We might all be AI engineers now
AI coding assistants and “agentic” tooling are rapidly reshaping software engineering, with many experienced developers reporting large productivity gains when they treat models as junior collaborators that execute well-scoped tasks under close human guidance. Others push back, arguing that current tools are unreliable, encourage shallow understanding and “vibe coding,” and risk flooding codebases and open source with low-quality, incoherent changes while dulling hard-won skills. Underneath the technical debate are broader worries about job displacement, environmental costs, changing expectations of what it means to be an engineer, and how future developers will build real expertise in a world where AI can already outperform many average coders on routine work.
Scope of “AI engineer” and role of agents
- Many see “agentic AI” as now core to software work: engineers design, decompose, review, and supervise; agents do bulk implementation.
- Others argue this is overblown marketing: most real-world use is still autocomplete, code search, and one-off helpers, not fully autonomous systems.
- Some liken the role shift to “architect/tech lead for machines” and find that exciting; others see it as devolving into middle-management of opaque tools.
Productivity, quality, and workflow
- Enthusiasts report big speedups: boilerplate, tests, glue code, migrations, and unfamiliar APIs done in minutes, enabling projects they’d never have attempted.
- They say the gains depend on tight specs, small scoped tasks, heavy testing, and strong fundamentals; AI output is treated as a hypothesis to validate.
- Skeptics see high cognitive load from constant review, more burnout, and lots of subtle bugs, incoherent architectures, and “locally ok, globally bad” code.
- Studies mentioned (without detail) reportedly show mixed or no net productivity gains; proponents counter that models and workflows have improved since.
Skills, learning, and junior engineers
- Many worry AI will hollow out fundamentals: juniors may “vibe-code” without ever learning design, debugging, or complexity management.
- Others argue AI can accelerate learning when used as a patient tutor, but only if people still do hard work (tests, tracing, refactors) themselves.
- There’s concern about how future experts will be trained when AI is already a better “junior developer” than most beginners.
Labor, economics, and power dynamics
- Commenters expect AI to deepen a K-shaped workforce: curious, strong engineers become far more productive; mediocre ones get exposed or displaced.
- Anxiety over layoffs, deskilling, and higher expectations with smaller teams is widespread; some see AI as a tool to break worker leverage.
- Debate over whether companies will build more or simply cut staff; many suspect the latter, at least initially.
Ethics, environment, and regulation
- Environmental impact of large-scale/agentic AI is raised, but concrete numbers in the thread are disputed or hand-waved.
- Some call for strict liability and possibly licensure for software, especially as AI-generated failures trigger public backlash.
Cultural and emotional reactions
- Old-school programmers mourn “losing the fun part” of carefully crafting code.
- Others describe a “golden age” of empowerment for nontraditional developers and domain experts.
- Accusations of hype, gaslighting, and emerging “AI priesthood” are common alongside genuine enthusiasm.