The friction between AI-generated code and traditional software development workflows has reached a critical point in code review pipelines. As one developer lamented on Hacker News, "I hate reviewing huge PRs" where AI output floods repositories with redundant or suboptimal solutions instead of leveraging existing libraries. This sentiment underscores a growing industry tension: When should an entire pull request be rejected outright as 'garbage'?

The New Review Reality

Previously, large pull requests implied significant human effort—hours of problem-solving and implementation. Reviewers would tread carefully, knowing the submitter had invested "brain juice" into the work. But with AI tools generating code on demand:
- Effort becomes invisible: It's impossible to gauge a contributor's intellectual investment
- Quality signals blur: Distinguishing between deliberate engineering decisions and AI "fluff" proves challenging
- Alternatives get overlooked: Teams risk reinventing wheels when existing OSS solutions exist

"How will I know which part reflects their genuine opinion and intellect versus AI-fluff I can rip apart without stepping on toes?" the developer questioned, highlighting the human dimension of technical critique.

Community Responses: Pragmatism Over Perfection

Hacker News commenters offered starkly practical approaches:
1. Business need as gatekeeper: "Does it fill an immediate business need? Then ship it. Most code is slop anyway." Otherwise, reject to avoid maintenance debt.
2. AI-vs-AI escalation: Some suggest using AI tools to automatically analyze and reject low-quality PRs
3. Collective resignation: "Remember all your competitors are also using AI slop. You're in good company..."

The Unresolved Tension

This dilemma exposes deeper workflow fractures:
- Process failure: When meetings become the default conflict resolution, it signals broken review protocols
- Quality erosion: Accepting "slop" normalizes technical debt accumulation
- Human disconnect: Reviewers struggle to balance psychological safety with code quality demands

As AI democratizes code generation, teams must redefine collaboration contracts. The answer may lie not in rejecting PRs, but in preventing them—through stricter pre-commit standards, AI-linter integrations, and cultural shifts that prioritize strategic code over volume.

Source: Hacker News discussion (https://news.ycombinator.com/item?id=46308807)