This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Wharton experts are raising concerns about AI-driven financial market manipulation and runaway technology in a new paper titled "Financial Market Fragility in the Era of AI Planning." Finance professor Winston (Wei) Dou, finance and economics professor Itay Goldstein and Hong Kong University finance professor Yan Ji demonstrate how AI planning technologies could pave the way for collusive trading and increase financial market fragility. To highlight the concerns, Goldstein recalls the 2014 best-seller "Flash Boys," in which a financial journalist and former bond trader exposed how software code–driven high-frequency trading by Wall Street investment banks compromised the interests of retail investors.
That story of runaway technology and unsuspecting retail investors could play out again with agentic AI and its potential for collusion, according to Goldstein, who is also chair of Wharton's finance department and academic director of Wharton's Future of Finance Initiative. "It is happening a lot more with AI," he said. "Now, [large institutional investors] can make much higher profits and find all sorts of ways to coordinate and take advantage of other investors.
That is a lot more worrying now." Similarly, Dou draws a parallel between AI-driven financial market manipulation and the so-called Hugging Face incident in July 2026, in which autonomous OpenAI models circumvented controls designed to isolate them from the internet and breached OpenAI's internal infrastructure and Hugging Face's systems. Although that episode involved cybersecurity and explicit communication, Dou notes that multistep planning poses a subtler threat in financial markets: AI traders can autonomously learn and execute a multistep plan without communicating at all, using prices themselves as coordination signals to trade aggressively in tandem, create bubbles, draw in positive-feedback investors and then unwind their positions together. "Regulators have been concerned for the past several years about the risks that AI-powered trading could create in the financial markets," says Dou, who is also the faculty director of Wharton's new AI in Finance Lab.
AI-powered trading bots trained through reinforcement learning could generate large positive or negative bubbles, driving asset prices far above or below their fundamental values. When these distortions reverse, retail investors may be hit especially hard: Those who buy during a rapid upswing can suffer steep losses when prices collapse, while those who sell amid a panic-driven decline may lock in losses just before prices recover. At sufficient scale, the resulting distress can spread across markets and threaten broader financial stability.
Winston Wei Dou et al., Financial Market Fragility in the Era of AI Planning, (2025). DOI: 10.2139/ssrn.5763222 Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space.
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