Nonaka and Takeuchi's SECI model describes knowledge creation as a spiral process of Socialization, Externalization, Combination, and Internalization—converting between tacit and explicit knowledge. This paper proposes that Large Language Models (LLMs) can serve as partners in this spiral, not by possessing tacit knowledge themselves, but by accelerating the externalization of human tacit knowledge through dialogue. When a human engages in sustained dialogue with an LLM, the human's tacit knowing is drawn out, articulated, and reflected back in structured form. This process resembles Shimizu's sōgo yūdō gōchi (mutual inductive coherence): two fields—human and AI—resonate and converge toward articulations neither could produce alone. The result is not artificial intelligence creating knowledge, but human-AI mutual induction accelerating the SECI spiral. This paper documents this phenomenon through autoethnographic analysis and proposes implications for research methodology, organizational knowledge creation, and understanding of human-AI collaboration.