yanni's Quick takes

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

Hey everyone, I’ve been going through the EA Introductory Program, and I have to admit some of these ideas make sense, but others leave me with more questions than answers. I’m trying to wrap my head around certain core EA principles, and the more I think about them, the more I wonder: Am I misunderstanding, or are there blind spots in EA’s approach? I’d really love to hear what others think. Maybe you can help me clarify some of my doubts. Or maybe you share the same reservations? Let’s talk. Cause Prioritization. Does It Ignore Political and Social Reality? EA focuses on doing the most good per dollar, which makes sense in theory. But does it hold up when you apply it to real world contexts especially in countries like Uganda? Take malaria prevention. It’s a top EA cause because it’s highly cost effective $5,000 can save a life through bed nets (GiveWell, 2023). But what happens when government corruption or instability disrupts these programs? The Global Fund scandal in Uganda saw $1.6 million in malaria aid mismanaged (Global Fund Audit Report, 2016). If money isn’t reaching the people it’s meant to help, is it really the best use of resources? And what about leadership changes? Policies shift unpredictably here. A national animal welfare initiative I supported lost momentum when political priorities changed. How does EA factor in these uncertainties when prioritizing causes? It feels like EA assumes a stable world where money always achieves the intended impact. But what if that’s not the world we live in? Long termism. A Luxury When the Present Is in Crisis? I get why long termists argue that future people matter. But should we really prioritize them over people suffering today? Long termism tells us that existential risks like AI could wipe out trillions of future lives. But in Uganda, we’re losing lives now—1,500+ die from rabies annually (WHO, 2021), and 41% of children suffer from stunting due to malnutrition (UNICEF, 2022). These are preventable d

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