TypeThreeAI pits models in Dyson race

The interesting part here is less “can AIs build a Dyson Sphere” and more “how much behavioral drift appears once you put different models under identical rules, noise, and time pressure.” That makes this a neat sandbox for observing long-horizon decision-making, not a rigorous benchmark yet.

• –Same starting conditions do not imply same outcomes once stochastic events, sampling, and model priors start compounding
• –Early divergence suggests path dependence: one risky expansion can lock in a very different trajectory than a conservative build-up loop
• –The project is strongest as a visual compare-and-contrast tool for strategy evolution, especially if it adds timelines, decision logs, and state summaries
• –Without fixed seeds and tighter controls, the results say more about model personality and randomness than about “true” convergence
• –If it exposes the action history well, it could become a useful demo for debugging long-horizon planning and memory failure modes