Compliance boundaries on AI-generated product testimonials

Generative artificial intelligence (generative AI) is transforming how online marketing content is created. Conventional “product recommendation notes” rely on real-life use, photography, editing and posting. Now, modern generative AI tools can mass produce first-person user testimonials, review summaries, and buying tips with little more than a product name, a target demographic and a platform’s house style. The efficiency gains are significant, but they raise fresh concerns about fair market competition and broader market order.

The 2025 revision of the Anti-Unfair Competition Law extends its reach to data, algorithms, technology and platform rules, and introduces a new clause outlawing the improper acquisition and use of others’ lawful data. The Interim Provisions on Anti-Unfair Competition on the Internet impose liability for online confusion, false advertising and algorithm-driven market interference. The Labeling Method for Content Generated by Artificial Intelligence, effective 2025, and its supporting national standards require AI-generated content to carry a source identifier and remain traceable. The Cyberspace Administration of China initiated a special enforcement action in 2026, prioritising the elimination of commercial operations that mass-produce fabricated consumer experience posts.

Red lines

Judicial intervention in the AI content sphere is underway. In a Zhejiang case, an AI writing tool providing RedNote-style recommendation copywriting services was held to be engaging in unfair competition and ordered to pay damages to the platform. The ruling suggests that when AI tools are used at scale in targeted commercial marketing, claims of technological neutrality may no longer hold, and courts will focus on how they are applied.

In this context, a platform’s ecosystem of genuine content – built over time through user reviews, trust and governance – becomes a competitive asset worthy of legal protection. While platforms cannot assert proprietary control over all user-generated content, the business advantages and technical assets built by the platform’s investment should be safeguarded by the Anti-Unfair Competition Law once mass AI-generated fake experience posts drive up governance costs, dilute authentic material and mislead consumers.

AI-generated recommendation writing tools can be assessed against the following four factors.

1. 1. Does the tool target a specific platform by mimicking or hinting at its name, formats, or style?
   2. Does it fabricate a first-person experience in a way that misleads users?
   3. Does it run at scale for profit, for example via subscriptions, template packages, or matrix accounts?
   4. Does it cause substantive harm to the platform’s ecosystem by undermining content quality, driving up governance costs and weakening user trust?

If all four are present, the practice can be characterised as “contextualised inducement-based unfair competition”.

Claims that AI tools are “technologically neutral” do not remove the need to respect defined safety boundaries. In practice, generic writing tools should be restricted to tasks such as editing language or drafting advertising material; users may draw on AI only to help convey authentic experiences; AI outputs must be clearly identified as commercial advertising or labelled as AI-generated; and AI should not be used to underpin bulk content operations or degrade the content ecosystem. AI-assisted writing is neutral in technical terms, but configuring AI as a pipeline for fabricated user experiences may be found unlawful.

Recommendations

In-house counsel and compliance teams are advised to convert the four-factor test into practical internal rules. They need first to assess the risks of using AI-generated content in marketing and spell out which types of output may count as contextualised inducement, such as fake first-person reviews, copying a platform’s style or running content at scale. Then companies should put in place review and labelling systems to ensure that the AI-generated material is clearly tagged with its origin and nature without misleading consumers, and that marketing and multi-channel network (MCN) teams receive regular training on compliance red lines.

Businesses should also establish a complaints and dispute resolution workflow, enabling swift response and remediation when a platform or consumer flags potentially offending content, with full operational records retained for any subsequent regulatory inquiry or legal proceedings. Contracts and service terms must define the AI provider’s duty of care and liability boundaries, guard against compliance risks from third-party tools, and be subject to periodic review for performance and risk.

On liability, AI service providers are expected to exercise design-level due care. They should refrain from using inducive templates, build in labelling and moderation tools, and respond to regulatory demands. Where users, businesses or MCN organisations knowingly mass-distribute fabricated experience content, they may face liability for misleading promotion or as accessories. Platforms, for their part, must introduce rules on AI content disclosure, systems to flag anomalous accounts and merchant governance measures, alongside effective channels for complaints and dispute handling.

In practice, businesses deploying AI-generated content need three safeguards: compliant design of the tools themselves, oversight of how they are used and collaboration with platforms on governance. Through internal audits and key performance indicator tracking – covering output volume, posting frequency, complaint rates and remediation records – companies can measure compliance and identify legal exposure. Executives should build these arrangements into yearly strategy and risk reviews so that AI use advances business goals while remaining within legal boundaries.

Going forward, AI developers, platforms and regulators must act in concert so that generative AI fosters innovation while preserving market order, bringing commercial efficiency and regulatory compliance into balance.

Ethan Zhang is a senior partner at Joint-Win Partners