We’ve been asking “can AI approximate human understanding?” But this question hides another: good enough for what purpose, judged by whose standards, serving whose interests?
“Good enough” isn’t universal. It depends on who’s judging, their resources and constraints, and what’s at stake.
We’re building AI that approximates human understanding. But something strange is happening: the approximation is changing what it approximates.
Humans adapt to AI. We change how we communicate to be better understood by algorithms. We modify our behavior to work with recommendation systems. We reshape our preferences based on what AI surfaces.
Not everyone benefits equally from AI that approximates human understanding. Some people will be approximated accurately because they match the patterns in training data. Others will be systematically misunderstood because they don’t fit dominant patterns. This isn’t a technical problem to solve. It’s a political reality that shapes whose understanding counts.
This is where I’m supposed to wrap everything up. Ten articles exploring how AI approaches understanding, what it can approximate, what remains beyond reach. Time for the synthesis, the grand conclusion, the neat ending.