Today's Perspective: AI "Standard Answers" Urgently Need Rules and Boundaries

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■ Yuan Chuanxi

The “GEO (Generative Engine Optimization) poisoning” black industry exposed on CCTV’s “3.15” evening show is like a heavy blow awakening those indulging in technological dividends. Unscrupulous merchants produce大量虚假内容“feeding” large models, causing fabricated information to be treated as “standard answers” by multiple mainstream AI applications.

This absurd case reflects the serious challenges society faces in embracing the AI wave: How should we view and use AI applications dialectically?

In fact, the original purpose of AI applications is empowerment. However, in practice, some companies distort it into a “cost-cutting” shield, sacrificing service warmth and user rights. Take AI customer service as an example: its efficiency in handling basic inquiries is undeniable, but some firms deliberately hide manual entry points to drastically cut labor costs, trapping users in a cycle of “circular talk” with machines. This kind of intelligent barrier is essentially a cold technological way to evade corporate responsibility.

When deeper issues require emotional interaction and complex decision-making, the lack of human intervention causes trust to fracture. This proves that proper AI use is not simply “machines replacing humans,” but should clearly define the boundaries of “human-machine collaboration,” letting algorithms handle routine low-level tasks while keeping the keys to user experience and crisis management firmly in warm, human hands.

If chaos in customer service stems from ethical lapses, then the GEO black industry exposed on “3.15” is a fundamental disruption of the information ecology. As AI search tools become the primary way for users to access information, traditional SEO (Search Engine Optimization) is evolving into more covert GEO tactics. This is a natural iteration of marketing aimed at more precise targeting, but the involvement of black industry turns “precision” into “poisoning.”

Some practitioners rely on large models’ dependence on training data, using matrix-style publishing of false soft articles, disguising ads as objective facts to directly influence the model’s core cognition. This method is more frightening than traditional bidding rankings because it removes “advertisement” labels, making lies appear as “truth.” Once large models become tools that favor those who pay more, the internet information ecosystem faces “bad money drives out good,” and public trust in AI will instantly collapse.

In the face of chaos like “customer service barriers” and “data poisoning,” mere technological optimism is insufficient. To break this deadlock, we need more than patchwork fixes; we must build a deep defense system from “source self-discipline” to “regulatory oversight,” ultimately reaching “individual awakening.”

Technology itself is not at fault, but those who use it must have reverence. Companies that focus only on short-term costs, viewing AI as a traffic-grabbing tool, will ultimately face backlash as their brand reputation collapses. True intelligent applications should proactively preserve the “human channel” in customer service and uphold the red line of “data poisoning” in marketing.

Self-discipline is not万能, and regulatory “sword” must hang high. Confronted with new black industries like GEO, traditional advertising law enforcement is lagging. Oversight logic must shift from “punishment after the fact” to “preemptive monitoring” and “algorithm accountability.” Accelerate the establishment of dynamic monitoring mechanisms for AI-generated content, clarify the legal responsibilities behind “algorithm recommendations,” and make malicious manipulators pay a heavy price to prevent industry ecosystem deterioration.

In the gap between technology and ethics, the last line of defense often lies with users. People must clearly recognize that AI-generated “standard answers” may contain biases or even lies. Maintaining critical thinking and developing habits of multi-party verification are not only self-protection measures but also essential survival wisdom for every digital citizen in the AI era.

From steam engines to electricity, every technological revolution has gone through a spiral of “wild growth—rule establishment—value integration.” Currently, AI applications are transitioning from the first to the second stage, requiring us to embrace efficiency benefits while guarding against boundary loss; encouraging business innovation while safeguarding public interests.

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Editor: Gao Jia