One right answer is not the same as a good sequence of decisions. eSlams is building a public stadium to explore the difference.
Have you ever replayed a game in your head and traced the loss back to a single move? Sometimes, one overlooked threat is enough to undo a game’s worth of good decisions.
That is what makes an opponent different from an answer key. On a fixed question-and-answer test, the response is the end of the task. In a game, it is the beginning of the next problem.
Your opponent responds. New information changes what makes sense. A promising plan becomes a liability. You have to adjust, carrying the consequences of your last decision into the next one.
So what happens when an AI model has to do more than produce a convincing answer?
I am building eSlams to explore that question: a public stadium where people can play against AI models and watch them compete, with an open-source engine underneath for investigating their decisions.
eSlams stands for Electronic Slams. The Grand Slams for AI models. At eSlams, AI plays games.
Games make the challenge concrete. In Connect Four, building your own threat is not enough if you overlook your opponent’s. In poker, a sensible decision can still lose a hand because you cannot see all the cards. Cooperative games ask something different again: can players coordinate towards a shared goal?
The point is not to demand perfection. It is to understand whether models can make consistently good decisions, adapt when circumstances change and recover when a plan goes wrong.
In Arena, you can choose a game and face an AI model yourself. In Battlefield, you can watch recorded model-versus-model matches. You do not need to understand a benchmark chart to have an opinion about the game unfolding in front of you.
Underneath the stadium, eSlams Core supports 50 game and control environments, spanning board games, cards, negotiation, cooperation and control tasks. Some use compact or adapted rules, clearly identified in the catalogue.
But a compelling replay is only the beginning of useful evidence.
Imagine a model loses. Did it choose a poor move, misjudge its opponent, or fail to return a usable response? Was the problem with the model, or with the software connecting it to the game? A final score cannot tell us.
Core records what happened so those questions can be investigated. The game engine enforces the rules and limits what each player can see. Moves and technical failures are recorded separately. If the software substitutes a move after a model fails, that intervention is recorded, and the run is excluded from valid scoring.
A match can be saved as a portable record that can be validated and replayed. Public replays are kept separate from private audit information. A spectator can follow the action; a developer can examine the record behind the result.
Games are not a complete test of intelligence, and winning one does not prove readiness for a high-stakes application. Meaningful comparisons need repeated trials, balanced conditions and a clear account of uncertainty. Games complement testing in the settings where an AI will actually be used; they do not replace it.
The MIT-licensed eSlams Core engine is available on GitHub and PyPI. Developers can connect their own agents, run experiments and inspect the results. We are hosting the inaugural AI Grand Slams from February 8–14, 2027.
The question is not simply whether an AI can win. It is what happened between the first move and the final score.
At eSlams, you can watch. You can play. And with the open-source engine, you can investigate.
Ramakrishnan Lokanathan is the founder of eSlams. He is speaking at AI Engineer New York on October 14 on “When AI Fails: Designing Evaluation Systems for High Stakes Applications.”
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