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Stanford HAI AI Index Report 2026

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Comprehensive annual survey of artificial intelligence progress, applications, and societal impact in 2025, covering research and development, technical performance, responsible AI, economy, science, medicine, education, policy and governance, and global public opinion.

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Description

Main Topic

Comprehensive annual survey of artificial intelligence progress, applications, and societal impact in 2025, covering research and development, technical performance, responsible AI, economy, science, medicine, education, policy and governance, and global public opinion.

Key Benefits

  • •Data-driven analysis of AI capabilities across 30+ benchmark categories with human performance comparisons
  • •Comprehensive global AI investment tracking including $285.9B US private investment in 2025
  • •Detailed coverage of AI policy and governance developments across 46 countries and major regulatory frameworks including the EU AI Act
  • •In-depth analysis of AI applications in medicine, science, and education with empirical outcome data
  • •Longitudinal trend data from 2010-2025 enabling year-over-year progress tracking
  • •Global public opinion data from Ipsos and other major surveys across 30+ countries
  • •Actionable insights for policymakers, investors, researchers, and executives on AI's transformative trajectory

Target Audience

  • •AI researchers and academics
  • •Technology executives and CIOs
  • •Policy makers and government officials
  • •Investors and venture capitalists
  • •Healthcare professionals and medical researchers
  • •Educators and academic administrators
  • •Journalists and science communicators
  • •Students in AI, computer science, and related fields

Use Cases

  • •Benchmark AI capability progress and compare against human performance baselines
  • •Track global AI investment trends and identify high-growth sectors
  • •Understand and navigate AI regulatory requirements across major jurisdictions
  • •Assess AI's productivity impact and ROI for business adoption decisions
  • •Stay current on AI safety, bias, and responsible AI developments
  • •Evaluate AI's scientific and medical applications for research planning
  • •Inform AI curriculum and education program development
  • •Guide national AI strategy and policy formulation with comparative data

Unique Value Propositions

  • •The most comprehensive and rigorous annual AI progress measurement, in its 9th edition with established credibility
  • •Stanford HAI's authoritative academic and research credentials
  • •Covers all major AI dimensions in one unified report — no need for multiple sources
  • •Longitudinal data spanning 2010-2025 provides unprecedented historical context
  • •423 pages of charts, data, and analysis representing hundreds of original data compilations
  • •Policy-neutral, evidence-based framing trusted by governments, industry, and academia globally

Slide Pages (423)

Detailed view of each slide page, including layout, key content and visual elements.

Page 1
cover

Cover Page

Content

Full cover of the Artificial Intelligence Index Report 2026, 9th edition, featuring Stanford HAI branding with a red/coral accent and abstract geometric pattern.

Layout Structure

Full-bleed cover with centered title, edition number, and Stanford HAI logo at bottom

Key Visual Elements

  • •Stanford HAI logo
  • •Red/coral geometric abstract design
  • •Title: Artificial Intelligence Index Report 2026
  • •9th Edition label
  • •Publication year 2026
Page 2
front_matter

Title Page and Publication Credits

Content

Formal title page listing Stanford Human-Centered AI (HAI) as publisher, editorial team credits, copyright notice, and publication details for the 9th annual edition.

Layout Structure

Clean white page with centered text hierarchy, HAI logo top, publication metadata bottom

Key Visual Elements

  • •Stanford HAI wordmark
  • •Editorial credits list
  • •Copyright 2026 notice
  • •ISBN/DOI reference
  • •Publication date
Page 3
front_matter

Table of Contents — Part 1

Content

First page of the table of contents listing chapters 1 through 5 with page numbers, chapter titles, and brief one-line descriptions of each chapter's scope.

Layout Structure

Two-column layout with chapter numbers on left, titles and page references on right; color-coded chapter tabs

Key Visual Elements

  • •Chapter color tabs
  • •Page number references
  • •Chapter title list
  • •Brief chapter descriptions
  • •HAI logo header
Page 4
front_matter

Table of Contents — Part 2

Content

Second page of the table of contents covering chapters 6 through 9 plus appendix, with page numbers and one-line chapter descriptions.

Layout Structure

Continuation of two-column TOC layout matching page 3 style

Key Visual Elements

  • •Chapter color tabs
  • •Appendix listing
  • •Page number references
  • •Chapter title list
Page 5
front_matter

Foreword — Page 1

Content

Opening foreword from HAI directors including Fei-Fei Li, introducing the significance of the 9th annual AI Index and contextualizing AI's rapid advancement in 2025.

Layout Structure

Single-column text with director photo inset top-right, HAI header banner

Key Visual Elements

  • •Director headshot photo
  • •HAI branding header
  • •Pull quote highlight box
  • •Signature line
Page 6
front_matter

Foreword — Page 2

Content

Continuation of the foreword discussing AI's transformative societal impact, the importance of data-driven AI policy, and HAI's mission to guide human-centered AI development.

Layout Structure

Single-column text layout with section subheading

Key Visual Elements

  • •Pull quote callout
  • •Section subheading
  • •Body text columns
Page 7
front_matter

Foreword — Page 3

Content

Third foreword page covering HAI's approach to tracking AI progress across multiple dimensions and acknowledging the global team of contributors to the 2026 report.

Layout Structure

Single-column text with acknowledgment paragraph and contributor credits

Key Visual Elements

  • •Contributor acknowledgment section
  • •Institutional partner logos
  • •Body text
Page 8
front_matter

Foreword — Page 4

Content

Closing page of the foreword with final remarks from HAI leadership on the stakes of AI development in 2025-2026 and a call to action for policymakers and researchers.

Layout Structure

Single-column text closing with director signatures block

Key Visual Elements

  • •Director signatures
  • •Closing statement
  • •HAI footer branding
Page 9
executive_summary

Executive Summary — Introduction

Content

Opening of the executive summary presenting the overarching narrative of AI in 2025: rapid capability gains, accelerating investment, growing policy activity, and societal debates.

Layout Structure

Full-width intro paragraph followed by numbered list preview of 10 key takeaways

Key Visual Elements

  • •Section header
  • •Numbered takeaway preview list
  • •Highlight color bar
  • •Summary introduction paragraph
Page 10
executive_summary

Executive Summary — Takeaways 1–3

Content

Key takeaways 1 through 3: (1) AI capabilities surging with benchmark saturation; (2) US private AI investment hits $285.9B; (3) AI incidents and harms tracking shows 1000% increase since 2019.

Layout Structure

Three-panel layout each with takeaway number, bold headline, supporting data point, and small icon

Key Visual Elements

  • •Takeaway number badges
  • •Bold headline text
  • •Supporting statistics
  • •Small data icons
  • •Color-coded panels
Page 11
executive_summary

Executive Summary — Takeaways 4–6

Content

Key takeaways 4 through 6: (4) AI agents now resolve 50%+ of real GitHub issues; (5) FDA approved 258 AI-enabled medical devices in 2025; (6) 46 countries have adopted national AI strategies.

Layout Structure

Three-panel layout matching page 10 style

Key Visual Elements

  • •Takeaway number badges
  • •Bold headline text
  • •Supporting statistics
  • •Small data icons
Page 12
executive_summary

Executive Summary — Takeaways 7–9

Content

Key takeaways 7 through 9: (7) 80% of students use generative AI for coursework; (8) EU AI Act prohibited practices ban took effect February 2025; (9) 59% of global respondents optimistic about AI.

Layout Structure

Three-panel layout matching pages 10-11

Key Visual Elements

  • •Takeaway number badges
  • •Bold headline text
  • •Supporting statistics
  • •Small data icons
Page 13
executive_summary

Executive Summary — Takeaway 10 and Highlights

Content

Final executive summary takeaway (10): AI Nobel Prize recognition marks historic milestone for science, plus a highlights chart showing key metrics across all nine chapters at a glance.

Layout Structure

Single takeaway panel top-half; summary metrics dashboard bottom-half

Key Visual Elements

  • •Takeaway 10 panel
  • •Multi-metric summary dashboard
  • •Chapter color-coded metric bars
  • •Nobel Prize callout
Page 14
executive_summary

Executive Summary — Data Highlights Chart

Content

Visual summary chart displaying year-over-year growth metrics across major report dimensions: investment, publications, model releases, policy actions, and adoption rates.

Layout Structure

Full-width grouped bar chart with color-coded categories and year labels on x-axis

Key Visual Elements

  • •Grouped bar chart
  • •Color-coded categories
  • •YoY growth percentages
  • •Legend
  • •Axis labels
Page 15
executive_summary

Executive Summary — Global AI Activity Map

Content

World map visualization showing AI activity levels by country across dimensions of research output, private investment, policy adoption, and talent concentration.

Layout Structure

Full-width world map with color gradient intensity scale and country callouts for top nations

Key Visual Elements

  • •World choropleth map
  • •Color intensity scale
  • •Country callout labels
  • •Legend with four dimensions
  • •Title and source note
Page 16
executive_summary

Executive Summary — Report Methodology Overview

Content

Brief explanation of the AI Index's data collection methodology, defining what constitutes AI in the report's scope, and listing primary data sources used across chapters.

Layout Structure

Two-column text with source list in sidebar and methodology diagram inset

Key Visual Elements

  • •Methodology diagram
  • •Data source list
  • •Definition box
  • •Two-column layout
Page 17
front_matter

Executive Summary — How to Use This Report

Content

Navigation guide for readers explaining chapter structure, how to find specific topics, online interactive data access, and citation guidelines for the AI Index 2026.

Layout Structure

Single-column text with call-out boxes for online resources and chapter navigation tips

Key Visual Elements

  • •Navigation tip boxes
  • •Online resource callout
  • •Chapter guide diagram
  • •Citation format example
Page 18
chapter_opener

Chapter 1 Opener — Research and Development

Content

Full-bleed chapter opener page with dark navy/slate grey background, large white Chapter 1 numeral, and title 'Research and Development' with a brief one-paragraph chapter overview.

Layout Structure

Full-bleed dark background, oversized chapter number left-aligned, title and description right-aligned

Key Visual Elements

  • •Dark navy/slate background
  • •Large white chapter number '1'
  • •Chapter title in white
  • •Brief overview paragraph
  • •Stanford HAI logo
  • •Red/coral accent line
Page 19
chapter_content

Chapter 1 — Introduction and Overview

Content

Introductory text for Chapter 1 summarizing key R&D findings: approximately 200 notable AI models released in 2025, benchmark saturation accelerating, open-source models closing the gap with proprietary systems.

Layout Structure

Single-column introduction with highlighted key statistics pull quotes

Key Visual Elements

  • •Key stat callout boxes
  • •Section introduction text
  • •Chapter color accent bar
  • •Footnote citations
Page 20
data_visualization

Chapter 1 — AI Model Releases: Annual Count

Content

Bar chart showing the number of notable AI model releases per year from 2019 to 2025, illustrating the dramatic acceleration with approximately 200 notable models released in 2025 alone.

Layout Structure

Full-width bar chart with year labels on x-axis, count on y-axis, bars colored in chapter navy/slate theme

Key Visual Elements

  • •Vertical bar chart
  • •Year x-axis 2019-2025
  • •Model count y-axis
  • •Data labels on bars
  • •Source attribution
  • •Chart title
Page 21
data_visualization

Chapter 1 — AI Model Releases: By Organization Type

Content

Stacked bar chart breaking down 2025 AI model releases by organization type: industry, academia, government, and civil society, showing industry's dominant share.

Layout Structure

Stacked horizontal bar chart with organization type categories and legend

Key Visual Elements

  • •Stacked bar chart
  • •Organization type legend
  • •Percentage labels
  • •Color coding by type
  • •Source note
Page 22
data_visualization

Chapter 1 — AI Model Releases: Geography

Content

Geographic breakdown of AI model origins in 2025 showing US, China, UK, Europe, and other regions, with the US producing the largest share of notable frontier models.

Layout Structure

World map with bubble sizes indicating model release volume per country, plus accompanying bar chart

Key Visual Elements

  • •Bubble map
  • •Country labels
  • •Bar chart inset
  • •Color-coded by region
  • •Legend
Page 23
chapter_content

Chapter 1 — Foundation Models Overview

Content

Overview section on foundation models in 2025, defining the term, listing key frontier models (GPT-4o, Gemini 2.0, Claude 3.5, Llama 3, DeepSeek), and explaining their architectural significance.

Layout Structure

Text-heavy section intro with model name callout boxes and a comparison table

Key Visual Elements

  • •Model name callout boxes
  • •Comparison table
  • •Section header
  • •Definition box
  • •Timeline inset
Page 24
data_visualization

Chapter 1 — Foundation Model Timeline 2025

Content

Horizontal timeline visualization showing the release dates of major foundation models throughout 2025, organized by organization and model family.

Layout Structure

Horizontal timeline with model labels above/below the line, organization color-coding

Key Visual Elements

  • •Horizontal timeline
  • •Model name labels
  • •Organization color legend
  • •Release date markers
  • •Model family groupings
Page 25
data_visualization

Chapter 1 — Open-Source vs. Proprietary Models

Content

Line chart comparing performance trajectories of open-source versus proprietary AI models from 2020-2025 on standardized benchmarks, showing the narrowing gap by late 2025.

Layout Structure

Dual-line chart with open-source (dashed) and proprietary (solid) lines, benchmark score on y-axis, year on x-axis

Key Visual Elements

  • •Dual-line chart
  • •Open-source vs proprietary legend
  • •Benchmark score axis
  • •Gap annotation arrows
  • •Key model labels on lines
Page 26
chapter_content

Chapter 1 — Open-Source AI Ecosystem

Content

Analysis of the open-source AI ecosystem growth in 2025, including GitHub star counts, download volumes for top models, and the role of platforms like Hugging Face in democratizing AI access.

Layout Structure

Mixed layout with text analysis left and bar charts right showing download/star metrics

Key Visual Elements

  • •Bar charts for downloads
  • •GitHub star count data
  • •Hugging Face reference
  • •Text analysis column
  • •Growth rate callouts
Page 27
data_visualization

Chapter 1 — AI Publications: Global Volume

Content

Line chart showing total AI-related academic publications from 2010-2025 by region, demonstrating China's lead in volume and the US's continued leadership in highly-cited papers.

Layout Structure

Multi-line chart with regional color coding, log-scale y-axis option, year on x-axis

Key Visual Elements

  • •Multi-line chart
  • •Regional color coding
  • •Publication count y-axis
  • •Year x-axis 2010-2025
  • •Legend with country/region names
Page 28
data_visualization

Chapter 1 — AI Publications: Citation Analysis

Content

Scatter plot of AI publication volume versus citation impact by country, illustrating the US-China publication volume-quality dynamic and the EU's citation performance.

Layout Structure

Scatter plot with country bubbles sized by total publication count, x-axis volume, y-axis citations per paper

Key Visual Elements

  • •Bubble scatter plot
  • •Country name labels
  • •X-axis: publication volume
  • •Y-axis: citations per paper
  • •Bubble size legend
  • •Quadrant lines
Page 29
data_visualization

Chapter 1 — AI Publications: By Research Domain

Content

Stacked area chart showing AI publication growth by research subdomain (NLP, computer vision, robotics, ML theory, etc.) from 2015-2025, with NLP and generative AI showing explosive growth.

Layout Structure

Stacked area chart with subdomain color layers, year x-axis, publication count y-axis

Key Visual Elements

  • •Stacked area chart
  • •Subdomain color legend
  • •Year x-axis
  • •Publication count y-axis
  • •Annotation for NLP/GenAI surge
Page 30
data_visualization

Chapter 1 — AI Publications: Conference vs. Journal

Content

Bar chart comparing AI research publication venues — top conferences (NeurIPS, ICML, CVPR, ICLR, ACL) versus journals — and their relative share of high-impact AI research in 2025.

Layout Structure

Side-by-side bar chart comparison with venue names on y-axis, paper count on x-axis

Key Visual Elements

  • •Side-by-side bars
  • •Venue name labels
  • •Paper count axis
  • •Conference vs journal color coding
  • •Top venue callouts
Page 31
chapter_content

Chapter 1 — Benchmark Saturation: Overview

Content

Introductory analysis of benchmark saturation in AI, explaining how many previously challenging benchmarks have been largely solved by 2025, necessitating harder evaluation tasks.

Layout Structure

Text section with benchmark saturation diagram showing tasks moving from 'unsolved' to 'saturated' zones

Key Visual Elements

  • •Saturation diagram
  • •Text analysis
  • •Benchmark examples list
  • •Human performance reference lines
  • •Section header
Page 32
data_visualization

Chapter 1 — Benchmark Saturation: Performance Chart

Content

Scatter plot showing AI performance versus human performance across 50+ benchmarks, with color-coding indicating benchmarks where AI has surpassed human-level performance.

Layout Structure

Large scatter plot with human-parity diagonal line, benchmark name annotations, color-coding for above/below human parity

Key Visual Elements

  • •Scatter plot
  • •Human-parity diagonal line
  • •Color coding above/below parity
  • •Benchmark name annotations
  • •Axes: AI score vs human score
Page 33
data_visualization

Chapter 1 — Training Compute Scaling

Content

Log-scale line chart tracking training compute (in FLOPs) used for frontier AI models from 2012-2025, showing the approximately 6-month doubling time and orders-of-magnitude increases.

Layout Structure

Log-scale line chart with model name annotations, time on x-axis, log FLOPs on y-axis

Key Visual Elements

  • •Log-scale line chart
  • •Model name annotations
  • •Doubling time reference line
  • •FLOPs y-axis (log scale)
  • •Year x-axis
  • •Trend line
Page 34
data_visualization

Chapter 1 — Training Compute: Cost Trends

Content

Analysis of AI training cost trends, showing that while compute per model increases, the cost per unit of compute has fallen dramatically, with a dual-axis chart illustrating both trends.

Layout Structure

Dual-axis chart with compute cost per FLOP on left axis and training cost per model on right axis

Key Visual Elements

  • •Dual-axis chart
  • •Cost per FLOP trend
  • •Training cost per model trend
  • •Year x-axis
  • •Annotation boxes for key milestones
Page 35
data_visualization

Chapter 1 — Inference Efficiency Progress

Content

Charts showing improvements in AI inference efficiency from 2020-2025: tokens per second, cost per million tokens, and model size required for equivalent performance all improving dramatically.

Layout Structure

Three-panel small multiple charts each showing one efficiency metric over time

Key Visual Elements

  • •Small multiple layout
  • •Three metric panels
  • •Year x-axis each
  • •Efficiency metric y-axes
  • •Trend annotations
Page 36
chapter_content

Chapter 1 — AI Hardware Landscape

Content

Overview of AI hardware developments in 2025: GPU/TPU advances, new custom AI accelerators from major tech firms, and the emergence of inference-optimized chips.

Layout Structure

Text overview with comparative hardware specs table and market share pie chart

Key Visual Elements

  • •Hardware specs table
  • •Market share pie chart
  • •Chip vendor comparison
  • •Text analysis
  • •Timeline of chip releases
Page 37
data_visualization

Chapter 1 — Data: Training Dataset Growth

Content

Bar chart and text analysis showing growth in training dataset sizes used for frontier models, from billions to trillions of tokens, and the shift toward synthetic data generation.

Layout Structure

Bar chart showing dataset size by model with text commentary column

Key Visual Elements

  • •Bar chart dataset sizes
  • •Synthetic data callout
  • •Token count labels
  • •Model name labels
  • •Text commentary
Page 38
data_visualization

Chapter 1 — Multimodal Model Capabilities

Content

Analysis of multimodal AI capabilities in 2025 — models integrating text, image, audio, and video — with capability matrix showing which models support which modalities.

Layout Structure

Capability matrix table with model names as rows, modalities as columns, checkmarks/scores in cells

Key Visual Elements

  • •Capability matrix table
  • •Modality column headers
  • •Model name rows
  • •Checkmark/score cells
  • •Color-coded performance tiers
Page 39
chapter_content

Chapter 1 — Reasoning Model Emergence

Content

Section on the emergence of reasoning-focused models in 2025 (e.g., OpenAI o-series, DeepSeek-R1), explaining chain-of-thought at inference time and benchmark improvements on hard reasoning tasks.

Layout Structure

Text section with before/after comparison chart showing reasoning model vs standard model performance

Key Visual Elements

  • •Before/after comparison chart
  • •Reasoning model callouts
  • •Text analysis
  • •Performance improvement annotations
  • •Model name labels
Page 40
chapter_content

Chapter 1 — AI Agent Systems Overview

Content

Introduction to AI agent systems released in 2025, covering architectures that enable multi-step autonomous task completion, tool use, and agentic workflows.

Layout Structure

Conceptual diagram of agent architecture (perception → reasoning → action loop) with text explanation

Key Visual Elements

  • •Agent architecture diagram
  • •Component labels
  • •Text explanation
  • •Example agent systems list
  • •Loop diagram
Page 41
data_visualization

Chapter 1 — Geographic R&D Leadership

Content

Comparative analysis of AI R&D leadership by country across dimensions: publication volume, citation impact, patent filings, model releases, and private investment.

Layout Structure

Radar/spider chart per top country (US, China, UK, EU, Canada, India) with five-dimension axes

Key Visual Elements

  • •Multi-country radar charts
  • •Five R&D dimension axes
  • •Country color coding
  • •Small multiple layout
  • •Summary rankings table
Page 42
data_visualization

Chapter 1 — Patent Filings in AI

Content

Bar chart showing AI patent filings by country from 2015-2025, with China surpassing the US in raw patent volume while the US maintains leadership in highly-cited patents.

Layout Structure

Grouped bar chart by country with year groupings, patent count on y-axis

Key Visual Elements

  • •Grouped bar chart
  • •Country color coding
  • •Patent count axis
  • •Year groupings
  • •Trend annotations
Page 43
data_visualization

Chapter 1 — AI Startups and New Entrants

Content

Data on new AI company formations in 2025, showing the surge in AI-native startups and the geographic distribution of new AI ventures across the US, EU, and Asia.

Layout Structure

World map bubble chart for startup formation plus bar chart of top startup hubs

Key Visual Elements

  • •World bubble map
  • •Startup count bubbles
  • •Top hub bar chart
  • •Geographic color coding
  • •Year-over-year growth callout
Page 44
data_visualization

Chapter 1 — Foundation Model Concentration

Content

Analysis of market concentration in frontier AI model development, showing that a small number of organizations (OpenAI, Google, Anthropic, Meta, Mistral, DeepSeek) account for most frontier releases.

Layout Structure

Pie chart showing share of frontier model releases by organization plus text analysis

Key Visual Elements

  • •Pie chart
  • •Organization name labels
  • •Share percentages
  • •Concentration index callout
  • •Text analysis
Page 45
data_visualization

Chapter 1 — AI Research Talent

Content

Analysis of AI research talent flows: graduate program enrollment trends, geographic migration of AI researchers, and the concentration of top AI talent in a few institutions and companies.

Layout Structure

Sankey diagram showing talent flows between countries plus bar chart of top institutions

Key Visual Elements

  • •Sankey flow diagram
  • •Country flow labels
  • •Institution ranking bars
  • •Talent migration annotations
  • •Text commentary
Page 46
data_visualization

Chapter 1 — AI Research Talent: Top Institutions

Content

Ranked list and bar chart of top AI research institutions by publication output and citation impact in 2025, including universities and industry labs.

Layout Structure

Ranked bar chart horizontal with institution names on y-axis, dual metrics on x-axis

Key Visual Elements

  • •Horizontal bar chart
  • •Dual metric bars
  • •Institution name labels
  • •Rank numbers
  • •Academic vs industry color coding
Page 47
data_visualization

Chapter 1 — Notable AI Milestones 2025

Content

Timeline of the most significant AI research and development milestones in 2025, from model launches to scientific breakthroughs and policy events.

Layout Structure

Vertical timeline with milestone entries, dates on left, descriptions on right, category color-coding

Key Visual Elements

  • •Vertical timeline
  • •Date labels
  • •Milestone descriptions
  • •Category color coding
  • •Icon per milestone type
Page 48
chapter_content

Chapter 1 — AI in Robotics and Embodied AI

Content

Overview of AI progress in robotics and embodied AI in 2025: foundation models for manipulation, locomotion improvements, and the emergence of generalist robot policies.

Layout Structure

Text overview with robot capability benchmark chart and illustrative photos

Key Visual Elements

  • •Robot capability chart
  • •Text overview
  • •Performance benchmark bars
  • •Example system callouts
Page 49
chapter_content

Chapter 1 — Generative AI: Image and Video

Content

Analysis of generative AI progress for image and video synthesis in 2025, including diffusion models, video generation quality improvements, and real-time generation capabilities.

Layout Structure

Text analysis with quality metric progression chart and example capability descriptions

Key Visual Elements

  • •Quality metric chart
  • •Text analysis
  • •Model capability comparison table
  • •Example output descriptions
Page 50
chapter_content

Chapter 1 — Generative AI: Audio and Speech

Content

Overview of generative AI advances in audio and speech synthesis in 2025: voice cloning, music generation, real-time translation, and ambient speech recognition improvements.

Layout Structure

Text overview with performance metric charts for speech recognition error rates and voice quality scores

Key Visual Elements

  • •Error rate trend chart
  • •Voice quality score chart
  • •Text overview
  • •Model comparison table
  • •Use case callouts
Page 51
chapter_content

Chapter 1 — AI Code Generation

Content

Section on AI code generation advances in 2025, covering model capabilities on HumanEval and SWE-bench, and the integration of AI coding assistants across developer workflows.

Layout Structure

Performance benchmark progression chart plus text analysis with developer adoption statistics

Key Visual Elements

  • •Benchmark progression chart
  • •Developer adoption stats
  • •Text analysis
  • •Model comparison
  • •Use case examples
Page 52
chapter_content

Chapter 1 — Emerging Research Themes

Content

Analysis of emerging AI research themes gaining traction in 2025: mechanistic interpretability, AI alignment, continual learning, test-time compute scaling, and neuromorphic approaches.

Layout Structure

Topic cluster visualization with publication growth charts per theme

Key Visual Elements

  • •Topic cluster diagram
  • •Publication growth charts
  • •Theme callout boxes
  • •Text analysis
  • •Citation trend lines
Page 53
data_visualization

Chapter 1 — International AI Collaboration

Content

Data on international AI research collaboration measured by co-authorship across borders, showing patterns of collaboration and increasing US-China research decoupling.

Layout Structure

Chord diagram of international co-authorship flows with text commentary

Key Visual Elements

  • •Chord diagram
  • •Country arc labels
  • •Flow width legend
  • •Text commentary
  • •Trend annotation
Page 54
data_visualization

Chapter 1 — AI Research Funding Sources

Content

Breakdown of AI research funding sources in 2025: government grants, industry funding, philanthropic investment, and the growing share of industry-funded academic research.

Layout Structure

Stacked bar chart over time showing funding source shares, with text analysis

Key Visual Elements

  • •Stacked bar chart
  • •Funding source legend
  • •Year x-axis
  • •Dollar amount y-axis
  • •Industry vs public funding callout
Page 55
chapter_content

Chapter 1 — Compute Access and Inequality

Content

Analysis of disparities in AI compute access between large tech firms, academic institutions, and researchers in lower-income countries, highlighting the compute divide.

Layout Structure

Comparative bar chart showing compute allocation with text analysis on access inequality

Key Visual Elements

  • •Comparative bar chart
  • •Access inequality callout
  • •Text analysis
  • •Institution type color coding
  • •Gap annotation
Page 56
data_visualization

Chapter 1 — AI Energy and Environmental Impact

Content

Data on AI's growing energy consumption in 2025: estimated energy use of frontier model training, inference at scale, and data center power demand growth attributed to AI workloads.

Layout Structure

Line chart of AI-attributed energy demand with carbon equivalence annotations and text commentary

Key Visual Elements

  • •Energy demand line chart
  • •Carbon equivalence callouts
  • •Text commentary
  • •Year x-axis
  • •MWh/GWh y-axis
Page 57
chapter_content

Chapter 1 — Open Problems in AI R&D

Content

Discussion of major open research problems in AI as of 2025: long-horizon reasoning, reliable planning, robustness to distribution shift, interpretability, and compositional generalization.

Layout Structure

Structured text with problem-statement boxes and citation counts per research challenge

Key Visual Elements

  • •Problem statement boxes
  • •Citation count bars
  • •Text analysis
  • •Research challenge icons
  • •Open question annotations
Page 58
data_visualization

Chapter 1 — R&D Chapter Summary Statistics

Content

Summary statistics page for Chapter 1 presenting the most important quantitative findings in a dashboard format: model releases, compute growth, publication volumes, and open-source metrics.

Layout Structure

Dashboard grid of large-number statistics with supporting sparklines and brief text labels

Key Visual Elements

  • •Large-number stat boxes
  • •Sparkline charts
  • •Color-coded metric panels
  • •Text labels
  • •Chapter color theme
Page 59
appendix

Chapter 1 — R&D Endnotes and Data Sources

Content

Reference notes for Chapter 1 citing primary data sources, methodology notes for publication and patent data, and definitions of key terms used in the R&D chapter.

Layout Structure

Two-column footnote/endnote format with source citations and numbered references

Key Visual Elements

  • •Numbered reference list
  • •Source URLs
  • •Methodology notes
  • •Two-column layout
Page 60
appendix

Chapter 1 — R&D Further Reading

Content

Curated list of further reading resources and key papers referenced in Chapter 1, organized by section topic, with brief annotations.

Layout Structure

Structured reading list with section headers and annotated entries

Key Visual Elements

  • •Structured reading list
  • •Section headers
  • •Paper/report citations
  • •Brief annotations
Page 61
chapter_opener

Chapter 2 Opener — Technical Performance

Content

Full-bleed chapter opener with dark navy/blue background, large white Chapter 2 numeral, title 'Technical Performance', and brief overview of AI performance advances measured in 2025.

Layout Structure

Full-bleed dark navy background, oversized chapter number, title and description in white

Key Visual Elements

  • •Dark navy/blue background
  • •Large white '2'
  • •Chapter title in white
  • •Chapter overview text
  • •Stanford HAI logo
  • •Accent line
Page 62
chapter_content

Chapter 2 — Technical Performance Introduction

Content

Introduction to the technical performance chapter, explaining the benchmarking framework, caveats about benchmark saturation, and the organization of performance data across task categories.

Layout Structure

Single-column introduction text with chapter section overview diagram

Key Visual Elements

  • •Section overview diagram
  • •Introduction text
  • •Benchmark framework explanation
  • •Key caveat callout boxes
Page 63
data_visualization

Chapter 2 — Language Understanding Benchmarks

Content

Overview of language understanding benchmark performance in 2025 across MMLU, MMLU-Pro, BIG-Bench Hard, and HellaSwag, showing AI models at or above human performance on most tasks.

Layout Structure

Multi-bar comparison chart with benchmark names on y-axis and performance scores on x-axis for top models

Key Visual Elements

  • •Multi-bar comparison chart
  • •Benchmark name labels
  • •Human performance reference line
  • •Model name color coding
  • •Score annotations
Page 64
data_visualization

Chapter 2 — Language Understanding: MMLU Trends

Content

Line chart tracking top model performance on MMLU and MMLU-Pro benchmarks from 2020-2025, demonstrating rapid progression from 60% to near-human and superhuman levels.

Layout Structure

Dual-line chart with MMLU and MMLU-Pro trends, model milestone annotations, human performance reference

Key Visual Elements

  • •Dual-line chart
  • •Model milestone annotations
  • •Human performance reference line
  • •Year x-axis
  • •Accuracy y-axis
Page 65
data_visualization

Chapter 2 — Mathematical Reasoning: Competition Math

Content

Bar chart showing AI performance on competition mathematics benchmarks (AIME, AMC, MATH) in 2025, with leading reasoning models achieving top human-competitive scores.

Layout Structure

Grouped bar chart by benchmark with model comparison bars, human expert baseline references

Key Visual Elements

  • •Grouped bar chart
  • •Benchmark name groups
  • •Human expert baseline
  • •Model name legend
  • •Score percentage labels
Page 66
data_visualization

Chapter 2 — Mathematical Reasoning: Trends Over Time

Content

Line chart tracking AI performance on MATH dataset from 2020-2025, showing the inflection point in 2024-2025 when reasoning models dramatically improved mathematical problem-solving.

Layout Structure

Line chart with key model annotations, inflection point highlighted, human score reference line

Key Visual Elements

  • •Line chart
  • •Model annotation labels
  • •Inflection point highlight
  • •Human score reference
  • •Year x-axis
  • •Accuracy y-axis
Page 67
data_visualization

Chapter 2 — Coding: HumanEval and Related Benchmarks

Content

Comparison of AI coding performance on HumanEval, HumanEval+, and MBPP benchmarks in 2025, showing near-saturation on original HumanEval and strong performance on harder variants.

Layout Structure

Bar chart comparing top coding models across three benchmarks with human developer baseline

Key Visual Elements

  • •Multi-benchmark bar chart
  • •Model comparison bars
  • •Human developer baseline
  • •Benchmark name labels
  • •Pass@1 metric annotation
Page 68
data_visualization

Chapter 2 — Coding: SWE-bench Performance

Content

Line chart and bar chart showing AI agent performance on SWE-bench (real GitHub issue resolution) from 2023-2025, with leading agents now resolving over 50% of issues.

Layout Structure

Left: line chart of best performance over time. Right: bar chart of top agents with resolution rate

Key Visual Elements

  • •Time series line chart
  • •Agent comparison bar chart
  • •50% milestone annotation
  • •Year x-axis
  • •Resolution rate y-axis
  • •Agent name labels
Page 69
data_visualization

Chapter 2 — Coding: Agentic Code Tasks

Content

Analysis of AI performance on agentic coding tasks requiring multi-file edits, test writing, debugging across large codebases, and tool use — going beyond single-function HumanEval style tasks.

Layout Structure

Benchmark comparison table with task category rows and model performance columns

Key Visual Elements

  • •Performance table
  • •Task category rows
  • •Model columns
  • •Performance score cells
  • •Color-coded tier bands
Page 70
data_visualization

Chapter 2 — Scientific Reasoning: GPQA

Content

Bar chart showing top model performance on GPQA (Graduate-Level Google-Proof Q&A) across physics, chemistry, and biology subdomains, with frontier models surpassing PhD-level human performance.

Layout Structure

Grouped bar chart by science domain with model comparison and PhD-level human baseline

Key Visual Elements

  • •Grouped bar chart
  • •Science domain groups
  • •PhD human baseline line
  • •Model name legend
  • •Score labels
Page 71
data_visualization

Chapter 2 — Scientific Reasoning: Benchmark Suite

Content

Multi-benchmark comparison across scientific reasoning tasks (GPQA, SciQ, ARC-Challenge, OlympiadBench) showing AI model performance relative to expert human baselines.

Layout Structure

Radar chart per top model showing multi-benchmark performance profile

Key Visual Elements

  • •Radar charts
  • •Multi-benchmark axes
  • •Model overlay comparison
  • •Human expert reference
  • •Score annotations
Page 72
data_visualization

Chapter 2 — Logical Reasoning and Formal Tasks

Content

Analysis of AI performance on formal logic, theorem proving, and structured reasoning tasks in 2025, including performance on LeanDojo, FrontierMath, and logical deduction benchmarks.

Layout Structure

Bar chart of performance by reasoning task category with text analysis

Key Visual Elements

  • •Bar chart
  • •Task category labels
  • •Performance scores
  • •Text analysis column
  • •Human/expert baseline markers
Page 73
data_visualization

Chapter 2 — Long-Context Understanding

Content

Performance analysis of AI models on long-context tasks requiring comprehension of documents up to 1 million tokens, showing significant improvement but remaining challenges in very long contexts.

Layout Structure

Line chart of performance versus context length with model comparison lines

Key Visual Elements

  • •Performance vs context length chart
  • •Model comparison lines
  • •Context length x-axis (tokens)
  • •Accuracy y-axis
  • •Model name labels
  • •Degradation annotation
Page 74
data_visualization

Chapter 2 — Factual Accuracy and Hallucination

Content

Analysis of AI model factual accuracy and hallucination rates across 2023-2025, showing improvements in grounded generation but persistent hallucination challenges in open-domain settings.

Layout Structure

Bar chart comparing hallucination rates across models with improvement trend line

Key Visual Elements

  • •Hallucination rate bar chart
  • •Model comparison
  • •Year-over-year trend
  • •Text analysis
  • •Mitigation technique callouts
Page 75
data_visualization

Chapter 2 — Instruction Following and Alignment

Content

Data on AI model performance on instruction-following benchmarks (IFEval, MT-Bench) in 2025, showing strong gains in following complex, multi-constraint instructions.

Layout Structure

Bar chart with instruction complexity tiers on x-axis and compliance rate on y-axis for top models

Key Visual Elements

  • •Bar chart
  • •Complexity tier x-axis
  • •Compliance rate y-axis
  • •Model comparison
  • •Benchmark name labels
Page 76
data_visualization

Chapter 2 — Multimodal: Vision-Language Performance

Content

Benchmark results for vision-language models in 2025 across tasks like visual QA, image captioning, and chart understanding, showing AI approaching or exceeding human performance.

Layout Structure

Multi-benchmark comparison bar chart for top vision-language models

Key Visual Elements

  • •Multi-benchmark bar chart
  • •Vision-language model names
  • •Human baseline references
  • •Task category groups
  • •Score labels
Page 77
data_visualization

Chapter 2 — Multimodal: Video Understanding

Content

Performance analysis of AI video understanding models on temporal reasoning, action recognition, and video QA benchmarks in 2025.

Layout Structure

Bar chart comparing top video models on multiple benchmarks with human performance baseline

Key Visual Elements

  • •Bar chart
  • •Video benchmark names
  • •Model comparison
  • •Human baseline
  • •Task type labels
Page 78
data_visualization

Chapter 2 — Multimodal: Audio and Speech Recognition

Content

Word error rate trends for AI speech recognition systems from 2015-2025 across multiple languages and accent groups, showing near-human performance in high-resource languages.

Layout Structure

Multi-line chart of WER over time by language group

Key Visual Elements

  • •Multi-line WER chart
  • •Language group color coding
  • •Year x-axis
  • •WER percentage y-axis
  • •Human parity reference
  • •Low-resource language callout
Page 79
data_visualization

Chapter 2 — Multimodal: Image Generation Quality

Content

Human evaluation scores for AI image generation quality from 2021-2025, showing dramatic improvements in photorealism, coherence, and prompt adherence.

Layout Structure

Line chart of quality scores over time with annotated model releases and sample output descriptions

Key Visual Elements

  • •Quality score line chart
  • •Model release annotations
  • •Year x-axis
  • •Quality score y-axis
  • •Human evaluation note
Page 80
data_visualization

Chapter 2 — Autonomous Agents: Task Completion

Content

Bar chart showing AI agent task completion rates on standardized agentic benchmarks (WebArena, OSWorld, AgentBench) in 2025, highlighting progress toward general autonomous task execution.

Layout Structure

Grouped bar chart by benchmark with agent system comparison bars and baseline references

Key Visual Elements

  • •Grouped bar chart
  • •Benchmark groups
  • •Agent system legend
  • •Completion rate y-axis
  • •Baseline reference lines
Page 81
data_visualization

Chapter 2 — Autonomous Agents: Web and Computer Use

Content

Analysis of AI agent performance on web browsing, computer use, and GUI interaction tasks, showing success rates and failure mode analysis for leading agent systems.

Layout Structure

Bar chart of success rates by task type with failure mode breakdown chart

Key Visual Elements

  • •Success rate bar chart
  • •Task type breakdown
  • •Failure mode chart
  • •Agent system comparison
  • •Text analysis
Page 82
data_visualization

Chapter 2 — Autonomous Agents: Multi-Step Planning

Content

Data on AI agent performance on long-horizon planning tasks requiring 10+ sequential steps, showing current limitations and the gap between short-task and long-task performance.

Layout Structure

Line chart of performance degradation with increasing task steps for multiple agent systems

Key Visual Elements

  • •Performance vs task length chart
  • •Multiple agent lines
  • •Step count x-axis
  • •Success rate y-axis
  • •Degradation annotation
Page 83
data_visualization

Chapter 2 — AI Translation Performance

Content

Analysis of AI translation quality across 200+ language pairs using BLEU and human evaluation scores, showing near-professional quality for high-resource pairs and significant gaps for low-resource languages.

Layout Structure

World language heat map for translation quality plus bar chart of top/bottom performing pairs

Key Visual Elements

  • •Language heat map
  • •Quality score color scale
  • •Bar chart top/bottom pairs
  • •Low-resource callout
  • •Professional baseline reference
Page 84
data_visualization

Chapter 2 — Summarization and Generation Quality

Content

Benchmarks for AI text summarization and long-form generation quality in 2025, including human evaluation consistency scores and factual accuracy metrics.

Layout Structure

Bar chart comparing summarization quality across models with human evaluation results

Key Visual Elements

  • •Summarization quality bars
  • •Model comparison
  • •Human evaluation scores
  • •Factual accuracy metric
  • •Benchmark source labels
Page 85
data_visualization

Chapter 2 — Robustness and Out-of-Distribution Performance

Content

Analysis of AI model robustness to distribution shift, adversarial inputs, and out-of-distribution test cases, showing persistent fragility despite overall capability gains.

Layout Structure

Grouped bar chart comparing in-distribution vs OOD performance gaps across model types

Key Visual Elements

  • •In-dist vs OOD bar chart
  • •Model type comparison
  • •Performance gap annotation
  • •Text analysis
  • •Robustness benchmark names
Page 86
data_visualization

Chapter 2 — AI vs. Human Performance: Consolidated View

Content

Consolidated comparison chart showing AI versus human performance across 30+ distinct tasks, with visual markers for tasks where AI now exceeds human performance.

Layout Structure

Horizontal dot plot with task names on y-axis, AI and human scores as paired dots, 'AI exceeds' shading

Key Visual Elements

  • •Horizontal dot plot
  • •Task name list
  • •AI score dots
  • •Human score dots
  • •AI-exceeds shading zone
  • •Legend
Page 87
data_visualization

Chapter 2 — Performance on Emerging Hard Benchmarks

Content

Performance data on newly introduced hard benchmarks designed to resist saturation in 2025: FrontierMath, MMMU-Pro, and Humanity's Last Exam, showing current frontier model capabilities.

Layout Structure

Bar chart of top model scores on three hard benchmarks with human expert baseline

Key Visual Elements

  • •Multi-benchmark bar chart
  • •Hard benchmark labels
  • •Human expert baseline
  • •Model comparison
  • •Score annotations
Page 88
data_visualization

Chapter 2 — Test-Time Compute Scaling

Content

Analysis of the test-time compute scaling paradigm in 2025: how additional inference compute through chain-of-thought, search, and self-verification improves performance on hard tasks.

Layout Structure

Line chart showing performance vs inference compute budget for reasoning models

Key Visual Elements

  • •Performance vs compute chart
  • •Model comparison lines
  • •Compute budget x-axis
  • •Accuracy y-axis
  • •Scaling trend annotation
Page 89
data_visualization

Chapter 2 — Model Calibration and Uncertainty

Content

Analysis of AI model calibration — how well model confidence scores match actual accuracy — showing improvements in calibration for top models but remaining challenges.

Layout Structure

Calibration curve plots for multiple models with ideal calibration diagonal reference

Key Visual Elements

  • •Calibration curves
  • •Ideal diagonal reference
  • •Model comparison lines
  • •Reliability diagram format
  • •Text analysis
Page 90
data_visualization

Chapter 2 — Multilingual Performance Gaps

Content

Heat map showing AI model performance across 100+ languages on standard NLP benchmarks, highlighting the significant performance gap between high-resource and low-resource language groups.

Layout Structure

Language-by-benchmark heat map with performance color gradient and regional groupings

Key Visual Elements

  • •Language heat map
  • •Performance color gradient
  • •Regional groupings
  • •Language name labels
  • •Benchmark column headers
Page 91
data_visualization

Chapter 2 — AI Performance in Specialized Domains

Content

Benchmark performance summary across specialized professional domains: law (Bar exam), medicine (USMLE), finance (CFA), and accounting, showing frontier AI models at or above professional passing scores.

Layout Structure

Bar chart of professional exam pass scores vs AI model scores across four domains

Key Visual Elements

  • •Professional exam comparison bars
  • •Pass score reference lines
  • •AI model score bars
  • •Domain name labels
  • •Performance annotations
Page 92
data_visualization

Chapter 2 — AI in Games and Strategic Reasoning

Content

Overview of AI performance in strategic games and simulations in 2025, including chess, Go, poker, and real-time strategy games, showing superhuman performance across most game categories.

Layout Structure

Rating comparison chart for AI vs top human players across game types

Key Visual Elements

  • •Rating comparison chart
  • •Game type labels
  • •AI vs human rating bars
  • •Superhuman annotation zones
  • •Text analysis
Page 93
data_visualization

Chapter 2 — Embodied AI and Robotics Performance

Content

Benchmark performance data for embodied AI systems on manipulation, navigation, and dexterous control tasks, showing substantial progress but remaining gap to human performance.

Layout Structure

Bar chart of success rates by task category for top robotic AI systems

Key Visual Elements

  • •Success rate bar chart
  • •Task category groups
  • •Robotic system comparison
  • •Human benchmark reference
  • •Text commentary
Page 94
data_visualization

Chapter 2 — AI Performance Cost Efficiency

Content

Analysis of AI performance per dollar of inference cost in 2025, showing dramatic improvements in cost efficiency that are democratizing access to high-capability AI.

Layout Structure

Scatter plot of performance vs cost per query for top models with time-based trend arrows

Key Visual Elements

  • •Performance vs cost scatter
  • •Model name labels
  • •Trend direction arrows
  • •Year annotations
  • •Frontier model callouts
Page 95
data_visualization

Chapter 2 — Frontier Model Capability Comparison

Content

Head-to-head capability comparison of the top 10 frontier models of 2025 across 15 benchmark categories, providing a structured overview of the current AI capability landscape.

Layout Structure

Large comparison table with model names as rows, benchmark categories as columns, color-coded performance tiers

Key Visual Elements

  • •Large comparison table
  • •Model name rows
  • •Benchmark category columns
  • •Color-coded performance tiers
  • •Tier legend
Page 96
data_visualization

Chapter 2 — Performance Trends: Summary

Content

Summary line charts showing performance trends across five major task categories (language, math, code, science, multimodal) from 2018-2025, illustrating the acceleration of AI progress.

Layout Structure

Five-panel small multiple line charts each showing one category trend over time

Key Visual Elements

  • •Five-panel small multiples
  • •Category label per panel
  • •Year x-axis
  • •Performance score y-axis
  • •Human parity reference
  • •Trend acceleration annotation
Page 97
chapter_content

Chapter 2 — Benchmark Design and Limitations

Content

Methodological discussion of AI benchmark design limitations, including data contamination concerns, benchmark overfitting, and the challenge of measuring general intelligence.

Layout Structure

Text analysis with illustrative contamination diagram and checklist of benchmark quality criteria

Key Visual Elements

  • •Contamination risk diagram
  • •Quality criteria checklist
  • •Text analysis
  • •Callout boxes
  • •Section header
Page 98
data_visualization

Chapter 2 — AI Safety Performance Benchmarks

Content

Data on AI model performance on safety and alignment-relevant benchmarks: TruthfulQA, BBQ bias evaluation, and jailbreak resistance testing, showing progress and remaining challenges.

Layout Structure

Bar chart comparing safety benchmark scores across top models with text commentary

Key Visual Elements

  • •Safety benchmark bar chart
  • •Model comparison
  • •TruthfulQA score bars
  • •Bias evaluation results
  • •Jailbreak resistance scores
Page 99
appendix

Chapter 2 — Technical Performance Endnotes

Content

Reference notes for Chapter 2 with data source citations, benchmark methodology references, and definitions of performance metrics used throughout the chapter.

Layout Structure

Two-column endnote format with numbered citations and source references

Key Visual Elements

  • •Numbered citations
  • •Source references
  • •Benchmark links
  • •Two-column layout
Page 100
appendix

Chapter 2 — Technical Performance Further Reading

Content

Curated further reading list for Chapter 2, organized by benchmark category, with key papers and leaderboard resources for continued exploration of AI technical performance.

Layout Structure

Structured reading list with category headers and annotated entries

Key Visual Elements

  • •Reading list
  • •Category headers
  • •Paper citations
  • •Annotation notes
Page 101
data_visualization

Chapter 2 — AI Reasoning: Chain-of-Thought Analysis

Content

Deep-dive analysis of chain-of-thought prompting effects on reasoning performance, showing that explicit step-by-step reasoning improves performance across math, logic, and science benchmarks.

Layout Structure

Bar chart comparing with/without CoT performance plus text analysis of mechanism

Key Visual Elements

  • •With/without CoT bar chart
  • •Task category breakdown
  • •Performance uplift annotations
  • •Text analysis
  • •Mechanism diagram
Page 102
data_visualization

Chapter 2 — Retrieval-Augmented Generation Performance

Content

Analysis of retrieval-augmented generation (RAG) system performance in 2025, showing how combining LLMs with external knowledge retrieval improves factual accuracy significantly.

Layout Structure

Bar chart of factual accuracy with and without RAG across knowledge domains

Key Visual Elements

  • •RAG vs no-RAG comparison bars
  • •Knowledge domain groups
  • •Accuracy improvement annotation
  • •Text analysis
  • •Model comparison
Page 103
data_visualization

Chapter 2 — AI for Structured Data and Tabular Tasks

Content

Benchmark performance of AI models on structured data tasks: SQL generation, table QA, and financial data interpretation, showing strong but imperfect performance.

Layout Structure

Bar chart of structured data task performance with task type breakdown

Key Visual Elements

  • •Structured data performance bars
  • •Task type breakdown
  • •SQL generation accuracy
  • •Table QA scores
  • •Model comparison
Page 104
chapter_content

Chapter 2 — AI Performance Reproducibility

Content

Analysis of reproducibility challenges in AI performance benchmarking, including sensitivity to prompt phrasing, temperature settings, and evaluation protocol variations.

Layout Structure

Variance analysis chart showing performance sensitivity to prompt variation with text commentary

Key Visual Elements

  • •Prompt sensitivity chart
  • •Variance box plots
  • •Text commentary
  • •Reproducibility guidelines
  • •Callout boxes
Page 105
chapter_content

Chapter 2 — Emergent Capabilities Analysis

Content

Discussion of emergent AI capabilities observed in 2025 — skills that appear discontinuously as model scale increases — with examples and debate about whether emergence is real or artifact of evaluation.

Layout Structure

Text analysis with emergence examples table and scale versus capability scatter plot

Key Visual Elements

  • •Scale vs capability scatter
  • •Emergence examples table
  • •Text analysis
  • •Debate callout boxes
  • •Model scale annotations
Page 106
chapter_content

Chapter 2 — AI Benchmark Ecosystem Overview

Content

Overview of the AI benchmarking ecosystem in 2025: key leaderboards, evaluation organizations (HELM, BIG-bench), and the challenge of maintaining meaningful evaluation as models improve.

Layout Structure

Ecosystem diagram showing major benchmarks, organizations, and their relationships

Key Visual Elements

  • •Ecosystem diagram
  • •Organization name nodes
  • •Benchmark name nodes
  • •Relationship arrows
  • •Timeline of key evaluations
Page 107
data_visualization

Chapter 2 — Multimodal Reasoning Performance

Content

Performance data on multimodal reasoning tasks requiring integration of text and visual information, including science diagrams, charts, and math with figures.

Layout Structure

Benchmark comparison bar chart for top multimodal models across reasoning task types

Key Visual Elements

  • •Multimodal reasoning bar chart
  • •Task type groups
  • •Model comparison
  • •Human baseline
  • •Score annotations
Page 108
data_visualization

Chapter 2 — AI in Real-World Deployment: Performance Gap

Content

Analysis of the gap between benchmark performance and real-world deployment performance, including discussion of distribution shift, edge cases, and the benchmark-deployment performance correlation.

Layout Structure

Scatter plot comparing lab benchmark score to real-world performance estimates for applications

Key Visual Elements

  • •Benchmark vs real-world scatter
  • •Application name labels
  • •Gap annotation
  • •Text analysis
  • •Correlation note
Page 109
chapter_summary

Chapter 2 — Technical Performance Chapter Summary

Content

Chapter 2 summary dashboard presenting the headline performance findings: AI at human parity on X tasks, surpassing human experts on Y tasks, and key outstanding challenges.

Layout Structure

Summary dashboard with large-number statistics, task category breakdown, and key-finding callout boxes

Key Visual Elements

  • •Large stat boxes
  • •Task category breakdown
  • •Key finding callouts
  • •Chapter color theme
  • •Progress indicator graphics
Page 110
data_visualization

Chapter 2 — Advanced Coding: Competitive Programming

Content

AI performance on competitive programming benchmarks (CodeForces, IOI problems) in 2025, showing frontier models reaching high-Codeforces-rated performance levels.

Layout Structure

Rating comparison chart with AI system levels mapped to human percentile equivalents

Key Visual Elements

  • •Rating comparison chart
  • •Human percentile reference
  • •AI system labels
  • •Competition level markers
  • •Text analysis
Page 111
data_visualization

Chapter 2 — Math Olympiad Performance

Content

Detailed analysis of AI performance on International Mathematical Olympiad (IMO) style problems in 2025, with frontier models achieving gold-medal-equivalent performance on selected problem sets.

Layout Structure

Problem difficulty vs solution rate chart with model comparison and IMO medal threshold annotations

Key Visual Elements

  • •Difficulty vs solution rate chart
  • •IMO medal thresholds
  • •Model comparison lines
  • •Problem type labels
  • •Text analysis
Page 112
data_visualization

Chapter 2 — AI for Scientific Literature Review

Content

Performance of AI systems on scientific literature review tasks: accurately summarizing papers, identifying methodology errors, and synthesizing findings across multiple papers.

Layout Structure

Accuracy comparison bar chart for literature review sub-tasks with model comparison

Key Visual Elements

  • •Review task bar chart
  • •Sub-task breakdown
  • •Model comparison
  • •Accuracy scores
  • •Text analysis
Page 113
data_visualization

Chapter 2 — Cross-Lingual and Multilingual Benchmarks

Content

Detailed multilingual benchmark performance (XTREME, XGLUE, mMMLU) showing performance gaps between English and non-English tasks, with analysis of low-resource language challenges.

Layout Structure

Heat map of performance by language and benchmark with language family groupings

Key Visual Elements

  • •Performance heat map
  • •Language family groups
  • •Benchmark column headers
  • •Performance color scale
  • •Low-resource callout
Page 114
chapter_content

Chapter 2 — AI Persuasion and Influence Capabilities

Content

Research findings on AI persuasion capabilities in 2025: studies showing AI-generated persuasive text outperforming human-written text in A/B tests, raising concerns about manipulation.

Layout Structure

Bar chart of persuasion study results with text analysis of implications and risk discussion

Key Visual Elements

  • •Persuasion study bar chart
  • •Human vs AI comparison
  • •Risk implication callouts
  • •Text analysis
  • •Research reference boxes
Page 115
chapter_content

Chapter 2 — AI Memory and State Management

Content

Analysis of AI model memory capabilities in 2025: in-context memory limits, external memory augmentation, and stateful agent systems enabling longer-horizon tasks.

Layout Structure

Comparative diagram of memory architectures with performance data on memory-dependent tasks

Key Visual Elements

  • •Memory architecture diagram
  • •Performance comparison
  • •Context window size comparison
  • •External memory callouts
  • •Text analysis
Page 116
data_visualization

Chapter 2 — AI Tool Use and Function Calling

Content

Performance data on AI models using external tools (APIs, calculators, search engines, code interpreters) in 2025, showing substantial improvements in reliable tool use.

Layout Structure

Success rate bar chart for tool use tasks by tool type and model

Key Visual Elements

  • •Tool use success rate bars
  • •Tool type breakdown
  • •Model comparison
  • •Text analysis
  • •Reliability callout
Page 117
data_visualization

Chapter 2 — Specialized Scientific AI Models

Content

Overview of domain-specific AI models for scientific tasks: protein structure, materials science, climate, genomics, and their performance relative to general-purpose models.

Layout Structure

Performance comparison table: specialist vs generalist models across scientific domains

Key Visual Elements

  • •Specialist vs generalist table
  • •Scientific domain rows
  • •Performance comparison columns
  • •Advantage annotation
  • •Text analysis
Page 118
data_visualization

Chapter 2 — AI for Document Processing

Content

Benchmark performance for AI document understanding tasks: PDF parsing, form extraction, contract analysis, and complex document QA in 2025.

Layout Structure

Bar chart of document task performance with task type breakdown and model comparison

Key Visual Elements

  • •Document task bar chart
  • •Task type labels
  • •Model comparison
  • •Accuracy metrics
  • •Text analysis
Page 119
data_visualization

Chapter 2 — Adversarial Robustness

Content

Analysis of AI model robustness against adversarial attacks in 2025, including image perturbation attacks, prompt injection for LLMs, and defense mechanism effectiveness.

Layout Structure

Attack success rate bar chart with defense method comparison and text analysis

Key Visual Elements

  • •Attack success rate bars
  • •Defense method comparison
  • •Attack type breakdown
  • •Text analysis
  • •Risk callout
Page 120
chapter_summary

Chapter 2 — Technical Performance Conclusion

Content

Concluding analysis of Chapter 2 key themes: the acceleration of AI capability gains, the challenge of keeping benchmarks meaningful, and the implications of AI surpassing human performance on an expanding set of tasks.

Layout Structure

Text conclusion with summary chart showing benchmark categories by human-parity status

Key Visual Elements

  • •Summary categorization chart
  • •Human parity status breakdown
  • •Text conclusion
  • •Key finding callouts
  • •Forward-looking notes
Page 121
chapter_opener

Chapter 3 Opener — Responsible AI

Content

Full-bleed chapter opener with dark teal background, large white Chapter 3 numeral, title 'Responsible AI', and brief overview of the chapter's focus on AI safety, bias, harms, and governance.

Layout Structure

Full-bleed dark teal background, oversized chapter number, title and description in white

Key Visual Elements

  • •Dark teal background
  • •Large white '3'
  • •Chapter title in white
  • •Chapter overview text
  • •Stanford HAI logo
  • •Accent line
Page 122
chapter_content

Chapter 3 — Responsible AI Introduction

Content

Introduction to the responsible AI chapter explaining the framework for measuring AI harms, safety research, bias evaluation, and governance in practice across 2025.

Layout Structure

Single-column introduction with section overview diagram and key definitions box

Key Visual Elements

  • •Section overview diagram
  • •Key definitions box
  • •Introduction text
  • •Chapter framework explanation
Page 123
data_visualization

Chapter 3 — AI Incidents Database: Volume Trends

Content

Line chart showing the number of AI-related incidents tracked by the AIAAIC database from 2012-2025, demonstrating more than 1000% growth over the period with steep acceleration from 2022.

Layout Structure

Full-width line chart with year x-axis, incident count y-axis, key incident spikes annotated

Key Visual Elements

  • •Incident count line chart
  • •Year x-axis 2012-2025
  • •Incident count y-axis
  • •Key spike annotations
  • •1000% growth callout
  • •Source attribution
Page 124
data_visualization

Chapter 3 — AI Incidents: By Category

Content

Stacked bar chart breaking down AI incidents in 2025 by harm category: disinformation/deepfakes, privacy violations, discrimination, autonomous system failures, and cybersecurity breaches.

Layout Structure

Stacked horizontal bar chart with harm category color coding and percentage labels

Key Visual Elements

  • •Stacked bar chart
  • •Harm category legend
  • •Category color coding
  • •Percentage labels
  • •Year comparison
Page 125
data_visualization

Chapter 3 — AI Incidents: Geographic Distribution

Content

World map and bar chart showing the geographic distribution of reported AI incidents in 2025, with the US, EU, and China accounting for the majority of tracked incidents.

Layout Structure

World map with incident density bubbles plus bar chart of top 15 countries by incident count

Key Visual Elements

  • •World incident map
  • •Density bubbles
  • •Country bar chart
  • •Geographic color coding
  • •Data source note
Page 126
chapter_content

Chapter 3 — Disinformation and Deepfakes

Content

Analysis of AI-generated disinformation incidents in 2025: deepfake videos targeting elections, synthetic audio fraud, and AI-generated news articles spread at scale.

Layout Structure

Timeline of major 2025 deepfake incidents with text analysis and detection capability chart

Key Visual Elements

  • •Incident timeline
  • •Detection capability chart
  • •Case study callout boxes
  • •Text analysis
  • •Statistics panel
Page 127
data_visualization

Chapter 3 — AI Deepfake Detection Capabilities

Content

Comparison of deepfake detection technology performance in 2025 versus deepfake generation quality, showing the ongoing arms race with detection consistently lagging generation.

Layout Structure

Dual-line chart of generation quality vs detection accuracy over time with arms-race gap annotation

Key Visual Elements

  • •Generation vs detection line chart
  • •Arms race gap annotation
  • •Year x-axis
  • •Performance score y-axis
  • •Technology label annotations
Page 128
chapter_content

Chapter 3 — Bias in AI Systems: Overview

Content

Overview of bias measurement and mitigation research in AI systems in 2025, covering hiring algorithms, credit scoring, healthcare triage, and facial recognition across demographic groups.

Layout Structure

Framework diagram of bias types and mitigation approaches with text overview

Key Visual Elements

  • •Bias taxonomy diagram
  • •Mitigation approach framework
  • •Text overview
  • •Domain examples
  • •Research citation boxes
Page 129
data_visualization

Chapter 3 — Bias Evaluation: Hiring and Employment

Content

Experimental results from bias audits of AI hiring tools in 2025, showing differential screening rates by race, gender, and age in automated resume screening systems.

Layout Structure

Bar chart of screening rates by demographic group with disparity ratio annotations

Key Visual Elements

  • •Demographic disparity bar chart
  • •Group comparison
  • •Disparity ratio annotations
  • •Legal threshold reference
  • •Text analysis
Page 130
data_visualization

Chapter 3 — Bias Evaluation: Healthcare Algorithms

Content

Analysis of bias in healthcare AI algorithms in 2025, focusing on differential accuracy of diagnostic AI across racial and socioeconomic groups and the causes of performance gaps.

Layout Structure

Bar chart of diagnostic accuracy by demographic group across multiple AI tools

Key Visual Elements

  • •Accuracy by demographic chart
  • •Algorithm comparison
  • •Disparity gap annotation
  • •Text analysis
  • •Healthcare domain callouts
Page 131
data_visualization

Chapter 3 — Bias Evaluation: Credit and Finance

Content

Data on AI bias in credit scoring and financial services, showing differential loan approval rates and risk score distributions across demographic groups in AI-assisted lending.

Layout Structure

Bar chart of approval rates and score distributions by demographic with text analysis

Key Visual Elements

  • •Approval rate bar chart
  • •Score distribution chart
  • •Demographic group breakdown
  • •Disparity annotation
  • •Text analysis
Page 132
data_visualization

Chapter 3 — Facial Recognition Accuracy Disparities

Content

Benchmark results for facial recognition systems across demographic groups in 2025, showing continued accuracy disparities despite progress, with analysis of regulatory responses.

Layout Structure

Error rate comparison chart by demographic group with regulatory response callouts

Key Visual Elements

  • •Error rate by demographic chart
  • •Group comparison bars
  • •Disparity annotation
  • •Regulatory callout boxes
  • •Text analysis
Page 133
data_visualization

Chapter 3 — Bias Mitigation Research Trends

Content

Line chart tracking the volume of AI fairness and bias mitigation research publications from 2015-2025, showing the field's rapid growth alongside increasing corporate fairness commitments.

Layout Structure

Line chart of publication volume plus bar chart of corporate fairness initiative announcements by year

Key Visual Elements

  • •Publication volume line chart
  • •Corporate initiative bar chart
  • •Year x-axis
  • •Dual y-axes
  • •Key milestone annotations
Page 134
data_visualization

Chapter 3 — AI Safety Research: Growth

Content

Line chart showing the growth of AI safety research publications and dedicated safety research organizations from 2016-2025, demonstrating the field's rapid institutionalization.

Layout Structure

Dual-line chart of safety publications and safety org count over time

Key Visual Elements

  • •Safety research line chart
  • •Org count line
  • •Year x-axis
  • •Dual y-axes
  • •Key org founding annotations
Page 135
chapter_content

Chapter 3 — AI Safety Research: Key Organizations

Content

Overview of major AI safety research organizations active in 2025: Anthropic, DeepMind Safety, ARC Evals, Redwood Research, METR, Apollo Research, and others.

Layout Structure

Organization profile cards in a grid with founding date, focus area, and key outputs per org

Key Visual Elements

  • •Organization profile cards
  • •Grid layout
  • •Focus area labels
  • •Founding date
  • •Key output descriptions
Page 136
chapter_content

Chapter 3 — Red-Teaming: Adoption and Standards

Content

Analysis of AI red-teaming adoption across major AI labs in 2025, showing red-teaming has become standard practice, with emerging standards from NIST and government frameworks.

Layout Structure

Adoption timeline chart plus text analysis of red-teaming methodologies and findings

Key Visual Elements

  • •Adoption timeline
  • •Red-teaming methodology diagram
  • •NIST framework callout
  • •Lab comparison table
  • •Text analysis
Page 137
data_visualization

Chapter 3 — Red-Teaming: Findings Summary

Content

Summary of disclosed red-teaming findings across major AI labs in 2025: common vulnerability categories, jailbreak technique prevalence, and the efficacy of different defense approaches.

Layout Structure

Vulnerability category bar chart with defense efficacy comparison table

Key Visual Elements

  • •Vulnerability category bars
  • •Defense efficacy table
  • •Category breakdown
  • •Lab comparison
  • •Text analysis
Page 138
chapter_content

Chapter 3 — Watermarking and AI Detection

Content

Overview of AI-generated content watermarking and detection technologies in 2025: C2PA provenance standards, AI text detectors, and image watermarking adoption by major platforms.

Layout Structure

Technology landscape diagram with adoption metrics and text analysis of effectiveness

Key Visual Elements

  • •Technology landscape diagram
  • •C2PA callout
  • •Adoption metrics
  • •Detection accuracy data
  • •Text analysis
Page 139
chapter_content

Chapter 3 — Privacy and AI Systems

Content

Analysis of AI privacy risks and protective measures in 2025: training data memorization, model inversion attacks, differential privacy adoption, and regulatory compliance challenges.

Layout Structure

Risk taxonomy diagram with adoption rate chart for privacy-preserving techniques

Key Visual Elements

  • •Privacy risk taxonomy
  • •DP adoption chart
  • •Regulatory callout
  • •Memorization risk data
  • •Text analysis
Page 140
chapter_content

Chapter 3 — AI and Cybersecurity

Content

Analysis of AI's dual role in cybersecurity in 2025: AI-powered threat detection and defense versus AI-enabled attack automation, with data on incident rates for both.

Layout Structure

Side-by-side bar charts for AI defense applications and AI attack applications with text analysis

Key Visual Elements

  • •Dual bar charts
  • •Defense vs attack framing
  • •Incident rate data
  • •Text analysis
  • •Risk callout boxes
Page 141
chapter_content

Chapter 3 — AI Safety: Capability Evaluations

Content

Overview of capability evaluation frameworks used by frontier AI labs in 2025 to assess dangerous capabilities before model deployment, including CBRN uplift testing.

Layout Structure

Evaluation framework diagram with capability category definitions and lab adoption table

Key Visual Elements

  • •Evaluation framework diagram
  • •Capability category list
  • •Lab adoption table
  • •CBRN callout
  • •Text analysis
Page 142
data_visualization

Chapter 3 — AI Risk Taxonomies and Frameworks

Content

Comparison of major AI risk taxonomies and frameworks in 2025: NIST AI RMF, EU AI Act risk tiers, OECD AI risk framework, and industry-developed risk classifications.

Layout Structure

Side-by-side comparison table of four frameworks with risk categories mapped across frameworks

Key Visual Elements

  • •Framework comparison table
  • •Risk category rows
  • •Framework column headers
  • •Overlap annotation
  • •Text commentary
Page 143
data_visualization

Chapter 3 — NIST AI Risk Management Framework Adoption

Content

Data on NIST AI RMF adoption rates across US federal agencies and private sector organizations in 2025, showing growing but uneven uptake.

Layout Structure

Adoption rate bar chart by sector and organization size with text analysis

Key Visual Elements

  • •Adoption rate bar chart
  • •Sector breakdown
  • •Organization size comparison
  • •Year-over-year growth
  • •Text analysis
Page 144
data_visualization

Chapter 3 — AI Model Cards and Transparency

Content

Analysis of model card and AI transparency documentation adoption in 2025, showing increased disclosure by major labs but inconsistent standards and depth across the industry.

Layout Structure

Adoption metrics bar chart plus quality scorecard comparing model card depth across major labs

Key Visual Elements

  • •Adoption metrics bars
  • •Quality scorecard
  • •Lab comparison
  • •Disclosure depth rating
  • •Text analysis
Page 145
chapter_content

Chapter 3 — Autonomous Weapons and Military AI

Content

Overview of AI applications in military and defense contexts in 2025: autonomous weapons development, targeting AI, AI-enabled surveillance, and international law challenges.

Layout Structure

Text analysis with global deployment map and risk factor callout boxes

Key Visual Elements

  • •Global deployment map
  • •Risk factor callouts
  • •Text analysis
  • •International law reference
  • •Defense spending callout
Page 146
data_visualization

Chapter 3 — AI in Criminal Justice

Content

Data on AI use in criminal justice settings in 2025: predictive policing, sentencing algorithms, bail decision tools, and documented bias concerns and legal challenges.

Layout Structure

Adoption map by US state with bias audit results chart and legal challenge timeline

Key Visual Elements

  • •US state adoption map
  • •Bias audit results
  • •Legal challenge timeline
  • •Text analysis
  • •Reform callout
Page 147
data_visualization

Chapter 3 — AI Surveillance Technologies

Content

Global analysis of AI-powered surveillance technology deployment in 2025: facial recognition in public spaces, behavior monitoring, and country-level adoption and restrictions.

Layout Structure

World map of surveillance technology adoption with regulatory restriction overlay

Key Visual Elements

  • •World surveillance adoption map
  • •Restriction overlay
  • •Country color coding
  • •Text analysis
  • •Civil liberties callout
Page 148
data_visualization

Chapter 3 — Environmental Concerns in AI Development

Content

Analysis of growing AI environmental impact concerns in 2025, focusing on data center water usage, carbon emissions from AI workloads, and sustainability initiatives by major AI companies.

Layout Structure

Bar chart of environmental metrics per company with sustainability commitment comparison table

Key Visual Elements

  • •Environmental metrics bar chart
  • •Water usage data
  • •Carbon emissions data
  • •Sustainability commitment table
  • •Text analysis
Page 149
chapter_content

Chapter 3 — AI and Labor Rights

Content

Overview of labor concerns related to AI in 2025: data labeling worker conditions, content moderation worker mental health, and AI-driven job displacement discussions.

Layout Structure

Text analysis with survey data on labeler working conditions and job displacement projection charts

Key Visual Elements

  • •Working conditions survey data
  • •Displacement projection chart
  • •Text analysis
  • •Labor rights callout
  • •Geographic distribution
Page 150
data_visualization

Chapter 3 — Content Moderation and AI

Content

Analysis of AI's role in content moderation on major platforms in 2025, including detection accuracy for harmful content categories and the challenge of false positives for marginalized communities.

Layout Structure

Accuracy metrics bar chart by content type with false positive rate breakdown by community

Key Visual Elements

  • •Content moderation accuracy bars
  • •False positive breakdown
  • •Content type labels
  • •Community group comparison
  • •Text analysis
Page 151
data_visualization

Chapter 3 — AI Responsible Use Policies: Corporate

Content

Overview of corporate AI responsible use policies at major AI companies in 2025, including usage policies, prohibited use cases, and enforcement mechanisms.

Layout Structure

Policy comparison table across major AI companies with use-case prohibition checklist

Key Visual Elements

  • •Policy comparison table
  • •Company name rows
  • •Use case columns
  • •Prohibition checkmarks
  • •Text analysis
Page 152
data_visualization

Chapter 3 — AI Governance in Practice: Industry

Content

Survey data on AI governance practices in industry: percentage of firms with AI ethics boards, responsible AI teams, impact assessments, and governance review processes.

Layout Structure

Bar chart of governance practice adoption rates by firm size and sector

Key Visual Elements

  • •Governance adoption bar chart
  • •Firm size breakdown
  • •Sector comparison
  • •Adoption rate y-axis
  • •Text analysis
Page 153
chapter_content

Chapter 3 — International AI Safety Coordination

Content

Overview of international AI safety coordination efforts in 2025: the Paris AI Safety Summit, the Bletchley Park follow-up, the Seoul AI Declaration, and multilateral safety research initiatives.

Layout Structure

Timeline of international safety summits and agreements with text analysis of commitments

Key Visual Elements

  • •International summit timeline
  • •Agreement callout boxes
  • •Country participation map
  • •Commitment description text
  • •Text analysis
Page 154
chapter_content

Chapter 3 — AI Incident Response: Case Studies

Content

Three detailed case studies of significant AI incidents in 2025: a major deepfake election disinformation campaign, a healthcare AI diagnostic error cluster, and an autonomous vehicle accident series.

Layout Structure

Three-panel case study layout with incident description, response, and lessons learned per case

Key Visual Elements

  • •Case study panels
  • •Incident description text
  • •Response timeline
  • •Lessons learned bullets
  • •Impact metrics callout
Page 155
data_visualization

Chapter 3 — AI Safety Benchmarks: Dangerous Capabilities

Content

Data on AI model performance on dangerous capability evaluation benchmarks in 2025, including CBRN uplift assessments and biosecurity risk evaluations used in pre-deployment testing.

Layout Structure

Bar chart of capability evaluation results with risk tier thresholds and lab comparison

Key Visual Elements

  • •Capability evaluation bars
  • •Risk tier thresholds
  • •Lab comparison
  • •Text analysis
  • •Safety threshold annotations
Page 156
chapter_content

Chapter 3 — AI Alignment Research Overview

Content

Overview of AI alignment research approaches in 2025: RLHF/RLAIF, Constitutional AI, debate and amplification, mechanistic interpretability, and scalable oversight techniques.

Layout Structure

Research approach taxonomy diagram with publication volume chart per approach

Key Visual Elements

  • •Alignment research taxonomy
  • •Publication volume bars
  • •Approach description text
  • •Technique comparison
  • •Text analysis
Page 157
data_visualization

Chapter 3 — Interpretability and Explainability Research

Content

Analysis of AI interpretability and explainability research growth in 2025, covering mechanistic interpretability techniques, saliency maps, and their practical deployment in regulated industries.

Layout Structure

Publication growth line chart plus adoption rate in regulated industries bar chart

Key Visual Elements

  • •Interpretability research line chart
  • •Industry adoption bars
  • •Technique taxonomy
  • •Year x-axis
  • •Text analysis
Page 158
data_visualization

Chapter 3 — AI Mental Health Applications and Harms

Content

Data on AI mental health applications and associated risks in 2025: chatbot-based mental health support deployment, efficacy data, and incidents involving harmful AI mental health interactions.

Layout Structure

Deployment metrics with efficacy chart and incident category breakdown

Key Visual Elements

  • •Deployment metrics
  • •Efficacy comparison chart
  • •Incident breakdown
  • •Text analysis
  • •Regulatory callout
Page 159
chapter_content

Chapter 3 — Children and AI Safety

Content

Overview of child safety concerns related to AI systems in 2025: CSAM detection, age-appropriate AI experiences, children's AI use in education, and regulatory protections.

Layout Structure

Text analysis with regulatory landscape map and safety measure adoption chart

Key Visual Elements

  • •Regulatory landscape overview
  • •Safety measure adoption
  • •Text analysis
  • •Age-appropriate AI callout
  • •Policy reference boxes
Page 160
data_visualization

Chapter 3 — AI Bias Mitigation: Effectiveness

Content

Experimental data on the effectiveness of leading bias mitigation techniques in 2025, comparing pre-processing, in-processing, and post-processing approaches across bias metrics.

Layout Structure

Comparative bar chart of bias metric reduction by technique with accuracy cost analysis

Key Visual Elements

  • •Bias reduction bar chart
  • •Technique comparison
  • •Accuracy cost analysis
  • •Bias metric labels
  • •Text analysis
Page 161
data_visualization

Chapter 3 — Responsible AI: Workforce and Roles

Content

Data on responsible AI job roles in 2025: AI ethicist, AI safety engineer, responsible AI program manager postings, salaries, and the supply-demand gap in the field.

Layout Structure

Job posting trend chart and salary comparison with supply-demand gap callout

Key Visual Elements

  • •Job posting trend
  • •Salary comparison
  • •Supply-demand gap
  • •Role category labels
  • •Text analysis
Page 162
chapter_content

Chapter 3 — AI Auditing and Third-Party Evaluation

Content

Overview of the emerging AI auditing ecosystem in 2025: third-party auditors, algorithmic audit standards, mandatory audit requirements in various jurisdictions, and industry self-assessment tools.

Layout Structure

Ecosystem diagram of AI audit organizations plus regulatory requirement comparison table

Key Visual Elements

  • •Audit ecosystem diagram
  • •Regulatory requirement table
  • •Organization list
  • •Standard comparison
  • •Text analysis
Page 163
chapter_content

Chapter 3 — Open-Source AI Safety Concerns

Content

Analysis of responsible AI concerns specific to open-source model releases in 2025, including dual-use risks, post-release safety, and community safety practices.

Layout Structure

Risk taxonomy for open-source AI with mitigation approach comparison and text analysis

Key Visual Elements

  • •Open-source risk taxonomy
  • •Mitigation comparison
  • •Dual-use risk callout
  • •Community practice examples
  • •Text analysis
Page 164
chapter_content

Chapter 3 — AI Safety Summit Outcomes

Content

Summary of outcomes from the Paris AI Safety Summit (February 2025) and subsequent international safety commitments, including the GPAI Code of Practice milestones.

Layout Structure

Outcome summary cards per summit with commitment tracking status indicators

Key Visual Elements

  • •Summit outcome cards
  • •Commitment status indicators
  • •GPAI Code callout
  • •Country signatory list
  • •Text analysis
Page 165
chapter_summary

Chapter 3 — Responsible AI Chapter Summary

Content

Summary dashboard for Chapter 3 presenting key responsible AI metrics: AI incident growth, bias audit results, safety research growth, and governance adoption rates.

Layout Structure

Dashboard grid with large-number statistics, trend sparklines, and key finding callout boxes

Key Visual Elements

  • •Large stat boxes
  • •Trend sparklines
  • •Key finding callouts
  • •Chapter teal color theme
  • •Progress indicators
Page 166
data_visualization

Chapter 3 — AI Toxicity and Harmful Output

Content

Analysis of AI system tendencies to generate toxic, harmful, or inappropriate outputs in 2025, including toxicity benchmark results and the effectiveness of safety fine-tuning.

Layout Structure

Toxicity rate comparison bars with model improvement trends and text analysis

Key Visual Elements

  • •Toxicity rate bars
  • •Model comparison
  • •Improvement trend
  • •Text analysis
  • •Safety fine-tuning callout
Page 167
chapter_content

Chapter 3 — Copyright and Intellectual Property in AI

Content

Overview of AI copyright and intellectual property challenges in 2025: training data rights, AI-generated content IP status, major legal cases, and emerging regulatory frameworks.

Layout Structure

Legal case timeline with policy framework comparison and text analysis

Key Visual Elements

  • •Legal case timeline
  • •Policy framework comparison
  • •Court ruling callouts
  • •Text analysis
  • •Jurisdiction comparison
Page 168
chapter_content

Chapter 3 — AI in Disinformation: Election Impact

Content

Analysis of documented AI-enabled disinformation campaigns targeting elections in 2025, including scale estimates, platform responses, and detection challenges.

Layout Structure

Campaign timeline with scale estimates and platform response comparison table

Key Visual Elements

  • •Disinformation campaign timeline
  • •Scale estimates
  • •Platform response table
  • •Detection challenge callout
  • •Text analysis
Page 169
chapter_content

Chapter 3 — Accountability Gaps in AI Deployment

Content

Analysis of accountability gaps in AI deployment chains in 2025: diffuse responsibility across developers, deployers, and users, and efforts to assign liability.

Layout Structure

Accountability gap diagram with liability framework comparison across jurisdictions

Key Visual Elements

  • •Accountability gap diagram
  • •Liability framework comparison
  • •Jurisdiction comparison
  • •Text analysis
  • •Reform callout
Page 170
data_visualization

Chapter 3 — AI and Human Rights

Content

Overview of AI systems' impact on human rights in 2025, from surveillance and free expression to access to justice and the right to explanation in automated decisions.

Layout Structure

Rights impact matrix table with AI application rows and human rights columns, with impact rating cells

Key Visual Elements

  • •Rights impact matrix
  • •Application rows
  • •Rights columns
  • •Impact rating cells
  • •Text analysis
Page 171
chapter_content

Chapter 3 — AI Safety: Technical Appendix Preview

Content

Preview of technical safety evaluation methodologies referenced in Chapter 3, including red-team protocols, capability evaluation procedures, and bias measurement standards.

Layout Structure

Methodology overview diagram with reference list and text explanations

Key Visual Elements

  • •Methodology overview diagram
  • •Reference list
  • •Evaluation procedure callouts
  • •Technical note boxes
  • •Text analysis
Page 172
data_visualization

Chapter 3 — Responsible AI Global Landscape

Content

World map visualization of responsible AI regulatory and governance activity in 2025, showing regulation density by country and the contrast between EU, US, and Asian approaches.

Layout Structure

World choropleth map with regulation intensity color coding and regional callout boxes

Key Visual Elements

  • •World choropleth map
  • •Regulation intensity colors
  • •Regional callout boxes
  • •Legend
  • •Text analysis
Page 173
data_visualization

Chapter 3 — AI Ethics Principles Comparison

Content

Comparative analysis of AI ethics principles adopted by governments, companies, and international organizations in 2025, mapping overlaps and divergences.

Layout Structure

Venn/overlap diagram of ethics principle themes across 20+ major frameworks

Key Visual Elements

  • •Ethics principle overlap diagram
  • •Principle theme labels
  • •Framework source list
  • •Overlap statistics
  • •Text analysis
Page 174
appendix

Chapter 3 — Responsible AI Endnotes

Content

Reference notes for Chapter 3 including primary data sources for incident data, bias studies, safety research, and governance framework citations.

Layout Structure

Two-column numbered endnote format

Key Visual Elements

  • •Numbered citations
  • •Source references
  • •Two-column layout
Page 175
appendix

Chapter 3 — Responsible AI Further Reading

Content

Curated further reading for Chapter 3 organized by section: safety research, bias and fairness, governance, and incident analysis, with brief annotations.

Layout Structure

Structured reading list with section headers and entries

Key Visual Elements

  • •Reading list
  • •Section headers
  • •Annotated entries
Page 176
chapter_opener

Chapter 4 Opener — Economy

Content

Full-bleed chapter opener with deep navy blue background, large white Chapter 4 numeral, title 'Economy', and brief chapter description covering AI investment, labor, and adoption trends.

Layout Structure

Full-bleed deep navy background, oversized chapter number, white title and description

Key Visual Elements

  • •Deep navy blue background
  • •Large white '4'
  • •Chapter title in white
  • •Chapter overview text
  • •Stanford HAI logo
  • •Accent line
Page 177
chapter_content

Chapter 4 — Economy Introduction

Content

Introduction to the economy chapter, framing AI's economic footprint in 2025 across private investment, labor markets, firm adoption, and productivity effects.

Layout Structure

Single-column introduction with chapter highlights callout boxes and section overview

Key Visual Elements

  • •Highlights callout boxes
  • •Section overview
  • •Introduction text
  • •Key stat callouts
Page 178
data_visualization

Chapter 4 — Global Private AI Investment: Trends

Content

Line chart showing global private AI investment from 2013-2025, reaching over $470 billion globally in 2025, with the US accounting for $285.9 billion, a tripling from 2022 levels.

Layout Structure

Line chart with global and US-specific investment lines, year x-axis, dollar amount y-axis, key milestone annotations

Key Visual Elements

  • •Global investment line
  • •US investment line
  • •Year x-axis
  • •Dollar amount y-axis
  • •$285.9B callout
  • •$470B+ annotation
  • •Tripling annotation
Page 179
data_visualization

Chapter 4 — Global Private AI Investment: By Country

Content

Bar chart ranking countries by private AI investment in 2025, with the US leading significantly, followed by China, UK, India, and Canada.

Layout Structure

Horizontal ranked bar chart with country names on y-axis and investment amounts on x-axis

Key Visual Elements

  • •Horizontal bar chart
  • •Country name labels
  • •Investment amount labels
  • •Color coding by geography
  • •Year label
Page 180
data_visualization

Chapter 4 — Private AI Investment: By Sector

Content

Breakdown of AI private investment by industry sector in 2025: enterprise software, healthcare, autonomous vehicles, financial services, cybersecurity, education, and retail.

Layout Structure

Pie chart and accompanying bar chart showing sector investment shares and amounts

Key Visual Elements

  • •Pie chart sectors
  • •Bar chart amounts
  • •Sector name labels
  • •Dollar amount labels
  • •Year-over-year change
Page 181
data_visualization

Chapter 4 — Generative AI Investment Surge

Content

Analysis of the generative AI investment surge in 2025, with a bar chart showing generative AI's share of total AI investment growing from 15% in 2023 to over 45% in 2025.

Layout Structure

Bar chart of generative AI investment share by year with total investment overlay line

Key Visual Elements

  • •GenAI share bar chart
  • •Total investment overlay line
  • •Year x-axis
  • •Percentage y-axis
  • •Share growth annotation
  • •Key company investments callout
Page 182
data_visualization

Chapter 4 — AI Mega-Rounds and Unicorns

Content

Data on AI company funding mega-rounds ($100M+) in 2025 and the global AI unicorn count, showing acceleration in billion-dollar valuations for AI-native companies.

Layout Structure

Bubble chart of mega-round deals by company with table of top 20 AI unicorns

Key Visual Elements

  • •Mega-round bubble chart
  • •Unicorn count table
  • •Deal size bubbles
  • •Company name labels
  • •Year comparison
Page 183
data_visualization

Chapter 4 — AI Mergers and Acquisitions

Content

Analysis of AI-related mergers and acquisitions activity in 2025, showing record M&A volumes and the strategic consolidation of AI talent and technology by large technology companies.

Layout Structure

M&A deal volume bar chart by year with deal type breakdown and key deal callouts

Key Visual Elements

  • •M&A volume bar chart
  • •Deal type breakdown
  • •Key deal callout boxes
  • •Year x-axis
  • •Strategic motivation analysis
Page 184
data_visualization

Chapter 4 — AI Investment: Geographic Concentration

Content

World map and Gini coefficient analysis showing the geographic concentration of AI private investment, with the US, China, and UK accounting for over 80% of global AI investment.

Layout Structure

World choropleth map of investment concentration plus concentration metrics panel

Key Visual Elements

  • •Investment concentration map
  • •Gini coefficient callout
  • •Country share percentages
  • •Regional bar chart
  • •Text analysis
Page 185
data_visualization

Chapter 4 — Fortune 500 AI Adoption

Content

Analysis of Fortune 500 companies' AI adoption in 2025: over 90% mention AI in earnings calls, with McKinsey survey data showing 70% using AI in at least one business function.

Layout Structure

Adoption trend line chart plus sector breakdown bar chart with mentions-to-adoption funnel diagram

Key Visual Elements

  • •AI mention trend line
  • •Sector adoption bars
  • •Earnings call mention callout
  • •70% adoption stat
  • •Funnel diagram
Page 186
data_visualization

Chapter 4 — AI Adoption by Firm Size

Content

Bar chart comparing AI adoption rates across firm sizes (large, mid-size, small, micro) showing significant adoption gaps and the challenges facing smaller firms in accessing AI tools.

Layout Structure

Grouped bar chart by firm size with adoption metric breakdown and text analysis

Key Visual Elements

  • •Firm size bar chart
  • •Adoption rate breakdown
  • •Size group comparison
  • •Gap annotation
  • •Text analysis
Page 187
data_visualization

Chapter 4 — AI Adoption by Industry Sector

Content

Sectoral breakdown of AI adoption in 2025: financial services and technology leading adoption, followed by healthcare, retail, and manufacturing, with education and government trailing.

Layout Structure

Horizontal bar chart ranking sectors by AI adoption rate with technology, use-case, and investment data

Key Visual Elements

  • •Sector adoption ranking bars
  • •Adoption rate percentages
  • •Leading sector callout
  • •Lagging sector callout
  • •Year comparison
Page 188
data_visualization

Chapter 4 — AI Productivity Effects: Evidence

Content

Summary of empirical studies measuring AI productivity effects across industries in 2025, showing documented gains in coding productivity, knowledge work output, and customer service efficiency.

Layout Structure

Study results summary table with productivity gain estimates and text analysis of methodology

Key Visual Elements

  • •Productivity gain table
  • •Study source citations
  • •Gain estimate ranges
  • •Sector breakdown
  • •Text analysis
Page 189
data_visualization

Chapter 4 — AI Productivity: Knowledge Work

Content

Analysis of AI productivity gains in knowledge work tasks: writing, coding, research, analysis, and customer service, with field study estimates ranging from 20-80% efficiency gains.

Layout Structure

Bar chart of productivity gains by task type with study source citations and error bars

Key Visual Elements

  • •Task productivity bar chart
  • •Gain percentage labels
  • •Study citations
  • •Error bars
  • •Task type breakdown
Page 190
data_visualization

Chapter 4 — AI and Labor Markets: Job Posting Trends

Content

Line chart showing AI-related job postings growth from 2019-2025, with approximately 30% year-over-year growth in 2025 and AI skills appearing in a growing share of non-AI-specialist postings.

Layout Structure

Dual-line chart of AI specialist postings and AI-skill-required postings over time

Key Visual Elements

  • •AI job postings line chart
  • •AI-specialist vs AI-skill lines
  • •Year x-axis
  • •Job count y-axis
  • •30% growth callout
Page 191
data_visualization

Chapter 4 — AI Wage Premium

Content

Bar chart showing wage premiums for AI-skilled workers in 2025, with AI roles commanding 30-40% wage premiums over comparable non-AI roles across software engineering, data science, and research.

Layout Structure

Wage premium bar chart by role type with comparison to non-AI equivalents

Key Visual Elements

  • •Wage premium bar chart
  • •Role type comparison
  • •30-40% premium callout
  • •Non-AI baseline bars
  • •Text analysis
Page 192
data_visualization

Chapter 4 — Occupational Exposure to AI

Content

Analysis of occupational groups' exposure to AI automation and augmentation in 2025, showing high-wage cognitive work highly exposed to augmentation and routine task workers at displacement risk.

Layout Structure

Scatter plot of occupational wage versus AI exposure with occupation cluster labels

Key Visual Elements

  • •Wage vs exposure scatter
  • •Occupation cluster labels
  • •Augmentation vs displacement zones
  • •Text analysis
  • •Risk tier color coding
Page 193
data_visualization

Chapter 4 — AI and Employment: Macro Evidence

Content

Analysis of macro-level employment data in AI-leading economies, showing strong employment in AI-intensive sectors despite automation concerns, with nuanced sectoral patterns.

Layout Structure

Employment trend charts by sector with AI-intensity overlay and text analysis

Key Visual Elements

  • •Employment trend lines
  • •Sector breakdown
  • •AI-intensity overlay
  • •Text analysis
  • •Macro employment callout
Page 194
data_visualization

Chapter 4 — AI and Wages: Distributional Effects

Content

Analysis of AI's distributional effects on wages: evidence of wage gains concentrated in high-education, high-skill workers, with limited wage growth for low-skill workers.

Layout Structure

Wage growth comparison chart by education level with distribution curve insets

Key Visual Elements

  • •Wage growth by education bars
  • •Distribution curve insets
  • •Text analysis
  • •Inequality callout
  • •Year comparison
Page 195
data_visualization

Chapter 4 — AI and Job Displacement: Current Evidence

Content

Summary of current empirical evidence on AI-related job displacement in 2025, distinguishing documented cases from projected displacement scenarios, with sector-level displacement data.

Layout Structure

Displacement evidence table with sector rows, documented vs projected columns, and text commentary

Key Visual Elements

  • •Displacement evidence table
  • •Sector rows
  • •Documented vs projected columns
  • •Text commentary
  • •Uncertainty callout
Page 196
data_visualization

Chapter 4 — AI Skills Gap Analysis

Content

Analysis of the AI skills gap in 2025: shortage of AI-skilled workers across virtually all sectors, the gap between AI job demand and qualified supply, and retraining challenges.

Layout Structure

Supply-demand gap chart by role and region with text analysis of gap causes

Key Visual Elements

  • •Supply-demand gap chart
  • •Role type comparison
  • •Regional gap breakdown
  • •Text analysis
  • •Retraining callout
Page 197
chapter_content

Chapter 4 — AI and Economic Inequality

Content

Discussion of AI's potential role in exacerbating economic inequality between high-AI-capability nations and lower-income countries, and between high-skill and low-skill workers within economies.

Layout Structure

Text analysis with inequality projection chart and regional capability gap visualization

Key Visual Elements

  • •Inequality projection chart
  • •Regional gap visualization
  • •Text analysis
  • •Policy recommendation callout
  • •Data reference boxes
Page 198
data_visualization

Chapter 4 — AI Infrastructure Investment

Content

Data on AI infrastructure investment in 2025: data center construction, GPU/chip procurement, electricity grid investments, and hyperscaler capital expenditure driven by AI demand.

Layout Structure

Infrastructure investment bar chart by category with year-over-year growth annotations

Key Visual Elements

  • •Infrastructure investment bars
  • •Category breakdown
  • •Year-over-year growth
  • •Hyperscaler capex callout
  • •Text analysis
Page 199
data_visualization

Chapter 4 — AI and Financial Markets

Content

Analysis of AI's impact on financial markets in 2025: AI company stock performance, AI-driven trading algorithm concerns, and investor sentiment data for AI sector stocks.

Layout Structure

AI sector stock index performance chart with market cap callouts and text analysis

Key Visual Elements

  • •AI sector stock chart
  • •Market cap callouts
  • •Year-over-year return
  • •Text analysis
  • •Investor sentiment note
Page 200
data_visualization

Chapter 4 — AI in Customer Service

Content

Data on AI deployment in customer service in 2025: chatbot adoption, resolution rate improvements, cost reduction metrics, and consumer satisfaction with AI customer service.

Layout Structure

Adoption metrics chart with satisfaction comparison and cost reduction analysis

Key Visual Elements

  • •Adoption metrics
  • •Satisfaction comparison
  • •Cost reduction bars
  • •Industry comparison
  • •Text analysis
Page 201
data_visualization

Chapter 4 — AI in Marketing and Advertising

Content

Analysis of AI adoption in marketing and advertising in 2025: generative AI for content creation, AI-driven ad optimization, personalization at scale, and ROI evidence.

Layout Structure

Adoption rate chart with ROI evidence table and content creation cost reduction data

Key Visual Elements

  • •Adoption rate chart
  • •ROI evidence table
  • •Cost reduction data
  • •Personalization metric
  • •Text analysis
Page 202
data_visualization

Chapter 4 — AI in Legal Services

Content

Overview of AI adoption in legal services in 2025: contract review automation, legal research AI, e-discovery tools, and the impact on legal work hours and billable rates.

Layout Structure

Time savings bar chart by legal task type with adoption rate and text analysis

Key Visual Elements

  • •Legal task time savings
  • •Adoption rate bars
  • •Billable rate impact
  • •Text analysis
  • •Tool name callouts
Page 203
data_visualization

Chapter 4 — AI in Manufacturing and Supply Chain

Content

Data on AI applications in manufacturing and supply chain in 2025: predictive maintenance, quality control vision systems, logistics optimization, and productivity gains.

Layout Structure

ROI and efficiency gains chart by application type with adoption rate breakdown

Key Visual Elements

  • •Efficiency gain bars
  • •Application type breakdown
  • •Adoption rate chart
  • •ROI estimates
  • •Text analysis
Page 204
data_visualization

Chapter 4 — AI in Financial Services

Content

Analysis of AI deployment in financial services in 2025: fraud detection, credit underwriting, algorithmic trading, compliance, and the regulatory landscape for financial AI.

Layout Structure

Use case adoption chart with fraud reduction metrics and regulatory callout boxes

Key Visual Elements

  • •Use case adoption bars
  • •Fraud reduction metrics
  • •Regulatory callout
  • •Text analysis
  • •Financial sector breakdown
Page 205
data_visualization

Chapter 4 — AI and Small Business

Content

Survey data on AI adoption among small and medium enterprises in 2025, showing growing adoption of AI tools for marketing, customer service, and operations, with access barriers.

Layout Structure

Adoption rate trend with barrier analysis chart and SME sector breakdown

Key Visual Elements

  • •SME adoption trend
  • •Barrier analysis
  • •Sector breakdown
  • •Tool usage survey
  • •Text analysis
Page 206
data_visualization

Chapter 4 — AI Startup Ecosystem

Content

Data on the AI startup ecosystem in 2025: new company formation rates, sector focus areas, funding rounds, and success metrics for AI-native versus AI-enabled startups.

Layout Structure

Startup ecosystem diagram with funding stage funnel and sector focus pie chart

Key Visual Elements

  • •Startup ecosystem diagram
  • •Funding stage funnel
  • •Sector focus pie chart
  • •Formation rate data
  • •Text analysis
Page 207
data_visualization

Chapter 4 — National AI Economic Strategies

Content

Comparison of national AI economic development strategies in 2025: US AI investment, EU AI industrial policy, China AI economy plan, India AI mission, and others.

Layout Structure

Comparative strategy table with investment amounts and priority areas per country

Key Visual Elements

  • •Strategy comparison table
  • •Country rows
  • •Investment amount column
  • •Priority areas column
  • •Text analysis
Page 208
chapter_content

Chapter 4 — AI and Trade

Content

Analysis of AI's role in international trade in 2025: AI in customs and logistics, AI export controls, chip trade restrictions, and AI's impact on comparative advantage.

Layout Structure

Export control timeline with trade flow impact chart and text analysis

Key Visual Elements

  • •Export control timeline
  • •Trade flow impact
  • •Text analysis
  • •Chip restriction callout
  • •Comparative advantage note
Page 209
data_visualization

Chapter 4 — AI Economic Impact: Projections

Content

Summary of major economic projections for AI's economic impact by 2030, from McKinsey, Goldman Sachs, and IMF, showing consensus on significant but uncertain GDP impact.

Layout Structure

Projection comparison chart with scenario ranges and methodology notes

Key Visual Elements

  • •GDP impact projection chart
  • •Scenario range bars
  • •Source comparison
  • •Year x-axis
  • •Uncertainty range annotation
Page 210
chapter_content

Chapter 4 — AI Enabling New Business Models

Content

Analysis of AI-enabled new business models in 2025: AI-as-a-service, vertical AI solutions, autonomous agent services, and platform businesses built on foundation model APIs.

Layout Structure

Business model taxonomy diagram with example companies and revenue model descriptions

Key Visual Elements

  • •Business model taxonomy
  • •Example company callouts
  • •Revenue model descriptions
  • •Text analysis
  • •Market size estimates
Page 211
data_visualization

Chapter 4 — AI and Energy Sector

Content

Data on AI applications in the energy sector in 2025: grid optimization, renewable energy management, demand forecasting, and oil and gas exploration AI use.

Layout Structure

Application bar chart with efficiency gain metrics and sector deployment map

Key Visual Elements

  • •Energy application bars
  • •Efficiency metrics
  • •Sector deployment map
  • •Text analysis
  • •Energy savings callout
Page 212
data_visualization

Chapter 4 — AI and Agriculture

Content

Overview of AI adoption in agriculture in 2025: precision farming, crop disease detection, yield prediction, and agricultural robotics deployment and productivity data.

Layout Structure

Adoption trend chart with productivity gain data and geographic distribution map

Key Visual Elements

  • •Agriculture adoption trend
  • •Productivity gain data
  • •Geographic map
  • •Application type breakdown
  • •Text analysis
Page 213
data_visualization

Chapter 4 — AI and Real Estate

Content

Analysis of AI applications in real estate in 2025: property valuation algorithms, market prediction tools, construction AI, and smart building management systems.

Layout Structure

Application adoption chart with valuation accuracy metrics and text analysis

Key Visual Elements

  • •Application adoption chart
  • •Valuation accuracy
  • •Text analysis
  • •Market prediction callout
  • •Smart building metrics
Page 214
data_visualization

Chapter 4 — AI Compute Economics

Content

Analysis of AI compute market economics in 2025: GPU rental prices, cloud AI service pricing trends, hyperscaler AI revenue, and the economics of training vs inference.

Layout Structure

Price trend charts for compute with market size estimates and hyperscaler AI revenue comparison

Key Visual Elements

  • •Compute price trend
  • •Market size estimate
  • •Hyperscaler revenue comparison
  • •Training vs inference economics
  • •Text analysis
Page 215
data_visualization

Chapter 4 — AI Talent Economics

Content

Salary and compensation data for AI roles in 2025, including ML engineers, AI researchers, data scientists, and AI product managers across different company types and geographies.

Layout Structure

Salary comparison chart by role and geography with total compensation breakdown

Key Visual Elements

  • •Salary comparison chart
  • •Role type breakdown
  • •Geographic comparison
  • •Comp component breakdown
  • •Text analysis
Page 216
chapter_content

Chapter 4 — AI and Intellectual Property Economy

Content

Analysis of the AI IP economy in 2025: AI-generated IP valuation, training data licensing markets, patent royalties for AI techniques, and legal uncertainty impacts on investment.

Layout Structure

IP market size estimate chart with legal uncertainty timeline and text analysis

Key Visual Elements

  • •IP market size estimate
  • •Legal uncertainty timeline
  • •Text analysis
  • •Policy callout
  • •Market development note
Page 217
data_visualization

Chapter 4 — AI Economy: Regional Spotlights

Content

Regional AI economy spotlights covering US AI corridor cities, EU AI hubs, China's AI technology parks, India's AI ecosystem, and emerging AI economies in Southeast Asia.

Layout Structure

Regional map with city/hub callout cards and key metrics per region

Key Visual Elements

  • •Regional map
  • •City hub callout cards
  • •Key metrics per region
  • •Investment comparison
  • •Text analysis
Page 218
chapter_summary

Chapter 4 — AI Economy Chapter Summary

Content

Chapter 4 summary dashboard with headline economic figures: $285.9B US AI investment, 90%+ Fortune 500 AI mention rate, 30-40% AI wage premium, and documented productivity gains.

Layout Structure

Summary dashboard with large-number statistics and chapter color-themed graphics

Key Visual Elements

  • •Large stat boxes
  • •Key finding callouts
  • •Chapter navy theme
  • •Progress indicators
  • •Summary charts
Page 219
appendix

Chapter 4 — Economy Endnotes

Content

Reference notes for Chapter 4 with investment data sources, labor market study citations, and productivity research methodology notes.

Layout Structure

Two-column numbered endnote format

Key Visual Elements

  • •Numbered citations
  • •Source references
  • •Two-column layout
Page 220
appendix

Chapter 4 — Economy Further Reading

Content

Curated further reading for Chapter 4 covering AI investment, labor economics, productivity research, and economic impact projections with brief annotations.

Layout Structure

Structured reading list with section headers

Key Visual Elements

  • •Reading list
  • •Section headers
  • •Annotated entries
Page 221
chapter_opener

Chapter 5 Opener — Science and AI

Content

Full-bleed chapter opener with dark teal/forest background, large white Chapter 5 numeral, title 'Science and AI', and brief overview of AI's transformative role in scientific research in 2025.

Layout Structure

Full-bleed dark teal/forest background, oversized chapter number, white title and description

Key Visual Elements

  • •Dark teal/forest background
  • •Large white '5'
  • •Chapter title in white
  • •Chapter overview paragraph
  • •Stanford HAI logo
  • •Accent line
Page 222
chapter_content

Chapter 5 — Science and AI Introduction

Content

Introduction to Chapter 5 framing AI as a new tool for scientific discovery in 2025, with overview statistics on AI-assisted research growth and the AI Nobel Prize milestone.

Layout Structure

Single-column introduction with key statistics callout boxes and section overview

Key Visual Elements

  • •Key statistics callout
  • •Section overview diagram
  • •AI Nobel Prize callout
  • •Introduction text
Page 223
data_visualization

Chapter 5 — AI-Assisted Science: Growth Overview

Content

Line chart showing the growth of AI-assisted science publications from 2015-2025 across major disciplines, with 71% growth in AI-assisted biology and life science research highlighted.

Layout Structure

Multi-line chart by scientific domain with year x-axis and publication count y-axis

Key Visual Elements

  • •Multi-line chart
  • •Domain color coding
  • •Year x-axis
  • •Publication count y-axis
  • •71% growth biology callout
  • •AI Nobel Prize annotation
Page 224
data_visualization

Chapter 5 — Protein Structure Prediction

Content

Analysis of AlphaFold 3 and subsequent protein structure prediction advances in 2025, with benchmark accuracy data and the growth of AI-assisted protein research.

Layout Structure

Accuracy benchmark chart with structural prediction examples table and text analysis

Key Visual Elements

  • •Accuracy benchmark chart
  • •Structural prediction comparison
  • •AlphaFold 3 callout
  • •Text analysis
  • •Research growth annotation
Page 225
chapter_content

Chapter 5 — Biomolecular Structure Prediction

Content

Overview of AI prediction of complex biomolecular structures beyond proteins in 2025: protein-DNA, protein-ligand, RNA structure, and antibody design using foundation models.

Layout Structure

Capability comparison chart with text analysis and key breakthrough callout boxes

Key Visual Elements

  • •Capability comparison
  • •Breakthrough callouts
  • •Structure type breakdown
  • •Text analysis
  • •Research growth chart
Page 226
data_visualization

Chapter 5 — AI Drug Discovery: Pipeline

Content

Analysis of AI-discovered drug candidates in 2025: number entering clinical trials, disease areas targeted, cost reduction estimates (30-50% savings), and key AI-drug-discovery companies.

Layout Structure

Drug pipeline funnel diagram with disease area breakdown and cost reduction bar chart

Key Visual Elements

  • •Drug pipeline funnel
  • •Disease area breakdown
  • •Cost reduction bars
  • •Company callout boxes
  • •Clinical trial count
Page 227
data_visualization

Chapter 5 — AI Drug Discovery: Clinical Progress

Content

Data on AI-discovered drugs currently in clinical trials in 2025, including Phase I/II/III status, therapeutic areas, and the first AI-discovered drugs approaching regulatory approval.

Layout Structure

Clinical trial status table with pipeline chart and milestone timeline

Key Visual Elements

  • •Clinical trial status table
  • •Pipeline chart
  • •Phase breakdown
  • •Therapeutic area labels
  • •Regulatory milestone timeline
Page 228
data_visualization

Chapter 5 — AI-Designed Materials

Content

Overview of AI applications in materials science in 2025: AI-designed battery materials, semiconductors, catalysts, and structural materials, with examples entering production.

Layout Structure

Materials application bar chart with discovery timeline and text analysis

Key Visual Elements

  • •Materials application bars
  • •Discovery timeline
  • •Application type breakdown
  • •Text analysis
  • •Production entry callout
Page 229
chapter_content

Chapter 5 — AI in Genomics and Precision Medicine

Content

Analysis of AI applications in genomics in 2025: variant effect prediction, gene regulation modeling, single-cell analysis, and AI-guided CRISPR targeting.

Layout Structure

Application capability matrix with genomics AI timeline and text analysis

Key Visual Elements

  • •Application capability matrix
  • •Genomics AI timeline
  • •Text analysis
  • •Precision medicine callout
  • •CRISPR AI reference
Page 230
data_visualization

Chapter 5 — AI in Climate and Earth Science

Content

Overview of AI applications in climate and earth science in 2025: AI climate models showing 10,000x speedups, downscaling global models, extreme event prediction, and carbon cycle monitoring.

Layout Structure

Speedup comparison chart with application domain breakdown and text analysis

Key Visual Elements

  • •Speedup comparison chart
  • •10,000x annotation
  • •Domain breakdown
  • •Text analysis
  • •Climate model callout
Page 231
data_visualization

Chapter 5 — AI Weather Forecasting

Content

Performance comparison of AI weather forecasting models (GraphCast, Pangu-Weather, FourCastNet) versus traditional NWP models in 2025, showing AI exceeding traditional models on most metrics.

Layout Structure

Performance comparison bar chart for multiple forecast metrics with traditional model baseline

Key Visual Elements

  • •Forecast accuracy comparison
  • •Traditional model baseline
  • •AI model name labels
  • •Metric breakdown
  • •Text analysis
Page 232
data_visualization

Chapter 5 — AI and Earthquake Prediction

Content

Analysis of AI earthquake prediction improvements in 2025: early warning time extensions, magnitude estimation accuracy, and aftershock prediction using deep learning on seismic data.

Layout Structure

Performance improvement chart with geographic deployment map and text analysis

Key Visual Elements

  • •Performance improvement chart
  • •Deployment map
  • •Warning time annotation
  • •Text analysis
  • •Accuracy metric callout
Page 233
chapter_content

Chapter 5 — AI in Physics

Content

Overview of AI applications in physics research in 2025: particle physics data analysis at CERN, quantum system simulation, plasma physics for fusion, and AI-guided experimental design.

Layout Structure

Application domain timeline with key discovery callouts and text analysis

Key Visual Elements

  • •Physics domain timeline
  • •Discovery callouts
  • •Text analysis
  • •CERN reference
  • •Fusion research callout
Page 234
data_visualization

Chapter 5 — AI and Astronomy

Content

Analysis of AI applications in astronomy in 2025: galaxy classification at scale, gravitational wave signal detection, exoplanet discovery from telescope data, and cosmological simulation acceleration.

Layout Structure

Discovery count chart with application domain breakdown and text analysis

Key Visual Elements

  • •Discovery count chart
  • •Domain breakdown
  • •Text analysis
  • •Telescope data callout
  • •Simulation speedup annotation
Page 235
data_visualization

Chapter 5 — AI in Chemistry

Content

Overview of AI in chemistry research in 2025: reaction prediction, synthesis planning, catalyst design, and AI-assisted chemical safety screening, with publication growth data.

Layout Structure

Publication growth chart with application breakdown and key tool callouts

Key Visual Elements

  • •Publication growth chart
  • •Application breakdown
  • •Tool callout boxes
  • •Text analysis
  • •Chemistry AI milestone timeline
Page 236
chapter_content

Chapter 5 — AI Nobel Prize Milestone

Content

Discussion of the historic 2024 Nobel Prize recognition of AI contributions to science (Geoffrey Hinton — Physics; Demis Hassabis and John Jumper — Chemistry), marking official scientific establishment recognition of AI.

Layout Structure

Timeline of Nobel Prize announcements with laureate profiles and significance analysis

Key Visual Elements

  • •Nobel Prize timeline
  • •Laureate profiles
  • •Significance analysis text
  • •AlphaFold callout
  • •Neural network reference
Page 237
data_visualization

Chapter 5 — AI-Enabled Scientific Literature Processing

Content

Data on AI tools processing scientific literature in 2025: semantic search over millions of papers, automated hypothesis generation, claim extraction, and citation graph analysis.

Layout Structure

Tool capability comparison table with adoption rate chart and text analysis

Key Visual Elements

  • •Capability comparison table
  • •Adoption rate chart
  • •Text analysis
  • •Tool name callouts
  • •Literature scale reference
Page 238
chapter_content

Chapter 5 — AI Autonomous Science Agents

Content

Overview of AI systems conducting autonomous scientific experiments in 2025: robotic lab systems guided by AI, self-directed hypothesis generation, experiment design, and result interpretation.

Layout Structure

Autonomous science pipeline diagram with capability milestone timeline and text analysis

Key Visual Elements

  • •Autonomous science pipeline
  • •Capability milestone timeline
  • •Text analysis
  • •Example lab system callouts
  • •Discovery example descriptions
Page 239
data_visualization

Chapter 5 — AI in Neuroscience

Content

Analysis of AI applications in neuroscience in 2025: brain mapping at scale, neural signal decoding, brain-computer interface improvements, and AI models of neural computation.

Layout Structure

Application capability chart with brain mapping scale data and text analysis

Key Visual Elements

  • •Capability chart
  • •Brain mapping scale data
  • •BCI performance metrics
  • •Text analysis
  • •Neural decoding callout
Page 240
data_visualization

Chapter 5 — AI in Biomedical Imaging Research

Content

Performance data for AI in biomedical imaging research (distinct from clinical use) in 2025: single-cell imaging analysis, cryo-EM processing, and super-resolution microscopy AI.

Layout Structure

Performance benchmark chart by imaging modality with text analysis

Key Visual Elements

  • •Imaging modality benchmark
  • •Performance comparison
  • •Text analysis
  • •Cryo-EM callout
  • •Resolution improvement data
Page 241
chapter_content

Chapter 5 — AI and Social Sciences

Content

Overview of AI applications in social science research in 2025: large-scale survey analysis, natural language processing of historical texts, and AI-assisted behavioral experiments.

Layout Structure

Application type overview with publication growth chart and text analysis

Key Visual Elements

  • •Application overview
  • •Publication growth
  • •Text analysis
  • •Methodological note
  • •Example study callouts
Page 242
chapter_content

Chapter 5 — AI Research Infrastructure

Content

Analysis of AI research infrastructure supporting scientific AI in 2025: national AI research clouds, open-access computing resources for science, and the role of NSF, DOE, and international equivalents.

Layout Structure

Infrastructure map with resource comparison table and funding analysis

Key Visual Elements

  • •Infrastructure map
  • •Resource comparison table
  • •Funding analysis bars
  • •National program callouts
  • •Text analysis
Page 243
data_visualization

Chapter 5 — AI in Mathematics Research

Content

Analysis of AI tools for mathematical research in 2025: automated theorem proving, AI-assisted proof verification, conjecture generation, and the growth of AI-assisted mathematics publications.

Layout Structure

Capability progress chart with theorem proving benchmark data and text analysis

Key Visual Elements

  • •Theorem proving progress
  • •Benchmark data
  • •Text analysis
  • •Lean proof checker callout
  • •AI conjecture examples
Page 244
chapter_content

Chapter 5 — AI Reproducibility in Science

Content

Discussion of AI tools for improving scientific reproducibility in 2025: automated protocol documentation, code and data sharing tools, and AI-assisted result verification.

Layout Structure

Reproducibility improvement diagram with adoption metric charts and text analysis

Key Visual Elements

  • •Reproducibility improvement diagram
  • •Adoption metrics
  • •Text analysis
  • •Tool comparison
  • •Policy reference boxes
Page 245
chapter_content

Chapter 5 — AI and Scientific Peer Review

Content

Analysis of AI tools in scientific peer review in 2025: automated review assistance, paper screening, ethical violation detection, and debates about AI reviewer reliability.

Layout Structure

Adoption rate chart with reliability data and ethical debate callout boxes

Key Visual Elements

  • •Adoption rate chart
  • •Reliability data
  • •Debate callout boxes
  • •Text analysis
  • •Journal policy comparison
Page 246
data_visualization

Chapter 5 — AI Science: Geopolitics and Access

Content

Analysis of geopolitical dimensions of AI-enabled science in 2025: research collaboration patterns, AI science capability gaps between nations, and open-access AI science tools.

Layout Structure

Collaboration network diagram with capability gap map and text analysis

Key Visual Elements

  • •Collaboration network
  • •Capability gap map
  • •Text analysis
  • •Open-access callout
  • •Geopolitical tension note
Page 247
data_visualization

Chapter 5 — AI Foundation Models for Science

Content

Overview of science-specific foundation models in 2025: biology (ESM3, Geneformer), chemistry (ChemBERTa), materials (MatBERT), earth science (ClimaX), and cross-domain scientific LLMs.

Layout Structure

Model taxonomy table with capability comparison and domain coverage diagram

Key Visual Elements

  • •Science FM taxonomy table
  • •Domain coverage diagram
  • •Capability comparison
  • •Model name callouts
  • •Text analysis
Page 248
data_visualization

Chapter 5 — AI-Accelerated Clinical Research

Content

Data on AI's impact on clinical trial design and execution in 2025: patient recruitment optimization, protocol design assistance, adverse event detection, and trial timeline reductions.

Layout Structure

Timeline reduction chart with application breakdown and text analysis

Key Visual Elements

  • •Timeline reduction chart
  • •Application breakdown
  • •Text analysis
  • •Cost savings callout
  • •Trial phase comparison
Page 249
chapter_content

Chapter 5 — AI in Epidemiology and Public Health Research

Content

Overview of AI applications in epidemiology and public health research in 2025: disease surveillance, outbreak prediction, genomic epidemiology, and AI-assisted vaccination strategy modeling.

Layout Structure

Application capability chart with surveillance deployment map and text analysis

Key Visual Elements

  • •Capability chart
  • •Surveillance deployment map
  • •Text analysis
  • •Outbreak prediction callout
  • •Public health data reference
Page 250
data_visualization

Chapter 5 — AI and Energy Science

Content

Analysis of AI contributions to energy science research in 2025: fusion plasma control with AI, battery discovery, carbon capture material design, and solar cell optimization.

Layout Structure

Application timeline with performance improvement data and text analysis

Key Visual Elements

  • •Application timeline
  • •Performance improvement data
  • •Text analysis
  • •Fusion AI callout
  • •Battery discovery reference
Page 251
data_visualization

Chapter 5 — AI Science Cross-Disciplinary Impact

Content

Analysis of cross-disciplinary impact of AI science tools, showing how AI is enabling breakthroughs at the interfaces between biology, chemistry, physics, and engineering.

Layout Structure

Cross-disciplinary impact diagram showing AI tool flows between scientific domains

Key Visual Elements

  • •Cross-disciplinary diagram
  • •Domain interface nodes
  • •Impact flow arrows
  • •Breakthrough examples
  • •Text analysis
Page 252
chapter_content

Chapter 5 — Science and AI: Risks and Challenges

Content

Discussion of risks and challenges in AI-enabled science: AI hallucinations in scientific contexts, over-reliance on AI models, black-box scientific reasoning, and the deskilling of researchers.

Layout Structure

Risk taxonomy with case study examples and mitigation discussion

Key Visual Elements

  • •Risk taxonomy
  • •Case study callouts
  • •Mitigation discussion
  • •Text analysis
  • •Researcher concern survey
Page 253
chapter_summary

Chapter 5 — AI Science Chapter Summary

Content

Chapter 5 summary dashboard: key statistics on AI-assisted science publication growth, breakthrough milestones, and sectors where AI is having the greatest scientific impact.

Layout Structure

Summary dashboard with large-number statistics and chapter teal-themed graphics

Key Visual Elements

  • •Large stat boxes
  • •Key finding callouts
  • •Chapter teal theme
  • •Breakthrough milestone list
  • •Progress indicators
Page 254
appendix

Chapter 5 — Science and AI Endnotes

Content

Reference notes for Chapter 5 with primary data sources for scientific publication data, AlphaFold citations, and domain-specific research references.

Layout Structure

Two-column numbered endnote format

Key Visual Elements

  • •Numbered citations
  • •Source references
  • •Two-column layout
Page 255
appendix

Chapter 5 — Science and AI Further Reading

Content

Curated further reading for Chapter 5 organized by scientific domain, including key papers and review articles on AI in biology, chemistry, earth science, and cross-domain AI science agents.

Layout Structure

Structured reading list with domain section headers and annotated entries

Key Visual Elements

  • •Reading list
  • •Domain section headers
  • •Annotated entries
Page 256
chapter_opener

Chapter 6 Opener — Medicine and AI

Content

Full-bleed chapter opener with deep blue background, large white Chapter 6 numeral, title 'Medicine and AI', and brief description of AI's growing role in clinical care and biomedical research.

Layout Structure

Full-bleed deep blue background, oversized chapter number, white title and description

Key Visual Elements

  • •Deep blue background
  • •Large white '6'
  • •Chapter title in white
  • •Chapter overview text
  • •Stanford HAI logo
  • •Accent line
Page 257
chapter_content

Chapter 6 — Medicine and AI Introduction

Content

Introduction to Chapter 6 presenting AI in medicine as a transformative force in 2025, with key statistics: 258 FDA-approved AI devices, 85.5% diagnostic accuracy for MAI-DxO, 300K+ clinicians using AI scribes.

Layout Structure

Single-column introduction with headline statistics callout boxes and section overview

Key Visual Elements

  • •Headline statistics callout
  • •258 FDA devices callout
  • •85.5% accuracy callout
  • •300K clinicians callout
  • •Section overview
Page 258
data_visualization

Chapter 6 — AI Medical Diagnostics: Performance

Content

Benchmark comparison of AI diagnostic systems versus specialist physicians in 2025 across radiology, pathology, dermatology, and ophthalmology, showing AI achieving or surpassing specialist accuracy.

Layout Structure

Multi-domain accuracy comparison bar chart with specialist baseline and AI performance bars

Key Visual Elements

  • •Multi-domain accuracy chart
  • •Specialist baseline bars
  • •AI performance bars
  • •Domain labels
  • •Text analysis
Page 259
data_visualization

Chapter 6 — MAI-DxO Diagnostic Accuracy

Content

Detailed analysis of MAI-DxO achieving 85.5% diagnostic accuracy on standardized clinical cases, surpassing the average specialist physician performance and validating AI diagnostic potential.

Layout Structure

Accuracy comparison chart with case type breakdown and statistical significance analysis

Key Visual Elements

  • •MAI-DxO accuracy chart
  • •Specialist comparison
  • •Case type breakdown
  • •Statistical significance
  • •Text analysis
Page 260
data_visualization

Chapter 6 — FDA AI-Enabled Medical Device Approvals

Content

Bar chart showing FDA-approved AI-enabled medical device approvals by year from 2016-2025, reaching 258 approvals in 2025 and totaling 950+ cumulative approvals.

Layout Structure

Bar chart with year x-axis and annual approval count on y-axis, cumulative line overlay

Key Visual Elements

  • •Annual approval bar chart
  • •Cumulative line overlay
  • •Year x-axis
  • •Approval count y-axis
  • •258 highlight annotation
  • •Trend callout
Page 261
data_visualization

Chapter 6 — FDA AI Devices: By Medical Specialty

Content

Breakdown of FDA-approved AI medical devices by specialty in 2025: radiology dominating at ~75%, followed by cardiology, pathology, and other specialties.

Layout Structure

Pie chart and bar chart of device distribution by medical specialty

Key Visual Elements

  • •Specialty pie chart
  • •Bar chart breakdown
  • •Radiology dominance callout
  • •Specialty name labels
  • •Percentage labels
Page 262
data_visualization

Chapter 6 — AI in Radiology

Content

Detailed analysis of AI in radiology in 2025: CT analysis, chest X-ray interpretation, mammography screening, and MRI analysis, with clinical deployment data and diagnostic accuracy benchmarks.

Layout Structure

Performance benchmark chart by imaging modality with deployment statistics and text analysis

Key Visual Elements

  • •Imaging modality benchmark
  • •Deployment statistics
  • •Text analysis
  • •Clinical trial callout
  • •FDA clearance reference
Page 263
data_visualization

Chapter 6 — AI in Pathology

Content

Overview of AI computational pathology in 2025: digital pathology slide analysis, cancer grading assistance, biomarker detection, and the growing clinical deployment of AI pathology tools.

Layout Structure

Capability comparison chart with deployment data and text analysis

Key Visual Elements

  • •Capability comparison
  • •Deployment data
  • •Text analysis
  • •Cancer grading accuracy
  • •Clinical adoption callout
Page 264
data_visualization

Chapter 6 — AI in Ophthalmology

Content

Analysis of AI applications in ophthalmology in 2025: diabetic retinopathy screening, glaucoma detection, age-related macular degeneration diagnosis, and retinal image analysis.

Layout Structure

Diagnostic accuracy bar chart by condition with geographic deployment map and text analysis

Key Visual Elements

  • •Diagnostic accuracy bars
  • •Condition comparison
  • •Deployment map
  • •Text analysis
  • •WHO screening reference
Page 265
data_visualization

Chapter 6 — AI Ambient Clinical Documentation

Content

Analysis of ambient AI clinical documentation scribes in 2025, used by 300,000+ clinicians to reduce documentation burden, with data on time savings and burnout reduction.

Layout Structure

Adoption trend line with time savings metrics and burnout reduction survey data

Key Visual Elements

  • •Adoption trend line
  • •300K clinicians callout
  • •Time savings metrics
  • •Burnout reduction data
  • •Text analysis
Page 266
data_visualization

Chapter 6 — AI Clinical Documentation: Outcomes

Content

Clinical outcome data from AI documentation scribe deployments in 2025: physician time savings (1-2 hours per shift), patient interaction time increases, and documentation accuracy improvements.

Layout Structure

Before/after comparison charts for time allocation and documentation accuracy with text analysis

Key Visual Elements

  • •Time allocation comparison
  • •Documentation accuracy chart
  • •Text analysis
  • •Physician survey data
  • •Patient interaction time callout
Page 267
data_visualization

Chapter 6 — AI in Drug Discovery: Overview

Content

Overview of AI drug discovery progress in 2025, including the number of AI-discovered drug candidates, leading companies (Insilico Medicine, Recursion, Exscientia), and cost reduction data.

Layout Structure

Company landscape diagram with pipeline count and cost comparison charts

Key Visual Elements

  • •Company landscape diagram
  • •Pipeline count comparison
  • •Cost reduction charts
  • •Text analysis
  • •Company name callouts
Page 268
chapter_content

Chapter 6 — Virtual Cell Models

Content

Overview of virtual cell modeling in 2025: AI models simulating individual cell responses to drugs and interventions, enabling personalized medicine research and reducing animal testing requirements.

Layout Structure

Virtual cell model capability diagram with application areas and text analysis

Key Visual Elements

  • •Virtual cell model diagram
  • •Application area breakdown
  • •Personalized medicine callout
  • •Text analysis
  • •Animal testing reduction reference
Page 269
data_visualization

Chapter 6 — AI in Mental Health

Content

Data on AI applications in mental health in 2025: chatbot therapy tools deployment, efficacy data from clinical studies, crisis detection capabilities, and ethical concerns.

Layout Structure

Efficacy study comparison chart with deployment statistics and ethical concern callout boxes

Key Visual Elements

  • •Efficacy comparison chart
  • •Deployment statistics
  • •Ethical concern callouts
  • •Text analysis
  • •Clinical study references
Page 270
chapter_content

Chapter 6 — AI in Genomics Medicine

Content

Analysis of AI applications in precision genomic medicine in 2025: polygenic risk score prediction, pharmacogenomics, rare disease diagnosis from genomic data, and AI-guided gene therapy design.

Layout Structure

Capability comparison table with clinical application examples and text analysis

Key Visual Elements

  • •Capability comparison table
  • •Clinical application examples
  • •Text analysis
  • •Rare disease callout
  • •Gene therapy reference
Page 271
data_visualization

Chapter 6 — AI and Clinical Decision Support

Content

Overview of AI clinical decision support systems (CDSS) deployment in 2025: sepsis prediction, deterioration alerts, drug interaction checking, and their impact on clinical outcomes.

Layout Structure

Outcome improvement bar chart by CDSS type with deployment data and text analysis

Key Visual Elements

  • •Outcome improvement bars
  • •CDSS type comparison
  • •Deployment statistics
  • •Text analysis
  • •Alert accuracy callout
Page 272
data_visualization

Chapter 6 — AI Surgical Assistance

Content

Data on AI-assisted surgery in 2025: robotic surgery AI guidance, real-time skill assessment, operative image analysis, and outcomes data for AI-assisted procedures.

Layout Structure

Outcome comparison chart for AI-assisted vs traditional surgery with text analysis

Key Visual Elements

  • •Outcome comparison chart
  • •Surgery type breakdown
  • •Text analysis
  • •Robot surgery market callout
  • •Skill assessment reference
Page 273
data_visualization

Chapter 6 — AI in Emergency Medicine

Content

Analysis of AI in emergency medicine in 2025: triage assistance, chest pain risk stratification, stroke detection on CT, and ED patient flow optimization.

Layout Structure

Performance metric chart by application type with deployment data and text analysis

Key Visual Elements

  • •Emergency AI performance chart
  • •Application type breakdown
  • •Deployment data
  • •Text analysis
  • •Triage accuracy callout
Page 274
chapter_content

Chapter 6 — AI and Health Equity

Content

Analysis of AI's impact on health equity in 2025: potential to expand diagnostic access in underserved areas, but also risk of perpetuating or amplifying existing disparities if trained on biased data.

Layout Structure

Equity impact dual-column analysis with geographic access expansion map and bias risk chart

Key Visual Elements

  • •Access expansion map
  • •Bias risk chart
  • •Equity impact analysis
  • •Text analysis
  • •Policy callout
Page 275
data_visualization

Chapter 6 — AI in Global Health

Content

Overview of AI applications in global health and low-resource settings in 2025: mobile AI diagnostics in LMICs, AI for neglected tropical disease, and telehealth AI deployment.

Layout Structure

Geographic deployment map with application type breakdown and cost-effectiveness data

Key Visual Elements

  • •Global deployment map
  • •Application breakdown
  • •Cost-effectiveness data
  • •LMIC callout
  • •Text analysis
Page 276
data_visualization

Chapter 6 — AI Medical Device Regulation

Content

Analysis of AI medical device regulation in 2025 across major jurisdictions: FDA's predetermined change control pathway, EU MDR for AI, China NMPA AI device approvals, and international harmonization efforts.

Layout Structure

Regulatory framework comparison table with approval timeline and text analysis

Key Visual Elements

  • •Regulatory comparison table
  • •Approval timeline
  • •Jurisdiction comparison
  • •Text analysis
  • •FDA pathway callout
Page 277
data_visualization

Chapter 6 — AI and Electronic Health Records

Content

Analysis of AI integration with EHR systems in 2025: predictive analytics embedded in EHRs, NLP for unstructured notes, AI-driven care gap identification, and interoperability challenges.

Layout Structure

EHR AI integration capability chart with adoption rate and text analysis

Key Visual Elements

  • •EHR integration chart
  • •Capability breakdown
  • •Adoption rate data
  • •Text analysis
  • •Interoperability callout
Page 278
data_visualization

Chapter 6 — AI in Wearables and Remote Monitoring

Content

Data on AI-powered wearable health devices in 2025: continuous glucose monitoring, cardiac rhythm detection, respiratory monitoring, and population-level health monitoring programs.

Layout Structure

Device capability comparison chart with clinical validation data and deployment statistics

Key Visual Elements

  • •Device comparison chart
  • •Clinical validation data
  • •Deployment statistics
  • •Text analysis
  • •Market size callout
Page 279
data_visualization

Chapter 6 — AI Patient Communication Tools

Content

Overview of AI patient communication tools in 2025: AI chatbots for patient education, medication adherence support, appointment scheduling, and post-discharge follow-up.

Layout Structure

Tool type comparison chart with efficacy data and patient satisfaction survey results

Key Visual Elements

  • •Tool comparison chart
  • •Efficacy data
  • •Patient satisfaction survey
  • •Text analysis
  • •Adoption rate callout
Page 280
data_visualization

Chapter 6 — Clinician Attitudes Toward AI

Content

Survey data on clinician attitudes toward AI in 2025: trust levels by specialty, concerns about liability, time to adopt new AI tools, and views on AI replacing versus augmenting clinical roles.

Layout Structure

Survey result bar chart by specialty and concern category with text analysis

Key Visual Elements

  • •Survey bar chart
  • •Specialty breakdown
  • •Concern category bars
  • •Text analysis
  • •Trust level callout
Page 281
data_visualization

Chapter 6 — AI Medical Education

Content

Overview of AI in medical education in 2025: AI clinical case simulators, AI-assisted board exam preparation, virtual patient interactions, and AI-guided surgical skills training.

Layout Structure

Adoption rate chart with learning outcome data and tool type breakdown

Key Visual Elements

  • •Adoption rate chart
  • •Learning outcome data
  • •Tool type breakdown
  • •Text analysis
  • •Simulation example callout
Page 282
data_visualization

chapter 6 — AI in Hospital Operations

Content

Data on AI applications in hospital operations in 2025: patient flow optimization, bed management, staffing prediction, supply chain management, and revenue cycle AI.

Layout Structure

Efficiency gain bar chart by operational domain with deployment statistics

Key Visual Elements

  • •Operational efficiency bars
  • •Domain breakdown
  • •Deployment statistics
  • •Text analysis
  • •Cost savings callout
Page 283
data_visualization

Chapter 6 — AI in Preventive Medicine

Content

Overview of AI in preventive medicine in 2025: population health risk stratification, cancer screening optimization, lifestyle intervention personalization, and public health AI tools.

Layout Structure

Risk stratification performance chart with screening optimization data and text analysis

Key Visual Elements

  • •Risk stratification chart
  • •Screening optimization data
  • •Text analysis
  • •Population health callout
  • •Intervention personalization reference
Page 284
data_visualization

Chapter 6 — AI-Enabled Clinical Trials

Content

Analysis of how AI is transforming clinical trials in 2025: synthetic control arms, patient matching, endpoint prediction, and real-world evidence generation, with cost and timeline impact data.

Layout Structure

Cost and timeline reduction chart with trial design methodology diagram and text analysis

Key Visual Elements

  • •Cost reduction chart
  • •Timeline reduction
  • •Trial design diagram
  • •Text analysis
  • •Synthetic control callout
Page 285
data_visualization

Chapter 6 — AI and Rare Disease Diagnosis

Content

Data on AI's impact on rare disease diagnosis in 2025: diagnostic time reduction, whole genome sequencing analysis AI, phenotype-to-genotype matching, and diagnosis odyssey shortening.

Layout Structure

Diagnostic time comparison chart with case study examples and text analysis

Key Visual Elements

  • •Diagnostic time comparison
  • •Case study callouts
  • •Text analysis
  • •Genome analysis reference
  • •Diagnosis odyssey callout
Page 286
data_visualization

Chapter 6 — AI in Pharmacy and Medication Management

Content

Overview of AI in pharmacy and medication management in 2025: automated dispensing AI, drug interaction checking, personalized dosing optimization, and medication adherence tools.

Layout Structure

Application capability chart with outcome data and text analysis

Key Visual Elements

  • •Pharmacy application chart
  • •Outcome data
  • •Text analysis
  • •Drug interaction callout
  • •Adherence improvement data
Page 287
data_visualization

Chapter 6 — AI Healthcare Cost Impact

Content

Analysis of AI's impact on healthcare costs in 2025: documented cost reductions in radiology, documentation, and operations, against implementation and integration costs.

Layout Structure

Cost impact analysis chart with ROI estimates by application type

Key Visual Elements

  • •Cost impact chart
  • •ROI estimates
  • •Application type breakdown
  • •Text analysis
  • •Net savings callout
Page 288
data_visualization

Chapter 6 — AI in Dermatology

Content

Performance data for AI dermatology systems in 2025: melanoma detection accuracy versus dermatologist, acne and skin condition classification, and AI-assisted teledermatology deployment.

Layout Structure

Accuracy comparison chart by condition type with deployment statistics and text analysis

Key Visual Elements

  • •Dermatology accuracy chart
  • •Condition type comparison
  • •Deployment statistics
  • •Text analysis
  • •Melanoma detection callout
Page 289
data_visualization

Chapter 6 — AI in Cardiology

Content

Analysis of AI in cardiology in 2025: ECG interpretation AI, echocardiography analysis, cardiac MRI segmentation, and AI-guided heart failure management.

Layout Structure

Performance benchmark chart by cardiology task with clinical deployment data

Key Visual Elements

  • •Cardiology benchmark chart
  • •Task type comparison
  • •Clinical deployment data
  • •Text analysis
  • •ECG AI callout
Page 290
data_visualization

Chapter 6 — AI in Oncology

Content

Comprehensive overview of AI in oncology in 2025: tumor detection, treatment response prediction, radiation planning optimization, genomic biomarker identification, and precision cancer medicine.

Layout Structure

Multi-application capability matrix with clinical evidence summary and text analysis

Key Visual Elements

  • •Oncology capability matrix
  • •Application rows
  • •Evidence level columns
  • •Text analysis
  • •Precision medicine callout
Page 291
data_visualization

Chapter 6 — AI in Infectious Disease

Content

Data on AI applications in infectious disease in 2025: pathogen genomic surveillance, outbreak detection algorithms, antimicrobial resistance prediction, and pandemic preparedness AI tools.

Layout Structure

Surveillance capability chart with deployment map and text analysis

Key Visual Elements

  • •Surveillance capability chart
  • •Deployment map
  • •AMR prediction data
  • •Text analysis
  • •Pandemic preparedness callout
Page 292
chapter_content

Chapter 6 — AI and Medical Data Infrastructure

Content

Analysis of medical data infrastructure supporting AI in 2025: federated learning deployments, synthetic data for training, data sharing frameworks, and the role of national health data programs.

Layout Structure

Infrastructure landscape diagram with federated learning deployment map and text analysis

Key Visual Elements

  • •Infrastructure landscape diagram
  • •Federated learning map
  • •Text analysis
  • •Data sharing framework comparison
  • •National program callouts
Page 293
chapter_content

Chapter 6 — AI Medicine: Ethical Challenges

Content

Discussion of key ethical challenges in AI medicine in 2025: algorithmic accountability in diagnosis, patient consent for AI tools, liability when AI errs, and transparency requirements.

Layout Structure

Ethical challenge taxonomy with regulatory response comparison and text analysis

Key Visual Elements

  • •Ethical challenge taxonomy
  • •Regulatory response comparison
  • •Text analysis
  • •Liability callout
  • •Consent framework reference
Page 294
chapter_summary

Chapter 6 — Medicine and AI Chapter Summary

Content

Chapter 6 summary dashboard with key statistics: 258 FDA approvals, 300K+ clinicians using AI scribes, 85.5% MAI-DxO accuracy, AI drug candidates in Phase II/III trials.

Layout Structure

Summary dashboard with large-number statistics and chapter blue-themed graphics

Key Visual Elements

  • •Large stat boxes
  • •Key finding callouts
  • •Chapter blue theme
  • •Progress indicators
  • •Summary charts
Page 295
appendix

Chapter 6 — Medicine and AI Endnotes and Further Reading

Content

Reference notes and further reading for Chapter 6 with FDA data sources, clinical study citations, and curated reading list organized by medical specialty and AI application.

Layout Structure

Two-column endnote format followed by structured reading list

Key Visual Elements

  • •Numbered citations
  • •Reading list
  • •Section headers
  • •Specialty organization
  • •Annotated entries
Page 296
chapter_opener

Chapter 7 Opener — Education

Content

Full-bleed chapter opener with forest green background, large white Chapter 7 numeral, title 'Education', and brief overview of AI's growing influence on learning, teaching, and academic institutions.

Layout Structure

Full-bleed forest green background, oversized chapter number, white title and description

Key Visual Elements

  • •Forest green background
  • •Large white '7'
  • •Chapter title in white
  • •Chapter overview text
  • •Stanford HAI logo
  • •Accent line
Page 297
chapter_content

Chapter 7 — Education Introduction

Content

Introduction to Chapter 7 presenting AI in education as a pivotal issue in 2025, with headline findings: 80% of students using generative AI, 11% CS enrollment decline, 82% growth in AI master's programs.

Layout Structure

Single-column introduction with headline statistics callout boxes and section overview

Key Visual Elements

  • •80% student AI use callout
  • •11% enrollment decline callout
  • •82% AI masters growth callout
  • •Section overview
  • •Introduction text
Page 298
data_visualization

Chapter 7 — CS Enrollment Trends

Content

Line chart showing CS enrollment trends at top US universities from 2015-2025, including an 11% decline in 2024-2025, interpreted as a market saturation signal for traditional CS degrees.

Layout Structure

Multi-institution enrollment line chart with year x-axis and enrollment count y-axis, 11% decline annotation

Key Visual Elements

  • •Enrollment line chart
  • •11% decline annotation
  • •Year x-axis
  • •Multiple institution lines
  • •Market saturation callout
Page 299
data_visualization

Chapter 7 — AI-Specific Program Growth

Content

Bar chart showing growth in AI/ML-specific graduate programs from 2018-2025, with 82% growth in AI master's programs and new AI undergraduate concentrations at major universities.

Layout Structure

Bar chart of program count by year with institution type breakdown and geographic distribution

Key Visual Elements

  • •Program count bar chart
  • •82% growth annotation
  • •Year x-axis
  • •Institution type breakdown
  • •Geographic map inset
Page 300
data_visualization

Chapter 7 — Student AI Use: Survey Data

Content

Survey data showing 80% of college students report using generative AI for coursework in 2025, broken down by use type: writing assistance, research, coding, studying, and problem-solving.

Layout Structure

Usage type bar chart with frequency distribution and demographic breakdown

Key Visual Elements

  • •Usage type bar chart
  • •80% callout
  • •Frequency distribution
  • •Demographic breakdown
  • •Text analysis
Page 301
data_visualization

Chapter 7 — Student AI Use: Academic Integrity

Content

Data on academic integrity concerns related to student AI use in 2025: detected AI-generated submissions, institutional policy evolution, and student views on AI use ethics.

Layout Structure

Policy adoption chart with integrity incident data and student opinion survey

Key Visual Elements

  • •Policy adoption chart
  • •Integrity incident data
  • •Student opinion survey
  • •Text analysis
  • •Policy examples callout
Page 302
data_visualization

Chapter 7 — Faculty Attitudes Toward AI

Content

Survey data on faculty attitudes toward AI in 2025: 60% allow AI use with restrictions, varying policies by discipline, and faculty concerns about assessment validity.

Layout Structure

Faculty survey bar chart by discipline with policy category breakdown and text analysis

Key Visual Elements

  • •Faculty survey bars
  • •60% allow with restrictions callout
  • •Discipline breakdown
  • •Policy category colors
  • •Text analysis
Page 303
data_visualization

Chapter 7 — AI Tutoring Systems: Efficacy

Content

Experimental evidence on AI tutoring system efficacy in 2025: Khan Academy Khanmigo, Duolingo Max, and similar AI tutors showing documented learning gains in controlled studies.

Layout Structure

Learning gain comparison chart by tool with study methodology notes and text analysis

Key Visual Elements

  • •Learning gain comparison
  • •Tool name callouts
  • •Study methodology notes
  • •Control condition reference
  • •Text analysis
Page 304
data_visualization

Chapter 7 — AI Tutoring: Deployment and Adoption

Content

Data on AI tutoring tool deployment in 2025: number of students using AI tutoring platforms, geographic reach, subject area coverage, and usage frequency patterns.

Layout Structure

Adoption chart with geographic map and subject area breakdown

Key Visual Elements

  • •Adoption trend chart
  • •Geographic map
  • •Subject area breakdown
  • •Usage frequency data
  • •Text analysis
Page 305
data_visualization

Chapter 7 — K-12 AI Curriculum

Content

Analysis of K-12 AI curriculum adoption worldwide in 2025, with 30+ countries implementing national AI/CS curriculum standards and data on teacher AI training availability.

Layout Structure

World map of K-12 AI curriculum adoption with country count bar chart and text analysis

Key Visual Elements

  • •World adoption map
  • •Country count bar chart
  • •30+ countries callout
  • •Teacher training data
  • •Text analysis
Page 306
data_visualization

Chapter 7 — K-12 AI Curriculum: Content Analysis

Content

Content analysis of K-12 AI curriculum standards in 2025: what AI topics are taught (machine learning concepts, ethics, data literacy, prompt engineering) and at which grade levels.

Layout Structure

Curriculum topic matrix by grade level with country comparison and text analysis

Key Visual Elements

  • •Curriculum topic matrix
  • •Grade level rows
  • •Country comparison columns
  • •Text analysis
  • •Ethics education callout
Page 307
data_visualization

Chapter 7 — Online AI Learning: MOOCs and Certifications

Content

Data on online AI learning platform growth in 2025: Coursera and edX AI courses up 50%+, new AI certification programs from major tech companies, and completion rate analysis.

Layout Structure

Growth trend chart with platform comparison and certification type breakdown

Key Visual Elements

  • •MOOC growth trend
  • •50%+ callout
  • •Platform comparison
  • •Certification type breakdown
  • •Completion rate data
Page 308
chapter_content

Chapter 7 — University AI Research and Teaching Integration

Content

Analysis of how top universities are integrating AI research findings into teaching in 2025, including new AI ethics curricula, interdisciplinary AI programs, and AI research center growth.

Layout Structure

University AI program comparison chart with research-teaching integration model diagram

Key Visual Elements

  • •University comparison chart
  • •Integration model diagram
  • •Research center count
  • •Text analysis
  • •Interdisciplinary program callout
Page 309
chapter_content

Chapter 7 — AI Literacy Education

Content

Overview of AI literacy education initiatives in 2025: general public AI literacy programs, government-funded AI upskilling, workplace AI training, and the growing AI literacy certification market.

Layout Structure

Program type overview with reach metrics and text analysis

Key Visual Elements

  • •Program type overview
  • •Reach metrics
  • •Government program callouts
  • •Text analysis
  • •Literacy definition box
Page 310
data_visualization

Chapter 7 — AI in Higher Education Administration

Content

Data on AI use in higher education administration in 2025: AI in student admissions, enrollment management, financial aid optimization, and student success prediction systems.

Layout Structure

Application adoption chart with bias audit results and text analysis

Key Visual Elements

  • •Application adoption chart
  • •Bias audit results
  • •Admissions AI callout
  • •Text analysis
  • •Ethical concern boxes
Page 311
chapter_content

Chapter 7 — AI Writing Assistance and Academic Writing

Content

Analysis of AI writing assistance use in academic contexts in 2025, including effects on writing quality, learning outcomes for writing skills, and evolving academic norms.

Layout Structure

Study results comparison chart with writing quality assessment and text analysis

Key Visual Elements

  • •Writing quality comparison
  • •Study results
  • •Academic norm evolution
  • •Text analysis
  • •Skill development concern
Page 312
chapter_content

Chapter 7 — AI for Students with Disabilities

Content

Overview of AI accessibility tools in education in 2025 benefiting students with disabilities: real-time captioning, AI reading assistance, personalized learning adaptations, and speech-to-text tools.

Layout Structure

Accessibility tool capability chart with adoption data and student outcome evidence

Key Visual Elements

  • •Accessibility tool chart
  • •Adoption data
  • •Outcome evidence
  • •Text analysis
  • •Disability category breakdown
Page 313
data_visualization

Chapter 7 — Educator AI Training

Content

Data on teacher and educator AI training programs in 2025: professional development participation rates, training content quality, and educator confidence in using AI tools.

Layout Structure

Training adoption chart with confidence survey and content coverage comparison

Key Visual Elements

  • •Training adoption chart
  • •Confidence survey bars
  • •Content coverage comparison
  • •Text analysis
  • •Gap callout
Page 314
chapter_content

Chapter 7 — AI and Educational Equity

Content

Analysis of AI's potential impact on educational equity in 2025: personalized learning reducing achievement gaps, but device and connectivity access gaps creating new divides.

Layout Structure

Equity impact dual analysis with access gap map and learning outcome data

Key Visual Elements

  • •Access gap map
  • •Learning outcome data
  • •Equity analysis text
  • •Digital divide callout
  • •Policy recommendation boxes
Page 315
data_visualization

Chapter 7 — AI in STEM Education

Content

Data on AI tools specifically supporting STEM education in 2025: AI math tutors, coding education platforms, science simulation tools, and lab safety AI applications.

Layout Structure

Tool type comparison chart with learning outcome evidence and adoption data

Key Visual Elements

  • •STEM tool comparison
  • •Learning outcome evidence
  • •Adoption data
  • •Text analysis
  • •Platform name callouts
Page 316
data_visualization

Chapter 7 — AI Language Learning Applications

Content

Analysis of AI-powered language learning applications in 2025: Duolingo, Rosetta Stone AI features, and other platforms showing learning acceleration data and engagement metrics.

Layout Structure

Learning rate comparison chart with engagement metrics and text analysis

Key Visual Elements

  • •Learning rate comparison
  • •Engagement metrics
  • •Text analysis
  • •Platform comparison
  • •AI feature callouts
Page 317
data_visualization

Chapter 7 — Global AI Education Policy

Content

Overview of national AI education policies in 2025: UNESCO's AI education guidance, EU AI education strategy, US AI literacy executive orders, and OECD AI education framework.

Layout Structure

Policy framework comparison table with geographic adoption map and text analysis

Key Visual Elements

  • •Policy comparison table
  • •Geographic adoption map
  • •Text analysis
  • •UNESCO callout
  • •National strategy references
Page 318
data_visualization

Chapter 7 — AI and Research Universities

Content

Analysis of AI's impact on research university priorities in 2025: AI research center funding, industry-academia AI partnerships, AI patent activity from universities, and AI-related research revenue.

Layout Structure

Research funding comparison chart with partnership data and text analysis

Key Visual Elements

  • •Research funding chart
  • •Partnership data
  • •Patent activity callout
  • •Text analysis
  • •Revenue comparison
Page 319
chapter_summary

Chapter 7 — Education Chapter Summary

Content

Chapter 7 summary dashboard presenting headline education statistics: student AI use rates, enrollment trends, AI tutoring efficacy, and K-12 curriculum adoption.

Layout Structure

Summary dashboard with large-number statistics and chapter forest green-themed graphics

Key Visual Elements

  • •Large stat boxes
  • •Key finding callouts
  • •Chapter green theme
  • •Progress indicators
  • •Summary charts
Page 320
appendix

Chapter 7 — Education Endnotes and Further Reading

Content

Reference notes and further reading for Chapter 7 including enrollment data sources, AI tutoring study citations, and curated reading list organized by education sector.

Layout Structure

Two-column endnote format followed by structured reading list

Key Visual Elements

  • •Numbered citations
  • •Reading list
  • •Section headers
  • •Education sector organization
Page 321
chapter_opener

Chapter 8 Opener — Policy and Governance

Content

Full-bleed chapter opener with dark purple/indigo background, large white Chapter 8 numeral, title 'Policy and Governance', and brief overview of AI regulatory and policy developments in 2025.

Layout Structure

Full-bleed dark purple/indigo background, oversized chapter number, white title and description

Key Visual Elements

  • •Dark purple/indigo background
  • •Large white '8'
  • •Chapter title in white
  • •Chapter overview text
  • •Stanford HAI logo
  • •Accent line
Page 322
chapter_content

Chapter 8 — Policy and Governance Introduction

Content

Introduction to Chapter 8 framing 2025 as a watershed year for AI policy globally, with headline statistics: $20.4B US federal AI investment, 25 AI bills signed, 46 countries with national AI strategies.

Layout Structure

Single-column introduction with headline statistics callout boxes and section overview

Key Visual Elements

  • •$20.4B US investment callout
  • •25 bills signed callout
  • •46 countries callout
  • •Section overview
  • •Introduction text
Page 323
data_visualization

Chapter 8 — Global AI Policy Timeline 2016-2025

Content

Comprehensive timeline visualization of major AI policy milestones from 2016 to 2025, from the Obama AI report (2016) through EU AI Act implementation (2025), organized by geography.

Layout Structure

Horizontal multi-track timeline with country/region lanes, milestone markers, and brief annotations

Key Visual Elements

  • •Multi-track timeline
  • •Country lane labels
  • •Milestone markers
  • •Year x-axis
  • •Color coding by region
  • •Key policy name labels
Page 324
data_visualization

Chapter 8 — National AI Strategies: Global Overview

Content

World map showing the 46 countries that have adopted formal national AI strategies by 2025, with adoption date shading and regional clusters of AI governance activity.

Layout Structure

World choropleth map with adoption date shading and country name callouts for major strategies

Key Visual Elements

  • •World strategy adoption map
  • •Adoption date shading
  • •Country callouts
  • •46 countries annotation
  • •Regional cluster highlights
Page 325
data_visualization

Chapter 8 — US AI Policy: Federal Investment

Content

Analysis of US federal AI investment reaching $20.4 billion in 2025, broken down by agency (DOD, DARPA, NSF, NIH, DOE, NIST) and priority research areas.

Layout Structure

Agency breakdown bar chart with priority area analysis and year-over-year growth annotation

Key Visual Elements

  • •Agency investment bar chart
  • •$20.4B callout
  • •Priority area breakdown
  • •Year-over-year growth
  • •DOD/NSF/NIH labels
Page 326
data_visualization

Chapter 8 — US AI Policy: Legislative Activity

Content

Analysis of US AI-related legislation in 2025: 25 AI bills signed into law at federal level, topic breakdown (safety, procurement, workforce, deepfakes, national security), and state-level AI bills.

Layout Structure

Legislative topic bar chart with timeline of signed bills and state-level activity map

Key Visual Elements

  • •Legislative topic bars
  • •25 bills callout
  • •Signed bill timeline
  • •State-level activity map
  • •Topic category labels
Page 327
chapter_content

Chapter 8 — US AI Policy: Executive Actions

Content

Summary of major US executive branch AI actions in 2025: NIST AI Safety Institute activities, White House AI policy directives, and federal agency AI implementation mandates.

Layout Structure

Policy action timeline with text analysis of key executive orders and agency responses

Key Visual Elements

  • •Policy action timeline
  • •Executive order callouts
  • •Agency response summary
  • •Text analysis
  • •NIST AI Safety Institute reference
Page 328
data_visualization

Chapter 8 — US AI Policy: Sector-Specific Regulations

Content

Analysis of US sector-specific AI regulations in 2025: FDA AI medical device guidance, FTC AI-in-advertising rules, CFPB AI credit scoring guidance, and EEOC AI hiring guidance.

Layout Structure

Regulatory matrix by sector and agency with text analysis

Key Visual Elements

  • •Sector regulatory matrix
  • •Agency labels
  • •Regulation type breakdown
  • •Text analysis
  • •Key guidance callouts
Page 329
chapter_content

Chapter 8 — EU AI Act: Overview

Content

Overview of the EU AI Act implementation milestones reached in 2025, including the prohibited practices ban that took effect February 2025, with a risk-tier classification diagram.

Layout Structure

Risk tier pyramid diagram with implementation timeline and prohibited practices list

Key Visual Elements

  • •Risk tier pyramid
  • •Implementation timeline
  • •February 2025 callout
  • •Prohibited practices list
  • •Text analysis
Page 330
data_visualization

Chapter 8 — EU AI Act: Risk Categories

Content

Detailed breakdown of EU AI Act risk categories: unacceptable risk (prohibited), high risk, limited risk, and minimal risk, with examples of AI applications in each category.

Layout Structure

Four-category diagram with examples per risk level and regulatory obligation summary

Key Visual Elements

  • •Risk category diagram
  • •Category labels
  • •Application examples per tier
  • •Regulatory obligation summary
  • •Color coding by risk level
Page 331
chapter_content

Chapter 8 — EU AI Act: High-Risk AI Requirements

Content

Analysis of compliance requirements for high-risk AI systems under the EU AI Act in 2025: technical documentation, human oversight, data governance, and conformity assessment obligations.

Layout Structure

Compliance checklist diagram with implementation cost estimates and sector-specific guidance

Key Visual Elements

  • •Compliance checklist
  • •Implementation cost estimates
  • •Sector guidance callouts
  • •Text analysis
  • •Timeline for high-risk compliance
Page 332
data_visualization

Chapter 8 — EU AI Act: Industry Compliance

Content

Data on EU AI Act compliance status among large enterprises in 2025, showing early adoption by large firms, compliance challenges for SMEs, and enforcement body establishment progress.

Layout Structure

Compliance rate bar chart by firm size with enforcement progress metrics and text analysis

Key Visual Elements

  • •Compliance rate bars
  • •Firm size comparison
  • •Enforcement progress
  • •Text analysis
  • •SME challenge callout
Page 333
chapter_content

Chapter 8 — EU AI Act: GPAI Code of Practice

Content

Analysis of the General Purpose AI Code of Practice under the EU AI Act, covering transparency requirements, systemic risk assessments, and how frontier AI developers are adapting.

Layout Structure

Code of Practice obligations diagram with frontier model developer compliance status table

Key Visual Elements

  • •GPAI obligations diagram
  • •Developer compliance table
  • •Transparency requirement callout
  • •Systemic risk definition
  • •Text analysis
Page 334
chapter_content

Chapter 8 — China AI Policy

Content

Overview of China's national AI policy in 2025: the Next Generation AI Development Plan implementation, $15B+ AI investment commitment, AI industry standards, and regulatory guidelines for generative AI.

Layout Structure

Policy framework diagram with investment chart and regulatory timeline

Key Visual Elements

  • •Policy framework diagram
  • •$15B+ investment callout
  • •Regulatory timeline
  • •GenAI regulation reference
  • •Text analysis
Page 335
chapter_content

Chapter 8 — China AI Governance: Generative AI Regulations

Content

Analysis of China's Generative AI Regulation (2023, updated 2025) provisions: content filtering requirements, watermarking mandates, real-name registration, and compliance enforcement.

Layout Structure

Regulation provision summary with compliance burden analysis and text analysis

Key Visual Elements

  • •Regulation provision summary
  • •Compliance burden analysis
  • •Content filtering callout
  • •Watermarking requirement
  • •Text analysis
Page 336
chapter_content

Chapter 8 — UK AI Policy

Content

Overview of UK AI policy in 2025: AI Safety Institute evolution, the pro-innovation regulatory approach, AI sector-specific guidance, and post-Brexit AI governance divergence from EU.

Layout Structure

Policy approach comparison (UK vs EU) with AI Safety Institute activity summary

Key Visual Elements

  • •UK vs EU comparison
  • •AI Safety Institute summary
  • •Pro-innovation approach callout
  • •Text analysis
  • •Sector guidance reference
Page 337
chapter_content

Chapter 8 — G7 and G20 AI Governance

Content

Analysis of G7 Hiroshima AI Process outcomes and G20 AI governance framework adoption in 2025, including implementation status of the Hiroshima Code of Conduct for advanced AI developers.

Layout Structure

Governance framework summary with implementation status indicators and country adoption table

Key Visual Elements

  • •Implementation status indicators
  • •Country adoption table
  • •Hiroshima AI Process callout
  • •G20 framework summary
  • •Text analysis
Page 338
chapter_content

Chapter 8 — International AI Safety Summits

Content

Summary of the Paris AI Safety Summit (February 2025) outcomes: key commitments, participating nations, working group results, and the follow-up schedule for the global AI safety process.

Layout Structure

Summit outcome summary with participating nation list and commitment tracking table

Key Visual Elements

  • •Summit outcome summary
  • •Nation participation list
  • •Commitment tracking table
  • •Paris Summit callout
  • •Follow-up schedule
Page 339
chapter_content

Chapter 8 — UN AI Governance

Content

Analysis of UN AI governance activities in 2025: the Global Digital Compact, UN AI resolution adoption, the UN Secretary-General's AI Advisory Board, and the debate over a new UN AI agency.

Layout Structure

UN governance activity timeline with text analysis and country position comparison

Key Visual Elements

  • •UN governance timeline
  • •Country position comparison
  • •Global Digital Compact callout
  • •Text analysis
  • •Advisory Board reference
Page 340
data_visualization

Chapter 8 — AI Sovereignty Concept

Content

Analysis of the emerging AI sovereignty concept in 2025, defining its five dimensions: compute sovereignty, data sovereignty, talent sovereignty, standards sovereignty, and geopolitical AI independence.

Layout Structure

Five-dimension framework diagram with country assessment per dimension and text analysis

Key Visual Elements

  • •Five-dimension framework
  • •Country assessment table
  • •Sovereignty definition boxes
  • •Text analysis
  • •Geopolitical context
Page 341
data_visualization

Chapter 8 — AI Compute Sovereignty

Content

Data on national efforts to build sovereign AI compute infrastructure in 2025: EU AI gigafactory plans, India's national AI compute grid, UAE's Falcon AI infrastructure, and others.

Layout Structure

Compute investment comparison chart with geographic deployment map and text analysis

Key Visual Elements

  • •Compute investment comparison
  • •Geographic deployment map
  • •National initiative callouts
  • •Text analysis
  • •Investment amount labels
Page 342
chapter_content

Chapter 8 — AI Export Controls

Content

Analysis of AI-related export control policies in 2025: US semiconductor export restrictions expanded, chip-to-China rules, AI software export controls, and allied country coordination.

Layout Structure

Export control timeline with country impact analysis and technology restriction map

Key Visual Elements

  • •Export control timeline
  • •Country impact analysis
  • •Technology restriction map
  • •Text analysis
  • •Chip restriction callout
Page 343
chapter_content

Chapter 8 — AI Standards Development

Content

Overview of AI standards development activity in 2025: ISO/IEC AI standards, IEEE AI ethics standards, NIST AI standards, and international standards harmonization efforts.

Layout Structure

Standards landscape diagram with adoption tracking chart and text analysis

Key Visual Elements

  • •Standards landscape diagram
  • •Adoption tracking
  • •Organization comparison
  • •Text analysis
  • •ISO/IEC callout
Page 344
data_visualization

Chapter 8 — AI Liability Frameworks

Content

Comparative analysis of AI liability frameworks emerging in 2025: EU AI Liability Directive, US product liability approaches for AI, and industry self-regulatory liability frameworks.

Layout Structure

Liability framework comparison table with jurisdiction-by-jurisdiction analysis

Key Visual Elements

  • •Liability framework table
  • •Jurisdiction comparison
  • •Approach category labels
  • •Text analysis
  • •Key provision callouts
Page 345
chapter_content

Chapter 8 — AI and Democracy

Content

Analysis of AI's impact on democratic processes in 2025: AI in elections (deepfakes, voter targeting, AI-generated political content), regulatory responses, and international elections monitoring.

Layout Structure

Impact analysis diagram with regulatory response timeline and text analysis

Key Visual Elements

  • •Impact analysis diagram
  • •Regulatory response timeline
  • •Election AI callout
  • •Text analysis
  • •International monitoring reference
Page 346
data_visualization

Chapter 8 — AI Policy: National Investment Comparison

Content

Cross-national comparison of public AI investment commitments in 2025: US $20.4B, EU €20B+ AI Act enforcement funding, China $15B+, UK £1.5B, Canada C$2.4B, and others.

Layout Structure

Comparative investment bar chart by country with text analysis

Key Visual Elements

  • •Investment comparison bar chart
  • •Country labels
  • •Investment amount labels
  • •Text analysis
  • •Relative comparison annotation
Page 347
chapter_content

Chapter 8 — AI and National Security

Content

Overview of AI applications in national security in 2025: intelligence analysis, cyber defense, logistics optimization, autonomous systems, and the US-China AI competition in defense.

Layout Structure

Application domain overview with defense AI investment data and text analysis

Key Visual Elements

  • •Defense AI application overview
  • •Investment data
  • •Text analysis
  • •US-China competition callout
  • •Policy framework reference
Page 348
data_visualization

Chapter 8 — AI Regulation: State-Level US Activity

Content

Analysis of US state-level AI regulation in 2025: California AI bills, New York AI hiring rules, Colorado AI consumer protection, and the patchwork vs federal preemption debate.

Layout Structure

State-by-state regulation activity map with bill count and topic breakdown

Key Visual Elements

  • •State regulation map
  • •Bill count by state
  • •Topic breakdown
  • •Federal preemption debate callout
  • •Text analysis
Page 349
data_visualization

Chapter 8 — AI Procurement Policy

Content

Analysis of government AI procurement policies in 2025: US federal AI acquisition rules, EU public sector AI requirements, and standards for responsible government AI procurement.

Layout Structure

Procurement policy comparison table by jurisdiction with text analysis

Key Visual Elements

  • •Procurement policy table
  • •Jurisdiction comparison
  • •Requirement categories
  • •Text analysis
  • •Government AI use callout
Page 350
chapter_content

Chapter 8 — AI and Healthcare Regulation

Content

Overview of AI-specific healthcare regulatory developments in 2025 beyond FDA device approvals: CMS AI coverage decisions, HIPAA-AI guidance, AI in clinical practice guidelines.

Layout Structure

Regulatory development timeline with text analysis of key decisions

Key Visual Elements

  • •Regulatory timeline
  • •Key decision callouts
  • •Agency label comparison
  • •Text analysis
  • •Coverage decision reference
Page 351
chapter_content

Chapter 8 — AI and Financial Regulation

Content

Analysis of AI regulation in financial services in 2025: SEC AI trading rules, banking regulator AI model risk guidance, CFPB AI in lending oversight, and global financial AI standards.

Layout Structure

Regulatory framework comparison by financial sector with text analysis

Key Visual Elements

  • •Financial sector comparison
  • •Regulatory framework summary
  • •SEC rules callout
  • •Text analysis
  • •Global standards reference
Page 352
data_visualization

Chapter 8 — AI and Labor Regulation

Content

Overview of AI labor regulation in 2025: EU AI Act worker surveillance provisions, US NLRB AI in workplace guidance, EEOC AI in hiring enforcement, and union AI negotiating demands.

Layout Structure

Labor regulation comparison table with text analysis

Key Visual Elements

  • •Labor regulation table
  • •Jurisdiction comparison
  • •Worker protection provisions
  • •Text analysis
  • •Union demand callout
Page 353
chapter_content

Chapter 8 — AI Policy Effectiveness Analysis

Content

Analysis of early evidence on AI policy effectiveness in 2025: which regulatory approaches are showing compliance results, unintended consequences of regulations, and policy gaps.

Layout Structure

Policy effectiveness assessment matrix with text analysis

Key Visual Elements

  • •Effectiveness assessment matrix
  • •Policy approach rows
  • •Outcome columns
  • •Text analysis
  • •Evidence quality rating
Page 354
data_visualization

Chapter 8 — AI and Privacy Regulation

Content

Overview of AI-specific privacy regulatory developments in 2025: GDPR AI guidance, US federal privacy bill AI provisions, data minimization for AI training, and biometric data restrictions.

Layout Structure

Privacy regulatory comparison table with text analysis

Key Visual Elements

  • •Privacy regulation table
  • •Jurisdiction comparison
  • •AI-specific provisions
  • •Text analysis
  • •GDPR AI guidance callout
Page 355
chapter_content

Chapter 8 — AI Policy: Civil Society and NGO Landscape

Content

Overview of civil society and NGO activity in AI policy in 2025: advocacy organizations, public interest AI research, coalition campaigns, and their influence on AI regulation.

Layout Structure

Landscape diagram of civil society AI organizations with policy influence analysis

Key Visual Elements

  • •Civil society landscape diagram
  • •Organization type nodes
  • •Policy influence analysis
  • •Text analysis
  • •Key organization callouts
Page 356
chapter_summary

Chapter 8 — AI Policy Chapter Summary

Content

Chapter 8 summary presenting headline policy figures: US federal AI investment, global legislation counts, EU AI Act milestones, AI summit outcomes, and the AI sovereignty concept emergence.

Layout Structure

Summary dashboard with large-number statistics and chapter purple/indigo-themed graphics

Key Visual Elements

  • •Large stat boxes
  • •Key finding callouts
  • •Chapter purple theme
  • •Progress indicators
  • •Policy milestone list
Page 357
chapter_summary

Chapter 8 — AI Governance Conclusions

Content

Concluding analysis of Chapter 8 identifying key governance trends: regulatory fragmentation risk, race to the top/bottom dynamics, AI sovereignty tensions, and the challenge of keeping pace with AI progress.

Layout Structure

Text conclusion with key tension diagram and forward-looking analysis

Key Visual Elements

  • •Key tension diagram
  • •Forward-looking text
  • •Governance gap callout
  • •Text analysis
  • •Policy recommendation boxes
Page 358
appendix

Chapter 8 — Policy and Governance Endnotes

Content

Reference notes for Chapter 8 with policy document citations, legislative data sources, and investment figure methodology notes.

Layout Structure

Two-column numbered endnote format

Key Visual Elements

  • •Numbered citations
  • •Source references
  • •Two-column layout
Page 359
appendix

Chapter 8 — Policy and Governance Further Reading

Content

Curated further reading for Chapter 8 organized by policy region and topic: US policy, EU policy, China policy, international governance, and AI sovereignty.

Layout Structure

Structured reading list with region and topic headers

Key Visual Elements

  • •Reading list
  • •Region headers
  • •Topic sections
  • •Annotated entries
Page 360
chapter_opener

Chapter 9 Opener — Public Opinion

Content

Full-bleed chapter opener with blue-teal background, large white Chapter 9 numeral, title 'Public Opinion', and brief overview of global public attitudes toward AI surveyed in 2025.

Layout Structure

Full-bleed blue-teal background, oversized chapter number, white title and description

Key Visual Elements

  • •Blue-teal background
  • •Large white '9'
  • •Chapter title in white
  • •Chapter overview text
  • •Stanford HAI logo
  • •Accent line
Page 361
chapter_content

Chapter 9 — Public Opinion Introduction

Content

Introduction to Chapter 9 framing the state of global public opinion toward AI in 2025: 59% global optimism from Ipsos, notable anxiety about disinformation, and significant expert-public attitude gaps.

Layout Structure

Single-column introduction with headline statistics callout boxes and section overview

Key Visual Elements

  • •59% optimism callout
  • •Disinformation anxiety callout
  • •Expert-public gap callout
  • •Section overview
  • •Introduction text
Page 362
data_visualization

Chapter 9 — Global AI Sentiment: Overview

Content

World map of global AI sentiment in 2025 showing the percentage expressing optimism about AI by country, with higher optimism in emerging economies and lower optimism in Europe.

Layout Structure

World choropleth map of AI optimism with color gradient scale and country callout boxes

Key Visual Elements

  • •World AI sentiment map
  • •Optimism color gradient
  • •Country callout boxes
  • •59% global average
  • •Regional pattern annotations
Page 363
data_visualization

Chapter 9 — Global AI Sentiment: Ipsos Survey Data

Content

Detailed Ipsos survey results on AI attitudes in 2025 across 30+ countries: optimism, worry, trust, and awareness levels, broken down by country and world region.

Layout Structure

Multi-country bar chart for each attitude dimension with regional aggregates

Key Visual Elements

  • •Multi-country bars
  • •Four attitude dimension panels
  • •Regional aggregate callouts
  • •Year comparison
  • •Source attribution
Page 364
data_visualization

Chapter 9 — AI Sentiment: Regional Differences

Content

Regional deep-dive on AI sentiment: China and India showing highest optimism (80%+), Western Europe most cautious, US moderate, and Africa showing high optimism tied to development hopes.

Layout Structure

Regional grouped bar chart with optimism/worry balance and text analysis

Key Visual Elements

  • •Regional comparison bars
  • •Optimism vs worry split
  • •80%+ China/India callout
  • •Western Europe caution callout
  • •Text analysis
Page 365
data_visualization

Chapter 9 — Expert vs Public AI Attitude Gap

Content

Comparison of AI attitude surveys for AI experts versus general public in 2025, showing experts more concerned about long-term risks while the public focuses on near-term economic impacts.

Layout Structure

Side-by-side comparison chart with concern category breakdown for experts vs public

Key Visual Elements

  • •Expert vs public comparison
  • •Concern category breakdown
  • •Long-term vs near-term contrast
  • •Text analysis
  • •Survey methodology note
Page 366
data_visualization

Chapter 9 — Trust in AI Institutions

Content

Survey data on public trust in various institutions to regulate AI fairly: EU institutions most trusted globally, tech companies least trusted, national governments mixed results.

Layout Structure

Horizontal bar chart of trust levels by institution type across multiple countries

Key Visual Elements

  • •Trust level bar chart
  • •Institution type comparison
  • •Multi-country overlay
  • •EU most trusted callout
  • •Tech company least trusted callout
Page 367
data_visualization

Chapter 9 — US Public AI Trust and Regulation

Content

US-specific public opinion data on AI: only 31% of Americans trust the government to regulate AI well, 68% worried about AI-generated misinformation, and views on AI in various government roles.

Layout Structure

US opinion bar chart with trust and concern breakdowns and text analysis

Key Visual Elements

  • •US opinion bar chart
  • •31% trust callout
  • •68% misinformation worry callout
  • •Government role breakdown
  • •Text analysis
Page 368
data_visualization

Chapter 9 — AI Anxiety: Sources and Distribution

Content

Analysis of AI anxiety sources in 2025: job loss concern, misinformation fear, privacy worry, autonomous weapons concern, and existential risk — broken down by country and demographic.

Layout Structure

Stacked bar chart of anxiety sources by country group with demographic breakdown insets

Key Visual Elements

  • •Anxiety source stacked bars
  • •Country group comparison
  • •Demographic breakdown insets
  • •Top anxiety source callout
  • •Text analysis
Page 369
data_visualization

Chapter 9 — AI Awareness and Understanding

Content

Data on public AI awareness and understanding levels in 2025: rising awareness but limited technical understanding, with awareness-understanding gap analysis by education level and age.

Layout Structure

Awareness vs understanding comparison chart with education and age breakdown

Key Visual Elements

  • •Awareness vs understanding chart
  • •Education level breakdown
  • •Age group comparison
  • •Gap annotation
  • •Text analysis
Page 370
data_visualization

Chapter 9 — Generational Differences in AI Attitudes

Content

Survey data comparing AI attitudes across generations (Gen Z, Millennials, Gen X, Boomers) in 2025: younger generations more optimistic and higher daily AI usage, older generations more cautious.

Layout Structure

Generational comparison bar chart for multiple attitude dimensions

Key Visual Elements

  • •Generational comparison bars
  • •Multiple attitude dimensions
  • •Gen Z vs Boomer contrast
  • •Daily usage comparison
  • •Text analysis
Page 371
data_visualization

Chapter 9 — Education Level and AI Attitudes

Content

Analysis of the correlation between education level and AI attitudes in 2025: higher education associated with more nuanced views rather than simple optimism or pessimism.

Layout Structure

Correlation chart of education level versus AI attitude dimensions with text analysis

Key Visual Elements

  • •Education vs attitude correlation
  • •Nuanced view callout
  • •Bar chart by education level
  • •Multiple attitude dimensions
  • •Text analysis
Page 372
data_visualization

Chapter 9 — AI Use and Attitudes

Content

Analysis of the relationship between personal AI tool use and attitudes toward AI in 2025: regular users significantly more positive about AI's benefits, but also more aware of risks.

Layout Structure

Use frequency vs attitude comparison chart with text analysis

Key Visual Elements

  • •Use frequency vs attitude
  • •Regular vs non-user comparison
  • •Benefit awareness callout
  • •Risk awareness note
  • •Text analysis
Page 373
data_visualization

Chapter 9 — AI Attitudes: Income and Class

Content

Survey data on AI attitudes by income level in 2025: lower-income respondents more concerned about job displacement, higher-income respondents more optimistic about AI's economic benefits.

Layout Structure

Income level attitude comparison chart with job displacement concern breakdown

Key Visual Elements

  • •Income level comparison
  • •Job displacement concern bars
  • •Economic benefit optimism
  • •Text analysis
  • •Class division callout
Page 374
data_visualization

Chapter 9 — AI Attitudes: Gender Differences

Content

Analysis of gender differences in AI attitudes in 2025: men slightly more optimistic on average, women more concerned about privacy and employment impacts, with variation by country.

Layout Structure

Gender comparison bar chart across attitude dimensions with country variation insets

Key Visual Elements

  • •Gender comparison bars
  • •Multiple attitude dimensions
  • •Country variation insets
  • •Privacy concern gender gap
  • •Text analysis
Page 375
data_visualization

Chapter 9 — Longitudinal AI Attitude Trends

Content

Line charts tracking AI attitude trends over time from 2019-2025: optimism rising through 2022, plateauing with more mixed views as AI becomes more personally relevant in 2024-2025.

Layout Structure

Multi-line longitudinal chart with attitude category lines and event annotation

Key Visual Elements

  • •Longitudinal line chart
  • •Attitude category lines
  • •Year x-axis
  • •Event annotations
  • •Trend direction callouts
Page 376
data_visualization

Chapter 9 — AI and Employment Concerns: Survey Data

Content

Global survey data on AI job displacement concerns in 2025, showing about half of global respondents worried about AI displacing their own job, varying significantly by sector and country.

Layout Structure

Global concern bar chart by country with sector-specific variation analysis

Key Visual Elements

  • •Job concern global bars
  • •Country comparison
  • •Sector variation chart
  • •50% global concern callout
  • •Text analysis
Page 377
data_visualization

Chapter 9 — AI in Everyday Life: Public Awareness

Content

Data on the extent to which people recognize AI in their daily products and services in 2025, showing high usage of AI tools but limited recognition that underlying technology is AI.

Layout Structure

Usage vs recognition comparison chart with product category breakdown

Key Visual Elements

  • •Usage vs recognition comparison
  • •Product category breakdown
  • •AI recognition gap callout
  • •Text analysis
  • •Daily tool examples
Page 378
data_visualization

Chapter 9 — AI and Social Cohesion

Content

Analysis of survey data on AI's perceived impact on social cohesion in 2025: concerns about AI exacerbating polarization, inequality, and reducing human connection.

Layout Structure

Concern category bar chart with polarization and inequality callouts and text analysis

Key Visual Elements

  • •Social concern category bars
  • •Polarization callout
  • •Inequality concern
  • •Text analysis
  • •Cross-country comparison
Page 379
data_visualization

Chapter 9 — AI Values and Ethics: Public Views

Content

Survey data on public views about AI ethics in 2025: what values should guide AI development (safety, transparency, fairness, privacy) and who should have authority over AI ethics decisions.

Layout Structure

Ranked values bar chart with authority preference breakdown and text analysis

Key Visual Elements

  • •Values ranking bars
  • •Authority preference breakdown
  • •Safety/transparency/fairness leaders
  • •Text analysis
  • •Demographic variation note
Page 380
data_visualization

Chapter 9 — AI and Democratic Values

Content

Survey data on public concerns about AI's impact on democratic values in 2025: election integrity, free speech, surveillance, and the rule of law, with significant concern levels globally.

Layout Structure

Democratic concern category bar chart with country comparison and text analysis

Key Visual Elements

  • •Democratic concern bars
  • •Country comparison
  • •Election integrity callout
  • •Free speech concern
  • •Text analysis
Page 381
chapter_summary

Chapter 9 — Public Opinion Chapter Summary

Content

Chapter 9 summary presenting headline public opinion findings: global optimism rate, trust rankings, top AI anxieties, and key demographic divides in AI attitudes.

Layout Structure

Summary dashboard with large-number statistics and chapter blue-teal themed graphics

Key Visual Elements

  • •Large stat boxes
  • •Key finding callouts
  • •Chapter blue-teal theme
  • •Attitude summary infographic
  • •Progress indicators
Page 382
appendix

Chapter 9 — Public Opinion Endnotes and Further Reading

Content

Reference notes for Chapter 9 with survey data source citations and curated further reading on public AI attitudes organized by topic and methodology type.

Layout Structure

Two-column endnote format followed by structured reading list

Key Visual Elements

  • •Numbered citations
  • •Reading list
  • •Survey source references
  • •Topic organization
Page 383
appendix

Appendix — Methodology Overview

Content

Overview of the AI Index 2026 research methodology, explaining data collection approaches across all nine chapters, the definition of 'AI' used in the report, and general methodological principles.

Layout Structure

Structured methodology overview with section-by-section approach diagram

Key Visual Elements

  • •Methodology overview diagram
  • •Section approach summary
  • •AI definition box
  • •Data collection principles
  • •Text
Page 384
appendix

Appendix — Chapter 1 R&D Methodology

Content

Detailed methodology for the R&D chapter: publication data sources (Semantic Scholar, Dimensions), patent data (WIPO, USPTO), model release tracking criteria, and compute data collection.

Layout Structure

Methodology description sections with data source tables and limitation notes

Key Visual Elements

  • •Data source tables
  • •Limitation notes
  • •Collection criteria
  • •Text methodology
Page 385
appendix

Appendix — Chapter 2 Technical Performance Methodology

Content

Methodology notes for the Technical Performance chapter: benchmark selection criteria, data retrieval from Papers With Code and leaderboards, human baseline collection, and benchmark inclusion/exclusion rules.

Layout Structure

Methodology description sections with benchmark selection criteria table

Key Visual Elements

  • •Benchmark selection criteria
  • •Data retrieval description
  • •Human baseline methodology
  • •Limitation notes
Page 386
appendix

Appendix — Chapter 3 Responsible AI Methodology

Content

Methodology for the Responsible AI chapter: AIAAIC incident data collection, bias study inclusion criteria, safety research publication search methodology, and governance framework coding procedures.

Layout Structure

Methodology description sections with inclusion/exclusion criteria

Key Visual Elements

  • •AIAAIC data methodology
  • •Inclusion criteria
  • •Coding procedures
  • •Limitation notes
Page 387
appendix

Appendix — Chapter 4 Economy Methodology

Content

Methodology for the Economy chapter: investment data from NetBase Quid, job posting data from Lightcast, wage data sources, productivity study selection criteria, and firm adoption survey methodology.

Layout Structure

Methodology description with data source reference table and limitation notes

Key Visual Elements

  • •Investment data source
  • •Job posting data source
  • •Wage data methodology
  • •Survey methodology
  • •Limitation notes
Page 388
appendix

Appendix — Chapter 5 Science Methodology

Content

Methodology for the Science and AI chapter: science publication AI-assisted classification methodology, domain-specific search criteria, and clinical trial data collection from ClinicalTrials.gov.

Layout Structure

Methodology description with domain criteria and classification explanation

Key Visual Elements

  • •Publication classification methodology
  • •Domain search criteria
  • •ClinicalTrials data source
  • •Limitation notes
Page 389
appendix

Appendix — Chapter 6 Medicine Methodology

Content

Methodology for the Medicine and AI chapter: FDA device data collection, clinical AI study inclusion criteria, diagnostic accuracy aggregation methods, and AI scribe adoption data sources.

Layout Structure

Methodology description sections with FDA data approach and study criteria

Key Visual Elements

  • •FDA data collection
  • •Study inclusion criteria
  • •Diagnostic accuracy methodology
  • •Scribe adoption data source
Page 390
appendix

Appendix — Chapter 7 Education Methodology

Content

Methodology for the Education chapter: enrollment data sources (IPEDS, international equivalents), student survey methodology, faculty survey design, and K-12 curriculum data collection approach.

Layout Structure

Methodology description with survey design notes and data source references

Key Visual Elements

  • •Enrollment data sources
  • •Survey methodology
  • •K-12 curriculum data collection
  • •Limitation notes
Page 391
appendix

Appendix — Chapter 8 Policy Methodology

Content

Methodology for the Policy chapter: legislative database sources, AI bill identification criteria, investment figure compilation sources, national strategy coding methodology, and policy timeline construction.

Layout Structure

Methodology description sections with legislative data source references

Key Visual Elements

  • •Legislative data sources
  • •Bill identification criteria
  • •Investment compilation
  • •National strategy coding
  • •Timeline construction
Page 392
appendix

Appendix — Chapter 9 Public Opinion Methodology

Content

Methodology for the Public Opinion chapter: survey data sources (Ipsos, Edelman Trust Barometer, Stanford survey), survey sampling criteria, cross-national comparability notes, and attitudinal measure definitions.

Layout Structure

Survey methodology description with sampling criteria and comparability notes

Key Visual Elements

  • •Survey source list
  • •Sampling criteria
  • •Cross-national comparability
  • •Attitudinal definitions
  • •Limitation notes
Page 393
appendix

Appendix — Data Definitions and Glossary

Content

Glossary of key terms and data definitions used throughout the AI Index 2026, including definitions for 'foundation model', 'AI incident', 'training compute', 'benchmark saturation', and other core concepts.

Layout Structure

Alphabetical glossary layout with term and definition entries

Key Visual Elements

  • •Alphabetical glossary
  • •Term entries
  • •Definition text
  • •Cross-reference notes
Page 394
appendix

Appendix — Benchmark Reference Table

Content

Comprehensive reference table of all AI benchmarks cited in the report, including benchmark name, domain, task type, human baseline, and year introduced.

Layout Structure

Multi-column reference table sorted by chapter then benchmark name

Key Visual Elements

  • •Reference table
  • •Benchmark names
  • •Domain column
  • •Task type column
  • •Human baseline column
  • •Year introduced column
Page 395
appendix

Appendix — AI Model Reference Table

Content

Reference table of all notable AI models mentioned in the AI Index 2026, including model name, organization, release date, modality, and access type (open/closed).

Layout Structure

Multi-column reference table sorted by organization and release date

Key Visual Elements

  • •Model reference table
  • •Organization column
  • •Release date column
  • •Modality column
  • •Access type column
Page 396
appendix

Appendix — AI Policy Reference Table

Content

Reference table of all AI policies, regulations, and legislation cited in the report, organized by country, including policy name, type, year, and status.

Layout Structure

Policy reference table sorted by country and year

Key Visual Elements

  • •Policy reference table
  • •Country column
  • •Policy name column
  • •Type column
  • •Year column
  • •Status column
Page 397
appendix

Appendix — Investment Data Reference

Content

Supplementary investment data tables providing detailed private AI investment figures by country, sector, and year from 2013-2025 as referenced in Chapter 4.

Layout Structure

Data table with country rows, year columns, and investment amount cells

Key Visual Elements

  • •Investment data table
  • •Country rows
  • •Year columns
  • •Dollar amount cells
  • •Source footnotes
Page 398
appendix

Appendix — Publication Data Reference

Content

Supplementary publication data tables providing detailed AI publication counts by country, domain, and year from 2010-2025 as referenced in Chapter 1.

Layout Structure

Data table with country/domain rows and year columns

Key Visual Elements

  • •Publication data table
  • •Country/domain rows
  • •Year columns
  • •Count cells
  • •Source footnotes
Page 399
appendix

Appendix — Survey Data Reference

Content

Supplementary tables presenting complete survey results from public opinion surveys cited in Chapter 9, including country-level breakdowns not shown in the main chapter.

Layout Structure

Survey data tables with country rows and attitude metric columns

Key Visual Elements

  • •Survey data tables
  • •Country rows
  • •Attitude metric columns
  • •Percentage cells
  • •Source footnotes
Page 400
appendix

Appendix — FDA AI Device Data Reference

Content

Supplementary tables of FDA AI-enabled medical device approval data cited in Chapter 6, including specialty, device type, 510(k)/PMA pathway, and applicant organization.

Layout Structure

FDA device data table with multiple attribute columns

Key Visual Elements

  • •FDA device table
  • •Specialty column
  • •Device type column
  • •Pathway column
  • •Applicant column
  • •Year column
Page 401
bibliography

Works Cited — A through D

Content

Bibliography entries A through D for all sources cited in the AI Index 2026, formatted in academic citation style with author, title, publication venue, and year.

Layout Structure

Two-column bibliography format with alphabetical entries

Key Visual Elements

  • •Two-column bibliography
  • •Alphabetical entries
  • •Author-date format
  • •Publication venue text
Page 402
bibliography

Works Cited — E through H

Content

Bibliography entries E through H for all sources cited in the AI Index 2026.

Layout Structure

Two-column bibliography format with alphabetical entries

Key Visual Elements

  • •Two-column bibliography
  • •Alphabetical entries
Page 403
bibliography

Works Cited — I through L

Content

Bibliography entries I through L for all sources cited in the AI Index 2026.

Layout Structure

Two-column bibliography format with alphabetical entries

Key Visual Elements

  • •Two-column bibliography
  • •Alphabetical entries
Page 404
bibliography

Works Cited — M through P

Content

Bibliography entries M through P for all sources cited in the AI Index 2026.

Layout Structure

Two-column bibliography format with alphabetical entries

Key Visual Elements

  • •Two-column bibliography
  • •Alphabetical entries
Page 405
bibliography

Works Cited — Q through T

Content

Bibliography entries Q through T for all sources cited in the AI Index 2026.

Layout Structure

Two-column bibliography format with alphabetical entries

Key Visual Elements

  • •Two-column bibliography
  • •Alphabetical entries
Page 406
bibliography

Works Cited — U through Z

Content

Bibliography entries U through Z completing the full works cited list for the AI Index 2026, followed by web resources and data platform references.

Layout Structure

Two-column bibliography format with alphabetical entries followed by web resources section

Key Visual Elements

  • •Two-column bibliography
  • •Alphabetical entries
  • •Web resources section
Page 407
appendix

Acknowledgments — Research Team

Content

Acknowledgment of the Stanford HAI research team members who contributed to the AI Index 2026, including research leads for each chapter and data analysts.

Layout Structure

Structured acknowledgment list by chapter with contributor names and roles

Key Visual Elements

  • •Chapter contribution list
  • •Contributor names
  • •Role descriptions
  • •HAI affiliation labels
Page 408
appendix

Acknowledgments — External Contributors

Content

Acknowledgment of external contributors, data providers, survey partners (Ipsos, NetBase Quid, Lightcast), and expert reviewers who contributed data or reviewed chapter drafts.

Layout Structure

Acknowledgment list by contributor type with organization affiliations

Key Visual Elements

  • •Contributor type sections
  • •Organization names
  • •Partner logos
  • •Data provider list
Page 409
appendix

Acknowledgments — Advisory Board

Content

Listing of the Stanford HAI AI Index Advisory Board members who provided guidance for the 2026 report, with names, titles, and institutional affiliations.

Layout Structure

Advisory board member listing in alphabetical order with titles and affiliations

Key Visual Elements

  • •Advisory board list
  • •Member names
  • •Titles
  • •Institutional affiliations
Page 410
appendix

Appendix — AI Incident Case Studies Database

Content

Extended case study summaries for 20 significant AI incidents in 2025 tracked in the AIAAIC database, providing detailed context beyond what appears in Chapter 3.

Layout Structure

Case study cards in two-column grid with incident name, date, category, and summary

Key Visual Elements

  • •Case study cards
  • •Two-column grid
  • •Incident category labels
  • •Date reference
  • •Impact summary
Page 411
appendix

Appendix — Country AI Profiles: US and Canada

Content

Detailed AI profiles for the US and Canada covering R&D output, investment, policy, talent, and adoption metrics with year-over-year comparisons.

Layout Structure

Country profile cards with radar charts and key metric tables

Key Visual Elements

  • •Country profile cards
  • •Radar charts
  • •Key metric tables
  • •YoY comparison
  • •Highlight callouts
Page 412
appendix

Appendix — Country AI Profiles: UK and EU

Content

Detailed AI profiles for the UK and major EU countries (Germany, France, Netherlands) covering R&D, investment, policy, talent, and adoption metrics.

Layout Structure

Country profile cards with radar charts and key metric tables

Key Visual Elements

  • •Country profile cards
  • •Radar charts
  • •Key metric tables
  • •EU policy callout
  • •Regional comparison
Page 413
appendix

Appendix — Country AI Profiles: China

Content

Detailed AI profile for China covering R&D output, private investment, national AI policy, talent pool, model releases, and adoption metrics with year-over-year comparisons.

Layout Structure

Country profile card with radar chart, key metric table, and policy highlight section

Key Visual Elements

  • •China profile card
  • •Radar chart
  • •Key metric table
  • •Policy highlights
  • •R&D output comparison
Page 414
appendix

Appendix — Country AI Profiles: India and Southeast Asia

Content

Detailed AI profiles for India and key Southeast Asian AI economies (Singapore, South Korea, Japan) covering R&D, investment, policy, and adoption metrics.

Layout Structure

Country profile cards with radar charts and key metric tables

Key Visual Elements

  • •Country profile cards
  • •Radar charts
  • •Key metric tables
  • •Emerging market callout
  • •Regional comparison
Page 415
appendix

Appendix — Country AI Profiles: Middle East and Africa

Content

AI profiles for key Middle East AI actors (UAE, Saudi Arabia) and emerging African AI economies, with AI investment, national strategy, and talent development data.

Layout Structure

Country profile cards with key metric summaries and national strategy highlights

Key Visual Elements

  • •Country profile cards
  • •Key metric summaries
  • •National strategy highlights
  • •Investment comparison
  • •Emerging AI callout
Page 416
appendix

Appendix — AI Technical Benchmarks: Full Results

Content

Comprehensive tables of AI technical benchmark results for all models and benchmarks cited in Chapter 2, providing full data behind the chapter's charts and comparisons.

Layout Structure

Large multi-column data tables with model rows and benchmark score columns

Key Visual Elements

  • •Multi-column data tables
  • •Model name rows
  • •Benchmark score columns
  • •Score cells
  • •Source footnotes
Page 417
appendix

Appendix — AI Education Data: Full Tables

Content

Supplementary enrollment, program, and survey data tables for Chapter 7, including institution-level CS enrollment data and full faculty/student survey cross-tabulations.

Layout Structure

Data tables with institution and demographic rows

Key Visual Elements

  • •Enrollment data tables
  • •Survey cross-tabulations
  • •Institution rows
  • •Demographic breakdown columns
  • •Source footnotes
Page 418
appendix

Appendix — AI Economy: Full Investment Tables

Content

Detailed investment data tables providing company-level, sector-level, and geographic breakdown of private AI investment for 2023-2025 as referenced in Chapter 4.

Layout Structure

Multi-level investment data tables by geography, sector, and year

Key Visual Elements

  • •Investment data tables
  • •Geography rows
  • •Sector breakdown
  • •Year columns
  • •Dollar amount cells
Page 419
appendix

Appendix — AI Safety Research: Full Publication Data

Content

Supplementary data tables for AI safety research publication analysis in Chapter 3, including search query protocols, keyword lists, and venue-level publication counts.

Layout Structure

Safety research data tables with venue and year breakdown

Key Visual Elements

  • •Safety research data tables
  • •Venue rows
  • •Year columns
  • •Count cells
  • •Search protocol description
Page 420
appendix

Appendix — AI Policy: Full Legislative Data

Content

Supplementary legislative data tables for Chapter 8, providing country-level counts of AI bills, legislative topic coding scheme, and key provisions summary for major AI laws.

Layout Structure

Legislative data tables with country rows and bill category columns

Key Visual Elements

  • •Legislative data tables
  • •Country rows
  • •Bill category columns
  • •Count cells
  • •Key provisions summary
Page 421
appendix

Appendix — AI Medical Devices: Full FDA Data

Content

Complete FDA AI-enabled medical device approval data through 2025 supplementing Chapter 6, with device-level detail on specialty, applicant, and approval pathway.

Layout Structure

FDA device data tables with full detail columns

Key Visual Elements

  • •FDA device full table
  • •Specialty column
  • •Applicant column
  • •Pathway column
  • •Year column
  • •Cumulative count
Page 422
back_matter

Report Back Matter — About Stanford HAI

Content

About page for Stanford Human-Centered AI Institute: mission, research focus areas, faculty and fellows, affiliated programs, and how to engage with HAI research and events.

Layout Structure

Institutional overview with HAI program list and contact/engagement information

Key Visual Elements

  • •HAI mission statement
  • •Program list
  • •Faculty affiliations
  • •Engagement callout
  • •HAI logo
Page 423
back_matter

Back Cover

Content

Back cover of the AI Index 2026 featuring Stanford HAI branding, report title, ISBN/DOI, website URL (aiindex.stanford.edu), and a brief one-paragraph summary of the report's purpose.

Layout Structure

Back cover with report branding, summary paragraph, Stanford HAI logo, and URL

Key Visual Elements

  • •Stanford HAI logo
  • •Report title
  • •Brief summary paragraph
  • •Website URL: aiindex.stanford.edu
  • •ISBN/DOI
  • •Red/coral design accent

Frequently Asked Questions

Common questions about this slide and the underlying presentation content.

What is the Stanford HAI AI Index Report 2026?

The Stanford HAI AI Index Report 2026 is the 9th edition of the most comprehensive annual survey of artificial intelligence progress published by Stanford University's Human-Centered AI Institute. The 423-page report covers AI research and development, technical performance benchmarks, responsible AI, economic impact, science applications, medicine, education, policy and governance, and global public opinion — all grounded in original data analysis and hundreds of visualizations.

What are the most important findings in the AI Index 2026?

Key findings include: US private AI investment reaching \85.9 billion in 2025 (triple 2022 levels); AI agents now resolving over 50% of real software engineering issues on SWE-bench; the FDA approving 258 AI-enabled medical devices in 2025; 80% of college students using generative AI for coursework; 46 countries adopting national AI strategies; AI incidents tracked by AIAAIC increasing 1000% since 2019; and the EU AI Act's prohibited practices ban taking effect in February 2025.

Who publishes the AI Index and why is it credible?

The AI Index is published by Stanford University's Human-Centered AI (HAI) Institute, one of the world's leading AI research centers. Now in its 9th annual edition, the report is produced by a team of researchers with an advisory board of leading AI scientists, policymakers, and industry experts. It draws on primary data from dozens of established sources including Ipsos, NetBase Quid, Lightcast, the FDA, WIPO, and academic publication databases, making it a trusted reference for governments, corporations, and researchers worldwide.

How is the AI Index Report structured and what does each chapter cover?

The report contains 9 chapters: (1) Research and Development — model releases, publications, compute scaling; (2) Technical Performance — benchmarks across language, math, coding, science, multimodal; (3) Responsible AI — incidents, bias, safety, governance; (4) Economy — investment, labor, productivity, adoption; (5) Science and AI — biology, climate, materials, physics; (6) Medicine and AI — diagnostics, FDA devices, drug discovery; (7) Education — enrollment, student AI use, tutoring; (8) Policy and Governance — national strategies, EU AI Act, US policy; (9) Public Opinion — global attitudes, trust, anxiety. Front matter includes an executive summary with 10 key takeaways.

Is the AI Index 2026 free to download?

Yes, the Stanford HAI AI Index Report is made freely available to the public at aiindex.stanford.edu. The full 423-page PDF report, interactive data visualizations, and underlying datasets are provided at no cost, consistent with Stanford HAI's mission of ensuring AI's benefits and risks are widely understood.

How does the AI Index 2026 compare to previous editions?

The 9th edition is the most comprehensive yet, expanding coverage to include new sections on AI in science (Chapter 5), medicine (Chapter 6), and a dedicated chapter on public opinion (Chapter 9). The 2026 edition introduces new concepts like AI sovereignty (Chapter 8), adds coverage of the EU AI Act implementation, tracks the explosion in generative AI investment, and extends longitudinal data series back to 2010 in some cases — enabling a richer historical perspective on AI's progress trajectory than any prior edition.

What data and visualizations are included in the report?

The report contains hundreds of original data visualizations across 423 pages including line charts tracking AI capability and investment trends from 2010-2025, scatter plots comparing AI versus human performance on 30+ benchmarks, world maps showing global AI activity, stacked area charts of publication growth, capability comparison matrices for frontier models, policy timelines, survey data breakdowns by country and demographic, FDA approval trends, and many more. Each chapter includes endnotes and further reading, and the appendix provides full supplementary data tables.

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