The asymptote of intent - by Adit Gupta - Before the prompt
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The asymptote of intent<br>Why intent cannot be captured, only constructed
Adit Gupta<br>Apr 28, 2026
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In "Formalise the Road," I made the case that structured specifications produce exponentially better outcomes than prose. The argument holds. But it was scoped to the mechanism, to what formalisation does for the system downstream. It did not ask what formalisation does to the intent itself. That turns out to be the more consequential question. Because no one begins by formalising. Everyone begins with intent. And intent, when you try to make it precise, does not hold still.
Have you ever sat down to write something and discovered, mid-sentence, that you didn’t know what you thought until you wrote it?<br>The writing was not transcribing a thought that already existed in complete form. The writing was the thinking. The sentence, once written, showed you something about what you meant that you could not have seen before you began. You revised. The revision showed you something else. By the end, you had not expressed an intent. You had constructed one.<br>Intent is a disposition. A partially formed, context-sensitive orientation toward an outcome that becomes more determinate through the act of expression, the resistance of the medium, and the responses of the people involved. It is not the ghost in the machine- the immaterial, fully-formed thought driving behaviour from behind the scenes. Gilbert Ryle spent a career dismantling that picture. The ghost was never fully formed. It takes shape through the attempt to express it.<br>This matters enormously when the entity receiving your expression is not a collaborator who pushes back, but AI that resolves your incompleteness silently, fills every gap with its most probable answer, and builds from the result. The act of building software with AI has quietly removed the rehearsal from the process. And the rehearsal, it turns out, was where intent was constructed.
What a distribution cannot hold
A language model assigns probabilities to what comes next. More data extends the range of what it has seen. A larger model stores finer-grained patterns within that range. The nature of the function does not change.<br>The problem this creates is not about coverage. It is about representation.<br>A probability distribution has no mechanism for encoding unresolvedness as a first-class object. When someone describes what they want to build, the description may not be a poor encoding of a clear intent. It may be an accurate expression of an intent that is genuinely unresolved, because the decisions that would resolve it have not yet been made. But the model cannot output “this intent is unresolved.” It can only output the most probable completion. The architecture forces resolution where none exists. Unresolvedness is not surfaced but papered over with confidence, and that confidence is the most dangerous thing about it, because it looks exactly like understanding.<br>More scale does not change this. Doubling the training corpus extends coverage. It does not give the distribution the capacity to distinguish between “this is resolved” and “this has not been decided yet.”
Structure determines behaviour
In molecular biology, function follows from structure. A protein is a chain of amino acids, but the chain does nothing until it folds. The three-dimensional shape of the folded protein determines what it binds to, what it catalyzes, what signals it transmits. Change the fold, and you change the function. The amino acid sequence is the same. The behaviour is entirely different.<br>I see a parallel to this model in what a specification template does to intent.<br>Templates sound static and dead but they are an abstraction of experience. Let’s consider the process of order fulfilment. Someone has studied how order fulfilment actually works across warehouses and logistics networks, how inventory states change, how handoffs between teams succeed and fail, how exceptions propagate through a pipeline. They have watched these processes run, watched them break, and identified the structural elements that recur: the states an order can be in, the roles that act on it, the rules that govern transitions, the handoffs that connect one decision to the next. The template encodes that experience into a reusable shape. It is not a theory about how fulfilment should work. It is a compression of how it does work, distilled into a structure that new teams can inhabit.<br>But the template is still generic. It carries the standard states, knows the common transitions and the roles that typically act on them. That is the value of abstraction. What the template does not know is how this particular business deviates from the standard shape. Perishable goods that cannot be restocked on return. Subscription orders that cycle rather than terminate. Oversized items that skip the standard packing queue. High-value orders that...