AI is creating a generation of illiterate programmers

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.”