Business 6 min read By Bethany Hadley
Pricing algorithms can quietly end competition, economists warn
Research on German fuel markets and laboratory experiments shows that independent pricing software can sustain higher prices without any agreement between firms, leaving antitrust law struggling to respond.
Companies that hand pricing decisions to software may be presiding over markets where competition has quietly stopped, without any meeting, message or agreement between rivals. New research and recent enforcement cases suggest that autonomous pricing systems can produce the economic outcome of a cartel — sustained higher prices — while leaving none of the evidence that antitrust law was written to detect.
The clearest real-world evidence comes from Germany, where automated pricing software became widely available to petrol stations in 2017. Economists later found that in markets where two competing stations both adopted the software, margins rose by about 38%. Market-level margins did not move at all when only one station in a market adopted the tool. The increase appeared only when two algorithms were left to set prices against each other, a pattern consistent with each system learning on its own that it earned more by backing off. The study was published in the Journal of Political Economy in 2024 and is among the first real-world measurements of a problem previously shown mostly in simulation.
Laboratory work points the same way. In a paper published in the American Economic Review in 2020, four economists set reinforcement-learning algorithms to compete in a standard model of repeated pricing. The algorithms could not communicate and were told only to maximise profit. They consistently learned to charge above the competitive level and to enforce that outcome. When one lowered its price to gain share, the others cut theirs, then returned to the higher level once it fell back into line. The pattern held even when firms differed in cost or demand and when the number of competitors changed. The four authors, joined by Wharton economist Joseph Harrington, later set out the policy stakes in Science, warning that delegating pricing to algorithms opens a backdoor to collusion because artificial intelligence can learn collusive rules with no human oversight or awareness.
Harrington has argued that competition law must be rethought for coordination that arises without agreement. That is the core difficulty. Antitrust enforcement was designed around human agreement, meaning evidence of a meeting or an understanding between competitors. Coordination a machine learns on its own provides none of that. The problem takes several forms. Independently deployed algorithms, each pursuing its own profit, can learn over repeated encounters to stop undercutting one another, with no one designing the outcome and no data changing hands. A single firm can use software to anticipate how rivals will react, raising a price where it predicts they will follow. Or competitors can feed data into a common provider whose algorithm guides them all, the pattern enforcers find easiest to challenge.
That last model is the only one regulators have managed to touch. In 2024, the US Department of Justice and several states sued RealPage, whose software recommended rents using data from competing properties, along with landlords that used it. In November 2025 the DOJ filed a proposed settlement. RealPage paid no penalty and admitted no wrongdoing. The terms mainly restrict the data the software may draw on, barring recent competitor data and the fine-grained local geography that made neighbourhood-level coordination possible, and install a court-appointed monitor. The settlement still needs court approval, and the wider litigation continues.
The deliberate version of the problem is easier to describe. In its antitrust suit against Amazon, the Federal Trade Commission described a pricing tool internally named Project Nessie. The system identified products where competitors were likely to follow an Amazon price increase, raised the price, and held it once rivals matched. The agency alleges the tool generated more than $1 billion in excess profit and that Amazon paused it during periods of heightened scrutiny, then switched it back on. Amazon disputes this and says the tool was discontinued years ago.
For executives, the practical warning is that the failure may not appear on any dashboard. An algorithm that sets an obviously wrong price is easy to catch. The harder case is one that does exactly what it was designed to do, optimise margin, and reaches an outcome the company would struggle to justify in public. Executives usually judge competition by the pressure they feel, and a market where prices hold and margins stay comfortable reads as one they have won. When autonomous agents set prices, that same calm picture can mean the opposite: competition has quietly stopped because the algorithms have learned that leaving each other alone pays better than fighting. Researchers still debate how readily laboratory results carry over to live markets, but that uncertainty is itself a reason for boards to watch behaviour now rather than wait for regulators to settle the question.
