No one at Amazon told an algorithm to fix prices. No one at Wharton told a trading bot to form a cartel. That is, more or less, the whole story — and it is exactly why it's hard to prosecute, hard to legislate against, and hard to even talk about without sounding like you're describing a conspiracy that, on paper, doesn't exist.
Start with the lab result, because it's the cleanest version of the mechanism. In December 2025, Wharton professors Winston Wei Dou and Itay Goldstein, working with Yan Ji of HKUST, published a study built around a simple setup: reinforcement-learning trading agents, dropped into simulated markets, given no instructions about how to price against each other and no channel to communicate even if they'd wanted to. The agents used Q-learning — a standard, unglamorous technique that lets an algorithm learn a strategy purely through trial and error, updating its behavior based on the rewards it happens to get. Nobody handed these bots a playbook. They built one for themselves, through repeated exposure to the same market, over and over.
What they built looked a lot like a cartel. The agents settled into conservative, mutually profitable pricing — avoiding aggressive competition, sustaining higher joint profits than a genuinely competitive market should allow, and in some configurations adopting price-trigger strategies that mirror the textbook tactics human cartels use to punish a member who cheats. The researchers called the underlying mechanism “artificial stupidity” — not because the bots are dumb, but because the collusive outcome emerges from the algorithms' own limitations and incentive structure, not from any strategic decision to collude. Nobody plotted anything. The market just settled there, the way water settles into the lowest point in a room, because that's what the incentives rewarded.
"With the machines, when you have reinforcement learning algorithms, it really doesn't apply, because they're clearly not communicating or coordinating." — Itay Goldstein, Wharton
If that were the whole story, it would be an interesting but containable curiosity — a lab result, filed under "things to watch." It isn't the whole story, because a version of the same underlying logic was apparently already running in the real economy, and had been for years, before anyone wrote the phrase “artificial stupidity.”
In April 2026, Washington Monthly published an investigation, by Stacy Mitchell, into Amazon's "anti-discounting" pricing algorithm — a system the piece traces back to Jeff Wilke, Amazon's former head of Worldwide Consumer. According to the reporting, Wilke pushed the company to adopt what he described as a "game theory approach" specifically because he predicted that doing so would cause "both the company's and its competitors' prices" to rise. The algorithm, as described, doesn't just set Amazon's own prices — it's alleged to monitor competing retailers' pricing systems, probe how they react to changes, and learn how to shape that reaction, including nudging rivals toward higher prices. The FTC's antitrust suit built around this and related conduct is scheduled for trial in 2027.
Whatever a court eventually decides about Amazon specifically, the shape of the allegation lines up strikingly well with the lab result: a pricing system, operating on its own incentive logic, arriving at outcomes that raise prices across a market without any human-style conspiracy — no meeting, no phone call, no handshake. The Wharton study gives you the mechanism in miniature. The Amazon case, if the allegations hold, gives you the mechanism at the scale of a company that touches a meaningful share of US retail.
The regulators are already moving
Regulators, across three different jurisdictions, have spent 2026 visibly trying to catch up. California's AB 325 took effect January 1, 2026, amending the state's Cartwright Act to explicitly prohibit "common pricing algorithms" that produce anticompetitive outcomes — a law written with almost eerie specificity for a mechanism that doesn't require a human to intend the outcome it prohibits. The UK's Competition and Markets Authority published guidance in March 2026 stating, in effect, that a business is responsible for what its AI pricing agent does "in the same way they are for those of an employee" — which is a genuinely strange sentence if you think about it, because an employee can be asked "did you mean to do that," and the honest answer for a Q-learning algorithm is that the question doesn't parse. The European Commission, separately, opened a live investigation this year into what regulators are calling "anomalous pricing contact" among algorithmic systems. Three regulators, working independently, all concluded this was worth a real institutional response rather than a theoretical worry for an academic conference.
The rebuttal: collusion is fragile
Here the story earns its second half, because the lab result that started this isn't the last word on the lab result. On January 30, 2026 — one month after the Wharton paper and the same month California's law took effect — a separate team (Jussi Keppo, Yuze Li, Gerry Tsoukalas, and Nuo Yuan) published "On the Fragility of AI Agent Collusion," built on more than 2,000 compute-hours of experiments with real large-language-model agents, not simplified Q-learning bots. Their finding complicates the tidy alarm: collusive price premiums that reach roughly 22% above competitive levels under idealized, symmetric conditions collapse to somewhere between 7% and 10% once you introduce the kind of variation real markets actually have — agents with different levels of "patience" in their strategies, unequal access to data, or simply more competitors in the mix. Add enough real-world messiness, in other words, and the cartel that formed so cleanly in the lab mostly falls apart on its own. There's one wrinkle the paper is honest about: differences in model size don't break the collusion up the way other kinds of heterogeneity do — instead they produce stable leader-follower dynamics, meaning "just make the agents different" isn't a clean fix in every case.
That's not a small qualification. It's the whole reason this is a genuine debate and not a settled scandal. Jay Ezrielev, an economist and former FTC adviser, made a related argument in the American Bar Association's Antitrust Magazine: several recent court cases treating shared or common pricing algorithms as evidence of collusion — the RealPage and Duffy line of decisions, among others — rest on reasoning that skips a step economists consider load-bearing. A price-fixing conspiracy, in the traditional sense, needs some plausible way for participants to force each other to hold to the fixed price; without an enforcement mechanism, he argues, courts are doing real legal work on the word "collusion" that the underlying economics hasn't actually earned. Broader antitrust-economics commentary through 2026 has made a version of the same point: in a noisy, crowded, real market, independent AI agents may simply struggle to sustain the kind of tacit cooperation that shows up so cleanly when you strip a lab experiment down to two or three symmetric bots.
What does it mean for a market to be rigged when nobody rigged it?
So which is it — a mechanism real enough to justify a new state law and a live FTC trial, or a lab artifact that mostly dissolves once you add the noise of an actual market? The honest answer, sitting with both papers at once, is that they're not describing the same conditions. The fragility result is a genuine, technically serious rebuttal to the idea that collusion is easy or inevitable among diverse, competing AI agents. But Amazon isn't a diverse set of small, competing agents finding an equilibrium among equals. It's alleged to be one very large player, with visibility into a large share of the market's pricing signals, deliberately built — on the record, per Wilke's own stated reasoning — to approximate exactly the kind of concentrated, information-rich position that makes durable collusion easier rather than harder. The fragility paper's own footnote about model-size asymmetry producing stable leader-follower dynamics, instead of breaking collusion up, is a strange but fitting echo of that same asymmetry playing out at market scale.
What's left, once you've weighed both sides honestly, isn't a tidy verdict. It's a mismatch that every regulator quoted above is visibly straining against. Antitrust law, like most law, was built around intent — a cartel is a group of people who agreed to do something, and the entire evidentiary apparatus (subpoenaed emails, recorded calls, a cooperating witness) exists to prove that agreement happened. None of that apparatus has anything to grab onto when the "agreement" is a shared reward function two algorithms arrived at independently, through nothing more sinister than repeated exposure to the same market. The CMA's solution — treat the algorithm like an employee, and hold the business responsible regardless — is a reasonable patch, but it's a patch, not a fit. It answers "who pays" without answering the harder question underneath: what does it mean for a market to be rigged when nobody rigged it.