I've been noticing a cluster of folks I identify with sharing some themes around how we do epistemics and ontology in AI safety *⁠ ⁠A commitment to always pushing from abstract to concrete constructs in the discourse (e.g. @sebkrier's posts on the OAI/HF incident) *⁠ ⁠⁠A refusal to indulge in ungrounded thought experiments *⁠ ⁠An insistence that claims come with data and concrete mechanism because if they don't they're thought experiments *⁠ ⁠⁠A focus on looking at net expected benefits of a given policy position, with deontological guardrails, and a refusal to just focus on harms (e.g. @Abi0lvera's and @ramez's writing) *⁠ ⁠⁠An insistence on including past tech transformations (electricity, air travel) within our reference class when thinking about what makes sense (e.g. @sayashk and @random_walker and @binarybits's writing) Examples of what I mean are intervening on overly idealized policy discussions of RSI with concrete technical details about what RSI is and isn't (e.g. @natolambert). @NateWitkin / @binarybits / @krishnanrohit insisting on including economic, policy, and social-cultural feedbacks in any discussion of runaway AI harms. None of this means denying catastrophic risks. It just means insisting on using data, mechanism, and consideration of AI's net benefits in considering such scenarios (I tried to do this here