International AI Safety Report 2026

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.

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.

Deep learning is foundational to general-purpose AI

Researchers build general-purpose AI models using a process called ‘deep learning’, which trains models to learn from examples. Unlike software engineering, deep learning models learn to accomplish tasks from data instead of relying on hand-written instructions. By processing large amounts of data, such as images, text, or audio, these models discover ways to represent that data, creating internal representations of patterns (such as shapes, word associations, or sound structures) that help the model recognise relationships and generate outputs aligned with its training objective. They then use these learned internal representations as abstract features to analyse new, similar data and generate outputs in the same style. For example, a general-purpose AI model trained on enough examples of 19th-century romantic English poetry can recognise new poems in that style and produce new material in a similar style.

On a more granular level, deep learning works by processing data through layers of interconnected information-processing nodes. These nodes are often called ‘neurons’ because they are loosely inspired by neurons in biological brains (‘neural networks’) (Figure 1.1). As information flows from one layer of neurons to the next, the model progressively transforms the data into more abstract representations as groups of learned features – patterns the model has automatically discovered in the data, rather than hand-coded ones. For example, in an image-processing model, the first layers might learn to detect simple features such as edges or basic shapes, while deeper layers combine these features to pick out more complex patterns such as faces or objects.

The features at all layers are discovered through the optimisation process that defines the training procedure. During training, when the model makes mistakes, deep learning algorithms adjust the strength of various connections between neurons to improve the model’s performance. The strength of each connection between nodes is often called a ‘weight’. This layered approach gives deep learning its name.

Deep learning has proven very effective at allowing AI systems to accomplish tasks that were previously considered difficult for traditional hand-programmed computational systems and other earlier symbolic or rule-based AI methods. Most state-of-the-art general-purpose AI models are now based on a specific neural network architecture known as the ‘transformer’. Transformers use an ‘attention’ mechanism that helps the model to focus on the most relevant parts of the input data when processing information, such as determining which words in a sentence are most important for understanding its meaning. This particular way of building models has led to significant improvements in translation, natural language processing, image recognition and speech recognition, ultimately leading to the development of today’s most advanced models.

General-purpose AI is developed in stages

Developing a general-purpose AI system involves multiple stages, from initial model training to post-deployment monitoring and updates (Figure 1.2). In practice, these steps often overlap in an iterative manner. Each stage requires different resource inputs (e.g. data, labour, compute) and different techniques, and they are sometimes undertaken by different developers (Figure 1.2 and Table 1.2).

For example, model pre-training generally requires large amounts of compute and data, making this stage particularly sensitive to policies that affect access to computational resources or training data. Similarly, data curation and some model fine-tuning methods currently involve large amounts of human labour for initial data labelling. This stage is therefore sensitive to changes in labour costs, platform policies, or regulations affecting cross-border contracting arrangements.

Reasoning systems generate ‘chains of thought’ during inference to improve performance

Inference happens when someone uses the AI model after it is trained. For example, inference occurs when a person asks an AI system to plan a trip and the model behind it draws on relevant aspects of what it has learned regarding geography, transportation, and cuisine to generate an itinerary.

In the past decade, advances in AI capabilities have largely come from larger training runs; that is, increasing the amount of compute used to train an AI model. Recently, however, researchers have made more gains by allowing models to process information for longer and by training them to produce explicit reasoning steps as they accomplish a task. AI systems that work like this are called ‘reasoning systems’, and the intermediate explanations they go through while solving a problem or answering a question are called ‘chains of thought’. Reasoning systems require more computational resources at the time of use to generate these sophisticated chains of thought, and more resources during training so that they learn to reason better. In practice, these reasoning capabilities let AI systems solve more complex problems by iteratively decomposing a task into smaller steps. Table 1.3 shows an example of a non-reasoning system and a reasoning system solving the same problem.

Reasoning systems have achieved major breakthroughs in capabilities on challenging problems. For example, in 2025, reasoning systems specialised for mathematical problem-solving, such as Google’s Gemini Deep Think and an unreleased, experimental model from OpenAI, solved International Mathematical Olympiad problems (in a structured test setting) at a level equivalent to human gold-medal performance. Reasoning systems have demonstrated significant progress in formal domains such as mathematics, logic puzzles, and structured scientific questions, where step-by-step reasoning can be explicitly verified. However, reasoning systems can also fail by producing irrelevant, unproductive, or repetitive chains of thought.

Updates on training methods

Since the publication of the last Report (January 2025), a training method called ‘distillation’ has greatly increased the efficiency with which some models can be fine-tuned. Distillation involves training a ‘student’ model on the outputs of a more powerful (and usually larger) ‘teacher’ model, allowing the student model to directly imitate the outputs of the teacher. For example, DeepSeek developed a large model called DeepSeek-R1, which excels at chain-of-thought reasoning. R1 produced reasoning outputs which were then used to fine-tune smaller student models, including DeepSeek-V3. DeepSeek-V3 maintains much of R1’s mathematical, coding, and document-analysis capabilities and was reportedly fine-tuned for approximately $10,000 USD (though its pre-training costs were not reported). This is likely orders of magnitude lower than the cost of fine-tuning similarly capable, larger models.

Distillation can thus be a cheap and efficient way for models to gain more powerful capabilities. Some researchers have used distillation to fine-tune highly capable models using as few as 1,000 examples generated from state-of-the-art models. Since distillation requires a pre-existing teacher model, it cannot be directly used to advance state-of-the-art model capabilities. However, it can speed up the proliferation of advanced AI capabilities, even from closed-source models.

Together with technological advances in ‘distributed compute’ and decentralised training (approaches where developers use multiple processors, servers, or data centres working together to perform AI training or inference, the degree to which many AI development projects depend on large-scale, centralised compute infrastructure has been reduced. This increasingly enables less well-resourced actors to develop and deploy powerful systems.

Updates on AI agents

Since the last Report (January 2025), advances in how developers combine AI models with tools have enabled the development of increasingly powerful AI agents. AI agents are designed to pursue goals, which are often specified by users in natural language. To achieve these goals, they are given access to tools, such as memory, a computer interface, and web browsers. These tools and the code used to combine them with the model are referred to as ‘scaffolding’, and they help AI agents autonomously interact with the world, make plans, remember important details, and pursue goals with much less oversight or assistance from humans. For example, Manus AI is a popular AI agent that can automate various tasks, including Web search, software development, and online purchases. Figure 1.3 illustrates a simple example of an AI agent composed of a general-purpose AI model ‘brain’ that can iteratively plan, reason, and use tools for memory, web browsing, and computer use.

Digital infrastructure for AI agents is expanding, and they are increasingly common across industries. AI agents have been developed for tasks such as research, software engineering, robotic control, and customer service. Ongoing research and development has resulted in steadily more capable and more autonomous AI agents or multi-agent systems. Researchers have estimated that the complexity of software benchmark tasks that AI agents can accomplish doubles approximately every seven months (see also §1.2. Current capabilities). Experts argue that increasingly capable AI agents will give rise to both major opportunities and risks (see §2.2.1. Reliability challenges).

Evidence gaps

The main evidence gaps around the general-purpose AI system development process stem from a lack of publicly available information regarding how they are developed. Some developers are highly transparent about how they develop general-purpose AI systems. However, in general, there is a limited degree of public and policymaker knowledge about how most advanced models are developed, safeguarded, evaluated, and deployed. This is particularly true for internally deployed AI systems that are used within AI companies but not used or understood by outside stakeholders. This limited external visibility creates challenges for transparency and oversight. Various researchers have pointed to limited and inconsistent transparency around training data, general-purpose AI models, AI agents, evaluations, development pipelines, and safety. Limitations to external disclosure are sometimes necessary to protect companies’ trade secrets and intellectual property. At the same time, low transparency makes it more difficult for independent researchers and policymakers to study general-purpose AI models and systems.