Verification is the constraint, not capability
A rewrite of half a million lines in eleven days, a licence that changes with your revenue, and a result that took a week to find and a month to trust.
How we organise
A Bun-to-Rust rewrite of more than half a million lines, once estimated at twelve months, was finished in eleven days. Verification now takes more time than implementation. Projects are capped at two engineers.
We tell a client that the practices thrown out are the ones that existed to coordinate people writing code, and the ones that survived are the ones that decide what to build and establish whether it works. Plan headcount on the assumption that AI removes engineering effort and you have the shape wrong. It relocates the effort onto the scarcer function. A firm that could not staff good review before will find that constraint binding much harder now.
Described everywhere as open weights, the licence is source-available. Any model-as-a-service operator above 20 million dollars of group revenue must sign a separate agreement before commercial use. Internal use is exempt.
Our reading is that open weights has stopped being a licensing answer and become a licensing question, whose answer changes with your revenue. Anyone building on a self-hosted frontier model needs a lawyer on the licence text.
How we build
The agent becomes a single Python class. Methods are capabilities, docstrings are prompts, and type annotations are enforced contracts. Tool results pass by reference as live objects rather than being serialised into the context window, so nothing needs compacting.
We would steal that last decision, which is mentioned almost in passing. As engineering it is a token argument. As assurance it is something else, because a live object can be inspected afterwards and a compacted summary cannot. Every harness that summarises its own history to fit the window is quietly destroying the evidence trail, and nobody writes that down as a trade-off.
Across nearly 6,000 paired runs, the best skill libraries win mainly by regressing less rather than by gaining more. A skill alters behaviour merely by sitting in context. It need never be invoked.
Our position is that a skill library needs a removal process as much as an addition process. Almost none have one. Somebody writes a skill, it helps, it stays, and nobody measures what it cost the runs where it was irrelevant.
How we assure
Discovering cryptographic weaknesses
A model cut the small-key security of a NIST post-quantum candidate from 2^64 to 2^38. It took about sixty hours. A separate result took the model a week to conceive and two researchers close to a month to trust. The vendor states plainly that human researchers may become the bottleneck.
We read the two halves as scaling differently. The output side scales. The checking side does not, because it needs the specific expert who can hold the problem, and that person does not become available faster because the model got cheaper. One detail matters for anyone designing multi-agent systems. The winning idea was rejected by one worker and recovered by a second, so the discovery was a property of the pair, and so was the near miss.
Anthropic’s position on open-weight models
Anthropic has never advocated banning open weights, and calls such models a public good where they lack dangerous capabilities. What it backs is export controls, a crackdown on industrial-scale distillation, and mandatory pre-release testing for every capable model.
We judge the rosters worth keeping apart. The open-weights letter carries two of the three majors and not the third, while a separate alliance launched the same month carries none of them at all. Anyone merging the two will state something false in one direction or the other. The strongest evidence in the alliance’s case is that when closed tools refused the forensic work after July’s intrusion, an open-weight model did it.