Why I left poker. Why I came back.

I played poker professionally for eight years. Not as a hobby that paid, as the job. Tournaments online and live, staking other players, coaching, the whole apparatus of treating a card game as a business.

Then I quit. Not because I stopped loving the game. Because I’d learned what it had to teach me about playing, and I wanted to see the machine from the other side.

The leaving

By 2014 I’d won a live main event, built a staking stable, and ground out tens of thousands of online tournaments. The results were tracked and public. That mattered to me. Poker is one of the few professions where your entire track record sits in a database anyone can query.

But the ceiling was visible. The games were getting harder every year. Solvers were coming. And I kept noticing that the most interesting decisions I made each week weren’t at the tables. They were about the business around the tables: who to stake, how to price a coaching package, when a player’s leak was worth fixing and when it wasn’t.

So I crossed the table. Unibet Poker at Kindred, working on how a poker room actually grows. Then Entain, GTM across one of the biggest gaming groups in the world. The company behind PartyPoker, which was once the biggest poker site on the planet.

Then further out: Web3, decentralized networks, and eventually AI. Building in public, marketing for AI-native companies, working with agents daily.

The coming back

Here’s what I didn’t expect. The further I got from poker, the more often poker came up.

Every AI lab that wants to prove its systems can handle the real world reaches for poker. Libratus. Pluribus. DeepStack. Chess and Go fell first, but they’re perfect information games. Everything on the board is visible. Poker is the benchmark that matters because it’s the one that looks like life: you decide with money on the line while the most important information stays hidden.

That’s also the job description of everyone I worked with in business. Launch or wait. Hire or pass. Price high or price low. Nobody has the full picture. Everybody acts anyway.

Poker players train that muscle deliberately, thousands of times a night, with immediate financial feedback. It’s the purest training ground for decision-making under uncertainty that exists. And AI is now industrializing exactly that skill.

So the two halves of my life stopped being separate. The game I spent eight years mastering turned out to be the laboratory for the problem AI is spending billions to solve. I didn’t leave AI to come back to poker. AI is the reason I’m back.

What this place is for

This site and this blog are where I write it down. Three threads:

• What eight years as a player, staker, and coach actually taught me, with the receipts.
• What running the other side of the table looks like, from inside Unibet and Entain. Most poker content is written by players. Very little is written by people who did both.
• Decision-making under incomplete information: poker as the framework, AI as the accelerant.

No strategy content for its own sake. No hand histories for nostalgia. The question that interests me is bigger than the game: how do you decide well when you can’t know enough?

Poker taught me the answer once. AI is teaching it to everyone now. I want to be writing from the seat where those two things meet.