● Covered by 3 sources · 15 reportsLow impact12 negative3 neutral
The Rise of "AI;DR" as a Response to Unedited AI-Generated Content
Key points
- "AI;DR" is a new acronym for ignoring unedited AI-generated content.
- There is growing frustration with the volume of unreviewed AI writing.
- The policy suggests not reading content if the creator didn't bother to edit it.
- This applies to professional communications, newsletters, and social media.
- The advice is against copying and pasting AI chatbot outputs as responses.
- Recipients of communication seek personal judgment and context.
- AI should be used as a drafting tool, not a final answer generator.
- The advice suggests adding individual insights to AI-generated content.
- The advice applies to DMs, Slack, or PR reviews.
- The phenomenon is called 'AI-blindness'.
- AI-blindness causes refusal to focus on content.
- AI-blindness leads to endless back-and-forth with sender.
- AI-generated documents contain Claude-specific analysis and lingo.
- AI-generated marketing decks mix strategy with technical gibberish.
- AI excels in structured tasks like coding.
- AI-generated prose uses robotic cadence and clichés.
- AI writing lacks actual understanding and insight.
- The article critiques the dichotomy between "serious" engineers and "artisanal" coders.
- "Serious" engineers value the end result most.
- "Artisanal" coders value the coding experience over the final product.
- The author wants programs to be reliable.
- Patience and care are important for reliable programs.
- Unit tests, LLM reviews, and provers guarantee code is 99% right.
- The author wants 100% correct, maintainable, and readable code.
- The author refuses to use LLMs because they isolate from low-level details.
- Senior engineers are experiencing burnout from reviewing AI-generated code.
- Reviewing AI-generated code is harder than human-written code.
- Review times increased by 91% for teams adopting AI.
- Merged pull requests increased by 98% for teams adopting AI.
- 77% of engineers spend less time writing code now, more time reviewing AI output.
- GitHub Podcast discusses common AI hot takes.
- AI shifts development effort but doesn't eliminate human oversight.
- The GitHub Podcast explores hot takes and their underlying assumptions.
- The GitHub Podcast discusses the hot take: 'You do not need to read AI-generated code'.
- The GitHub Podcast states that developers are still responsible for AI-generated code.
- The GitHub Podcast suggests different review processes for different types of code.
- AI-generated content uses specific vocabulary and structural patterns.
- AI writing makes online content sound uniform and artificial.
- AI writing uses phrases like 'a powerful shift' and 'unlocks a new way to think'.
- AI writing uses words like 'wedge' frequently.
- The article argues against using AI for initial content generation.
- Writing is a critical thinking process AI cannot replicate.
- AI should be used for refining human-generated text.
- AI is bad at thinking and coming up with new ideas.
- AI-generated content is appearing in design proposals, business plans, documentation, presentations, tickets, and meeting summaries.
- AI output prioritizes detail over meaning and human connection.
- AI-generated design documents are summaries made by a machine, lacking context or perspective.
- Authors of AI-generated documents become impatient when people are not engaged.
- AI cannot assess long-term code quality.
- AI is not trained on maintainability metrics.
- Bad architecture and unmaintainable code effects take months or years to notice.
- Bad code is hard to read, understand, and evolve.
- Bad code breaks programs in non-deterministic ways.
- Bad code requires serious undertaking to add features.
- A developer used AI coding tools for a month.
- The developer observed a decline in their coding abilities.
- The developer became more reliant on AI for tasks.
- The developer banned AI contributions from their FOSS project, LibreWeddingPlanner.
- The developer used enhanced autocomplete in VSCode.
- The developer downloaded and used other code generation models.
- The article argues LLMs have not 'solved' coding.
- The majority of software cost is in maintenance, reliability, and security.
- LLMs cannot be held accountable for errors.
- Accountability is critical for software in high-risk industries.
- The author has 4 years of experience building with AI and AI systems.
- The author was an early adopter of LLM-powered coding tools.
- The author built their own harness for LLM-powered coding tools.
- The author teaches topics related to LLM-powered coding tools.
- The author builds LLM-powered products.
- The article challenges the narrative that 'coding is solved' and engineering is about 'taste'.
- Non-functional requirements (NFR) include maintenance, reliability, security, and scalability.
- Engineers are losing understanding of system architecture and design intent due to AI reliance.
- Teams are shipping code without proper comprehension or problem-solving.
- Engineering quality and job satisfaction are declining.
- AI-generated code is considered average.
- AI can improve below-average codebases to average.
- A new role at a big company found all specs, code, tests, PRDs, tickets, and reports were made by Claude Code.
- The author previously wrote an essay on how to kill code review.
- The author's previous proposal for AI code verification included five layers of trust.
- The previous proposal included comparing multiple AI options, deterministic guardrails, human-defined acceptance criteria, permission systems, and adversarial verification.
- The author now believes their previous replacement for code review was wrong.
- The core purpose of code review is shifting towards knowledge transfer and shared understanding.
The Emergence of AI;DR
The acronym "AI;DR" (AI; Didn't Read) has been introduced as a response to the increasing volume of unedited AI-generated content. This term is gaining popularity among individuals who are encountering AI output that has not been reviewed or refined by a human.
Growing Frustration with Unedited AI
There is a growing sentiment of frustration regarding the proliferation of AI-written material that lacks human oversight. This applies to various forms of communication, including professional exchanges, newsletters, and social media content. The concern is that unedited AI output often contains repetitive phrasing or lacks the nuance of human-crafted text.
A New Policy for Engagement
A proposed policy suggests that if a creator does not take the time to review and edit AI-generated content, then the recipient should not feel obligated to read it. This policy aims to encourage more thoughtful use of AI tools, emphasizing the importance of human intervention in the content creation process, especially in professional and public-facing communications.
Distinguishing Appropriate AI Use
While acknowledging that 100% AI-generated content may be acceptable in specific contexts, such as customer support, the policy draws a distinction for other areas. For instance, in professional discussions or personal branding, the expectation is that content should reflect human care and editing, rather than being raw AI output.
Updates
- The author previously wrote an essay on how to kill code review.
- The author's previous proposal for AI code verification included five layers of trust.
- The previous proposal included comparing multiple AI options, deterministic guardrails, human-defined acceptance criteria, permission systems, and adversarial verification.
- The author now believes their previous replacement for code review was wrong.
- The core purpose of code review is shifting towards knowledge transfer and shared understanding.
- Engineers are losing understanding of system architecture and design intent due to AI reliance.
- Teams are shipping code without proper comprehension or problem-solving.
- Engineering quality and job satisfaction are declining.
- AI-generated code is considered average.
- AI can improve below-average codebases to average.
- A new role at a big company found all specs, code, tests, PRDs, tickets, and reports were made by Claude Code.
- The article argues LLMs have not 'solved' coding.
- The majority of software cost is in maintenance, reliability, and security.
- LLMs cannot be held accountable for errors.
- Accountability is critical for software in high-risk industries.
- The author has 4 years of experience building with AI and AI systems.
- The author was an early adopter of LLM-powered coding tools.
- The author built their own harness for LLM-powered coding tools.
- The author teaches topics related to LLM-powered coding tools.
- The author builds LLM-powered products.
- The article challenges the narrative that 'coding is solved' and engineering is about 'taste'.
- Non-functional requirements (NFR) include maintenance, reliability, security, and scalability.
- A developer used AI coding tools for a month.
- The developer observed a decline in their coding abilities.
- The developer became more reliant on AI for tasks.
- The developer banned AI contributions from their FOSS project, LibreWeddingPlanner.
- The developer used enhanced autocomplete in VSCode.
- The developer downloaded and used other code generation models.
- AI cannot assess long-term code quality.
- AI is not trained on maintainability metrics.
- Bad architecture and unmaintainable code effects take months or years to notice.
- Bad code is hard to read, understand, and evolve.
- Bad code breaks programs in non-deterministic ways.
- Bad code requires serious undertaking to add features.
- AI-generated content is appearing in design proposals, business plans, documentation, presentations, tickets, and meeting summaries.
- AI output prioritizes detail over meaning and human connection.
- AI-generated design documents are summaries made by a machine, lacking context or perspective.
- Authors of AI-generated documents become impatient when people are not engaged.
- The article argues against using AI for initial content generation.
- Writing is a critical thinking process AI cannot replicate.
- AI should be used for refining human-generated text.
- AI is bad at thinking and coming up with new ideas.
- AI-generated content uses specific vocabulary and structural patterns.
- AI writing makes online content sound uniform and artificial.
- AI writing uses phrases like 'a powerful shift' and 'unlocks a new way to think'.
- AI writing uses words like 'wedge' frequently.
- GitHub Podcast discusses common AI hot takes.
- AI shifts development effort but doesn't eliminate human oversight.
- The GitHub Podcast explores hot takes and their underlying assumptions.
- The GitHub Podcast discusses the hot take: 'You do not need to read AI-generated code'.
- The GitHub Podcast states that developers are still responsible for AI-generated code.
- The GitHub Podcast suggests different review processes for different types of code.
- Senior engineers are experiencing burnout from reviewing AI-generated code.
- Reviewing AI-generated code is harder than human-written code.
- Review times increased by 91% for teams adopting AI.
- Merged pull requests increased by 98% for teams adopting AI.
- 77% of engineers spend less time writing code now, more time reviewing AI output.
- The article critiques the dichotomy between "serious" engineers and "artisanal" coders.
- "Serious" engineers value the end result most.
- "Artisanal" coders value the coding experience over the final product.
- The author wants programs to be reliable.
- Patience and care are important for reliable programs.
- Unit tests, LLM reviews, and provers guarantee code is 99% right.
- The author wants 100% correct, maintainable, and readable code.
- The author refuses to use LLMs because they isolate from low-level details.
- AI excels in structured tasks like coding.
- AI-generated prose uses robotic cadence and clichés.
- AI writing lacks actual understanding and insight.
- The phenomenon is called 'AI-blindness'.
- AI-blindness causes refusal to focus on content.
- AI-blindness leads to endless back-and-forth with sender.
- AI-generated documents contain Claude-specific analysis and lingo.
- AI-generated marketing decks mix strategy with technical gibberish.
- The advice is against copying and pasting AI chatbot outputs as responses.
- Recipients of communication seek personal judgment and context.
- AI should be used as a drafting tool, not a final answer generator.
- The advice suggests adding individual insights to AI-generated content.
- The advice applies to DMs, Slack, or PR reviews.
✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →
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Primary sources
GitHub khaosdoctor/dontquotetheai GitHub purplesyringa/site GitHub en/get-started GitHub software-development/innersource GitHub pollinations/pollinations GitHub Twarner491/AvianVisitorsHow outlets covered it
The traditional view of code review primarily for defect detection is being challenged by AI-generated code, as studies show defect finding is a minor part of reviews. The core purpose of code review is shifting towards knowledge transfer and building a shared understanding of the system within a team. This re-evaluation is critical as AI increases code output, making human understanding more expensive.
The increasing reliance on AI for code generation is causing engineers to lose understanding of system architecture and design intent. This trend results in teams shipping code without proper comprehension or problem-solving, leading to a decline in engineering quality and job satisfaction.
This article argues against the idea that large language models (LLMs) have "solved" coding, emphasizing that the majority of software cost lies in maintenance, reliability, and security, not just initial code creation. It highlights that LLMs cannot be held accountable for errors, which is critical for software in high-risk industries.
A developer recounts their experience of using AI coding tools for a month, detailing how it negatively impacted their skills and work habits. The developer observed a decline in their coding abilities and an increased reliance on AI for tasks they previously handled independently.
The article argues that relying solely on AI for code generation without human oversight leads to unmaintainable projects because AI cannot assess long-term code quality. It contends that AI is not trained on maintainability metrics, which manifest over months or years, unlike immediate reward signals.
The author expresses frustration with the increasing volume of AI-generated content in professional and personal communication, finding it unreadable and lacking context. This trend is observed in design proposals, pull requests, and personal messages, where AI output prioritizes detail over meaning and human connection.
An opinion piece argues against using AI for initial content generation, stating that writing is a critical thinking process that AI cannot replicate. It suggests that AI should instead be used for refining and improving human-generated text, similar to a proofreader.
This article criticizes the increasingly prevalent and recognizable writing style generated by AI, characterized by specific vocabulary and structural patterns. It argues that this 'AI tone' makes online content sound uniform and artificial, contrasting it with natural human writing.
The GitHub Podcast addresses common AI hot takes, specifically focusing on the necessity of reviewing AI-generated code and the role of AI proficiency in developer hiring. The discussion emphasizes that while AI shifts development effort, it does not eliminate the need for human oversight and judgment.
Senior engineers are experiencing burnout due to the increased burden of reviewing AI-generated code, which is harder to evaluate than human-written code. This shift in workload has led to a 91% increase in review times and a 98% increase in merged pull requests for teams adopting AI.
This article critiques the emerging dichotomy between "serious" engineers and "artisanal" coders, particularly in the context of AI tools. It argues that valuing deep understanding and reliability, even without AI, aligns with engineering principles, contrary to how the "artisanal" label is often used.
Large Language Models (LLMs) have not achieved human-level prose, leading to the conclusion that writing is a relatively safe profession from AI displacement. While AI excels in structured tasks like coding, the subjective and complex nature of good writing presents a significant barrier for current AI capabilities.
An individual describes a phenomenon of becoming 'AI-blind' when encountering documents that show strong traces of AI generation, leading to a refusal to focus on the content. This issue arises from exposure to numerous low-effort AI-generated texts, causing the brain to disregard such content as empty of meaning.
An opinion piece advises against simply copying and pasting AI chatbot outputs as responses, arguing that recipients seek personal judgment and context. It suggests using AI as a drafting tool but adding individual insights to provide value.
The acronym "AI;DR" (AI; Didn't Read) is gaining traction as a way to dismiss unedited AI-generated content. This reflects a growing frustration among readers with the proliferation of AI output that lacks human review and refinement.