---
layout: post
title: "The Glibberish Problem"
date: 2026-08-18 12:00:00 +0100
image: /images/2026/08/lotus-eaters-cover.png
categories: [current, ai, writing, work]
---

There's a big change happening in our inboxes and channels. Suddenly, everything is *load-bearing*. Every advantage is *durable*. Every company sits at the *intersection* of three enormous ideas. And every observation arrives with its own drum roll. It is superficially good, but there's nothing beneath the surface. If you try to polish it, entire paragraphs collapse. This is worse than being flooded with "AI slop". It's sometimes convincing enough, but it is never great, and it bypasses the opportunity for people to make it great.

<!--more-->

 Everyone has a list, so here's my list of glib rhetorical gibberish constructions (let's call them "glibberisms") that AI loves to write and humans are learning to hate:

- **The Colon Reveal**
  *"The product exists for one reason: to make complexity legible."*
  The punctuation equivalent of a mid-sentence cliffhanger.
- **The Pregnant Pause**
  *"The problem is no longer technical. It is existential."*
  A short declarative sentence lands after an ordinary claim to announce that something profound has just occurred. Works equally well with a dash.
- **The Wild Dash**
  *"The stakes are high—we need to act."*
  Em dash as wildcard punctuation, allowing an impressionistic approach to sentence construction.
- **The Metaphor Mixologist**
  *"We are scaling across parallel tracks over growing foundations."*
  Reaches for several incongruous metaphors all at once. You go along a track, not *across* it; tracks don't normally have foundations; foundations don't grow. Incongrous in the physical world, but perhaps not in LLM latent space.
- **The Extinction Escalator**
  *"This is not merely a nuisance for one country. It could be a disaster for all humanity."*
  Drags every consequence to the edge of a civilisational cliff.
- **The Cold-Open Carousel**
  *"The dam has cracked. The tide is turning. A sleeping giant has awakened."*
  Keeps beginning over with new scene-chewing metaphors because the last one didn't do the trick.
- **Sentence Confetti**
  *"The issue? Trust. The answer? Governance. Simple. Powerful. Inevitable."*
  Scatters fragments around to simulate pace and conviction.
- **The Bottomless Bottom Line**
  *"The short version… Bottom line up front… TL;DR… In one sentence…"* followed by five more paragraphs.
  Tries to summarise without leaving anything out.
- **The Gen Cliché**
  *"We need a durable, load-bearing foundation for a rapidly evolving landscape."*
  Adjectives that have gone from rare to cliché in less than 3 years.
- **The Honesty Declaration**
  *"Let me be honest with you. Honestly, that's on me. Sam put it honestly when it said X."*
  Honesty is normally assumed; flagging it implies the rest is a lie.
- **The Reluctant Concession**
  *"I'll be the first to admit this isn't perfect."*
  Performative humility without revising anything. 
- **The Authenticity Sticker**
  *"This is not speculation. It has been verified by multiple independent sources."*
  Awards itself a certificate of reliability.
- **The Commentary Track**
  *"That is not an exaggeration. It is the uncomfortable truth."*
  Tells the reader how to judge what's being said.
- **The Structure Trailer**
  *"Three facts. Ninety-three words. Only one heading."*
  Narrates the structure of the material rather than the subject. Often seen in AI slide decks.
- **The Two-Sided Bet**
  *"The project is making meaningful progress, although challenges may require prioritisation."*
  Tries to take both sides of the same bet and win.
- **The Intersection Roundabout**
  *"The company sits at the intersection of AI, sovereignty and the future of work."*
  Offers plenty of approaches, but no exit.

[There is empirical evidence that LLMs](https://arxiv.org/pdf/2406.07016) have acquired their own vocabulary, with sudden surges in words like *delve, intricate, pivotal, crucial, comprehensive* and *underscore* in post-gen scientific literature. But the broader pattern is in the stagecraft. The LLM continually narrates itself, announcing that something significant is happening. These glibberisms aren't just bad style, they are a cover-up for the absence of real thinking.

![Google's AI answers The Simpson's quote "Do you ever think anything you don't say?" with a hilarious lack of irony.](/images/2026/08/lotus-eaters-simpsons-google.png){: .captioned }

## Blame the training

In 2023 I wrote about the risk [that models would increasingly "chew their own cud"](https://seanblanchfield.com/2023/03/chatgpt-chewing-the-cud), training on the output of their predecessors. This is a widely acknowledged problem now. Perhaps some of the strange rhetorical devices in AI-speak are artifacts caused by positive interference in the training data, with the natural inclinations of LLMs getting reinforced across generations of models.

The other possible culprit is human feedback during language models post-training. Reviewers who are basically paid by the task are shown two outputs and asked to pick one. What they can assess quickly is whether the prose *feels* authoritative and well-structured. The model gets fine-tuned on the appearance of quality, not quality itself.

No wonder the big labs are [buying up pre-2022 books, guaranteed human](https://www.news.com.au/technology/online/internet/ai-labs-buy-scan-shred-millions-of-rare-books/news-story/0c3b45a67093ab462a587a0348538ce9). Everything since 2022 is descending in a downward spiral. Models are increasingly pre-trained on slop that was generated by earlier models. Then they are post-trained by superficial human feedback. Now people are beginning to emulate the writing style of LLMs.  Worse, I worry that many people are beginning to think like LLMs... which is to say not to think at all.

## We don't write so we can have more files

People sometimes compliment me for writing well (I don't know why they always seem so surprised!). But the little voice inside my head always says "*the writing wasn't difficult; the thinking was the hard part*". I can spend weeks trying to understand a thing well enough to be ready to write it down. The process involves reading, writing, drawing, talking, arguing and repeatedly trying to explain it to real (or if necessary imaginary) people.  

[Educational researchers](https://www.cambridge.org/core/journals/studies-in-second-language-acquisition/article/methodological-advances-in-investigating-l2-writing-processes/3A57F6F5C1CCFDBBEE6A5897918FB0DF) have talked about the distinction between *knowledge telling* and *knowledge transforming*. Novices write down what they know, but expert writers develop what they know as they write it. Trying to explain something organises your knowledge and helps you understand it.

Writing is not about having more documents. By writing something well, you are discovering and distilling a new understanding, and perhaps even making it teachable. But when you skip to the end with an LLM, and you cheat both yourself and your audience. 

## The Evasion Engine

A year ago I [wrote](https://seanblanchfield.com/2025/05/hitchhikers-guide-to-ai-collaboration) that sending a colleague generated text that takes longer to read than it took to create is anti-collaborative. The perpetrator gets a shallow feeling of productivity, while externalising outsized costs to everyone else. Since then, LLMs got a lot more convincing, and the problem has got trickier. It's not just that we're spraying semi-convincing slop at each other, or that we're leaving our brains at home, but that AI creates the illusion that knowledge work is happening while masking the absence of vital decisions.

Consider this prime sample of Q2 2026 frontier AI drivel: "*While the authentication workstream has presented some unforeseen complexities, the team continues to make meaningful progress across several key areas. The current timeline remains achievable, although some prioritisation decisions may be required as we work through the remaining dependencies.*" So... how bad is it? What was the problem? How achievable? What decisions do we need to make and who needs to make them?

It reminds me of Sir Humphrey Appleby, the mastermind of evasion in *Yes Minister*. ![Sir Humphrey Appleby, dissembler extraordinaire (Yes Minister!)](/images/2026/08/sir-humphrey.jpg){: .captioned .right-half }

> **Humphrey**: "Well, Minister, if you ask me for a straight answer, then I shall say that, as far as we can see, looking at it by and large, taking one time with another in terms of the average of departments, then in the final analysis it is probably true to say, that at the end of the day, in general terms, you would probably find that, not to put too fine a point on it, there probably wasn't very much in it one way or the other. As far as one can see, at this stage."
>
> **Hacker**: "Is that yes or no?"
>
> **Humphrey**: "Yes and no."

If Sir Humphrey and LLMs have one thing in common, it is their dislike of the straight answer. This is what's piling up in our emails, documents, blog posts, marketing copy, requirements documents and specifications. This will soon be the diet we will feed AI agents so they can do better work, so we must urgently realise that when it comes to context, *more* is not always more.

{: .callout }
> ### The parable of the em dash
> 
> The explosion in generated em dashes is a perfect little diorama of the problem. It's an ambiguous bit of punctuation that "*can do the job of a comma, a colon, parentheses, or a semicolon — and it does it with considerably more drama*" according to the [Ireland Publishing House](https://irelandpublishinghouse.ie/blog/em-dash-en-dash-how-to-use/). I know LLMs love the drama, but I suspect they love the ambiguity even more. I picture an LLM sentence with an em dash as two clauses sitting across a table gesturing at each other. This wildcard allows the LLM to gloss over cause-and-effect and carry on generating. Editing out the em dashes often requires a lot of work, committing the text to a one direction or another, and often forcing a re-examination of everything that follows. 
>
> I asked my agent about its em dash habit, and it included 14 em dashes in its response. It has a skill to "lint" its output for this kind of thing, but it gave itself a pass without fixing a single one. When I leaned on it a bit, it removed some of the offending sentences, missed others, and put some new ones in. I asked it to introspect its behaviour, and it claimed that the "smooth" feeling of the em dash is "irresistable" to it. It said that it aligns so well with its training that they are effectively invisible to it. They seem to be the filler words that an LLM mutters but cannot hear.

![Two clauses shrugging at each other across an em dash](/images/2026/08/lotus-eaters-emdash.png){: .captioned }

At organisation scale, this might be terrifying. Plans and analyses and strategies get widely shared. Every meeting is promptly followed by slightly incorrect notes and misassigned next steps. People spend days feeding slop back into the machine, engaged in the theatre of knowledge work. But humans get cut out of vital decisions, too removed to effectively say no to things before they happen, their contributions reduced to "**LGTM** &#128077;" rubberstamps of the machine's work. 

## The way back

Thanks to AI, writing lots of words is no longer impressive. Our human contribution has moved upstream.  Things like these become more valuable:

- **Creativity**. Finding a fresh angle, not generating yet another variation on a theme.
- **Taste**. Saying no to almost everything, and yes to the one thing that belongs.
- **Clarity**. Articulating the essentials with alacrity. 

If you don't contribute at this level, you are basically doing the work of an AI harness, and you'll soon be replaced by a YC startup. You should definitely squeeze every drop of productivity out of your LLMs, but not at the expense of your own unique contribution. Your LLM can be your army of researchers, a tireless sounding board, an expert form-filler, a rapid experimenter, an automatic tester, a ruthless guardian of your inbox and calendar. But we need to make it work for us, not to think for us. We must stay involved at the creative level, decide what is important and what isn't, and what's authentic to our mission and team, and what's just not for us. And we should want our colleagues and collaborators to do the same. 

Here's a litmus test to help us stay on course. Hundreds of years ago, Blaise Pascal apologised *"I would have written a shorter letter, but I did not have the time."*  Out of respect for ourselves and each other, let's make sure we only send each other short letters from now on.