The Paradox of 'Code'
Solving a coded puzzle does not require the same type of 'thought' needed to play chess at a grandmaster level
It’s a striking irony: a large language model (LLM) can churn out a functional chess engine—complete with move generation, board evaluation, and search algorithms—in a matter of seconds, yet teaching that same model to play chess at a grandmaster level is a far harder challenge.
This paradox reveals profound insights about the nature of artificial intelligence, the difference between ‘creation’ and mastery, and the limits of pattern recognition in complex domains. Let’s unpack the strongest points behind this phenomenon and what they tell us about LLMs, chess, and intelligence itself.
FORWARD
PLEASE GO MAKE A FREE ACCOUNT ON PRODUCTHUNT.COM, VISIT [Mimiic.app & UP VOTE THE PROJECT BEFORE IT IS LIVE 9/9: Mimiic Product Hunt] ITS FREE AND HELPS ME A TON!
Point 1: Writing Code Is a Well-Defined Task; Playing Chess Is a Battle of Intuition
Writing a chess engine is a structured problem. It involves translating well-known algorithms—minimax, alpha-beta pruning, or evaluation heuristics—into code. These are deterministic, rule-based systems that LLMs can replicate by drawing on their vast training data, which includes countless examples of programming patterns and chess engine designs.
An LLM doesn’t need to understand chess deeply to produce a working engine; it just needs to stitch together familiar code snippets. The task is akin to solving a puzzle with clear instructions. Playing chess at an expert level, however, demands something far more elusive: intuition, strategic foresight, and adaptability.
Chess is a game of imperfect information—not in the technical sense, but in the cognitive one. A grandmaster doesn’t just calculate moves; they feel the position, recognize patterns, and make decisions under uncertainty. LLMs, despite their pattern-matching prowess, struggle to replicate this.
Their training data—text, code, and even annotated games—lacks the experiential depth of a human player’s intuition. While an LLM can mimic tactical calculations, it falters in the nuanced, long-term planning that defines elite play.
Point 2: Chess Engines Are Tools; Playing Chess Is a Skill
A chess engine is a tool designed to brute-force solutions. It evaluates millions of positions per second, using predefined heuristics to score board states and a search algorithm to find the best move.
An LLM can generate the code for such a tool because it’s a matter of assembling logical components: move generators, evaluation functions, and search trees. These are well-documented in computer science literature, and LLMs excel at regurgitating structured knowledge.
Playing chess, on the other hand, is a skill that requires balancing competing priorities—king safety, piece activity, pawn structure—while adapting to an opponent’s style.
Even if an LLM generates a chess engine, using that engine effectively (or playing without one) demands a level of contextual awareness that LLMs lack. It can’t “feel” the flow of a game or anticipate an opponent’s psychological blunders. This gap highlights a core limitation: LLMs are excellent at producing tools but struggle with tasks requiring dynamic, real-time decision-making (stop giving your brain over to the Wizard of Oz).
Point 3: Training for Coding vs. Training for Play
LLMs are trained on vast corpora of text, including code repositories, technical papers, and game annotations. This makes them adept at synthesizing code for a chess engine, as they can draw on explicit examples of similar programs. The process is almost mechanical: identify the task, retrieve relevant patterns, and output syntactically correct code.
Errors, if any, are usually minor and fixable through iteration. Training an LLM to play chess at a high level, however, requires a different kind of data: not just game records, but the reasoning behind each move, the trade-offs considered, and the strategic intent.
While some datasets (like PGN files with annotations) provide this, they’re incomplete. Human grandmasters learn through experience, trial and error, and studying opponents—processes that don’t translate neatly into text.
Fine-tuning an LLM for chess would require a massive, specialized dataset of expert-level decision-making, coupled with reinforcement learning to simulate real-game experience. Even then, the model might overfit to common positions or fail to generalize to novel ones.
Point 4: The Computational vs. Cognitive Divide
Chess engines rely on computational power to outperform humans, not cognitive insight. Stockfish, for example, doesn’t “think” like Bobby Fischer; it calculates billions of positions and applies heuristics. An LLM can replicate this computational approach in code because it’s a matter of translating math into syntax.
But playing chess without an engine—like a human—requires cognitive shortcuts that LLMs can’t naturally possess.
We use heuristics honed through years of practice, like recognizing key squares or sensing tactical opportunities. LLMs, despite their ability to generate code, lack this embodied knowledge.
This divide underscores a broader truth about AI: LLMs are phenomenal at tasks with clear rules and explicit knowledge, but they stumble in domains requiring tacit, experiential understanding.
Chess play, at its highest levels, is as much art as science—a realm where LLMs, are out of their depth.
People argue against by saying “for now”, but don’t realize they are just blindly turning their brains and decision-making over to what is essentially just a reddit super user masquerading as a Hive Mind.
Instead, this should force you to look within and realize…its you who is the ‘super intelligence’ (if honed & prioritized with God properly).
Point 5: The Illusion of Understanding
Perhaps the most powerful insight is that LLMs’ ability to write chess engines creates an illusion of understanding. Generating a working engine might make it seem like the model “knows” chess, but this is a mirage. The LLM isn’t reasoning about the game; it’s parroting patterns from its training data. When asked to play chess, it can’t lean on the same computational crutches as an engine.
It must rely on its own “reasoning,” (haha what a joke) which is often shallow or inconsistent in dynamic contexts like a chess game. This illusion has broader implications.
It reminds us that LLMs are tools for knowledge synthesis, not true comprehension. Their strength lies in their ability to mimic expertise in structured tasks, but their weaknesses emerge in fluid, open-ended challenges like playing a game of chess against a skilled opponent.
Conclusion: A Mirror to AI’s Limits
Solving a code puzzle with instructions does not require the same type of 'thought' needed for someone to play chess at an expert level
Writing a chess engine is a triumph of pattern recognition; playing chess like a grandmaster requires something closer to wisdom. Which AI knows nothing of.
For now, LLMs excel at the former but only graze the surface of the latter. This dichotomy isn’t just about chess—it’s about the future of AI.
And Truth Himself…
What do you think?
God-Willing, see you at the next letter
GRACE & PEACE
VISIT JoeGuglielmucci.com TODAY
Didn’t realize how much code affects different situations. Thanks for the tips keeps me learning.