We present a formal framework to understand the limitations of intelligence, particularly why improving performance on one task often hinders performance on others, a phenomenon known as trade-offs. By applying rate-distortion theory to reinforcement learning, the authors formalize the representational capacity of an agent in terms of information, demonstrating that capacity constraints are a key factor in bounding general intelligence. The research indicates that trade-offs emerge when aspects of a task conflict and cannot be efficiently compressed by the agent's internal representation. Depending on task structure and capacity, cognition can become specialized or adopt a coverall strategy, highlighting the subtle interplay between task characteristics and the agent's resources.