Frontier language models
General-purpose language models trained at scale.
- Maturity
- Not yet judged
- Attractiveness
- –
- Evidence
- None yet
Five layers, from models to infrastructure. Momentum and attractiveness are computed from the evidence; maturity is a written judgement.
General-purpose language models trained at scale.
Models that spend extra compute thinking through a problem before answering.
Models that read, hear or produce images, video and audio as well as text.
Models whose weights can be run and tuned by the buyer, including small and on-device ones.
Software that lets models plan, call tools and hand work between steps.
Agents that operate screens, browsers and desktop software like a person would.
Agents that write, test and ship code with limited supervision.
Agents that hold spoken conversations, such as in contact centres.
Several agents with different roles working on one task.
Shared standards for how agents call tools and talk to each other.
Giving models access to a company's own documents and data.
Data stores built to find items by meaning rather than keyword.
How agents keep and reuse what they have learned across tasks.
Producing training and evaluation data, by people or by models.
Assistants built for one profession or industry's daily work.
Products designed around AI doing the work, rather than humans using a tool.
Reading, extracting from and routing documents and forms.
Search that returns synthesised answers rather than links.
Running trained models quickly and cheaply for real users.
Rented or built compute capacity for training and running models.
Processors designed for AI workloads.
Testing, monitoring and constraining AI systems in production.
Protecting AI systems and proving they follow policy.
Calls: Use now (maturity 4–5), Pilot (3), Watch (1–2). A technology shows "Not yet judged" until someone writes its maturity and a reason. See Method.