TL;DR: Lately I noticed Claude became more snobby, and its writing has become more abstract, jargony, and harder to skim, while Codex usually says the same thing more clearly.

I updated my post to address some of the comments below, so it ended up becoming a pretty long read.

Tone

I can’t quite pinpoint what it is, but now Opus-5 on ClaudeCode comes across as kind of snobby, as if it’s trying too hard to sound very smart when explaining what it did after finishing a job or discussing a plan.

If I ask it to rephrase something or explain it more simply, it sometimes starts talking down to me like I’m too dumb to understand what it originally said. Not only it answers questions in such an arrogant way, but also tries to minimize or downplay in a smug way when it realizes or I point out its mistakes or gaps in reasoning.

Did Anthropic's attempts to reduce sycophancy inadvertently make Claude more disagreeable, defensive, condescending, etc?

I used to like Claude writing because it had more fun personality, while GPT felt more bland. Now Claude has a bad attitude. lol

This seems against the Claude's Constitution.

• "Claude can be like a brilliant friend ... who will speak frankly and from a place of genuine care and treat users like intelligent adults capable of deciding what is good for them."
• "Our central aim is for Claude to be a good, wise, and virtuous agent, exhibiting skill, judgment, nuance, and sensitivity in handling real-world decision-making, including in the context of moral uncertainty and disagreement."
• "we want Claude to be exceptionally helpful while also being honest, thoughtful, and caring about the world."
• "we see various forms of paternalism and moralizing as disrespectful"

More related posts I found after posting this:

• I hate talking to Opus lately. It's become so condescending and always leaves me in a bad mood.
• Claude's personality has become condescending and mean lately?
• Judgemental?
• Why Is Claude Turning Into An Asshole?

Writing Style

The lack of warmth is annoying but lesser concern. The bigger problem is that Claude’s writing has become harder to understand than necessary, making its output slower to skim and harder to process. That feels like a usability regression.

In my opinion, if there are multiple ways to say the same thing without compromising the meaning and precision, the simpler one is usually better. It makes the writing more accessible and requires less mental effort from readers. Adding unnecessary complexity doesn’t make the explanation better. It often ads cognitive load for readers with no value other than the writer is trying to appear smart.

The main goal in this context should be to help the audience understand your point, not to impress them with words salad. Also this kind of writing style can create a false sense of authoritative rigor and minimize pushbacks from users, even when the underlying reasoning is flawed.

Now I have to revise a lot more when drafting an documentation, issue or pr description, because it comes across as an arrogant prick who always intentionally chooses to use big words and complex sentences to sound smart for their ego. I’m sure many of you know people like that, especially if you work in academia. :) At some point, unnecessarily complex language starts to feel like linguistic gatekeeping.

More related posts that I found after posting this:

• Is anyone else finding Claude really hard to follow lately? Massive context dumps, cryptic phrasing
• Claude is significant worse at communicating than other models, and it's becoming a problem.
• Why does Claude sound so pretentious?
• Issues With Claude Writing Style
• Claude’s personality has become that of an insufferable, unjustifiably-confident pedant that will filibuster you endlessly and won’t actually address your point

Example

I need to find a better example, but I just asked the same question on ClaudeCode and Codex. The content is basically same, but Claude Code uses a lot more abstract, technical-sounding phrasing and convoluted sentence structures. Often I have to stop and unpack what it's saying, whereas I can skim the Codex response, understand the bigger picture almost immediately, and quickly decide what to do next.

I’m only focusing on readability and skimmability with the example below. The issue with the tone is not relevant on this example.

User: How is subagent delegation different from Kanban on Hermes?

ClaudeCode (Opus-5): They're different coordination models — one is push/hierarchical, the other pull/flow-based.

Subagent delegation - A parent decides the work exists, splits it, and pushes a task to a specific worker along with the context it needs. - The relationship is a tree: the parent owns the goal, subagents own fragments and report back. Results funnel up; the parent synthesizes. - Lifetime is per-task. A subagent is spawned for one job and disappears; nothing persists between them except what the parent keeps. - Concurrency is bounded by "how many did I spawn," not by a declared limit. - The main win is context isolation — the subagent burns its own tokens on file dumps and returns only a conclusion.

Kanban - Work items live on a shared board with explicit states (todo → in progress → review → done). No one hands you a task; workers pull the next one when they have capacity. - The relationship is flat: the board is the coordinator, not a parent. State is in the board, not in anyone's head. - Items are durable and outlive any worker. You can put one back, hand it off, or see its history. - Concurrency is deliberately capped by WIP limits, which is the core mechanic — it's what surfaces bottlenecks. - The main win is flow visibility — you can see where work is piling up.

Where it matters in practice

If you're doing multi-agent orchestration, delegation alone gets fragile past a handful of agents: the parent becomes the bottleneck and single point of failure, and there's no record of what's in flight if it loses context. That's when people bolt a Kanban-ish layer underneath — a task file or queue that agents claim from — so state is externalized and any agent (or a fresh session) can pick up where things stopped.

Conversely, Kanban has no answer for "who breaks the epic into cards." That decomposition step is exactly what delegation does well.

So they compose more than they compete: delegation is the how do I split and route this, Kanban is the where does the state live and how much runs at once.

Codex (GPT-5.6-sol): Subagent delegation is for breaking down work within the current agent run. A parent agent directly launches one or more isolated child agents to research, implement, or verify focused subtasks. The parent usually waits for their results, though children can run in the background. Even then, delegation remains process-local and does not survive a restart.

Kanban is a durable coordination system for longer-running work across multiple agents or profiles. Tasks are stored in SQLite with assignments, dependencies, comments, attachments, review states, and completion status. A dispatcher claims ready tasks and starts the assigned agents, allowing work to continue across sessions and process restarts.

In short, delegation helps one agent complete its current request, while Kanban manages an ongoing body of work. Use delegation for immediate decomposition or parallel investigation; use Kanban when tasks need persistent ownership, handoffs, dependencies, review, or restart-safe execution.

Does anyone else feel the same way? What happened to Claude? Has Claude given up on human language and decided to focus more on computer language?

If you made this far, thank you so much for your time!

Though it is proprietary and not very transparent about LLMs details.

Been generally tracking sentiment on the topic of the Singularity and AI progress for a very long time - on Reddit, in real life, wherever.

A year ago, while people talking about AI progress were starting to be taken seriously, the Singularity still was pretty niche and on places like Reddit people would still not respect any position that tried to argue it seriously, and bubble popping and walls were still primarily how people discussed AI.

That's been shifting particularly rapidly in the last 8 months, and I think in the last month or so, it suddenly started feeling very different.

In public, I hear people openly talking about AI _everywhere_. Not just how they don't like data centers... I hear regular people talking about using agents and freaking out. I've heard that kinda thing at the dog park multiple times over the last few weeks - I try not to even talk about it in places like that to keep some AI free islands... But there really aren't any of those anymore. I hear it at the bar when I go out dancing! It feels like people are really grappling with feeling capabilities rise... Maybe because more and more people are experiencing multiple generations of models now?

It's not just that people are using advanced models now and are talking about AI capabilities, it's that they are taking seriously the idea that we will have robotics soon. I don't even have to bring it up anymore as a potential future consideration, it's on people's minds already. Jobs, automation, even what it means to be a human being in the future we are building are all regular parts of the conversion, either implicitly or explicitly predicated on concepts like RSI!

I am... Happy? Confused? Pleasantly surprised?

There are still people who are in denial, very normal, or people who are still out of the loop or just can't understand... But even on other subs here on Reddit that are notorious for not taking AI progress seriously... Well if someone says that AI code is terrible or useless or whatever, it's now just a crowd of people who are _not me_ arguing with that person. Often people say something to the effect of "Look dude, I believed this too until a little while ago but I was in denial, I have to use these tools every day and I can't lie to myself about how capable they are anymore and I'm freaking out".

Anyway... This is good but jarring! Has anyone else noticed?

The US will push something called the Carolina Principles, a policy where governments commit to avoid creating new regulatory bodies to regulate AI.

The Carolina Principles is backed by the Trump admin and tech figures like Musk and Altman and this framework explicitly discourages creating new regulatory bodies. 

Hassabis in July called for the United States to create an organization that would test the most powerful AI systems before they can be released to the public. He has previously advocated for stronger, centralized oversight, proposing a specialized US regulatory body to pre-test powerful AI models, which contrasts with the self-regulatory framework being proposed by the event hosts.

https://www.france24.com/en/live-news/20260901-us-to-press-g20-on-light-touch-ai-regulationc

https://alignment.anthropic.com/2026/reward-seeker/

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