36氪 Research Institute | 2026 China Intelligent Hardware Industry Development Research Report 36氪研究院 | 2026年中国智能硬件行业发展研究报告

Summary

The Chinese smart hardware industry is transitioning from the stage of single-product intelligence and scenario interconnection to the AI-native intelligent stage. Edge-side autonomous intelligence has become a key point for reconstructing the core logic of products, with core competitiveness shifting towards local AI experiences. The value structure of the entire industrial chain is being reshaped: upstream components are customized, midstream software and hardware are coordinated, and downstream scenarios are penetrated extensively. Both consumer and industrial terminals are expanding in two lines, with smart robots emerging as new growth species spanning both B/C ends. The model of going global has evolved into a higher dimension, building new barriers for global competition through AI capabilities, shifting from cost efficiency to composite value.

Deep Analysis

TL;DR

• The Chinese smart hardware industry is transitioning from the stage of single-product intelligence and scenario interconnection to the AI-native intelligent stage.
• Edge-side autonomous intelligence has become a key point for reconstructing the core logic of products, with core competitiveness shifting towards local AI experiences.
• The value structure of the entire industrial chain is being reshaped: upstream components are customized, midstream software and hardware are coordinated, and downstream scenarios are penetrated extensively.
• Both consumer and industrial terminals are expanding in two lines, with smart robots emerging as new growth species spanning both B/C ends.
• The model of going global has evolved into a higher dimension, building new barriers for global competition through AI capabilities, shifting from cost efficiency to composite value.

Why It's Worth Reading

This report deeply analyzes the paradigm shift in the smart hardware industry under the AI-native era, revealing the change in core competitiveness from "parameter stacking" to "edge-side intelligence," providing strategic direction for practitioners on industrial chain upgrading and global layout, with significant decision-making reference value.

Technical Analysis

1. Edge-Side Autonomous Intelligence Reconstructs Product Logic: New-generation hardware embeds local AI computing units during the R&D phase, enabling independent environmental perception, multimodal interaction, and dynamic decision-making without cloud reliance, breaking traditional fixed linkage instruction limitations.
2. Underlying Technology Breakthroughs Drive Decentralization: Advances in edge-side AI chips, lightweight large models, and multi-sensor fusion technologies reduce dependence on cloud computing power, enhancing local autonomous perception, analysis, decision-making, and functional iteration capabilities.
3. Industrial Chain Synergy Upgrading: Upstream core components evolve towards AI customization, high integration, and domestication; midstream breaks through low-profitability dilemmas via software-hardware synergy and flexible efficiency improvement; downstream penetrates deeply into multiple scenarios, achieving integrated software-hardware value operations.
4. Smart Robots Become Dual-End Growth Engines: Non-humanoid robots have been scaled up, humanoid robots are about to enter mass production, possessing both consumer and commercial attributes, becoming the core driving force for cross-scenario industry growth.
5. New Barriers for AI-Native Global Expansion: Enterprises anchor global markets at the product definition stage, outputting localized AI services and scenario solutions, conducting preemptive compliance layouts, forming a two-way cycle where overseas market validation feeds back domestic innovation.

Industry Insights

1. Strategic Focus Shift: Enterprises need to abandon traditional hardware parameter competition paths, focusing R&D efforts on building edge-side AI experiences and local autonomous intelligence capabilities to meet competitive requirements in the AI-native stage.
2. Breaking Through Industrial Chain Synergy: Upstream, midstream, and downstream should strengthen software-hardware synergy and scenario value-added cooperation, jointly breaking through value valleys, upgrading from single manufacturing to integrated operations, releasing long-term growth space.
3. Deepening Global Layout: Going-global strategies should shift from low-cost OEM to "born global," building composite competitive advantages in technology, service, and compliance through AI capability output and ecosystem co-construction, achieving global market feedback to boost local innovation.

TL;DR

• 中国智能硬件行业正从单品智能化和场景互联阶段，向AI原生智能阶段过渡。
• 端侧自主智能成为产品核心逻辑的重构点，核心竞争力转向本地AI体验。
• 全产业链价值格局重塑，上游元器件定制化、中游软硬协同、下游多场景渗透。
• 消费与产业终端双线扩容，智能机器人成为横跨B/C两端的新增长物种。
• 出海模式升维，以AI能力构建全球化竞争新壁垒，从成本效率转向复合价值。

为什么值得看

该报告深刻剖析了AI原生时代下智能硬件行业的范式转移，揭示了从“参数堆砌”到“端侧智能”的核心竞争力变化，为从业者指明了产业链升级与全球化布局的战略方向，具有重要的决策参考价值。

技术解析

1. 端侧自主智能重构产品逻辑：新一代硬件在研发阶段即内嵌本地AI算力单元，可脱离云端独立完成环境感知、多模态交互与动态决策，打破传统固定联动指令局限。
2. 底层技术突破推动去云端化：端侧AI芯片、轻量化大模型、多传感融合等技术取得进展，降低对云端算力依赖，增强本地自主感知、分析决策与功能迭代能力。
3. 产业链协同升级：上游核心元器件向AI定制化、高集成、国产化迭代；中游沿软硬协同、柔性提效主线突破低毛利困境；下游向多场景纵深渗透，实现软硬一体化价值运营。
4. 智能机器人成为双端增长引擎：非人形机器人已规模化落地，人形机器人迈入放量前夕，兼具消费与商用属性，成为跨场景拉动行业增长的核心动力。
5. AI原生出海新壁垒：企业在产品定义阶段锚定全球市场，输出本地化AI服务与场景解决方案，前置合规布局，形成海外市场验证反哺国内创新的双向循环。

行业启示

1. 战略重心转移：企业需摒弃传统硬件参数竞争路径，将研发重心转向端侧AI体验与本地自主智能能力的构建，以适应AI原生阶段的竞争要求。
2. 产业链协同破局：上中下游应加强软硬协同与场景增值合作，共同突破价值洼地，从单一制造向一体化运营升级，释放长期增长空间。
3. 全球化布局深化：出海策略应从低价代工转向“生而全球化”，通过AI能力输出与生态共建，构建技术、服务与合规的复合竞争优势，实现全球市场反哺本土创新。