In recent years, the global scientific research field has witnessed an intense emergence of disruptive achievements. In 2024, using artificial intelligence (AI) technology, a Chinese-Australian team discovered over 160,000 entirely new RNA viruses, a figure nearly 30 times the number of previously known virus species. In April 2026, the Chinese Academy of Sciences officially released the "Panshi 100" scientific large model system, establishing intelligent clusters across eight major disciplines to empower the entire chain of scientific research. During the same period, a Chinese self-developed AI for Science ultra-large computing with 60,000-GPU cluster was completed, accelerating and empowering research in the fields of materials, aerospace, and life sciences.
Based on long-term observation and research, ANBOUND’s founder Kung Chan pointed out that behind this series of landmark events lies a global wave of scientific research paradigm reshaping, driven centrally by AI. The focus of such a shift is on the underlying logic of scientific research operations, manifesting primarily as three systemic transitions of research methods, organizational models of research, and the participating subjects of scientific research.
However, before analyzing the three systemic transitions mentioned by Kung Chan, it is first necessary to understand the four critical iterations that the global scientific research paradigm has undergone. In history, every paradigm shift has been driven by core technological breakthroughs, adapted to the social development needs of different stages, and shaped differentiated research models and industry characteristics. These shifts have also laid a solid technical foundation and provided developmental experience for the current new paradigm driven by artificial intelligence.
Before the 17th century, global scientific research was in the developmental stage of the empirical paradigm. During the era of the Renaissance, scientists such as Copernicus and Galileo broke through the medieval tradition of speculative philosophy, pioneering the primitive research model of "observation—experiment—induction". In that period, scientific research activities were primarily based on individual exploration, relying on manual experimental operations and human sensory observation to accumulate research experience. There was no professional or systematic research organization back then. The scale of research was small, and research efficiency was relatively low, which only adapted to the foundational exploration needs of the embryonic stage of natural science.
From the 17th century to the mid-20th century, the theoretical paradigm gradually replaced the empirical paradigm to become the mainstream of scientific research. The United Kingdom, France, and Germany successively became world scientific centers, and modern foundational science experienced explosive growth. Major scientific theories, such as Newtonian mechanics, Maxwell's equations of the electromagnetic field, and Einstein's theory of relativity, were introduced one after another. This shifted the logic of scientific research from empirical induction to rational deduction, forming a standardized deductive research model of "mathematical modeling—logical deduction—theoretical validation". At the organizational level, universities and private laboratories became the main vehicles for research, small-scale and closed research teams became the mainstream form of study, and governments began to intervene marginally in the field of foundational scientific research. This paradigm established the rigor and logic of modern science, building a solid technological foundation for the advancement of the Industrial Revolution and the construction of the modern industrial system, and driving a leap-forward surge in humankind's modern science and technology.
In the 1950s, the advent of computer technology ushered in a new era of the computational paradigm, which first emerged in the United States and long dominated global scientific research development. Relying on the powerful computing capabilities of computers, researchers could perform digital simulations of complex systems, solving scientific conundrums that traditional theoretical deductions found difficult to analyze, and adding a new scientific research path of "numerical computation—simulation prediction". The organizational form of scientific research began to exhibit cross-institutional collaboration characteristics, and the government officially became the core subject of scientific research funding investment. Relying on the National Science Foundation (NSF), the U.S. coordinated the layout of major scientific research projects, gradually forming an embryonic scientific research structure of division of labor and collaboration among universities, national laboratories, and technology enterprises. The computational paradigm expanded the boundaries of human scientific exploration, aided breakthrough developments in complex fields such as nuclear fusion, aerospace, and high-end manufacturing, and drove the implementation and shaping of high-tech industries such as semiconductors, nuclear energy, and precision instruments.
As the world entered the 21st century, the popularization of the internet and the rapid explosion of massive data gave rise to the data-driven paradigm. Global scientific research stepped into a developmental stage characterized by "massive data—statistical analysis—pattern mining", with digitalization and informatization becoming the core features of scientific research. This stage remained centered on human-dominated data analysis. Scientific research data gradually achieved digital sharing, and open-source research platforms began to sprout and develop. Tech corporations such as Google and IBM entered the scientific research field by virtue of their massive data resources, constructing a diversified structure of scientific research subjects comprising "government + institutes of higher learning + enterprises". However, this paradigm still prolonged the older hypothesis-driven logic of scientific research. When facing highly complex and strongly coupled research fields such as biomedicine and novel materials, it exhibited shortcomings such as low data analysis efficiency and insufficient pattern mining capabilities, making it difficult to adapt to the R&D demands of cutting-edge, hardcore technologies.
In the past decade, especially since 2020, along with the iterative upgrading of large language models (LLMs), the continuous improvement of computing power infrastructure, and the increasing maturity of automated experimental technologies, the AI-driven paradigm has officially exploded, becoming the fifth-generation scientific research paradigm and the core nucleus of the current global scientific research transformation. Kung Chan emphasized that AI technology is thoroughly overturning the traditional operational logic of scientific research, driving a systemic transition across research methods, organizational models, and participating subjects, and reshaping the global landscape of technological innovation. The 2024 Nobel Prizes in Physics and Chemistry, respectively, recognized research related to the application of machine learning in physics and the AI prediction of protein structures, marking the authoritative recognition of the AI-driven research paradigm by the global scientific community and officially establishing its mainstream scientific research status.
At the level of research methods, global scientific research logic has also undergone a fundamental reversal, transitioning from the dominance of deductive methods to the dominance of AI-inductive methods. Conventional scientific research follows an inherent pattern of "subjective hypothesis—repeated validation", which presents long R&D cycles, high costs of trial and error, and significant difficulties in achieving breakthroughs within complex scientific research. In 2021, the AlphaFold model developed by DeepMind precisely solved the puzzle of predicting three-dimensional protein structures, compressing what used to be a months-long analysis period down to the hour level, marking the upgrade of AI from a research auxiliary tool to a core research engine. Currently, the U.S., the European Union, and China all position AI for Science as a focus of their technological strategies, relying on artificial intelligence to mine massive scientific literature and experimental data, autonomously generate research hypotheses, and predict experimental results, thereby substantially compressing R&D cycles. The Massachusetts Institute of Technology in the U.S. utilized AI technology to screen novel battery materials, boosting material R&D screening efficiency by 90%. Insilico Medicine in the European Union developed the GENTRL intelligent model, completing the entire process of designing, synthesizing, and validating a novel drug molecule in just 46 days. In April 2026, the Chinese Academy of Sciences released the "Panshi 100" scientific large model system, building intelligent model clusters for eight major professional disciplines to achieve AI empowering the entire chain of the research process, which officially marks the historic transition of research logic from “pattern-searching by humans” to "data and intelligence collaboratively mining patterns".
At the institutional level, the paradigm of scientific research has fundamentally shifted from a closed-source mode to an open-source, collaborative ecosystem. During the mid-to-late 20th century, mainstream research institutions in Europe and the U.S. predominantly operated under a closed approach. Research data and experimental code were strictly guarded, which led to widespread duplication of effort and significant resource inefficiencies across the sector. The turn of the millennium marked a transition, as the gradual rise of open-source platforms like GitHub provided the necessary infrastructure for sharing research assets. By the 2010s, global research collaboration began to accelerate rapidly. This trend culminated during the COVID-19 pandemic, when research institutions worldwide shared viral genome sequencing data and experimental findings in real time. This unprecedented level of cooperation drastically shortened vaccine development timelines and served as a definitive proof of concept for open, collaborative research. Currently, there are also focuses on refining open-science infrastructure. The U.S. is building shared scientific research platforms that consolidate public research resources, including computing power, data, and experimental equipment. Meanwhile, the European Union, leveraging the European Research Council, has established a transnational research collaboration framework that defines explicit guidelines for the advancement of open science. Today, global researchers leverage open-source foundational models and public scientific databases to build distributed collaborative networks that transcend geographical barriers and disciplinary boundaries. Consequently, co-creation of knowledge and resource pooling have become the dominant operational models for research organizations. By 2026, the utilization of ultra-large-scale computing power across the world’s top ten biological AI research projects has continued to climb, with the U.S., China, and Europe accounting for 38%, 31%, and 19% of these computing resources, respectively. The sharing of computational capacity has thus emerged as the core foundation sustaining open and collaborative scientific research.
At the level of research entities, it has been shifted from being government- and university-led to being enterprise-driven, marked by the deep integration of industry, academia, and research. In the 20th century, basic research in Western countries was heavily reliant on government fiscal appropriations, with universities serving as the primary executors of scientific inquiry. This resulted in a protracted technology transfer chain and significant inefficiencies in bringing research to market. In the 21st century, leveraging their advantages in capital, computational power, and market applications, leading technology firms have gradually become the core force of R&D. These enterprises focus on real-world market demands to tackle technical bottlenecks, effectively bridging the complete innovation chain from basic research and applied development to industrial commercialization. In the U.S., companies like Google, Microsoft, and Tesla continue to increase investment in foundational research. Notably, Google’s DeepMind developed the Cell2SentenceScale27B model, which successfully and autonomously identified entirely new research directions for cancer treatment. In Europe, Siemens and AstraZeneca are deeply invested in industrial technology and biopharmaceuticals, utilizing corporate capital to drive the implementation of frontier technologies. Concurrently, China’s research industry has undergone a parallel upgrade. Dawning Information Industry has constructed the nation’s largest AI research computing cluster, featuring 60,000 GPUs, driving the deep integration of supercomputing and intelligent computing to empower local corporate innovation. Under this new paradigm, governments focus on top-level strategic planning and policy guidance, while universities specialize in basic theoretical research and professional talent cultivation. Enterprises now lead the charge in technical breakthroughs and the commercialization of findings, forming a modernized ecosystem of research entities defined by enterprise-centric, industry-academic-research synergy.
The current evolution of research paradigms has emerged as the central battlefield in the global strategic competition for science and technology, with nations formulating distinct AI research strategies tailored to their specific industrial foundations and technical advantages. The U.S. continues to lead in AI research by leveraging its profound technical accumulation and corporate dominance. The European Union focuses on ethics and open science to cultivate a collaborative ecosystem. China is rapidly aligning with global research trends, integrating "AI for Science" as a core priority within its 15th Five-Year Plan and continuously deepening its integrated industry-academia-research system. Meanwhile, Japan and South Korea are maintaining a precise focus on niche sectors such as advanced materials and biopharmaceuticals to drive the practical application of AI technologies. As it stands, the global research landscape is undergoing a structural reconfiguration. While the United Kingdom and the U.S. have begun to scale back budgets for certain traditional areas of basic research, France, Germany, and the European Union as a whole have increased investment in talent acquisition and research funding. This has accelerated the mobility of elite scientific talent, computational resources, and data assets, resulting in a development climate where open collaboration and geopolitical competition coexist. The industry has defined 2025 as the strategic inaugural year for AI4S (AI for Science), as global competition intensifies across all fronts, from computational infrastructure, research data, intelligent models, and industry standards, marking a period of unprecedented heat in the technological Great Game.
All in all, the ongoing shift in global research paradigms is the inevitable result of the convergence of technological iteration, market demand, and international competition. At this moment, the AI-driven research paradigm is still in a phase of refinement and deepening. The industry continues to grapple with systemic challenges, including non-standardized research data, a lack of regulatory frameworks for AI ethics, and a critical shortage of high-end, interdisciplinary scientific talent. Nevertheless, it is undeniable that human-machine collaboration, open sharing, and demand-driven innovation have become the defining characteristics of modern inquiry. The scientific community has officially entered a new era of development. Moving forward, nations will continue to increase investments in intelligent research infrastructure and optimize their innovation systems to secure the commanding heights of global technological development. Under these multifaceted forces, the global technological landscape will accelerate its departure from unipolar dominance, evolving instead toward a mature ecosystem of pluralistic symbiosis and collaborative checks and balances. This transition will see sustained momentum in global scientific innovation and the advancement of human civilization.
Final analysis conclusion:
The global community has officially entered the "Fifth Paradigm" of scientific research, driven by AI. This transformation is currently undergoing three major transitions involving research methodology, organizational structures, and participating entities. Methodologically, the logic of inquiry is shifting from human-led hypothesis deduction to AI-driven pattern discovery. Organizationally, the research model is evolving from closed, siloed efforts toward global open-source collaboration. In terms of the broader landscape, the framework has transitioned into an enterprise-led system characterized by the deep integration of industry, academia, and research. Currently, major powers including China, the U.S., and Europe are intensifying their strategic positioning in AI-driven research, leading to increasingly fierce global technological competition. While the sector still faces systemic challenges like fragmented data standards, a void in ethical oversight, and a shortage of specialized talent, the future of global research is moving towards human-machine collaboration and open-source sharing. Consequently, the global scientific landscape will accelerate its evolution toward a model of pluralistic symbiosis.
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He Yan is a researcher at ANBOUND, an independent think tank.
