What the “AI Plus” plan actually does
The State Council’s AI Plus blueprint is not a single statute but a cross-government program that directs agencies and provinces to integrate AI across priority sectors, upgrade national data and compute infrastructure, and accelerate an open-source ecosystem. In practice, that means:
- Sectors: targeted AI deployment in industry (smart factories, quality control), healthcare (assisted diagnosis), agriculture (precision farming), finance (risk modeling), education and public services.
- Data & compute: improving data supply (opening select public datasets), strengthening intelligent computing capacity, and backing model R&D.
- Ecosystem & funding: encouraging open-source, talent pipelines, and channeling both state and “patient” private capital toward long-horizon AI projects.
For global companies, the headline is operational: the plan signals continued state support for applied AI—tying procurement, subsidies, and standards to production-grade deployments rather than frontier demos.
The new AI labeling law: explicit & implicit marks are now mandatory
Effective now as the labeling law came live on September 1, 2025, China’s nationwide Measures for Labeling AI-Generated Synthetic Content require platforms and providers to label AI-generated text, images, audio, and video in two ways:
- Explicit labels (clear, visible disclosures to users such as badges or on-screen marks), and
- Implicit identifiers (e.g., metadata tags, watermarks) embedded so that downstream systems and crawlers can detect synthetic origin.
The law applies across mainstream platforms (social, short-video, messaging) and aims to curb misinformation and undisclosed synthetic media. Providers are expected to stand up governance processes for labeling accuracy, user reporting, and timely takedowns—under the supervision of the Cyberspace Administration of China (CAC).
Where PIPL fits: AI compliance is also privacy compliance
China’s Personal Information Protection Law (PIPL) has governed personal-data processing in China since 2021. For AI builders and deployers, three pillars matter most:
- Lawful basis & transparency: Processing that identifies or relates to individuals requires a legal basis (often consent) and clear notice. Automated decision-making must be fair, unbiased, and explainable at a high level—especially where it significantly affects individuals.
- Sensitive data & minimization: Extra protections apply to biometric, health, financial, and under-age data. Collect only what is necessary and retain it briefly.
- Cross-border transfers: Moving personal data out of mainland China generally triggers security assessments, SCCs, or certification. Recent adjustments have eased some pathways but still expect transfer impact assessments, consent, and contract terms with overseas recipients.
Combined with the labeling law, PIPL pushes AI programs toward traceability (prove what was generated by AI), accountability (who labeled, when, using what method), and lawful data sourcing (document dataset provenance, licenses, and consent).
Side-by-side: what each instrument demands
| Instrument | Primary goal | Who is in scope? | Key duties |
|---|---|---|---|
| AI Plus (State Council program) | Integrate AI into the real economy; upgrade data & compute | Agencies, SOEs, private firms in priority sectors | Adopt AI in production; open select public datasets; foster open-source; build talent & compute |
| AI Labeling Measures (nationwide rule) | Make synthetic media detectable by people & machines | Platforms, model providers, content publishers | Apply explicit labels and embedded identifiers; set up user flags/takedowns; ensure accuracy & logs |
| PIPL (privacy law) | Protect personal information & rights | “Personal information handlers” (controllers) and processors | Legal basis, notice/consent, minimization, security, rights handling; rules for cross-border transfers |
Implications for product, data, and legal teams
- Build the labeler into the pipeline. Treat explicit/implicit labels as blocking controls at generation and publishing, not post-hoc add-ons.
- Prove provenance. Maintain signed manifests for model outputs and training material licenses. Watermark where feasible and record label integrity checks.
- Segment China data. Keep mainland datasets, logs, and label keys in-region and prepare a cross-border playbook (TIA, SCCs, consent) for any exports.
- Harden for minors & sensitive data. Apply high-privacy defaults, automated filtering, and human review paths for biometric or youth-related content.
- Vendor contracts. Push labeling, watermarking, and PIPL clauses into agreements with model/API and content-moderation partners—plus audit rights.
Design choices for age-appropriate and low-leakage deployments
- Privacy-preserving Prefer on-device labeling and cryptographic provenance (e.g., signed metadata) to reduce server-side exposure.
- Defense-in-depth Combine visible marks with robust metadata so edits/screenshots still leave a detectable trail.
- Explainability Offer plain-language notices for synthetic media and short retention windows for raw IDs or faces encountered during verification or moderation.
FAQ (for global teams shipping into China)
Do we need to label everything our model outputs? If content is AI-generated and distributed to users, expect to label it explicitly and embed machine-readable markers. Drafts kept internally may be out of scope, but anything public-facing should be labeled.
What if our watermark is stripped? The rules contemplate implicit identifiers in metadata/watermarks and explicit user-visible indicators. Use multiple, redundant methods and keep issuance logs for audits.
Can we train on China-sourced data and export the model? If personal information is involved, you’ll need a lawful basis and cross-border mechanism (security assessment, SCCs, certification, or a qualified exemption). Conduct a transfer impact assessment and document data lineage.
AI Plus China Compliance Solution
“AI Plus” puts the Chinese economy on a deliberate track toward applied AI at national scale, while the labeling law makes synthetic media auditable by design. PIPL then ties the loop: if AI touches personal data, you must meet strict privacy duties and cross-border controls. Companies that wire labeling into their content pipelines, localize data flows for China, and document consent/licensing at the training and output stages will be positioned to grow with fewer compliance shocks. Those that don’t risk takedowns, fines, and market-access headaches. If you want a turnkey package—labeling SOPs, PIPL transfer templates, and vendor clauses—Captain Compliance can help with AI Plus compliance and privacy requirements for not only China but all over Asia and the world.
Implementation help: Need an AI labeling SOP, a PIPL cross-border checklist, and contract addenda for model providers? We can tailor and deliver above and beyond your expectations to cover your domestic and international privacy and AI regulaotry requirements. Book a demo below to learn more about getting compliant with the privacy and compliance leader Captain Compliance’s help.
