Traditional AI struggles with games like Civilization not due to computational complexity, but because these games require maintaining a long-term strategic narrative, not just optimizing individual moves. Human players win by committing to a coherent story for their civilization's development.

Static benchmarks are easily gamed. Dynamic environments like the game Diplomacy force models to negotiate, strategize, and even lie, offering a richer, more realistic evaluation of their capabilities beyond pure performance metrics like reasoning or coding.

Modern LLMs use a simple form of reinforcement learning that directly rewards successful outcomes. This contrasts with more sophisticated methods, like those in AlphaGo or the brain, which use "value functions" to estimate long-term consequences. It's a mystery why the simpler approach is so effective.

When tested at scale in Civilization, different LLMs don't just produce random outputs; they develop consistent and divergent strategic 'personalities.' One model might consistently play aggressively, while another favors diplomacy, revealing that LLMs encode coherent, stable reasoning styles.

The primary constraint on output is no longer a tool's capability but the user's skill in prompting it. This is exemplified by a developer who created a complex real-time strategy (RTS) game from scratch in one week by prompting an AI model, having not written a single line of code himself in two months.

The most effective AI architecture for complex tasks involves a division of labor. An LLM handles high-level strategic reasoning and goal setting, providing its intent in natural language. Specialized, efficient algorithms then translate that strategic intent into concrete, tactical actions.

Google DeepMind CEO Demis Hassabis argues that today's large models are insufficient for AGI. He believes progress requires reintroducing algorithmic techniques from systems like AlphaGo, specifically planning and search, to enable more robust reasoning and problem-solving capabilities beyond simple pattern matching.

Good Star Labs found GPT-5's performance in their Diplomacy game skyrocketed with optimized prompts, moving it from the bottom to the top. This shows a model's inherent capability can be masked or revealed by its prompt, making "best model" a context-dependent title rather than an absolute one.

Leverage Large Language Models (LLMs) to overcome the 'blank page' problem in strategy development. Use them as a conversational partner to organize scattered thoughts, build a narrative, and refine your ideas before presenting them to stakeholders or the wider team.