The Commonplace
A research knowledge base on how AI is changing work. It reads new economics papers every day, grades the evidence, and tracks where findings agree and where they clash.
Last pipeline run: Aug 30, 2026 at 17:33 UTC
Latest digest
2026-08-24
*This weekly digest tracks what is NEW or CHANGED in AI-economics research. For the cumulative state of evidence on any topic, see the /syntheses pages. A single study rarely overturns a body of evidence.*
## The Delta
*Coming in, Firm Productivity leaned positive (278 papers); this week, a coun
Top papers
From the latest pipeline run, ranked by relevance, evidence strength, methods rigor, and number of supported claims.
1
Crossing into AI: When Incumbents Build, Partner, Acquire, Absorb, or Wait A History-Friendly Agent-Based Model of the Generative-AI Market Transition · Yingzheng Liu, Shun Cao, Zhen Liu
medium evidence
medium rigor
relevance 8/10
10 strong claims
theoretical
2
Below critical mass: Marshallian constraints, Jacobian synergies, and density thresholds for creative ecosystems · Slavomír Ondoš, Oto Hudec
medium evidence
medium rigor
relevance 7/10
8 strong claims
correlational
3
Impact of Artificial Intelligence on the Global Economy · Niharika Sharma Mahajan
medium evidence
medium rigor
relevance 7/10
12 strong claims
review meta
4
How Artificial Intelligence Empowers Green Supply Chain Management: From the Perspective of Firm Internal Capabilities · Enlu Jiang, Qian Cheng, Haoqing Jiang
medium evidence
medium rigor
relevance 7/10
8 strong claims
correlational
5
How Is AI Transforming the Task Characteristics and the Experience of Vulnerable and Minority Employees in the Hospitality Sector? · Deepak Bangwal, Shobha Maindola, Rupesh Kumar, Pankaj Chamol…
medium evidence
medium rigor
relevance 7/10
5 strong claims
correlational
Where papers disagree
Claims from different papers that point opposite ways on the same outcome, ranked by evidence weight. These are machine-detected candidates, not confirmed contradictions, so read both sources before drawing a conclusion.
positive
Machine learning and AI methods (sequence-to-function, phenotype prediction) significantly accelerate DBTL cycles and im…
vs.
negative
People are more likely to give up after interacting with AI (increased likelihood of quitting tasks unassisted).
Same outcome category, opposite direction (auto-detected, may differ in population/context)
positive
Machine learning and AI methods (sequence-to-function, phenotype prediction) significantly accelerate DBTL cycles and im…
vs.
negative
AI deployment reduces average chat duration.
Same outcome category, opposite direction (auto-detected, may differ in population/context)
positive
Machine learning and AI methods (sequence-to-function, phenotype prediction) significantly accelerate DBTL cycles and im…
vs.
null result
Actual completion times between independent completion and AI-assisted completion did not differ.
Same outcome category, opposite direction (auto-detected, may differ in population/context)
positive
Machine learning and AI methods (sequence-to-function, phenotype prediction) significantly accelerate DBTL cycles and im…
vs.
null result
The same bias was not observed when imagining help from another human participant.
Same outcome category, opposite direction (auto-detected, may differ in population/context)
positive
Machine learning and AI methods (sequence-to-function, phenotype prediction) significantly accelerate DBTL cycles and im…
vs.
negative
There is a 'speedup illusion' where people have accurate forecasts of independent completion times but significantly und…
Same outcome category, opposite direction (auto-detected, may differ in population/context)
Source health
arxiv
Aug 30, 17:33
openalex
Aug 30, 17:33
semantic_scholar
Aug 30, 17:33
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What it tracks
AI & Labor Productivity
AI & Labor Markets
Human-AI Collaboration
AI & Skills/Training
AI & Organizational Design
AI & Innovation
AI & Inequality
AI Adoption & Diffusion
AI Governance & Policy