International AI Safety Report 2026

Leading general-purpose AI systems now pass professional licensing exams in law and medicine, write functional software when given simple prompts, and answer PhD-level science questions as well as subject-matter experts. Just three years ago, when ChatGPT launched, they could not reliably do any of these things. The pace of this transformation has been remarkable, and while the pace of future changes is uncertain, most experts expect that AI will continue to improve.

Almost a billion people now use general-purpose AI systems in their daily lives for work and learning. Companies are investing hundreds of billions of dollars to build the infrastructure to train and deploy them. In many cases, AI is already reshaping how people access information, make decisions, and solve problems, with applications in industries from software development to legal services to scientific research.

But the same capabilities that make these systems useful also create new risks. Systems that write functional code also help create malware. Systems that summarise scientific literature might help malicious actors plan attacks. As AI is deployed in high-stakes settings – from healthcare to critical infrastructure – the impacts of deliberate misuse, failures, and systemic disruptions can be severe.

For policymakers, the rate of change, the breadth of applications, and the emergence of new risks pose important questions. General-purpose AI capabilities evolve quickly, but it takes time to collect and assess evidence about their societal effects. This creates what this Report calls the ‘evidence dilemma’. By acting too early, policymakers risk implementing ineffective or even harmful interventions. But waiting for conclusive evidence can leave societies vulnerable to potential risks.

The role of this report

This Report aims to help policymakers navigate that dilemma. It provides an up-to-date, internationally shared scientific assessment of general-purpose AI capabilities and risks.

The writing team included over 100 independent experts, including an Expert Advisory Panel comprising nominees from more than 30 countries and intergovernmental organisations including the EU, OECD, and UN. The Report also incorporates feedback from reviewers across academia, industry, government, and civil society. While contributors differ on some points, they share the belief that constructive and transparent scientific discourse on AI is necessary for people around the world to realise the technology’s benefits and mitigate its risks.

Because the evidence dilemma is most acute where scientific understanding is thinnest, this Report focuses on ‘emerging risks’: risks that arise at the frontier of general-purpose AI capabilities. Its analysis focuses on issues that remain particularly uncertain, aiming to complement efforts that consider the broader social impacts of AI. While this Report draws on international expertise and aims to be globally relevant, readers should note that variation in AI adoption rates, infrastructure, and institutional contexts mean that risks may manifest differently across countries and regions.

The evidence base for these risks is uneven. Some risks, such as harms from AI-generated media or cybersecurity vulnerabilities, now have robust empirical evidence. Evidence for others – particularly risks that may arise from future developments in AI capabilities – relies on modelling exercises, laboratory studies under controlled conditions, and theoretical analysis. The analysis here draws on a broad range of scientific, technical, and socioeconomic evidence published before December 2025. Where high uncertainty remains, it identifies evidence gaps to guide future research.

Changes since the 2025 Report

This edition of the International AI Safety Report follows the publication of the first Report in January 2025. Since then, both general-purpose AI and the research community’s understanding of it have continued to evolve, warranting a revised assessment.

Over the past year, AI developers have continued to train larger and more capable AI models. However, they have also achieved significant capability gains through new techniques that allow systems to use more computing power to generate intermediate steps before giving a final answer. These new ‘reasoning systems’ show particularly improved performance in mathematics, coding, and science. In addition, AI agents – systems that can act in the world with limited human oversight – have become increasingly capable and reliable, though they remain prone to basic errors that limit their usefulness in many contexts.

General-purpose AI systems have also continued to diffuse, faster than many previous technologies in some places, though unevenly across countries and regions. Improved performance in capabilities related to scientific knowledge has also prompted multiple developers to release new models with additional safeguards, as they were unable to confidently rule out the possibility that these models could assist novices with weapon development.

This Report covers all these developments in greater depth, and incorporates several new structural elements to improve its usefulness and accessibility. It includes capability forecasts developed with the Forecasting Research Institute and scenarios developed with the OECD. Each section includes updates since the last Report, key challenges for policymakers, and evidence gaps to guide future research.

How this Report is organised

This Report is organised around three central questions:

• What can general-purpose AI do today, and how might its capabilities change? Chapter 1 covers how general-purpose AI is developed (§1.1. What is general-purpose AI?), current capabilities and limitations (§1.2. Current capabilities), and the factors that will shape developments over the coming years (§1.3. Capabilities by 2030).

• What emerging risks does general-purpose AI pose? Chapter 2 covers risks from malicious use, including the use of AI systems for criminal activities (§2.1.1. AI-generated content and criminal activity), manipulation (§2.1.2. Influence and manipulation), cyberattacks (§2.1.3. Cyberattacks), and developing biological or chemical weapons (§2.1.4. Biological and chemical risks); risks from malfunctions, including operational failures (§2.2.1. Reliability challenges) and loss of control (§2.2.2. Loss of control); and systemic risks,* including disruptions to labour markets (§2.3.1. Labour market impacts) and threats to human autonomy (§2.3.2. Risks to human autonomy).

• What risk management approaches exist, and how effective are they? Chapter 3 covers the distinctive policymaking challenges that general-purpose AI poses (§3.1. Technical and institutional challenges), current risk management practices (§3.2. Risk management practices), the various techniques developers use to make AI models and systems more robust and resistant to misuse (§3.3. Technical safeguards and monitoring), the particular challenges of open-weight models (§3.4. Open-weight models), and efforts to make society more resilient to potential AI shocks and harms (§3.5. Societal resilience).

* In this report, systemic risks are risks that result from widespread deployment of highly-capable general-purpose AI across society and the economy. Note that the EU AI Act uses the term differently, to refer to risks from general-purpose AI models that pose “risks of large-scale harm”.

Many aspects of how general-purpose AI will develop remain deeply uncertain. But decisions made today – by developers, governments, communities, and individuals – will shape its trajectory. This Report aims to ensure that those decisions are made with the best possible understanding of AI capabilities, risks, and options for risk management.