Anthropic's Scion accused of deliberately copying Meta's Mononoke data model
In a follow-up post about Anthropic's Scion version control system, blogger sunshowers says the project did not merely borrow unintentionally from Meta's Mononoke but deliberately and blatantly ripped it off, claiming he privately received confirmation and that there was overlap in personnel. He argues the resemblance goes far beyond the general idea of derived data, extending to the entire data model: Scion's changesets are described as essentially identical to Mononoke's Bonsai changesets, and he notes even the name "Scion" reverses the syllables of "Bonsai" and drops the "B". The author says he has no problem with good ideas being copied, only with credit being stripped, and that crediting Sapling with "hard-won lessons" is not enough — which makes Anthropic's "our LLM rebuilt and productionized this in five months" story less impressive, though still notable. As of the post, Anthropic had not updated its original write-up or responded.
The dispute focuses attention on how AI companies reuse open source and peers' prior designs, and on attribution when model-built systems are presented as original work.
Update: Nick G. from Anthropic has responded on Hacker News, and sufficient attribution has been added to Anthropic’s blog post. I stand by my posts as of the time I wrote them.
Discuss on Hacker News.
In response to yesterday’s post about Anthropic’s Scion ripping off Mononoke, I received a number of questions and concerns. Here’s a followup to answer the big ones.
This was a ripoff. My post yesterday was trying to be as charitable as possible given what I knew, and in particular that the similarity to Mononoke was mostly unintentional and the lack of credit was the result of not doing due diligence. However, after publishing the post I privately received confirmation that Scion intentionally and blatantly ripped off Mononoke, and in particular that there was overlap in personnel. There is no chance that this was an unintentional homage. This is something I had a hunch about but was not comfortable saying yesterday, given what I knew back then.
The resemblance is much deeper than just the notion of derived data. Some people pointed out that derived data is just an index, which is a well-known concept. I want to clarify that derived data was merely the most striking resemblance. It goes much deeper than that, and extends to the entire data model, as well as a number of the structures built around it.