‘Equity Analysts Are Dead’: Bridgewater CIO Says AI Already It Does Better

Trey Thoelcke

Fri, September 11, 2026 at 5:40 PM GMT+3 5 min read

Quick Read

Bridgewater CIO Greg Jensen says AI already outperforms human equity analysts at ingesting filings and producing forward estimates.
Bridgewater's token spend jumped roughly 200x year-over-year, funding a flywheel where AI profits reinvest into smarter AI.
Jensen argues AI won't make markets efficient because frontier capability keeps leapfrogging, rewarding firms that stay ahead of competitors.
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Greg Jensen, a chief investment officer at Bridgewater Associates, used a recent appearance on Bloomberg's Odd Lots podcast to argue that one of Wall Street's most familiar jobs is already gone in practice. His claim is that AI systems now do the core equity analyst function, ingesting both structured filings and unstructured company disclosures and producing forward estimates, better than the humans who have historically done that work. He framed it as an assessment of where the technology stands inside his own firm today.

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That is a notable thing to hear from the investment chief of one of the largest hedge funds in the world. Bridgewater's clients are pensions, sovereign wealth funds, and endowments that have paid for exactly the kind of human research judgment Jensen is describing as displaced. And the audience most exposed to his claim is anyone who reads sell-side notes, tracks analyst ratings, or leans on consensus estimates to make decisions.

Inside Bridgewater's AI Buildout

Jensen offered a concrete signal of how seriously the firm is investing in this shift. He said Bridgewater's token spend is up roughly 200x year-over-year, a figure he presented as approximate rather than precise. That spend funds the compute behind the firm's in-house models.

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He then described the economic loop Bridgewater is trying to build. "We have a value creating AI that's generating more value than we're paying it. And one of the ways we set it up is as it makes money, we put more into making the intelligence better," Jensen said. His moat argument follows directly from that setup. "The people that can use intelligence, that generate revenue that could then put that revenue back into generating better intelligence can create this moat," he said, describing the design as a flywheel where better prediction funds still-better prediction.

The organizational implication is direct. Jensen described the goal as building an investment firm with "the AI at the center," with human risk controls and human oversight of data acquisition maintained around it for safety reasons.

His Forecast on Firm-Wide Capability

Jensen went further and offered a forecast, framed as his own view rather than an established fact. He said the machine side of Bridgewater is already producing outputs that stand up next to the firm's long-running human process. "The AI is making the investment decisions and doing that in a better and better way, such that now we've got these two intelligences, this human intuition system that we've worked on for 50 years, compounding all of our understanding, this AI system that's now been at it for two and a half years," he said. The unstated trajectory is that the newer system keeps compounding.

Why He Says Markets Still Won't Be Efficient

The investing takeaway is where Jensen pushes back on the easy conclusion. He does not think universal AI adoption makes markets perfectly efficient. He argues the frontier keeps moving, and that firms competing on capability at that frontier will still generate durable edges over firms running one step behind. In his framing, the top model providers themselves keep leapfrogging one another on capability, and the same competitive dynamic applies to the investors deploying those models. Being at the frontier still pays.

He is candid about the risks that come with all of this. A separate 24/7 Wall St. piece covers his warnings about AI risk from the same episode and can be read here.

What It Means for Readers Who Follow Analyst Research

For investors who rely on published sell-side research and analyst price targets, Jensen's argument suggests treating those outputs as one input among several rather than a definitive read. If the estimate-building and disclosure-ingestion work is increasingly automated at large buy-side shops, consensus figures may compress faster after events, and the useful lifespan of a given rating change may shrink. Cross-checking against primary filings, transcripts, and management guidance matters more. Jensen's full remarks are available on the Bloomberg Odd Lots podcast page, and readers can judge his framing directly.

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