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 antitrust
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 arguments
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 technology
requires federal courts to revisit the letter and spirit of antitrust law’s treatment
of predatory pricing claims.
