LLMs and Sneaky Big Markets - Scale Venture Partners

• Citations

Key takeaway

• For entrepreneurs: Large Language Models (LLMs) present significant venture opportunities in niche, text-heavy verticals by expanding the total addressable market (TAM) through their ability to automate and enhance specialized tasks traditionally performed by white-collar professionals. This expansion into "sneaky big" markets can drive substantial growth and innovation for startups focusing on vertical SaaS with applied AI.
• For investors: LLMs are not only transforming traditional software markets but are also uncovering substantial new venture opportunities in niche, text-heavy verticals. By expanding the TAM through their ability to automate and enhance specialized tasks, LLMs are creating "sneaky big" markets that were previously overlooked, making them highly attractive for venture investment. Investors should focus on how deeply and effectively LLMs are integrated into a startup's core operations.

Summary

The article "LLMs and Sneaky Big Markets" by John Gianakopoulos, published by Scale Venture Partners, explores the transformative potential of Large Language Models (LLMs) in creating new venture opportunities within niche, text-heavy verticals. Gianakopoulos highlights the immediate impact LLMs have on software businesses, particularly in specialized domains where their ability to process and understand complex queries can revolutionize industry practices. The piece underscores the role of LLMs in expanding the total addressable market (TAM) by not just focusing on technology spend but also on labor spend, thereby uncovering "sneaky big" markets previously overlooked for venture investment.

Insights

• LLMs are driving optimism in the startup community by demonstrating their applicability across various markets, especially after the post-2021 valuation hangover.
• The technology's prowess in understanding context within specific domains and integrating with vertical-specific datasets is seen as revolutionary, potentially automating tasks traditionally performed by white-collar jobs.
• Gianakopoulos discusses the importance of evaluating TAM in vertical software markets, emphasizing that a larger TAM generally equates to more significant outcomes and attractiveness to early-stage investors.
• The article provides a framework for thinking about AI applications in vertical markets, including the critical questions Scale Venture Partners considers before making an investment.

Implications

• The advent of LLMs signifies a shift towards more specialized, verticalized applications of AI, moving beyond general-purpose use cases to impact narrow, specialized domains deeply.
• For entrepreneurs and startups, the expansion of TAM through LLMs opens up new avenues for innovation and venture opportunities in sectors previously deemed unattractive due to perceived market size limitations.
• Investors are encouraged to reassess their criteria for venture investments in light of LLMs' potential to redefine market boundaries and create value in "sneaky big" markets.
• The broader adoption of LLMs across various industries could lead to significant shifts in labor dynamics, with software increasingly "eating" into tasks and roles traditionally reserved for human workers, thus necessitating a reevaluation of workforce strategies and training programs.