Sean Goedecke explains how to build real-time AI systems using "System One" models—fast classifiers that pick from multiple-choice options instead of generating arbitrary text. He shares two practical techniques: tiered goals (breaking decisions into hierarchical loops) and tournament sampling (narrowing large option sets through multiple passes).

• You can convert any LLM into a fast classifier by batching single-token outputs with structured choices, getting predictable inference timing without retraining the model
• Tiered goal hierarchies (strategic → tactical → specific targets) let System One models handle complex tasks like game-playing by providing short-term context, similar to how LLM reasoning chains work
• Tournament sampling outperforms absolute scoring for large option sets—the Wikiracing demo found the optimal 3-link Wikipedia path by sampling 100 links at a time rather than trying to rank all 1,000+ options at once