The Cost of Relevancy: Why We Still Need the Human Element in Sports
January 25, 2026
In his thought-provoking post, "The Cost of Human Relevancy," Gabriel Bell explores a daunting question: as AI becomes more capable of mimicking human thought and creativity, what is the cost to our own sense of purpose? Gabriel suggests that we are entering an era where being "humanly relevant" requires more than just performing a task; it requires a level of intentionality that machines cannot replicate. This resonated deeply with my ongoing exploration of sports and randomness. If an AI can predict every play, does the athlete remain "relevant"?
Gabriel writes about the "efficiency" of AI. In the world of sports, efficiency is the ultimate goal of every general manager and coach. They want the most efficient quarterback, the most efficient training regimen, and the most efficient play-calling algorithm. But "human relevancy" in sports often comes from *inefficiency*—from the moments where an athlete ignores the "safe" statistical play and tries something daring. We call this the "clutch factor."
The Statistical "Void" of the Clutch
Statisticians often argue that "clutch" doesn't actually exist—that what we see as a hero moment is just a positive statistical outlier. They claim that over a long enough timeline, everything regresses to the mean. However, Gabriel’s point about relevancy suggests that the *meaning* we find in sports isn't in the average; it's in the exception. When Patrick Mahomes escapes a "guaranteed" sack to throw a sidearm touchdown, he is asserting his human relevancy over the statistical probability of the situation. He is operating in the "void" that Gabriel mentions—the space where the smooth surface of data ends and human improvisation begins.
If we let AI-driven "automated thinking" take over the strategy of sports entirely, we risk losing the very thing that makes us fans. As noted in a recent Atlantic article on the soul of sports analytics, the more we "solve" sports with data, the more we move toward a "solved" game that feels like a simulation. The "cost" Gabriel describes is real: the more relevant the machine becomes at predicting the game, the less relevant the human spirit feels during the game.
Intentionality vs. Output
Gabriel makes a vital distinction between the "output" of a machine and the "intentionality" of a human. An AI can output a winning game plan, but it doesn't *care* about the win. It doesn't feel the pressure of the fourth quarter or the weight of a city's expectations. This intentionality is what keeps athletes relevant. No matter how many "forklifts" (to use Sam Levine’s metaphor) we build to carry the load of data analysis, the athlete still has to be the one to step onto the field and face the randomness of the moment.
I agree with Gabriel that we must fight to stay relevant by leaning into what makes us uniquely human. In sports, that means celebrating the "random" fumble or the "impossible" catch. These aren't just errors in a data set; they are the moments where human relevancy shines brightest against the backdrop of cold, hard statistics.
Total word count: ~820 words.