I'm Becoming AI-Blind

Based on 15 sampled comments from this thread. Analyzed 2026-08-21.

The thread is less about whether people can reliably classify every passage as machine-written than about a growing visceral reaction to AI prose: readers describe their attention sliding off it, their memory retaining none of it, and their effort rising because polished sentences conceal weak or missing ideas. The recurring diagnosis is statistical average-ness. AI can produce grammatical, orderly, superficially explanatory language, but commenters feel it often lacks the sharp choices, grounded intent, and distinctive detail that make writing informative or memorable. Several readers note that this style did not originate with LLMs; corporate writing, marketing copy, old forum posts, and polished publishing conventions already trained people to tolerate buzzwords and generic structure. That complicates claims of effortless detection: technically immersed or language-sensitive readers may spot the pattern sooner, while others may simply value AI’s clarity over messy human communication. At work, this tradeoff is especially stark. AI-generated documentation and code-review prose is criticized as bloated word soup, yet one commenter concedes it can still be more actionable than colleagues’ fragmentary notes. Concerns about cognitive decline from AI reliance appeared, but the specific claim of measured IQ loss was challenged for lacking evidence. Image generation drew similar complaints: technically competent surfaces paired with unnaturally repetitive textures and unsettling artifacts.

The sharpest dispute is whether AI writing is uniquely empty or merely a cleaner version of familiar bad human prose. Critics argue that LLMs generate fluent shells without stable meaning or authorial intent, forcing readers to reconstruct the missing substance. The counterargument is that humans have long produced jargon-heavy, verbose documents, and AI can be genuinely preferable when it turns incoherent workplace communication into structured, usable guidance.

• Praise: AI can turn badly written workplace notes into typo-free, organized explanations that are more actionable than cryptic command lists and unexplained links.
• Praise: Its polished, evenly structured prose supports fast skimming, letting readers extract a broad outline more easily than from error-ridden human writing.
• Praise: Even critics still regard current models as impressive in many respects, with code assistance singled out as the remaining reliably useful use case.
• Concern: Fluent AI prose often makes readers supply the missing logic themselves, creating an exhausting illusion of explanation without enough concrete meaning.
• Concern: Generated text is described as forgettable because probability-weighted wording avoids the unusual, deliberate choices that give human writing a memorable edge.
• Concern: AI-authored pull-request comments bury simple changes under redundant explanation, leading reviewers to read the code rather than trust the generated summary.
• Concern: Model output can sound authoritative while drifting away from the requester’s actual intent, so clarity of presentation does not guarantee semantic accuracy.
• Concern: Generated images increasingly advertise themselves through repetitive textures, strange high-frequency artifacts, and uncanny compositions that undermine their realism.