You’ve been using AI tools for months. So why do you have less time than before?

The answer is 150 years old. In 1865, an economist named William Stanley Jevons discovered something deeply counterintuitive: when a technology becomes more efficient, total consumption of the resource it saves tends to go up — not down. More efficient coal engines didn’t reduce coal use. They made coal cheaper to run, so demand exploded.

The same mechanism is running on your calendar right now. Researchers call it workload creep — and it follows a predictable pattern. The faster AI lets you produce, the more output gets expected of you. That efficiency gain doesn’t go to you. It gets absorbed into the new baseline before you ever had a chance to keep it.

In this episode, we break down the Jevons Paradox and what it actually means for leaders deploying AI tools across their organizations. We look at why 95% of large enterprise AI investments are generating zero measurable return — while 90% of workers are successfully using AI on their own outside company systems. We examine the jagged frontier: where AI performs brilliantly and where it silently fails. And we get to the one architectural shift that actually breaks the cycle — the difference between automating a task and automating a workflow.

About the host

Laurence Gill is an IT leader with more than two decades of experience overseeing technology implementation across the U.S. government.. He is a doctoral candidate in cybersecurity with a dissertation focused on federal IT spending, and has spent years training youth and adults in workforce development skills including financial literacy, cybersecurity, entrepreneurship, and AI. That training mission is the reason this podcast exists: making complex, high-stakes knowledge accessible to the people who need it most, without requiring a technical background to benefit from it.