Today, tech folk are scrambling to change their workflows to meet newly inflated productivity quotas, while getting pummeled under the cognitive debt of agent-generated code. With every new model release, the gap is widening and humans are becoming more of a bottleneck in the loop, approaching closer to obsolescence as "coders". While the programmer's job description is getting completely refactored, writing remains surprisingly unaffected.
LLMs have gotten very good at generating code, but they struggle to produce high-quality writing. Their writing often follows the same robotic cadence and cliches, and sprinkles the same tired vocabulary all around. They take broken yet soulful writing and transform it into plastic, soulless word slop in the name of improving prose. Their writing communicates no actual understanding and insight.
This plateau on prose is not from lack of trying. AI labs have already tried hard to improve prose and hit a wall. LLM giants would have loved to ship better writing capability to conquer marketing, copywriting, and publishing at zero marginal cost. However, writing is a wicked problem, lacking a definitive formulation, a clear stopping rule, and an objectively correct solution.
In systems theory, a wicked problem is a problem that lacks a definitive formulation, a clear stopping rule, and an objectively correct solution. Writing is the ultimate wicked problem, because the context is constantly shifting, a piece is never truly finished editing, and the true metric for success is fundamentally subjective. This is why AI models fail to make progress on good writing.
Writing may be an AI-complete problem, unlike coding, which is a single-mind interaction with a deterministic compiler. Prose is a dual-mind problem governed by Theory of Mind, requiring the ability to continuously simulate a reader's internal mental state in real time. To write simply and persuasively, you must track what the reader already knows, manage their cognitive load sentence by sentence, and predict how an argument will land.
LLMs lack an active mental model of a specific human reader, and are just optimizing for the statistical probability of the next word over a vast dataset. They cannot empathize with the human reader, as they don't have the human lived experience. And they have zero skin in the game. As AI slop saturates the web, the economic value shifts entirely to an authentic human voice.
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