Quick takes

I recently created a simple workflow to allow people to write to the Attorneys General of California and Delaware to share thoughts + encourage scrutiny of the upcoming OpenAI nonprofit conversion attempt. Write a letter to the CA and DE Attorneys General I think this might be a high-leverage opportunity for outreach. Both AG offices have already begun investigations, and AGs are elected officials who are primarily tasked with protecting the public interest, so they should care what the public thinks and prioritizes. Unlike e.g. congresspeople, I don't AGs often receive grassroots outreach (I found ~0 examples of this in the past), and an influx of polite and thoughtful letters may have some influence — especially from CA and DE residents, although I think anyone impacted by their decision should feel comfortable contacting them. Personally I don't expect the conversion to be blocked, but I do think the value and nature of the eventual deal might be significantly influenced by the degree of scrutiny on the transaction. Please consider writing a short letter — even a few sentences is fine. Our partner handles the actual delivery, so all you need to do is submit the form. If you want to write one on your own and can't find contact info, feel free to dm me.

Notes on some of my AI-related confusions[1] It’s hard for me to get a sense for stuff like “how quickly are we moving towards the kind of AI that I’m really worried about?” I think this stems partly from (1) a conflation of different types of “crazy powerful AI”, and (2) the way that benchmarks and other measures of “AI progress” de-couple from actual progress towards the relevant things. Trying to represent these things graphically helps me orient/think.  First, it seems useful to distinguish the breadth or generality of state-of-the-art AI models and how able they are on some relevant capabilities. Once I separate these out, I can plot roughly where some definitions of "crazy powerful AI" apparently lie on these axes:  (I think there are too many definitions of "AGI" at this point. Many people would make that area much narrower, but possibly in different ways.) Visualizing things this way also makes it easier for me[2] to ask: Where do various threat models kick in? Where do we get “transformative” effects? (Where does “TAI” lie?) Another question that I keep thinking about is something like: “what are key narrow (sets of) capabilities such that the risks from models grow ~linearly as they improve on those capabilities?” Or maybe “What is the narrowest set of capabilities for which we capture basically all the relevant info by turning the axes above into something like ‘average ability on that set’ and ‘coverage of those abilities’, and then plotting how risk changes as we move the frontier?” The most plausible sets of abilities like this might be something like:  * Everything necessary for AI R&D[3] * Long-horizon planning and technical skills? If I try the former, how does risk from different AI systems change?  And we could try drawing some curves that represent our  guesses about how the risk changes as we make progress on a narrow set of AI capabilities on the x-axis. This is very hard; I worry that companies focus on benchmarks in ways that

Holden Karnofsky has joined Anthropic (LinkedIn profile). I haven't been able to find more information.

It's the first official day of the AI Safety Action Summit, and thus it's also the day that the Seoul Commitments (made by sixteen companies last year to adopt an RSP/safety framework) have come due. I've made a tracker/report card for each of these policies at www.seoul-tracker.org. I'll plan to keep this updated for the foreseeable future as policies get released/modified. Don't take the grades too seriously — think of it as one opinionated take on the quality of the commitments as written, and in cases where there is evidence, implemented. Do feel free to share feedback if anything you see surprises you, or if you think the report card misses something important. My personal takeaway is that both compliance and quality for these policies are much worse than I would have hoped. I believe many peoples' theories of change for these policies gesture at something about a race to the top, where companies are eager to outcompete each other on safety to win talent and public trust, but I don't sense much urgency or rigor here. Another theory of change is that this is a sort of laboratory for future regulation, where companies can experiment now with safety practices and the best ones could be codified. But most of the diversity between policies here is in how vague they can be while claiming to manage risks :/ I'm really hoping this changes as AGI gets closer and companies feel they need to do more to prove to govts/public that they can be trusted. Part of my hope is that this report card makes clear to outsiders that not all voluntary safety frameworks are equally credible.

Both Sam and Dario saying that they now believe they know how to build AGI seems like an underrated development to me. To my knowledge, they only started saying this recently. I suspect they are overconfident, but still seems like a more significant indicator than many people seem to be tracking.

Quick thoughts on investing for transformative AI (TAI) Some EAs/AI safety folks invest in securities that they expect to go up if TAI happens. I rarely see discussion of the future scenarios where it makes sense to invest for TAI, so I want to do that. My thoughts aren't very good, but I've been sitting on a draft for three years hoping I develop some better thoughts and that hasn't happened, so I'm just going to publish what I have. (If I wait another 3 years, we might have AGI already!) When does investing for TAI work? Scenarios where investing doesn't work: 1. Takeoff happens faster than markets can react, or takeoff happens slowly but is never correctly priced in. 2. Investment returns can't be spent fast enough to prevent extinction. 3. TAI creates post-scarcity utopia where money is irrelevant. 4. It turns out TAI was already correctly priced in. Scenarios where investing works: 1. Slow takeoff, market correctly anticipates TAI after we do but before it actually happens, and there's a long enough time gap that we can productively spend the earnings on AI safety. 2. TAI is generally good, but money still has value and there are still a lot of problems in the world that can be fixed with money. (Money seems much more valuable in scenario #5 than #6.) What is the probability that we end up in a world where investing for TAI turns out to work? I don't think it's all that high (maybe 25%, although I haven't thought seriously about this). You also need to be correct about your investing thesis, which is hard. Markets are famously hard to beat. Possible investment strategies 1. Hardware makers (e.g. NVIDIA)? Anecdotally this seems to be the most popular thesis. This is the most straightforward idea but I am suspicious that a lot of EA support for investing in AI looks basically indistinguishable from typical hype-chasing retail investor behavior. NVIDIA already has a P/E of 56. There is a 3x levered long NVIDIA ETP. That is not the sort of thin

I'd love to see an 'Animal Welfare vs. AI Safety/Governance Debate Week' happening on the Forum. The risks from AI cause has grown massively in importance in recent years, and has become a priority career choice for many in the community. At the same time, the Animal Welfare vs Global Health Debate Week demonstrated just how important and neglected the cause of animal welfare remains. I know several people (including myself) who are uncertain/torn about whether to pursue careers focused on reducing animal suffering or mitigating existential risks related to AI. It would help to have rich discussions comparing both causes's current priorities and bottlenecks, and a debate week would hopefully expose some useful crucial considerations.

Some of my thoughts on funding. It's giving season and I want to finally get around to publishing some of my thoughts and experiences around funding. I haven't written anything yet because I feel like I am mostly just revisiting painful experiences and will end up writing some angry rant. I have ideas for how things could be better so hopefully this can lead to positive change not just more complaining. All my experiences are in AI Safety. On Timing: Certainty is more important than speed. The total decision time is less important than the overdue time. Expecting a decision in 30 days and getting it in 35 days is worse than if I expect the decision in 90 days and I get it in 85 days. Grantmakers providing statistics about timing expectations makes things worse. If the mean or median response time is N days it is now N+5 days is it appropriate for me to send a follow-up email to check on the status? Technically it's not late yet. It could come tomorrow or in N more days. Imagine if the Uber app showed you the global mean wait time for the last 12 months and there was no map to track your driver's arrival. "It doesn't have to reduce the waiting time it just has to reduce the uncertainty" - Rory Sutherland My conversations about people's expectations and experiences with people in Berkeley are at times very different to those outside of Berkeley. After I posted my announcement about shutting down AISS and my comment on the LTFF update several people reached out to me about their experiences. Some people I already knew well, some I had met and others I didn't know before. Some of them had received funding a couple of times but their negative experiences led them to not reapply and walk away from their work or the ecosystem entirely. At least one mentioned having a draft post about their experience that they did not feel comfortable publishing. There was definitely a point for me where I had already given up but just not realised it. I had already run out of fundi