The ghost cartel — your pricing algorithm may have stopped competing without your knowledge

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.

That is the deliberate version of this problem: a company designing a system to anticipate rivals. The harder version is the one nobody designs at all. In 2017, when automated pricing software became widely available to German gas stations, economists later found that in markets where two competing stations both adopted it, margins rose by about 38% — with no meeting, no message, and no agreement between them. Market-level margins didn’t move at all when only one station in a market adopted the software. The rise appeared only when two algorithms were left to set prices, in effect, against each other, a pattern consistent with each one learning on its own that it earned more by backing off.

That study, published in the Journal of Political Economy in 2024, is among the first real-world measurements of a problem previously shown mostly in simulation.

Pricing algorithms can produce the economic outcome of a cartel, meaning higher prices sustained over time, without the conduct antitrust law was written to detect. It matters for any company that has handed pricing to software, because the behavior may not appear on the dashboards used to judge whether the software works.

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. Automated pricing breaks that instinct. When autonomous agents set prices, the same calm picture can mean the opposite, a sign that competition has quietly stopped because the algorithms have learned that leaving each other alone pays better than fighting.

The failure that should concern leaders is subtle. 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, optimize margin, and reaches an outcome the company would struggle to justify in public.

Three ways competition quietly disappears

Competition can fade in more than one way. 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.

Call it the ghost: no agreement, no data exchange, no one who designed it — just two systems that arrived at........

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