Artificial Intelligence Index Report 2026

Comments on U.S. National AI Research Resource Interim Report

The AI-Enabled Scientific Frontier

The paper "The AI-Enabled Scientific Frontier" analyzes 2,507 head‑to‑head comparisons of AI versus other scientific methods across 27 disciplines from 2000 to early 2025. It finds that AI often outperforms traditional statistics but at higher computational cost, while in about a quarter of cases AI is both more expensive and less effective. Compared to scientific computing, AI usually underperforms but at lower cost, though since 2020 its performance has improved, now surpassing computing in over half of the comparisons.

AI in Science: Early Insights

The paper "AI in Science: Early Insights" analyzes AI’s impact on scientific work using data from 15 million Gemini interactions, 2,600 specialized AI models, and a survey of 600 scientists. It finds widespread AI adoption, complementary use of large language models and specialized tools, significant productivity gains of about seven hours per week, and a shift in research bottlenecks toward hypothesis backlog and verification needs. The study suggests AI can boost scientific productivity but its full effect depends on addressing new downstream challenges.

Measuring Biological Capabilities and Risks of AI Agents

arXiv:2606. 19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks.

The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI

arXiv:2608. 05173v1 Announce Type: cross Abstract: As AI capabilities advance, AI systems will pose greater risks to national security and potentially humanity as a whole.

From AGI to ASI

arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.

Import AI 452: Scaling laws for cyberwar; rising tides of AI automation; and a puzzle over gDP forecasting

How much could AI revolutionize the economy?

Who Delegates to AI? Evidence from 53,000 Agent Configurations

The paper introduces the Agentic Adoption Index (AAI), a new metric that captures whether workers actually delegate tasks to AI within their workflows, rather than merely measuring potential AI applicability. Using 53,000 agent skill specifications and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those previously deemed most at risk, that AAI aligns more closely with AI’s capabilities than current usage, and that adoption peaks at mid‑wage, bachelor’s‑level occupations while declining at both ends of the wage and education spectrum. The study highlights that technical availability explains much of the variation, but other factors—such as resistance to specification or professional discretion—also influence who adopts AI. whyItMatters":"The findings suggest that actual AI adoption patterns differ from prior risk assessments, indicating that factors beyond technical feasibility shape who delegates to AI, which has implications for workforce planning and policy."

Introducing AI stories: daily benefits shine a light on bigger opportunities

Sam Altman has written that we are entering the Intelligence Age, a time when AI will help people become dramatically more capable. The biggest problems of today—across science, medicine, education, national defense—will no longer seem intractable, but will in fact be solvable.

Atria Dawn: The Dawn of Agentic Superintelligence

The paper introduces Atria Dawn Preview, a foundation agentic language model aimed at scientific research and engineering workflows. Trained through a Verifiable Experience Pipeline, it performs competitively across 16 real‑world benchmarks, achieving the highest scores on five. The authors also present a detailed case study of human–AI collaboration, showing that while AI proposes methods and revisions, humans retain final decision‑making and guide the research direction.

The End of AI Exponentiation: Fluttering Inside and Outside AI Bubble

arXiv:2609.05894v1 Announce Type: new Abstract: The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large lang...

The Past and Future of AI Scientists

arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.