An AI sourcing platform's primary function is to secure goods, but a valuable byproduct is proprietary, real-time data on commodity pricing, freight, and factory output. This data is highly valuable to financial institutions like hedge funds, creating an entirely new revenue stream for the company.

The key to explosive AI revenue growth is shifting from per-seat SaaS models to monetizing inference. This "inference waterfall" creates a usage-based revenue stream that removes growth ceilings, enabling companies to scale at unprecedented rates by capturing value directly tied to AI consumption.

As AI consumes content directly, traditional monetization like subscriptions weakens. The new model involves licensing high-quality, underlying data to AI developers. This includes usage-based pricing (tokens) and sophisticated outcome-based models where revenue is shared based on the value AI creates.

As AI agents and developers operate increasingly within the terminal (CLI), demand for programmatic, API-driven data access will explode. This will replace clunky web UIs and credit card subscriptions with seamless, micro-transaction-based data consumption.

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.

The rise of AI agents enables a move away from traditional per-seat SaaS pricing. Instead of selling access to a tool, entrepreneurs can sell a specific, guaranteed outcome delivered by an agent (e.g., a daily brief of competitor activity), transitioning to an outcome-based revenue model.

As AI commoditizes software creation, the primary source of sustainable value shifts from the software itself to the unique, high-quality data that AI agents use for decision-making. Businesses must re-center their strategy around data as the core asset.

The initial AI boom was fueled by scraping the public internet. Cuban predicts the next phase will be dominated by exclusive data deals. Content owners, like medical journals, will protect their IP and auction it to the highest-bidding AI companies, creating valuable data silos.

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.