US tech companies should “look to build a monopoly” because… “Monopoly is the condition of every successful business.” —A US Tech Billionaire Footnote 1
Over the past decade, the rapid advancement of artificial intelligence (AI) technologies has spurred a wave of ambitious initiatives from leading technology giants, as well as significant policy responses from governments worldwide (Taeihagh, Reference Taeihagh2021). Companies such as Google, Microsoft, Amazon, and OpenAI have invested heavily in AI research and development, aiming to push the boundaries of machine learning, natural language processing, computer vision, and other AI-driven innovations (Odhabi & Abi-Raad, Reference Odhabi, Abi-Raad and Soliman2024; van der Vlist et al., Reference van der Vlist, Helmond and Ferrari2024). These advancements are not only transforming industries but are also reshaping workplace dynamics such as talent management (Vaiman et al., Reference Vaiman, Cascio, Collings and Swider2021) and organizational behavior (Mudunuri et al., Reference Mudunuri, Hullurappa, Vemula, Selvakumar and Özsungur2025), creating new challenges and opportunities for industrial-organizational (I-O) psychology (see Asfahani, Reference Asfahani2022 for a review). As AI technologies become increasingly integrated into various human resource (HR) practices and decision-making processes (Vrontis et al., Reference Vrontis, Christofi, Pereira, Tarba, Makrides and Trichina2022), I-O psychologists are uniquely positioned to address the implications of these changes for workforce development and organizational effectiveness.
Although nations worldwide have recognized the strategic importance of AI and have sought to establish comprehensive policies to foster its development (Radu, Reference Radu2021; Schiff, Reference Schiff2022), the USA, as a global leader in AI, has also implemented comprehensive national strategies aimed at fostering innovation, strengthening its technological ecosystem, and maintaining its competitive edge in the rapidly evolving AI landscape (Bareis & Katzenbach, Reference Bareis and Katzenbach2022). These efforts are likely to have profound implications for I-O psychology. For example, AI-driven tools are reshaping job roles, creating new skill requirements, and influencing how organizations attract, develop, and retain talent (Ekuma, Reference Ekuma2024). However, the integration of AI into the workplace also raises critical questions about bias, fairness, and equity, as AI algorithms may inadvertently perpetuate or exacerbate existing disparities in hiring, promotion, and performance evaluation (Tambe et al., Reference Tambe, Cappelli and Yakubovich2019). These shifts and developments emphasize the need for I-O psychologists to better understand the influence of government policies on how AI technologies are implemented in ways that enhance, rather than hinder, effective talent management and organizational outcomes.
Indeed, as AI continues to advance globally, the US government has taken increasingly assertive measures to safeguard its position at the forefront of AI development, reflecting a broader effort to secure economic and geopolitical advantages (Schmidt, Reference Schmidt2022). This reinforces the nation’s role as a dominant force in shaping AI research, commercialization, and deployment. At the same time, American AI companies aspire to operate in a unipolar environment where the USA and its allies maintain exclusive control over cutting-edge AI technologies (Geopolitical Economy, 2025). Federal policies that implicitly or explicitly promote AI monopolization raise important considerations for I-O psychology, particularly regarding their potential effects on workforce dynamics. Thus, by examining the potential impacts of US national AI policies and regulations on talent management, this discussion seeks to bridge the gap between AI advancements and I-O psychology, highlighting the critical role of I-O psychologists in navigating the complexities of an AI-driven workplace.
National AI policies and changes in the USA
Over the past decade, successive US administrations have issued multiple official documents related to AI. Although the overarching theme of “America First” has consistently remained a priority, with a focus on achieving American technological hegemony and monopoly in AI, the approaches outlined in these documents vary when it comes to addressing issues like tech monopolies and regulatory competition. Recently, President Donald J. Trump administration’s 2025 EOs, “Initial Rescissions of Harmful Executive Orders and Actions” (EO 14148) and “Removing Barriers to American Leadership” (EO 14179), just represented a decisive departure from the Biden administration’s structured oversight model.
Indeed, this policy shift by Trump is a continuation of the “light-regulatory-touch” AI regulatory policies from his first term. Prioritizing technological breakthroughs and US global dominance (Federal Register, 2019), his 2019 Executive Order (EO) on “Maintaining American Leadership in Artificial Intelligence” urged federal agencies to avoid “unnecessary barriers” to AI research and development while streamlining commercialization (Meltzer, Reference Meltzer2019, p. 4). However, while maintaining the goal of “ensuring American leadership,” the Biden administration shifted to a “govern first, then lead” strategy: The 2023 EO 14110 emphasized risk mitigation, equity, and international collaboration, mandating antidiscrimination measures, red-teaming exercises for high-risk AI models, and safety test sharing by developers (Federal Register, 2023). It also enforced structured oversight, transparency, and accountability, including risk management frameworks, compliance reports, and antidiscrimination laws in hiring and healthcare (Wörsdörfer, Reference Wörsdörfer2024), balancing innovation with public trust and ethical standards. These measures tend to have important implications for talent management, as they directly address issues such as bias in recruitment, fairness in performance evaluations, and equity in career advancement opportunities (Baum, Reference Baum2023).
Nonetheless, as AI technologies rapidly advanced globally, the Biden’s administration, in its later stages, realized that a values-based alliance strategy would not suffice to ensure “America First,” and US federal policies evolved to maintain the nation’s leadership and supremacy in the field. Thus, by January 2025, the Biden administration issued the EO “Advancing United States Leadership in Artificial Intelligence Infrastructure,” which marked a pivotal moment in AI policy. This order sought to bolster US economic competitiveness, ensure access to advanced AI models, and reduce reliance on foreign infrastructure (White House, 2025a). Domestically, it adopted a selective easing approach to AI regulation, retaining oversight only for AI systems procured by the federal government. Although this shift could benefit talent management to a certain extent by allowing private-sector organizations additional flexibility in deploying AI-driven tools for recruitment, performance evaluation, and workforce planning, the lack of comprehensive oversight raised concerns about potential biases in AI systems, which could undermine efforts to promote diversity, equity, and inclusion in the workplace. Internationally, it shifted toward a strategy of technological coercion, unilaterally demanding that allies adopt American technical standards. This approach may create significant challenges for multinational organizations, particularly in aligning talent management practices across regions with differing regulatory frameworks.
Moving forward, the Trump administration’s 2025 EOs revoked many of Biden’s earlier policies, including the 2023 EO 14110 on “Safe, Secure, and Trustworthy Artificial Intelligence,” which was deemed “burdensome” (White House, 2025b). The Trump administration’s deregulatory agenda prioritized rapid innovation and private-sector growth, aiming to outpace international competitors and maintain US dominance in AI. However, this approach raised concerns about eroding public trust and fragmenting global AI standards (Sarokhanian & Menges, Reference Sarokhanian and Menges2025; White House, 2025c). Such a shift from technological multilateralism toward zero-sum competition in the AI area tends to further complicate efforts to create cohesive and equitable talent management strategies in a globalized economy. Indeed, under a multilateral approach, international collaboration can foster shared standards and ethical frameworks for AI, enabling aligned talent management strategies across borders (Kashefi et al., Reference Kashefi, Kashefi and Ghafouri Mirsaraei2024). However, zero-sum competition—prioritizing dominance over cooperation—fragments these efforts. Especially when countries like the USA, unilaterally impose technical standards or restrict AI access, multinational organizations struggle to harmonize talent practices, which creates more inconsistencies in AI-driven recruitment, performance evaluation, and workforce development, leading to additional disparities in fairness, transparency, and inclusivity.
Although most of these AI policies are “soft or semi-hard-law documents” that may not have legally binding governance mechanisms (Wörsdörfer, Reference Wörsdörfer2024, p. 1), the influence of US federal-level AI-related EOs on state policy and legislation reflects a dynamic interplay between centralized strategic goals and decentralized governance. On the one hand, state-level measures tend to reflect how federal directives can catalyze localized regulatory innovation, particularly when aligned with socio-economic priorities like civil rights and public trust. On the other hand, rapid shifting federal priorities have also led to a fragmented regulatory landscape (Liebig et al., Reference Liebig, Güttel, Jobin and Katzenbach2024). For example, Arizona has positioned itself as a hub for AI chip manufacturing to reduce reliance on foreign suppliers (Nguyen, Reference Nguyen2025), aligning more closely with the latest Trump administration’s EO on AI deregulation and the goal of maintaining US AI supremacy. However, states like California enacted laws addressing algorithmic bias in hiring and housing, mirroring the previous mandates for federal agencies to audit AI systems for discriminatory impacts (Dwyer, Reference Dwyer2025). Similarly, Colorado passed legislation requiring transparency in AI-driven public services (Colorado General Assembly, 2024), aligning with the Biden’s order’s emphasis on accountability. This patchwork approach to AI governance at the state level has significant implications for the workforce, particularly from an I-O psychology perspective. In particular, the implications of the policy swing and changes for talent management will be discussed in detail in the section below.
Assessing implications for talent management
Reflecting on the above AI policy swing and changes in the USA, we suggest that a deeply relevant factor to I-O psychology research and practice is the implications of US AI policies for talent management. This is because the development of the AI industry—particularly the advancement and deployment of AI technologies—depends heavily on a skilled and diverse workforce (Chuang, Reference Chuang2024; Jaiswal et al., Reference Jaiswal, Arun and Varma2023). For example, the creation of cutting-edge AI systems demands expertise in fields such as machine learning, data science, software engineering, and ethics, underscoring the critical importance of talent acquisition and retention for fostering innovation (Malik et al., Reference Malik, De Silva, Budhwar and Srikanth2021). Scholars have emphasized that effective government policies are essential to cultivating a robust pipeline of AI professionals (e.g., Dwivedi et al., Reference Dwivedi, Hughes, Ismagilova, Aarts, Coombs, Crick, Duan, Dwivedi, Edwards, Eirug, Galanos, Vigneswara Ilavarasan, Janssen, Jones, Kumar, Kizgin, Kronemann, Lal, Lucini, Medaglia and Williams2021; Valle-Cruz et al., Reference Valle-Cruz, Criado, Sandoval-Almazán and Ruvalcaba-Gomez2020). However, the Trump administration’s aggressive pursuit of American monopoly through deregulatory policies has created a landscape dominated by major US technological giants, which could inadvertently undermine talent management in three important ways, with significant implications for I-O psychology.
First, the monopolistic approach to AI dominance risks neglecting workforce diversity and inclusion, which represents a critical concern for talent management and I-O psychology research and practice. Recent US AI policies, such as those under the Trump’s administration, emphasize securing a dominant, if not monopolistic, position in the global AI race, driven by the goal of outpacing international competitors and maintaining technological and economic supremacy. However, this narrow focus on achieving AI dominance often prioritizes immediate technological gains over the broader societal and ethical implications of AI development. For instance, the deregulatory nature of EO 14179 (2025) reflects a tendency to pursue higher efficiency and competitiveness but overlooks critical issues like diversity and inclusion that were addressed in EO 14110 (2023). By incentivizing rapid innovation without addressing systemic barriers, these policies may further marginalize underrepresented groups in science, technology, engineering, and mathematics (STEM) fields, such as women and minorities (Griffith, Reference Griffith2010; Varma, Reference Varma2018), who are already underrepresented in the AI workforce (Young et al., Reference Young, Wajcman and Sprejer2023). From an I-O psychology perspective, this lack of deliberate efforts to promote workforce and talent diversity through inclusive policies tends to risk the USA in perpetuating a homogeneous talent pool, which can limit creativity, innovation, and the ability to address biases in AI systems. Consequently, a monopolistic strategy that fails to address these issues may ultimately weaken the USA’s ability to develop equitable and representative AI technologies, undermining long-term innovation and competitiveness. In this sense, we suggest that, under such a context, I-O psychologists should play an active role in advocating for inclusive policies and designing necessary interventions to promote equitable talent pipelines.
Second, policies prioritizing dominance in AI are likely to exacerbate fierce competition and talent poaching among companies, compromising effective talent utilization and retention. As the USA pushes for rapid advancements, companies may resort to aggressive recruitment tactics, such as offering inflated salaries or benefits to lure top talent from competitors. Although this may benefit individual employees in the short term, it fosters an unsustainable talent ecosystem marked by reduced flexibility and heightened financial risks (DeVaro, Reference DeVaro2020). This approach aligns with I-O psychology research on the potential impacts of excessive competition on employee turnover rates and a lack of long-term career development opportunities (e.g., Idris, Reference Idris2014; Van der Heijden et al., Reference Van der Heijden, De Vos, Akkermans, Spurk, Semeijn, Van der Velde and Fugate2020), as employees frequently shift roles to compete for limited resources. Indeed, in line with the resource-based view (Hobfoll et al., Reference Hobfoll, Halbesleben, Neveu and Westman2018), under the siphon effect, where talent increasingly aggregates in powerful corporations, smaller firms and startups, which are often hubs for innovation (Adler et al., Reference Adler, Florida, King and Mellander2019), may struggle to compete with larger corporations for skilled professionals. This concentration of talent in a few dominant players harms innovation across the broader AI ecosystem and undermines collaborative efforts needed to tackle complex AI challenges. Along the same vein, when policy directions shift from “responsible innovation” to purely technological competition, companies may face organizational cultural challenges such as increased attrition rates among core research and development personnel, and intensified friction in cross-departmental collaboration. These hidden losses often prove more destructive than visible costs. Thus, although the pursuit of a monopoly position may yield short-term gains, it risks eroding the foundation of a resilient and inclusive talent pipeline essential for sustained AI leadership. Given that, I-O psychology practitioners are likely to play an essential role in addressing these challenges. For example, measures should be taken to foster collaborative talent ecosystems, promote long-term career development, and mitigate the hidden costs of attrition and cross-departmental friction (e.g., Cheng et al., Reference Cheng, Yu, Dong and Zhong2024; Galetić & Klindžić, Reference Galetić and Klindžić2020; Snell et al., Reference Snell, Swart, Morris and Boon2023), which should help cultivate sustainable talent management.
Third, similarly, the AI monopoly policies of the USA have inadvertently created barriers to effective international talent exchange. AI policies enacted under Trump’s administration are designed to protect national security and maintain technological dominance, imposing stringent restrictions on the sharing of AI technologies and expertise with non-American entities, in particular those from target countries perceived as strategic competitors. By doing this, the USA has fostered an environment of protectionism that limits the free flow of knowledge across borders and, consequently, the ripple effects extend far beyond, impacting researchers, academics, and professionals worldwide. For instance, visa restrictions and limitations on collaborative research projects from US government have made it increasingly difficult for international AI experts to work in the USA or engage in joint activities with American institutions (Chen & Katzke, Reference Chen and Katzke2024). This not only hampers the career prospects of talented individuals but also deprives the global AI community of diverse perspectives and ideas that are crucial for tackling complex challenges. Within the USA, the monopoly on cutting-edge AI technologies and the reluctance to share advancements with the international community have fragmented the global AI ecosystem, isolating the nation from effective talent exchange and collaboration. This further accelerates the aforementioned siphon effect and exacerbates ineffective talent utilization. Thus, from an I-O psychology research perspective, we highlight that it is important to consider talent management by adopting a global view (Wang et al., Reference Wang, Huang, Yang and Huang2022). Especially, I-O psychologists may appeal for a change of policies to balance national security with the benefits of international collaboration, ensuring that organizations can attract and retain diverse talent while fostering cross-border innovation.
Additionally, the abovementioned fragmented regulatory policies that vary widely across jurisdictions at the state level also create significant challenges for effective talent management, utilization, and deployment. Especially those businesses and professionals attempting to operate across state borders have to navigate a complex maze of compliance issues such as licensing requirements, data privacy laws, and labor regulations. Therefore, the lack of uniformity of AI regulatory policies at the state level (Liebig et al., Reference Liebig, Güttel, Jobin and Katzenbach2024) is likely to discourage the mobility of skilled workers, as they may face barriers to relocating or working in states with differing regulatory environments. Further, from an organizational level perspective, the inconsistency in policies weakens the organizational capabilities to deploy talent strategically (Obaji & Olugu, Reference Obaji and Olugu2014), as they must tailor their practices to meet the specific demands of each state. As a result, the lack of national synergy tends to lead to unequal distribution of talent across the nation, with some states benefiting from robust talent pools, whereas others struggle to attract and retain skilled professionals. I-O psychologists can help organizations navigate the complexities of regulatory differences and inequitable access to opportunities by developing adaptive talent management strategies. Specifically, by leveraging their expertise in organizational behavior and workforce dynamics, they can design frameworks that align with varying state and international regulations while promoting fairness and inclusivity (Soekotjo et al., Reference Soekotjo, Kuswanto, Setyadi and Pawirosumarto2025). For instance, they can create tailored recruitment, training, and retention programs that account for regional compliance requirements and cultural nuances (e.g., Allen & Vardaman, Reference Allen and Vardaman2017). Additionally, I-O psychologists can advocate for policies that reduce barriers to talent mobility and foster equitable access to opportunities (Groenewald et al., Reference Groenewald, Groenewald, Uy, Kilag, Abendan and Pernites2024), ensuring that organizations can attract and retain diverse talent. This approach not only enhances organizational agility but also supports a more inclusive and innovative workforce, ultimately driving long-term success in a competitive global landscape.
Conclusion
Although the rapid advancement of AI technologies has positioned the USA as a global leader, its pursuit of AI dominance through monopolistic policies has raised significant concerns about talent management—a salient and prevalent research topic of I-O psychology. To sum up, the policy shifts toward deregulation and protectionism under the Trump administration have prioritized maintaining a monopoly in AI at the expense of workforce diversity, talent retention, and global cooperation. These policies undermine critical principles of equitable talent management, organizational effectiveness, and talent mobility. By neglecting diversity and inclusion, exacerbating talent poaching, and restricting international collaboration, these policies risk eroding the foundation of a resilient and innovative workforce.
To address these challenges, a more balanced approach is needed—one that safeguards national interests while promoting open talent management and global collaboration. I-O psychologists can play a pivotal role in shaping this approach by advocating for policies that prioritize diversity, equity, and inclusion; designing adaptive talent management strategies that account for regulatory differences; and fostering frameworks for secure knowledge sharing. For instance, revising visa policies to attract and retain international talent, creating equitable recruitment practices, and promoting multilateral agreements for AI research can help bridge gaps in the global talent ecosystem. By integrating I-O psychology principles into AI policy and practice, organizations can unlock the full potential of their talent pools, fostering innovation, and addressing the pressing challenges of the AI era. Only through a collaborative and inclusive mindset can the global AI community achieve sustainable progress and equitable outcomes.
Competing interests
The authors declare that they have no known competing or conflicting interests that could have appeared to influence the work reported in this paper.
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