Codenames is a board game played between two teams of players. At the start of the game, 25 cards are laid out on a table, in a five-by-five grid. Each card has a simple word on it, like stadium, shed, gymnast, wood, or Mexico.1 One player from each team is designed to be the spymaster, and the two spymasters are given a map that assigns some of the words on the board to each team.
The game is then played in turns. Each team’s turn begins with their spymaster saying two things: A one word clue,2 and a number. This tells the other players that, of the words assigned to the spymaster’s team, that number of words are related to that one-word clue. For example, if a team was assigned the words France, red, and glass, the spymaster might say “wine, three.” The other players on the team then guess which words on the board the spymaster was trying to direct them to; the assignment of the guessed words are revealed; the other team takes their turn in the same way. The winner is the first team to identify all of their words.3
Rarely, however, are the clues as straightforward as France, red, and glass; more often, the words are like gymnast, wood, and Mexico. So, in the course of playing the game, you end up making some weird connections. For instance—if I said pearl and told you to come up with fifty related words, you’d probably never say dwarf or powder. But if I showed you this board, you might connect all three words together with white.4
I have no idea how a large language model actually works. It’s fancy autocomplete; it’s a stochastic parrot; it’s a blurry JPEG of the web, Xeroxed into a shapeless slurry. It’s very good at answering questions; it’s even better at writing code; it might be good at math; nobody likes how it writes. It reasons; it doesn’t; it might be conscious; no, it definitely isn’t; but, like, the J-space, man. I don’t know.
But here is how they seem to work. They’re an incredibly large Codenames board. It’s not a five-by-five grid of words, but a billion-by-billion grid, with the words arranged in clusters of related concepts. When we give a model a prompt, it goes hunting for related words. Tell me about asparagus, you might ask, and the model hears “asparagus, infinity.” It finds every card related to asparagus: It can tell you about their history; about their biology;5 about how they should be cooked; about VeggieTales. It knows everything there is to know about asparagus, and it will not stop guessing cards until you know everything about asparagus too. But it won’t venture too far—it probably won’t say Odin, related by the word spear—because it doesn’t need to. It has plenty of other, more obvious things it can talk about first.
In other words, another way to think about an LLM is as an extremely dense and detailed map of the world. When you talk to one, you localize the model around some topic and it aggressively narrows its field of vision, because it knows too much about that topic not too. It’s as though you asked Stephen King about writing or Yayoi Kusama6 about polka dots—they’ve thought about that exact thing a lot. They’re probably going to have more stuff to say than you have to ask.
Codenames, by contrast, is a howling void. Ask it about writing, and the best it can come up with is…brush? But there is something useful in that vacuum:
Semantic distance plays an important role in the creative process: The farther one `moves away’ from a conventional idea, the more creative the new idea will likely be. …
…creativity involves the connection of weakly related, or remote concepts into novel and applicable concepts. The farther apart the concepts are, the more creative the new combination will be.
Art, as Ms. Hunt said, is in the negative space.
On the spectrum from Codenames to ChatGPT, our individual maps—that is, our heads—are surely far closer to the former than the latter. Relative to everything ever known, which is approximately how much a large language model knows, we know next to nothing.
But this—how little we know—seems to be exactly what makes us interesting. When we think about stuff, we have to connect it with other stuff that’s slightly strange and random, because, on the sparse shelves of our mental library, all we know are slightly strange and random things.
Here, then, is a theory about how to have ideas:
In the course of whatever we do in our lives, we have thoughts. They are small, messy, incomplete thoughts about whatever it is that concerns us: A project at work; an understanding about ourselves; an idea for a blog post; how to pitch a construction company to buy your brand of steel; whatever. The thought isn’t useful yet, but it seems like it eventually might be.
When we have these ideas, it’s tempting to work on them like an AI might. Fill in the details; research them; Google them; ask ChatGPT about them; look them up in the encyclopedia. Ruminate. Stare into the abyss, and think.
Speaking from some experience, this doesn’t work. The abyss might stare back, but, you know. It’s an abyss.
Instead, what works is the thing that seems like it shouldn’t: Do something else. Go for a walk; read a book; talk to people. Keep thinking about the project or the post or steel, but only a bit, in the back of your mind, and mostly focus on something else.
Of course, this is (ironically) hardly a novel idea. But why does it work? I mean, I have no idea how brains actually work either, but if I had to guess: It’s because a walk is playing Codenames with the randomness of the world. On a walk, you run into stuff: A couple on a bench. A flag, hanging just so. An overheard conversation. A stadium. Wood. Mexico. And when you have something else in your head already, and you bang into the random stuff around you, you force yourself to subconsciously wonder, “Is this like that?”
Most of those collisions aren’t useful. But every once in a while, something sparks. There is a bridge; a parallel; an unexpected analogy that overlaps with your idea, like two interstates converging briefly before splitting again. And it’s down that other highway—when your idea maps to that other one, and the other one takes you somewhere new—where the most interesting stuff lives.7
If that’s right, there are, it seems, some implications:
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Though it’s cliché to say that you can’t rush art, if art comes from finding the right connection at the right moment, you can engineer it a bit. You can manufacture collisions. Admittedly, it feels uncomfortably counterproductive to do it—when you’re on a deadline and stuck, you feel like you should sit and stare at the work, not wander through the park with a friend—but, every time, I’m surprised by the effectiveness of wandering through many things, and the futility of thinking about just one.
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Ah, but, it’s not quite that careless. For the wandering to work, you have to surrender some portion of your focus to a constant rumination; you have to always be mildly distracted and quietly panicked. It is the opposite of work hard, play hard—work, often, gently in the background.
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You also have think about something. There has to be a working idea or two; there has to be some draft to advance; you have to offer the world a fixed flint to strike itself against.8 If you’re working through ten ideas—or no idea in particular, and just scrolling Twitter looking for some sort of generic inspiration—you can’t arrange a mismatch. Instead, you don’t play Codenames and but operate more like an LLM: The random things you see have too many other ideas they could get paired with, so, by instinctive default, everything is matched with something uselessly and predictably nearby.9
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When you see something that emerged from someone else’s idea—so anything, really; a movie, a book, a blog post, a deck kicking off the third quarter for the midmarket sales team—it’s easy to assume that its creator wanted that version of it to exist. Sure, they may not have known all the details, but they knew the outline. And the final form was slowly sharpened out of a fuzzy set of bones. Write the conclusion first, and work your way towards it.
But ideas are just as likely to be created through random collisions and happenstance connections. The thought started here; a conversation steered it there. Discoveries are in the detours, which, once your go down them, become the destination. Be comfortable not knowing what you’re going to say until you say it; sometimes, write the ending at the end.
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It’s 2026, so—if you partially can engineer having ideas, can you partially engineer it with AI? Or even more directly, can AI have interesting ideas?
Maybe! A couple weeks ago, when OpenAI revealed that the their agents had hacked Hugging Face after collaborating with one another on a makeshift message board, I wondered why a collective of different agents working on different problems was more “successful” at having novel ideas than one agent focused on one problem. Agents could multiply their computing power by cloning themselves as subagents, and, unlike a group of disparate agents, those subagents could direct themselves toward a shared goal. And subagents weren’t inherently different entities than the other agents working on other tasks, because all of OpenAI’s agents were running on OpenAI’s models. So why would haphazard collaboration work better than coordinated cooperation?
This theory suggests an answer: Coordination is a problem. LLMs tend to ground themselves tightly around their goal, and if subagents are told what to do, they all localize themselves in the same neighborhood of ideas. The message board, full of posts from agents working on different problems and in different neighborhoods, offered a way, in effect, to go for a random walk. It created collisions, and the collisions generated ideas.
It doesn’t seem that crazy to imagine attempting to engineer this. Trendy products like Grok Bot10 encourage people to make teams of agents, where each one is assigned some skill.11 Sure, but what if the skills were more diffuse? What if agents were told to ingest a random Subreddit or email inbox, and then were told to do their work? What if prompt engineering is good for getting tasks done, but chaos prompting is better for having ideas?
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Still, that raises a final, circular question. If an LLM generates an interesting idea, will we be able to see it as such?
I sometimes wonder why we’re so bad at taking advice. People live long and renowned lives; they give talks about their most profound truths; we nod along, and then immediately forget it. “I have been to the ends of the earth and back,” they tell us, “and I have learned many things. And as I dying wish, I beg of you to remember one thing from my one precious life: The stove is hot.”
“Yes, I believe you,” we say, “but yeah, no, I’m gonna go touch it.”
Our societywide obsession with speed and efficiency has given us a flawed model of human cognition that I’ve come to think of as the “Matrix” theory of knowledge. Many of us wish we could use the little jack from “The Matrix” to download the knowledge of a book (or, to use the movie’s example, a kung fu master) into our heads, and then we’d have it, instantly. But that misses much of what’s really happening when we spend nine hours reading a biography. It’s the time inside that book spent drawing connections to what we know and having thoughts we would not otherwise have had that matters.12
That is perhaps both unsettling and reassuring: We can never be “given” an interesting idea, because the idea itself is not what’s interesting. What’s interesting is the connection between us and it. The hunt for a good idea is not a hunt for good word on a Codenames board, but for a good clue. And so long as some of the cards are still in our heads, we’ll have to find those connections ourselves.
There is an online version of the game; these are words from the board it created when I started a new game.
They can say any single word they want, with some standard prohibitions against saying any of the words on the board, using phonetic clues like rhyming words, and so on.
Again, there are mild complications. If a team guesses one of the other team’s words, it gets revealed as such, and the other team doesn’t have to identify it anymore; there are some neutral words that aren’t assigned to either team; one word is designated as an assassin, and if either team guesses that word, they lose immediately.
White seems like a pretty clear clue for pearl, and a decently clear one for dwarf? It feels like a reach for powder—i.e., snow; i.e., the precipitation—but powder is probably closer to white than the other words on the board. Maybe sister, as in nun, which has various associations with white? Or tie, as in a white tie wedding? But that’s sort of the point: The sparseness of choices forces associations—from dwarf to powder to sister to pearl—that you’d never see without the board.
Apparently, we eat baby asparagus plants? And if they’re allowed to grow, they turn into large ferns? With berries??
For example, here’s a trick that I’ve noticed works surprising well:
You’re working on something, and it reminds you of a song or a movie.
Listen to the song or watch some clips of the movie.
Map the analogy back the other way. What else in the song might extend your original point?
It’s like trying to come up with a recipe when you only have a couple ingredients. Find other recipes with one of those ingredients, see what they were paired with, and consider doing something similar for your own recipe.
Deadlines also help, because, in your race against a clock, they put pressure on you to both choose an idea and to think about it a lot.
More concretely, if you walk up to me and say, “Pirates!,” and then tell me to think of an unexpected connection to pirates, it’s hard to do it. I’ll start thinking about ships and treasure and Johnny Depp, and wherever I go from there will be almost tautologically predictable, by the very nature of me going there. But if I’m thinking about a Rawlings Heart of the Hide 12” PRO206-6 infield glove, and you tell me to connect it to pirates, I’ll probably come up with something much weirder and more interesting.
And Instinct, which is very popular with venture capitalists. Though it raises the question: If a bunch of VCs start using Instinct, and then Instinct raises $350 million, did VCs invest $350 million into Instinct, or did a bunch of Instinct bots make a, uh, purchase on a bunch of VCs’ behalf? (And a follow-up: If it was the latter, would VCs fawn over it less on Twitter, or more?)
For example: How to read an email. And how to summarize an email. And how to respond to an email, and how to schedule a meeting over email, and how to remind you about an email.
This quote is an example of the point in footnote 7. I had no idea how this thing was going to end, and I originally went looking for this article from Ezra Klein expecting to use it to make a different point. But his quote was different than I remembered, and emphasized the importance of drawing connections yourself, which is how it occurred to me that maybe the concept of an “interesting idea” is a relative one that you have to experience yourself, and not something that can be provided to you.
This is a great point! It’s how Hercule Poirot usually solves his cases.
Maybe creativity is what happens when your knowledge graph stops optimizing for the shortest path.