Barry Duong, a former hedge fund portfolio manager at Balyasny Asset Management, left Wall Street to become the lead AI strategist for public equities at Hebbia, a professional services AI startup. His transition was driven by a growing fascination with artificial intelligence, sparked during a noncompete period after leaving Balyasny. He began experimenting with early-stage AI tools, including stealth products, and was struck by the technology’s potential. As he reflected on the future of finance, he realized he wanted to be part of the innovation rather than just using it. At Hebbia, Duong leads a team that develops custom AI prompts and works directly with financial services firms to integrate AI into their workflows. Unlike many AI tools that focus on automating repetitive tasks, Hebbia’s approach aims to enhance the capabilities of professionals at every level—from junior analysts to senior managing directors. The goal is not just efficiency, but elevating human judgment and decision-making through intelligent augmentation. Duong’s team conducts large-scale training sessions for hundreds of employees at once, teaching them how to write effective prompts and build tailored workflows. They also run one-on-one sessions with senior bankers and investors to design specific AI solutions—such as automating financial models or generating presentations—freeing up time for higher-value work. The process is highly iterative, involving context engineering, prompt refinement, and selecting the right AI model for each task. Success depends on the human element: creativity, problem-solving, and deep domain expertise. His team is composed of professionals from top-tier financial firms across buy-side and sell-side roles, bringing deep knowledge of investment banking, credit, and other finance verticals—not just technical AI skills. One of the most striking metrics at Hebbia is the volume of information processed: over a billion pages for clients, equivalent to roughly 3,000 years of reading and 2,000 years of analysis. These capabilities allow firms to move faster, make more informed decisions, and focus on the most promising opportunities. Duong believes AI won’t reduce headcount but will change the nature of work. Junior staff will become managers of AI agents, while mid- and senior-level leaders must learn to manage AI themselves. The future of finance will require a new kind of expertise—orchestrating AI systems, not just using them. He sees AI as a tool that enhances, rather than replaces, the art and science of finance. While large language models are powerful, there’s still much to explore—especially in foundational quant models that could outperform LLMs in specific mathematical or analytical tasks. For Duong, the move from Wall Street to AI wasn’t a departure from finance, but a natural evolution. He’s now at the forefront of reshaping how financial professionals work—making them smarter, faster, and more strategic.