International AI Safety Report 2026

• Historical analogue: Passenger aircraft speed, which climbed quickly from 1930 to 1960 before levelling off at 500 knots due to practical limitations.

Scenario 2: Progress slows

A scenario in which incremental gains within existing approaches to training AI systems deliver continued but slower progress.

• Scenario: In 2030, AI systems are comparable to useful assistants. They have a deep knowledge base, excel at standard forms of structured reasoning, and can usefully perform tasks that require them to use a computer, navigate the Web, or undertake limited interaction with people or services on behalf of the user. They can retain relevant memories, maintain coherent thinking, and error-correct to perform longer or more complex tasks. They lack robust abilities to learn new skills and can handle physical or embodied social tasks only in limited, controlled environments (such as factories or laboratories).

• Pathway: After 2025, the approaches of frontier model developers struggle to overcome limitations in continual learning, metacognition and agency, problem-solving, creativity, physical tasks, and social interaction, with existing training paradigms providing imperfect solutions. Scaling of pre-training, inference and post-training combined with some algorithmic innovations continue to deliver progress, but it is slower than in recent years and reasoning systems fail to generalise as well as hoped. The ability to continue scaling is slowed as investors see lower returns from continued investments. Bottlenecks in hardware, infrastructure, natural resources, data supply, and energy limit the ability to rapidly scale compute and data.

• Historical analogue: Antibiotic discovery, which saw a ‘golden era’ of rapid breakthroughs from the 1940s to 1960s, then slowed as the low-hanging fruit from existing discovery methods were exhausted.

Scenario 3: Progress continues

A scenario in which continued rapid progress occurs.

• Scenario: In 2030, AI systems are comparable to expert collaborators. They can successfully perform many professional tasks in digital environments that might take humans a month to complete. AI systems typically rely upon humans to provide high-level directions, but can often work with high autonomy towards a given objective, including autonomously interacting with a range of stakeholders. They can effectively form and retrieve memories and can ‘learn on the job’ to some extent. They can successfully handle some physical tasks and embodied social tasks beyond controlled environments.

• Pathway: After 2025, AI capabilities continue to grow rapidly through larger training runs, more powerful reasoning systems, and new algorithmic innovations. Compute and data inputs continue to scale and do not hit substantial limits before 2030, matching current estimates of the possible scope for continued growth. Iteration and extension of existing approaches or novel algorithmic innovations enable developers to overcome current limitations in areas such as continual learning.

• Historical analogue: Moore’s law, where computing power on chips doubled approximately every two years over five decades.

Scenario 4: Progress accelerates

A scenario in which dramatic progress leads to AI systems as or more capable than humans across most or all capability dimensions.

• Scenario: In 2030, AI systems are comparable to human-level remote workers. AI systems’ autonomy and cognitive capability match or surpass humans in cognitive tasks. They capably and autonomously work towards broad strategic goals that they can reflect upon and revise if circumstances change, while also collaborating with humans where necessary. AI systems can seamlessly learn new information and skills during deployment. AI-guided robots can handle complex physical or social tasks in dynamic real-world environments in many industries and roles. AI performance still largely lags humans’ in these physical and embodied tasks, unless the system was developed specifically for a given task, due to challenges in generalisation across physical tasks.

• Pathway: After 2025, there are continued exponential gains in AI capabilities within existing paradigms via continued or accelerated scaling of pre-training, post-training, and inference. These are amplified by significant algorithmic breakthroughs and increasingly substantial contributions from AI coding assistants to the development of AI.

• Historical analogue: DNA sequencing saw superexponential improvements from 2000 to 2020 due to the development of new sequencing paradigms.

This scenario analysis suggests that, by 2030, AI progress could plausibly range from stagnation to rapid improvement to levels that exceed human cognitive performance. The full analysis supporting these scenarios is available in OECD (2026) AI Progress Scenarios: Exploring possible pathways through 2030.

Updates

Since the publication of the last Report (January 2025), observed developments have largely remained consistent with the rapid AI progress trajectories outlined in that Report. General-purpose AI systems have become substantially more capable, affordable, and widely adopted, with particularly notable advances in scientific reasoning and autonomous task execution. Major AI companies and cloud providers have announced unprecedented data centre investments totalling hundreds of billions of dollars, demonstrating sustained commitment to the compute scaling trends anticipated in the previous Report. AI developers have made substantial progress in developing agents that can more reliably execute longer multi-step tasks with reduced human oversight, including advancements in computer use and tool use. The adoption of inference-time compute scaling has become widespread across multiple developers. AI tools are now routinely integrated into AI development workflows for writing training code, designing hardware architectures, and generating synthetic training data.

Evidence gaps

The main evidence gaps around future AI capabilities include limited scientific evidence relevant to forecasting, insufficient data about real-world constraints on AI progress, and limited understanding of whether and to what extent automation could accelerate AI development. First, researchers cannot reliably predict when AI systems will have certain capabilities, or where diminishing returns to scaling key inputs will constrain progress. The relationship between benchmark performance and real-world performance also remains poorly understood; so even if benchmark performance was easily predictable, the associated real-world impacts would be highly uncertain.

Second, there is limited evidence around the real-world constraints that could limit AI progress. These constraints include unclear availability of training data beyond 2030 and whether energy production, chip manufacturing, and capital expenditures can keep pace with the demands of AI development.

Third, there is minimal empirical understanding of feedback loops from AI automating its own research and development. In particular, there are major uncertainties about how much human oversight will be needed in this process, and about whether slow feedback loops in large-scale experiments could constrain acceleration.

These evidence gaps force policymakers to navigate between two pitfalls: underestimating rapidly emerging capabilities on the one hand, and overreacting to technical advances that may not translate into practical applications on the other. This makes contingency planning across multiple scenarios essential.

Challenges for policymakers

For policymakers working on AI capability forecasting, key challenges include unreliable measurement tools and uncertainty about when certain capabilities will be developed. Current benchmarks often fail to accurately represent real-world capabilities, prompting increased efforts to develop more challenging and realistic evaluations. For example, even if a model achieves 90% accuracy on a programming benchmark, this does not imply that it can build functional software applications. Estimates of algorithmic efficiency progress are highly uncertain due to limited data on key indicators, such as training efficiency improvements, inference-time optimisations, and architectural innovations. For example, while studies of algorithmic efficiency in language models suggest efficiency improvements of 3x per year based on previous data points, they are unable to rule out rates ranging from 2–6x per year.

This forecasting problem compounds the uncertainty about capability trajectories, which have vastly different policy implications. If algorithmic progress continues at the upper bound of current estimates, models could achieve equivalent capabilities with 10–100x less compute by 2030. Regulators will therefore need to consider frameworks that can adapt or remain robust to rapid changes in the rate of AI progress and in what AI development looks like, particularly in terms of the required resources. To reduce uncertainty, it will be important to monitor concrete indicators including real-world task evaluations, the rate of algorithmic innovation, and the emergence of qualitatively new capabilities.

Technical supplement

Scaling laws are often used as empirical guidance

‘Scaling laws’ describe predictable relationships between model size, computational resources, and performance. When model developers increase training compute by 10x, model performance tends to improve by a predictable amount across diverse tasks such as language understanding, image recognition, and code generation. This predictable relationship has held across six orders of magnitude of model size – from small research models to today’s frontier AI systems, which cost hundreds of millions of dollars to train – suggesting that these patterns reflect fundamental properties of how neural networks learn. This consistency has led many developers and investors to treat scaling laws as useful empirical guidance, informing major investment decisions. However, scaling laws are empirical regularities, not mathematical guarantees. They are inferred from observed behaviour and may break down at levels of compute or data beyond current experience. And because they predict technical metrics – not end-user value – real-world performance or economic returns may not increase smoothly with training compute. For example, OpenAI discontinued GPT-4.5 although it achieved technical improvements consistent with scaling laws, suggesting that additional scaling may not always translate into proportionate economic value.

Physical infrastructure can constrain the scaling of computational resources

AI computation has massive energy demands, and current growth rates in AI power consumption could persist for several years. Global AI computation is projected to require electricity consumption similar to that of Austria or Finland by 2026. Based on current growth rates in power consumption for AI training, the largest AI training runs in 2030 will need 4–16 gigawatts (GW) of power, enough to power millions of US homes. Even today, OpenAI’s planned Stargate data centre reaches 1.2 GW scales, and Meta’s planned Louisiana data centre is projected to exceed 2 GW. Experts in a forecasting survey by the Forecasting Research Institute predict that, by the end of 2030, 7.4% of US electricity consumption will be devoted to training or deploying AI systems in the median scenario. Although these energy demands are large, the US (where most frontier AI models are being developed) is building out power infrastructure to meet them and to connect data centres across different regions. These efforts are likely enough to support training runs on the scale of 10 GW, so, at least until the end of the decade, energy bottlenecks will likely not prevent compute scaling.

Challenges to producing and improving AI chips exist, but can likely be overcome. It typically takes three to five years to build a computer chip fabrication plant, and supply chain shortages sometimes delay the production of important chip components. However, major AI companies can still sustain compute growth in the near term by capturing large fractions of the AI chip stock. For example, one study estimates that the share of the world’s data centre AI chips owned by a single AI company at any point in time is somewhere between 10% and 40%. Moreover, existing trends and technical possibilities in chip production suggest that it is possible to train AI systems with 100,000x more training compute than GPT-4 (the leading language model of 2023) by 2030. This is sufficient to support existing growth rates in training compute, which imply a total increase of 10,000x over the same period. Hence, chip production constraints are significant, but they are unlikely to prevent further scaling of the largest models at current rates until 2030, if investment is sustained. However, it is unknown whether similar levels of investment will continue, and this is a major reason that AI capabilities in coming years are uncertain.

Understanding current hard benchmarks

As discussed above, an informative metric of AI progress is the length of tasks that models can complete: in software engineering, this length doubles roughly every seven months. In order to study this trend, researchers created 170 tasks relating to research or software engineering, ranging from quick bug fixes that take minutes to feature implementations requiring days. Models must solve problems within constraints that mirror human work. Results show a consistent exponential pattern: for example, at 50% success rates, the maximum solvable task duration has grown from a few seconds in 2019 to 2.5 hours in 2025, while at 80% success rate task lengths are much lower – currently around 20–30 minutes. Beyond these limits, success rates drop sharply: models that maintain 50% success at 2.5 hours fall below 25% at four hours. The evaluation also highlights capability asymmetries: models excel at code generation and syntax transformation but continue to have low performance with architectural decisions and cross-file refactoring that human software developers handle more naturally.

FrontierMath is another difficult benchmark that tests the limits of AI mathematical reasoning through problems created by leading mathematicians specifically to challenge AI systems. The benchmark contains original research-level mathematics problems that require deep conceptual understanding, creative proof strategies, and the ability to combine techniques from multiple mathematical domains, such as number theory, real analysis, and algebraic geometry. These problems are unpublished and vetted by over 60 mathematicians to prevent models from viewing them before they are tested. The problems are divided into three main tiers: about 25% are at the level of the International Mathematical Olympiad, ~50% require graduate-level knowledge, and the toughest ~25% are research-level questions demanding hours or even days from top mathematicians to solve. When the benchmark was released in 2024, state-of-the-art AI systems scored under 2% overall on the full set. However, recent models show promise: according to Epoch AI’s evaluations, OpenAI’s GPT-5 reached ~25%, and the new o4-mini achieved roughly 20%, with some capability even on the hardest tier, signalling rapid progress from baseline levels. Importantly, these successful models used new inference scaling techniques.