Cognitive Scaffold

Preparing your thinking workspace

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MENTAL MODEL · M6488

AI

AI
CultureHigh supportAnthropology
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Updated 2026-08-08

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INTRODUCTION

English translation pending.

CORE DEFINITION

An AI-complete problem is one whose solution would require capabilities equivalent to those of a general artificial intelligence. The label is a difficulty rating rather than a formal complexity class: it signals that no known algorithm can solve the problem without the kind of general understanding, common sense and creativity that current systems lack. Natural language understanding, humour and literary translation are commonly cited examples. The practical consequence is that such problems should be decomposed into constrained sub-problems or approximated, not attacked head on.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Rate the difficulty: ask whether solving this needs general understanding or common sense. - Mark it AI-complete: treat the task as blocked on general intelligence rather than on more data. - Carve out a sub-problem: reduce it to a constrained version that current systems can handle.

anchor

Anchor fast decisions

The classification works by reduction: if a task cannot be solved without the same capacities that general intelligence would supply, then progress on the task is bounded by progress on general intelligence. Current systems excel where the problem can be specified and constrained, but they lack the background knowledge and flexible inference that open-ended understanding demands. That is why adding more data or parameters yields diminishing returns on these tasks. Recognizing the ceiling lets teams redirect effort toward partial solutions instead of waiting for a breakthrough.

MINIMUM ACTION

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Practice this model in one real situation:

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Source support: Explicit

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    en.wikipedia.orghttps://en.wikipedia.org/wiki/Artificial_intelligenceZH · Explicit
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