Hide table of contents


After a good long while of reading, thinking, and working at the edges of AI governance (I am an historian of international law and global governance as well as an AI capabilities evaluator and qualitative red-teamer), I am finally taking the plunge and starting to write about it. 

My main contention is this: It seems to me that now, more than ever, we need to rapidly expand the field of AI governance and, specifically, work on its international dimensions. Right now, world governments and international institutions, as well as AI labs developing AI systems that will (and are) finding private and public applications across the world, are in dire need of this talent. Yet the pipelines for producing the talent necessary to implement international AI governance are not sufficiently developed to meet that demand. Without that talent, there will be measurably little progress in efforts to achieve any international AI governance, and what success we do have may be misdirected and therefore ineffective.

If we cannot solve for today's AIs, which are orders of magnitude less complex than advanced AIs that seem in-range by 2030, we may be highly unlikely to be able to implement any meaningful international governance mechanisms that we do happen to theorize later on.

We must start taking this international governance gap much more seriously, and now. 

In this and a series of subsequent posts, I hope to lay out this case as comprehensively as I can, albeit from my own limited perspective. The trajectory of my argument, for now, is the following:

  1. International governance is a high-viscosity fluid
  2. International governance is responsive to certain authorities
  3. International governance requires institutional coordination
  4. International AI governance can help reduce suffering risks today
  5. International governance may have to change considerably to accommodate advanced AI
  6. A reassessment of prevailing international governance models as they pertain to AI and AGI
    1. On “strong” versus “weak” internationalisms
    2. Reconsidering the nonproliferation analogy
    3. The role of INGOs
    4. Where is the "compliance surface" of international AI governance?

I want this to be as useful to others as I can make it, of course. If there is a topic you'd like to see sooner than others, please comment and/or vote below. 

International governance is a high-viscosity fluid

I want to begin my argument with a simple point. 

We have to appreciate in greater depth a fundamental property of international governance: it is highly viscous. It moves slowly. And it requires a great deal of sustained effort to generate sufficient momentum.

This viscosity arises from three sources:

  1. Even today, in the afterglow of the post-war human rights revolution,  international governance inherently proceeds on a voluntary basis. Roughly speaking, any such governance depends on mutual agreements between equals (legally speaking, at least). And while sometimes those agreements are subject to oversight and arbitration from international entities (like UN bodies) or other states and INGOs, there is no authority that can absolutely compel a state to honor international agreements. This, still, is the domain of natural law. 
  2. International agreements are often formalized by treaties or conventions. However, these treaties or conventions are often subject to ratification and implementation by the municipal authorities of signatory states. In the United States, the Senate must ratify any treaty negotiated by the Executive Branch. And, despite current scholarship on the 'secret Congress' that reassures us that important business still manages to get done in hyperpolarized times, this is no guarantee that international agreements will stick. For instance, the U.S. Senate infamously voted not to ratify the Treaty of Versailles (in which allied states normalized relations with Germany and formed the League of Nations), which prevented the United States from joining the League. The U.S. and Germany only made peace in 1921.
  3. International governance initiatives, whether achieved through institutions, conventions, or treaties, compete for political will and attention with several other high-profile needs. In other words, it takes substantial amounts of time, resources, and willpower to reach even a singly multilateral treaty agreement. A pertinent example is the Iran nuclear treaty formalized in July of 2015, marking the end of a nearly 12 year combined effort to achieve the P5+1 framework. I was at the Wilson Center in Washington, D.C. when the success of the treaty was announced, attending a workshop. I asked a leader of the workshop, also a longtime mentor,  what he thought of the news, and he replied, with tears forming in his eyes, "I never thought I would live to see it."

Time is not on our side. We must start building international frameworks, institutions, conventions, and will. That, in my opinion, begins by expanding the AI governance talent pipeline.

There is difficult work to do. There are exceedingly few solutions to problems of AI governance, and the window for effecting them may be closing faster than we realize. This is especially true for projects of international governance, which regard only certain sources as authoritative. 

In my next post, I will return to explore these claims in more detail.

 

(image: Carel Willink, “Late visitors to Pompeii,” 1931)

Comments


No comments on this post yet.
Be the first to respond.
Curated and popular this week
LintzA
 ·  · 15m read
 · 
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
Dr Kassim
 ·  · 4m read
 · 
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
Rory Fenton
 ·  · 6m read
 · 
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
Recent opportunities in AI safety
20
Eva
· · 1m read