Public internet data has been largely exhausted for training AI models. The real competitive advantage and source for next-generation, specialized AI will be the vast, untapped reservoirs of proprietary data locked inside corporations, like R&D data from pharmaceutical or semiconductor companies.

Instead of patenting its sauce recipe—which requires public disclosure and expires in 20 years—Raising Cane's uses costly operational secrecy. This protects the formula indefinitely and, more importantly, transforms the sauce from a simple condiment into a valuable, unifying brand myth.

Mark Cuban warns that patenting work makes it public, allowing any AI model to train on it instantly. To maintain a competitive data advantage, he suggests companies should increasingly rely on trade secrets, keeping their valuable IP out of the public domain and away from competitors' models.

China's durable advantage isn't just its massive workforce but the collective "process knowledge" generated on factory floors. This expertise in solving countless small manufacturing problems cannot be easily written down or encoded in equipment, creating a powerful, hard-to-replicate competitive moat.

A key competitive advantage for AI companies lies in capturing proprietary outcomes data by owning a customer's end-to-end workflow. This data, such as which legal cases are won or lost, is not publicly available. It creates a powerful feedback loop where the AI gets smarter at predicting valuable outcomes, a moat that general models cannot replicate.

The choice between a patent and a trade secret is a strategic decision based on vulnerability. If a product can be purchased and deconstructed to reveal its innovation, a patent is the necessary path. Trade secrets are only viable for innovations that are impossible to discover through reverse engineering.

The "golden era" of big tech AI labs publishing open research is over. As firms realize the immense value of their proprietary models and talent, they are becoming as secretive as trading firms. The culture is shifting toward protecting IP, with top AI researchers even discussing non-competes, once a hallmark of finance.

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.

The concept of "sovereignty" is evolving from data location to model ownership. A company's ultimate competitive moat will be its proprietary foundation model, which embeds tacit knowledge and institutional memory, making the firm more efficient than the open market.