Building ChatGPT for Finance — Joseph Kim, OpenAI's Head of Financial Services

What does it take to build AI for finance on top of a general-purpose model? Joseph Kim, who leads work on ChatGPT for Financial Services at OpenAI, joins swyx and Richard at AI Engineer New York to discuss the decisions behind the product: training financial reasoning into a shared model, connecting market data, and building workflows financial professionals can review and trust. Joseph explains why OpenAI bundled financial data even when connectors already existed, what startups can build with the same plugin primitives, and why human taste and expert judgment are hard to measure. The conversation moves from analyst workflows and review chains to investment ideas and the problem of building useful financial evaluations. Chapters 0:00 Cold open 0:25 Introducing Joseph Kim 0:56 ChatGPT for Financial Services 2:19 Training financial reasoning into the model 3:31 Codex adoption inside OpenAI 4:27 Building a finance product 5:29 Why bundle financial data? 6:39 What can startups build? 9:18 Safety and human review 12:04 The banking review chain 12:36 Financial mistakes and AI adoption 14:56 Judgment, taste and the limits of accuracy 15:46 Training models on human taste 18:40 Code quality as a taste benchmark 19:04 Investment ideas and moving up the analyst stack 22:14 Why financial evaluations are hard 25:25 OpenAI’s finance team is hiring Joseph’s AI Engineer keynote: youtube.com/watch?v=jiUC6IUMdvc&t=4350s ChatGPT for Financial Services: openai.com/index/introducing-chatgpt-financial-services/ AI Engineer: ai.engineer/ Latent Space: latent.space/ Sponsorship and business inquiries: business@latent.space