"We've finished the model... when do we get to the value?" Here's what I'm watching happen in real time: DevOps teams are spending $$$$$$ annually on cloud egress fees just to move ML models between environments, and flow data to centralized hosting. Platform engineers are debugging why a 1.1GB model requires a 7GB container. SREs are explaining why edge inference needs a 30-second cold start. Everyone knows something's wrong. But we keep reaching for the same solution: bigger containers, faster networks, more centralized infrastructure. WebAssembly 3.0 , released last week, MAY have another way. I hope! The Container Tax We Normalized Docker revolutionized deployment by making environments portable. From nightmare- runbooks to docker run changed how we ship software. But watch what happened to model deployment: A transformer model that's 1.11GB becomes 7.05GB once you add Python, PyTorch, and CUDA libraries. The actual inference code? Maybe 2MB.…