metadata: authors: 0: author: Liu,Yan-000529044
1: author: He Wang
count: World
docna: 0: docna: Who on Earth Is Using Generative AI ?
docty: Policy Research Working Paper
abstracts: cdata!: Leveraging 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 country-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 hundreds of
            millions of users. ChatGPT alone commanded 82.5 percent of
            the traffic, yet reaching only one-eightieth of
            Google&#x2019;s 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 artificial intelligence has achieved
            unprecedentedly rapid global diffusion, reaching almost all
            economies worldwide within 16 months of ChatGPT&#x2019;s
            release. Middle-income economies 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 generative 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.
display_title: Who on Earth Is Using Generative AI ?
listing_relative_url: /research/2024/08/34379070/earth-using-generative-ai
topicv3: ICT Innovation and Transformation m1326189 2399,Digital Economy Strategy m1327584 2345
publishtoextweb_dt: 2024-08-19T00:00:00Z
dois: 10.1596/1813-9450-10870
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 58
author_keys: 0: author_key: 1676956
1: author_key: NA
model_extractions: Policy Research Working Paper 10870 Who on Earth Is Using Generative AI?

_Yan Liu He Wang_ Digital Development Global Practice August 2024
Policy Research Working Paper 10870

# **Abstract**

Leveraging 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 country-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 hundreds of millions of users. ChatGPT alone commanded 82.5 percent of the traffic, yet reaching only one-eightieth of Google’s 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 artificial intelligence has achieved unprecedentedly rapid global diffusion, reaching almost all economies worldwide within 16 months of ChatGPT’s release. Middle-income economies 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 generative 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 yanliu@worldbank.org.

_The 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._ Produced by the Research Support Team
# Who on Earth Is Using Generative AI?<sup>∗</sup>

Yan Liu<sup>†*</sup> and He Wang<sup>‡*</sup>

**JEL codes:** O30, O31, O14

**Key words:** Generative AI, Technology Adoption, Geographic Disparities, Digital Divide

> ∗We would like to thank Stephane Straub, Davide Strusani, and Estefania Vergara-Cobos for helpful comments and suggestions.

> †Email: yanliu@worldbank.org

> ‡Email: hwang21@worldbank.org
# **1 Introduction**

Generative AI (GenAI) holds the potential to transform economies and societies. Taking traditional AI’s predictive power one step further, generative AI is capable of creating new content in all forms of 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 significant efficiency 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 of generative 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, social structures, and global competitiveness. Despite extensive discussions about the applications and potentials of generative AI on societies (Korinek 2023; Chui, Hazan, Roberts, Singla, and Smaje 2023; Brynjolfsson, Li, and Raymond 2023; Jha, Qian, Weber, and Yang 2024; Kim, Muhn, and Nikolaev 2024), research on the scale of generative AI usage is rare. This paper seeks to unveil the real-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 crucial for policymakers, businesses, and researchers.

To measure AI adoption globally, several organizations have been publishing AI indexes and reports, tracking trends in AI development, and focusing on assessing countries’ readiness for AI integration. Since 2017, Stanford University has been publishing the AI Index Report (Maslej, Fattorini, Perrault, et al. 2024) annually, providing a comprehensive overview of developments in AI research, investment, technology performance, education, and governance. Tortoise Media also publishes the Global AI Index to benchmark 62 countries’ performance on AI innovation, investment, and implementation. The Government AI Readiness Index 2023 by Oxford Insights ranks countries on their readiness to integrate AI into government operations across 193 economies. The IMF’s recent report emphasizes AI’s transformative potential for labor markets and the need for AI readiness, highlighting the necessity of robust digital infrastructure and skilled workforces in advanced economies, alongside the challenges faced by less developed countries (IMF 2024). Most of these reports and indexes focus on the supply side of AI, offer limited coverage of developing 2
countries, and often lack specific emphasis on generative AI technologies.

Tracking the usage of generative AI tools is a crucial first step in understanding their economic and social implications. Despite progress in connecting more people to the internet, the multi-faceted digital divide is widening both within and between economies, amplifying disparities in productivity and consumer welfare (Sorbe, Gal, Nicoletti, and Timiliotis 2019). Applying innovative data to summarize stylized facts is fundamental. This includes understanding the types of users and economies that are leading in generative AI adoption, those that are lagging, how people are using generative AI tools, and the underlying factors driving these trends. Such insights are essential for policymakers, businesses, and researchers to make informed decisions and foster equitable development in the AI landscape and the broader economy.

However, there is a glaring data gap in monitoring the diffusion and adoption of generative AI, 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-consuming and expensive, often requiring extensive timelines to gather and analyze data. Many surveys are also one-off and not comparable over time or across countries. For instance, a survey conducted by Humlum and Vestergaard (2024) in Denmark reveals widespread usage, with half of the surveyed workers reporting they use ChatGPT. This adoption has led to significant time savings in daily tasks, particularly for younger and less experienced workers. Despite these insights, similar detailed data from developing countries are lacking, underscoring a notable gap in our global understanding of generative AI’s impact. Additionally, the definition of AI varies widely, encompassing a broad spectrum of technologies from computer vision to robotics and natural language processing, which complicates consistent reporting. Moreover, AI is frequently embedded within products and services, leading to significant under-reporting as respondents may not explicitly recognize or disclose their use of AI. The data gap is even more pronounced in developing countries, which lack the capacity and resources to conduct in-depth surveys. These challenges hinder the collection of accurate, comparable, and up-to-date data on AI adoption, further obscuring the global landscape of AI 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. It processes approximately 25 billion URLs daily across a global database that contains over 43 trillion 3
links and 500 TB of raw data. By employing proprietary machine learning algorithms, Semrush analyzes this data to generate estimated metrics such as the number of visits, unique visitors, average session duration, etc. Furthermore, Semrush disaggregates these metrics by economies using users’ IP addresses, providing globally comparable data across both advanced and developing economies. 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 websites’ traffic globally. Despite the inherent limitations of using website traffic as a proxy, it provides an effective means to monitor generative AI usage from a globally comparable perspective.

Utilizing Semrush data complemented with Google Trends data, this paper offers an in-depth analysis of generative AI usage by individuals across countries, focusing on both temporal trends and geographic disparities. We examine the adoption rates of various generative AI tools, identify key socioeconomic factors influencing usage, and explore the early impact of generative AI tools on traffic to other websites, people’s online activities, and broader implications. By highlighting outperforming and underperforming economies and surfacing factors shaping generative AI adoption, this study provides valuable insights into the global diffusion of generative AI technologies and their potential to drive transformative change. The paper is designed to provide descriptive insights that may prompt further questions and exploration. Our analysis seeks not to establish causal 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 generative AI tools exist as of March 2024. The top 40 most visited tools have nearly 3 billion monthly visits. Chatbots dominate the generative AI landscape due to their versatility and wider applicability, accounting for 95% of traffic among the top 40 tools. ChatGPT alone commands 82.5% of total 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 since mid-2023, hinting at market saturation and intensifying competition.

2. Generative AI user demographics skew toward young, educated males. Youth and male bias is most pronounced for video generation tools, while chatbot users are more highly educated than Google users. Generative AI tools are primarily used as productivity tools, as these tools are 4
predominantly accessed via desktop computers during weekdays.

3. Unprecedented global diffusion and widespread usage in middle-income economies. Just 16 months since ChatGPT’s 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 ChatGPT’s debut. Middle-income economies now contribute over 50% of traffic, showing disproportionately high adoption of generative AI relative to their GDP, electricity consumption, and search engine traffic. 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, and stronger human capital are key predictors of higher generative AI uptake. Specialization in digitally tradable services and English fluency are strongly associated with higher chatbot usage.<sup>1</sup> 5. 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 immediately after GPT-4 launch, though some recovery has been observed as sites integrate generative AI capabilities. People are increasingly using ChatGPT for information acquisition and aggregation, skill learning, and language processing, while more complex cognitive and analytical tasks have been augmented by ChatGPT.

Existing evidence in advanced countries shows that AI adoption by firms remains very low and is dominated by large firms and young firms. Acemoglu (2024), Babina, Fedyk, He, and Hodson (2024), and Goldfarb, Taska, and Teodoridis (2023) used online job posting data to measure firms’ AI adoption and subsequent effects. Miric, Jia, and Huang (2023) and Webb (2019) used patent 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 among businesses. Even though AI development and use has been ongoing for decades, AI usage among US businesses remains very low and highly uneven. Fewer than 6% of firms used any of the AIrelated technologies, and adoption is heavily tilted towards very large businesses, businesses owned by more educated and younger owners, and in superstar cities. Bonney, Breaux, Buffington, et

> 1Digitally tradable services include, for example, information and communication technology and business process outsourcing, finance and insurance, professional services, and research activities.

5
al. (2024) provided more current evidence on AI use, which includes generative AI, based on the Business Trends and Outlook Survey (BTOS). They found that the AI use rate rose from 3.7% in September 2023 to 5.4% in February 2024, with an expected rate of 6.6% by early fall 2024. AI users often utilize AI to substitute for work tasks and equipment or software, though few firms report reductions in employment due to AI use yet. IBM Global AI Adoption Index 2023 is among the few studies assessing firms’ AI adoption around the world. Globally, 42% of enterprises with more 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 AI usage. Companies in the financial services industry, industrial equipment, and telecommunications are most likely to be using AI.

While most firms are still trying to figure out how AI can add value for their businesses, individuals and employees have been embracing the technology much faster. Pew Research Center has conducted two rounds of surveys among US adults to shed light on ChatGPT usage by individuals. By Feburary 2024, 23% of US adults report ever using ChatGPT, up from 18% in July 2023. Users are concentrated in those aged 18-29 and highly educated. Most users use ChatGPT for tasks at work, learning, and entertainment (McClain 2024). The 2024 Work Trend Index Annual Report by Microsoft and LinkedIn highlights that 75% of knowledge workers in 31 surveyed economies use AI at work in 2024. Four out of five AI users bring their own AI tools to work, and this is even more common at small and medium-sized companies. Individuals in administrative and support services, real estate and retail industries report the highest generative AI adoption. Reported generative AI usage is the highest in India, Thailand, Indonesia and China (above 90%).

The contribution of this paper is threefold. First, to the best of our knowledge, this is the first paper that uses website traffic data to offer real-time and granular insights into the usage of various generative AI tools across countries. While there is a growing body of research on AI adoption in advanced countries (Acemoglu 2024; Babina, Fedyk, He, and Hodson 2024; McElheran, Li, Brynjolfsson, et al. 2024; Goldfarb, Taska, and Teodoridis 2023), evidence in developing countries remains scarce. Many existing studies only offer a snapshot of AI adoption instead of dynamic trends. There is also a lack of cross-country comparisons of AI adoption. This paper addresses this gap by innovatively leveraging website traffic data obtained from Semrush. This type of data is particularly valuable in a rapidly evolving research domain like AI. By harnessing granular traffic 6
metrics for individual websites and subpages, our study offers rich, timely insights into how emerging technologies are diffused and adopted globally, user characteristics and usage patterns.

Second, this study contributes to a strand of literature on the geography of technology diffusion, deepening the understanding of the country level factors that influence the adoption of generative AI. Previous research has broadly identified economic and infrastructural factors as influencers of technology adoption at the country level (Keller 2004; Comin and Hobijn 2010; Czernich, Falck, Kretschmer, and Woessmann 2011; Delera, Pietrobelli, Calza, and Lavopa 2022), but lacks specific analysis related to generative AI. This paper uses regression analysis to explore how demographics, economic development, industry structure, human capital, and digital infrastructure affect the adoption of various generative AI tools. Our findings highlight the risks of AI exacerbating the already growing digital divide and call for collaborate efforts to ensure the technology is accessible to all.

Finally, by combining website traffic data with Google Trends data, this study also sheds light on early impacts of generative AI tools on individuals’ online activities, and discusses the implications for public preferences, digital firms’ business model and competition dynamics.

The remainder of the paper is organized as follows. Section 2 introduces the website traffic data. Section 3 identifies a list of the most popular generative AI tools and shows the rapid adoption of generative AI by individuals. Section 4 analyzes user characteristics of different types of generative AI tools and usage patterns. Section 5 demonstrates the geographical disparities in generative AI usage and highlights the record-breaking global diffusion of generative AI. Section 6 delves into the socioeconomic and infrastructural factors that influence these geographic disparities in adoption. Section 7 briefly explores early impacts of generative AI tools on individuals’ online activities, and discusses the broader implications. Section 8 concludes and discusses avenues for future research.

# **2 Website Traffic Data to Monitor Generative AI Usage**

Thanks to advancements in digital analytics technologies, we are now able to monitor generative AI usage in real-time through website traffic data. Website traffic data typically include metrics such as number of visits, time spent on site, bounce rates, and user demographics. This data has been widely used by website owners, digital marketers, business analysts, and researchers to gain insights into 7
online behavior and website performance. Common applications include optimizing user experience, improving content strategy, measuring marketing campaign effectiveness, and informing business decisions. Large companies often use this data to refine their digital strategies, while smaller businesses and content creators use it to understand their audience better. Investors and analysts also leverage this data to assess the performance and growth potential of online businesses. As digital presence becomes increasingly important across industries, website traffic data has become a crucial tool for understanding and responding to online user behavior.

Website traffic data offers several unique advantages in monitoring generative AI usage compared to other data sources. First, website traffic data provides near-instantaneous information on user engagement. Unlike survey data which tend to lag behind actual usage trends, traffic data reflects real-time user behavior. Traffic data also directly measures actual usage, eliminating the self-reporting biases that can affect survey responses. Traffic data has comprehensive coverage, capturing a wide range of users from casual experimenters to frequent users across countries. The data is also more granular, metrics are at the website level and can be broken down by geographic location, date and time, device type, visit type and other variables. In addition, even periodic surveys have major time gaps in between, while traffic data provides a continuous stream of information allowing trend analysis and quick identification of changes in user patterns.

For our analysis, we obtained traffic data from Semrush, a leading provider of such data. Semrush collects raw data through clickstream analytics, tracking codes, and server log files. It processes approximately 25 billion URLs daily across a global database that contains over 43 trillion links and 500 TB of raw data. By employing proprietary machine learning algorithms, Semrush analyzes this data to generate estimated metrics such as the number of visits, unique visitors, average session duration, and further disaggregates these metrics by countries using users’ IP addresses. For each website, Semrush also infers user profiles based on patterns of online behaviors.

We accessed the monthly traffic data for various tools from January 2022 to March 2024 using the Semrush API. The data includes total visits and unique users per domain from both desktop and mobile devices across geographies. It also encompasses traffic originating from mobile applications. However, it is important to note that this measure of consumer usage does not reflect professional usage through APIs or indirect access via third-party apps. Nonetheless, since most generative AI tools are designed for productivity and work-related tasks for individual usage, our website usage 8
data can serve as a reliable proxy.

As a cross check, in Appendix Figure A1, we compare the website traffic of ChatGPT against its popularity on the internet, as measured by Google Trend index.<sup>2</sup> Semrush’s estimates of ChatGPT’s monthly website visits closely align with the trends observed in the weekly Google Trends data, a rapid rise following the release of GPT 3.5, a subsequent slowdown after mid-2023, and some short-term fluctuations during holiday seasons. The strong correlation between Google Trends data and Semrush’s traffic estimates substantiates the reliability of the website traffic data. We also compared Semrush data with similar data obtained from other sources, the patterns for popular websites with over 1 million sessions per day are also largely consistent.

While website traffic data is valuable for tracking individuals’ AI usage, it has several limitations. First, individuals’ use of VPNs can obscure the actual geographic origin of traffic. This may bias the usage data for economies with small populations, particularly island territories, though the impact on larger economies is negligible. Second, most of the traffic analytics tools Semrush relies on only work with Google search engine. While traffic estimates from other search engines like Bing, DuckDuckGo, Yandex, and Baidu can be available, the accuracy is less ideal. Results on Baidu is particularly limited, compromising data quality in China. Third, traffic data can inflate user numbers as the same individual with several digital devices could be counted as different users, leading to overestimation of engagement. Fourth, traffic data does not necessarily reflect the depth or quality of engagement with AI tools, what people are using them for, offline AI usage or enterprise-level AI usage. Fifth, as generative AI is increasingly integrated into various new applications, such as search engines or devices like Siri as announced by Apple, the indirect usage of generative AI websites—such as traffic via APIs—cannot be captured by this dataset.

# **3 The Rapid Adoption of Generative AI**

Artificial Intelligence (AI) broadly refers to systems or machines that mimic human intelligence to perform tasks. Generative AI, a specific subset of AI, involves algorithms that create new content—whether text, code, images, audio, videos, and more—drawing from extensive datasets to generate outputs that were not explicitly programmed. The study of AI has been ongoing for

> 2The Google Trends index reflects global weekly searches for the keyword “ChatGPT” from November 2022 to March 2024. Further details are provided in Section 7.

9
decades, with generative AI also developing over this time, albeit previously in more nascent forms. The transformative capabilities of these technologies, however, truly gained prominence with the emergence of ChatGPT 3.5.

The debut of ChatGPT 3.5 in November 2022 has marked a significant milestone in AI development. Its successor GPT-4, released in March 2023, showcases enhanced conversational abilities and multimodal capabilities. The transformer model, originally proposed by Alphabet in 2017, has revolutionized neural networks and given rise to the large language models (LLMs) and multimodal models that can interpret natural language prompts correctly and generate original text, code, audio, image, and video often indistinguishable from human-made content. The breakthrough has propelled generative AI to the forefront of AI research and commercialization.

Generative AI tools have proliferated and witnessed rapid adoption across various domains. As of June 2024, at least hundreds of new generative AI tools have been developed around the world. In the realm of text generation, ChatGPT, Gemini, Claude, and Microsoft Copilot have profoundly affected writing, editing, language translation, chatbots, search and information aggregation. Image generation tools, such as Midjourney, Stable Diffusion, and Leonardo have democratized digital art creation, enabling both professionals and amateurs to produce high-quality visuals from text descriptions. In the audio domain, AI-powered tools have emerged for music composition, voice synthesis, and podcast creation. Video generation has seen the rise of tools capable of creating short-form content, deepfakes, and even rudimentary animations. Code generation tools such as GitHub Copilot and Devin AI have gained traction in software development, assisting programmers with task automation and bug detection. Additionally, multimodal AI systems that combine text, image, and sometimes audio capabilities have begun to blur the lines between these categories, offering more versatile and powerful creative tools. The proliferation of generative AI tools and quick user adoption has not only transformed some occupations and industries already, but has also raised important questions about copyright, ethics, security, misinformation, labor market and inequality, the future of human creativity and beyond.

We compiled a list of the 40 most popular websites specifically designed for generative AI functions. As technologies continue to evolve and increase in complexity, distinguishing between generative AI and other AI applications, and even non-AI applications, can be challenging. In this paper, we first gathered a longer list from multiple sources and verified each site’s functionality. We 10
distinguished between tools exclusively designed for generative AI tasks and those with embedded generative AI functions, such as traditional chatbots, Bing.com and Notion.ai. The latter were excluded from our primary list but considered for comparative analysis. We then selected the top 40 tools with the highest traffic in March 2024 for our analysis. The tools were categorized into three types: chatbot, image, and video. It is worth noting that chatbots increasingly include additional functions such as image and video creation, while some video generation tools can also generate images. Although these features complicated our classification, we attempted to categorize each tool based on its original purpose and most popular functions.

11
Table 1: List of selected Generative AI tools

||GenAI tool|Target website domain|Type|Trafc in March 2024 (million visits)|
|---|---|---|---|---|
|1|ChatGPT|chat.openai.com|Chatbot|2,343.2|
|2|Gemini|gemini.google.com|Chatbot|132.9|
|3|Poe|poe.com|Chatbot|43.4|
|4|Perplexity<br>|perplexity.ai|Chatbot<br>|40.2|
|5|Claude|claude.ai|Chatbot|32.3|
|6|DeepAI|deepai.org|Chatbot|31.1|
|7|Copilot|copilot.microsoft.com|Chatbot|26.2|
|8|Midjourney|midjourney.com|Image|24.7|
|9|Prezi|prezi.com|Image|18.0|
|10|Nightcafe|creator.nightcafe.studio|Image|13.9|
|11|Leonardo|app.leonardo.ai|Image|13.6|
|12|Gamma|gamma.app|Image|11.6|
|13|Pixai|pixai.art|Image|9.6|
|14|Runway|runwayml.com|Video|9.0|
|15|Ideogram|ideogram.ai|Image|8.9|
|16|Playground|playground.com|Image|8.8|
|17|Youchat|you.com|Chatbot|8.7|
|18|Blackbox AI|blackbox.ai|Chatbot|8.7|
|19|ChatPDF|chatpdf.com|Chatbot|6.8|
|20|MaxAI|maxai.me|Chatbot|6.1|
|21|Craiyon|craiyon.com|Image|5.8|
|22|OpusClip|opus.pro|Video|4.7|
|23|Synthesia|synthesia.io|Video|4.1|
|24|Phind|phind.com|Chatbot|3.9|
|25|Writesonic|writesonic.com|Chatbot|3.8|
|26|Stability AI|stability.ai|Video|3.2|
|27|Le chat|chat.mistral.ai/chat/|Chatbot|3.0|
|28|Designs.AI|designs.ai|Image|1.9|
|29|Ernie Bot|yiyan.baidu.com|Chatbot|1.5|
|30|Liner|getliner.com|Chatbot|1.5|
|31|Artbreeder|artbreeder.com|Image|1.4|
|32|Kimi chatbot|kimi.moonshot.cn|Chatbot|1.3|
|33|Fliki|app.fiki.ai|Video|1.2|
|34|Easy-peasy|easy-peasy.ai|Chatbot|1.2|
|35|Genmo AI|genmo.ai|Video|1.0|
|36|Tongyi Qianwen|tongyi.aliyun.com/qianwen/|Chatbot|0.9|
|37|Meta image|imagine.meta.com|Image|0.4|
|38|Ifytek spark|xinghuo.xfyun.cn|Chatbot|0.4|
|39|InVideo|invideo.io/ai/|Video|0.3|
|40|Doubao|doubao.com|Chatbot|0.3|

12
<!-- Start of picture text --> 2500<br>§ 2000<br>€<br>o<br>gs<br>5 1500<br>£ === Monthly visits<br>= s== Unique users<br>2 GPT 3.5 GPT 4.0<br>Ss 1000<br>&<br>bE<br>a<br>25a<br>500<br>0<br>2022m11 2023m1 2023m3 2023m5 2023m7 2023m9 2023m11 2024m1 2024m3<br><!-- End of picture text -->
<!-- Start of picture text --> 1005 110b<br>=—— Google<br>2. 10b 6.8b<br>2> eS ourTube<br>2 —= Wikipedia<br>25 2.3b ChatGPT<br>1.8b Quora<br>1b<br>100m<br> ai |<br>2022m7 2023m1 2023m7 2024m1<br><!-- End of picture text -->
<!-- Start of picture text --> Chatbot<br>150 ; / 2500<br>FSfe}Cc 2000 — ChatGPT (RHS)<br>E 100 se Gemini<br>g 1500<br>g : : +000 = Poe<br>Z 50 —— Perplexity<br>3CcS 500 — Claude<br>ot: : Lo<br>a a<br>2022m1 2022m7 2023m1 2023m7 2024m1<br>Image GenAl<br>80 : : : :<br>2a2§7 ee : =——mee MidjPrezi Midjourne'° y<br>2 40>: : . == Nightcafe<br>==> == Leonardo<br>5 205: =—— Gamma<br>3<br>0<br>ass<br>2022m1 2022m7 2023m1 2023m7 rs2024m1|<br>Video GenAl<br>15 ' ‘<br>Cc . .<br>& = Runway<br>e 10 === OpusClip<br>g —— Synthesia<br>2 55 —— StabilityAl<br>3ic}Cc . — Flikiiki<br>0 - 7 : :<br>To]—>,n<br>2022m1 2022m7 2023m1 2023m7 2024m1<br><!-- End of picture text -->
# **4 Generative AI User Profile and Usage Patterns**

This section examines the demographic characteristics and usage patterns of generative AI tool users. Drawing from clickstream data aggregated by Semrush, we analyze key user attributes including age, gender, education, and employment status, alongside usage metrics such as daily traffic fluctuations and device preferences. This analysis offers a nuanced view of who is using generative AI tools and how they’re being integrated into daily routines and workflows. By exploring these factors, we aim to illuminate current trends in AI tool adoption across various population segments.

The gender gap in digital tool usage remains a significant aspect of the digital divide, with females representing 48% of global internet users. Figure 5 illustrates the share of female users of five popular generative AI tools across each category, as well as five other widely used websites. Generally, female users are underrepresented in generative AI tools compared to male users, with video generation tools showing the lowest female participation. ChatGPT has a female user share of 33%, lower than Google (48%), Quora (54%), and Wikipedia (52%).<sup>4</sup> Runway, a popular video generation tool, has an even lower female user share at 21%. Midjourney, the most popular image generation tool in our sample, bucks this trend with 52% female users.

> 4The gender disparity in ChatGPT adoption is also found in Humlum and Vestergaard (2024). In their Danish survey, female white-collar workers are 20.6% less likely to use ChatGPT compared to their male counterparts within the same occupation categories.

17
<!-- Start of picture text --> GenAlI-Chatbot GenAl-Image GenAlI-Video Others<br>70<br>= 50<br>8®a<br>gB8<br>2Se<br>2 30<br>10 | |<br>aX%Y®®% “SY‘yg, hoy,“Sng,—%,GOPe %es.my&“% &“Ye,%, MeO&Ge,% ousYeGe, %,jp, “ay &Ge 2"%,, %Yx“eza% b%, & |%%,<br>6% Sey i i i a<br><!-- End of picture text -->
<!-- Start of picture text --> 40 |<br>30<br>5 @@TM Male users<br>é "Female users<br>20<br>10 Fi I i<br>ie || ee ||P i<br>0 29.3595 Xp Sp Oy BEIEIGIR I TAIT, SRI,<br>Sage = “tg ae Og<br>Chatbot GenAl Image GenAl Video GenAl Google<br><!-- End of picture text -->
<!-- Start of picture text --> 50<br>40<br>30 “® Chatbot GenAl<br>5 = Image GenAl<br>é ~ Video GenAl<br>20 ~~ Google<br>10<br>i)<br> |<br>No school High shool and below College Post-graduate<br><!-- End of picture text -->
<!-- Start of picture text --> 140<br>g5 120 x ——~_e“<br>8= a ® . » 4 4 \ © ChatGPT<br>5§ 100 LX= /\\~ 5 /\ -_.9-2-8. Se/\\~ oyoa © Google<br>° New, \, Ni s KANG 0 —e- Netflix<br>i “A ~~” NY p =e= YouTube<br>2 80 " b<br>aa PF) g<br>60Otapr2024a 08apr2024 18apr2024 a22apr2024 29apr2024 |<br><!-- End of picture text -->

<!-- Start of picture text --> 140<br>8§ 120 - ee<br>&S ° axA, of.| . R I =e ChatGPT<br>5x 100 4 : 4 ; \yfp 4 © Google<br>° . \p Ah, \» \ } . -©- Netflix<br>3 ~o—-4 oe Ne? =©= YouTube<br>3 80 ! 4 INS<br>27 & & F } ©<br>60Otapr2024a|08apr2024 18apr2024 22apr2024 29apr2024<br><!-- End of picture text -->
<!-- Start of picture text --> Nightcafe A Quora A YouTube<br>80 @ Lechat © Gemini 4 Google<br>© Deepal A Wikipedia<br>© Easy-peasya CraiyorTM<br>@ Phind<br>=5S 60 + FHMeeSealyBS © Blaxreetyntyesiacat 0 oe, ° Chatbot GenAl<br>g @GenmoAl Bigzi s cnacer<br>=S Designs Al Gamma® Perplexity = Image GenAl<br>£ 40 * Video GenAl<br>—5o Metleta image © Writesonit© RuNaysijouneyLeonardo© Copilot aOther websitesi<br>3<br>© OpusClip<br>20 @ Invideo<br>@ Kimi chatbot<br>0 © Boittyaek© SpaIK TAH Einie CanindB, MaxAL<br>aa—WDMIIT oY]<br>4 6 8 10 12<br>log10(Monthly traffic)<br><!-- End of picture text -->
<!-- Start of picture text --> 2 / ~~ i ° cd c \ 4 + ~<br>ae : 3 yy <a See ns ot ws :<br>7 Pre: cates<br>~ . oa. 2<br>a “a ee.<br>VISITS (MILLIONS)482.9 ~ oSoi € De7 Na: fyweg<br>0.16 \ 6,<br>ne an.)<br>IBRD 48264 |<br><!-- End of picture text -->
# Table 2: Ranking of ChatGPT traffic, March 2024

|**No.**|**Economy**|**Monthly**<br>**trafc,**<br>**million**<br>**visits**|**Share**<br>**i**<br>**global**<br>**trafc, **|**n**<br>**%**|**No.**|**Economy**|**Monthly**<br>**trafc per**<br>**internet**<br>**user**|**No.**|**Economy**|**Monthly**<br>**trafc**<br>**per**<br>**person**<br>**aged 15-64**|
|---|---|---|---|---|---|---|---|---|---|---|
|**1**|United States|482.9|20.6||**1**|Singapore|2.67|**1**|Brunei Darussalam|3.82|
|**2**|India|260.0|11.1||**2**|Brunei Darussalam|2.67|**2**|Singapore|3.51|
|**3**|Brazil|159.3|6.80||**3**|Estonia|2.12|**3**|British Virgin Islands|3.28|
|**4**|Philippines|94.38|4.03||**4**|Canada|1.92|**4**|Estonia|3.07|
|**5**|Indonesia|88.37|3.77||**5**|Moldova|1.88|**5**|Guam|2.90|
|**6**|Canada|70.04|2.99||**6**|Suriname|1.71|**6**|Canada|2.71|
|**7**|Germany|67.99|2.90||**7**|Iceland|1.69|**7**|Iceland|2.52|
|**8**|United Kingdom|62.16|2.65||**8**|Australia|1.61|**8**|Australia|2.32|
|**9**|Mexico|58.53|2.50||**9**|United States|1.54|**9**|United States|2.23|
|**10**|France|54.95|2.35||**10**|Malta|1.50|**10**|Netherlands|2.18|
|**11**|Viet Nam|54.66|2.33||**11**|Netherlands|1.50|**11**|Malta|2.16|
|**12**|Colombia|44.84|1.91||**12**|Norway|1.33|**12**|Norway|2.03|
|**13**|Japan|44.78|1.91||**13**|Switzerland|1.32|**13**|Tuvalu|2.02|
|**14**|Spain|44.78|1.91||**14**|Liechtenstein|1.22|**14**|Switzerland|1.99|
|**15**|Australia|39.71|1.70||**15**|Bulgaria|1.22|**15**|French Polynesia|1.96|
|**16**|Taiwan, China|30.76|1.31||**16**|Luxembourg|1.21|**16**|Suriname|1.95|
|**17**|Italy|29.69|1.27||**17**|Panama|1.20|**17**|Cayman Islands|1.88|
|**18**|Malaysia|28.18|1.20||**18**|Philippines|1.18|**18**|Moldova|1.84|
|**19**|Poland|28.01|1.20||**19**|Colombia|1.18|**19**|Liechtenstein|1.82|
|**20**|Pakistan|27.55|1.18||**20**|New Zealand|1.09|**20**|New Zealand|1.74|
|**21**|T ̈urkiye|26.55|1.13||**21**|St. Kitts and Nevis|1.09|**21**|Luxembourg|1.72|
|**22**|Netherlands|24.94|1.07||**22**|Sweden|1.08|**22**|Sweden|1.65|
|**23**|Argentina|22.99|0.98||**23**|Finland|1.05|**23**|Gibraltar|1.61|
|**24**|Ukraine|20.81|0.89||**24**|United Arab Emirates|1.03|**24**|Bulgaria|1.61|
|**25**|Peru|20.81|0.89||**25**|Dominica|1.03|**25**|Finland|1.59|
|**26**|Russian Federation|19.80|0.85||**26**|Belgium|1.02|**26**|Belgium|1.54|
|**27**|Korea, Rep.|19.57|0.84||**27**|Czechia|1.02|**27**|Denmark|1.49|
|**28**|Chile|15.06|0.64||**28**|Latvia|1.01|**28**|Ireland|1.48|
|**29**|Singapore|14.86|0.63||**29**|Denmark|0.98|**29**|Latvia|1.48|
|**30**|Morocco|14.25|0.61||**30**|Portugal|0.98|**30**|United Kingdom|1.47|
<!-- Start of picture text --> 100<br>80 mm US<br>mmm India<br>mm Brazil<br>x «0 Ne mm Philippines<br>8 | Indonesia<br>ng mm Canada<br>cae | |YS m mmm GermanyMexicoUK<br>France<br>20 Others<br>02022m11oo2023m1 2023m3oo2023m5oo2023m7 2023m9 2023m11 2024m1 2024m3<br><!-- End of picture text -->

<!-- Start of picture text --> 100<br>90<br>80<br>0<br>+0 mm HICs<br>PS == UMICs<br>8 50<br>ng “ =m m L ICsMICs<br>30<br>20<br>10<br>0 2023m1———_.aoSerre renee2023m4re _T—_>2023m7 2023m10 2024m1<br><!-- End of picture text -->
<!-- Start of picture text --> fT)<br>Indiaig United States = 100<br>Brazil<br>Indonesia<br>Philippines<br>TM GenAl Traffic<br>‘Search Engine Traffic<br>Canada<br>TM Electricity Consumption<br>TM GDP<br>Germany<br>Mexico<br>United Kingdom<br>France<br> |<br>0 20 40 60 80 100<br>Percentage relative to US values<br><!-- End of picture text -->
<!-- Start of picture text --> 60<br>UMIC+LMIC+LIC<br>50<br>=== Chatbot GenAl<br>40<br>5 == Image GenAl<br>oI :<br>a LMIC+LIC Video GenAl<br>30<br>== Google+Bing+Yahoo<br>. > ~ aa<br>10<br>2023m1Too2023m4 2023m7M——2023m10 2024m1<br><!-- End of picture text -->
<!-- Start of picture text --> — C r ss a . e<br>_ TRAFFIC PER USER: ' WA, .<br>2.66 X : 5 wi Q N<br>1.2 ]<br>08 ) g<br>. 0 qLjy 7"w &<br>IBRD 48263 |<br>JULY 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 3<br>a=) © Brunei © Singapore<br>N<br>=2<br>=os © Estonia<br>=3 2 Moldova © Canada<br>3 Suriname @ Iceland<br>A emata «SUS© Australia<br>= ee<br>oa © Philippines . . °<br>2 1 ° o%s° “a0° ° e<br>5 Brazil © 8 oe 8 RRS many<br>> ee e<br>=£ e e e Mexico?ee @e e<br>5 e ee? Indonesia eo 20?<br>Ss @e India ge ee ee*<br>2 oe Sa5 °<br>0 ee ° aA ot, & 08 °.<br>aa |<br>6 8 10 12<br>In(GDP per capita, 2022)<br>e High-income economies Upper-middle-income economies<br>e Lower-middle-income economies Low-income economies<br><!-- End of picture text -->
<!-- Start of picture text --> 3 3<br>Brunei © Singapore Brunei © Singapore<br>55<br>gg<br>3 Estonia s © Estonia<br>§? # Moldova Canada 5? e868<br>Ss ® Suriname # lebland Ss Suriname<br>3 © Augipla Natta 5 © Australia ewata © US<br>=#2g24= © Philippines° . .@ © - oewage >oeFT. feet. ¢ URSeeGomanfiane®e FS2g245 bd :: go cerePerey.*PtfippinesoS Kamae°. © oo * .<br>= ee ss 2 ry te Mexicog . . me “s . ° . = e . esse fe 4e etexiog e ° ¢€ .<br>Poery Ipdonesia @ a. 7. *. 6 ° . oe.Seg© © S ypdonesia,eet, eea . e<br>=0 20 Oot40 60 —————_?p-1 2 1.3 4>$£ 15<br>Fixed broadband penetration rate (%) In(Fixed broadband download speed, Mbps)<br>33<br>©Brunei ® Singapore © Singapore!<br>55<br>=3 @ Estonia Pad3 Estonia<br>€ © Suriname Iceland ss sustvata E « Iceland<br>2g © Malta°.2 ous £g eiMata°« .<br>2!3eS.° ws # Philippintsrs) 2 ! © Philippines wai % adimany oe 8 .°Epa<br>g . et “SO s are rad . ee<br>fe 6 0e Be oeee © e%e%Nexicngge indonesia 5 © india . oe es:% a Somerte‘elngonesiaoe e .<br>rt er oe a “ o 8%e . : .<br>——0 >.5 10 —@—§ 15 —.0>20 40 60<br>In(Digitally delivered service exports) %Population with intermediate digital skills<br>e High-income economies e Upper-middle-income economies<br>e Lower-middle-income economies ° Low-income economies<br><!-- End of picture text -->
economy _i_ , %Internet users _i_ , log GDP per capita ln(GDPpc _i_ ), and youth (age 20-35) population share, Youth share _i_ . This specification allows us to control for the scale effect and interpret the results as the correlation of other independent variables with the intensity of generative AI usage. We include the potential channels that may affect generative AI usage at the country level, indicated as vector _Xi_ . We also include the month fixed effects _δt_ to control for seasonality as well as global macro conditions in this panel regression specification. Notably, we use the GDP per capita and other indicators from 2022 or the most recent available data prior to 2022. This selection is driven by data availability and aims to mitigate reverse causality where the widespread adoption of generative AI adoption might influence key economic indicators, such as GDP per capita and exports of digitally delivered services. The summary statistics for the dependent variables are presented in Table A1, and the summary statistics and data source of independent variables are listed in Appendix Table A2.

Table 3 juxtaposes factors affecting the usage of ChatGPT and Google as a comparison. In Column (1), we regress ChatGPT’s monthly traffic (log) on four basic variables of an economy: GDP per capita, the percentage of population using the internet, population, and the percentage of the population aged 20-35. The coefficient of log population is slightly over 1, indicating an approximate unity scale effect. Internet user share is strongly related to monthly visits. A one percentage point increase in internet user share is associated with a 3.8% rise in ChatGPT visits. This coefficient is above 1%, implying a potential spillover effect among internet users: ChatGPT spreads faster and is more intensively used as the internet user base grows. We include the percentage of the population aged 20-35 to control for the demographic structure of an economy. The positive coefficient is consistent with the previous descriptive evidence that youth population is the main user of ChatGPT. Before adding other factors, the coefficient of log GDP per capita reads 0.538. The same specification is applied to Google’s monthly traffic in Column (6), yielding results very close to Column (1), except that the younger population is negatively correlated with Google’s traffic, as Google users are distributed more evenly across age groups (Figure 6).

We group other control variables into three categories: (1) digital infrastructure, (2) economy and industry structure, and (3) human capital. We explore the correlation between each category and ChatGPT’s usage in Columns (2)-(4) of Table 3. Fixed broadband penetration and median download speed are both positively correlated with ChatGPT usage. The significant correlation 31
highlights the fundamental role of digital infrastructure availability and quality as the key supplyside barriers to generative AI usage. Column (3) shows that trade openness, service sector share of GDP, and digitally delivered service exports in total exports are all positively and significantly associated with ChatGPT usage. Column (4) reports positive and significant coefficients of human capital variables: dummy for English speaking country, literacy rate, percentage of the population with intermediate digital skill.<sup>7</sup> We choose intermediate digital skills as ChatGPT is most relevant for workers with such skills.

Column (5) includes all three sets of variables together and shows similar and robust results as the previous columns. The coefficient of log GDP per capita becomes weaker as we include more factors into regressions. It turns insignificant and close to zero in Column (5). Together with the increased adjusted R squared from Column (1) to Column (5), this pattern indicates that the correlation between income level and ChatGPT usage can be broken down into our identified three areas.

The comparison with Google yields additional insights. Specialization in digitally deliverable services, white collar workers, English fluency and digital skills are highly correlated with ChatGPT traffic but not Google traffic. This corroborates our previous findings that ChatGPT is by and large used by white collar workers in tradable, knowledge-intensive industries.

The strong correlation between country-level English fluency and ChatGPT usage can be attributed to several interconnected factors. Primarily, ChatGPT’s training on predominantly English-language data results in better performance and user experience in English. This is compounded by the internet’s English-centric content and the tech industry’s use of English as a lingua franca. Countries with higher English fluency often have education systems emphasizing technology and digital literacy, fostering a more tech-savvy population primed for AI adoption. Early adoption patterns, initially driven by English-language marketing and outsourcing demand emanating from advanced English-speaking countries, may have set a precedent for ongoing usage trends. Additionally, many professional fields that benefit from ChatGPT, such as programming and research, predominantly operate in English, further skewing usage towards English-proficient populations.

> 7This measure is based on the question “% creating electronic presentations with presentation software” in ITU ICT SDG indicator 4.4.1. Digital skills are classified into three levels: basic, intermediate, and advanced, referring to foundational digital abilities, goal-specific technological uses, and specialized ICT professional expertise, respectively (ITU 2020).

32
Table 3: Correlation between monthly traffic of ChatGPT and Google with other factors

||(1)|(2)|(3)<br>ChatGPT|(4)|(5)|(6)|(7)|(8)<br>Google|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|
|ln(GDP per capita)|0.538<sup>_∗∗∗_</sup>|0.318<sup>_∗∗∗_</sup>|0.431<sup>_∗∗∗_</sup>|0.292<sup>_∗∗∗_</sup>|0.030|0.566<sup>_∗∗∗_</sup>|0.289<sup>_∗∗∗_</sup>|0.569<sup>_∗∗∗_</sup>|0.353<sup>_∗∗∗_</sup>|0.198<sup>_∗∗∗_</sup>|
||(0.042)|(0.062)|(0.054)|(0.057)|(0.074)|(0.028)|(0.041)|(0.033)|(0.047)|(0.053)|
|Internet user share (% pop)|0.038<sup>_∗∗∗_</sup>|0.035<sup>_∗∗∗_</sup>|0.036<sup>_∗∗∗_</sup>|0.031<sup>_∗∗∗_</sup>|0.028<sup>_∗∗∗_</sup>|0.044<sup>_∗∗∗_</sup>|0.040<sup>_∗∗∗_</sup>|0.043<sup>_∗∗∗_</sup>|0.035<sup>_∗∗∗_</sup>|0.033<sup>_∗∗∗_</sup>|
||(0.002)|(0.003)|(0.003)|(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|ln(Population)|1.172<sup>_∗∗∗_</sup>|1.146<sup>_∗∗∗_</sup>|1.211<sup>_∗∗∗_</sup>|1.185<sup>_∗∗∗_</sup>|1.201<sup>_∗∗∗_</sup>|1.131<sup>_∗∗∗_</sup>|1.094<sup>_∗∗∗_</sup>|1.161<sup>_∗∗∗_</sup>|1.133<sup>_∗∗∗_</sup>|1.133<sup>_∗∗∗_</sup>|
||(0.021)|(0.021)|(0.023)|(0.021)|(0.024)|(0.010)|(0.010)|(0.013)|(0.011)|(0.012)|
|20-34 age group share (% pop)|0.030<sup>_∗∗∗_</sup>|0.054<sup>_∗∗∗_</sup>|0.046<sup>_∗∗∗_</sup>|0.026<sup>_∗∗∗_</sup>|0.055<sup>_∗∗∗_</sup>|-0.024<sup>_∗∗∗_</sup>|0.003|-0.009|-0.017<sup>_∗∗∗_</sup>|0.011|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.008)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|ln(Fixed broadband download speed)||0.244<sup>_∗∗∗_</sup>|||0.205<sup>_∗∗∗_</sup>||0.365<sup>_∗∗∗_</sup>|||0.282<sup>_∗∗∗_</sup>|
|||(0.046)|||(0.044)||(0.033)|||(0.031)|
|Fixed broadband penetration (%)||0.015<sup>_∗∗∗_</sup>|||0.010<sup>_∗_</sup>||0.016<sup>_∗∗∗_</sup>|||0.010<sup>_∗∗∗_</sup>|
|||(0.005)|||(0.005)||(0.003)|||(0.003)|
|Trade share of GDP (%)|||0.0011<sup>_∗∗_</sup>||0.0011<sup>_∗∗_</sup>|||0.0017<sup>_∗∗∗_</sup>||0.0013<sup>_∗∗∗_</sup>|
||||(0.0006)||(0.0006)|||(0.0005)||(0.0004)|
|Service sector share of GDP (%)|||0.020<sup>_∗∗∗_</sup>||0.013<sup>_∗∗∗_</sup>|||0.024<sup>_∗∗∗_</sup>||0.018<sup>_∗∗∗_</sup>|
||||(0.003)||(0.003)|||(0.002)||(0.002)|
|Digitally delivered service exports (% exports)|||0.0006<sup>_∗_</sup>||0.0005|||0.0002||0.0001|
||||(0.0003)||(0.0003)|||(0.0003)||(0.0003)|
|Share of white-collar + sales jobs (%)|||0.007||0.017<sup>_∗∗_</sup>|||-0.012<sup>_∗∗∗_</sup>||-0.001|
||||(0.007)||(0.007)|||(0.004)||(0.003)|
|English speaking country (=1)||||0.188<sup>_∗∗_</sup>|0.224<sup>_∗∗∗_</sup>||||-0.048|0.004|
|||||(0.078)|(0.077)||||(0.047)|(0.045)|
|Literacy rate (%)||||0.027<sup>_∗∗∗_</sup>|0.023<sup>_∗∗∗_</sup>||||0.029<sup>_∗∗∗_</sup>|0.022<sup>_∗∗∗_</sup>|
|||||(0.004)|(0.004)||||(0.002)|(0.002)|
|Digital skill: intermediate (% pop)||||0.007<sup>_∗∗_</sup>|0.004||||0.005<sup>_∗_</sup>|0.000|
|||||(0.003)|(0.003)||||(0.003)|(0.003)|
|Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<br>|2136|2136|2136|2136|2136|2136|2136|2136|2136|2136|
|Adjusted _R_<sup>2</sup>|0.7617|0.7656|0.7672|0.7697|0.7761|0.8966|0.9045|0.9052|0.9064|0.9156|

_Note:_ Robust standard errors are in parentheses. The dependent variable in this table is the monthly visits of ChatGPT or Google from April 2023 to March 2024. Missing values in independent variables are imputed with predictions from the corresponding auxiliary cross-country regression on * ** *** ln(GDP per capita) and ln(Population). _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.
Table 4: Correlation between monthly visits of GenAI and Search Engine with other factors

||(1)<br>Cha|(2)<br>tbot|(3)<br>Im|(4)<br>age|(5)<br>Vi|(6)<br>deo|(7)<br>Search|(8)<br> engine|
|---|---|---|---|---|---|---|---|---|
|Dependent variable|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|ln(Visits)|
|ln(GDP per capita)|0.567<sup>_∗∗∗_</sup>|0.056|0.651<sup>_∗∗∗_</sup>|0.229<sup>_∗∗_</sup>|0.823<sup>_∗∗∗_</sup>|0.586<sup>_∗∗∗_</sup>|0.571<sup>_∗∗∗_</sup>|0.202<sup>_∗∗∗_</sup>|
||(0.041)|(0.071)|(0.066)|(0.107)|(0.068)|(0.117)|(0.028)|(0.052)|
|Internet user share (% pop)|0.036<sup>_∗∗∗_</sup>|0.025<sup>_∗∗∗_</sup>|0.062<sup>_∗∗∗_</sup>|0.042<sup>_∗∗∗_</sup>|0.051<sup>_∗∗∗_</sup>|0.039<sup>_∗∗∗_</sup>|0.043<sup>_∗∗∗_</sup>|0.032<sup>_∗∗∗_</sup>|
||(0.002)|(0.003)|(0.004)|(0.005)|(0.004)|(0.005)|(0.002)|(0.002)|
|ln(Population)|1.173<sup>_∗∗∗_</sup>|1.206<sup>_∗∗∗_</sup>|1.404<sup>_∗∗∗_</sup>|1.369<sup>_∗∗∗_</sup>|1.532<sup>_∗∗∗_</sup>|1.507<sup>_∗∗∗_</sup>|1.126<sup>_∗∗∗_</sup>|1.129<sup>_∗∗∗_</sup>|
||(0.021)|(0.024)|(0.025)|(0.028)|(0.023)|(0.029)|(0.010)|(0.012)|
|20-34 age group share (% pop)|0.035<sup>_∗∗∗_</sup>|0.061<sup>_∗∗∗_</sup>|0.038<sup>_∗∗∗_</sup>|0.104<sup>_∗∗∗_</sup>|0.007|0.068<sup>_∗∗∗_</sup>|-0.024<sup>_∗∗∗_</sup>|0.010|
||(0.008)|(0.008)|(0.014)|(0.015)|(0.015)|(0.016)|(0.007)|(0.007)|
|ln(Fixed broadband download speed)||0.215<sup>_∗∗∗_</sup>||0.529<sup>_∗∗∗_</sup>||0.435<sup>_∗∗∗_</sup>||0.284<sup>_∗∗∗_</sup>|
|||(0.040)||(0.076)||(0.091)||(0.030)|
|Fixed broadband penetration (%)||0.007||0.016<sup>_∗∗_</sup>||0.019<sup>_∗∗∗_</sup>||0.010<sup>_∗∗∗_</sup>|
|||(0.005)||(0.007)||(0.007)||(0.003)|
|Trade share of GDP (%)||0.0018<sup>_∗∗∗_</sup>||0.0015<sup>_∗_</sup>||0.0007||0.0013<sup>_∗∗∗_</sup>|
|||(0.0005)||(0.0008)||(0.0011)||(0.0004)|
|Service sector share of GDP (%)||0.015<sup>_∗∗∗_</sup>||0.024<sup>_∗∗∗_</sup>||0.029<sup>_∗∗∗_</sup>||0.018<sup>_∗∗∗_</sup>|
|||(0.003)||(0.005)||(0.005)||(0.002)|
|Digitally delivered service exports (% exports)||0.0007<sup>_∗∗_</sup>||-0.0005||-0.0007||0.0001|
|||(0.0003)||(0.0006)||(0.0007)||(0.0003)|
|Share of white-collar + sales jobs (%)||0.018<sup>_∗∗_</sup>||-0.003||-0.010||-0.001|
|||(0.007)||(0.007)||(0.007)||(0.003)|
|English speaking country (=1)||0.176<sup>_∗∗_</sup>||-0.298<sup>_∗∗∗_</sup>||-0.195<sup>_∗_</sup>||0.012|
|||(0.073)||(0.109)||(0.117)||(0.044)|
|Literacy rate (%)||0.025<sup>_∗∗∗_</sup>||0.039<sup>_∗∗∗_</sup>||0.020<sup>_∗∗∗_</sup>||0.021<sup>_∗∗∗_</sup>|
|||(0.004)||(0.006)||(0.006)||(0.002)|
|Digital skill: intermediate (% pop)||0.005||-0.014<sup>_∗∗∗_</sup>||-0.018<sup>_∗∗∗_</sup>||0.001|
|||(0.003)||(0.005)||(0.005)||(0.003)|
|Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<br>|2136|2136|2136|2136|2136|2136|2136|2136|
|Adjusted _R_<sup>2</sup>|0.7736|0.7899|0.7271|0.7556|0.7281|0.7446|0.8984|0.9175|

_Note:_ Robust standard errors are in parentheses. The dependent variable in this table is the aggregated monthly visits of chatbots, image GenAI, video GenAI, and search engines (Google, Bing, and Yahoo) from April 2023 to March 2024. Missing values in independent variables are imputed * ** *** with predictions from the corresponding auxiliary cross-country regression on ln(GDP per capita) and ln(Population). _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.
We then study how these country-level factors affect traffic to different types of generative AI tools and search engines,<sup>8</sup> and present regression results in Table 4. Results in Columns (1)-(2) and (7)-(8) are close to the corresponding columns in Table 3, as ChatGPT dominates traffic to all chatbots and Google for search engines. Coefficient on GDP per capita increases from Chatbot to Image to Video, reflecting the concentration of users for these tools in richer economies. Also, fixed broadband download speed has a larger impact on image and video generation tools’ traffic, highlighting the need for high speed internet to deploy image and video generation applications. Specialization in high-skill tradable services, white collar jobs, and English fluency are also not significantly correlated with image and video generation tool usage.

Tables 5 and 6 apply the same framework on the other two website usage indicators. The correlations between monthly unique users and other factors shown in Table 5 are very similar to those in Table 3. Digitally delivered service exports become more significantly correlated to ChatGPT’s traffic, strengthening our finding that specialization in skilled tradable services drives demand for ChatGPT usage at work.

In addition to the extensive margin of ChatGPT’s usage–the number of people and visits, we are also interested in the intensive margin–average time spent on ChatGPT per visit. Table 6 reports the regression results on factors affecting the intensive margin. In contrast to the positive correlation between GDP per capita and ChatGPT’s monthly visits, the average duration per visit negatively correlates to GDP per capita in Column (1). However, the magnitude of the intensive margin coefficient is about one-tenth of that of the extensive margin, guaranteeing that the total usage time is positively correlated to income level. Fixed broadband penetration, English-speaking country dummy, and literacy rate remain significant and positive. Users in English-speaking countries tend to spend 8-9% more time on ChatGPT per visit. The overall explaining power of all variables together is lower, indicated by the lower adjusted R squared. This is partly because of less variation in the intensive margin across economies. Table A1 shows that the standard deviation of time per visit is about half of the mean, while the standard deviation of visits or users per internet user about equals the means. This indicates that the digital divide in generative AI is primarily at the extensive margin–how many people are accessing ChatGPT. Once people have used ChatGPT, the intensive margin–time spent on the tool per visit–have lower variations.

> 8This category contains three globally used search engines, Google, Bing, and Yahoo.

35
Table 5: Correlation between monthly users of ChatGPT and Google with other factors

||(1)|(2)|(3)<br>ChatGPT|(4)|(5)|(6)|(7)|(8)<br>Google|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable|ln(Users)|ln(Users)|ln(Users)|ln(Users)|ln(Users)|ln(Users)|ln(Users)|ln(Users)|ln(Users)|ln(Users)|
|ln(GDP per capita)|0.520<sup>_∗∗∗_</sup>|0.307<sup>_∗∗∗_</sup>|0.420<sup>_∗∗∗_</sup>|0.272<sup>_∗∗∗_</sup>|0.039|0.455<sup>_∗∗∗_</sup>|0.236<sup>_∗∗∗_</sup>|0.437<sup>_∗∗∗_</sup>|0.288<sup>_∗∗∗_</sup>|0.156<sup>_∗∗∗_</sup>|
||(0.038)|(0.057)|(0.048)|(0.048)|(0.065)|(0.028)|(0.040)|(0.032)|(0.042)|(0.051)|
|Internet user share (% pop)|0.035<sup>_∗∗∗_</sup>|0.033<sup>_∗∗∗_</sup>|0.034<sup>_∗∗∗_</sup>|0.031<sup>_∗∗∗_</sup>|0.028<sup>_∗∗∗_</sup>|0.040<sup>_∗∗∗_</sup>|0.037<sup>_∗∗∗_</sup>|0.039<sup>_∗∗∗_</sup>|0.034<sup>_∗∗∗_</sup>|0.033<sup>_∗∗∗_</sup>|
||(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|ln(Population)|1.133<sup>_∗∗∗_</sup>|1.111<sup>_∗∗∗_</sup>|1.176<sup>_∗∗∗_</sup>|1.152<sup>_∗∗∗_</sup>|1.174<sup>_∗∗∗_</sup>|1.098<sup>_∗∗∗_</sup>|1.070<sup>_∗∗∗_</sup>|1.126<sup>_∗∗∗_</sup>|1.105<sup>_∗∗∗_</sup>|1.106<sup>_∗∗∗_</sup>|
||(0.018)|(0.018)|(0.020)|(0.018)|(0.021)|(0.009)|(0.010)|(0.011)|(0.010)|(0.011)|
|20-34 age group share (% pop)|0.017<sup>_∗∗_</sup>|0.042<sup>_∗∗∗_</sup>|0.034<sup>_∗∗∗_</sup>|0.008|0.039<sup>_∗∗∗_</sup>|-0.002|0.020<sup>_∗∗∗_</sup>|0.012<sup>_∗∗_</sup>|-0.001|0.025<sup>_∗∗∗_</sup>|
||(0.008)|(0.008)|(0.007)|(0.008)|(0.007)|(0.006)|(0.007)|(0.006)|(0.006)|(0.006)|
|ln(Fixed broadband download speed)||0.208<sup>_∗∗∗_</sup>|||0.175<sup>_∗∗∗_</sup>||0.285<sup>_∗∗∗_</sup>|||0.228<sup>_∗∗∗_</sup>|
|||(0.041)|||(0.038)||(0.034)|||(0.032)|
|Fixed broadband penetration (%)||0.016<sup>_∗∗∗_</sup>|||0.011<sup>_∗∗_</sup>||0.013<sup>_∗∗∗_</sup>|||0.009<sup>_∗∗∗_</sup>|
|||(0.004)|||(0.005)||(0.003)|||(0.003)|
|Trade share of GDP (%)|||0.0009<sup>_∗_</sup>||0.0011<sup>_∗∗_</sup>|||0.0005||0.0003|
||||(0.0005)||(0.0005)|||(0.0004)||(0.0004)|
|Service sector share of GDP (%)|||0.024<sup>_∗∗∗_</sup>||0.017<sup>_∗∗∗_</sup>|||0.026<sup>_∗∗∗_</sup>||0.021<sup>_∗∗∗_</sup>|
||||(0.002)||(0.003)|||(0.002)||(0.002)|
|Digitally delivered service exports (% exports)|||0.0007<sup>_∗∗_</sup>||0.0005<sup>_∗_</sup>|||-0.000||-0.0001|
||||(0.0003)||(0.0003)|||(0.0003)||(0.0003)|
|Share of white-collar + sales jobs (%)|||0.003||0.012<sup>_∗∗_</sup>|||-0.010<sup>_∗∗∗_</sup>||-0.001|
||||(0.006)||(0.006)|||(0.003)||(0.003)|
|English speaking country (=1)||||0.306<sup>_∗∗∗_</sup>|0.333<sup>_∗∗∗_</sup>||||0.054|0.078<sup>_∗_</sup>|
|||||(0.069)|(0.068)||||(0.045)|(0.043)|
|Literacy rate (%)||||0.023<sup>_∗∗∗_</sup>|0.019<sup>_∗∗∗_</sup>||||0.022<sup>_∗∗∗_</sup>|0.015<sup>_∗∗∗_</sup>|
|||||(0.003)|(0.004)||||(0.002)|(0.002)|
|Digital skill: intermediate (% pop)||||0.008<sup>_∗∗∗_</sup>|0.005<sup>_∗_</sup>||||0.003|-0.001|
|||||(0.003)|(0.003)||||(0.002)|(0.002)|
|Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<br>|2136|2136|2136|2136|2136|2136|2136|2136|2136|2136|
|Adjusted _R_<sup>2</sup>|0.7897|0.7936|0.7979|0.7982|0.8065|0.8923|0.8979|0.9021|0.8990|0.9087|

_Note:_ Robust standard errors are in parentheses. The dependent variable in this table is the monthly visits of ChatGPT or Google from April 2023 to March 2024. Missing values in independent variables are imputed with predictions from the corresponding auxiliary cross-country regression on * ** *** ln(GDP per capita) and ln(Population). _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.
Table 6: Correlation between duration per visit of ChatGPT and Google with other factors

||(1)|(2)|(3)<br>ChatGPT|(4)|(5)|(6)|(7)|(8)<br>Google|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable|ln(Time)|ln(Time)|ln(Time)|ln(Time)|ln(Time)|ln(Time)|ln(Time)|ln(Time)|ln(Time)|ln(Time)|
|ln(GDP per capita)|-0.057<sup>_∗∗_</sup>|-0.110<sup>_∗∗∗_</sup>|-0.046|-0.081<sup>_∗∗∗_</sup>|-0.104<sup>_∗∗∗_</sup>|0.039<sup>_∗∗∗_</sup>|0.030<sup>_∗∗∗_</sup>|0.026<sup>_∗∗∗_</sup>|-0.002|-0.020<sup>_∗∗_</sup>|
||(0.025)|(0.027)|(0.030)|(0.031)|(0.037)|(0.005)|(0.007)|(0.008)|(0.006)|(0.010)|
|Internet user share (% pop)|0.009<sup>_∗∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|0.010<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|-0.000|-0.000|-0.001<sup>_∗∗_</sup>|-0.001<sup>_∗∗_</sup>|-0.001<sup>_∗∗∗_</sup>|
||(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|ln(Population)|0.092<sup>_∗∗∗_</sup>|0.089<sup>_∗∗∗_</sup>|0.091<sup>_∗∗∗_</sup>|0.097<sup>_∗∗∗_</sup>|0.093<sup>_∗∗∗_</sup>|0.005|0.007<sup>_∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|0.010<sup>_∗∗∗_</sup>|0.016<sup>_∗∗∗_</sup>|
||(0.011)|(0.011)|(0.011)|(0.011)|(0.012)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|20-34 age group share (% pop)|0.012<sup>_∗∗∗_</sup>|0.019<sup>_∗∗∗_</sup>|0.012<sup>_∗∗_</sup>|0.009<sup>_∗∗_</sup>|0.017<sup>_∗∗∗_</sup>|-0.007<sup>_∗∗∗_</sup>|-0.004<sup>_∗∗∗_</sup>|-0.005<sup>_∗∗∗_</sup>|-0.010<sup>_∗∗∗_</sup>|-0.006<sup>_∗∗∗_</sup>|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|ln(Fixed broadband download speed)||0.019|||0.014||-0.029<sup>_∗∗∗_</sup>|||-0.034<sup>_∗∗∗_</sup>|
|||(0.022)|||(0.022)||(0.006)|||(0.006)|
|Fixed broadband penetration (%)||0.006<sup>_∗∗∗_</sup>|||0.006<sup>_∗∗∗_</sup>||0.003<sup>_∗∗∗_</sup>|||0.002<sup>_∗∗∗_</sup>|
|||(0.002)|||(0.002)||(0.001)|||(0.001)|
|Trade share of GDP (%)|||-0.0002||-0.0002|||0.0002<sup>_∗∗∗_</sup>||0.0003<sup>_∗∗∗_</sup>|
||||(0.0002)||(0.0002)|||(0.0001)||(0.0001)|
|Service sector share of GDP (%)|||0.003||0.001|||0.003<sup>_∗∗∗_</sup>||0.002<sup>_∗∗∗_</sup>|
||||(0.002)||(0.002)|||(0.0005)||(0.0005)|
|Digitally delivered service exports (% exports)|||-0.0001||-0.0002|||-0.00002||-0.00002|
||||(0.0002)||(0.0002)|||(0.0001)||(0.0001)|
|Share of white-collar + sales jobs (%)|||-0.003||-0.001|||0.001||0.002<sup>_∗_</sup>|
||||(0.002)||(0.003)|||(0.001)||(0.001)|
|English speaking country (=1)||||0.080<sup>_∗∗_</sup>|0.089<sup>_∗∗_</sup>||||0.090<sup>_∗∗∗_</sup>|0.092<sup>_∗∗∗_</sup>|
|||||(0.035)|(0.035)||||(0.009)|(0.009)|
|Literacy rate (%)||||0.005<sup>_∗∗∗_</sup>|0.004<sup>_∗∗_</sup>||||0.004<sup>_∗∗∗_</sup>|0.004<sup>_∗∗∗_</sup>|
|||||(0.002)|(0.002)||||(0.000)|(0.000)|
|Digital skill: intermediate (% pop)||||-0.001|-0.003<sup>_∗∗_</sup>||||0.001<sup>_∗∗_</sup>|0.001<sup>_∗∗_</sup>|
|||||(0.001)|(0.001)||||(0.0005)|(0.0005)|
|Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<br>|2101|2101|2101|2101|2101|2136|2136|2136|2136|2136|
|Adjusted _R_<sup>2</sup>|0.1284|0.1317|0.1287|0.1351|0.1371|0.1061|0.1173|0.1286|0.1868|0.2169|

_Note:_ Robust standard errors are in parentheses. The dependent variable in this table is the monthly visits of ChatGPT or Google from April 2023 to March 2024. Missing values in independent variables are imputed with predictions from the corresponding auxiliary cross-country regression on * ** *** ln(GDP per capita) and ln(Population). _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.
# **7 Early Impacts of Generative AI on Website Traffic**

In this section, we explore the early impacts of generative AI on individuals’ online activities and the implications on the digital ecosystem. Generative AI tools can disrupt the current traffic patterns and advertising-based business models through multiple channels: First, chatbots can provide direct answers, potentially reducing the need for users to click through to multiple websites and aggregate information by themselves. This may divert traffic from traditional search engines and information websites. Second, AI-generated content may compete with human content creators, reducing traffic to blogs, news sites, and other content platforms. However, high-quality, curated content will become more valuable, benefiting some content creators that offer authoritative, novel, and unique content. Third, AI assistants could act as intermediaries, potentially reducing direct traffic to e-commerce websites. If traffic is diverted, it could significantly affect the current attention-advertising based business model. Fewer page views and clicks could shrink ad revenue for many websites. However, traditional websites and platforms will integrate generative AI features to remain competitive. New monetization models will also emerge to leverage AI capabilities. Eventually, a new balance will be struck between AI-mediated and direct web interactions.

Analysis of monthly traffic patterns across various websites reveals distinct impacts potentially correlated with the emergence and updates of ChatGPT. Figure17 compares the normalized traffic trends of three popular information-acquiring websites: Google, Wikipedia, and Quora. Despite ongoing speculation about whether Bing or ChatGPT could supplant Google as the go-to portal for internet users seeking information, we observe no clear trend in Google’s traffic before and after the launch of GPT 4. As of March 2024, Google’s traffic remains 80 times higher than ChatGPT’s (Figure 3). This substantial gap suggests that the fluctuations in Google’s traffic are more likely attributable to seasonality rather than ChatGPT’s influence. Similarly, Quora’s traffic exhibits high volatility without clear correlations to ChatGPT’s trajectory. Wikipedia’s traffic, however, was quite stable before the launch of GPT 4, but displays a downward trend since the launch of GPT 4. This shift in Wikipedia’s traffic suggests a potential migration of users towards ChatGPT for information inquiries and learning purposes.

These observations indicate that while generative AI tools like ChatGPT are making inroads in the information-seeking landscape, their impact on established platforms varies. Google’s resilience 38
<!-- Start of picture text --> 140<br>120<br>.<br>SsS iN NI<br>= 100+4----->> AT 7SS<br>E wy ChatGPT<br>® 80<br>g Quora<br>2>ia 60 — Wikipedia<br>= — Google<br>£<6<br>= 40<br>GPT 3.5 GPT 4<br>20<br>0<br>2022m7 2022m10 2023m1 2023m4 2023m7 2023m10 2024m1 2024m4<br><!-- End of picture text -->
<!-- Start of picture text --> 140<br>120<br>s ele Nee Na<br>e — ChatGPT<br>g 80<br>s —_ Google Translate<br>2 i<br>3> 60 — Notion<br>2 — Grammly<br>=40<br>GP’ AT 4<br>20<br>0<br>2022m7 2022m10 2023m1 —-2023m4_«—- 2023m7 —-2023m10_2024m1 ~—-2024m4<br><!-- End of picture text --> <!-- Start of picture text --> 140<br>120<br>3 100 fp----g52=== Eh ---------sgh ------- ee A -f--------\----f--------<br>e<br>g 80 = ChatGPT<br>s |<br>2 — Huggingface<br>3> 60 — Stackoverflow<br>2<br>= 40<br>GP’ 4<br>20<br>0<br>2022m7 —2022m10--2023m1 =. 2023m4._-——-2023m7 ~—-2023m10_2024m1 20244.<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>S 80 \<br>2<br>S<br>an<br>2o 70<br>E<br>2<br>D<br>fefo}<br>© 60<br>GP PyT-4<br>50<br>—— Google Trend: Translate<br>40 === Google Trend: Write<br>O1julz022 O10ct2022 O1jan2023 Otapr2023 O1jul2023 O10ct2023 Otjan2024 O1apr2024<br><!-- End of picture text -->
is not very clear just from observation on the figures, and there are many reasons other than the release of ChatGPT that affect the popularity of the keywords on Google. To investigate the correlation of ChatGPT usage and the Google Trend of each keyword more rigorously, we collect the Google Trends of each keyword searched in each economy and run panel data regression. We first take the monthly average of the Google Trend index to match Semrush monthly traffic of ChatGPT. For each keyword, we run the following regression: In the equation, the dependent variable GoogleTrend<sup>(keyword</sup> _it_<sup>_k_)</sup> is the Google Trend index of keyword _k_ of economy _i_ in month _t_ . The independent variable of interest is the intensity of ChatGPT usage. Specifically, ln(ChatGPT _it−_ 1) stands for the log of ChatGPT’s monthly visits per internet user, of economy _i_ in the previous month _t −_ 1. We use the one-month lag of ChatGPT usage to avoid the simultaneity problem due to the reverse impacts from Google Trend to ChatGPT. Control variables _Xit_ include monthly traffic of Google of economy _i_ in month _t_ , as well as an economyspecific trend. Google traffic controls for the overall Google search activity, and the economyspecific trend controls for the underlying trend of a certain keyword happening in the country, which may not be impacted by the usage of ChatGPT. We also control for month fixed effects _δt_ and economy fixed effects _θi_ . It is worth noting that as the dependent variable is normalized within an economy and country fixed effects are added in the regressions, the coefficient of interest, _α_ , can be interpreted as the within-economy change of popularity of a keyword searched on Google.

Figure 20 presents the regression results, showing point estimates of the effect of ChatGPT usage intensity on Google Trends for various keyword. For most keywords, the correlations are insignificant, which is expected given the early stage of generative AI adoption. However, three keywords stand out with significant correlations: “Chat” and “AI” are positively correlated with ChatGPT usage intensity. Unsurprisingly, as they directly relate to ChatGPT and generative AI, Google searches for these terms increase as ChatGPT usage intensifies.

Notably, the keyword “Learn” shows a strong negative and significant correlation with ChatGPT usage intensity. The point estimate of -2.4 indicates that a one percent increase in ChatGPT’s monthly visits per internet user in the previous month is associated with a 0.024 percentage point 42
<!-- Start of picture text --> : : Chat :<br>: : Secure :<br>: : Optimize : :<br>: Detect :<br>:: Assist :<br>: :: ComposeDiagnose : ::<br>: : Drive : :<br>:: :: ‘AutomateAnalyze :: ::<br>: : Code : :<br>: Invest :<br>: Shop :<br>: Guide : © Point estimate<br>: Pign<br>: : : == 90% confidence interval<br>: : Copk : :<br>: : Stream : : —— 95% confidence interval<br>:: :: DesigPlai :: ::<br>: Read :<br>: Build :<br>: : Translate : :<br>: : Write : :<br>: : Control : :<br>: Predict<br>: : Teach : :<br>: : Edit : :<br>: Navigate :<br>: Recognize :<br>: Learn: :<br>-4 -2 0 2 4<br>Change of Google Trends<br><!-- End of picture text -->
ing, such as “Optimize”, “Diagnose”, “Analyze”, and “Code”. These complementary effects may indicate ChatGPT’s current limitations in these areas, prompting users to verify information on Google. On the contrary, words related to language processing and simpler tasks like “Translate”, “Write”, and “Edit” show negative correlations, highlighting ChatGPT’s strengths in these areas.

While acknowledging the imperfections in our measures and results, these trends provide valuable direction for further exploration as more comprehensive data becomes available. Moreover, this analysis reinforces our confidence in the website traffic data and the insights derived from our previous sections. The observed patterns suggest a nuanced impact of ChatGPT on information-seeking behaviors, with varying effects across different types of tasks and knowledge domains.

# **8 Conclusion**

This paper leverages unconventional data, including website traffic data and Google Trends, to shed light on generative AI adoption patterns by individuals across countries. Our analysis yielded several key findings: First, we documented a proliferation of generative AI tools and their rapid adoption, noting the dominance of chatbots in the generative AI landscape, signs of market saturation and intensifying competition. As of March 2024, There are at least hundreds of generative AI tools around the world. The top 40 most visited new generative AI tools collectively have nearly 3 billion visits per month and hundreds of millions of unique users. Among the top 40 tools, chatbots occupy the top seven positions and account for a staggering 95% of the traffic, with ChatGPT alone commanding 82.5% of total traffic. Nonetheless, traffic to some generative AI tools has plateaued since mid-2023, hinting at market saturation, intensifying competition, and potential challenges for future growth.

Second, users of generative AI tools heavily skew toward young, highly educated males compared to general internet and Google users. The youth and male bias is particularly pronounced for video generation tools. While users of image and video generation tools have similar education backgrounds as Google users, chatbot users are more highly educated, likely due to frequent usage by college-educated white-collar workers for professional purposes. In contrast, usage of image and video generation tools tend to be more experimental and recreational. Generative AI tools are also primarily accessed via desktop computers and used during weekdays, indicating that they are 44
predominantly used for productivity purposes and tasks related to work and education.

Third, generative AI tools have achieved unprecedented global diffusion both in terms of scale and speed, though low-income countries are falling further behind middle-income countries. ChatGPT is used in 209 of 218 economies worldwide. The United States accounted for over 70% of total traffic for ChatGPT upon its launch, but that share dropped to 25% within one month. Traffic originating from middle-income economies surpassed that of high-income economies within six months, and middle-income economies now contribute more than half of the traffic. In addition to the US, India, Brazil, the Philippines, and Indonesia round out the top five economies in terms of ChatGPT traffic. The over-representation of middle-income economies is remarkable when put into perspective: for example, India only accounts for 14% of US GDP, 35% of US electricity consumption, but over 50% of US ChatGPT traffic. Similar patterns are observed across many other countries, attesting to the widespread interest and uptake of generative AI. Still, low-income economies only represent less than 1% of ChatGPT traffic.

Forth, panel data regressions reveal that countries’ income level, share of youth population, the availability and quality of digital infrastructure, and human capital are strongly correlated with higher uptake of generative AI. Specialization in the digital sector and white-collar occupations, and English fluency predict higher chatbot usage but not other types of generative AI usage. Higher internet speed is more important for image and video generation tools than chatbots. Youth population, better digital infrastructure, higher literacy rate and English fluency also predict more intensive usage of ChatGPT, measured with average duration per session.

Finally, we found indicative evidence that generative AI tools are disrupting the current traffic patterns for certain websites and altering individuals’ online activities. Information websites like Wikipedia, translation and writing tools like Grammarly and Google Translate, IT professionals’ community Stack Overflow have experienced substantial drops in traffic immediately after the launch of GPT-4. Traffic to some of these sites have recovered as they integrated generative AI capabilities, underscoring the dynamic nature of the tech landscape and the importance of adaptability and continuous innovation for sustaining relevance. Cross country panel data regressions also suggest that ChatGPT is changing how people acquire information, learn new skills, and conduct language-processing tasks. Such tasks are increasingly conducted with the help of ChatGPT, while more complex cognitive and analytical tasks seem to have been augmented by ChatGPT.

45
These findings have significant implications for policymakers, businesses, and researchers. The impact of generative AI on economic development and societal structures presents both opportunities and challenges. For developing regions, targeted investments in digital infrastructure, digital skills, English language proficiency, and the development of domestic white-collar service industry are imperative to leverage the full potential of AI technologies. Even though ChatGPT and many other generative AI tools support hundreds of languages, English fluency is a highly significant predictor of generative AI uptake. Governments and organizations need to prioritize multilingual AI development, investing in datasets and research for diverse languages. Digital literacy programs combining English skills with AI literacy could help bridge the gap for non-English speakers. Localization efforts, cultural adaptation of AI tools, and equitable access initiatives are crucial to prevent language barriers from exacerbating existing digital divides.

As the generative AI landscape continues to evolve rapidly, ongoing research is crucial to monitor adoption trends and their societal impacts. However, as generative AI becomes integrated into existing applications and new devices, monitoring its usage will be more challenging. Researchers need to develop innovative methods to effectively track generative AI usage in the new era. Future studies could also focus on the long-term impacts of AI on labor markets, education systems, and economic structures across different regions. Additionally, investigating the ethical implications and potential biases in AI adoption patterns will be essential for ensuring that these technologies contribute to inclusive and sustainable growth.

46