@shirendev15Founder & CEO, MCBI, LLC | Mongolia Circular Battery Initiative
Can AI Help Track the World's Critical Minerals?
As AI and data technologies advance, could we use them to make global mineral supply chains more transparent? Imagine combining AI, digital tracking, and circular economy systems to follow valuable materials throughout their entire lifecycle. What do you think? Is this the next big opportunity for AI?
AICritical MineralsSupply ChainCircular Economy
Comments3
The missing link isn't the desire for transparency; it’s the lack of rigorous, measurable metrics.
For a long time, nobody treated this through a strict engineering lens because defining those metrics requires heavy, foundational intellectual labor. Two years ago, as an engineer, I realized these exact metrics didn't exist yet, so I started developing and mathematically grounding them myself.
When you build Human-AI Coupled Systems and apply rigorous physics-based metrics to supply chains and operational loops, transparency stops being a buzzword and becomes a mathematically verifiable architecture. Everything is already calculated and running.
Thank you, Tetyana I agree, without clear metrics transparency stays just a word. I would be interested to learn which metrics you use for supply chains.
Enkhtsetseg Shirendev Exactly. You don't need a coupled system everywhere—that would introduce unnecessary friction.
Routine data processing, tracking, and telemetry across the supply chain can be fully delegated to autonomous agents. Human-AI Coupled Systems should be strategically placed only at the core controlling checkpoints where critical decisions, compliance shifts, or high-risk forks occur.
As for the metrics, I use a core universal framework that gets calibrated and fine-tuned for specific domain requirements like battery lifecycle tracking. It bridges raw agent data with verified structural integrity
3 comments
The missing link isn't the desire for transparency; it’s the lack of rigorous, measurable metrics.
For a long time, nobody treated this through a strict engineering lens because defining those metrics requires heavy, foundational intellectual labor. Two years ago, as an engineer, I realized these exact metrics didn't exist yet, so I started developing and mathematically grounding them myself.
When you build Human-AI Coupled Systems and apply rigorous physics-based metrics to supply chains and operational loops, transparency stops being a buzzword and becomes a mathematically verifiable architecture. Everything is already calculated and running.
Thank you, Tetyana I agree, without clear metrics transparency stays just a word. I would be interested to learn which metrics you use for supply chains.
Enkhtsetseg Shirendev Exactly. You don't need a coupled system everywhere—that would introduce unnecessary friction.
Routine data processing, tracking, and telemetry across the supply chain can be fully delegated to autonomous agents. Human-AI Coupled Systems should be strategically placed only at the core controlling checkpoints where critical decisions, compliance shifts, or high-risk forks occur.
As for the metrics, I use a core universal framework that gets calibrated and fine-tuned for specific domain requirements like battery lifecycle tracking. It bridges raw agent data with verified structural integrity