2024年到底谁在使用人工智能？（英）.pdf

Policy Research Working Paper10870Who on Earth Is Using Generative AI?Yan LiuHe WangDigital Development Global PracticeAugust 2024 Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedProduced by the Research Support TeamAbstractThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues.An objective of the series is to get the findings out quickly,even if the presentations are less than fully polished.The papers carry the names of the authors and should be cited accordingly.The findings,interpretations,and conclusions expressed in this paper are entirely those of the authors.They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations,or those of the Executive Directors of the World Bank or the governments they represent.Policy Research Working Paper 10870Leveraging unconventional data,including website traffic data and Google Trends,this paper unveils the real-time usage patterns of generative artificial intelligence tools by individuals across countries.The paper also examines coun-try-level factors driving the uptake and early impacts of generative artificial intelligence on online activities.As of March 2024,the top 40 generative artificial intelligence tools attract nearly 3 billion visits per month from hun-dreds of millions of users.ChatGPT alone commanded 82.5 percent of the traffic,yet reaching only one-eightieth of Googles monthly visits.Generative artificial intelligence users skew young,highly educated,and male,particularly for video generation tools,with usage patterns strongly indicating productivity-related activities.Generative arti-ficial intelligence has achieved unprecedentedly rapid global diffusion,reaching almost all economies worldwide within 16 months of ChatGPTs release.Middle-income econo-mies have disproportionately high adoption of generative artificial intelligence relative to their economic scale,now contribute more than 50 percent of global traffic,while low-income economies contribute less than 1 percent.Regression analysis reveals that income level,share of youth population,digital infrastructure,specialization in high-skill tradable services,English proficiency,and human capital are strongly correlated with higher uptake of gen-erative artificial intelligence.The paper also documents disruptions in online traffic patterns and emphasizes the need for targeted investments in digital infrastructure and skills development to harness the full potential of artificial intelligence.This paper is a product of the Digital Development Global Practice.It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world.Policy Research Working Papers are also posted on the Web at http:/www.worldbank.org/prwp.The authors may be contacted at yanliuworldbank.org.Who on Earth Is Using Generative AI?Yan Liu*and He Wang*World BankJEL codes:O30,O31,O14Key words:Generative AI,Technology Adoption,Geographic Disparities,Digital DivideWe would like to thank Stephane Straub,Davide Strusani,and Estefania Vergara-Cobos for helpful commentsand suggestions.Email:yanliuworldbank.orgEmail:hwang21worldbank.org1IntroductionGenerative AI(GenAI)holds the potential to transform economies and societies.Taking traditionalAIs predictive power one step further,generative AI is capable of creating new content in all formsof media-text,code,images,audio,video,and more.Generative AI tools like ChatGPT,Co-Pilot,and Midjourney are expected to revolutionize how certain tasks are performed,leading to significantefficiency gains and new opportunities for innovation(Eloundou,Manning,Mishkin,and Rock 2023;Humlum and Vestergaard 2024).Since the debut of ChatGPT in November 2022,various types ofgenerative AI tools have proliferated and many have amassed a huge user base within record time.The widespread use of generative AI offers the possibility to transform economic development,socialstructures,and global competitiveness.Despite extensive discussions about the applications andpotentials of generative AI on societies(Korinek 2023;Chui,Hazan,Roberts,Singla,and Smaje2023;Brynjolfsson,Li,and Raymond 2023;Jha,Qian,Weber,and Yang 2024;Kim,Muhn,andNikolaev 2024),research on the scale of generative AI usage is rare.This paper seeks to unveil thereal-time global scale of generative AI usage,including its demographic and country distribution,and to explore the barriers to and impacts of its adoption.As these technologies continue to evolve,understanding their adoption patterns,both in advanced and developing economies,becomes crucialfor policymakers,businesses,and researchers.To measure AI adoption globally,several organizations have been publishing AI indexes andreports,tracking trends in AI development,and focusing on assessing countries readiness for AIintegration.Since 2017,Stanford University has been publishing the AI Index Report(Maslej,Fattorini,Perrault,et al.2024)annually,providing a comprehensive overview of developmentsin AI research,investment,technology performance,education,and governance.Tortoise Mediaalso publishes the Global AI Index to benchmark 62 countries performance on AI innovation,investment,and implementation.The Government AI Readiness Index 2023 by Oxford Insightsranks countries on their readiness to integrate AI into government operations across 193 economies.The IMFs recent report emphasizes AIs transformative potential for labor markets and the needfor AI readiness,highlighting the necessity of robust digital infrastructure and skilled workforces inadvanced economies,alongside the challenges faced by less developed countries(IMF 2024).Mostof these reports and indexes focus on the supply side of AI,offer limited coverage of developing2countries,and often lack specific emphasis on generative AI technologies.Tracking the usage of generative AI tools is a crucial first step in understanding their eco-nomic and social implications.Despite progress in connecting more people to the internet,themulti-faceted digital divide is widening both within and between economies,amplifying disparitiesin productivity and consumer welfare(Sorbe,Gal,Nicoletti,and Timiliotis 2019).Applying in-novative data to summarize stylized facts is fundamental.This includes understanding the typesof users and economies that are leading in generative AI adoption,those that are lagging,howpeople are using generative AI tools,and the underlying factors driving these trends.Such insightsare essential for policymakers,businesses,and researchers to make informed decisions and fosterequitable development in the AI landscape and the broader economy.However,there is a glaring data gap in monitoring the diffusion and adoption of generativeAI,especially in developing countries.Tracking AI adoption is challenging due to several factors.First,conducting comprehensive and representative surveys on AI usage is both time-consumingand expensive,often requiring extensive timelines to gather and analyze data.Many surveys arealso one-off and not comparable over time or across countries.For instance,a survey conducted byHumlum and Vestergaard(2024)in Denmark reveals widespread usage,with half of the surveyedworkers reporting they use ChatGPT.This adoption has led to significant time savings in dailytasks,particularly for younger and less experienced workers.Despite these insights,similar detaileddata from developing countries are lacking,underscoring a notable gap in our global understandingof generative AIs impact.Additionally,the definition of AI varies widely,encompassing a broadspectrum of technologies from computer vision to robotics and natural language processing,whichcomplicates consistent reporting.Moreover,AI is frequently embedded within products and ser-vices,leading to significant under-reporting as respondents may not explicitly recognize or disclosetheir use of AI.The data gap is even more pronounced in developing countries,which lack thecapacity and resources to conduct in-depth surveys.These challenges hinder the collection of ac-curate,comparable,and up-to-date data on AI adoption,further obscuring the global landscape ofAI adoption.This paper aims to fill this data gap by utilizing novel website traffic data from Semrush.Semrush collects raw data through clickstream analytics,tracking codes,and server log files.Itprocesses approximately 25 billion URLs daily across a global database that contains over 43 trillion3links and 500 TB of raw data.By employing proprietary machine learning algorithms,Semrushanalyzes this data to generate estimated metrics such as the number of visits,unique visitors,average session duration,etc.Furthermore,Semrush disaggregates these metrics by economiesusing users IP addresses,providing globally comparable data across both advanced and developingeconomies.For each website,Semrush also infers user profiles based on patterns of online behaviors.This dataset offers a unique opportunity to monitor a selected sample of generative AI websitestraffic globally.Despite the inherent limitations of using website traffic as a proxy,it provides aneffective means to monitor generative AI usage from a globally comparable perspective.Utilizing Semrush data complemented with Google Trends data,this paper offers an in-depthanalysis of generative AI usage by individuals across countries,focusing on both temporal trendsand geographic disparities.We examine the adoption rates of various generative AI tools,identifykey socioeconomic factors influencing usage,and explore the early impact of generative AI toolson traffic to other websites,peoples online activities,and broader implications.By highlightingoutperforming and underperforming economies and surfacing factors shaping generative AI adop-tion,this study provides valuable insights into the global diffusion of generative AI technologiesand their potential to drive transformative change.The paper is designed to provide descriptiveinsights that may prompt further questions and exploration.Our analysis seeks not to establishcausal relationships but to highlight correlations and trends that could inform subsequent research.Several key findings have emerged from our analysis:1.Rapid proliferation of generative AI tools and quick adoption.At least hundreds of genera-tive AI tools exist as of March 2024.The top 40 most visited tools have nearly 3 billion monthlyvisits.Chatbots dominate the generative AI landscape due to their versatility and wider applica-bility,accounting for 95%of traffic among the top 40 tools.ChatGPT alone commands 82.5%oftotal traffic and boasts 500 million users per month,representing 12.5%of the global workforce.Impressively,it took only five months for ChatGPT to reach 500 million monthly unique users.However,traffic and users of several generative AI tools including ChatGPT have plateaued sincemid-2023,hinting at market saturation and intensifying competition.2.Generative AI user demographics skew toward young,educated males.Youth and malebias is most pronounced for video generation tools,while chatbot users are more highly educatedthan Google users.Generative AI tools are primarily used as productivity tools,as these tools are4predominantly accessed via desktop computers during weekdays.3.Unprecedented global diffusion and widespread usage in middle-income economies.Just 16months since ChatGPTs release,it has reached 209 of 218 economies worldwide.As of March 2024,the top five economies for ChatGPT traffic are the US,India,Brazil,the Philippines,and Indonesia.The US share of ChatGPT traffic dropped from 70%to 25%within one month of ChatGPTsdebut.Middle-income economies now contribute over 50%of traffic,showing disproportionatelyhigh adoption of generative AI relative to their GDP,electricity consumption,and search enginetraffic.Low-income economies,however,represent only less than 1%of global ChatGPT traffic.4.Higher income levels,a higher share of youth population,better digital infrastructure,andstronger human capital are key predictors of higher generative AI uptake.Specialization in digitallytradable services and English fluency are strongly associated with higher chatbot usage.15.Generative AI tools are already disrupting online traffic patterns and altering user habits.Information,language processing,and professional question and answer websites like Wikipedia,Grammarly,Google Translate,and Stack Overflow experienced significant traffic drops immediatelyafter GPT-4 launch,though some recovery has been observed as sites integrate generative AIcapabilities.People are increasingly using ChatGPT for information acquisition and aggregation,skill learning,and language processing,while more complex cognitive and analytical tasks havebeen augmented by ChatGPT.Existing evidence in advanced countries shows that AI adoption by firms remains very lowand is dominated by large firms and young firms.Acemoglu(2024),Babina,Fedyk,He,andHodson(2024),and Goldfarb,Taska,and Teodoridis(2023)used online job posting data to measurefirms AI adoption and subsequent effects.Miric,Jia,and Huang(2023)and Webb(2019)usedpatent data as a proxy for AI adoption.Zolas,Kroff,Brynjolfsson,et al.(2021)and McElheran,Li,Brynjolfsson,et al.(2024)used the US Annual Business Survey data to track AI use amongbusinesses.Even though AI development and use has been ongoing for decades,AI usage amongUS businesses remains very low and highly uneven.Fewer than 6%of firms used any of the AI-related technologies,and adoption is heavily tilted towards very large businesses,businesses ownedby more educated and younger owners,and in superstar cities.Bonney,Breaux,Buffington,et1Digitally tradable services include,for example,information and communication technology and business processoutsourcing,finance and insurance,professional services,and research activities.5al.(2024)provided more current evidence on AI use,which includes generative AI,based on theBusiness Trends and Outlook Survey(BTOS).They found that the AI use rate rose from 3.7%inSeptember 2023 to 5.4%in February 2024,with an expected rate of 6.6%by early fall 2024.AIusers often utilize AI to substitute for work tasks and equipment or software,though few firmsreport reductions in employment due to AI use yet.IBM Global AI Adoption Index 2023 is amongthe few studies assessing firms AI adoption around the world.Globally,42%of enterprises withmore than 1000 employees reported having actively deployed AI in their business.India(59%),the United Arab Emirates(58%),Singapore(53%),and China(50%)are leading the way in AIusage.Companies in the financial services industry,industrial equipment,and telecommunicationsare most likely to be using AI.While most firms are still trying to figure out how AI can add value for their businesses,indi-vid