In every technological wave, there is a phase when scarcity is mistaken for strategy. The early web had its "walled gardens," when portals such as AOL and MSN believed exclusivity would preserve user value. Cable television clung to proprietary networks long after audiences had moved online. Even music labels, faced with the rise of Napster, thought restricting access would protect value. It never did. The gravity of technology has always pulled toward openness, interoperability, and scale.
The same pattern is repeating in artificial intelligence, particularly regarding training data. Companies are increasingly claiming that exclusive, proprietary datasets represent competitive advantages that will ensure their dominance. This assumption mirrors past miscalculations about how technology markets actually evolve.
The belief that restricted training data creates lasting advantage overlooks a fundamental truth: the most valuable AI models emerge from broad, diverse information sources. Exclusivity doesn't enhance capability—it constrains it. The companies that will define the next decade of AI won't be those hoarding data, but those building systems trained on the richest possible corpus of human knowledge.
History suggests this trajectory. Open-source software didn't fail because of proprietary alternatives; it thrived while commercial software stagnated. The internet's openness outpaced closed platforms. The fallacy persists because scarcity feels secure. But in technology, openness ultimately wins.