Model access for third-parties — it's a big deal!

Over time, there might be an increasingly large gap between insider model access and outsider model access. By insiders, I mean employees at the frontier lab.[1] By "outsiders", I mean external safety researchers, third-party auditors, and other actors trying to make the future go well. I will call this a model access gap — and when the gap is small, I'll call this model access parity.[2]I think that one of the top priorities for the external AI safety community over the next 6-12 months should be ensuring model access parity. Main reasons:This would allow us to direct billions of dollars in AI labour towards making things go well. This seems robustly good, regardless of what activities we decide to actually direct the labour towards.I think publicly available models will probably lag 3-6 months behind the best internal models. Hence, as R&D uplift grows superexponentially, we might see the differential uplift grow from 2x to 60x. In short: I think achieving model access parity might be preferable to scaling the headcount of outsider orgs by ten-fold.Model access parity isn't too far from the status quo, but it's the kind of thing that we could lose soon. I think precedent here might be sticky, so it seems like a good time to push for it.To be clear: I think this is a big deal, it probably won't happen by default, and we are not on track to achieve it.Which outsiders?"Outsiders" includes the AI safety community outside the frontier labs, along with other actors trying to make the future go well. To make thing concrete, I've included a list below of orgs this might include.I want to be clear that this list is intentionally expansive — it's not supposed to indicate which outsiders are top priority for model access parity. One might believe there are just 3-5 high-priority orgs, such that ensuring model access parity for these orgs would capture most of the value from a widespread model access parity. Alternatively, you might believe that the returns diminish slowly, a