Source: Fortune
Summary
The Federal Trade Commission alleges Amazon used an internal pricing tool, Project Nessie, to raise prices when competitors followed, generating over $1 billion in excess profit. Amazon disputes the claims, stating the tool was discontinued years ago. A 2024 study in the Journal of Political Economy found that automated pricing software in German gas stations led to higher margins without explicit coordination. The research highlights how algorithms can mimic cartel behavior, raising concerns for antitrust enforcement. The article outlines three ways competition can disappear, including autonomous systems and shared platforms, with the law struggling to address these issues.
Our Reading
The numbers tell one story.
Amazon’s Project Nessie allegedly boosted profits by predicting competitor behavior.
Algorithms in Germany drove up margins without coordination or communication.
Competition can vanish through ghost, mirror, or hub mechanisms.
The law struggles to catch what it was never designed to see.
Author: Evan Null
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 the same truce independently.
A single firm can instead use software to anticipate how rivals will react, raising a price where it predicts they will follow, a unilateral strategy rather than a pact.
Call it the mirror: Amazon’s Nessie belongs here — no pact, just a system built to predict a rival’s reflection and act first.
Or competitors feed their data into a common provider whose algorithm guides them all, the pattern enforcers find easiest to challenge.
Why the law struggles with this
Antitrust enforcement was designed around human agreement, evidence of a meeting or understanding between competitors. Coordination a machine learns on its own provides none of that, which is why even the most prominent recent case, built around a shared vendor, proved so hard to resolve.
In 2024, the 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 settlement still needs court approval, and the wider litigation continues. RealPage is the easier case, a common provider pooling competitors’ nonpublic data into one recommendation. The harder case begins when independently deployed systems reach the same result using nothing but the prices they can all observe.
Two recent appellate rulings, both involving the same vendor’s software, drew this line for us. The Ninth Circuit dismissed a case against Las Vegas hotels because the tool did not pool their confidential data.
The Third Circuit revived a near-identical case against Atlantic City casinos, where competitors did feed nonpublic data into the shared system and followed its output about nine times in ten. Pooled competitor data on one side and independent use of the same tool on the other is the boundary between RealPage and the harder case.
The question leaders skip
Most pricing teams judge their systems on performance. Margins and conversion improve, and the software is called a success. But a coordinated market and a competitive one produce the same figures, so those metrics cannot reveal the risk.
The sharper question is behavioral. What has the system learned about competitors, and would the company defend that behavior to a regulator, or to customers who found that rival suppliers had somehow stopped undercutting one another?
A board that cannot explain why prices across its category have converged, beyond pointing to the algorithm, has delegated a decision it never intended to make.
What leadership can do now
Turning the systems off is neither realistic nor necessary. The task is to govern what they are permitted to learn, and the research points to several measures.
The first is to establish where the systems can observe competitors. A pricing agent that reacts to a rival’s price in real time has the input coordination needs.
The second is to introduce constraints that make coordination harder to sustain. Some evidence suggests it is more fragile when competing systems differ from one another or face more rivals.
So leaders should treat these steps as risk reduction rather than a guarantee.
The third is to audit behavior rather than results alone. Reviewing only financial performance will not detect this.
