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.
This looks like a genuinely useful experiment. I’m especially curious whether making the process public leads to better decisions, rather than just more transparent ones.
One thing I’d watch for is reviewers anchoring on the first few comments or on recognizable names. It might be interesting to collect people’s initial views privately, then compare them with how they update after reading the discussion.
You’d end up learning quite a lot about when public deliberation actually improves grantmaking, and when it just creates herding.
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.
This looks like a genuinely useful experiment. I’m especially curious whether making the process public leads to better decisions, rather than just more transparent ones.
One thing I’d watch for is reviewers anchoring on the first few comments or on recognizable names. It might be interesting to collect people’s initial views privately, then compare them with how they update after reading the discussion.
You’d end up learning quite a lot about when public deliberation actually improves grantmaking, and when it just creates herding.