly effective leaders already have efficient ways of working. They know how to write, analyze, plan, and decide. When AI feels slower, more iterative, or slightly error-prone, it is labeled as not worth the effort.

This is the efficiency paradox.

Some of the most competent people are often the first to walk away from AI, because their baseline of efficiency is already so high. They judge it by a very reasonable question: Did this save me time right now?

The leaders who get the most out of AI tend to approach it differently. They lead with curiosity — about what the tool can do, where it struggles, and how it responds when pushed. Over time, that curiosity builds intuition.

They stop asking, "Is this faster?"

They start asking, "What can this help me think through?"

AI only becomes efficient after you've been inefficient with it for a while.

That tension reflects how high performers have been trained to work and why this shift can feel counterintuitive at first. What matters is that this early investment doesn't just affect today's output. It changes what future first attempts look like as the tools continue to evolve.