This research paper explores the extent to which the United States and China can realize the transformative economic potential of Artificial Intelligence (AI) given significant structural, political, and strategic constraints. It begins by examining the growth potential of AI in both countries, highlighting China's state-led investment and strategic planning under the Next-Generation AI Development Plan, and the United States' market-driven model led by private sector innovation. It then evaluates structural barriers to AI adoption, including labor market mismatches, regulatory bottlenecks, and disparities in research talent. Drawing on Daron Acemoglu’s task-based model, the analysis challenges overly optimistic macroeconomic forecasts by estimating the limited scope of automation and modest gains in total factor productivity. The paper further discusses the impact of protectionist trade policies—especially under the Trump administration—and their effects on supply chains, investment flows, and technological decoupling. A case study of NVIDIA CEO Jensen Huang’s 2025 visit to China illustrates how corporate strategies diverge from national policies, underscoring the tension between geopolitical rivalry and economic interdependence. The conclusion emphasizes that the realization of AI’s full economic gains—particularly in terms of GDP growth, productivity improvements, and global competitiveness—depends not only on technological innovation but also on international cooperation and the establishment of stable, inclusive policy environments. The paper acknowledges limitations due to its reliance on secondary sources and rapidly evolving global conditions.

1. Overview 

The recent development of Artificial intelligence (AI) has become a central driver of future economic growth narratives for both the United States and China. Both countries are positioning AI at the heart of their national innovation agendas, seeing it not only as a driver of productivity and industrial transformation, but also as a strategic asset in global power dynamics. According to projections from the International Monetary Fund (IMF), the implementation of AI would raise productivity, reshape labor markets, and amplify income and wealth inequality particularly benefiting advanced economies and high-income workers. (IMF, 2024) Similarly, a report done by PwC estimates that AI could contribute up to $15.8 trillion to the global economy by 2030, with China and the United States together accounting for nearly 70% of these gains. Such forecasts have fuelled optimism about AI as the next general-purpose technology capable of driving a fourth industrial revolution. (PwC, 2017) However, while optimistic projections paint AI as an engine of transformative productivity, a closer look at recent academic findings suggests a more measured reality that the actual macroeconomic gains from AI may be far more limited. While AI may eventually improve its task level efficiency and contribute to the economy, the total factor productivity (TFP) does not increase as a result of the AI boom. (Acemoglu, 2024) 

This tension between technological promise and structural limitations is further complicated by rising economic nationalism and protectionist trade policies, particularly in the United States. Under the Trump administration the imposition of aggressive tariff policy has a significant impact on the global supply chains and investment flows. This raises a very important question: how do the tariff policies eventually affect the sustainable AI Growth between economies?

Within this context, the strategic competition between the United States and China over AI leadership is not only technological or economic—it is deeply geopolitical and systemic. This paper explores the evolving economics of AI through the lens of this competition, focusing on three key dimensions: (i) the true economic growth potential of AI between US and China, (ii) the distributional and inequality effects within and between nations, and (iii) the strategic policy choices—including trade, investment, and regulatory decisions—that will shape the global balance of power in the AI era.

2.The Growth potential of AI In China and USA

2.1 China’s State-Led AI Strategy

To understand the economic implications more precisely, it is essential to examine how China and the U.S. are individually approaching AI development. Many reports from multinational corporations and global institutions have demonstrated significant optimism regarding the growth of artificial intelligence (AI) in both the Chinese and U.S. economies. In particular, China has made AI development a central pillar of its long-term economic and technological strategy. “Beijing plans to invest 10tn Chinese yuan ($1.4tn; £1tn) in the next 15 years as it competes with Washington to gain the edge in advanced tech. (BBC News, 2025) This massive investment is part of a broader national ambition outlined in key policy frameworks such as the Next-Generation AI Development Plan (2017) which aims to become the world's major AI innovation centre by 2030, with the scale of its AI core industry exceeding 1 trillion yuan (about 140.9 billion U.S. dollars), and the scale of related industries exceeding 10 trillion yuan.” (Xinhua,2024). Recent analysis from Goldman Sachs on What Advanced AI Means for China's Economic Outlook further suggests that the emergence of new AI models in China, such as the release of DeepSeek, could accelerate the country's AI development and adoption beyond earlier projections (Goldman Sachs 2025)— which may have been developed at a lower cost than other leading models could accelerate the country's AI development and adoption speed beyond earlier projections. Faster adoption of generative AI is expected to lower labor costs and enhance productivity as automation expands across sectors, ultimately driving greater economic gains for China.

However, despite these opportunities, economists from Goldman Sachs caution that China’s labor market remains less prone to AI-driven automation compared to the United States. “In China, sectors highly exposed to AI-driven tasks, such as finance, insurance, and professional services, constitute less than 3% of jobs in China, compared with 14% in the US. Moreover, while AI-driven task automation could improve overall efficiency, the implementation of these technologies must be managed carefully, given China's current structural challenges, including persistent deflationary pressures, a 15% young unemployment rate, and an annual influx of over 10 million new college graduates” (Goldman Sachs, 2025). This highlights that while China’s massive AI investments drive rapid progress, labor market constraints, including high youth unemployment and deflationary pressures, may limit economic gains and destabilize employment.

The substantial investment in AI is not merely aimed at boosting domestic productivity; by accelerating AI adoption across strategic sectors and integrating AI advancements into military modernization, digital infrastructure exports and standards-setting efforts, China seeks to transform its technological scale into systemic global influence. Despite U.S. export controls on semiconductor technology, these restrictions may be accelerating its push for technological self-sufficiency. (Asia Pacific Task Force, 2025) In this sense, AI development is not merely an economic project but a strategic lever designed to erode U.S. dominance and reposition China as a leading architect of the next technological revolution. Rather than simply overtaking the United States, China aspires to become a technology leader rather than a follower and more than capable of innovation. (Bork, 2025)

2.2 The United States’ Market-Driven Innovation Model

While China pursues a state-led innovation strategy, the United States adopts a markedly different approach, emphasizing market-driven innovation. The market-driven approach is based on the premise that markets provide the best incentives for innovation, technological progress and economic growth. (APCO Worldwide, 2023) Economists have long emphasized the critical role of innovation in driving economic growth, a link originally established in growth models such as Romer's (1990) endogenous growth theory. Recent work also positions “AI, along with advanced robotics and sensor technologies, as a potential general-purpose technology (GPT) capable of triggering waves of follow-on innovation and productivity gains” (Cockburn et al., forthcoming). Based on the Goldman Sachs report the baseline estimation for the widespread adoption of generative AI could raise US labour productivity by over 15% within the next decade and unlock about $4.5 trillion of annual US GDP (in today’s dollars) (Goldman Sachs, 2025) Together, these theoretical and empirical insights suggest that US had a solid foundation that provided the necessary conditions for widespread diffusion and complementary investments are met, AI has the potential to deliver economic gains to the US economy in the coming decade.

However, despite this optimism, structural constraints remain. Although both theoretical and empirical evidence suggest that AI indeed will improve the overall US economy, there is “a notable lag between technological progress and the commercialization of new innovative ideas building on this progress. (Furman and Seamans, 2019)” Moreover, AI may be hitting performance limits (Marcus 2018), and commercial applications have not yet had any dramatic impact on economic productivity. (Brynjolfsson et al., forthcoming) Realizing the full economic benefits would therefore require proactive policy interventions to accelerate the adoption speed and resolve institutional bottlenecks.

A further critical concern is The United States is struggling to maintain its share of the top 2 per cent (or “elite”) of AI talent produced globally. Furthermore, the growth in the number of top AI researchers originating from the United States is slowing compared to China. According to Long (2025), while the United States initially produced around 35% of the world's top AI researchers in 2019, this share declined by 7% over the following three years, reflecting China's accelerating capacity to nurture elite AI talent. This trend suggests that the U.S. is becoming a less attractive destination for top researchers, a development that could seriously undermine its long-term technological leadership compared to China as “AI is not just a technological battleground but also a catalyst for reshaping the global order and redefining the dynamics of cooperation and competition on the world stage.” (Al Qutbah, 2025) 

3. Arguments from Acemoglu: A More Measured Assessment

Although both the United States and China invest heavily in AI, significant structural constraints limit their ability to fully realize its economic potential. A more measured assessment of AI’s macroeconomic potential comes from Daron Acemoglu’s recent work that challenges the idea that AI will significantly improve the nation's economy, The Simple Macroeconomics of AI (2024), which challenges prevailing narratives of explosive productivity growth. Using a task-based model grounded in Hulten’s theorem, Acemoglu finds that only a fraction of labour tasks—approximately 20%—are currently exposed to AI, and even within these, only 23% are expected to be profitably automated in the next decade. Factoring in realistic cost savings and automation limits, he estimates that AI-driven total factor productivity (TFP) growth in the U.S. will be no more than 0.66% over ten years—or as low as 0.53% when adjusting for the limited productivity gains from “hard-to-learn” tasks that require contextual judgment.

Acemoglu also warns while AI may complement certain labor tasks, its macro impact on wages is likely to be uneven. Rather than reducing inequality, AI is projected to widen the gap between capital and labor income, with high-income earners benefiting disproportionately. These findings provide a crucial counterweight to the optimistic forecasts by organizations like PwC and the IMF, which envision sweeping economic transformations. When considered in conjunction with rising trade protectionism in the U.S.—particularly under recent Trump-era tariff escalations that inject volatility into global supply chains—the economic promise of AI appears increasingly fragile. For both the U.S. and China, realizing the full potential of AI may depend less on raw technological capability and more on creating stable, innovation-friendly policy environments that avoid undermining long-term investment with short-term economic uncertainty.

Complementing Acemoglu’s cautious view, a recent report by Goldman Sachs (The Global Impact of AI: Mind the Gap, 2025) suggests that AI-driven productivity gains could increase global GDP by up to 4% over the next decade under a high total factor productivity (TFP) growth scenario. However, even this optimistic projection rests on the assumption of evenly distributed gains—a premise increasingly questionable given structural inequalities both within and between countries. Moreover, from a macroeconomic perspective in this report, there is evidence suggesting that AI will affect both non-tradable and tradable sectors and could generate complex real exchange rate dynamics potentially leading to a depreciation which contrasts with the traditional theories. These complexities highlight the urgent need for scenario-specific analysis and strategically targeted fiscal and monetary interventions to shape the pace and direction of AI adoption. 

4. Policy Risks: Impact of Trump-Era Tariffs and Ongoing Uncertainty

For decades, the United States held undisputed supremacy in the creation and expansion of new technologies (Ministerio de Defensa de España, 2025) but the recent Trump administration's aggressive protectionist policies, including tariffs on Chinese goods and restrictions on semiconductor exports, introduced significant volatility into global technology markets and heavily affected the US AI development.

According to a recent Washington Post report (De Vynck, 2025), renewed threats of broader tariffs targeting Silicon Valley firms could further destabilize the AI innovation ecosystem by limiting international investment and collaboration. These actions inject volatility into the global market, reducing predictability for AI R&D investment and raising the costs of cross-border AI adoption, especially in sectors heavily dependent on hardware components. The long-term implications of protectionist measures could slow AI diffusion globally, weaken supply chain resilience, and intensify technological decoupling between the U.S. and China.

Trump’s significant tariff increases prompted Chinese commentators to describe the situation as a "number game" (Global Times, 2025) where there is no economic significance behind these numbers. The escalating reciprocal tariff war has further amplified global market uncertainties and slowed the pace of AI adoption, which suggests that the US may require a longer timeline to realize its full economic potential through AI-driven innovation. While the Trump administration justified the broad increase in tariffs as a legitimate response to allegedly unfair trade practices (Naves, 2025) the ultimate impact of these policies remains uncertain, it is evident that tariffs were strategically employed not solely for economic correction but also as instruments to advance the administration’s "America First" agenda, with far-reaching consequences for the future competitiveness of the U.S. AI sector.

This strategy has extended beyond trade policy into education and talent mobility. Recent developments—such as a temporary suspension of student visa issuance for Chinese nationals and proposed reductions in the proportion of international students at elite institutions like Harvard—signal a further escalation of this nationalist agenda. These actions are not merely symbolic; they actively erode the United States’ historical advantage in attracting global AI talent, particularly from China. As AI development depends critically on high-skill human capital, such restrictions could diminish the U.S.’s long-term competitiveness in AI innovation. Ultimately, these policies amplify global uncertainty and risk delaying the realization of AI’s transformative economic potential.

The broader implication is that during Trump’s administration, any economic, educational, or diplomatic channel may be weaponized to reinforce nationalistic objectives. This creates a volatile policy environment that not only alienates global talent but also undermines long-term U.S. competitiveness in AI. Since AI’s transformative potential depends heavily on high-skill labor, international collaboration, and global supply chain efficiency, the cumulative effect of these strategies may significantly delay or diminish the economic gains that AI could otherwise deliver to the United States.

5. Recent Developments: Jensen Huang’s Visit to China and Emerging AI Diplomacy

These tensions were further highlighted by the surprise visit of NVIDIA CEO Jensen Huang to China in 2025. According to an article by The Economic Times (2025), Huang met with Liang Wenfeng, the CEO of DeepSeek, as well as with several Chinese government officials. Despite escalating trade barriers and semiconductor export restrictions. Huang’s visit sent a strong signal to global markets: leading U.S. AI firms may not fully align themselves with the Trump administration's confrontational policies. As Huang stated during his visit that: “The China market is very important to us, and it's important to our growth” (Global Times, 2025) His remarks underscore the reality that, despite the intensifying technological competition between the United States and China, significant avenues for international cooperation remain. Moreover, Huang’s engagement illustrates the growing divergence between U.S. market-driven corporate interests and U.S. government protectionist strategies under Trump, further complicating the dynamics of the bilateral AI rivalry. As a result, the path toward realizing the full transformative potential of AI will not only be shaped by technological advances, but increasingly by the interplay of political choices, institutional environments, and cross-border corporate strategies.

6. Conclusion: Strategic Implications and Research Limitations

This paper critically assessed the extent to which the United States and China can realize the transformative economic potential of AI amidst mounting structural, political, and strategic constraints. While early projections from PwC and the IMF forecasted sweeping productivity gains, more recent academic analyses—particularly Acemoglu’s task-based framework—suggest that the macroeconomic impact of AI will likely be far more constrained than initial optimism implied. Structural rigidities, protectionist trade policies, and intensifying political frictions—exemplified by the Trump administration's tariff escalations—have slowed AI diffusion and undermined global supply chain resilience. Although instances of corporate pragmatism, such as NVIDIA CEO Jensen Huang’s visit to China, reveal lingering economic interdependence, the broader trajectory points toward a fragmented and politically volatile AI landscape. Ultimately, unlocking AI’s full economic potential will hinge not merely on technological breakthroughs but on the establishment of stable, innovation-friendly policy environments and sustained international cooperation. Absent such alignment, both the United States and China risk diminishing the very transformative gains they seek.

It must also be acknowledged that this paper faces limitations. This paper relies on secondary-source analysis, drawing on academic literature, institutional reports, and market data to examine macroeconomic trends and policy implications surrounding AI development in the United States and China.As a single-author study conducted without external peer review, there are inherent risks regarding source validation and potential interpretative bias. Moreover, given the accelerating pace of geopolitical and technological change, any conclusions drawn remain provisional, subject to the evolving dynamics of the global system.