Generative AI and Platform Systems' Structural Restructuring of Communication Mechanisms

1. Executive Summary

This study focuses on the structural transformation of "communication research" in the context of platformization and generative AI. The core conclusions are as follows:

• Information dissemination is shifting from a "link distribution model" to an "answer generation model," with systems represented by Google and OpenAI reshaping the structure of information entry points.
• Recommendation-driven dissemination mechanisms (e.g., TikTok Feed) and generative systems form a parallel competitive relationship rather than a one-way replacement.
• RAG (Retrieval-Augmented Generation) and embedding retrieval are becoming the infrastructure layer for cross-platform information dissemination.
• User behavior is migrating from "multi-node retrieval" to "single-turn interaction acquisition," but verification behavior has not disappeared simultaneously—it has moved outward.
• Dissemination systems are entering a three-layer coexistence stage of "search—recommendation—generation," rather than a linear evolutionary replacement.

2. Background

Industry Background

Communication research has long been based on the linear model of "information source—channel—audience," but platform-based systems (Search / Feed / AI Assistant) have shifted dissemination mechanisms toward algorithm-driven structures.

Research Questions

1. Is information dissemination shifting from search-engine dominance to generative systems?
2. Do recommendation systems and generative AI constitute a substitutional relationship?
3. How do RAG and embedding mechanisms restructure communication pathways?
4. Is user behavior shifting from "retrieval-based communication" to "dialogue-based communication"?

3. Key Trends

Trend 1: Restructuring of Information Entry from "Link Distribution" to "Answer Generation"

【Fact】

• Google has introduced AI Overviews in Search, presenting some query results directly as generative summaries.
• OpenAI's ChatGPT transforms information acquisition from a "search page" to a "conversational generation interface."

【Consensus】

The industry generally agrees that search entry points are being "de-linked," but the underlying indexing structure remains.

【Inference】

The communication chain has shifted from "Query → Page → Click" to "Query → Generated Answer."

A. Pro-Trend

• Reduces the cost of information acquisition
• Decreases reliance on multi-hop clicks
• Enhances efficiency of real-time dissemination

B. Anti-Trend

• Rising computational costs limit full replacement
• Insufficient source transparency undermines credible dissemination
• Long-tail information still relies on traditional search structures#### C. Alternative Hypothesis

Search has not been replaced but has evolved into a "generative interface layer," still relying on the underlying web index structure.

Behavioral Evidence

• Users have shifted from "clicking multiple links" to "obtaining summaries in a single round."
• The "search and verify" behavior has moved to different platforms (Google ↔ ChatGPT).

Trend 2: Feed as the Primary Communication Structure (Feed-first Communication)

【Fact】

• TikTok's For You Feed is entirely driven by recommendation systems for content distribution.
• Meta's Instagram Reels emphasizes algorithmic distribution rather than social relationship chains.

【Consensus】

Recommendation systems have become the main content consumption entry point, rather than a supplementary module.

【Inference】

The logic of communication has shifted from "active acquisition" to "passive reception first."

A. Pro-Trend

• Improves content distribution efficiency.
• Reduces user selection costs.
• Strengthens personalized communication paths.

B. Anti-Trend

• Increases the risk of information homogenization.
• Amplifies cold-start bias.
• Reduces users' active exploration ability.

C. Alternative Hypothesis

The feed does not replace search but serves as a "low-cognitive-cost information consumption layer."

Behavioral Evidence

• User dwell time increases but search behavior decreases.
• Shift from "keyword input" to "infinite scrolling consumption."

Trend 3: RAG and Embedding as the Communication Infrastructure Layer

【Fact】

• OpenAI adopts RAG architecture to enhance the model's ability to trace information.
• Google uses embedding ranking to optimize search relevance.

【Consensus】

RAG has become the standard structure for connecting "external information sources with generative models."

【Inference】

The communication system is transitioning from "content distribution" to "semantic retrieval + generative fusion."

A. Pro-Trend

• Expands information coverage.
• Supports dynamic knowledge updates.
• Reduces the probability of model hallucination.

B. Anti-Trend

• Retrieval bias introduces structural errors.
• Increases reliance on information sources.
• Increases system complexity.

C. Alternative Hypothesis

RAG is not a new communication paradigm, but a "model-based reconstruction version" of the search system.

Behavioral Evidence- Users no longer manually visit source pages, but rely on model-integrated results.

• The behavior of "citing sources" decreases, but internal system calls increase.The three together constitute a "tripartite structure" of the communication system, rather than a substitution relationship.

Alternative Systemic Interpretation (System-Level Counter-Interpretation)

The current changes should not be interpreted as "search or recommendations being replaced," but a more reasonable interpretation is:

• The search system is being strengthened as a "semantic entry layer"
• The recommendation system still serves as the "attention distribution core"
• The generative system is an "interaction optimization layer"

The communication structure is not converging, but expanding into a multi-layer parallel system.