Most people think the hard part of AI is training the model. It isn’t. Training is expensive, but it happens occasionally. Inference happens constantly. Every ChatGPT response, every recommendation on Netflix, every autocomplete suggestion, every AI copilot interaction — all of it depends on inference systems serving models efficiently in real time. That is where modern AI infrastructure actually lives. A model sitting on disk is just a collection of weights and configuration files. It becomes useful only when a production system can: load it efficiently, execute it at scale, manage memory correctly, schedule requests intelligently, and deliver responses with low latency at acceptable cost. This is why inference engineering has become one of the most important layers in AI. The challenge is no longer only: “How do we train large models?” The challenge is increasingly: “How do we serve intelligence economically?” That shift changes everything. It changes: hardware design, GPU arc...