Ask HN: How is AI-assisted coding going for you professionally?

Hacker News Discussion ↗

Overall sentiment & divergence

  • Experiences range from “game‑changer, 5–10x faster” to “net negative, I want to quit.”
  • Many describe simultaneously feeling empowered on side projects and burned out or anxious at work.
  • Strong split between people who feel they’ve learned to harness tools and those who find them inconsistent, myopic, or unusable on complex systems.

Where AI tools help

  • Greenfield / small, well‑scoped tasks: scaffolding apps, CRUD APIs, CI/CD, glue scripts, data munging, infra boilerplate, tests, refactors, local tools.
  • Understanding large or legacy codebases: “what touches X?”, “how is auth done?”, summarizing flows, exploring unfamiliar repos, generating diagrams.
  • Debugging: pinpointing bugs, reading logs and traces, fixing test fallout after refactors, triaging build failures.
  • Non‑coding: design docs, ticket drafting, documentation clean‑up, research and architecture brainstorming.

Where they fail or cause harm

  • Complex, interconnected systems: client/server interactions, legacy monoliths, niche domains, performance‑critical or numerical code.
  • Architecture and design: tendency to over‑engineer, duplicate logic, add layers and custom parsers, drift from existing patterns.
  • Reliability: hallucinated APIs, wrong docs, subtle security bugs, timing issues, and brittle refactors.
  • “AI slop” PRs: huge diffs that superficially look fine but are conceptually wrong, bloated, or unmaintainable.

Team and organizational dynamics

  • Some orgs mandate “AI‑first” or even “100% AI‑generated code,” others ban or heavily discourage it.
  • Senior engineers report becoming “code janitors,” cleaning up AI‑generated mess from managers or peers.
  • Code review is a new bottleneck: more and bigger PRs, reviewers overwhelmed, tension over quality vs velocity.
  • Management sometimes uses AI to mass‑produce design docs, tickets, and performance text that nobody really reads.

Impact on careers & skills

  • Many fear skill atrophy, loss of “craft,” and hollowing out of mid‑level roles; others lean into design/architecture and accept less typing.
  • Juniors can now produce large change sets without understanding them, making mentorship and review harder.
  • Some see solo‑dev and small‑team opportunities exploding; others anticipate layoffs or a sharp rise in expectations without matching rewards.

Emerging best practices

  • “Spec → plan → critique → implement → review” workflows; plan mode before code.
  • Strong tests, CI, and e2e coverage as guardrails; reject AI code that doesn’t move tests from red to green.
  • Repo hygiene for “AI‑native” development: AGENTS.md/CLAUDE.md, coding style guides, skills, and scripts for common workflows.
  • Use AI heavily for understanding, small increments, and boring work; keep humans fully responsible for design and final quality.