Effective today, I’ve left Open Philanthropy and joined the Carnegie Endowment for International Peace[1] as a Visiting Scholar. At Carnegie, I will analyze and write about topics relevant to AI risk reduction. In the short term, I will focus on (a) what AI capabilities might increase the risk of a global catastrophe; (b) how we can catch early warning signs of these capabilities; and (c) what protective measures (for example, strong information security) are important for safely handling such capabilities. This is a continuation of the work I’ve been doing over the last ~year.
I want to be explicit about why I’m leaving Open Philanthropy. It’s because my work no longer involves significant involvement in grantmaking, and given that I’ve overseen grantmaking historically, it’s a significant problem for there to be confusion on this point. Philanthropy comes with particular power dynamics that I’d like to move away from, and I also think Open Philanthropy would benefit from less ambiguity about my role in its funding decisions (especially given the fact that I’m married to the President of a major AI company). I’m proud of my role in helping build Open Philanthropy, I love the team and organization, and I’m confident in the leadership it’s now under; I think it does the best philanthropy in the world, and will continue to do so after I move on. I will continue to serve on its board of directors (at least for the time being).
While I’ll miss the Open Philanthropy team, I am excited about joining Carnegie.
- Tino Cuellar, Carnegie’s President, has been an advocate for taking (what I see as) the biggest risks from AI seriously. Carnegie is looking to increase its attention to AI risk, and has a number of other scholars working on it, including Matt Sheehan, who specializes in China’s AI ecosystem (an especially crucial topic in my view).
- Carnegie’s leadership has shown enthusiasm for the work I’ve been doing and plan to continue. I expect that I’ll have support and freedom, in addition to an expanded platform and network, in continuing my work there.
- I’m generally interested in engaging more on AI risk with people outside my existing networks. I think it will be important to build an increasingly big tent over time, and I’ve tried to work on approaches to risk reduction (such as responsible scaling) that have particularly strong potential to resonate outside of existing AI-risk-focused communities. The Carnegie network is appealing because it’s well outside my usual network, while having many people with (a) genuine interest in risks from AI that could rise to the level of international security issues; (b) knowledge of international affairs.
- I resonate with Carnegie’s mission of “helping countries and institutions take on the most difficult global problems and advance peace,” and what I’ve read of its work has generally had a sober, nuanced, peace-oriented style that I like.
I’m looking forward to working at Carnegie, despite the bittersweetness of leaving Open Phil. To a significant extent, though, the TL;DR of this post is that I am continuing the work I’ve been doing for over a year: helping to design and advocate for a framework that seeks to get early warning signs of key risks from AI, accompanied by precommitments to have sufficient protections in place by the time they come (or to pause AI development and deployment until these protections get to where they need to be).
- ^
I will be at the California office and won’t be relocating.
> Besides RSPs, can you give any additional examples of approaches that you're excited about from the perspective of building a bigger tent & appealing beyond AI risk communities? This balancing act of "find ideas that resonate with broader audiences" and "find ideas that actually reduce risk and don't merely serve as applause lights or safety washing" seems quite important. I'd be interested in hearing if you have any concrete ideas that you think strike a good balance of this, as well as any high-level advice for how to navigate this.
I'm pretty focused on red lines, and I don't think I necessarily have big insights on other ways to build a bigger tent, but one thing I have been pretty enthused about for a while is putting more effort into investigating potentially concerning AI incidents in the wild. Based on case studies, I believe that exposing and helping the public understand any concerning incidents could easily be the most effective way to galvanize more interest in safety standards, including regulation. I'm not sure how many concerning incidents there are to be found in the wild today, but I suspect there are some, and I expect there to be more over time as AI capabilities advance.
> Additionally, how are you feeling about voluntary commitments from labs (RSPs included) relative to alternatives like mandatory regulation by governments (you can't do X or you can't do X unless Y), preparedness from governments (you can keep doing X but if we see Y then we're going to do Z), or other governance mechanisms?
The work as I describe it above is not specifically focused on companies. My focus is on hammering out (a) what AI capabilities might increase the risk of a global catastrophe; (b) how we can try to catch early warning signs of these capabilities (and what challenges this involves); and (c) what protective measures (for example, strong information security and alignment guarantees) are important for safely handling such capabilities. I hope that by doing analysis on these topics, I can create useful resources for companies, governments and other parties.
I suspect that companies are likely to move faster and more iteratively on things like this than governments at this stage, and so I often pay special attention to them. But I’ve made clear that I don’t think voluntary commitments alone are sufficient, and that I think regulation will be necessary to contain AI risks. (Quote from earlier piece: "And to be explicit: I think regulation will be necessary to contain AI risks (RSPs alone are not enough), and should almost certainly end up stricter than what companies impose on themselves.")