How does game theory work when everyone is a computer program who can read everyone else's source code? This is the problem of 'program equilibria'. In this episode, I talk with Caspar Oesterheld on work he's done on equilibria of programs that simulate each other, and how robust these equilibria are.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2026/02/18/episode-49-caspar-oesterheld-program-equilibrium.html

Note from Caspar on 2:00:06: At least given my current interpretation of what you say here, my answer is wrong. What actually happens is that we're just back in the uncorrelated case. Basically my simulations will be a simulated repeated game in which everything is correlated _because I feed you my random sequence_ and your simulations will be a repeated game where everything is correlated. Halting works the same as usual. But of course what we end up actually playing will be uncorrelated. We discuss something like this later in the episode.

Topics we discuss, and timestamps:

0:00:44 Program equilibrium basics

0:14:20 Desiderata for program equilibria

0:24:35 Why program equilibrium matters

0:33:35 Prior work: reachable equilibria and proof-based approaches

0:53:26 The basic idea of Robust Program Equilibrium

1:07:47 Are ϵGroundedπBots inefficient?

1:15:06 Compatibility of proof-based and simulation-based program equilibria

1:18:32 Cooperating against CooperateBot, and how to avoid it

1:44:43 Making better simulation-based bots

2:01:22 Characterizing simulation-based program equilibria

2:21:24 Follow-up work

2:29:49 Following Caspar's research

Links for Caspar:

Academic website: https://www.andrew.cmu.edu/user/coesterh/

Google Scholar: https://scholar.google.com/citations?user=xeEcRjkAAAAJ&hl=en

Blog: https://casparoesterheld.com/

X / Twitter: https://x.com/c_oesterheld

Research we discuss:

Robust program equilibrium: https://link.springer.com/article/10.1007/s11238-018-9679-3

Characterising Simulation-Based Program Equilibria: https://arxiv.org/abs/2412.14570

Manifold open-source prisoner's dilemma tournament: https://manifold.markets/IsaacKing/which-240-character-program-wins-th

Results of Alex Mennen's open source prisoner's dilemma tournament: https://www.lesswrong.com/posts/QP7Ne4KXKytj4Krkx/prisoner-s-dilemma-tournament-results-0

A General Counterexample to Any Decision Theory and Some Responses: https://arxiv.org/abs/2101.00280

Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory: https://arxiv.org/abs/2208.07006

Parametric Bounded Löb's Theorem and Robust Cooperation of Bounded Agents: https://arxiv.org/abs/1602.04184

A Note on the Compatibility of Different Robust Program Equilibria of the Prisoner's Dilemma: https://arxiv.org/abs/2211.05057

Episode art by Hamish Doodles: hamishdoodles.com

In this episode, Guive Assadi argues that we should give AIs property rights, so that they are integrated in our system of property and come to rely on it. The claim is that this means that AIs would not kill or steal from humans, because that would undermine the whole property system, which would be extremely valuable to them.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2026/02/15/episode-48-guive-assadi-ai-property-rights.html

Topics we discuss, and timestamps:

0:00:28 AI property rights

0:08:01 Why not steal from and kill humans

0:15:25 Why AIs may fear it could be them next

0:20:56 AI retirement

0:23:28 Could humans be upgraded to stay useful?

0:26:41 Will AI progress continue?

0:30:00 Why non-obsoletable AIs may still not end human property rights

0:38:35 Why make AIs with property rights?

0:48:01 Do property rights incentivize alignment?

0:50:09 Humans and non-human property rights

1:02:18 Humans and non-human bodily autonomy

1:16:59 Step changes in coordination ability

1:24:39 Acausal coordination

1:32:37 AI, humans, and civilizations with different technology levels

1:41:39 The case of British settlers and Tasmanians

1:47:22 Non-total expropriation

1:53:47 How Guive thinks x-risk could happen, and other loose ends

2:03:46 Following Guive's work

Guive on Substack: https://guive.substack.com/

Guive on X/Twitter: https://x.com/GuiveAssadi

Research we discuss:

The Case for AI Property Rights: https://guive.substack.com/p/the-case-for-ai-property-rights

AXRP Episode 44 - Peter Salib on AI Rights for Human Safety: https://axrp.net/episode/2025/06/28/episode-44-peter-salib-ai-rights-human-safety.html

AI Rights for Human Safety (by Salib and Goldstein): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4913167

We don't trade with ants: https://worldspiritsockpuppet.substack.com/p/we-dont-trade-with-ants

Alignment Fine-tuning is Character Writing (on Claude as a techy philosophy SF-dwelling type): https://guive.substack.com/p/alignment-fine-tuning-is-character

Claude's charater (Anthropic post on character training): https://www.anthropic.com/research/claude-character

Git Re-Basin: Merging Models modulo Permutation Symmetries: https://arxiv.org/abs/2209.04836

The Filan Cabinet: Caspar Oesterheld on Evidential Cooperation in Large Worlds: https://thefilancabinet.com/episodes/2025/08/03/caspar-oesterheld-on-evidential-cooperation-in-large-worlds-ecl.html

Episode art by Hamish Doodles: hamishdoodles.com

When METR says something like "Claude Opus 4.5 has a 50% time horizon of 4 hours and 50 minutes", what does that mean? In this episode David Rein, METR researcher and co-author of the paper "Measuring AI ability to complete long tasks", talks about METR's work on measuring time horizons, the methodology behind those numbers, and what work remains to be done in this domain.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2026/01/03/episode-47-david-rein-metr-time-horizons.html

Topics we discuss, and timestamps:

0:00:32 Measuring AI Ability to Complete Long Tasks

0:10:54 The meaning of "task length"

0:19:27 Examples of intermediate and hard tasks

0:25:12 Why the software engineering focus

0:32:17 Why task length as difficulty measure

0:46:32 Is AI progress going superexponential?

0:50:58 Is AI progress due to increased cost to run models?

0:54:45 Why METR measures model capabilities

1:04:10 How time horizons relate to recursive self-improvement

1:12:58 Cost of estimating time horizons

1:16:23 Task realism vs mimicking important task features

1:19:50 Excursus on "Inventing Temperature"

1:25:46 Return to task realism discussion

1:33:53 Open questions on time horizons

Links for METR:

Main website: https://metr.org/

X/Twitter account: https://x.com/METR_Evals/

Research we discuss:

Measuring AI Ability to Complete Long Tasks: https://arxiv.org/abs/2503.14499

RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts: https://arxiv.org/abs/2411.15114

HCAST: Human-Calibrated Autonomy Software Tasks: https://arxiv.org/abs/2503.17354

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity: https://arxiv.org/abs/2507.09089

Anthropic Economic Index: Tracking AI's role in the US and global economy: https://www.anthropic.com/research/anthropic-economic-index-september-2025-report

Bridging RL Theory and Practice with the Effective Horizon (i.e. the Cassidy Laidlaw paper): https://arxiv.org/abs/2304.09853

How Does Time Horizon Vary Across Domains?: https://metr.org/blog/2025-07-14-how-does-time-horizon-vary-across-domains/

Inventing Temperature: https://global.oup.com/academic/product/inventing-temperature-9780195337389

Is there a Half-Life for the Success Rates of AI Agents? (by Toby Ord): https://www.tobyord.com/writing/half-life

Lawrence Chan's response to the above: https://nitter.net/justanotherlaw/status/1920254586771710009

AI Task Length Horizons in Offensive Cybersecurity: https://sean-peters-au.github.io/2025/07/02/ai-task-length-horizons-in-offensive-cybersecurity.html

Episode art by Hamish Doodles: hamishdoodles.com

Could AI enable a small group to gain power over a large country, and lock in their power permanently? Often, people worried about catastrophic risks from AI have been concerned with misalignment risks. In this episode, Tom Davidson talks about a risk that could be comparably important: that of AI-enabled coups.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/08/07/episode-46-tom-davidson-ai-enabled-coups.html

Topics we discuss, and timestamps:

0:00:35 How to stage a coup without AI

0:16:17 Why AI might enable coups

0:33:29 How bad AI-enabled coups are

0:37:28 Executive coups with singularly loyal AIs

0:48:35 Executive coups with exclusive access to AI

0:54:41 Corporate AI-enabled coups

0:57:56 Secret loyalty and misalignment in corporate coups

1:11:39 Likelihood of different types of AI-enabled coups

1:25:52 How to prevent AI-enabled coups

1:33:43 Downsides of AIs loyal to the law

1:41:06 Cultural shifts vs individual action

1:45:53 Technical research to prevent AI-enabled coups

1:51:40 Non-technical research to prevent AI-enabled coups

1:58:17 Forethought

2:03:03 Following Tom's and Forethought's research

Links for Tom and Forethought:

Tom on X / Twitter: https://x.com/tomdavidsonx

Tom on LessWrong: https://www.lesswrong.com/users/tom-davidson-1

Forethought Substack: https://newsletter.forethought.org/

Will MacAskill on X / Twitter: https://x.com/willmacaskill

Will MacAskill on LessWrong: https://www.lesswrong.com/users/wdmacaskill

Research we discuss:

AI-Enabled Coups: How a Small Group Could Use AI to Seize Power: https://www.forethought.org/research/ai-enabled-coups-how-a-small-group-could-use-ai-to-seize-power

Seizing Power: The Strategic Logic of Military Coups, by Naunihal Singh: https://muse.jhu.edu/book/31450

Experiment using AI-generated posts on Reddit draws fire for ethics concerns: https://retractionwatch.com/2025/04/28/experiment-using-ai-generated-posts-on-reddit-draws-fire-for-ethics-concerns/

Episode art by Hamish Doodles: hamishdoodles.com

In this episode, I chat with Samuel Albanie about the Google DeepMind paper he co-authored called "An Approach to Technical AGI Safety and Security". It covers the assumptions made by the approach, as well as the types of mitigations it outlines.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/07/06/episode-45-samuel-albanie-deepminds-agi-safety-approach.html

Topics we discuss, and timestamps:

0:00:37 DeepMind's Approach to Technical AGI Safety and Security

0:04:29 Current paradigm continuation

0:19:13 No human ceiling

0:21:22 Uncertain timelines

0:23:36 Approximate continuity and the potential for accelerating capability improvement

0:34:29 Misuse and misalignment

0:39:34 Societal readiness

0:43:58 Misuse mitigations

0:52:57 Misalignment mitigations

1:05:20 Samuel's thinking about technical AGI safety

1:14:02 Following Samuel's work

Samuel on Twitter/X: x.com/samuelalbanie

Research we discuss:

An Approach to Technical AGI Safety and Security: https://arxiv.org/abs/2504.01849

Levels of AGI for Operationalizing Progress on the Path to AGI: https://arxiv.org/abs/2311.02462

The Checklist: What Succeeding at AI Safety Will Involve: https://sleepinyourhat.github.io/checklist/

Measuring AI Ability to Complete Long Tasks: https://arxiv.org/abs/2503.14499

Episode art by Hamish Doodles: hamishdoodles.com

In this episode, I talk with Peter Salib about his paper "AI Rights for Human Safety", arguing that giving AIs the right to contract, hold property, and sue people will reduce the risk of their trying to attack humanity and take over. He also tells me how law reviews work, in the face of my incredulity.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/06/28/episode-44-peter-salib-ai-rights-human-safety.html

Topics we discuss, and timestamps:

0:00:40 Why AI rights

0:18:34 Why not reputation

0:27:10 Do AI rights lead to AI war?

0:36:42 Scope for human-AI trade

0:44:25 Concerns with comparative advantage

0:53:42 Proxy AI wars

0:57:56 Can companies profitably make AIs with rights?

1:09:43 Can we have AI rights and AI safety measures?

1:24:31 Liability for AIs with rights

1:38:29 Which AIs get rights?

1:43:36 AI rights and stochastic gradient descent

1:54:54 Individuating "AIs"

2:03:28 Social institutions for AI safety

2:08:20 Outer misalignment and trading with AIs

2:15:27 Why statutes of limitations should exist

2:18:39 Starting AI x-risk research in legal academia

2:24:18 How law reviews and AI conferences work

2:41:49 More on Peter moving to AI x-risk research

2:45:37 Reception of the paper

2:53:24 What publishing in law reviews does

3:04:48 Which parts of legal academia focus on AI

3:18:03 Following Peter's research

Links for Peter:

Personal website: https://www.peternsalib.com/

Writings at Lawfare: https://www.lawfaremedia.org/contributors/psalib

CLAIR: https://clair-ai.org/

Research we discuss:

AI Rights for Human Safety: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4913167

Will humans and AIs go to war? https://philpapers.org/rec/GOLWAA

Infrastructure for AI agents: https://arxiv.org/abs/2501.10114

Governing AI Agents: https://arxiv.org/abs/2501.07913

Episode art by Hamish Doodles: hamishdoodles.com

In this episode, I talk with David Lindner about Myopic Optimization with Non-myopic Approval, or MONA, which attempts to address (multi-step) reward hacking by myopically optimizing actions against a human's sense of whether those actions are generally good. Does this work? Can we get smarter-than-human AI this way? How does this compare to approaches like conservativism? Listen to find out.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/06/15/episode-43-david-lindner-mona.html

Topics we discuss, and timestamps:

0:00:29 What MONA is

0:06:33 How MONA deals with reward hacking

0:23:15 Failure cases for MONA

0:36:25 MONA's capability

0:55:40 MONA vs other approaches

1:05:03 Follow-up work

1:10:17 Other MONA test cases

1:33:47 When increasing time horizon doesn't increase capability

1:39:04 Following David's research

Links for David:

Website: https://www.davidlindner.me

Twitter / X: https://x.com/davlindner

DeepMind Medium: https://deepmindsafetyresearch.medium.com

David on the Alignment Forum: https://www.alignmentforum.org/users/david-lindner

Research we discuss:

MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking: https://arxiv.org/abs/2501.13011

Arguments Against Myopic Training: https://www.alignmentforum.org/posts/GqxuDtZvfgL2bEQ5v/arguments-against-myopic-training

Episode art by Hamish Doodles: hamishdoodles.com

Earlier this year, the paper "Emergent Misalignment" made the rounds on AI x-risk social media for seemingly showing LLMs generalizing from 'misaligned' training data of insecure code to acting comically evil in response to innocuous questions. In this episode, I chat with one of the authors of that paper, Owain Evans, about that research as well as other work he's done to understand the psychology of large language models.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/06/06/episode-42-owain-evans-llm-psychology.html

Topics we discuss, and timestamps:

0:00:37 Why introspection?

0:06:24 Experiments in "Looking Inward"

0:15:11 Why fine-tune for introspection?

0:22:32 Does "Looking Inward" test introspection, or something else?

0:34:14 Interpreting the results of "Looking Inward"

0:44:56 Limitations to introspection?

0:49:54 "Tell me about yourself", and its relation to other papers

1:05:45 Backdoor results

1:12:01 Emergent Misalignment

1:22:13 Why so hammy, and so infrequently evil?

1:36:31 Why emergent misalignment?

1:46:45 Emergent misalignment and other types of misalignment

1:53:57 Is emergent misalignment good news?

2:00:01 Follow-up work to "Emergent Misalignment"

2:03:10 Reception of "Emergent Misalignment" vs other papers

2:07:43 Evil numbers

2:12:20 Following Owain's research

Links for Owain:

Truthful AI: https://www.truthfulai.org

Owain's website: https://owainevans.github.io/

Owain's twitter/X account: https://twitter.com/OwainEvans_UK

Research we discuss:

Looking Inward: Language Models Can Learn About Themselves by Introspection: https://arxiv.org/abs/2410.13787

Tell me about yourself: LLMs are aware of their learned behaviors: https://arxiv.org/abs/2501.11120

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data: https://arxiv.org/abs/2406.14546

Emergent Misalignment: Narrow fine-tuning can produce broadly misaligned LLMs: https://arxiv.org/abs/2502.17424

X/Twitter thread of GPT-4.1 emergent misalignment results: https://x.com/OwainEvans_UK/status/1912701650051190852

Taken out of context: On measuring situational awareness in LLMs: https://arxiv.org/abs/2309.00667

Episode art by Hamish Doodles: hamishdoodles.com

What's the next step forward in interpretability? In this episode, I chat with Lee Sharkey about his proposal for detecting computational mechanisms within neural networks: Attribution-based Parameter Decomposition, or APD for short.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/06/03/episode-41-lee-sharkey-attribution-based-parameter-decomposition.html

Topics we discuss, and timestamps:

0:00:41 APD basics

0:07:57 Faithfulness

0:11:10 Minimality

0:28:44 Simplicity

0:34:50 Concrete-ish examples of APD

0:52:00 Which parts of APD are canonical

0:58:10 Hyperparameter selection

1:06:40 APD in toy models of superposition

1:14:40 APD and compressed computation

1:25:43 Mechanisms vs representations

1:34:41 Future applications of APD?

1:44:19 How costly is APD?

1:49:14 More on minimality training

1:51:49 Follow-up work

2:05:24 APD on giant chain-of-thought models?

2:11:27 APD and "features"

2:14:11 Following Lee's work

Lee links (Leenks):

X/Twitter: https://twitter.com/leedsharkey

Alignment Forum: https://www.alignmentforum.org/users/lee_sharkey

Research we discuss:

Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition: https://arxiv.org/abs/2501.14926

Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html

Towards a unified and verified understanding of group-operation networks: https://arxiv.org/abs/2410.07476

Feature geometry is outside the superposition hypothesis: https://www.alignmentforum.org/posts/MFBTjb2qf3ziWmzz6/sae-feature-geometry-is-outside-the-superposition-hypothesis

Episode art by Hamish Doodles: hamishdoodles.com

How do we figure out whether interpretability is doing its job? One way is to see if it helps us prove things about models that we care about knowing. In this episode, I speak with Jason Gross about his agenda to benchmark interpretability in this way, and his exploration of the intersection of proofs and modern machine learning.

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/03/28/episode-40-jason-gross-compact-proofs-interpretability.html

Topics we discuss, and timestamps:

0:00:40 - Why compact proofs

0:07:25 - Compact Proofs of Model Performance via Mechanistic Interpretability

0:14:19 - What compact proofs look like

0:32:43 - Structureless noise, and why proofs

0:48:23 - What we've learned about compact proofs in general

0:59:02 - Generalizing 'symmetry'

1:11:24 - Grading mechanistic interpretability

1:43:34 - What helps compact proofs

1:51:08 - The limits of compact proofs

2:07:33 - Guaranteed safe AI, and AI for guaranteed safety

2:27:44 - Jason and Rajashree's start-up

2:34:19 - Following Jason's work

Links to Jason:

Github: https://github.com/jasongross

Website: https://jasongross.github.io

Alignment Forum: https://www.alignmentforum.org/users/jason-gross

Links to work we discuss:

Compact Proofs of Model Performance via Mechanistic Interpretability: https://arxiv.org/abs/2406.11779

Unifying and Verifying Mechanistic Interpretability: A Case Study with Group Operations: https://arxiv.org/abs/2410.07476

Modular addition without black-boxes: Compressing explanations of MLPs that compute numerical integration: https://arxiv.org/abs/2412.03773

Stage-Wise Model Diffing: https://transformer-circuits.pub/2024/model-diffing/index.html

Causal Scrubbing: a method for rigorously testing interpretability hypotheses: https://www.lesswrong.com/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing

Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition (aka the Apollo paper on APD): https://arxiv.org/abs/2501.14926

Towards Guaranteed Safe AI: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-45.pdf

Episode art by Hamish Doodles: hamishdoodles.com