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on Artificial Intelligence |
| By: | Jingyi Cui; Gabriel Dias; Justin Ye |
| Abstract: | We study how generative AI affects labor market signaling using the introduction of an AI-powered cover letter writing tool on Freelancer.com. Our data track both access to the tool and usage at the application level. Difference-in-differences estimates show that access to the AI tool increased textual alignment between cover letters and job posts--which we refer to as cover letter tailoring--and raised callback likelihoods. Workers with weaker pre-AI writing skills saw larger improvements in cover letters, indicating that AI substitutes for workers' own skills. Although only a minority of applications used the tool, the overall correlation between cover letter tailoring and callbacks fell by 51%, implying that cover letters became less informative signals of worker ability in the age of AI. Employers correspondingly shifted toward alternative signals, such as workers' past reviews, which became more predictive of hiring. Finally, within the treated group, greater time spent editing AI drafts was associated with higher hiring success. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.25054 |
| By: | Shan Jiang |
| Abstract: | Generative artificial intelligence (AI) has begun to reshape academic publishing by enabling the rapid production of submission-ready manuscripts. While such tools promise to enhance productivity, they also raise concerns about overwhelming journal systems that have fixed acceptance capacities. This paper uses simulation modeling to investigate how AI-driven surges in submissions may affect desk rejection rates, review cycles, and faculty publication portfolios, with a focus on business school journals and tenure processes. Three scenarios are analyzed: a baseline model, an Early Adopter model where a subset of faculty boosts productivity, and an AI Abuse model where submissions rise exponentially. Results indicate that early adopters initially benefit, but overall acceptance rates fall sharply as load increases, with tenure-track faculty facing disproportionately negative outcomes. The study contributes by demonstrating the structural vulnerabilities of the current publication system and highlights the need for institutional reform in personnel evaluation and research dissemination practices. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.16925 |
| By: | George Gui; Seungwoo Kim |
| Abstract: | Pre-experiment stratification, or blocking, is a well-established technique for designing more efficient experiments and increasing the precision of the experimental estimates. However, when researchers have access to many covariates at the experiment design stage, they often face challenges in effectively selecting or weighting covariates when creating their strata. This paper proposes a Generative Stratification procedure that leverages Large Language Models (LLMs) to synthesize high-dimensional covariate data to improve experimental design. We demonstrate the value of this approach by applying it to a set of experiments and find that our method would have reduced the variance of the treatment effect estimate by 10%-50% compared to simple randomization in our empirical applications. When combined with other standard stratification methods, it can be used to further improve the efficiency. Our results demonstrate that LLM-based simulation is a practical and easy-to-implement way to improve experimental design in covariate-rich settings. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.25709 |
| By: | Abril Arteaga, Andres Sebastian; Rangel, Marcos; Zanoni, Wladimir |
| Abstract: | We developed a Labor Market Turing Test (LMTT) to measure human-AI decision alignment using data from 277 human recruiters engaged in a field experiment set in Quito, Ecuador. We augmented the pool of recruiters by creating AI teams, each of which with differing impersonation of human-like traits, and compared their choices to humans and a benchmark AI model. While AI teams were more consistent, they selected candidates with a pattern that markedly different from human choices. In fact, random decisions mir- rored human choices more closely than our most human-like AI agents. These findings reveal a fundamental tension between algorithmic consistency and human judgment. That humans were closer to a random process when com- paring candidates with equal productivity might be seen as a fairer outcome. Our LMTT framework, which involves isolating and estimating a machina la- tent trait, provides a quantitative tool for assessing human-AI alignment which can be employed across critical domains, such as healthcare, justice, and edu- cation, thereby informing the design and AI governance. |
| Keywords: | Algorithmic Fairness;Human-AI Alignment;Latent Trait Analysis |
| JEL: | J71 M51 C91 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14296 |
| By: | Adam Feher (University of Lausanne); Emilia Garcia-Appendini (Norges Bank; University of St. Gallen - School of Finance; Swiss Finance Institute); Roxana Mihet (Swiss Finance Institute - HEC Lausanne) |
| Abstract: | We leverage a comprehensive dataset on U.S. data center energy loads, utility electricity prices, and establishment-level revenues, employment, and carbon emissions from 2010 to 2023 to examine whether rising data center demand affects local retail energy prices or other spillovers. For identification, we employ an instrumental variables continuous difference-indifferences design, exploiting exogenous variation in data center location attractiveness. We find no detectable local spillover effects from data center energy growth. A regional model calibrated to these null results suggests that shocks larger than those observed through 2023 could still result in noticeable increases in household utility bills if not offset by regulation or external supply. |
| Keywords: | AI, energy prices, spillovers, data centers, energy, electricity |
| JEL: | Q55 Q58 D24 O33 O44 L94 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2573 |
| By: | Cynthia Hajj; Christophe Schmitt (CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine); Nehme Azoury |
| Abstract: | The adoption of artificial intelligence has risen, yet research on its impact on innovation processes between actual businesses remains sparse. This research fills the present gap by investigating ten workers from a tech startup who utilize artificial intelligence tools in operational and creative activities. The paper analyzes business-related AI functionality through a qualitative analysis of ten tech start-up employees. The examination reveals that AI produces significant enhancements in problem resolution by executing mundane actions while analyzing large datasets to deliver data-driven suggestions to users. The interview respondents mentioned that AI's role in diminishing supply chains is 15%, while allowing AI to manage customer service without employee engagement in 80% of interactions. The implementation costs, along with data dependency and occasional contextual blindness in AI systems, represented some of the problems in this system. Analysis demonstrated that AI tools enable the development of innovative concepts and challenge established viewpoints, prompting participants to create a gamified loyalty system and dynamic content planning. Participants in the study emphasized the need for human involvement to refine AI-based insights, recognizing how human imagination complements AI capabilities effectively. The work enhances academic discussions about AI-related problem-solving and creativity while offering specific business-related recommendations for implementation. The recommendations begin with establishing initial experimental programs, while providing support for employee's skills development, and fostering strong alliances between technical AI personnel and professional subject matter experts. Research topics focused on AI application fields and the anticipated impacts on company decision-making, as well as the ethical ramifications, need further exploration. This research confirms the revolutionary potential of artificial intelligence systems for problem-solving methods, but requires proper execution, along with human supervision, to fully realize their advantages. |
| Keywords: | human–AI collaboration, data-driven insights, AI tools, case study, qualitative research, business innovation, problem-solving skill, Artificial Intelligence (AI) |
| Date: | 2025–10–06 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05300779 |
| By: | Danqing Chen; Carina Kane; Austin Kozlowski; Nadav Kunievsky; James A. Evans |
| Abstract: | Large Language Models have spread rapidly since the release of ChatGPT in late 2022, accompanied by claims of major productivity gains but also concerns about job displacement. This paper examines the short-run labor market effects of LLM adoption by comparing earnings and unemployment across occupations with differing levels of exposure to these technologies. Using a Synthetic Difference in Differences approach, we estimate the impact of LLM exposure on earnings and unemployment. Our findings show that workers in highly exposed occupations experienced earnings increases following ChatGPT's introduction, while unemployment rates remained unchanged. These results suggest that initial labor market adjustments to LLMs operate primarily through earnings rather than worker reallocation. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.15510 |
| By: | Maurice E. Stucke (University of Tennessee Winston College of Law) |
| Abstract: | Generative artificial intelligence (AI) is reshaping how companies profile individuals, create and target ads, and influence behavior—often in ways that undermine privacy, autonomy, and democracy. This article explores a critical but overlooked question: how AI affects the relationship between competition and privacy. Increased competition in the AI supply chain may seem like a solution to Big Tech's dominance, but when firms are rewarded for surveillance and manipulation, more competition can actually make things worse. Drawing on recent market trends and twenty state privacy laws, the Article shows how the existing legal frameworks-even those designed to protect privacy—fall short and may unintentionally entrench the power of few data-opolies. It argues that privacy and competition must be addressed together, not in silos, and offers specific legislative reforms to help align business incentives with public interests. Without stronger guardrails, AI risks accelerating a race to the bottom—fueled not only by powerful technologies, but by well-intentioned, but flawed policies. |
| Keywords: | Antitrust, privacy, monopolies, data, artificial intelligence |
| JEL: | K21 K24 L40 L41 L50 O33 |
| Date: | 2025–06–25 |
| URL: | https://d.repec.org/n?u=RePEc:thk:wpaper:inetwp236 |
| By: | Arenas Díaz, Guillermo (Università Cattolica del Sacro Cuore); Piva, Mariacristina (Università Cattolica del Sacro Cuore); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| Abstract: | This study investigates the relationship between Artificial Intelligence (AI) and innovation inputs in Spanish manufacturing firms. While AI is increasingly recognized as a driver of productivity and economic growth, its role in shaping firms’ innovation strategies remains underexplored. Using firm-level data, our analysis focuses on whether AI complements innovation inputs - specifically R&D and Embodied Technological Change (ETC) - and whether AI can be considered as a Method of Invention, able to trigger subsequent innovation investments. Results show a positive association between AI adoption and both internal R&D and ETC, in a static and a dynamic framework. Furtheremore, empirical evidence also highlights heterogeneity, with important peculiarities affecting large vs small firms and high-tech vs low-tech companies. These findings suggest that AI may act as both a complement and a catalyst, depending on firm characteristics. |
| Keywords: | innovation inputs, R&D, method of invention, Artificial Intelligence, innovative complementarities |
| JEL: | O31 O32 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18175 |
| By: | Leonardo Gambacorta; Tullio Jappelli; Tommaso Oliviero |
| Abstract: | We present findings from a specialized module on generative artificial intelligence (gen AI) included in the Italian Survey of Consumer Expectations (ISCE), conducted in 2024 with a representative sample of Italian individuals. This analysis offers novel insights into current and anticipated interactions with gen AI tools and the potential benefits from adoption. As of April 2024, 75.6% of the Italian population aged 18–75 was aware of gen AI, 36.7% had used it in the previous 12 months, and 20.1% reported monthly usage. Socio-economic factors significantly influence adoption rates, with higher usage observed among men, individuals with college degrees, and younger individuals, particularly students. Looking ahead, gen AI is expected to be used more frequently for education and leisure activities in the coming months. Finally, using a Mincer earnings regression, we highlight that the income return associated with gen AI usage is around 2%. |
| Keywords: | generative AI, households' survey |
| JEL: | D10 O33 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1298 |
| By: | Parma Bains; Gabriela E Conde; Rangachary Ravikumar; Ebru S Iskender |
| Abstract: | This paper discusses the imperative for financial supervisory authorities to enhance their toolkit through the adoption of Artificial Intelligence in response to the growing digitalization of financial services. It aims to assist authorities in safely and effectively overseeing applications of Artificial Intelligence in the financial sector by proposing a tailored project management methodology for implementation of Artificial Intelligence by financial supervisory authorities that address unique risks and align initiatives with strategic goals. Key challenges, including ensuring explainability and mitigating bias, with a focus on stakeholder collaboration, are emphasized, alongside prerequisites for successful deployment, such as robust governance frameworks and adequate resources. |
| Keywords: | Artificial Intelligence; Machine Learning; Deep Learning; Generative AI; DevOps; MLOps; AI governance; Data governance |
| Date: | 2025–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/199 |
| By: | Servaas Storm (Delft University of Technology) |
| Abstract: | The U.S. is undergoing an extraordinary, AI-fueled economic boom: The stock market is soaring thanks to exceptionally high valuations of AI-related tech firms, which are fueling economic growth by the hundreds of billions of U.S. dollars they are spending on data centers and other AI infrastructure. The AI investment boom is based on the belief that AI will make workers and firms significantly more productive, which will in turn boost corporate profits to unprecedented levels. But evidence is piling up that generative AI (GenAI) is failing to deliver. This paper argues that (i) we have reached "peak GenAI" in terms of current Large Language Models (LLMs); scaling (building more data centers and using more chips) will not take us further to the goal of "Artificial General Intelligence" (AGI); returns are diminishing rapidly; (ii) the AI-LLM industry and the larger U.S. economy are experiencing a speculative bubble, which is about to burst (because of the first point); and (iii) the U.S. bet the farm on a future dominated by U.S.-owned AGI, because, for geopolitical reasons, it could not afford to risk to lose the AI-race with China; this geopolitical bet on AGI is now going bad. |
| Keywords: | Artificial intelligence; generative AI; AI bubble; ChatGPT; LLMs; productivity impacts; profitability; price-earnings ratio; scaling; hallucinations; energy and water use; geopolitics of AI race. |
| JEL: | E24 F52 G01 O30 O33 |
| Date: | 2025–09–03 |
| URL: | https://d.repec.org/n?u=RePEc:thk:wpaper:inetwp240 |