Artificial Intelligence Index Report 2026

As AI continues to advance rapidly, the question becomes whether the systems built around it can keep up. Governance frameworks, evaluation methods, education systems, and the data infrastructure needed to track AI’s impact are struggling to match the pace of the technology itself. That gap—between what AI can do and how prepared we are to manage it—runs through every chapter of this year’s report. New in this edition, the report tracks how AI is being tested more ambitiously across reasoning, safety, and real-world task execution, and why those measurements are increasingly difficult to rely on. It also features new estimates of generative AI’s economic value alongside emerging evidence of its labor market effects, an analytical framework on AI sovereignty, and a science chapter developed in collaboration with Schmidt Sciences. For the first time, the report features standalone chapters on AI in science and AI in medicine, reflecting AI’s growing impact across these two domains. For close to a decade, the AI Index has worked to bring reliable global data to a field that is evolving faster than most efforts to measure it. The report equips policymakers, researchers, executives, journalists, and the public with the necessary evidence to make informed decisions about AI. As the technology moves deeper into classrooms, clinics, and legislatures—and reshapes how people work, learn, and govern—the cost of incomplete data continues to rise. Key takeaways:

1. AI capability is not plateauing. It is accelerating and reaching more people than ever.
2. The U.S.-China AI model performance gap has effectively closed. U
3. The United States hosts the most AI data centers, with the majority of their chips fabricated by one Taiwanese foundry.
4. AI models can win a gold medal at the International Mathematical Olympiad but cannot reliably tell time—an example of what researchers call the jagged frontier of AI. 
5. Robots still fail at most household tasks, even as they excel in controlled environments. 
6. Responsible AI is not keeping pace with AI capability, with safety benchmarks lagging and incidents rising sharply. 
7. The United States leads in AI investment, but its ability to attract global talent is declining.
8. AI adoption is spreading at historic speed, and consumers are deriving substantial value from tools they often access for free. 
9. Productivity gains from AI are appearing in many of the same fields where entry-level employment is starting to decline. 
10. AI’s environmental footprint is expanding alongside its capabilities.
11. AI models for science can outperform human scientists, though bigger models do not always perform better.
12. AI is transforming clinical care, but rigorous evidence remains limited. 
13. Formal education is lagging behind AI, but people are learning AI skills at every stage of life.
14. AI sovereignty is becoming a defining feature of national policy, but capabilities remain uneven, even as open-source development helps to redistribute who participates. 
15. AI experts and the public have very different perspectives on the technology’s future, and global trust in institutions to manage AI is fragmented.