By Reese Wong

AI has quietly shifted critical problem-solving into private chat interfaces. A worker can research a problem with AI and return to colleagues with a polished recommendation. The team sees the answer. But the underlying thinking – the sources consulted, assumptions tested or uncertainty that remains – often remains invisible.

The raw chat thread itself does not need to be shared, but the context behind the output eventually does. To realize the true value of AI at work, organizations must move from individual, “single-player” AI to shared, collaborative environments.

When does private AI become a team problem?

The central challenge is determining which context must be shared once others rely on the work. A reviewer rarely needs every exploratory prompt; they just need enough context to evaluate the reasoning and challenge the output.

Researchers at the MIT Media Lab have begun exploring this balance throughInquiryBits, a prototype that shares limited traces of people’s AI conversations with colleagues. In a June 2026 preprint describing a study of 80 professionals, participants were broadly willing to share these traces within bounded groups, but comfort fell as the audience widened.

Striking that balance matters. Without basic traces, teams risk duplicating analysis, scrambling at handovers or acting on untested assumptions.