GoKawiilFor serious poker players, the ability to sniff out the “tells” that expose an opponent’s intentions is nearly as important to winning as the cards themselves.
Many gamblers have made careers out of their ability to decipher the meaning of everything other players do at the table—their conscious movements, their body language, and their subconscious tics, all of which might reveal their strategy—as a method of gaining an edge in this game of incomplete information.
It’s understandable, then, that ESPN’s use of a new “AI tells detection” tool during the 2026 World Series of Poker Main Event broadcast stoked some serious debate within the poker community.
The tool began appearing periodically during the first few days of the tournament’s live broadcast in early July. A text overlay displayed various live metrics on a player’s movements, plus a “hand strength model” chart breaking down different possibilities of the type of hand a player might be holding. The tool looks slick, but a viewer might naturally wonder how accurate its data is, or how the AI came to know the players’ tics and gestures well enough to venture such a guess.
Is the tool just a neat party trick—or a silly one, depending on your sensibilities? Or is it an attempt to haphazardly stuff AI into the inherently human pursuit of poker, threatening the game’s soul and future?
Do Tell
Hundreds of pros on the poker circuit specialize in spotting tells. This new tool, designed by an AI engineer for the US Air Force named Luke Geel, purports to digitize that process. It’s watched every hand captured on camera in the 2026 WSOP Main Event to build a tells database on various players.
The system gathers inputs on the players ranging from eye movements and the rate at which they blink, to the players’ posture, chip handling movements, “hand fidget” metrics, and more. It analyzes that data and the outcomes of each hand to predict the likelihood of which general hand type a player might have: A strong made hand, a drawing hand, a bluff, and so on.
The poker experts I spoke to are skeptical about the tool’s effectiveness—especially since it was trained on such a small amount of data. The 2026 edition of the WSOP Main Event tournament drew over 9,000 entries, but the vast majority of those players never spent time at one of three tables that were being recorded by cameras. (The same camera feeds used for the broadcast were also used to train the AI tool). Even those who did sit at those tables weren’t there long enough for the system to build a robust dataset that covers the vast range of situations possible in poker.
“The streams are varied enough that you don't get the same players too frequently,” says Michael Gagliano, a 17-year poker professional who made the Main Event final table this year and is playing for the $10 million top prize this week.
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