I don’t really understand this post; I suspect the LLM has made it a little confused.
You say:
> When we mistake an early-stage research or field-building problem for a last-mile delivery problem, we set ourselves up for disappointment.
Cultivated meat was, for years, implicitly treated as if it was close to a last-mile problem: a few years from cost parity, just needing scale and distribution. It wasn’t. Something similar could happen with welfare tech.
Isn’t most of the focus already on development?
> Leafleting was perhaps the original example. In the early-to-mid 2010s, it was often treated as one of the most effective things people could do for animals, based largely on evidence that later looked much weaker than initially believed. The story was wonderfully legible: distribute leaflets, convert people to vegetarianism, reduce animal suffering. Simple, measurable, scalable. However, the reality was messier. The foundational studies were methodologically flawed (e.g., self-reporting on diets leading to social-desirability bias) and long-term effects were unreliably extrapolated. When these flaws were found, there was a rapid downward adjustment, and now leafleting has effectively been fully removed from EA animal work.
I don’t see why this was named as a “bubble”, my impression is that this was basically good. People thought leafletting was good; people tried leafletting; it wasn’t very good; people stopped doing it. That seems very functional to me.
My impression is that bubbles are bad because they are ex-post indications of overcorrections (on entry or exit or both) that have a bunch of harmful externalities—but I think one needs to make an actual argument at the object level for why there could be an overcorrection towards welfare tech rn (e.g. maybe you think that too much funding is going into it too quickly, but are you sure that money would actually be counterfactually spent on other animal stuff? Or maybe you think that people are moving away from other interventions to work on this stuff, but the main people I know doing AW tech dev are unsurprisingly technical people and weren’t working on other AW interventions before that, so there’s not much of a tradeoff there).
The early research behind leafleting was low-quality in a way that should have been noticeable, and a better-calibrated version of the movement would not have spent so much time and money on it. Whereas the cage-free bubble was a necessary way of getting a massive worldwide campaign off the ground. I’d like use to have higher standards for our fads than we did for leafletting but I think the fad phenomenon is inevitable for a movement as constrained on talent and resources as we are.
1. “Isn’t most of the focus [for cultivated meat] already on development?” Yes, it is. However, our experience was that during the peak hype period, clean meat was communicated as almost ready to be on store shelves worldwide. So we are arguing that there is pressure for ideas to be presented as close to last-mile delivery, even when this is not the case.
2. “I don’t see why [leafletting] was named as a ‘bubble’” For us, a bubble just refers to “a situation where expected impact in a given timeframe substantially exceeds realistic impact”, so it does not necessitate that the downward adjustment is wrong. Though maybe your point is more “why include it if the downward adjustment was justified?”. To that, we would say there is damage caused on the upswing and the downswing. In this instance, the damage was in the detraction from other strategies and the unnecessary disillusionment. Also, we meant for the historical examples to be more illustrative of the pattern, and definitely don’t mean to be disparaging to those efforts or decisions.
3. “I think one needs to make an actual argument at the object level for why there could be an overcorrection towards welfare tech” Interesting point for clarification! In part, we mean for this post to be a flag, giving a name to a pattern and signalling that welfare tech shares many of the hallmarks. Similarly, we don’t mean to say that any given effort is necessarily a mistake. That said, we do think that there is a significant amount of resources currently being directed towards welfare tech (for example here, here, here) in ways that are oriented towards a belief that welfare tech is either ready for implementation and scaling or close to it. That could totally be right in some instances! But it could also be wrong. Even the fact that we are talking about welfare tech as a monolith is, for us, a signal that it is being simplified in the way that we describe as part of the cycle of a bubble.
I don’t really understand this post; I suspect the LLM has made it a little confused.
You say:
> When we mistake an early-stage research or field-building problem for a last-mile delivery problem, we set ourselves up for disappointment.
Cultivated meat was, for years, implicitly treated as if it was close to a last-mile problem: a few years from cost parity, just needing scale and distribution. It wasn’t. Something similar could happen with welfare tech.
Isn’t most of the focus already on development?
> Leafleting was perhaps the original example. In the early-to-mid 2010s, it was often treated as one of the most effective things people could do for animals, based largely on evidence that later looked much weaker than initially believed. The story was wonderfully legible: distribute leaflets, convert people to vegetarianism, reduce animal suffering. Simple, measurable, scalable. However, the reality was messier. The foundational studies were methodologically flawed (e.g., self-reporting on diets leading to social-desirability bias) and long-term effects were unreliably extrapolated. When these flaws were found, there was a rapid downward adjustment, and now leafleting has effectively been fully removed from EA animal work.
I don’t see why this was named as a “bubble”, my impression is that this was basically good. People thought leafletting was good; people tried leafletting; it wasn’t very good; people stopped doing it. That seems very functional to me.
My impression is that bubbles are bad because they are ex-post indications of overcorrections (on entry or exit or both) that have a bunch of harmful externalities—but I think one needs to make an actual argument at the object level for why there could be an overcorrection towards welfare tech rn (e.g. maybe you think that too much funding is going into it too quickly, but are you sure that money would actually be counterfactually spent on other animal stuff? Or maybe you think that people are moving away from other interventions to work on this stuff, but the main people I know doing AW tech dev are unsurprisingly technical people and weren’t working on other AW interventions before that, so there’s not much of a tradeoff there).
The early research behind leafleting was low-quality in a way that should have been noticeable, and a better-calibrated version of the movement would not have spent so much time and money on it. Whereas the cage-free bubble was a necessary way of getting a massive worldwide campaign off the ground. I’d like use to have higher standards for our fads than we did for leafletting but I think the fad phenomenon is inevitable for a movement as constrained on talent and resources as we are.
Hey Caleb!
Going through each point in turn:
1. “Isn’t most of the focus [for cultivated meat] already on development?”
Yes, it is. However, our experience was that during the peak hype period, clean meat was communicated as almost ready to be on store shelves worldwide.
So we are arguing that there is pressure for ideas to be presented as close to last-mile delivery, even when this is not the case.
2. “I don’t see why [leafletting] was named as a ‘bubble’”
For us, a bubble just refers to “a situation where expected impact in a given timeframe substantially exceeds realistic impact”, so it does not necessitate that the downward adjustment is wrong.
Though maybe your point is more “why include it if the downward adjustment was justified?”.
To that, we would say there is damage caused on the upswing and the downswing. In this instance, the damage was in the detraction from other strategies and the unnecessary disillusionment.
Also, we meant for the historical examples to be more illustrative of the pattern, and definitely don’t mean to be disparaging to those efforts or decisions.
3. “I think one needs to make an actual argument at the object level for why there could be an overcorrection towards welfare tech”
Interesting point for clarification!
In part, we mean for this post to be a flag, giving a name to a pattern and signalling that welfare tech shares many of the hallmarks. Similarly, we don’t mean to say that any given effort is necessarily a mistake.
That said, we do think that there is a significant amount of resources currently being directed towards welfare tech (for example here, here, here) in ways that are oriented towards a belief that welfare tech is either ready for implementation and scaling or close to it. That could totally be right in some instances! But it could also be wrong. Even the fact that we are talking about welfare tech as a monolith is, for us, a signal that it is being simplified in the way that we describe as part of the cycle of a bubble.