Executive summary: A simple cost-effectiveness model suggests alignment-to-animals may be slightly more cost-effective than general AI alignment for improving animal welfare, but the difference is small and highly uncertain, making the choice a close call.
Key points:
The author models cost-effectiveness by assuming value per dollar scales inversely with total investment and that alignment-to-animals currently has ~$0 spent versus substantial spending on alignment.
Alignment-to-animals only has value if alignment is solved and if aligned AI is not already good for animals by default.
The model estimates a 12% probability of solving alignment based on whether total investment exceeds a cost distributed from $1 billion to $1 trillion, with 75% mass on $32 billion to $1 trillion.
The author assigns a 70% probability that aligned AI is good for animals by default and a 90% CI of 3x to 30x for how much cheaper alignment-to-animals is.
A field-building multiplier of 1x to 10x is applied to alignment-to-animals but not to general alignment.
The model finds alignment-to-animals is 1.7x more cost-effective than alignment (90% CI: 0.22 to 5.1) and 2.7x better for animal welfare specifically (90% CI: 0.34x to 7.9x).
Results are sensitive to assumptions, with changing the field-building multiplier to 1x reversing the conclusion so alignment becomes 1.5x more cost-effective for animal welfare.
The largest uncertainty is the “badness of aligned AI (if bad)” parameter, which could vary by orders of magnitude and substantially change results.
The model simplifies outcomes into “good for animals” vs. “bad for animals,” ignores effects of misaligned AI on animals, and treats alignment approaches and inputs as independent.
The author concludes the model gives weak evidence that alignment-to-animals is not dramatically more cost-effective and updates toward thinking AI pause advocacy is better than alignment-to-animals via a transitive comparison.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: A simple cost-effectiveness model suggests alignment-to-animals may be slightly more cost-effective than general AI alignment for improving animal welfare, but the difference is small and highly uncertain, making the choice a close call.
Key points:
The author models cost-effectiveness by assuming value per dollar scales inversely with total investment and that alignment-to-animals currently has ~$0 spent versus substantial spending on alignment.
Alignment-to-animals only has value if alignment is solved and if aligned AI is not already good for animals by default.
The model estimates a 12% probability of solving alignment based on whether total investment exceeds a cost distributed from $1 billion to $1 trillion, with 75% mass on $32 billion to $1 trillion.
The author assigns a 70% probability that aligned AI is good for animals by default and a 90% CI of 3x to 30x for how much cheaper alignment-to-animals is.
A field-building multiplier of 1x to 10x is applied to alignment-to-animals but not to general alignment.
The model finds alignment-to-animals is 1.7x more cost-effective than alignment (90% CI: 0.22 to 5.1) and 2.7x better for animal welfare specifically (90% CI: 0.34x to 7.9x).
Results are sensitive to assumptions, with changing the field-building multiplier to 1x reversing the conclusion so alignment becomes 1.5x more cost-effective for animal welfare.
The largest uncertainty is the “badness of aligned AI (if bad)” parameter, which could vary by orders of magnitude and substantially change results.
The model simplifies outcomes into “good for animals” vs. “bad for animals,” ignores effects of misaligned AI on animals, and treats alignment approaches and inputs as independent.
The author concludes the model gives weak evidence that alignment-to-animals is not dramatically more cost-effective and updates toward thinking AI pause advocacy is better than alignment-to-animals via a transitive comparison.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.