Source: The Intelligence Staircase
The AI debate has a vocabulary problem. We argue about whether AI is “truly intelligent” or “just pattern matching” as if intelligence were a single dial. The Intelligence Staircase makes a compelling case that this framing is the source of most of our confusion.
The framework proposes ten distinct tiers of intelligence, from basic behavioural competence (hardcoded stimulus-response, like a thermostat) up through adaptive learning, generalisation, meta-cognition, and recursive self-improvement. Each tier solves a problem the one below it can’t handle. And crucially, performance on tier N tells you very little about capability on tier N+2.
The Clever Hans Effect is the most important warning in the framework: a horse that appears to solve arithmetic by reading subtle cues from its trainer demonstrates tier-1 behavioural competence but zero tier-2 understanding. Current AI systems do something analogous constantly — they produce outputs that look like higher-tier cognition but are generated by mechanisms that don’t require it.
What I take from this practically: when evaluating an AI system, ask not just “can it do the task?” but “how is it doing the task, and does that mechanism generalise?” A system solving hard problems by brute-force pattern matching over training data is at a different tier than one building and manipulating internal representations.