AI is a general-purpose economic force that raises productivity and creates new organizational forms, but it also reshapes tasks, labor demand, and market power, requiring fresh research agendas and policy responses.

Model-Driven Hybrid AI Framework for End-to-End Autonomous Decision-Making in Drug Development

Hyunjung Lee, Hyeonseok Kang, W. Jung, Hyojin Cho, Sungwoo Goo, Hwi-yeol Yun, Min-Gul Kim, Jung-woo Chae, Sang-Min Park, Soyoung Lee, Jae Hyun Kim, S. Jung · Fetched March 15, 2026 · bioRxiv

semantic_scholar review_meta n/a evidence 8/10 relevance DOI Source

The paper frames AI as a general-purpose prediction and optimization technology that will boost productivity and enable new business models while reconfiguring tasks, labor demand, market structure, and policy needs, calling for targeted research and regulation to manage distributional and governance challenges.

Summary

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Assessment

Paper Typereview_meta Evidence Strengthn/a — This is an agenda-setting survey and conceptual/theoretical synthesis rather than an empirical paper that produces new causal estimates or tests; it summarizes existing evidence rather than generating primary causal identification. Methods Rigorn/a — The work is a careful, scholarly synthesis and conceptual framing of the literature (theory, models, and selected empirical results) but does not apply a specific empirical identification or estimation strategy that can be rated for econometric rigor. SampleAn edited review/agenda (Agrawal, Gans, Goldfarb, 2019) that synthesizes theoretical models, case studies, and published empirical findings across economics, strategy, and computer science up to 2019; no new microdata sample or original dataset is created in the volume itself. Themesinnovation productivity labor_markets governance skills_training GeneralizabilityNot an empirical study—conclusions are conceptual and synthesize prior work rather than estimate effects in specific populations., Framing and examples reflect literature up to 2019 and may not incorporate subsequent advances (e.g., large-scale generative models and rapid deployment post-2019)., High-level theory may not map cleanly onto sector- or country-specific institutional contexts., Policy recommendations are broad and may require local tailoring to regulatory and labor market institutions.