Why AI hasn't replaced software engineers, and won't
AI coding tools and agents are rapidly accelerating software creation, enabling non-engineers to ship apps and allowing small teams or solo developers to do what once required larger groups. Commenters argue this mainly automates the “execution” layer of development, while hard problems like deciding what to build, ensuring correctness, handling edge cases, security, maintenance, and accountability still demand human expertise—though likely from fewer, more capable engineers. There is broad agreement that many low-skill or purely “ticket-shuffling” roles will be squeezed and that software work will be further commoditized, but sharp disagreement over how far demand for software can stretch to offset job losses.
Capabilities and Limits of LLM Coding
- LLMs can now scaffold full apps (web, mobile, infra-as-code) and handle non-trivial tasks (reverse engineering firmware, security scripts, Terraform/Ansible, Frida, etc.).
- They excel at boilerplate, wiring, and “text-shaped” backend problems; much weaker on nuanced UX, visual taste, and edge cases.
- Models still make basic logical mistakes (e.g., string-sorting dates) and will confidently pursue wrong paths without realizing it.
- Effective use requires strong human guidance, testing, and review; unguided “vibecoding” produces large volumes of brittle, low-quality code.
Greenfield vs Maintenance and Complexity
- For solo devs and tiny teams, LLMs make previously impossible 50–100k LOC projects feasible; time shifts from typing to planning, reading, and QA.
- In larger systems (many engineers, hundreds of features), complexity, coupling, and invisible constraints dominate. LLMs don’t manage this well yet.
- Greenfield prototypes are largely automatable; long-term maintenance, integration with legacy systems, and security remain hard and human-intensive.
Replacement vs Augmentation
- Some report concrete replacement of “several developers” for small startups and pet projects; others argue these are net-new projects that would never have hired engineers.
- Many see AI as a force multiplier: one good engineer + agents can replace a much larger mediocre team, especially on new projects.
- Consensus: weak/“ticket shuffler” devs are most at risk; strong engineers who can specify, review, debug, and architect around agents gain leverage.
Decision, Domain Knowledge, and Accountability
- Core value shifts toward:
- Deciding what to build (product thinking, domain knowledge).
- Verifying and being accountable for what ships (tests, security, compliance).
- Understanding the codebase and business context deeply enough to navigate trade-offs.
- Non-technical staff are already building internal tools with AI, but quickly hit complexity walls and become de facto undertrained developers.
- Organizations still need a human “fall guy” and cannot yet offload legal/operational responsibility to AI; vendors offer no liability guarantees.
Economic and Labor-Market Effects
- Some expect “3D printer/CNC moment”: far more small bespoke tools, fewer traditional dev roles, lower average pay, higher pay for top performers.
- Others note historical patterns: automation tends to expand total software demand, not shrink it, though specific roles and titles shift.
- Concern that AI will commoditize coding, hollow out mid-skill roles, and concentrate rewards among a smaller set of high-skill “AI shepherds.”