AI로부터 가장 안전한 직업은 글쓰기일지도 모른다

The Safest Job from AI may be Writing Get link Facebook X Pinterest Email Other Apps - August 31, 2026 Today, tech folk are scrambling to change their workflows to meet newly inflated 5X 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 I am appalled at the absolute shit they spew as prose. They always follow the same robotic cadence and cliches, and sprinkle the same tired vocabulary all around. They take my broken yet soulful writing and transform it into a plastic soulless word slop in the name of improving prose. Their writing communicates no actual understanding and insight. I think we are all developing a visceral ick reaction to AI writing. It is trapped in the uncanny valley, and it may be stuck there for a long time. I am increasingly convinced LLMs will not threaten decent writers anytime soon. My prediction rests on the three observations below. Tell me where my logic breaks. The plateau on prose The stuck-in-slop state of LLM writing is not from lack of trying. AI labs already tried hard on improving 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. Look at image, voice, and heck video models. They got good quickly, because they can be scaled with more parameters and compute. Compared to their rapid progress, text models plateaued hard on expression, depth, and authenticity. I think it is wicked hard to bride the final 20% (also applies for image, voice, video models). Writing is a wicked problem 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. Mapping domains along this wickedness spectrum explains why AI dominates certain fields but produces utter slop in others: Math sits on the left of the spectrum. Specifications are concise, and verification is binary and automated. Since the feedback loop is perfectly closed, AI models can rapidly solve math problems and prove results automatically. Code relies on formal logic and test-driven feedback. An LLM can generate functional code because compilers, unit tests, and model checkers can instantly catch errors. Verification is largely mechanical. Structured domains like law and finance also have some explicit boundaries as they are governed by regulatory frameworks and evaluated on empirical data. Writing sits at the far extreme of the spectrum. There is no well defined specification to the task at hand, and there is no ground truth or objective verification of the output. The ultimate measure of success is resonance inside another human mind. 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 . It requires 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. Since LLMs lack an active mental model of a specific human reader, they 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. Comparative advantage and costly signals To land this plane, let's pull in David Ricardo and classical economics. The law of comparative advantage states that even if one party can produce everything more efficiently than another, both still benefit from specializing where their relative opportunity cost is lowest. In other words, even if AI has an absolute advantage in typing speed and generating volume at zero marginal cost, our human labor is still governed by the opportunity cost of where our scarce resources would be least wasted. The opportunity cost for a human burning their scarce cognitive capacity on generic and repetitive tasks is now infinitely high. Instead, human effort shines where AI fails, that is for navigating wicked-problems and pushing for creativity. This is where economics meets evolutionary biology, as "human proof-of-work" becomes the ultimate costly signal. In nature, a costly signal (like a peacock's tail or an elk's antlers) works because it is expensive to produce and impossible to fake. As AI slop saturates the web, with the same token (pardon my pun), the economic value shifts entirely to an authentic human voice. Unlike tech folk, writers don't have to change a damn thing about how they work to optimize their comparative advantage and capture this costly signal. As AI is stripping away the accidental complexity of software to expose its inherent complexity , tech workers are struggling to adjust. But good writers have always been wrestling with the inherent complexity of communication at the wicked frontier, and they remain untouched. And maybe programming itself is turning into a form of creative writing, getting more opinionated and more architectural. Get link Facebook X Pinterest Email Other Apps Comments Post a Comment