Thinking In Bets

The thinking-in-bets skill applies Annie Duke’s decision-making framework to help AI agents analyze product scenarios as probabilistic bets rather than certainties. It structures responses to highlight potential outcomes, underlying assumptions, and the specific information needed to resolve uncertainty before committing to a strategy.

When to use it

• Evaluating high-stakes product launches where market feedback is currently unavailable.
• Reviewing strategic pivots to ensure the team acknowledges risks rather than assuming guaranteed success.
• Conducting post-mortems on failed experiments to distinguish between bad luck and flawed logic.
• Simulating stakeholder discussions by generating arguments for multiple competing hypotheses.

Key capabilities

• Reframes binary "yes/no" decisions into a spectrum of possible outcomes with assigned probabilities.
• Identifies hidden assumptions that could invalidate the current strategic approach.
• Generates specific data points or experiments required to reduce uncertainty effectively.
• Separates the quality of past decisions from their actual outcomes to encourage learning.

Example prompts

• "Analyze our plan to enter the Asian market using the thinking-in-bets framework. What are the plausible outcomes and what information do we lack?"
• "We are debating whether to sunset Feature X. Apply the betting mindset to list the scenarios where this decision succeeds versus fails."
• "Act as a skeptical stakeholder. Challenge our roadmap by identifying the biggest 'bet' we are making that might be wrong and how to test it."

Tips & gotchas

This skill is most effective when paired with specific product context; without details on your current situation, the agent may generate generic advice. Ensure you provide clear constraints on available resources so the suggested "bets" remain realistic for your team's capacity.