Arcade game machines of the 1980s and 90s faced a unique challenge: creating a compelling and fair experience in player-versus-CPU gameplay. The primary goal was to maximize revenue by encouraging players to insert more coins. This led to the development of sophisticated, albeit sometimes ruthless, balancing techniques.

A core strategy was Adaptive Difficulty. Games like "Gauntlet" or "R-Type" would subtly adjust the CPU's aggressiveness and the number of enemies based on player performance. A player on a winning streak would face a sudden, sharp increase in challenge, while a struggling player might catch a brief respite. This "rubber banding" effect kept games tense and engaging.

Another method was Structured Difficulty Scaling. Instead of being adaptive, many games employed a fixed but steep difficulty curve. Early levels served as a tutorial, luring players in with a false sense of security. As players progressed, the CPU-controlled opponents would become exponentially more accurate, faster, and aggressive. This design encouraged mastery and repeated plays to conquer later stages.

The AI itself was often Rule-Based and Predictable. Unlike modern AI that learns, arcade game AI operated on predefined patterns. The balance came from how these patterns were executed. The CPU could have "perfect" reaction times or inhuman accuracy, but its actions were frequently telegraphed. Skilled players could learn these patterns, turning an seemingly impossible challenge into a predictable puzzle to be solved. This rewarded observation and practice.

Finally, a blunt but effective tool was Resource Management. Games balanced the experience by limiting player resources, such as lives and continues. The CPU's inherent advantage was counterbalanced by the player's ability to try again—for a price. This direct link between challenge and revenue was the ultimate balancing mechanism, ensuring the machine remained profitable while providing a satisfying, if demanding, test of skill.