I like stories where characters wear suits.

Since I like suits so much, I realized that I should just wear one.

The result has been overwhelmingly positive. Everyone loves it: friends, strangers, dance partners, bartenders. It makes them feel like they're in a Kingsmen film. Even teenage delinquents and homeless beggars love it. The only group that gives me hateful looks is the radical socialists.

1. The first time I go somewhere wearing a suit, people ask me why I'm wearing a suit.
2. The second time, nobody asks.
3. After that, if I stop wearing a suit, people ask why I'm not wearing a suit.

If you wear a suit in a casual culture, people will ask "Why are you wearing a suit?" This might seem to imply that you shouldn't wear a suit. Does...

Outlive: The Science & Art of Longevity by Peter Attia (with Bill Gifford^[1]) gives Attia's prescription on how to live longer and stay healthy into old age. In this post, I critically review some of the book's scientific claims that stood out to me.

This is not a comprehensive review. I didn't review assertions that I was pretty sure were true (ex: VO2 max improves longevity), or that were hard for me to evaluate (ex: the mechanics of how LDL cholesterol functions in the body), or that I didn't care about (ex: sleep deprivation impairs one's ability to identify facial expressions).

First, some general notes:

• I have no expertise on any of the subjects in this post. I evaluated claims by doing shallow readings of relevant scientific literature, especially meta-analyses.
• There

Glenn Beck is the only popular mainstream news host who takes AI safety seriously. I am being entirely serious. For those of you who don't know, Glenn Beck is one of the most trusted and well-known news sources by American conservatives. 

Over the past month, he has produced two hour-long segments, one of which was an interview with AI ethicist Tristan Harris. At no point in any of this does he express incredulity at the ideas of AGI, ASI, takeover, extinction risk, or transhumanism. He says things that are far out of the normie Overton Window, with no attempt to equivocate or hedge his bets. "We're going to cure cancer, and we're to do it right before we kill all humans on planet Earth". He just says things...

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...

This is more speculative and confusing than my typical posts and I also think the content of this post could be substantially improved with more effort. But it's been sitting around in my drafts for a long time and I sometimes want to reference the arguments in it, so I thought I would go ahead and post it.

I often speculate about how much progress you get in the first year after AIs fully automate AI R&D within an AI company (if people try to go as fast as possible). Natural ways of estimating this often involve computing algorithmic research speed-up relative to prior years where research was done by humans. This somewhat naturally gets you progress in units of effective compute — that is, as defined by...

Not saying we should pause AI, but consider the following argument:

1. 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.
2. 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

A long essay about LLMs, the nature and history of the the HHH assistant persona, and the implications for alignment.

Multiple people have asked me whether I could post this LW in some form, hence this linkpost.

~17,000 words. Originally written on June 7, 2025.

(Note: although I expect this post will be interesting to people on LW, keep in mind that it was written with a broader audience in mind than my posts and comments here.  This had various implications about my choices of presentation and tone, about which things I explained from scratch rather than assuming as background, my level of comfort casually reciting factual details from memory rather than explicitly checking them against the original source, etc.

Although, come of think of it, this was also true of most of my early posts on LW [which were crossposts from my blog], so maybe it's not a big deal...)

In this post I want to highlight a small puzzle for causal theories of mechanistic interpretability. It purports to show that causal abstractions do not generally correctly capture the mechanistic nature of models. 

Consider the following causal model $M$:

Assume for the sake of argument that we only consider two possible inputs: $(0,0)$ and $(1,1)$, that is, $X_1$ and $X_2$ are always equal.^[1]

In this model, it is intuitively clear that $X_1$ is what causes the output $X_5$, and $X_2$ is irrelevant. I will argue that this obvious asymmetry between $X_1$ and $X_2$ is not borne out by the causal theory of mechanistic interpretability.

Consider the following causal model $M^{\ast }$:

Is $M^{\ast }$ a valid causal abstraction of the computation that goes on in $M$? That seems to depend on whether $Y_1$ corresponds to $X_1$ or to $X_2$. If $Y_1$ corresponds to $X_1$, then it seems that $M^{\ast }$ is a faithful representation of $M$. If $Y_1$ corresponds to $X_2$, then $M^{\ast }$ is not intuitively a faithful representation of $M$. Indeed, if $Y_1$ corresponds...