Artificial intelligence helped shape one of the world’s most influential hedge funds long before today’s AI boom.

Early use of algorithms allowed complex information to be processed "far more quickly," forming the foundation of Bridgewater Associates’ investment approach, according to founder Ray Dalio.

In a recent post on X, Dalio shared how those computer-based systems gradually became central to how the firm evaluated data and made decisions.

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Dalio said that he began working with early forms of AI 35-40 years ago after translating his decision-making principles into equations. Over time, feeding those rules into an "expert system" allowed machines to analyze large volumes of data efficiently while still applying his decision-making principles.

"I could have inspiration and logic and so on. It was a great partnership," Dalio said. He said the approach to investment decision-making "is what made Bridgewater successful."

How Algorithms Became A Second Brain

In the same X post, Dalio said that the early systems were designed to mirror how he evaluated markets, risks, and probabilities. In Dalio’s view, aligning the algorithms with his thinking allowed the technology to handle vast amounts of data, while he focused on judgment and strategy.

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In Dalio’s view, that approach later evolved into a broader framework at Bridgewater, where investment decisions were guided by clearly defined principles rather than intuition alone. He said that the structure helped limit emotional bias and enabled repeatable processes as the firm scaled.

Over time, those principles were formalized into tools others could use. Dalio said that he further computerized his thinking by building a digital "coach," allowing colleagues to ask questions and receive guidance rooted in his decision rules. The goal, according to Dalio, was consistency, speed, and clarity across the organization.

Before Chatbots, AI Was Already Doing The Work

Dalio said in the same post that early AI systems were far more limited than today’s tools, but in his view, they laid the groundwork for what followed. He said recent advances in large language models have made the process "much more seamless and useful" than earlier versions.

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According to Dalio, newer models differ from rigid, rule-based systems by absorbing nuance and context while still applying structured logic. He said that this shift has expanded how AI can support decision-making beyond financial markets, including management and broader problem-solving.

More broadly, those observations reflect wider changes across finance, where firms increasingly rely on AI to support research and analysis. Many asset managers now use machine learning to scan earnings calls, economic releases, and alternative data sources faster than traditional methods allow.

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