What does this equation mean?

None of these ten is exotic. Each is a specific, dated, sourced instance of a general property: an open-weight release multiplies the number of places a given property of the model can fail, without multiplying the number of parties responsible for checking it. A safety-trained closed model has one deployment surface a provider monitors; a safety-trained open model has as many deployment surfaces as there are people who downloaded it, and monitoring responsibility does not travel with the file. If a base model preserves a given desirable property — genuine safety training, license compliance, unmodified provenance — with independent probability p at each of n downstream forks, the probability that all n forks still have it is $p^n$ , which falls quickly even when p is fairly high and n is modest; the releasing lab’s own diligence bounds only the first term. This is not a claim that any specific p or n has been measured for a real ecosystem — it has not, and Section 3 above is partly an argument that it cannot be, given what is publicly observable. It is a statement about why fragmentation compounds every other failure mode on this list rather than merely adding to it: license drift, safety degradation, and provenance confusion are not competing explanations for what goes wrong with open weights. They are the same structural gap, observed from different angles.