人工智能对产出和通货膨胀的影响

The impact of artificialintelligence on output andinflation byIñakiAldasoro,SebastianDoerr,LeonardoGambacorta and Daniel Rees Monetary and Economic Department April 2024 JEL classification: E31, J24, O33, O40. Keywords: artificial intelligence, generative AI, inflation,output, productivity, monetary policy. BISWorking Papers are written by members of the Monetary and EconomicDepartment of the Bank for International Settlements, and from time to time by othereconomists, and are published by the Bank. The papers are on subjects of topicalinterest and are technical in character. The views expressed in them are those of theirauthors and not necessarily the views of the BIS. This publication is available on the BIS website (www.bis.org). ©Bank for International Settlements 2024. All rights reserved. Brief excerpts may bereproduced or translated provided the source is stated. The impact of artificial intelligence onoutput and inflation I AldasoroBISS DoerrBIS & CEPRL GambacortaBIS & CEPRD ReesBIS April 11, 2024 Abstract This paper studies the effects of artificial intelligence (AI) on sectoral and aggregateemployment, output and inflation in both the short and long run. We construct anindex of industry exposure to AI to calibrate a macroeconomic multi-sector model.Building on studies that find significant increases in workers’ output from AI, wemodel AI as a permanent increase in productivity that differs by sector. We findthat AI significantly raises output, consumption and investment in the short andlong run.The inflation response depends crucially on households’ and firms’ an-ticipation of the impact of AI. If they do not anticipate higher future productivity,AI adoption is initially disinflationary. Over time, general equilibrium forces leadto moderate inflation through demand effects.In contrast, when households andfirms anticipate higher future productivity, inflation rises immediately. Inspectingindividual sectors and performing counterfactual exercises we find that a sector’sinitial exposure to AI has little correlation with its long-term increase in output.However, output grows by twice as much for the same increase in aggregate pro-ductivity when AI affects sectors producing consumption rather than investmentgoods, thanks to second round effects through sectoral linkages.We discuss howpublic policy should foster AI adoption and implications for central banks. 1Introduction Recent advances in artificial intelligence (AI) have raised hopes of a boost to economicgrowth.Many scholars believe that AI has the potential to be “the most importantgeneral-purpose technology of our era” (Brynjolfsson et al., 2023). The recent inroads ofgenerative AI in everyday applications in particular promise widespread efficiency gains.1Unlike automation through robots, which can accomplish only explicitly understood (i.e.,’routine’) tasks, AI can infer tacit relationships that are not fully specified by underlyingsoftware (Autor, 2022).By transforming occupational tasks, altering corporate strate-gies, and affecting production efficiency, AI may have significant consequences for labourmarkets, firms, and whole industries (Agrawal et al., 2019). A key channel through which AI affects economic growth is through improvements inproductivity (Acemoglu and Restrepo, 2018; Aghion et al., 2018). Micro-economic stud-ies find that generative AI can make workers tremendously more productive, especially inoccupations that require cognitive work (Brynjolfsson et al., 2023; Noy and Zhang, 2023).AI also boosts firm growth and innovation (Babina et al., 2024). At the macro-economiclevel, analyses suggest that AI could raise annual productivity growth by around 1 per-centage point (pp) per annum over the next decade (Baily et al., 2023; Goldman Sachs,2023). The adoption of AI can hence be thought of as an increase in productivity thatexpands an economy’s output capacity. Compared to information technology (IT), whoseimpact took years to be reflected in aggregate productivity numbers (Fernald and Wang,2015), AI is considerably easier to use and implement in processes as it is a general-purpose technology that does not require the deployment of new hardware, deep userknow-how, or a substantial reconfiguration of business practices. As a consequence, theimpact of AI on productivity will likely be felt in the coming years already (Brynjolfssonet al., 2018; Furman and Seamans, 2019).2 In this paper, we investigate the effects of AI on aggregate output and inflation, as well as on output and employment in different sectors. We do so by first constructing ameasure of exposure to AI at the industry level. We then embed this exposure measureinto a macroeconomic multi-sector model, calibrated to the US economy using input-output tables. We also use the model to perform counterfactual exercises. We start by constructing an industry-level measure of exposure to AI (AIIE) at the2-digit NAICS level.Building on the indicator developed in