To succeed with $Micron Technology(MU)$ , one must distinguish between the initial AI training phase and the shift towards the Inference Stage. The training phase focused on uploading data to build Large Language Models, while the industry is moving towards deployment—applying those models to massive datasets to generate real-time business value. This 'download' phase shifts the focus from the data centre backbone to the edge, where AI is integrated into practical applications. Because the inference stage is considerably more memory-intensive, this transition represents the primary catalyst for memory manufacturers to capture peak market value.

Inference versus Training: While training requires massive compute power, inference requires rapid data retrieval. This shift prioritises High-Bandwidth Memory and high-capacity DDR5, which are core strengths for Micron.

Edge Computing: Moving AI models from centralised clouds to edge devices, such as AI PCs and smartphones, necessitates a substantial increase in local RAM to handle real-time processing without latency.

Memory Intensity: Industry experts estimate that AI-enabled devices require 2x to 3x more memory than standard hardware, creating a structural tailwind for Micron's stand-alone valuation.