Episode 17: Do We Really Need All These Data Centers?

Data centers have dominated the AI news cycle lately, from local concerns about noise and heat to questions about who pays for the added strain on the electric grid. In this solo episode, I take stock of the build-out and why I think it’s happening with very little mindfulness. A conversation last week likened it to the railroad boom of the Industrial Revolution, when tracks went down everywhere, towns boomed or withered depending on where the line ran, and nobody planned for the environmental damage. Two questions rarely come up in the coverage. First, how much of this capacity is actually needed? Every frontier lab is building its own, with no real effort to treat compute as a shared resource that could lower the environmental impact. Second, why are we running every query through massive general-purpose language models? I compare them to department stores, where everything is on the shelf and none of it is as good as it could be. Running makes a good example. The AI coaching features in Strava, Runna, and Garmin lean on frontier models, and as a coach I think they suffer for it. A narrow model trained only on running knowledge would likely coach better and draw far less from the grid. So where’s the hope? I’d like to see companies build data centers alongside environmental groups and communities, pay for their own grid impact instead of passing it to taxpayers, and invest in narrower, more efficient models. For the rest of us, it comes down to mindfulness. Before reaching for a chatbot, ask whether the use case is warranted or just a dopamine hit. Is the scratch really worth it? 0:58 Environmental and grid concerns 2:22 How much capacity do we really need? 4:11 AI as a department store 6:12 Where the hope lies