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on Artificial Intelligence |
| By: | Francesco Filippucci; Peter Gal; Matthias Schief |
| Abstract: | The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon. It builds a novel micro-to-macro framework by combining existing estimates of micro-level performance gains with evidence on the exposure of activities to AI and likely future adoption rates, relying on a multi-sector general equilibrium model with input-output linkages to aggregate the effects. Its main estimates for annual aggregate total-factor productivity growth due to AI range between 0.25-0.6 percentage points (0.4-0.9 pp. for labour productivity). The paper discusses the role of various channels in shaping these macro-level gains and highlights several policy levers to support AI's growth-enhancing effects. |
| Keywords: | Artificial Intelligence, Productivity, Technology adoption |
| JEL: | E1 O3 O4 O5 |
| Date: | 2024–11–22 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:29-en |
| By: | Anton Korinek; Jai Vipra |
| Abstract: | This paper examines the evolving structure and competition dynamics of the rapidly growing market for foundation models, with a focus on large language models (LLMs). We describe the technological characteristics that shape the AI industry and have given rise to fierce competition among the leading players. The paper analyzes the cost structure of foundation models, emphasizing the importance of key inputs such as computational resources, data, and talent, and identifies significant economies of scale and scope that may create a tendency towards greater market concentration in the future. We explore two concerns for competition, the risk of market tipping and the implications of vertical integration, and we evaluate policy remedies that aim to maintain a competitive landscape. Looking ahead to increasingly transformative AI systems, we discuss how market concentration could translate into unprecedented accumulation of power, highlighting the broader societal stakes of competition policy. |
| JEL: | D43 K21 L4 L86 O33 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33139 |
| By: | Thomas Licht; Klaus Wohlrabe |
| Abstract: | This paper examines the adoption of Artificial Intelligence (AI) among German firms, leveraging firm-level data from the ifo Business Survey. We analyze the diffusion of AI across sectors and firm sizes, showing a significant increase in AI usage from 2023 to 2024, particularly in manufacturing and services. The survey data allows us to explore not only sectoral patterns of adoption but also the drivers and barriers that firms face, including firm-specific characteristics and industry dynamics. Additionally, we investigate the role of managerial traits, such as risk tolerance and patience, in shaping AI adoption decisions. Finally, we assess the potential pro-ductivity impacts of AI at the firm level, with a focus on the expected long-term benefits of AI for different sectors of the German economy. Our findings contribute to the growing body of research on AI adoption by providing new evidence from a non-US context, offering valuable insights for both academia and politics. |
| Keywords: | artificial intelligence, AI, ifo business survey, productivity |
| JEL: | M15 O30 C83 L20 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11459 |
| By: | Stefania Albanesi; Wabitsch Alena; António Dias da Silva; Juan F. Jimeno; Ana Lamo |
| Abstract: | We examine the link between labour market developments and new technologies such as artificial intelligence (AI) and software in 16 European countries over the period 2011-2019. Using data for occupations at the 3-digit level, we find that on average employment shares have increased in occupations more exposed to AI. This is particularly the case for occupations with a relatively higher proportion of younger and skilled workers. While there exists heterogeneity across countries, only very few countries show a decline in employment shares of occupations more exposed to AI-enabled automation. Country heterogeneity for this result seems to be linked to the pace of technology diffusion and education, but also to the level of product market regulation (competition) and employment protection laws. In contrast to the findings for employment, we find little evidence for a relationship between relative wages across occupations and potential exposures to new technologies |
| Keywords: | Artificial intelligence; Employment; Occupations; Skills |
| JEL: | J23 O33 |
| Date: | 2024–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedmoi:99164 |
| By: | Thomas MELONIO; Peter Martey ADDO; Anastesia TAIEB; Laura LANDREIN |
| Abstract: | This paper examines the potential of artificial intelligence (AI) investment to drive sustainable development across diverse national contexts. By evaluating critical factors, including AI readiness, social inclusion, human capital, and macroeconomic conditions, we construct a nuanced and comprehensive analysis of the global AI landscape. Employing advanced statistical techniques and machine learning algorithms, we identify nations with significant untapped potential for AI investment.We introduce the AI Investment Potential Index (AIIPI), a novel instrument designed to guide financial institutions, development banks, and governments in making informed, strategic AI investment decisions. The AIIPI synthesizes metrics of AI readiness with socio-economic indicators to identify and highlight opportunities for fostering inclusive and sustainable growth. The methodological novelty lies in the weight selection process, which combines statistical modeling and also an entropy-based weighting approach. Furthermore, we provide detailed policy implications to support stakeholders in making targeted investments aimed at reducing disparities and advancing equitable technological development. |
| JEL: | Q |
| Date: | 2024–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:avg:wpaper:en17595 |
| By: | Tianyu Zhou; Pinqiao Wang; Yilin Wu; Hongyang Yang |
| Abstract: | As financial markets grow increasingly complex, there is a rising need for automated tools that can effectively assist human analysts in equity research, particularly within sell-side research. While Generative AI (GenAI) has attracted significant attention in this field, existing AI solutions often fall short due to their narrow focus on technical factors and limited capacity for discretionary judgment. These limitations hinder their ability to adapt to new data in real-time and accurately assess risks, which diminishes their practical value for investors. This paper presents FinRobot, the first AI agent framework specifically designed for equity research. FinRobot employs a multi-agent Chain of Thought (CoT) system, integrating both quantitative and qualitative analyses to emulate the comprehensive reasoning of a human analyst. The system is structured around three specialized agents: the Data-CoT Agent, which aggregates diverse data sources for robust financial integration; the Concept-CoT Agent, which mimics an analysts reasoning to generate actionable insights; and the Thesis-CoT Agent, which synthesizes these insights into a coherent investment thesis and report. FinRobot provides thorough company analysis supported by precise numerical data, industry-appropriate valuation metrics, and realistic risk assessments. Its dynamically updatable data pipeline ensures that research remains timely and relevant, adapting seamlessly to new financial information. Unlike existing automated research tools, such as CapitalCube and Wright Reports, FinRobot delivers insights comparable to those produced by major brokerage firms and fundamental research vendors. We open-source FinRobot at \url{https://github. com/AI4Finance-Foundation/FinRobot}. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08804 |
| By: | Hoyoung Lee; Youngsoo Choi; Yuhee Kwon |
| Abstract: | Recent advancements in Large Language Models (LLMs) have the potential to transform financial analytics by integrating numerical and textual data. However, challenges such as insufficient context when fusing multimodal information and the difficulty in measuring the utility of qualitative outputs, which LLMs generate as text, have limited their effectiveness in tasks such as financial forecasting. This study addresses these challenges by leveraging daily reports from securities firms to create high-quality contextual information. The reports are segmented into text-based key factors and combined with numerical data, such as price information, to form context sets. By dynamically updating few-shot examples based on the query time, the sets incorporate the latest information, forming a highly relevant set closely aligned with the query point. Additionally, a crafted prompt is designed to assign scores to the key factors, converting qualitative insights into quantitative results. The derived scores undergo a scaling process, transforming them into real-world values that are used for prediction. Our experiments demonstrate that LLMs outperform time-series models in market forecasting, though challenges such as imperfect reproducibility and limited explainability remain. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08404 |