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).

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).

According to God of Prompt on Twitter, a new research paper from PayPal and NVIDIA demonstrates that significant performance improvements in agentic AI do not require massive general-purpose language models. Instead, PayPal achieved a 49% reduction in agent latency, a 58% improvement in retrieval latency, and a 45% decrease in GPU costs by replacing a slow, large LLM with a smaller, domain-specific model fine-tuned for commerce search tasks using NVIDIA’s NeMo framework. This approach, which involved targeted fine-tuning and infrastructure-grade experimentation, maintained or improved output quality. The findings highlight a shift in AI deployment strategies toward specialized small models and modular, multi-agent system architectures, providing concrete business opportunities for enterprises seeking scalable, efficient AI solutions without the overhead of large models (source: God of Prompt, Twitter; PayPal & NVIDIA research paper).