One Big Team
The Power of Proximity to Coworkers
Natalia Emanuel, Emma Harrington & Amanda Pallais
Quarterly Journal of Economics, forthcoming
Abstract:
How does proximity to coworkers affect training and productivity? We study software engineers at a Fortune 500 firm from 2019 to 2024, leveraging two shocks to proximity: (i) the office closures in 2020 and (ii) the subsequent return-to-office mandates in 2022 and 2023. In both cases, co-located teams experienced bigger changes in proximity than distributed ones, facilitating difference-in-differences designs. We find that sitting near teammates increases coding feedback by 18.3% and improves code quality. Gains are concentrated among less-tenured and younger employees, who are building human capital. However, there is a tradeoff: experienced engineers write less code when sitting near teammates. In national US data, we find evidence that the rise of remote work has had scarring effects on young college graduates. In remotable jobs, young graduates’ unemployment rate increased relative to older graduates’ post-pandemic (2022−2024) compared to pre-pandemic (2017−2019), a pattern we do not observe in non-remotable jobs.
Through the Lens of Clarity: Perceived Organizational Tightness Boosts Creativity for Men, but Not for Women
Grace Lim & Roy Chua
Organization Science, forthcoming
Abstract:
A commonly held perspective in the cultural tightness literature is that cultural tightness tends to negatively impact creativity. Yet some findings indicate that this relationship is not strictly negative and that a more nuanced perspective should be considered. Drawing on social information processing (SIP) theory and social role theory, we build theory on how the perception of organizational cultural tightness can increase creativity for some employees, but not for others. Specifically, we propose that perceived organizational tightness -- the extent to which one perceives that an organization is characterized by strong norms and sanctions for deviation -- increases clarity on creativity evaluation standards and that gender moderates this relationship such that the effect is stronger for men than for women. Clarity on creativity evaluation standards subsequently enhances creative self-efficacy, which, in turn, boosts employee creativity. Across four studies (a two-wave online study, a two-wave multisource field study, and two online experiments), we found evidence supporting our theory. This research extends our understanding of when, how, and why the perceived tightness of an organization’s culture influences employee creativity. Theoretical and practical implications are discussed.
Backfiring AI? AI Deployment in Workplace
Di Yuan, Manmohan Aseri & Narayan Ramasubbu
Management Science, forthcoming
Abstract:
Seeking value from artificial intelligence (AI) technologies, firms are rapidly deploying them to augment employees and improve business performance. The diffusion of AI into a firm’s business processes affords the tracking of task actions performed by high-performing employees and the codification of best practices into recommendation systems and training programs. The rising trend in AI deployment reveals managers’ expectations that AI-facilitated knowledge transfer would elevate overall firm performance. However, deploying AI in a workplace has the potential to change the competitive dynamics among employees. The AI system can learn from high-performing employees and make that knowledge available to others. In a competitive environment, this can disincentivize high-performing employees and ultimately backfire, leading to a decline in overall firm productivity. In this paper, we study this problem of employee incentive issues when deploying AI in a competitive workplace, using a game-theoretic model. Our results show that when employees compete using both tangible (“hard”) and intangible (“soft”) skills, firm policies that favor AI-facilitated knowledge transfer and task outcome-based compensation may lower firm performance. We illustrate that payoffs from AI deployments depend on workforce heterogeneity, reliance on tangible skills, the skill disparity between employees, and AI efficacy. Using our model, we develop policy recommendations for maximizing the return on organizational AI deployments. Our results suggest that some ostensibly simple solutions, like guaranteeing or increasing the wages of adversely affected employees, may not solve the problem effectively, and firms would have to judiciously choose optimal AI efficacy levels for achieving better outcomes.
Managers and Gatekeepers in the Age of AI
Cassandra Merritt et al.
University of Notre Dame Working Paper, April 2026
Abstract:
Artificial intelligence (AI) is frequently cast as a transformative technology that will raise productivity while displacing human work, yet organizational adoption remains uneven and aggregate effects are mixed. We examine whether middle managers contribute to this gap by acting as gatekeepers to AI adoption. In a pre-registered survey experiment of 2,000 managers in the United States and United Kingdom, respondents were randomly assigned to view videos summarizing recent evidence on AI's productivity benefits, its labor-displacing potential, or a placebo control. Exposure to information about labor displacement leads to a large reduction in intended AI adoption and advocacy (by 0.4-0.5 standard deviations) and a moderate reduction in staffing intentions (by 0.2 standard deviations). In contrast, information about productivity benefits has no significant average effect, although it increases advocacy among managers with low prior familiarity with AI. These findings indicate that middle managers' responses to the information environment shape both technology adoption and employment intentions. Rather than inducing substitution away from labor and toward AI, information about AI's labor-displacing potential leads managers to scale back both planned AI adoption and their staffing intentions.
Generative AI and Entrepreneurship
Abhinav Gupta et al.
University of North Carolina Working Paper, April 2026
Abstract:
This paper studies how Generative AI (Gen AI) is reshaping the U.S. startup ecosystem. Exploiting the release of ChatGPT, we show that startups with greater pre-release Gen AI task exposure reduced employment within two quarters, primarily among junior and implementation roles. Displaced workers experienced longer unemployment spells and moved to lower-paying but less exposed jobs. Conversely, exposed startups increased productivity, scaled faster, and accelerated through financing rounds. Venture capital shifted toward frequent, smaller investments, boosting new firm formation. Overall, incumbent contraction was offset by new firm formation, leaving aggregate employment unchanged but shifting composition to senior roles.
Remote Work and AI Use: Evidence from a Worker Panel
Christos Makridis
Arizona State University Working Paper, April 2026
Abstract:
Does remote work drive AI adoption? Using longitudinal worker data, I document that fully remote and hybrid workers are 6.8 and 6.1 percentage points more likely to report frequent AI use than otherwise similar, remote-feasible onsite workers. Both differences collapse to near zero once identification exploits within-person changes in remote status. Event-study estimates show no pre-trends and no post-transition shifts, and the within-worker null holds even in the most AI-exposed, remote-capable occupations. The remote-work premium in AI use reflects sorting on worker type and task content, not a causal effect of the arrangement.
Talent Flows in Venture Capital Networks
Daniel Kim & Elaine Seoyoung Pak
University of Pennsylvania Working Paper, May 2026
Abstract:
Venture capital (VC) investors provide startups with financial and strategic resources, yet their role in facilitating talent acquisition remains under-explored. We examine talent flows among startups within a VC's portfolio network. Using individual career history data for 24,006 U.S. VC-backed startups (2000-2022), we document that a significant share of new hires hail from the investor's own portfolio. These talent flows are strategically timed: departures to peer startups occur primarily when the source firm exits the portfolio through failure, acquisition, or IPO. Leveraging quasi-exogenous variation from staggered changes in conflict-of-interest laws, we find that such portfolio-based hiring significantly improves startup survival, growth, and exit outcomes. Together, these findings imply that VCs help improve startup performance by facilitating the inter-temporal redeployment of human resources from retiring holdings to more active ones.
Artificial intelligence adoption and the demand for managerial expertise
Liudmila Alekseeva et al.
Strategic Management Journal, forthcoming
Abstract:
This paper examines how firms' adoption of artificial intelligence (AI) relates to the demand for managers and managerial skills. Using a skills-based measure of AI adoption derived from Lightcast job postings, we show that firms with greater AI adoption post more managerial vacancies and a higher share of such vacancies than less intensive adopters. These relationships are strongest in manufacturing and among firms with higher research & development intensity. Greater AI adoption is also associated with shifts in managerial skill requirements toward interpersonal and growth-oriented skills, including stakeholder management, creativity, and sales management, and away from routine administrative skills such as budgeting, planning, staff management, and customer service. Overall, the results suggest a reconfiguration of managerial roles toward capabilities facilitating scaling, coordination, and adaptation in AI-enabled environments.