Did GitHub Copilot increase my productivity?

Original Article ↗ Hacker News Discussion ↗

Perceived Productivity Effects

  • Many report clear productivity gains, especially in common stacks (TypeScript/React, JS, Python, Rust, C on small scopes): faster boilerplate, tests, refactors, and “first draft” code.
  • Others find net slowdown: waiting for suggestions, over-relying on autocomplete, or spending more time reviewing and debugging AI output than writing it themselves.
  • Several say the main benefit is reduced cognitive load and increased enjoyment, not raw speed. It helps them stay productive longer and tackle more side projects.
  • A common pattern: very useful early in greenfield projects and for routine tasks; value drops as codebases become more complex and idiosyncratic.

How Developers Actually Use Copilot/LLMs

  • Strongest uses:
    • Boilerplate and “0-entropy” code (loops, mappings, CRUD, scaffolding, Dockerfiles).
    • Tests (suggesting additional cases, Given/When/Then patterns).
    • Docstrings, type hints, simple refactors and renames.
    • Quick scripts, CSV/data munging, and API examples for unfamiliar libraries/frameworks.
  • Many treat chat-based LLMs as a better search engine / “super StackOverflow” for explanations and examples, then refine manually.
  • Effective workflows often limit suggestions to small fragments or manual invocation; whole-function or multi-file generation is viewed as risky and time-consuming to verify.

Accuracy, Trust, and Review Burden

  • Recurrent complaints: hallucinated APIs, paths, methods, and partial or subtly wrong logic, especially in C/C++ and complex business logic.
  • Some argue reading AI-generated code is still less work than typing it; others insist reviewing is harder than writing and erodes mental models of the system.
  • Tests are suggested as a mitigation, but there’s concern about “tests that test nothing” and long‑term maintainability of AI-heavy codebases.

Impact on Learning and Craft

  • Some feel LLMs accelerate learning and expose them to techniques and APIs they “should have known years ago.”
  • Others worry about shallower understanding, fewer “side quests,” and skills atrophying (GPS/phone-number analogies).
  • Comparison to interns: LLMs are like ultra-fast juniors who don’t improve over time; training them doesn’t compound into future benefit.

Workplace, Economics, and Ethics

  • Reports of outsourcing shops and junior-heavy teams seeing contracts canceled or roles redefined as seniors plus AI, especially for frontend and integration work.
  • Concerns about copyright, proprietary code leakage, corporate policies, and Copilot’s use of public code for training.
  • Broader AI hype and cost are debated: some see a genuine shift; others see a bubble subsidized by legacy profits and cloud/GPU economics.