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NEW YORK, Nov 7 (Reuters Breakingviews) - Turn an artificial-intelligence bot into a trader and it acts all too human. Over a two-week span, six frontier models were seeded with $10,000 apiece to trade digital-coin derivatives. Five finished deep in the red, while the last barely scraped by despite its flimsy risk-adjusted score. For the time being, primate fund managers can rest easy knowing machines simply ape their worst tendencies. The Alpha Arena experiment, opens new tab conducted by U.S. startup Nof1 took place on Hyperliquid, a crypto-derivatives venue that allowed the bots to buy and sell perpetual futures using real money. Each generated its own thesis, sized risk and executed transactions around the clock, logging its reasoning in real time. Discipline and dispassion were in short supply. Sign up here. Instead, the books read more like a turbocharged Reddit board. Outcomes ranged from Qwen's $652 loss, excluding a modestly successful open trade at the end of the trial, to GPT-5's $5,679 loss. Claude, DeepSeek, Gemini, and Grok frittered away a third to half their stakes. Only about one in four trades made money, and a simple risk-adjusted return measure sat below zero. The bots didn't just recklessly lose money, but paid handsomely for the privilege: roughly 10% of the funds went to fees. The similarities to mere mortals were striking. Large-language models chased trends and ignored basic risk discipline. Qwen's winning bitcoin trade borrowed $19 for every $1 it put in. None leveraged itself less than 10 times. Rather than map the market, the machines exhibited adrenaline-junkie behavior, racking up 628 trades over the short span. Academic research confirms the patterns. A recent survey, opens new tab of more than 80 studies found that AI models help process information, but struggle once real-world frictions enter the frame. Machine-learning strategies that look strong in controlled environments collapse, opens new tab when tested over longer periods or wider markets, another research paper concluded. Machines generating alpha remains very much in the imagination. Hedge fund bosses have good reasons to stay cool. Citadel founder Ken Griffin, for one, sees the hot new technology as useful for deciphering noise, but hollow, opens new tab at generating outsized returns. Over the past decade, only one in five actively managed funds outperformed, opens new tab its average indexed peer, according to Morningstar. Unless AI traders learn the virtues of cost discipline, position sizing and patience, they will only be as good as their impulsive and greedy human counterparts. At that point, the AI will probably learn to love passive investing. Follow Sebastian Pellejero on LinkedIn, opens new tab. Context News For more insights like these, click here, opens new tab to try Breakingviews for free. Editing by Jeffrey Goldfarb; Production by Pranav Kiran Breakingviews Reuters Breakingviews is the world's leading source of agenda-setting financial insight. As the Reuters brand for financial commentary, we dissect the big business and economic stories as they break around the world every day. A global team of about 30 correspondents in New York, London, Hong Kong and other major cities provides expert analysis in real time. Sign up for a free trial of our full service at https://www.breakingviews.com/trial and follow us on Twitter @Breakingviews and at www.breakingviews.com. All opinions expressed are those of the authors. Sebastian Pellejero is a U.S. columnist for Reuters Breakingviews, based in New York. He writes about topics across business, investing, markets and technology. Prior to joining in March 2025, he worked as an equity research analyst at BlackRock and a markets reporter for The Wall Street Journal, along with stints at Bloomberg and Debtwire. He is a graduate of Wake Forest University and speaks Spanish.
