中国AI如何从追赶者到定义者？2026年深度分析发展路径

The Present and Future of Chinese AI: The Transition from Follower to Definitive Force

发布时间：2026-03-11 10:42 文章来源：光明网-《光明日报》

Release Date: 2026-03-11 10:42 Source: Guangming Online - Guangming Daily

2025年年初，DeepSeek以“高性能、低成本”的双重震撼力亮相世界舞台，被国际上称为“DeepSeek时刻”……我国开源模型全球下载量首度实现历史性超越，在新兴市场呈现“爆炸式增长”态势……我们正在见证的，不仅仅是一次技术突破，更是一个智能文明新极的加速崛起。中国的人工智能，正以其独特的路径与节奏，在全球坐标系中刻下越来越深的印记，展现从追赶者到定义者的转变。

At the beginning of 2025, DeepSeek made a stunning global debut with its dual impact of "high performance and low cost," an event internationally dubbed the "DeepSeek moment"... China's open-source models achieved a historic milestone by surpassing others in global downloads for the first time, showing an "explosive growth" trend in emerging markets... What we are witnessing is not merely a technological breakthrough but the accelerated rise of a new pole in intelligent civilization. Chinese artificial intelligence, with its unique path and rhythm, is carving an increasingly profound mark on the global coordinate system, demonstrating a transition from a follower to a definer.

算力、数据与算法：“铁三角”的破局与超越

Computing Power, Data, and Algorithms: Breakthroughs and Surpassing the "Iron Triangle"

人工智能的竞争，本质是算力、数据与算法“铁三角”的竞争。中国在这场竞争中，经历了从“大力出奇迹”到“精益求精”的转变，在基础研究层面实现了系统性突破。

The competition in artificial intelligence is, in essence, a competition of the "iron triangle": computing power, data, and algorithms. In this race, China has undergone a transition from "brute force yielding miracles" to "striving for perfection," achieving systematic breakthroughs at the foundational research level.

算力：从“卡脖子”到“软硬协同”的突围

Computing Power: Breaking Through from "Stranglehold" to "Hardware-Software Synergy"

算力层面，实现了从“卡脖子”到“软硬协同”的突围。国外凭借英伟达GPU硬件与CUDA（统一计算设备架构）构筑的技术“护城河”，牢牢掌控全球高端训练算力的制高点。客观来看，我国与国外存在硬件代差，然而，我国在2025年展现出了强大的战略韧性，以华为昇腾（Ascend）为代表的国产算力芯片和CANN（神经网络计算架构）软件生态，通过“芯片+集群+软件栈”的系统工程，已在政务、金融等核心领域推理场景实现广泛替代，在部分典型场景训练任务上达到可用乃至好用的水平，如DeepSeek-R2在昇腾910B集群训练、微调。

At the computing power level, a breakthrough has been achieved from a "stranglehold" situation to "hardware-software synergy." Foreign entities, leveraging the technological "moat" built by NVIDIA's GPU hardware and CUDA (Compute Unified Device Architecture), firmly control the high ground of global high-end training computing power. Objectively, a hardware generation gap exists between China and foreign counterparts. However, in 2025, China demonstrated formidable strategic resilience. Domestic computing power chips represented by Huawei's Ascend and the CANN (Compute Architecture for Neural Networks) software ecosystem have achieved widespread substitution in inference scenarios within core sectors like government affairs and finance through a "chip + cluster + software stack" systems engineering approach. They have reached usable, even good, levels for training tasks in some typical scenarios, such as the training and fine-tuning of DeepSeek-R2 on Ascend 910B clusters.

我国的独特优势，在于探索出一条“软件定义算力、算法驱动效能、人工智能+赋能场景”的创新路径。DeepSeek-R1的成功证明：通过算法优化（如MoE架构、稀疏注意力机制），可以在既定算力约束下显著提升模型效能上限，为算力受限条件下的大模型训练提供了可验证路径。这种“低资源消耗、工程能力引领、高智力产出”的研究方向，正是我国对全球AI基础研究的最大贡献。

China's unique advantage lies in exploring an innovative path of "software-defined computing power, algorithm-driven efficiency, and AI+ empowered scenarios." The success of DeepSeek-R1 proves that through algorithm optimization (e.g., MoE architecture, sparse attention mechanisms), the upper limit of model efficiency can be significantly improved under given computing power constraints, providing a verifiable path for large model training under limited computing resources. This research direction—characterized by "low resource consumption, engineering capability leadership, and high intellectual output"—represents China's most significant contribution to global AI foundational research.

数据：从“规模红利”迈向“合成质量”

Data: Transitioning from "Scale Dividend" to "Synthetic Quality"

数据层面，我国正从“规模红利”迈向“合成质量”。数据是AI的“燃料”，燃料的质量决定引擎的效能。国外依托其全球互联网主导地位，在高质量英文语料、科学文献及代码库积累上拥有天然优势。我国则拥有全球最庞大的数字化应用场景与用户群体，但在面向大模型训练的优质中文语料库构建上曾面临结构性挑战。

At the data level, China is transitioning from a "scale dividend" to "synthetic quality." Data is the "fuel" for AI, and the quality of the fuel determines the engine's performance. Foreign entities, relying on their dominant position in the global internet, possess inherent advantages in accumulating high-quality English corpora, scientific literature, and code repositories. China, on the other hand, boasts the world's largest digital application scenarios and user base but has faced structural challenges in constructing high-quality Chinese corpora for large model training.

破局之道在于技术创新和工程突破。过去一年，我国科研力量在“合成数据”与“数据课程学习”领域取得引领性进展。针对中文数据质量不均的痛点，国内团队开发了先进的数据清洗与合成管线，通过AI生成教科书级的高质量数据反哺训练，显著提升了数据效率与模型性能，使我国在该技术方向上跻身全球第一梯队。美国麻省理工学院与开源平台Hugging Face的联合报告显示，2025年中国开源模型全球下载量占比17.1%，反超美国的15.8%，位居全球第一。这进一步证实，我国将形成引领全球人工智能工程创新的中国方阵，为世界人工智能的健康发展作出更大贡献。

The solution lies in technological innovation and engineering breakthroughs. Over the past year, China's research forces have achieved leading progress in the fields of "synthetic data" and "data curriculum learning." Addressing the pain point of uneven Chinese data quality, domestic teams have developed advanced data cleaning and synthesis pipelines. By using AI to generate textbook-quality high-standard data for training, they have significantly improved data efficiency and model performance, propelling China into the global first tier in this technological direction. A joint report from MIT and the open-source platform Hugging Face shows that in 2025, Chinese open-source models accounted for 17.1% of global downloads, surpassing the US's 15.8% to rank first globally. This further confirms that China is forming a leading contingent in global AI engineering innovation, poised to make greater contributions to the healthy development of world AI.

算法与模型：告别“套壳”，确定“中国流派”

Algorithms and Models: Moving Beyond "Re-shelling" and Establishing the "Chinese School"

算法与模型层面，告别“套壳”，确定“中国流派”。两年前，业界诟病我国AI多为Llama架构的微调，如今这一论调已成历史。DeepSeek-V3/R1、阿里Qwen2.5等模型，在网络架构、多头隐式注意力机制（MLA）、混合专家（MoE）负载均衡等，对生成式大语言模型架构变革方面作出了原创性贡献。我国在推理效率优化和长文本处理上展现了惊人的创新力。

At the algorithm and model level, China has moved beyond "re-shelling" and established the "Chinese school." Two years ago, the industry criticized that much of China's AI involved fine-tuning the Llama architecture. This narrative is now history. Models like DeepSeek-V3/R1 and Alibaba's Qwen2.5 have made original contributions to the architectural evolution of generative large language models in areas such as network architecture, Multi-head Latent Attention (MLA), and Mixture of Experts (MoE) load balancing. China has demonstrated remarkable innovative capabilities in inference efficiency optimization and long-context processing.

相比国外模型一味追求模型规模化法则的做法，我国研究更侧重于“性价比”，即如何用更小的参数、更少的显存，实现同等的效果，这使得国产模型在端侧部署和中小企业应用中极具竞争力。

Compared to foreign models' singular pursuit of scaling laws, Chinese research places greater emphasis on "cost-effectiveness"—how to achieve comparable results with fewer parameters and less GPU memory. This makes domestic models highly competitive for edge-side deployment and applications in small and medium-sized enterprises.

综观“铁三角”的竞争，我国已在算力基建规模、算法效能优化及数据治理技术上初步形成独特优势。当然，客观差距依然存在：国产单卡性能与互联带宽仍落后于英伟达H200/B200等前沿产品，大规模万卡集群的稳定性仍需持续攻坚，但我国在算力基建规模与系统集成能力上已形成独特优势，为后续突破奠定了坚实基础。

Surveying the competition within the "iron triangle," China has preliminarily formed unique advantages in computing infrastructure scale, algorithm efficiency optimization, and data governance technology. Objective gaps, of course, persist: the performance of domestic single GPUs and interconnect bandwidth still lags behind cutting-edge products like NVIDIA's H200/B200, and the stability of large-scale ten-thousand-card clusters requires ongoing攻坚. However, China has established distinct advantages in computing infrastructure scale and system integration capabilities, laying a solid foundation for subsequent breakthroughs.

全球人工智能形成“双核驱动”竞争格局

Global Artificial Intelligence Forms a "Dual-Core Driven" Competitive Landscape

全球人工智能已形成我国与国外“双核驱动”的竞争格局，各自的技术发展路径，显现出明显分野。

Global artificial intelligence has formed a "dual-core driven" competitive landscape between China and foreign entities, with their respective technological development paths showing distinct divergences.

• 基础模型架构领域：国外继续扮演基础性、颠覆性架构主要定义者的角色。从Transformer深度学习模型架构到潜在的下一代范式，其原始创新活力依然强劲。我国的代表性技术则体现在对主流架构的增量式深度优化与效能革新上，如在注意力机制加速、训练稳定性提升等方面取得了突出进展。
  • Foundational Model Architecture: Foreign entities continue to play the primary role as definers of foundational, disruptive architectures. From the Transformer deep learning model architecture to potential next-generation paradigms, their original innovative vitality remains strong. China's representative technologies are reflected in incremental deep optimization and efficiency innovation of mainstream architectures, achieving notable progress in areas like attention mechanism acceleration and training stability enhancement.
• 多模态与通用大模型领域：国外OpenAI、谷歌等持续在复杂推理与多模态深度整合上设立标杆。我国模型在性能逼近的同时，突出特点在于对训练与推理成本效益的极致追求，以及在特定领域应用的突破。目前，总体处于“并跑”与“追赶”并存的状态。除了研发大模型超级通用智能技术，中国也在发展差异化技术路线：分布式智能体系统，以通专融合、大小协同、端云组合、群体协作等技术，面向物理世界和硬件终端发展执行智能。
  • Multimodal and General-Purpose Large Models: Foreign entities like OpenAI and Google continue to set benchmarks in complex reasoning and deep multimodal integration. While Chinese models are closing the performance gap, their distinguishing features lie in the extreme pursuit of training and inference cost-effectiveness, as well as breakthroughs in specific domain applications. Currently, the overall situation is one of "running in parallel" and "catching up" coexisting. Beyond developing large-model super-general intelligence, China is also pursuing differentiated technological routes: distributed agent systems, utilizing technologies like general-specialized fusion, large-small collaboration, edge-cloud combination, and swarm collaboration, to develop executive intelligence for the physical world and hardware terminals.
• 前沿与交叉探索领域：各国侧重明显。国外在AI安全与对齐、量子计算与AI的结合以及受神经科学启发的新范式等前沿基础探索上更为活跃。我国则在具身智能、科学智能（AI for Science）及存算一体芯片、光子计算芯片等硬件基础方向，实现了世界级的点状突破，展现出强大的任务攻坚能力。
  • Frontier and Interdisciplinary Exploration: Different countries have distinct focuses. Foreign entities are more active in frontier foundational explorations like AI safety and alignment, the integration of quantum computing and AI, and neuroscience-inspired new paradigms. China, however, has achieved world-class point breakthroughs in hardware foundation directions such as embodied intelligence, AI for Science, compute-in-memory chips, and photonic computing chips, demonstrating formidable task-oriented攻坚 capabilities.
• “人工智能+”产业应用技术层面：我国展现出全球领先的优势。得益于庞大的市场、丰富的场景和海量的数据，在计算机视觉、自然语言处理、语音对话、知识图谱构建与应用，以及移动支付、智能推荐等商业化落地方面，我国形成了强大的工程化能力和快速迭代优势。相比之下，国外在机器人智能、企业级解决方案平台以及基础算法框架的生态建设上根基更深。这种差异不是简单的“领先”与“追随”，而是不同创新模式的体现。国外擅长“从0到1”的原始创造，我国精于“从1到N”的工程优化与场景适配。
  • "AI+" Industrial Application Technology: China demonstrates globally leading advantages. Benefiting from a vast market, diverse scenarios, and massive data, China has developed strong engineering capabilities and rapid iteration advantages in the commercial implementation of computer vision, natural language processing, voice dialogue, knowledge graph construction and application, as well as mobile payments and intelligent recommendations. In contrast, foreign entities have deeper roots in robot intelligence, enterprise-level solution platforms, and the ecosystem development of foundational algorithm frameworks. This difference is not a simple matter of "leading" and "following," but rather a reflection of different innovation models. Foreign entities excel at "0-to-1" original creation, while China is adept at "1-to-N" engineering optimization and scenario adaptation.

企业生态：升维至全栈体系竞争

Enterprise Ecosystem: Competition Escalates to Full-Stack Systems

在企业生态维度，竞争已升维至全栈体系。国外企业形成以微软、谷歌、OpenAI、英伟达为核心的强大闭环生态，从芯片设计、云计算基础设施、基础大模型研发到最终的应用商店，构筑了“护城河”。我国企业则呈现多元化、差异化发展态势：

At the enterprise ecosystem dimension, competition has escalated to full-stack systems. Foreign companies have formed a powerful closed-loop ecosystem centered around Microsoft, Google, OpenAI, and NVIDIA, constructing a "moat" spanning from chip design and cloud computing infrastructure to foundational large model R&D and ultimately application stores. Chinese enterprises, however, exhibit a diversified and differentiated development landscape:

• 华为、浪潮等聚焦AI算力基础设施的突破。
  • Huawei, Inspur, etc. focus on breakthroughs in AI computing power infrastructure.
• 百度、阿里、腾讯依托云服务与数据优势，构建大模型及产业赋能平台。
  • Baidu, Alibaba, Tencent leverage their cloud service and data advantages to build large models and industry empowerment platforms.
• 科大讯飞、商汤、思必驰、云知声等在垂直领域持续深耕。
  • iFlytek, SenseTime, AISpeech, Unisound, etc. continue to deepen their expertise in vertical domains.
• 联想集团等，正通过“混合式人工智能”战略，将AI深度嵌入智能终端与实体经济，探索规模化落地新路径。
  • Lenovo Group, etc., through a "Hybrid AI" strategy, are deeply embedding AI into intelligent terminals and the real economy, exploring new paths for scaled implementation.
• 以DeepSeek、智谱AI为代表的开源力量，则通过高性价比、易获取的技术，在发展中国家开辟新航道，成为AI软实力出海的亮眼名片。
  • Open-source forces represented by DeepSeek and Zhipu AI are opening new avenues in developing countries through cost-effective, easily accessible technology, becoming a bright名片 for the出海 of AI soft power.

其次，我国大模型在搜索方面的应用独具特色：豆包、Kimi、元宝、DeepSeek App等AI助手，已经成为人们日常搜索和解决常见问题的得力帮手。

Furthermore, the application of China's large models in search is distinctive: AI assistants like Doubao, Kimi, Yuanbao, and the DeepSeek App have become powerful aids for people's daily searches and solving common problems.

产业应用：深度融合与规模化爆发

Industrial Application: Deep Integration and Scaled Breakout

刚刚过去的2025年，被业界定义为“大模型应用落地元年”，人工智能进入“深耕”之年。当下，我国AI技术正以前所未有的深度和广度，系统性地融入经济社会各领域，呈现出从试点探索向规模化、深度融合方向发展的鲜明特征。

The recently concluded year 2025 was defined by the industry as the "first year of large model application落地," marking the beginning of AI's "deep cultivation" phase. Currently, China's AI technology is systematically integrating into all sectors of the economy and society with unprecedented depth and breadth, exhibiting distinct characteristics of transitioning from pilot exploration to scaled and deep integration.

• 金融领域：AI已从效率工具演进为业务内核。智能风控借助大模型解析海量非结构化数据，风险评估愈发精准。为跨越“数字鸿沟”，AI数字人柜员能以自然语言交互办理业务，极大提升服务可及性与温度。“大模型+金融”全链路解决方案，已贯穿投资顾问、营销获客、风控全流程。
  • Finance Sector: AI has evolved from an efficiency tool to a business core. Intelligent risk control leverages large models to parse massive unstructured data, making risk assessment increasingly precise. To bridge the "digital divide," AI digital tellers can handle business through natural language interaction, significantly enhancing service accessibility and warmth. The "Large Model + Finance" full-chain solution now spans the entire process from investment advisory and marketing/customer acquisition to risk control.
• 教育领域：AI正推动教育走向个性化和智能化。AI不仅能辅助教师生成高质量教案，还能实现因材施教，帮助学生高效个性化学习。北京等地发布的“AI+教育”典型案例，显示了该技术从单点工具向系统性教学解决方案的演进。此外，在企业内部，AI知识库功能将企业专属知识与大模型深度结合，可大幅缩短新员工培训时间，提升整体专业水平与组织智慧。
  • Education Sector: AI is driving education towards personalization

常见问题（FAQ）

中国AI发展在算力、数据和算法这'铁三角'中取得了哪些关键突破？

中国在算力上通过华为昇腾等国产芯片和CANN软件生态实现'软硬协同'突围；数据层面正从规模红利转向合成质量；算法上告别'套壳'模式，通过MoE架构等优化形成'中国流派'。

中国AI在全球'双核驱动'竞争格局中有哪些独特优势？

中国展现出工程性价比高、产业应用落地速度快、规模化能力强等优势，形成了强调效能优化与深度融合现实场景的特色发展路径，如DeepSeek的'高性能、低成本'突破。

中国AI如何从'追赶者'转变为'定义者'？具体表现是什么？

中国AI已从追逐技术参数转向定义技术价值，通过'软件定义算力、算法驱动效能'的创新路径，在基础研究层面实现系统性突破，并在开源模型全球下载量上实现历史性超越。