The rapid development of artificial intelligence has transformed the way people search for and interact with information. Traditional search engines primarily retrieve documents, web pages, and other information sources, while modern large language models can interpret questions, reason over information, and generate answers. This paper explores a possible next step: a search paradigm in which artificial intelligence systems communicate with other AI systems to search for, compare, evaluate, and synthesize answers.The paper introduces the concept of AI-to-AI Communication for Intelligence Search, in which a user's question is distributed among multiple specialized AI systems rather than being answered by a single model. The participating systems may independently reason about the problem, retrieve external information, critique other responses, and contribute evidence to a final synthesis layer. The proposed framework is conceptually related to retrieval-augmented generation, AI agents, and multi-agent systems, but shifts the focus from retrieving information for an AI toward retrieving and evaluating intelligence from multiple AIs.Therefore, the proposed architecture moves beyond answer-level agreement toward claim-level verification. Individual AI responses are decomposed into claims, evidence is retrieved independently, sources are evaluated for independence, and correlated errors are explicitly considered before a final answer is produced.Based on this perspective, AI-to-AI Intelligence Search can be viewed as a potential new layer between artificial intelligence systems: a layer for discovering, coordinating, evaluating, and verifying distributed machine intelligence.