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.

Distributors possess a long-standing "secret weapon"—a massive repository of clean, well-understood data on partner behavior and transactions. As AI becomes prevalent, distributors are uniquely positioned to leverage this data to provide superior business intelligence, solidifying their role in the channel ecosystem.

To solve the classic marketplace problem where buyers and sellers connect and then transact offline, Sorcerer acts as the supplier itself. It operates a 'blind escrow marketplace,' ensuring all transactions flow through its platform and protecting its business model, rather than just acting as a connector.

Constant changes in international tariffs force businesses to rapidly find alternative suppliers to avoid collapsing their margins. This chaos makes platforms that can quickly source and switch factories on a dime indispensable, turning geopolitical instability into a significant business advantage.

Companies controlling proprietary data, even if publicly accessible but hard to collect (like FlightAware), can use AI to deliver a 'finished meal' instead of just the 'raw vegetables.' This moves them up the value chain from a data provider to a solutions provider, unlocking significant pricing power.

The long-theorized "data network effect" is now a powerful reality in the age of AI. Access to a proprietary and, most importantly, *live* data stream creates a significant moat. A commodity AI model trained on this unique, dynamic data can outperform a state-of-the-art model that lacks it.

A proven strategy for monetizing AI within existing products is to develop and launch task-specific 'agents.' These agents, as demonstrated by THL's portfolio companies, are sold as additional SKUs or modules, enhancing the core product's value and creating new, direct revenue streams from AI.

As algorithms become more widespread, the key differentiator for leading AI labs is their exclusive access to vast, private data sets. XAI has Twitter, Google has YouTube, and OpenAI has user conversations, creating unique training advantages that are nearly impossible for others to replicate.

YipitData had data on millions of companies but could only afford to process it for a few hundred public tickers due to high manual cleaning costs. AI and LLMs have now made it economically viable to tag and structure this messy, long-tail data at scale, creating massive new product opportunities.