Many businesses reach a million in revenue through sheer effort but then stall. The shift to scaling requires achieving product-market fit, which creates leverage and pulls in customers, leading to exponential profitability instead of diminishing returns from just pushing harder.

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'.

Unlike traditional SaaS, achieving product-market fit in AI is not enough for survival. The high and variable costs of model inference mean that as usage grows, companies can scale directly into unprofitability. This makes developing cost-efficient infrastructure a critical moat and survival strategy, not just an optimization.

The coaching software market primarily serves individual 'prosumers.' While there are multi-coach practices, they are not numerous enough or willing to pay exponentially more to constitute a true enterprise segment. This structural limitation makes it a difficult space for VC-backed companies who rely on expansion revenue and high ACV to justify valuations.

Unlike traditional SaaS, achieving product-market fit in AI doesn't guarantee a viable business. The high cost of goods sold (COGS) from model inference can exceed revenue, causing companies to lose more money as they scale. This forces a focus on economical model deployment from day one.

The traditional SaaS growth metric for top companies—reaching $1M, $3M, then $10M in annual recurring revenue—is outdated. For today's top-decile AI-native startups, the new expectation is an accelerated path of $1M, $10M, then $50M, reflecting the dramatically faster adoption cycles and larger market opportunities.

OpenAI's path to 2.6 billion users relies on high-growth markets like India and Brazil. However, these regions have historically low average revenue per user (ARPU), creating a major challenge, as massive user growth won't necessarily translate into the revenue needed to hit ambitious financial targets.

The first million can be achieved unprofitably with random projects just to hit the number. Breaking through the $10M barrier is far more difficult because it requires a sustainable, profitable business model, real momentum, and a scalable structure, which is where most service-based companies get stuck.

Large-sounding enterprise AI adoption metrics, like Google's '150 enterprises processing a trillion tokens,' can translate to surprisingly low revenue—less than $1M per enterprise annually. This suggests headline adoption numbers may not yet reflect significant financial impact for cloud providers.