Recent acquisitions of slow-growth public SaaS companies are not just value grabs but turnaround plays. Acquirers believe these companies' distribution can be revitalized by injecting AI-native products, creating a path back to high growth and higher multiples.
A unique dynamic in the AI era is that product-led traction can be so explosive that it surpasses a startup's capacity to hire. This creates a situation of forced capital efficiency where companies generate significant revenue before they can even build out large teams to spend it.
The slow growth of public SaaS isn't just an execution failure; it's a structural problem. We created so many VC-backed companies that markets became saturated, blocking adjacent expansion opportunities and creating a 'Total Addressable Market (TAM) trap'.
Traditional valuation models assume growth decays over time. However, when a company at scale, like Databricks, begins to reaccelerate, it defies these models. This rare phenomenon signals an expanding market or competitive advantage, justifying massive valuation premiums that seem disconnected from public comps.
Recent security breaches (e.g., Gainsight/Drift on Salesforce) signal a shift. As AI agents access more data, incumbents can leverage security concerns to block third-party apps and promote their own integrated solutions, effectively using security as a competitive weapon.
Contrary to the 'get in early' mantra, the certainty of a 3-5x return on a category-defining company like Databricks can be a more attractive investment than a high-risk seed deal. The time and risk-adjusted returns for late-stage winners are often superior.
The operating model for SaaS has inverted post-2021. Previously, growth came at the cost of declining efficiency ('200% headcount to grow 100%'). The new benchmark is to achieve hyper-efficiency at the margin, demanding teams grow revenue at double the rate of their headcount expansion.
In a new, high-risk category, betting on infrastructure ('shovels') isn't necessarily safer. If the category fails, both app and infra lose. But if it succeeds, the application layer captures disproportionately more value, making the infrastructure a lower-upside bet for the same level of existential risk.
The push for AI-driven efficiency means many companies are past 'peak employee.' This creates a scenario analogous to a country with a declining population, where the total number of available seats is in permanent decline, making per-seat pricing a fundamentally flawed long-term business model.