What if AI tutors displace teachers and teaching assistants?
AI tutors at district scale is a real edtech labor hit but a weak semis tailwind; inference for tutoring is cheap, so the Nvidia/Micron read is marginal. The actionable chain is short legacy edtech and adjunct-heavy staffing on margin compression, akin to Chegg's 2023 collapse when ChatGPT gutted its homework moat (-40%+ in a day). Forward angle: districts adopt slower than markets price, so the displacement is a multi-year drip, not a shock.
Every number ships with its receipt — the odds, the range, the precedents, and a public grade at Reality Check. The statistical machinery that produces it is proprietary.
The butterfly cascade
How this trigger trickles across markets, left → right — the root shock, its first‑order moves, then the ripple effects. Drag any node; tap a market for its real price history.
Resolution timeline — how this probability is moving
Our model's odds (electric blue) over time vs the market's (Polymarket, amber), from the past toward the 1–3 years horizon. Each dot is a real macro event that nudged the probability — green pushed it up, red pushed it down. Tap a dot for the source. Loading the probability audit trail…
What it would mean
If this plays out, it is a risk-off shock. Districts and universities adopt AI tutors at scale, shrinking adjunct and teaching-assistant rolls and pressuring edtech labor models. The trigger decomposes into signed root‑shocks — AI capex ▲ · Job displacement ▲ — which propagate through our causal graph to the markets below.