The barriers to AI engineering are crumbling fast
Claims that “we can all be AI engineers” prompt sharp debate over what that role actually means and how much expertise it truly requires. Commenters contrast low-bar LLM app wiring and prompt work with high-stakes machine learning engineering in areas like fintech, question hype-driven titles such as “AI developer,” and note that many strong engineers have little public online footprint. Others critique the ubiquity and quality of AI-generated content and images, doubt LLMs’ reliability for complex logic or math, and worry that shallow AI integration may dilute software skills rather than deepen them.
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.