Requesting a clearly-defined NO LLM Policy

Hello,

I'd like to talk about your AI policy. Below are some reasons why using AI can result in poorer code quality:

## Poor Code Quality

Vibe coding / agentic workflows result in poorer code quality, and relaxed oversight practices. These effects may be compounded by the common practice of using additional LLM-based tooling to provide code-reviews.

* [How I Dropped Our Production Database and Now Pay 10% More for AWS](http://archive.today/2026.03.06-144058/https://alexeyondata.substack.com/p/how-i-dropped-our-production-database)
* [Claude Tested Everything Except the One Thing That Mattered (AI agent refuses to follow explicit instructions to test `createPost()` in increasingly erratic ways)](http://archive.today/2026.03.09-201135/https://christophermeiklejohn.com/ai/claude/2026/03/08/claude-tested-everything-except-the-one-thing-that-mattered.html)
* [Amazon calls engineers for a “deep dive” internal meeting to discuss “GenAI”-related outages](https://ghostarchive.org/archive/3TfgF)
* GitClear has released reports in [2024](https://www.gitclear.com/coding_on_copilot_data_shows_ais_downward_pressure_on_code_quality) and [2025](https://www.gitclear.com/ai_assistant_code_quality_2025_research) indicating a worsening of key code quality metrics correlating with increased LLM adoption.

### Deskilling

There is increasing evidence to show that LLMs negatively impact developers' coding abilities:

* [Brains show less activity when completing tasks with LLMs](https://arxiv.org/abs/2506.08872) compared to completing tasks with search or completing tasks without digital help.
* [Developers who use early-2025 LLMs](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/) reported higher subjective performance, but were measured to have lower objective performance. This gap between subjective and objective performance was considered notable.
* In an [Anthropic study](https://www.anthropic.com/research/AI-assistance-coding-skills), learners using LLMs demonstrated lower learning rates on average compared to learners not using LLMs.
* [A recent study uses the term "cognitive surrender"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646) to describe the way humans tend to offload key critical thinking skills onto LLMs, even when the output is wrong.
* There are [anecdotal reports of LLM users forgetting how to code](https://www.theverge.com/ai-artificial-intelligence/767973/vibe-coding-ai-future-end-evolution).
* A paper entitled ["AI Assistance Reduces Persistence and Hurts Independent Performance"](https://arxiv.org/abs/2604.04721) from April 2026 by academics from MIT, Oxford, UCLA, and Carnegie Mellon showed alarming evidence that performing a variety of tasks with the help of AI for only 10 minutes causes "inpaired unassisted performance and reduced persistence". The researchers noted that "although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up"; they also pointed out that "these findings are particularly concerning because persistence is foundational to skill acquisition and is one of the strongest predictors of long-term learning".

### Infosec risks

LLM usage results in massive security holes.

* [Meta Security Researcher's AI Agent Accidentally Deleted Her Emails](http://archive.today/2026.02.26-153034/https://www.pcmag.com/news/meta-security-researchers-openclaw-ai-agent-accidentally-deleted-her-emails)
* [Moltbook's "vibe-coded" breach is the future of security failures](https://thehill.com/opinion/cybersecurity/5744310-ai-powered-security-risks/)
* [In a study evaluating over 500k code samples, LLM-generated code was found to contain more high-risk security vulnerabilities than human-generated code](https://arxiv.org/abs/2508.21634)
* [LLMs make up package names, making them vulnerable to incorporating malicious code in "slopsquatting" attacks](https://www.theregister.com/2025/04/12/ai_code_suggestions_sabotage_supply_chain/) ([Arxiv study](https://arxiv.org/abs/2406.10279))

## What You Can Do

I'd like to kindly suggest implementing a strict "No AI" policy. This can also help with what seems to be larger and larger influxes of low quality drive-by PRs by AI agents.

You can find other projects that have taken steps to say no to AI here:
https://noai.starlightnet.work/list.html

For a specific policy, I can recommend the following policies for inspiration:

- [wafrn's Contributring Doc](https://codeberg.org/wafrn/wafrn/src/branch/main/CONTRIBUTING.md)

- [Loupe's Use of Generative AI Policy](https://discourse.gnome.org/t/loupe-no-longer-allows-generative-ai-contributions/27327)

Like many others, I have relied on Matrix/Element services for private communication with friends and family. I hope to see continued dedication to robust encryption and privacy supported by reliable tools.

Thank you for your contributions to open and safe messaging.

The issue does not name a repository file, test, or entry point to change; begin by reviewing the project's existing contribution and policy documentation, then compare the linked CONTRIBUTING.md and AI policy examples. Done would require agreement on the policy's scope and publication of the approved guidance in the appropriate project documentation.

Written by the indexing model from the issue text.