In the Forbes interview, George Civula, CEO of Hebbia, discusses how his AI company is revolutionizing the M&A market by enabling financial professionals to process vast amounts of unstructured data, uncover new investment opportunities, and streamline due diligence. He emphasizes that Hebbia’s AI acts as a reliable reasoning engine, not just automating tasks but driving revenue and insights, and predicts that AI will ultimately enhance job satisfaction and create new opportunities in finance.
In this Forbes interview, Katherine Schwab speaks with George Civula, CEO of Hebbia, an AI company focused on transforming the finance industry, particularly in the M&A (mergers and acquisitions) market. Civula reflects on the company’s rapid growth in 2025, noting that while 2024 was a year of cautious experimentation with AI, 2025 saw widespread adoption and multi-year enterprise deals, especially among leading financial institutions. Hebbia’s AI tools are designed for investors, bankers, and lawyers, aiming to provide a competitive edge by not just automating routine tasks but also generating new revenue opportunities.
Civula explains that Hebbia’s platform serves three main use cases in finance: assistant tasks (quick, chat-based queries), analyst tasks (complex deliverables like building Excel models or PowerPoint presentations), and managing director (MD) level tasks that directly drive revenue by uncovering new deals and insights. Unlike many AI tools that focus on cost savings or simple automation, Hebbia’s unique value lies in its ability to process vast amounts of unstructured data—such as legal documents and data rooms—to identify investment opportunities and streamline due diligence, making high finance more accessible and efficient.
Addressing concerns about AI reliability and hallucinations, Civula distinguishes Hebbia’s approach as a “reasoning engine” rather than a “generation engine.” Instead of expanding on small prompts, Hebbia’s AI synthesizes large volumes of information down to the most critical statistics, grounding its outputs in source data and reducing the risk of fabricated results. The platform offers multiple interfaces, including a chat product, a “matrix” data grid that condenses millions of pages into key numbers, and proactive “deal spaces” that automatically generate presentation-ready outputs without user prompting.
Civula shares his founding story, describing how his fascination with large language models during his Stanford PhD led him to leave academia and start Hebbia. Although the company began with a technology-first mindset, it quickly found its strongest product-market fit in finance, where the value of truth and information throughput is highest. He argues that as AI matures, its greatest impact will be in fields like finance that demand new insights and competitive edge, rather than in repetitive tasks like legal or medical summarization.
On broader industry trends, Civula is optimistic about AI’s potential to create jobs and enhance job satisfaction, rather than simply causing white-collar job loss. He believes AI will free professionals from grunt work, allowing them to focus on creative and strategic tasks. While acknowledging some short-term displacement, he predicts that AI will ultimately lead to more fulfilling roles and new opportunities. Civula also downplays concerns about an AI bubble, viewing current market excitement as a necessary phase for building transformative infrastructure. His long-term vision is for Hebbia’s innovative interfaces to become ubiquitous, enabling half the world’s workforce to collaborate with AI agents in new and powerful ways.