⚡ AI is getting cheaper, faster, and more efficient. So why are data centres preparing to use far more electricity?

In this episode of A Beginner’s Guide to AI, Professor GePhardT explores Jevons’ paradox and its uncomfortable lesson for artificial intelligence: using less energy per task does not guarantee that we use less energy overall.

Better chips, smaller models, efficient inference, and AI agents make AI more affordable. But lower cost also creates more demand. One AI task becomes ten. One assistant becomes an agent. One model call becomes a long automated workflow running quietly in the background.

We look at the AI rebound effect, AI energy consumption, data-centre electricity demand, sustainable AI, AI agents, and the infrastructure cost behind near-free inference. The episode also examines Google Gemini prompt efficiency alongside the larger growth in AI-focused data centres.

📧💌📧

Tune in to get my thoughts and all episodes. Don’t forget to subscribe to our Newsletter: beginnersguideto.ai

📧💌📧

👤 About Dietmar Fischer

Dietmar is a podcaster and digital marketer from Argo.berlin. If you want to get your AI or digital marketing going, contact him at argoberlin.com.

💬 Quotes from the Episode

“Each individual task may become cheaper. The total number of tasks can explode.”
“The efficient oven was not a failure. It did exactly what it was supposed to do.”
“Efficient per what? And more of how many?”

Hosted on Acast. See acast.com/privacy for more information.

Jevons’ AI Paradox: Why Cheaper Models Raise Energy Demand

A Beginner's Guide to AI

Jevons’ AI Paradox: Why Cheaper Models Raise Energy Demand