Summary of Key Points
80 years ago, the United States established its technological dominance through a model that involved government funding for basic research at universities and subsequent commercialization by enterprises (as outlined in the Bush report). However, facing the challenges of the AI era and competition from China, a new White House report has overturned this approach, shifting to a focus on a comprehensive competitive framework that spans from the laboratory to the factory. The emphasis is no longer solely on the number of published papers but on who can quickly transform scientific discoveries into products and build viable industries. Key aspects of the reform include allocating research funds in a manner similar to venture capital (willing to invest in high-risk projects), incorporating engineers and technicians as core research personnel, and using AI to revamp the research process (for example, the "Genesis Project" aims to integrate national research resources). At the same time, the U.S. is addressing its own weakness in the integration of research and manufacturing by trying to bridge this gap.
Why Has the Old Model Suddenly Become Ineffective? The Root of American Anxiety
The traditional American research model relied on universities conducting basic research (leading to Nobel Prize-winning papers) followed by enterprises commercializing the results. This model gave rise to innovations such as the internet and semiconductors. However, it has now encountered significant problems: there is a severe disconnect between research and industry. For instance, while American scholars made breakthroughs in lithium battery technology, Japanese companies developed commercially viable products, and China later became the global leader in battery production due to its complete industrial chain. The same applies to liquid crystal displays and semiconductor packaging and testing—America had the inventions but failed to gain control over manufacturing and industry leadership.
The competitive landscape in the AI era has changed. No longer is it about who discovers new principles first; instead, the focus is on who can quickly turn those principles into products, optimize processes, and build industries. Having numerous papers without practical applications is of little use, which is at the heart of American anxiety.
The New Report's Innovative Measures: How to Accelerate Research Transformation
The core of the new report is to accelerate the transformation of research findings. Three key areas have been revised:
1. Allocating research funds like venture capital: Previously, funding was decided by expert peer reviews, which were conservative and slow, preventing many disruptive projects (such as early mRNA research) from receiving support. Now, a "golden vote" system has been introduced, allowing individual experts to fund promising projects without consensus. The application process has also been simplified, with funds allocated within one month and providing five years of stable funding, freeing scientists from the constant task of managing projects.
2. Redefining research personnel: Traditionally, only doctors and professors were considered key researchers, but now engineers, technicians, and equipment operators are also recognized as essential. In the AI era, designing solutions is relatively easy; the challenge lies in implementing and optimizing them (for example, improving semiconductor yields requires on-site factory experience). Therefore, the U.S. plans to integrate engineering training into universities and open national laboratories to enterprises, re-establishing the multidisciplinary collaboration seen during World War II.
3. Reengineering the research process with AI: The "Genesis Project" has been launched to connect the supercomputers, databases, and AI systems of 17 national laboratories, creating a "scientific operating system." AI will act as the brain, supercomputers as the nerves, and laboratories as the experimental components, automatically conducting experiments and analyzing data. The goal is to double U.S. research productivity within ten years, as the future competition will not be about the number of scientists but about the efficiency of scientific production systems.
Why Has China Become a Benchmark for the U.S.?
Although not explicitly mentioned in the report, China's influence is evident everywhere:
- China's R&D investment (in terms of purchasing power parity) is already on par with that of the U.S.
- China's advantage lies in its ability to address market needs, drive corporate innovation, solve engineering problems, and optimize supply chains. For example, DJI has rapidly dominated the global drone market through its Shenzhen-based supply chain, and the cost of photovoltaic technology has been reduced by 90% in ten years, thanks to engineering and scaling.
- The U.S. is concerned that in fields like AI and robotics, the competition will not be about the number of papers in laboratories but about the speed at which entire ecosystems are implemented. China's "market-to-industry" model targets the U.S.'s weakness in the integration of research and manufacturing.
Two Major Barriers to Reform: Funding and Scientific Freedom
1. Where will the money come from? The report outlines various plans, but the Trump administration cut the R&D budget by 6% in 2026, with the Department of Energy’s Science Office cutting by 14% and ARPA-E (for advanced energy research) by 56.5%. There is a significant contradiction between the desire for reform and the lack of funding.
2. Will scientific freedom be compromised? Supporters argue that the old model was too conservative, and government-led strategies could focus resources on major initiatives (like the Manhattan Project). Critics worry that if research becomes too aligned with national strategies, fundamental areas like quantum mechanics may be neglected, as many great discoveries originated from free exploration.
The New Game of Sino-U.S. Technological Competition: A Full-Chain Race
In the past, both countries had their strengths—America excelled in basic research, while China was strong in engineering and manufacturing. Now, both are working to address their weaknesses: China is increasing investment in basic research, and the U.S. is focusing on improving manufacturing and commercialization capabilities. The future competition will not be about the number of papers or the size of laboratories but about the speed at which the entire process from scientific discovery to market application can be completed. Whoever can integrate these steps most efficiently will emerge as the winner.
For China, this indicates that in the AI era, while coming up with new ideas is becoming easier, turning them into reality is the true test of strength. China must maintain its advantages and continue to invest in basic research. For the U.S., this represents the most significant overhaul of its research system in 80 years, and success will depend on funding and effective implementation.
In summary, this reform is an attempt by the U.S. to adapt to the AI era and compete with China. It marks a shift from a focus on papers to practical applications, and from free exploration to collaborative efforts across the entire research and development chain. The winner will be the one who can most quickly transform ideas from laboratories into products in factories.