The hollowing out of U.S. manufacturing has reduced the availability of large-scale, real-world application scenarios, constraining the translation of technological advances into broad operational deployment. In contrast, China has pursued a “new type of whole-nation system” that concentrates resources on strategic bottlenecks—such as semiconductors, industrial software, and aero-engines—while advancing domestic substitution through tightly coordinated ecosystems. Platforms like Huawei’s HarmonyOS, Euler, and Ascend exemplify a full-stack approach to self-reliance, reinforced by scenario-driven deployment in areas such as industrial 5G, smart grids, and vehicle–road collaboration, where applications continuously feed back into R&D.
This closed loop—bottleneck breakthroughs, domestic substitution, ecosystem collaboration, and scenario-driven iteration—has reshaped the logic of technological competition by accelerating learning and diffusion at scale. As a result, China has cultivated a dense set of “ready-to-deploy” scenarios that allow AI and digital technologies to penetrate daily life and business more rapidly. By comparison, the United States continues to lead in frontier innovation but faces structural challenges in system-level deployment, challenges that complicate its response to China’s increasingly integrated and application-rich model of technological competition.
The Strategic Cost of Manufacturing Hollowing: How the Loss of Application Scenarios Erodes America’s Technological Competitiveness
The hollowing out of American manufacturing has produced consequences far deeper than a simple reduction in factory count or industrial employment. More fundamentally, it has deprived the United States of dense, high-frequency application scenarios that are essential for transforming scientific breakthroughs into durable industrial advantage. This erosion has weakened the country’s systemic capabilities across technology transfer, industrial iteration, innovation ecology, and strategic resilience, thereby reshaping the long-term balance in Sino–US competition in science, technology, and industry.
First, the absence of robust domestic application scenarios has created a widening gap between laboratory innovation and market realization. The United States retains world-leading strengths in basic research and early-stage prototyping, yet increasingly lacks the intermediate layer of engineering validation, productization, and scalable manufacturing. As a result, many cutting-edge technologies remain suspended at the level of academic demonstrations and patents, unable to enter a self-reinforcing cycle of real-world deployment, problem exposure, and iterative improvement. Innovation thus concentrates in isolated peaks rather than accumulating into broad, industrialized plateaus of capability.
Second, the decline of manufacturing has slowed the nation’s capacity for “evolution through use.” Advanced technologies mature not primarily through theoretical optimization, but through repeated exposure to complex, high-stress, real-world environments. Without domestic industrial systems that continuously generate demanding operational data, iteration becomes dependent on simulations or fragmented overseas feedback. This weakens model generalization, engineering robustness, and adaptability in frontier scenarios, and lengthens the cycle from concept to reliable deployment. In contrast, environments that embed technologies directly into large-scale operations accelerate learning and compress innovation timelines.
Third, the loss of application scenarios has distorted the innovation ecosystem itself. Capital and talent increasingly favor light-asset, fast-return activities that do not require physical validation, long development cycles, or production-line integration. Hardware-intensive and manufacturing-linked innovation—precisely the domains that depend most on real-world scenarios—struggles to attract sustained investment and top engineering talent. The result is an imbalanced structure: a vibrant upper layer of conceptual and digital innovation resting on a weakened middle layer of engineering realization and a hollowed-out industrial foundation.
Fourth, the erosion of manufacturing-linked scenarios has undermined strategic resilience. In moments of crisis—whether public health emergencies, geopolitical shocks, or supply-chain disruptions—the absence of domestically exercised production pathways slows response times and limits credible alternatives. Supply chains appear efficient in normal conditions but reveal blind spots under stress, as components, materials, and processes lack domestically validated substitutes. Strategic planning thus becomes more theoretical and less grounded in tested industrial realities.
Finally, the long-term detachment from complex manufacturing environments has shaped a rigid cognitive framework among policymakers and analysts. Overreliance on technological determinism and market spontaneity leads to systematic underestimation of scenario-driven, system-level innovation paths pursued elsewhere. When application scenarios are dismissed as secondary or distorted, strategic assessments lag behind actual capability evolution, resulting in delayed and reactive policy responses.
In sum, the hollowing out of American manufacturing has deprived the United States of the application scenarios that function as the crucible of industrial learning. This loss has weakened the feedback loops that convert knowledge into power, innovation into systems, and advantage into resilience. Rebuilding competitiveness therefore requires not only breakthroughs at the laboratory frontier, but the deliberate reconstruction of dense, real-world application environments in which technologies can be tested, stressed, refined, and scaled.
The Erosion of America’s Engineering Intuition
In contemporary discussions of technological competition, a recurring contrast is drawn between China’s AI ecosystem and that of the United States. This contrast is often framed in terms of innovation versus application, but at a deeper level it reflects divergent “training grounds” within modern industrial civilization. The difference is not simply about who invents better algorithms, but about which society continuously cultivates engineers who internalize technology through daily, embodied interaction with complex systems.
China’s advantage lies in the abundance of real-world application scenarios for AI. Its massive, highly digitized consumer and enterprise markets—spanning mobile payments, e-commerce, logistics, transportation, healthcare, and manufacturing—provide fertile ground for deploying AI at scale. Strong state support accelerates adoption across sectors, while large, centralized data pools reduce friction between experimentation and implementation. As a result, AI is woven into everyday economic and social operations, reinforcing a tight feedback loop between theory, deployment, and iteration.
By contrast, the United States remains dominant in foundational AI innovation—models, chips, and algorithms—but operates within a narrower band of applied contexts. Decades of industrial offshoring have thinned domestic manufacturing capacity, limiting opportunities to integrate AI deeply into production environments such as advanced machine tools, consumer electronics assembly, or large-scale industrial automation. AI deployment is thus concentrated in sectors like software, finance, and healthcare, while privacy constraints and fragmented data ecosystems further slow diffusion into operational settings.
The consequence is more profound than the loss of manufacturing jobs or GDP share. Manufacturing is a tangible expression of a nation’s technological consciousness. When engineers can debug domestically produced machine tools, optimize real transportation control networks, or observe battery cells being assembled on a factory floor, they develop tacit knowledge—an intuitive grasp of system coupling, failure modes, and iterative rhythms that cannot be learned from abstraction alone. This embodied understanding forms the substrate of long-term innovation.
As scholars such as Gary P. Pisano and Willy C. Shih argue in Producing Prosperity, removing manufacturing capabilities ultimately undermines a nation’s ability to innovate. What the United States has gradually lost is not merely industrial capacity, but a civilizational reservoir of engineering intuition. Meanwhile, China’s dense web of industrial and digital scenarios is cultivating a generation of composite engineers—fluent in technology, grounded in industry, and attuned to users. This accumulation of tacit knowledge, difficult to measure and even harder to replicate, may prove decisive in long-term technological competition.
China’s New Whole-Nation System: Institutional Design for Reducing Innovation Friction
China has not reverted to a traditional planned economy. Instead, it has constructed a hybrid innovation system that combines state-defined strategic direction, market-based competition, and ecosystem-level coordination. The core logic of this “new type whole-nation system” lies in institutional engineering: reducing friction between research, industry, and application so that scientific capability can be converted into scalable engineering outcomes with minimal loss. Rather than replacing markets, the system reshapes incentives, sequencing, and coordination across actors.
In the semiconductor sector, this approach is visible in the restructuring of state capital and industrial organization. The third phase of the National Integrated Circuit Industry Investment Fund concentrates resources on foundational bottlenecks—equipment, materials, and EDA—rather than diffuse expansion. Under policy guidance, firms such as SMIC, YMTC, and CXMT pursue differentiated technological paths, while domestic equipment suppliers iterate rapidly through verification inside local fabs. This “task-oriented R&D plus closed-loop process validation” shortens feedback cycles and accelerates engineering maturity.
A similar pattern appears in industrial software. Indigenous ERP, CAD/CAM, and PLM systems have advanced not primarily through isolated laboratory breakthroughs, but through sustained stress testing in real industrial environments. Large state-owned enterprises provide complex, high-stakes application scenarios that force domestic software to meet operational standards, creating a feedback loop in which usability, reliability, and scalability are proven through deployment rather than abstract benchmarks.
In aero-engines, the system reaches its most integrated form. Program-level goals, such as the CJ-1000A, act as organizing cores that align research institutes, enterprises, and universities around unified models, standards, and supply chains. Materials research, manufacturing processes, and system integration are coordinated through model-driven collaboration, transforming fragmented expertise into cumulative engineering capability. Across sectors, the defining advantage of China’s approach is its ability to compress the distance from laboratory insight to production deployment, converting dispersed innovation potential into concentrated, system-level momentum—an integration capacity that many advanced economies, including the United States, struggle to replicate at scale.
From Indigenous Substitution to System-Level Rulemaking: China’s Push for Ecosystem Sovereignty
China’s strategy of “domestic substitution plus ecosystem collaboration” has moved well beyond isolated technological breakthroughs. It is increasingly a coordinated effort to build a fully controllable digital foundation and, more importantly, to compete for system-definition and rule-setting power. This transition marks a shift from replacing individual foreign technologies to reconstructing entire technological stacks and ecosystems that can operate independently while scaling across national markets and critical sectors.
Huawei’s integrated architecture—spanning HarmonyOS, Euler, Ascend, MindSpore, and the Pangu large model—illustrates this transformation. HarmonyOS NEXT’s complete departure from AOSP is not merely an operating system upgrade; it represents a decisive break from long-standing dependencies on the Google ecosystem. By developing its own kernel, compiler, and development tools, China has weakened the “soft lock-in” mechanisms embedded in global mobile platforms and reasserted control over foundational layers of digital infrastructure.
Equally significant is the redefinition of compatibility and migration costs. The “one-time development, multi-terminal deployment” model lowers the barrier for developers to enter a new ecosystem, making transition less costly than rewriting applications from scratch. With millions of developers already engaged, compatibility is no longer defined by adherence to legacy standards, but by the capacity of a new platform to absorb, adapt, and redeploy applications at scale. This reframing shifts power away from incumbent ecosystems toward those that can offer credible alternatives with lower switching friction.
At the infrastructure level, the ecosystem’s institutional embedding further strengthens its durability. Euler’s widespread adoption in telecom, energy, and finance, alongside Ascend’s role in supercomputing, smart cities, and autonomous driving, creates high conversion barriers rooted in real-world deployment rather than abstract standards. These systems are no longer experimental substitutes; they are becoming default choices in mission-critical environments, reinforcing ecosystem cohesion through usage rather than policy alone.
For the United States, this evolution challenges the traditional model of technological hegemony built on standards dominance and ecosystem lock-in. China’s ability to leverage large-scale domestic scenarios and essential public needs enables it to cultivate endogenous ecosystems that external restrictions struggle to disrupt. As system-definition power gradually shifts, containment through bans becomes increasingly costly and less effective, signaling a structural change in how technological leadership and rule-setting are contested in the digital era.
From Scale to Speed: How China’s Scenario-Driven Model Accelerates Technological Iteration
China has increasingly leveraged its ultra-large national scale and highly complex real-world application environments as a structural advantage for technological development. Rather than relying primarily on laboratory validation or small-scale pilots, China embeds emerging technologies directly into nationwide, mission-critical scenarios. These large, demanding deployments function as natural accelerators: they compress feedback loops, force early exposure to edge cases, and rapidly drive technologies from prototype to maturity. In this sense, national scale is transformed into an engine of accelerated iteration.
A prominent example is the deployment of 5G-R in high-speed rail. The Beijing–Zhangjiakou High-Speed Railway became the world’s first commercial 5G-R system, enabling fully IP-based train control, dispatching, and maintenance with sub-10-millisecond latency and five-nines reliability. Operating under such stringent conditions compelled domestic vendors to harden ultra-reliable low-latency communication (uRLLC) protocols and network slicing at scale. These capabilities are now being exported alongside Chinese equipment and operational models to overseas rail projects, forming an integrated output of standards, infrastructure, and operations refined through real-world stress testing.
A similar dynamic is evident in China’s smart grid transformation. By connecting hundreds of millions of smart meters and building the world’s largest distribution-level Internet of Things, the State Grid has created an environment where cloud-edge-device collaboration is continuously validated under extreme reliability and safety requirements. This has driven iterative improvements across the technology stack—from AI edge inference platforms and power-optimized chips to operating systems and databases—each cycle shaped by live operational feedback rather than abstract benchmarks.
Urban vehicle-to-infrastructure (V2X) deployments further illustrate this scenario-driven path. Large-scale roadside unit rollouts in multiple cities allow autonomous driving systems to operate in dense, unpredictable urban conditions without heavy reliance on high-precision maps. The result is a low-cost, infrastructure-assisted approach that prioritizes robustness and scalability, offering an alternative technological trajectory to perception-heavy, compute-intensive models developed elsewhere.
In contrast, the United States faces structural constraints that limit the formation of comparable large-scale testing grounds. Aging infrastructure, fragmented ownership models, and resistance to coordinated upgrades reduce opportunities for sustained, system-level validation in areas such as power electronics, rail communications, and urban V2X. The absence of dense, unified scenarios slows feedback, lengthens iteration cycles, and increases the risk that advanced technologies drift away from real operational needs. The divergence highlights a core lesson: when scale, complexity, and deployment are aligned, they can turn national infrastructure itself into a powerful catalyst for technological evolution.
System Resilience versus Breakthrough Innovation: The Structural Asymmetry Between China and the United States
The evolving technological competition between China and the United States is less a race along a single dimension than a manifestation of deep structural asymmetry. China’s strength lies in system-level resilience—the ability to mobilize, coordinate, absorb failure, and iteratively improve across an entire industrial ecosystem—while the United States continues to excel at single-point, frontier-breaking innovation. These contrasting models shape not only the pace of technological advance, but also how breakthroughs are translated into scalable, reliable, and politically durable capabilities.
China’s innovation system is organized around task-oriented, state-coordinated consortia that integrate enterprises, universities, and research institutes toward clearly defined technological objectives. This model enables rapid resource aggregation and collective problem-solving in strategic sectors such as semiconductors, telecommunications, and energy. By contrast, the U.S. innovation landscape is fragmented by firm-level competition, intellectual property constraints, and limited government authority to coordinate private R&D. While this preserves strong incentives for originality, it weakens collective response speed and system-wide integration, particularly in industries that require synchronized advances across equipment, materials, processes, and manufacturing scale.
This asymmetry is reinforced by differences in trial-and-error tolerance and market structure. China’s vast, multi-layered domestic market provides a buffer for immature technologies to be deployed in non-critical or policy-supported environments, enabling “learning by doing” and application-driven refinement. The U.S. capital market, however, is highly sensitive to short-term performance; prolonged uncertainty or early-stage failure often leads to rapid capital withdrawal. As a result, American firms may hesitate to pursue high-risk, long-cycle technologies, even when their long-term strategic value is substantial.
Talent composition and infrastructure further widen the gap. China continues to benefit from a large cohort of engineers focused on implementation, process optimization, and manufacturing integration—skills essential for turning designs into dependable systems. The U.S., while attracting exceptional theoretical and entrepreneurial talent, faces shortages in mid-level engineering and technical roles critical to advanced manufacturing. Meanwhile, China’s infrastructure development is tightly coupled with industrial policy, standards, and application scenarios, creating reinforcing feedback loops between infrastructure, data, and industry. In the U.S., fragmented governance and politicized funding processes constrain the timely provision of comparable systemic infrastructure.
The strategic outcome is increasingly clear. The United States retains leadership in cutting-edge discoveries and singular technological leaps, but struggles to convert them into broadly deployed, resilient systems. China, by contrast, is steadily narrowing gaps in system-level availability and reliability, even where it lags at the absolute technological frontier. This divergence is now spilling beyond bilateral competition: elements of China’s system-centric development model are being adopted across the Global South, while the United States selectively emulates aspects of Chinese industrial policy under the politically safer language of security and resilience. The contest, therefore, is not simply about innovation speed, but about which system proves more capable of sustaining technological power over time.
Final Thoughts
The global competition has moved beyond a narrow “technology war” into a contest over system-generating capability. China is constructing a scalable, replicable, and iterative national system that emphasizes organizational rationality over individual genius, scenario feasibility over theoretical optimization, and proactive ecosystem shaping over short-term market expediency. This approach enables continuous collective iteration, embedding innovation directly into complex real-world contexts rather than isolating it within discrete breakthroughs.
By contrast, the hollowing out of American manufacturing has eroded not only productive capacity but also the engineering civilization that anchors innovation in material reality. Without rebuilding this foundation, even world-class basic science and abundant financial capital risk becoming a “castle in the air,” lacking depth and resilience in system-level competition. Chips and tools may be embargoed, but the daily needs of 1.4 billion people—and the iterative force of millions of engineers—cannot be blocked or silenced.