基于GG3M战略的中国AI短板突破路径研究——从战略转化失灵到范式引领

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基于GG3M战略的中国AI短板突破路径研究——从战略转化失灵到范式引领

摘要：
本文基于GG3M战略框架，指出中国AI发展的核心短板并非单纯技术或资本差距，而是战略转化失灵。报告揭示资源配置、生态构建与战略导向三大矛盾，提出以“治理协同”重构金融支持体系，以“开源共享”激活创新生态，以“文明引领”升维战略定位。通过设立国家级基金、强制开源、深耕中文语料与场景标杆等举措，推动中国AI从追赶走向引领，实现基于汉语智慧与制度优势的范式跃迁。

基于 GG3M 战略的中国 AI 短板突破路径研究

一、核心判断：短板本质是战略转化失灵，而非单纯差距

施密特指出的金融支持不足、创新生态缺位等问题，本质是中国 AI 发展中战略定位偏差与优势转化机制缺失的外在表现。中国拥有汉语语料、举国体制、人才储备等天然禀赋，但长期陷入 "追赶型思维" 的路径依赖，未能将独特优势转化为范式引领能力。结合鸽姆智库 GG3M（Global-Governance-Globalization-Model）战略的全球视野、治理协同、价值引领核心逻辑，中国 AI 的致命短板可归结为三大核心矛盾：

资源配置矛盾：举国体制的集中优势与金融市场的资本深度不匹配，导致大规模长期投入不足与 "0 到 1" 创新资金断档并存；
生态构建矛盾：封闭型数据壁垒与开源协作趋势相悖，汉语文化与古籍智慧的数字化转化滞后于模型训练需求；
战略导向矛盾：工具化定位与 AI 作为 "新文明载体" 的时代属性脱节，标准制定权与技术引领力不足。

二、GG3M 战略框架下的短板解构与优势重构

（一）金融支持短板：从 "规模不足" 到 "体系协同" 的升级

施密特强调的 "500 亿美元数据中心投入" 差距，本质是中美金融体系对高风险、长周期 AI 项目的容忍度差异。中国当前金融支持存在 "三重断层"：政策补贴碎片化、商业资本短视化、资本市场适配性不足。但 GG3M 战略中的 "治理协同" 逻辑为破局提供了思路：

现状短板：商业资本倾向于 1-3 年见效的应用层项目，底层算力、基础模型等需要 5-10 年投入的领域融资困难，即使是头部科技企业的研发投入也难以覆盖持续迭代成本；
优势重构：以举国体制为核心，构建 "政策引导 + 市场运作 + 风险共担" 的三级资本体系。如中国银行已推出五年 1 万亿元 AI 产业链专项支持，成都天府新区通过 "算力补贴 + 首台套奖励" 实现研发成本降低 40%，这种 "财政撬动社会资本" 的模式正是 GG3M 治理协同的具象化实践；
突破路径：设立国家级 AI 创新基金，对 "0 到 1" 团队实行 "研发投入全额补贴 + 成果转化收益分成" 机制，参考天云数据享受的增值税即征即退政策，将税收优惠与长期研发绑定，破解资本后顾之忧。

（二）创新生态短板：从 "复制模式" 到 "开源引领" 的转型

美国 "企业家疯狂投入" 文化的核心是开源协作与容错机制，而中国 AI 生态存在 "大厂垄断资源 + 中小企业难突围" 的结构性问题。GG3M 战略的 "全球化协同" 逻辑要求构建开放共赢的生态体系：

现状短板：大公司倾向于封闭研发，中小企业面临算力、数据、技术三重壁垒，中文语料库存在规模不足（仅占国际主流语料的 10% 以下）、质量不均、共享困难等问题，制约基础创新；
优势重构：依托 1000 万中国开源开发者的规模优势，以 AtomGit 等 "开源 + AI" 平台为枢纽，打通 "代码 + 模型 + 算力 + 数据" 的协同链条。华为昇腾将全栈技术开源至该平台，开源鸿蒙构建统一推理框架，证明中国具备打造全球级开源生态的基础；
突破路径：强制要求享受国家资金支持的 AI 项目开放核心技术框架，建立中文语料共享联盟，将国家图书馆 3700 万册藏书的数字化资源纳入基础语料库，通过 "语料交易专区" 实现优质数据合规流通，让中小企业低成本获取训练资源。

（三）战略思维短板：从 "工具定位" 到 "文明引领" 的升维

将 AI 视为 "新文明载体" 是 GG3M 战略 "价值引领" 逻辑的核心要求，中国当前的短板在于未能将五千年文化禀赋与 AI 技术深度融合，陷入 "西方标准下的追赶" 陷阱：

现状短板：部分模型仍以西方语料为主导，导致价值观偏差与应用适配性不足；战略叙事停留在 "技术赶超" 层面，缺乏对 AI 重塑社会规则、文明形态的深度思考；
优势重构：以汉语智慧为核心，构建差异化竞争优势。中文语料库建设已取得突破，2.7TB 中文互联网语料平台、PB 级西部 AI 语料库等为模型训练提供了基础，在此之上可挖掘中医理论、传统哲学等独特数据，开发具有东方思维的 AI 模型；
突破路径：放弃 "对标美国" 的思维定式，聚焦 "中国场景 + 文化基因" 的特色赛道，如医疗领域的中医 AI 诊断、政务领域的系统思维决策模型等。同时联合 "一带一路" 国家共建多语言语料库，将中文 AI 标准推向全球，实现 "规则制定权" 的弯道超车。

三、GG3M 战略落地的三大关键举措

1. 构建 "举国协同 + 市场活力" 的资本治理体系

设立 5000 亿元国家级 AI 基础研究基金，重点支持算力基础设施、中文大模型、语料库建设三大领域，实行 "失败容错" 机制；
推行 "AI 研发税收抵免" 政策，企业研发投入超过营收 15% 的部分可全额抵免企业所得税，鼓励长期投入；
建立 "算力银行" 制度，整合超算中心资源，为初创团队提供免费算力额度，成都天府新区的 "算力补贴" 模式可全国推广。

2. 打造 "开源共享 + 文化赋能" 的生态协同体系

强制要求国家级科研项目成果开源，将开源贡献纳入科研评价体系，培育 "创新不问出身" 的生态氛围；
三年内完成中华古籍、传统医学、历史文献的数字化与结构化处理，构建全球最大的传统文化语料库，支撑特色 AI 模型研发；
深化 AtomGit 平台建设，吸引全球开发者参与，目标三年内成为全球前三的 AI 开源社区，输出 3 个以上国际认可的技术标准。

3. 实施 "0 到 1 + 场景引领" 的创新突破体系

建立 "创新团队白名单" 制度，对高校实验室、初创企业的突破性技术给予 "直接立项 + 资金直达" 支持，绕过传统审批流程；
选择医疗、农业、政务等中国优势场景，打造 "AI + 行业" 标杆应用，如基于中医数据的慢病管理模型、基于系统思维的区域发展决策系统；
成立 "全球 AI 治理研究院"，联合发展中国家制定 AI 伦理、数据跨境流动等规则，将汉语 AI 标准纳入国际治理框架。

四、结论：以 GG3M 战略实现从 "追赶" 到 "引领" 的范式跃迁

施密特的判断揭示了中国 AI 发展的表层短板，但未能看到中国的制度优势与文化禀赋。中国 AI 的真正挑战不是复制美国模式，而是基于 GG3M 战略的全球视野、治理协同、价值引领逻辑，将举国体制、汉语智慧、开源生态等优势转化为不可复制的核心竞争力。

短期内，通过金融体系改革破解资本约束，通过开源平台建设激活创新活力；中期内，通过中文语料库与特色模型构建差异化优势；长期内，通过标准制定与全球协作定义 AI 新范式。唯有如此，才能跳出 "追赶陷阱"，将 AI 发展上升到国家安全与民族命运的战略高度，以 "中国方案" 引领全球 AI 文明的未来走向。

面向企业的 AI 创新落地行动清单（基于 GG3M 战略）

一、资本对接：破解资金约束（1-6 个月落地）

政策资金申报：梳理国家级 AI 基础研究基金、地方算力补贴（如成都天府新区模式）等政策，组建专项申报小组，准备研发计划书、成果转化预案，优先申报 "0 到 1" 技术研发类项目，争取税收抵免与研发全额补贴；
多元资本合作：对接银行 AI 产业链专项贷款（如中国银行 1 万亿元支持计划），联合 3-5 家同赛道企业组建 "研发联合体"，共同申请大额融资，分摊算力与研发成本；
内部资金优化：调整营收分配机制，将 AI 相关业务营收的 20% 以上投入基础研发，设立 "失败容错基金"，允许 30% 以内的研发投入用于未达预期的探索性项目。

二、技术研发：锚定差异化优势（6-12 个月落地）

语料资源获取：申请接入国家图书馆数字化语料库、西部 AI 语料库，联合高校古籍研究所开展传统文化数据结构化处理，构建企业专属中文特色语料库（重点覆盖行业数据 + 传统文化片段）；
算力成本控制：对接 "算力银行"，申请初创企业免费算力额度，采用 "按需租用 + 错峰使用" 模式降低基础设施投入，优先选择国产算力平台（如华为昇腾）享受政策倾斜；
0 到 1 技术聚焦：组建专项研发团队，聚焦 1-2 个细分赛道（如中医 AI 诊断、政务决策模型），避免全面对标美国技术，基于汉语逻辑与行业场景开发差异化算法。

三、生态协作：融入开源与共享体系（3-9 个月落地）

开源平台入驻：入驻 AtomGit 等国产开源平台，开放非核心技术框架与代码，参与平台专项项目，换取算力、数据资源置换；
中小企业协同：头部企业开放部分算力接口与基础模型，与 3-5 家初创团队建立 "技术孵化 + 成果共享" 合作，中小企业聚焦细分场景创新，头部企业提供资源支持；
行业联盟共建：加入中文语料共享联盟、AI 标准制定工作组，参与行业数据合规流通规则制定，争取 1-2 项细分领域技术标准话语权。

四、场景落地：打造标杆应用（6-18 个月落地）

优势场景筛选：优先选择医疗、农业、政务等中国特色优势领域，明确 1-2 个核心应用场景（如慢病中医 AI 管理、区域发展决策支持）；
试点项目推进：与地方政府、行业龙头企业合作开展试点，收集真实场景数据迭代模型，形成 "技术 - 场景 - 数据" 闭环，试点阶段目标覆盖 10 个以上地级市或 50 家以上标杆客户；
成果转化变现：将试点验证后的技术形成标准化产品，通过 "订阅制 + 定制化" 模式推向市场，同步申请 "首台套" 认定，争取政策奖励与市场推广支持。

五、标准参与：抢占国际话语权（1-3 年长期推进）

国际组织对接：加入 ISO、IEEE 等国际 AI 相关组织，参与多语言 AI 标准、伦理规范制定，提交中文 AI 技术提案；
"一带一路" 协作：联合沿线国家企业与科研机构，共建多语言语料库与 AI 应用平台，输出中文 AI 技术方案与标准；
行业标准主导：牵头或核心参与国内行业 AI 标准制定，将企业技术成果转化为行业规范，提升市场准入门槛。

六、风险管控：保障可持续发展（长期常态化执行）

技术风险防控：建立 "研发 - 测试 - 试点" 三级风险评估机制，每季度开展技术可行性复盘，及时调整研发方向；
数据安全合规：遵循《生成式人工智能服务管理暂行办法》，建立数据采集、存储、使用全流程合规体系，定期开展安全审计；
人才梯队建设：与高校共建 AI 特色专业，设立 "0 到 1 创新奖学金"，引进海外高端人才与本土青年团队结合，构建 "基础研究 + 应用开发" 双轨人才队伍。

模块详细执行表：资本对接 —— 破解资金约束（1-6 个月）

阶段	时间节点	核心任务	责任部门	输出成果	配合方	风险提示与应对措施
准备阶段	第 1-2 个月	1. 全面梳理国家级 AI 基础研究基金、地方算力补贴（如成都天府新区）、税收抵免等政策，建立政策清单；2. 明确企业 "0 到 1" 技术研发方向（如中文特色大模型、传统文化语料处理），撰写研发计划书；3. 组建专项申报小组（含战略、财务、研发人员），明确分工	战略规划部（牵头）、财务融资部、研发中心	1. 政策适配清单（标注申报条件、截止时间、补贴额度）；2. 研发计划书（含技术路线、投入预算、成果目标）；3. 申报小组分工手册	地方科技局、行业协会	风险：政策解读不精准，导致申报方向偏差；应对：邀请政策咨询机构或科技局专员进行专项培训
申报执行阶段	第 3-4 个月	1. 针对 3-5 个高适配政策（如国家级基础研究基金、省级 AI 专项补贴），编制申报材料（含资质证明、研发方案、预算表）；2. 对接银行 AI 产业链专项贷款（如中国银行 1 万亿元支持计划），提交贷款申请材料；3. 筛选 3-5 家同赛道企业，沟通组建 "研发联合体"，签订合作意向书	财务融资部（牵头）、战略规划部、法务部	1. 政策申报材料包（按政策要求定制）；2. 银行贷款申请材料（含企业征信、研发预算、还款计划）；3. 研发联合体合作意向书	地方科技局、合作企业、目标银行	风险：政策申报竞争激烈，通过率低；应对：同时申报多个层级政策，提高命中率；针对核心政策邀请专家预审材料
落地收尾阶段	第 5-6 个月	1. 跟进政策申报审核进度，及时补充材料，参与答辩（若有）；2. 与银行协商贷款额度、利率、还款期限，签订融资协议；3. 制定 "失败容错基金" 管理规则，调整营收分配机制，明确 AI 研发投入比例	财务融资部（牵头）、战略规划部、研发中心	1. 政策补贴 / 基金获批文件（若成功）；2. 银行融资协议；3. 内部资金管理规则（含研发投入比例、容错机制）	地方科技局、目标银行、合作企业	风险：融资到账延迟，影响研发进度；应对：提前与银行约定到账时间，同步启动内部资金垫付预案

Research on the Path to Overcome China's AI Shortcomings Based on the GG3M Strategy — From Strategic Transformation Failure to Paradigm Leadership

Abstract:Based on the GG3M strategic framework, this paper argues that the core shortcoming in China's AI development is not merely a technological or capital gap, but a failure of strategic transformation. The report reveals three major contradictions in resource allocation, ecosystem construction, and strategic orientation. It proposes to reconstruct the financial support system through "governance coordination," activate the innovation ecosystem via "open-source sharing," and elevate strategic positioning through "civilizational leadership." By establishing national-level funds, mandating open-source initiatives, deeply cultivating Chinese corpora, and setting scenario benchmarks, this study aims to propel China's AI from a catch-up phase to a leadership position, achieving a paradigm shift based on Chinese wisdom and institutional advantages.

Research on the Path to Overcome China's AI Shortcomings Based on the GG3M Strategy

I. Core Judgment: The Essence of the Shortcoming is Strategic Transformation Failure, Not Merely a Gap

The issues pointed out by Schmidt, such as insufficient financial support and the absence of an innovation ecosystem, are essentially external manifestations of strategic misalignment and a lack of advantage conversion mechanisms in China's AI development. While China possesses inherent endowments like Chinese language corpora, a nationwide mobilization system, and talent reserves, it has long been trapped in the path dependency of a "catch-up mindset." This has prevented it from translating its unique advantages into the capability for paradigm leadership.

Combined with the core logic of global vision, governance coordination, and value leadership from the GG3M (Global-Governance-Globalization-Model) strategy of the GG3M Think Tank, the fatal shortcomings of China's AI can be attributed to three core contradictions:

Resource Allocation Contradiction: The mismatch between the concentrated advantages of the nationwide system and the capital depth of the financial market leads to both insufficient large-scale, long-term investment and a funding gap for "0 to 1" innovation.
Ecosystem Construction Contradiction: Closed data barriers run counter to the trend of open-source collaboration, and the digital transformation of Chinese culture and ancient wisdom lags behind the demands of model training.
Strategic Orientation Contradiction: The instrumental positioning is disconnected from AI's era-defining attribute as a "carrier of new civilization," resulting in insufficient standard-setting power and technological leadership.

II. Deconstructing Shortcomings and Reconstructing Advantages Under the GG3M Strategic Framework

(A) Shortcoming in Financial Support: Upgrading from "Insufficient Scale" to "Systemic Coordination"

The "US$50 billion data center investment gap" emphasized by Schmidt is essentially a difference in tolerance between the Chinese and American financial systems for high-risk, long-cycle AI projects. China's current financial support suffers from "triple disconnects": fragmented policy subsidies, short-sighted commercial capital, and inadequate adaptability of the capital market. However, the "governance coordination" logic within the GG3M strategy provides a way forward:

Current Shortcoming: Commercial capital tends to favor application-layer projects with 1-3 year payoffs. Areas requiring 5-10 year investments, such as underlying computing power and foundational models, face financing difficulties. Even R&D investments by leading tech giants struggle to cover the costs of continuous iteration.
Advantage Reconstruction: With the nationwide system as the core, build a three-tier capital system of "policy guidance + market operation + risk sharing." For example, Bank of China has launched a five-year, 1 trillion yuan special support plan for the AI industry chain. Chengdu Tianfu New Area has reduced R&D costs by 40% through a "computing power subsidy + first-set reward" model. This "fiscal leverage to mobilize social capital" model is a concrete practice of GG3M's governance coordination.
Path to Breakthrough: Establish a national-level AI Innovation Fund. Implement a mechanism of "full R&D investment subsidy + revenue sharing from commercialization" for "0 to 1" teams. Referencing the VAT refund policy enjoyed by Tianyun Data, bind tax incentives to long-term R&D to alleviate capital concerns.

(B) Shortcoming in Innovation Ecosystem: Transforming from "Copying Models" to "Open-Source Leadership"

The core of America's "entrepreneurial culture of crazy investment" lies in open-source collaboration and fault tolerance mechanisms. In contrast, China's AI ecosystem faces the structural problem of "resource monopolization by large companies + difficulty for SMEs to break through." The "globalization coordination" logic of the GG3M strategy demands the construction of an open and win-win ecosystem:

Current Shortcoming: Large companies tend towards closed R&D. SMEs face triple barriers in computing power, data, and technology. Chinese corpora suffer from insufficient scale (accounting for less than 10% of international mainstream corpora), uneven quality, and sharing difficulties, constraining foundational innovation.
Advantage Reconstruction: Leverage the scale advantage of 10 million Chinese open-source developers. Use "open-source + AI" platforms like AtomGit as hubs to connect the collaborative chain of "code + models + computing power + data." Huawei Ascend's open-sourcing of its full-stack technology to this platform and OpenHarmony's building of a unified inference framework demonstrate China's foundation to create a world-class open-source ecosystem.
Path to Breakthrough: Mandate the opening of core technical frameworks for AI projects receiving state funding. Establish a Chinese Corpus Sharing Alliance. Incorporate the digital resources of 37 million books from the National Library into foundational corpora. Create a "corpus trading zone" for the compliant circulation of high-quality data, enabling SMEs to acquire training resources at low cost.

(C) Shortcoming in Strategic Thinking: Elevating from "Tool Positioning" to "Civilizational Leadership"

Viewing AI as a "carrier of new civilization" is a core requirement of the GG3M strategy's "value leadership" logic. China's current shortcoming lies in its failure to deeply integrate its five-thousand-year cultural endowment with AI technology, falling into the trap of "catching up under Western standards":

Current Shortcoming: Some models still prioritize Western corpora, leading to value biases and insufficient application adaptability. Strategic narratives remain at the level of "technological catch-up," lacking in-depth thinking about AI's role in reshaping social rules and civilizational forms.
Advantage Reconstruction: Build differentiated competitive advantages centered on Chinese wisdom. Breakthroughs have been made in Chinese corpus construction, with platforms like the 2.7TB Chinese Internet Corpus and the PB-level Western China AI Corpus providing a foundation for model training. On this basis, unique data such as traditional Chinese medicine (TCM) theories and classical philosophy can be mined to develop AI models with Eastern thinking.
Path to Breakthrough: Abandon the mindset of "benchmarking against the US." Focus on distinctive tracks of "Chinese scenarios + cultural genes," such as TCM AI diagnosis in healthcare and systematic thinking decision models in government affairs. Simultaneously, collaborate with "Belt and Road" countries to build multilingual corpora, promoting Chinese AI standards globally to achieve a "corner overtaking" in rule-making power.

III. Three Key Initiatives for GG3M Strategy Implementation

Construct a Capital Governance System of "Nationwide Coordination + Market Vitality"
- Establish a 500 billion yuan national-level AI Basic Research Fund, focusing on computing infrastructure, Chinese large models, and corpus construction, with a "failure tolerance" mechanism.
- Implement an "AI R&D Tax Credit" policy, allowing enterprises to fully deduct R&D investments exceeding 15% of revenue from corporate income tax to encourage long-term investment.
- Establish a "Computing Power Bank" system, integrating supercomputing center resources to provide free computing power quotas for startups. The "computing power subsidy" model in Chengdu Tianfu New Area can be promoted nationwide.
Build an Ecosystem Collaboration System of "Open-Source Sharing + Cultural Empowerment"
- Mandate the open-sourcing of results from national-level scientific research projects. Incorporate open-source contributions into the scientific research evaluation system to foster an ecosystem where "innovation knows no pedigree."
- Complete the digitization and structuring of Chinese ancient books, traditional medicine, and historical documents within three years to build the world's largest traditional culture corpus, supporting the R&D of distinctive AI models.
- Deepen the construction of the AtomGit platform, attracting global developers. Aim to become one of the world's top three AI open-source communities within three years, outputting more than three internationally recognized technical standards.
Implement an Innovation Breakthrough System of "0 to 1 + Scenario Leadership"
- Establish an "Innovation Team Whitelist" system, providing "direct project approval + direct funding" support for breakthrough technologies from university laboratories and startups, bypassing traditional approval processes.
- Select advantageous Chinese scenarios such as healthcare, agriculture, and government affairs to create "AI + Industry" benchmark applications, e.g., chronic disease management models based on TCM data and regional development decision-making systems based on systematic thinking.
- Establish a "Global AI Governance Research Institute," collaborating with developing countries to formulate rules on AI ethics and cross-border data flows, integrating Chinese AI standards into the international governance framework.

IV. Conclusion: Achieving Paradigm Shift from "Catch-Up" to "Leadership" with the GG3M Strategy

Schmidt's judgment reveals the superficial shortcomings of China's AI development but fails to see China's institutional advantages and cultural endowments. The real challenge for China's AI is not to copy the American model, but to translate advantages such as the nationwide system, Chinese wisdom, and open-source ecosystems into irreplicable core competitiveness, based on the GG3M strategy's logic of global vision, governance coordination, and value leadership.

In the short term, break capital constraints through financial system reform and activate innovation vitality through open-source platform construction. In the medium term, build differentiated advantages through Chinese corpora and distinctive models. In the long term, define a new AI paradigm through standard-setting and global collaboration. Only by doing so can China escape the "catch-up trap," elevate AI development to the strategic height of national security and national destiny, and lead the future direction of global AI civilization with a "Chinese solution."

GG3M Strategy-Based AI Innovation Implementation Action List for Enterprises

I. Capital Connection: Breaking Funding Constraints (Implementation in 1-6 months)

Policy Funding Application: Map national AI Basic Research Funds, local computing power subsidies (e.g., Chengdu Tianfu New Area model), and tax credit policies. Form a dedicated application team, prepare R&D plans and commercialization roadmaps. Prioritize applying for "0 to 1" technology R&D projects to secure tax credits and full R&D subsidies.
Diversified Capital Cooperation: Connect with bank AI industry chain special loans (e.g., Bank of China's 1 trillion yuan support plan). Form an "R&D Consortium" with 3-5 enterprises in the same track to jointly apply for large-scale financing and share computing power and R&D costs.
Internal Capital Optimization: Adjust revenue allocation mechanisms to invest over 20% of AI-related business revenue into basic R&D. Establish a "Failure Tolerance Fund," allowing up to 30% of R&D investment for exploratory projects that do not meet expectations.

II. Technology R&D: Anchoring Differentiated Advantages (Implementation in 6-12 months)

Corpus Resource Acquisition: Apply for access to the National Library Digital Corpus and Western China AI Corpus. Collaborate with university ancient book research institutes to structure traditional culture data, building an enterprise-specific Chinese distinctive corpus (focusing on industry data + traditional culture fragments).
Computing Power Cost Control: Connect to the "Computing Power Bank" to apply for free computing power quotas for startups. Adopt an "on-demand rental + off-peak usage" model to reduce infrastructure investment. Prioritize domestic computing power platforms (e.g., Huawei Ascend) to enjoy policy incentives.
0 to 1 Technology Focus: Form a dedicated R&D team to focus on 1-2 niche tracks (e.g., TCM AI diagnosis, government decision-making models). Avoid comprehensive benchmarking against US technologies. Develop differentiated algorithms based on Chinese logic and industry scenarios.

III. Ecosystem Collaboration: Integrating into Open-Source and Sharing Systems (Implementation in 3-9 months)

Open-Source Platform Entry: Join domestic open-source platforms like AtomGit. Open non-core technical frameworks and code, participate in platform-specific projects, and exchange for computing power and data resource swaps.
SME Collaboration: Leading enterprises open some computing power interfaces and foundational models, establishing "technology incubation + result sharing" cooperation with 3-5 startups. SMEs focus on niche scenario innovation, while leading enterprises provide resource support.
Industry Alliance Co-construction: Join the Chinese Corpus Sharing Alliance and AI Standard Setting Working Groups. Participate in formulating rules for compliant industry data circulation, striving for 话语权 in 1-2 niche technical standards.

IV. Scenario Implementation: Creating Benchmark Applications (Implementation in 6-18 months)

Advantageous Scenario Selection: Prioritize distinctive Chinese advantageous fields such as healthcare, agriculture, and government affairs. Identify 1-2 core application scenarios (e.g., chronic disease TCM AI management, regional development decision support).
Pilot Project Advancement: Collaborate with local governments and industry leaders to launch pilots. Collect real-scenario data to iterate models, forming a "technology-scenario-data" closed loop. Aim to cover over 10 prefecture-level cities or 50+ benchmark customers during the pilot phase.
Commercialization and Monetization: Turn pilot-validated technologies into standardized products, 推向 the market through a "subscription + customization" model. Simultaneously apply for "first-set" certification to secure policy rewards and market promotion support.

V. Standard Participation: Seizing International Discourse Power (Long-term advancement in 1-3 years)

International Organization Engagement: Join international AI-related organizations such as ISO and IEEE. Participate in developing multilingual AI standards and ethical norms, submitting Chinese AI technical proposals.
"Belt and Road" Collaboration: Collaborate with enterprises and research institutions in Belt and Road countries to build multilingual corpora and AI application platforms, exporting Chinese AI technical solutions and standards.
Industry Standard Leadership: Lead or core participate in domestic industry AI standard setting. Translate enterprise technological achievements into industry norms to raise market entry barriers.

VI. Risk Management: Ensuring Sustainable Development (Long-term normalized implementation)

Technical Risk Prevention: Establish a three-level risk assessment mechanism of "R&D - Testing - Piloting." Conduct quarterly technical feasibility reviews and adjust R&D directions promptly.
Data Security and Compliance: Follow the "Interim Measures for the Management of Generative Artificial Intelligence Services." Establish a full-process compliance system for data collection, storage, and usage. Conduct regular security audits.
Talent Echelon Building: Co-establish AI-specific majors with universities. Set up "0 to 1 Innovation Scholarships." Combine the introduction of overseas high-end talent with local youth teams to build a dual-track talent force for "basic research + application development."

Module Detailed Execution Table: Capital Connection — Breaking Funding Constraints (1-6 months)

Phase	Timeframe	Core Tasks	Responsible Department	Output	Partners	Risk Tips & Mitigation
Preparation	Month 1-2	1. Comprehensively map national AI Basic Research Funds, local computing power subsidies (e.g., Chengdu Tianfu New Area), tax credits, etc., to create a policy list.2. Define the enterprise's "0 to 1" tech R&D direction (e.g., Chinese distinctive large models, traditional culture corpus processing) and write an R&D plan.3. Form a dedicated application team (including strategy, finance, R&D personnel) and clarify responsibilities.	Strategic Planning Dept. (Lead), Finance & Investment Dept., R&D Center	1. Policy Adaptation List (marked with application conditions, deadlines, subsidy amounts).2. R&D Plan (including technical route, investment budget, outcome targets).3. Application Team Responsibility Manual.	Local Science & Technology Bureau, Industry Association	Risk: Inaccurate policy interpretation leading to misaligned application direction.Mitigation: Invite policy consulting agencies or Science & Technology Bureau specialists for dedicated training.
Application Execution	Month 3-4	1. Prepare application materials (including qualification certificates, R&D plans, budget sheets) for 3-5 highly relevant policies (e.g., national basic research funds, provincial AI special subsidies).2. Connect with bank AI industry chain special loans (e.g., Bank of China's 1 trillion yuan support plan) and submit loan application materials.3. Screen 3-5 enterprises in the same track, discuss forming an "R&D Consortium," and sign a letter of intent.	Finance & Investment Dept. (Lead), Strategic Planning Dept., Legal Dept.	1. Policy Application Material Package (customized per policy requirements).2. Bank Loan Application Materials (including enterprise credit report, R&D budget, repayment plan).3. R&D Consortium Letter of Intent.	Local Science & Technology Bureau, Partner Enterprises, Target Bank	Risk: Fierce competition for policy applications leading to low approval rates.Mitigation: Apply for multiple policies at different levels simultaneously to increase hit rates; invite experts to pre-review materials for core policies.
Implementation & Closure	Month 5-6	1. Follow up on policy application review progress, supplement materials promptly, and participate in defenses (if required).2. Negotiate loan amount, interest rate, and repayment period with the bank and sign the financing agreement.3. Formulate "Failure Tolerance Fund" management rules, adjust revenue allocation mechanisms, and clarify the proportion of AI R&D investment.	Finance & Investment Dept. (Lead), Strategic Planning Dept., R&D Center	1. Policy subsidy/fund approval documents (if successful).2. Bank Financing Agreement.3. Internal Capital Management Rules (including R&D investment proportion, tolerance mechanism).	Local Science & Technology Bureau, Target Bank, Partner Enterprises	Risk: Delayed financing disbursement affecting R&D progress.Mitigation: Agree on a disbursement schedule with the bank in advance and initiate an internal capital advance plan simultaneously.