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• Kairos-HomeWorld is purpose built for embodied intelligence and represents the first unified framework capable of generating a complete, fully interactive home environment from a single text prompt. Extending indoor scene generation beyond individual rooms, it enables whole-home simulation in which every object is fully manipulable within an integrated simulation engine.
• Kairos-HomeWorld employs a four-stage hierarchical architecture encompassing floorplan generation, 2D-to-3D lifting, recursive refinement, and manipulable object placement. This approach enables the production of globally coherent, physically accurate, and simulation-ready scenes. Each environment contains more than 15 manipulable objects and achieves a Footprint Object Density of 4.16, the highest among compared methods.
• The accompanying open-source dataset is purpose-built for Chinese households, pairing 300,000 real residential floor plans with 5,000 fully furnished, simulation-ready homes and 50,000 physics-enabled interactive object assets. Already deployed in ACE ROBOTICS’ daily robot training, it significantly accelerates the simulation-to-reality transfer cycle.

SHANGHAI, CHINA – Media OutReach Newswire – 5 June 2026 – ACE ROBOTICS, in collaboration with the Multimedia Laboratory at The Chinese University of Hong Kong (CUHK) and Shenzhen Loop Area Institute, today announced the open-source release of Kairos-HomeWorld, the industry’s first unified World Model framework capable of generating full home-scale, object-level interactive 3D environments from a single text prompt. The solution addresses longstanding limitations in indoor scene generation, which has typically been restricted to single-room outputs with weak global consistency and limited interactivity. Kairos-HomeWorld overcomes these constraints by delivering structurally coherent, physically plausible, and functionally complete residential environments. These high-fidelity, large-scale simulations provide a robust foundation for advancing embodied intelligence applications and accelerating real-world robot training.

The long-term vision for embodied intelligence is the home environment. However, residential settings are inherently diverse and highly personalized, requiring robots to be trained across a broad range of realistic and differentiated scenarios before they can reliably operate in even a single household. High-fidelity simulation offers the most practical pathway to achieving this at scale, yet existing approaches typically involve a trade-off: synthetic environments lack realism, while scanned real-world scenes offer limited interactivity. Kairos-HomeWorld, together with its accompanying dataset, is designed to bridge this gap, delivering both realistic and interactive environments within a unified framework.

A four-stage architecture for whole-home, object-level generation
Conventional approaches to indoor scene generation remain constrained to single-room outputs, often exhibiting weak global consistency, frequent physical inaccuracies, and limited or no interactivity. Kairos-HomeWorld takes a fundamentally different approach. It decomposes whole-home generation into a structured, four-stage process, redefining the underlying architectural paradigm from the ground up.

Stage 1 — Floor Plan Generation. A K-D tree-based approach translates real-world floor plans into a hierarchical text representation that can be efficiently processed by large language models (LLMs). This method mitigates common issues in conventional layout generation, including room overlaps and fragmented topologies, resulting in more coherent and structurally consistent spatial configurations.

Stage 2 — 2D-to-3D Lifting & Furniture Layout Generation. A “top-down global initialization combined with a first-person detail walkthrough” approach anchors the process to the 3D building shell generated in Stage 1. This methodology mitigates the geometric drift commonly associated with conventional 2D-to-3D lifting techniques, enabling more stable and spatially consistent scene generation.

Stage 3 — Recursive Refinement. A fine-tuned vision-language model performs iterative validation and correction, automatically identifying and resolving physical inconsistencies, such as obstructed doorways or object collisions. This recursive process materially reduces spatial errors, achieving among the lowest reported furniture-collision rates in the industry.

Stage 4 — Manipulable Object Placement. A surface-centric placement algorithm assigns each object detailed physical properties, including material composition, density, friction, and structural support relationships. Each generated scene incorporates an average of more than 15 manipulable objects and achieves a Footprint Object Density (FOD) of 4.16, a metric reflecting the concentration of items across furniture surfaces. All objects are natively compatible with simulation engines, enabling direct interaction for tasks such as grasping, movement, and stacking.

The resulting environments move beyond “viewable but not actionable” outputs. Their coherent spatial structure enables seamless, continuous navigation across multiple rooms, while objects embedded with realistic physical properties allow robots to simulate complex household tasks end-to-end. Taken together, this approach addresses key limitations in existing data pipelines, resolving the scarcity of high-quality 3D simulation data, the lack of realism in synthetic environments, and the limited interactivity of scanned scenes within a single unified framework.

The dataset: 300,000 real floor plans, 5,000 fully interactive homes, built for Chinese households
ACE ROBOTICS and CUHK are open-sourcing a dataset of 300,000 structurally annotated residential floor plans, sourced from real-world listings and processed through a multi-stage automated pipeline. The pipeline vectorizes and labels key spatial elements, including door and window positions, room geometry, functional zoning, and connectivity. By comparison, widely used benchmarks such as RPLAN and ResPlan contain approximately 80,000 and 17,000 floor plans, respectively, underscoring the scale and comprehensiveness of the Kairos-HomeWorld dataset.

Building on this foundation, the dataset also includes 5,000 fully furnished residential environments, each featuring a complete furniture layout and an average of more than 15 physics-enabled, manipulable objects, powered by the PhysX-Omni model. All assets are sim-ready and can be directly imported into a simulation engine, enabling immediate use in interactive training scenarios.

Most existing open indoor-scene datasets are centered on North American and European residential formats, typically featuring open-plan kitchens, the absence of service balconies, and layouts and design elements that capture only a narrow segment of global housing. As a result, robots trained on these datasets often exhibit limited transferability when deployed in environments outside their scope. Kairos-HomeWorld ‘s dataset is purpose-built for Chinese households, with deliberate coverage of historically under-represented housing typologies. It spans a wide range of unit sizes, from approximately 30 m² (around 320 sq ft) studio apartments to residences exceeding 200 m² (approximately 2,150 sq ft). The dataset accurately reflects key architectural features common in these settings, including north-south cross-ventilated layouts, enclosed kitchens, dedicated service balconies, wet-and-dry-separated bathrooms, and entryway storage, as well as the irregular room configurations often found in older housing stock.

The dataset is being openly released to both academic and industry communities. Going forward, the team plans to expand its scope to include additional regions, interior styles, and interaction scenarios, further lowering barriers to real-world-ready training for embodied intelligence.

See it in action: one prompt to a fully interactive home
Kairos-HomeWorld runs the full end-to-end pipeline, from initial text input to a fully interactive home environment, delivering global spatial consistency, physical realism, and seamless interactivity from a single prompt.

The system begins with a single-line prompt: “Generate a 90 m² (approximately 970 sq ft) two-bedroom apartment in neo-Chinese style.” Leveraging real floor plan data and its K-D tree representation, Kairos-HomeWorld first constructs an empty spatial layout aligned with real-world living patterns, incorporating cross-ventilation and well-defined functional zoning. Building on this foundation, a hierarchical “global layout plus first-person detail” approach furnishes the environment with stylistic coherence, while a PhysX-Omni rendering pass assigns full physical properties to all surfaces and objects, including articulated behavior, ensuring the scene is fully interactive and sim-ready.

A single natural-language instruction, “tidy the whole home”, is decomposed by the robot into a variety of discrete sub-tasks, executed sequentially along a complete navigation path spanning the living room, bedrooms, kitchen, bathroom, and dining area. The robot recognizes objects, plans efficient routes, and performs precise manipulation tasks, including articulated-object interactions for opening refrigerator and cabinet doors, fluid interactions for pouring laundry detergent, soft-body interactions for drawing curtains, irregular-object interactions for grasping apples, and gravity-based physical interactions for placing snacks.

Conventional simulation environments typically support navigation-focused training in isolation. By contrast, Kairos-HomeWorld integrates globally consistent spatial structures with objects that embody realistic physical properties. This enables robots to interact naturally with more than 15 object types, accurately modeling real-world dynamics such as collision, gravity, and friction, and to rehearse the full lifecycle of complex household tasks entirely within a virtual environment.

Across the industry, the same structural bottleneck, the scarcity of home-scale training data, is being addressed through multiple approaches. For example, Figure AI’s collaboration with Brookfield focuses on collecting human activity data across more than 100,000 residential units. Kairos-HomeWorld addresses this challenge through on-demand synthetic generation, delivering scalable training environments enhanced with object-level physical realism, capabilities that real-world data capture alone cannot fully provide.

In contrast, Kairos-HomeWorld delivers significantly lower costs and higher efficiency for household robot training. Powered by its world model, it can programmatically generate diverse Chinese home simulation scenes and physics-enabled interactive objects at scale.

Robots can complete a full range of household tasks training entirely within the virtual environment. New scene generation incurs near-zero marginal cost, eliminating substantial real-world testing expenses such as site operation and maintenance and furniture damage. Meanwhile, unconstrained by the limited stock of physical residential properties, it outperforms real-world data collection approaches in both training efficiency and scalable expansion.

Kairos-HomeWorld is already deployed in ACE ROBOTICS’ embodied intelligence training workflows, enabling full-pipeline simulation of long-horizon household tasks, including cross-room navigation and multi-room tidying. By allowing robots to rehearse complete task sequences in a virtual environment, the platform significantly shortens the simulation-to-reality transfer cycle. This approach lowers barriers to developing embodied intelligence systems and supports the accelerated, large-scale deployment of home robotics, particularly within the Chinese market. Kairos-HomeWorld is now available on GitHub.Hashtag: #ACEROBOTICS

The issuer is solely responsible for the content of this announcement.

About ACE ROBOTICS

Equipping robots with intelligent “brains” and engaging “souls”.

ACE ROBOTICS is a pioneering robotics company dedicated to advancing the field of embodied intelligence. Through breakthrough technological innovations and deep insights into embodied intelligence scenarios, we aim to empower robots with the ability to autonomously understand and explore the physical world, thereby accelerating their commercial implementation.

The company pioneered the ACE R&D paradigm and built a vision-based “environmental data engine, real-world cognition, embodied interaction generalization” technology chain. Using full spatiotemporal and multi-perspective environmental capture as its engine, along with Kairos 3.0 – China’s first open-source and commercially applicable world model – plus the Embodied Foundation Model as its technical backbone, ACE ROBOTICS addresses core industry challenges such as data scarcity, common sense gaps, poor generalization, and limited versatility. Simultaneously, the company unveiled its flagship A1 Embodied Super Brain Module, accelerating the large-scale commercial deployment of embodied intelligence across diverse scenarios.

ACE ROBOTICS is both a technology pioneer and an ecosystem builder. Through strategic cooperation with top hardware manufacturers, cloud service providers, and vertical scenario partners, we have broken through the “model-hardware-scenario” industrial deadlock, providing standardized and customized solutions that are driving the development of China’s embodied intelligence industry.

The plan, worth about $4.3 billion, reflects a sharp turn by Gulf from its traditional base as a power producer into a broader infrastructure group linking electricity generation, telecommunications, cloud services and industrial logistics. The investment push comes as Thailand seeks to position itself as a regional data hub, helped by rising demand for cloud computing, artificial intelligence workloads and data-localisation capacity across Southeast Asia.

Gulf’s strategy is built around a simple commercial premise: AI facilities need vast, reliable and increasingly low-carbon electricity. Data centres are among the most power-intensive assets in the digital economy, and operators are under pressure to secure stable energy supply while meeting sustainability commitments from global technology clients. Gulf’s existing position in gas-fired power, renewable energy and utilities gives it an advantage that pure technology infrastructure firms may struggle to match.

Sarath, Gulf’s chief executive and controlling figure, has spent years reshaping the group from a conventional energy developer into a conglomerate with exposure to telecoms, satellites, digital platforms and cloud infrastructure. The merger of Gulf Energy Development and Intouch Holdings created a larger listed platform linked to Advanced Info Service, Thailand’s biggest mobile operator, and Thaicom, the satellite company. That structure gives Gulf access to telecom traffic, enterprise clients and digital distribution channels that can support its data-centre ambitions.

The investment programme is expected to support new data-centre capacity, related infrastructure and power projects needed to serve hyperscale and enterprise customers. Gulf has already moved into the sector through partnerships involving global technology names and regional operators, while its digital arm has explored cloud and AI services designed for corporate and government clients. The company’s first major data-centre project has become a reference point for its expansion into facilities that can host high-density computing workloads.

Thailand’s broader investment environment has also shifted in Gulf’s favour. Authorities have approved large-scale data infrastructure projects from global technology players, including server and data-processing investments across Bangkok, Samut Prakan and Chachoengsao. Google, Amazon Web Services, Microsoft and ByteDance-linked operations have all announced or pursued major commitments in the country, intensifying competition but also validating Thailand’s role in the regional digital economy.

For Gulf, the opportunity lies not only in renting data-centre capacity but also in selling bundled infrastructure: power, connectivity, cloud access and long-term operational reliability. That model could create recurring income and reduce dependence on regulated returns from power generation. It may also help Gulf deepen its relationship with large technology clients seeking local partners that understand land, energy approvals, grid constraints and domestic regulatory requirements.

The group’s financial position gives it room to move, though the scale of the programme will be closely watched by investors. Gulf reported strong first-quarter earnings for 2026, helped by its energy operations and profit contribution from Advanced Info Service. Revenue growth and core profit gains have strengthened confidence in its ability to finance new projects, but data centres require heavy upfront spending and returns can depend on utilisation rates, long-term contracts and energy costs.

The timing is also sensitive. Across Asia, governments are competing for data-centre investment while confronting pressure on power grids, water use and land availability. Singapore’s cautious approach to new data-centre capacity has encouraged developers to look at Malaysia, Thailand, Indonesia and Vietnam, but each market faces its own constraints. Thailand has industrial land, policy incentives and improving connectivity, yet grid readiness and clean-energy availability remain decisive issues for hyperscale clients.

Gulf’s renewable energy targets are likely to form part of its sales pitch. The company has set out plans to raise the share of renewables in its generation portfolio and reduce carbon intensity, while continuing to rely on gas as a stable baseload fuel. Its long-term LNG supply arrangements also point to a strategy that balances energy security with transition commitments, even as customers demand cleaner electricity for digital operations.

Sarath’s expansion plan underscores how AI is redrawing corporate strategy beyond Silicon Valley. Energy groups, telecom operators and infrastructure funds are increasingly competing for the physical assets behind the digital boom. Gulf’s bet is that Thailand’s AI economy will be shaped as much by power plants, fibre networks and industrial sites as by software models and cloud applications.

The Tokyo-based maker of NAND flash memory and solid-state drives has become one of the most closely watched stocks on the Tokyo Stock Exchange after a sharp rally tied to demand for data-centre storage, AI servers and high-performance memory systems. Its market capitalisation has climbed into the tens of trillions of yen, placing it near Toyota and behind SoftBank Group in a reshuffled ranking that would have seemed unlikely when Kioxia priced its initial public offering at 1,455 yen a share in December 2024.

The shift marks a wider challenge to the long-standing dominance of Japan’s manufacturing champions. Toyota, the global carmaker that symbolised the strength of Japan Inc. for decades, has been displaced from the top position by SoftBank during the latest AI-led surge, while Kioxia has risen rapidly from a former Toshiba memory unit backed by Bain Capital into a central player in the market’s technology rotation.

Kioxia’s climb reflects investor expectations that memory will remain a bottleneck in AI infrastructure. Generative AI systems require vast amounts of storage and high-speed data movement, pushing demand beyond the older cycle driven mainly by smartphones and personal computers. Data-centre operators, cloud companies and chip designers are competing for components needed to train and run large AI models, giving memory producers stronger pricing power after years of volatility.

The company’s earnings have strengthened sharply. Kioxia has projected operating profit of about 1.3 trillion yen for the April-June quarter, a figure that puts it among the most profitable listed companies in Japan. Its latest annual results showed strong momentum in revenue, margins and cash generation, helping ease earlier concerns about debt and cyclicality. Rating upgrades to investment grade have added to the perception that the balance sheet is improving at a time when investors are rewarding semiconductor exposure.

Kioxia’s origins give its rise broader industrial significance. The company traces its roots to Toshiba’s memory business, which was sold to a Bain-led consortium in 2018 after Toshiba’s financial crisis. The group was renamed Kioxia in 2019, drawing on the Japanese word for memory and the Greek word for value. Its public listing in 2024 valued it below 800 billion yen, after earlier IPO attempts were delayed by weak valuation conditions and pressure in the memory market.

That cautious debut contrasts sharply with its current status. Shares have multiplied as investors reassessed the role of NAND flash in AI systems, especially as large-scale data centres require higher storage density, faster access and more energy-efficient hardware. Kioxia has also been developing advanced BiCS FLASH technology and promoting next-generation SSDs for AI storage infrastructure.

The rally has also lifted questions over valuation risk. Memory chips remain one of the semiconductor industry’s most cyclical segments, with prices exposed to shifts in capital spending, inventory levels and customer demand. A sharp rise in market value leaves Kioxia vulnerable if hyperscaler investment slows, if competitors add capacity faster than expected, or if customers push back against higher pricing.

Competition is intense. Samsung Electronics, SK Hynix, Micron Technology and SanDisk remain key players across memory markets, with SK Hynix holding a dominant position in high-bandwidth memory used in AI accelerators. Kioxia’s strength is more closely tied to NAND flash and SSDs, where demand from AI infrastructure has broadened the market but does not eliminate the risk of oversupply once new capacity comes on stream.

Toyota’s relative decline in the rankings does not point to operational weakness alone. The carmaker remains one of the world’s largest manufacturers by sales and a major profit generator, but investors have been rotating away from traditional industrial leaders towards companies with direct AI exposure. Concerns over electric-vehicle competition, currency movements and the cost of future technologies have weighed on auto valuations, even as Toyota continues to command global scale in hybrid vehicles and supply-chain management.

SoftBank’s ascent has further highlighted the changing market narrative. Its exposure to Arm and AI-related investments has drawn investors seeking Japan-based access to global AI infrastructure. Together, SoftBank and Kioxia now represent a market story centred on chips, data centres, software platforms and computing capacity rather than the export-led industrial model that long defined Japan’s equity market.

Foreign investor flows have amplified the shift. Japan’s equity market has benefited from corporate-governance reforms, stronger capital returns and renewed interest in companies linked to the AI supply chain. The Nikkei 225 has reached record territory, though the rally has been uneven, with gains concentrated in a limited group of technology-linked names while broader market performance has been more mixed.

Kioxia’s next test will be whether it can convert favourable pricing and sold-out capacity into durable earnings. Investors will look for clearer shareholder-return plans, disciplined capital expenditure and evidence that AI storage demand can remain strong beyond the current investment wave. The company’s contemplated US share listing would widen access to global technology investors and could further raise its profile if market conditions remain supportive.

The stock climbed about 6 per cent on Tuesday as investors reacted to signs that Tencent is moving closer to embedding a task-performing assistant inside WeChat, the messaging, payments and social platform used by more than 1.4 billion people. The prototype is designed to help users complete everyday actions within the app, including finding venues, interacting with mini-programmes and placing customised orders, rather than merely answering questions in chatbot form.

The planned agent would mark a significant step in Tencent’s push to convert its AI investments into consumer products at scale. WeChat already functions as a digital operating system for daily life in China, covering messaging, payments, shopping, food delivery, public services, entertainment, ride-hailing and enterprise communication. That reach gives Tencent an advantage that few rivals can match: a single platform with a vast user base, heavy daily engagement and a dense network of third-party services.

People familiar with the prototype have indicated that users would be able to access the AI agent through a swipe gesture on the main WeChat screen. Tencent is expected to begin the regulatory compliance process before wider testing, with a small group of external users likely to receive access before any phased public launch. A firm release date has not been set, reflecting both regulatory uncertainty and the technical demands of deploying agentic AI across a platform of WeChat’s size.

The market response underscores how investors are reassessing Tencent’s AI position after months of concern that the company was trailing Alibaba, ByteDance and Baidu in the race to commercialise generative AI. Tencent has promoted Yuanbao, its AI chatbot, developed its Hunyuan foundation model and introduced productivity-focused AI tools, but WeChat remains the asset most capable of turning those efforts into a daily consumer habit.

Tencent’s first-quarter earnings showed both the strength and the cost of that strategy. Revenue rose 9 per cent year on year to RMB196.5 billion, while IFRS net profit attributable to equity holders increased 21 per cent to RMB58.1 billion. Marketing services revenue rose 20 per cent to RMB38.2 billion, helped by AI-driven advertising improvements, while domestic games revenue grew 6 per cent and international games revenue climbed 13 per cent. Capital expenditure reached RMB31.9 billion, up 16 per cent, as spending on AI infrastructure continued to rise.

The company has told investors that new AI products are weighing on margins, even as core businesses continue to generate strong cash flow. Excluding new AI products, non-IFRS operating profit rose 17 per cent in the first quarter, compared with a 9 per cent increase overall. That gap highlights the near-term drag from AI investment, but also explains why investors are sensitive to any sign that Tencent can place AI directly into revenue-generating services.

Competition is intensifying across China’s technology sector. Alibaba has pushed Qwen into consumer and enterprise markets, ByteDance has expanded AI features across its content ecosystem, and Baidu continues to build around Ernie and AI cloud services. The key battleground is shifting from chatbots to agents that can perform tasks, connect with apps and act across digital services with limited user input.

WeChat’s mini-programme ecosystem could be central to Tencent’s approach. Millions of merchants, developers and service providers already operate within the platform, allowing an AI agent to navigate services without pushing users outside Tencent’s environment. For merchants, an effective agent could increase conversion rates by shortening the path from search to purchase. For Tencent, it could deepen engagement, improve advertising relevance and strengthen its fintech and business services ecosystem.

Regulation remains a critical constraint. China requires generative AI services offered to the public to comply with rules covering data security, content control and algorithmic governance. Any WeChat agent would need to satisfy those requirements before broad release, especially because it could interact with payments, commerce and public services.

The Provisions on the Protection of Trade Secrets, issued by the State Administration for Market Regulation under Order No. 126, came into force on 1 June 2026 and replace rules first introduced in 1995. The overhaul gives regulators clearer authority to handle disputes involving digital assets that now sit at the centre of corporate value, from AI model architecture and training datasets to platform recommendation systems, software code and commercially valuable operational data.

The change marks a significant shift in China’s treatment of intangible technology assets. Earlier rules were drafted before cloud computing, AI systems and large-scale data operations became central to business competition. The new framework defines trade secrets as technical or business information that is not publicly known, has commercial value and has been subject to confidentiality measures by the rights holder. Technical information now expressly covers data, algorithms, computer programs and code, while business information can include management, sales, finance, planning, customer and operational data.

The timing gives the regulation wider strategic weight. China and the United States are locked in a prolonged technology rivalry involving semiconductor controls, restrictions on advanced computing hardware, limits on investment in sensitive sectors and scrutiny of cross-border technology transfers. Washington has tightened export controls on high-end chips and chipmaking equipment, while Beijing has expanded its own legal toolkit to protect critical technology, secure data flows and prevent overseas transfer of capabilities it regards as strategically important.

The new rules do not create a separate trade secret law, but provide more detailed administrative guidance under China’s Anti-Unfair Competition Law. They clarify how regulators should assess whether information is secret, whether it has commercial value, what counts as reasonable confidentiality measures and how infringement should be investigated. For companies, the central message is that protection will depend not only on the value of the information, but also on whether internal controls, access limits, confidentiality agreements, digital safeguards and evidence trails are in place.

The provisions also recognise that unsuccessful research can have commercial value. Stage-by-stage technical results, failed experimental data and development paths that save time or cost in future research may qualify for protection if they meet the legal conditions. That is especially relevant for AI labs, pharmaceutical developers, electric vehicle makers, chip design firms and advanced manufacturing groups, where failed trials can reveal important technical boundaries and prevent competitors from repeating expensive mistakes.

Regulators have been given practical enforcement tools, including powers to inspect business premises, question individuals, copy documents, examine electronic records and take action against materials linked to suspected infringement. Penalties can include orders to stop unlawful conduct, confiscation of illegal gains and fines that may reach CNY5 million in serious cases. The framework also covers improper acquisition, disclosure, use or permission to use trade secrets obtained through theft, bribery, fraud, coercion, electronic intrusion or breach of confidentiality obligations.

For multinational companies operating in China, the new rules create a mixed picture. Stronger protection could help firms defend proprietary data, software systems and industrial processes in a market where trade secret disputes have often been difficult to pursue. At the same time, wider protection for data and algorithms may increase compliance complexity, particularly in joint ventures, outsourced research, cloud services, employee mobility, cross-border collaboration and licensing arrangements.

Technology companies are likely to face tougher expectations over documentation. Firms seeking protection will need to show that the information was not easily accessible, carried economic value and was actively protected. Generic claims that an algorithm or dataset is confidential may not be enough. Companies will be expected to classify sensitive assets, restrict access by role, track downloads and transfers, maintain logs, use encryption or watermarking where appropriate, and ensure that employment and partnership contracts define confidentiality obligations with precision.

The rules also sit alongside China’s broader data and security regime, including laws on data security, personal information protection, cybersecurity and export control. That overlap could make enforcement more powerful but also more complicated. A dataset may be a trade secret, a regulated data asset and a cross-border transfer concern at the same time. Businesses handling AI training material, user behaviour data or industrial performance data will need to assess those obligations together rather than treating trade secret protection as a standalone issue.