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Tom Dwan aka Durrr, an example in the online poker world of a master of accelerated learning curves.

Learning Curves & Compounding Feedback Loops in Poker, Machine Learning, Product Development & Startups

Adam Breckler
May 31, 2019 · 4 min read

A collection of quotes and idea snippets

You can listen to the podcast here

I think it’s very important to have a feedback loop, where you’re constantly thinking about what you’ve done and how you could be doing it better. I think that’s the single best piece of advice: constantly think about how you could be doing things better and questioning yourself. — Elon Musk

Learning curves are all around us, but are often misunderstood phenomenon.

Here are a few of them.

Learning Curves in Poker

How did Tom Dwan come out of seemingly nowhere to become the world’s most feared online poker player?

Dwan began playing online poker with a $50 bankroll in 2007. He initially focused on sit-and-go tournaments, later switching to multiplayer cash games then to heads-up cash games.

According to, a site that tracks high-stakes online poker, Dwan earned $312,800 in 2007 on Full Tilt Poker and $5.41 million in 2008.[4]

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According to HighStakesDB, Tom (Durrr) Dwan had played over 1M hands, averaging a take of $2 on each hand, over time.


  • Assuming each hand lasted around 5 minutes on average, that’s well over the 10,000 hours required for expertise. In fact it's over 500x that amount.
  • The key innovation of online poker was to accelerate the pace of play (and potential profit for the casino) by allowing players to “multi-table” hence shortening the feedback loop for learning in the process.
  • What took someone like Doyle Bruson a lifetime to master, was suddenly possible for any kid with an internet connection, an allowance-sized bankroll and the willingness to dedicate the time to learn the game.

This explains how someone like Durrr could have burst onto the scene and immediately have been able to go toe to toe and outplay the world’s best players so quickly.

He simply had more had seen many more situations than them. In machine learning terms, he had a much larger training set.

Machine “Learning” Curves & AI

AIs have long dominated games such as chess, and last year one conquered Go, but they have made relatively lousy poker players. In DeepStack researchers have broken their poker losing streak by combining new algorithms and deep machine learning, a form of computer science that in some ways mimics the human brain, allowing machines to teach themselves.

Fast forward to 2017, ten years since the popularizing of online poker, benefiting from the subsequent stream of digital hand-history available to train computer models on, researchers have been able to accomplish what was previously thought impossible and develop an AI (known as “Deepstack”) that could consistently beat the best human poker players.

Only one year later, AI advancements could now eclipse top human achievement

Even more impressive was the fact that Deepstack was able to outmaneuver its human pro opponents at No-Limit Texas hold-em, which is much more difficult to train a model for than its simpler Limit Holdem variation.

Learning Curves in the Economy, Organizations & Individual Products

Learning = Knowledge = Wealth

Wealth is created by learning curves that result from millions of falsifiable experiments in entrepreneurship by people in free market economies. The most important role money is as the measure of that learning. Manipulating the value of money by printing currency or artificially suppressing interest rates, does not create wealth. — George Gilder in “A 21st Century Case for Gold: A New information Theory of Money

Fast feedback loops can compound to deliver superior business results

Metric feedback loops clearly lead to optimization on the production side of systems and markets. But, the most efficiently self-optimizing systems are those with compounding feedback loops on the consumption side. These happen when use of a product begets more use of a product, thus coopting consumers into alignment with the producer’s goal metric. The result is a flywheel effect. — Jim Collins

Having tighter feedback loops allow for more creativity to be exercised.

“Here’s something I’ve come to believe: creators need an immediate connection to what they are creating. That’s my principle. Creators need an immediate connection to what they create. And what I mean by that is, when you are making something, if you make a change or you make a decision, you need to see the effect of that immediately. There can’t be any delay and there can’t be anything hidden.” Bret Victor — Inventing on Principle from CUSEC on Vimeo.

What learning curves and feedback loops are most exciting to you? Please leave yours in the comments.

About the Author

Adam Breckler is the Founder of Prism Labs, building Xpo.Network and Bridge.Academy.

Thanks to Ankit Kumar Singh for feedback on this post.

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