AI Made Writing Code Easier. It Made Being an Engineer Harder

Perceived AI authorship and “slop” writing

• Many commenters are convinced the blog post is largely or fully LLM‑generated, citing its cadence, repetitive “this is not X, it’s Y” rhetoric, buzzwordy labels, and padded paragraphs that restate the same point.
• Several mention AI-detection tools (especially one service) claiming 100% AI authorship, though others caution that such detectors are often unreliable.
• There is strong dislike of AI prose: described as long‑winded, vacuous, formulaic, and “LinkedIn‑style,” with little substance for the word count.
• Some argue that when text uses first‑person experience, AI authorship becomes a trust problem; readers feel misled if it wasn’t actually someone’s lived experience.
• A minority say the article is still insightful regardless of how it was generated.

How AI is changing engineering work

• Many agree AI has made coding faster but shifted emphasis toward design, architecture, specification, review, and supervision.
• Senior engineers report their job was already more about planning, reviewing, and training; AI mostly amplifies that.
• Others argue the hard parts were always non‑coding skills; AI mainly removes illusion that “writing code” was the core difficulty.
• Some worry expectations have quietly ratcheted up: same or more scope, faster timelines, plus AI‑usage metrics, without more support or pay, leading to burnout.

Juniors, training, and jobs

• Multiple commenters fear juniors lose crucial “simple” tasks that once built foundations; unclear how they will gain experience.
• Some say new grads already struggle to find entry‑level jobs, and AI may worsen this.
• Concern that management will try to replace teams (e.g., 5 devs) with a single engineer plus AI.

Diverging attitudes toward AI tools

• Enthusiasts say AI makes programming far more fun: it handles boilerplate, lets them jump languages and frameworks, and focus on system design and ideas.
• Others value the craft of writing code itself; they see an identity crisis in being pushed into “code supervisor” roles.
• Several distinguish “engineers” who design and reason about systems from “code monkeys” who just produce code; AI is seen as squeezing out the latter.

Quality, safety, and engineering rigor

• Some argue AI accelerates both good and bad practices: it can write tests and structured code, but also mass‑produce “slop” if users lack judgment.
• One anecdote describes a non‑coder using AI to build a medical web app with serious security mistakes, illustrating “unknown unknowns.”
• Commenters stress that AI code still requires human architecture, constraints, review, and responsibility.

Impact on online discourse and writing

• Many feel HN and the broader web are being flooded with AI‑generated articles and even comments, making reading more tedious.
• There are calls for explicit tagging or flagging of AI‑generated content, and for readers to seek smaller, more curated communities.
• Some use AI as a proofreading or documentation aid but avoid letting it “speak for them” in opinionated writing.