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.

According to @godofprompt, OpenAI's O1 model demonstrates that enhancing model intelligence can be effectively achieved by increasing inference-time computation rather than simply expanding model size (source: @godofprompt, https://x.com/godofprompt/status/2011722597797675455). Major industry players including DeepSeek, Google, and Anthropic are now shifting their strategies to focus on test-time compute, signaling a paradigm shift away from the traditional 'training wars' and towards an 'inference war.' This trend opens up significant business opportunities for AI companies to develop optimized inference frameworks and infrastructure, catering to the growing demand for smarter, more efficient AI applications. The move towards test-time compute is expected to drive innovation in AI deployment, reduce costs, and enable more scalable commercial solutions.

According to DeepLearning.AI, independent lab Artificial Analysis has found that the cost of evaluating advanced chain-of-thought reasoning models, such as OpenAI o1, is rapidly escalating beyond the reach of resource-limited AI researchers. Benchmarking OpenAI o1 across seven widely used reasoning tests consumed 44 million tokens and incurred expenses of $2,767, highlighting a significant barrier for academic and smaller industry groups. This trend poses critical challenges for AI research equity and the development of robust, open AI benchmarking standards, as high costs may restrict participation to only well-funded organizations (source: DeepLearning.AI, June 18, 2025).