This is a crosspost for The Case for Insect Consciousness by Bob Fischer, which was originally published on Asterisk in January 2025. [Subtitle.] The evidence that insects feel pain is mounting, however we approach the issue. For years, I was on the fence about the possibility of insects feeling pain — sometimes, I defended the hypothesis;[1] more often, I argued against it.[2] Then, in 2021, I started working on the puzzle of how to compare pain intensity across species. If a human and a pig are suffering as much as each one can, are they suffering the same amount? Or is the human’s pain worse? When my colleagues and I looked at several species, investigating both the probability of pain and its relative intensity,[3] we found something unexpected: on both scores, insects aren’t that different from many other animals.  Around the same time, I started working with an entomologist with a background in neuroscience. She helped me appreciate the weaknesses of the arguments against insect pain. (For instance, people make a big deal of stories about praying mantises mating while being eaten; they ignore how often male mantises fight fiercely to avoid being devoured.) The more I studied the science of sentience, the less confident I became about any theory that would let us rule insect sentience out.  I’m a philosopher, and philosophers pride themselves on following arguments wherever they lead. But we all have our limits, and I worry, quite sincerely, that I’ve been too willing to give insects the benefit of the doubt. I’ve been troubled by what we do to farmed animals for my entire adult life, whereas it’s hard to feel much for flies. Still, I find the argument for insect pain persuasive enough to devote a lot of my time to insect welfare research. In brief, the apparent evidence for the capacity of insects to feel pain is uncomfortably strong.[4] We could dismiss it if we had a consensus-commanding theory of sentience that explained why the apparent evidence is ir

Introduction I have been writing posts critical of mainstream EA narratives about AI capabilities and timelines for many years now. Compared to the situation when I wrote my posts in 2018 or 2020, LLMs now dominate the discussion, and timelines have also shrunk enormously. The ‘mainstream view’ within EA now appears to be that human-level AI will be arriving by 2030, even as early as 2027. This view has been articulated by 80,000 Hours, on the forum (though see this excellent piece excellent piece arguing against short timelines), and in the highly engaging science fiction scenario of AI 2027. While my article piece is directed generally against all such short-horizon views, I will focus on responding to relevant portions of the article ‘Preparing for the Intelligence Explosion’ by Will MacAskill and Fin Moorhouse.  Rates of Growth The authors summarise their argument as follows: > Currently, total global research effort grows slowly, increasing at less than 5% per year. But total AI cognitive labour is growing more than 500x faster than total human cognitive labour, and this seems likely to remain true up to and beyond the point where the cognitive capabilities of AI surpasses all humans. So, once total AI cognitive labour starts to rival total human cognitive labour, the growth rate of overall cognitive labour will increase massively. That will drive faster technological progress. MacAskill and Moorhouse argue that increases in training compute, inference compute and algorithmic efficiency have been increasing at a rate of 25 times per year, compared to the number of human researchers which increases 0.04 times per year, hence the 500x faster rate of growth. This is an inapt comparison, because in the calculation the capabilities of ‘AI researchers’ are based on their access to compute and other performance improvements, while no such adjustment is made for human researchers, who also have access to more compute and other productivity enhancements each year.

I am Jason Green-Lowe, the executive director of the Center for AI Policy (CAIP). Our mission is to directly convince Congress to pass strong AI safety legislation. As I explain in some detail in this post, I think our organization has been doing extremely important work, and that we’ve been doing well at it. Unfortunately, we have been unable to get funding from traditional donors to continue our operations. If we don’t get more funding in the next 30 days, we will have to shut down, which will damage our relationships with Congress and make it harder for future advocates to get traction on AI governance. In this post, I explain what we’ve been doing, why I think it’s valuable, and how your donations could help.  This is the first post in what I expect will be a 3-part series. The first post focuses on CAIP’s particular need for funding. The second post will lay out a more general case for why effective altruists and others who worry about AI safety should spend more money on advocacy and less money on research – even if you don’t think my organization in particular deserves any more funding, you might be convinced that it’s a priority to make sure other advocates get more funding. The third post will take a look at some institutional problems that might be part of why our movement has been systematically underfunding advocacy and offer suggestions about how to correct those problems. OUR MISSION AND STRATEGY The Center for AI Policy’s mission is to directly and openly urge the US Congress to pass strong AI safety legislation. By “strong AI safety legislation,” we mean laws that will significantly change AI developers’ incentives and make them less likely to develop or deploy extremely dangerous AI models. The particular dangers we are most worried about are (a) bioweapons, (b) intelligence explosions, and (c) gradual disempowerment. Most AI models do not significantly increase these risks, and so we advocate for narrowly-targeted laws that would focus their att