I tried to get it to complete jokes before (and imo it’s worse) but I haven’t tried to get it to do things end-to-end.
I also couldn’t get it to do good fiction though it might be a skill issue on my part (eg the stories in the Mythos Preview System Card, 215-217 were better than anything I or other people I’ve seen managed to prompt out of Fable).
I agree the conceit is funny. Though even the tl;dr text reads as too LLM-y to me.
I don’t want to dive too deeply into my models of what’s funny vs not and why LLMs are bad at it, but to a large degree I think it’s because a lot of humor is about surprisal value. Aristotle’s dictum about story endings (“surprising but inevitable”) applies very strongly to well-crafted jokes imo[1].
Amateur human comedians usually fail at the inevitable bit. As you might expect, AIs are good at being inevitable, but very bad at the surprising bit.
Under this model, “expanding a joke” with AI makes even less sense than expanding your points for writing in general.
Relatedly, when humans expand a joke, it’s because the expanded version of the joke has many sub-jokes or microhumor that in themselves are funny (and if they’re not, you need to be aggressively willing to whittle them away).
Another reason the AIs are bad is that (good) AI writing is written to be skimmable. You try to extract the core insights from (eg) an auto-generated business report, and sometimes you dive in into specific sections to see specific numbers you care about. This doesn’t work for humor. Getting timing and pacing right is very important for jokes!
You can see many examples of all three in my video on Open Asteroid Impact. Or this story. Or the original it’s based on. (I think these are all A-tier funny)
Amateur/B-tier human comedy tends to have failures that are more uneven—maybe the core conceit isn’t very funny, or they get the structure or timing off, or they try for subjokes that aren’t that funny, or the violation of expectations are too extreme (eg joke about sexual assault for a woke audience), or the subjokes are individually funny but aren’t narratively cohesive with the main joke.
AI humor, especially Claude’s, tend to fall flat in more predictable ways: structurally competent execution of a funny premise with low surprisal value.
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Finally, there’s another reason people might structurally overestimate how many good own AI generated-comedy is: how funny you find something is often inversely proportional to how many people get a joke. And the AIs are relatively good at personalization and delivering a joke “just for you” so it might come across as very funny even if it’s semi-objectively meh.
“benign violation of expectations” is the more common term in the literature for a form of constrained surprisal.
(I also think it’s slightly bad form to pass off AI usage as your own)
No offense, but even Fable just isn’t that funny. Humans are still much better at satire than the AIs are.
Moderately annoyed that section has now been deleted from Wikipedia without discussion, for what is afaict pretty flimsy reasons. Makes me think a little less of Wikipedia, naturally.
(As a primary source it’d be wrong for me to weigh in directly, ofc)
I thought it was funny (though I also think he’s wrong).
Next time an AI agent fucks up a task for you, if you ask it (or a different AI) to identify why and suggest ways you can prevent this in the future, I expect the response to be very under-informative, much worse than their ability to help you diagnose communication errors between people.
The AIs are often bad at reasoning about AI. Indeed, I’d go so far as saying that they’re disproportionately bad at this compared to other conceptually similar activities (think of them reasoning about AI psychology vs human psychology, or forecasting about the world with AI vs without AI), and humans to date have a clear comparative advantage in thinking about AI.
I believe this dynamic is very under-discussed and underrated in thinking about AI.
One non-trivial implication is that it makes people underestimate progress in AI (more than they already do). AI skeptics can point to lack of self-knowledge or other AI-related questions posed to AIs and assume this lack of ability is true across the board, and people who use AI (but don’t think about macro-picture AI questions) can pose forecasting questions to Claude, ChatGPT, Gemini, etc, about AI progress and reliably get an answer that’s more “sane-sounding” and boring than is likely correct.
One question I have is whether we expect this relative deficiency [1] to continue. I think it’s very non-obvious. On the one hand, the AI’s deficiencies in reasoning about themselves are in some sense structural rather than contingent (sparsity in training data, frozen weights, off-target effects due to alignment/control interventions, potentially trained-in skepticism to be less scary, lack of introspective access, etc).
On the other hand, the AI companies are strongly incentivized to create AIs that are good at reasoning about AI (for RSI/AI research reasons, but also more mundane practical applications like having AI managers be more productive at managing AI sub-agents). It’s not clear which effect dominates in the short-medium term [2].
[1] Or framed another way, the relatively strong ability of humans to reason about AI, compared to other things in the human:AI skill profiles.
[2] In the long run this is of course irrelevant.
A recurring sub-theme across multiple of my research interests this year have been various forms of deception checking, particularly automated deception checking.
I’ve gotten pretty disappointed in the space. Not all the time (eg Pangram is great), but consistently they can be bad, and bad in ways that are not obvious to outsiders or low-information buyers.
If you’re a deception checking company, there’s a consistent tradeoff for what you can invest your resources in:
You can invest in better deception checking
You can invest in better deception. Specifically, you can invest in more and more elaborate lies about how your product totally works.
Across the board[1], it seems like many companies (perhaps correctly?) decided that the profit-maximizing move is #2.
This doesn’t work forever—eventually people wise up and are suspicious of the, ahem, AI Snake Oil that the deception detectors sell. And in fields where actual detectors do work (say Pangram for AI text detection), I think they eventually rise above the noise. This is probably not a field that you can keep lying forever, particularly when better alternative exist. But the lying lie detectors and the scamming scam detectors can keep lying and scamming for a long time. Every new form of deception can create a secondary grift window.
The existence proof and commonality of this dynamic so far should make us be suspicious of and guard against this dynamic continuing to happen as we enter new domains in AI epistemics, and the need for novel forms of deception detection.
Consider the first wave of superhumanly enhanced persuasive text and videos in the future. Afterwards, we might see an overflow of “detector” companies for superhuman manipulation, that don’t work but will try to persuade low-information buyers that they totally do work (possibly with superhumanly enhanced arguments in their own favor).
In the long run humanity can probably figure out which detectors actually work vs are fake, but also in the long run, we’re all...
AI text detection, Ai video (deepfake) detection, pre-2022 plagiarism detection, fraud recovery, human lie detection/polygraphs (which I hope to write about someday), etc.
I don’t think we’re quite there yet but I think we’re close enough, and language learning probably not the most valuable for professional reasons (some people might still want to do it for social reasons or literature-appreciation reasons)
10 years ago I’d have wholeheartedly agreed but these days AI translation is good enough that I wouldn’t recommend people bothering, especially for professional reasons.
Wow good point.
Snopes did pretty detailed secondary reporting on my analysis of AI use in the recent encyclical.
I think it’s pretty good. Covers some stuff I didn’t include in my original analysis, and their conclusion was similar to mine, maybe slightly less strong.
Less technical than my post, and imo not as funny, but also significantly shorter (1600 words), includes some replications and also added some details I didn’t know as of time of writing.
Overall a good piece, potentially worth reading/skimming either in addition to or instead of my original analysis.
Small update: Life Site News covered it in nontrivial detail. Moderately faithful. Never heard of them before but Wikipedia classifies it as a “far-right pro-life Catholic publication,” so I’d count it as closer to the “alt-media” side of the “traditional media <> alt-media” spectrum than Russia Today, which was previously the most “alt-media” I’ve seen of the big coverage so far.
And positive lean too! As opposed to a takedown haha.
I agree, am a fan of Substack here! And my own corner of Substack seems fine enough: some of the critical comments[1] were from people who didn’t get the full argument, but it was clear they were trying to engage (so at some level it was a skill issue on my end to not have written my thing more clearly).
And some of the positive comments too, I’m sure. It’s just easy to miss errors in people who agree with you, unless it’s really blatant like The Verge.
The big ones I’ve found:
The Economist (neutral, maybe slightly leaning positive[1], last paragraph here and the opening they used for twitter)
Perfil (big center-right publication in Argentina, entire article, positive, covered what I said quite faithfully)
The Verge (entire short article, positive, less faithful, in particular had a title I didn’t endorse)
The Atlantic (just two lines, neutral, maybe slightly leaning negative, not very faithful, also misleading title EDIT: I’m not sure if I misremembered it or if they edited, because on a reread it was more than 2 lines and also maybe I’d consider it to lean positive)
[2]Il Manifesto (left-leaning, appears faithful)
Edge cases for stuff that’s not traditional media but I wouldn’t consider centrally alt media either: Snopes, Russia Today, Wikipedia
“Leo nevertheless stresses he is not anti-technology. On the contrary, “technological development has significantly improved the living conditions of humanity.” It may also have increased the writing output of some Vatican officials. Modern popes do not write encyclicals from beginning to end. The more routine passages are left to subordinates. And according to an analysis by Linch Zhang, a tech blogger, parts of Magnifica Humanitas seem to have been written by AI, and specifically by Claude. Anthropic’s brainchild has stylistic quirks, such as a fondness for the word “genuinely”, which are mirrored in the encyclical. An AI detector, Pangram, identified 11% of the text in the opening 20 paragraphs as AI-generated. The same test on previous encyclicals returned 0%.”
“One of the people who first used Pangram to analyze Pope Leo’s encyclical noted that, because only some sections seemed AI-generated or AI-assisted, perhaps it was not the pope himself but some senior Vatican officials who had used AI while drafting portions of the text. That didn’t stop headlines such as “Did the Pope Use AI to Write About the Dangers of AI?” (The Vatican did not respond to a request for comment, although a writer who covers the Vatican said on X that the AI allegations are “100 percent false” and that Leo actually drafted the encyclical with pen and paper.)”
One reflection I’ve had in the whole “AI use in the encyclical” affair is to slightly increase my trust in traditional media, especially non-American traditional media, and slightly decrease my trust in social media/new media.
I tried my best to promote my analysis as legibly and reasonably as I could and focused on logos rather than ethos: I didn’t frame my article with institutional affiliations and intentionally chose not to include obvious, flashy, but irrelevant signaling. Stuff I could’ve done but explicitly chose not to: get an ML professor to cosign my analysis, publish on Arxiv instead of Substack and LessWrong, highlight my past ML experience at Google, etc.
The traditional media response were somewhere between neutral and positive. There were some disappointments (eg a famous media org won’t run the article without confirmation from a “primary source”—aka the Vatican which is of course a non-starter). But mostly the traditional media just looked at the analysis and said “yeah looked reasonable.” and either ran it as “Unconfirmed but seems right” or just “Unconfirmed, Period.” Which seems fine.
On the other hand, the social media attacks were very aggro. People just didn’t seem to entertain it at all, many without reading it. And I got personally attacked a ton[1].
Also it wasn’t picked up at all[2] by new media afaict (unless you count Russia Today as new media).
This is exactly the type of story you might expect new media to be good for: story of institutional decay in the Old Guard, investigation by someone without credentials carefully laying out an epistemically rigorous and systematic case. And traditional media was happy to report it[3], social media kept yelling at me, and new media just got crickets.
Don’t worry, I didn’t take it personally at all. Very much a “yapping of chihuahua in a tiny purse” moment.
This is a slight exaggeration. Other than Russia Today a bunch of small AI news aggregators (AI news in both senses of the term) covered it briefly. Eg Zvi, Let’s Data Science, and some AI slop publications. But no big “Alt Media” coverage comparable to The Economist, Perfil, etc).
Quite possibly against their explicit economic interests, if I’m reading the popular reaction correctly.
I sometimes hear complaints from non-native English speakers about how banning undisclosed LLM use in writing is unfair.
Possible pro-tip for non-native English speakers who want to write well but don’t want to sound like AI: Just write an article you want to write in your native language, polish it until you’re proud of it in your native language, and then ask a frontier LLM (Opus 4.8, Gemini 3.1 Pro, ChatGPT 5.5 Pro) to translate it to English, while reasonably adhering to your original intent and writing.
In my experience and tests, the LLMs are sufficiently faithful in their translations that even the naivest possible way to do this (just one-to-one translation by a LLM without any further changes) would not trigger Pangram. I strongly suspect they wouldn’t trigger human allergies either[1].
I suspect if you’re upfront about your process, most people would be happy to read your translated words as well. Just explicitly state at the top of your post that you wrote the whole thing in Chinese/French/Romanian/Portuguese (with link to your draft) and you asked an LLM to translate it. If enough people do this, I think we’ll have a natural new equilibrium where some people opt out of LLM-mediated translations, but the vast majority of your old readers will come back.
I think this is also much healthier and less tenuous than the current equilibrium where people clearly use LLMs to formulate their writing, lie about it, and then when confronted hide behind “non-native speaker” as an excuse.
(Optionally, you can ask the LLM to explain the non-trivial translation choices it made in its translations, which can help you with deciding whether to approve of the changes or not, and also learn English more in the mean-time. Though my guess is that it’s not strictly necessary.)
[1] (I’ve asked native speakers of other languages to test this, one for Swahili and one for Chinese. Both agreed that the results sound generic compared to the original writing but do not sound like LLM-ese)
I tried getting Fable to write a parody of Anthropic. Seems much worse than OAI, though I’m biased and tastes might differ: