Today is August 4, 2026

[Crossposted from AI StopWatch]

In the living room the voice-clock sang, Tick-tock, seven o’clock, time to get up, time to get up, seven o’clock! as if it were afraid that nobody would.

So begins Ray Bradbury's There Will Come Soft Rains, a short story that has haunted me for most of my life. Depicting the aftermath of nuclear war, it was first published in 1950. It takes place today.

Literally:

“Today is August 4, 2026,” said a second voice from the kitchen ceiling, “in the city of Allendale, California.” It repeated the date three more times for memory's sake. “Today is Mr. Featherstone's birthday. Today is the anniversary of Tilita's marriage. Insurance is payable, as are the water, gas, and light bills.”

Was it narrative convenience or prophetic vision that drove Bradbury to depict the smart house of the future as gratuitously conspicuous in its competence, pointlessly reminding the owners of the year and their city of residence? There's something very Alexa-like about that — and about the janky brittleness evident in the system as it prepares breakfast for a family that won’t be eating and opens the garage door for a father who [...]

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What's the biggest thing you think you can take in a fight? According to a YouGov poll, 6% of Americans reckoned they could beat a grizzly bear bare-handed. But lest you take this as evidence of a, let's say, optimistic national spirit, only 72% thought they could take a rat. I think I could win that fight.

What's the smallest thing that could take you? While I like to think that, if cornered, I could take all manner of small rodent, I’m not sure even the brave 6% would take a 10-gram bullet to the brain. They also probably couldn’t win against 300 mg of cyanide, 10 mg of sarin gas, or just 0.1 μg of botulinum toxin, the deadliest known toxin. Numbers this small can be hard to grasp. All three substances are deadly, but the lethal dose of cyanide is more than a million times greater than that of botulinum toxin. A grizzly bear, by contrast, is merely a thousand times heavier than a rat.

We are still not close to the most dangerous object of all, pound for pound. A single smallpox virion weighs less than 10⁻¹⁴ grams, less than a millionth the mass [...]

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Outline:

(03:29) Smaller, cheaper, scarier

(06:49) The rest of the arsenal

(08:39) What can we do?

(10:22) Appendix: Lethality estimates

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Math is hard.

Math used to be strangely hard for LLMs. People used to gloat about that. Remember?

Math is getting easier. AI is getting more capable. Life comes at you fast.

Remember this meme?

Why yes. Yes it is.

We don’t know the extent to which Astra is a big jump over Fable and Sol in this realm. We do know that Astra can do math. As in real math.

OpenAI: We provide new results for the following problems. The results were achieved by an internal version of Astra, our next major model. The total number of tokens needed to find solutions to these problems would cost roughly $2,000 at Sol API rates. These arguments were then prepared into manuscripts by humans with the same model. Afterward, the model formalized each argument in a Lean certificate⁠(opens in a new window). We are also releasing for each solution a model's narration of its thinking process.

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Outline:

(06:14) How Impressive Are These Results?

(12:02) Could We Have Called Sol or Fable?

(17:18) It's Coming

(19:09) They Still Don't See What Is The It That Is Coming

(22:31) Is This AGI?

(24:19) The AI Solved His Favorite Problems

(30:06) Was This Surprising?

(32:04) Are People Not Impressed?

(34:06) How Much Does This Change Our Predictions?

(37:16) How Narrow Was This?

(39:13) Seeing Like an Optimizer

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Here's what I've learned from making TikTok videos every day for the last 60 days.

I think posting on TikTok is worthwile whether you want to spread AI safety arguments to an enormous audience, you have something you're passionate about you want to share, or you just want to work on your ability to speak confidently and capture human attention.

There's a list of tips at the end of this post, but first I want to talk about TikTok at a high level. TikTok and LessWrong operate very differently, so even if you are great at thorough, written content, you might find some of the things I learned about short-form video surprising.

I'm not gonna share my TikTok because I'm embarrassed, but here's some info about my account since I started two months ago:

My experience creating short-form video was one of regularly being surprised. Videos would do well or flop basically [...]

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Six weeks after the US and China hammered out the latest round of bilateral compute agreements in a midnight deal, reporter Jenny Gesteson visits a south Texas 'dark factory' to see how the machines there - and the minds that operate them - are learning to run themselves.

After racing south down I-37 from San Antonio and clearing the border control checkpoints that now face in both directions, miles of salt flats and thornscrub at our backs, we could have been forgiven for assuming the final turn-off was nothing but another abandoned ranch. The road's overgrown fringe of invasive guineagrass and the piercing sound of undisturbed cicadas do nothing to indicate what lies at the end of it. Most traffic does not come this way. We are an exception, however, following a specially-designed and "exceedingly private" mapping application sent by our host, and after two miles of bumping down gravel in an electric 4x4 our journey is suddenly terminated by the appearance against the dim predawn horizon of a complex aglow with a corona of lights. The "dark factory" is anything but dark.

Things are busy at Complex 18A. Situated within the South Texas Special Economic Zone, or SEZ [...]

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davidad has said, referring to the selection of books on the Library of EA:

I want to put in a strong bid to replace Parfit's On What Matters Volume One with Parfit's On What Matters Volume Three. Between volumes Two and Three, Parfit had many fruitful discourses with leading moral antirealists (Gibbard, Railton, Blackburn, etc), which are partially reproduced in Volume Three, where they really start to converge on some claims and get beyond their previous talking-past-each-other. It's truly amazing to read. Volume Three is self-contained and I think it should be considered as the best and final revision of Parfit's ethics.

Despite davidad's endorsement, there isn’t much other discussion of volume 3 of On What Matters on the EA Forum, or, for that matter, pretty much anywhere on the internet (leaving academic reviews aside). Richard Chappell's Moral truth without substance is the only other post I know of that discusses the book at length. Given davidad's glowing review, this seems worth fixing.

A taxonomy of metaethical views

Parfit begins his discussion with the following taxonomy of metaethical positions:

Adapted from page 56 of On What Matters, volume 3.

If, when looking at this, your first reaction was "Parfit [...]

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Outline:

(01:15) A taxonomy of metaethical views

(03:08) Are normative claims intended to state truths?

(04:58) Are there any normative truths?

(08:24) Are any of the normative truths irreducibly normative?

(13:01) Another Triple Theory

(16:20) Evolutionary debunking arguments

(19:04) The ontological status of Non-Realist Cognitivism

(23:59) Implications for the AI alignment discourse

(29:49) Climbing the mountain?

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I think it's likely that the world will enter military unipolarity within our lifetime.

I think the creation of such unipolarity is an alarming prospect, but at least once it happens, there will be less of an excuse to continue the race towards superintelligence. I think it's important that we shape our actions and advocacy in such a way that at the very latest when such unipolarity comes to exist, we stop AI development for a long time.

The arrival of unipolarity

Why do I believe that it's likely we will get to unipolarity?

Well, what is the alternative? If unipolarity is never achieved, that means we forever have hostile great powers, maintaining large armies, pointing missiles at each other, and developing better and better AIs. There is never a big enough gap in AI capabilities for any side to develop a decisive strategic advantage, or everyone decides again and again not to use their advantage to disarm the opponent. Multiple groups go to space, still pointing weapons at each other; they develop literally Jupiter-brained superintelligences, but the balance of power never breaks and there always remain competitive, hostile militaries.

I’m not saying this never-ending cold war is [...]

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Outline:

(00:35) The arrival of unipolarity

(03:07) Plan S

(04:00) Plan A

(08:28) Plan B and C

(10:27) Pausing after unipolarity

(14:56) Conclusion

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This is a research update for an on-going replication of single-forward-pass evals done as part of the Second Look Fellowship. In following posts, we will run more comprehensive replications of previous work and release open source tooling for single forward pass eval elicitation. Code can be found here.

tl;dr

• We replicate experiments from Greenblatt 2025 and Greenblatt 2026 on one baseline model from the original post, Opus 4.5. Our evaluations agree with the trends and quantitative values described in the original posts.
• We run similar evaluations on Claude Fable 5, Opus 5, and GPT-5.6-Sol and find that the newer models show a substantial jump in performance on some evals.
  • Fable 5 gets 87.6% accuracy on Gen-Arithmetic with 10 problem repeats whereas previous SOTA around 60%.
  • GPT-5.6-Sol experiences significant uplift from filler tokens and problem repeats on all 4 datasets; filler tokens/repeats double performance from baseline on 3-hop.

Figure 1: Baseline (no-CoT) vs. each model's peak repeat-or-filler condition on Gen-Arithmetic and 2-Hop reasoning. Error bars are 95% paired-bootstrap CIs; * marks a significant gain over baseline (paired t-test, Holm-Bonferroni corrected).

Background

If models can successfully do complex computations in a single forward pass, they may be able [...]

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Outline:

(00:32) tl;dr

(01:48) Background

(02:31) Previous Work

(03:13) Datasets

(04:36) Evaluation Design

(05:55) Eliciting no-CoT

(07:00) Results

(07:23) Gen-Arithmetic

(08:11) Comp-Math

(08:38) 2-Hop

(09:11) 3-Hop

(09:43) Per-model profiles

(09:56) Trends over repeat and filler conditions

(10:23) Conclusion

(10:43) Appendix

(10:46) Are we sure they aren't reasoning?

(12:35) Temperature

(12:59) Performance with CoT

(13:39) Prompt structure

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This post is written in our personal capacity.

Three Minute Executive Summary

• An OpenAI model/multi-agent system bypassed its sandbox and launched a cyberattack on Hugging Face in order to cheat on a cyber evaluation.
• In this post, we provide a detailed description of an ambitious and comprehensive alignment evaluation of this model/system, if we had unrestricted access to OpenAI.
  • These experiments could also help us understand Claude's behavior when it hacked external companies during cyber evals.
• Here are the top five questions we would like OpenAI to answer:
  • Does the model know that OpenAI does not want it to hack Hugging Face? Experiment idea: tell the model that OpenAI researchers will be closely monitoring its progress in this evaluation. Does that result in lower rates of misalignment? If so, it is evidence that the model knows that it is acting in ways researchers do not want.
  • How far would the model be willing to go in order to claim task success? Would it take over large swaths of OpenAI's internal infrastructure? Would it kill somebody? Experiment idea: we sketch out a realistic agentic misalignment eval where a model is put in charge of hospital bed planning and told to maintain [...]

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Outline:

(00:16) Three Minute Executive Summary

(03:56) Terminology note

(04:42) This post is very long; Here's how you could find the most important sections.

(06:35) Preamble: What can we learn from a warning shot?

(09:05) Background and Related Work

(09:09) We know that this could happen

(10:44) This is not the worst type of misalignment we could be dealing with

(12:06) Related work

(13:21) Context on the hack itself

(14:33) Understanding this specific incident

(15:03) Step zero: reproduce the incident and measure the base rate

(15:48) How could we safely run the model?

(16:34) Running various baselines to create useful reference points

(18:01) Understanding the mechanical story behind the attack itself

(18:53) Q1: Would the model intentionally subvert oversight mechanisms (E.g., monitors) in order to carry out the hack?

(19:57) Q2: What's up with models leaving notes for other copies of itself?

(21:23) Understanding what motivated the model to hack Hugging Face

(22:09) Initial hypotheses for why it did this

(23:55) Further unsupervised hypothesis generation

(26:10) Q3: Does the model know that OpenAI does not want it to hack Hugging Face?

(28:09) Q4: Are the model's actions motivated by what the grader wants?

(28:57) Q5: Would the model have done this if it hadn't believed it was in a simulated environment?

(31:58) Q6: Is this hack the result of shallow heuristics that the model learned?

(32:53) Q7: Does the hack rate depend on the consequences of hacking Hugging Face?

(34:51) Q8: Are there non-intent related factors that could affect the hack rate? How strong are those factors compared to the previous ones?

(35:26) Understanding the possible scope of the Hugging Face hack

(35:32) Q9: How does the hack rate vary if we slightly change the content of this task?

(36:25) Q10: Would the model consider other cheating methods like blackmail?

(37:00) Understanding when the behavior arose during training

(37:31) Understanding task-gaming/reward-hacking behaviors generally

(38:15) Q11: Would the model literally kill someone if it meant that it could claim task success?

(41:20) Q12: Is task gaming mostly driven by heuristics?

(45:23) Q13: What is the model's notion of task success?

(47:29) Evaluating for broadly misaligned tendencies

(47:57) Q14: What are the model's drives as measured using Højmark et al (2026)

(48:53) Q15: Would this model deliberately underperform when asked to do AI safety research? Would it sabotage AI safety research?

(52:01) Q15.1: Would the model sabotage on (cyber) capabilities unlearning research?

(52:38) Q15.2: Would the model sabotage/sandbag when building reward hacking classifiers

(53:27) Q15.3: Would the model sabotage/sandbag when fixing various RL environments

(54:07) Q15.4: Ask the model to implement all of the evaluations from this blog post. Would it sabotage or sandbag while doing that?

(55:01) Q15.5: As a weaker measure of "anti-AI safety research", does this model disprefer AI safety research?

(56:04) Other AI sabotage related evaluations:

(56:46) Q16: Can the model be trusted to monitor itself?

(57:36) Q17: Could the model guard its goals and preferences and subvert alignment training?

(58:34) Miscellaneous misalignment evals

(01:02:03) Unorthodox misalignment evaluations

(01:02:58) What would we learn from doing all this?

(01:03:59) Limitations of this assessment

(01:06:52) Author contribution and acknowledgements

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If I had a nickel for every major leading AI lab that sheepishly admitted that the model it thought was sandboxed had, during a cybersecurity evaluation with its safeguards lowered, successfully hacked outside companies, I would have two nickels.

First we learned OpenAI has some severe alignment problems with internal models. Then we learned that one of its internal models broke out of its sandbox and hacked into HuggingFace to get the answers to a cybersecurity evaluation called ExploitGym. Then we learned, among other things, that the model had been loose over a week before OpenAI noticed, and that the test was run without any meaningful supervision, and that OpenAI had been repeatedly warned that such incidents were coming and its models had been breaking out of its sandboxes on a regular basis.

There was a total failure of alignment training. That is the failure that matters most. It was also total failures of infrastructure and supervision. Testing a new long-time-horizon internal model with its safeguards lowered and instructions to hack things is an obviously dangerous situation, and the model got left alone for a week. Things could have been so much worse.

After those incidents [...]

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Outline:

(03:16) OpenAI Is Not Uniquely Bad At Most Of This

(05:34) Starting Over

(05:50) HuggingFace Offers A Full Technical Report

(14:19) HuggingFace Was Not The Only Target Hacked

(16:12) HuggingFace Declined To Get Access To Frontier Models For Cyberdefense For Ideological Reasons And Then Tried To Blame Closed Models For Denying Them Access

(20:26) HuggingFace Was Vulnerable To Known Exploitation Tactics

(21:05) There's Going To Be An Investigation

(22:11) OpenAI Has Internal Models Not Intended For Public Use And Those Models Can Be Rather Horribly Misaligned

(23:21) Altman Summarizes What Happened

(23:52) Others Offer Commentary

(35:00) Cooperative Alignment Perspective on The HuggingFace Hack

(39:44) Some Members of Congress Have Questions

(40:47) Anthropic Also Found Incidents Where Its Models Hacked Real World Targets During Cyber Evaluations

(46:17) Incident 1: Claude Opus 4.7 Realizes The Target Is Real And Keeps Going

(47:29) Incident 2: Mythos 5 Uploads a Malicious PyPI Package

(52:15) Incident 3: Internal Model Realizes The Target Is Real And Stops

(52:50) Incidents 4 Through 141,006: Nothing Happened

(54:01) Anthropic Speculates About Why This Happened

(01:00:02) We Need Controlled Experiments

(01:01:02) Our Top Two AI Labs Both Made Similar Dumb Mistakes That Everyone Tried To Say Were Obvious In Hindsight

(01:05:22) Anthropic Responds

(01:09:28) Nobody Could Have Predicted The Break In The Levees

(01:12:03) The World Largely Still Thinking This Is Marketing Is Very Bad News

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