In 1962, economist Kenneth Arrow identified a fundamental flaw in the marketplace of ideas: information cannot be valued until it is disclosed, but once disclosed, the buyer acquires it without paying. To solve this “information paradox,” modern societies created patents—a legal framework allowing inventors to reveal their ideas while retaining the right to profit from them. Today, as frontier artificial intelligence models reach the limits of public internet data, Arrow’s paradox has returned with unprecedented force.
Having consumed much of the readily available stock of public human knowledge, AI developers are turning toward the next frontier: the tacit, unwritten knowledge embedded within corporate organizations and specialized workforces. This proprietary intelligence—housed in internal workflows, institutional memory, and complex problem-solving routines—is the true foundation of organizational value.
The central economic question of the coming decade is no longer just about copyright or public web-scraping; it is about who owns the internal expertise extracted to train enterprise AI models. When employees interact with generative tools, they unwittingly transfer decades of domain-specific mastery into centralized algorithms. Once digitized, that knowledge is permanently absorbed by the platform provider, diluting the firm’s competitive edge and rendering human expertise increasingly redundant.
Without new legal mechanisms or updated intellectual property frameworks tailored to algorithmic learning, organizations risk surrendering their core competitive advantages in exchange for short-term efficiency gains. Whoever sets the terms of how this tacit knowledge is teased out, digitized, and transferred will set the terms of the next global economy.
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