Source note · Independent commentary
When the tool learns from the experts and hands it to the novices
It is one of the few large field studies of a generative assistant in real work, and its most quoted result is also its most misread: the newcomers gained most, and the experts were the ones the tool was trained on.
Research reviewed
Generative AI at Work
- Authors
- Erik Brynjolfsson, Danielle Li, Lindsey Raymond
- Institution
- National Bureau of Economic Research
- Publication
- NBER Working Paper 31161; later published in The Quarterly Journal of Economics
- Date
- 2023-04
- Type
- Working paper
A field study of a generative AI assistant rolled out to several thousand customer support agents. The tool raised issues resolved per hour, with the largest gains among newer and lower-skilled agents and small effects for the most experienced.
Read the original sourceWhat the source supports
- In this setting, a generative AI assistant increased average productivity, measured as issues resolved per hour.
- The gains were concentrated among novice and lower-skilled agents. Experienced, high-skill agents saw minimal improvement.
- The authors interpret the tool as spreading the practices of more able workers to newer ones.
What the source does not support
- That experienced workers in other fields will see the same pattern. The study covers one company and one kind of work.
- That AI assistance makes experience worthless. The tool learned from the experienced agents' own behavior.
- Any claim about job loss, wages, or long-term careers. The study measures task performance, not employment outcomes.
What Sougata is doing with it
Sougata uses this as evidence that the visible output gap between novice and expert can shrink quickly, while noting that the expert's judgment is what the tool was trained to imitate. That distinction between output and judgment is central to Episode 1 and is Sougata's synthesis, not the paper's claim.
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Why it matters here