CAS Academician E Weinan: Independent Researchers Will Be the Main Force in Scientific Research in the Future; in the AI Era, Every Team and Individual with Ideas and Capabilities Can Participate in Technological Innovation
At the Guanghua Thought Power Forum, Academician E Weinan stated that AI will greatly lower the barriers to scientific research and promote research democratization, and proposed building an AI-native industrial software system to achieve overtaking on a new track.
Key Takeaways
- Key Highlight：At the Guanghua Thought Power Forum, Academician E Weinan stated that AI will greatly lower the barriers to scientific research and promote research democratization, and proposed building an AI-native industrial software system to achieve overtaking on a new track.
- Innovation & Tech：Highlights advancements in CAS, Academician, Weinan, demonstrating rapid progress in model capabilities.
- Industry Impact：Reported via IT之家 (CN), offering actionable signals for developers and technology leaders.
E Weinan, Academician of the Chinese Academy of Sciences and Chair Professor at Peking University, stated at the Guanghua Thought Power Forum on October 17 that artificial intelligence is essentially a combination of mathematics and computer engineering, and will greatly lower the barriers to scientific research and promote research democratization. He predicted that through open laboratories and cloud services, every team and individual with ideas and capabilities can participate in technological innovation, and that independent researchers are expected to become the main force in scientific research in the future.
The core of this viewpoint lies in AI's reshaping of the productive relations in scientific research. Traditional research heavily relies on large institutions, expensive equipment, and long-term training, while the proliferation of AI toolchains enables individuals and small teams to carry out computational simulations, data analysis, and model building that were previously only possible in top-tier laboratories. This lowering of barriers may change the distribution landscape of research talent.
E Weinan also pointed out that enterprises engaged in actual production lack original innovation and can only compete in terms of management and pricing, with the root cause of involution being insufficient innovation capability. He believes that high-end industrial software and high-end CNC systems for machine tools are currently subject to external control, but that autonomy and controllability can be achieved in a relatively short period, and even overtaking on a new track is possible by building an AI-native industrial software system.
AI-native industrial software means no longer layering AI features on top of traditional software architectures, but instead redesigning the software system from the ground up with AI at its core. If this path proves viable, it could provide China with new opportunities to bypass traditional technological barriers in the industrial software field, but it also faces long-term challenges such as data accumulation, engineering validation, and ecosystem development.
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Industry Insights & Analysis
As artificial intelligence rapidly evolves, breakthroughs surrounding CAS, Academician, Weinan, Independent are shifting toward scalable, robust real-world implementations.
Driven by both open-source ecosystems and proprietary model architectures, the integration between compute optimization, data engineering, and agentic workflows is accelerating. This development provides a strategic benchmark for upcoming AI tooling and developer workflows.