AI doesn’t generate economic value in a training cluster. It creates value when it runs—inference, not training. That means snap decisions in a self-driving car, speech interpreted locally on a wearable, or quality control in a factory running autonomously. And it’s opening a new avenue for investors who understand that performance is increasingly a question of where and how AI runs, not only what it knows.Over the last year, headlines around artificial intelligence have fixated on one thing: scale. Bigger models, bigger clusters, bigger training runs. But in the rush to measure progress by parameter counts and GPU hours, one dimension has remained critically underdiscussed.For investors tracking AI, deployment is now the litmus test. It reveals which companies can operate in edge environments, scale without ballooning costs, and serve sectors where latency and power matter more than FLOPs. How costly is AI deployment ?

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