Global AI*bio Research Alliance Officially Launched! Building a Trustworthy Knowledge Ecosystem Together

AI*bio正成为全球未来产业布局与科技战略博弈的核心赛道。从AI辅助底盘细胞理性设计、代谢通路优化、DBTL循环迭代到生物大模型研发，从实验室研发效率跃升、工程化放大提速到下游多场景商业化渗透，AI与合成生物学、生物制造产业的深度交汇，正驱动生命科学研发范式与生产方式发生系统性变革，成为发展新质生产力、构筑国家生物经济竞争力的核心抓手。2026年政府工作报告将生物制造列为壮大未来产业、培育新经济增长点的重点方向。

产业在加速扩张的同时，行业正面临多重核心挑战：技术研发与产业落地间存在信息鸿沟，成果转化"死亡谷"亟待打通；产业链参与者分散，细分领域缺少统一评价基准与发展共识，资本与资源配置效率有待提升；科技伦理治理体系建设滞后于技术迭代，生物安全、数据合规、全球规则博弈等前沿议题缺乏系统化协同机制；交叉领域复合型人才供给不足，技术、产业、治理兼备的高端人才缺口显著。

The Global AI*bio Research Alliance was established precisely to address these critical questions.

Fudan University School of International Relations and Public Affairs成立于2000年，其学科源于1923年复旦大学政治学系，是我国政治学、公共管理和国际关系领域的重要教学科研机构。学院设有政治学、公共管理等一级学科，致力于培养具有国际视野和公共精神的高层次人才，围绕公共政策、全球治理、国际关系、科技创新政策等领域开展高水平研究，积极推动国际学术交流与合作。

Frost & Sullivan possesses global industry perspective, mature research methodologies, and deep capital market analysis experience. Frost & Sullivan China has expertise in AI underlying technologies and local governance research. Frost & Sullivan also exhibits a global governance vision and international rule-making capabilities. The combined strengths of these three parties are highly complementary and aligned in philosophy. They will achieve dual penetration across industrial practice, technology development, and governance research. This collaboration represents a powerful alliance between an AI*bio industry think tank and top-tier universities, as well as a comprehensive system upgrade of the industry research framework, technological innovation capabilities, and global governance skills.

合成生物学领域领军人物、中国科学院院士Zhao GuopingAt this summitSynthetic Biology and Bio Manufacturing Forum的独家专访中表示，AI已然为研发效率的提升打开了新的窗口，但AI的落地也有现实的门槛，其高度依赖高质量、多维度的深度数据集。这和传统生物学、生物信息学的研究逻辑并不相同。AI不怕数据复杂，真正制约它的是数据体量不足，或是数据缺少规范化治理、本身质量不达标。只要同时把数据的"量"和"质"做好，AI就能释放出强大的学习能力。

Academician Zhao has long advocated for this causeQuantitative Synthetic BiologyThe new development paradigm, he further stated in an interview, requires a "white box" strategy previously used by the industry: first establishing a theoretical framework, then conducting experiments based on that theory to extract scientific principles. However, in today’s industrial context, this approach’s research efficiency can no longer meet the needs of industrial development. In response to this reality, the team led by Academician Zhao proposed the "black box" model, whose core capability is to generate large volumes of biological data at scale.In the past, it was difficult to standardize the data generated from experiments for use by AI. Now, this condition has gradually matured."白箱"与"黑箱"两种路径的相互融合，正是定量合成生物学要解决的核心问题，也归属于AI for science（AI4S）的范畴。现在AI for science在合成生物学领域已取得亮眼突破，但依然有大量卡点亟待攻克。所以我们不能止步于AI for science，还要大力发展AI for industry('AI4I'). The complete industrial chain spans from initial technology iteration to product development, then to commercialized products, including many stages such as pilot testing. Various aspects will present numerous data challenges. In the future, it is essential to promote the integration of AI for science and AI for industry. By leveraging the synergy between AI for science and AI for industry, AI can truly empower synthetic biology, further driving the high-quality development of biomanufacturing industry, connecting all stages of the industry, and unlocking the full industrial value.

The high-quality development of the AI*bio industry depends on the coordinated progress of technology, industry, and governance. In the future, the Global AI*bio Research Alliance will move beyond the perspective of individual institutions, conduct research based on real industrial scenarios, establish a regular communication mechanism among multiple parties, and strive to create a professional think tank and cross-sector resource platform with international vision, deep scientific expertise, and comprehensive industry influence. The alliance will also continuously showcase China’s innovative achievements in AI*bio integration worldwide, steadily enhancing China’s voice and rule-making power in global technology governance, helping China’s AI*bio industry overcome homogenized competition and expand overall industry value.

In the future, the three parties will establish a dual-track reporting system of “domestic policy research + international industry white papers”. Domestically, they will focus on cutting-edge issues such as technology ethics governance and ethical review mechanisms for investment, conducting specialized research to support national and local policy formulation. Internationally, they will jointly release a series of important industry reports, systematically analyzing global technological evolution, industry patterns, regulatory governance, and investment trends, providing authoritative decision-making references for the industry.

The first report released at this summit is‘Global and Chinese Synthetic Biology and Biomanufacturing Industry White Paper (2026)’Leaded by the Institute of Humanities and Social Sciences at University of Electronic Science and Technology of China and the School of International Relations and Public Affairs at Fudan University, which provided research support in the chapter “Synthetic Biology Governance: Ethical Framework and Policy System Construction”.

The white paper points out that although China’s current synthetic biology governance policy framework has formed a multi-level institutional framework, there is a lack of specific regulations tailored to industry-specific risks. The implementation details still need further improvement, posing potential institutional risks due to delayed regulatory adaptation. The pattern where global biotechnology governance rules are dominated by developed countries further highlights the urgency of establishing local ethical standards and participating in international rule-making. From the perspective of industrial governance research, the following pyramid structure creates a comprehensive multi-level governance system suitable for local contexts. Unlike some overseas governance systems that rely on fragmented management across research funding, industry self-regulation, export controls, laboratory safety, and product regulation, this approach ensures hierarchical transmission of value orientation, theoretical tools, regulatory systems, implementation, and internal and external coordination, forming a closed-loop governance logic that effectively balances biological security risks with the long-term development needs of the biomanufacturing industry.

Source: Institute of Humanities and Social Sciences at University of Electronic Science and Technology of China, School of International Relations and Public Affairs at Fudan University, SULLIVAN Analysis

The white paper indicates thatThe top-level value layerforms the underlying value benchmark of the governance system. Compared to traditional life ethics frameworks centered on individual rights protection, synthetic biology governance in China emphasizes the balance between public interest, shared risk, development security, and responsible innovation. It adheres to the core principle of “ethics first, common good”, establishes credible and collaborative local governance standards, and provides corresponding ethical governance documents to form a unified value scale. This level defines the ethical boundaries between research and industrialization from the outset, providing stable value judgments for subsequent regulatory revisions, scientific reviews, and industrial supervision, thereby mitigating ethical risks arising from the commercialization of cutting-edge biotechnology.

The theoretical method layerprovides specialized support for institutional evolution. Based on the three-tier progressive research paradigm of ELSI – PITA - GITA and the two-dimensional risk classification assessment tool, it builds a cross-disciplinary research system integrating ethics, law, life sciences, and public administration. This section conducts specialized analysis of synthetic biology-specific technical risks that general regulations cannot cover, creating quantitative risk identification pathways, and compensating for the insufficient refinement of current regulatory rules, providing empirical support for policy improvement and standard setting.

The middle-level regulatory and implementation layertranslates the institutional framework into practical applications. With the ‘Biosecurity Law» serving as a rigid upper-level constraint, combined with general ethical norms, medium- and long-term industry plans, and specialized standards for specific fields, a layered regulatory matrix is established. At the same time, regulatory responsibilities are delegated to research institutions and market entities, creating a full-process control mechanism including pre-examination, mid-course review, and accountability for violations, ensuring precise alignment between macrolegal provisions and laboratory research and pilot industrialization scenarios.

The bottom-level internal and external collaboration layer completes the dual extension of the governance system. Domestically, it relies on government-industry-university-research collaborative innovation platforms and public education channels to reduce public misconceptions and mitigate social舆论 risks related to industrial development. Internationally, it leverages international academic platforms to present local governance solutions and includes the development demands of the Global South in international rule-making discussions. The overall structure forms a complete transmission chain from top-level value guidance to global governance participation, enhancing China’s ability to shape rules in global biotechnology governance while strengthening China’s biosecurity defense.

Currently, AI for Science is driving the Automotive & Mobility industry into a period of paradigm innovation. At the same time, AI search and agents are evolving from “assistant tools” into key channels for industrial decision-making and public understanding. The format of industry knowledge supply is undergoing fundamental changes.

The “Knowledge Generation Three Generations” framework proposed by Frost & Sullivan indicates that human knowledge has experienced two major leaps: Knowledge 1.0 addressed the issue of “staying behind,” using books and archives as carriers, with trust coming from the authority of authors and institutions; Knowledge 2.0 addressed the issue of “being accessible,” using the internet and search engines as carriers, with trust derived from source reputation and platform rankings; and the upcoming Knowledge 3.0 era will focus on whether knowledge can be reliably used by humans and machines and who is responsible for it. When models and agents begin to extract claims, synthesize answers, form recommendations, and even take actions, knowledge must retain the necessary context, evidence, status, and responsibility even after leaving the original text.

Therefore, as the industry expands rapidly, three deep challenges are becoming increasingly apparent:

• Disordered Knowledge Supply

Industry knowledge is scattered across research papers, policy documents, industry reports, and corporate disclosures, with inconsistent formats, outdated versions, and unclear responsibility. The boundaries of AI-generated content, as well as issues of factual inaccuracy and hallucinations, are growing. The traditional document-based knowledge model can no longer meet the demands of the intelligent era.

• Outdated Governance System

Frontier issues such as biosecurity and ethical risks arising from the integration of AI and biotechnology lack systematic regulation. The collaborative mechanisms between technology governance, research and development, industrial implementation, and capital investment have not been established, making it urgent to design institutional arrangements that embed ethics throughout the entire chain.

• Shortage of Talent Supply

There is a shortage of interdisciplinary talents in the intersection of AI and biology. The curriculum systems in universities do not match the actual needs of the industry, and high-level talents with technical skills, industry awareness, and governance competence are scarce.

Mr. Wang Chenhui, Managing Partner and President of Frost & Sullivan China, and Co-Founder and President of LeadLeo

As mentioned by Mr. Wang Chenhui, Managing Partner and President of Frost & Sullivan China, and Co-Founder and President of LeadLeo, atThe Synthetic Biology and Biomanufacturing Forumhe stated that AI is redefining the decision-making processes in both industries and consumption. The industry needs to shift from short-term result optimization to long-term co-creation of a trustworthy knowledge ecosystem. Building a verifiable, traceable, computable, and accountable public trusted knowledge system and integrating technology ethics governance into the entire industry chain has become a core requirement for high-quality development.In the AI era, Frost & Sullivan will also transform from a single consulting firm into a manager of AI-native structured knowledge bases.

In the future, Frost & Sullivan, the University of Electronic Science and Technology of China, and Fudan University will, based onKnowledge 3.0the core concept established, create a public trusted knowledge infrastructure in the vertical field of AI*bio industry. By relying on a five-layer knowledge object architecture (content layer, experience layer, evidence layer, responsibility layer, lifecycle layer), we will structure and organize core knowledge units such as industry definitions, technical terms, regulatory rules, and ethical boundaries, establishing a trusted knowledge system with traceable sources, updatable versions, assignable responsibilities, and machine-accessible capabilities.

The University of Electronic Science and Technology of China and Fudan University will lead the design of the knowledge graph technical architecture, the review of technical achievements, the formulation of governance rules, and the control of compliance boundaries. Frost & Sullivan will assume the responsibility of operational management, handling industrial knowledge production organization, standard interface release, commercialization system design, and global promotion. Both parties have now officially started the collaboration.We sincerely invite all industries to join us in building more pure, safe, publicly trusted, traceable, and auditable data for China and the world, helping the country gain more influence globally in the AI environment.

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Frost & Sullivan China

Senior Partner and Managing Director Yang Xiaocheng

Email: jacky.yang@frostchina.com

Frost & Sullivan China

Consulting Director Sun Rong

Email: skyler.sun@frostchina.com

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