Deep in Book VII of Plato's Republic, Socrates describes prisoners chained inside a cave, mistaking shadows cast on a wall by firelight for reality itself. They name the shadows, debate them and develop expertise about them. The prisoners are completely, sincerely wrong, and they have no idea. The cave isn't a place of stupidity; it's a place of convincing, well-organized illusion.

But Plato's real interest wasn't the cave; it was in the periagoge—a Greek word meaning the turning of the soul away from shadows and toward the light. For Plato, this was education itself: not the filling of an empty vessel with facts, but a fundamental reorientation of how a person relates to truth and how they come to know that truth.

The shadows persist, but today they aren't cast by firelight; they are generated by machines. Large language models (LLMs), image-making and AI-powered search produce outputs that are fluent, confident and immediate.

But here's the crucial difference from Plato's original problem: His shadows were at least connected to something real. What AI produces is different in that a language model has no built-in commitment to truth, only a statistical relationship to an enormous quantity of text. When it tells you something, it isn't reporting; it's composing.

The outputs can be correct. But they can also be wrong in ways that are structurally indistinguishable from being right. The shadow no longer flutters on a cave wall. It speaks now, and sometimes it speaks beautifully.

This is why periagoge—turning toward the light—matters more now than ever and why AI threatens it so quietly. Knowledge isn't merely true belief; it's true belief held for the right reasons, connected to the world through justification, evidence and process.

AI disrupts this at the root. It is useful precisely because it decouples output quality from the slow, demanding work of verification. You don't need to consult a primary source, triangulate between perspectives or sit with the discomfort of not yet knowing.

Bypassing learning
GenAI poses many problems for learning. When an AI hands us an answer, we risk bypassing the process through which learning happens. We've received a product that looks like knowledge from the outside but is hollow at its core; it's a shadow that convinces us of something we haven't actually understood.

GenAI doesn't just help us think. It thinks instead of us. And there's growing evidence it's making us measurably worse at doing it ourselves.