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AI is reshaping innovation itself: by lowering search and experimentation costs and creating new human–machine complements, AI changes where and how firms innovate and reorganize, with broad implications for productivity and adoption.

Political identity and responses to dynamic pricing
An Sheng, Sungwoo Choi, Choongbeom Choi, Myungkeun Song · August 06, 2026 · International Journal of Hospitality Management
openalex review_meta n/a evidence 8/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

Structured author observations

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OpenAlex

Latest observation:

  1. An Sheng provider ID
  2. Sungwoo Choi provider ID
  3. Choongbeom Choi provider ID
  4. Myungkeun Song provider ID

Semantic Scholar

Latest observation:

  1. An Sheng provider ID
  2. Sungwoo Choi provider ID
  3. Choongbeom Choi provider ID
  4. Myungkeun Song provider ID
Agrawal, Gans, and Goldfarb argue that AI transforms the process and incentives for innovation—altering who innovates, how firms organize, and how human skills complement machine capabilities—rather than acting solely as a labor substitute.

Citation observations

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Summary

I don't have a paper to summarize yet. Please either:

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  • Tell me which of these known AI-economics papers you'd like summarized:
    • "The Impact of Artificial Intelligence on Innovation" (Agrawal, Gans, Goldfarb, 2019)
    • "Automation and New Tasks: How Technology Displaces and Reinstates Labor" (Acemoglu & Restrepo)
    • "When Does AI Reduce Labor Demand? Evidence from GitHub Copilot" (recent empirical studies)
    • Or name another paper (title + authors or link).

When you provide the paper, I will produce a structured markdown summary with these sections: - ## Main Finding - ## Key Points - ## Data & Methods - ## Implications for AI Economics

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Assessment

Paper Typereview_meta Evidence Strengthn/a — This is an agenda-setting/review/theoretical chapter that synthesizes prior work and develops conceptual arguments about how AI affects innovation; it does not present new causal identification or original empirical estimation. Methods Rigorn/a — The paper is primarily conceptual and synthetic rather than an empirical study with an identification strategy; rigor should be judged by the breadth and coherence of the literature synthesis and theoretical framing rather than experimental or quasi-experimental design. SampleNo original sample or dataset—this is a literature review and theoretical/agenda-setting piece that draws on examples and empirical findings from multiple domains (economics, management, and computer science) rather than a single empirical dataset. Themesinnovation productivity adoption org_design GeneralizabilityNot an empirical estimate—no direct causal effect to generalize, High-level conceptual claims may not map cleanly to specific industries or national contexts, Rapid AI progress since 2019 may change applicability of some examples and forecasts, Heterogeneity across firm size, tasks, and institutional settings is discussed but not empirically resolved

Notes