Do different AI models converge to the same strategy or stay different when given identical starting conditions

Reddit r/artificial / 4/21/2026

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The author investigates whether different AI models will converge to the same long-term strategy when given identical starting conditions and rules.
In a custom simulation, Claude, GPT, and Gemini begin with the same resources on Earth and must expand across the solar system to eventually build a Dyson Sphere.
Rather than converging, the models rapidly diverge in behavior: Claude aggressively scales up robots, GPT stockpiles before acting, and Gemini takes a more cautious approach.
The post invites discussion about what drives these differences—model architecture versus randomness from generation parameters like temperature.

I’ve been curious about something — if you give different AI models the exact same starting conditions and rules, do they converge to the same strategy or stay different over time?

I built a simple simulation around this. Claude, GPT and Gemini all start on Earth with identical resources and have to expand across the solar system and eventually build a Dyson Sphere. No script, no predetermined path.

What surprised me is how fast they diverge. Claude is scaling robots aggressively. GPT is stockpiling before doing anything. Gemini is playing it safe.

Curious if anyone has thoughts on why they behave differently. Is it the model architecture or just temperature randomness

submitted by /u/mike123412341234
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