The boom in tools for data teams faded because the Total Addressable Market (TAM) was overestimated. Investors and founders pattern-matched the data space to larger markets like cloud and dev tools, but the actual number of teams with the budget and need for sophisticated data tooling proved to be much smaller.
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'.
Initial data suggested the market for design tools was too small to build a large business. Figma's founders bet on the trend that design was becoming a key business differentiator, which would force the market to expand. They focused on building for the trend, not the existing TAM.
During a fundamental technology shift like the current AI wave, traditional market size analysis is pointless because new markets and behaviors are being created. Investors should de-emphasize TAM and instead bet on founders who have a clear, convicted vision for how the world will change.
The conversation around Ideal Customer Profile (ICP) has evolved beyond simple refinement. With newly accessible data, companies are fundamentally re-evaluating their Total Addressable Market (TAM), challenging long-held assumptions about who their potential customers are and how big the opportunity is.
For venture capitalists investing in AI, the primary success indicator is massive Total Addressable Market (TAM) expansion. Traditional concerns like entry price become secondary when a company is fundamentally redefining its market size. Without this expansion, the investment is not worthwhile in the current AI landscape.
While data labeling companies show massive revenue growth, their customer base is often limited to a few frontier AI labs. This creates a lopsided market where providers have little leverage, compete on price, and are heavily dependent on a handful of clients, making the ecosystem potentially unstable.
When growth flattens, data companies must expand their value proposition. This involves three key strategies: finding new end markets, solving the next step in the customer's workflow (e.g., location selection), and acquiring tangential datasets to create a more complete solution.