Hwang's insight—that significant innovation happens at cultural intersections—challenges everything we thought we knew about creativity. In the industrial age, innovation meant improving within paradigms. In the AI age, innovation means synthesizing across them.
This shift is profound. AI excels at optimization within defined parameters. Give it rules, data, and objectives, and it will find optimal solutions faster than any human. But AI struggles at the boundaries—where different rule systems meet, where success means different things to different stakeholders, where value itself must be redefined.
Consider why this matters now more than ever:
The Data Paradox: AI trained on data from one culture often fails spectacularly in another. Amazon's same-day delivery, transformative in America, initially flopped in Japan where customers valued reliability over speed³⁸. The algorithm couldn't see what humans understood: different cultures define "good service" differently.
The Innovation Imperative: McKinsey research shows that companies with culturally diverse executive teams are 33% more likely to outperform on profitability³⁹. But diversity alone isn't enough—the magic happens when different perspectives synthesize into something new.
The Meaning Crisis: As AI handles more execution, human value shifts to meaning-making. And meaning is always culturally embedded. What inspires Americans might confuse Germans. What delights Japanese might offend Brazilians. Synthesizing across these differences creates universal resonance.