You can't pay me to prompt

Scope of AI Use in Programming

• Supporters describe LLMs as powerful assistants: navigating complex codebases, discovering APIs and edge cases, refactoring under constraints, generating glue code, and helping with rarely used tools (sed/awk/regex, Sheets APIs, etc.).
• Others argue discourse fixates on “code/content vomit,” missing these subtler but real productivity gains.
• Some say senior dev + AI ≈ “superpowers”; AI is likened to dynamic languages or power tools that reduce boilerplate and speed experimentation.

Quality, “Slop,” and Model Collapse

• Many report AI-driven “slop”: superficially plausible but buggy, incoherent, or redundant code; low-quality content flooding the web; harder differentiation between real expertise and “cosplay.”
• Concerns about LLMs training on their own output, leading to an Internet-scale echo chamber and stagnation.
• Some insist careful curation, testing, and discipline can make AI output useful; others argue that, in practice, most usage accelerates low-quality work.

Workplace Pressure and Identity

• Several mention top-down mandates (“AI use is mandatory”) and link them to leadership that already tolerates low quality and outsourcing.
• Some devs see refusing AI as preserving craft, skills, and joy in programming; others see resistance as fear, unfairness about eroded scarcity, or simple change aversion.
• Both “cheerleaders” and “haters” are described as exhausting; multiple comments call for nuanced, non-absolutist takes.

Fatigue, Hype, and Badges

• Many are tired of ubiquitous AI marketing and AI-centric posts; others are equally tired of anti-AI rants.
• AI is compared to past hype cycles (blockchain, NFTs, Kubernetes) and to long-running “AI effect” debates.
• The author’s “no AI” badge and similar “Not By AI” branding spark mixed reactions: some see principled signaling and a source of cleaner training data, others see confusion, performativity, or even a monetized gimmick.