According to Stanford AI Lab (@StanfordAILab), leveraging test-time training combined with open-source AI models now enables researchers and businesses to achieve state-of-the-art (SOTA) scientific discoveries with only a modest investment. This approach surpasses prompt engineering methods used with closed frontier models like Gemini and GPT-5 for complex discovery tasks in mathematics, kernel engineering, algorithms, and biology (source: Stanford AI Lab, Twitter, Jan 22, 2026). The practical implication is a democratization of advanced AI capabilities, lowering costs for organizations aiming to innovate in scientific research. This trend opens up significant business opportunities for startups and enterprises to build specialized AI solutions for scientific domains, using open models and custom training to outperform proprietary alternatives.