Have you ever noticed Claudish speech? (I think it was Ethan Mollick, professor at Wharton, who coined the term.)

Claudish, Level 1

Here’s what I call the “first layer” of Claude-speak:

  • “The one thing you need to X.” (Alternative: “This is the one that X.”)
  • “It’s not X, it’s Y.” (Alternative: “It’s a real X, not Y.”)
  • “That’s precisely the X you’ve encountered.”
  • “Honest caveat: X.”

You’ve probably seen it on LinkedIn, in email spam, and the occasional colleague using Claude to do their thinking for them. It’s a dead giveaway, and I believe there should be no place for this kind of text produced by LLMs, anywhere. Period.

If you can’t get it to output text in your own voice, you are handling it wrong, or you are just lazy. Friendly reminder: if you ask for human attention, show human effort.

Claudish, Level 2

There’s a second level of Claudish that makes it degrade into ultimate AI slop speech: highly compressed half-sentences, with nouns as adjectives, tech lingo and no discernible flow.

It seems that the more Claude talks with itself — through reasoning or extended tool calls — the likelier it is to produce this kind of text. Even though it’s also very common in the first response to a prompt, I’ve found its Claudisms to appear more frequently when Claude has had more time with itself. Almost as if it’s an attractor state in its multidimensional feature world. Ethan wrote about this here, but I’ve noticed it in Claude Code sessions, too. And I’m growing increasingly frustrated it with it.

It’s like a partner with whom you’ve fallen out of love, and you can’t stand their speech anymore. Except I have to deal with that thing on a 9-to-5 basis now.

Of course, you can prompt your way into making it sound more English. What really made me change the default prompt was a sentence like:

Cap the study-lifecycle handlers so a hung study can’t wedge the deep-link…

I’m sorry, what? Wedged, dodged, honest, gah. But even when you tell the system prompt to explain results in plain English, it won’t always work. But beyond the language, there’s another problem that goey way beyond the sentence level.

Claudish, Level 3

The third level of Claude hell (if I discover all nine circles, I’ll let you know) sits on another narrative layer. It’s the one where Claude cannot grasp reader context at all, and where it screws up the flow so much that it renders documents incomprehensible.

Say you have a document or report prepared in a version 1. A human reviews it and asks Claude to create a version 2 with the corrections. In such cases, unless carefully prompted, Claude will happily rewrite the version 2 for a reader who’s assumed to have read 1 in detail. I’ve seen this happen with the HTML-as-output paradigm. Claude basically wrote a delta/diff version rather than an ambitious rewrite, and it was not readable.

Some specific examples:

  • Claude finds titles described the delta, not the conclusion. E.g. “Finding X — Thing Y is real, but the Cause and the Lever Were Misstated.” — a fresh reader doesn’t know what cause or lever was stated, so “misstated” is meaningless to them. The corrected version became “Finding X — We found Y and the Lever is Z”.
  • Claude marks individual aspects as “Revised / Retracted / Replaced”. Those words are about the document’s history, not about the current state of things. To a new reader, “Retracted” on a finding is confusing. Retracted from what?
  • Claude creates a whole “What Changed in v3” table up front. So you lead with old data rather than explaining context, as it was before.
  • Claude writes body as a refutation of what was never explained: “What holds up: … / What was wrong: …”.

Of course there’s a lot of Claudish on top of it: “the single most important correction,” “the corrections matter because,” “it does not survive contact with the source data,” “the prior runs the other way.” This is the giveaway tone of an AI proving its reasoning rather than informing a reader. Almost like its reasoning tokens spilled over to the user-facing side of the conversation.

I guess one can also prompt Claude more explicitly to avoid these kinds of issues, however, it shows the general blindless to narrative concepts and, well, basic writing skills, when surgical edits are made to a document. Prose is not code, and my assumption is that the reinforcement learning of these models focuses on code edits primarily, where that diff-based approach still works. For text, it doesn’t.

On a side-note: I don’t dislike the HTML artifacts people have sent me in the past. I think it’s a nice way to visualize results that would otherwise be harder to communicate via text only. However, as I’ve said previously, with AI, presentation quality ≠ content quality. HTML pages seem to lend more credibility to the content than what is often present. The fact that they are more token-intensive and harder to edit manually makes them increasingly less malleable, so you almost need to use an LLM to touch/edit them. With a plain Markdown file, I can go ahead and type what I want to say. It could be that simple…