LLM applications: an investing framework
Key takeaway
- For entrepreneurs: Focus on creating AI applications that solve unique, economically valuable problems to stand out in a crowded market.
- For investors: Prioritize investments in AI-native companies with novel product approaches and strong market potential, even in a competitive landscape.
Summary
The article "LLM applications: an investing framework" by Chandar Lal explores the evolving landscape of generative AI applications and provides a framework for evaluating investment opportunities. It emphasizes the importance of identifying unique market opportunities, developing innovative products, and ensuring defensibility in a rapidly growing and competitive field.
Insights
- Market Opportunity: Generative AI offers both greenfield and brownfield opportunities. Greenfield opportunities create entirely new markets, while brownfield opportunities enhance or replace existing human activities.
- Product Development: Successful AI applications must offer new capabilities and intuitive user interactions. The model alone is not sufficient; the product must be user-friendly and provide durable value.
- Defensibility: Investors need to identify the "moat" or competitive advantage of AI applications. This includes assessing whether the product is merely a thin layer on top of a general-purpose LLM or if it offers a unique, defensible position in the market.
- Go-to-market: Lean heavily towards founding teams that can demonstrate strong commercial instincts, with a clear understanding of who their buyer is, and how to reach them most effectively.
Implications
- For Entrepreneurs: The focus should be on solving hard problems with economically valuable solutions that are difficult to replicate. This requires a deep understanding of the market and user needs.
- For Investors: The investment strategy should include a thorough evaluation of the market potential, product innovation, and defensibility. Investors must be judicious in selecting startups that can sustain a competitive edge.