The barriers to AI engineering are crumbling fast

Perception of “AI engineer” and job titles

• Many commenters are immediately suspicious of labels like “AI developer/engineer,” comparing them to past hype titles (“Scrum master,” “web developer”).
• Some see “AI engineer” as just “software engineer using AI APIs,” not someone building models. Others say the term has stabilized as: building products that integrate LLMs, distinct from AI research.
• Broader skepticism about overinflated titles (“data engineer,” “UX engineer”) and whether “engineer” should imply formal credentials.

GitHub, LinkedIn, and signaling competence

• Several argue great engineers often have little or no public GitHub or LinkedIn; their real work is closed-source and under NDA.
• Others (especially hiring managers) treat LinkedIn and GitHub as useful but imperfect signals when screening many candidates.
• Debate over whether lack of online presence is a negative, neutral, or positive signal.

Definition and difficulty of AI engineering

• Disagreement on whether this work is “just software engineering with AI integration” vs a distinct discipline needing skills like evaluation and fine‑tuning.
• One view: building with LLMs is lower technical difficulty but still valuable; difficulty ≠ value.
• Another view: casually wiring up APIs is akin to fragile early web dev and not production‑ready engineering.

Usefulness of LLM apps and OS‑level AI

• Some struggle to find compelling LLM app ideas beyond codegen, content generation, and natural language control of existing products.
• Vision of future OS‑integrated assistants that can operate apps via high-level commands, given proper APIs and language‑to‑action models.
• Others note frustration with current “AI” features (e.g., beta OS assistants) despite good experiences with local models and tooling.

AI imagery and content quality

• Strong criticism of AI hero images as ugly, fake‑looking, and a signal of low‑effort “slop,” justifying skipping such articles.
• Others think the images are fine, cost‑effective illustrations and that dislike is mostly taste or bias.

Industry reality, pay, and hype

• Senior AI/ML roles at big tech are described as highly paid and very demanding, with significant research catch‑up.
• Practitioners in high‑stakes ML (e.g., fraud detection) see LLM‑centric résumés as weak compared to traditional, rigorous models with strict latency/accuracy constraints.
• Multiple comments frame the article and career path as hype‑aligned and not attractive to all developers.

LLM limitations vs humans

• Discussion on LLMs’ poor reliability in logic/math: they pattern‑match text rather than truly reason.
• Some argue humans also rely on trained patterns; others counter that even pre‑modern societies showed stronger innate mathematical and logical abilities than current LLMs.