Stanford's 'Terminator' AI Just Beat 93% Of Human Fund Managers Using Just Public Data, Professor Warns These Entry-Level Number Crunchers Risk Extinction

Stanford's "Terminator" AI fund manager beat most Wall Street pros and now threatens junior analysts' jobs, researchers say.

What Happened: The bot, built by Stanford Graduate School of Business professor Ed deHaan and Boston College colleagues, retro-tested more than 3,300 diversified mutual-fund portfolios from 1990 to 2020 and outperformed 93% of their human managers after adjusting each fund every quarter.

Researchers fed the algorithm only public data — financial statements, analyst forecasts and daily prices—mirroring what real managers saw in the pre-Internet era. Using what IBM defines as a “random-forest” model, the AI repeatedly split the data into new patterns, swapped out risky positions and steered more cash into broad index funds, a move that boosted risk-adjusted returns across nearly every test case.

The bot never relied on "secret signals," deHaan told Fortune. Instead, it uncovered "information hiding in plain sight."

DeHaan warns that the real losers are entry-level number-crunchers. "Sitting around, crunching Excel spreadsheets is a job that will barely exist in five years," he said, arguing that automation can do a week's work in hours.

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That said, it is worth adding that the study is a "thought experiment" and not live trading. The bot never faced rival algorithms or the compliance limits that constrain real funds. DeHaan says its edge would shrink once the technology spreads, echoing other research showing AI gains erode when everyone uses the same signals.

Why It Matters: The Stanford study suggests funds would have needed to quintuple fees to match the AI's performance, highlighting the pressure on managers to adopt smarter tools or vanish. Or as DeHaan puts it, the shake-out could create a market that favors the "clever human who thinks like a human and can ‘out-human' the AI.”

Goldman Sachs believes AI automation could disrupt up to 300 million jobs worldwide, a scenario that would land hardest on entry-level finance roles. Early adopters contend the shift is already underway, with trading desks integrating specialist models for analysis, risk checks and execution, shaving days of work to mere minutes.

That backdrop is fueling a boom in robo-advisors and other low-cost digital platforms, while a separate report shows AI systems are already parsing market data with great speed and accuracy.

Photo Courtesy: Wanan Wanan on Shutterstock.com

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