The AI Index Report 2026 is the ninth annual edition of the AI Index, an independently sourced survey of AI progress produced by Stanford HAI (the Stanford Institute for Human-Centered Artificial Intelligence). The 2026 edition runs 423 pages across 9 chapters, covering research and development, technical performance, responsible AI, the economy, science, medicine, education, policy and governance, and public opinion. The report frames its 15 top takeaways around the claim that AI capability is accelerating rather than plateauing while responsible-AI practice, labor markets, and public understanding lag behind.
Capability and performance
The report states that AI capability is accelerating rather than plateauing, with industry producing more than 90% of notable frontier models in 2025. On the SWE-bench Verified coding benchmark, performance rose from 60% to near 100% of the human baseline within one year. Organizational AI adoption reached 88%.
The report describes the US-China model performance gap as having effectively closed, with models trading the lead multiple times since early 2025. DeepSeek-R1 briefly matched top US models in February 2025, and the report puts the leading US model (Anthropic) ahead by just 2.7% as of March 2026. The United States produces more top-tier models, while China leads in publication volume, citations, patent output, and industrial robot installations. The United States hosts the most AI data centers at 5,427; almost every leading AI chip is fabricated by a single Taiwanese foundry (TSMC), which the report characterizes as an acute supply-chain dependency.
The report describes a "jagged frontier" of uneven capability: AI wins gold at the International Mathematical Olympiad yet reads analog clocks correctly only 50.1% of the time. AI agents improved from 12% to roughly 66% task success on OSWorld while still failing about one in three attempts. Robots fail at most household tasks, with a 12% success rate, even as they reach 89.4% success in controlled lab simulations.
Responsible AI
The report states that responsible AI is not keeping pace with capability. Documented AI incidents rose to 362 in 2025, up from 233 in 2024. It notes that improving one responsible-AI dimension, such as safety, can degrade another, such as accuracy, a trade-off relevant to evaluation frameworks including FMF cyber thresholds and safety cases.
Economy and labor
The report records US private AI investment of $285.9B in 2025, 23 times China's $12.4B, and counts 1,953 newly funded US AI companies in 2025, ten times the next country. It states that the US ability to attract global talent is declining.
Generative AI reached 53% population-level adoption within three years, which the report describes as faster than the personal computer or the internet. The United States ranks 24th at 28.3% adoption. The consumer value of generative AI in the United States was estimated at $172B annually by early 2026, with median value per user tripling between 2025 and 2026.
The report links productivity gains of 14-26% (in customer support and software development) to declining entry-level employment in the same fields. In software development, US developers ages 22-25 saw employment fall nearly 20% from 2024, even as headcount for older developers grew. The report presents this co-occurrence of productivity gains and entry-level employment decline in the same fields as a recurring tension in the labor data, bearing on the displacement predictions associated with Dario Amodei tracked under AI Labor Disruption.
Environment
The report documents an expanding environmental footprint for AI. It cites Grok 4 training emissions of 72,816 tons CO₂e and AI data center power capacity of 29.6 GW, which it compares to the peak demand of New York state. It states that GPT-4o inference water use may exceed the drinking water needs of 12 million people.
Science and medicine
The report states that AI for science can outperform human scientists, though larger models do not always win; it cites a 111M-parameter protein model that beat previous leaders. In clinical care, ambient AI scribes reduced note-writing time by up to 83%, but the report cautions that rigorous evidence remains limited, noting that nearly half of clinical AI studies use exam questions rather than real patient data.
Education
The report states that 80% of US students use AI for schoolwork, while only 50% of schools have AI policies. New AI PhDs in the US and Canada increased 22% between 2022 and 2024, with new PhDs going to academia rather than industry.
Policy and public opinion
The report identifies AI sovereignty as a defining feature of national policy, with national AI strategies expanding, especially in developing economies, and covers developing-country AI strategies and global standards convergence. It describes open-source development redistributing participation, with contributions from the rest of the world approaching the United States on GitHub.
On public opinion, the report records a 50-point expert-public gap on AI's future: 73% of experts expect a positive job impact versus 23% of the public. It states that global trust in AI governance varies widely by country.
Key quantitative data points
| Metric | Value |
|---|---|
| US private AI investment (2025) | $285.9B |
| China private AI investment (2025) | $12.4B |
| US:China investment ratio | 23:1 |
| Organizational AI adoption | 88% |
| Population-level generative AI adoption | 53% (3 years) |
| US adoption rate | 28.3% (24th globally) |
| Consumer value of gen AI (US, early 2026) | $172B/year |
| Productivity gains (customer support, software dev) | 14-26% |
| Young developer (22-25) employment decline | ~20% from 2024 |
| AI incidents documented (2025) | 362 (up from 233) |
| AI data center power capacity | 29.6 GW |
Relation to other topics
Several of the report's findings bear on recurring topics elsewhere. The 2.7% US-China gap as of March 2026 quantifies the qualitative gap described under the Fast-Follow Problem and in China and the US Are Running Different AI Races. The OSWorld jump from 12% to 66% quantifies agentic capability growth discussed under Agentic AI, while the persistent one-in-three failure rate is consistent with a capability-deployment gap. The report's finding of 53% adoption in three years, faster than the PC or internet, bears on the slow-diffusion thesis in AI as Normal Technology, while its "jagged frontier" of uneven capability cuts the other way. The TSMC single-foundry dependency relates to supply-chain questions in Compute Governance. The 14-26% productivity gains are larger-scale figures alongside the 15% finding of Brynjolfsson et al. discussed under Generative AI at Work.
Provenance
Published in 2026 by the Stanford Institute for Human-Centered Artificial Intelligence (HAI). 423 pages, 9 chapters. Ninth annual edition of the AI Index.