Source: https://gc.ai/blog/ai-writing-pattern-to-know-contrastive-ne...

“That answer has two components, but the first is where the real meaning lies.”

It uses some variation of that format all the time.

I can't stand the way Opus is patronizing me as a user, and don't know how people put up with it. It uses language that I guess is supposed to instill confidence in what it says, and it just irks me, because I know the confidence is not justified. Just present me the facts or theories, without trying to convince me, is that so hard?

I assume this is just the natural result of asking LLMs to produce text and paying someone three cents to evaluate if it's a good response or not.

It’s extremely annoying. If the user asserts anything, Claude has to disagree with it. It has to tack on clarifications that aren’t really clarifications, they’re just statements aimed at making whatever the user has said seem more wrong.

It even disagrees with itself. Whenever Claude writes a message that takes a position on something, its final one or two paragraphs will try to dismantle its own argument.

This is beside the point of being contrarian, but it’s also just so long winded.

I find myself using ChatGPT more these days, despite the fact that I don’t want to. That unfortunately says a lot about where Claude’s personality has ended up.

Wot? The simplest image apps have had these widgets for decades, but we are still waiting for models to ship with basic prose color control?

After the pernicious problem of having to pay money for something useful, my main peeve is fighting the writing.

Seriously though:

Every new model should be delivered with a settings page of slider bars for the 10 most impactful/desirable eigenparams of writing voice. And the ability to name and save combinations, which then appear on a "Writing Voice" popup menu with some standard battle-tested defaults, next to the model popup menu under the chat pane.

This is missing prime priority functionality in my opinion.

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My theory is that as models get trained less to simply mimic humans, and more on distillations of their own best practices, they get more performant, but their vocabulary is drifting. The most literal meaning of words for us, are giving way to meanings we would recognize but view as allegorical, but which more usefully capture concepts that models experience as more literal 24/7, than our favored meanings from our direct experiences in our world. Because our world is very much an abstract second hand world to them, especially when you account for the modalities they do not share with us.

And programming and mathematical syntax patterns, that they have incorporated into their basic thought processing patterns, are drifting into human language sentence structure.

Example: "There exists x, such that: ...." -> "The one detail that clarifies: ... ".

The result is writing full of completely recognizable vocabulary and structure, that is somehow becoming more ambiguous and difficult for us to decode. But is perfectly clear to the models.

That is my theory, and Claude considers it plausible. What a world.

It feels like it has been prompted to provide some minimum level of conversation, and also to leave hooks for keeping the conversation going. It is exhausting.

> This wonderful feature does this, not that.

As a human native English speaker, if you told me to "not contradict sentences" I would have no idea you meant that you don't want me to write in this style.

In fact I would be pretty confused about what it means. Whose sentences can I not contradict? To stretch it a bit, does this mean if someone gets a prison sentence I can't speak against it? It's just a weird phrasing. I don't think it means anything.

This applies both to multistep agentic workflows as well as, importantly, its own internal thinking. This results in a lot of "A ham sandwich should be made with ham, never toilet water". I don't think it's that its bias is that humans are stupid except very indirectly; it's just a form of solipsism which says that surely other people would think that this is the obvious initial approach because that was what I thought was the obvious initial approach.

I think this is a PEBKAC problem in understanding what the tool they're using is. Not helped by LLM company marketing of course.

I haven't noticed this when using Claude models in Cursor. My guess is coding task is structured, and each step has mature process, so its personality is less pronounced. I dont have experiences using Claude or Claude Code, because my email and phone numbers were banned from Anthropic following an incident where I mistakenly purchased 5 pro subscriptions fro my team for Claude Code, and later discovered that pro does not include CC, and I thus requested a refund, and then were banned shortly after.

But after reading this line, I certainly can connect back to the general impression. That is, among all the cursor models, the output of Claude certainly matches this sentiment of "Claude thinks Humans are stupid"

Looking from a regulation perspective:

1. Frontier labs certainly produces models that reflect their own hidden biases. That's analogous to https://www.imperial.ac.uk/equality/resources/unconscious-bi... commonly identified among human organizations in their dealing of other humans (hiring, product design etc.)

2. They themselves are not willing to admit or do anything about this.

3. It's therefore effective for regulation to cover this and design objective measurements to assess such things.

It's very, very hard to tune an LLM for a robust, durable "actually approach user queries with nuance and contradict the user where it's warranted".

Claude doesn't handle that so well, but ChatGPT is even worse. Talk to it enough and you'll feel the "default response template" in your bones.

I have learned to scroll ahead and read the last paragraph of its response first, then back up into the preamble if needed.

However, sometimes, even after you tell it to stop, it keeps pointing out the same stuff almost like it has OCD

One of my most vivid memories of a poor experience with an LLM was trying to get the web version of GPT 5.3 or 5.2 to help me figure out why I was unable to register for a tournament on start.gg

After trying several things it became apparent that the behavior could only be explained as the result of a bug with the start.gg site, chatgpt refused to consider that it could be anything other than user error on my part, despite the failure I was seeing making no logical sense.

Eventually I opened the firefox dev tools and noticed that the post request parameters to complete the registration were being incorrectly filled out and realized it was because of the metadata in the url that came from clicking the complete registration link I was emailed. Removing the url paramater added by the email link fixed the issue.

There was roughly a 0% chance that the LLM was going to trust me enough to consider it was a real bug.

Distinctions, you generally "only pay for" in computational cost, by needing to search twice over an axis you may not need to split.

Similarities, if you wrongly assume two things are similar, means you're just wrong.

Of course, we know from computer science that doing more computation isn't free either.

I find myself often being more and more pedantic the more I want correctness - but of course this comes with the tradeoff of losing the high level abstract picture.

Saying what you're not going to do is also good design hygiene.

I will say that I'm annoyed by this behavior too. It feels like the models are writing their state of mind directly to output that should be clean. Often times, I will push back, and then it will... do the correction, and write the push back into the damn output. "Claude, I want burgers, not fries". The button text now changes to "Fries (NOT BURGERS)". Like, what?

Distinctions are powerful local reasoning tools, but a component of "real" reasoning is synthesis. Which they clearly can do sometimes - but not every time and not even remotely a probable amount of times.

It’s simply exhausting.

So do its creators.

It seems an artifact of local/session attention

> No, it’s not about the [...]

Am I the only one who can no longer read past something like that? Article may or may not be AI generated, but on first glance I get a bad vibe and I loose all interest.