According to God of Prompt (@godofprompt), a recent paper demonstrates that AI model performance can be significantly improved by implementing a more efficient cache mechanism. This innovative approach eliminates the need for adding extra words or retraining the model, thus preserving the original input length while enhancing the model’s comprehension and output quality. The findings highlight a practical optimization strategy for businesses seeking to maximize AI model efficiency without incurring additional training costs or complexity, offering immediate benefits for large-scale AI deployments and inference workloads (source: God of Prompt, Jan 17, 2026).

According to @godofprompt, OpenAI's o1 model demonstrates that increasing a model's intelligence can be achieved by enabling it to 'think longer' during inference, rather than simply making models larger through more extensive training (source: Twitter, Jan 15, 2026). Leading AI companies such as DeepSeek, Google, and Anthropic are now shifting their focus toward test-time compute, investing in inference-time strategies to optimize and enhance model performance. This marks a significant industry pivot from the so-called 'training wars'—where competition centered on dataset size and model parameters—to a new era of 'inference wars' where maximizing the effectiveness and efficiency of models during deployment becomes crucial. This paradigm shift opens up new business opportunities for providers of inference optimization tools, hardware tailored for extended compute, and services aimed at reducing cost per query while delivering higher intelligence at runtime.