Billionaire Ken Griffin once dismissed AI as ‘garbage.’ Here’s why he changed his mind—and why he’s ‘depressed’

(SeaPRwire) –   Just months after characterizing artificial intelligence as “garbage,” Citadel CEO Ken Griffin is now issuing a warning that the technology will fundamentally alter society, admitting he returned home “depressed” after witnessing its capabilities.

Griffin, a billionaire hedge fund manager overseeing one of the world’s leading trading firms, had previously been a notable skeptic of AI in the financial sector. As recently as January 22 at the World Economic Forum in Davos, he stated to a panel that while AI might appear impressive on the surface, upon closer examination, “it’s all garbage.”

This shift in perspective occurred rapidly and, according to Griffin himself, had a significant impact.

What changed his mind

Earlier this month, during a discussion at Stanford Business School, Griffin recounted a profound personal realization. “I’ve got to tell you, I went home one Friday, actually fairly depressed,” he stated. “You could just see how this was going to have such a dramatic impact on society.”

The turning point was observing AI’s actual applications within Citadel. Griffin noted that the technology had become “profoundly more powerful” than it was just a few months prior, allowing the firm to “unleash” a wider array of use cases that were previously unattainable.

Griffin’s remarks at Stanford followed his participation in the Milken Institute’s Global Conference, where he asked CEOs to share how they were leveraging AI to transform their businesses and reported receiving “six or seven extraordinary stories” in response.

The finance jobs reckoning

For Griffin, the most compelling evidence lies not in coding or content creation, but in high-level financial research. Tasks that Citadel would have previously assigned to teams holding master’s and PhD degrees in finance, and which took weeks or months to complete, are now being accomplished by AI agents in a matter of hours or days.

“To be blunt, work that we would usually do with people with master’s and PhDs in finance over the course of weeks or months is being done by AI agents over the course of hours or days,” Griffin said at Stanford.

He differentiated between the more modest productivity improvements AI has brought to software engineering—which he estimated at 15% to 25%—and the far more disruptive transformation occurring in knowledge work and research. “When you’re seeing really high-level research being done by AI engines, it’s quite eye-opening,” he commented.

Griffin’s change of heart is particularly noteworthy given his recent pushback against the widespread enthusiasm for AI on Wall Street. At Davos in January, he cautioned that the prediction of 50% of entry-level jobs disappearing within five years was “hype pushed to justify data center spending,” according to LinkedIn commentary on his statements. He also pointed out that U.S. data center spending could exceed $500 billion this year, suggesting that the investment narrative was outpacing tangible results. It was at this time that he made the remarks that AI seemed to be “garbage” under the surface. What a difference a few months can make.

The broader warning

Griffin is now drawing attention to a higher level of skill automation—the automation of work previously considered secure due to advanced degrees and specialized expertise. His message to the workforce is both urgent and direct: adaptability is now the sole enduring advantage.

“The success in your career will be defined as to whether or not you will be a lifelong learner or not,” Griffin stated at Stanford, “and AI will just make this all the more important.”

It remains an open question whether young adults will be receptive to Griffin’s observations. Almost concurrently with Griffin’s change of perspective, commencement speakers have faced boos from Gen Z audiences, seemingly whenever they mention the advantages of AI.

For this story, journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.