Grief and the AI split
Generative AI tools for writing code are exposing a long‑hidden split among software engineers: those who prize the craft and cognitive challenge of programming versus those who primarily care about shipping results. Commenters debate whether AI meaningfully improves quality and productivity or simply accelerates the production of brittle “slop,” raising concerns about long‑term maintainability, professional standards, and overreliance by less experienced developers. Underneath the arguments are deeper anxieties about job security, the loss of satisfying work, concentration of power in AI vendors, and how far organizations should go in automating the software development lifecycle.
Craft vs result framing
- Many like the “craft lovers vs result chasers” framing, but a lot of commenters see it as an oversimplification or outright wrong.
- Several argue the real axis is what you care about: enduring quality, correctness, and maintainability vs speed and “good enough for now”.
- Others say the deeper split is between those who enjoy understanding and designing systems vs those happy to delegate that to tools.
Quality, risk, and maintainability
- Strong worry that unreviewed or lightly reviewed AI code is just a new way to accrue massive tech debt and hidden bugs.
- Some report seeing AI-heavy codebases that are huge, verbose “slop” requiring rewrites; others report quality improving when AI is used under strict docs, tools, and human review.
- There’s tension between contexts: quick MVPs and experiments vs regulated, safety‑ or money‑critical systems where AI shortcuts are seen as unacceptable.
Productivity and workflows
- Many report substantial productivity gains: faster prototyping, debugging, refactoring, writing tests, resurrecting old side projects.
- Others say the productivity story is overstated; coding was never the main bottleneck compared to understanding problems, design, and coordination.
- “Agentic” workflows (agents modifying codebases) divide people: some see them as a new platform layer, others as inherently brittle and non‑deterministic.
Emotional responses and identity
- A recurring theme is grief, but for different things:
- Loss of daily hands‑on coding as a paid craft.
- Loss of professional identity built around being “good at code”.
- Fear of obsolescence vs excitement about new creative possibilities.
- Some worry AI use erodes deep focus and reasoning, making people reach for tools instead of thinking through hard problems.
Economic, ethical, and power concerns
- Concerns about:
- Job security and the collapse of “knowledge work” as a moat.
- Proprietary AI platforms capturing dev tooling, with per‑line costs and data/control implications.
- Hype‑driven abandonment of basic engineering process (reviews, tests) and the long‑term fallout.
- Use of AI in critical domains (healthcare, finance, infrastructure) without adequate safeguards.
Future of craft and work
- Some think craft will “move up a level” (requirements, architecture, system prompts); others think the craft dies and so does its paid livelihood.
- Many expect a messy correction: current FOMO‑driven overuse will blow up, leading to more sustainable, process‑bound AI usage that still needs skilled engineers.