AI and the Low-Altitude Economy: Structural Changes in Global Airspace Competition

AI and the Low-Altitude Economy: Structural Shifts in Global Airspace Competition

The low-altitude economy is becoming the most imaginative strategic emerging industry after the digital economy. From drone logistics to air taxis, from precision agriculture to emergency rescue, the airspace from 1,000 meters up to below 3,000 meters above the ground is being redefined as a new frontier for economic growth. And artificial intelligence (AI), as the most critical technological variable of this era, is widely regarded as the key to unlocking the potential of the low-altitude economy. But a recent study based on panel data from 30 Chinese provinces from 2012 to 2022 points out that the relationship between AI and the low-altitude economy is far more complex than a simple "technology empowerment" narrative: it is neither a uniformly rising straight line nor does it have universal positive spillover effects. This conclusion is challenging the existing assumptions of global policymakers about AI and the low-altitude economy.

The Low-Altitude Economy: The Next Strategic High Ground of Global Competition

The low-altitude economy encompasses multiple areas, including drones, electric vertical takeoff and landing aircraft (eVTOL), air traffic management, and low-altitude infrastructure. The International Civil Aviation Organization and national regulators have not yet formed a unified definition, but major economies such as China, the United States, the European Union, and Japan have all incorporated the low-altitude economy into their national strategic visions. In 2024, the global drone market reached US$88.9 billion, with civil drones accounting for as much as 76%. This is only one segment—drones. If one adds low-altitude aircraft manufacturing, operational services, infrastructure, and the resulting applications in logistics, agriculture, and urban management, the potential scale of the entire low-altitude economy ecosystem would be even larger.

China's approach is particularly aggressive. Currently, more than 70% of drone-related patents worldwide originate from China. At the end of 2024, China's National Development and Reform Commission formally established the Low-Altitude Economy Development Department and proposed reaching an industrial scale of 3.5 trillion yuan by 2035. This top-down administrative push, combined with a massive manufacturing base and local pilot cities, gives China a unique institutional advantage in the low-altitude arena. However, this latest study reminds us that a lead in scale does not imply an unobstructed path for technological empowerment.

AI Empowerment: A Complex Mechanism Beyond Linear Narratives

From a technological standpoint, AI's role in the low-altitude economy is unquestionable. Computer vision allows drones to identify obstacles in complex environments; autonomous navigation algorithms enable aircraft to position themselves accurately in GPS-denied environments; large language models and generative AI give low-altitude platforms real-time decision-making and swarm coordination capabilities. Together, these technologies significantly enhance the efficiency, safety, and scalability of low-altitude operations while reducing energy consumption and operational costs.

However, empirical research reveals a counterintuitive phenomenon: AI's impact on the development of the low-altitude economy follows an "inverted U-shape." In the early stages, AI's technological penetration clearly drives low-altitude economic growth—autonomous flight reduces labor costs, intelligent scheduling optimizes airspace use, and data-driven maintenance extends the lifespan of aircraft. But when AI development surpasses a certain critical point, its marginal contribution to the low-altitude economy begins to decline, and may even turn into a negative constraint.The root of this nonlinear relationship lies in the fact that growth in the low-altitude economy does not depend solely on algorithmic breakthroughs; it also requires matching physical infrastructure, airworthiness certification systems, airspace management institutions, and operator skills. When AI capabilities expand rapidly while "institutional capacity" is insufficient, the technology falls into "idle spinning." For example, a low-altitude logistics network with a top-tier autonomous driving algorithm, if lacking mature low-altitude route planning and urban takeoff/landing support, may achieve lower operational efficiency than a simpler but reliable solution.

Spatial Divergence: One Region's AI Dividend Can Be Another Region's Pressure

What deserves more attention is the spatial spillover effect of AI on the low-altitude economy. Research shows that the AI development level of a province has a "U-shaped" impact on the development of the low-altitude economy in neighboring regions. This means that in the early stage, the siphoning effect of local AI deprives surrounding areas of resources—talent flows to technology centers, capital concentrates in pioneering regions, and policy resources are tilted toward demonstration zones, causing short-term damage to surrounding low-altitude industries. However, as AI technology matures in core regions and industries spill over, diffusion effects begin to emerge, and neighboring areas can benefit by undertaking technology transfer, building industrial chain support, and coordinating airspace.

This U-shaped spatial trajectory provides an important warning for balanced regional development in China and even globally. In the stage when the low-altitude economy has not yet formed a stable profit model, if all regions simultaneously pursue independent development of cutting-edge AI, it may exacerbate duplicate construction and resource waste. Conversely, a division-of-labor system based on comparative advantages—one region focusing on AI algorithms, another building test airspace, and a third developing operational services—may be more conducive to overall long-term growth. However, in reality, competition among administrative regions tends to favor the "whole-industrial-chain model," which may precisely cause the inverted-U turning point to arrive prematurely.

The Dual Paradox of Human Capital and Technological Innovation

The research further points out that the two transmission channels of human capital and technological innovation show weak or even negative effects in the short term. This deviates from the traditional economic assumption that technology drives growth by enhancing human capital and promoting innovation. The reason lies in short-term "resource mismatch" and "skill mismatch": the rapid introduction of AI technology requires workers to quickly master new skills, but the education and training system has an obvious lag. At the same time, the low-altitude economy is still an early-stage industry; a large influx of capital into AI research and development may suppress investment in traditional hardware such as aircraft bodies, battery technology, and air traffic control radar—which are precisely the physical foundation of the low-altitude economy.

The policy implication of this finding for all countries is that one cannot simply "prioritize AI over the low-altitude economy." The rise of the low-altitude economy requires balanced development across artificial intelligence and fields such as aerospace engineering, materials science, energy storage, and communication networks. If policies tilt excessively toward AI, they may distort resource allocation and instead delay the substantive maturation of the industry.

Policy Experiments and Global Path Differences## Policy Experiments and Divergent Global Pathways

To verify the robustness of the conclusions, the researchers treated the National Big Data Comprehensive Pilot Zone policy as an exogenous shock and employed a spatial difference-in-differences model for testing. The results show that the Big Data Pilot Zone policy has a significant positive promoting effect on the low-altitude economy, further highlighting the importance of data infrastructure in this context. However, this policy effect also has a threshold—the scaling up of data centers does not automatically translate into the prosperity of low-altitude application scenarios.

Major global economies exhibit marked differences in their pathways for the low-altitude economy. The United States, drawing on strong AI algorithms and Silicon Valley capital, emphasizes commercial closed loops for drone logistics and urban air mobility; Europe prioritizes airworthiness certification and social acceptance, leaning toward a gradualist low-altitude traffic management system; China, by contrast, advances the entire industrial chain through central-local coordination and its vast market capacity. These pathways have no absolute superiority or inferiority, but research demonstrates that the degree of coupling between AI and the low-altitude economy depends on each economy's institutional flexibility, factor endowments, and geospatial structure.

Long-Term Trends: Airspace Authority and Governance Challenges

Viewed from a broader perspective, the low-altitude economy heralds a power shift over airspace resources. Traditionally, airspace has been strictly controlled by the state, with military and commercial aviation occupying an overwhelmingly dominant position. The low-altitude economy, however, calls for treating airspace as a public resource that can be operated with fine granularity and opening it to drones and urban air vehicles. This involves complex issues such as national security, privacy protection, noise pollution, and insurance liability—far beyond what any single technical department can resolve.

In this context, AI is both an enabler and a potential source of risk. Highly autonomous low-altitude systems, if lacking explainability and reliability, could produce catastrophic consequences under extreme weather, cyberattacks, or system failures. Therefore, the long-term development of the low-altitude economy inevitably requires establishing a set of cross-regional and cross-national technical standards and governance frameworks. As the research points out, if the influence of AI has spatial boundaries, then cross-border low-altitude flight—such as cross-border logistics and international emergency rescue—will depend even more on regulatory coordination at the global level.

For the future, the story of AI and the low-altitude economy is not simply one side driving the other. It more closely resembles a complex chemical reaction: artificial intelligence provides the catalyst, but the reaction rate is jointly determined by airspace institutions, infrastructure, human capital, and social acceptance. Only those countries or regions that can first find the equilibrium point will truly seize the commanding heights in the next round of global airspace competition.