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on Artificial Intelligence |
| By: | Nicholas Bloom; Christos Makridis |
| Abstract: | Using new data from the Gallup Workforce Panel, we document a persistent partisan gap in self-reported AI use at work: Democrats are consistently more likely than Republicans to report frequent use. In 2025:Q4, for example, 27.8% of Democrats report using AI weekly or daily, compared with 22.5% of Republicans. Democrats also report deeper task-level integration, using AI in 16% more work activities than Republicans. Consistent with this, Democrats are employed in occupations with higher predicted AI exposure based on task-content measures and report larger perceived differences in AI-related job displacement risk. However, in regression models the partisan gap in AI use disappears once we control for education, industry, and occupation, indicating that observed differences primarily reflect compositional variation rather than political affiliation per se. |
| JEL: | J0 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34813 |
| By: | Miklos Koren; Zsofia Barany; Ulrich Wohak |
| Abstract: | Generative AI is a directional technology: it excels at some task combinations and performs poorly at others. Knowledge work is also directional and endogenous: workers can satisfy their job requirements with different combinations of tasks. Studying AI adoption by knowledge workers hence requires comparing two vectors.We develop a high-dimensional model of task choice and technology adoption, with otherwise standard neoclassical assumptions. AI is adopted when its direction is aligned with what the worker values at the margin -- the worker's shadow prices, rather than with what the worker actually does -- their activity vector. This yields a cone of adoption that widens as AI capability grows; near the entry threshold, small improvements in capability translate into large expansions in the set of adopted directions. Adoption also has a structured intensive margin: a tool can be worth using but not worth using all the time, generating a region of stable hybrid production between an entry threshold and an all-in threshold. We also show how to derive shadow prices as explicit functions of observable skill and requirement vectors. The framework explains rapid adoption in aligned occupations, heterogeneous adoption elsewhere, and weak correlation with one-dimensional skill measures: the key heterogeneity is directional alignment, not skill level. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.12958 |
| By: | Willem THORBECKE |
| Abstract: | Artificial intelligence (AI) use and its energy requirements are skyrocketing. This paper finds that the market capitalizations of Amazon, Google, Meta, and Microsoft have increased by more than $500 billion above predicted values since ChatGPT was launched in 2022. Nevertheless they negotiate aggressively to lower energy costs and transfer electricity expenses to other ratepayers. Their appetite for energy is also met by burning fossil fuels including coal. This paper considers how to incentivize Big Tech companies to internalize the externalities associated with data center electricity use. It also recommends innovations that can reduce AI energy demand. These include using AI itself to save energy at data centers and in the production of batteries, steel, glass, hydrogen, ammonia, and copper. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:26013 |
| By: | Christopher Kops; Elias Tsakas |
| Abstract: | This paper proposes a model of choice via agentic artificial intelligence (AI). A key feature is that the AI may misinterpret a menu before recommending what to choose. A single acyclicity condition guarantees that there is a monotonic interpretation and a strict preference relation that together rationalize the AI's recommendations. Since this preference is in general not unique, there is no safeguard against it misaligning with that of a decision maker. What enables the verification of such AI alignment is interpretations satisfying double monotonicity. Indeed, double monotonicity ensures full identifiability and internal consistency. But, an additional idempotence property is required to guarantee that recommendations are fully rational and remain grounded within the original feasible set. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.04526 |
| By: | Zercher, Désirée; Jussupow, Ekaterina; Heinzl, Armin |
| Abstract: | Generative AI has advanced capabilities, enabling these systems to participate as teammates in human teams. Yet, the potential consequences of including an AI teammate for team climate have yet to be explored. Thus, we investigate how shared decisional ownership between humans and AI, as well as the perception of AI as a teammate affect team climate (including its subdimensions). We conducted an experiment with 85 participants in 35 teams collaborating with a generative AI teammate on a team decision-making task. We demonstrate that human decisional ownership improves team climate, while AI decisional ownership has a non-significant negative impact. However, when AI is perceived as a teammate, its decisional ownership also enhances team climate. The qualitative analysis provides additional insights into how these perceptions emerge. Our findings provide a nuanced understanding of the mechanisms of team-AI collaboration that shape team climate and offer practical guidance for fostering a positive team climate. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:158981 |
| By: | Marius Guenzel; Shimon Kogan; Marina Niessner; Kelly Shue |
| Abstract: | Human capital—encompassing cognitive skills and personality traits—is central for labor-market success, yet personality remains difficult to measure at scale. Leveraging advances in AI and comprehensive LinkedIn microdata, we extract the Big 5 personality traits from facial images of 96, 000 MBA graduates, and demonstrate that this novel “Photo Big 5” predicts school rank, job matching, compensation, job transitions, and career advancement. The Photo Big 5 provides predictive power comparable to race, attractiveness, and educational background, and is only weakly correlated with cognitive measures such as test scores. We show that individuals systematically sort into occupations where their personality traits are valued and earn higher wages when traits align with occupational demands. While the scalability of the Photo Big 5 enables new academic insights into the role of personality in labor markets, its growing use in industry screening raises important ethical concerns regarding statistical discrimination and individual autonomy. |
| JEL: | D91 J2 M5 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34808 |
| By: | Deepak Mishra (Indian Council for Research on International Economic Relations (ICRIER)); Mansi Kedia; Aarti Reddy |
| Abstract: | This study examines the employment implications of generative AI in India's IT sector. Drawing on a survey of 651 IT firms across 10 Indian cities conducted between November 2025 and January 2026, it finds no evidence to support apocalyptic predictions of large-scale job losses following AI adoption. Instead, the results point to a fairly modest though broad-based moderation in hiring, concentrated primarily at the entry level. Occupations most exposed to AI—particularly technical and analytically intensive roles—are found to experience the strongest growth in demand. Consistent with this pattern, software-related divisions have seen the least moderation in employment relative to other business functions. A majority of firms also report productivity gains in the form of higher and better-quality output, along with time and cost savings. Overall, the evidence suggests that the generative AI revolution is not fundamentally different from earlier general-purpose technologies, which reduced costs, expanded markets, and generated net positive employment opportunities. Taken together, these findings indicate that rising global demand for AI-enabled goods and services should support significant net job creation in India's IT sector. These conclusions, however, may warrant reassessment if the scope of generative AI use expands dramatically or if artificial general intelligence and smart humanoids are deployed at scale. |
| Keywords: | AI, Artificial Intelligence, Jobs, icrier, employment |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:bdc:report:26-r-06 |
| By: | Donghyun Suh; Samil Oh |
| Abstract: | Using a representative survey of Korean workers, we provide evidence on the adoption of Generative AI (GenAI) and how GenAI reallocates time at work. We find that 51.8\% of workers use GenAI for work and GenAI reduces working time by 3.8\%. However, these gains may not materialize in aggregate productivity statistics yet: the correlation between time savings and output changes is near zero. We show this disconnect arises because workers capture efficiency gains primarily as on-the-job leisure, rather than increasing their output. These findings suggest that standard productivity measures may understate AI's impact by missing non-pecuniary welfare channels. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.12695 |
| By: | Sebastian, Raquel; Salas-Rojo, Pedro (London School of Economics and Political Science); C. Palomino, Juan; G. Rodríguez, Juan |
| Abstract: | Technological change fuels economic growth, but its impact on wage inequality remains contested. This study presents a unified empirical framework that isolates the effects of new technologies such as automation and AI on the entire wage distribution. We develop a continuous and task-sensitive automation index and propose a distributional counterfactual-based method. Applying the approach to Spanish micro-data for 2000-2019 and instrumenting technology variables, we find automation to be a key driver of inequality: without task displacement the Gini coefficient would be 21.5% lower and significant wage shares would shift from the top 10% towards middle and bottom groups. Automation is found to barely affect the gender gap in the period studied, yet to widen the education premium. Like automation, AI exposure increases inequality, although the mechanisms to impact wages differ: automation tends to negatively impact wages in the middle of the distribution, while AI tends to increase wages at the top. Trade, offshorability, educational attainment, employment rates and mark-ups play secondary, period specific roles. The results can inform policies on skill formation and inclusive innovation. |
| Keywords: | automation; AI; wage inequality; structural change; job tasks |
| JEL: | O33 D33 J21 J24 J31 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:amz:wpaper:2026-04 |
| By: | Shaukat, Hira; Ali, Amjad; Audi, Marc |
| Abstract: | The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, labor markets, and government institutions across the globe. This study explores the multifaceted impact of AI on economic growth, employment, and workforce skills. Drawing on sectoral data and comparative literature, the paper analyzes how AI-driven technologies shape growth and labor outcomes. While technological progress creates new opportunities for individuals equipped with advanced skills, it simultaneously displaces traditional, routine-based jobs, resulting in potential unemployment for less adaptable segments of the workforce. The study emphasizes the critical role of governance in addressing the challenges posed by AI and underscores the importance of proactive policy measures to ensure inclusive growth. It further explores the potential of AI in education, particularly in developing countries, where its integration can enhance students' employability skills. However, challenges such as affordability, ethical concerns, and overdependence on technology are also highlighted. The paper advocates for increased investment in reskilling initiatives, AI literacy programs, and the development of adaptive governance structures to facilitate the equitable integration of AI technologies. Overall, the findings offer strategic guidance for policymakers to design adaptive governance structures and reskilling programs. |
| Keywords: | Artificial Intelligence, Economic Growth, Employment, Skills Development, Governance |
| JEL: | O3 O4 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127488 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper evaluates the causal impact of Generative Artificial Intelligence (GenAI) adoption on productivity and systemic risk in the U.S. banking sector. Using a novel dataset linking SEC 10-Q filings to Federal Reserve regulatory data for 809 financial institutions over 2018--2025, we employ two complementary identification strategies: Dynamic Spatial Durbin Models (DSDM) to capture network spillovers and Synthetic Difference-in-Differences (SDID) for causal inference using the November 2022 ChatGPT release as an exogenous shock. Our findings reveal a striking ``Productivity Paradox'': while DSDM estimates show that AI-adopting banks are high performers ($\beta > 0$), the causal SDID analysis documents a significant ``Implementation Tax'' -- adopting banks experience a 428-basis-point decline in ROE as they absorb GenAI integration costs. This tax falls disproportionately on smaller institutions, with bottom-quartile banks suffering a 517-basis-point ROE decline compared to 129 basis points for larger banks, suggesting that economies of scale provide significant advantages in AI implementation. Most critically, our DSDM analysis reveals significant positive spillovers ($\theta = 0.161$ for ROA, $p |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.02607 |
| By: | Yang ZHANG (Faculty of Business Administration, University of Macau); Ziang QIU Ziang (Faculty of Business Administration, University of Macau); Donghyun PARK (The South East Asian Central Banks (SEACEN) Research and Training Centre); Shu TIAN (Economic Research and Development Impact Department, Asian Development Bank) |
| Abstract: | This paper provides a comprehensive systematic review of the transformative impact of Artificial Intelligence (AI) on the global financial landscape. By synthesising 249 peer-reviewed studies published between 1990 and 2025, the research categorises AI’s contributions into three primary domains: asset pricing and portfolio management; financial markets and institutions; and corporate finance and governance. Furthermore, the review offers a specialised assessment of AI’s implications for financial stability within Asia. The findings reveal that while AI acts as a "stabilising intelligence" by enhancing efficiency, predictive precision, and financial inclusion, it simultaneously introduces "adaptive fragility" by concentrating market power, embedding algorithmic biases, and intensifying systemic linkages. |
| Keywords: | Artificial Intelligence, literature review, financial markets, financial stability, Asia, |
| JEL: | G10 G20 G30 M15 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:sea:wpaper:wp61 |
| By: | David Hirshleifer; Lin Peng; Qiguang Wang; Weichen Zhang; Xiaoyan Zhang |
| Abstract: | Generative AI use for content generation is associated with divergent outcomes on different financial social media platforms: indications of reasoning enhancement on Seeking Alpha, and of belief distortions on WallStreetBets. On Seeking Alpha, adoption is associated with information frictions. AI-assisted postings tilt toward analysis/credibility, and their sentiment positively predicts future returns. Use of AI is associated with more informative retail order flow and lower bid-ask spreads. In contrast, AI adoption on WallStreetBets follows surges in retail buying, and AI-assisted content is associated with emotionality and sentiment contagion. Such content precedes higher trading volume, greater volatility, and more lottery-like return distributions. |
| JEL: | D02 D82 D83 D84 D9 G11 G12 G14 G2 G4 G5 K22 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34807 |
| By: | Rodríguez-Pose, Andrés; You, Zhuoying |
| Abstract: | Few studies have examined the economic consequences of deploying artificial intelligence (AI) and robotics in less-developed cities, where policies have often failed. To address this gap, we analyse a panel of 270 Chinese cities (2009–2019) using OLS, IV-2SLS, and quantile regression techniques. We find that AI and robotics significantly promote technological innovation in China, with especially pronounced implications for cities at or below the technological frontier. These technologies also enhance the returns to science and technology (S&T) investment. Its novelty lies in framing AI and robotics as policy substitutes and tools for narrowing innovation divides among Chinese cities. |
| Keywords: | AI; robotics; technological innovation; Chinese cities |
| JEL: | O31 O33 R11 R58 |
| Date: | 2026–02–05 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137040 |
| By: | Ozili, Peterson K; Obiora, Kingsley; Onuzo, Chinwe |
| Abstract: | Artificial intelligence is disrupting the financial sector globally. Artificial intelligence will also affect financial regulation and financial system stability in several ways. Little is known about how artificial intelligence might affect the stability of the financial system. Using a contextual framework and discourse analysis methodology, this article identifies some risks that artificial intelligence could pose to financial system stability in Nigeria. The study focused on how AI risks affect those directly involved in financial stability work in Nigeria. If these risks are mitigated, the adoption of AI for financial stability work will yield positive benefits for financial stability in Nigeria. |
| Keywords: | Nigeria, artificial intelligence, financial stability, algorithm, banking supervision, financial regulation, financial sector. |
| JEL: | G21 G23 O31 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127370 |
| By: | Quimba, Francis Mark A.; Caboverde, Christopher Ed C.; Salazar, Alliah Mae C. |
| Abstract: | Artificial intelligence (AI) is expected to make substantial contributions to Philippine growth and productivity in the coming years. Local government units (LGUs) may leverage this new technology to provide enhanced public service delivery to their respective constituencies. The question now is how ready these units are to adopt AI. This study assesses the preparedness of Philippine LGUs for AI adoption, identifies key barriers to implementation, and derives policy-relevant insights to support inclusive and sustainable AI adoption. Using an AI Readiness Index complemented by qualitative interviews with LGU officials, the study finds that, among other things, LGUs exhibit generally low to moderate readiness for AI adoption, with critical bottlenecks including shortages of ICT and AI-related skills, limited last-mile internet connectivity, and minimal budget allocations for digital initiatives. LGUs also face the challenge of balancing the provision of essential public services that yield immediate political dividends with the longer-term process of implementing and adopting AI for local governance. Policy implications highlight the need for coordinated, multi-level interventions that align infrastructure investment, skills development, governance reforms, and resource allocation. Comments to this paper are welcome within 60 days from the date of posting. Email publications@pids.gov.ph. |
| Keywords: | Artificial Intelligence, AI adoption, AI readiness, local government units, Philippine LGUs, local governance modernization |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:phd:dpaper:dp_2025-48 |
| By: | Qiankun Zhong; Thomas F. Eisenmann; Julian Garcia; Iyad Rahwan |
| Abstract: | Reliance on generative AI can reduce cultural variance and diversity, especially in creative work. This reduction in variance has already led to problems in model performance, including model collapse and hallucination. In this paper, we examine the long-term consequences of AI use for human cultural evolution and the conditions under which widespread AI use may lead to "cultural collapse", a process in which reliance on AI-generated content reduces human variation and innovation and slows cumulative cultural evolution. Using an agent-based model and evolutionary game theory, we compare two types of AI use: complement and substitute. AI-complement users seek suggestions and guidance while remaining the main producers of the final output, whereas AI-substitute users provide minimal input, and rely on AI to produce most of the output. We then study how these use strategies compete and spread under evolutionary dynamics. We find that AI-substitute users prevail under individual-level selection despite the stronger reduction in cultural variance. By contrast, AI-complement users can benefit their groups by maintaining the variance needed for exploration, and can therefore be favored under cultural group selection when group boundaries are strong. Overall, our findings shed light on the long-term, population-level effects of AI adoption and inform policy and organizational strategies to mitigate these risks. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.03541 |