Chess engines didn't replace Magnus Carlsen, and AI won't replace you (coding-with-ai.dev)

The piece argues that, like Magnus Carlsen using chess engines as post-game coaches rather than in-game autopilots, developers should treat coding assistants as learning partners—not replacements. Chess engines (and AlphaZero’s novel ideas) expanded human play by revealing new strategies that top players studied and integrated; similarly, LLMs can generate solutions and surface approaches programmers might not have considered. The key lesson: don’t blindly accept generated output—use it to learn, question, and improve your own judgment. Practically, this reframes workflows: generated code should go through human review for correctness, requirements fit, idiomatic style, edge cases and hallucinated/imaginary APIs. That gatekeeping preserves responsibility and prevents subtle bugs that look plausible but fail in production. The net effect is faster prototyping and greater ambition for those whose stacks are well-represented in training data, while also creating a risk of skill atrophy if engineers stop interrogating suggestions. True autonomous agents or AGI remain distant per experts, so the near-term impact is augmentation—tools that raise productivity and change how we learn, but still demand skilled human oversight.