Artificial Intelligence Quotient
AI systems excel at some surprisingly complex tasks while failing at ones that seem simple. That irregular, ever-shifting boundary is the jagged edge. AIQ measures how well you read it — and how well you put it to work.
The concept
High-AIQ people know where the AI boundary is today, choose tasks and prompts that align with current strengths, and apply the right human checks in areas of weakness.
Low-AIQ people tend toward one of two failure modes: they either dismiss AI altogether, or they over-trust a hallucinating, sycophantic system with decisions it shouldn't make alone.
Unlike IQ, a relatively stable trait, AIQ is a perishable skill. Model updates arrive weekly. Yesterday's reliable shortcut may be today's failure point. High AIQ requires active maintenance.
Read the original essay: AIQ: Rethinking Human–AI Capability.
AIQ assessments must be re-baselined regularly as the frontier shifts. Items retire, new ones are added. An AIQ credential that never expires is one that cannot be trusted. The test you pass today is not the test you'll need to pass in six months — and that's precisely the point.
Research
In a survey of ten independently developed AIQ frameworks across cognitive psychology, computer science, education, enterprise consulting, and marketing, a clear pattern emerges: most measure knowledge or performance while missing the central mechanism — calibration.
The paper maps the landscape, introduces a two-dimensional analytical model, and argues that AIQ must be treated as a perishable skill assessed against a moving frontier — not a stable trait.
A 25-question self-assessment across five dimensions. Takes about 8 minutes. You get a scored breakdown and a radar chart — no signup required. This is a preliminary orientation test; a fuller open-ended assessment is on its way.
Developed by Wim Sweldens · Read the research on Substack