Hedge funds have a constant, daily need to make informed buy, sell, or hold decisions, creating a clear business problem that data solves. Corporations often lack this frequent, high-stakes decision-making cycle, making the value proposition of external data less immediate and harder to justify.

Prediction markets are not just for betting. They are becoming a valuable source of predictive data for enterprises, as shown by new partnerships with media giants like CNN and CNBC. This dual-purpose model, functioning as both a consumer product and a B2B data service, creates two distinct revenue streams.

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.

A powerful, overlooked competitive moat exists in the "outsourced R&D" model. These companies, like Core Labs in energy or Christian Hansen in food, become so integral to clients' innovation that they command high margins and valuations that appear expensive when viewed only through the lens of their specific industry.

Palantir commands a massive valuation premium because it is both well-run and unique, with no clear alternatives. This lack of competition dramatically reduces churn risk and increases the durability of future cash flows, justifying a higher multiple than other software companies that operate in more crowded markets.

When approached by large labs for licensing deals, GI's founder advises against simply selling the data. He argues the only way to accurately value a unique dataset is to model it yourself to understand its true capabilities. Without this, founders risk massively undervaluing their core asset, as its potential is unknown.

Stack Overflow structures its AI data licensing deals as recurring revenue streams, not one-time payments. AI labs pay for ongoing rights to train new models on the entire cumulative dataset, ensuring the corpus's value is monetized continuously as the AI industry evolves.

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.