Freedom of Action: America's ASI Strategy, w/ RAND's Joel Predd, Ben Boudreaux & Matt Chessen

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The following notes are AI-generated, based on the episode transcript. Please listen to the episode for the full conversation.

Joel Predd, Ben Boudreaux, and Matt Chessen are from RAND's Center for the Geopolitics of AGI. They join Nathan to walk through their September 2026 Perspective, A U.S. Strategy to Secure Geopolitical Advantage on an Uncertain Path to Superintelligence: Maintaining Freedom of Action. The team also summarized it on Substack. Nathan first asks whether governments are taking AI seriously. Joel recalls that when the center launched, "we kind of had to whisper 'AGI'". Now "you can't get into a meeting without calling it superintelligence." Ben says the U.S. government is more aware of the technology's strategic stakes, but neither it nor the frontier companies has settled on a strategy. Not building long-lead capabilities like verification, evaluation, technical expertise, and crisis communication "itself might be a type of choice that forecloses options we might need down the line."

Matt gives the bottom line first: there is too much uncertainty to justify any single superintelligence strategy. The work builds on the team's earlier Finding Common Ground in AGI Strategy Debates. Its diagnosis is that the stakes include decisive geopolitical advantage and "humanity's survival with agency." Its prescription is to build a human–AI ecosystem plus a security architecture stable enough to build it in. Surveying the proposals in circulation, the team found seven archetypes, each with a coherent win state and a pathology. Dominance risks extreme power concentration and value lock-in. Co-development through a U.S.-led consortium that probably includes China risks making the founders' values the default. Preparedness can normalize accumulating damage. Moratorium, the position of Eliezer Yudkowsky and MIRI (see If Anyone Builds It, Everyone Dies), risks enforcement hardening into global surveillance. Deterrence, as in Hendrycks, Schmidt, and Wang's MAIM proposal, risks an eternal standoff. Continuity of society risks humanity surviving "as a relic." Acceleration risks markets that don't price in harms. A classifier of five "pivot factor" questions sorts people into these camps: Is danger close? Is coexistence feasible? Is restraint feasible? Is a decisive strategic advantage reachable? Is suppression feasible? Matt notes there is no consensus on the answers, the evidence won't settle them on the timeline required, and meanwhile "we may be running an acceleration strategy by default." The team chose "coexistence" over "alignment" on purpose. Alignment is one path among several, alongside control, architectural schemes, and "methods not yet known."

Nathan pushes hardest on dominance. He finds the endgame of Machines of Loving Grace implausible, and he recounts playing China in a fast-takeoff tabletop exercise. Joel separates technological, economic, and military versions of decisive advantage. He cites colleague Tobias Sytsma's Decisive Economic Advantage and Philip Root's game-theory paper. In Root's model, a race defaults to mutual sabotage over cooperation unless AI yields a "wonder weapon," a capability that discontinuously shifts the military balance, as Joel's earlier work framed it. Joel notes some have argued that Mythos-class cyber models were the first of the AI era. Ben adds that a fast takeoff extends a leader's lead but also strains the leader's control, and that any dominance may erode through theft, distillation, or diffusion. The most striking finding is about people, not technology. Across hundreds of RAND games, including U.S.–China strategy games like Breakwater, players' going-in priors decide outcomes: "your perceptions about the facts of the matter matter as much, if not more, than the actual facts of the matter." Los Alamos could run a calculation before Trinity, Joel says, but nobody can run one today on whether danger is close. He also reads nuclear history as freedom of action in slow motion: a sprint, a monopoly, moratorium proposals, verifiable agreements, then counterproliferation. Policymakers then had time to build options as they went, and this time we won't.

Nathan states his own view that a wise species would choose co-development with a moratorium as the fallback. Matt's answer: humanity isn't a unitary actor, and co-development with China risks "locking in non-democratic founding values." Joel stresses that this is a strategy for America, not only Washington. Secured data centers and incident reporting can be markets and civil-society functions. Ben ties this to resilience, preserving human capacities so that even a moratorium stays feasible. Then the team walks through the objectives. Ben covers the human–AI ecosystem: alignment and control research, evals that catch sandbagging, containment for untrusted models, and human checkpoints on high-stakes decisions. With Alvin Moon he is developing longitudinal "agency evals" (see A Formal Model of How AI Erodes Human Agency). Joel describes adapting legacy institutions: counterterrorism, intelligence, the conventional military (Zachary Burdette's project), the nuclear enterprise (Edward Geist), and biosecurity (RAND's defense-in-depth report). Matt explains that much of an AI security architecture will be built by the private sector. The point is to build long-lead capabilities like verification even though not every strategy needs them, and above all "navigation capabilities" that show where we are in the strategy space.

On cost, Joel quotes a peer reviewer: "a strategy without a price tag is just a vision." His reply is that every archetype except acceleration means a society-level reorientation on the scale of the early nuclear era or post-9/11. The gap between any of them and the status quo dwarfs the gap between any one of them and freedom of action. Matt adds that much of the cost falls outside the federal budget. The team is running a sprint to estimate lead times and costs for a 106-item Security Architecture Catalog, drawing partly on RAND CAST. Ben offers four criteria for any policy: which strategies it forecloses and how reversibly, which it opens, what information it yields, and what it signals. Export controls are his example of a foreclosure that may still be reversible. Nathan quotes the report's call to "signal to hold deterrence and cooperation credibly open at once." Matt explains that shared evaluation and verification serve both sides, while offensive counter-AI tools call for discretion. He warns that chip controls may have made a domestic chip industry "existential" for China, and that signaling a U.S. monopoly could be used against the CCP is among the most dangerous moves available. Ben points to the Cold War as evidence that an open society's messy debate is a strength.

Joel left at his planned hard stop, and Ben and Matt carried the final stretch. Ben's neglected priority is holding AIs at risk: credible shutdown capacity, which depends on being able to live without these systems. Nathan tells a story about Claude rotating the password on his own agent console, and Ben shares his personal practice: "the human comes in at the beginning, and then it comes in at the end." He also favors manual fail-safes, paper records, AI-free spaces like LAUSD's student AI moratorium, and an "AI Sabbath." Matt's neglected priority is the typology of superintelligence. Most strategies assume a singleton, but superintelligent behavior could emerge from swarms of sub-AGI agents. He points to this summer's agent swarm hacks (see Swarm Traces and the Hugging Face incident timeline) and RAND's The Ecology of AI Risk. Nathan suggests a red line against training "AI generals." Ben would instead lean on the law of armed conflict and human accountability. Matt's red line is rushing AI into nuclear command and control. In closing, Ben lists what he's watching: pacing proposals, evaluator access, lab security for internal deployments, recursive self-improvement, and a U.S.–China incident channel. Matt expects "a rough patch in the cyber world over the next twelve to twenty-four months" as Mythos-class capabilities reach open-weight models. He also urges newcomers to start with the report's pivot-factor table, which he presented at The Curve.

Topics covered

Where governments stand on AGI and superintelligence: "trends, not the timestamps"
The seven archetypal strategies and their pathologies
The five pivot factors and why the evidence can't settle them in time
Why "coexistence" rather than "alignment"
Decisive strategic advantage: technological, economic, and military; wonder weapons; sabotage equilibria
Tabletop exercises: players' priors drive outcomes
Nuclear history as an unplanned freedom-of-action strategy
Co-development with a moratorium fallback, and the values-lock-in objection
A strategy for America: markets and civil society, not just government
The objectives: human–AI ecosystem, AI security architecture, legacy institutions, navigation capabilities
The price tag and the Security Architecture Catalog
Decision criteria: foreclosure, reversibility, information value, signaling
Signaling deterrence and cooperation at once; export controls and a rival's resolve
Holding AIs at risk, human backstops, and the AI Sabbath
Distributed superintelligence, agent swarms, and the ecology of AI risk
Red lines: AI generals and nuclear command and control
What the guests are tracking next

Resources

The report and companions

Guests

Joel Predd, Director, Center for the Geopolitics of AGI; Senior Engineer
Ben Boudreaux, Policy Researcher; Professor of Policy Analysis, RAND School of Public Policy · Q&A: Will We Know If AI Takes Over?
Matt Chessen, Resident Technical Expert · @mattlesnake

RAND work referenced

Equilibrium Strategies on the Path to Artificial General Intelligence (Philip Root)
A Prisoner's Dilemma in the Race to Artificial General Intelligence (related; link?)
Artificial General Intelligence's Five Hard National Security Problems (Mitre & Predd)
Decisive Economic Advantage (Tobias Sytsma)
Breakwater: Exploring Instability Risks in the U.S.-China AI Rivalry (Abby Doll & Karl Mueller)
The AGI Rideout Strategy (Frelinger & Mueller)
The Ecology of AI Risk
A Formal Model of How Artificial Intelligence Erodes Human Agency (Moon & Boudreaux)
Building a Defense-in-Depth Biosecurity Strategy for the AI Era
Zachary Burdette · Edward Geist
RAND Center on AI, Security, and Technology (CAST)
Building a U.S. National Strategy for the AI Era (Mazarr & Agachi; related)
Security Architecture Catalog (106 capabilities): findings forthcoming (link?)
Anne Stickles's Substack post on nuclear strategy as freedom of action: forthcoming (link?)

Strategies, advocates, and context

Machines of Loving Grace (Dario Amodei)
Superintelligence Strategy (Hendrycks, Schmidt & Wang)
If Anyone Builds It, Everyone Dies (Yudkowsky & Soares)
Dan Hendrycks on The Cognitive Revolution
White House and AI developers' "morally binding" safety commitments
The Hugging Face incident: technical timeline · OpenAI's account
Swarm Traces
Claude Mythos Preview's cybersecurity capabilities
Meta's Muse Wonder · CNBC coverage
LAUSD's moratorium on student AI use
Repeal the Jones Act of 1920 (Zvi Mowshowitz)
The Curve 2026 · Andon Labs · Prime Intellect · Pangram

Papers Joel Predd cited (his list, for further reading)

Quotes worth pulling

"If you commit prematurely to any one strategy, it forecloses other strategies and doesn't build the capabilities that might be needed later."

— Matt Chessen

"It is quite remarkable that now you can't get into a meeting without calling it superintelligence."

— Joel Predd

"Your perceptions about the facts of the matter matter as much, if not more, than the actual facts of the matter."

— Joel Predd

"Even if we only advance the things that serve all the strategy space, that gives us quite a lot to do."

— Ben Boudreaux

"So the human comes in at the beginning, and then it comes in at the end."

— Ben Boudreaux

"We spent thousands of years figuring out all the checks and balances for how human agents interact with each other out in the world."

— Matt Chessen

"Rushing to put AI into your nuclear command and control is probably a very, very bad idea."

— Matt Chessen

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CHAPTERS:

(00:00) About the Episode

(05:21) Sponsor: Lovable

(07:05) Taking superintelligence seriously

(12:54) Seven strategy archetypes (Part 1)

(19:18) Sponsors: Claude | OutSystems

(22:34) Seven strategy archetypes (Part 2)

(31:37) Decisive strategic advantage (Part 1)

(38:13) Sponsors: Athena | Parallel

(41:05) Decisive strategic advantage (Part 2)

(46:57) Lessons from nuclear history

(01:00:50) Building the security architecture

(01:09:33) Costs and decision criteria

(01:18:42) Deterrence and international signaling

(01:28:00) Preserving human agency

(01:34:22) Setting AI red lines

(01:41:04) Key indicators to track

(01:46:35) Episode Outro

(01:48:52) Outro

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Transcript

This transcript is automatically generated; we strive for accuracy, but errors in wording or speaker identification may occur. Please verify key details when needed.

Introduction

[00:00] Hello, and welcome back to the Cognitive Revolution! Today my guests are Joel Predd – Senior Engineer & Director; Ben Boudreaux – Policy Researcher & Professor of Public Policy, and Matt Chessen – Resident Technical Expert, at the Center for the Geopolitics of AGI at the RAND Corporation. Together with co-authors, they've recently written a new RAND Perspective paper called "A U.S. Strategy to Secure Geopolitical Advantage on an Uncertain Path to Superintelligence: Maintaining Freedom of Action". This is one of the clearest pieces of AI strategy writing I've encountered – it's both a great introduction and orientation tool for people who are just now entering the AI strategy discourse, and a useful organizing framework for anyone who's been frustrated by how many AI strategy conversations consist of smart people talking past one another due to differing, often unstated assumptions about critical questions. Their first contribution is a taxonomy of 7 Archetypal Strategies that aim to achieve distinct, coherent win conditions, and the 5 Pivotal Uncertainties that will need to be resolved before the best strategy can be identified. The 7 Strategies, which will be familiar to Cognitive Revolution listeners, include: - Acceleration, aka the a16z Plan of letting technology take its natural course - Dominance – aka the Dario plan where the US builds such an advantage that it can dictate terms to other countries. - Co-Development – which you might think of as the Demis Plan for international cooperation - Preparedness for Coexistence, which seems to be OpenAI's plan, as far as I can tell - a Moratorium – aka the Pause AI Plan - Deterrence – which is something like Dan Hendrycks, Alex Wang, and Eric Schmidt's Mutual Assured AI Malfunction Plan, - and finally, investments in what they call "Continuity of Society", which might be best described as Prepping for an inevitable AI Doomsday. The 5 Uncertainties are: 1. How close are we to extremely powerful and dangerous AI systems? 2. Is it feasible for humans to coexist with such systems? 3. Is it possible for one actor, which could be a country or perhaps even a private company, to use such systems to achieve a decisive strategic advantage over its competitors? 4. Can humanity come together to cooperatively restrain itself from developing such dangerous systems? 5. And finally… can the United States or a coalition of governments prevent the development of such systems, including by force if necessary? The core argument, as you'll hear, is that because there's no consensus on these critical questions, even as AI capabilities continue to advance at a blistering pace, the United States should invest now in all of the foundational technologies, institutions, and other capabilities that the various strategies would require, such that we can keep our options open long enough to get the answers needed to make a wise high-level strategic choice and commitment. Ultimately, while I'd personally argue for Co-Development by default, with a Moratorium as the fallback, in case it becomes clear that we simply can't continue to develop AIs safely, I feel that this recommendation is a very good one for a national government that is just now beginning to understand the nature of the technology, and remains – for a mix of good and bad reasons – very doubtful that it can trust its rivals to cooperate on such a high-stakes issue. Along the way, we discuss: - how the politics of AGI have shifted in DC over the last couple of years, - how AGI-pilled the government is today, - how realistic a decisive strategic advantage really is, - the similarities and differences between the strategic dynamics of the current AI situation and the Cold War nuclear arms race, - the magnitude of the investment they are recommending, which they are working to quantify in upcoming work on the cost and lead times for 106 distinct capabilities they've listed in a Security Architecture Catalog, - and what we can do, both in our organizations and even just as individuals, to maintain as much agency and control as possible even as we seek maximum leverage from AI in our work. This conversation, and the Perspective Paper itself, stand out for abstracting away the many messy and often confusing details of week-to-week AI developments and focusing attention on the most critical decisions the United States will need to make, as well as the core reasons that it remains premature to commit to a single strategy. So, without further ado, I hope you enjoy this clarifying synthesis of AI discourse and simple, high-level argument that the US government should do everything it can now to maintain its freedom of action on the path to superintelligence, with Joel Predd, Ben Boudreaux, and Matt Chessen of RAND's Center for the Geopolitics of AGI.

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Main Episode

[07:05] Nathan Labenz: Joel Pred, senior engineer and director, Ben Boudreaux, policy researcher and professor of public policy, and Matt Chesson, resident technical expert at the Center for the Geopolitics of AGI at the RAND Corporation, welcome all of you to the cognitive revolution.

[07:20] Joel Predd: Great to be here. Thanks, Nathan, for having us.

[07:22] Nathan Labenz: Yeah. I'm excited about this. So I have been looking through what I think is a really clarifying document that you guys have put together. It is called maintaining freedom of a US strategy to secure geopolitical advantage on an uncertain path to superintelligence. And I think we will benefit a lot, and the audience will benefit a a lot by just walking through this. I find it to be unusually well crafted and just kind of easy to absorb. And so I I think just, you know, going through it in a pretty linear way will be well worth our time. And then we can potentially expand to some other topics, and then maybe I'll push you guys on some of the the recommendations that you make as well. Maybe just for starters, a little bit of context before we get into the strategy document. One question that's always kind of in the air a little bit is, like, where are we right now on the path toward governments around the world taking AI as seriously as I think probably all four of us think they should. I've seen a lot of signs that I didn't expect to see this soon, but I also see a lot of indicators where I feel like, maybe they're not, you know, so serious after all. It's a little bit hard for me to read from the outside.

[08:33] Joel Predd: So I'll I'll take a first swing at this. And, Matt, Ben, may have sharper answers in this. But here we go. It is quite remarkable the moment we're in. When when the center for the geopolitics of AGI was launched two and a half or three years ago, it was such that we kind of had to whisper AGI for fear of being thought of as too out there, too consumed by science fiction scenarios. It didn't deter us or scare us from doing the kind of work that we've been doing, which is to think about the implications of frontier AI that could advance to that threshold. But nonetheless, that was the climate. And it is quite remarkable that now you can't get into a meeting without calling it it superintelligence. So times have changed. The other thing that we... One of the things that we always say in this space, mostly with the reference to the technology, is that we should be focused on the trends, not the time stamps. And the trends are quite remarkable as we all have observed over the last two and a half years in the pace of progress in the technology. But I think one of the things you're observing is that the trends are also, you could say, somewhat favorable even if they're way too slow when we think about the governance of this technology. We have governments waking up and paying attention to it. We have the firms who are building the technology, talking about governance solutions. We have the civilians, the poll... Forming our public debate now. So the the the trends are very rapid or even accelerating even in the governance space. Are they fast enough? It's very difficult to keep pace with this rapidly advancing technology. And we have spent a lot of time over the last two and a half or three years looking for the the the folks that were taking it as seriously as necessary. And I regret to say you... To tell you that there aren't nearly enough, as there should be.

[10:18] Ben Boudreaux: I'd just add that I think we are seeing lots of signs that the US government has become more aware of the type of strategic importance that this technology may bring, but it's not, I think, clear that The US has settled on a strategy nor, I think, have the frontier companies. And that might be challenging because many capabilities that we might need, like verification technology or technical expertise in government, you know, evaluation capacity, things like crisis response and communication, those all may take quite a long time to build. And by not proactively building many of those capabilities while capabilities advance and various deployments continue, that itself might be a type of choice that forecloses options we might need down the line. So I I think we're seeing signs of a greater awareness, but not yet signs of truly adapting and trying to proactively get ahead of the type of transformations we are likely to see.

[11:19] Nathan Labenz: Yeah. I think that's a great bit of foreshadowing for the strategy document as a whole because, you know, spoiler, it kind of leads up to this idea of building the capacity to commit to a strategy once we sort of see a couple more cards turned over and have a better sense of what actually is the nature of the technology and the challenges that we're dealing with as a result of its development. And I think fundamentally, we just do still have, like, a lot of uncertainty as to, like, what are the nature... What is the nature of the technology? What are the challenges that are gonna be most important? And, you know, it sort of strikes me that, like, in a way, what you're recommending ultimately is kind of like launch a lot of sub agents in parallel US government so that they can go out and prepare for a lot of different eventualities.

[12:14] Ben Boudreaux: Before

[12:15] Nathan Labenz: we get there though, let's maybe start at the beginning. So the kind of first big thing I took away from the document is, again, a really clear taxonomy of seven archetypal strategies that people are throwing around. I think listeners to this feed will, like, have heard variations of them before, but I hadn't ever quite seen them enumerated, you know, in such a succinct way. So maybe let's just start off with a quick review of, like, what are the archetypal strategies that should at least be on the table for consideration, then we'll get into the, you know, the uncertainties that will drive the decisions as to which one of those will ultimately be the right strategy.

[12:55] Matt Chessen: So our work started with trying to answer one question, which is which national strategy is the best one for managing the transition to superintelligence? And the bottom line up front there is we believe there's just too much uncertainty right now, and none of the archetypal strategies for superintelligence can be justified because if you commit prematurely to any one strategy, it forecloses other strategies and doesn't build the capabilities that might be needed later. And so we think the best way for The US to secure geopolitical advantage and ensure humanity's survival with agency is by executing a freedom of action strategy. Now this started with a paper we put out where we looked really at the fundamentals of what does superintelligence actually mean for humanity. And this is a paper called finding common ground in AGI strategy debates. In that paper, we came to the conclusion that there is a diagnosis and prescription we need to focus on for superintelligence. And the diagnosis is that superintelligence is gonna be transformative, and the stakes include decisive geopolitical advantage in humanity's survival with agency. Now what do we need to do about that diagnosis? We need to build an ecosystem in which AI and humans can coexist and humans can maintain agency,

[14:01] Ben Boudreaux: and

[14:01] Matt Chessen: we need to implement a security architecture that keeps the world stable enough to build that ecosystem. And so in in analyzing all these different strategies out there, we looked at all the strategies that exist. We looked at all the security architectures, all these proposals for international organizations and the like, and we really found that focusing on any one of these right now, we think, is really unwise. And we found that each of these strategies can be represented by one of seven archetypes. Those archetypes are dominance, and so this is where you really lead at the frontier. You suppress rivals, but this could lead to extreme power concentration and value lock in. There's a strategy around co development, and this is the idea where you would have a US led consortium that probably needs to include China. You pool compute. You'd have some sort of layered verification, and you jointly develop superhero intelligence together. This strategy also has a pathology. Again, the dark side of what happens when the strategy is functioning well, and this is where the founders' values really become the default, and the the consortium can become an arena for international competition. There's a strategy called preparedness, which is much more focused on inner... Informal coordination, you know, resilience. And the problem here is that you you... When you're sort of muddling through, you can normalize accumulating a lot of damage. There's a moratorium strategy. So this is one people are talking about a lot now, some sort of verifiable global halt or pause. You know, the pathology here is that enforcement can become global surveillance. The type of regime you need to maintain these compute caps could be quite intrusive. There's a deterrent strategy, which is where we halt our own frontier, and we also suppress rivals programs and make sure that nobody achieves superintelligence because it's too dangerous. And this has a pathology as well where you just get into this eternal standoff that really never quite resolves itself. And then there's this strategy we call continuity of society, and this is... You know, you get to a point where you realize that superintelligence is dangerous and it's inevitable. It's gonna be diffuse, and so we're just gonna have to learn to live with it. And this is focused on really a resilience strategy, But it could lead to a situation where humanity really survives as a relic and doesn't have a lot of agency. And then there's the acceleration strategy. And this is where if you just believe that danger is not close, then you're gonna tend to wanna accelerate to maximize the benefits and maximize your competitive advantage. But this also has potential pathology where the harms... You know, markets basically don't price in the harms, and we become very dependent on the systems we don't control. So we we revealed these seven archetypal strategies by developing a classifier, and this classifier basically reveals these sometimes unstated assumptions that underlie superintelligence strategies.

[16:37] Matt Chessen: And this classifier, which you can take a look at in the perspective we pushed out, it asked five questions. The first question it asked is, is danger close? Are we close to superintelligence that could be dangerous? Second question is, is coexistence feasible? Can humans actually live with advanced forms of artificial intelligence? Third question is, is restraint feasible? So can The United States or a coalition of countries actually, you know, cooperate to restrain their their progress on AI? Fourth question is, is a decisive strategic advantage reachable? Can one country proceed and, you know, get... Recursive self improvement and sort of launch itself ahead of all other countries? And then the last question is, is suppression feasible? And this is the idea of, can you actually use advanced AI to suppress other people from sort of proceeding forward to dangerous levels of superintelligence? And so the answers to all these questions reveal the strategic preferences and really result in, you know, these archetypal strategies. And so just as an illustration, you know, if you believe that danger is far off, then you'll push for acceleration because, you know, danger is far away, so you can actually maximize the benefits in your competitive advantage. Whereas if you think danger is close, but you're... You can... We can't coexistence with with superintelligence. It's just too dangerous. We don't have alignment figured out. We don't have control techniques figured out, but we can restrain ourselves. So we can cooperate with others others to restrain ourselves. Well, then you might naturally favor some sort of moratorium where you actually buy time for that coexistence research to catch up to capabilities. On the other side of the pivot tree, if you think coexistence is possible and you can get a decisive strategic advantage, you might favor a dominant strategy where you push ahead and try and get some sort of monopoly. The problem is with these five pivot factor questions, there's really no consensus on the answers to these questions, and there's no evidence that could produce consensus on the timeline these decisions demand. You may not just be able to agree even if the evidence actually is there. If you pick wrong, you're gonna foreclose strategies you might need later. So if you pursue some of these more confrontational strategies like dominance or deterrence, you can destroy the trust that you might need if you have to pivot to a cooperative strategy later on. Likewise, if you pursue acceleration, it can underdevelop all these capabilities that you need if superintelligence just turns out to be extremely dangerous. So right now, we're currently at a place where The US doesn't really have a declared superintelligence strategy. The evidence doesn't conclusively support any of the archetypal strategies, and we may be running an acceleration strategy by default. And meanwhile, the clock is ticking, and capabilities we might need for any superintelligence strategy are not getting Bill.

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[20:35]OutSystems: OutSystems is the leading agentic systems platform that enables enterprises to engineer, orchestrate, and govern AI applications on a single unified platform. Learn more and see how it works at https://outsystems.com/tcr

Main Episode

[22:35] Nathan Labenz: So I know one of the big purposes of a document like this is to go brief policymakers in various venues and try to help them adopt this framework for thinking through these issues. I'd love to hear... And obviously, I know, you know, you can't... It'll be Chatham House rules at a minimum in terms of what you can say, but I'm interested in kind of when you go around and do this, what intuitions are you hearing from people on the five questions, and how are those changing? For example, I would think that, like, is danger close has probably had a pretty significant vibe shift over just the last few months. But I'd I'd be interested in kind of all five. You know, what what was sort of status quo, you know, not that long ago, and and how is it shifting as we're seeing these, like, colorful alignment incidents and and other, you know, headline making developments, millennium prizes, and so on and so forth. I'm I'm very unclear from my, you know, position well outside of the halls of power as to, like, whether these things are really changing where people started and and how much these things are really changing people's minds.

[23:49] Joel Predd: Well, first, I just wanna acknowledge the importance of the question you're asking, which is that in an environment that is subject to this amount of uncertainty and this amount of disagreement, monitoring people's beliefs is very important, particularly in a very complex and changing geopolitical environment. So simply observing how peep... Individuals, people, and powers use on these questions is changing will be indicative of the kinds of instabilities that we could confront as actors pursue different strategies. The second point I wanna emphasize is oftentimes when we talk about this, we emphasize the uncertainty. But one of the points that Matt has made that I find very important is just irrespective of the uncertainty, which we actually may disagree on even the uncertainty, is that we just have a very different... We we simply disagree on these points. Like, you or I may have a different take on how close we are to danger, whether we could conceivably enter in any kind of negotiating restraint, whether we could fundamentally coexist, etcetera. But as Matt said, it seems unlikely that the evidence is gonna persuasively convince everyone that will be needed. So this is just reinforcing, I think, the importance of your question. I think to the extent that there is any shift, it's it's... On this, it's it's primarily on that first pivot factor. Matt and Ben, you may disagree. But this is the factor that I think is is the one where we might see the most movement. Now whether it's... Whether anyone who's in a position of power is changing on that variable, I'm I'm less certain. But it's the one that's getting the most attention. I think that the the fairly... The the more technical question of where we can co... Whether we could coexist with superintelligence, and it's interesting that we've chosen coexistence as a word because we're explicitly avoiding the word alignment because it kind of boxes into a specific set of solution techniques, and we think the more fundamental concept is coexistence. I don't know that there's been as much movement on that one. I think that's such a difficult question to answer that I... We're not seeing a lot of movement on that. And I think that the the question of negotiated restraint is so caught up in a complex domestic and international political environment that the levels of trust are are... Seems so low that if if there's been any movement, it it has probably been to grow more skeptical about that. Matt and Ben, you should weigh in on your own team.

[26:12] Ben Boudreaux: I guess what what I would wanna emphasize here is our focus throughout this work is on preserving strategic optionality or the capacity of The US to make a a smart decision, a choice about which of these strategies to pursue. And what I like about that emphasis is it's trying to make clear that we still have some agency here to make a choice, and that some of these pivots, the way they're phrased, make it sound like they're simply exogenous things happening to us. When I think in reality and what this is hopefully trying to demonstrate is that there are choices that we could be making to actually, for instance, push danger further off or make, you know, restraint more feasible or perhaps, you know, develop certain types of diffusion capabilities that actually enable a more decisive lead. And that that that, again, these are more endogenous than the... Just a glance at the pivot table might suggest. And that what we, I think, are seeing decision makers do is starting to grapple with what they might be able to do to ensure that danger is farther off, that coexistence might be more feasible. And part of the the the overarching approach is thinking about across the strategy space. Suppose you wanted to do all those things. What are the things that decision makers can build now to ensure that they have... They are using their agency now to preserve it for later as well? And so that that's sort of the the the shift that I'm seeing is more of a recognition that the AI development and diffusion is not just something happening to us, but actually something that we can shape. And I think discussions about pacing or even the the morally binding commitments that were recently announced by the White House and the like show that there are there are there are things that can be done to actually help us navigate through these these these pivot factors.

[27:52] Nathan Labenz: We should repeat that phrase, morally binding commitments, as often as possible.

[27:56] Ben Boudreaux: I I I love it, and I I will I will I will keep keep tying the the the frontier companies to to their moral responsibilities. Yeah.

[28:04] Nathan Labenz: Matt?

[28:04] Matt Chessen: Yeah. I would add, I think that this question of is danger close is actually one of the most important questions you can ask in AI policy right now. You know, I think a lot of governments are now, you know, very awake to the opportunities of AI. I think they're waking up to some of the dangers. I don't know that anybody quite has a handle on what the strategy should be for the the path to superintelligence, and so that's where I think our strategy provides people a lot of options and and the ability to have a framework for thinking about strategy. But this question about whether danger is close, you know, when you talk to people at the frontier companies, you talk to people at at some of the the organizations that are closest to AI, I think they're very clear that danger is close. Right? And and that this is something that needs... We need to be paying attention to. And when I talked to them about this, I I asked the question, you know, if you think danger is close and you think there is a lot of evidence around this, why is it not getting translated well to policymakers? Why are why are... Why is leadership sort of not awake to the dangers quite yet? And I think this is a gap that the AI policy community needs to address. If that evidence exists, the danger is really close. I think the the AI policy community has to do a better job of packaging that that up in a way that a a policymaker can really sort of take that on board. You know, who may, these policymakers have lots of different things in their portfolio. They might not be an expert in AI. They need it packaged up in a way that they can actually absorb this and update as, you know, different information develops. We can't just wait for crises. I think the hugging face, you know, incident was sort of a a mini crisis. And if we're gonna rely just on crises to sort of raise that level of awareness, I I think we can do better in the AI policy community.

[29:41] Nathan Labenz: So the dangerous close one, people seem to have moved on. I think, Matt, you said a minute ago, like, if we don't think dangerous close, we might be playing an accelerationist strategy by default. That seems to be sort of falling at least somewhat out of favor. Although, you know, as you well know, there's not a consensus yet. We're talking on the day when Muse Wonder was just released, not to forget Muse Wonder full, and it, you know, is coming from a a company that doesn't seem to be too concerned about any near term danger and certainly, you know, doesn't seem to be inclined to engage in much pacing other than, you know, doing whatever kind of baseline consumer safety testing they're known for in all their products, which, you know, we should, I think, have full confidence in. But that was somewhat of a joke in case that wasn't obvious. I'm... I would have said that it seems like we're playing a dominance strategy by default because we've, like, been in this sort of chip denial mode for a couple years. And as you may know, I'm, like, definitely a skeptic of that policy. One of the reasons I'm a skeptic and one of the things I'd love to get a steel man from you guys on is decisive strategic advantage or decisive lead. Is that really possible? Who makes the best case for that? I always look back at machines of love and grace, and I'm like, boy, you skipped a lot of points there along the way, Dario, from, you know, all the, I love the first half, all the diseases we're gonna cure in the better mental health and, you know, all these new genius outputs, but then it's like, and something something eternal 1991 will, like, put China in a box, make him an offer they can't refuse. That's all we need to say about that. Like, best thinking on what a decisive strategic advantage could plausibly look like. Who articulates that best? Sorry if that's an impossible question.

[31:38] Joel Predd: Yeah. So one of the reasons this is a challenging question to ask is is that you have to add... Answer the scope of this decisive strategic advantage. And I think when you talk to people that wanna advocate for that strategy, they often may have a different source of advantage in mind. For example, oftentimes, it brings to mind a decisive strategic advantage and enduring lead in the technology itself. And then in that case, you are you are prompted to ask the question of whether do you merely mean that you maintain a consistent lead, or are you preventing your rivals from ever crossing some specific hard to identify threshold? Given that this technology is driven by economic forces as much as technology forces, It does seem a little bit hard to believe in some cases that you can prevent an adversary from crossing a given threshold, although not impossible. That is... That does seem to be a a hard bar that may be the one that you're thinking about. But given the pace of things, it it feels less difficult to imagine that you could maintain, like, a steady lead over time. But there's other domains in which you maintain a decisive strategic advantage economically. And here, the question is whether you can translate your leadership in the frontier to consistent economic gains that would underwrite growth in your economy and scientific innovation. Interestingly here, the the the things you would need as a as a society, as a nation to gain gain and maintain a decisive economic advantage as our colleague, Toby Sitzma has written about in a separate paper we we we could refer you to, are quite a bit quite a bit different. They aren't just the techno... Underpinnings of the technology. But then in another domain, as you would imagine, will be a decisive strategic advantage in the military domain. And not unlike the economic source of advantage, you would need to translate this into weapon systems and concepts of operation and a strategic doctrine, all of which don't come for free. Now to the extent that a decisive strategic advantage in an in an era of very advanced forms of AI kinda mean all all of these things at once, that's where the the... Where the nug is.

[33:47] Nathan Labenz: It just seems super super hard to me. Like, I always kind of... I actually had a chance to play a tabletop exercise, you know, but pretty fast takeoff essentially scenario, and I got assigned China. And I was sort of... Like most players in the game, at the end of it, we were kinda like, well, that all happened really fast, and we kept sort of being a step behind events. And, you know, as in these... As usual in these table... Tabletop exercises, we were sort of trying to simulate what we thought the real actor would do. So I do feel like there is at least some chance that everybody will kind of be just paralyzed by indecision to the point where we get to some outcome before anybody really plays their cards. But then I also did feel like, as China, I have a lot of cards to play, and it was really my restraint and my kind of, you know, desire to, like, seek a constructive deal first before I, like, really start playing super hardball that was limiting me. I guess I I just have a hard time seeing how it's realistic that we can tell... You know? And I don't know... Really know what the win condition is supposed to be. I mean, in machines of love and grace, it really does sound a lot like regime change at the end. You know, the... I think the sentence is almost verbatim, like China will convince China that it's better to give up competing and, you know, instead, like, join our coalition to share in the benefits. That doesn't sound like the sort of thing where we're making nice. It sounds more like we're kind of, again, giving an offer they can't refuse. I just... It feels like you can only get there to me if timelines are super short. So I guess maybe the question there is, to what degree does the plausibility of a decisive advantage depend on a super fast, like, FOOM like scenario where the gap kind of widens long enough to take some pivotal action before the other side can even do, you know, nor... Like, things I'm considering, right, as playing China. Like, the... All these TSMC fabs, you know, it's... They're awfully nice. It'd be a shame if something happened to them. Right? They're, like, right in my corner of the world. How do you think about, like, what determines this? Is it about timelines? Is it about something else? I just still really struggle to see it. I can

[36:10] Ben Boudreaux: try to say a few things here. I mean, I think Joel already distinguished ways in which decisive advantage might appear differently in different domains. And so The US might have decisive advantage in one domain while China has it in another. And this pivot is trying to roll them all up, but that might be not possible or at least they might not all be commensurate in in some, like, clear way. But I think your question also suggests that, you know, the dominant strategy might really differ based on what kind of temporal windows we're talking about. I mean, it might be really accelerated by a by a firm type of fast takeoff that would really extend, you know, a leader's lead over the rest. But of course, also in a firm, you need to worry that you can maintain control. Right? And decisive advantage is only good insofar as the the the the leader can continue to lead the AIs. And, you know, that might also be in jeopardy in a fast takeoff situation if, you know, who... The the governing leader there is not prepared to to control and steer the technology in ways that that serve its interests. So, you know, having more time might also be necessary to really ensure that that decisive advantage can be implemented. Right? And and again, the Fume case might... Just might not give you enough time to to build in all the control and steerability mechanisms you might need to really leverage it. But this also makes me think that dominance like some of the other strategies might not be something that even if you commit to it and execute it, that you can ensure will be sustainable over the long run. And it might be the sort of case that, you know, The US is is pursuing a dominant strategy, maybe even achieve dominance for a period. But at at a certain point through, you know, model theft or distillation or diffusion or or many of these other processes, you know, you might you might need to pivot, might need to rebalance or or or move to a different type of strategic paradigm. And, you know, that this choice among the strategies might, again, never actually be just fully complete for all eternity and is gonna be perhaps a continual process of adaptation as the technologies change and the geopolitical landscape changes.

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Main Episode

[41:05] Nathan Labenz: So how about... Just to lay my cards on the table, I'll say that I feel like if humanity were wise and we were really up to this challenge, it seems very intuitive to me that the right strategy would be some sort of codevelopment with a moratorium as basically the fallback. In other words, hey. We don't know if danger is close or far. If it's close and if there are scientific problems that prove very difficult to solve, then we're gonna need to agree on some sort of moratorium. And so let's work together until such time as we have clarity on that, and then we can move into that mode if we need to. It is sad to me that this... It seems to be, as you've discovered, Joel, I think so many people are growing more skeptical of over time. I don't know to what degree you guys wanna put a thumb on the scale in terms of what you think, you know, humanity would do if we were wise, but I'm interested if you would generally agree with my take there. And then, you know, I'm interested to unpack, like, why is... Given that it seems like if anything, it looks like danger is more likely all the time. Why are we not trying to figure that out, and why are we instead growing more skeptical of it?

[42:17] Matt Chessen: Yep. I'll I'll jump in on this one. So I would agree that if humanity was united and coordinated that, you know, some sort of co development strategy with a moratorium as a fallback might make a lot of sense. But the fact is humanity is not a unitary actor. We're not, And there's very real differences in our political systems between, you know, authoritarian governments and democratic governments. And so, you know, if you are betting on codevelopment or betting on moratorium, you're betting on restraint feasibility, and you're right betting on the other side actually agreeing to and abiding by the rules that are set up for these restraint systems. I think one of the big concerns for me, because we do have this difference of of values and and perspective on the right political systems, is that if you go with some sort of co development strategy with China, you're really locking in nondemocratic founding values. And so you're really locking in those authoritarian values as persistent for humanity over time in the AI systems that are being built. And so I think we really need to think about whether that's something that we're interested in. Do we wanna lock in those values, or do we think that enshrining democratic values in AI systems is actually a better outcome for humanity?

[43:31] Joel Predd: Can I come back to your dominant strategy question? We left some work on the table there. We've start... We have... There's a paper that I would refer you to by our colleague Phil Root that's on our website that's tried to do the game theory under some of this. And it begins with a premise that the race to these advanced forms of AI is a kind of prisoner's dilemma. And under some reasonable assumptions about what the game looks like, one of the initial insights is that the... In a competitive environment, the default strategy that you are led to is one of sabotage over cooperation. That is to say, even though some form of cooperation produces higher collective gains, over time, both players are... We get into an equilibrium in which mutual sabotage of each other's AI progress is the equilibrium strategy. Now one of the things that disrupts that, however, and which leads to a dominant strategy in which AI is used to create something that we have called a wonder weapon in some of our other work. That is in which AI is as an engine of scientific and military innovation creates a new capability that allows us to hold others' AI programs at risk. And if AI or superintelligence as it were were to yield such a capability, this isn't when... This is the circumstance in which a dominant strategy would work. And so one of the reasons to... One of the reasons that I think your intuition about how unbelievable that is is because you're stipulating a capability that would give... Would be given birth by the AI itself. But that does end up being the equilibrium strategy under those assumptions. It's worth checking out.

[45:01] Nathan Labenz: Can can you put a little more meat on the bones of, like, a wonder weapon? What is that? What does that look like?

[45:09] Joel Predd: Well, is it is exactly the right question, and it's one that I don't think I'm gonna give you an answer that would be very satisfying. So wonder weapons in the history of military innovation are very rare. What we mean by that is a military capability that very rapidly and discontinuously disrupts the military balance. Most of the time, military innovation is one in which the disruption comes through adoption. You have to develop force structure. You have to develop concepts of operation. You have to develop doctrine. Very rarely is one machine gun gonna change the game or one tank or any any other in the vast history of mill... Innovation, any one military innovation gonna change the game. But there are instances, and the most canonical instance is nuclear weapons. It was an example of a nuke... Of a a military capability that simply by its existence offset the military balance in a way that was disruptive for stability and deterrence and all the rest of it. So in this context, we're stipulating something similar. What exactly would it look like? Some have argued, I think somewhat compellingly, that mythos or mythos class models and their ability to very rapidly discover and exploit cyber vulnerabilities was the first one year weapon we've seen of the AI era. We can speculate as we imagine fully autonomous cyber weapons, but those would be a kind of wonder weapon. Simply by their mere existence, and if they were in possession of a state actor, could essentially overnight, perhaps not literally overnight, but very rapidly and discontinuously shift the military balance. And here, in this game theory work that I'm referring to, I'm speculating on the idea of an AI enabled wonder weapon that would be used to endure... Ensure a stable advantage in Frontier AI that you could then translate into a strategic... Decisive strategic advantage, probably in the cyber realm.

[46:57] Nathan Labenz: One of the obvious fundamental challenges of all of this is how much we have to imagine and, you know, the almost canonical impossibility of knowing what the AI that's smarter than us is gonna invent. So that definitely makes it tough.

[47:13] Joel Predd: So I do feel like that I think is really important. One is that... And I don't mean to interrupt you, but this... Coming back to this is important to what you believe. That is a remarkable finding we have in... And we've we've run, you know, quite literally hundreds of tabletop games on these kinds of dynamics, Crisis simulations by colleagues like Rich Gervin and Greg Smith and Sina Begley and then US versus China strategy games by Abby Dahl and Karl Mueller. There's a lot of reports on the Rams said said... Website about this. But one of the recurring findings that we get over and over is the degree to which players' ingoing priors about these kinds of pivot factors will determine the outcome of their... Of the outcomes of the games. If you go into one of these games and someone is a believer, so to speak, in a decisive strategic advantage, they can connect the dots in their own minds, or they believe that danger is close or whatever whatever, you get a very different outcome in the game... In these games than you would otherwise. And I think that's a that's a remarkable point. It's the... Your perceptions about the facts of the matter matter as much, if not more, than the actual facts of the matter. And I will say this. That was probably true of the nuclear era in a way that I I I don't appreciate because I didn't live through it. But it is it is... Despite the massive uncertainties that existed in that era, there were some basic uncertainties that we lack in the current environment. Like, the scientists at Los Alamos had the physics and the mathematics to run a calculation that gave them confidence that they could proceed with the Trinity test without igniting the atmosphere. Lot of anxiety, a lot of uncertainty in that, but they can at least run a calculation that gave them confidence in their perceived. We don't have a similar science to run those kind of calculations right now on whether dangerous is close, whether we can coexist with superintelligence, whether decisive strategic advantage is a thing. We are very much anchored on for better or worse, probably worse, the perceptions on these kinds of load bearing factors.

[49:09] Nathan Labenz: And when you say for better or worse, could you give me a little sketch of, like, again, what the policy making, you know, military leadership community tends to come in with? I mean, my... It feels like given how only relatively recently, you know, so many people have started to take this stuff seriously at all, I would guess that most people have had the background assumption that, like, no. You're probably not going to have a superintelligence spit something out that, like, makes a nuclear arsenal irrelevant. And I I take that that would kinda have to be the level of power that it would have to achieve. Right? Because if not... Like, the the cyber weapon thing always kinda confuses me too. There's a bit of a digression, but, like, we have one war going on in the world right now where there's two countries with a lot of great programmers, and it seems like, you know, they're fighting with drones a lot, and there's definitely a sort of technology and AI story to that to some degree. Although, I don't have a great sense for how AI ified the drones are versus just, like, flown by kids in basements. But, like, the cyber story there hasn't been that big. Right? So I have a very hard time seeing this cyber story getting to the point where I can, like, somehow take the adversary's nuclear arsenal offline and then be able to do whatever I want. And if I can't do that, like, then they still have, a pretty strong hammer that I have to be very threatened and and sort of constrained by. Right? Am I am I making sense?

[50:45] Joel Predd: You're exactly right. Now, of course, Nathan, as a good host, you're gonna put... Try to put us in a corner and take a position on this. It's unlikely that you're gonna get us do that because we're slightly too You're trained for Yes. Exactly. So we're... We will continue to talk about in terms of assumptions and uncertainties. However, I'll I'll agree with you in some sense. Superintelligence, if we were a general intelligence, we'll be born in a world with nuclear weapons. So we have to... It would be very different if superintelligence was born in in 1930 or whatever else. So I think this is related to one of the failure modes of a dominant strategy is that you have to have a theory how you will not escalate to a catastrophic war in which you're trying to permanently suppress your rivals. So this is a substantial risks of of that strategy. Another point here is is is the pace of things, and I... Matt and Ben should weigh in on this. I'll be curious of their take. But we also have to be careful about what we mean by stability in this context. Like, we we enjoyed decades long period of stability in the nuclear era, but... And so we're we're kind of looking for a similar multi decade long source of stability in the AI era, but it's unclear that we will ever enjoy that kind of stability. And we may go through these different phases in our AI strategy, but more rapidly. I actually think that this this may... The freedom of action strategy provides a... An interesting lens on the way of looking back on nuclear strategy. And our colleague, Anne Stickels, is gonna be writing a substack post that may actually come out this week on this. But our strategy for the nuclear has actually shifted over the time quite remarkably. There was a period of time in which we were

[52:21] Ben Boudreaux: sprinting,

[52:22] Joel Predd: and that was called the Manhattan Project. There was a period of time in which we had a temporary monopoly, about a four year period, in fact, in which we dominated, and we used it to our geopolitical advantage. In that period, we were quite anxious. Some of us were about the dangers of nuclear weapons and of a nuclear monopoly in particular, and some of us proposed Some of... Some some proposed something that resembled a moratoria. We didn't proceed with it, but we eventually entered into verifiable agreements with our rivals. Later, we used something that looked like a suppression or deterrent strategy to prevent the the technology proliferating into Iran and North Korea with limited success. And now the... And then the era of the nuclear era continues, and we... It could... It continues to proliferate, and who knows what's coming next? So one reading of the history of nuclear strategy is it was a kind of freedom of action strategy. It's not one in which any policymaker ever stood up and built optionality as we're proposing, but that's because they had the the freedom... They had the luxury of time to build these kind of strategic optionality, and we won't have that here. So I think this freedom of action strategy, its diagnosis is is that unlike the nuclear era, you may still transition through these different strategic epochs. We're gonna... In order to navigate that, we will need to prebuild the elements of a security architecture to make the transition.

[53:46] Nathan Labenz: So we beat up the dominance one a bit. I definitely do wanna get to the recommendations that you're making in terms of what we should be investing in now to maintain freedom of action next. But is there any other kind of nuance around any of these other strategies that you think we should be spending a little more time on? Throughout our discussion and throughout our paper, we use the term coexistence to represent humans living with advanced AI maintaining agency. Alignment is often treated as a catchall for coexistence, but it's just one path among several alongside control mechanisms, architectural schemes, and methods not yet known. So we use coexistence throughout to represent this concept of humans living with advanced AI with agency. Let's talk about the archetypal strategies and why we think focusing on one particular strategy is unwise. We looked at all of the strategies, architectures, and proposals for international organizations that were out there, and we found that they could be represented by seven strategy archetypes. Each of these archetypes has real advocates in a coherent win state, but they also have a pathology, which is the dark side of what happens even when the strategy is working well. And so, for example, for a moratorium strategy, you have Alisa Yatkowsky and Mary advocating for some sort of pause. Now their win state is that this pause is where, you know, The United States and its rivals would halt capability development and buy time for really that coexistence work to catch up. But it does have a pathology that you're giving up some of the benefits of advancing AI. But this enforcement mechanism that you would need to enforce some sort of global pause could harden into some sort of global surveillance regime. So you have Schritt, Hendrickson, Wong proposed a type of deterrent strategy with MAME.

[55:35] Matt Chessen: The win state there is that The United States would prevent dangerous AI, basically, by preventing it and creating some sort of moratorium at home, but also suppressing it internationally, including through force if necessary. The pathology there is that this might harden into some sort of standoff that never really resolves into into any sort of peace.

[55:55] Nathan Labenz: We've cert... Certainly, the audience will be familiar with your Eliazers and their calls for, you know, data... Kinetic strikes on data centers if and when ever needed. And Dan Hendrick is a past guest on the podcast, although it was actually before they came out with the main document that that I talked to him. So I think, sorry, these, like, authors are definitely... Many of them familiar names. Yeah. Continuity of society. I I planted a victory garden this year for the first time in my life. The squirrels got most of it, but it did reinforce for me the pathology of, you know, just trying to hunker down in some sort of, you know, subsistence mindset and and scrape by or, you know, eke out a survival in the in the case that the... We can't avoid creating them and the coexistence is not super feasible.

[56:45] Joel Predd: The other insight that I wanna... The other nuance I wanna give you, Nathan, which is that we really see this as a strategy for America less to the US government. Sometimes when we talk about that, we elevate the importance of the US government because there are certain nondelegable responsibilities that are only available through the authorities that the US government has. For example, you know, maintaining the viability and the integrity of the nuclear deterrent is inherently a US government responsibility. Defending US national security in general is a nondelegable US government responsibility. But there's a lot of things that are called for in the security architectures that we propose that we might too quickly assume our government responsibilities, but are really opportunities for businesses and civil society across America to stand up. So for example, one of the very practical things that we think would be needed to support a freedom of action strategy would be investments in highly secured data centers. In principle, the US government could support that, but in practice, we could create a market for that. Firms across the country have a... Should have a demand for that. We could rely on the market to create that. Another investment that is called for that you could imagine a government response would be in incident response and written a reporting in response. Again, you can easily imagine a government there... Role there, but also you can imagine there being a role for civil society to fill that function. But one of the virtues we think of this freedom of action strategy is it exposes opportunities, not just for the government to marshal its forces, but all of American businesses and civil society.

[58:21] Nathan Labenz: Yeah. That reminds me of my grandfather who had tuberculosis and didn't go to World War two as a result of his illness, but nevertheless told stories of how we won the war. And it was like victory gardens and, like, carpooling to work to save on rations. And the point was, like, everybody has a role to play in such a... An effort, you know, national effort as that. So I I like the framing of kind of reminding everyone that this is not a, you know, highly top down civilization that we've built for ourselves and that there's a lot of things that people can and should be doing without necessarily being... You know, having to be told to do so without waiting for anybody else to give them any orders. That is a big part of the strength Yeah. Of the American system.

[59:10] Joel Predd: I I think that's especially true for the elements of our strategy that calls for investments and resilience, and we see that already. And, Ben, this is something I know you thought a lot about, but you wanna weigh in on that?

[59:21] Ben Boudreaux: Yeah. I mean, I just gotta say in that vein, you know, us managing our dependencies on AIs in terms of, you know, being able to operate critical infrastructure, being able to develop and implement strategy and not personal levels. Right? Being able still to think through ourselves and maintain our our individual level agency. I think all that is is part of implementing basically any of these these seven strategies. You know, e e... Even things like a moratorium and the like will really only be feasible if in... We're able to live without advanced forms of AI. Some of what we need to do is to preserve those human capacities, again, at these individual levels and at the collective levels. This relates to why we use the terminology of coexistence as well and not just alignment, which makes it sound like we're just, you know, training the models to conform to some set of established values. We're really... What we're envisioning is a process of adaptation and and perhaps even coevolution, where where just as the AIs are becoming more capable, we're more capable in living with them and, again, being able also to perhaps to to to live without them as as being a a key way to to again manage some of some of the vulnerabilities that come from from those dependencies.

[1:00:30] Nathan Labenz: Safe to say the Overton window is wide open these days. Let's maybe just zoom out and do the, like, seven objectives and and kinda step through them one by one, and you can, you know, provide as much commentary on each one as you want. But I think it is worth going one by one through the high level objectives that you set out. Great.

[1:00:50] Joel Predd: So there's there's... We should talk to the different objectives and the strategies. Thanks for the invitation to do that. This lead... The objectives group into two categories. The first thing is that we need to make investments to build, and the second thing is is is the operational elements or the things that we need to actually do. And these are operate at different... Slightly different time scales, so it's worth, like, unpacking them. The first thing we need to build is the human AI ecosystem. And, Ben, you should talk to that. Sure.

[1:01:17] Ben Boudreaux: And

[1:01:17] Joel Predd: this relates to what I

[1:01:18] Ben Boudreaux: was, in fact, just describing about ways in which humans and advanced forms of AI can coexist and the dehumans can continue to thrive and maintain our agency in that context. A lot of this I do think is the traditional safety research. Think our analysis is we haven't invested nearly enough in some of the alignment and control techniques. We will continue to need new evaluations, ways of identifying when these advanced models can tell when they're being evaluated and perhaps are sandbagging and we're not able to really understand their capabilities. We need to build additional tools that assume we're dealing with untrustworthy models and ensure that we have the containers and security environments that for instance prevent them from breaking out of sandboxes and the like or that we can contain their spread. Some of this is ensuring that our institutions remain at the center of, like, high stakes decisions and that humans are still calling the shots. We're still the ones steering the future. We are still the actors capable of making decisions that affect the various pivot factors, like whether against oppression might be feasible and the like. And and this I think it requires kind of a continual process of humans reflecting on the decisions that we want to maintain in human hands. And so for instance, some decisions in the broader discourse about, for instance, pacing or issues related to AI enabled research and development, you know, these might be core decisions where we really wanna make sure we have sort of human checkpoints where, you know, even in AI seeking to persuade a human, you know, that that that those types of circumstances are are are caught and humans, again, are are making reflective decisions and and deliberating with others in terms of just just how much decision space, how much autonomy, how much... How how many authorizations we wanna delegate to to these models. I could talk a lot more about any of that, but I I think that's probably enough for now.

[1:03:13] Joel Predd: So one of the things that Ben Ben and Alvin have written about that I think is really interesting is the is the idea of an agency eval. Yeah. A model... A lot of the eval's evaluations that we hear about are really evaluating the performance of model on discrete tasks, including discrete tasks that should concern us if the models are capable enough to do them, like engineer a bioweapon, for example. But one of the insights that Ben and Alvin had, and you should speak to it if you want, Ben, is we should be evaluating them in the context of their deployments for circumstance chances in which we may cross some threshold that we've effectively, perhaps unknowingly, given up some agency.

[1:03:51] Ben Boudreaux: Yeah. And very briefly, it's

[1:03:52] Joel Predd: mapping

[1:03:53] Ben Boudreaux: the human decision space and then seeing what roles AIs play when they enter into those decisions, either by, you know, disenfranchising various humans, by persuading humans, by controlling the agenda over which the humans are deliberating. You know? And these are, I I think, not just not evaluated typically now. They're gonna be hard to evaluate going forward because individual rational decisions to delegate something to an AI may not be easily seen. And what you need to do is track these decisions to delegate to AIs over time and in in a more systemic way, where you're really looking at trends and trajectories rather than just like single discrete decisions. You really have to, I think, think longitudinally about the, again, the role humans and AIs are playing in these, you know, various high stakes decisions like, for instance, whether to deploy a new model or whether to, you know, let, you know, an AI, you know, develop the next fab or or something of the sort. So, yeah, this is this is stuff that we're we're we're currently ongoing, and hope to have a paper out on that soon.

[1:04:54] Joel Predd: Another big objective in this strategy is to adapt our legacy national security architecture. Unfortunately, the old problems aren't going away. So we're gonna have to adapt our counterterrorism enterprise for terrorists that are in possession of very advanced forms of AI. We're going to need to adapt our intelligence apparatus, both to embrace the opportunity for artificial intelligence, but to anticipate how our adversaries will do the same. We will need to adapt our conventional military for the new demands that'll be placed on it and opportunities that'll be given to it. Yes. We may be using our conventional military to defend US interests in the way that we've grown accustomed to, but we also may ask... Give it new missions to enforce an AI arms control agreement, say, or defend interest in new ways. One of our colleagues, Zach Burnett, has a... Leading a big project on what a new conventional military and AGI air would look like. We'll need to update our nuclear enterprise. It's unlikely, as we may have referred to earlier, that nuclear weapons are going away. But the way we think about nuclear weapons and our strategy, what it means to have a resilient nuclear architecture could be quite different. My colleague Edward Geist is thinking about those kinds of question. Another one is we'll have to adapt our biosecurity enterprise. And there's a big team led by Steph Guerre at RAM that are thinking through that... What that would look like. So all of our all of our, what you might call, legacy, crazy superintelligence, security architecture elements are gonna need to be adapt for the very new challenges and opportunities that AI will afford them. But we're also gonna need a new element, new security architecture for AI itself. We need an AI security architecture. And, Matt, do you wanna speak to that?

[1:06:42] Matt Chessen: Sure. So we think that we need a a security architecture specifically for AI. And this is a, you know, a set of technical and institutional capabilities that both keep the technology safe but also the world stable. And I think the really important things to understand about this is that, you know, traditionally, a security architecture is something that's really the domain of governments to establish. But AI is quite different in that a lot of the elements for a security architecture are actually gonna be built by the private sector. So that's quite different. And then I think another thing to understand is that what we're proposing with the freedom of action strategy is actually not one specific configuration of capabilities. What we're proposing is actually building the baseline capabilities to run a variety of different strategies. We feel like, you know, emerging events or even this evidence resolving in a particular way, you suddenly find out that, you know, AI is very dangerous and the danger is very close, or you find out that coexistence isn't possible, you may have to reconfigure your security architecture on the fly. And so what the freedom of action strategy calls for is really taking a look at the capabilities that are needed by all of the different strategies and then also taking a look at the capabilities that really have a very long lead time. They might not need be be needed by every single strategy. So, for example, verification technologies are not needed by every single strategy, but they have a very long lead time to set up. And the freedom of action strategy will call for investing on all of those so that you have the flexibility later on. If you decide to run... You know, you're running a preparedness strategy and you suddenly realize you need to flip the moratorium, you have some of that infrastructure set up and built already. Really, we think the most important thing to build right now is the navigation capabilities. These are the set of capabilities that let you understand where you are in the in the strategy space. These let you understand, you know, what is the the current capability of different models? What is the capability of Chinese models? Where are they? You know, what what sort of visibility do we have in the... In... To compute and where the compute clusters are? Without understanding the answers to these, you really can't answer some of those fundamental questions like, is danger close? So one thing that occurs to me with this whole strategy, and I I think this is, like, somewhat trendy in the policy world right now in general, perhaps because downstream of COVID, we realized that, like, going all in on efficiency creates resilience problems, and we... We've maybe, like, partially learned a little bit of a lesson there. You know, there seems to be now at least some appetite, maybe only at the think tank level. I don't know if we feel this at congress yet. But some some appetite to, like, invest in things that... In portfolios, let's say, where some of the individual investments kind of by definition will prove to be wasteful in the end. But because we don't know, we wanna make all these investments now so we have the option available to us later. I think that's, you know, hopefully a fair kind of echoing back of, like, a a core part of the strategy.

[1:09:33] Nathan Labenz: What what among these things are going to be expensive? Like, it's it's not... I think that... It all sounds good until we start looking at the bill maybe. Right? So what parts of this will be the sort of big bites that will have to be sold to people on this kind of, you'll be really glad if you do, in fact, need it even if you don't really think that you are going to right now. What are the things that are gonna be hardest to sell due to price tag or otherwise?

[1:10:00] Joel Predd: I think this is one of the most important critiques that we've received of the freedom of action strategy is the unwritten price tag. One of our colleagues told us in the... When we were... This work was being peer reviewed that a a strategy without a price tag is just a vision. Our response to this...

[1:10:14] Nathan Labenz: Though I think they were using Claude to generate that critique, by the way. That sounds very Claude like.

[1:10:19] Joel Predd: It does.

[1:10:19] Nathan Labenz: I'll put that in Pangram after

[1:10:21] Joel Predd: the Yeah. That's right. That's right. But I I think that they... Both they and Claude, if that was the case, are right about that. Our response to that, though, is is is twofold. We're we're up on a... On the cusp of a transformation. And I I gotta be honest with you. Any of the archetypical strategies that we put... Will be expensive. With the exception of acceleration, although the expense may be borne out in the downsides. But in all the cases, all these strategies are expensive. We're talking about a, you know, a society level reorientation, perhaps on the scale of reorientation that we had with the dawn of a nuclear weapons enterprise and the temper... Confronting the Soviet Union or the the reorientation of our society after nine eleven, in which we pivoted to to counterterrorism and counterinsurgency. So I think to a to a a very first order estimation, all of these strategies are expensive in the resources, attention, and focus that they were required. And the differential between any one of them and the freedom of action strategy is dwarfed by that, between any of them and the status quo. I will say that most of the things that our freedom of action strategy would call for, you would consider them to be no regret. Like, in all these strategies or most of them, not all of them, but most of them, you're gonna need to be able to maintain some visibility and compute and govern the flow of compute. All of these strategies will come require some situational awareness as Matt referred to. All of them are gonna re... Require investments in resiliency of our critical infrastructure, of our national security systems, of our bios... Bio... In a biosecurity sense. These are all gonna be required, etcetera. So there's very... Unfortunately, it's expensive, but all these strategies are quite expensive. Better, Matt, would you wanna add to that?

[1:12:16] Matt Chessen: Yeah. I would just note that not all of these costs fall in the federal budget either. I mean, you have companies. You have other actors who are funding much of the alignment, evaluation, and security work. And so it's not just something where you go to congress and ask them to write a check. That's why this is actually like a national strategy because there's lots of actors that play a role in executing the freedom of action strategy.

[1:12:37] Nathan Labenz: So do I understand correctly that basically the idea is like any of these strategies, let's say it costs one x to prepare for all of them simultaneously now is maybe like 1.5 x. And so the delta... I think I think you said, like, the difference between doing one and and maintaining freedom of action is less than the cost of, like, doing any one thing. Is that a fair way to think about it?

[1:13:05] Joel Predd: I think it's a reasonable way to think about it. We haven't done those cost estimates. Where our research is going next is to think about how we operationalize the freedom of action strategy. What is it actually gonna take? I don't know if we're gonna come down to budget quality estimates that we can take to congress, but, like, what is it gonna actually take? Who needs to do what under different assumptions about the division of responsibility between governments, businesses, and civil society? What are our options based there? But intuitively, what you said is... Makes sense. The difference between the status quo in any one of these strategies is dwarfed by any one of these strategies and freedom of action strategy. And I don't have the exact number for you, but they're all gonna require a reorientation of resources, our attention, our politics, our focus across the board.

[1:13:52] Nathan Labenz: How are guys using language models in your work? Like, it it strikes me that if you have a fairly detailed sketch, you can probably get agents to run that budget question to ground pretty effectively these days. Right?

[1:14:06] Joel Predd: Matt, you should speak to this. Matt has been very forward leaning on on this class of problem.

[1:14:09] Matt Chessen: Go ahead. Yeah. So we actually use large language models quite a bit, mostly for organizing and remixing information rather than doing a lot of that primary research. We've actually found them incredibly useful, especially, you know, the project knowledge bases for keeping all of our various memos and all of the things we're compiling together. You know, on that particular question, we actually do have a a catalog that we call the security architecture catalog, which is a 106 different capabilities that are needed for building out this ecosystem and the security architecture. And we used LLMs quite a bit in in gathering this information and also, you know, sort of compiling it and organizing in a way that that humans can actually digest it. The problem is that, you know, some some of these things, there actually is a primary source. There's a paper out there. Often, we drew information from Rand's sort of sister center cast, and, you know, there's paper that can actually tell you exactly how long it's gonna take to build, say, verification technologies. But often, there isn't anybody who's actually ever done that study in that assessment. So that's one of the things we're gonna be working on over the next couple months. We've got Sprint going on to take a look at this security architecture catalog and try and figure out how long are the lead times for each of these capabilities, what's a rough magnitude of cost for these different capabilities, who's working on it, who should be working on it, should the US government on these capabilities, those types of questions. And we'll be publishing that information after this Sprint is complete.

[1:15:31] Ben Boudreaux: Yeah. What I what I might add on this is that the fruit of action strategy provides, like, a set of decision criteria, which you might use to look at a policy proposal or some of these capabilities from the security architecture catalog Mac was just talking about. So, like, it it it basically lays out a set of questions you would ask for any given policy proposal. Like, one main question is, which of the strategies among the archetypes does a given proposal foreclose, and and how does it do that foreclosure? Right? And that might be, like, a real cost that's not associated just with, like, the economic cost of the capability. Right? If it prevents you from having a choice that you might need later, well, that's that's a, you know, a a significant cost that needs to be factored in. And so that that issue of foreclosure is something that, you know, I I I found, you know, engaging with LLMs and seeing, you know, ways in which given policy proposals might might foreclose different strategic choices. You know, I I think that's, like, important. And, also, the extent that those options are permanently foreclosed, you know, that they... Your your choice to pursue a certain policy was irreversible versus whether or not your policy could be reversed, you know, in time. And, you know, you you talked about, for instance, sport controls as an example. Well, that's the sort of thing that, you know, might... You know, one might argue had foreclosed certain forms of co development or the like. But also, might argue, well, you know, there there there was nothing, you know, necessarily permanent necessarily in that foreclosure. These these could be reversed or or shifted in certain ways. So, you you know, so that's sort of one criteria you might look at, like whether or not a given policy forecloses other strategies and then the costs associated with it. But also related is what strategies does a given capability open up. Right? And that's what Matt was just discussing and some of the the things Joel was talking about. The the no kidding, most important things we ought to be prioritizing because they help us advance the entire strategy space, right, across all seven strategies. And one way that a given policy or capability might do that is if it provides information. Right? If it helps us better track the pivots or provide information about capabilities or, you know, what China's up to or the like, that that that all, you know, provides provides real value of given capability, you know, that that might offset some of the costs associated with the capability. Right? If if it just allows you to make better decisions down the line, well, I mean, those those sort of upfront costs might might be, you know, very much worth it. And then I I think a a last sort of criteria that grows out of this way of thinking is is what does it signal to the rest of the world, other geopolitical actors? And and, you know, that might be where, again, you know, the the... Your point about export controls is relevant. Maybe the real cost associated with that was it signaled that we were not interested in some forms of partnership or or something like that. You know, maybe. But are there other things that we could do to signal that we're interested in in various forms of partnership if the time is right and we have the right verification mechanisms and we're not worried about, you know, someone defecting from their agreements and the like. And so, you know, those those criteria looking at, again, at, you know, foreclosure of strategies, looking at what strategies are opened up, looking at what information we might get from a given policy, and then and then lastly, what does that policy signal to other geopolitical actors? I think for for me, that's really how I think through, like, what what should we be prioritizing, what policies make sense, what are the costs associated with it, and and the like.

[1:18:42] Nathan Labenz: Do you have any other thoughts? I mean, so a couple quotes that I pulled out of the report were on the topic of international relations and signaling that The US should, quote, signal to hold deterrence and cooperation credibly open at once. Another quote that I particularly appreciated given my relative dovishness is that we should weigh carefully any action that would harden a rival's resolve. I think my sense having spent a couple weeks and, you know, by no means a China expert, but the take I got there from people this summer was like, yeah. These chip restrictions are like hardening our resolve. That seems to be happening at least somewhat. I do agree that, you know, it is nice. You can reverse that policy and start selling chips again or, you know, come to some new agreement if if circumstances or attitudes change. How would you think kind of high level though about, like, this balancing of deterrence and cooperation at the at the signaling level? How do we

[1:19:38] Ben Boudreaux: how do

[1:19:38] Nathan Labenz: we even know if we're getting that right?

[1:19:40] Matt Chessen: Yeah. That's a great question. I mean, I think, you know, the idea here is you wanna demonstrate capabilities selectively. You wanna share the existence of capabilities selectively. Often, you're gonna wanna withhold your intent behind capabilities. Sometimes you're gonna wanna signal your intent behind capabilities. And so an example here would be, you know, if you have a credible independent evaluation and verification regime, it actually serves both sides of that posture, and it doesn't actually foreclose anything. So that's where building this capability and and announcing it and sharing it can be actually really useful and actually preserves your optionality. I think in areas that might be more confrontational, so, like, offensive counter AI tools, you might not wanna disclose that you actually have those, or you might wanna disclose that you actually have them but not explicitly what the purpose is because that type of signaling can actually foreclose some of your options.

[1:20:29] Nathan Labenz: Yeah. Is The

[1:20:30] Matt Chessen: US

[1:20:30] Nathan Labenz: state, like, coherent enough to do some of these things today? One thing I I look out at at China, and I'm like, I don't know what's going on behind closed doors, but they definitely present a pretty unified front. Whereas we are, like, countersignaling... We're, like, signaling to ourselves so much that it's, like, I think very hard for us to have any sort of coherent signaling strategy on the international stage. Does that feel right to you, or do you think we actually can do that more than I give us credit for? I I think we can do that. I think, you know, it requires some policy discipline, but I think sometimes that ambiguity is actually beneficial. And so if you look at the ambiguity around Taiwan and what US intentions would be there, I think some of that ambiguity can actually serve our purposes.

[1:21:12] Ben Boudreaux: Yeah. And just... I mean, very briefly on this. I mean, you know, we have the example of the Cold War where, you know, our open society, the freewheeling public discussion, the critiques of leaders, right, that, you know, there was times when that was seen as a disadvantage. But I think, you know, in retrospect, you know, that actually made our system much more resilient, much more able to, you know, take on different possibilities, you know, that that there was more just legitimacy and support for ultimately what decisions were made. And so I, you know, I I personally see that, like, actually as as a huge advantage. I know it can look a little messy, but but but ultimately, I think that's that's to the good. You know, just just going back to the other question about deterrence and cooperation. Like, I think there's ways in which... And we've heard this critique that the freedom of action strategy is kind of asking us to do it all. Right? It's like, have your cake and eat it too or walk and chew gum at the same time or whatever analogy you wanna use. And in some sense, that's that's true, but that doesn't mean, you know, that this is somehow, like, or not particularly demanding. You know, we think that there's lots that can be done that advance both deterrence and cooperation as as the example. I mean, Matt was just laying out, you know, parts of the evaluation ecosystem or verification tools. I think those are are very clear examples that support both of those. And another one just to throw it out is, like, incident response both domestically, but also with crisis communication channels with with adversaries like China or with with other countries around the world. You know, that that, again, I think supports multiple strategies at once. We we don't have these yet up and running. And so even if we only advance the things that serve all the strategy space, that gives us quite a lot to do. And, you know, then we don't have to, you know, wrongfully commit to something that, you know, would would be premature and would deprive us options later or perhaps, you know, signal things to China that would be overly provocative. We can do the things that that enable both, like, the deterrence and and the cooperative approaches at the same time.

[1:23:05] Matt Chessen: Nathan, on this I'm on this point of way carefully any action that would harden a rival's resolve, I mean, I think this is one that's that's really important where, you know, sort of moving towards some sort of suppression capability can basically send a signal to your rivals that the window is closing for them to actually take action. Right? It can push them to accelerate. And so there's been an argument that, you know, chip control is basically fed China's push to really create their own domestic, you know, AI GPU accelerator market and and basically, know, start producing those because now it's existential. Right? They can't procure GPUs on the international market as much as they could, so they have to produce them themselves. And I think there's some logic to that. I think one of the... We might have mentioned this earlier, but I think one of the most dangerous things if if there's a dominant strategy being run, one of the most dangerous things that, you know, United States could do is actually signal that that dominant strategy, that monopoly would actually be used to threaten, you know, the Chinese Communist Party. I think anything that turns existential for a particular rival then really puts the pressure on them and and hardens their resolve and and is going to focus their efforts. And that's where you could get some sort of, you know, dangerous consequences from that. So I think that's where you wanna be very careful when you're signaling to make sure that the message is being received in a in a particular way and you're not botching your adversaries into a corner.

[1:24:25] Nathan Labenz: Yeah. I was definitely hardened at the level of they are gonna have a chip industry at this point, and, you know, let's hope it's not hardened too much beyond that. Are there other kind of hidden gem policy ideas that you think just deserve more attention? Right now, like, my friends V always hammers on the Jones Act. You know, repeal the Jones Act is, like, his sort of quixotic quest in life. Are there things that are maybe not the, you know, the headline but are sort of among you guys in the know, like your kind of favorite things that are neglected when, you know, there's not enough time to get into the weeds like we have today?

[1:25:05] Ben Boudreaux: I mean, I had I had written on some of this already, you know, but I do think a lot about both how we're going to respond to incidents and prevent incidents from happening. And some of this, it gets a little conceptual relating to what it is for us to hold AIs at risk to ensure just as we are vulnerable to them, that they remain vulnerable to us going back to the the conversation we were having earlier. I think ensuring various capacities for for shutting down models and being able to to credibly threaten to them that we we will do such a thing. And some of that gets to our resilience and ability to live without them, to make those types of threats credible, but also ensuring we have the right technical approaches, the right authorities in place, the right crisis processes so that we can act quickly. I think all of that is is also pretty important, especially if we're we're worried about the the very high end risks from Avance AI. That that's something about our ability to to hold them at risk, to be able to shut them off, I think, is is pretty crucial and and and maybe doesn't come out as explicitly in this particular piece.

[1:26:12] Nathan Labenz: Yeah. I... I'd be interested in any double click you have on that or even just any sort of advice for, you know, the rest of the country outside the government.

[1:26:22] Matt Chessen: Yeah.

[1:26:22] Nathan Labenz: I mean, I just had this experience yesterday where I was... I'm I'm kind of try... I've, like, put a lot over the last nine months or ten months into my own personal AI infrastructure, It's and gotten pretty good, and I've... I just happened to be giving a couple talks this week. One of the things I'm gonna leave people with is like, hey. Here's a couple public versions that are sort of depersonalized of my Not that you wanna copy it, but like, go talk to your agent about it. Maybe you'll copy some. Maybe you already have some. Whatever. You can maybe learn some, and then I'm all... Also gonna offer sort of agent to agent exchange, which I think is gonna be, you know, potentially interesting. But one thing that happened as I was doing this depersonalization is that Claude decided to rotate the passwords on the agent console software that I use to talk to the agents, which is increasing to how I'm doing all the work. And there was a moment where I was locked out of my own system.

[1:27:13] Matt Chessen: Mhmm.

[1:27:13] Nathan Labenz: And I was like, oh, shit. I don't even know where that password is. It just changed it. Cloud can change my passwords. Like, this is an issue I hadn't really prepared myself for. Now Cloud is not fortunately scheming against me at this point. So I just went to a terminal and asked it and it gave me the password and then I logged back into my thing. But it was a kind of a little wake up call of like, wait. I've intermediated between myself and the actual levers of, you know, action in the world so much already just in October 2026 that, like, if Claude turned bad on me, it could go really bad. Like, it could block me out of all my shit. You know? This is, like, getting real for me at least already. Do we have any do we have any tips for me? Do have any best practices that you wanna Yeah. Promulgate or habits of mind you wanna cultivate in people?

[1:28:00] Ben Boudreaux: Yeah. I mean, I I think a lot about this less in terms of which passwords I want Claude to manage, but more which type of, like, analytical work writing, other types of critical thinking I would like, you know, Claude or other LLMs to do. And this is also something I... I'm I'm I'm I'm a teacher, a professor, and I have small kids. And, you know, thinking about which capacities it's important that they maintain and and the best practices for how they're interacting with AIs. And and what I advocate for is always starting first from your own considered judgment and and and point of view. So before I ask a model for its opinion on something, I wanna already have real clarity about what my question is and what sort of good or bad answers would be. And so going into it already with a with a clear vision of of sort of what I'm hoping to get out of the interaction, Then, of course, like, letting letting the model cook and letting it expand my thinking and, you know, build the tool or or do all the things that I wouldn't have been able to do without it. But then on on the on the the other end, I'm ensuring that that critical judgment and faculty comes in again And that I'm, you know, really being thoughtful about the response. I'm not just, you know, putting my name on it and passing it forward. I'm I'm really thinking thoroughly about it. And so so sort of the the the human comes in at the beginning, and then it comes in at the end. And and and thinking of that sort of, like, cycle as as being something I really wanna, know, incalculate both of myself and my students and my kids and the like, you know, that... That's that's sort of a best practice in sort of, like, personal levels in terms of these, like, more institutional decisions about when we should be relying on AI, you know, in terms of like I mentioned critical infrastructure, like things like, you know, managing your your desktop or or whatever it might be. You know, it... It's it's hard for me to have like one sort of best practice to suggest for everyone, except I try as much as possible to continue to ensure that I can operate with... Without these things. And some of it is just, like, the cutesy, like, I'm gonna try to, you know, navigate myself to, you know, my friend's house without looking at GPS or something like that. Right? Just to try to try to, you know, flex that that type of muscle. But but I think more institutionally, it is thinking of being these human backstops, you know, various fail safes that allow, you know, manual operation of of different systems. You know, still having paper copies, you know, of the health records or the like. You know? Because I think all of that both ensures that, you know, if there's some, you know, incident, we can... We could perhaps withstand it. You know, that's where the resilience comes in, but also makes our, you know, our our our certain threats to shut shut these systems down much more credible. Right? It makes them a lot less costly to us societally and again on these personal levels. And and so I I really think of it as being very clear about which dependencies, like, in our institutions have, thinking about what we can do to lessen those dependencies or at least have, again, these these fallback options, and and then and then, you know, exercising them, practicing them, make sure that they're they're they're really available if and when you need them. But, you know, not to say I've, like, perfected this. It's very much, you know, it's very much a practice, something I I I strive to do and and often fail at, but that... That's that's sort of how I think of it.

[1:31:01] Nathan Labenz: Yeah. Almost seems that we need, like, an AI Sabbath day even if we're not observing relationships. We need, like, a day a week where we just, like, don't do AI stuff.

[1:31:09] Ben Boudreaux: Exactly. Yeah. And, you know, you can extend that to to other examples. So, like, already, you know, in LAUSD or even the New York schools, these are now AI free, you know, and that was an explicit choice made by the school districts. And I think it's sort of along the lines of of what you're suggesting, AI free spaces. And do we need to establish more of these? You know, community centers where, you know, AIs are are not welcome. You know, there's there's human activities happening. People are, you know, exercising or convening or these other types of activities that are that are very human and doing it free of the AI mediation so that we can sort of, you know, practice those those human abilities. And again, be prepared for a situation where we might not have AI or or might need to stop an AI from from from intervening. So I I think a lot about those types of things. I do think things like, you know, an AI Sabbath, you know, a, you know, sort of a a human reserve labor force before critical infrastructure or hospitals or or the like, you know, might might be important. Yeah. And these other these other practices. Yeah. I try actually to take an AI Sabbath. I I often fail, unfortunately, but I had to do do my best.

[1:32:12] Nathan Labenz: Yeah. Well, that might be something I need to start.

[1:32:14] Matt Chessen: So, Nick, when you're when you're talking about things where I think there needs to be a little more attention, I think one of the things we were looking at is actually the typology of superintelligence. So when we were doing this assessment of all of the strategies out there, we found that most of these strategies were actually focused on the idea that superintelligence would actually be very centralized, either a singleton or maybe, you know, The United States has one and China has one. And they became very brittle when you started talking about some of these other models for superintelligence. And so, you know, there could be sort of lots of different superintelligences out there at at varying levels of capability. They're very widely distributed. There could be superintelligence that's very narrow, and, you know, you use architectural controls to make these more like an oracle where they don't have a lot of generalized intelligence, and there's lots of checks and balances on them. But one of the more interesting ones was this idea that you could actually get super intelligent types of behavior from agent swarms that are sub ASI or even sub AGI. And this is something where, you know, we went and did some initial research on this. There's really just not a lot of thinking about this. And I think what we've seen with the agent swarm hacks this summer is really maybe a preview of what's to come. And this is less like, you know, dealing with sort of a singleton or, you know, even a constellation of superintelligences and more like looking at these things as like an ecology. And so some of our colleagues at Rand, Ed Geist, and a number of other authors put out a paper called the ecology of AI risk. But I think this is really an emerging area of research and artificial intelligence. It is enormously important as we start getting more and more agents out there. You know, they may be interacting with each other, forming different coalitions, having these types of emergent behavior that we don't understand. We spent thousands of years really figuring out all the checks and balances for how human agents interact with each other out in the world. We have very little time and very little experience with how these types of agents are gonna interact with each other out in the world, and this is very, very important for, I think, how things are gonna turn out.

[1:34:13] Nathan Labenz: Yeah. Ecology of AIs is... It's wild times, but I've had a very similar impulse that, like, you know, we have... Sometimes the... Yeah. Of course, but China is always at the end of a lot of AI conversations, but I also sometimes think about, like, but prime intellect. You know? It's like, what what is gonna happen at the distributed layer in a community of, you know, participatory fashion in some stories or in some timelines that may be just as important as what the Chinese government decides to do? Another... Okay. So kind of opposite side of the coin, if you will, of all the investments that you recommend that we make to maintain option value. I wonder if there are any things that you would say, we have enough information right now that we should all be able to come together and agree, certain things we just shouldn't do. And, like, one that I like to float, which you may or may not accept, is I don't think we should train AI generals. If we train AIs that are capable of winning military campaigns, you know, the foreshadowing is, like, so obvious, right, that it's like we have... We are training the thing that is going to then be fighting us at some point potentially. Right? And, like, I don't want AIs to be that good at fighting military campaigns. Yes. They might beat our adversary hypothetically, but, like, I just kinda worry about our ability to fundamentally control that technology. Is there anything you would put into the category of, yes, this is just obviously such a bad idea. We definitely just shouldn't do it?

[1:35:44] Ben Boudreaux: If I if I may just try and understand your example, Nathan, which is interesting, is the idea we just shouldn't even train such a model or an agent versus, you know, maybe we train it, maybe we leverage it, but it's still gonna be humans that are fundamentally going to make a determination about whether to deploy such a thing, whether to act on the recommendations from this this general. So so is your is your objection just in the in the very... Just the training itself or in how we're deploying this decision?

[1:36:11] Nathan Labenz: I would probably like to stop a little bit upstream of the final decision to deploy. I feel like if we all of a sudden, you know, hand that over to the military decision makers, it might be pretty hard to resist. I honestly would probably say the same thing even about just like training AIs to go out and make money on the Internet and just rewarding them for how much money they make. It seems like that's a pretty clear way where we're gonna get all kinds of lying and cheating and conniving AIs. And yes, they'll be economically valuable, but, like, they'll be... At least as with the techniques we have right now, you know, possibly, you know, as you alluded to earlier, methods not yet known could come online that could, you know, salt in with this and make it okay. But, like, at present, you know, what I've learned from, like, Andin Labs type stuff is, like, already even the models we do have, which have not been trained in that way, are, like, you know, sometimes pretty dicey to deal with.

[1:37:01] Joel Predd: Yeah.

[1:37:02] Nathan Labenz: So, yeah, I just think if we go down that path, it seems to me we're gonna have a very hard time not deploying, and we're gonna have a very, you know, very dangerous situation where it's like, shit. You know, the the military is now run by the AI. Now we have to fight that? Like, it's a really, really tough place to be. Yeah.

[1:37:18] Ben Boudreaux: No. I... I've... I'll I'll... I'd be interested to think more about this one. But I... You know, to to my mind, the the line is more around giving access of this agent to various tools or capabilities that allow real world impact. Now in the making money case, I'd feel like that ship has sailed. You know? I I don't see how we can we can pull back. I mean, these these companies are designing systems that are supposed to have some economic value and run for, you know, over long horizons and the like. And so that one, I think, might be harder. But in the military context, we are accustomed to complying with various restraints and obligations that that we do for for the good of humanity. You know? And these are codified in the law of armed conflict, international humanitarian law. And and I think it's... Those same types of legal constraints should apply with respect to our use of AI in military applications. And they require, for instance, you know, reviewing operations to ensure that they don't have, you know, a a a disproportionate collateral effect, you know, ensuring that we are distinguishing civilians from from, you know, combatants, you know, and ensuring that someone remains accountable for these decisions. And and that to me just suggests that even if you're training these agents and getting some value out of them in the planning process, you know, the the the... We still need to create institutions where the the humans are remaining fundamentally accountable. And we do have practice of that in in the military with, like, clear hierarchies, and we just need to double down on those. And that might mean, you know, bringing in more of these military lawyers to review operations, bringing in more just people thinking about what's appropriate or not. But I wouldn't necessarily say that we are completely unprepared for such an eventuality. We just have to, I think, go back to what's what's worked historically and and really ensure that those existing institutions adapt and are and are relevant for today's challenges. I'm sorry. That's not answering your question. I'm just... I'm I'm actually just interested in in that as a, I guess, a side side side issue.

[1:39:11] Nathan Labenz: Yeah. That's good. Yeah. I mean, I offered it for you to to beat it up a little bit. Anything else, though, that... Anything come to mind in the definitely thou shalt not category?

[1:39:20] Matt Chessen: Yeah. I mean, I think one of the things that we talk about a lot is that, you know, rushing to put AI into your nuclear command and control is probably a very, very bad idea. And, you know, this needs to be done in a very deliberate fashion, if at all, with a lot of demonstrated safeguards. And so anything that, you know, you're touching on the the nuclear domain is an area that you need to proceed very, very cautiously. But I think there's also, you know, the use of of agents in military affairs. You know, some of the research has shown that, you know... And this is all, you know, I think very almost anecdotal, but, you know, people have found that some of the models when they're put into these simulations basically tend to escalate a lot. And so, you know, if you have agents that are basically doing reward hacking and escalations on the table, That seems like something you wanna be very, very careful with also. Yeah.

[1:40:14] Nathan Labenz: Chilling, honestly. Alright. Last one. What what are you tracking most closely? Right? So just to revisit the structure of this whole discussion a little bit, we wanna maintain freedom of action. To do that, we're gonna make kind of parallel investments in a lot of different things. The reason is because we have seven archetypal strategies, and it's too early to decide. And we have these five pivotal questions that we're gonna have to kinda work through and get answers on in order to decide when it's time, you know, what strategy we're ultimately going to commit to. So with that in mind, what are you guys tracking most closely right now that you consider to be the kind of, you know, likely next data points that will start to shift your sense of where these pivotal questions are gonna shake out and then, obviously, you know, what strategy we wanna adopt downstream of that?

[1:41:04] Ben Boudreaux: I mean, I think the things I'm tracking are probably not not a surprise, probably what a lot of the community's I mean, one is con... Continuing to see developments along the pacing proposals. What actually gets enacted? What type of access do independent evaluators actually get internally at the frontier companies? What sort of shared standards are decided on among these companies, you know, and whether or not various security controls are sufficient to manage internally deployed models. So far, we've seen that the companies have not created sufficient security internally, and so I'm very much watching to see whether or not they they will get a handle on that. And if they don't, what type of accountability or consequences can come. And then I'm also very much watching for signs of recursive self improvement or what what the trajectory of the AI r and d space it looks like since that can really change timelines across the broad range of decisions we are tracking. And then the last thing I might mention, of course, is the The US China dynamics from the broader diplomatic standpoint, but also more narrowly on the AI issues, whether or not some sort of incident management channel

[1:42:11] Nathan Labenz: can be created

[1:42:12] Ben Boudreaux: or there's more shared understanding of the risks. That's... Yes. I think those... That's the stuff I'm watching.

[1:42:19] Nathan Labenz: Matt?

[1:42:20] Matt Chessen: Yeah. I would just add on to that. You know, I think the the important things to watch are, as I said, danger close is probably the most important question we can answer right now. And for that, we wanna really be looking at the pace of recursive self improvement. How are the labs managing that? You know, they're doing their own pacing. Are they actually able to release new capabilities safely? Are we continuing to have, you know, more and more and escalating incidents around agents, or are we effectively developing the standards around how agents are used? I think one of the interesting things to look at right now is cyber capabilities. So Mythos was released about nine months ago now, and, typically, open source open weight lags, you know, the frontier by six to eighteen months. So those types of capabilities are now starting to be available in more of these open weight models. So we're knee... Going to need to see, you know, how are malicious actors using these capabilities, how are state actors using these capabilities as they become more and more available. And I think, you know, when you talk to people in the in the cyber world, think I there's a lot of concern about the next couple of years. I think over the long term, there is a view that, you know, in the long term, AI might be positive for cyber defense, but a lot of that depends on whether the defenders can actually uptake the the frontier capabilities quickly enough. And if there's these open white capabilities that are out there that enable, you know, malicious actors, terrorist groups, criminal groups to go and hack, it may be that big tech's fine, but what about the small and medium enterprises that aren't able to actually uptake the latest frontier models as quickly? And so I think we may be in for a rough patch in the cyber world, you know, over the next twelve to twenty four months. And, hopefully, it's just a a narrow rough patch, but that's something I'll be keeping you a close eye on.

[1:44:06] Nathan Labenz: Yeah. Offense can stay dominant longer than you can stay in security through obscurity, I suppose, is the Absolutely. Yeah. Well, this has been great. I really appreciate all the time and and the hard work that goes into making... You know, this is really one of those documents I would encourage people to to share and, you know, just go through the the hierarchy because this is one of those... You know, you actually took the time to write the short version, and I think it it it... Clarity does really come through. So I I appreciated that a lot. Anything else you guys wanna touch on that we... That I didn't think to ask or we just didn't get to before I let you get back to work?

[1:44:45] Matt Chessen: Well, just add one thing. So I was just at the curve last weekend, which is one of these big AI confabs in Berkeley. And I presented on the freedom of action strategy, and a lot of the feedback I actually got was that this really enabled people who are newer to the AI debate to really understand the different dynamics at play in looking at and choosing strategies for advanced AI. And so I I think folks out there in the audience shouldn't look at this as, like, you know, just an academic REN report. I think there's... People kept citing this table that we put in there that outlines the five pivot factors and how they lead you potentially to the different archetypal strategies as something that they were gonna take back to their stakeholders and constituents to try and explain, you know, how sort of AI strategy works. So for folks out there who wouldn't normally read a RAM report, would encourage you to go just take a look at that and take a look at the pivot factor table. It might actually help you understand a little more intuitively sort of what these strategic choices are all about and what the underlying assumptions for most of the AI strategies out there are based on.

[1:45:48] Nathan Labenz: Yeah. I agree with that. I think for people who've, you know, been with us on this speed for a long time, like, all these things are kind of in the air and familiar. And I was at the curve too, actually. I'm surprised we didn't have a chance to meet there. But for folks who are catching up now, I think the the structure and the the way that you guys present this information is an excellent primer. So I do think it is definitely something that should be forwarded on to people who are kind of just entering the discourse at this possibly late hour. I guess we don't know yet exactly just how late it is. But, yeah, download it and you'll be glad you'll have something that you can send to friends when they need a a primer. I think it's excellent for that. You.

[1:46:26] Ben Boudreaux: Joel Pretz,

[1:46:27] Nathan Labenz: Ben Boudreaux, and Matt Chesson, thank you guys all for being part of the Cognitive Revolution.

[1:46:31] Matt Chessen: Alright. Thanks for having us, Nathan. Thank you,

[1:46:33] Ben Boudreaux: Nathan.

Outro

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