Global AI*bio Research Alliance officially launches! Building a trustworthy knowledge ecosystem together

AI*bio is becoming the core area of global future industrial layout and technological strategy competition. From rational design of chassis cells assisted by AI, optimization of metabolic pathways, iterative improvement of DBTL, to the development of bio-large models, from an increase in laboratory research efficiency, to accelerated engineering amplification, to commercialization in multiple downstream scenarios, the deep integration of AI and synthetic biology, and bio manufacturing industries is driving systematic changes in life science research paradigms and production methods, becoming a key approach for developing new productive forces and building national biotech competitiveness. The 2026 Government Work Report listed bio manufacturing as a key direction for strengthening future industries and cultivating new economic growth points.

While the industry is expanding rapidly, it faces multiple core challenges: there is a information gap between technology research and development and industrial implementation, the "valley of death" for achievement transformation needs to be resolved; the participants in the industrial chain are scattered, there is a lack of unified evaluation criteria and development consensus in specific fields, and the efficiency of capital and resource allocation needs to be improved; the construction of a technology ethics governance system lags behind technological iteration, and there is a lack of a systematic collaborative mechanism for emerging issues such as biosecurity, data compliance, and global rule negotiations; there is a shortage of composite talents in interdisciplinary fields, and a significant gap exists in high-end talents with both technical, industrial, and governance capabilities.

The Global AI*bio Research Alliance was established to address these key issues.

School of International Relations and Public Affairs, Fudan UniversityEstablished in 2000, its disciplines originated from the Department of Political Science at Fudan University in 1923, making it an important teaching and research institution in the fields of political science, public management, and international relations in China. The school has first-level disciplines such as political science and public management, aiming to cultivate high-level talents with an international perspective and public spirit, conducting high-level research in areas such as public policy, global governance, international relations, and science and technology innovation policies, and actively promoting international academic exchanges and cooperation.

One party has a global industrial perspective, mature research methodology, and deep analysis experience in the capital market; the other party has mastery of AI underlying technologies and local governance research experience; the third party has a global governance perspective and international rule research capabilities. The three parties' abilities are highly complementary, and their concepts are highly consistent. They will achieve two-way penetration in industrial practice, technology research and governance studies. This is a strong combination of an industry think tank and a top university in the AI*bio field, as well as a systematic upgrade of the industry research system, technological innovation capabilities, and global governance capabilities.

Leading figure in synthetic biology, Academician of the Chinese Academy of SciencesZhao Guopingat this summitSpecial Forum on Synthetic Biology and Bio Manufacturingsaid in an exclusive interviewAI has opened a new window for improving research efficiency, but the implementation of AI also has practical barriers, as it highly depends on high-quality, multi-dimensional deep datasets. This is different from the research logic of traditional biology and bioinformatics.AI is not afraid of complex data; what truly restricts it is insufficient data volume or lack of standardized governance and poor data quality. As long as both the "quantity" and "quality" of data are improved, AI can unleash its strong learning ability.

Academician Zhao has long advocatedQuantitative Synthetic BiologyA new development paradigm. In his interview, he further stated that in the past, the industry generally adopted a "white box" strategy: first, establish a theoretical framework, then conduct experiments based on the theory guidance, and extract scientific laws from them. However, under today's industrial reality, the research efficiency of this model can no longer meet the needs of industrial development. Based on this reality, Academician Zhao's team proposed a "black box" model, whose core ability is to produce large amounts of biological data on a scale.In the past, it was difficult for us to supply AI with standardized data processed from experimental outputs. Now, the conditions are gradually maturing.The integration of the "white box" and "black box" approaches is precisely the core issue that quantitative synthetic biology aims to solve, and it also falls withinAI for science(AI4S) scope. Currently, AI for science has made remarkable breakthroughs in the field of synthetic biology, but there are still many bottlenecks that need to be overcome.Therefore, we cannot stop at AI for science; we also need to vigorously developAI for industry(AI4I). The complete industrial chain includes original technology iteration, product development, commercial products, and many other links. Each link will encounter numerous data problems. In the future, we must promote the two-way combination of AI for science and AI for industry. By leveraging the synergy of AI for science and AI for industry, AI can truly empower synthetic biology, further drive the high-quality development of the bio manufacturing industry, open up the entire industrial chain, and release the complete 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 limitations of a single institution's perspective, conduct research based on industrial realities, establish a regular communication mechanism for multi-party collaboration, and strive to create a professional think tank and cross-sector resource collaboration platform with international forward-looking vision, deep scientific foundation, and full-chain industrial influence. The alliance will also continue to showcase China's innovative practices in AI*bio fusion to the world, steadily enhancing China's voice and rule-making influence in global technology governance, helping China's AI*bio industry avoid homogenized competition and jointly expand the overall industrial value.

In the future, the three parties will create a dual-track report system of "internal policy research + external industry white papers". Internally, they will focus on cutting-edge issues such as technology ethics governance and ethical review mechanisms for investment, form special research on internal policies to support the formulation of relevant national and local policies; externally, they will jointly release a series of major industry reports, systematically analyzing global technology iteration, industrial patterns, regulatory governance, and investment and financing trends, providing authoritative decision-making references for the industry.

The first release at this summit was''Global and Chinese Bio Manufacturing Industry Development White Paper (2026)'',The Institute of Humanities and Social Sciences, University of Electronic Science and Technology of China and the School of International Relations and Public Affairs, Fudan University provided research support in the chapter "Synthetic Biology Governance: Ethical Framework and Policy System Construction".

The white paper indicates that although the current Chinese synthetic biology governance policy system has formed a multi-level institutional framework, there is a lack of specialized regulations tailored to the industry's unique risks. The detailed implementation rules still need further improvement, posing institutional risks due to lag in 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-building. From the perspective of industrial governance research, the following pyramid structure has created a comprehensive full-chain governance system with local adaptability, different from some overseas governance systems that are fragmented across research funding, industry self-regulation, export controls, laboratory safety, and product regulation, achieving hierarchical transmission of value orientation, theoretical tools, regulatory systems, implementation, and internal and external coordination, forming a closed-loop governance logic, effectively balancing biosecurity risks and the long-term development requirements of the bio manufacturing industry.

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

The white paper indicates,Top Value Layerforms the underlying value benchmark of the governance system. Compared with the traditional life ethics framework centered on protecting individual rights, synthetic biology governance in the Chinese context emphasizes the balance of public interest, risk sharing, development security, and responsible innovation. It anchors the core orientation of "ethics first, common good", establishes credible and local governance guidelines, and provides a unified value standard with special ethical governance documents. This level defines the ethical boundaries of technology research and development and industrialization from the source, providing a stable value judgment basis for subsequent regulatory revisions, scientific research reviews, and industrial supervision, offsetting the potential ethical deviations derived from the commercialization of frontier biotechnology.

Theory and Method Layerprovides professional support for institutional iteration. relying on the three-level progressive research paradigm of ELSI - PITA - GITA and a two-dimensional risk classification assessment tool, it builds a cross-ethics, law, life science, and public management research system. This section conducts special analysis of the risks of synthetic biology technologies that general regulations cannot cover, forms a quantitative risk identification path, compensates for the shortcomings of the current regulatory rules' refinement level, and provides empirical research support for improving policy details and standards.

Middle Regulatory System Layer and Implementation Layercomplete the transformation of the institutional framework into practical scenarios. With theBiosecurity Law, as a rigid upper-level constraint, combined with general ethical norms, medium- and long-term industrial plans, and special standards for specific fields, forming a layered regulatory matrix. At the same time, regulatory responsibilities are decentralized to research institutions and market entities, establishing a full-process control mechanism including pre-examination, process review, and violation accountability, ensuring precise matching of macro-law provisions with laboratory research, pilot industrialization scenarios.

Bottom Internal and External Synergy Layer completes the bidirectional extension of the governance system. Internally, relying on government-industry-university-research collaborative innovation platforms and popularization channels, it alleviates public perception biases and mitigates social public opinion risks of industrial development. Externally, relying on international academic platforms, it exports local governance solutions and incorporates the development demands of the Global South into international rule dialogue. The overall structure forms a complete transmission chain from top-value guidance to global governance participation, strengthening China's biosecurity defense while continuously enhancing China's rule-making ability in global biotechnology governance.

Currently, AI for Science is driving life science and bio manufacturing industries into a paradigm innovation period. At the same time, AI search and agents are evolving from "assistant tools" into a key entry point for industrial decision-making and public cognition, and the supply of industry knowledge is undergoing fundamental changes.

Sullivan's "knowledge generation cycle" framework indicates that human knowledge has undergone two major leaps: Knowledge 1.0 solved the problem of "preserving knowledge", using books and archives as carriers, with trust coming from the authority of authors and institutions; Knowledge 2.0 solved the problem of "finding knowledge", using the internet and search engines as carriers, with trust coming from the reputation of sources and platform rankings; the upcoming Knowledge 3.0 era will focus on solving the problem of "whether it can be reliably used by humans and machines, and who is responsible". When models and agents start to extract claims, combine answers, form suggestions, and even take actions, knowledge must still carry necessary context, evidence, status, and responsibility even after leaving the original text.

Therefore, while the industry is expanding rapidly, three deep challenges are becoming increasingly prominent:

• Disordered Knowledge Supply

Industry knowledge is scattered in research papers, policy documents, industrial reports, and corporate disclosures, with inconsistent formats, outdated versions, and unclear responsibility entities. The boundaries of AI-generated content, fact distortion, and hallucination are becoming increasingly prominent. The traditional knowledge format based on documents can no longer meet the demand for use in the intelligent era.

• Lagging Governance System

Frontier issues such as biosecurity and ethical risks brought about by the integration of AI and biotechnology lack systematic regulation. The collaborative mechanism between technology governance, technology research and development, industrial implementation, and capital investment has not been established, and the institutional design for ethical embedding throughout the whole chain is urgent.

• Shortage of Talent Supply

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

Mr. Wang Chenhui, Senior Partner and President of S Sullivan China, Co-founder of Head豹

As mentioned by Mr. Wang Chenhui, Senior Partner and President of S Sullivan China, Co-founder of Head豹Special Forum on Synthetic Biology and Bio Manufacturingin his speech, he said that AI is redefining the decision-making processes in the industry and consumption sectors. The industry needs to shift from short-term result optimization to the joint construction of a credible knowledge ecosystem in the long term. Building a verifiable, traceable, calculable, and accountable public knowledge system and integrating technology ethics governance into the entire industrial chain has become a core requirement for the high-quality development of the industry.In the AI era, S Sullivan will also transform from a single consulting firm to a manager of an AI-native structured knowledge library.

In the future, S Sullivan, University of Electronic Science and Technology of China, and Fudan University will base onKnowledge 3.0the core concept built, to create a public credible knowledge infrastructure for the vertical field of AI*bio industry. Relying on a five-layer knowledge object architecture (content layer, experience layer, evidence layer, responsibility layer, lifecycle layer), it will structurally sort core knowledge units such as industry definitions, technical terms, regulatory rules, and ethical boundaries, creating a credible knowledge system that is traceable in origin, updatable in versions, accountable in responsibility, and usable by machines.

University of Electronic Science and Technology of China and Fudan University will lead the design of the knowledge graph technology architecture, review of technical achievements, formulation of governance rules, and control of compliance boundaries; S Sullivan will be responsible for the entrusted operation, including organizing industrial knowledge production, releasing standard interfaces, designing the commercialization system, and global promotion. The two parties have already officially started the cooperation.We sincerely invite the entire industry to join us, to build more pure, secure, publicly trustworthy, traceable, and auditable data for China and the world, helping the country gain more influence globally in the AI era.

Participating in cooperation

Please contact us.

Frost & Sullivan China

Senior Partner and Managing Director Yang Xiaoceng

Email: jacky.yang@frostchina.com

Frost & Sullivan China

Consulting Director Sun Rong

Email: skyler.sun@frostchina.com

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