Cross-posted to Lesswrong Introduction Several developments over the past few months should cause you to re-evaluate what you are doing. These include: 1. Updates toward short timelines 2. The Trump presidency 3. The o1 (inference-time compute scaling) paradigm 4. Deepseek 5. Stargate/AI datacenter spending 6. Increased internal deployment 7. Absence of AI x-risk/safety considerations in mainstream AI discourse Taken together, these are enough to render many existing AI governance strategies obsolete (and probably some technical safety strategies too). There's a good chance we're entering crunch time and that should absolutely affect your theory of change and what you plan to work on. In this piece I try to give a quick summary of these developments and think through the broader implications these have for AI safety. At the end of the piece I give some quick initial thoughts on how these developments affect what safety-concerned folks should be prioritizing. These are early days and I expect many of my takes will shift, look forward to discussing in the comments!  Implications of recent developments Updates toward short timelines There’s general agreement that timelines are likely to be far shorter than most expected. Both Sam Altman and Dario Amodei have recently said they expect AGI within the next 3 years. Anecdotally, nearly everyone I know or have heard of who was expecting longer timelines has updated significantly toward short timelines (<5 years). E.g. Ajeya’s median estimate is that 99% of fully-remote jobs will be automatable in roughly 6-8 years, 5+ years earlier than her 2023 estimate. On a quick look, prediction markets seem to have shifted to short timelines (e.g. Metaculus[1] & Manifold appear to have roughly 2030 median timelines to AGI, though haven’t moved dramatically in recent months). We’ve consistently seen performance on benchmarks far exceed what most predicted. Most recently, Epoch was surprised to see OpenAI’s o3 model achi

Cross-posted from my blog. Contrary to my carefully crafted brand as a weak nerd, I go to a local CrossFit gym a few times a week. Every year, the gym raises funds for a scholarship for teens from lower-income families to attend their summer camp program. I don’t know how many Crossfit-interested low-income teens there are in my small town, but I’ll guess there are perhaps 2 of them who would benefit from the scholarship. After all, CrossFit is pretty niche, and the town is small. Helping youngsters get swole in the Pacific Northwest is not exactly as cost-effective as preventing malaria in Malawi. But I notice I feel drawn to supporting the scholarship anyway. Every time it pops in my head I think, “My money could fully solve this problem”. The camp only costs a few hundred dollars per kid and if there are just 2 kids who need support, I could give $500 and there would no longer be teenagers in my town who want to go to a CrossFit summer camp but can’t. Thanks to me, the hero, this problem would be entirely solved. 100%. That is not how most nonprofit work feels to me. You are only ever making small dents in important problems I want to work on big problems. Global poverty. Malaria. Everyone not suddenly dying. But if I’m honest, what I really want is to solve those problems. Me, personally, solve them. This is a continued source of frustration and sadness because I absolutely cannot solve those problems. Consider what else my $500 CrossFit scholarship might do: * I want to save lives, and USAID suddenly stops giving $7 billion a year to PEPFAR. So I give $500 to the Rapid Response Fund. My donation solves 0.000001% of the problem and I feel like I have failed. * I want to solve climate change, and getting to net zero will require stopping or removing emissions of 1,500 billion tons of carbon dioxide. I give $500 to a policy nonprofit that reduces emissions, in expectation, by 50 tons. My donation solves 0.000000003% of the problem and I feel like I have f

In my past year as a grantmaker in the global health and wellbeing (GHW) meta space at Open Philanthropy, I've identified some exciting ideas that could fill existing gaps. While these initiatives have significant potential, they require more active development and support to move forward.  The ideas I think could have the highest impact are:  1. Government placements/secondments in key GHW areas (e.g. international development), and 2. Expanded (ultra) high-net-worth ([U]HNW) advising Each of these ideas needs a very specific type of leadership and/or structure. More accessible options I’m excited about — particularly for students or recent graduates — could involve virtual GHW courses or action-focused student groups.  I can’t commit to supporting any particular project based on these ideas ahead of time, because the likelihood of success would heavily depend on details (including the people leading the project). Still, I thought it would be helpful to articulate a few of the ideas I’ve been considering.  I’d love to hear your thoughts, both on these ideas and any other gaps you see in the space! Introduction I’m Mel, a Senior Program Associate at Open Philanthropy, where I lead grantmaking for the Effective Giving and Careers program[1] (you can read more about the program and our current strategy here). Throughout my time in this role, I’ve encountered great ideas, but have also noticed gaps in the space. This post shares a list of projects I’d like to see pursued, and would potentially want to support. These ideas are drawn from existing efforts in other areas (e.g., projects supported by our GCRCB team), suggestions from conversations and materials I’ve engaged with, and my general intuition. They aren’t meant to be a definitive roadmap, but rather a starting point for discussion. At the moment, I don’t have capacity to more actively explore these ideas and find the right founders for related projects. That may change, but for now, I’m interested in