What happened
Lin Le, CEO of Ling Shu Technology, described trusted data spaces as the core hub for data circulation and utilization, providing security and rule safeguards from raw data to token output and application deployment.
He warned that without a trusted infrastructure, raw data cannot safely enter dataset processing, making model training, token production and business applications impossible.
The company supports city-level and industry-level trusted data spaces, connecting national data infrastructure nodes with government and enterprise business systems, and has deployed city data hubs in more than 20 cities.
Why it matters
The statement signals a push to treat AI data preparation as a standardized, assembly-line-style process, with trusted data spaces acting as the quality-control and compliance layer.
If adopted widely, this approach could unlock safe use of public and enterprise data for AI training, especially in regulated sectors where traceability and auditability are mandatory.
Key facts
Lin Le is the CEO of Ling Shu Technology, a data circulation service provider.
Trusted data spaces are being built by local governments, data groups, trading institutions and tech companies.
Ling Shu processes public and industry data into high-quality datasets for model training and token production under compliance.
The company has implemented city-level data circulation hubs in more than 20 cities.
What to watch next
Whether Ling Shu's standardized and compliant data supply model is replicated across other regions and industries.
How trusted data spaces evolve to support interoperability between AI data suppliers and token-producing enterprises.