Why China's Prodigy Programs Have Increased Enrollment by 8x

In part 3, I briefly mentioned that DeepSeek founder Liang Wenfeng is an alumnus of Zhejiang University.

However, following this series to the end, I realized that line was actually part of a much larger story.

China didn’t just stumble upon AI talent; it has spent decades designing a state-level pipeline that makes such talent inevitable.

In this installment, I want to trace that blueprint from start to finish.

Having examined policies, factories, AI models, and finance, the final remaining question was how the people who create all of this are cultivated.

University at 15, PhD in your 20s: The USTC Gifted Youth Program

The symbol of China’s genius-nurturing system is the ‘Shaonianban’ (Gifted Youth Program).

It is an undergraduate program for gifted students around the age of 15, operated by six prestigious universities in Beijing, Zhejiang, Jiangsu, Shaanxi, and Anhui.

By Korean standards, this is a system where middle schoolers or high school freshmen sit in university lecture halls—a concept difficult to imagine.

Most famous is the program at the University of Science and Technology of China (USTC) in Anhui, often called the ‘KAIST of China.’ It was established in 1978 at the suggestion of Nobel laureate Dr. Tsung-Dao Lee. The average age of the first cohort was 14, with the youngest being just 11.

USTC itself is no ordinary university.

Founded in Beijing in 1958 with the goal of fostering scientific talent, it moved to Hefei, Anhui in 1970 due to the Cultural Revolution. Despite limiting enrollment to around 1,860 students per year to focus on elite education, it produces one academician (yuan-shi) per 1,000 graduates and over 700 master’s/PhD holders per 1,000, reflecting extreme density.

Professor Pan Jianwei, who led the research on the quantum computer ‘Zuchongzhi’ mentioned in Part 4, is also from this university. While the campus is spread across six locations, the Gifted Youth Program students attend classes alongside science, humanities, and medical students at the main campus. The quantum security from Part 4 and the talent cultivation in this part were rooted in the same university.

For the first two years, students do not declare a major, instead building ‘research stamina’ by strengthening their foundations in mathematics and physics.

It is common for them to define their specific research interests and earn a PhD within 3–4 years. Since they finish their studies in their early 20s, while Korean gifted students are obsessed with high school entrance exams, these students are already immersed in research. By the time their peers are college freshmen, they have already earned their PhDs and are diving into tech entrepreneurship or national strategic research.

Why the 8x increase in enrollment

It is worth noting that the scale of the Gifted Youth Program has expanded eightfold over the last decade.

From its inception in 1978 until 2009, enrollment hovered around 40–50 students. It jumped to around 200 starting in 2010 and reached a record high of 380 in 2024. Last year’s enrollment also reached 375, which can be read as a sign of continued government interest.

I don’t believe this timing is a coincidence.

Analysts suggest that as China accelerated its efforts to cultivate AI and semiconductor talent, the Gifted Youth Program was re-evaluated as a strategic national resource.

The early 2010s overlapped with China’s preparation for industrial upgrading strategies like ‘Made in China 2025,’ and the recent expansion coincides with the intensification of U.S. semiconductor and AI export controls.

It is natural to view this as desperation to cultivate top-tier talent domestically. As it becomes harder to bring in talent from the outside, the center of gravity has shifted toward cultivating more talent internally, starting at a younger age.

However, some evaluate that as the number of universities operating these programs has shrunk from six to three and enrollment is being capped again, the function is narrowing from a mass-talent training tool to an elite track for selecting and managing the absolute top tier.

It appears to be a constant balancing act between quantitative expansion and qualitative management.

The ‘Strong Foundation Plan’ and nationwide early selection

If the Gifted Youth Program is a track for the extreme few, the ‘Strong Foundation Plan (Qiangji Jihua)’ is the mechanism that filters for top-tier students from a much broader base. It replaced the previous ‘autonomous recruitment’ system in 2020 following equity concerns.

It is a system where top-tier universities like Peking University and Tsinghua University early-select top talent in basic sciences, humanities, and AI/computer/electronic information fields.

The scale is not small. As of 2023, 26% of Tsinghua’s freshmen and a staggering 29% of Peking University’s freshmen were admitted via the Strong Foundation Plan. Considering that one in three or four freshmen enters through a process that evaluates foundational academic potential separately from the general Gaokao score, it is a significant portion.

It is also characterized by the fact that students admitted through this plan are restricted from easily switching to non-basic science or humanities majors, suggesting a design intent to narrow their career paths early and focus them on those fields.

Combined with the ‘New Gaokao’ (China’s college entrance exam, 3+1+2 format) that structures entry into STEM fields, and the Gifted Youth tracks, some analyze that certain students gain experience equivalent to university lab projects as early as late high school.

One research institute analyzed that while the central Ministry of Education manages the total university enrollment, this system has expanded institutional autonomy, allowing universities to design major operations and specific programs according to industrial demand. This structure, where the center sets the broad framework and universities handle detailed implementation, bears a resemblance to the resource allocation method of the 15th Five-Year Plan examined in Part 1.

Yao Class and Turing Class: Elite tracks for AI

Once in university, even more exclusive special classes await. Tsinghua’s ‘Yao Class’ is the most representative.

Created by Professor Andrew Yao, who won the Turing Award—the ‘Nobel Prize of Computer Science’—in 2004, when he joined Tsinghua, it is known as a gathering of the world’s brightest undergraduates. Peking University has its corresponding ‘Turing Class.’ In an interview, a professor from the Turing Class calmly stated, “We are simple people. We just want to be really good at artificial intelligence, and we want to teach our students to be that way too.” It is a method of pouring the best education into the best talent without being constrained by equity with other departments.

After the Turing Class established its footing, efforts to spread its results more widely have been underway. Once the Turing Class’s internal education system succeeded, Peking University reportedly expanded it beyond computer-related departments to the humanities and social sciences to boost AI education effects across the entire university. It serves as a sort of laboratory and standard model that propagates verified methods from elite tracks to the entire institution. This diffusion method resembles the logic of the Strong Foundation Plan mentioned earlier. I get the impression that the pattern of experimenting for the few first, and then gradually expanding the method once results are verified, is being repeated throughout the education system.

Results are also visible in the numbers.

Looking at the number of authors of papers published in top-tier international AI conferences (ICML, NeurIPS, ICLR) by institution, Chinese institutions account for 31 of the top 100, ranking second after the U.S. (37). Among them, Tsinghua ranked 2nd, and Peking University and Zhejiang University tied for 6th. This means universities sit at the center of the Chinese AI ecosystem.

Tsinghua had already laid this foundation even before the Chinese government designated AI as a core national future technology in 2018. Recently, there is news that Tsinghua has even released the first university-wide comprehensive guidelines on how to use AI in education, which can be read as an attempt to redesign the way talent is cultivated using AI itself.

The Brain Supply Chain: Designing the entire life cycle

The concept that binds all these tracks together is the ‘Brain Supply Chain.’

It means the state directly designs talent specialized for every stage of the AI value chain, from algorithm design to data preprocessing, model training, and industrial application. One research institute summarized this as a ‘Life Cycle × Value Chain’ model.

From early selection in primary and secondary stages to special university classes, and subsequent collaboration with big tech firms, an individual’s entire growth process is structured like a supply chain.

It is easy to understand this concept by comparing it to a component supply chain.

Just as the dark factories examined in Part 2 procure parts to assemble finished products,

this brain supply chain identifies ‘raw’ talent in primary and secondary education,

polishes them through the Strong Foundation Plan and Gifted Youth Program,

processes them into near-finished products in the Yao and Turing Classes,

and finally connects them to big tech firms, the final demand source.

It is as if the process of raising a person has been meticulously designed in stages, just like an industrial supply chain.

While using the word ‘supply chain’ for humans might sound a bit cold, it seems like an accurate metaphor in that each stage connects to supply raw materials for the next.

The results of this strategy are clearly visible in patent and paper statistics.

Analysts suggest that China accounts for over 23% of global AI papers and around 70% of AI patents, with this full-cycle talent strategy at the core. Since a structure is in place where big tech firms like Tencent collaborate with universities to nurture talent specialized for the entire AI value chain, the path for graduates is already pre-designed.

It gives the impression that career paths are somewhat guided by the grain of one’s talent: algorithm design talent to research institutes, data preprocessing experts to industrial sites, and model training specialists to big tech research teams.

The evidence of Liang Wenfeng

The most concrete example that this system actually works is DeepSeek founder Liang Wenfeng.

As seen in Part 3, he pursued a bachelor’s in electronics and a master’s in information and communication engineering at Zhejiang University in Hangzhou.

Although Liang Wenfeng did not go through elite tracks like the Gifted Youth Program or the Strong Foundation Plan, it is worth noting that Zhejiang University itself grew within this state-level STEM cultivation current. Considering his background of experimenting with AI in quant trading since his graduate school days before founding DeepSeek, it can be read as a path where he applied the fundamental capabilities learned at school to the industrial field on his own.

Industry insiders do not view Liang Wenfeng’s emergence as a coincidence of a single geeky genius, but as the result of the government’s AI talent strategy. This means it is the result of a system where special classes gather and nurture top talent from across the country like an elite force, while simultaneously running a strategy to expand the base by establishing AI departments in over 580 universities nationwide.

The key is that these two strategies—’elite force’ and ‘base expansion’—work together.

If tracks like the Gifted Youth Program, Yao Class, and Turing Class nurture the top 1% with extreme precision, general AI departments at 580+ universities create a much larger labor pool below them.

The fact that talent can emerge without going through top-tier elite tracks, like Liang Wenfeng, can be attributed to the fact that this base is sufficiently thick.

Unitree founder Wang Xinxing (from Part 2) and Liang Wenfeng (from Part 3) were both individuals who emerged on top of this state-level educational design.

The shadows of the Gifted Youth Program and Strong Foundation Plan

However, this system is not without its shadows.

This is the exact point of contrast with the youth unemployment discussed in Part 5. While a minority of elites are entering university at 15 and becoming PhDs in their early 20s with full state support, one in seven young people on the other side cannot find a job, leading to the creation of self-deprecating buzzwords.

The fact that the autonomous recruitment system before the Strong Foundation Plan was abolished due to equity controversies shows the fairness issues that this early selection system inevitably carries.

Strategies that concentrate resources on the top tier may help national competitiveness, but to the majority outside that, it can manifest as another form of inequality.

One expert made a very interesting point about this elite education.

They argued that if talent is scarce, the answer isn’t to create more graduate schools to graduate students faster, or to force AI advanced learning at a younger age. The key, they point out, is to cultivate foundational learning capabilities to pierce through phenomena and the challenging spirit to create new changes that others haven’t. It seems that USTC’s Gifted Youth Program’s requirement for students to focus on foundational studies for about two years without declaring a major is aligned with this critical awareness. It is a design that prioritizes foundational stamina over speed.

This is also a suggestion for Korea’s talent policy, reading as a message that the competition for cutting-edge strategic technologies like AI, bio, and quantum is ultimately a competition for talent, and that we must break free from outdated customs.

Another side effect of this early selection system is the lowering of the age of entrance exam competition.

Top-tier students and parents have no choice but to feel the pressure to start preparing for the Gifted Youth Program or Strong Foundation Plan from middle school. Just as the competition for specialized high schools and gifted schools in Korea has already seen a lower age threshold, there is a high possibility that as these early selection tracks expand in China, the competition in the preceding stages will intensify as well. The fact that the Gifted Youth Program enrollment has increased eightfold likely means the number of students and parents preparing for this track has also increased, and it is worth noting that the burden borne by the remaining students who failed to make the cut is not well captured in statistics.

From Part 1 to Part 6, this series has followed the pieces of China’s great transformation—policy, factories, AI models, quantum security, the shadows of the economy, and the flow of money.

Part 7 was a story about the people who create all those pieces—how the brains themselves are cultivated.

Policies set the direction, and factories, AI models, and financial infrastructure materialize that direction, but it is ultimately people who design and operate all of this.

If you think about the 15-year-old selected for the Gifted Youth Program becoming a bureaucrat who designs the next version of the ’new quality productive forces’ policy 20 years later, an engineer operating a dark factory, or a founder building the next DeepSeek, it becomes a bit clearer why this talent pipeline is designed so precisely.

Comparing it to the situation in Korea, many thoughts arise.

While systems like science gifted schools and early admission to KAIST are already in place in Korea, there is a significant difference in scale and the level of national resource input compared to the pipeline connecting China’s Gifted Youth Program, Strong Foundation Plan, Yao Class, and Turing Class.

Korea’s gifted schools or early KAIST admissions are closer to individual school-level programs, and there isn’t a structure where the state meticulously designs career paths beyond that.

On the other hand, China is different in that it connects an individual’s entire growth path to national strategic industries, from primary/secondary selection to university special classes and post-graduation industrial placement. Above all, China is different in that it has prepared clear demand sources—national strategic industries (semiconductors, AI, robotics)—at the end of this pipeline.

Cultivating talent and growing the industries that will utilize that talent are happening simultaneously within the same policy design.

In the next installment, I plan to conclude this series by examining the strategies Korea can choose in the face of all these currents, such as the ‘hinge theory’ and supply chain early warning systems.

The final question will likely be where Korea, having faced all these pieces—policy, factories, AI, quantum, economy, finance, and now talent—can stand.

What I confirmed throughout this journey was the fact that not one of these pieces was a coincidence.