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Prices for consumer goods as wide-ranging and essential as groceries, over-the-counter drugs, and rental housing are now commonly set by automated algorithmic systems, which process enormous volumes of data to rapidly adjust prices in response to market conditions. Antitrust regulators have raised concerns that these systems can be used, intentionally or inadvertently, to facilitate anticompetitive collusion, ultimately harming consumers by raising prices and reducing competition on the merits. In this Note, I argue that the imposition of a collusion framework designed around human behavior is misguided, and that algorithmic systems should instead be analyzed in terms of their structural characteristics. By thinking of pricing algorithms in terms of traits like strategic commitment, the scope of data use, and platform intermediation, regulators can better understand the true extent of algorithm-driven pricing harms and design more effective, targeted regulations to address them.