Leave Me Behind

Craft, Community, and Loss

• Many agree the author is expressing grief over losing a beloved craft and sense of community (pairing, labs, meetups), not just complaining about tools.
• Some argue this nostalgia is real and valid; others see it as an existential crisis or sunk-cost issue that ignores that tools have always changed crafts.
• Several note that even before LLMs, industrial practices, Jira, remote work, and career progression already eroded the “joyful, communal” phase of programming.

AI as Tool vs Threat to Learning

• One camp treats LLMs as “Iron Man suits”: accelerators for typing, boilerplate, search, refactoring, tests, and small maintenance, while humans still design and reason.
• Another camp fears “outsourcing thinking,” deskilling, and a drug‑like dependence where people stop doing things they once could do, leading to “cognitive surrender.”
• Some solo or geographically isolated devs say AI is the first “collaborator/mentor” they’ve ever had and is empowering rather than dehumanizing.

Code Quality, Maintenance, and “Slop”

• Critics say stochastic code generation lowers the floor: it enables cheap, disposable, low‑quality software and encourages “vibecoding” without understanding.
• Others report the opposite in their own projects: more tests, better CI, more refactoring, and AI code review catching subtle bugs.
• Several stress that quality depends on how tools are used: careful review vs. blindly accepting agent output. The long‑term effect on the median codebase is seen as unclear.

Jobs, Inequality, and Historical Analogies

• Repeated comparisons to machinists→CNC operators, artisans→mass production, and furniture makers→IKEA: AI may make “hand‑crafted” software a niche craft.
• Disagreement over whether this is “just another automation” (manual labor analogy) or fundamentally different because it targets cognition and learning.
• Some warn of job devaluation and token‑usage metrics; others argue skills in specification, review, and system design will remain central.

Human Intelligence, Distraction, and Meaning

• Several worry AI plus existing media tech accelerate distraction, lazy thinking, and societal “dumbing down,” even if aggregate knowledge and GDP rise.
• Others argue human heedlessness predates AI; these tools mostly expose and scale existing tendencies.
• Multiple comments stress that people derive meaning from work; large‑scale removal of meaningful craft and autonomy is seen as socially dangerous.

Workplace Pressures and Power Concentration

• Reports of management demanding maximum AI usage, PR throughput, and token metrics create a zero‑sum, fear‑driven environment.
• Concerns that proprietary AI centralizes power and rents, unlike tools like git or Postgres; calls for open models and skepticism about further corporate control.
• A minority is enthusiastic about entrepreneurial upside and personal experimentation; another group prefers to “opt out” or stick to non‑AI workflows.