Where do visionary ideas come from? Although the products of vision as manifested in technical innovation are readily observed, the ideas that eventually change the world are often obscured. Here we develop a novel method that uses deep learning to identify visionary ideas from the language used by individuals and groups. Quantifying vision this way unearths prescient ideas, individuals, and documents that prevailing methods would fail to detect. Applying our model to corpora spanning the disparate worlds of politics, law, and business, we demonstrate that it reliably detects vision in each domain. Moreover, counter to many prevailing intuitions, vision emanates from each domain’s periphery rather than its center. These findings suggest that vision may be as much a property of contexts as of individuals.
