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AI headlines are everywhere—and many claim they know exactly what’s coming next.
In this episode, Teresa Torres and Petra Wille push back on that certainty. They explain why people are bad at predicting the future and why betting on a single outcome can be risky.
Instead, they share a more useful approach: scenario planning. Explore multiple possible futures, extract what matters, and use it to make better decisions today.
If you’re navigating AI-driven change, this episode will help you stay grounded without ignoring what’s coming.
Key Takeaways
- Confident predictions are often wrong
- Early adopters don’t represent everyone
- Treat predictions as one possible future
- Scenario planning > trying to be right
- Focus on patterns, not hype
Timestamps
- 00:00 – The problem with future predictions
- 04:00 – Why experts get it wrong
- 06:00 – Scenario planning explained
- 12:00 – Early adopters vs. reality
- 20:00 – AI, GUIs, and extreme takes
- 27:00 – Using scenarios in product work
- 34:00 – Final thoughts
The Core Idea
- We’re in a period of change—but no one can predict exactly how it plays out
- Strong predictions often ignore uncertainty
A Better Approach
- Treat every prediction as a scenario
- Ask: what else could happen?
- Use multiple futures to guide decisions
What to Watch For
- “My experience = everyone’s future” thinking
- Over-indexing on early adopters
- Ignoring real-world constraints
How to Apply It
- Run quick scenario exercises with your team
- Push ideas to extremes to explore implications
- Extract the underlying insight (not the exact prediction)
Resources & Links:
- Follow Teresa Torres: https://ProductTalk.org
- Follow Petra Wille: https://Petra-Wille.com
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