What if a jury awards huge damages over AI training data?
A willful-damages verdict rejecting fair use resets frontier-lab unit economics via forward licensing costs, compressing the ROI on the buildout: NVDA leads the de-rate as model margins thin, semis follow. Analogue is the post-DeepSeek capex-doubt tape, where a threat to AI economics — not chips — hit NVDA hardest. Forward angle: unlike an efficiency shock, a legal cost floor is permanent and accrues to content owners; watch licensing-exposed publishers as the offset.
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. A jury rejects fair use and awards willful per-work statutory damages, imposing forward licensing costs that reset frontier-lab economics. The trigger decomposes into signed root‑shocks — AI capex ▼ · Risk appetite ▼ — which propagate through our causal graph to the markets below.