How Magnus Carlsen’s chess AI strategy applies to coding

A software developer draws parallels between how chess grandmaster Magnus Carlsen uses chess engines for training and how developers can effectively work with AI coding assistants. The comparison reveals that both tools serve as powerful learning partners rather than replacements, fundamentally changing how professionals approach their craft while requiring human expertise to distinguish good suggestions from poor ones.

The chess engine parallel: Magnus Carlsen doesn’t use engines during games but leverages them extensively for post-game analysis and training.

• After DeepMind’s AlphaZero (an AI system) beat Stockfish (a traditional chess engine), Carlsen studied those games deeply, saying: “I have become a very different player in terms of style than I was a bit earlier, and it has been a great ride.”
• AlphaZero revealed unconventional strategies like sacrificing pieces for long-term advantage and using the king as an active fighter—moves human experts initially considered unsound.
• The key insight: Carlsen learns why these moves work through review, using the engine as a coach rather than an autopilot.

Code review as post-game analysis: Developers can apply similar principles when working with AI coding assistants.

• Instead of blindly accepting AI-generated code, treat it like reviewing a junior developer’s pull request with healthy skepticism.
• LLMs (large language models—the AI systems behind coding assistants) frequently invent imaginary API endpoints and miss edge cases that require human oversight.
• The review process becomes an opportunity to learn new patterns and approaches while ensuring code quality and consistency.

How the work has shifted: AI assistants change the development process rather than eliminating human involvement.

• Traditional workflow focused on planning, coding, debugging, reviewing, and testing.
• With AI tools, developers can attempt more ambitious projects because they have a “sparring partner” to catch mistakes and suggest shortcuts.
• The learning happens during the review phase, where developers analyze AI suggestions and decide what to implement.

The broader impact: Chess engines didn’t kill chess—they made it more popular and accessible.

• Chess.com membership doubled from 100 million in December 2022 to 200 million by April 2025.
• Engines made the best players even better while making high-level play more accessible to everyone.
• Similarly, coding assistants are augmenting developers rather than replacing them, though primarily for those working with widely-covered technology stacks.

What they’re saying: Experts see AI as augmentation rather than replacement, though true autonomy remains distant.

• “It will take about a decade to work through all of those issues” regarding autonomous agents, according to Andrej Karpathy, a leading AI researcher, in an October 2025 interview with Dwarkesh Patel.
• The author notes: “The option to copy-paste without understanding has always existed (StackOverflow, now LLMs). The tool doesn’t decide whether I learn or not. I do.”