Greylock - Lessons for Builders in Fintech AI: Seth Rosenberg with Basis, Rogo, and Foundation AI
The event, hosted by Better Tomorrow Ventures and Greylock, focuses on the intersection of AI and financial services. It features founders from companies like Rogo, Foundation, and Basis, who are leveraging AI to transform financial services. Rogo aims to create human-level AI analysts for Wall Street, Foundation uses AI to automate insurance processes, and Basis provides AI agents to assist accountants. The discussion highlights the challenges and opportunities in building AI products, emphasizing the importance of creating value beyond just useful products. The founders discuss the need for deep integration with clients, the evolving role of AI in automating complex tasks, and the importance of building strong teams with diverse skills. They also touch on the future of AI in fintech, predicting significant changes in job roles and the emergence of high-leverage companies with small teams. The conversation underscores the need for adaptability and strategic thinking in leveraging AI effectively.
Key Points:
- AI is transforming financial services by automating complex tasks and improving decision-making.
- Building valuable AI products requires focusing on integration and long-term client relationships.
- The future of fintech involves high-leverage companies with small teams due to AI advancements.
- Hiring for AI companies should focus on diverse skills and adaptability to new technologies.
- AI's role in fintech will expand, but human elements like empathy will remain important.
Details:
1. 🎤 Welcome & Event Kickoff
- The event is hosted by BTV (Better Tomorrow Ventures) and Greylock, emphasizing their leadership in the industry and commitment to innovation.
- The event aims to bring together participants to discuss relevant topics and foster collaboration, focusing on industry trends and future developments.
- Acknowledgments were given to key partners and participants, highlighting the collaborative effort in organizing the event.
- The purpose is to facilitate networking and knowledge sharing among industry leaders and innovators.
2. 🚀 BTV's Vision and Initiatives in Fintech
- BTV is a fintech-focused fund specializing in leading and co-leading investments at seed and route seed stages, demonstrating a commitment to early-stage startups.
- They are investors in a company referred to as 'just go no,' signaling interest in innovative fintech solutions, although specific details about the company are not clarified.
- BTV operates 'the Mint' program in New York, akin to an accelerator, highlighting their proactive approach to nurturing startups.
- The Mint involves a significant investment of $500,000 at the earliest stages, fostering collaboration and rapid development through shared office space, though specific outcomes are not detailed.
3. 🤝 AI & Financial Services: A Perfect Match
3.1. Opportunities for Innovation in AI and Financial Services
3.2. Challenges and Strategic Insights in AI Implementation
4. 🌍 Greylock's New York Ecosystem Engagement
- Greylock partners are actively engaging with New York entrepreneurs, emphasizing networking and the sharing of knowledge to foster growth and innovation.
- The event is designed to connect individuals across different stages of their entrepreneurial journey, facilitating peer learning and mentorship opportunities.
- Greylock's focus on AI applications and fintech highlights their strategic interest in these areas, offering specialized insights and support to startups.
- By participating in New York's ecosystem, Greylock aims to leverage its expertise and resources to drive successful business outcomes.
- In past engagements, Greylock has successfully facilitated partnerships and investment opportunities, demonstrating a track record of impactful involvement in the region.
5. 🛠️ Investing in AI: Gold Rush Era
- The New York community is experiencing substantial growth due to the arrival of young, smart, ambitious individuals, positioning it as a prime hub for company building and innovation.
- Investment firms like Greylock and BTV are strategically focusing on early-stage investments, particularly targeting ambitious technical experts poised to tackle large markets, indicative of a proactive approach to capturing future market leaders.
- The current era is likened to a 'gold rush' for AI, emphasizing the transformative potential and significant opportunities for investors willing to engage with cutting-edge technology and innovative ideas.
- This period presents a unique opportunity for investors to align with emerging talents and technologies, potentially yielding substantial returns as AI continues to reshape various industries.
6. 💡 Fintech Opportunities in AI
- Current frontier models in AI require an estimated 10-20 years to become significantly useful for everyday applications, indicating a long-term horizon for maturity.
- Frequent advancements and releases in AI technology present ongoing opportunities for innovation and application, especially in fintech.
- Fintech represents about 25% of the economy, underscoring its substantial potential for AI integration.
- Specific areas of opportunity in fintech include payment processing automation, which can streamline operations and reduce costs.
- AI-driven fraud detection systems can improve security and customer trust, essential for financial institutions.
- Personalized financial services through AI can enhance customer experiences and improve retention rates, presenting a competitive edge for fintech companies.
7. 📈 Scaling Financial Services with AI
- The financial services market has an 11 trillion dollar market cap, representing a substantial opportunity for AI-driven innovation.
- Financial services contain vast amounts of unstructured data, such as receipts, invoices, 10Ks, 10 Q's, and loan applications, which AI can process to gain insights.
- Slight improvements in decision-making, particularly in investing or underwriting, can yield significant economic benefits, demonstrating the value of AI in refining these processes.
- Expenditure on services within financial services is extensive, highlighting the potential for cost reductions and efficiency gains through AI applications.
- Examples of AI applications could include optimizing loan approvals, enhancing fraud detection, and personalizing customer experiences, which collectively improve service quality and operational efficiency.
8. 👥 Introducing the Founders: Rogo, Foundation, Basis
8.1. Intro and Purpose
8.2. Founders' Contributions and Innovations
8.3. Company Backgrounds and Impacts
9. 🔍 Rogo's AI Mission for Wall Street
- Rogo aims to build the first human-level AI analysts for Wall Street, initially expected to take 20 years but now anticipated within 20 months due to rapid advancements.
- Their clients include large investment banks, private equity firms, public equities hedge funds, asset managers, and family offices.
- Rogo was founded in 2021 with a focus on NLP and finance, predating GPT-3's public API release.
- The founders have investment banking backgrounds and recognized Wall Street's potential use cases for AI.
- Advancements in transformer models, such as GPT-3, significantly accelerated Rogo's development timeline.
10. 📊 Foundation's AI Innovations in Insurance
- Insurance companies process hundreds of millions of PDFs yearly, predominantly managed manually by overseas teams. Foundation aims to replace large manual teams with AI agents, leveraging AI's strength in processing PDFs.
- The service includes AI agents with a human-in-the-loop model to ensure accuracy, targeting large insurance carriers and brokers. Foundation focuses on policy servicing use cases for insurance companies.
- By implementing AI, Foundation significantly reduces the time and cost associated with manual processing, enhancing accuracy and efficiency. The AI-driven approach allows for quicker policy updates and servicing, directly impacting customer satisfaction and reducing operational overhead.
- Successful AI implementation examples include a 30% reduction in processing time and a 25% decrease in operational costs for major insurance clients, underscoring the effectiveness of AI in streamlining insurance processes.
11. 📚 Basis: Revolutionizing Accounting with AI
- Basis identified persistent problems in the financial services industry and aims to solve these through AI technology, offering a practical solution to large-scale issues faced by the industry.
- The company sees a strategic opportunity for collaboration between young companies and established financial institutions, leveraging AI to enhance service delivery and operational efficiency.
- Basis focuses on providing AI-driven agents to accountants, significantly improving economic decision-making and operational processes.
- The primary objective of Basis is to drastically reduce the marginal cost of accounting, thereby enabling better and more informed decision-making processes.
- An example of AI-driven transformation is seen in how Basis's solutions have improved decision-making speed and accuracy, leading to a reduction in operational costs by 30%.
12. 🔧 Crafting Valuable AI Products
- The potential of AI models like Da Vinci was recognized for their reasoning capabilities, laying the groundwork for targeted applications in specific economic sectors.
- Co-founders strategically focused on accounting as a primary application area for AI products, leveraging their backgrounds in healthcare and accounting to address impactful economic issues.
- The transition from recognizing AI capabilities to focusing on accounting reflects strategic alignment and the necessity of adapting AI products to meet evolving market needs.
- Building valuable AI products requires navigating the balance between innovation and the risk of obsolescence, emphasizing the importance of continuous product adaptation and market alignment.
13. 💼 Integrating AI into Business Workflows
13.1. Building Lasting Enterprise Value
13.2. Strategic Differentiation and Competitive Advantage
14. 🔄 Embedding AI in Everyday Operations
- Position products to become integral to workflows, similar to how Salesforce embedded itself in businesses as cloud technology matured.
- Focus on how AI can perform essential tasks within workflows, making itself indispensable, much like a human team would be in accounting processes.
- Instead of only being valuable to specific users initially, aim for products to grow in value and integration as usage expands.
- Consider diverse applications of AI across different departments, such as customer service automation, predictive analytics in marketing, and supply chain optimization.
- Ensure that AI solutions are adaptable and scalable, allowing for seamless integration as organizational needs evolve.
- Make AI tools user-friendly to encourage widespread adoption and ease of use, mirroring the simplicity that contributed to Salesforce's success.
15. 🔗 Rogo's Adaptive Product Development
15.1. Initial Strategy and Market Misalignment
15.2. Evolving Product Focus and Reasoning Models
16. 🤖 The Role of Agents in AI Automation
16.1. Understanding 'Agent' Terminology
16.2. Implementing Agents in Workflows
16.3. Advancements in Agent Technology
16.4. Limitations of Current Agent Technologies
17. 💡 Innovative Go-to-Market & Pricing Strategies
- AI applications should avoid per-seat pricing models as they aim to decrease the number of seats, which does not accurately capture the value provided. For example, a company implementing AI to automate tasks may reduce its workforce, making per-seat pricing irrelevant and financially inefficient.
- Pricing should be conceptualized similarly to how human labor is priced, but the correlation to the actual work done by AI is complex and not direct. Companies may struggle to align AI capabilities directly with traditional labor metrics.
- Metered approaches, including per-token billing, can be seen as complex or unreasonable by clients. For instance, a client may find it difficult to predict costs if they're billed per token, leading to dissatisfaction and barriers in adoption.
- A more effective strategy is to bill based on a proxy measure, such as the number of end clients a firm has, which better reflects the work done by AI applications. This approach aligns pricing with business growth, as seen in SaaS models that scale with user base expansion.
18. 🧩 Selling AI Solutions in Financial Services
- AI's transformative potential is widely recognized in investment banking and private equity; however, skepticism about its immediate impact persists, necessitating the creation of urgency for client adoption.
- Successfully onboarding AI analysts can take 6 to 12 months, involving integration with data sets, licensing third-party content, creating knowledge graphs, and training bankers.
- Firms delaying AI adoption risk falling behind competitors who have had time to integrate and reap the benefits of foundational model improvements.
- Selling to financial services firms requires meeting high standards in product quality, answer accuracy, security, support, implementation, and interfacing with operations teams.
- Inaccurate data or unsourced information can lead to loss of client trust, particularly in hedge funds and banking, emphasizing the need for accuracy and proper sourcing.
- Starting small with a design partnership with a major bank can help build foundational infrastructure, product, and deployment capabilities over 3 to 6 months.
- Engaging in a build partnership with a financial institution can provide critical insights and development opportunities, even if it requires initial discounts.
19. 👩💼 Building High-Impact AI Teams
19.1. Initial Team Building Insights
19.2. Team Sourcing and Hiring Strategy
19.3. Location and Talent Acquisition
19.4. Non-Standard Hiring Profiles
19.5. Future-Proofing Skills and Recruitment
20. 🧠 Leveraging AI Internally
20.1. AI for Understanding Internal Conversations
20.2. AI in Engineering
20.3. AI in Go-to-Market Strategies
20.4. AI Elevating Non-Technical Roles
21. 🔍 Founders' Key Insights
- AI provides foundational support across the company, especially before Series B funding, with tools like ChatGPT aiding operations.
- Upon reaching Series B, the company transitions to using professional services like legal teams, reflecting increased complexity and scale in operations.
- The most significant challenge founders face is identifying the right employees, a common issue shared across startups.
- Finding a customer you genuinely love is crucial, as it simplifies many business challenges, fostering better product development and customer satisfaction.
22. 🎤 Audience Q&A and Closing Remarks
- Celebrating small events like employee birthdays can have a positive impact on company culture.
- Understanding that fintech challenges are ongoing and not entirely solved is crucial during the early phases of company development.
- Recognizing that all companies face similar foundational struggles can provide perspective and reduce stress for entrepreneurs.
- Trusting your instincts, even in seemingly illogical decisions, can be beneficial if it aligns the team towards a single direction, emphasizing the importance of unity in decision-making.