Definition
General-purpose AI (GPAI) refers to artificial intelligence systems that can perform a wide range of tasks across multiple domains, similar to human cognitive abilities but without necessarily achieving the full human-like understanding and adaptability of Artificial General Intelligence (AGI). GPAI systems are designed to be flexible and capable of handling diverse tasks without being explicitly programmed for each one.
General-purpose AI describes AI systems that can perform a wide range of tasks rather than a single narrow function. Often called foundation models, these systems, including tools such as ChatGPT, are reshaping how AI is built, deployed, and used. They are becoming core infrastructure that other companies build on to deliver services to end users.
These technologies are expected to drive innovation across sectors, but they also raise policy questions around privacy, intellectual property, accountability and the spread of misinformation. Regulators, particularly in the EU, are under pressure to support innovation while ensuring appropriate safeguards.
GPAI vs narrow AI vs AGI
AI is commonly discussed in three broad categories.
Artificial narrow intelligence focuses on specific tasks in defined environments. Examples include image recognition and speech recognition systems. These models are trained on well-labelled data and usually cannot generalise beyond their original purpose.
Artificial general intelligence (AGI) refers to the idea of machines that could perform a wide range of intelligent tasks, think abstractly, and adapt to new situations in human-like ways. AGI is widely discussed but remains unclear and not yet realised.
General-purpose AI sits between these. Foundation models are trained on very large and diverse datasets and can be adapted to many tasks with minimal fine-tuning. They generalise more than narrow AI, but they do not have human-like understanding, reasoning or consciousness.
How GPAI models are built and used
General-purpose AI systems are developed as large base models and then made accessible to others through APIs or open-source releases. Many organisations use them as a starting point for their own applications. Common uses include:
- text generation, translation and summarisation
- image, audio or video generation from text prompts
- coding assistance and content creation
- potential applications in education, health, and materials design
Investment in generative and general-purpose AI is growing rapidly, and these tools are now widely available to businesses and the public.
Key characteristics of GPAI
GPAI models share several defining features:
- training on massive and diverse datasets, often from the internet
- ability to perform multiple tasks and adapt to new ones with little extra data
- use as underlying infrastructure for many downstream services
- potential to show capabilities beyond those originally intended, though the extent of this is still debated
- linked to ideas such as knowledge transfer, few-shot learning and adapting quickly to new tasks.
Risks, regulation, and societal impact
The size, opacity, and flexibility of GPAI systems create significant risks. Research shows they can produce biased or discriminatory outputs, generate toxic or harmful content, and spread false or misleading information. Users may also overestimate their abilities and use them in unsafe contexts.
The main areas of concern include:
- privacy, consent, and lawful use of data
- intellectual property and use of copyrighted material
- liability when AI systems cause harm
- effects on education, creative industries, and the labour market
Experts are calling for stronger oversight, testing and monitoring, and for regulation that reflects the complex value chains behind foundation models.
GPAI and the path towards AGI
Recent advances in large language models are sometimes seen as steps towards AGI because they can handle tasks they were not directly trained for. This has created both excitement and fear.
However, it is still unclear whether these systems truly show general intelligence. Their abilities differ significantly from human cognition, and AGI itself is not clearly defined. For now, general-purpose AI is best seen as a more practical stage, where systems can handle many tasks and generalise beyond narrow AI without reaching human-level intelligence.
Key takeaways
- General-purpose AI, or foundation models, can perform diverse tasks and be adapted with minimal additional training.
- They sit between narrow AI and the idea of AGI, offering broader capabilities without human-like intelligence.
- These systems are widely used as infrastructure through APIs and power many new AI applications.
- They bring major benefits but also serious risks related to bias, misinformation, privacy, intellectual property and liability.
- Policymakers are working to balance innovation with safeguards as general-purpose AI becomes central to the AI ecosystem.