Public figures—CEOs, politicians, analysts, and other influential voices—constantly make predictions about markets, technology, and global trends. Yet there's no reliable way for businesses, investors, or the public to assess who has a strong track record and who doesn't. This gap can lead to costly decisions based on unreliable sources. A platform that systematically tracks and scores the accuracy of predictions could help users filter out noise and identify trustworthy voices.

How it could work

The platform would function like a "credit score" for prediction accuracy. First, it would collect verifiable predictions from public statements—earnings calls, interviews, social media—and categorize them by topic and time horizon. For example, if a CEO predicts their company will grow revenue by 20% in a year, the platform would later check the actual results. Over time, individuals would receive scores based on how often their past predictions were correct. The system could also flag new predictions from historically accurate figures, giving users an edge in decision-making.

Potential applications and incentives

Different stakeholders could benefit in distinct ways:

• Investors could prioritize insights from analysts with proven accuracy.
• Businesses might adjust strategies based on the track records of industry forecasters.
• Experts would have an incentive to participate, as high scores could enhance their credibility.

Monetization could come from premium features like real-time alerts on high-confidence predictions or enterprise access for financial firms.

Execution and challenges

A minimal version could start manually—say, by tracking predictions from 50 prominent figures—before automating data collection. Early challenges would include defining objective verification criteria (e.g., how to score ambiguous predictions) and ensuring legal protections against disputes. However, existing tools like prediction markets focus on collective forecasts, whereas this concept uniquely evaluates individual reliability.

By turning subjective opinions into measurable data, such a platform could provide a missing filter for decision-makers drowning in forecasts. Starting small with high-impact predictions (e.g., in finance) would allow for testing before scaling to other domains.