The Attention Allocation Problem

The tension between keeping up with AI and actually building things is itself a Pareto problem. Most of what drops in the AI news cycle is noise — but a vital few developments genuinely change what's possible. The difficulty is that you can't know which is which without paying some attention, and paying attention has a real cost measured in hours not spent building.

Pareto's principle suggests a strategy here: find the 20% of information sources that surface 80% of what actually matters, and ignore the rest. But the math underneath complicates that intuition. In a landscape that moves as fast as AI, the distribution isn't stable — what counts as the vital few shifts week to week. The muscle described in both posts is the same muscle: the ability to quickly assess whether something deserves your full attention or just a scan, and to trust that judgment enough to move on.