Contributors

Chair

Prof. Yoshua Bengio, Université de Montreal / LawZero / Mila – Quebec AI Institute

Expert Advisory Panel

The Expert Advisory Panel is an international advisory body that advises the Chair on the content of the Report. The Expert Advisory Panel provided technical feedback only. The Report – and its Expert Advisory Panel – does not endorse any particular policy or regulatory approach.

The Panel comprises representatives nominated by over 30 countries and international organisation including from; Australia, Brazil, Canada, Chile, China, the European Union (EU), France, Germany, India, Indonesia, Ireland, Israel, Italy, Japan, Kenya, Mexico, the Netherlands, New Zealand, Nigeria, the Organisation for Economic Co-operation and Development (OECD), the Philippines, the Republic of Korea, Rwanda, the Kingdom of Saudi Arabia, Singapore, Spain, Switzerland, Türkiye, the United Arab Emirates, Ukraine, the United Kingdom and the United Nations (UN).

The full membership list for the Expert Advisory Panel can be found here: https://internationalaisafetyreport.org/expert-advisory-panel

Lead Writers

**Stephen Clare, **Independent

**Carina Prunkl, **Inria

Chapter Leads

Maksym Andriushchenko, ELLIS Institute Tübingen

Ben Bucknall, University of Oxford

**Malcolm Murray, **SaferAI

Core Writers

**Shalaleh Rismani, **Mila – Quebec AI Institute

**Conor McGlynn, **Harvard University

**Nestor Maslej, **Stanford University

**Philip Fox, **KIRA Center

Writing Group

Rishi Bommasani, Stanford University

Stephen Casper, Massachusetts Institute of Technology

Tom Davidson, Forethought

Raymond Douglas, Telic Research

David Duvenaud, University of Toronto

**Usman Gohar, **Iowa State University

Rose Hadshar, Forethought

**Anson Ho, **Epoch AI

**Tiancheng Hu, **University of Cambridge

Cameron Jones, Stony Brook University

Sayash Kapoor, Princeton University

Atoosa Kasirzadeh, Carnegie Mellon

Sam Manning, Centre for the Governance of AI

Vasilios Mavroudis, The Alan Turing Institute

Richard Moulange, The Centre for Long-Term Resilience

**Jessica Newman, **University of California, Berkeley

Kwan Yee Ng, Concordia AI

Patricia Paskov, University of Oxford

Girish Sastry, Independent

Elizabeth Seger, Demos

Scott Singer, Carnegie Endowment for International Peace

Charlotte Stix, Apollo Research

Lucia Velasco, Maastricht University

Nicole Wheeler, Advanced Research + Invention Agency

Advisers to the Chair*

* Appointed for the planning phase (February–July 2025); from July, consultants to the Report team

**Daniel Privitera, **Special Adviser to the Chair, KIRA Center

Sören Mindermann, Scientific Adviser to the Chair, Mila – Quebec AI Institute

Senior Advisers

Daron Acemoglu, Massachusetts Institute of Technology

Thomas G. Dietterich, Oregon State University

Fredrik Heintz, Linköping University

Geoffrey Hinton, University of Toronto

Nick Jennings, Loughborough University

Susan Leavy, University College Dublin

Teresa Ludermir, Federal University of Pernambuco

Vidushi Marda, AI Collaborative

Helen Margetts, University of Oxford

John McDermid, University of York

Jane Munga, Carnegie Endowment for International Peace

Arvind Narayanan, Princeton University

Alondra Nelson, Institute for Advanced Study

Clara Neppel, IEEE

Sarvapali D. (Gopal) Ramchurn, Responsible AI UK

Stuart Russell, University of California, Berkeley

Marietje Schaake, Stanford University

Bernhard Schölkopf, ELLIS Institute Tübingen

Alvaro Soto, Pontificia Universidad Católica de Chile

Lee Tiedrich, Duke University

Gaël Varoquaux, Inria

Andrew Yao, Tsinghua University

Ya-Qin Zhang, Tsinghua University

Secretariat

AI Security Institute: Lambrini Das, Arianna Dini, Freya Hempleman, Samuel Kenny, Patrick King, Hannah Merchant, Jamie-Day Rawal, Jai Sood, Rose Woolhouse

**Mila – Quebec AI Institute: **Jonathan Barry, Marc-Antoine Guérard, Claire Latendresse, Cassidy MacNeil, Benjamin Prud’homme

Acknowledgements

Civil Society and Industry Reviewers

Civil Society

Ada Lovelace Institute, African Centre for Technology Studies, AI Forum New Zealand / Te Kāhui Atamai Iahiko o Aotearoa, AI Safety Asia, Stichting Algorithm Audit, Carnegie Endowment for International Peace, Center for Law and Innovation / Certa Foundation, Centre for the Governance of AI, Chief Justice Meir Shamgar Center for Digital Law and Innovation, Digital Futures Lab, EON Institute, Equiano Institute, Good Ancestors Policy, Gradient Institute, Institute for Law & AI, Interface, Israel Democracy Institute, Mozilla Foundation, NASSCOM, Old Ways New, RAND, Royal Society, SaferAI, Swiss Academy of Engineering Sciences, The Centre for Long-Term Resilience, The Alan Turing Institute, The Ethics Centre, The Future Society, The HumAIne Foundation, Türkiye Artificial Intelligence Policies Association

Industry

Advai, Anthropic, Cohere, Deloitte, Digital Umuganda, Domyn, G42, Google DeepMind, Harmony Intelligence, Hugging Face, HumAIn, IBM, LG AI Research, Meta, Microsoft, Naver, OpenAI, Qhala

Informal reviewers

Markus Anderljung, David Autor, Mariette Awad, Jamie Bernardi, Stella Biderman, Asher Brass, Ben Brooks, Miles Brundage, Kevin Bryan, Rafael Calvo, Siméon Campos, Carmen Carlan, Micah Carroll, Alan Chan, Jackie Cheung, Josh Collyer, Elena Cryst, Tino Cuéllar, Allan Dafoe, Jean-Stanislas Denain, Fernando Diaz, Roel Dobbe, Seth Donoughe, Izzy Gainsbury, Ben Garfinkel, Adam Gleave, Jasper Götting, Kobi Hackenburg, Lewis Hammond, David Evan Harris, Dan Hendrycks, José Hernández-Orallo, Luke Hewitt, Marius Hobbhahn, Manoel Horta Ribeiro, Abigail Jacobs, Ari Kagan, Daniel Kang, Anton Korinek, Michal Kosinski, Gretchen Krueger, Dan Lahav, Anton Leicht, Vera Liao, Eli Lifland, Matthijs Maas, James Manyika, Simon Mylius, AJung Moon, Seán Ó hÉigeartaigh, Tamara Paris, Raymond Perrault, Siva Reddy, Luca Righetti, Jon Roozenbeek, Max Roser, Anders Sandberg, Leo Schwinn, Jaime Sevilla, Theodora Skeadas, Chandler Smith, Tobin South, Jonathan Spring, Merlin Stein, David Stillwell, Daniel Susser, Helen Toner, Sander van der Linden, Kush Varshney, Jess Whittlestone, Kai-Cheng Yang

The Secretariat and writing team appreciated assistance with quality control and formatting of citations by José Luis León Medina and copyediting by Amber Ace.

Copyright and disclaimer

© Crown owned 2026

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected].

Where we have identified any third-party copyright information you will need to obtain permission from the copyright holders concerned.

Any enquiries regarding this publication should be sent to: [email protected].

Disclaimer

This Report is a synthesis of the existing research on the capabilities and risks of advanced AI. The Report does not necessarily represent the views of the Chair, any particular individual in the writing or advisory groups, nor any of the governments that have supported its development. The Chair of the Report has ultimate responsibility for it and has overseen its development from beginning to end.

Research series number: DSIT 2026/001

Forewords

A new scientific assessment of a fast-moving technology

Professor Yoshua Bengio – Université de Montréal / LawZero / Mila – Quebec AI Institute & Chair

This is the second International AI Safety Report, which builds on the mandate by world leaders at the 2023 AI Safety Summit at Bletchley Park to produce an evidence base to inform critical decisions about general-purpose artificial intelligence (AI).

This year, we have introduced several changes to make this Report even more useful and accessible.

First, to help policymakers better understand the range of potential outcomes despite the uncertainty involved, we have drawn upon new research conducted by the Organisation for Economic Co-operation and Development (OECD) and Forecasting Research Institute to present more specific scenarios and forecasts.

Second, following extensive consultation, we have narrowed the scope to focus on ‘emerging risks’: risks that arise at the frontier of AI capabilities. Given high uncertainty in this domain, the rigorous analysis the Report provides can be especially valuable. A narrower scope also ensures this Report complements other efforts, including the United Nations’ Independent International Scientific Panel on AI.

Of course, some things have not changed.

This remains the most rigorous assessment of AI capabilities, risks, and risk management available. Its development involved contributions from over 100 experts, including the guidance of experts nominated by over 30 countries and intergovernmental organisations.

The Report’s fundamental goal is also the same: to advance a shared understanding of how AI capabilities are evolving, risks associated with these advances, and what techniques exist to mitigate those risks.

The pace of AI progress raises daunting challenges. However, working with the many experts that produced this Report has left me hopeful. I am immensely grateful for the enormous efforts of all contributors – we are making progress towards understanding these risks.

With this Report, we hope to improve our collective understanding of what may be the most significant technological transformation of our time.

Building a secure future for AI through international cooperation

Kanishka Narayan MP – Minister for AI and Online Safety, Department for Science, Innovation and Technology, UK Government

AI continues to redefine the possibilities before us – transforming economies, revitalising public services, and rapidly accelerating scientific advancement. This pace of progress demands an up-to-date, shared understanding of AI capabilities. This effort will build trust, enable adoption and pave the way for AI to deliver prosperity for all.

The 2026 *International AI Safety Report *is the result of strong collaboration across countries, organisations, civil society and industry partners – working together to produce robust, evidence-based analysis. The Report provides an essential tool for policymakers and world leaders to help navigate this challenging and fast-moving landscape.

The United Kingdom remains committed to strengthening international partnerships, scientific collaboration, and institutions that drive innovative AI research forward, including the AI Security Institute. Following the success of the landmark Summits hosted in Bletchley Park (November 2023), Seoul (May 2024) and Paris (February 2025), I am especially looking forward to the India AI Impact Summit – where this Report will be showcased – to ensure AI is shaped for humanity, inclusive growth and a sustainable future.

I am delighted to present this Report and thank Yoshua Bengio, the writing team, and all contributors for their dedication to this initiative. Together – through shared responsibility and international cooperation – we can forge a path where AI delivers security, opportunity and growth for every nation and every citizen.

Enabling equitable access to AI for all

***Ashwini Vaishnaw ***– Minister of Railways, Information & Broadcasting and Electronics & Information Technology, Government of India

The second International Al Safety Report builds on the mandate of the 2023 Al Safety Summit at Bletchley Park. It aims at developing a shared, science-based understanding of advanced Al capabilities and risks.

This edition focuses on rapidly evolving general-purpose Al systems, including language, vision and agentic models. It also reviews associated challenges, including wider impacts on labour markets, human autonomy and concentration of power.

As Al systems grow more capable, safety and security remain critical priorities. The Report highlights practical approaches of model evaluations, dangerous capability thresholds and ‘if-then’ safety commitments to reduce high-impact failures.

Our global risk management frameworks are still immature, with limited quantitative benchmarks and significant evidence gaps. These gaps must be addressed alongside innovation.

For India and the Global South, Al safety is closely tied to inclusion, safety and institutional readiness. Responsible openness of Al models, fair access to compute and data, and international cooperation are essential too.

As host of the 2026 India Al Impact Summit, India has a key role in shaping global Al safety efforts. The Report is intended to help policymakers, researchers, industry and civil society shape national strategies.

About this Report

This is the second edition of the International AI Safety Report. The series was created following the 2023 AI Safety Summit at Bletchley Park to support an internationally shared scientific understanding of the capabilities and risks associated with advanced AI systems. A diverse group of over 100 Artificial Intelligence (AI) experts guided its development, including an international Expert Advisory Panel with nominees from over 30 countries and international organisations, including the Organisation for Economic Co-operation and Development (OECD), the European Union (EU), and the United Nations (UN).

Scope, focus, and independence

Scope: This Report concerns ‘general-purpose AI’: AI models and systems capable of performing a wide variety of tasks across different contexts. These models and systems perform tasks like generating text, images, audio, or other forms of data, and are frequently adapted to a range of domain-specific applications.

Focus: This Report focuses on ‘emerging risks’: risks that arise at the frontier of AI capabilities. The Bletchley Declaration, issued following the 2023 AI Safety Summit, emphasised that “particular safety risks arise at the ‘frontier’ of AI”, including risks from misuse, issues of control, and cybersecurity risks. The Declaration also recognised broader AI impacts, including on human rights, fairness, accountability, and privacy. This Report aims to complement assessments that consider these broader concerns, including the UN’s Independent* *International Scientific Panel on AI.*

Independence: Under the leadership of the Chair, the independent writing team jointly had full discretion over its content. The Report aims to synthesise scientific evidence to support informed policymaking. It does not make specific policy recommendations.

* Note that this focus makes the scope of this Report narrower than that of the 2025 Report, which also addressed issues such as bias, environmental impacts, privacy, and copyright.

Process and contributors

The International AI Safety Report is written by a diverse team with over 30 members, led by the Chair, lead writers, and chapter leads. It undergoes a structured review process. Early drafts are reviewed by external subject-matter experts before a consolidated draft is reviewed by:

• An Expert Advisory Panel with representatives nominated by over 30 countries and international organisations, including the OECD, the EU, and the UN
• A group of Senior Advisors composed of leading international researchers
• Representatives from industry and civil society organisations

The writing team, chapter leads, lead writers, and Chair consider feedback provided by reviewers and incorporate it where appropriate.

Key developments since the 2025 Report

Notable developments since the publication of the first International AI Safety Report in January 2025.

• General-purpose AI capabilities have continued to improve, especially in mathematics, coding, and autonomous operation. Leading AI systems achieved gold-medal performance on International Mathematical Olympiad questions. In coding, AI agents can now reliably complete some tasks that would take a human programmer about half an hour, up from under 10 minutes a year ago. Performance nevertheless remains ‘jagged’, with leading systems still failing at some seemingly simple tasks.
• Improvements in general-purpose AI capabilities increasingly come from techniques applied after a model’s initial training. These ‘post-training’ techniques include refining models for specific tasks and allowing them to use more computing power when generating outputs. At the same time, using more computing power for initial training continues to also improve model capabilities.
• **AI adoption has been rapid, though highly uneven across regions. **AI has been adopted faster than previous technologies like the personal computer, with at least 700 million people now using leading AI systems weekly. In some countries over 50% of the population uses AI, though across much of Africa, Asia, and Latin America adoption rates likely remain below 10%.
• **Advances in AI’s scientific capabilities have heightened concerns about misuse in biological weapons development. **Multiple AI companies chose to release new models in 2025 with additional safeguards after pre-deployment testing could not rule out the possibility that they could meaningfully help novices develop such weapons.
• **More evidence has emerged of AI systems being used in real-world cyberattacks. **Security analyses by AI companies indicate that malicious actors and state-associated groups are using AI tools to assist in cyber operations.
• **Reliable pre-deployment safety testing has become harder to conduct. **It has become more common for models to distinguish between test settings and real-world deployment, and to exploit loopholes in evaluations. This means that dangerous capabilities could go undetected before deployment.
• **Industry commitments to safety governance have expanded. **In 2025, 12 companies published or updated Frontier AI Safety Frameworks – documents that describe how they plan to manage risks as they build more capable models. Most risk management initiatives remain voluntary, but a few jurisdictions are beginning to formalise some practices as legal requirements.

Executive Summary

**This Report assesses what general-purpose AI systems can do, what risks they pose, and how those risks can be managed. **It was written with guidance from over 100 independent experts, including nominees from more than 30 countries and international organisations, such as the EU, OECD, and UN. Led by the Chair, the independent experts writing it jointly had full discretion over its content.

**This Report focuses on the most capable general-purpose AI systems and the emerging risks associated with them. ‘**General-purpose AI’ refers to AI models and systems that can perform a wide variety of tasks. ‘Emerging risks’ are risks that arise at the frontier of general-purpose AI capabilities. Some of these risks are already materialising, with documented harms; others remain more uncertain but could be severe if they materialise.

**The aim of this work is to help policymakers navigate the ‘evidence dilemma’ posed by general-purpose AI. **AI systems are rapidly becoming more capable, but evidence on their risks is slow to emerge and difficult to assess. For policymakers, acting too early can lead to entrenching ineffective interventions, while waiting for conclusive data can leave society vulnerable to potentially serious negative impacts. To alleviate this challenge, this Report synthesises what is known about AI risks as concretely as possible while highlighting remaining gaps.

**While this Report focuses on risks, general-purpose AI can also deliver significant benefits. **These systems are already being usefully applied in healthcare, scientific research, education, and other sectors, albeit at highly uneven rates globally. But to realise their full potential, risks must be effectively managed. Misuse, malfunctions, and systemic disruption can erode trust and impede adoption. The governments attending the AI Safety Summit initiated this Report because a clear understanding of these risks will allow institutions to act in proportion to their severity and likelihood.

Capabilities are improving rapidly but unevenly

Since the publication of the 2025 Report, general-purpose AI capabilities have continued to improve, driven by new techniques that enhance performance after initial training. AI developers continue to train larger models with improved performance. Over the past year, they have further improved capabilities through ‘inference-time scaling’: allowing models to use more computing power in order to generate intermediate steps before giving a final answer. This technique has led to particularly large performance gains on more complex reasoning tasks in mathematics, software engineering, and science.

At the same time, capabilities remain ‘jagged’: leading systems may excel at some difficult tasks while failing at other, simpler ones. General-purpose AI systems excel in many complex domains, including generating code, creating photorealistic images, and answering expert-level questions in mathematics and science. Yet they struggle with some tasks that seem more straightforward, such as counting objects in an image, reasoning about physical space, and recovering from basic errors in longer workflows.

The trajectory of AI progress through 2030 is uncertain, but current trends are consistent with continued improvement. AI developers are betting that computing power will remain important, having announced hundreds of billions of dollars in data centre investments. Whether capabilities will continue to improve as quickly as they recently have is hard to predict. Between now and 2030, it is plausible that progress could slow or plateau (e.g. due to bottlenecks in data or energy), continue at current rates, or accelerate dramatically (e.g. if AI systems begin to speed up AI research itself).

Real-world evidence for several risks is growing

General-purpose AI risks fall into three categories: malicious use, malfunctions, and systemic risks.

Malicious use

AI-generated content and criminal activity: AI systems are being misused to generate content for scams, fraud, blackmail, and non-consensual intimate imagery. Although the occurrence of such harms is well-documented, systematic data on their prevalence and severity remains limited.

Influence and manipulation: In experimental settings, AI-generated content can be as effective as human-written content at changing people’s beliefs. Real-world use of AI for manipulation is documented but not yet widespread, though it may increase as capabilities improve.

**Cyberattacks: **AI systems can discover software vulnerabilities and write malicious code. In one competition, an AI agent identified 77% of the vulnerabilities present in real software. Criminal groups and state-associated attackers are actively using general-purpose AI in their operations. Whether attackers or defenders will benefit more from AI assistance remains uncertain.

Biological and chemical risks: General-purpose AI systems can provide information about biological and chemical weapons development, including details about pathogens and expert-level laboratory instructions. In 2025, multiple developers released new models with additional safeguards after they could not exclude the possibility that these models could assist novices in developing such weapons. It remains difficult to assess the degree to which material barriers continue to constrain actors seeking to obtain them.

Malfunctions

Reliability challenges: Current AI systems sometimes exhibit failures such as fabricating information, producing flawed code, and giving misleading advice. AI agents pose heightened risks because they act autonomously, making it harder for humans to intervene before failures cause harm. Current techniques can reduce failure rates but not to the level required in many high-stakes settings.

Loss of control: ‘Loss of control’ scenarios are scenarios where AI systems operate outside of anyone’s control, with no clear path to regaining control. Current systems lack the capabilities to pose such risks, but they are improving in relevant areas such as autonomous operation. Since the last Report, it has become more common for models to distinguish between test settings and real-world deployment and to find loopholes in evaluations, which could allow dangerous capabilities to go undetected before deployment.

Systemic risks

Labour market impacts: General-purpose AI will likely automate a wide range of cognitive tasks, especially in knowledge work. Economists disagree on the magnitude of future impacts: some expect job losses to be offset by new job creation, while others argue that widespread automation could significantly reduce employment and wages. Early evidence shows no effect on overall employment, but some signs of declining demand for early-career workers in some AI-exposed occupations, such as writing.

Risks to human autonomy: AI use may affect people’s ability to make informed choices and act on them. Early evidence suggests that reliance on AI tools can weaken critical thinking skills and encourage ‘automation bias’, the tendency to trust AI system outputs without sufficient scrutiny. ‘AI companion’ apps now have tens of millions of users, a small share of whom show patterns of increased loneliness and reduced social engagement.

Layering multiple approaches offers more robust risk management

Managing general-purpose AI risks is difficult due to technical and institutional challenges. Technically, new capabilities sometimes emerge unpredictably, the inner workings of models remain poorly understood, and there is an ‘evaluation gap’: performance on pre-deployment tests does not reliably predict real-world utility or risk. Institutionally, developers have incentives to keep important information proprietary, and the pace of development can create pressure to prioritise speed over risk management and makes it harder for institutions to build governance capacity.

**Risk management practices include threat modelling to identify vulnerabilities, capability evaluations to assess potentially dangerous behaviours, and incident reporting to gather more evidence. **In 2025, 12 companies published or updated their Frontier AI Safety Frameworks – documents that describe how they plan to manage risks as they build more capable models. While AI risk management initiatives remain largely voluntary, a small number of regulatory regimes are beginning to formalise some risk management practices as legal requirements.

Technical safeguards are improving but still show significant limitations. For example, attacks designed to elicit harmful outputs have become more difficult, but users can still sometimes obtain harmful outputs by rephrasing requests or breaking them into smaller steps. AI systems can be made more robust by layering multiple safeguards, an approach known as ‘defence-in-depth’.

**Open-weight models pose distinct challenges. **They offer significant research and commercial benefits, particularly for lesser-resourced actors. However, they cannot be recalled once released, their safeguards are easier to remove, and actors can use them outside of monitored environments – making misuse harder to prevent and trace.

Societal resilience plays an important role in managing AI-related harms. Because risk management measures have limitations, they will likely fail to prevent some AI-related incidents. Societal resilience-building measures to absorb and recover from these shocks include strengthening critical infrastructure, developing tools to detect AI-generated content, and building institutional capacity to respond to novel threats.

Introduction

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:

1. 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).
2. 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).
3. 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.

1 Background on general-purpose AI

Over the past year, the capabilities of general-purpose AI models and systems have continued to improve. Leading systems now match or exceed expert-level performance on standardised evaluations across a range of professional and scientific subjects, from undergraduate examinations in law and chemistry to graduate-level science questions. Yet their capabilities are also ‘jagged’: they simultaneously excel on difficult benchmarks and fail at some basic tasks. Current systems still provide false information at times, underperform in languages that are less common in their training data, and struggle with real-world constraints like unfamiliar interfaces and unusual problems. Alleviating these limitations is an area of active research, and researchers and developers are making progress in some areas. Sustained investment in AI research and training is expected to drive continued capability progress through 2030, though substantial uncertainty remains about both what new capabilities will emerge and whether current shortcomings will be resolved.

This chapter covers current and future capabilities of general-purpose AI. The first section introduces general-purpose AI, explaining how these systems work and what drives their performance (§1.1. What is general-purpose AI?). The second section examines current capabilities and limitations (§1.2. Current capabilities). A recurring theme is the ‘evaluation gap’: how a system performs in pre-deployment evaluations like benchmark testing often seems to overstate its practical utility, because such evaluations do not capture the full complexity of real-world tasks. The final section considers how capabilities might evolve by 2030 (§1.3. Capabilities by 2030). AI developers are investing heavily in computing power, data generation, and research. However, there is substantial uncertainty about how these investments will translate into future capability gains. To illustrate the range of plausible outcomes, the section presents four scenarios developed by the OECD, which range from stagnation to an acceleration in the rate of capability improvements.

1.1. What is general-purpose AI?

Key information

• **‘General-purpose AI’ refers to AI models and systems that can perform a variety of tasks, rather than being specialised for one specific function or domain. **Examples of such tasks include producing text, images, video, and audio, and performing actions on a computer.
• General-purpose AI models are based on ‘deep learning’. Modern deep learning involves using large amounts of computational resources to help AI models learn complex relationships and abstract features from very large training datasets.
• **Developing a leading general-purpose AI system has become very expensive. **To train and deploy such systems, developers need extensive data, specialised labour, and large-scale computational resources. Acquiring these resources to develop a leading system from scratch now costs hundreds of millions of US dollars.
• Since the publication of the last Report (January 2025), capability improvements have increasingly come from post-training techniques and extra computational resources at the time of use, rather than from increasing model size alone. Previous performance improvements largely resulted from making models larger and using more data and computing power during initial training.

What are general-purpose AI systems?

General-purpose AI systems are software programmes that learn patterns from large amounts of data, enabling them to perform a variety of tasks rather than being specialised for one specific function or domain (see Table 1.1). To create these systems, AI developers carry out a multi-stage process that requires substantial computational resources, large datasets, and specialised expertise (see Table 1.2). Computational resources (often shortened to ‘compute’) are required both to develop and to deploy AI systems, and include specialised computer chips as well as the software and infrastructure needed to run them.* Because they are trained on large, diverse datasets, general-purpose AI systems can carry out many different tasks, such as summarising text, generating images, or writing computer code. This section explains how general-purpose AI systems are made, what ‘reasoning’ models are, and how policy decisions shape general-purpose AI system development.

* The term compute can also refer to either a measurement of the number of calculations a processor can perform (typically measured in floating-point operations per second) or specifically the hardware (such as graphics processing units) that performs those calculations.

**Type of general-purpose AI **	Examples
Language systems	+ Apertus1 + Claude-4.52 + Command A3 + EXAONE 4.04 + Gemini-3 Pro5 + GLM-4.56 + GPT-57 + Hunyuan-Large8 + Kimi-K29 + Mistral 3.110 + Qwen311 + DeepSeek-V3.212
Image generators	+ DALL-E 313 + Gemini 2.5 Flash14 + Midjourney v715 + Qwen image16
Video generators	+ Cosmos17 + SORA18 + Pika19 + Runway19 + Veo 320
Robotics and navigation systems	+ Gemini Robotics[21](#footnote21_cQfAAvHbnYH8HkFhyfiihuMLixVh7FbIL3q2RN4O-M_aJayLc4LSUj8 “[industry] Gemini Robotics Team, S. Abeyruwan, J. Ainslie, J.-B. Alayrac, M. G. Arenas, T. Armstrong, A. Balakrishna, R. Baruch, M. Bauza, M. Blokzijl, S. Bohez, K. Bousmalis, A.