AI is creating a generation of illiterate programmers
AI coding assistants are reshaping how people learn and practice programming, raising concerns that over-reliance on tools like ChatGPT and Copilot will erode fundamental skills in reading, reasoning about, and debugging code. Commenters compare this shift to past abstractions such as high-level languages and calculators, but stress a key difference: AI output is often wrong or non-deterministic, so developers still need deep understanding to validate and maintain what it generates. Many see value in AI for boilerplate, learning and prototyping, yet worry it will encourage "prompt jockeys" and management to mistake code generation for true engineering expertise, with long-term implications for software quality, education, and jobs.
Skill Atrophy and Dependency
- Several commenters say coding skills atrophy quickly when relying on AI or not coding for months; others feel they can ramp back up in days, especially for greenfield projects.
- Many distinguish between “typing code” and deeper abilities: understanding systems, existing codebases, architecture, debugging, and design.
- Some propose “No-AI days” or rules like “read the docs first” to avoid losing core skills, but others think occasional abstinence isn’t enough.
Non-Programmers and “Prompt Programmers”
- One camp argues AI lets non-programmers build useful scripts/apps, expanding who can create software (similar to spreadsheets or no-code tools).
- Critics say many of these users can’t read, debug, or reason about the code, likening them to past “Stack Overflow programmers” or drag‑and‑drop tool users.
- A few report personal success stories: starting as copy‑paste users, then gradually learning syntax, structure, and best practices via AI and trial‑and‑error.
AI vs Traditional Abstractions
- Some compare AI-assisted coding to previous abstraction jumps (assembly → C, C → higher-level languages, spreadsheets, visual tools).
- Others insist LLMs are fundamentally different from compilers: non-deterministic, probabilistic, and not guaranteed to map a precise specification to correct code.
- A recurring distinction: higher-level languages still demand precise thought; LLMs can generate plausible but subtly wrong solutions.
Tooling, Quality, and Limits
- Many note LLMs are great for boilerplate, brainstorming, and “getting started,” but often fail on edge cases, nuanced logic, or large/complex projects.
- Complaints include hallucinated APIs, type errors, weird language constructs, and “reasoning loops” where the model can’t escape a dead end.
- Some advocate tighter integration with IDEs, type checkers, tests, and up-to-date docs so tools auto-correct and iterate rather than just autocomplete.
Jobs, Roles, and Future Outlook
- Strong view that AI will reduce demand for average coders, especially those doing mostly glue/CRUD work; emphasis will shift to architecture, product thinking, and managing AI agents.
- Others doubt near-term full replacement, citing AI’s unreliability and the enduring need for problem framing, domain understanding, and system design.
- Education and hiring may need to distinguish between true understanding and AI-augmented performance; some propose norms like “no AI without understanding the solution.”