'The perils of AI safety's insularity' — why outside contributions get ignored

What it does

N/A

Honest assessment

Include this in the playbook as the honest counterweight. It explains structurally why a good submission from an unaffiliated person often lands nowhere: the field's absorption mechanisms are social and network-based, not procedural. The practical implication is that formal mechanisms with published duties — select committee evidence, regulations.gov comments, regulatory complaints, a permanent AIID record — are more reliable for an outsider than trying to be heard inside the field, because they do not depend on already being known.

How to file

N/A — read as diagnosis. Its claims: AI safety has built a parallel research universe outside academia that trades external scrutiny for speed; the network is geographically and financially concentrated (Berkeley/San Francisco, EA-aligned funders); researchers do not meaningfully engage with mainstream ML venues and consequently reinvent existing methods; funding shapes research direction more than consensus does; and there is a reported lower publication bar for optimistic than for critical claims about AI. It cites work arguing 'scheming' research risks repeating failures ape-language researchers made 50 years ago, and Dan Hendrycks calling mechanistic interpretability misguided.

Format

N/A

Timing

N/A

What happens after

N/A

What it accepts

N/A

What it does not accept

N/A

Operated by

Transformer (news outlet; primary funder Open Philanthropy / Coefficient Giving)