⚡ Quick Summary
r/programming (6.9M members) banned all LLM topic posts in April 2026
Stack Overflow has banned AI-generated answers since late 2022
SDL, Gentoo, NetBSD, Redox OS, and OpenJDK banned AI-generated code contributions
Oracle's OpenJDK bans AI contributions while its sibling project GraalVM allows them
New Relic research (June 2026) found AI code scores higher in review but causes MORE production incidents
Microsoft cancelled Claude Code licenses for thousands of engineers by June 30, 2026
The core issue is NOT anti-AI sentiment it's quality, maintenance burden, security, and copyright
What's still allowed: Deep technical ML content, human-authored code, AI-assisted code with disclosure
Introduction
If you tried to post about ChatGPT on a developer forum recently and your post disappeared, you are not alone. The trend of why coding communities are banning AI content has turned into a full movement in 2025 and 2026.
It is not one quirky subreddit mod making an unusual rule. It is a coordinated wave hitting Reddit, Stack Overflow, major open source repositories, and now even enterprise giants like Oracle and Microsoft. And the developers behind these decisions are not Luddites. Many of them use AI tools every single day.
This article explains what is actually happening, why it keeps happening, which communities have banned what, and what you can do about it whether you are a beginner just entering the field or a senior engineer navigating these spaces daily.
What Does "Banning AI Content" Actually Mean?
Before diving into the reasons, it helps to be precise. The word "ban" gets used loosely, and the restrictions vary widely.
There are three main types:
1. Banning AI-generated code contributions (open source projects) You cannot submit a pull request, patch, or fix where the code was generated by a large language model like ChatGPT, Claude, or GitHub Copilot. Only human-authored code is accepted.
2. Banning LLM-related discussion topics (forums and subreddits) You cannot post news, guides, or opinion pieces about LLMs. The community still does programming — it just does not want ChatGPT or Copilot as a topic of conversation.
3. Banning AI tools at the company or team level (enterprise) Organizations restricting which AI coding tools employees can use, or banning them entirely over security, cost, or governance concerns.
All three are happening simultaneously in 2026, driven by overlapping but distinct reasons.
Why Coding Communities Are Banning AI: The Real Reasons
1. The Signal-to-Noise Problem
In April 2026, r/programming the largest coding community on Reddit with 6.9 million members announced a temporary ban on all LLM-related content. The mod team described the situation as "exhausting." Posts about new AI models, AI coding assistants, and vibe coding had grown so dominant that actual software engineering discussion was getting buried.
This is a classic signal-to-noise ratio problem. When one topic generates enormous hype, it can take over a community that was built for something else entirely. r/programming was built for expert-level software engineering discussion. By 2026, a large portion of its front page was indistinguishable from a general AI news feed. The moderators were not saying AI is bad. They were saying: this community has a specific purpose, and that purpose is being drowned out.
2. The "AI Slop" Epidemic in Developer Communities
A term that gained currency in early 2025 and became unavoidable by 2026 is "AI slop" — content generated quickly by an LLM that looks technically plausible on the surface but contains subtle errors, hallucinations, or shallow reasoning that becomes a liability when acted upon.
In developer communities, AI slop takes several specific forms:
Forum answers: ChatGPT-generated code pasted as an answer. It compiles. It has security holes, deprecated methods, or broken logic that is not obvious at first glance.
Tutorials: A detailed-looking guide generated in minutes, full of invented function names and incorrect API references.
Bug reports: Plausible-sounding reports in confident technical language where the bug itself was hallucinated.
Pull requests: Complete-looking contributions that crumble under review because the submitter cannot explain a single line.
One real incident captures this vividly: the OCaml open source community received a pull request with over 13,000 lines of AI-generated code. Maintainers rejected it, citing copyright concerns, lack of review capacity, and the long-term maintenance cost it would impose.
3. The Maintenance Burden That Is Breaking Volunteers
This is the most important and least discussed reason. Open source software runs on unpaid volunteers. The old model worked because contribution was genuinely hard: you had to read the codebase, understand the issue, write and test a fix, and craft a coherent pull request. That effort was an accidental quality filter.
AI has eliminated that filter. Anyone can now generate a large, plausible-looking pull request in minutes without understanding any of it. But reviewing those contributions did not get faster — it got harder. AI-generated code is subtly wrong rather than obviously wrong, and catching those subtle errors requires the same (or more) cognitive effort than writing the correct code from scratch.
Research from CodeRabbit found that AI-generated pull requests have 1.7 times more issues than human-written ones, and they arrive at far higher volume. The Jazzband Python project collective shut down entirely in 2025-2026, overwhelmed by AI-generated submissions. The curl project shut down its bug bounty program after AI-generated vulnerability reports consumed maintainer time without producing a single valid finding. The Godot game engine and Apache Log4j 2 reported similar problems.
The EFF's February 2026 policy on LLM contributions put it plainly: code reviews are "turning into code refactors for maintainers if contributors don't understand the code they submitted."
4. The Production Stability Paradox — AI Code Looks Good But Breaks Things
This is the most counterintuitive finding of 2026, and it directly explains why some communities are getting stricter even as AI code quality appears to improve.
In June 2026, New Relic released its State of AI Coding report. The finding that made waves: AI-generated code actually grades higher in code review than human-written code. Reviewers rate it as cleaner and more complete. Yet at the same time, organizations using AI-generated code are experiencing a rise in production incidents.
What this reveals is a dangerous gap between how code looks and how code performs. AI can generate clean, readable code that passes review processes — and then fails in production environments in ways that human-authored code with the same review score would not. The code is polished, but something in the underlying logic or edge-case handling is wrong in ways that only emerge at runtime.
For developer communities that care about production reliability, this is not reassurance — it is alarming. A review process that cannot catch AI-introduced failures is a broken review process, and the communities that ban AI content are in many cases reacting to exactly this gap.
5. Security: A Developer's Code Can Attack at Scale
Security researchers have consistently found that AI-generated code introduces significantly more security vulnerabilities than human-authored code. One dimension that gets less attention: a developer might write one vulnerable function. An AI agent, running autonomously, can generate hundreds of similarly vulnerable functions before anyone notices.
Legal and Copyright Minefields
Many open-source licenses require strict attribution. Because LLMs are trained on billions of lines of code without explicit consent from the original authors, utilizing AI-generated code introduces legal risks. Projects like OpenJDK ban AI contributions entirely to avoid potential copyright infringement lawsuits down the road.
What’s Still Allowed in Coding Communities?
Despite the heavy restrictions, developer forums and open-source projects are not entirely anti-AI. They are simply anti-hype and anti-spam. If you want to contribute or post without getting banned, here is what remains completely acceptable:
* Deeply Technical ML Content: Discussions about building, training, fine-tuning, and optimizing LLM architectures from scratch. * Human-Authored Explanations: Sharing how you integrated an AI API into your software, written completely in your own words.
* Disclosed AI Assistance: Using AI tools for basic syntax correction or minor boilerplate code, provided the final logic is 100% tested, understood, and disclosed to the maintainers.
Conclusion: Moving from Hype to Substance
The widespread ban on AI content in 2026 isn't a rejection of technology; it's a desperate attempt to save developer communities from drowning in low-effort "AI slop." Open-source maintainers and forum moderators are simply demanding substance over hype to protect their time and project stability. For developers, the lesson is clear: AI is a powerful assistant under the hood, but human oversight, deep technical understanding, and rigorous code review remain completely non-negotiable.
At the enterprise level, this risk is concrete. Companies banning AI coding tools internally are often responding to security audits that flagged AI-generated code containing privilege escalation paths, insecure authentication patterns, or subtle injection vulnerabilities that looked fine in isolation.
A 2026 Reddit thread from a developer whose company was about to ban AI coding tools entirely illustrates the pattern: the security team had audited production code and found that AI-generated modules had introduced multiple high-severity issues that had passed code review precisely because they were written in a clean, readable style that discouraged scrutiny.
6. The Trust Gap That Data Confirms
Stack Overflow's 2025 developer survey revealed a striking paradox. Over 84% of developers reported using or planning to use AI tools — up from roughly 70% in 2023. But only 29% said they trust those tools, down 11 percentage points from 2024.
More familiarity with AI tools is correlating with less trust, not more. This is the opposite of how technology adoption normally works. Usually, you learn the tool's quirks, develop best practices, and grow more confident. With AI coding tools, the more developers use them, the more they encounter the hallucinations, the confidently wrong explanations, the references to APIs that do not exist.
Stack Overflow described this as a "developer AI trust gap" that has direct implications for which communities will allow AI content. A community built around trust in technical answers cannot afford to let that trust erode by allowing unverified AI-generated answers into its corpus.
7. AI Tools Are Biased Toward Popular Frameworks
There is a dimension of the AI coding problem that does not get discussed in the context of community bans, but it should. AI coding tools are biased toward popular technologies. They are trained on vastly more data about React than about niche front-end frameworks, more about Python than about Zig or OCaml, more about common patterns than about edge-case architectures.
When someone uses AI to write code for a niche framework and posts it to a community focused on that framework, the result is often confidently wrong in framework-specific ways. The AI has generated code in the general shape of the framework but missed the details that matter to practitioners. For communities built around specialized technologies, this kind of AI-generated content is particularly corrosive because it looks authoritative to beginners and is subtly wrong to experts.
8. Copyright and Licensing Uncertainty
SDL (Simple DirectMedia Layer), one of the most widely used multimedia libraries in game development, formalized its AI policy in April 2026. The policy states directly: AI-generated code is based on sources of unknown origins and may not be compatible with the Zlib license, or may introduce conflicting license terms.
This is a genuine legal concern. LLMs are trained on vast amounts of code from across the internet, including code under restrictive licenses. When an LLM generates code, it is not possible to trace what training data influenced that output. For projects with specific licensing requirements — GPL, LGPL, MIT, Zlib — accepting AI-generated contributions means accepting unknown legal exposure.
QEMU addressed this in 2026, noting that with AI content generators, "the copyright and license status of the output is ill-defined with no generally accepted, settled legal foundation."
Which Communities Have Banned What: The Full Table
Community / Project | What Is Banned | What Is Still Allowed |
|---|---|---|
r/programming (April 2026) | All posts about LLMs, ChatGPT, Copilot as topics | Technical ML and machine learning algorithm discussions |
Stack Overflow (since 2022) | AI-generated answers | Verified, human-authored answers; AI-assisted research |
Gentoo (April 2024) | AI-assisted code contributions | Human-written contributions |
NetBSD (2024) | AI-generated code (treated as copyright violation) | Human-authored code under clear license |
Redox OS (February 2026) | All LLM-generated contributions | Human-authored patches with Certificate of Origin |
SDL (April 15, 2026) | LLM-generated code for contributions | Using AI to identify issues (not generate fixes) |
EFF (February 2026) | Undisclosed AI contributions | AI-assisted code with disclosure, understanding, and human docs |
Oracle OpenJDK (June 2026) | Generative AI contributions (interim policy) | Human-authored contributions under Oracle Contributor Agreement |
Oracle GraalVM (June 2026) | Nothing banned | AI-assisted contributions permitted |
Zig language (2026) | AI-generated code contributions | Human-authored contributions |
Debian (2026) | No formal ban reached | Current policies, pending formal guidelines |
Note: Oracle's situation is particularly interesting — two sister projects backed by the same company adopted opposite policies, making clear that even within a single organization there is no settled consensus.
The Enterprise Angle: When Companies Themselves Ban AI
The community bans described above come from open source volunteers and forum moderators. But a parallel wave of restrictions is hitting AI coding tools at the corporate level.
Microsoft Cancels Claude Code Licenses (June 2026)
In May 2026, Microsoft began cancelling most internal Claude Code licences for its Experiences and Devices division, the group responsible for Windows, Microsoft 365, Outlook, Teams, and Surface. Engineers were directed to transition to GitHub Copilot CLI by June 30, 2026. The directive came from Executive Vice President Rajesh Jha.
Claude Code had been made available to Experiences and Devices in December 2025, and by early 2026 it had become popular enough to start displacing GitHub Copilot CLI in daily engineer workflows. That created a problem that was both financial and strategic: Microsoft sells Copilot to the rest of the world and cannot credibly do that while its own engineers migrate away from it at scale.
The episode illustrates something broader: AI coding tools have moved from optional experimentation to strategic infrastructure with budget, governance, and platform consequences. The token-based pricing model of tools like Claude Code means costs scale directly with how useful the tool is making it financially volatile for large engineering organizations.
Uber experienced the same dynamic: the company reportedly burned through its entire 2026 AI tools budget on Claude Code and Cursor in just four months.
Companies Banning AI Coding Over Security
Multiple organizations have moved to restrict or ban AI coding tools following security audits. The pattern is consistent: AI-generated code passed review processes because it was clean and readable, then failed in production or was flagged in security reviews for introducing privilege escalation paths, insecure patterns, or subtle vulnerabilities.
The problem is that security review of AI-generated code is structurally harder than review of human-written code. Human developers make mistakes in ways that experienced reviewers recognize and know to look for. AI generates mistakes that are new, clean-looking, and do not fit the pattern-matching that experienced code reviewers rely on.
What Is the "Productivity Paradox" of AI Coding?
There is a counterintuitive finding at the heart of this entire discussion. Most developers believe AI tools make them faster. And in terms of raw code generation, that's true AI can generate boilerplate, draft functions, and scaffold projects far faster than typing from scratch.
But research published by METR in February 2026 found that when you account for the time spent finding and fixing AI-generated errors, steering the AI, and waiting on it to complete tasks, developers using AI were actually about 19% slower on average than developers working without it. Critically, the developers in the study did not know that they reported feeling more productive even while being measured as slower.
This matters for community bans because it explains why experienced developers are often the most skeptical. They have accumulated enough experience reviewing and debugging code of their own and others' to recognize the subtle wrongness of AI output. The developer who submits an AI-generated pull request feels productive. The maintainer who has to review and rewrite it is simply doing extra work.
Step-by-Step: How to Participate Without Getting Banned
If you use AI tools in your workflow which most developers do in 2026 here is how to engage productively in communities that have restrictions.
Step 1: Read the specific rules of the specific community. Not all bans are the same. r/programming banning LLM discussion topics is different from SDL banning LLM code contributions, which is different from Oracle OpenJDK banning generative AI pull requests. Check the rules of each community individually before participating.
Step 2: Use AI as a reference tool, not a submission tool. There is a meaningful difference between using an AI to understand a concept and then writing code yourself, versus prompting an AI for code and pasting the output. Most communities ban the latter. Use AI to learn, then produce your contribution from your own understanding.
Step 3: Verify everything before posting. If AI gives you information you want to share, verify it first. Run the code. Check the official documentation. Confirm function names, library versions, and version-specific behavior. Post your verification, not the AI output.
Step 4: Disclose when required. Projects like the EFF's allow AI-assisted contributions with three requirements: disclosure, demonstrated understanding, and human-authored documentation. If you used AI as part of your process, say so. Demonstrating that you understand the code you're submitting is the key signal maintainers are looking for.
Step 5: Write from your own experience. The most valued content in developer communities has always been experiential. "I ran into this specific problem, here is exactly what happened, here is what I tried, here is what worked." AI cannot write this for you because it did not have your experience. Posts grounded in real incidents are essentially immune to AI content bans.
Advanced: What to Do If Your Post or PR Gets Removed
For forum posts:
Contact moderators with a specific explanation of how the post was written
Offer to elaborate or answer follow-up questions that demonstrate your understanding
Ask what specifically triggered the removal — moderators are generally willing to explain
For open source contributions:
Do not resubmit immediately; read the project's contribution guidelines carefully
If the project has a no-LLM policy, rewrite the contribution yourself from scratch
When resubmitting, include a PR description that walks through your reasoning — this is the strongest evidence of genuine authorship
Be prepared to discuss the code in comments or a linked issue; maintainers may ask questions
For future contributions:
Write in a specific, personal voice — AI writing tends toward generic completeness with no rough edges
Include specific error messages, environment details, version numbers, and debugging steps you actually took — AI often invents or generalizes these
Reference concrete experience rather than abstract description
Real-World Examples That Show Why This Matters
The 225-Hour Rebuild: In early 2026, a developer named Pooja Rana spent 225 hours managing and debugging AI-generated code, then deleted all of it and rewrote it by hand. The original code looked complete and passed initial review. The maintenance cost revealed itself over months.
The Jazzband Shutdown: The Jazzband Python project collective — a well-established, productive open source ecosystem — shut down entirely in 2025-2026 after being overwhelmed by AI-generated contributions that its volunteer maintainers could not keep up with reviewing.
The 13,000-Line PR: The OCaml community received a single AI-generated pull request with over 13,000 lines. One maintainer warned that such submissions could bring the entire PR system to a halt.
The curl Bug Bounty: The curl project shut down its bug bounty program in early 2026 after AI-generated "vulnerability reports" flooded the system — all technically plausible, none valid.
The Ars Technica Retraction: In 2026, Ars Technica retracted an article after the AI a writer used hallucinated quotes from an open source library maintainer. The same maintainer had separately been harassed by someone's AI agent trying to get AI-generated code merged into his project.
The New Relic Production Crisis Finding: A June 2026 industry report found AI code scores higher in code review than human code — yet organizations using AI code are experiencing more production incidents. This is the clearest evidence yet that current review processes cannot catch the specific failure modes AI introduces.
Latest Updates (2026)
Here is what has changed most recently, directly relevant to this topic:
Oracle OpenJDK Bans Generative AI; Oracle GraalVM Allows It (June 2026)
The OpenJDK Governing Board approved an interim policy prohibiting contributions created with generative AI, while the sibling Coding Assistants policy from GraalVM permits them. Both projects require contributors to sign the Oracle Contributor Agreement. This split — within a single company — is one of the clearest signals that even major institutional players have not resolved where AI belongs in open source contribution workflows.
New Relic: AI Code Grades Higher in Review, Causes More Production Failures (June 10, 2026)
New Relic's 2026 State of AI Coding report found that vibe coding is now mainstream, but unverified trust in AI-generated code is producing a production crisis. The key finding: reviewers rate AI code higher than human code, but production incidents are rising among organizations using AI-generated code. This "looks good, breaks things" pattern is reshaping how technical communities think about AI content policies.
r/programming Temporary LLM Ban (April 2026)
The mod team of r/programming (6.9M members) tried a 2-to-4-week ban on all LLM-related discussion content. Technical machine learning content remained allowed. The community and industry watched closely to see if it would become permanent.
SDL Formal AI Policy (April 15, 2026)
SDL released a formal written policy explicitly prohibiting LLM-generated code contributions, naming ChatGPT, Claude, Copilot, and Grok specifically. AI may be used to identify code issues, but human-authored solutions are required. SDL underpins a significant portion of cross-platform game and multimedia development.
Microsoft Cancels Claude Code Licenses (May–June 2026)
Microsoft began cancelling most internal Claude Code licences for its Experiences and Devices division by June 30, 2026, directing thousands of engineers to GitHub Copilot CLI. The move reflected cost control (token-based pricing makes popular AI tools financially volatile), platform strategy (Microsoft sells Copilot and cannot have engineers migrating away from it), and governance standardization. Claude models remained available through Microsoft-controlled Azure channels.
Uber Burns Through 2026 AI Tools Budget in Four Months
Uber reportedly exhausted its entire 2026 AI tools budget on Claude Code and Cursor in just four months — a sharp illustration of how token-based pricing creates financial risk at scale that traditional per-seat software licensing does not.
Zig Programming Language AI Ban (2026)
The Zig programming language project adopted restrictions on AI-generated code contributions. For a language that attracts programmers specifically interested in deep systems control and explicit code, the rejection of AI contributions reflects the community's ethos as much as its quality standards.
Stack Overflow AI Trust Gap Report (February 2026)
Stack Overflow's developer survey found that AI tool usage rose to 84% while trust in those tools dropped to 29% — an 11-point fall from 2024. The more developers use AI, the less they trust it. Stack Overflow described this as the "developer AI trust gap" and identified hallucinations, non-determinism, and security concerns as the primary drivers.
EFF Three-Condition Middle Ground (February 2026)
The Electronic Frontier Foundation adopted the most clearly articulated middle-ground AI contribution policy: LLM use is allowed with mandatory disclosure, demonstrated understanding, and human-authored documentation. This model is being watched by other communities as a possible template.
METR Research: AI Makes Developers 19% Slower (February 2026)
AI research lab METR published findings showing developers who used AI tools were 19% slower on average than developers working without them, once error-correction time was accounted for. Critically, the developers did not know this — they felt more productive. The research also found that by February 2026, most developers were unwilling to work on tasks without AI tools at all, even limited tasks.
Troubleshooting Checklist
Before posting in any developer community, check these:
Have I read the specific AI content rules of this specific community?
Is my post written from my own knowledge and experience?
Have I run all code snippets myself and confirmed they work?
Are all function names, API references, and version numbers accurate?
Does my post include concrete details — error messages, environment info, debugging steps — that would be hard to fabricate?
If I used AI as a research or learning tool, do I actually understand what I'm sharing?
Am I sharing genuine insight, not reformatted AI output?
If disclosure is required by this community, have I included it?
When to Contact Moderators or Maintainers
Reach out directly when:
Your post was removed without explanation and you believe it was incorrectly flagged as AI-generated
You want to clarify the rules before posting something that might be borderline
You have a legitimate use case for AI-assisted content and want to know if disclosure would make it acceptable
You are a maintainer dealing with an AI contribution flood and need platform-level help (GitHub has been developing emergency PR management tools for exactly this)
Most moderators and maintainers are willing to engage with good-faith questions. The bans are not about punishing developers who use AI thoughtfully — they are about reducing the cost imposed on the community by low-effort, unverified, or misrepresented AI output.
FAQ
The bans are about discussion content and contributions, not your private workflow. Most communities do not care if you used Copilot to write code. They care if you paste AI output into their forum without verifying it, or submit an AI-generated pull request to a project you don't understand.
r/programming's April 2026 temporary ban covered all posts about LLMs — news, guides, product discussions. Technical machine learning discussions remained allowed. The policy was a trial; the mod team was evaluating whether to make it permanent.
It depends on context. For simple, well-scoped tasks, AI-generated code can be fine. For complex systems, AI-generated code tends to have 1.7x more issues (CodeRabbit research), introduces more security vulnerabilities, and is harder to maintain because contributors often don't understand it. Most strikingly, a June 2026 New Relic report found AI code actually grades higher in review but causes more production incidents.
It typically refers to posts substantially generated by a large language model — ChatGPT, Claude, Gemini — rather than written from the author's own knowledge. Some forums (like r/programming) also ban posts about LLM tools as a topic: news, announcements, product guides. These are different restrictions.
AI has separated effort from understanding. When someone submits code they generated but don't understand, they are asking unpaid volunteers to do the review, debugging, and teaching the contributor should have done themselves. The EFF noted code reviews "turn into code refactors" when contributors cannot explain or defend what they submitted.
Many projects allow this with conditions. The EFF requires disclosure, demonstrated understanding, and human-authored documentation. SDL requires human-authored code only. Oracle's OpenJDK bans generative AI contributions entirely; Oracle's GraalVM allows them. Always check each project's specific contribution guidelines.
AI slop is AI-generated content that looks technically credible but contains subtle errors, hallucinations, or shallow reasoning. It is damaging in developer communities because developers act on technical information. Code that looks correct but breaks in production causes real harm. RedMonk analyst Kate Holterhoff coined the related term "AI Slopageddon" in early 2025.
Yes. Stack Overflow's prohibition has been in place since late 2022. The site's traffic has declined sharply as developers increasingly ask chatbots instead of consulting forums. The ban reflects Stack Overflow's core value: verified human expertise, not probabilistic text generation.
AI tools perform well on popular frameworks and languages with large training corpora. They confidently generate wrong code for niche frameworks, specialized systems, or non-mainstream languages. Communities built around those niche technologies — like Zig — find AI-generated contributions particularly unreliable, which reinforces their decision to ban them.
The trend has been toward spread and formalization, not reversal. Unless AI-generated code quality improves substantially in ways that make the production incident rate go down, and unless the volume problem is addressed at the platform level, the economic argument for bans — protecting unpaid volunteer time — remains compelling. The June 2026 New Relic finding (higher review scores, more production failures) suggests quality improvement visible in review is not translating to production reliability, which will keep the pressure on communities to maintain restrictions.
Conclusion
The question of why coding communities are banning AI content has a genuine answer: it is not anti-AI sentiment. It is a response to a specific, measurable set of problems — quality that fails in production despite passing review, a maintenance burden that is burning out unpaid volunteers, security vulnerabilities that slip past standard review processes, copyright exposure that projects cannot accept, and a trust gap that keeps growing the more developers actually use AI tools.
The communities doing the banning are not opposed to AI. Many of their maintainers and moderators use AI tools daily. What they are opposed to is the specific pattern of low-effort AI submission that creates work for others without contributing genuine value.
If you navigate these spaces in 2026, the practical conclusion is clear: use AI to learn, then contribute your understanding. Verify everything before posting. Write from real experience. Understand the code you submit. These have always been the standards that made developer communities worth participating in. AI made it easier to skip them. The communities are pushing back — and the data says they have good reason to.