Today's guest is Annie Duke, who is a former world-class professional poker player and one of the world's top experts on decision-making. She's a bestseller author and coach of many tech founders and teams.
With Annie we talked about her journey from studying decision science to becoming a top poker player and back to decision-making. We explored how to make good decisions under uncertainty, alone and in a team. And we particularly focused on quitting decisions, what makes for a good versus a bad quit and why we are so bad at recognizing those.
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🎙️ Episode
You can watch the full episode on Youtube:
Or listen to it on Spotify, Apple, Overcast, or your podcast app of choice.
🥇 Interview Summary
If you are a 🔒 paid subscriber 🔒 you will find my own summary of the interview below.
It’s the 10-minute, handcrafted takeaways of what we talked about, with timestamps to the relevant video moments, for those who don’t have time to sit through the 1-hour chat.
Here is the agenda for today:
🎯 Annie's Journey from Academia to Poker to Decision Science (04:10)
🧠 The Problem of "Resulting" in Decision-Making (10:45)
⚡ Creating Shorter Feedback Loops (16:17)
📋 Four Principles of Good Decision-Making (26:50)
🚪 The Underrated Skill of Strategic Quitting (47:59)
Let's dive in 👇
1) 🎯 Annie's Journey from Academia to Poker to Decision Science (04:10)
Annie's path to becoming a decision-making expert started in cognitive science at UPenn, where she was pursuing a PhD focused on decision-making under uncertainty. When a chronic illness forced her to take time off, she needed income and turned to poker — a game she knew through her brother's success.
What began as a temporary income solution became an 18-year career. However, about eight years into her poker career, Annie began giving talks to options traders about risk management, merging her academic background with her poker experience.
"The work that I did in cognitive science is really all about decision-making under uncertainty. Poker is decision-making under uncertainty. My consulting is working with people on decision-making under uncertainty."
Annie eventually discovered she preferred positive-sum environments over poker's zero-sum nature. This led her to retire from poker in 2012 and focus on consulting, speaking, and writing — including her bestselling book "Thinking in Bets." She also co-founded the Alliance for Decision Education and became a partner at First Round Capital, where she coaches founders.
The key insight: poker became an incredible laboratory for studying decision-making under uncertainty, giving Annie intuitive understanding of cognitive biases that weren't getting much attention in academic circles.
2) 🧠 The Problem of "Resulting" in Decision-Making (10:45)
Annie's core insight from poker is the concept of "resulting" — judging decisions by their outcomes rather than the quality of the decision-making process itself.
She uses a game progression to illustrate this:
Chess: Pure skill, no luck — outcomes directly reflect decision quality
Backgammon: Skill + dice — luck makes feedback loops messier
Poker: Skill + luck + hidden information — closest to real-world decision-making
"The error that we make is that we tend to treat everything like chess. We just sort of background the fact that there's this uncertainty that exists and we'll assume if someone has a good outcome that they played well and if someone has a bad outcome, they must have made bad decisions."
This happens constantly in business: when salespeople crush their numbers, leadership assumes they're great; when they miss, there's a postmortem to figure out what went wrong. But outcomes alone don't tell us about decision quality.
The resulting bias creates a major impediment to learning because it prevents us from properly closing feedback loops and understanding the true reasons behind outcomes.
3) ⚡ Creating Shorter Feedback Loops (16:17)
When challenged about poker having faster feedback than venture capital (where exits take 10-15 years), Annie makes two crucial points:
First, even in poker, you only see your opponent's cards 11% of the time at professional levels — meaning 89% of hands don't provide clean feedback.
More importantly, Annie rejects the idea of inherently long feedback loops:
"It's not like you make a decision and then you go into a coma and then you wake up 10 or 15 years later and you find out how it turned out. There's all sorts of things that are happening in between."
Using venture capital as an example: while you may not know the final exit value for years, you can track whether companies fund their next round (usually within 12-16 months), achieve product-market fit, grow revenue, and hit other necessary-but-not-sufficient milestones.
The key is identifying these intermediate signals and treating them as feedback on decision quality. Annie emphasizes that almost any decision has shorter feedback loops if you actively look for the necessary checkpoints along the way.
4) 📋 Four Principles of Good Decision-Making (26:50)
Annie outlines four core principles for high-quality decision-making:
Speed Assessment
First, determine whether this decision deserves significant time and effort. Consider:
Long-term impact: How much will this matter?
Reversibility: How easily can you change course?
Don't spend forever choosing from a menu, but do invest time in hiring key personnel.
Make the Implicit Explicit
We often make decisions by "feel" or "gut," but this needs to be articulated:
"I actually had some people say to me, 'Oh, I just know a good founder when I see one.' And it's like, okay, maybe, but maybe we should make that explicit. What do you mean by that?"
Making criteria explicit helps you realize when your reasoning doesn't make sense, allows others to challenge your thinking, and makes it harder to rationalize poor decisions after the fact.
Quantify Qualitative Opinions
Instead of saying a market is "great," put a number on it: "On a scale of 0-10, how good is this market?" This forces precision and reveals hidden disagreements within teams.
📊 Precision: Forces exact opinions rather than vague language
🔍 Disagreement discovery: Reveals when team members actually disagree despite using similar words
📝 Accountability: Creates concrete predictions to evaluate later
Collect Opinions Independently
Never gather important information in group settings due to anchoring bias, influence effects, and groupthink.
"I don't think anybody should ever elicit any information that matters in a group setting. Ever."
Instead, collect individual perspectives first, then discuss the differences without trying to force consensus.
5) 🚪 The Underrated Skill of Strategic Quitting (47:59)
Annie argues that quitting is one of the most important skills in decision-making under uncertainty, yet it's severely undervalued due to cultural associations with failure.
The core logic is simple: since we're neither omniscient nor have time machines, we'll always learn new information after making decisions. Sometimes this information suggests we should change course.
"What more important skill could you have than the ability to quit what you're doing? You make a decision under conditions of uncertainty, afterwards you find out new stuff... and then you get to stop."
The Psychology Problem
People don't quit when they should because they:
Associate quitting with failure and lack of character
Fear judgment from others (though research shows people actually respect good quitting)
Fall victim to sunk cost fallacy
Annie uses the extreme example of Shavano Keith, who broke her leg at mile 8 of the London Marathon but continued running for 18 more miles despite medical advice to stop.
Practical Solutions: Kill Criteria and Pre-Commitment
You can't think your way out of cognitive biases, but you can create processes that help:
🎯 Pre-commitment contracts: Set stop-losses or other automatic triggers
📋 Kill criteria: Define in advance what signals would indicate it's time to quit
👥 Accountability partners: Have others help enforce your pre-commitments
Annie shares a detailed example of working with a sales team to define specific signals that indicate a deal should be abandoned (customer only cares about price, won't schedule demos, no decision-maker access after three meetings).
The key insight: there's a huge psychological difference between encountering a warning signal on the fly versus having pre-committed to act on that signal. The signal is identical, but your response will be dramatically different.
🙏 Thank you
Thank you so much for reading & listening! Let me know what you think about the podcast and the brand new summaries in the comments or via email!
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See you next week! 👋
Luca
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