Going full AI engineer, not touching code anymore

Original Article ↗ Hacker News Discussion ↗

Skill Atrophy and Understanding Code

  • Many worry that “not touching code” will erode the ability to program and reason about systems; reading diffs alone may not be enough practice.
  • Some argue long experience and continual code review will preserve skills; others note managers who stopped coding did lose technical sharpness.
  • Concern that relying on LLMs early in problem‑solving narrows one’s solution space and trains people into “mid” thinking.

LLMs vs Compilers and Determinism

  • Multiple commenters reject the analogy “LLMs are like compilers”: compilers are deterministic translations with clear semantics; LLMs produce best‑guess, sometimes wrong, designs.
  • LLM output is seen as closer to an intern’s work: sometimes helpful, never fully trustworthy, always requiring review.

Speed, Business Incentives, and Quality

  • Strong theme: businesses optimize for velocity and short‑term revenue. LLMs fit this by enabling fast, cheap MVPs whose hidden 5–15% of problems show up later.
  • Quality and long‑term maintainability are often deprioritized; LLMs may accelerate creation of fragile, Rube Goldberg codebases.

How People Actually Use LLMs for Coding

  • Experiences diverge: some get high‑quality, idiomatic code routinely; others find LLMs verbose, brittle on complex tasks, and slower than hand edits for small changes.
  • Common pattern: humans design architecture and core abstractions, then use LLMs to fill in boilerplate or extend patterns.
  • LLMs often struggle with refactoring, larger OOP systems, reuse of existing utilities, and avoiding duplicated helper functions.

Impact on Design, Architecture, and Solution Space

  • Advocates say the real value in software is architectural decisions and trade‑offs; LLMs free them from typing to focus on that.
  • Critics counter that if you no longer build architectures yourself, you lose the tacit knowledge needed to judge them or foresee their long‑term costs.

Career Identity and Role Shift

  • Some welcome becoming “AI orchestrators,” likening it to moving from manual craft to directing powerful tools.
  • Others feel this is effectively sliding into management and away from the craft they enjoy.
  • Broader worry that many are chasing AI hype, prompt tricks, and self‑promotion rather than doing solid engineering.