Real game AI, not a chatbot: why these opponents don't use an LLM

Syndicated from the original on lkforge.com. The engines are playable in your browser at lkforge.com/games; the harness that produced these numbers is public and seeded.

Every "AI" in a product now seems to mean a large language model. The AI that plays against you on my site doesn't — it's classical game-tree search: minimax, expectimax, breadth-first search. That's a deliberate engineering choice, and it's the difference between an opponent that's provably correct and instant and one that's plausible and slow.

The core point

My tic-tac-toe engine returns a provably-optimal move in about 0.3 ms, on your device, with zero network calls — and it has lost 0 of 1,200 test games. Those are properties a language model, by construction, cannot offer: determinism, a correctness proof, and sub-frame latency without a server.

"Why not just use an LLM?"

Fair question in 2026 — you could prompt a model with the board and ask for a move. The reason I don't: a language model is trained to predict the next token of text, not to search a game tree. It can explain tic-tac-toe strategy fluently and still play a losing move, because fluent text and optimal play are different objectives. Winning a solved game is a search problem, and we already have exact, fast algorithms for it.

The three engines — minimax + alpha-beta for tic-tac-toe, expectimax for 2048, and BFS for Color Lines — are textbook, deterministic, and run in well under a millisecond in a browser tab.

Search vs. a language model, point by point

Every row is an architectural difference — how each system decides — not a quoted benchmark. The only measured numbers here are mine.

The payoff: a strength number you can actually pin down

Because the engines are deterministic, I can put an exact figure on how strong they are — run the shipped code headlessly, hundreds of times, and count. That's far harder for a model whose output shifts with sampling and phrasing.

2048 solver, 250 self-play games: 69.6% of games reach the 2048 tile, 30% reach 4096, and none of the 250 reached 8192 — the honest ceiling of a corner-snake expectimax search at ~0.5 ms/move. A number, with error bars you could compute, precisely because the same board always drives the same search.

Tic-tac-toe is the cleaner case: full-depth minimax is provably optimal, so "unbeatable" is a theorem, not a vibe. Across 1,200 self-play games (1,000 vs random, 200 vs a perfect copy) it lost none. Alpha-beta keeps full depth cheap: 36,528 nodes instead of 549,945 at the opening move — a 93% cut — in about 0.3 ms.

The right tool, not the trendy one

None of this is anti-LLM. Language models are extraordinary at language — and a couple of the tools on my site that are genuinely language tasks could use one. But a board game with fixed rules and a finite tree is exactly the problem classical search was invented for.

Full write-up with charts: *lkforge.com/blog/game-ai-not-llms*. Related: Six Games, Three Classic Algorithms · Minimax & Alpha-Beta, Visualized · and the companion experiment, We Asked ChatGPT and Grok to Benchmark Our Game AI.