AI产业链的卡位战 海信为何成了重要玩家？

This article shares the core takeaways from Hisense's newly announced AI strategy and the current development trends of the AI industry:

1. The AI industry has moved beyond the technology demonstration phase and is now being deployed in real-world physical scenarios. Only AI that integrates into daily life can deliver truly inclusive benefits, and real-world scenario adoption is the defining line between AI hype and transformative AI revolution.

2. Hisense has completed a full AI industry value chain layout spanning underlying energy systems, AI infrastructure, chips, self-developed large language models, and end-user applications. It has launched a "1+5" industrial architecture to fully embrace AI transformation.

3. Consumers can already experience Hisense's AI-enabled products today, including smart air conditioners that automatically adjust airflow, refrigerators that automatically eliminate odors and disinfect, AI-powered TVs that can analyze plot context, and an AI companion product suite covering multiple home scenarios. AI is already delivering proactive services to households and transforming daily life experiences.

This article provides actionable insights for traditional manufacturing brands on AI transformation and brand marketing upgrades:

1. Shifting consumer trends: AI has expanded from digital spaces to physical scenarios, with the home emerging as the core adoption arena for physical AI. Consumer demand has evolved beyond basic connected device control to a requirement for devices that proactively sense environments, understand needs, and deliver targeted services. Brands need to adjust their R&D direction to align with this trend.

2. A reference model for brand transformation: Traditional brands do not need to build AI capabilities from scratch. Instead, they can reorganize their years of accumulated, fragmented industry assets around the AI value chain to complete transformation. Hisense's path from a traditional home appliance brand to an industrial technology enterprise offers a highly valuable reference.

3. A reference model for marketing and global expansion: Hisense launched its AI strategy during the World Cup to leverage global traffic, completed brand upgrade and exposure, and shifted from exporting products to exporting industrial technology capabilities. This marketing and globalization path is a useful reference for peer brands.

This article outlines market shifts in the new phase of AI industry adoption and shares opportunity alerts and actionable lessons for hardware sellers:

1. Analysis of market changes: The AI industry has shifted from an early focus on large model parameter competition to competition around real-world physical scenario adoption. New incremental market opportunities have emerged in both AI infrastructure and end-use applications, reshaping core growth logic.

2. Opportunity alerts: AI large model training and inference have boosted underlying supporting demand for data center power distribution, energy storage, optical chips, and optical modules. Relevant sellers can pursue incremental orders from AI infrastructure construction. Meanwhile, consumer-facing smart devices have entered an era of proactive intelligent service, driving strong new demand for multi-device collaborative products built for home scenarios.

3. Risk and experience takeaways: The core of AI competition has shifted to adoption capability, and sellers need to build up technology and scenario reserves in advance. Hisense's experience of building a global R&D, manufacturing and service network and sticking to independent core technology development is well worth learning for all types of sellers.

This article provides clear actionable insights for manufacturing factories on transformation and business expansion in the new AI cycle:

1. Shifting product design and manufacturing requirements: AI has reshaped the design logic of smart end devices. Where devices only needed to support connectivity and control in the past, they now require the ability to proactively sense environments, understand user needs, deliver natural interaction, and perform automatic operations. Factories need to adjust and upgrade their product design and manufacturing processes to align with this direction.

2. New business opportunities: Large-scale AI adoption has driven demand growth in supporting industries including underlying energy distribution, semiconductor chips, optical communication devices, and laser devices. Factories with relevant technology and production capacity can tap into new demand from AI infrastructure construction to expand incremental revenue.

3. Insights for digital transformation: Traditional manufacturing factories do not need to discard their existing layout to start AI transformation from scratch. Instead, they can reorganize their existing industrial assets around the AI value chain to align with the new industrial cycle. Hisense's path of gradually building core technology capabilities, expanding industrial segments, and ultimately achieving AI transformation is an ideal reference for manufacturing factories.

This article outlines industry changes after AI entered the mass adoption phase and provides insights on industry trends and client demand for AI-related service providers:

1. Industry development trends: AI has moved from lab technology to real-world physical deployment, creating new service demand across the entire industrial chain. Large unmet demand and broad market opportunities exist in underlying AI infrastructure construction, AI upgrading of traditional end products, and multi-device scenario collaboration.

2. Core client pain points: Many traditional manufacturing enterprises pursuing AI transformation face challenges of fragmented existing industrial capabilities that cannot meet end-to-end AI requirements. Physical AI adoption requires full-stack capability from R&D to mass production and after-sales service, which most enterprises cannot build on their own, creating demand for external service provider support.

3. Reference for business direction: Service providers can develop solutions aligned with the full AI adoption value chain to target the segmented demand for energy management, optical communication connectivity, scenario-based AI deployment, and multi-device collaboration, to serve manufacturing enterprises' AI transformation needs. They can also develop supporting technical services focused on home-based physical AI scenarios to capture new growth opportunities.

This article outlines industry changes in the new phase of AI development and provides insights on operation, merchant recruitment and risk mitigation for marketplace platforms:

1. New market demand: As AI expands into physical-world deployment, a large number of traditional manufacturing enterprises need to align their fragmented existing industrial capabilities with the AI value chain. They generally require platforms to provide support for industrial chain collaboration, technology matchmaking, and global brand exposure, creating new requirements for platform services.

2. Directions for merchant recruitment and platform operation: AI infrastructure construction has driven growth among merchants in energy, semiconductors, optical communication, and large model application services. Platforms can open targeted recruitment to expand their product categories, and build dedicated operation segments focused on physical AI adoption scenarios to help merchants connect with both B2B and B2C demand.

3. Risk mitigation: The current AI industry suffers from hype focused on parameter size rather than real-world adoption, which creates泡沫风险. Platforms should guide merchants to focus on delivering value in real scenarios, avoid pure concept hype, help merchants connect with genuine demand, and mitigate the platform's exposure to industry volatility risks.

This article outlines new developments in the AI industry as it enters a new phase, and provides a typical case and new research directions for industry research:

1. New industry dynamics: The AI industry is evolving toward integrating technology into daily life, expanding from the digital world to the physical world. Scenario adoption has become the core inflection point for industry differentiation. The key factor for industry positioning has shifted from model parameter scale to full value chain layout and scenario adoption capability. The long-held industrial assets of traditional manufacturing enterprises are being revalued in the new cycle, and the industrial ecosystem landscape is being reshaped.

2. Business model innovation: When AI is deployed in physical end products, the business model of traditional hardware enterprises may change—shifting from one-time revenue from device sales to recurring revenue from sustained user, scenario and service operations. At the same time, interaction data collected from physical end devices can be used to optimize AI models, creating a positive data-product feedback loop.

3. A research sample for transformation: This article presents a complete path for traditional manufacturing enterprises to transform into technology enterprises: through gradual advancement of independent core technology development and expansion of industrial layout, enterprises can ultimately align with the AI industry value chain to achieve upgrading. It provides a highly valuable, real-world case for research on high-end transformation of manufacturing enterprises and changes in the AI industrial ecosystem.