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.

Data businesses have high fixed costs to create an asset, not variable per-customer costs. This model shows poor initial gross margins but scales exceptionally well as revenue grows against fixed COGS. Investors often misunderstand this, penalizing data companies for a fundamentally powerful economic model.

History shows pioneers who fund massive infrastructure shifts, like railroads or the early internet, frequently lose their investment. The real profits are captured later by companies that build services on top of the now-established, de-risked platform.

Lyft's CEO argues the competition is not a binary battle with Uber for their combined 2.5 billion annual rides. Instead, the true target market is the 160 billion rides Americans take in their own cars. This reframes the opportunity from market share theft to massive market expansion and conversion.

While many see autonomous vehicles as a threat to Uber's ride-hailing, its delivery segment may be more important and defensible. Automating last-mile delivery of goods from varied locations is significantly more complex and less economical than automating passenger transport, providing a durable moat.

Beyond AI infrastructure providers (NVIDIA, AWS), a key opportunity lies in the 'layer below'—companies like Uber and Spotify. They leverage big tech's tools but dominate specific verticals because they possess superior, niche-specific user data, which AI then supercharges for monetization and personalization.

While massive "kingmaking" funding rounds can accelerate growth, they don't guarantee victory. A superior product can still triumph over a capital-rich but less-efficient competitor, as seen in the DoorDash vs. Uber Eats battle. Capital can create inefficiency and unforced errors.

Financial models struggle to project sustained high growth rates (>30% YoY). Analysts naturally revert to the mean, causing them to undervalue companies that defy this and maintain high growth for years, creating an opportunity for investors who spot this persistence.

This provides a simple but powerful framework for venture investing. For companies in markets with demonstrably huge TAMs (e.g., AI coding), valuation is secondary to backing the winner. For markets with a more uncertain or constrained TAM (e.g., vertical SaaS), traditional valuation discipline and entry price matter significantly.