The question comes up every time we describe the AI system at M10: if the opponents adapt to how you play, isn't that just a sophisticated way of making the game unfair? The concern makes sense on the surface. An opponent that knows your tendencies should, in theory, beat you more consistently than one that does not. That would make the game harder over time, which would make it less fair over time.
The problem with that reasoning is that it conflates adaptation with exploitation. A truly adaptive system does not aim to maximize its win rate against you. It aims to provide a match that is appropriately difficult given your current skill level. Those are different goals, and building the second one requires a set of constraints that the first one would never impose on itself.
Defining the Problem Space
Before we could design fair constraints for our AI, we had to be precise about what we meant by fairness in this specific context. There are at least three things a player might mean when they say a game feels fair:
First, outcome fairness: the result of a match reflects skill rather than luck or factors outside the player's control. Second, process fairness: the opponent plays by consistent, learnable rules rather than arbitrary or opaque ones. Third, information fairness: both sides operate under similar constraints regarding what they know about each other.
A standard competitive game typically tries to guarantee all three simultaneously by giving both sides identical initial conditions and symmetric rules. An adaptive AI system breaks that symmetry the moment it starts reading the player's habits and building counter-strategies. So the fairness question for us was: given that we have already broken informational symmetry by design, how do we preserve process fairness and outcome fairness strongly enough that players experience the result as legitimate?
The Three Constraints We Built
We landed on three design constraints that we treat as hard rules for the AI at every skill level.
The first is what we call the read-window delay. The AI cannot act on information about a player's habit until that habit has been observed over a minimum number of rounds. Early in a match, the AI behaves as a reasonable generic opponent. It does not start tailoring its behavior to you specifically until it has enough signal to do so accurately. This matters for fairness because it means the first few rounds of a match are genuinely neutral. You are not already behind before you have had a chance to establish yourself.
The second constraint is counter-strategy legibility. When the AI shifts its approach because it has read a habit, the change must be observable to the player within one or two subsequent engagements. We call this the tell requirement. The AI cannot make an invisible adjustment and suddenly start beating the player in ways they cannot diagnose. Every counter-strategy shift has a visible behavioral signal that a paying-attention player can detect and respond to. This is what keeps the system within the boundaries of process fairness: the opponent's logic remains learnable throughout the match.
The third constraint is adaptation speed capping. The AI can only update its model of the player at a fixed rate, regardless of how much behavioral data it accumulates. Even in a session where the player is being very consistent and readable, the AI cannot suddenly snap to a fully optimized counter-strategy in the middle of a match. The cap gives the player enough time to notice what is happening and adjust before the opponent's counter has fully locked in.
Where Things Get Complicated
These three constraints work well in the middle of the skill distribution. They start to show seams at the extremes.
At low skill levels, the read-window delay can feel punishing rather than fair, because low-skill players cycle through so many different failed strategies that the AI has trouble building a coherent read at all. The result is an opponent that feels inconsistent because it genuinely cannot stabilize its model. We handle this by widening the read window at low skill brackets, which means the AI takes longer to adapt but also means its adaptation, when it does come, is more grounded in actual behavioral data.
At high skill levels, the adaptation speed cap creates a different problem. Expert players pick up on the AI's counter-strategies very quickly, meaning they can identify and exploit the tell before the AI has finished adapting. In effect, they can stay one step ahead of the system by cycling their playstyle frequently enough to prevent the AI from ever fully committing to a counter. We consider this a feature rather than a problem for players at that level. Outmaneuver the adaptation system itself is a legitimate high-skill expression. Not every player will be able to do it, and that seems about right.
What We Are Not Claiming
We are not claiming that this framework solves the fairness problem completely. There are edge cases that still produce unfair-feeling outcomes. A player who has developed a very unusual playstyle that our behavioral model does not represent well can find themselves facing an AI that is simultaneously reading them incorrectly and adapting to the wrong signal, which produces erratic opponent behavior that can feel either too easy or too hard depending on the mismatch.
We are also not claiming that every player will feel the system is fair all the time. Player perception of fairness is only loosely correlated with actual fairness. A player who is losing consistently will report the system as unfair at rates much higher than the objective data supports. We see that in our playtest feedback and we are not trying to design our way around it. Our goal is to make the underlying reality fair, measure it carefully, and be honest about the gap between reality and perception.
Fairness as an Ongoing Calibration
The framework above is our current best answer to the design problem. It will change as we learn more from players in actual sessions. Some of the specific parameter values, like the exact length of the read window and the adaptation speed cap at each skill bracket, are calibrated from playtesting data and will be tuned again when we run additional sessions.
The broader principle, that fairness in an adaptive system requires explicit design constraints not just good intentions, is something we are confident will hold regardless of how the specifics change. An AI that adapts to players will naturally trend toward exploitation unless you actively prevent it. Building those active prevention mechanisms in early, and testing them rigorously, is what keeps the word "fair" in our product description honest.