Why effort scales superlinearly with the perceived quality of creative work

What diction is "appropriate to the subject matter" is a negotiation between author and reader.

I think the author is ok with it being inappropriate for many; it's clearly written for those who enjoy math or CS.

I believe that last-mile edits do not significantly improve the quality of (most) creative work. To produce high-quality work, one must have already "cached" their "motor heuristics," which, in simpler terms, means having dedicated thousands of hours to deep and deliberate practice in their field.

The definition of 'last-mile edits' is very subjective, though. If you're dealing with open systems, it's almost unthinkable to design something and not need to iterate on it until the desired outcome is achieved. In other domains, for example, playing an instrument, your skills need to have been honed previously: there's nothing that will make you sound better (without resorting to editing it electronically).

On a related note I wrote a few “poems” using anagrams. The principle is simple: take a short phrase and have each line in the poem be an anagram of it. You can’t do this with just any phrase; the letters need to be reasonably well balanced for the target language so you can still form pronouns, key grammatical verbs (to be, to have, etc.), and some basic structure.

It becomes interesting once sentences span multiple lines and you start using little tactical tricks to keep syntax, semantics, and the overall argument coherent while respecting the anagram constraint.

Using an anagram generator is of course a first step, but the landscapes it offers are mostly desert: the vast majority of candidates are nonsense, and those that are grammatical are usually thematically off relative to what you’ve already written. And yet, if the repeated anagram phrase is chosen well, it doesn’t feel that hard to build long, meaningful sentences. Subjectively, the difficulty seems to scale roughly proportionally with the length of the poem, rather than quadratically and beyond.

There’s a nice connection here to Sample Space Reducing (SSR) processes. The act of picking letters from a fixed multiset to form words, and removing them as you go, is a SSR. So is sentence formation itself: each committed word constrains the space of acceptable continuations (morphology, syntax, discourse, etc.).

Understanding scaling through history-dependent processes with collapsing sample space, https://arxiv.org/pdf/1407.2775

> Many such stochastic processes, especially those that are associated with complex systems, become more constrained as they unfold, meaning that their sample-space, or their set of possible outcomes, reduces as they age. We demonstrate that these sample-space reducing (SSR) processes necessarily lead to Zipf’s law in the rank distributions of their outcomes.

> We note that SSR processes and nesting are deeply connected to phase-space collapse in statistical physics [21, 30–32], where the number of configurations does not grow exponentially with system size (as in Markovian and ergodic systems), but grows sub-exponentially. Sub-exponential growth can be shown to hold for the phase-space growth of the SSR sequences introduced here. In conclusion we believe that SSR processes provide a new alternative view on the emergence of scaling in many natural, social, and man-made systems.

In my case there are at least two coupled SSRs: (1) the anagrammatic constraint at the line level (letters being consumed), and (2) the layered SSRs of natural language that govern what counts as a well-formed and context-appropriate continuation (from morphology and syntax up through discourse and argumentation). In practice I ended up exploiting this coupling: by reserving or spending strategic words (pronouns, conjunctions, or semantically heavy terms established earlier), I could steer both the unfolding sentence and the remaining letter pool, and explore the anagram space far more effectively than a naive generator.

Very hand-wavy hypothesis: natural language is a complex, multi-layered SSR engine that happens to couple extremely well to other finite SSR constraints. That makes it unusually good at “solving” certain bounded combinatorial puzzles from the inside—up to and including, say, assembling IKEA furniture.

One extra nuance here: in the anagrammatic setting, the coupling between constraints is constitutive rather than merely referential. The same finite multiset of letters simultaneously supports the combinatorial constraint (what strings are formable) and the linguistic constraint (what counts as a syntactically and discursively acceptable move), so every choice is doubly binding. That’s different from cases like following IKEA instructions, where language operates as an external controller that refers to another state space (parts, tools, assembly steps) without sharing its “material” degrees of freedom. This makes the anagram case feel like a toy model where syntax and semantics are not two separate realms but two intertwined SSR layers over one shared substrate—suggesting that what we call “reference” might itself be an emergent pattern in how such nested SSR systems latch onto each other.

I think it's a trick. It seems to be the article is just a series of ad-hoc assumptions and hypotheses without any support. The language aims to hide this, and makes you think about the language instead of its contents. Which is logically unsound: In a sharp peak, micro optimizations would give you a clearer signal where the optimum lies since the gradient is steeper.

Judging by the comments here, I'm the only one, but I have no idea what he's talking about. Even the abstract:

> The act of creation is fractal exploration–exploitation under optimal feedback control. When resolution increases the portion of parameter space that doesn't make the artifact worse (acceptance volume) collapses. Verification latency and rate–distortion combine into a precision tax that scales superlinearly with perceived quality.

Is this just saying that it's ok if doodles aren't good, but the closer you get to the finished work, the better it has to be? If your audience can't understand what the hell you're talking about for simple ideas, you've gone too far.