- Volume 1.8× its 7-day average
- Lowest level in 7 days
- Biggest AI / Tech mover today
Loading dashboard…
Loading dashboard…
Loading category…
Risk briefing
AI and tech prediction markets price questions like whether a frontier model ships by a date, whether a benchmark gets beaten, or how a major product launch lands. These markets are newer and often thinner than macro or politics, so a single informed trader can move them meaningfully. ProbCast's liquidity adjustment is especially useful here, helping you tell a genuine consensus shift from low-volume noise.
Meaningful moves in this sector — probability shifts that are liquidity supported
No meaningful moves in this category right now.
Aggregate movement and net direction across this category
Large shifts on thin markets, wide spreads, or stale prices — interpret with caution
No noisy moves flagged in this category right now.
Same question, different price across venues — a trust signal to weigh before trusting the number
No matched cross-venue questions disagree by more than 4 points here — venues are effectively in agreement on this sector.
Thin markets and stale prices where the read is fragile — track, do not rely on
Notable probability moves with derived context and trust scores
Every tracked market in this category, ranked by Meaningful Move Score
| Market | Venue | Prob | Δ 24h | Trust | Signal |
|---|---|---|---|---|---|
| Will Anthropic and OpenAI IPO in the same month? | Manifold | 20% | -3.9 pts | 17 | Watch |
Where activity is concentrated in this sector
| Market | Venue | Prob | Δ 24h | Trust | Signal |
|---|---|---|---|---|---|
| Will Anthropic and OpenAI IPO in the same month? | Manifold | 20% | -3.9 pts | 17 | Watch |
Every move above is scored for movement, liquidity, and trust so you can tell a meaningful move from a noisy one. Thin markets, wide spreads, and stale prices are labelled rather than hidden. This is a read-only intelligence layer over public prediction markets — informational only, never trading or betting advice.
Read the scoring methodology