Meta-Optimized Continual Adaptation for circular manufacturing supply chains in carbon-negative infrastructure The Moment I Realized Static Optimization Was Obsolete It was 3:47 AM on a Tuesday, and my laptop fan was screaming like a jet engine. I had been running my 47th experiment on reinforcement learning for supply chain optimization, and the results were... disappointing. The model had learned to optimize for cost reduction perfectly in a static environment, but when I introduced a 15% carbon tax shock (simulating a sudden policy change), the entire system collapsed. The agent kept trying to maximize throughput using the cheapest energy sources, completely ignoring the new carbon penalties. This wasn't just a bug—it was a fundamental limitation. Traditional optimization approaches, even those using meta-learning, assume the world changes slowly enough that you can retrain periodically. But in circular manufacturing supply chains for carbon-negative infrastructure, the world changes hourly .…