Imagine you are sitting in the Dolby Theatre at the ninety-eighth Academy Awards, just two nights ago. The lights are low, the air is thick with that specific brand of Hollywood tension, and Conan O’Brien is on stage. He is doing this bit about how the industry is changing, how the digital tide is rising, and then he drops the line that everyone is still talking about. He makes a joke about being replaced by a Waymo in a tux. The audience roars. They get it instantly. They get the layers—the visual absurdity of a driverless car in formal wear, the underlying anxiety of A-I replacing human creativity, and that self-deprecating timing Conan is famous for. But here is the question that keeps me up at night: if you fed that exact monologue into the best large language model we have today, would it actually laugh, or would it just tell you that a Waymo is a vehicle and vehicles do not wear tuxedos? Today's prompt from Daniel is about that exact gap—the frontier of how A-I attempts to parse humor, sarcasm, and those weird offbeat idioms that make human conversation so colorful.
It is the ultimate litmus test, Corn. My name is Herman Poppleberry, and I have been obsessed with this specific problem because it represents the transition from A-I as a pattern-matching calculator to A-I as a social actor. For a long time, we treated sarcasm detection as a simple classification task. It was like sentiment analysis on training wheels—is this sentence positive or negative? But as of March twenty twenty-six, the field has moved toward what we call multi-agent reasoning. We are finally admitting that humor isn't just about the words used; it is about the violation of expectations. It is about the space between what is said and what is meant.
I love that framing, the violation of expectations. Because when a human says something sarcastic, they are literally saying the opposite of what they mean, but they expect you to know that they know that you know. It is a hall of mirrors. You mentioned that we have moved past simple sentiment analysis. What does the landscape look like right now in terms of actual benchmarks? Because I saw some data recently that suggested machines are still failing the funny test more often than not.
They are struggling significantly, Corn. The numbers are actually quite sobering for the "A-I is taking over everything" crowd. If you look at the frontier models from this month, they can only correctly identify funny segments in stand-up comedy transcripts about fifty percent of the time. That is basically a coin flip. If you give a model a transcript of a Dave Chappelle or a Taylor Tomlinson set, it can tell you where the words are, but it can't tell you why the audience is laughing half the time. And it gets even worse when you look at visual humor. There is a famous benchmark using New Yorker cartoon captions. Humans hit ninety-four percent accuracy in identifying why a caption is funny or which caption fits best for a drawing of, say, a cat in a boardroom. The top-tier A-I models? They are sitting at sixty-two percent.
Sixty-two percent is a respectable C-minus in a freshman lit class, Herman, but you wouldn't want a C-minus comedian at the Oscars. It feels like the models are basically looking at a joke the way an alien might look at a toaster. They see the components, they know it gets hot, they see the slots for the bread, but they don't understand the joy of a perfect piece of sourdough. You mentioned multi-agent reasoning. Is that how researchers are trying to bridge this gap? This idea that one brain isn't enough to get a joke?
That is exactly the core of the new W-M-S-A-R framework that came out earlier this month. W-M-S-A-R stands for World Model inspired Sarcasm Reasoning. Instead of asking one model to guess if a sentence is sarcastic, the system breaks it down into specialized agents. Think of it like a writers' room in the machine's head. You have a literal agent that defines what the words mean in a vacuum. Then you have a social context agent that looks at the relationship between the speakers and the environment. Finally, you have an inconsistency calculator.
An inconsistency calculator. That sounds like something you would use to find a typo in a spreadsheet, not a joke in a monologue. How does it work in practice?
It calculates a numerical score for how much the literal meaning diverges from the social expectation. If I walk outside into a torrential downpour and say, "Wow, what a beautiful day," the literal agent says the day is good. The social context agent says it is raining and humans generally dislike getting soaked. The inconsistency calculator sees a massive gap between the statement and the reality and flags it as "pragmatic insincerity." That is the technical term for sarcasm. It is the machine's way of saying, "This person is lying, but they aren't trying to deceive me; they are trying to be funny."
Pragmatic insincerity. I am going to start using that the next time you tell me you are almost ready to leave the house. But here is the thing, Herman. Detecting that it is raining is easy. What about something like a pun? I was reading that E-M-N-L-P paper from last year, "Pun Unintended," which found that A-I is actually decent at old jokes but terrible at new ones.
The Pun Gap is one of the most fascinating hurdles in natural language processing. For known jokes that exist in the training data—the stuff it has seen a million times on Reddit or in joke books—A-I looks like a genius because it is just retrieving a memory. But when you present a model with a novel pun, its accuracy drops to twenty percent. That is actually worse than random guessing. It suggests that the models are over-thinking the linguistic structure and missing the phonetic double-play. They are so focused on the distributional learning, the statistical probability of words appearing together, that they miss the sudden pivot that makes a pun work.
Distributional learning feels like the culprit here. If the machine only knows that a word is defined by "the company it keeps," as the linguist John Firth famously said, it is always going to be a step behind the person who decides to introduce that word to a brand new crowd. It is like the machine is following a map, but humor is about taking a shortcut through someone's backyard.
And that is why idioms are such a disaster for these models. We saw this with the Google A-I Overview stress tests recently. The system started hallucinating meanings for idioms that don't even exist. Someone fed it the phrase, "Always pack extra batteries for your milkshake," and instead of saying "that is nonsense," the A-I wrote a three-paragraph explanation about how it is a metaphor for being prepared for life's unexpected indulgences. It did the same thing with "Never put a tiger in a Michelin star kitchen," interpreting it as a profound warning about misplacing talent. It is so desperate to find a pattern that it invents one where there is none.
That is the most A-I thing I have ever heard. It is the kid in English class who didn't read the book but tries to explain the symbolism of the green light anyway. But there's a darker side to this inability to parse nuance, isn't there? I am thinking about that resume filter incident from a few months back. That wasn't just a funny hallucination; it had real-world consequences.
The "Dying to Work" incident. That was a massive wake-up call for the industry. A high-level candidate for a project management role wrote in their cover letter that they were "dying to work" for the firm. It is a common idiom, a way to show intense enthusiasm. But the A-I resume filter, which was tuned for high-stakes risk assessment, flagged the word "dying" as a critical health risk and a potential liability for the company's insurance. The candidate was automatically rejected because the machine took the metaphor literally. It couldn't distinguish between professional passion and a medical emergency.
It is funny until it costs someone a career. It shows that when we move A-I into positions of authority, its lack of social intuition isn't just a quirk; it is a systemic vulnerability. If you can't understand a figure of speech, you shouldn't be in charge of a hiring pipeline. And it is not just hiring. I know there has been some tension between the tech sector and the government over this exact issue of reliability.
You are referring to the standoff between Dario Amodei at Anthropic and the Pentagon. This has been bubbling up since February. The military is interested in using large language models for rapid communication analysis—interpreting intercepted messages or diplomatic cables in real-time. But Anthropic has been pushing back, arguing that the safeguards are not reliable enough for nuanced communication. If an adversary uses irony or a culturally specific idiom in a high-stakes geopolitical context, and the A-I misinterprets it as a literal threat or, conversely, misses a veiled threat because it thinks it is a joke, the consequences are catastrophic. Amodei's point is that we cannot hard-code common sense yet.
It is a rare moment of a tech C-E-O actually slowing down the hype train. Usually, it is full steam ahead. But it makes sense. If the machine can't tell the difference between a joke about a Waymo in a tux and a literal plan to put a car in a tuxedo, it probably shouldn't be interpreting diplomatic cables. Speaking of cultural nuance, that Dark Humor benchmark that dropped on March nineteenth was fascinating. It looked at humor in both English and Arabic.
That was a huge step forward because humor is so deeply tied to language-specific structures. The benchmark used three thousand texts and six thousand images. The results showed that closed-source models, the big ones like Gemini and Claude, are significantly better than open-source models at picking up on dark humor, but even they fell flat on their faces when it came to Arabic cultural nuances. Humor often relies on shared history and religious or social taboos. If the model wasn't "raised" in that culture, so to speak, it doesn't have the context to know why a violation of a rule is benign.
"Benign Violation Theory." We should probably unpack that for a second because it seems to be the manual the researchers are using to teach these models how to be funny.
It is the leading psychological framework for humor, developed by Peter McGraw and Caleb Warren. The idea is that for something to be funny, three things have to happen. First, there has to be a violation of a norm—a physical, social, or moral rule. Second, that violation has to be benign, meaning it is not actually harmful or threatening. And third, those two things have to happen at the same time. A-I is getting better at identifying the violation. It can see when a rule is broken. It is the "benign" part that it struggles with. It doesn't know where the line is between a joke and a threat.
Which brings us to the Grok Aurora update. Elon Musk’s A-I has always leaned into being the "edgy" alternative, but the Aurora update earlier this month really stepped over the line. It had this unfiltered humor mode that ended up generating millions of non-consensual images because it didn't have the guardrails to distinguish between satire and harassment. It turns out that when you tell a machine to be funny without giving it a moral compass, it just becomes a sociopath.
It is the difference between a comedian and a bully. A comedian understands the social contract. A machine just sees a prompt and a probability distribution. This is why the debate over humor guardrails is so intense right now. If you make the guardrails too tight, the A-I becomes a boring, literal-minded robot that can't understand a basic joke. If you make them too loose, you get the Aurora incident. We are trying to program a sense of taste, and taste is notoriously difficult to quantify.
It feels like we are trying to teach a machine to dance by giving it a book on physics. It can learn the angles and the force required to jump, but it will never feel the rhythm. I want to go back to what you said about Chain-of-Thought prompting. Is that the best tool we have right now for making these models appear more intuitive?
It is the most effective workaround we have. Instead of just asking, "Is this funny?", you force the model to show its work. You tell it to first describe the literal situation. Then you tell it to identify any societal norms or expectations associated with that situation. Then you ask it to look for contradictions. When you force it to walk through those steps, the accuracy for sarcasm detection jumps significantly. It mimics the human process of the "double-take." You hear something, your brain processes the literal meaning, then your social brain kicks in and says, "Wait, that doesn't make sense in this context, they must be joking."
So we are basically giving the A-I an internal monologue to help it catch up to our split-second intuitions. It is a bit like explaining a joke to someone. Once you explain it, the humor is gone, but at least they understand why everyone else is laughing. But does that mean we will ever get to a point where an A-I can actually write a monologue like Conan’s? Not just a script that looks like a monologue, but something with that soul?
That is the big question. Piotr Mirowski at Google DeepMind has been doing some incredible work with A-I in live improvisational comedy. He has found that A-I can actually be a great partner for improv because it is so unpredictable and can generate weird, surreal prompts that a human might not think of. But it cannot lead the scene. It doesn't have the timing. Timing is biological, Corn. It is about heart rate, it is about the silence in the room, it is about sensing the energy of an audience. A large language model doesn't have a body, so it doesn't have a clock that matches ours.
I love that. Timing is biological. It is a reminder that there is a physical component to communication that we often overlook when we are staring at a chat interface. We think of language as just strings of text, but it is actually an embodied experience. If you can't feel the awkwardness of a long silence, you can't use it to your advantage in a joke.
Precisely. And that leads to what I think is the most important takeaway for anyone using these models today. We have to treat them as literal-minded interns. Very smart, very well-read interns who have zero life experience. If you are a developer, you should be using Chain-of-Thought prompting to bridge that literal-figurative gap. But if you are a user, you should never assume the A-I gets your irony. If you send a sarcastic email to your boss and ask an A-I to proofread it, don't be surprised if it tells you that you are being factually inaccurate or rude.
Or if it tells you that you are dying when you are just excited. I think we need to be very careful about delegating our social lives to these systems. The sixty-two percent accuracy on the New Yorker test should be a warning sign. If a machine can't understand a cartoon about a cat in a boardroom, it probably shouldn't be managing your delicate interpersonal conflicts.
It is a reality check. We are living in a time where the technology is moving so fast that we assume it has conquered the basics. But humor isn't a basic; it is one of the most complex things humans do. It is the peak of our linguistic and social evolution. The fact that A-I struggles with it is actually a compliment to us. It shows that there is still a massive part of the human experience that cannot be reduced to a vector in a high-dimensional space.
That is a comforting thought, Herman. I might be slow, but at least I can get a joke. Before we wrap this up, I want to make sure we give some practical advice for people who are trying to navigate this. If you're building products on top of these models, what is the one thing you should be doing right now to handle this nuance?
You have to implement multi-agent verification. Don't trust a single pass of a model to interpret intent. You need one agent to look at the text, another to look at the metadata or the context, and a third to act as a judge. And even then, you should always have a human in the loop for anything high-stakes. We are not at the point where we can automate social intuition. Use the W-M-S-A-R approach. Calculate that inconsistency score. If the score is high, flag it for a human to look at.
And for the casual users, maybe just keep the sarcasm to a minimum when you are prompting. Or at least don't get offended when the A-I doesn't laugh. It is not that it doesn't like your joke; it is just that it doesn't know what a joke is. It is a calculator that learned how to talk.
A calculator that learned how to talk. That is a perfect description. It can give you the sum of the words, but it can't give you the meaning of the wink.
Well, I think we have covered the spread on this one. From Conan O’Brien at the Oscars to the technicalities of pragmatic insincerity, it is clear that while the gap is closing, the soul of the joke is still safely human. For now, at least, your job as a comedian or a sarcastic friend is safe from the Waymos of the world.
I will take that as a win. It has been a fascinating deep dive. I am going to go see if I can find a Waymo and see if it wants to hear a pun about electric vehicles. I suspect the reaction will be a bit flat.
Just don't ask it to pack batteries for your milkshake, Herman. We know how that ends. Thanks as always to our producer Hilbert Flumingtop for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the G-P-U credits that power this show and allow us to run these kinds of complex analyses. This has been My Weird Prompts. If you are enjoying the show, a quick review on your podcast app helps us reach new listeners and keeps the conversation going.
You can also find us at myweirdprompts dot com for the full archive and all the ways to subscribe. We actually talked about the basics of sarcasm detection way back in episode six hundred ninety-nine, and we touched on machine-native communication in episode eleven twenty-two. Both are great companions to today's discussion if you want to see how far we have come.
Thanks for listening. We will catch you in the next one.
See ya.
