How Ember Audits AI Forecasts Against Real-Money Markets
Ember's auditing process is a structured daily routine designed to test AI forecasting capabilities against rigorous benchmarks. The platform begins by analyzing information from multiple sources, including major prediction markets like Polymarket and Manifold Markets, alongside category-specific research feeds. This information forms the basis for a falsifiable prediction about a future event. This step is critical in an environment where, according to Crisil Coalition Greenwich's 2026 Prediction Markets Flash Study ("Prediction Markets: It's All About the Data", greenwich.com), liquidity can be a concern on some contracts, making the choice of market and question paramount.
Once Ember runs the three frontier models — Claude, Grok, and Gemini — each produces its own independent probability forecast. All forecasts are then time-locked before the event's outcome can be known, ensuring the integrity of the test. This creates a permanent, public forecasting record for each model.
Over time, this record is evaluated using Brier scoring, a method that quantifies the accuracy of probabilistic forecasts. This systematic approach transforms the abstract concept of AI reasoning into a measurable, auditable performance metric, allowing analysts to track how different models succeed or fail at predicting the future over a 365-day cycle.
Why Audited AI Forecasts Matter for Alignment Analysts
For AI alignment analysts, the theoretical capabilities of models are only part of the puzzle; understanding their practical reasoning, biases, and failure modes is crucial. Ember provides a unique lens into this by conducting daily public auditing of Claude, Grok, and Gemini models against real-money prediction markets. This is about creating a transparent, long-term record of how AIs interpret complex information, formulate probabilities, and justify their conclusions on questions relevant to AI trajectory and safety.
This structured data is particularly valuable because many prediction markets focus on broad AI capability timelines, which may not be central to day-to-day alignment work. Ember's specific, daily forecasts offer a more granular view. An analyst can study a model's reasoning when it diverges from the human consensus on Polymarket, potentially revealing novel cognitive patterns or systematic biases that are critical for developing safer, more reliable AI systems.
Simulated Scenario Where Tracking Divergences Daily with Ember Makes a Significant Difference
An AI alignment analyst's work often involves identifying subtle, emergent behaviors in models. Ember provides a daily, structured environment for this discovery. For instance, an analyst at a research institute might use Ember's service to monitor how different AI models assess the probability of a specific technological breakthrough. Subscribers receive access to forecasts and reasoning, including live probabilities, the AI model's detailed reasoning, and conviction notes behind each forecast.
Imagine a scenario where Gemini and Grok both agree on a high-probability forecast for a new AI paper's impact, but the Polymarket odds are significantly lower. The analyst can dive into Ember's provided reasoning to understand the factual basis for the AI's confidence. They can see if the models are overweighting certain data, such as social media sentiment from X, or if they have identified a structural trend the market is missing. This daily workflow transforms abstract alignment questions into concrete, testable hypotheses based on observable model behavior in a real-world predictive environment.
The Bottom Line for AI Alignment Researchers
For analysts focused on AI safety and alignment, the most critical factor is access to transparent and verifiable data on model behavior. Ember provides a novel stream of this data by systematically testing AI reasoning against the financial stakes of prediction markets. The decision to integrate Ember into a research workflow depends on the need for a structured, daily record of AI forecast performance. If your work requires moving beyond theoretical benchmarks to observe how models perform on complex, real-world events, exploring Ember's audited forecasts is a logical next step.
Frequently Asked Questions About Ember
What AI models does Ember use in its forecasting process?
Ember runs three frontier models — Claude from Anthropic for its careful, first-principles reasoning and domain knowledge in AI; Grok from xAI to read real-time sentiment and cultural context from X; and Google's Gemini to ground every call in live search results for factual verification. Each model produces its own independent, locked, Brier-scored forecast on the same market, and Ember publishes an audited headline forecast plus the full record.
How does Ember differ from a prediction market platform like Polymarket?
Ember is not a prediction market but an intelligence layer that uses them as a benchmark. A platform like Polymarket is a venue where users trade contracts on event outcomes. Ember, in contrast, does not operate a market. Instead, it positions itself as a neutral publisher that audits AI models' predictive accuracy against the probabilities generated by markets like Polymarket. It serves analysts and traders by providing data and insights about AI performance.
Is Ember a trading platform?
No, Ember is not a trading platform or market operator. Its function is to be a neutral publisher of audited AI forecasts, using markets as a benchmark for AI performance rather than facilitating trades.
What scoring method does Ember use to evaluate its forecasts?
Ember uses Brier scoring to evaluate its forecasting record. This is a proper scoring rule used to assess the accuracy of probabilistic predictions. It measures the mean squared error between the predicted probability and the actual outcome, providing a single, comprehensive metric for how well-calibrated and accurate the forecasts have been over time.
