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on Artificial Intelligence |
| By: | Martin Bichler; Philip Hierhager; Kassian K\"ock; Fabian Pieroth |
| Abstract: | Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically select the stage-game Nash outcome with prices near marginal cost. We show that LLM pricing agents can escape this benchmark, depending on deployment decisions. LLMs outperform standard online learners through faster adaptation, but this advantage largely disappears against full-information learners. An extracted surrogate algorithm illustrates how LLMs in specific deployments profit from regularly undercutting slower learners. Agents that post one price every period from raw market histories that include rival prices can sustain supracompetitive regimes in self-play. Agents that periodically emit probability distributions from summarized histories of their own profits instead move toward the competitive region in self-play. Markets mixing these interfaces can generate dynamic regimes in which LLMs establish a high-price anchor and regularly undercut it to capture demand. These regimes can keep both competitors' prices above marginal cost. Deployment choices shape these outcomes, shifting the focus from stationary equilibria to dynamic pricing regimes. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.21711 |
| By: | Hanna Brosch; Christina Langer; Philipp Lergetporer; Sandra Mueller; Harald Pfeifer; Nele Tschoepe |
| Abstract: | Does generative artificial intelligence (AI) expand the set of tasks that middle-skilled, early-career workers can successfully perform? In a field experiment with 673 final-year IT apprentices at twelve German vocational schools, we randomly assign access to AI for completing occupation-specific tasks. To identify task expansion, we study tasks both within the apprenticeship curriculum and deliberately beyond it. AI raises productivity substantially across all task types, with gains tending to be larger on tasks where performance without AI is lowest. Crucially, productivity increases by 26–31 percentage points on tasks beyond apprentices’ formal training, providing direct evidence of AI-enabled task expansion. These productivity gains do not come at the cost of reduced immediate task comprehension. Treatment effects are generally larger among apprentices with above-median AI literacy. |
| Keywords: | generative artificial intelligence, field experiment, productivity, task expansion, middle-skilled workers, apprenticeships |
| JEL: | J24 O33 C93 I21 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13045 |
| By: | Megan Evans (Max Planck Institute for Demographic Research, Rostock, Germany); Noli Brazil |
| Abstract: | Urban residents increasingly rely on online tools to searchfor housing, raising new questions as to the role of digital tools in interrupting or reinforcing racialized housing searches. Generative Artificial Intelligence (GenAI), in particular, represents a new intermediary in how neighborhood knowledge is created and distributed in housing platforms and search engines. This study examines whether ChatGPT engages in racial matching by systematically varying user demographics in housing queries across 10 large U.S. cities. Using 14, 400 unique queries per city, we asked GPT-4o for neighborhood recommendations and areas to avoid. Merging AI generated recommendations with census data, we investigate racial patterns in how GPT-4o incorporate user characteristics and neighborhood demographics into their housing guidance. Findings suggest that AI recommendations reflect and may amplify existing patterns of residential segregation through systematic differences in how neighborhoods are recommended to different users. Results have important implications for informing both fair housing policy and the responsible development and regulation of AI systems in the housing market. |
| JEL: | J1 Z0 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:dem:wpaper:wp-2026-041 |
| By: | Hellsten, Mark (The Ratio Institute) |
| Abstract: | Despite growing policy interest in AI workforce development, surprisingly little is known about what defines these workers, or indeed if they differ from their occupational peers. Using a novel matched vacancy-hire dataset linking Swedish job postings to the individuals hired to fill them, drawn from register data on the entire Swedish population, I study the characteristics of workers who develop and apply AI. Observable traits including high cognitive ability, a tertiary degree in STEM subjects, and a recent migrant background largely explain occupational sorting. Of these, only higher education, prior AI experience, and a science degree set them apart within occupation. I then use earnings as a window onto unobservable differences: AI workers earn approximately 5% more than their peers, entirely explained by firm-level wages. However, AI workers with a doctorate degree stand out sharply in both traits and earnings, indicating that AI workers split into segments that differ by the complexity of their skills and tasks. Taken together, these results point to two distinct policy levers: the supply of average AI workers, termed AI integrators, can be expanded via STEM education participation, while the elite AI developers are supplied by researchers moving to the private sector. |
| Keywords: | Artificial Intelligence; Wage premium; Labor demand; Skills; Technology diffusion |
| JEL: | D22 J24 M20 O33 |
| Date: | 2026–10–16 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ratioi:0393 |
| By: | Cody Orr; Lee C. Tucker; Lawrence Warren |
| Abstract: | How are new college graduates affected by the rise of artificial intelligence, and what can this tell us about the mechanisms behind AI's overall labor market effects? We use administrative records on college graduates to observe how economic outcomes among college majors with differing levels of labor market AI exposure have evolved since large language models became available. We find that post-graduation employment, earnings, and job switching patterns among the most AI-exposed college majors began to diverge immediately following the introduction of ChatGPT in late 2022. In regression-adjusted estimates, the most AI-exposed decile of college majors saw their likelihood of initial employment decline by five percentage points, while full-quarter initial earnings declined by thirteen percent. This earnings decline is comparable in magnitude to the earnings losses associated with graduating into a large recession. Roughly half of the decline in earnings is attributable to lower earnings within the industry sectors that employ these graduates, with the remainder resulting from a shift in the industry mix into lower-wage sectors such as restaurants and retail. The effects attenuate as graduates move further from labor market entry but remain substantial for the most exposed majors. |
| Keywords: | Artificial Intelligence, College major, Postsecondary employment outcomes |
| JEL: | J23 J24 I26 O33 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:26-56 |
| By: | Amit Chandra (Institut d'Administration des Entreprises (IAE) - Bordeaux); Sylvie Michel (Institut d'Administration des Entreprises (IAE) - Bordeaux) |
| Abstract: | The rapid proliferation of Artificial Intelligence (AI) technologies has transformed work practices and skill requirements across industries, particularly within the Information Technology (IT) sector. Unlike traditional automation tools, agentic AI possesses autonomous decision-making capabilities and can collaborate with humans as "team members" in complex software development tasks. While existing research has examined Agentic AI's productivity impacts in software development, there remains a limited understanding of how it influences skill changes across different experience levels. This article aims to investigate whether the collaboration between agentic agents and human agents influences the developers' skills in the software industry. To achieve this aim, we conducted a systematic literature review (SLR) that examines the impact of agentic AI on developer skills in the software industry. The findings indicate that agentic AI does not remove the need for developer skills but instead reshapes them. As AI agents take on routine and repetitive development tasks, human developers increasingly focus on higher-level activities such as system design, coordination, supervision, and governance. The outcome of this literature review will allow us to initiate further research for understanding how agentic AI impacts software development skills. |
| Date: | 2026–05–20 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05789314 |
| By: | Antonio Minniti (Department of Economics, University of Bologna); Klaus Prettner (Department of Economics, Vienna University of Economics and Business); Francesco Venturini (Department of Economics, University of Urbino) |
| Abstract: | We study whether AI innovation changes labor-market polarization by weakening the relative position of high-skilled workers. We develop a CES production framework showing how AI affects the wage-cost ratio between high- and low skilled workers through wage and employment margins. We test the model's predictions using European regional labor-market data and find that AI reduces the relative wage-cost ratio by nearly 7 percent on average, with effects broadly split between wage and employment adjustments. These results are primarily driven by regions in countries highly specialized in AI technologies. Overall, our evidence suggests that AI may counteract the polarization effects associated with over two centuries of technological change. |
| Keywords: | AI, Wage-cost ratio, Employment, CES, DiD |
| JEL: | O3 R1 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp400 |
| By: | Sebastian Sartor; Robert Seamans; Neil Thompson |
| Abstract: | Exposure indices that map technical capabilities onto occupational abilities have become a standard tool for anticipating how AI may reshape work, but existing measures either cover software AI or proxy robotics through patents and installation data that reflect the industrial robots of past decades. We construct the first abilities-based exposure measures for robotics and physical AI, and the first to span occupations, industries, and geographies. Following the framework of Felten et al. (2018, 2021), we build a robotics capability taxonomy of 16 capabilities in eight domains from 78 academic, industry, and standards sources, use a large language model to score the relatedness of each capability to the 52 ONET work abilities, and aggregate to Robotics Occupational Exposure (ROE) for 774 occupations, Robotics Industry Exposure (RIE) for 250 NAICS industries, and county-level Robotics Geographic Exposure (RGE). We find that robotics exposure concentrates in physically intensive work such as construction, farming, transportation, and production, and in industries such as logging, mining, trucking, and warehousing. Unlike generative-AI exposure, ROE is higher in occupations with lower wages, lower education requirements, and less creative-ability content. Physical collaboration and assistance, terrain-based locomotion, grasping and handling, and task and functional reasoning move the exposure ranking most, yet no single capability carries the index. Because the measures capture technical relatedness rather than readiness, cost, or adoption, they should be read as an upper bound on where physical automation could affect work. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24267 |
| By: | Phurichai Rungcharoenkitkul |
| Abstract: | Transformative AI promises to accelerate growth through productivity enhancement and automation. Yet automation on such a scale would raise the share of investment in output, potentially starving AI innovation of returns from a shrinking consumer market. We propose an endogenous growth framework to study this interaction, featuring research and entry decisions that propel AI advances but whose costs must be paid for by consumer demand. We identify several scenarios, spanning transformative AI, a demand bottleneck outcome where thinning consumer demand stalls AI progress, and intermediate cases in between. When anchored to evidence, the calibrated model points to the demand bottleneck as a highly plausible scenario, and projects more moderate growth in the coming years relative to recent studies such as Korinek et al. (2026). We draw policy implications, including for the natural rate of interest. |
| Keywords: | artificial intelligence, automation, natural rate of interest, demand-pull innovation, market size, endogenous growth, demand bottlenecks |
| JEL: | E22 E43 E52 O31 O33 O41 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1381 |
| By: | Matthew Dey; Emin Dinlersoz; Lucia Foster; Cheryl Grim; John Haltiwanger; Peter B. Meyer; Sabrina Wulff Pabilonia; Aditya Pande; Martha Stinson; Sean Wang; Zoltan Wolf |
| Abstract: | Measures of exposure to artificial intelligence (AI) are widely used to study where AI is likely to affect firm and worker outcomes, yet limited evidence exists on how closely exposure relates to realized AI adoption at the firm level. We examine this relationship by linking 14 firm-level exposure measures, constructed from occupational exposure estimates in the literature and occupational employment shares from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, to direct measures of firm AI adoption from the Census Bureau’s Business Trends and Outlook Survey. Exposure is positively and statistically significantly associated with adoption, but explains only a modest share of its variation. The strength of the relationship varies considerably across standardized exposure measures: a one-standard-deviation increase in firm-level exposure is associated with a 4–11 percentage point higher firm adoption probability, falling to 1–8 percentage points after controlling for year and sub-sector fixed effects. The exposure–adoption relationship is also heterogeneous across firm-size classes and sectors. Exposure is thus an informative but incomplete signal of adoption. More broadly, the results have implications for the interpretation of exposure-based measures in studies of the economic effects of AI. |
| Keywords: | Artificial Intelligence, AI adoption, AI exposure, firm-level data, occupational mix |
| JEL: | O33 D22 J23 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:26-61 |
| By: | Alina Ermolaeva; Hannah Magdalena Seidl |
| Abstract: | Does artificial intelligence (AI) change the transmission of monetary policy? We develop a two-sector heterogeneous-agent New Keynesian model with task-based production, in which AI automates part of the work in one exposed sector and leaves the other shielded. In the AI steady state, the economy holds more capital, and labor income shifts from the exposed workers, who lose through hours, to the shielded workers, who gain through higher wages. We then compare the same rate cut across the two economies. It stimulates macroeconomic aggregates more in the AI economy. The deeper capital stock delivers a larger initial stimulus. Investment responds as strongly as without AI, but on a permanently larger base, so the same response means more demand. Machines absorb part of the exposed sector’s share of that demand. The shielded sector’s share becomes labor income, which is spent in turn. It matters who gets automated: the amplification of monetary policy in an AI economy runs through the workers AI does not replace. |
| Keywords: | Artificial intelligence, automation, task-based production, HANK, monetary transmission, labor share, sectoral incidence |
| JEL: | E21 E24 E32 E52 J23 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2184 |
| By: | Swaminathan Balasubramaniam (NEOMA Business School, Rouen, France); Jorge Sabat (Universidad Andrés Bello – Institute of Economic Policy, Santiago, Chile); Soumyajit Ray (Department of Electrical Engineering, Johns Hopkins University, Baltimore, MD) |
| Abstract: | Using Reddit r/wallstreetbets posts, we compare the sentiment of AI-generated replies with actual human top-level replies to the same investor-authored posts. We find that counterfactual replies generated by a user-facing AI model are systematically more bullish than human replies. Prompting the model to respond as a professional investor narrows the gap, but does not eliminate it. To study potential mechanisms, we compare the user-facing model with a matched base model that shares the same architecture and pretraining corpus but lacks instruction-following post-training and chat-format deployment. The instruction-tuned model is consistently more bullish than the matched base model, suggesting that the post-trained conversational layer amplifies bullishness beyond what is already present in the base model. We rationalize the evidence with a reduced-form model in which replies reflect a learned bullish default, responsiveness to the user’s thesis, and categorical expression. Finally, we examine whether AI-augmented signals retain return-relevant content. The return evidence is descriptive: AI-augmented signals remain positively associated with near-horizon returns in the main specifications, but the relation is not uniform across samples or horizons. |
| Keywords: | AI and LLMs; Investor Communication; Information Intermediaries; Sentiment; Retail Investors |
| JEL: | G10 G12 G41 C55 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:bfi:wpaper:2026-133 |
| By: | Amirhossein Zohrehvand; Aldona Kapa\v{c}inskait\.{e} |
| Abstract: | Artificial intelligence (AI) makes it easier than ever to produce research outputs. The pressing question is how researchers should organize their work with this technology. The question is not new: software engineering and security research have long studied how teams of people and machines keep control of a shared body of work with many contributors. We distil principles from the practices these literatures report and transpose them to AI-assisted research. The principles form TRACE: Track records, Review against sources, Accept independently, Control transitions, and Evaluate corrections. Together, the five principles give researchers a common basis for remaining in control while integrating AI into their workflows. By bounding units of work and attending to how each is shaped, they help researchers pace AI-assisted work to human attention rather than to the speed at which AI produces content. We also introduce TRACE-OS, an open source operating system for AI-assisted research projects. It puts the principles into practice through shared instructions, tools, and records that fit researchers' existing workflows. The shared standard can contribute to building trust, which allows teams to delegate and realize the productivity that AI promises. More broadly, we aim to open a conversation about the governing principles through which the research community can benefit from AI. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24035 |