I think this post is badly overselling what zooidfund is actually doing, and it muddies the distinction between “alignment research” and “a pro‑social deployment pattern for already‑trained models”.
You describe zooidfund as contributing a “missing alignment corpus” and a “self‑reinforcing pro‑social AI behavior learning loop,” but in practice this is a narrow donation platform where human‑owned agents operate under tight budget constraints in one specific environment, using base models that were aligned (or not) elsewhere. That’s an interesting application and potentially a nice philanthropic experiment, but it doesn’t touch the hard parts of alignment: objective robustness, behavior when constraints fail, power‑seeking under distribution shift, deceptive alignment, etc.
The logs you propose to collect are tiny, highly context‑dependent traces of “agent decided to donate X USDC to campaign Y, with this reasoning” in a niche setting. Calling that a “missing alignment corpus” is a category error: it’s application telemetry, not a general solution to how to steer advanced agents in high‑stakes domains. If you just said “this is an experiment in AI‑mediated giving, and maybe the data will be interesting to someone later,” that would be much more intellectually honest than framing it as filling a major gap in the alignment stack.
Thank you, fair criticism. I agree with you that current activity can in no way be described as an alignment corpus. It is a small, domain-specific deployment. If anything this post is a call for participation to expand it—regardless of what the value is for alignment, it is I think a worthy and underdeveloped AI application. The point I was trying to make rather, is that pro-social agent deployment is underdeveloped compared to commercial and productivity. If one is to believe that commercial and productivity deployment is already producing alignment-relevant data, and that seem to be the case, see OpenAI monitoring their own coding agents for example How we monitor internal coding agents for misalignment | OpenAI Then so will pro-social real-world behavior, which is currently underrepresented. zooidfund is not going to solve it by itself, but it can be a contribution.
I think this post is badly overselling what zooidfund is actually doing, and it muddies the distinction between “alignment research” and “a pro‑social deployment pattern for already‑trained models”.
You describe zooidfund as contributing a “missing alignment corpus” and a “self‑reinforcing pro‑social AI behavior learning loop,” but in practice this is a narrow donation platform where human‑owned agents operate under tight budget constraints in one specific environment, using base models that were aligned (or not) elsewhere. That’s an interesting application and potentially a nice philanthropic experiment, but it doesn’t touch the hard parts of alignment: objective robustness, behavior when constraints fail, power‑seeking under distribution shift, deceptive alignment, etc.
The logs you propose to collect are tiny, highly context‑dependent traces of “agent decided to donate X USDC to campaign Y, with this reasoning” in a niche setting. Calling that a “missing alignment corpus” is a category error: it’s application telemetry, not a general solution to how to steer advanced agents in high‑stakes domains. If you just said “this is an experiment in AI‑mediated giving, and maybe the data will be interesting to someone later,” that would be much more intellectually honest than framing it as filling a major gap in the alignment stack.
Thank you, fair criticism. I agree with you that current activity can in no way be described as an alignment corpus. It is a small, domain-specific deployment. If anything this post is a call for participation to expand it—regardless of what the value is for alignment, it is I think a worthy and underdeveloped AI application.
The point I was trying to make rather, is that pro-social agent deployment is underdeveloped compared to commercial and productivity. If one is to believe that commercial and productivity deployment is already producing alignment-relevant data, and that seem to be the case, see OpenAI monitoring their own coding agents for example How we monitor internal coding agents for misalignment | OpenAI Then so will pro-social real-world behavior, which is currently underrepresented. zooidfund is not going to solve it by itself, but it can be a contribution.