Classic no-trade theorems attribute trade to heterogeneous beliefs. We re-examine this conclusion for AI agents, asking if trade can arise from computational limitations alone, even with common beliefs. We model agents’ bounded computational rationality within an unfolding game framework, where an agent’s computational power determines the complexity of its strategy. Our central finding inverts the classic paradigm: we show that a stable no-trade outcome (a Nash equilibrium) is reached only when “almost rational” agents have slightly different computational power. Paradoxically, when agents possess perfectly identical power, they may fail to converge to an equilibrium, resulting in persistent strategic adjustments that constitute a form of trade. This instability is exacerbated if agents can strategically under-utilize their computational resources, which eliminates any chance of equilibrium in Matching Pennies scenarios. Our results suggest that the inherent computational limitations of AI agents can lead to situations where equilibrium is not reached, creating a more lively and unpredictable trade environment than traditional models would predict.