Historical milestone · 2023
Experimental evidence on productivity effects of generative AI
Reviewed through September 18, 2026
2023 · Historical milestone
Experimental evidence on productivity effects of generative AI
- Era
- 2020s
- Theme
- Human–AI interaction & adoption
- Evidence form
- Randomized controlled experiment
- School / paradigm
- Human-computer interaction / labor economics
- Institution / context
- MIT
- Researchers
- Shakked Noy; Whitney Zhang
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Assigned professionals to complete writing tasks with or without a generative language model and evaluated completion time and output quality.
Result / historical claim. Reported faster completion and higher evaluated quality in the assisted condition.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Randomized controlled experiment
Limitation / debate. Short controlled tasks, one model, and a selected participant pool limit generalization to whole jobs and long-run learning.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
No primary-source URL is recorded for this entry in our reviewed data. Rather than manufacture a citation, we link the Implement Agentic research page that carries the record.
Reproduce
A reproduction tutorial is linked only when one exists for this exact record.
A reproduction tutorial is not yet available for this entry. The closest reviewed material is .
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.
