Frontier language models
General-purpose language models trained at scale.
- Maturity
- Not yet judged
- Attractiveness
- 44
- Evidence
- 61 (3 in last 4 wks) · Higher
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 read, hear or produce images, video and audio as well as text.
Models that spend extra compute thinking through a problem before answering.
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.
Several agents with different roles working on one task.
Agents that hold spoken conversations, such as in contact centres.
Named deployments at volume: Viettel callbot ~33M calls (2024) and DBS digibot resolving 9 in 10 queries (H1 2026), both company-reported; no independent savings audit. Proposed by research run 2026-10-14; a person confirms.
Agents that write, test and ship code with limited supervision.
Measured gains reported in production (Sony Bank/Fujitsu: development period -30%, man-hours -40%; Techcombank: agent build time months to under a week; both company-reported) and strong lab benchmark scores, but an RCT found experienced developers 19% slower and benchmark contamination is documented. Proposed by research run 2026-10-14; a person confirms.
Agents that operate screens, browsers and desktop software like a person would.
OSWorld-Verified leaders now exceed the ~72% human baseline (lab and leaderboard figures), but independent work finds agents complete very little real paid work end to end (Remote Labor Index: best agent automated 2.5% of projects). Proposed by research run 2026-10-14; a person confirms.
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.
Protecting AI systems and proving they follow policy.
Testing, monitoring and constraining AI systems in production.
Processors designed for AI workloads.
Running trained models quickly and cheaply for real users.
Rented or built compute capacity for training and running models.
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.