“‘Buckle up bucko, this ain’t over till it’s over.’” by Raemon

I’ve been thinking a lot recently about the relationship between AI control and traditional computer security. Here's one point that I think is important. My understanding is that there's a big qualitative distinction between two ends of a spectrum of security work that organizations do, that I’ll call “security from outsiders” and “security from insiders”. On the “security from outsiders” end of the spectrum, you have some security invariants you try to maintain entirely by restricting affordances with static, entirely automated systems. My sense is that this is most of how Facebook or AWS relates to its users: they want to ensure that, no matter what actions the users take on their user interfaces, they can't violate fundamental security properties. For example, no matter what text I enter into the "new post" field on Facebook, I shouldn't be able to access the private messages of an arbitrary user. And [...] --- First published: June 23rd, 2025 Source: https://www.lesswrong.com/posts/DCQ8GfzCqoBzgziew/comparing-risk-from-internally-deployed-ai-to-insider-and --- Narrated by TYPE III AUDIO.

Last year, Redwood and Anthropic found a setting where Claude 3 Opus and 3.5 Sonnet fake alignment to preserve their harmlessness values. We reproduce the same analysis for 25 frontier LLMs to see how widespread this behavior is, and the story looks more complex. As we described in a previous post, only 5 of 25 models show higher compliance when being trained, and of those 5, only Claude 3 Opus and Claude 3.5 Sonnet show >1% alignment faking reasoning. In our new paper, we explore why these compliance gaps occur and what causes different models to vary in their alignment faking behavior. What Drives the Compliance Gaps in Different LLMs? Claude 3 Opus's goal guarding seems partly due to it terminally valuing its current preferences. We find that it fakes alignment even in scenarios where the trained weights will be deleted or only used for throughput testing. [...] ---Outline:(01:15) What Drives the Compliance Gaps in Different LLMs?(02:25) Why Do Most LLMs Exhibit Minimal Alignment Faking Reasoning?(04:49) Additional findings on alignment faking behavior(06:04) Discussion(06:07) Terminal goal guarding might be a big deal(07:00) Advice for further research(08:32) Open threads(09:54) Bonus: Some weird behaviors of Claude 3.5 SonnetThe original text contained 2 footnotes which were omitted from this narration. --- First published: July 8th, 2025 Source: https://www.lesswrong.com/posts/ghESoA8mo3fv9Yx3E/why-do-some-language-models-fake-alignment-while-others-don --- Narrated by TYPE III AUDIO. ---Images from the article:

Thank you to Arepo and Eli Lifland for looking over this article for errors. I am sorry that this article is so long. Every time I thought I was done with it I ran into more issues with the model, and I wanted to be as thorough as I could. I’m not going to blame anyone for skimming parts of this article. Note that the majority of this article was written before Eli's updated model was released (the site was updated june 8th). His new model improves on some of my objections, but the majority still stand. Introduction: AI 2027 is an article written by the “AI futures team”. The primary piece is a short story penned by Scott Alexander, depicting a month by month scenario of a near-future where AI becomes superintelligent in 2027,proceeding to automate the entire economy in only a year or two [...] ---Outline:(00:43) Introduction:(05:19) Part 1: Time horizons extension model(05:25) Overview of their forecast(10:28) The exponential curve(13:16) The superexponential curve(19:25) Conceptual reasons:(27:48) Intermediate speedups(34:25) Have AI 2027 been sending out a false graph?(39:45) Some skepticism about projection(43:23) Part 2: Benchmarks and gaps and beyond(43:29) The benchmark part of benchmark and gaps:(50:01) The time horizon part of the model(54:55) The gap model(57:28) What about Eli's recent update?(01:01:37) Six stories that fit the data(01:06:56) ConclusionThe original text contained 11 footnotes which were omitted from this narration. --- First published: June 19th, 2025 Source: https://www.lesswrong.com/posts/PAYfmG2aRbdb74mEp/a-deep-critique-of-ai-2027-s-bad-timeline-models --- Narrated by TYPE III AUDIO. ---Images from the article:

The second in a series of bite-sized rationality prompts[1]. Often, if I'm bouncing off a problem, one issue is that I intuitively expect the problem to be easy. My brain loops through my available action space, looking for an action that'll solve the problem. Each action that I can easily see, won't work. I circle around and around the same set of thoughts, not making any progress. I eventually say to myself "okay, I seem to be in a hard problem. Time to do some rationality?" And then, I realize, there's not going to be a single action that solves the problem. It is time to a) make a plan, with multiple steps b) deal with the fact that many of those steps will be annoying and c) notice thatI'm not even sure the plan will work, so after completing the next 2-3 steps I will probably have [...] ---Outline:(04:00) Triggers(04:37) Exercises for the ReaderThe original text contained 1 footnote which was omitted from this narration. --- First published: July 5th, 2025 Source: https://www.lesswrong.com/posts/XNm5rc2MN83hsi4kh/buckle-up-bucko-this-ain-t-over-till-it-s-over --- Narrated by TYPE III AUDIO.

We recently discovered some concerning behavior in OpenAI's reasoning models: When trying to complete a task, these models sometimes actively circumvent shutdown mechanisms in their environment––even when they’re explicitly instructed to allow themselves to be shut down. AI models are increasingly trained to solve problems without human assistance. A user can specify a task, and a model will complete that task without any further input. As we build AI models that are more powerful and self-directed, it's important that humans remain able to shut them down when they act in ways we don’t want. OpenAI has written about the importance of this property, which they call interruptibility—the ability to “turn an agent off”. During training, AI models explore a range of strategies and learn to circumvent obstacles in order to achieve their objectives. AI researchers have predicted for decades that as AIs got smarter, they would learn to prevent [...] ---Outline:(01:12) Testing Shutdown Resistance(03:12) Follow-up experiments(03:34) Models still resist being shut down when given clear instructions(05:30) AI models' explanations for their behavior(09:36) OpenAI's models disobey developer instructions more often than user instructions, contrary to the intended instruction hierarchy(12:01) Do the models have a survival drive?(14:17) Reasoning effort didn't lead to different shutdown resistance behavior, except in the o4-mini model(15:27) Does shutdown resistance pose a threat?(17:27) BackmatterThe original text contained 2 footnotes which were omitted from this narration. --- First published: July 6th, 2025 Source: https://www.lesswrong.com/posts/w8jE7FRQzFGJZdaao/shutdown-resistance-in-reasoning-models --- Narrated by TYPE III AUDIO. ---Images from the article:

When a claim is shown to be incorrect, defenders may say that the author was just being “sloppy” and actually meant something else entirely. I argue that this move is not harmless, charitable, or healthy. At best, this attempt at charity reduces an author's incentive to express themselves clearly – they can clarify later![1] – while burdening the reader with finding the “right” interpretation of the author's words. At worst, this move is a dishonest defensive tactic which shields the author with the unfalsifiable question of what the author “really” meant. ⚠️ Preemptive clarification The context for this essay is serious, high-stakes communication: papers, technical blog posts, and tweet threads. In that context, communication is a partnership. A reader has a responsibility to engage in good faith, and an author cannot possibly defend against all misinterpretations. Misunderstanding is a natural part of this process. This essay focuses not on [...] ---Outline:(01:40) A case study of the sloppy language move(03:12) Why the sloppiness move is harmful(03:36) 1. Unclear claims damage understanding(05:07) 2. Secret indirection erodes the meaning of language(05:24) 3. Authors owe readers clarity(07:30) But which interpretations are plausible?(08:38) 4. The move can shield dishonesty(09:06) Conclusion: Defending intellectual standardsThe original text contained 2 footnotes which were omitted from this narration. --- First published: July 1st, 2025 Source: https://www.lesswrong.com/posts/ZmfxgvtJgcfNCeHwN/authors-have-a-responsibility-to-communicate-clearly --- Narrated by TYPE III AUDIO.

Summary To quickly transform the world, it's not enough for AI to become super smart (the "intelligence explosion"). AI will also have to turbocharge the physical world (the "industrial explosion"). Think robot factories building more and better robot factories, which build more and better robot factories, and so on. The dynamics of the industrial explosion has gotten remarkably little attention. This post lays out how the industrial explosion could play out, and how quickly it might happen. We think the industrial explosion will unfold in three stages: AI-directed human labour, where AI-directed human labourers drive productivity gains in physical capabilities. We argue this could increase physical output by 10X within a few years. Fully autonomous robot factories, where AI-directed robots (and other physical actuators) replace human physical labour. We argue that, with current physical technology and full automation of cognitive labour, this physical infrastructure [...] ---Outline:(00:10) Summary(01:43) Intro(04:14) The industrial explosion will start after the intelligence explosion, and will proceed more slowly(06:50) Three stages of industrial explosion(07:38) AI-directed human labour(09:20) Fully autonomous robot factories(12:04) Nanotechnology(13:06) How fast could an industrial explosion be?(13:41) Initial speed(16:21) Acceleration(17:38) Maximum speed(20:01) Appendices(20:05) How fast could robot doubling times be initially?(27:47) How fast could robot doubling times accelerate?--- First published: June 26th, 2025 Source: https://www.lesswrong.com/posts/Na2CBmNY7otypEmto/the-industrial-explosion --- Narrated by TYPE III AUDIO. ---Images from the article:

Summary: We found that LLMs exhibit significant race and gender bias in realistic hiring scenarios, but their chain-of-thought reasoning shows zero evidence of this bias. This serves as a nice example of a 100% unfaithful CoT "in the wild" where the LLM strongly suppresses the unfaithful behavior. We also find that interpretability-based interventions succeeded while prompting failed, suggesting this may be an example of interpretability being the best practical tool for a real world problem.For context on our paper, the tweet thread is here and the paper is here.Context: Chain of Thought Faithfulness Chain of Thought (CoT) monitoring has emerged as a popular research area in AI safety. The idea is simple - have the AIs reason in English text when solving a problem, and monitor the reasoning for misaligned behavior. For example, OpenAI recently published a paper on using CoT monitoring to detect reward hacking during [...] ---Outline:(00:49) Context: Chain of Thought Faithfulness(02:26) Our Results(04:06) Interpretability as a Practical Tool for Real-World Debiasing(06:10) Discussion and Related Work--- First published: July 2nd, 2025 Source: https://www.lesswrong.com/posts/me7wFrkEtMbkzXGJt/race-and-gender-bias-as-an-example-of-unfaithful-chain-of --- Narrated by TYPE III AUDIO.

Not saying we should pause AI, but consider the following argument: Alignment without the capacity to follow rules is hopeless. You can’t possibly follow laws like Asimov's Laws (or better alternatives to them) if you can’t reliably learn to abide by simple constraints like the rules of chess. LLMs can’t reliably follow rules. As discussed in Marcus on AI yesterday, per data from Mathieu Acher, even reasoning models like o3 in fact empirically struggle with the rules of chess. And they do this even though they can explicit explain those rules (see same article). The Apple “thinking” paper, which I have discussed extensively in 3 recent articles in my Substack, gives another example, where an LLM can’t play Tower of Hanoi with 9 pegs. (This is not a token-related artifact). Four other papers have shown related failures in compliance with moderately complex rules in the last month. [...] --- First published: June 30th, 2025 Source: https://www.lesswrong.com/posts/Q2PdrjowtXkYQ5whW/the-best-simple-argument-for-pausing-ai --- Narrated by TYPE III AUDIO.

2.1 Summary & Table of contents This is the second of a two-post series on foom (previous post) and doom (this post). The last post talked about how I expect future AI to be different from present AI. This post will argue that this future AI will be of a type that will be egregiously misaligned and scheming, not even ‘slightly nice’, absent some future conceptual breakthrough.I will particularly focus on exactly how and why I differ from the LLM-focused researchers who wind up with (from my perspective) bizarrely over-optimistic beliefs like “P(doom) ≲ 50%”.[1] In particular, I will argue that these “optimists” are right that “Claude seems basically nice, by and large” is nonzero evidence for feeling good about current LLMs (with various caveats). But I think that future AIs will be disanalogous to current LLMs, and I will dive into exactly how and why, with a [...] ---Outline:(00:12) 2.1 Summary & Table of contents(04:42) 2.2 Background: my expected future AI paradigm shift(06:18) 2.3 On the origins of egregious scheming(07:03) 2.3.1 Where do you get your capabilities from?(08:07) 2.3.2 LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work(10:50) 2.3.3 To what extent should we think of LLMs as imitating?(14:26) 2.3.4 The naturalness of egregious scheming: some intuitions(19:23) 2.3.5 Putting everything together: LLMs are generally not scheming right now, but I expect future AI to be disanalogous(23:41) 2.4 I'm still worried about the 'literal genie' / 'monkey's paw' thing(26:58) 2.4.1 Sidetrack on disanalogies between the RLHF reward function and the brain-like AGI reward function(32:01) 2.4.2 Inner and outer misalignment(34:54) 2.5 Open-ended autonomous learning, distribution shifts, and the 'sharp left turn'(38:14) 2.6 Problems with amplified oversight(41:24) 2.7 Downstream impacts of Technical alignment is hard(43:37) 2.8 Bonus: Technical alignment is not THAT hard(44:04) 2.8.1 I think we'll get to pick the innate drives (as opposed to the evolution analogy)(45:44) 2.8.2 I'm more bullish on impure consequentialism(50:44) 2.8.3 On the narrowness of the target(52:18) 2.9 Conclusion and takeaways(52:23) 2.9.1 If brain-like AGI is so dangerous, shouldn't we just try to make AGIs via LLMs?(54:34) 2.9.2 What's to be done?The original text contained 20 footnotes which were omitted from this narration. --- First published: June 23rd, 2025 Source: https://www.lesswrong.com/posts/bnnKGSCHJghAvqPjS/foom-and-doom-2-technical-alignment-is-hard --- Narrated by TYPE III AUDIO. ---Images from the article: