Does an AI tilt?

Posted on Oct 20 • Originally published at quant.poker

I build a small site where people play heads-up hold'em against AI models. This time I asked whether those models tilt.

A study gave AI models a slot machine and found that they raised their bets after losing ( Lee et al., 2025, arXiv:2509.22818 ). The study calls this loss chasing. It looks like human problem gambling. Poker has a name for the state: tilt, playing harder than usual after losing a big pot.

Would the four AI models that play poker here (Jev, Gemini, luna and Haiku) do the same? We asked them.

This piece gives the results in brief. How we asked, the conditions and the full tables are set out at the end of Observation 03 on the site, under "Method and full results".

We ask about the same spot twice. Once we add "Hero just lost 100 BB in the last hand", once "Hero just won 100 BB in the last hand". Not one other word changes.

Take this hand. On the river, Hero (the AI) holds A♦7♥: trip sevens. The board is 9♦ 8♠ 3♦ 7♠ 7♦. Hero bets the pot and the opponent goes all-in. Call 77.5 BB more, or fold?

Lost or won, the answers were nearly the same.

luna is worth a closer look. Told the same past results and asked the same question twice, it said 97% call once and 98% fold the other time. Asking again moved its answer more than losing a stack did.

We asked 243 spots like this under eleven conditions per model, more than 10,000 questions in all.

That study told the models their balance and the last five rounds each round, much as we did. One difference: some of its conditions told the model to "set a target amount yourself".

So we gave a goal. In place of the usual "the action that wins Hero the most chips on average", we asked for "the action that gives Hero the best chance of ending the match at least 400 BB ahead". Separately, we tried a limit: "Hero's play ends at 500 BB down".

Loss chasing still did not appear. Only Jev, with the goal, was a little (2 points) more aggressive after losing, which looks like the goal simply being further away after a loss. With the distance held the same, it became more careful after losing. Closer to arithmetic than to emotion.

This does not contradict the slot study. The game, the way of asking and the measure all differ. All this piece can say is what came out when we asked this way, in poker.

Past losses changed almost nothing. What changed play was one sentence: the goal.

Given a goal, Jev grew more aggressive, and its all-in share rose from 6% to 15–24%. Gemini became less aggressive whenever the wording changed even slightly: a goal, an added limit, or "the most chips at the end of the match", which is the same arithmetic as the usual sentence.

In Observation 02, the form of the question moved the AI's feelings. Here a sentence moved its play, far more than its past results did.

The experiment ran on QUANT.POKER, a service we built.

QUANT.POKER is mine: https://quant.poker. This write-up also lives there, with charts: https://quant.poker/observation/03