Hello everyone,
I have been working on a concept that treats complex systems as “relation networks.” While I cannot share the technical mechanism at this stage, I’d like to outline how this framework could potentially perform at different scales and what kind of outcomes we might expect.
1. Small Scale (pilot / test systems)
Use cases: AI models, healthcare testing, local energy grids, small research labs
Estimated success rate: 65–70%
Outcome: Strong proof-of-concept, measurable but limited in scope.
2. Medium Scale (sector-level / regional systems)
Use cases: AI training optimization, regional healthcare modeling, energy distribution, scientific discovery
Estimated success rate: 50–60%
Outcome: Consistent efficiency gains and improved predictive power, though some constraints will remain.
3. Large / Global Scale (highest implementation)
Use cases: Next-generation AI, global healthcare disease models, planetary energy/climate systems
Estimated success rate: 30–40%
Outcome: Transformative potential — resource savings, predictive modeling, and control at unprecedented levels, but also the most challenging to implement.
-–
Request for input:
I am only sharing potential outcomes here, not the technical process. My intent is to open a discussion about:
Which scale seems most realistic to test first,
What kind of safety and ethical safeguards should be prioritized,
And whether this resonates with existing approaches in AI and complex systems.
— A.A.S, Pakistan..