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A released checkpoint is usually described as an endpoint. It is closer to a beginning — ten separately documented failures, from safety training stripped in weeks to weights carrying a hidden payload, show what actually happens once the download starts.
Two questions decide how open-weight AI matures by 2035: whether it closes the capability gap with closed frontier models, and whether regulators and license terms converge on treating it like closed deployment. Four scenarios, each with a falsifier.
Six labs call their releases open, and each grants a different bundle: some cap commercial scale, some restrict use categories, one ships weights behind a paper alone, and only one also publishes the data, code, and logs needed to rebuild what shipped.
Meta's own papers specify exactly how Llama encodes position, shares attention across heads, gates its activations and normalizes its signals — and exactly what changed, and did not change, from Llama 2 through Llama 3 to Llama 4.
A permissive-sounding grant still carries a usage threshold, a use policy and a naming rule. What checkpoint you start from, how you adapt it and what you can prove about the result decide what actually ships.
Meta did not set out to start an open-weight movement. A gated research release leaked within a week, and everything that followed — the commercial license, the benchmark claims, a rival's answer — was improvised in response to that accident.