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.

When Good Star Labs streamed their AI Diplomacy game on Twitch, it attracted 50,000 viewers from the gaming community. Watching AIs make mistakes, betray allies, and strategize made the technology more relatable and less intimidating, helping to bridge the gap between AI experts and the general public.

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.

AI struggles with tasks requiring long and wide context, like software engineering. Because adding a linear amount of context requires an exponential increase in compute power, it cannot effectively manage the complex interdependencies of large projects.

Even when AI performs tasks like chess at a superhuman level, humans still gravitate towards watching other imperfect humans compete. This suggests our engagement stems from fallibility, surprise, and the shared experience of making mistakes—qualities that perfectly optimized AI lacks, limiting its cultural replacement of human performance.

Large Language Models are uniquely suited for complex strategy games like Civilization. Their strength lies not in calculation, where traditional AI excels, but in maintaining long-term narrative consistency and strategic coherence, which is the actual bottleneck for game mastery.

AI systems often collapse because they are built on the flawed assumption that humans are logical and society is static. Real-world failures, from Soviet economic planning to modern systems, stem from an inability to model human behavior, data manipulation, and unexpected events.

The challenge in designing game AI isn't making it unbeatable—that's easy. The true goal is to create an opponent that pushes players to an optimal state of challenge where matches are close and a sense of progression is maintained. Winning or losing every game easily is boring.

Current AI world models suffer from compounding errors in long-term planning, where small inaccuracies become catastrophic over many steps. Demis Hassabis suggests hierarchical planning—operating at different levels of temporal abstraction—is a promising solution to mitigate this issue by reducing the number of sequential steps.