Everything Nestor has measured, written, and said about AI.

A new report from Stanford’s Tech Impact and Policy Center is offering one of the first cross-sector attempts to make sense of a technology moving faster than the research meant to understand it: AI companions.

The most recent version of the AI Index report unpacks the latest AI trends from technical performance to economics, geopolitics to public opinion and more. Some of the top highlights include the continued climb of AI capabilities, staggering rates of AI adoption and record revenues reported by major AI developers.

The second International AI Safety Report, published in February 2026, is the next iteration of the comprehensive review of latest scientific research on the capabilities and risks of general-purpose AI systems. Led by Turing Award winner Yoshua Bengio and authored by over 100 AI experts, the report represents the largest global collaboration on AI safety to date.

This paper analyzes global AI patenting from 2010 to 2023 to assess Europe’s position in an increasingly bipolar innovation landscape dominated by the United States and China. Using linked patent, firm, ownership, and citation data, the authors examine the geography, specialization, and international diffusion of AI innovation.

Put together by a team of Stanford, Princeton and MIT researchers, the 2025 Foundation Model Transparency Index outlines the state of AI transparency. In the last year, transparency progress deteriorated: developers became less open about training data, compute and the post-deployment impact of their models.

This second update to the 2025 International AI Safety Report assesses new developments in general-purpose AI risk management over the past year. It examines how researchers, public institutions, and AI developers are approaching risk management for general-purpose AI.

The first key update from the International AI Safety Report team: shorter, focused reports that highlight the most important developments between full editions. These updates aim to provide policymakers, researchers, and the public with up-to-date information to support wise decisions about AI governance.

The 2025 Index is the most comprehensive global AI stock-take to date, arriving at a time when AI is becoming more important than ever. New in this year’s report are in-depth analyses of the evolving landscape of AI hardware, novel estimates of inference costs, and new analyses of AI publication and patenting trends.

Small models get better, regulation moves to the states, and more. The 2025 AI Index Report shows a maturing field, improvements in AI optimization, and a growing saturation of use, and abuse, of this technology. This article covers the 2025 report in ten charts.

This paper presents the latest version of the Global AI Vibrancy Tool (GVT), an interactive suite of visualizations designed to facilitate the comparison of AI vibrancy across 36 countries, using 42 indicators organized into 8 pillars. The tool offers customizable features that allow users to conduct in-depth country-level comparisons and longitudinal analyses of AI-related metrics.

To codify how foundation model developers should provide transparency, we propose Foundation Model Transparency Reports, drawing upon the transparency reporting practices in social media. While external documentation of societal harms prompted social media transparency reports, our objective is to institutionalize transparency reporting for foundation models while the industry is still nascent.

To understand how the AI transparency landscape has changed, the 2024 Foundation Model Transparency Index conducts a follow up 6 months following the initial launch, scoring developers on the same 100 indicators. Developers now score 58 out of 100 on average, a 21 point improvement over the first version.

Text embedding is becoming an increasingly popular AI methodology, especially among businesses, yet the potential of text embedding models to be biased is not well understood. This paper examines the degree to which a selection of popular text embedding models are biased, particularly along gendered dimensions.

The cost of training frontier AI models has grown by a factor of 2 to 3x per year for the past eight years, suggesting that the largest models will cost over a billion dollars by 2027.

The costs of training frontier AI models have grown dramatically in recent years, but there is limited public data on the magnitude and growth of these expenses. This paper develops one of the first detailed cost models to address this gap, estimating training costs using three approaches that account for hardware, energy, cloud rental, and staff expenses.

The 2024 AI Index broadens its scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. The 2024 edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

The Foundation Model Transparency Index is a new project that assesses the transparency of foundation model developers. The Index is designed around 100 transparency indicators, which codify transparency for foundation models, the resources required to build them, and their use in the AI supply chain. The 2023 Index scores 10 leading developers providing a snapshot of AI transparency.

This article contributes to the understanding of how the legal system is reacting to the ongoing uptake of AI and the disputes or right infringements this uptake creates. Select legal cases regarding the use of AI technology for automated decisions are reviewed, with a focus on filings in Europe and the USA.

The 2023 AI Index report introduces more original data than any previous edition, including a new chapter on AI public opinion, a more thorough technical performance chapter, original analysis about large language and multimodal models, detailed trends in global AI legislation records, a study of the environmental impact of AI systems, and more.

The paper provides stakeholders (policymakers, advocates, workers, and unions) with insights into AI-powered algorithmic worker surveillance and productivity-scoring tools. Topics explored include the assumptions embedded in workplace surveillance and scoring technologies, how employers use these systems in ways that affect human rights, and actionable recommendations for federal agencies and labour unions.

The 2022 edition of Stanford’s annual AI Index includes an expanded technical performance chapter, a new survey of robotics researchers around the world, data on global AI legislation records in 25 countries, and a new chapter with an in-depth analysis of technical AI ethics metrics.