Standing on the frontier
We build on the frontier models, not against them. The work is the efficient, specialised layer they make possible.
It should be said plainly, because the opposite is easy to assume of a lab building its own models: we are not trying to beat the frontier. The largest general models are extraordinary, and we use them every day. They are the material this is built from and, in a real sense, the teachers. The agents that run our operations, and generate the data we intend to learn from, run on them today.
What we are building is a different thing, and it sits downstream. A frontier model is a generalist of remarkable range, and range is expensive. Set to a narrow, repetitive, cost-sensitive job, it is often far more capability than the job needs, and paying for that surplus, in money and in energy, adds up across millions of small tasks. The sensible answer is not a bigger generalist. It is the right tool for the job: keep the powerful general model for the genuinely hard problems it is uniquely good at, and run a small, specialised model, trained for one vertical, on the rest.
None of that is a quarrel with the frontier. Building a specialist for a domain is only possible because a generalist got good enough to run the business that produces the domain’s data in the first place. The frontier does the hard, open-ended reasoning. The specialist does the repeated, well-defined work underneath it, cheaply and reliably, and the hard cases escalate back up.
There is also a practical reason, not only a principled one. A company that positions itself against its own suppliers is telling on itself, and a lab that needs the frontier to exist has no business pretending it is the enemy.
The specialised models this is aiming at do not exist yet. What exists is the substrate that would train them, and the conviction that the future of applied AI is not only larger generalists but also smaller, sharper, cheaper specialists, standing on the frontier the large models built.