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Explore How Bridgewater Built an AI Analyst That Does Hours of Expert Research in Minutes with 168,193 views, 3,245 likes, and 72 comments. Experience the impact of this video content that has captured audience attention.

Brendan McManus, Michael Ran, and Santi Weight from Bridgewater Associates' applied AI team walk through PAT, the Pocket Analyst Tool — an internal AI analyst deployed to hundreds of investors that performs hours of deep exploratory research in minutes. Brendan explains how 50 years of written-down investment logic gave Bridgewater a unique advantage entering the AI era. Michael demos PAT live — covering time series search with human-like inspection (jumping from 50% to 90% accuracy), per-user security harnesses, and a Teach button that autonomously generates benchmarks and PRs to fix its own mistakes. Santi then goes deep on the architecture: treating agentic code generation as a compiler problem, parallel LLM code gen from a structured plan, DAG-based validation agents, and a static analysis caching layer that makes iterative re-runs nearly instantaneous. Chapters: 0:00 Introduction: 50 years of written-down investment logic as an AI advantage 1:52 Bridgewater's approach to AI: as investors and as practitioners 3:11 The research circle and the artificial investor roadmap 4:30 PAT: what it is and what it's not 5:01 What PAT had to do: data, tools, diagnosable analyses, context, and learning 6:24 How a 50-year-old hedge fund actually builds this 7:09 Multi-archetype teams: investors, technologists, and scientists side by side 8:32 Demo: PAT live — oil supply shocks and the Middle East conflict 9:33 The per-user security problem: every investor gets their own PAT 10:24 Time series search with human-like inspection: 50% to 90% accuracy 11:14 The plan is the analysis: clarifying questions and high-quality research plans 12:49 Parallel code generation: 3-task and 20-task plans take the same time 14:27 Self-correction: PAT checks its own work before returning results 15:24 The Teach button: autonomous benchmark creation and PR generation 18:08 Santi Weight: treating coding agents as a compiler problem 19:44 Chat agent architecture in LangGraph and why coding is a pure implementation detail 21:13 The natural language Python project: deterministic tasks and parallel LLM gen 22:37 Validation agents in parallel: enforcing correctness in the architecture 24:00 Execution layer: static analysis, caching annotations, and near-instant re-runs 25:29 Three takeaways: specialize agents, benchmark narrow workflows, think compiler Resources: → LangGraph: https://www.langchain.com/langgraph → LangSmith: https://www.langchain.com/langsmith → LangChain Academy: https://academy.langchain.com Bridgewater Disclaimer: The AI tools and capabilities discussed herein are for informational purposes only. There is no guarantee that any discussed capabilities or anticipated benefits will be achieved. See Important Disclosures and Other Information: https://www.bridgewater.com/important-disclosures-and-other-information Risks Associated with the Use of AI Tools: https://www.bridgewater.com/risks-associated-with-use-of-ai-tools

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