AI can't even fix a simple bug – but sure, let's fire engineers

Original Article ↗ Hacker News Discussion ↗

AI as a Tool vs Overhyped “Replacement”

  • Many frame AI as just another tool: powerful when used well, useless or harmful when misused.
  • Others argue this analogy breaks because vendors aggressively market AI as an autonomous replacement, not a simple productivity aid.
  • Several suggest the real criticism should be aimed at companies and marketing, not at the raw capability of the models themselves.

Coding, Debugging, and Technical Limits

  • Experiences are mixed: some report strong gains for boilerplate, refactors with good tests, DSL transpilers, and documentation help.
  • Others describe frequent hallucinations (fake options, joins, APIs), brittle debugging help, and broken or unmaintainable code, especially in complex domains like runtimes, compilers, and native platforms.
  • AI often needs extremely detailed, carefully curated prompts and context; once it’s “off,” the overhead to recover can exceed any time saved.
  • Several note that “funny failures will be gone in months” has been said for years, while quality appears to plateau or even regress in places.

SaaS, Control, and Data Concerns

  • Strong disagreement over whether cloud LLMs are truly “tools” when users can’t inspect, repair, train, or fully constrain them.
  • Concerns include codebase stomping, data leakage, brittle dependence on connectivity, and opaque experimentation by providers.
  • Local models are suggested as an answer, but many note the hardware and ops costs are prohibitive for most.

Jobs, Layoffs, and Productivity

  • Debate over whether engineers are actually being fired because of AI: some see AI as cover for a tech recession, Section 174, and prior over-hiring; others report orgs explicitly cutting junior roles and “downsizing 50 to 5” with AI.
  • Comparisons to spreadsheets and accountants: tools changed the work mix, reduced some roles, but didn’t eliminate the profession—yet accounting’s trajectory is cited as a cautionary tale.
  • Some argue that firing engineers for AI is “natural selection” for bad companies; others stress the human cost and note that C-suites rarely bear the consequences.

Adoption Dynamics and Hype Pressure

  • AI use is often driven top‑down for PR, KPIs, and “we are an AI company” narratives, sometimes disconnected from actual usefulness.
  • There is pressure to “be the AI expert on the team,” but skepticism about investing heavily in workarounds for rapidly obsolete tools.

Where AI Works Well Today

  • Commenters highlight embeddings, semantic search, log/error analysis, scaffolding boilerplate, and assisted test-writing as genuinely high-value uses.
  • The consensus across the thread: AI is a real accelerator in the hands of skilled engineers, but nowhere close to safely replacing them.