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VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Suproteem Sarkar | AI Agents and Higher-Order Work
X-WR-TIMEZONE:Pacific Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260617T131711Z
UID:tag:localist.com\,2008:EventInstance_52374333861267
DTSTART:20260406T190000Z
DTEND:20260406T200000Z
DESCRIPTION:How do AI agents influence knowledge work? This paper finds tha
 t agents shift worker effort from implementation to supervision\, which es
 pecially benefits verifiable work and expert workers. I use data from the 
 coding platform Cursor to study agents in software production.\n\nFirst\, 
 I find that workers have become less likely to produce output manually aft
 er AI agents were introduced\, and are more likely to delegate work to age
 nts. Second\, workers use agents for abstract\, higher-order tasks like de
 legation\, context gathering\, and planning. Third\, agents are used more 
 frequently in settings with easier-to-verify work outputs. Fourth\, experi
 enced workers appear more skilled at delegating work to agents—they ask 
 fewer questions\, seek more alignment through planning\, and accept agent 
 outputs at higher rates. Fifth\, using variation from the platform's featu
 re release timeline\, I find that agents increase software output\, especi
 ally for firms with more verifiable work and more experienced workers.\n\n
 The complementarity between AI agents and expertise contrasts with evidenc
 e on non-agentic AI tools\, which workers are more likely to use for suppo
 rt than for delegation. Taken together\, the results suggest that the retu
 rns to expertise may rise as knowledge work becomes more abstract.
GEO:37.429987;-122.17333
LOCATION:Gates Computer Science Building\, 119
SUMMARY:Suproteem Sarkar | AI Agents and Higher-Order Work
URL;VALUE=URI:https://events.stanford.edu/event/suproteem-sarkar-ai-agents-
 and-higher-order-work
CATEGORIES:Class/Seminar
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