AI is all about inference now (www.infoworld.com)

The AI story today is a shift from model creation to model application: enterprises are moving from “build” to “run.” Signals include big capacity deals between major AI labs and cloud providers (reported large commitments to lock in predictable compute), IDC’s forecast that spending on inference infrastructure will exceed training infrastructure by end of 2025, and predictions that most organizations will run dozens to hundreds of generative AI use cases in production. In short, training is episodic; inference is constant and now drives where dollars, ops, and product value concentrate. Technically, this changes priorities: you need systems to serve millions of low-latency inference calls cost-effectively (NVIDIA and new accelerator vendors are optimizing for that), and you must connect models to enterprise data. Retrieval-augmented generation, vector databases, and “bringing the model to the data” create an external memory that reduces hallucinations and improves relevance. Practical implications include choosing the right model size for the job (smaller fine-tuned models often beat oversized ones), investing in deployment plumbing (monitoring, access controls, prompt/output filtering), and focusing on high-impact use cases first. The winner in enterprise AI will be whoever makes inference on governed data cheap, reliable, and compliant—not whoever trains the largest model.