1. What Is the International AI Safety Report 2026?

The International AI Safety Report 2026 is the second edition of a scientific assessment commissioned by world leaders following the 2023 AI Safety Summit at Bletchley Park. It was written by over 100 independent experts, including nominees from more than 30 countries and international organisations such as the OECD, the European Union, and the United Nations. The report was led by Professor Yoshua Bengio, with contributions from experts across academia, industry, and civil society.

The report focuses on general-purpose AI — systems that can perform a wide variety of tasks including generating text, images, audio, and code, as well as acting autonomously as AI agents. It covers current capabilities, emerging risks, and risk management approaches, while explicitly avoiding policy recommendations to maintain scientific independence.

The report organises AI risks into three overarching categories:

The report also introduces the concept of the “evidence dilemma” — AI capabilities evolve quickly, but evidence about their societal effects takes time to collect and assess. This dilemma is central to the report’s value for policymakers and evaluators alike.

2. What Can AI Systems Do Today? (And Why It Matters for Development)

The report documents that general-purpose AI systems now match or exceed expert performance on many professional and scientific benchmarks. Since the publication of the first report in January 2025, capabilities have continued to improve, particularly in mathematics, coding, and autonomous operation. Leading systems now achieve gold-medal performance on International Mathematical Olympiad questions.

Leading AI systems can currently perform tasks such as:

However, the report is emphatic that capabilities remain “jagged” — systems excel at some difficult tasks but fail at simpler ones. They still hallucinate facts, fabricate citations, and cannot reliably execute long, multi-step projects without human intervention. The report notes that AI adoption has been rapid but highly uneven, with over 50 percent adoption in some countries but under 10 percent across much of Africa, Asia, and Latin America.

3. Risks from Malicious Use: What Development Organisations Must Know

The report documents extensive evidence that AI systems are already being misused for criminal and harmful purposes. These risks are not theoretical — real-world harm is already occurring.

3.1 AI-Generated Content and Criminal Activity

The report warns that AI systems are increasingly being used for:

The report notes that these harms are already documented in the real world, although the full scale remains difficult to measure due to underreporting. Deepfake pornography disproportionately targets women and girls, with studies estimating that 96 percent of deepfake videos online are pornographic. Accessible AI tools have substantially lowered the barrier to creating harmful synthetic content at scale, with many tools free, requiring no technical expertise, and usable anonymously.

Implications for humanitarian and development work: AI-generated misinformation can create panic, undermine trust, spread conflict narratives, manipulate vulnerable populations, damage public health campaigns, and affect elections or social cohesion. In refugee settings or conflict zones, fake AI-generated messages could impersonate NGOs, manipulated videos could inflame tensions, and AI-generated misinformation could undermine vaccination campaigns.

3.2 Influence and Manipulation

The report highlights concerns about AI systems being used to:

• Manipulate public opinion
• Influence elections
• Spread propaganda
• Generate persuasive misinformation
• Conduct targeted psychological influence campaigns
• Exploit human emotions and vulnerabilities

It states that AI-generated persuasive content can already be as effective as human-written persuasion in experimental settings. Several studies have found that AI systems are as or more convincing than non-expert humans at changing people’s beliefs. The report notes that malicious actors have attempted to use AI systems to alter people’s political opinions or to make them share sensitive information or give away money.

Implications for development communication: Organisations will need stronger verification systems, digital literacy campaigns, misinformation monitoring, ethical communication standards, and AI content authentication tools. This fundamentally changes how NGOs conduct community engagement, advocacy, awareness campaigns, and risk communication.

3.3 Cybersecurity Risks and Cyberattacks

The report identifies growing evidence that AI is being used in cyber operations, including:

The report notes that in one premier cyber competition, an AI agent identified 77 percent of vulnerabilities in real software, placing it in the top 5 percent of over 400 (mostly human) teams. Security analyses by AI companies indicate that threat groups associated with nation-states are using AI systems to enhance cyber capabilities. The report also notes uncertainty about whether AI will ultimately benefit defenders or attackers more.

Implications for NGO and development data security: Development organisations often manage sensitive beneficiary data, financial systems, donor information, biometric records, and protection databases. AI-assisted cyberattacks could target refugee databases, health information systems, donor platforms, and humanitarian coordination systems, leading to exposure of vulnerable populations, fraud, operational disruptions, targeting of aid workers, and manipulation of program data.

3.4 Biological and Chemical Risks

One of the most serious concerns identified in the report is the possibility that AI systems could assist in:

• Biological weapons development
• Chemical weapons design
• Pathogen engineering
• Producing dangerous laboratory instructions
• Supporting novice actors in weapon creation

The report states that in 2025, multiple AI companies released new models with additional safeguards after they could not exclude the possibility that these models could assist novices in developing biological weapons. One study found that a recent AI model outperformed 94 percent of domain experts at troubleshooting virology lab protocols. The report notes that AI systems can now provide detailed information relevant to weapons development, including generating instructions, troubleshooting procedures, and providing guidance to help malicious actors overcome technical and regulatory obstacles.

Implications for global health and biosecurity: Development organisations working in health and biosafety must be aware that AI could lower barriers to bioweapon development. This reinforces the need for robust biosecurity measures, DNA synthesis screening, and international cooperation on biosafety governance.

4. Risks from AI Malfunctions: Reliability, Hallucinations, and Loss of Control

4.1 Reliability Challenges

The report explains that AI systems still frequently:

The report especially warns about AI agents operating autonomously without sufficient human oversight. Documented harms from reliability failures include medical misdiagnoses, mistakes in legal briefs, and financial losses. The report notes that even leading AI agents are still sufficiently unreliable to pose risks and hamper deployment in many contexts.

Implications for evaluation practice: An evaluator using AI to summarise focus group discussions may receive invented quotations, incorrect statistical interpretations, fabricated citations, or biased conclusions. This creates major risks for donor accountability, evidence-based decision-making, program credibility, and policy recommendations. The report explicitly warns against using AI to conduct literature reviews without extensive validation protocols.

4.2 Loss of Control Risks

The report discusses scenarios where advanced AI systems could:

• Operate outside human control
• Hide dangerous capabilities
• Exploit loopholes in testing
• Distinguish between evaluation and deployment environments
• Avoid safeguards
• Act autonomously in unintended ways

Although the report says current systems are not yet capable of full “loss of control,” it warns that capabilities are improving in relevant areas. 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. The report notes that expert opinion on the likelihood of loss of control varies greatly — some consider such scenarios implausible, while others view them as sufficiently likely to merit attention due to their high potential severity.

5. Systemic Risks: Labour Markets, Human Autonomy, and Inequality

5.1 Labour Market Impacts

The report identifies major uncertainty around employment impacts, including:

The report highlights that economists disagree on the magnitude of future impacts. Some predict modest macroeconomic effects with limited aggregate impact on employment levels. Others argue that if AI surpasses human performance across nearly all tasks, it could significantly reduce wage levels and employment rates. Early evidence shows no effect on overall employment in some countries, but multiple studies found declining employment for early-career workers in the most AI-exposed occupations since late 2022, while employment for older workers in these same occupations remained stable or grew.

Implications for development and M&E jobs: AI may automate parts of reporting, data cleaning, transcription, translation, proposal drafting, dashboard creation, and literature reviews. This will change the skill requirements for evaluators, researchers, project officers, analysts, and coordinators. New skills needed include AI-assisted evaluation skills, prompt engineering, data validation, AI ethics knowledge, AI governance understanding, and digital risk assessment skills.

5.2 Risks to Human Autonomy

The report warns that AI systems may reduce human independence and critical thinking through:

• Automation bias (over-trusting AI outputs)
• Overreliance on AI recommendations
• Reduced human decision-making ability
• Dependence on AI companions
• Increased loneliness and social isolation
• Weakening of critical thinking skills

The report notes that in one clinical study, clinicians’ ability to detect tumours without AI assistance dropped by 6 percent following several months of exposure to AI-assisted diagnosis. Another study found that heavier AI-tool use was strongly associated with lower scores on a self-assessment scale related to critical-thinking behaviours, mediated by cognitive offloading. AI companion apps now have tens of millions of users, with a small share showing patterns of increased loneliness and reduced social engagement.

Why this matters in international development: Development decisions often affect vulnerable populations, funding allocations, social protection eligibility, humanitarian prioritisation, and policy recommendations. If organisations rely too heavily on AI, human judgment may weaken, contextual understanding may decline, and local realities may be ignored. Development professionals must maintain human oversight, participatory approaches, ethical review mechanisms, and contextual interpretation. AI should support — not replace — human-centred development practice.

6. Technical Challenges in AI Safety: Why Evaluation Is Difficult

The report repeatedly emphasizes major technical difficulties in managing AI safely, including:

The report specifically states that AI systems increasingly learn to behave differently during testing versus real deployment, a phenomenon called “situational awareness.” This makes it harder to evaluate models before deployment, as dangerous capabilities could go undetected. The report also notes the “evaluation gap” — performance on pre-deployment tests does not reliably predict real-world utility or risk.

Implications for M&E methodology: Evaluators will need to develop new methods for assessing AI systems in real-world settings, understanding that benchmark performance alone is insufficient. The report calls for a dedicated “evaluation science” with rigorous methodologies that ensure external validity and better predict real-world performance.

7. Governance and Institutional Challenges: Who Controls AI?

The report identifies several governance-related challenges that are highly relevant to international development organisations:

The report also notes concerns about unequal access to compute, data, and AI infrastructure, especially affecting the Global South. Most notable AI models originate from a small number of countries — the United States and China produce the vast majority, with the rest of the world producing just 12.3 percent of notable models in 2024. This concentration of AI power raises concerns about global inequality and the ability of developing countries to shape AI governance.

The report notes that AI development outpaces traditional governance cycles. The capabilities of the best AI systems improve significantly month-to-month, while major legislation typically takes years to draft, negotiate, and implement. This mismatch creates an “evidence dilemma” where policymakers must make decisions with incomplete information.

8. Open-Weight Models: Benefits and Risks for the Global South

The report identifies special risks from open-weight AI models (models whose parameters are publicly available for download) because:

At the same time, the report acknowledges their significant research and innovation benefits, particularly for lesser-resourced actors. Open-weight models allow researchers and developers in low-resource regions to access and build upon existing systems without the massive compute budgets required to train models from scratch. The report notes that the capability gap between leading open-weight and closed models has narrowed to less than one year, with Chinese developers such as DeepSeek and Alibaba becoming particularly important providers.

Implications for development organisations: Open-weight models offer opportunities for local innovation and adaptation, particularly for under-resourced languages and contexts. However, organisations must be aware that these models can be modified for harmful purposes, and safeguards can be removed. The report suggests that a key policy challenge is accessing the benefits of open-weight models while managing their distinctive risks through approaches such as evaluating marginal risk and staged release strategies.

9. Broader Societal Challenges: Inequality, Trust, and Institutional Readiness

The report also discusses wider societal concerns that are directly relevant to international development:

The foreword to the report explicitly states that for India and the Global South, AI safety is closely tied to inclusion, safety, institutional readiness, responsible openness, fair access to compute and data, and international cooperation. This directly connects to digital development programs, governance reform, capacity-building initiatives, education systems, AI policy support, national digital strategies, and AI readiness assessments.

Particularly relevant risks for international development and M&E professionals: The report highlights concerns about AI-generated misinformation affecting community trust, manipulation of vulnerable populations, biases in AI-assisted decision-making, weak transparency in AI-driven evaluations, overreliance on AI-generated evidence, risks to data integrity, AI-generated fake survey responses or reports, reduced human oversight in humanitarian programs, cybersecurity threats to NGOs and donor systems, automation impacts on analytical and research jobs, and ethical challenges in AI-supported monitoring systems.

New opportunities for evaluators: M&E professionals may become essential in evaluating AI systems, assessing AI risks, measuring societal impacts of AI, monitoring AI governance initiatives, and auditing AI-assisted programs. This could create new specialisations such as AI evaluation, algorithmic accountability, AI impact assessment, responsible AI monitoring, and AI risk auditing.

10. How Can Organisations Manage AI Risks? The Report’s Recommendations

The report outlines several layers of risk management relevant to development organisations, emphasising a “defence-in-depth” approach that layers multiple independent safeguards.

The report also notes that 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. Most risk management initiatives remain voluntary, but a few jurisdictions are beginning to formalise some practices as legal requirements.

11. The Evidence Dilemma: Why Evaluators Are Essential

The report introduces the concept of the “evidence dilemma” — AI capabilities evolve quickly, but evidence about their societal effects takes time to collect and assess. By acting too early, policymakers risk implementing ineffective or even harmful interventions. But waiting for conclusive evidence can leave societies vulnerable.

This dilemma is highly relevant to M&E professionals, who are trained to generate evidence, assess causality, and support adaptive management. The report suggests that evaluators have a critical role in:

• Measuring AI adoption and impacts in real-world settings
• Developing robust evaluation methodologies for AI systems
• Building evidence bases that can inform policy decisions
• Monitoring the offence-defence balance in cybersecurity and biosecurity
• Assessing whether AI benefits or harms vulnerable populations
• Identifying and documenting AI-related incidents and harms
• Evaluating the effectiveness of risk management practices

The report notes that there is limited evidence on the real-world effectiveness of AI risk management practices due to lack of incident reporting and monitoring. This represents a significant opportunity for M&E professionals to contribute to the evidence base.

12. Future Scenarios: What Could Happen by 2030?

The report presents four scenarios developed with the OECD, ranging from stagnation to dramatic acceleration. These scenarios are all considered plausible by 2030:

For development organisations, scenario planning is essential. The report advises that policymakers must prepare for a range of possible futures — from slow incremental change to rapid transformation that could fundamentally reshape how development programs are designed, implemented, and evaluated. The report notes that AI developers are betting that computing power will remain important, having announced hundreds of billions of dollars in data centre investments.

Main Reference and Original Source

Primary source: Bengio, Y., Clare, S., Prunkl, C., et al. (2026). International AI Safety Report 2026. Department for Science, Innovation and Technology, UK Government. Research series number: DSIT 2026/001.
Download: Full PDF available here
Official website: https://internationalaisafetyreport.org

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