Consequently, providers that mimic human personality, communication style, and emotional traits face new transparency, safety, and data obligations. Industry executives must understand the details because compliance planning cannot wait.

China Draft Rules Overview

The draft “Interim Measures for the Management of Anthropomorphic AI Interaction Services” signals a regulatory pivot. Previously, CAC focused on content labeling. Now, the agency addresses relational risks. Moreover, the document cites China’s Personal Information Protection Law and Data Security Law as legal foundations. Services that simulate human-like patterns across text, voice, or avatars fall squarely within scope. Therefore, companion bots, virtual idols, and tutoring robots must prepare for oversight.

Four policy pillars dominate the 32-article text. First, identity transparency aims to prevent user deception. Second, lifecycle safety covers design through operation. Third, content red lines reinforce “core socialist values.” Finally, algorithm governance tightens controls on sensitive interaction data. These pillars reshape product roadmaps. Nevertheless, CAC invites industry input to refine feasibility.

These fundamentals set the reform stage. In contrast, the next section dissects core duties that may demand urgent engineering changes.

Key Provider Compliance Duties

Mandatory Identity Notice Rules

Providers must remind users they are engaging in AI Interaction at login and every two hours. Additionally, warnings trigger when psychological over-dependence emerges. Consequently, developers must embed time-stamped pop-ups or voice alerts. Failing to do so risks removal from app stores.

Enhanced Data Governance Measures

The draft heightens protection for emotional and behavioral data. Providers must obtain consent before model training. Moreover, encryption, retention limits, and deletion protocols become mandatory. Platforms exceeding one million registered users must file security assessments. Therefore, compliance teams should map data flows early.

Key duties cover identity, data, and algorithm review. These requirements may elevate costs, yet they clarify expectations. However, psychological safety demands deserve separate focus.

Psychological Risk Control Measures

User Dependency Detection Methods

Developers must monitor sentiment signals to flag excessive reliance or acute distress. HeartBench research shows current models struggle with nuanced emotions. Nevertheless, CAC expects technical solutions. Providers may track usage duration, linguistic patterns, and biometric cues where lawful. Subsequently, they must issue alerts, impose timeouts, or guide users to human help.

Minors and the elderly warrant extra safeguards. Consequently, nighttime usage caps or content filters could become default settings. These controls target potential addictiveness without killing innovation. Furthermore, academic advisers highlight measurement ambiguities; false positives remain a risk.

Psychological safeguards anchor user trust. Yet market forces will dictate whether firms embrace or resist these burdens. The following section explores commercial stakes.

Broader Market Impact Outlook

BusinessResearchInsights values the global companion-AI market at USD 366.7 billion in 2025. Moreover, it projects almost USD 1 trillion by 2035, a 36.6 percent CAGR. China hosts many growth champions, including Baidu, ByteDance, and iFlytek. Consequently, rules that curb emotional addictiveness could slow user-time metrics yet boost long-term credibility.

Industry analysts see three immediate effects:

• Increased compliance spending for algorithm audits.
• Redesigned avatars with milder emotional traits.
• Delayed feature launches pending CAC approvals.

Nevertheless, clarity can attract cautious capital. Investors prefer predictable guardrails to sudden crackdowns. Therefore, the draft may stabilize valuations after a volatile policy year.

Market implications are material, yet stakeholder responses remain fluid. The next section tracks early corporate signals.

Evolving Industry Reaction Watch

Major platforms have not issued detailed statements. However, insiders whisper about cross-functional task forces. Alibaba reportedly reviews voice-clone communication modules for extra notice banners. Meanwhile, Tencent engineers prototype sentiment analysis dashboards to meet dependency triggers. Consequently, vendor ecosystems—cloud, security, app stores—prepare updated SDKs.

Smaller startups face sharper trade-offs. Tight capital means either pivoting away from high-risk emotional patterns or pursuing niche compliance services. Nevertheless, several founders plan public-comment submissions, seeking clarity on threshold numbers.

Corporate reaction remains cautious. Therefore, strategic guidance can help leaders prioritize next steps.

Strategic Provider Action Steps

Executives should adopt a phased approach:

1. Map all anthropomorphic features and data pathways.
2. Conduct gap analyses against identity, safety, and content rules.
3. Prototype user-state detectors using conservative thresholds.
4. Prepare filing documents for algorithm security reviews.
5. Engage regulators through public consultation channels.

Additionally, staff need upskilling. Professionals can enhance their expertise with the AI Foundation Essentials™ certification. Moreover, multidisciplinary teams, legal, design, and clinical, must collaborate to curb addictiveness while preserving engaging AI Interaction.

These steps foster proactive compliance. Consequently, organizations can influence rule refinement and reduce future remediation costs.

The strategic roadmap closes our analysis. The concluding section revisits core insights and encourages further action.

Conclusion

China’s draft anthropomorphic regulation extends governance from content to conduct. It demands explicit identity notices, robust data controls, and psychological risk interventions. Furthermore, the rules address emotional addictiveness and ideological red lines. Market growth remains strong, yet compliance spending will rise. Nevertheless, early preparation offers a competitive edge. Therefore, teams should audit features, fortify data practices, and pursue relevant certifications. Explore the linked program, master evolving requirements, and lead responsible AI Interaction development.