Can AI Predict the Best Move in Any Position?

In the age of artificial intelligence, the intersection of chess and AI has opened doors that previous generations could only dream of. With superhuman engines like Stockfish and Leela Chess Zero dominating analysis, the question arises: Can AI predict the best move in any position?

This article takes a detailed look into what “best move” really means, how AI evaluates positions and generates decisions, the limits of current technology, and what the future may hold for AI’s role in move prediction.

1. Defining the “Best Move”

Before we assess AI’s predictive power, we must understand what constitutes the “best move” in chess.

Theoretically best move: In a perfect information game, this would be the move that leads to a win (or draw if no win is possible) with optimal play from both sides.
Practically best move: In real games, the best move may balance strategic soundness with psychological or time-based advantages.

For AI, “best” is quantified numerically—usually as the move that results in the highest evaluation score for the side to move, assuming optimal replies.

2. How Do Chess AIs Work?

Modern AI-powered chess engines determine moves using two main components:

a. Search Algorithms

AI engines explore possible continuations using advanced search methods:

Minimax and Alpha-Beta Pruning: These look several moves ahead, trimming unpromising lines to improve efficiency.
Monte Carlo Tree Search (MCTS): Used by neural engines like Leela, MCTS uses statistical sampling to explore the most promising branches.
Quiescence Search: Extends analysis in volatile positions to avoid misjudging due to the “horizon effect.”

b. Evaluation Functions

An evaluation function estimates the quality of a position. Traditional engines use handcrafted criteria (material, king safety, pawn structure), while modern neural networks learn patterns from millions of games and self-play data.

The combination of deep search and precise evaluation allows AI to assess billions of positions in seconds.

3. Can AI Truly Predict the Best Move?

The answer is nuanced.

In Tactical Positions: Often Yes

In sharp, tactical situations with clear threats and forcing lines, AI engines like Stockfish or Leela can predict the best move with near-perfect accuracy. Their ability to calculate deeply and precisely means:

Blunders are immediately punished.
Forced mates are found almost instantly.
Tactical combinations are executed flawlessly.

For example, if there’s a forced checkmate in 10 moves, an engine will almost always find it—assuming it has sufficient search depth and time.

In Strategic Positions: Generally, But Not Always

In complex, quiet positions, things get murkier. The “best move” may not be obvious even to the engine, because:

Strategic nuances (e.g., a long-term king walk or subtle exchange sacrifice) can be hard to quantify.
Engines may disagree even among themselves.
Neural network engines like Leela might prefer different moves than classical engines like Stockfish, showing that “best” can depend on the evaluation model.

So while AI often finds excellent moves, it’s not always clear that these are objectively best.

4. The Role of Neural Networks

With the advent of deep learning, engines like AlphaZero and Leela Chess Zero changed the landscape. They don’t use brute-force tactics alone. Instead, they:

Learn position evaluations from scratch via reinforcement learning.
Rely less on deep calculation and more on intuitive patterns.
Excel at long-term planning and positional play.

This allows neural engines to sometimes “see” the best move where classical engines struggle—especially in closed or quiet positions.

For instance, AlphaZero famously sacrificed material for long-term pressure, a concept hard to quantify in traditional evaluation terms but proven effective through deep self-play.

5. The Human-AI Gap

One of the most interesting developments is how closely human top-level play matches engine recommendations. In general:

Super-GMs (2700+ rating) play moves that align with engine top choices around 60–70% of the time.
In classical time formats, accuracy increases significantly.
In blitz or bullet, discrepancies increase as time pressure affects human play more than engines.

Thus, while AI can often predict what a strong human would play, it doesn’t always mean the AI thinks it’s the best move.

6. How AI Prediction Helps in Practice

Even if AI can’t always predict the best move, it can still provide enormous practical value:

a. Game Preparation

Engines analyze opening novelties and refute suboptimal lines.
Precomputed databases (cloud evaluations) give near-perfect prep.

b. Training

AI highlights inaccuracies, mistakes, and blunders in training games.
Provides variations and insights into “why” certain moves work better.

c. Move Suggestion Tools

Apps and online platforms use engines to offer real-time feedback.
Tools like “Move Trainer” help players practice choosing optimal moves in various positions.

7. Limitations of AI Move Prediction

Despite the enormous capabilities, AI has limitations:

a. Horizon Effect

Even with deep calculation, engines can sometimes miss long-term ideas just beyond their search horizon.

b. Positional Ambiguity

In positions with many equal moves (e.g., drawn endgames), the engine may fluctuate between multiple “best” choices.

c. Engine Disagreement

Different engines might recommend different moves in the same position, especially in strategic contexts.

d. Overfitting to Data

Neural networks trained on specific datasets may develop evaluation biases that don’t generalize perfectly.

e. Lack of Human Context

AI doesn’t consider psychological, clock-based, or tournament-related factors. A theoretically best move may be practically risky against a particular opponent or in a time scramble.

8. How Close Are We to Perfect Prediction?

Chess is a finite game, but its complexity is astronomical: ~10^120 possible positions. Solving it completely is currently infeasible.

However, in certain domains, AI is already approaching perfection:

Endgames: With 7-piece tablebases, AI has solved all such positions—these are perfect move predictions.
Tactical puzzles: AI rarely misses forced lines.
Opening theory: Certain lines have been extensively mapped and engine-tested to dozens of moves.

For the rest of the game, especially middlegames with many options, AI continues to improve—but the notion of a universally best move remains elusive in many positions.

9. The Future of AI in Move Prediction

a. Scaling Neural Networks

Projects like Leela and Fat Fritz are expanding network sizes and training data to improve strategic understanding.

b. Hybrid Models

Engines like Stockfish now use NNUE (efficient updatable neural networks), combining classical search with learned evaluation for stronger results.

c. Quantum Computing (Theoretical)

If quantum computing becomes viable for chess, it could revolutionize evaluation by processing vast trees in parallel, potentially enabling closer approximation to perfect play.

d. Explainable AI (XAI)

One of the biggest challenges is making engine evaluations explainable—helping humans understand why a move is best. Future tools might prioritize interpretability as well as accuracy.

Conclusion: AI as a Near-Perfect Predictor—With Caveats

So, can AI predict the best move in any position? In many cases, yes—particularly in sharp or well-understood positions. In others, especially murky middlegames or deeply strategic battles, “best” becomes subjective, and even engines may differ.

What’s certain is that chess AI is the most powerful tool ever developed for understanding the game. Whether predicting forced mates or exploring the beauty of a quiet pawn push, AI enhances our ability to study, learn, and enjoy chess on a deeper level.

And as AI continues to evolve, so too will our understanding of what it means to find “the best move.”