Any AI looking at the footage would face the same issue, even for a tournament as long as the Main Event.
More Than Just Cookies
Tell detection is nuanced work, and pros are dubious that a camera-based AI tool can effectively do it better than a human.
Most nonplayers’ exposure to the importance of poker tells comes from the penultimate scene in the 1998 film Rounders. Matt Damon’s character, Mike McDermott, folds a monster hand to John Malkovich’s Teddy KGB after recognizing that the gangster has him beat—and McDermott discovers this after spotting a tell based on KGB’s habit of eating Oreos at the table. It’s arguably the most memorable poker scene in movie history because it perfectly expresses the battle of wits that underlies every poker game, even though in reality it’s quite reductive.
“To reference the Rounders Oreo cookie tell, it’s a little more abstract than that,” says Shaun Deeb, a two-time winner of the WSOP Player of the Year award and one of the most recognizable players in the game. (Deeb also made a deep run in the 2026 Main Event, finishing 15th.)
“Physical tells are so much more expansive than I think the public realizes,” Deeb says. “There are leg tells, checking tells, verbal tells, breathing tells, pulse tells. There’s an insane amount of tells available, and most of those can’t be picked up by a camera.”
An AI can track visual and audio patterns, but it can’t deduce intention; the former is only so valuable without the latter. Even if the tool was hypothetically perfect at determining when a player was projecting confidence or weakness, that alone isn’t a road map to deciphering their actual hand.
“How strong is two pair to one player versus another player?” says Gagliano. “Maybe someone is extra confident with a hand that’s actually weak for the situation, but for some reason they think they have the best hand, so they’re really confident.
“Maybe if I was playing a casual tournament, I would think my two pair is extremely strong. But in the Main Event I’m still a little nervous, because it’s a high-stakes situation. So maybe my body language is referencing the situation rather than the hand strength.”
For a broadcast entertainment tool, those flaws aren’t necessarily a deal-breaker. No one is expecting some all-knowing oracle—Geel, the tool’s creator, least of all. He’s transparent about the fact that a larger sample of hands would be better for his tool, telling WIRED via email that he’s run some blind tests on other poker competitions with mixed results.
Maybe the feature adds value for some ESPN viewers, though players like Deeb are skeptical even of that. “I think they randomly found something to try to make it like another sport, and I just think it was swing-and-a-miss,” Deeb says.
While viewers saw the tool in action during portions of the tournament broadcast in July, a representative from Omaha Productions, a company licensed by ESPN for WSOP and other sports coverage, said in a text message that the tool would not be used for the final table. The representative declined to provide any reasoning for that decision.
Watching the Detectives
As AI continues to improve, even skeptics concede it’s possible tools like these evolve rapidly and will likely be applied for financial gain. Within poker’s “high-roller” tournament scene, where the buy-ins frequently reach six figures, a small pool of mostly recognizable professionals play each other in events that are often broadcast. It’s possible that hundreds or even thousands of hours of footage exist of these top players, many of whom play dozens of such events every year. It’s already common for poker players to study streamed and broadcast footage to gather info on their regular opponents. Could improved AI optimize that very human process?
Deeb, for one, isn’t worried. As a top pro, he’s frequently been hired to coach players as they make deep runs in the Main Event; he says that process has often included bringing in a hand-picked live tells specialist to observe both opponents and the client themselves (to see if they have any glaring tendencies that should be corrected). A close friend of Deeb’s was watching the streams during his run this year as well, doing the same thing on his behalf.