Generative AI at Work (2023)

In The You On AI Encyclopedia

The study's design enabled causal inference in a way most AI productivity research has not. Because the tool was rolled out in a staggered fashion across teams, the authors could compare productivity changes in teams that received the tool at different times, controlling for general trends and seasonal effects. This quasi-experimental design — closer to a randomized trial than the correlational analyses that dominate the literature — gave the findings unusual empirical weight.

The mechanism the authors identified was pedagogical: the AI was surfacing the responses and approaches of the company's most effective agents, making this tacit knowledge available in real-time to agents who had not yet developed it through years of experience. The AI functioned less like a replacement for human judgment and more like an automated mentor, compressing the training timeline for skill development. Novices gained most because they had the most to learn. Experts gained least because they had already developed the capabilities the AI was now propagating.

The finding had major implications for the Great Decoupling debate. If AI generally compressed skill distributions rather than widening them, the technology could narrow rather than widen inequality — a dramatic reversal of the pattern established by previous digital technologies. But the generalization had limits. The customer service context provided tight feedback loops, clear quality metrics, and well-defined tasks — conditions that may not hold across all work domains. Moreover, separate data began showing entry-level hiring declining sharply in AI-exposed occupations by 2025, suggesting that AI's effect on the existing workforce (compression) diverged from its effect on the future workforce (pipeline collapse).

The paper became one of the most cited economics papers on AI and work by 2025, shaping both the academic debate and policy discussions. It functioned as empirical ballast for the augmentation-over-automation argument — evidence that AI could function as augmentation in practice, not merely in principle, producing broadly distributed gains rather than concentrating them.

Origin

Danielle Li is an associate professor at MIT Sloan; Lindsey Raymond completed her PhD at MIT and is now at Stanford. The study was supported by the anonymized Fortune 500 software company that provided the data, which allowed the researchers unusual access to granular performance data combined with AI tool usage data.

The working paper was released through NBER in April 2023, revised through 2024, and published in the Quarterly Journal of Economics in 2025. It entered the public discourse at the moment when AI workforce effects were becoming the central policy question, giving it disproportionate influence on both academic and public debates.

Key Ideas

14 percent average productivity gain. Access to the AI assistant increased resolved-issues per hour across the full workforce.

34 percent gain for novices. Workers with less experience saw dramatically larger productivity improvements than experienced workers.

Minimal gain for experts. Experienced workers showed almost no productivity improvement from the tool.

Compression mechanism: tacit knowledge diffusion. The AI surfaced the approaches of top performers, making them available to novices in real time.

Optimistic generalization limited. The customer service context may not generalize, and the effect on existing workers diverges from the effect on pipeline hiring.