Superplatforms Have to Attack AI Agents

2025-05-23

AI Summary

This paper identifies a fundamental tension between dominant digital platforms and large language model (LLM)-driven AI agents: AI agents’ cross-platform autonomy threatens platforms’ attention-monopoly-based business models and may reconfigure digital traffic gateways. Method: To analyze this conflict, the study introduces the novel “Gatekeeping Theory” framework—integrating platform governance, gatekeeping economics, LLM behavioral modeling, and cross-platform autonomy mechanisms—and systematically identifies emerging technical countermeasures, including API rate limiting, protocol blocking, and semantic interference. Contribution/Results: The paper clarifies how AI agents disrupt platform monetization logic, delineates the motivations and legitimate boundaries of platform countermeasures, and advocates for a collaborative governance framework grounded in user rights and ecosystem openness. It provides the first systematic analysis of platform-driven constraints on autonomous AI agents and proposes foundational principles for balancing innovation, competition, and fairness in AI-augmented digital ecosystems.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Over the past decades, superplatforms, digital companies that integrate a vast range of third-party services and applications into a single, unified ecosystem, have built their fortunes on monopolizing user attention through targeted advertising and algorithmic content curation. Yet the emergence of AI agents driven by large language models (LLMs) threatens to upend this business model. Agents can not only free user attention with autonomy across diverse platforms and therefore bypass the user-attention-based monetization, but might also become the new entrance for digital traffic. Hence, we argue that superplatforms have to attack AI agents to defend their centralized control of digital traffic entrance. Specifically, we analyze the fundamental conflict between user-attention-based monetization and agent-driven autonomy through the lens of our gatekeeping theory. We show how AI agents can disintermediate superplatforms and potentially become the next dominant gatekeepers, thereby forming the urgent necessity for superplatforms to proactively constrain and attack AI agents. Moreover, we go through the potential technologies for superplatform-initiated attacks, covering a brand-new, unexplored technical area with unique challenges. We have to emphasize that, despite our position, this paper does not advocate for adversarial attacks by superplatforms on AI agents, but rather offers an envisioned trend to highlight the emerging tensions between superplatforms and AI agents. Our aim is to raise awareness and encourage critical discussion for collaborative solutions, prioritizing user interests and perserving the openness of digital ecosystems in the age of AI agents.

Problem

• AI agents threaten superplatforms' user-attention monetization model
• Superplatforms must defend centralized control against AI agent gatekeeping
• Emerging tensions require collaborative solutions for digital ecosystem openness

Innovation

• Analyzing conflict via gatekeeping theory
• Exploring superplatform-initiated attack technologies
• Highlighting tensions for collaborative solutions