According to God of Prompt on Twitter, the AI industry's focus is shifting from building ever-larger models trained on massive datasets to developing smarter inference strategies that enable smaller models to achieve deeper reasoning. The discussion highlights that test-time compute scaling now allows models to dynamically increase their computational depth during inference, effectively rendering expensive $100 million training runs less critical. This paradigm shift presents significant business opportunities for companies to optimize inference techniques, reduce infrastructure costs, and deliver competitive AI applications without relying on massive model sizes. As a result, intelligence in AI is becoming defined by the efficiency and flexibility of inference rather than just the volume of training data or model parameters (Source: @godofprompt, Twitter, Jan 15, 2026).