20VC: Why VC Today is Worse Than 2021 | Why Vertical SaaS is...

Full Title

20VC: Why VC Today is Worse Than 2021 | Why Vertical SaaS is a Bad Investment Today | Why We Are Deluding Ourselves on Growth Expectations | Revolut Raises $3BN at a $75BN Valuation | Benchmark Adds Their Newest General Partner

Summary

The episode discusses the current state of VC, noting a shift from the exuberance of 2021 to a more challenging environment marked by TAM exhaustion and increased capital intensity.

Key topics include Benchmark's new partner, Revolut's valuation, the debate around vertical SaaS viability in the AI age, and the strategic implications of AI's impact on market expansion and investment theses.

Key Points

• Benchmark's hiring of Everett Randall signifies a steady, strategic approach to team building, contrasting with the hyper-growth strategies seen in the past, and highlights the firm's continued strength and ability to attract top talent despite market shifts.
• The discussion around Revolut's $75 billion valuation emphasizes the trend of private markets outperforming public markets for high-growth companies, and questions whether the massive valuations are sustainable given potential TAM exhaustion.
• There is a strong debate about the long-term viability of vertical SaaS, particularly in the context of AI, with concerns that current euphoria might lead to over-romanticization of niche markets and subsequent TAM exhaustion, mirroring past market cycles.
• The conversation delves into the critical importance of Total Addressable Market (TAM) and the "TAM exhaustion" phenomenon, suggesting that relying solely on aggressive growth projections without considering market saturation can be a fatal flaw in investment theses.
• The increasing capital intensity required for AI development, including the need for self-owned data centers, signifies a shift from software-centric businesses to fixed asset plays, raising the stakes and potentially limiting the number of viable players.
• The hosts express concern about investors overextrapolating current AI-driven growth rates, drawing parallels to the COVID-era surge in demand for certain software, and warn of potential miscalculations if market adoption slows or shifts.
• The discussion highlights that while AI is fundamentally improving business processes and creating new software categories, the rapid pace of development and current market euphoria could lead to a higher rate of investment mistakes than successes.
• The debate on whether to invest in early-stage (e.g., $1M ARR) or later-stage (e.g., $100M+ ARR) companies reveals differing investment philosophies, with one host favoring later-stage, more proven businesses due to a lack of unique value-add in early stages, while the other sees opportunity in nascent markets driven by AI's disintermediating power.
• The conversation touches on the ethical considerations and business strategies surrounding AI-generated content, particularly erotica, and the challenges of content moderation for AI platforms, suggesting that while demand exists, business decisions around supporting such content will be complex.
• The debate on the relative merits of Deal and Rippling as investments points to the critical role of TAM and international expansion versus deep product integration in the HR tech space, underscoring the difficulty in predicting long-term defensibility in a rapidly evolving market.

Conclusion

The current investment climate, driven by AI, is characterized by high expectations and capital intensity, but also carries significant risks of TAM exhaustion and market corrections.

Investors need to be mindful of not over-extrapolating current growth rates and to carefully assess TAM and entry valuations, especially as the AI landscape evolves rapidly.

Despite the challenges and potential for mistakes, the underlying technological advancements in AI present significant opportunities, but navigating this era requires a nuanced understanding of market dynamics and a disciplined investment approach.

Discussion Topics

• How do you balance the pursuit of aggressive growth in the AI era with the risk of TAM exhaustion and market saturation?
• What are the key indicators for identifying truly defensible vertical SaaS businesses in a landscape increasingly shaped by AI?
• Given the escalating capital intensity of AI development (e.g., data centers), how should investors re-evaluate their risk assessment and portfolio construction strategies?

Key Terms

TAM

Total Addressable Market; the total market demand for a product or service.

ARR

Annual Recurring Revenue; the predictable revenue a company expects to receive annually from its customers.

SaaS

Software as a Service; a software distribution model where a third-party provider hosts applications and makes them available to customers over the internet.

LLM

Large Language Model; a type of artificial intelligence that can understand and generate human-like text.

Inference

The process by which a trained machine learning model makes predictions on new data.

Compute

The processing power required for AI models to train and run.

EOR

Employer of Record; a service that handles payroll, compliance, and HR for companies employing workers in countries where they don't have a legal entity.

GTM

Go-to-Market; the strategy a company uses to bring a new product or service to customers.

ARR

Annual Recurring Revenue; a metric used in SaaS businesses to represent the normalized revenue from subscriptions over a 12-month period.

VC

Venture Capital; capital invested in a project or enterprise for which there is a typically mutual expectation of very high returns.

Timeline