At Amazon, some coders say their jobs have begun to resemble warehouse work

Shift from Writing Code to Reading/Reviewing It

• Several commenters say they now enjoy debugging, refactoring, and system design more than “green‑field” coding; AI can make the tedious parts disappear but risks turning engineers into code janitors or prompt jockeys.
• Others find AI-generated code (“vibe coding”) messy, inconsistent, and hard to review, making the job less satisfying and more like supervising a sloppy junior.
• Some like AI as a “super‑StackOverflow” for syntax, boilerplate, config, and refactors, but insist you must already understand what you’re doing for it to be safe or useful.

Factory / Warehouse Metaphor and Pre‑Existing Drudgery

• Many argue big‑company development was already factory‑like: JIRA tickets, story points, sprint throughput, and low autonomy. AI just accelerates an existing trend.
• The Amazon comparison to auto factories is widely attacked: factories rely on rigorously engineered designs, deterministic machines, and heavy QC; LLMs are stochastic and not at that standard.
• Some say the real “factory” is the dev process itself (standups, status reporting, metrics), not the act of typing code.

Deskilling, Class, and Automation

• Strong theme: developers aren’t a special elite but well‑paid workers whose jobs, like others, are being automated and Taylorized. Long subthread disputes whether SEs are “working class” or “middle class,” but consensus that they sell labor, not capital.
• Some see “poetic justice” in programmers being automated after decades of automating others; others call that dehumanizing and argue the real issue is who captures productivity gains.
• Multiple comments advocate unions, stronger labor rights, or UBI; others distrust unions but still want better systemic protections.

Code Quality, Maintainability, and “Vibe Coding” Risks

• Widespread fear that AI will accelerate production of “AI slop”: brittle, over‑patched code, shallow test coverage, and unknown security holes.
• Concern about a “shitpile singularity,” where short‑term productivity hides long‑term collapse in maintainability and reliability.
• Some report AI genuinely helping with non‑trivial refactors and pattern extraction in large codebases; others counter that if you can’t verify the change yourself, you’re just deferring the pain.

Amazon‑Specific Practices and Culture

• One Amazon engineer claims the article overstates AI pressure; another counters with specifics: AI browser extensions installed by default, non‑dismissible nags, leadership emails demanding daily AI use, and planning docs forced to include AI sections.
• Commenters note Amazon already treats many engineers as interchangeable ticket‑closers, with aggressive RTO, heavy monitoring, and strong output expectations; AI is seen as another lever to squeeze more work from fewer people.
• Others inside FAANG argue there is still substantial new feature work and surprising amounts of manual, unautomated process, especially at large scale.

Productivity Claims and Management Motives

• Skepticism toward studies like Microsoft’s 25% Copilot boost: commenters note small or negative effects for experienced devs and methodological caveats.
• Many believe executives are using AI as rhetorical cover for layoffs, higher quotas, and “doing more with less,” regardless of real efficiency or risk to core systems.
• Observers note the familiar pattern: any real productivity gain is quickly reset as the new baseline expectation for individual performance.

Changing Skill Profile and Education

• Multiple people predict that junior dev roles will shrink or change: if all you do is small, pre‑chewed tickets, AI can do much of that; the remaining work requires deeper reasoning, architecture, and domain understanding.
• There’s disagreement on education: some say curricula must fully embrace AI (even “AI‑only” assignments); others argue students must first learn to think and program without it or they’ll never progress past superficial use.
• Concern that overreliance on AI will stunt the pipeline of truly senior engineers who can design, debug, and secure complex systems without a model.

Broader Trend: Disempowering Knowledge Workers

• Commenters tie this to a wider shift: pandemic‑era “we’re all in this together” giving way to narratives of bloat, laziness, and the need to squeeze white‑collar workers.
• Many see AI tooling as part of a long‑running managerial project to deskill, measure, and control knowledge work—turning creative roles into standardized, surveilled workflows.