Lecture Notes: The Future of Vertical AI Agents

Introduction

• Speaker: Gary, Jared, Harj, and Diana.
• Topic: Discussion on vertical AI agents and their potential to transform industries.
• Context: Progression in AI and foundation models from single to multiple competing players like OpenAI and Claude.

Vertical AI Agents

• Definition: AI applications designed for specific industries or functions, replacing entire teams and tasks.
• Potential: Jared argues for the creation of $300 billion companies in this space.
• Comparison to SaaS: Analogous growth potential compared to the SaaS boom triggered by XML HTTP requests and technologies like Ajax.

Historical Context of SaaS

• Catalysts: Advent of technologies that moved software from CD-ROMs to web-based applications.
• Key Players: Paul Graham’s ViaWeb as an early SaaS example.
• Incumbents vs. Startups: Incumbents won obvious consumer categories but missed non-obvious mass consumer ideas (e.g., Uber) and B2B SaaS opportunities.

Potential of LLMs (Large Language Models)

• New Paradigm: LLMs as a revolutionary shift akin to SaaS technology, enabling new applications and efficiencies.
• Enterprise Adaptation: Enterprises exploring custom LLM applications through platforms like Sierra and VectorShift.

Vertical AI Agents vs. SaaS

• Market Dynamics: Vertical AI agents could disrupt SaaS by integrating software and human roles.
• Enterprise Hesitation: Initial reluctance due to unknown applications and perceived threats to existing roles.
• Vertical vs. Horizontal Solutions: The tension between specialized vertical solutions and broader SaaS platforms.

Case Studies & Examples

• Qualtrics and Surveys: Outset’s use of LLMs to improve survey interpretation and decision-making.
• QA Testing: Momentic’s AI agent replacing QA teams, mirroring historical tensions faced by Rainforest QA.
• Recruitment and Dev Tools: Examples of AI-driven efficiency replacing traditional recruitment and developer support roles.

Implications for Startups

• Operational Changes: Shift in startup growth strategies due to AI efficiencies, potentially fewer employees needed.
• Future Unicorns: Prediction of startups with minimal staff achieving high valuations due to AI leverage.

Voice AI and Real-World Applications

• Debt Collection: Salient uses AI voice calls for automating debt collection, a traditionally low-wage, high-churn job.
• Voice Technology Progress: Rapid advancements enabling practical AI-driven voice applications.

Future Trends and Considerations

• Enterprise Acceptance: Growing acceptance of vertical AI agents in enterprises, faster than previous tech waves.
• AI Agents in Management: Potential for AI agents to extend managerial capabilities and efficiency.
• Market Competition: Emergence of competition in foundation models fostering a fertile ecosystem.

Conclusion

• Advice for Founders: Focus on boring, repetitive tasks for vertical AI opportunities.
• Final Thoughts: Continued rapid progression in AI technologies with significant implications for the business landscape.