Lessons for Builders in Fintech AI: Seth Rosenberg with Basis, Rogo, and Foundation AI

The Fintech AI Opportunity

• 💡 The intersection of AI and financial services presents a massive opportunity, given finance is a quarter of the economy and rich in unstructured data (receipts, invoices, loan applications).
• 🚀 AI offers significant economic benefits by enabling slightly better decisions in areas like investing and underwriting, and can transform the enormous spend on financial services.
• 🏙️ New York City is highlighted as a growing epicenter for vertical fintech AI, attracting smart, ambitious builders and fostering a strong community.

Building AI-Native Products

• 🎯 Companies should aim to build valuable companies, not just useful products, by focusing on creating business moats, embedded workflows, and long-term stickiness.
• 🧠 Rogo evolved from automating rote tasks to developing human-level AI analysts for Wall Street, emphasizing core financial reasoning and insights.
• 🛠️ Foundation defines an AI agent as capable of autonomously reasoning through novel tasks, using standard operating procedures (SOPs) to process documents for insurance businesses.
• 💡 Basis focuses on providing AI agents to accountants to lower the marginal cost of accounting and improve economic decision-making.

Go-to-Market Strategies

• 💰 Pricing models must adapt as AI replaces headcount; per-seat pricing is ineffective. Basis prices based on a proxy of the work done for end clients.
• ✅ Selling to financial services requires navigating high standards for product quality, security, and support, and creating urgency by highlighting the long-term commitment needed for AI integration.
• 🤝 Early on, it's crucial to engage in design partnerships and build deeply with customers for 3-6 months to establish foundational infrastructure and ensure product utility.

Cultivating AI-First Talent

• 🧑‍💻 Successful teams prioritize hiring individuals with strong agency, taste, and thoughtfulness, focusing on engineers who can architect and mold data rather than just code.
• 📈 AI internally elevates non-technical staff, enabling them to perform tasks like writing SQL queries or creating product mocks, effectively providing a "floor" for various functions.
• 🚀 Deployed intelligence teams (or deployed engineering) are vital for vertical AI companies, as they span technical, sales, and domain expertise to solve complex customer problems.

The Future of Financial AI

• 🔮 Within 5-10 years, many current jobs may be transformed, leading to the creation of massive, high-leverage companies with smaller, highly effective teams.
• 🔗 Future advancements require greater interoperability with data and automation of underlying systems, with potential roles for technologies like blockchain in financial infrastructure.
• 🤔 The long-term role of human empathy and judgment in senior financial roles remains a key question as AI capabilities advance.