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on Artificial Intelligence |
| By: | Raša Karapandža; Yaw Nyarko |
| Abstract: | Large language models (LLMs) increasingly provide economic and financial advice. We develop a microeconomic framework for understanding this role. We prove that an LLM acts like a voting rule: each input text is an election, possible continuations are candidates, and output probabilities are vote shares derived from the training corpus. Such rules can generate Condorcet cycles, implying intransitive pairwise recommendations and potential money-pump exploitation. Using ChatGPT-4o, we identify millions of cycles in pairwise investment choices among S&P 100 firms. Our theorems establish the voting interpretation on the training data and support its use off-sample. We also show that correcting cycles ex post is generally infeasible. Finally, temperature changes the voting rule: at T = 1, outputs are sampled in proportion to vote shares; lower temperatures favor the plurality winner, whereas higher temperatures flatten probabilities toward uniformity. |
| JEL: | D81 D83 D91 G41 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35839 |
| By: | Daniel P. Gross; Dror Shvadron; Hansen Zhang |
| Abstract: | Large language models are diffusing rapidly into science, raising concerns that delegating research writing to AI may undermine the development of expertise that scientific training is supposed to build. We measure AI-generated writing in the dissertations of a near-census of US STEM PhD graduates from 2019 to 2026 and link graduates to their subsequent employment. AI writing is absent before 2023, present in 29% of dissertations filed in 2026, and rapidly growing. Use is more common among students from non-English-speaking countries and at lower-ranked programs. Among graduates in the same program and cohort, those whose dissertations contain AI writing are less likely to enter academic research careers and more likely to enter non-tenure-track roles or industry, with this pattern strongest among US-origin students. At the same time as AI may increase research productivity, it thus appears to also be displacing a potentially important portion of the work through which scientific expertise is developed. |
| JEL: | I23 J24 O31 O33 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35859 |
| By: | Jun Yeong Lee |
| Abstract: | Evidence that pricing algorithms collude comes from markets in which every seller runs the same algorithm. We ask what happens when they do not. Four language models from four providers, each at its cheapest tier, price in a four-firm logit Bertrand market without communication, in every homogeneous, two-by-two and fully mixed composi- tion (11 cells, 20 runs, 200 periods). Collusion is a property of the model: Claude and Gemini markets reach 72 to 79 percent of the monopoly rent, DeepSeek markets 24 percent, and GPT markets none, though GPT prices drift above the monopoly level rather than toward competition. Mixing does not reduce collusion by itself. Markets containing Gemini, which opens at the highest price and settles highest, are more col- lusive than the homogeneous markets they are built from; markets containing Claude, which opens lower and follows its rivals down, are less so; and only the fully mixed market is significantly less collusive than the average homogeneous one. Stability de- pends on the least stable participant: two GPT firms suffice to keep any market from converging. Inside mixed markets the rent is shared in a transitive order, DeepSeek over Claude over Gemini over GPT, that inverts the anchor ranking. The model that raises the price collects the least of the rent, as the price-leadership model predicts. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.11256 |
| By: | Haonan Huang; Joey Xiao |
| Abstract: | Personal AI assistants are beginning to transact for consumers, and early adopters capture real savings. Whether those savings survive, and what happens to consumers who have no assistant, depends on how sellers respond -- a question single-user evidence cannot answer. We build an agent-based rental market in which language models play consumers, assistants, and six adaptive sellers guided by an algorithmic pricing tool. Half the population receives an assistant under an advisory or an executing mandate; the contract pairs a fee only the renter's physical action avoids with a pre-selected add-on an authorised assistant can cancel online. An analytical benchmark and a behaviourally calibrated rule market supply ex-ante predictions, and paired branches with frozen versus adaptive sellers separate adoption effects from market feedback. Across thirty simulated markets, executing assistants cut adopters' spending by 13.7 USD per renter-day when sellers are frozen; adaptation claws back about a third, leaving 8.7, with the gains arriving both as lower bills and as rentals completed at all. Sellers raise headline rates while cutting fees, and the calibrated forecast of the burden on unassisted consumers (+3.6) does not transfer: their mean spending change is +0.4, confidence interval -0.6 to +1.3. Seller-model swaps and a within-market transfer of fee-setting to the pricing tool show that fee conduct, and with it the division of the gains, is decided on the seller side. Assistants, we conclude, should be evaluated at market level -- completion, total spending, and non-users included -- and the comparison layers locate exactly where a calibrated behavioural forecast fails in a language-model market. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.05823 |
| By: | Ajzenman, Nicolás; Del Toro Mijares, Ana Teresa; Elacqua, Gregory; Hermosilla, Catalina; Veleda, Santiago Nicolas |
| Abstract: | A central obstacle to scaling many interventions is implementer quality: as a program expands and the pool of implementers broadens, average quality falls. Conversation-based interventions (counseling, mentoring) are particularly sensitive to this constraint. LLMs offer a natural way out: personalized exchanges at zero marginal cost and uniform quality. We run a nationwide experiment in Chile (N42, 000) designed to reduce teacher shortages by increasing applications to education majors. We compare an AI chatbot, Kai, with trained human counselors, both reaching high school seniors via WhatsApp. Among students with baseline interest in education (the interventions target), Kai significantly increases first-ranked education majors and the share of education majors in students choice sets; estimates in the human arm are smaller and not significant. Although the best human counselors are highly effective, Kai matches or outperforms roughly two- thirds of them. Its advantage lies in what we call “controlled variance”: it compresses the dispersion in quality observed across human implementers. Finally, text analysis reveals different interaction patterns across arms. Kai concentrates on factual content and scores higher on semantic coherence, while counselors score substantially higher on motivational and empathetic language. |
| Keywords: | Teachers;teacher shortages;Sclae-up;behavioral interventions;AI chatbots;Generative AI |
| JEL: | D91 I23 I25 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14771 |
| By: | Will Aarons; Asani Sarkar |
| Abstract: | There is little consensus on the effect of AI adoption on employment. Economy-wide measures of pay and headcount have moved little even as AI adoption has spread rapidly, yet narrower measures document declining hiring for workers at the start of their careers or in AI-exposed occupations. In this post, we revisit this topic for U.S. small businesses. We use data from the 2025 Small Business Credit Survey (SBCS) to examine the twelve-month-forward employment and revenue expectations of AI users. We find that firms currently using AI are significantly more likely than non-users to expect increased employment and revenues over the next year, even after accounting for firm and owner characteristics and location. Revenue expectations are most optimistic for AI users that report facing operational challenges in utilizing technology. |
| Keywords: | artificial intelligence (AI); small business; employment; revenues |
| JEL: | O3 L2 D2 |
| Date: | 2026–10–08 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:103899 |
| By: | Asik Erpek, Gunes Arkadas; Hari, Siddharth; Ozen, Efsan Nas; Raju, Dhushyanth |
| Abstract: | Artificial intelligence (AI) is reshaping labor markets globally, less through immediate, economywide displacement than through a gradual and uneven reconfiguration of what workers do and how they do it. For Türkiye, the central question is not whether AI will matter, but whether the country is positioned to capture its productivity dividends while managing its distributional risks. This question is especially important because Türkiye combines substantial occupational exposure to AI with still-limited firm adoption, persistent skills mismatches, large gaps in labor market inclusion, and a constrained domestic AI talent base. The near-term policy challenge is therefore one of uneven adjustment. Some exposed workers may be well placed to benefit from AI-enabled productivity gains, while others face greater substitution risk or may remain excluded from the productivity gains that AI can generate. This note synthesizes global evidence with quantitative and qualitative findings for Türkiye and translates them into a targeted policy agenda. |
| Date: | 2026–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:hdnspu:212940 |