WhatA short interactive course that teaches non-technical users accurate mental models for when to trust AI output, using proven analogies and hands-on exercises where they catch AI errors in domains they know.
SignalTechnically literate users are struggling badly to explain AI unreliability to friends and family — they report trying multiple analogies (tour guides who fabricate, Russian roulette, blended books) with mixed results, revealing a massive gap in AI literacy tooling.
Why NowAI tools are now used daily by mainstream consumers for consequential tasks (health, finance, legal questions) but there is no scalable way to teach appropriate skepticism — the gap between AI capability and user calibration is at its widest point ever.
MarketB2B: enterprises onboarding employees to AI tools (~$5B corporate training market segment); B2C: could be viral/freemium; competes loosely with generic AI literacy content but no interactive product exists.
MoatProprietary pedagogical research on which analogies and exercises actually shift user behavior — this is a data moat built through A/B testing teaching methods at scale.
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