Is AI ruining our skills? Early results are in – and they're not good

Original Article ↗ Hacker News Discussion ↗

Perceived Skill Atrophy

  • Many participants report firsthand decline in skills (coding, planning, problem‑solving, even typing) after sustained AI use, often describing a “relearning to walk” phase when they stop.
  • Core worry: not just loss of narrow skills (like arithmetic or CLI commands) but erosion of broad reasoning, judgment, and “blank‑page” problem solving.
  • Several note that AI use can undermine users’ ability to evaluate AI output, creating a feedback loop of dependence.

Tool Use, Trade-offs, and Historical Analogies

  • Some compare LLMs to calculators, compilers, cars, or Ghost disk imaging: tools that make old skills unnecessary and let new layers of abstraction emerge.
  • Others argue the difference is scope: LLMs touch “basically all knowledge and communication skills,” not a narrow domain.
  • There’s general agreement that unused skills atrophy; dispute is whether that’s acceptable or existentially dangerous.

Impact on Software Engineering Practice

  • Multiple anecdotes of senior engineers “vibe‑coding,” shipping more but with worse code and weaker judgment.
  • Concerns that high-end, nuts‑and‑bolts expertise (systems, languages, compilers, architecture) will shrink, risking a slowdown in genuine innovation even as CRUD output explodes.
  • Reviewers struggle to mentor juniors because AI-written code no longer reflects the junior’s own thinking.
  • Others report the opposite: system design and architectural thinking improving because AI handles boilerplate and enables faster exploration and refactoring.

Learning, Education, and Cognitive Effects

  • Split views:
    • Some use AI as a tutor to tackle hard subjects (physics, quantum mechanics), new languages, and physical skills, emphasizing drills and verification.
    • Others say this usually leads to superficial “edutainment,” wide but shallow understanding, and kids doing homework by pasting prompts with zero reflection.
  • Breadth vs depth is a recurring theme: easy curiosity scratching vs hard, generative mastery.

Open-Source, Access, and Power Concentration

  • Debate over whether open models are “close enough” to frontier systems and whether that’s sufficient once a model is “good enough” for a task.
  • Worries that if only a few large providers control top models, society becomes cognitively dependent on a centralized, political, or corporate “oracle.”

Workplace Incentives and Career Dynamics

  • Many note that corporate incentives (velocity, cost-cutting) favor aggressive AI use, pushing deskilling while demanding more output.
  • Some foresee management paths opening sooner for mediocre but AI‑amplified developers; others predict a premium on those who retain deep skills.
  • There’s tension between short‑term productivity gains and long‑term professional and societal resilience.