RIP Old VC Playbook: How Investors Are Rethinking AI Startups Diligence
Investors are adopting new strategies for evaluating AI startups in the SaaS sector. The evolving landscape is challenging traditional investment norms. Diligence processes are being adjusted to accommodate the unique characteristics of AI companies. Investors are exploring innovative methods to assess the potential of these startups. This shift reflects the need to adapt to the changing dynamics of the industry.
Investment and Diligence Concept
The AI revolution is moving faster than previous technological shifts. While the mobile internet took nearly a decade to reach 90 percent household adoption, ChatGPT achieved the same user penetration in just two years. This accelerated cycle is creating companies that reach incredible scale in record time, but it’s also rewriting the venture capital playbook. The traditional rules of SaaS investing are being challenged, and the moats we once relied on are becoming less defensible. Here are ten ways how investors are rethinking their AI startups diligence today based on recent discussions with Eight Roads Ventures colleague, Michael Treskow.
Agents Over Co-Pilots
The first wave of AI applications was dominated by “co-pilots” — tools that assist humans. The next, more powerful wave is characterized by “agents” — autonomous systems that complete tasks from beginning to end. As an investor, the key question now goes beyond making a workflow more efficient to asking if this can automate the workflow entirely.
Erosion of Defensive Moats
The three defensive moats that defined the SaaS era are eroding: Implementation Friction, Workflow Stickiness, and Data Gravity. With the underlying models increasingly turning into an API-accessible commodity, differentiation is shifting up the stack to the application layer.
Building New Moats
The most defensible companies are building new moats around enterprise knowledge, trust, and observability. Investors must figure out how deeply the product is embedded within a customer’s core business processes or how well the agents internalize the enterprise knowledge.
Shifting Market Dynamics
A critical shift in the AI era is the expansion of the total addressable market (TAM) beyond traditional software budgets. AI companies can now tap into two distinct enterprise spending pools.
Operational Efficiency
AI-native companies are operating with unprecedented efficiency. In a confusing market with intense competition, GTM makes all the difference. On the tech side, having a Head of AI to stay on top of the latest feature releases is crucial.
Metrics and Monitoring
LTV/CAC is still relevant, but velocity matters more. Investors must be vigilant about how costs are reported and dig into the P&L to understand the true cost of goods sold. Look beyond NPS to metrics like product usage, which is a leading indicator of retention.