Ascending Friction

In The You On AI Encyclopedia

The principle provides a crucial corrective to two common misreadings of AI. The first treats AI as pure liberation — the removal of difficulty from creative work. This misreading underestimates the new demands that operating at a higher cognitive floor imposes. The second treats AI as displacement — the elimination of human judgment from the production process. This misreading misses that the judgment has not been eliminated but elevated.

In Smith's framing, ascending friction is consistent with the broader pattern of what specialization does to work. When a task is decomposed into simpler operations, the specialized worker becomes extraordinarily efficient at her narrow part. But the coordination of the specialized operations — deciding what to produce, in what quantities, to what quality standard — remains a higher-order task that the specialization itself does not solve. What AI does is invert the pattern: it handles the specialized execution and leaves the coordination entirely to the human, who must now operate at the judgment level for which her previous training often did not prepare her.

The psychological experience of ascending friction is often reported as exposure. The worker who arrives at the higher floor without the resources to meet its demands experiences the ascent not as liberation but as a form of difficulty for which nothing in her previous training has prepared her. This is not a failure of the individual; it is a structural consequence of the transition, and it requires a structural response — from educational institutions, from professional organizations, from employers who deploy the tools.

The principle has policy implications. Training programs that focus on AI tool use without developing judgment at the higher floor prepare workers to do the wrong work well. Programs that develop judgment alongside tool use prepare workers for the economy the tools are creating. The distinction is not subtle, but institutions have been slow to recognize it, and most AI-related training remains oriented toward the lower floor that the tools are rapidly making less valuable.

Origin

The concept appears in You On AI, Chapter 13 (pp. 102-110), where Edo Segal uses the laparoscopic surgery example to illustrate the principle: when tactile friction was removed from surgery, a new and more demanding friction (operating on a two-dimensional image of three-dimensional space) was introduced.

Don Norman

The underlying pattern — that abstraction relocates rather than eliminates difficulty — has been observed by many technology theorists, including Don Norman in his work on interaction design.

Key Ideas

Relocation, not elimination. Difficulty moves to a higher cognitive floor when AI handles the lower floor; the practice as a whole does not become easier.

Ascent as exposure. Workers who arrive at the higher floor without preparation experience the transition as vulnerability rather than liberation.

Structural response. The appropriate institutional response is to develop judgment at the higher floor, not to lament the lower friction's loss.

Policy implications. Training programs focused on AI tool use without judgment development prepare workers for the wrong economy.