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Bridgewater & Global Citizen—Forecasting the Future: A Modern Economics Challenge 2026 Terms & Conditions
June 2, 2026
Oliver Simon Recognized as a 2026 Institutional Investor Hedge Fund Rising Star
June 2, 2026
Oliver Simon, Head of AI & ML Investment Strategy, was recognized as a next-generation leader reshaping the industry for his work building AIA Labs and defining what investing looks like in an AI-driven future.
Karen Karniol-Tambour on the Twin Forces Reshaping Institutional Portfolios
June 5, 2026
At the 2026 Fiduciary Investors Symposium at Harvard, Co-CIO Karen Karniol-Tambour explains how modern mercantilism and AI are reshaping the investment landscape—and what institutional portfolios need to do differently. See important disclosures and other information.
Jasjeet Sekhon
June 15, 2026
Jasjeet (Jas) Sekhon is Chief Strategy Officer at Google DeepMind, where he leads cross-cutting initiatives spanning technical research, commercialization, and policy. Previously, Jas served as Bridgewater Associates’ Chief Scientist and Head of AI, where he co-founded AIA Labs, Bridgewater’s dedicated artificial intelligence research and investment lab, alongside co-CIO Greg Jensen. Jas serves on Bridgewater’s Board of Directors.
Bridgewater Associates, LP — 2025 Statement on principal adverse impacts of investment decisions on sustainability factors
June 29, 2026
Learning to Replicate Expert Judgment in Financial Tasks
June 30, 2026
Sarah Su, Kevin Zhu, Emily Xiao, Rohan Alur, Daniel Kang
Investor Judgement
Learning to Replicate Expert Judgment in Financial Tasks
September 21, 2026
Aspen Institute Selects Nina Lozinski for Its 2026 Technology Leaders Initiative
July 16, 2026
Nina Lozinski, Co-Head of AI & ML Investment Strategy at Bridgewater, was selected for the Aspen Institute's inaugural Technology Leaders Initiative—a fellowship convening leaders shaping the future of technology and its role in society.
Provable Generalization Bounds for RLVR at the Billion-Parameter Scale
July 20, 2026
Rohan Alur, Daniel Kang, Yuxuan Zhu
Reinforcement learning with verifiable rewards (RLVR) has become a critical tool for modern large language model (LLM) development. RLVR is used to improve the general-purpose reasoning abilities of frontier language models, and has proven particularly critical for mathematical problem solving and agentic coding. Beyond the frontier, a model which is trained via RLVR can outperform much larger frontier models in specialized domains, making this approach especially attractive for enterprise tasks involving proprietary data. For example, Databricks uses RLVR to train a text-to-SQL specialist that outperforms much larger frontier models in this domain.
From Our CIOs: Taking Stock of the New Paradigm
July 27, 2026
Since we identified AI and modern mercantilism as the key forces defining the new paradigm, they have accelerated and now increasingly dominate the global economy and markets. In our recent quarterly letter to clients, we discussed (1) how the current economic cycle is evolving, shaped by these forces, and (2) the questions they are prompting us to wrestle with in portfolios
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