The intrinsic qualities of basic research and its deep relationship with economic structure

Basic research, also known as "original innovation," is essentially an exploration of natural laws and scientific principles, not directly aimed at specific applications. This type of research has three core characteristics: high uncertainty, delayed explicit output, and strong externalities.

Firstly, the results of basic research are often difficult to predict. A seemingly "niche" study may become the starting point for disruptive technology a decade later. For example, early quantum mechanics was only meant to explain the behavior of microscopic particles, but today its results are driving new directions in industries like quantum communication and quantum computing.

Secondly, the return cycle of basic research is extremely long, and it rarely translates into economic benefits in the short term. However, precisely because of this, it has the ability to reshape the underlying structure of the technology stack. Once it enters the application phase, its driving effect often expands geometrically, having a multiplier effect on overall productivity improvement.

More critically, the externalities of basic research are very strong, meaning its results can be reused across multiple industries and regions, not owned by a single entity. This "knowledge sharing" mechanism makes it similar to public infrastructure in the economic structure, an indispensable "non-competitive asset" that drives long-term growth.

In this sense, basic research is not a marginal existence, but rather the core tension in the deep evolution of the economic system.

The scientific accumulation behind technological breakthroughs and industrial transitions

To understand how basic research becomes a lever for economic growth, it is helpful to trace the underlying paths of several major technological transformations:

——Information Revolution: The birth of the semiconductor industry is rooted in quantum physics research from the 1930s to the 1950s. The systematic exploration of solid-state physics by Bell Labs provided the theoretical foundation for the invention of the transistor, which in turn gave rise to entirely new industrial chains such as computers, communications, and integrated circuits. The entire process spanned nearly 30 years, yet its industrial value continues to be released to this day.

——Biotechnology Wave: The core of modern bioengineering comes from the development of molecular biology in the 1970s, particularly the revelation of the DNA double helix structure and the understanding of gene expression mechanisms. The rise of fields such as CRISPR gene editing, synthetic biology, and personalized medicine are all derivatives of basic biological research.

——Rise of Artificial Intelligence: The neural network models behind deep learning can be traced back to the perceptron theory of the 1950s and the backpropagation algorithm of the 1970s. When computing power was not yet mature, these theories were long regarded as "ivory tower" research, only to explode with significant industrial impact when hardware conditions matured in the 2010s.

These examples indicate that the rise of an industry often does not begin with market demand or commercial motivation, but rather with the validation of a concept in basic science that forms a new path. Once such breakthroughs occur, they can trigger a leap in the entire economic structure.

Case Analysis: The Fundamental Shortcomings in China's Investment in Science and Technology

In recent years, China's total R&D investment has jumped to the second highest in the world, but the issue of "low proportion of basic research" in its structure remains prominent. According to data from the National Bureau of Statistics, in 2023, the proportion of basic research in China's total R&D expenditure was only 6.57%, while countries like the United States and Israel were above 15%.

The reasons for this situation are multiple:

Firstly, the assessment orientation is biased towards short-term effectiveness, with local governments and enterprises more inclined to focus on "visible" results, such as the construction of industrial parks and the transformation of high-tech, while basic research, due to its "lack of clear output," is often overlooked.

Secondly, the dominance of the "project-based" system in the research framework leads researchers to favor topics with lower risks and clearer expected outcomes, making it difficult to concentrate efforts on tackling long-term, high-risk fundamental issues.

Thirdly, enterprises, as the main body of R&D investment, lack a tradition of basic research and focus more on product iteration and market competition, with very few making foundational breakthroughs.

These issues have led to a paradox: on one hand, national policies encourage "original technology," while on the other hand, the soil for basic research has not been sufficiently cultivated, resulting in a "gap between policy calls and institutional supply."

However, it should also be noted that in recent years, reforms in the National Natural Science Foundation, the restructuring of key laboratory systems, and the construction of "frontier interdisciplinary platforms" are attempting to correct this bias and create a more stable and resilient ecological environment for basic research.

Is the economic return of basic research quantifiable?

A common question is: Is basic research worth the high investment? Is its economic return predictable?

In fact, there has been in-depth research on this in the field of economics. For example, economists Zvi Griliches and Paul Romer have suggested that the return on investment in knowledge capital may far exceed that of traditional capital. Griliches found in his research on agricultural technology that for every dollar invested in basic research, there is an average long-term output of 20 to 50 dollars.

At the macro level, cross-country comparisons among OECD countries also indicate a significant positive correlation between the intensity of basic research (as a proportion of GDP) and the growth of total factor productivity in the medium to long term. For instance, countries like Finland, Israel, and South Korea have consistently maintained a high proportion of investment in basic research, which has led to industrial restructuring and a leap in high value-added exports over the subsequent two to three decades.

Moreover, the National Science Foundation of the United States has tracked a group of "neglected disciplines," including mathematics, particle physics, and fundamental chemistry. The results showed that the research outcomes in these fields have been widely utilized in subsequent industrial design, information algorithms, and new materials, with their actual economic impact far exceeding initial estimates.

Of course, the investment in basic research should not be evaluated solely based on "short-term ROI." Its value is more reflected in "directional guidance" and "expansion of technological options." Economic growth is not just a linear accumulation; it is the result of repeated leaps in innovation, and these leaps are often ignited by a new discovery in a particular laboratory.

How national policies establish a long-chain mechanism of "research and industry"

To achieve an effective connection between basic research and economic growth, a complete set of long-chain mechanisms that cross time, disciplines, and departmental boundaries must be constructed. This includes:

First, a stable financial support structure. Basic research should have a "financial safety net" mechanism to avoid falling into the short-sighted logic of the market. Establishing cross-cycle special funds and ensuring multi-year continuous investment in basic research teams is a prerequisite for ensuring that exploratory research is not interrupted.

Second, the construction of academic-industry crossover platforms. The results of basic research often need to be combined with engineering capabilities to be transformed into practical technologies, which requires the establishment of "knowledge translation" platforms between universities, research institutes, and enterprises. For example, MIT's Lincoln Laboratory and Huawei's Noah's Ark Lab are attempting to build such bridges.

Third, the optimization of the scientific talent training system. Basic research relies on long-term accumulation and academic freedom, necessitating the establishment of a more fault-tolerant evaluation system that encourages young scientists to take original risks. Initiatives such as the "Outstanding Youth Program" and "Leading Talent in Science and Technology" are actively exploring this.

Fourth, the reconstruction of corporate research culture. Encouraging leading enterprises to establish basic research departments and guiding them to undertake some basic research tasks through tax reductions and government co-construction of laboratories. Typical examples include Huawei's "2012 Laboratory" and Alibaba's Damo Academy, which are transforming into "science-based enterprises."

Only by connecting these links can basic research become a truly usable economic resource rather than an abstract academic stock.

From “Investment” to “Expectation”: The Establishment of Social Cognition and Policy Patience

Although the importance of basic research has increasingly been recognized, its “slow output” characteristic is still easily marginalized under political and social pressures. Therefore, it is crucial to establish reasonable expectations and patience for basic research across society.

This is not only a matter of research policy but also a shift in cognitive approach.

At the public level, efforts should be made through science popularization education, transparency in the research process, and scientific communication mechanisms to help the public understand that scientific research is not achieved overnight and is not always aimed at solving immediate problems. A research culture that is more accommodating of “failure” and “waiting” is the soil for the development of basic research.

At the government level, basic research should be regarded as an important component of national governance capability, maintaining stability and long-term investment, even if there are no “political achievements” in the short term, support should still be steadfast. This is a strategic belief and the foundation of national innovation capability.

Ultimately, the quality of economic growth depends on whether there is sufficient depth of “future variables.” Basic research is precisely the nurturing ground for these variables, the silent starting point of structural transitions, and an invisible lever to propel the future.