As AI learns directly from how people work, a new tension is emerging about expertise, power, and governance
In many workplaces, the newest addition to virtual meetings isn鈥檛 a colleague, but an AI assistant like Granola or Otter. Suddenly no one has to scramble for action items or wonder who said what. The tool fades into the background while work gets a little smoother. And somewhere downstream, the precise record of how capable people think through a problem, handle a difficult client, or navigate a complex negotiation becomes raw material for an AI model. The convenience is real, and the implications are enormous. The new working paper 鈥�,鈥� co-written by D^3 Associate , confronts this dynamic head-on. What happens when workers realize that their work habits, insights, and creativity are training the systems that could replace them? Combining survey evidence, a randomized experiment, and formal economic theory, the authors show that when workers understand that the information they give out to AI may strengthen the organization鈥檚 hand later, they may change how much they share.
Why This Matters
For business leaders, this research surfaces a friction that most AI adoption strategies don鈥檛 account for yet. The employees whose expertise you most need to encode could be precisely the ones most aware of what鈥檚 at stake when they share it. As AI tools become more capable and more visible in the workplace, worker awareness will only rise, and so could strategic withholding. This creates a clear managerial implication: organizations can improve AI adoption not just by deploying better tools, but by discussing employee career concerns directly and giving people more meaningful control over how their work data is used. Firms that treat data governance as part of talent strategy and innovation design, rather than a legal checkbox, may be better positioned to unlock mutual benefit: stronger AI performance, higher productivity, and gains that are shared more broadly by the people helping to build the organization鈥檚 future.
Link to the D^3 Insight Article
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