maps, territory and LMs

11 Apr 2026 culture

Borges has a very short story about an empire whose cartographers kept producing larger and larger maps, until they built one the size of the empire itself.¹ the following generations, less enchanted, saw it was useless and abandoned it to the weather. maps are useful precisely because they are reductions. when the pursuit of fidelity destroys the compression it destroys the point.

for the purposes of this text, LMs are our maps.²

i’ll cut to the chase (bear with me though). LMs have become really good. so good that they are now well beyond useful representations of the territory, and are in many ways beginning to reshape the territory itself. this means, i think, that we need to be much better at reading maps without losing our connection to the territory. we need more ways to stay engaged while reading and interacting with them. much of our (professional) interaction with computers is mediated through LMs now: when examining a new codebase, when reading a paper, when priming ourselves towards a task. sometimes even as an interface for thinking. this is an abstraction layer that we are not really willing to avoid at this point (and im not saying that we should) but it changes what we need to be good at.

Baudrillard, writing in 1981, proposed four stages that describe how representations relate to reality.³ i think they map (pun acknowledged) really nicely to LMs, and in a way that is unique, because LMs seem to occupy different stages all at once, depending on the use case.

in stage one, the image is a faithful copy of reality. LMs were in a sense designed this way: trained to predict and reproduce patterns in human-generated text as accurately as possible, a compressed but structurally faithful representation of what we’ve written and thought.⁴

in stage two, the image masks and distorts reality. LMs do this too. what you get back is a smoothed-out, averaged version of the territory, and subtle distortions are easy to miss precisely because the surface looks coherent. ask an LM to explain the causes of the 2008 financial crisis and you’ll get subprime mortgages and deregulation. ask again with different framing, same answer. the response feels authoritative, but it’s closer to a popularity-weighted consensus than to the still-unresolved debates among economists.⁵

in stage three, the image masks the absence of reality. once you have a good enough approximation, engagement with the territory itself becomes less needed. you (may) stop checking sources because the answer looks right. you stop exploring because the recommendation feels sufficient. the financial crisis question again: asking it feels like research but is really just consuming a pre-averaged explanation. the activity looks the same, but something has been hollowed out.

stage four, when the representation has no relation to reality at all, is trickier. i’m not sure we’re there yet, though part of what makes stage four unsettling is that you might not know when you’ve arrived. it possibly emerges when much of the content available for training new systems is mostly output of previous systems, or when “the chat” becomes everyone’s primary source of knowledge, “becoming both the image of god and god.”⁶

what’s uncomfortable is that the transitions between these stages aren’t clear. we can’t point to the exact moment when a useful map starts substituting for the territory. it’s slippery, and that slipperiness is what makes it hard to stay aware.

LMs are not like other maps.

a cartographic map looks the same to every reader. an LM doesn’t. the output changes with slight modifications to the prompt, and it has been shown that the sophistication of the model’s responses correlates with the educational background of the person prompting it.⁷ this map is very much in the eyes of the beholder. it’s also malleable in ways that static maps never were: you can zoom in and out of topics, approach them from different angles, connect information across fields that would be harder to bridge otherwise.

this is genuinely useful. it clears away clutter and creates space to truly reflect on a research project, on a complex codebase, on ideas that are half-formed. these “means of summarization,” as Henry Farrell called them,⁸ are useful even though we always have access to the full territory (the entire web), partly because of the flexibility of input and output, and mainly because they are much better at understanding what we want.

unlike any previous map, this one is also becoming an object of study in its own right. since these maps arrived, everyone wants to be a cartographer: building, tinkering, adapting these systems, exploring what’s inside them. in many areas of AI research, models are explored not as maps of reality but as something useful on their own terms. houellebecq’s protagonist in the map and the territory argued that the map is more interesting than the territory. in AI research, this is increasingly literally the case.⁹

so here is where i think the actually interesting part is.

using these maps well, knowing when to trust them, when to zoom in, when to stop and touch the territory, is a skill, and it is largely a tacit one. it’s also a personal one: because the map looks different to each reader, the intuitions you develop are calibrated to your own version of it, not to a shared public artifact. by tacit i mean something close to what Michael Polanyi meant when he wrote, with admirable directness, “we can know more than we can tell.”¹⁰

the tacit knowledge here isn’t about spotting obvious hallucinations. it’s subtler: a feeling that something hasn’t been verified, an uneasiness about a claim you’re not sure the data supports, a sense that the output is too smooth.¹¹ it’s the kind of thing a clinician means when they say a patient “looks sick” before the labs come back, or a developer means when they talk about “code smell.” you attend from pattern recognition you can’t fully articulate to a judgment that something needs checking.

Polanyi has a really nice example of a blind person learning to use a probe.¹⁰ at first you feel the impact of the probe against your hand. but as you learn, your awareness shifts: you stop feeling the probe and start feeling what the probe touches. the proximal sensation becomes distal perception. using LMs well might be something like this: at first you attend to the output itself (is this correct? does this look right?). over time, if you develop the skill, you begin to attend through the output to the “territory” behind it.

this skill is learned through practice and resists being codified into rules or checklists. a piece about maps, then, that arrives at the conclusion that the most important skill for navigating them can’t itself be mapped.¹²

i should probably note: the analogy of LMs as maps is itself a map, and i’ve tried to use it where it’s useful without forcing it where it isn’t. LMs are also tools, research subjects, economic forces, things that reshape the territory they represent. no single frame captures all of this.

the current LMs we use are the worst ones we’ll ever get to use. they will get better (in all aspects). the skill of reading maps well, of maintaining that productive uneasiness, will matter more next year than it does now.

Borges’ cartographers fell in love with map-making and forgot what it was for. the next generation, less enchanted, abandoned the map to the weather. neither response seems right to me. somewhere between obsession and indifference, there is the delicate practice of using these maps well, and of staying honest about how our own relationship to them keeps changing.

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