According to God of Prompt (@godofprompt), the economics of AI model deployment have shifted dramatically, with smaller models like a 7B parameter model capable of matching GPT-4-level intelligence by allowing for 100 times longer inference time. This approach offers significant cost savings—training GPT-4 requires over $100 million in compute, while running complex inference costs approximately $0.10 per query. By optimizing inference duration, businesses can deploy smaller, more efficient AI models that outperform larger ones at a fraction of the cost, opening up new opportunities for scalable and affordable AI solutions across industries (Source: @godofprompt, Twitter, Jan 15, 2026).