Hi! I'm Sophie, a gap year student at Stanford focusing on AI governance for catastrophic risk mitigation. I write a Substack, The Counterfactual -- https://thecounterfactual.substack.com/ -- and I'm currently an ERA Fellow on the AIxBiosecurity track working on tiered access controls for LLMs/cloud labs and interventions to stop China from open-weighting their models.
I'm in the early stages of co-founding a donor advisory initiative focused on neglected areas within AI safety, and would appreciate connections with anyone in the grantmaking/fundraising space!
Happy to chat / might be helpful if you're interested in learning more about:
Thanks for your comment! The base rate argument is reasonable, and I agree that in absolute numbers there are far more potential novice threat actors than expert ones.
But I think timing matters. Expert uplift is a leading indicator-- by the time a model is good enough to meaningfully help a novice with no bio background complete a reverse genetics workflow, it's been helping people with partial expertise clear their specific bottlenecks for much longer. So I'd frame it less as "novice uplift doesn't matter" and more as "if we wait for novice uplift to show up in studies, we've already missed the window where expert uplift became dangerous." Measuring expert uplift first gives us an earlier warning signal.
Also worth noting that the "crazy expert" bucket isn't as empty as the simple model suggests; Aum Shinrikyo's bio program was led by people with graduate degrees, and the 2001 anthrax attacks were likely carried out by a senior USAMRIID researcher. The base rate of "expert with intent" may be low, but it's not zero, and the expected damage per attempt is much higher.