We should expect that the incentives and culture for AI-focused companies to make them uniquely terrible for producing safe AGI.    From a “safety from catastrophic risk” perspective, I suspect an “AI-focused company” (e.g. Anthropic, OpenAI, Mistral) is abstractly pretty close to the worst possible organizational structure for getting us towards AGI. I have two distinct but related reasons: 1. Incentives 2. Culture From an incentives perspective, consider realistic alternative organizational structures to “AI-focused company” that nonetheless has enough firepower to host successful multibillion-dollar scientific/engineering projects: 1. As part of an intergovernmental effort (e.g. CERN’s Large Hadron Collider, the ISS) 2. As part of a governmental effort of a single country (e.g. Apollo Program, Manhattan Project, China’s Tiangong) 3. As part of a larger company (e.g. Google DeepMind, Meta AI) In each of those cases, I claim that there are stronger (though still not ideal) organizational incentives to slow down, pause/stop, or roll back deployment if there is sufficient evidence or reason to believe that further development can result in major catastrophe. In contrast, an AI-focused company has every incentive to go ahead on AI when the case for pausing is uncertain, and minimal incentive to stop or even take things slowly.  From a culture perspective, I claim that without knowing any details of the specific companies, you should expect AI-focused companies to be more likely than plausible contenders to have the following cultural elements: 1. Ideological AGI Vision AI-focused companies may have a large contingent of “true believers” who are ideologically motivated to make AGI at all costs and 2. No Pre-existing Safety Culture AI-focused companies may have minimal or no strong “safety” culture where people deeply understand, have experience in, and are motivated by a desire to avoid catastrophic outcomes.  The first one should be self-explanatory. The second one is a bit more complicated, but basically I think it’s hard to have a safety-focused culture just by “wanting it” hard enough in the abstract, or by talking a big game. Instead, institutions (relatively) have more of a safe & robust culture if they have previously suffered the (large) costs of not focusing enough on safety. For example, engineers who aren’t software engineers understand fairly deep down that their mistakes can kill people, and that their predecessors’ fuck-up have indeed killed people (think bridges collapsing, airplanes falling, medicines not working, etc). Software engineers rarely have such experience. Similarly, governmental institutions have institutional memories with the problems of major historical fuckups, in a way that new startups very much don’t.

Congratulations to the EA Project For Awesome 2024 team, who managed to raise over $100k for AMF, GiveDirectly and ProVeg International by submitting promotional/informational videos to the project. There's been an effort to raise money for effective charities via Project For Awesome since 2017, and it seems like a really productive effort every time. Thanks to all involved! 

[PHOTO] I sent 19 emails to politicians, had 4 meetings, and now I get emails like this. There is SO MUCH low hanging fruit in just doing this for 30 minutes a day (I would do it but my LTFF funding does not cover this). Someone should do this!

This could be a long slog but I think it could be valuable to identify the top ~100 OS libraries and identify their level of resourcing to avoid future attacks like the XZ attack. In general, I think work on hardening systems is an underrated aspect of defending against future highly capable autonomous AI agents.

Introducing Ulysses*, a new app for grantseekers.    We (Austin Chen, Caleb Parikh, and I) built an app! You can test the app out if you’re writing a grant application! You can put in sections of your grant application** and the app will try to give constructive feedback about your applicants. Right now we're focused on the "Track Record" and "Project Goals" section of the application. (The main hope is to save back-and-forth-time between applicants and grantmakers by asking you questions that grantmakers might want to ask. Austin, Caleb, and I hacked together a quick app as a fun experiment in coworking and LLM apps. We wanted a short project that we could complete in ~a day. Working on it was really fun! We mostly did it for our own edification, but we’d love it if the product is actually useful for at least a few people in the community! As grantmakers in AI Safety, we’re often thinking about how LLMs will shape the future; the idea for this app came out of brainstorming, “How might we apply LLMs to our own work?”. We reflected on common pitfalls we see in grant applications, and I wrote a very rough checklist/rubric and graded some Manifund/synthetic applications against the rubric.  Caleb then generated a small number of few shot prompts by hand and then used LLMs to generate further prompts for different criteria (e.g., concreteness, honesty, and information on past projects) using a “meta-prompting” scheme. Austin set up a simple interface in Streamlit to let grantees paste in parts of their grant proposals. All of our code is open source on Github (but not open weight 😛).*** This is very much a prototype, and everything is very rough, but please let us know what you think! If there’s sufficient interest, we’d be excited about improving it (e.g., by adding other sections or putting more effort into prompt engineering). To be clear, the actual LLM feedback isn’t necessarily good or endorsed by us, especially at this very early stage. As usual, use your own best judgment before incorporating the feedback. *Credit to Saul for the name, who originally got the Ulysses S. Grant pun from Scott Alexander. ** Note: Our app will not be locally saving your data. We are using the OpenAI API for our LLM feedback. OpenAI says that it won’t use your data to train models, but you may still wish to be cautious with highly sensitive data anyway.  *** Linch led a discussion on the potential capabilities insights of our work, but we ultimately decided that it was asymmetrically good for safety; if you work on a capabilities team at a lab, we ask that you pay $20 to LTFF before you look at the repo.