It’s true that much of the debate around AI swings between extremes — utopian promises on one side, dystopian collapse on the other. But institutions don’t operate well in extremes.
What matters is how we design governance that acknowledges uncertainty while still enabling progress. In practice, that means imperfect but adaptive frameworks — guardrails that evolve as technology and society evolve.
Instead of asking “which fallacy is right,” we might ask: how do we build systems that remain trustworthy even when our assumptions about AI turn out to be wrong?
What matters is how we design governance that acknowledges uncertainty while still enabling progress. In practice, that means imperfect but adaptive frameworks — guardrails that evolve as technology and society evolve.
Instead of asking “which fallacy is right,” we might ask: how do we build systems that remain trustworthy even when our assumptions about AI turn out to be wrong?