At Code Class 2026, Sun Weijie, founder of DP Technology, an early portfolio company of Source Code Capital, delivered a talk on “AI for Science.” From systematically building the field’s underlying infrastructure from the outset to seeing AI for Science become a national strategic priority shared by China, the United States, and Europe in 2025, DP Technology has remained focused on one difficult but necessary task: building a common scientific foundation for research and discovery worldwide.
The following is an edited and abridged selection from Sun Weijie’s talk.
Thank you very much for inviting me to Code Class. Every time I attend, I feel an immense sense of pride and warmth. DP Technology was one of Source Code Capital’s early investments, and our core mission is AI for Science.
The ultimate goal of AI for Science is to create AI scientists that surpass humans across the board. Just as AI may surpass humans at writing code this year or next, the core problem AI for Science aims to solve has now achieved broad global recognition.
From Early Pioneer to Global Consensus
Founded in 2018, DP Technology was an early pioneer in AI for Science. We began building the field’s underlying infrastructure systematically from day one. The early path of exploration was exceptionally difficult, which is why we are especially grateful for Source Code Capital’s sustained support and partnership from the beginning. It was not until 2025 that AI for Science finally achieved global consensus as an industry direction.
In 2025, China, the United States, and Europe all introduced policies that elevated AI for Science to the level of national strategy. China’s “Artificial Intelligence Plus” action plan, released in July, placed AI for Science at the top of its priority areas. Europe’s Horizon plan, released in August, ranked it as the second strategic priority. In November, the United States announced the Genesis Mission, elevating the field directly to the level of a fundamental national policy.
It is worth remembering that the United States has launched only two comparable top-tier basic-science initiatives in its history. The first was the Manhattan Project, which supported the development of the atomic bomb. The second was the Apollo Program, which helped the United States gain a technological advantage during the Cold War. The Genesis Mission is the third, and its strategic intent is unmistakable.
Why does AI for Science occupy such a high strategic position? Put simply, it will be the primary gateway through which humanity acquires scientific knowledge and conducts research. It will also become the underlying engine of technological innovation across industries and a critical lever in competition among major powers. Notably, the six dimensions outlined by the U.S. Genesis Mission are broadly aligned with the “four beams and N pillars” infrastructure framework for AI for Science that we proposed in 2022.
The Limits of Traditional Research and the Answer from AI Scientists
What fundamental problem is AI for Science meant to solve? Our answer is that it is not merely about enabling breakthroughs in one or two subfields. It is about creating a common scientific foundation for research and discovery across the world.
For a long time, the return on investment of traditional human research has been extremely low—far lower than in factories, data centers, or internet platforms. The numbers are striking: global annual research spending is approximately $2.8 trillion, while China alone spends RMB 3.6 trillion each year, equivalent to roughly 2.7% of GDP. Yet the scientific output generated by this enormous investment remains limited.
The root cause is a fundamental weakness in the traditional research paradigm. Traditional research places human scientists at the center and relies on tools to explore the world. Its core activities can be reduced to three words: “read, compute, and do”—read the literature, run calculations, and conduct experiments. This model has two major pain points:
First, the tools are outdated and inefficient. Even today, the way we read the literature is not fundamentally different from how scholars leafed through printed papers in the seventeenth century. Research software and laboratory instruments have also failed to fully carry forward the productivity gains of successive technological revolutions.
Second, talent cannot be replicated. Human beings are the agents of research, but their scientific judgment cannot be copied, scaled, or compounded, directly creating a severe shortage of top talent. There are an estimated 60 to 80 million frontline research professionals worldwide, yet fewer than two million top scientists are believed to be capable of making core breakthroughs in new drugs, new materials, and basic science.
AI for Science will fundamentally change this situation. On the one hand, it can upgrade and replace traditional research tools with intelligent systems. On the other, AI agents can already simulate human researchers, take on research tasks independently, and complete the full loop from question to result. This is the core vision of DP Technology: to build AI scientists that surpass humans across the board and solve the global shortage of top scientific talent.
Based on the trajectory of technological progress and our accumulated experience, we believe AI agents capable of surpassing human scientists across the board may take shape within the next two or three years. An equally clear opportunity today is to make traditional research infrastructure intelligent—upgrading it while equipping AI scientists with the knowledge, compute, and experimental capabilities they need.
Simply increasing compute and execution capacity cannot solve complex scientific problems. It requires a mature tool system for “reading, computing, and doing.” With this goal in mind, we have built a closed-loop research system centered on scientific agents and integrating all three capabilities. An AI scientist cannot work without any one of these four elements.
We have already built AI scientists with the equivalent of five to eight years of experienced research expertise in multiple subfields. One example is MatMaster, an AI materials scientist developed jointly with the Suzhou Materials Laboratory. These AI scientists are built on SciMaster, our general-purpose scientific intelligence foundation. Internal testing shows that SciMaster reaches postdoctoral-level performance across disciplines and already surpasses human postdocs in core tasks including literature review, scientific computing, experimental operations, and scientific writing.
Read, Compute, Do: Three Research Infrastructure Pillars and Commercial Deployment
Around “reading, computing, and doing,” we have systematically rebuilt the three pillars of research infrastructure.
Knowledge systems (read). We have fully structured a vast body of high-quality literature, patents, and data from around the world. AI agents can search across these scientific resources and produce literature reviews with precise source tracing and low hallucination rates. A deep research report that takes the system ten minutes is equivalent to a month of work by a core R&D professional—a tenfold gain in efficiency. The challenge lies in data cleaning: manually cleaning and annotating a single PDF is expensive, and hundreds of millions of papers worldwide cannot be processed by humans alone. Through human-machine collaborative annotation, we have reduced the cost to nearly one-thousandth of the original level. Since the launch of Bohrium Science Navigator, its registrations and visits have remained the highest in the industry, and 4.35 million researchers worldwide now use our products.
Computing systems (compute). Compute tools are the core foundation for iterating on scientific research. We have completed the intelligent adaptation and MCP integration of scientific computing tools from open-source communities and public platforms, and our platform now hosts more than 50,000 categories of scientific software and tools. Where public tools cannot deliver high-precision computation, we develop specialized scientific foundation models, including Uni-SMART for multimodal scientific literature, Uni-AIMS for molecular representation, Uni-Fold for proteins, Uni-RNA for genes, DPA for atoms, and Uni-MOL for molecular conformations. Together, they support high-precision computation in materials, chemistry, biology, and other fields. Notably, in May 2026, DP Technology and its partners jointly released DPA4, a new model architecture to which DP Technology was a core contributor. DPA4 ranked first worldwide on the CPS composite metric in Matbench Discovery, a leading international benchmark for materials discovery, making it the latest SOTA model. On the authoritative molecular benchmark SPICE-MACE-OFF, DPA4 also achieved a new SOTA result with fewer parameters, surpassing the previous leader eSEN to take the top spot.
Experimental systems (do). Every frontier discovery ultimately has to be synthesized, tested, and validated in the laboratory. The central pain point today is that agents cannot interact efficiently with physical laboratory hardware because there is no unified underlying operating system. We were among the first to enter this field and developed UniLab OS, an intelligent laboratory operating system. It now connects and standardizes more than 150 broad categories and nearly 2,000 types of laboratory instruments, enabling AI to take control of the laboratory and conduct experiments autonomously.
The efficiency gains from this system are substantial. In polymer-materials R&D, it used to take a PhD student an entire morning to organize 100 sets of reagents and tune the equipment. Our intelligent system can now deliver the equivalent of a full day’s output from dozens of PhD students in a single day. In Shanghai, the intelligent discovery platform for peptide drug molecules alone can match the around-the-clock output of 500 to 800 chemists. In Yibin, our intelligent R&D system for electrolytes and solid-state electrolytes delivers output equivalent to that of nearly 100 specialists.
Once the infrastructure is in place, how do we close the commercial loop? Our capabilities can be packaged as standardized software services for customers, while large enterprises and research institutions can receive customized R&D solutions. Here I want to highlight the FDE, or forward-deployed engineer, model. It is particularly well suited to scientific research because it addresses three major pain points in the industry:
First, R&D needs are highly fragmented and long-tailed. For a cathode-materials customer, we mapped more than 160 R&D steps across the full process—far too many for any single-point tool to cover. But saving 30% to 40% of the time at each step compounds into a transformative gain in end-to-end efficiency.
Second, the industry has three separate barriers: AI technology, scientific expertise, and engineering implementation. People who understand algorithms may not understand materials chemistry, while domain experts may lack an understanding of AI. A product alone cannot bridge all three gaps.
Third, R&D data is a company’s core intellectual property, and companies will never share it externally. A model that depends on acquiring customer data to iterate is therefore not sustainable.
These three factors point to an optimal commercial model: use 70% to 80% standardized platform capabilities as the foundation, then add 20% to 30% customized development to fit each company’s R&D workflow deeply. To deliver this, we assembled a “three-in-one FDE task force” whose members combine the attributes of domain scientists, AI algorithm experts, and product-delivery specialists. They can translate customer needs into problems AI can solve in practice and deliver quickly.
DP Technology’s core strengths can be summed up in three words: earliest, deepest, and fastest. We were among the first companies in the world to define AI for Science and systematically build its research infrastructure. We have gone deep on foundational infrastructure with exceptional generality and fundamental reach, while remaining among the industry leaders in market coverage and commercial deployment. Today, millions of researchers worldwide and more than 100 universities in China use our platform, and hundreds of leading companies in fields including life sciences and materials science work with us closely.
The Next Five Years and 2040: A Transformative Shift
The direction of scientific research is fully aligned with the national strategies set out in China’s 15th and 16th Five-Year Plans. China’s core goals are to achieve basic self-reliance and strength in science and technology and address key technology bottlenecks by 2030, reach a high level of self-reliance and strength by 2035, and share Chinese research infrastructure and approaches with the world. AI for Science will play a central supporting role in this process.
Over the next five years, the world will see a wave of investment in AI for Science infrastructure, with data, compute, and experimental infrastructure all undergoing comprehensive upgrades. Traditional research-literature databases and scientific software will be fully replaced by AI agents. Scientific instruments will not disappear, but they will be upgraded into AI-native intelligent devices. The industry has reached a consensus: within five years, every R&D-intensive company in biomedicine, new materials, chemicals, and related fields will have to become AI-driven, or fall irreversibly behind in R&D efficiency.
Looking further ahead, we may witness a transformative shift in scientific research before 2040. Scientific discovery could become as simple as using a search engine, organized into a standardized industrial pipeline. Long-standing scientific problems that humanity has pursued for generations may finally be solved. The barrier to research could fall dramatically: a high-school student might be able to distill and define a scientific question, while the population able to participate in scientific discovery could grow from 245 million today to more than 500 million by 2040. The traditional logic governing the distribution of scientific value would be restructured.
The goal of achieving a high level of self-reliance and strength in science and technology will be realized by our generation of researchers. Our core vision is to create AI scientists that surpass humans and solve the global shortage of research talent—to build the Anthropic of scientific research.
