Organizational Technology Ladders: Remote Work and Generative AI Adoption
Open accessAbstract
In this study, I propose that firms move along an "organizational technology ladder'': adopting one technology transforms hiring and work processes, which then changes the costs of adopting the next technology. I focus on the impact of firms' adoption of remote work technology on the proliferation of generative AI technology: using detailed job posting data and an IV approach based on differences in labor market pressure to allow remote work, I show that a 10 pp rise in remote work induced by the instrument in 2021-2022 causes an increase in job postings mentioning generative AI skill by 2024 of 0.4 pp across firms and 1.1 pp across occupations within firms. Generative AI adoption also changes firms' use of remote work: a synthetic difference-in-differences estimation around the release of ChatGPT shows that firms with greater exposure to generative AI reduced their remote hiring by 13% relative to a matched control group, with a more negative effect for tech firms than for non-tech firms. I explore the mechanism for these effects and show that remote work adoption shifts hiring towards technology and management skills that then enable more rapid generative AI adoption. Generative AI adoption skews remaining human tasks towards requiring more decision-making in the tech sector, which can explain the decline in remote work in tech, if decision-intensive tasks are less productive while working remotely. Firms that have low remote productivity, as evidenced by having a return-to-office mandate, are more likely to adopt generative AI after hiring remotely. I formalize this technology ladder mechanism using a task-based model of sequential firm investments in new technologies that change the importance of in-person coordination.
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Schubert (2025) shows that hiring for Generative AI skills in job postings interacts with the demand for remote work.
“Schubert (2025) further shows that hiring for Generative AI skills, as listed in job postings, interacts with the demand for remote work.”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Schubert (2025)
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Research works printed but not linked: 25
- Acemoglu, Daron, David Autor, Jonathon Hazell, and Pascual Restrepo, 2022, Artificial intelligence and jobs: evidence from online vacancies, Journal of Labor Economics 40, S293-S340.
- Bresnahan, Timothy F, Erik Brynjolfsson, and Lorin M Hitt, 2002, Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence, The quarterly journal of economics 117, 339-376.
- Brynjolfsson, Erik, and Lorin M Hitt, 2000, Beyond computation: Information technology, organizational transformation and business performance, Journal of Economic perspectives 14, 23-48.
- Brynjolfsson, Erik, and Kristina McElheran, 2016, The rapid adoption of data-driven decisionmaking, American Economic Review 106, 133-139.
- Brynjolfsson, Erik, Daniel Rock, and Chad Syverson, 2021, The productivity j-curve: How intangibles complement general purpose technologies, American Economic Journal: Macroeconomics 13, 333372.
- Choudhury, Prithwiraj, Cirrus Foroughi, and Barbara Larson, 2021, Work-from-anywhere: The productivity effects of geographic flexibility, Strategic Management Journal 42, 655-683.
- Deming, David J, 2017, The growing importance of social skills in the labor market, The quarterly journal of economics 132, 1593-1640.
- Dingel, Jonathan I, and Brent Neiman, 2020, How many jobs can be done at home?, Journal of Public Economics 189, 104235.
- Eisfeldt, Andrea L, and Gregor Schubert, Forthcoming, Generative ai and finance, The Annual Review of Financial Economics .
- Eisfeldt, Andrea L, Gregor Schubert, Miao Ben Zhang, and Bledi Taska, 2025, Generative ai and firm values, Available at SSRN 4436627 .
- Eloundou, Tyna, Sam Manning, Pamela Mishkin, and Daniel Rock, 2023, Gpts are gpts: An early look at the labor market impact potential of large language models, arXiv preprint arXiv:2303.10130 . · arXiv
- Emanuel, Natalia, and Emma Harrington, 2024, Working remotely? selection, treatment, and the market for remote work, American Economic Journal: Applied Economics 16, 528-559.
- Flynn, Sean, Andra C Ghent, and Vasudha Nair, 2024, Determinants and consequences of return to office policies, Available at SSRN .
- Garicano, Luis, and Esteban Rossi-Hansberg, 2006, Organization and inequality in a knowledge economy, The Quarterly journal of economics 121, 1383-1435.
- Gibbs, Michael, Friederike Mengel, and Christoph Siemroth, 2023, Work from home and productivity: Evidence from personnel and analytics data on information technology professionals, Journal of Political Economy Microeconomics 1, 7-41.
- Goldin, Claudia, 2014, A grand gender convergence: Its last chapter, American economic review 104, 1091-1119.
- Gulati, Piyush, Arianna Marchetti, Phanish Puranam, and Victoria Sevcenko, 2025, Generative ai adoption and higher order skills, arXiv preprint arXiv:2503.09212 . · arXiv
- Lichtinger, Guy, and Seyed Mahdi Hosseini Maasoum, 2025, Generative ai as seniority-biased technological change: Evidence from us resume and job posting data, Available at SSRN .
- Maestas, Nicole, Kathleen J Mullen, David Powell, Till Von Wachter, and Jeffrey B Wenger, 2023, The value of working conditions in the united states and implications for the structure of wages, American Economic Review 113, 2007-2047.
- Manson, Steven, Jonathan Schroeder, David Van Riper, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles, 2024, Ipums national historical geographic information system: Version 19.0 [dataset], http://doi.org/10.18128/D050.V19.0. · DOI
- Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean, 2013, Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems 26.
- Noy, Shakked, and Whitney Zhang, 2023, Experimental evidence on the productivity effects of generative artificial intelligence, Available at SSRN 4375283 .
- Olea, José Luis Montiel, and Carolin Pflueger, 2013, A robust test for weak instruments, Journal of Business & Economic Statistics 31, 358-369.
- Schubert, Gregor, Anna Stansbury, and Bledi Taska, 2024, Employer concentration and outside options, Available at SSRN 3599454 .
- Zheng, Lianmin, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al., 2023, Judging llm-as-a-judge with mt-bench and chatbot arena, Advances in neural information processing systems 36, 46595-46623.
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Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI
Humlum, A., & Vestergaard, E. (2025) · doi:10.3386/w33777 · Cited by 34 · see citing works
- Authors
- Schubert, G.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Schubert, Gregor
- Venue
- Academy of Management Proceedings
- Publisher
- Elsevier BV
- Published
- 2025
- Volume
- 2026
- Issue
- 1
- ISSN
- 0065-0668 (ISSN-L)
- OpenAlex
- W4409201899
- Cited by
- 5
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2
- Type
- conference abstract
- Language
- en
- Open access
- green
- doi.org
- Locations
- Academy of Management Proceedings (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Sociology and Political Science › Work-Family Balance Challenges
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Keywords
- remote work, generative AI adoption, job advertisements, remote hiring, ChatGPT, technological competence, decision-making tasks, labor market pressure, technology companies, task-based model
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