HF's Elie Bakouch: Sharing pre-training efficiency gains wouldn't really help rivals
eliebakouch · x · 2026-11-06
Hugging Face researcher Elie Bakouch argues there's a spectrum between full confidentiality and sharing nothing when labs discuss disclosure norms. Using pre-training AI R&D automation as an example, he contends that publishing efficiency gains over previous architectures — even attributing them to data, architecture, or other factors — wouldn't meaningfully speed up competitors, since everyone is watching everything anyway. He also notes other important findings in the METR report beyond its AI R&D metrics and looks forward to Anthropic's forthcoming report.
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