Investing in the Age of Generative AI

🌈 Abstract

The article discusses the current state of the AI investment landscape, highlighting the reasons behind the intense funding of AI startups, the challenges and weaknesses in the generative AI market, and a framework for early-stage funds to evaluate investment opportunities in this space.

🙋 Q&A

[01] WHY there's such an intensity in the funding of AI startups

1. Questions related to the content of the section?

• The article explains that the intense funding of AI startups is driven by two key factors:
  • The slowdown in the SaaS market, which has led traditional SaaS investors to turn to generative AI as a new growth opportunity.
  • The "AI startups: Sell work, not software" thesis, which suggests that generative AI can replace human labor and generate higher revenue per customer compared to traditional SaaS models.

[02] Weaknesses and wonkiness in the generative AI landscape

1. Questions related to the content of the section?

• The article highlights several early signs of weakness in the generative AI market:
  • Challenges at the foundation model layer, with early deaths and slowdowns of some startups, indicating a maturing and competitive market.
  • Increased availability of cloud GPU capacity, suggesting a lack of demand for generative AI applications.
  • A revenue shortfall at the application layer, where the bulk of revenues are accruing to chip designers and cloud providers, rather than generative AI startups.

[03] A framework for thinking about opportunities in generative AI

1. Questions related to the content of the section?

• The article presents a framework for early-stage funds to evaluate investment opportunities in generative AI:
  • The compute substrate and foundation model vendor categories are out of scope for most early-stage funds due to the high capital requirements.
  • Opportunities exist in vertical SaaS, where generative AI can automate "jobs to be done" that previously required human workers.
  • At the application layer, there are questions around the durability of generative AI revenues, the true size of the TAM, and the potential compression of margins as the market matures.

[04] Bifurcation in fund strategies and potential outcomes

1. Questions related to the content of the section?

• The article argues that there is a bifurcation in fund strategies and the size of potential outcomes in the generative AI space:
  • Large, multi-stage funds can deploy significant capital into coding automation startups, where the potential exit values are massive (over $1 trillion by 2030).
  • Smaller, early-stage funds are limited to vertical SaaS opportunities, where the TAM and potential exit values are more constrained (e.g., the US paralegal market only supports a single unicorn).

[05] Paths forward for funds rethinking their strategies

1. Questions related to the content of the section?

• The article suggests several strategies for venture funds to generate returns in the current generative AI cycle:
  • Adopting a PE-like mindset for vertical SaaS, being more valuation-sensitive and risk-averse.
  • Taking on more technical risk by looking at applications of AI in industries with additional barriers, such as robotics or biotech.
  • Backing smaller, profitable companies that can reach liquidity through acquisitions, rather than pursuing massive TAMs.