We’ve been consulting with several parties1 on responsible scaling2 policies (RSPs). An RSP specifies what level of AI capabilities an AI developer is prepared to handle safely with their current protective measures, and conditions under which it would be too dangerous to continue deploying AI systems and/or scaling up AI capabilities until protective measures improve.
We think RSPs are one of the most promising paths forward for reducing risks of major catastrophes from AI. We’re excited to advance the science of model evaluations to help labs implement RSPs that reliably prevent dangerous situations, but aren’t unduly burdensome and don’t prevent development when it’s safe.
This page will explain the basic idea of RSPs as we see it, then discuss:
Why we think RSPs are promising. In brief (more below):
A pragmatic middle ground. RSPs offer a potential middle ground between (a) those who think AI could be extremely dangerous and seek things like moratoriums on AI development, and (b) who think that it’s too early to worry about capabilities with catastrophic potential. RSPs are pragmatic and threat-model driven: rather than arguing over the likelihood of future dangers, we can implement RSPs that commit to measurement (e.g., evaluations) and empirical observation - pausing deployment and/or development if specific dangerous AI capabilities emerge, until protective measures are good enough to handle them safely.
Knowing which protective measures to prioritize. RSPs can help AI developers move from broad caution-oriented principles to specific commitments, giving a framework for which protective measures (information security; refusing harmful requests; alignment research; etc.) they need to prioritize to safely continue development and deployment.
Evals-based rules and norms. In the longer term, we’re excited about evals-based AI rules and norms more generally: requirements that AI systems be evaluated for dangerous capabilities, and restricted when that’s the only way to contain the risks (until protective measures improve). This could include standards, third-party audits, and regulation. Voluntary RSPs can happen quickly, and provide a testbed for processes and techniques that can be adopted to make future evals-based regulatory regimes work well.
What we see as the key components of a good RSP, with sample language for each component. In brief, we think a good RSP should cover:
Limits: which specific observations about dangerous capabilities would indicate that it is (or strongly might be) unsafe to continue scaling?
Protections: what aspects of current protective measures are necessary to contain catastrophic risks?
Evaluation: what are the procedures for promptly catching early warning signs of dangerous capability limits?
Response: if dangerous capabilities go past the limits and it’s not possible to improve protections quickly, is the AI developer prepared to pause further capability improvements until protective measures are sufficiently improved, and treat any dangerous models with sufficient caution?
Accountability: how does the AI developer ensure that the RSP’s commitments are executed as intended; that key stakeholders can verify that this is happening (or notice if it isn’t); that there are opportunities for third-party critique; and that changes to the RSP itself don’t happen in a rushed or opaque way?
Adopting an RSP should be a strong and reliable signal that an AI developer will in fact identify when it’s too dangerous to keep scaling up capabilities, and react appropriately.
I'm excited about labs adopting RSPs for several reasons:
Making commitments and planning-in-advance is good for safety
Making commitments is good for coordination and helping other labs act well
Making commitments is good for transparency and helping outsiders predict what labs will do
Making safety commitments conditional on risks is nice — everyone, including people who think risk is very small, should be able to agree on warning signs that should lead to stronger safety practices or pausing scaling
1. I like the idea of concrete (publicly stated) pre-defined measures, since it lowers the risk of moving safety standards/targets. It would be a substantial improvement over what we have today, especially if there's coordination between top labs.
2. The graph shows jumps where y increases at a rate greater than x. Has this ever happened before? What we've seen so far is more of a mirrored L. First we move along the x-axis, later (to a smaller degree) along the y-axis.
3. The line between the red and blue area should be heavily blurred/striped. This might seem like an aesthetic nitpick, but we can't map the edges of what we've never seen. Our current perceptions are thought up by human minds that are innately tuned to empathize with and predict human behavior, which unwittingly leads to thinking along the lines: "If I was an AI and thought like a psychopathic human, what would I do?". We don't do this explicitly, but that's what we're actually doing. The real danger lies in the unknown unknowns, which cannot be plotted on a graph a priori. At the moment, we're assuming progression of dangers/capabilities in a "logical order", i.e. the way humans gain abilities/learn things. If the order is thrown around, so are the warning signs.
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
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
1. I like the idea of concrete (publicly stated) pre-defined measures, since it lowers the risk of moving safety standards/targets. It would be a substantial improvement over what we have today, especially if there's coordination between top labs.
2. The graph shows jumps where y increases at a rate greater than x. Has this ever happened before? What we've seen so far is more of a mirrored L. First we move along the x-axis, later (to a smaller degree) along the y-axis.
3. The line between the red and blue area should be heavily blurred/striped. This might seem like an aesthetic nitpick, but we can't map the edges of what we've never seen. Our current perceptions are thought up by human minds that are innately tuned to empathize with and predict human behavior, which unwittingly leads to thinking along the lines: "If I was an AI and thought like a psychopathic human, what would I do?". We don't do this explicitly, but that's what we're actually doing. The real danger lies in the unknown unknowns, which cannot be plotted on a graph a priori. At the moment, we're assuming progression of dangers/capabilities in a "logical order", i.e. the way humans gain abilities/learn things. If the order is thrown around, so are the warning signs.