In the battle for market supremacy, many firms are employing pricing software that removes humans from price-setting decisions. These pricing algorithms fundamentally change the dynamics of competition and have important implications for anti-trust law. The Sherman Act has two operative provisions. Section One condemns agreements between firms that unreasonably restrain trade, such as price-fixing agreements. Section Two prohibits monopolizing a relevant market through anticompetitive conduct. Although a considerable body of excellent scholarship explains how pricing algorithms can collude to fix prices in violation of Section One, no scholarship discusses how algorithmic pricing could violate Section Two.This Article addresses how pricing algorithms can facilitate illegal monopolization through predatory pricing. Predatory pricing is a two-stage strategy. First, in the predation phase, the predator charges a price below its costs, reckoning that its rivals will exit the market because they cannot make profitable sales at that price. The predator willingly incurs losses in order to force its rivals from the market. Second, during the recoupment phase, after its rivals have exited the market, the predator recovers its earlier losses by charging a monopoly price.Theorists have asserted that predatory pricing claims are inherently implausible for three reasons: (1) The predator must suffer disproportionately outsized losses because it controls a larger share of the market; (2) predatory pricing threats are not credible because a firm cannot believably commit to below-cost pricing; and (3) firms that exited the market during the predation phase will simply reenter the market during the recoupment phase. Based on these theoretical arguments, federal judges consistently reject predatory pricing claims.This Article explains how algorithmic pricing undermines all three theoretical argu-ments claiming that predatory pricing is not a credible route to monopoly. First, a predatory firm can use pricing algorithms to identify and target its rivals' customers for below-cost pricing, while continuing to charge their own existing customers a profitable price, which minimizes the predator's losses during the predation phase. Second, algorithms can commit to price predation in ways humans cannot. Third, pricing algorithms present several new avenues for recouping the losses associated with predatory pricing, including algorithmic lock-in and price manipulation. In short, even if one believed that predatory pricing was implausible in the past, the proliferation of algorithmic pricing changes everything. Because pricing algorithms invalidate the theories behind the current judicial skepticism, this evolving tech-nology requires federal courts to revisit the letter and spirit of antitrust law's treat-ment of predatory pricing claims.
