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on Artificial Intelligence |
| By: | Dirk Bergemann (Yale University); Alessandro Bonatti (Massachusetts Institute of Technology); Alex Smolin (Toulouse School of Economics) |
| Abstract: | We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM). Our framework captures several key features of LLMs: variable operational costs of processing input and output tokens; the ability to customize models through fine-tuning; and high-dimensional user heterogeneity in terms of task requirements and error sensitivity. In our model, a monopolistic seller offers multiple versions of LLMs through a menu of products. The optimal pricing structure depends on whether token allocation across tasks is contractible and whether users face scale constraints. Users with similar aggregate value-scale characteristics choose similar levels of fine-tuning and token consumption. The optimal mechanism can be implemented through menus of two-part tariffs, with higher markups for more intensive users. Our results rationalize observed industry practices such as tiered pricing based on model customization and usage levels. |
| Date: | 2025–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2425 |
| By: | Fontanelli, Luca; Calvino, Flavio; Criscuolo, Chiara; Nesta, Lionel; Verdolini, Elena |
| Abstract: | We leverage a uniquely comprehensive combination of data sources to explore the enabling role of human capital in fostering the adoption of predictive AI systems in French firms. Using a causal estimation approach, we show that ICT engineers play a key role for AI adoption by firms. Our estimates indicate that raising the current average share of ICT engineers in firms not using AI (1.66%) to the level of AI users (6.7%) would increase their probability to adopt AI by 0.81 percentage points - equivalent to an 8.43 percent growth. However, this would imply substantial investments to fill the existing gap in ICT human capital, amounting to around 450.000 additional ICT engineers. We also explore potential mechanisms, showing that the relevance of ICT engineers for predictive AI is driven by the innovative nature of its use, make-vs-buy choices, large availability of data, ICT and R&D intensity. |
| Keywords: | artificial intelligence; human capital; technological diffusion |
| JEL: | J24 O33 |
| Date: | 2024–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126787 |
| By: | Menaka Hampole; Dimitris Papanikolaou; Lawrence D.W. Schmidt; Bryan Seegmiller |
| Abstract: | We leverage recent advances in NLP to construct measures of workers' task exposure to AI and machine learning technologies over the 2010 to 2023 period that vary across firms and time. Using a theoretical framework that allows for a labor-saving technology to affect worker productivity both directly and indirectly, we show that the impact on wage earnings and employment can be summarized by two statistics. First, labor demand decreases in the average exposure of workers' tasks to AI technologies; second, holding the average exposure constant, labor demand increases in the dispersion of task exposures to AI, as workers shift effort to tasks that are not displaced by AI. Exploiting exogenous variation in our measures based on pre-existing hiring practices across firms, we find empirical support for these predictions, together with a lower demand for skills affected by AI. Overall, we find muted effects of AI on employment due to offsetting effects: highly-exposed occupations experience relatively lower demand compared to less exposed occupations, but the resulting increase in firm productivity increases overall employment across all occupations. |
| JEL: | E20 J01 J24 O3 O33 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33509 |
| By: | Paul E. Soto |
| Abstract: | This paper introduces a novel measure of firm-level Artificial Intelligence (AI) Research & Development—the AIR Index—derived from the semantic similarity between earnings conference call transcripts and leading AI research papers. The AIR Index varies widely across industries, with sustained strength in computer and electronic manufacturing, and accelerating growth in computing infrastructure and educational services seen after the introduction of ChatGPT in November 2022. I find that the AIR Index is associated with an immediate increase in Tobin’s Q and can help explain the cross-section of cumulative absolute returns following the conference call, suggestive of investors valuing substantive AI discussions in the near-term. A sharp rise in the AIR Index leads to persistent increases in year-over-year capex growth, lasting about a year before tapering off, indicative of the life cycle of AI-induced capital deepening. However, I find no significant effects of AI R&D on productivity or employment. Using industry level survey data from Census, I find that recent growth in the AIR Index correlates with broader AI adoption trends. The positive association of the AIR Index with capex and valuation holds across previous time periods, suggesting that Generative AI may be the latest form of an ongoing technical innovation process, albeit at an accelerated pace. |
| Keywords: | Artificial intelligence; Capital expenditure; Corporate finance; Natural language; Processing, productivity |
| JEL: | O32 E22 C49 |
| Date: | 2025–02–12 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-11 |
| By: | Jeffery Piao; K. Philip Wang; Diana L. Weng |
| Abstract: | Utilizing confidential microdata from the Census Bureau’s new technology survey (technology module of the Annual Business Survey), we shed light on U.S. banks’ use of artificial intelligence (AI) and its effect on their small business lending. We find that the percentage of banks using AI increases from 14% in 2017 to 43% in 2019. Linking banks’ AI use to their small business lending, we find that banks with greater AI usage lend significantly more to distant borrowers, about whom they have less soft information. Using an instrumental variable based on banks’ proximity to AI vendors, we show that AI’s effect is likely causal. In contrast, we do not find similar effects for cloud systems, other types of software, or hardware surveyed by Census, highlighting AI’s uniqueness. Moreover, AI’s effect on distant lending is more pronounced in poorer areas and areas with less bank presence. Last, we find that banks with greater AI usage experience lower default rates among distant borrowers and charge these borrowers lower interest rates, suggesting that AI helps banks identify creditworthy borrowers at loan origination. Overall, our evidence suggests that AI helps banks reduce information asymmetry with borrowers, thereby enabling them to extend credit over greater distances. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-07 |
| By: | Adewopo, Julius Babatunde; Andree, Bo Pieter Johannes; Peter, Helen; Solano-Hermosilla, Gloria; Micale, Fabio |
| Abstract: | High-frequency monitoring of food commodity prices is important for assessing and responding to shocks, especially in fragile contexts where timely and targeted interventions for food security are critical. However, national price surveys are typically limited in temporal and spatial granularity. It is cost prohibitive to implement traditional data collection at frequent timescales to unravel spatiotemporal price evolution across market segments and at subnational geographic levels. Recent advancements in data innovation offer promising solutions to address the paucity of commodity price data and guide market intelligence for diverse development stakeholders. The use of artificial intelligence to estimate missing price data and a parallel effort to crowdsource commodity price data are both unlocking cost-effective opportunities to generate actionable price data. Yet, little is known about how the data from these alternative methods relate to independent ground truth data. To evaluate if these data strategies can meet the long-standing demand for real-time intelligence on food affordability, this paper analyzes open-source daily crowdsourced data (104, 931 datapoints) from a recently published data set in Nature Journal, relative to complementary ground truth sample. The paper subsequently compares these data to open-source monthly artificial intelligence–generated price data for identical commodities over a 36-month period in northern Nigeria, from 2019 to 2022. The results show that all the data sources share a high degree of comparability, with variation across commodity and market segments. Overall, the findings provide important support for leveraging these new and innovative data approaches to enable data-driven decision-making in near real time. |
| Date: | 2024–04–23 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10758 |