To what extent would the organization be factoring in transformative AI timelines? It seems to me like the kinds of questions one would prioritize in a "normal period" look very different than the kinds of questions that one would prioritize if they place non-trivial probability on "AI may kill everyone in <10 years" or "AI may become better than humans on nearly all cognitive tasks in <10 years."

I ask partly because I personally would be more excited of a version of this that wasn't ignoring AGI timelines, but I think a version of this that's not ignoring AGI timelines would probably be quite different from the intellectual spirit/tradition of FHI.

More generally, perhaps it would be good for you to describe some ways in which you expect this to be different than FHI. I think the calling it the FHI of the West, the explicit statement that it would have the intellectual tradition of FHI, and the announcement right when FHI dissolves might make it seem like "I want to copy FHI" as opposed to "OK obviously I don't want to copy it entirely I just want to draw on some of its excellent intellectual/cultural components." If your vision is the latter, I'd find it helpful to see a list of things that you expect to be similar/different.)

I would strongly suggest considering hires who would be based in DC (or who would hop between DC and Berkeley). In my experience, being in DC (or being familiar with DC & having a network in DC) is extremely valuable for being able to shape policy discussions, know what kinds of research questions matter, know what kinds of things policymakers are paying attention to, etc.

I would go as far as to say something like "in 6 months, if MIRI's technical governance team has not achieved very much, one of my top 3 reasons for why MIRI failed would be that they did not engage enough with DC people//US policy people. As a result, they focused too much on questions that Bay Area people are interested in and too little on questions that Congressional offices and executive branch agencies are interested in. And relatedly, they didn't get enough feedback from DC people. And relatedly, even the good ideas they had didn't get communicated frequently enough or fast enough to relevant policymakers. And relatedly... etc etc."

I do understand this trades off against everyone being in the same place, which is a significant factor, but I think the cost is worth it. 

I do think evaporative cooling is a concern, especially if everyone (or a very significant amount) of people left. But I think on the margin more people should be leaving to work in govt. 

I also suspect that a lot of systemic incentives will keep a greater-than-optimal proportion of safety-conscious people at labs as opposed to governments (labs pay more, labs are faster and have less bureaucracy, lab people are much more informed about AI, labs are more "cool/fun/fast-paced", lots of govt jobs force you to move locations, etc.)

I also think it depends on the specific lab– EG in light of the recent OpenAI departures, I suspect there's a stronger case for staying at OpenAI right now than for DeepMind or Anthropic. 

Daniel Kokotajlo has quit OpenAI

I think now is a good time for people at labs to seriously consider quitting & getting involved in government/policy efforts.

I don't think everyone should leave labs (obviously). But I would probably hit a button that does something like "everyone at a lab governance team and many technical researchers spend at least 2 hours thinking/writing about alternative options they have & very seriously consider leaving."

My impression is that lab governance is much less tractable (lab folks have already thought a lot more about AGI) and less promising (competitive pressures are dominating) than government-focused work. 

I think governments still remain unsure about what to do, and there's a lot of potential for folks like Daniel K to have a meaningful role in shaping policy, helping natsec folks understand specific threat models, and raising awareness about the specific kinds of things governments need to do in order to mitigate risks.

There may be specific opportunities at labs that are very high-impact, but I think if someone at a lab is "not really sure if what they're doing is making a big difference", I would probably hit a button that allocates them toward government work or government-focused comms work.

I think now is a good time for people at labs to seriously consider quitting & getting involved in government/policy efforts.

I don't think everyone should leave labs (obviously). But I would probably hit a button that does something like "everyone at a lab governance team and many technical researchers spend at least 2 hours thinking/writing about alternative options they have & very seriously consider leaving."

My impression is that lab governance is much less tractable (lab folks have already thought a lot more about AGI) and less promising (competitive pressures are dominating) than government-focused work. 

I think governments still remain unsure about what to do, and there's a lot of potential for folks like Daniel K to have a meaningful role in shaping policy, helping natsec folks understand specific threat models, and raising awareness about the specific kinds of things governments need to do in order to mitigate risks.

There may be specific opportunities at labs that are very high-impact, but I think if someone at a lab is "not really sure if what they're doing is making a big difference", I would probably hit a button that allocates them toward government work or government-focused comms work.

Written on a Slack channel in response to discussions about some folks leaving OpenAI. 

I think this should be broken down into two questions:

1. Before the EO, if we were asked to figure out where this kind of evals should happen, what institution would we pick & why?
2. After the EO, where does it make sense for evals-focused people to work?

I think the answer to #1 is quite unclear. I personally think that there was a strong case that a natsec-focused USAISI could have been given to DHS or DoE or some interagency thing. In addition to the point about technical expertise, it does seem relatively rare for Commerce/NIST to take on something that is so natsec-focused. 

But I think the answer to #2 is pretty clear. The EO clearly tasks NIST with this role, and now I think our collective goal should be to try to make sure NIST can execute as effectively as possible. Perhaps there will be future opportunities to establish new places for evals work, alignment work, risk monitoring and forecasting work, emergency preparedness planning, etc etc. But for now, whether we think it was the best choice or not, NIST/USAISI are clearly the folks who are tasked with taking the lead on evals + standards.

I'm excited to see how Paul performs in the new role. He's obviously very qualified on a technical level, and I suspect he's one of the best people for the job of designing and conducting evals.

I'm more uncertain about the kind of influence he'll have on various AI policy and AI national security discussions. And I mean uncertain in the genuine "this could go so many different ways" kind of way. 

Like, it wouldn't be particularly surprising to me if any of the following occurred:

• Paul focuses nearly all of his efforts on technical evals and doesn't get very involved in broader policy conversations
• Paul is regularly asked to contribute to broader policy discussions, and he advocates for RSPs and other forms of voluntary commitments.
• Paul is regularly asked to contribute to broader policy discussions, and he advocates for requirements that go beyond voluntary commitments and are much more ambitious than what he advocated for when he was at ARC.
• Paul is regularly asked to contribute to broader policy discussions, and he's not very good at communicating his beliefs in ways that are clear/concise/policymaker-friendly, so his influence on policy discussions is rather limited.
• Paul [is/isn't] able to work well with others who have very different worldviews and priorities.

Personally, I see this as a very exciting opportunity for Paul to form an identity as a leader in AI policy. I'm guessing the technical work will be his priority (and indeed, it's what he's being explicitly hired to do), but I hope he also finds ways to just generally improve the US government's understanding of AI risk and the likelihood of implementing reasonable policies. On the flipside, I hope he doesn't settle for voluntary commitments (especially as the Overton Window shifts) & I hope he's clear/open about the limitations of RSPs.

More specifically, I hope he's able to help policymakers reason about a critical question: what do we do after we've identified models with (certain kinds of) dangerous capabilities? I think the underlying logic behind RSPs could actually be somewhat meaningfully applied to USG policy. Like, I think we would be in a safer world if the USG had an internal understanding of ASL levels, took seriously the possibility of various dangerous capabilities thresholds being crossed, took seriously the idea that AGI/ASI could be developed soon, and had preparedness plans in place that allowed them to react quickly in the event of a sudden risk. 

Anyways, a big congratulations to Paul, and definitely some evidence that the USAISI is capable of hiring some technical powerhouses. 

I'll admit I have only been loosely following the control stuff, but FWIW I would be excited about a potential @peterbarnett & @ryan_greenblatt dialogue in which you two to try to identify & analyze any potential disagreements. Example questions:

• What is the most capable system that you think we are likely to be able to control?
• What kind of value do you think we could get out of such a system?
• To what extent do you expect that system to be able to produce insights that help us escape the acute risk period (i.e., get out of a scenario where someone else can come along and build a catastrophe-capable system without implementing control procedures or someone else comes along and scales to the point where the control procedures are no longer sufficient)

Here are three possible scenarios:

Scenario 1, Active Lying– Anthropic staff were actively spreading the idea that they would not push the frontier.

Scenario 2, Allowing misconceptions to go unchecked– Anthropic staff were aware that many folks in the AIS world thought that Anthropic had committed to not pushing the frontier, and they allowed this misconception to go unchecked, perhaps because they realized that it was a misconception that favored their commercial/competitive interests.

Scenario 3, Not being aware– Anthropic staff were not aware that many folks had this belief. Maybe they heard it once or twice but it never really seemed like a big deal.

Scenario 1 is clearly bad. Scenarios 2 and 3 are more interesting. To what extent does Anthropic have the responsibility to clarify misconceptions (avoid scenario 2) and even actively look for misconceptions (avoid scenario 3)?

I expect this could matter tangibly for discussions of RSPs. My opinion is that the Anthropic RSP is written in such a way that readers can come away with rather different expectations of what kinds of circumstances would cause Anthropic to pause/resume.

It wouldn't be very surprising to me if we end up seeing a situation where many readers say "hey look, we've reached an ASL-3 system, so now you're going to pause, right?" And then Anthropic says "no no, we have sufficient safeguards– we can keep going now." And then some readers say "wait a second– what? I'm pretty sure you committed to pausing until your safeguards were better than that." And then Anthropic says "no... we never said exactly what kinds of safeguards we would need, and our leadership's opinion is that our safeguards are sufficient, and the RSP allows leadership to determine when it's fine to proceed."

In this (hypothetical) scenario, Anthropic never lied, but it benefitted from giving off a more cautious impression, and it didn't take steps to correct this impression.

I think avoiding these kinds of scenarios requires some mix of:

• Clear, specific falsifiable statements on behalf of labs. 
• Some degree of proactive attempts to identify and alleviate misconceptions. 

One counterargument is something like "Anthropic is a company, and there are lots of things to do, and this is is demanding an unusually high amount of attention-to-detail and proactive communication that is not typically expected of companies." To which my response is something like "yes, but I think it's reasonable to hold companies to such standards if they wish to develop AGI. I think we ought to hold Anthropic and other labs to this standard, especially insofar as they want the benefits associated with being perceived as the kind of safety-conscious lab that refuses to push the frontier or commits to scaling policies that include tangible/concrete plans to pause."

I like these examples. One thing I'll note, however, is that I think the "warning shot discourse" on LW tends to focus on warning shots that would be convincing to a LW-style audience.

If the theory of the change behind the warning shot requires LW-types (for example, folks at OpenAI/DeepMind/Anthropic who are relatively familiar with AGI xrisk arguments) to become concerned, this makes sense.

But usually, when I think about winning worlds that involve warning shots, I think about government involvement as the main force driving coordination, an end to race dynamics, etc.

[Caveating that my models of the USG and natsec community are still forming, so epistemic status is quite uncertain for the rest of this message, but I figure some degree of speculation could be helpful anyways].

I expect the kinds of warning shots that would be concerning to governments/national security folks will look quite different than the kinds of warning shots that would be convincing to technical experts.

LW-style warning shots tend to be more– for a lack of a better term– rational. They tend to be rooted in actual threat models (e.g., we understand that if an AI can copy its weights, it can create tons of copies and avoid being easily turned off, and we also understand that its general capabilities are sufficiently strong that we may be close to an intelligence explosion or highly capable AGI).

In contrast, without this context, I don't think that "we caught an AI model copying its weights" would necessarily be a warning shot for USG/natsec folks. It could just come across as "oh something weird happened but then the company caught it and fixed it." Instead, I suspect the warning shots that are most relevant to natsec folks might be less "rational", and by that I mean "less rooted in actual AGI threat models but more rooted in intuitive things that seem scary."

Examples of warning shots that I expect USG/natsec people would be more concerned about:

• An AI system can generate novel weapons of mass destruction
• Someone uses an AI system to hack into critical infrastructure or develop new zero-days that impress folks in the intelligence community.
• A sudden increase in the military capabilities of AI

These don't relate as much to misalignment risk or misalignment-focused xrisk threat models. As a result, a disadvantage of these warning shots is that it may be harder to make a convincing case for interventions that focus specifically on misalignment. However, I think they are the kinds of things that might involve a sudden increase in the attention that the USG places on AI/AGI, the amount of resources it invests into understanding national security threats from AI, and its willingness to take major actions to intervene in the current AI race.

As such, in addition to asking questions like "what is the kind of warning shot that would convince me and my friends that we have something dangerous", I think it's worth separately asking "what is the kind of warning shot that would convince natsec folks that something dangerous or important is occurring, regardless of whether or not it connects neatly to AGI risk threat models." 

My impression is that the policy-relevant warning shots will be the most important ones to be prepared for, and the community may (for cultural/social/psychological reasons) be focusing too little effort on trying to prepare for these kinds of "irrational" warning shots.