The 194-Comment Revolt Against ChatGPT's Wall-of-Text Disease
Everyone's treating AI text generation backwards. While the industry celebrates longer context windows and more sophisticated responses, users are screaming into the void about something much simpler: AI won't shut up.
The noslopgrenade.com post that triggered this debate didn't need fancy research or complex arguments. It hit a nerve with a simple truth - people are dumping unedited AI walls of text into conversations, emails, and documentation. The result? 325 upvotes and 194 comments of collective frustration.
<> "A model that is fluent but verbose is often perceived as lower quality than a model that is concise and directly useful. For many applications, 'less text' is not a downgrade, it is the product."/>
The numbers tell the story. Power users are maintaining "exclude lists" of AI-generated phrases they never want to see again. Words like "delve," "leverage," and "embark" have become digital poison - instant signals that a human checked out and let the machine ramble.
Matrix Group now advises clients to build custom ChatGPT configurations that ban specific terms. Louis Bouchard has documented an entire workflow for de-ChatGPT-ifying drafts. The community has essentially created an immune response to AI verbosity.
The Hidden Economics of Bloated AI
Here's what the conversation missed: verbose AI isn't just annoying, it's expensive. Every extra token costs money. Longer responses mean:
- Higher API bills
- Slower processing times
- Wasted context windows
- More user editing overhead
Companies are paying premium prices for AI to generate text that users immediately delete. It's like ordering a luxury car and getting the engine plus 500 pounds of decorative chrome.
The technical fix exists. Developers can enforce structured output schemas, add brevity constraints, and separate "draft mode" from "final answer mode." But most don't bother because the models default to verbose, and verbose feels more intelligent.
The Elephant in the Room
We're not actually mad at AI for being verbose. We're mad at ourselves for accepting it.
The real controversy buried in those 194 comments? Some developers argue that "AI-sounding" writing is just bad corporate jargon that humans have been producing for decades. The models learned verbosity from us.
But that misses the scale problem. When humans write boring corporate speak, it's limited by typing speed and attention spans. AI can generate infinite amounts of polished-sounding nothing. We've automated the worst parts of business communication.
The HN discussion split predictably:
- "Stop blaming the tool for user incompetence"
- "The tool should be better by default"
Both sides are wrong. This isn't about blame - it's about recognizing that conciseness is now a competitive differentiator for AI products.
What Actually Works
The revolt has produced practical solutions. Effective prompt engineering now includes:
1. Explicit brevity requirements ("Answer in under 50 words")
2. Format constraints (bullet points, numbered lists)
3. Banned phrase lists (maintained per organization)
4. Role definitions that emphasize directness over politeness
Some teams are building post-processing layers that automatically trim repetitive content. Others use browser extensions to summarize AI responses before reading them.
The irony is perfect: we need AI tools to fix AI output.
Enterprise customers are driving this shift hardest. When an AI assistant generates a 400-word email response that should be 50 words, it creates legal and compliance risks. Brand voice matters. Customer-facing professionalism matters.
The market opportunity is obvious: whoever solves AI verbosity first wins the enterprise.
The 194-comment thread wasn't really about text generation. It was about respect for reader attention. In a world drowning in content, brevity isn't just better writing - it's basic human courtesy.
