Since 2024, Meta has been developing its own search capabilities to support Meta AI and reduce reliance on external engines, shifting crawlers from model training to real-time indexing. Resource investment is concentrated on high-frequency, low-return web scraping, motivated by the desire to control the source of answers and data loops, avoiding leakage of search requests that competitors could use for training, while enhancing timeliness and controllability of AI responses. This phase mirrors early expansion of Google's indexing or crawlers from platforms like OpenAI, moving from reliance on external searches to building vertical indexes independently, with the AI search industry shifting from open web pages to closed data moats. Essentially, this represents a restructuring of the industry chain: web content is transformed from a user traffic entry point into raw materials for AI training and indexing, with high-intensity scraping bringing almost no return traffic, leading to further imbalance between costs and revenues for content providers. The expansion of AI indexing comes at the expense of content providers' computing power, and building a self-owned search is a strategic choice to cut off data leakage. High-frequency scraping with zero return visits is a new value extraction model.