WORK
The Berkeley Study
Xingqi Maggie Ye and Aruna Ranganathan's 2026 Harvard Business Review ethnography of an AI-augmented workplace — the most rigorous empirical documentation to date of positive feedback dynamics in human-machine loops.
In the summer of 2025, doctoral student Xingqi Maggie Ye and Associate Professor Aruna Ranganathan of UC Berkeley's Haas School of Business began an eight-month embedded ethnography of a 200-person technology company integrating generative AI tools into its workflow. Their findings, published in Harvard Business Review in February 2026, produced what Segal calls the most rigorous empirical confirmation available of what Byung-Chul Han had diagnosed philosophically. Workers using AI did not work less. They worked more, took on more, expanded into domains beyond their roles, filled previously protected pauses with AI-assisted tasks, and reported rising burnout even as they described themselves as more productive. In cybernetic terms, the study documented a social system transitioning from negative to positive feedback — the removal of implementation friction collapsing the governor that had previously constrained the achievement loop.
In The You On AI Field Guide
The study's methodology was ethnographic rather than statistical: the researchers embedded themselves in the company, attended meetings, watched screens, talked with