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.