I'm Becoming AI-Blind
cymerys.com
August 21, 2026
3 min read
51/100
Summary
A software professional reports becoming less able to concentrate on workplace documents that show what they perceive as strong signs of low-effort AI generation. They describe rereading such material without absorbing it, then asking senders questions already answered in the documents. Examples include a design document using Claude-like phrasing, a 20-page marketing deck that combines strategy with implausible technical jargon, and a verbose requirements document that resembles uncertain internal reasoning. The writer says AI-generated workplace text can be recognizable through distinctive wording, sentence flow, excessive verbosity, and efforts to portray routine details as major breakthroughs. They disagree with research suggesting people generally cannot reliably identify AI-generated text, arguing that low-effort output is easier to spot. They compare their reaction to banner blindness: repeated exposure to AI-generated LinkedIn posts, emails, and websites that they consider wordy but lacking meaning has trained them to mentally filter similar material. The writer says this filtering creates an unexpected productivity cost, because AI tools intended to improve efficiency can instead make them disengage from documents. They also recount noticing an apparently AI-generated restaurant image on the Baltic coast that seemed to depict moldy quiche.
Key Takeaways
- The writer reports losing focus on work documents they believe contain obvious traces of low-effort AI generation.
- Examples cited include Claude-like design language, marketing materials with technical-architecture jargon, and excessively verbose technical requirements.
- The writer attributes their response to repeated exposure to AI-generated online content and compares it with banner blindness.
- The writer says AI-generated text can reduce their productivity by prompting them to dismiss or mentally filter workplace documents.
What the discussion said
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.
Where opinion split
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.
Community Sentiment
Positives
- AI can turn badly written workplace notes into typo-free, organized explanations that are more actionable than cryptic command lists and unexplained links.
- Its polished, evenly structured prose supports fast skimming, letting readers extract a broad outline more easily than from error-ridden human writing.
- Even critics still regard current models as impressive in many respects, with code assistance singled out as the remaining reliably useful use case.
Concerns
- Fluent AI prose often makes readers supply the missing logic themselves, creating an exhausting illusion of explanation without enough concrete meaning.
- Generated text is described as forgettable because probability-weighted wording avoids the unusual, deliberate choices that give human writing a memorable edge.
- AI-authored pull-request comments bury simple changes under redundant explanation, leading reviewers to read the code rather than trust the generated summary.
- Model output can sound authoritative while drifting away from the requester’s actual intent, so clarity of presentation does not guarantee semantic accuracy.
- Generated images increasingly advertise themselves through repetitive textures, strange high-frequency artifacts, and uncanny compositions that undermine their realism.
