Artificial Intelligence http://lists.repec.org/mailman/listinfo/nep-ain Artificial Intelligence 2026-04-20 Strategic Algorithmic Monoculture:Experimental Evidence from Coordination Games https://d.repec.org/n?u=RePEc:arx:papers:2604.09502&r=&r=ain AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- baseline action similarity -- from strategic algorithmic monoculture, whereby agents adjust similarity in response to incentives. We implement a simple experimental design that cleanly separates these forces, and deploy it on human and large language model (LLM) subjects. LLMs exhibit high levels of baseline similarity (primary monoculture) and, like humans, they regulate it in response to coordination incentives (strategic monoculture). While LLMs coordinate extremely well on similar actions, they lag behind humans in sustaining heterogeneity when divergence is rewarded. Gonzalo Ballestero Hadi Hosseini Samarth Khanna Ran I. Shorrer 2026-04 Scaffolding Human-AI Collaboration: A Field Experiment on Behavioral Protocols and Cognitive Reframing https://d.repec.org/n?u=RePEc:arx:papers:2604.08678&r=&r=ain Organizations have widely deployed generative AI tools, yet productivity gains remain uneven, suggesting that how people use AI matters as much as whether they have access. We conducted a field experiment with 388 employees at a Fortune 500 retailer to test two scaffolding interventions for human-AI collaboration. All participants had access to the same AI tool; we varied only the structure surrounding its use. A behavioral scaffolding intervention (a structured protocol requiring joint AI use within pairs) was associated with lower document quality relative to unstructured use and substantially lower document production. A cognitive scaffolding intervention (partnership training that reframed AI as a thought partner) was associated with higher individual document quality at the top of the distribution. Treatment participants also showed greater positive belief change across the session, though sensitivity analyses suggest this likely reflects recovery from carry-over effects rather than genuine training-induced shifts. Both findings are subject to design limitations including an AM/PM session confound, differential attrition, and LLM grading sensitivity to document length. Alex Farach Alexia Cambon Lev Tankelevitch Connie Hsueh Rebecca Janssen 2026-04 Extrapolating Volition with Recursive Information Markets https://d.repec.org/n?u=RePEc:arx:papers:2604.08606&r=&r=ain One of the impediments to the efficiency of information markets is the inherent information asymmetry present in them, exacerbated by the "buyer's inspection paradox" (the buyer cannot mitigate the asymmetry by "inspecting" the information, because in doing so the buyer obtains the information without paying for it). Previous work has suggested that using Large Language Model (LLM) buyers to inspect and purchase information could overcome this information asymmetry, as an LLM buyer can simply "forget" the information it inspects. In this work, we analyze this mechanism formally through a "value-of-information" paradigm, i.e. whether it incentivizes information to be priced and provided in accordance with its "true value". We focus in particular on our new recursive version of the mechanism, which we believe has a range of applications including in AI alignment research, where it is related to Extrapolated Volition and Scalable Oversight. Abhimanyu Pallavi Sudhir Long Tran-Thanh 2026-04 AI and Worker Well-being: Evidence from a Nationally Representative Study https://d.repec.org/n?u=RePEc:rif:wpaper:137&r=&r=ain Abstract Utilizing nationally representative cross-sectional and longitudinal data from Finland (2018–2023), we provide a population-level assessment of the relationship between AI and worker well-being. Contrary to international evidence suggesting a positive or an inverted U-shaped relationship, we find no systematic association between AI use intensity and job satisfaction. However, we do find that work engagement is higher among employees who are personally involved with AI, with the strongest association among intensive users for whom AI is an essential part of their work. Furthermore, technology-replacement fears have remained stable despite rapid AI advancement and do not predict subsequent labour market transitions. An interpretation is that Finland’s high-trust institutional environment and robust social safety nets may effectively moderate the disruptive psychological and economic shocks typically associated with rapid technological change. Bryson, Alex Kauhanen, Antti Rouvinen, Petri Artificial intelligence, Job satisfaction, Work engagement, Technology-related fears, Labour market transitions 2026-04-07 LR-Robot: An Human-in-the-Loop LLM Framework for Systematic Literature Reviews with Applications in Financial Research https://d.repec.org/n?u=RePEc:arx:papers:2604.14793&r=&r=ain The exponential growth of financial research has rendered traditional systematic literature reviews (SLRs) increasingly impractical, as manual screening and narrative synthesis struggle to keep pace with the scale and complexity of modern scholarship. While the existing artificial intelligence (AI) and natural language processing (NLP) approaches often often produce outputs that are efficient but contextually limited, still requiring substantial expert oversight. To address these challenges, we propose LR-Robot, a novel framework in which domain experts define multidimensional classification taxonomies and prompt constraints that encode conceptual boundaries, large language models (LLMs) execute scalable classification across large corpora, and systematic human-in-the-loop evaluation ensures reliability before full-dataset deployment.The framework further leverages retrieval-augmented generation (RAG) to support downstream analyses including temporal evolution tracking and label-enhanced citation networks. We demonstrate the framework on a corpus of 12, 666 option pricing articles spanning 50 years, designing a four-dimensional taxonomy and systematically evaluating up to eleven mainstream LLMs across classification tasks of varying complexity. The results reveal the current capabilities of AI in understanding and synthesizing literature, uncover emerging trends, reveal structural research patterns, and highlight core research directions. By accelerating labor-intensive review stages while preserving interpretive accuracy, LR-Robot provides a practical, customizable, and high-quality approach for AI-assisted SLRs. Wei Wei Jin Zheng Zining Wang Weibin Feng 2026-04 AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows https://d.repec.org/n?u=RePEc:arx:papers:2604.10529&r=&r=ain We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO's AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976-2023) and Chinese patents (2010-2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa. Hanming Fang Xian Gu Hanyin Yan Wu Zhu 2026-04 Forecasting the Economic Effects of AI https://d.repec.org/n?u=RePEc:nbr:nberwo:35046&r=&r=ain We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% and the labor force participation rate falling from its current level of 62% to 55% by 2050, with roughly half of that decline—equivalent to around 10 million lost jobs—attributable to AI. A variance decomposition suggests that expert disagreement about these effects is driven primarily by different beliefs about the economic effects of highly capable AI systems rather than by disagreement about the pace of AI progress. These forecasts map onto notably different policy preferences across groups: experts strongly favor targeted measures such as worker retraining, whereas the general public supports both targeted programs and broader interventions, including a job guarantee and universal basic income. Ezra Karger Otto Kuusela Jason Abaluck Kevin A. Bryan Basil Halperin Todd R. Jones Connacher Murphy Philip Trammell Matt Reynolds Dan Mayland Ria Viswanathan Ananaya Mittal Rebecca Ceppas de Castro Josh Rosenberg Philip Tetlock 2026-04 AI unbound: digital infrastructure, AI adoption, and firm performance https://d.repec.org/n?u=RePEc:cep:cepdps:dp2172&r=&r=ain We study how digital infrastructure relaxes constraints on the diffusion and economic impact of artificial intelligence (AI). Using administrative data and a nationally representative enterprise survey from Turkey (2021-2024), we document significant disparities in AI adoption. Adoption is concentrated among large firms and in regions with high-speed broadband and proximity to data centers, particularly for software-intensive and cloud-based applications. To identify causal effects, we exploit the staggered expansion of Turkey's national natural gas pipeline network, which serves as a conduit for fiber-optic deployment. Because pipeline routing is determined by energy distribution priorities rather than digital demand, it provides plausibly exogenous variation in connectivity. Difference-in-differences estimates show that improved connectivity significantly increases AI adoption, particularly for software-intensive technologies and among small and medium-sized enterprises. Instrumental-variable estimates indicate that infrastructure-driven AI adoption raises labor productivity and export intensity while shifting labor composition toward ICT-related roles. These findings highlight digital infrastructure as a primary determinant of both the pace of AI diffusion and its resulting economic returns. Nuriye Melisa Bilgin Gianmarco Ottaviano artificial intelligence, digital infrastructure, broadband, technology diffusion, firm productivity, cloud computing 2026-04-15 Trade in AI-Related Products https://d.repec.org/n?u=RePEc:nbr:nberwo:35053&r=&r=ain This paper documents facts about international trade in AI-related products. I develop a large language model (LLM) classification tool that maps HS10 codes in U.S. trade data to products used in the construction and operation of AI infrastructure. AI-related products account for 23 percent of U.S. imports in 2025, and imports of these products have grown by 73 percent since 2023. Over the same period, imports of non-AI-related products have grown by only 3 percent, with the divergence between the two categories beginning in early 2024. Mexico is a key market on both the import and export side, and together with Taiwan these two countries account for about half of all U.S. trade in AI-related products. Trade policy has treated these products lightly with product-level exemptions shielding much of AI-related imports from tariffs. Absent the AI boom, a simple accounting exercise suggests that the U.S. goods trade deficit would have been nearly $200 billion smaller in 2025. Michael E. Waugh 2026-04