The Hidden Multiplier: Unraveling the True Cost of the Global AI Skills Gap

AI technologies neither develop nor operate in isolation—they remain deeply intertwined with existing social, economic, and political frameworks. The AI value chain, while powered by technological innovation, is fundamentally shaped by human decisions, institutional arrangements, and established power relations (see Table 1). These human and institutional factors determine how AI technologies are developed, who controls them, and how their benefits are distributed. Far from being a neutral technological process, the AI value chain has emerged as a powerful global divergence catalyst, expanding existing inequalities and creating new ones. The chain, which covers many links such as data collection, annotation, model development, and deployment, is not merely a sequence of technical steps but a complex ecosystem that mirrors and magnifies global socioeconomic disparities. As the foundation of this chain, data are the core of AI systems. The data sources are diverse, ranging from publicly available documents to proprietary information collected by corporations. However, in the process of transforming raw data into usable input data for AI systems, the existence of the first layer of the skills gap is revealed. Although data collection itself may not require advanced skills, the crucial tasks of data annotation and labeling are often undertaken by workers in developing countries. These workers, despite usually possessing university degrees, find themselves in low-paying jobs with limited career advancement prospects [5]. Therefore, a paradoxical situation has been formed: developing countries have contributed essential inputs to the AI value chain and reap minimal benefits from the high-value outputs.

Table 1 Accumulation of Inequalities Across the AI Value Chain

Going up the value chain, we encounter more specialized roles in data engineering, model design, and system deployment. These high-skill, high-value activities are mainly concentrated in developed economies, especially the United States and China. The United States with nearly 6,300 patents filed since 2014 lags in a distant second place, less than one-sixth of China’s total [6]. While the quantity of patents does not necessarily reflect their quality or the impact of the innovations, this imbalance in patents translates directly into a capacity gap for advanced AI research and development. In training cutting-edge AI models, the related costs are staggering. Estimates range from U.S. $78 million for GPT-4 of OpenAI to U.S. $191 million for Gemini Ultra of Google [7]. The astronomical figures of cost exclude most developing countries and even many developed ones from participating in the frontier AI research, creating a de facto “AI oligarchy.”

When advanced AI capabilities remain concentrated among select countries and corporations, far-reaching implications emerge. Such consolidation strengthens these nations’ technological dominance while granting them unprecedented influence over AI’s future trajectory. Across the globe, their reach shapes technical standards, ethical frameworks, and governance mechanisms for artificial intelligence. A self-reinforcing cycle emerges as initial advantages in AI expertise and infrastructure cement their leadership in innovation, attracting fresh talent and investment. Ultimately, these compounding factors risk creating a world where powerful AI technologies rest in the hands of a privileged few nations and companies. Yet, it reshapes the global power dynamics and exacerbates the existing geopolitical tensions.

How AI affects labor markets proves far more nuanced and complex than initial predictions suggested. By 2025, AI may replace 85 million jobs while creating 97 million new jobs [8]. At first glance, this appears promising, yet closer examination reveals significant disparities. Developing countries, while currently less exposed to AI-driven job displacement, also stand to gain less from the productivity improvements and employment opportunities associated with AI adoption. While automation poses risks to existing jobs, society faces the simultaneous challenge of developing skills needed for an AI-driven economy. The disparity becomes particularly evident in the distribution of job displacement risks across different sectors and regions, creating unique challenges for workforce adaptation and economic planning.

Moreover, the AI skills gap is reflected in the creation of AI technology, in addition to its application across various economic sectors of the economy. With the increasing popularity of AI systems in healthcare, finance, and education, countries that lack the necessary skills to implement and adapt these technologies risk falling further behind. The gap in application skills could result in a situation in which developing countries become passive consumers of AI technologies developed elsewhere, rather than active participants in shaping how these technologies are used in their specific contexts. Limiting these countries from obtaining economic benefits from AI will also cause serious concerns about technological dependence and data sovereignty.

The AI value chain’s role as a catalyst for divergence goes beyond economic considerations to impact social and cultural domains as well [2]. As AI systems increasingly influence decision-making processes in areas such as loan approvals, hiring, and criminal justice, the lack of diversity in AI development teams can perpetuate and amplify existing biases [9]. The concentration of AI development in a limited number of geographical and cultural contexts risks creating AI systems that are less effective or even harmful when applied in different cultural settings [10]. Modern technological frameworks within society serve not only to mirror but to amplify existing social disparities [11]. The cultural mismatch may exacerbate global inequalities across multiple levels from basic access to advanced AI capabilities, through the skills needed to effectively engage with these technologies, to the ultimate benefits and harms derived from AI systems [1], [12]. As demonstrated in recent studies, AI systems trained predominantly on Western data sets show significantly reduced performance when applied in different cultural contexts [13]. The systematic bias in AI development primarily benefits populations whose cultural contexts dominate the training processes.

In essence, the AI value chain is not a greater equalizer, but a powerful mechanism to amplify global disparities. It has created a multitiered global AI ecosystem, in which a small group of countries and corporations are in a leading position in innovation and value capture, a larger group struggles to catch up, and a significant portion of the world risks being left behind entirely. Existing institutional structures and power dynamics shape this divergence, leading to deepening patterns of economic and social inequality. Without careful consideration of how AI technologies are developed and deployed within different socioeconomic contexts, these patterns may further amplify existing power imbalances in the global economy. To solve it, we need both technical solutions and a comprehensive reevaluation of how we approach AI development, education, and global cooperation in the age of artificial intelligence. AI technologies continue to advance rapidly, creating an urgent need to balance innovation with inclusiveness and fairness. How we address this balance today will significantly shape global society’s future development.

The ramifications of the global AI skills gap extend far beyond purely economic concerns and create a ripple effect that touches every aspect of society and threatens to reshape the global order. Innovation sparks a cascade of widespread changes, affecting everything from social structures to international relations and human development trajectories. The core of the AI skills gap is to amplify the existing disparities and turn small initial differences into yawning chasms that could take generations to bridge. Concentrated within a few countries and companies, AI skills and resources create limited perspectives for innovation while hampering AI’s potential to address global challenges. The concentration of skills and resources further solidifies the dominant position of leading AI hubs, as they continue to attract global talent and investment. As a result, the scope of the global AI research agenda has narrowed which overlooks key areas of investigation that can benefit developing nations or solve local unique challenges. For example, AI solutions developed mainly in Silicon Valley or Beijing may not adequately address the specific needs of healthcare systems in sub-Saharan Africa or the agricultural challenges in South Asia. A significant opportunity cost arises from the misalignment between global AI development and local needs, potentially delaying revolutionary AI solutions for global health, education, and sustainable development [14].

The ramifications of the global AI skills gap extend far beyond purely economic concerns and create a ripple effect that touches every aspect of society and threatens to reshape the global order.

With the increasing penetration of AI into all aspects of daily life, from social media algorithms to automated decision-making systems in public services, those who do not have the skills to understand or critically participate in these technologies may be increasingly marginalized. Digital disenfranchisement could cause a new type of social stratification, in which AI literacy has become the key factor in determining social mobility and civic participation. Wang et al. have identified distinct user groups based on their AI knowledge, skills, and attitudes, revealing significant disparities that correlate with age, education level, and gender [12]. The gap in AI skills exacerbates existing social inequalities and creates a new underclass of “AI have-nots,” who are increasingly excluded from economic opportunities and decision-making processes. The gender dimension of social differences emerges as a critical concern in the AI transformation landscape. Women face particularly acute challenges, being disproportionately represented in occupations susceptible to AI-driven automation. In most regions, women’s exposure to the automating effects of generative AI technology is more than double that of men’s exposure [3]. Historical patterns of occupational segregation explain these disparities, as women predominantly occupy administrative, customer service, and clerical positions—sectors where AI advancement is particularly rapid. The implications extend beyond immediate job displacement, threatening to erode decades of progress in workplace gender equality and potentially reinforcing traditional gender roles in the digital economy. Without targeted policy interventions and reskilling programs, the ongoing technological shift may worsen gender wage gaps and create additional barriers to women’s economic progress. Gender disparity in AI skills and job vulnerability threatens to reverse decades of progress in workplace gender equality and lead to the reentrenchment of gender roles and economic disparities.

AI is a critical driving force of economic growth and military capability. Countries with advanced AI skills and infrastructure gain significant strategic advantages. Technological edge can be translated into greater geopolitical influence, which potentially changes the balance of power on the global stage. The concentration of AI capabilities in a few countries has raised concerns about technological colonialism, that is, developing countries have begun to rely on AI technologies and services provided by more advanced nations. Such dependence threatens to extend beyond economic spheres, potentially compromising political autonomy and national sovereignty. For example, countries lacking robust AI capabilities may be forced to rely on foreign-developed AI systems to manage key infrastructure, financial systems, and even national defense technologies, which compromise their autonomy and security [15]. What is more, the AI skills gap can exacerbate existing tension around data sovereignty and digital governance. Countries with advanced AI capabilities have significant advantages in shaping global norms and standards for AI development and use, which could make AI governance frameworks mainly reflect the values and interests of a small group of technologically advanced countries, conflicting with the needs and cultural contexts of other countries.

In the process of AI systems actively mediating our interactions with information and culture, these systems developed primarily in a handful of cultural contexts, inadvertently promoting cultural homogenization. Content recommendation systems, language models, and even creative tools driven by AI are usually trained on data sets that overrepresent certain cultural perspectives while underrepresenting others. AI development’s bias causes a gradual erosion of cultural diversity because less-represented cultures are trying to maintain their digital presence and influence [13]. Also, the AI skills gap could undermine progress toward global development goals. Although AI accelerates the progress of many goals in the United Nations Sustainable Development Goals (SDGs), from improving healthcare outcomes to improving agricultural productivity, this potential can only be realized if countries have the skills and infrastructure to develop and deploy AI solutions tailored to their specific challenges. The current disparity in AI capabilities means that the benefits of AI in dealing with global challenges are likely to be unevenly distributed, which widens the gap between developed and developing countries in achieving SDGs [16].

Beyond economic considerations, ethical challenges arise when examining how unevenly distributed AI capabilities affect different populations and societies. AI systems are constantly involved in making or influencing decisions that affect human lives. From credit scoring to criminal justice, the lack of diverse perspectives in AI development has raised serious concerns about bias and fairness. Some certain groups are underrepresented in AI development teams, which results in AI systems inadvertently perpetuating or even amplifying existing societal prejudice. The ethical dimension of the AI skills gap not only raises questions of justice and equality but also may undermine the public trust in AI technologies. Without a broadly AI-literate population, there is a risk of creating a divide between some AI “high priests” who know and control these technologies and a larger population that is subject to their decisions without the ability to scrutinize or challenge them [17]. For example, in the medical field, AI systems are increasingly widely used in diagnosis, treatment planning, and resource allocation, but if these systems are developed by teams without multicultural backgrounds, the health needs and cultural habits of specific people may be ignored. For example, if the dermatology diagnosis AI is mainly trained based on the data of light skin, it may not perform well in diagnosing patients with dark skin. Beyond its technical implications, the situation presents profound ethical challenges concerning human life itself.

On top of that, political campaigns, public discourse, and voting systems are increasingly influenced by AI technologies. Disparities in AI literacy could bring about new forms of political inequality. People with greater AI skills may have advantages in navigating the information landscapes, detecting misinformation, and participating in digital civic spaces. In this scenario, AI literacy becomes a prerequisite for effective civic participation, potentially excluding most of the population from meaningful engagement in democratic processes [18]. The potential application of AI in complex disinformation campaigns or microtargeted political advertising further highlights the importance of extensive AI literacy as a safeguard of democratic integrity. What is even more worrying is that AI technology may be used to manipulate voters’ emotions and behaviors. The progress of deep forgery technology makes it easier to make realistic false videos, which may be used to spread false information or slander political opponents. In the meantime, AI-driven social media algorithms may create an echo chamber effect and aggravate political polarization. Citizens who lack AI literacy may be more easily misled and manipulated. We cannot help but ask: in this new era dominated by AI, how to ensure the inclusiveness and representativeness of democracy? How to prevent technical elites from dominating public discourse while ordinary citizens cannot participate effectively?

Global disparities in AI capabilities mirror broader patterns of economic inequality, yet they evolve and expand at an unprecedented pace that demands immediate attention. As AI technologies continue to advance, their transformative power increasingly shapes economic opportunities, social mobility, and geopolitical influence. To meet the multifaceted challenges posed by the global AI skill gap, a comprehensive and coordinated approach must be adopted which should cover international collaboration, educational reform, policy innovation, ethical considerations, and social dialog.

The core is to enhance international collaboration, especially in the field of AI development and knowledge sharing. The United Nations and other international institutions can play a central role in facilitating this cooperation and creating platforms for the exchange of AI research, best practices, and resource sharing. Such collaboration should aim at democratizing the acquisition of AI technologies and skills, ensuring that the benefits of AI are more equitably distributed globally. It may involve the establishment of international AI research centers to bring together experts from different backgrounds and geographical regions to foster a more inclusive approach to AI development. Similarly, creating global open-source AI projects will help to level the playing field so that developers all over the world can contribute to and benefit from cutting-edge AI advancements [19].

Global disparities in AI capabilities mirror broader patterns of economic inequality, yet they evolve and expand at an unprecedented pace that demands immediate attention.

Educational reform must transcend the conventional approach of merely introducing specialized AI courses or basic coding classes. Introducing the concept of “AI skill premium,” Grant and Ungor demonstrate how workers with AI-related skills increasingly command higher wages than those with traditional educational backgrounds [20]. Labor market projections further underscore this transformation, revealing a significant restructuring of employment patterns where routine tasks face automation, while new roles demanding AI-related competencies continue to emerge [21]. Responding to these shifts demands a comprehensive reimagining of educational systems that extend beyond technical training. Educational institutions must foster critical thinking, creativity, and ethical reasoning capabilities alongside AI literacy [22]. Current labor data reveal gender disparities, with women facing more than double the risk of job displacement from AI automation compared to men [8]. Addressing these inequities requires targeted programs supporting underrepresented groups in AI-related fields, combined with comprehensive digital literacy training that enables effective engagement with AI technologies across various contexts [5].

Policy frameworks governing AI development must evolve beyond traditional regulatory approaches, adapting to the unique challenges posed by rapidly advancing technologies. Analysis by Acemoglu and Restrepo [23] reveals that automation-driven displacement of routine tasks has fundamentally altered wage structures, with their findings suggesting that between 50% and 70% of wage changes over four decades stem from this technological shift [23]. Responding to these challenges, contemporary policy proposals advocate for innovative approaches that balance innovation with equity. Guerreiro et al. [24] envision frameworks incorporating targeted incentives for corporate investment in workforce development while maintaining robust mechanisms for algorithmic accountability. Complementing these proposals, recent comprehensive reviews of global AI regulation emphasize the critical importance of data governance structures that protect public interests while fostering competitive markets [21]. Such sophisticated policy frameworks must additionally address questions of data sovereignty and cross-border AI deployment, ensuring that regulatory measures support rather than hinder international collaboration.

Ethical considerations in AI development extend far beyond theoretical frameworks, demanding practical implementation strategies that address diverse societal impacts. Recent demographic analysis reveals distinct patterns in how AI technologies affect various population groups, highlighting the urgent need for inclusive development practices [12]. Within healthcare applications, for instance, AI systems trained predominantly on data from certain demographic groups have shown reduced effectiveness when applied to underrepresented populations, raising serious concerns about healthcare equity. Effective ethical frameworks must translate abstract principles into concrete development guidelines, incorporating diverse perspectives from the earliest stages of AI system design [25]. Questions of algorithmic fairness, transparency, and accountability take on particular significance in applications affecting fundamental human rights and opportunities, requiring systematic approaches to ethical assessment and monitoring [26]. Moving beyond superficial diversity initiatives, AI development teams must reflect genuine cultural and demographic diversity, ensuring that technological solutions address the needs and values of varied populations.

Social dialog emerges as a crucial mechanism for ensuring AI development aligns with diverse societal needs and values. Comprehensive studies of technological transitions highlight how countries maintaining robust social dialog mechanisms prove better equipped to manage technological change while preserving social cohesion [27]. Evidence from recent UN/ILO analysis demonstrates that successful AI integration depends heavily on sustained communication among workers, employers, policymakers, and civil society organizations, enabling early identification of potential impacts and collaborative development of mitigation strategies [28]. Such dialog has proven particularly effective when supported by formal structures for ongoing consultation and clear mechanisms for incorporating stakeholder feedback into policy and development decisions. Through structured dialog, communities can better articulate their needs and concerns, ensuring that AI development serves broader societal interests rather than narrow technical or commercial objectives.

Today, we are at a crossroads where our collective choice will determine whether AI becomes a powerful force to promote global progress and equality, or a tool to further entrench power in the hands of a few, leaving vast swathes of humanity behind.

The global AI skill gap is far from a simple technological challenge, but a decisive issue of our time that threatens to completely reshape the structure of our global society. As we have discussed, AI skill gaps act as an invisible multiplier, amplifying the existing inequalities and creating new divides that span economic, social, and even geopolitical dimensions. Today, we are at a crossroads where our collective choice will determine whether AI becomes a powerful force to promote global progress and equality, or a tool to further entrench power in the hands of a few, leaving vast swathes of humanity behind. The mitigation strategies that we discussed—from international cooperation and educational reform to ethical considerations and social dialog—offer a way forward.

While our analysis has primarily focused on the challenges and risks of AI-driven inequalities, it is important to acknowledge that AI technologies, when properly governed and implemented, can also help reduce existing gaps and inequalities. For example, the democratization of AI tools through free or low-cost solutions can potentially level the playing field in various domains. AI-powered educational tools can assist students with learning difficulties, while language models can help non-native speakers improve their writing skills. The key lies in ensuring that these beneficial applications of AI are accessible to and usable by diverse populations. Future research should examine more specifically how different types of AI applications—from general-purpose language models to specialized domain applications—may either exacerbate or mitigate existing inequalities, as the impacts likely vary significantly across different AI technologies and contexts.