Research as Product: How AI Reshapes the Boundaries Between Science and Industry

Thomas Kuhn, in The Structure of Scientific Revolutions, proposed that the development of science is not a linear accumulation of knowledge, but a disruption and leap from old paradigms to new ones. The announcement of the 2024 Nobel Prizes in Physics and Chemistry brought "AI for Science (AI4S)" from a specialized scientific topic into broader public view.

Following experimental science, theoretical science, computational science, and data-intensive science, a growing number of researchers now regard AI4S as the "fifth scientific research paradigm." Over the past two years, AI4S has continued to evolve at an unprecedented pace, profoundly reshaping humanity's methodology for exploring the unknown on a larger scale.

Today, scientists are re-examining complex systems such as proteins, materials, and climate through the lens of models, mapping these complex physical entities into a computable, searchable, optimizable, verifiable, and reusable digital space. This fundamentally breaks through the marginal cost of traditional knowledge production.

Its rapid progress continues to remind people that AI is no longer just a supporting tool for searching literature, writing reviews, generating reports, or creating presentations. It is increasingly intervening in the formulation of scientific questions, hypothesis generation, experiment design, result validation, and even knowledge production itself.

The history of human technological progress shows that every fundamental change in scientific methodology will trigger enormous industrial dividends. Just as the invention of the microscope in the 17th century not only expanded Leeuwenhoek's scale of observing life but also directly gave birth to modern medicine and the pharmaceutical industry, the von Neumann computing paradigm of the 20th century not only brought the Turing machine to life but also nurtured a tens-of-trillion-dollar information industry. AI4S is no different. It is rewriting the current and future landscape of productivity by seamlessly embedding "knowledge production" into industrial processes.

If we break down the productivity transformation brought by AI4S layer by layer, we can identify at least three progressive waves of industrial impact: first, direct industrial outcomes from scientific discoveries; second, an industrial ecosystem driven by full-chain demand; and third, the deepest layer — knowledge production itself is evolving into a brand-new form of industry.

The First Wave: Breakthroughs under a New Paradigm

AI4S enables researchers to reverse-generate scientific hypotheses from massive data, significantly improve the design and operation of scientific experiments, drive breakthroughs in complex systems and high-dimensional problems, promote deep interdisciplinary integration, and even conduct fully automated "self-driven research." It can sift candidate molecules from millions to thousands, transform material search from "trial and error" to "navigation-style" discovery, and simulate specific environmental contingencies thousands of times in virtual space.

This transformation reshapes traditional scientific research — which was highly dependent on individual experience and long-cycle trial and error — into a computable, iterable, platform-based engineering system, offering new breakthrough possibilities for problems that were once "untestable," "unaffordable in time," "unsolvable," or "unbearable."

Taking stock of the major breakthroughs AI4S has achieved in recent years in materials engineering, drug discovery, climate science, and other fields, its industrial disruptive power is already astonishing:

Google DeepMind's GNoME can predict 2.2 million new crystal structures at once — equivalent to the total output of human scientists over the past 800 years — delivering a dimensionality-reduction strike against traditional R&D in solid-state battery materials and new chip materials. Microsoft's MatterGen can automatically generate new materials directly from design requirements, almost completely颠覆ing traditional R&D pathways in batteries, catalysts, and semiconductors.

In drug discovery, the traditional process from target discovery to preclinical candidate drug confirmation takes 4-5 years, while AI can shorten this to 1-1.5 years. It is widely expected to break the "Double Ten Law" of new drug R&D (an average investment of $1 billion and a 10-year development cycle).

In meteorological services, models such as Google DeepMind's GraphCast and GenCast, Microsoft's Aurora, and NVIDIA's FourCastNet have demonstrated overwhelming performance advantages over traditional state-of-the-art numerical weather prediction systems, leading to their adoption by meteorological agencies in multiple countries.

It is clear that AI's involvement has significantly activated the "blockbuster" potential of frontier scientific research for industrialization, making scientific research once again a source of the "Midas touch" for industrial sectors. This is precisely why tech giants worldwide have focused in recent years on life sciences, materials science, and other fields where AI4S is most active, establishing AI4S laboratories — aiming to secure early positioning for the commercial value of frontier fields.

The Second Wave: Full-Chain Industrial Demand

If the first wave stems from the direct monetization of scientific discoveries, then from a broader industrial chain perspective, the second wave originates from the enormous "water seller" opportunities that the AI4S operating mechanism itself brings to the entire upstream and downstream.

AI4S couples large-scale data, computing power, engineering systems, and research problems in an unprecedented way. No single AI4S breakthrough is the victory of a single model. Behind it all lies the need for high-quality scientific data, specialized models, computing platforms, automated experimental systems, instrument interfaces, standard systems, validation platforms, and the pilot-scale and industrialization capabilities to carry forward research成果.

Specifically, is there an AI-ready high-quality data system? Can automated experiments and automated validation be conducted in a closed loop? Can model outputs be automatically converted into process parameters? ... Every technical node aimed at turning scientific discoveries into producible, regulatable, and deliverable products is generating strong industrial demand.

Around this core, a new AI4S industrial ecosystem is taking shape: scientific data services, automated laboratories, new materials pilot platforms, high-performance computing services, specialized model toolchains, instrument data interfaces, and standardization platforms have all become new industrial directions and battlegrounds for venture capital.

Multiple investment institutions estimate that the entire AI4S market and its surrounding supporting markets will give rise to a trillion-level market. This judgment is not simply betting on the explosion of a single market; rather, it should be understood as a systemic opportunity formed by the叠加 of multiple segments: data, computing power, models, experimentation, engineering, and industry applications.

In other words, AI4S is not an isolated track, but a collection of new industrial infrastructure. The trillion-level consensus is precisely the advance pricing by the keen-eyed capital market of the value of next-generation industrial infrastructure.

The Third Wave: The "New Industrialization" of Knowledge Production

While the first two opportunities are certainly noteworthy, if we shift our focus away from specific discoveries and products to examine the deep deconstruction AI4S is performing on "knowledge production" itself, we will find an even more disruptive trend:

AI4S endows knowledge production with direct productivity value, making "research itself" a brand-new form of industry.

This is the third and most far-reaching wave of industrial impact brought by AI4S. Specifically, in the past, there was often a long chain between basic research and industrial transformation: research required paper publication, publication required engineering verification and reproduction, and engineering had to go through industrial mass production before reaching the market. Each of these steps not only incurred significant time loss and capital consumption but also risked getting stuck or disconnected.

However, driven strongly by AI4S, the three steps of "discovery — validation — mass production" are seamlessly compressed into a digital闭环 of "data — model — validation." Knowledge, from the moment of its generation, has already undergone application constraints and validation screening. This not only improves the efficiency of knowledge production but also greatly shortens the chain from basic research to industrial transformation.

The significance of this transformation is no less than AI4S's reshaping of scientific research paradigms. AI not only bridges the long and模糊 gap between research and industry but also fundamentally eliminates, from a technical standpoint, the defects of traditional science that hindered transformation.

And the essential reason behind this is: Through AI4S, the "intermediate state" between scientific discovery and industrial application has disappeared.

This precisely explains why Hassabis can win a Nobel Prize while simultaneously raising $600 million through his founded company Isomorphic Labs. Because under the AI4S paradigm, Nobel-level scientific achievements and sellable products can even be the same file in physical form. This is epoch-making in the history of modern technology — it means that knowledge production is no longer just a distant starting point for industrialization, but itself possesses complete productivity attributes.

Similarly, GNoME mentioned earlier generates not just a theoretical crystal structure, but 2.2 million candidate structures that can be directly screened downstream. GraphCast's achievement is not discovering new equations of atmospheric dynamics, but an inference model that meteorological agencies worldwide can immediately adopt.

This brings profound structural changes to industry. The time lag between "publishing a paper" and "starting a company" is visibly disappearing. A striking example is that in recent years, after publishing AI4S papers in top journals, Chinese research teams generally concurrently set up companies for industrial implementation. This phenomenon, once very rare, is becoming the norm in the AI4S field.

Perhaps this "research as product" characteristic stems from the unique gene of computer science — where the research object and research method are one and the same — which, through the methodological载体 of AI4S, spills over into all disciplines that can be digitally represented. At the same time, it injects the underlying logic of software's "zero marginal cost of replication" into the ancient and expensive human activity of "knowledge production" for the first time, finally making it an object that can be transformed by "industrialization."

Thus, the deep integration of "computable scientific objects" and "engineered research workflows"徹底 pulls knowledge production out of the "handicraft era" of individual inspiration and into the "era of large-scale industry" — standardized, high-throughput, and scalable — making knowledge production itself an explosively imaginative industry. This is the key to the third wave of AI4S.

AI4S: Ushering in a New Era of Productivity

Admittedly, AI4S is still in its early stages. Behind those well-known success stories, from scientific feasibility to engineering feasibility, commercial feasibility, and regulatory feasibility, AI4S still faces multiple barriers including process stability, cost control, industrial supporting systems, and market access. In many disciplines, there is also a scarcity of high-quality, reusable data, high costs of building automated experimental platforms, and issues with the reproducibility and interpretability of model results.

However, the trend is clear. Just as the steam engine in the 19th century amplified human physical strength through machines, and the computer in the 20th century amplified human brainpower through computation, in the 21st century, AI4S may be driving an even more profound transformation — amplifying the value of "discovery" itself through AI.

Following this thread, we can more clearly see the progressive线索 revealed by these three waves:

The first wave is AI4S accelerating scientific discovery, making new drugs, new materials, and new models more惊艳.

The second wave is the formation of new infrastructure around data, computing power, models, experimentation, and pilot platforms.

The third wave is knowledge production itself beginning to be engineered, platformized, and assetized.

These three waves do not exist in isolation. Discovery offers new possibilities, infrastructure improves transformation efficiency, and the reconstruction of knowledge production methods turns discovery from an偶然 breakthrough in a few projects into an organizable, iterable, and verifiable continuous process. As knowledge production is increasingly incorporated into this "new industrialization" engineering workflow, scientific discovery is no longer just the living water at the source of industry, but a majestic river that continuously generates new industries, new tools, and new infrastructure.

Just as Kuhn asserted at the beginning: the disruption of old paradigms will surely give birth to the rules of a new world. Perhaps this is the most exciting aspect of AI4S — not just the dawn of a new scientific era, but the arrival of a new era of productivity.

(Author Qian Xuesheng is a Ph.D. in Intelligent Systems, Senior Researcher at the Smart City Research Center of Fudan University, and Deputy Director of the AI Special Committee of the Science Pictorial Editorial Board.)