Why Bigger AI Isn't Always Better

Microsoft just unveiled a monster of a machine built for local AI. More memory. More horsepower. More everything.

Which led Rob and Justin to a question that has almost nothing to do with the hardware.

Are we already using more AI than the job actually requires?

This conversation starts with Microsoft's latest announcement but quickly turns into something much bigger. When do you actually need a frontier model? When is a smaller model just as good? And what happens when companies stop optimizing for the smartest AI and start optimizing for the right AI?

It's a familiar pattern. New technology shows up, everyone assumes bigger is better, and eventually we learn that the best solution isn't the most powerful one. It's the one that's powerful enough. AI may be reaching that point faster than anyone expected.

Along the way, Rob and Justin dig into the economics of tokens, why developers should think differently than everyday AI users, and why Microsoft's latest hardware announcement feels like it's missing a piece of the story. They don't pretend to have all the answers. Instead, they do what this podcast does best: pull on an interesting thread until a much better conversation emerges.

If your first instinct has been to reach for the biggest model every time, this episode might convince you that the future belongs to the people who know when not to.

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