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.

Startups often fail to displace incumbents because they become successful 'point solutions' and get acquired. The harder path to a much larger outcome is to build the entire integrated stack from the start, but initially serve a simpler, down-market customer segment before moving up.

Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.

A common mistake is building a visually impressive data product (like Google Earth) that is interesting but doesn't solve a core, recurring business problem. The most valuable products (like Google Maps) are less about novelty and more about solving a frequent, practical need.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

Investors and acquirers pay premiums for predictable revenue, which comes from retaining and upselling existing customers. This "expansion revenue" is a far greater value multiplier than simply acquiring new customers, a metric most founders wrongly prioritize.

As AI makes building software features trivial, the sustainable competitive advantage shifts to data. A true data moat uses proprietary customer interaction data to train AI models, creating a feedback loop that continuously improves the product faster than competitors.