Arif Hilali of Bain Capital Ventures warns investors against mistaking Silicon Valley hype for mainstream adoption. He uses cloud computing's slow, multi-decade rollout as a parallel for AI, suggesting that even when a trend seems obvious inside the tech bubble, its true market penetration takes much longer than anticipated.

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'.

Companies like Sierra can't justify a 100x ARR valuation by targeting the existing software market (e.g., $8B Service Cloud). The bet is that they will capture a significant portion of the much larger human labor market ($200B+ for support agents). This represents a fundamental transition of spend from human capital to software.

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.

The true market opportunity for AI is not merely replacing existing software but automating human labor. This reframes the total addressable market (TAM) from the ~$400 billion global software industry to the $13 trillion US-only labor market, representing a thirty-fold increase in potential value.

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.

The rise of public cloud was driven by a business model innovation as much as a technological one. The core battle was between owning infrastructure (capex) and renting it (opex) with fractional consumption. This shift in how customers consume and pay for services was the key disruption.

When analyzing a true market disruptor with a long growth runway, the bigger analytical error is being too conservative. A forecast that is too low and prevents an investment is more damaging to long-term returns than an overly optimistic one that is later adjusted. The goal is to "get it right," not just be safe.

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.