Software Developers Say AI Is Rotting Their Brains

Overall split: empowerment vs. erosion

• Many report feeling dramatically more productive with AI-assisted coding (especially agents), describing large speed-ups on some tasks and new ability to ship ideas quickly.
• Others find AI coding slower, more frustrating, or net‑negative once review and debugging are included, and some have stopped using it for code generation entirely.
• Several comments stress that effectiveness depends heavily on task type, codebase size/age, and how the tools are used.

Where AI helps vs. where it fails

• Works well for:
  • Boilerplate, tests, simple scripts, glue code, internal tools.
  • Debugging in messy legacy systems, “rubber‑ducking,” planning tasks, and quickly summarizing or traversing docs and wikis.
  • Raising the floor for weak areas (e.g., frontend polish, sysadmin chores).
• Works poorly for:
  • Large, long‑lived codebases with complex invariants and performance constraints.
  • Frameworks with many breaking versions (e.g., Odoo), where models mix APIs.
  • High-quality refactors or situations where code bloat and architectural clarity matter.

Quality, review burden, and velocity

• Big concern: explosive growth in code volume without proportional review capacity.
• Reviewers describe huge AI-generated PRs (thousands of LOC), often poorly tested, with duplicated functionality or unnecessary wrappers.
• Some fear devs will become full‑time reviewers for bots; others hope bots will eventually do review too.
• Several expect more errors in production because every line of code is a liability and AI is a “firehose.”

Skills, cognition, and “brain rot”

• One camp: AI lets you “outsource thinking but not understanding”; you still need strong intuition, judgment, and line‑by‑line review.
• Another camp: heavy reliance clearly atrophies mechanical coding skills and syntax recall; people report flunking basic interview tasks after months of agentic coding.
• Some describe a psychological shift: coding without AI feels pointless or inefficient, leading to dependency and reduced motivation.
• Others compare it to calculators or cars: capabilities atrophy, but that doesn’t automatically make the tool illegitimate.

Job satisfaction, craft, and industry direction

• Many who loved “crafting” software feel reduced to chatting with a bot, editing slop, and chasing velocity metrics; some plan to leave the field or already have.
• Others enjoy an “editor” role: using experience to guide agents while offloading rote work.
• There is anxiety that commercial development will become “industrial programming,” dominated by quantity over understanding, turning every new system into legacy from day one.

Evidence, hype, and organizational pressure

• Strong skepticism about claims of 5–50× productivity; commenters note lack of credible, reproducible studies and no obvious surge in high‑quality software.
• Some point out that large companies may be mandating AI usage and then touting adoption metrics.
• Several emphasize that current discourse underplays long‑term maintenance, tech debt, and human burnout from constantly “steering a rocket ship with chopsticks.”