Do AI models make top performers harder to identify?
Core argument: The author argues that large language models are compressing variance across knowledge domains including writing, coding, finance, reasoning, and creating, pushing the tails of performance distributions closer to the mean and thinning them.
All distributions are compressing at once under the evolutionary pressure of LLMs. Like batting averages in baseball, variance in many/most knowledge domains is collapsing. The consequence: our ability to identify true outliers—our “.400 hitters” of thought, design, or creativity—erodes. The tails of the distribution exist, but they are closer to the mean, less populated, and masked by the rising competence of everyone else. This is not about tools “making us better.” It is about the compression of variance in creative domains where variance mattered: writing, coding, finance, reasoning, and creating. Just as baseball lost its .400 hitters—not because top hitters no longer existed, but because their relative distinctiveness disappeared—intellectual and creative .400 hitters will become much harder to detect. Evolution does not necessarily march toward higher peaks but toward tighter distributions around a plateau. Once variance collapses, the outliers vanish into the baseline. And societies that cannot recognize outliers cannot reward or sustain them. Or they seize upon stranger differentiators, divorced from actual performance, which is a topic for another day.





