The Eternal Sloptember
Skepticism is growing around AI coding agents as developers report that while they can rapidly generate working code and boilerplate, they also encourage large, poorly understood changes, architectural slop, and subtle bugs that are hard to trace. Supporters counter that, in the hands of experienced engineers and with careful “harness” design and code review, LLMs can massively boost productivity, especially for routine tasks, refactoring, and small greenfield projects. The broader concern is not whether models can program at all, but how their widespread, uncritical use may erode engineering skill, code quality, and the shared mental models needed to maintain complex systems over time.
What LLMs and agents can do today
- Many commenters say current models can program: they produce compilable, idiomatic code, handle boilerplate, migrations, tests, refactors, and are especially strong in mainstream stacks.
- Others report success building full applications with agents plus custom “harness engineering,” treating them as powerful but constrained tools.
- A minority claim they still get better and faster results by hand, especially on highly novel, niche, or low-level work (e.g., unusual SDKs, complex netcode, EMR systems).
Limits, slop, and long‑term maintainability
- Strong concern that agents overproduce “slop”: large, overcomplicated PRs, hacks, bad abstractions, and subtle bugs that are hard to detect.
- LLMs are seen as good at syntax and local correctness but weak at architecture, modelling, and choosing the “right problem” to solve.
- People worry about legacy codebases degenerating into unreadable AI output and about future maintainability once context windows or models change.
Human‑in‑the‑loop, harnesses, and process
- Repeated theme: tools are valuable only with strict human review, tight scopes, tests, guardrails, and rollback/canary practices.
- “Harness engineering” (agents, memory, review skills, staged plans, TDD, integration tests) is presented as key to scale their use safely.
- Critics note that without disciplined review, organizations will rubber‑stamp huge AI PRs and ship regressions.
Impact on developers and organizations
- Seniors often find LLMs a major force multiplier; some claim ~5–10× individual productivity for standard business work.
- Others report skill atrophy, burnout, and feeling like they’re starting with a legacy codebase from day one.
- Debate over whether agentic coding will net‑improve quality or simply accelerate production of mediocre software, especially by low performers.
Broader analogies and societal concerns
- Comparisons to crypto, industrial automation, Luddites, Eternal September, digital cameras, and cars vs horses.
- Some argue AI is clearly more useful than crypto; others stress that social and economic choices, not raw capability, determine real impact.
- Energy use, GPU constraints, climate implications, and concentration of power are recurring worries.