Ember

Ember is an innovative and highly transparent prediction platform designed to test whether artificial intelligence can consistently outperform the crowd in real-world forecasting. At its core, Ember runs a live, ongoing experiment where multiple AI models independently analyze markets and make daily predictions. The platform focuses on measurable outcomes, strict accountability, and complete transparency, creating a system where every prediction is publicly tracked and permanently recorded.

What makes Ember unique is its “Arena” concept, where three different AI models — each with distinct strengths — generate independent probability-based predictions on live markets such as Polymarket. These models do not collaborate or share information during the decision-making process. Instead, they operate separately, and their disagreements are treated as valuable signals rather than problems to solve. When the models diverge significantly, especially by 10 or more percentage points compared to real-money market consensus, Ember flags these situations as high-conviction opportunities worth attention.

The platform relies on a powerful intelligence stack that processes data from more than 19 different sources every day. These sources include prediction markets, bookmaker odds, AI research publications, real-time sentiment from social platforms, and emerging technology trends. By combining financial data, academic research, and live public sentiment, Ember creates a multi-dimensional view of each prediction scenario. This synthesis happens early each day, ensuring that all predictions are made before outcomes are known.

A key pillar of Ember is its commitment to transparency. Every prediction includes a clearly defined outcome, a deadline, and a disproof condition — meaning there is no ambiguity about what counts as being right or wrong. Once a prediction is published, it is permanently locked. There are no edits, no deletions, and no retroactive changes. This approach eliminates bias and ensures that the track record reflects genuine performance rather than selective reporting.

The platform also introduces a scoring system based on Brier scores, which objectively measure the accuracy of probabilistic predictions. Over time, this scoring system reveals which model performs best and whether AI can truly develop reliable predictive intelligence. Importantly, both successes and failures are recorded, and incorrect predictions are followed by detailed post-mortem analyses to understand what went wrong.

Ember offers different levels of access depending on how early and deeply users want to engage with the data. The free tier allows users to view past predictions and outcomes, while paid tiers provide early access to signals, full visibility into model reasoning, and additional tools such as APIs and custom prediction requests. This structure emphasizes that timing is a key advantage — those who see the signals earlier may gain a strategic edge.

Ultimately, Ember is more than just a prediction tool; it is a live experiment in AI accountability and decision-making. By forcing models to disagree, recording every outcome, and making the entire process public, the platform challenges traditional ideas about forecasting. It invites users to observe, analyze, and question whether artificial intelligence can consistently identify opportunities where the broader market might be wrong.