We're living through a two-sided revolution in the technology of prediction: 1. Prediction markets (@Polymarket, @Kalshi). 2. AI forecasters. How do these compare? We benchmarked @_Mantic_AI against 4,000+ Polymarkets. Results: 🔵 $100k volume is the crossover point where the AI loses its superiority over the market. 🔵 At >$1M volume, the optimal forecast still requires giving Mantic 14% weight. 🔵 Mantic is better calibrated. This is for geopolitics, economics, elections etc -- we exclude sports and crypto. Personal thoughts: 1. "At what market volume do you lose your Brier advantage over the market?" could be the new METR time horizons graph. 👀 2. AI wins on scale. We can apply the "$100k-volume-level accuracy" to a universe of millions of questions. 🚀

Ben & I started Mantic with the mission to solve forecasting. This summer, Mantic out-predicted all 676 humans in the preeminent forecasting tournament. We’ve raised $25M to scale. We’re serving the brave decision-makers who want to grapple with uncertainty. Join the mission.

We ran a $25,000 contest to uncover: where do the best AI forecasting systems disagree about the future? The contest attracted superforecasters and hedge fund quants, who spent months probing for wedge questions. What were the winning tactics?

Mantic now makes *conditional* forecasts! This is a capability @itsmebenday and I have been excited about for a long time. What's conditional forecasting? You're looking at a Mantic forecast — e.g. of the oil price — and you want to see how it would vary under different scenarios, e.g. the Strait of Hormuz reopening. This is one of our most asked-for features, and it's taken some research to get right. We're currently treating it as an explainability technique. We give some examples in today's Friday Forecasts post (link in reply).