[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-194760-105":53,"doc-detail-194760-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","trends-in-artificial-intelligence-ai-user-usage-capex-and-monetization-trends","Trends in Artificial Intelligence - AI User, Usage, CapEx and Monetization Trends","","Artificial Intelligence trends are analyzed through user adoption, usage growth, capital expenditure, compute economics, and monetization pressure. The material tracks shifts from early adoption toward unprecedented developer and enterprise engagement, including geographic user distribution and changing cost structures. It highlights rising competition from open-source momentum and China’s model advances, while comparing revenue versus compute expense and showing how inference costs relate to performance convergence. Additional coverage addresses AI’s rapid integration into the physical world and related data-driven execution.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/trends-in-artificial-intelligence-ai-user-usage-capex-and-monetization-trends/194760/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/trends-in-artificial-intelligence-ai-user-usage-capex-and-monetization-trends/194760.png","ImageObject",442,249,{"name":88,"@type":89},"Chloe Bennett","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-06","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":79},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"How does the document explain AI user growth using AI user and usage metrics?","Question",{"text":108,"@type":109},"It ties AI adoption to user counts and usage expansion across platforms, including comparisons between internet access and leading USA-based LLM users, as well as weekly active users for major LLMs.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What does the document say about compute and inference costs?",{"text":113,"@type":109},"It presents the idea that model compute costs are high and rising while inference costs per token are falling, leading to performance convergence and higher developer usage.",{"name":115,"@type":106,"acceptedAnswer":116},"What factors drive AI monetization threats in the document?",{"text":117,"@type":109},"It attributes monetization threats to rising competition, open-source momentum, and China’s rise, supported by market-share comparisons between leading USA and China desktop LLM usage.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},194760,1788442339,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":140},962084925782,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","| Seem Like Change Happening Faster Than Ever?\u003Cbr>Yes, It Is |  |  |\n| --- | --- | --- |\n| Developers in Leading Chipmaker’s Ecosystem |  |  |\n| Number of Developers, MM | | 6MM |\n| 2005 2025\u003Cbr>Details on\u003Cbr>Source: Leading Chipmaker Page 38 |  |  |\n\n| AI User + Usage + CapEx Growth =\u003Cbr>\u003Cbr>Unprecedented |  |  |  |\n| --- | --- | --- | --- |\n| Internet vs. Leading USA-Based LLM: Total Current Users Outside North America |  |  |  |\n| Share of Total Current Users,% | 100%\u003Cbr>50%\u003Cbr>0% | \u003Cbr>90%\u003Cbr>@ Year 3\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr> 90%\u003Cbr> @ Year 23 |  |\n| 0  Internet  LLM\u003Cbr>Years In\u003Cbr>Note: LLM data is for monthly active mobile app users. App not available in select countries, including China and Russia, as of 5/25.\u003Cbr>Source: United Nations / International Telecommunications Union (3/25), Sensor Tower (5/25) |  |  | 33 Years In\u003Cbr>Details on Page 56 |\n\n\n| AI User + Usage + CapEx Growth =\u003Cbr>\u003Cbr>Unprecedented |  |  |\n| --- | --- | --- |\n| Leading USA-Based LLM Users |  |  |\n| Weekly Active Users, MM | | 800MM |\n| 10/22 4/25\u003Cbr>Details on\u003Cbr>Source: Company disclosures Page 55 |  |  |\n\n|  |  |  |\n| --- | --- | --- |\n| .2\u003Cbr>AI User + Usage + CapEx Growth =\u003Cbr>Unprecedented |  |  |\n| Big Six* USA Technology Company CapEx |  |  |\n| CapEx, $B | |  |\n| 2014 2024 |  |  |\n| *Apple, NVIDIA, Microsoft, Alphabet, Amazon (AWS only), & Meta Platforms Source: Capital IQ (3/25), Morgan Stanley |  | Details on Page 97 |\n\n|  |  |\n| --- | --- |\n| AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising |  |\n| Cost of Key Technologies Relative to Launch Year |  |\n| % of Original Price By Year (Indexed to Year 0) | \u003Cbr>Electric Power\u003Cbr>Computer Memory AI Inference\u003Cbr>\u003Cbr>|\n| 0 Years 72 Years\u003Cbr>Note: Per-token inference costs shown. Details on\u003Cbr>Source: Richard Hirsh; John McCallum; OpenAI Page 138 |  |\n\n|  |  |  |\n| --- | --- | --- |\n| AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise |  |  |\n| Desktop User Share,% | Leading USA LLMs vs. China LLM Desktop User Share\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>2/24\u003Cbr>2/25\u003Cbr>4/25\u003Cbr>\u003Cbr>75%\u003Cbr>60%\u003Cbr>\u003Cbr>USA – LLM \\#1 China USA – LLM \\#2 |  |\n| Note: Data is non-deduped. Share is relative, measured across six leading global LLMs.\u003Cbr>Source: YipitData (5/25) |  | Details on Page 293 |\n\n| AI Usage + Cost + Loss Growth = Unprecedented |  |  |  |\n| --- | --- | --- | --- |\n| Leading USA-Based AI LLM Revenue vs. Compute Expense |  |  |  |\n| Revenue (Blue) & Compute Expense (Red) | |  | +$3.7B\u003Cbr>-$5B |\n| 2022 2023 2024 |  |  |  |\n| Note: Figures are estimates.\u003Cbr>Source: The Information, public estimates |  | Details on Page 173 |  |\n\n|  |  |  |\n| --- | --- | --- |\n| .1\u003Cbr>AI Monetization Threats = Rising Competition +\u003Cbr>Open-Source Momentum + China’s Rise |  |  |\n| China vs. USA vs. Rest of World Industrial Robots Installed |  |  |\n| Industrial Robots Installed | |  |\n| 2014 2023 |  |  |\n| Note: Data as of 2023.\u003Cbr>Source: International Federation of Robotics |  | Details on Page 289 |\n\n| AI & Physical World Ramps = Fast + Data-Driven |  |  |\n| --- | --- | --- |\n| % of San Francisco\u003Cbr>Gross Bookings | 8/23\u003Cbr>A Ride Share vs. Autonomous Taxi Provider, San Francisco Operating Zone Market Share\u003Cbr>\u003Cbr>\u003Cbr>34%\u003Cbr>0%\u003Cbr> Ride Share  Autonomous Taxi 4/25 | 27%\u003Cbr>19%\u003Cbr>Details on\u003Cbr>Page 302 |\n| Source: YipitData (4/25) |  |  |\n\n| Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before |  |  |\n| --- | --- | --- |\n| Mobile App Monthly Active Users, MM | Leading USA-Based LLMApp Users by Region\u003Cbr>\u003Cbr>Sub-Saharan Africa\u003Cbr>South Asia\u003Cbr>North America\u003Cbr>Middle East & North Africa Latin America & Caribbean\u003Cbr>Europe & Central Asia\u003Cbr>East Asia & Pacific |  |\n| 5/23 4/25 |  |  |\n| Note: Region definitions per World Bank definitions. China not included in East Asia figures.\u003Cbr>Data for standalone app only. Source: Sensor Tower (5/25) |  | Details on Page 315 |\n\n|  |  |  |\n| --- | --- | --- |\n| AI & Wo","cbCaitfay4FCfjKZ","https://ap.wps.com/l/cbCaitfay4FCfjKZ","pdf",12746971,340,"English","# AI User + Usage + CapEx Growth\n## Internet vs. leading USA-based LLM user share\n## Leading USA-based LLM weekly active users\n# Compute costs and performance convergence\n## Key technology cost index\n# AI monetization threats\n## Competition, open-source momentum, and China’s rise\n# AI usage, revenue, and expense dynamics\n## Revenue vs. compute expense\n# AI in the physical world and work evolution\n## Ride share vs. autonomous taxi market share\n## USA IT job postings: AI vs. non-AI","[{\"question\":\"How does the document explain AI user growth using AI user and usage metrics?\",\"answer\":\"It ties AI adoption to user counts and usage expansion across platforms, including comparisons between internet access and leading USA-based LLM users, as well as weekly active users for major LLMs.\"},{\"question\":\"What does the document say about compute and inference costs?\",\"answer\":\"It presents the idea that model compute costs are high and rising while inference costs per token are falling, leading to performance convergence and higher developer usage.\"},{\"question\":\"What factors drive AI monetization threats in the document?\",\"answer\":\"It attributes monetization threats to rising competition, open-source momentum, and China’s rise, supported by market-share comparisons between leading USA and China desktop LLM usage.\"}]","Trends in Artificial Intelligence - AI User, Usage, CapEx and Monetization Trends | PDF",119]