I am a programmer, not a rubber-stamp that approves Copilot generated code

Original Article ↗ Hacker News Discussion ↗

Programmer Identity and Craft

  • Several comments draw a line between “programmers” who understand what they ship and “developers/script kiddies” who paste code they don’t grasp.
  • A “proper programmer” is described as: using Stack Overflow only as input to reasoning, maintaining personal templates, reading library code, keeping private forks, and upstreaming fixes.
  • Others push back that “just get it working” is often rational for business or for small scripts, and not inherently a moral failing.

Forced AI Adoption and Surveillance

  • Multiple reports of companies tracking LLM/Copilot usage, tying it to performance reviews, and maintaining “naughty lists” for low usage.
  • Large vendors (Microsoft, Amazon, Oracle, Google Workspace) are cited as enabling or encouraging such metrics; some smaller companies copy the practice.
  • Many see this as a “company-switching issue” and an example of management chasing hype and justifying AI spend, not genuine productivity.
  • There’s debate over performance tracking in general; some countries and union environments restrict it, leading to arguments about promotions, fairness, and metrics gaming.

AI Autocomplete, Agents, and Developer Flow

  • Many find inline AI completions aggressively distracting— likened to a toddler, a mosquito, or an interrupting coworker. Opt‑out defaults are widely resented.
  • Some mitigate by enabling AI only on keypress, using CLI agents, or disabling inline suggestions entirely.
  • Opinions split: short 1–2 line completions and boilerplate/test generation are often praised; multi-line “vibe coding” and comment autocompletion are seen as wrong, generic, or actively misleading.
  • A common pattern: use AI for throwaway code, refactors, or unfamiliar stacks; avoid it for core logic or when deep thinking is required.

Code Quality, Maintainability, and “Workslop”

  • Strong concern that LLM code “looks fine” and passes shallow tests but is architecturally weak, fragile, or odd, pushing long‑term cost to reviewers and maintainers.
  • This is compared to sloppy freelancers/consultants or inexperienced juniors; AI mainly accelerates an existing problem and scales it.
  • Some advocate pairing AI with careful specs, RFCs, and human-written tests, possibly using the LLM in a TDD loop; others argue this still demands as much diligence as hand‑coding.
  • Term “workslop” is used for output that superficially satisfies metrics but offloads real work to whoever comes next—including one’s future self.

Productivity, Tools, and Coercion

  • A recurring question: if AI is truly a huge productivity win, why mandate usage and measure token burning instead of just measuring outcomes?
  • Comparisons are made to RTO mandates and earlier forced shifts (React, cloud, microservices, Vim/IDE wars, Dvorak). Some say people irrationally resist tools; others say management routinely misjudges what actually helps.
  • Many argue editors and AI assistants are deeply personal workflow choices; standardizing on infrastructure is not the same as standardizing on how individuals think.

Jobs, Economics, and the Future of Programming

  • Some predict AI will eventually handle not just typing but design, risking many programming careers; others insist current LLMs can’t reliably reason, and scaling trends look limited.
  • Analogies are drawn to looms, compilers, and pilots with autopilot: humans shift from “doing everything” to supervising, especially in failure modes.
  • There’s anxiety about layoffs, lifestyle inflation, and engineers in HCOL cities tied to employers that now enforce AI usage. Others respond that developers are still relatively privileged and can (or should) change jobs or reduce lifestyle risk.
  • A sizable group expects new niches: cleaning up bad AI codebases, consulting on architecture, or building businesses that “just ship code that works” against sloppier AI‑driven competitors.

Personal Strategies and Cultural Split

  • Some commenters enthusiastically report 4–8x speedups with careful use of agents and detailed specs, seeing skepticism as a competitive advantage for them.
  • Others report AI kills their joy of coding, breaks flow, and turns them into reviewers of code they didn’t think through—precisely what they don’t want their job to become.
  • The community appears to be polarizing into those who enjoy using AI as a core tool and those who either resist on principle, dislike the experience, or only trust it in narrow, low‑risk contexts.