Trends in Artificial Intelligence - AI User, Usage, CapEx and Monetization Trends

Sep 3, 2026

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.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages340

Reading Time1 h 59 min

AccessFree to download

AI User + Usage + CapEx Growth

Internet vs. leading USA-based LLM user share

Leading USA-based LLM weekly active users

Compute costs and performance convergence

Key technology cost index

AI monetization threats

Competition, open-source momentum, and China’s rise

AI usage, revenue, and expense dynamics

Revenue vs. compute expense

AI in the physical world and work evolution

Ride share vs. autonomous taxi market share

USA IT job postings: AI vs. non-AI

| Seem Like Change Happening Faster Than Ever?
Yes, It Is | | | | --- | --- | --- | | Developers in Leading Chipmaker’s Ecosystem | | | | Number of Developers, MM | | 6MM | | 2005 2025
Details on
Source: Leading Chipmaker Page 38 | | | | AI User + Usage + CapEx Growth =

Unprecedented | | | | | --- | --- | --- | --- | | Internet vs. Leading USA-Based LLM: Total Current Users Outside North America | | | | | Share of Total Current Users,% | 100%
50%
0% |
90%
@ Year 3

90%
@ Year 23 | | | 0 Internet LLM
Years In
Note: LLM data is for monthly active mobile app users. App not available in select countries, including China and Russia, as of 5/25.
Source: United Nations / International Telecommunications Union (3/25), Sensor Tower (5/25) | | | 33 Years In
Details on Page 56 | | AI User + Usage + CapEx Growth =

Unprecedented | | | | --- | --- | --- | | Leading USA-Based LLM Users | | | | Weekly Active Users, MM | | 800MM | | 10/22 4/25
Details on
Source: Company disclosures Page 55 | | | | | | | | --- | --- | --- | | .2
AI User + Usage + CapEx Growth =
Unprecedented | | | | Big Six* USA Technology Company CapEx | | | | CapEx, $B | | | | 2014 2024 | | | | *Apple, NVIDIA, Microsoft, Alphabet, Amazon (AWS only), & Meta Platforms Source: Capital IQ (3/25), Morgan Stanley | | Details on Page 97 | | | | | --- | --- | | AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising | | | Cost of Key Technologies Relative to Launch Year | | | % of Original Price By Year (Indexed to Year 0) |
Electric Power
Computer Memory AI Inference

| | 0 Years 72 Years
Note: Per-token inference costs shown. Details on
Source: Richard Hirsh; John McCallum; OpenAI Page 138 | | | | | | | --- | --- | --- | | AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise | | | | Desktop User Share,% | Leading USA LLMs vs. China LLM Desktop User Share

2/24
2/25
4/25

75%
60%

USA – LLM \#1 China USA – LLM \#2 | | | Note: Data is non-deduped. Share is relative, measured across six leading global LLMs.
Source: YipitData (5/25) | | Details on Page 293 | | AI Usage + Cost + Loss Growth = Unprecedented | | | | | --- | --- | --- | --- | | Leading USA-Based AI LLM Revenue vs. Compute Expense | | | | | Revenue (Blue) & Compute Expense (Red) | | | +$3.7B
-$5B | | 2022 2023 2024 | | | | | Note: Figures are estimates.
Source: The Information, public estimates | | Details on Page 173 | | | | | | | --- | --- | --- | | .1
AI Monetization Threats = Rising Competition +
Open-Source Momentum + China’s Rise | | | | China vs. USA vs. Rest of World Industrial Robots Installed | | | | Industrial Robots Installed | | | | 2014 2023 | | | | Note: Data as of 2023.
Source: International Federation of Robotics | | Details on Page 289 | | AI & Physical World Ramps = Fast + Data-Driven | | | | --- | --- | --- | | % of San Francisco
Gross Bookings | 8/23
A Ride Share vs. Autonomous Taxi Provider, San Francisco Operating Zone Market Share

34%
0%
Ride Share Autonomous Taxi 4/25 | 27%
19%
Details on
Page 302 | | Source: YipitData (4/25) | | | | Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before | | | | --- | --- | --- | | Mobile App Monthly Active Users, MM | Leading USA-Based LLMApp Users by Region

Sub-Saharan Africa
South Asia
North America
Middle East & North Africa Latin America & Caribbean
Europe & Central Asia
East Asia & Pacific | | | 5/23 4/25 | | | | Note: Region definitions per World Bank definitions. China not included in East Asia figures.
Data for standalone app only. Source: Sensor Tower (5/25) | | Details on Page 315 | | | | | | --- | --- | --- | | AI & Wo

FAQs

How does the document explain AI user growth using AI user and usage metrics?

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.

What does the document say about compute and inference costs?

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.

What factors drive AI monetization threats in the document?

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.

Sep 3, 2026

Upload by Chloe Bennett