Learning to code is still worthwhile

LLMs and the Future of Coding

• Many argue current and near‑future LLMs greatly reduce the need to hand‑write routine code, especially for CRUD apps and “outer layer” business software.
• Others counter that LLMs are non‑deterministic, brittle, and poor at high‑level abstraction, so skilled programmers remain crucial, especially for core infrastructure, compilers, frameworks, and safety‑critical systems.
• Some foresee a small elite of deep experts overseeing powerful AI tools, with far fewer traditional coding jobs; others expect Jevons‑style effects where cheaper software creation increases total demand.

Why Learning to Code Still Matters (According to Supporters)

• Teaches problem decomposition, debugging, and systematic thinking; often compared to learning math or philosophy for reasoning skills.
• Essential to judge, guide, and constrain LLM output; “prompt‑only” users can’t reliably tell good solutions from bad.
• Foundational understanding (from logic gates up to high‑level languages) helps when abstractions leak and AI can’t fix edge‑case failures.
• Seen as a way to fully engage with and shape AI systems rather than passively delegating everything.

Skepticism: Is It Still a Good Career Bet?

• Some compare future programming work to poetry, early music, or niche crafts: rewarding but economically precarious.
• Concern that LLM‑driven productivity improvements will reduce demand for average coders, amplifying only top performers.
• Worries about advising new students to take on debt for a field that may be heavily commoditized within 5–20 years; analogies to blacksmiths, tool‑and‑die workers, and outsourced IT.

Abstraction, Fundamentals, and Understanding

• Broad agreement that “learning to code” is really about understanding systems, not memorizing syntax.
• Debate over how deep one must go (assembly, CPU design, etc.), but many fear AI will encourage shallow, cargo‑cult knowledge.

Coding as Craft, Art, or Plumbing

• Some see code as a creative medium on par with music, literature, or painting; others say most industry work is closer to plumbing or carpentry—necessary, constrained craft.
• Several note that LLMs risk stripping away the “flow” and joy of hand‑crafting abstractions, turning senior work into reviewing and “babysitting” machine‑generated code.

Quality, “Slop,” and Long‑Term Risks

• Reports that weak developers still produce bad code with LLMs, just faster; good developers can learn faster and explore designs more.
• Fears of an ecosystem filled with opaque, bloated, AI‑generated code that humans (and later models) struggle to maintain.
• Minority view claims AI‑generated code is already often cleaner than typical human code, challenging assumptions about quality decline.