Computerworld Pakistan takes a look at Global AI Policies, exploring the policies from U.S., China and Pakistan.
Artificial intelligence has shifted from being a futuristic concept to becoming a pivotal force in shaping global power, commerce, and governance. Across continents, national strategies are emerging that reveal not only technological ambitions but also deep philosophical differences in how societies intend to harness this transformative tool. The United States has framed its AI agenda in uncompromising terms, with its leadership declaring that “whoever has the largest AI ecosystem will set global AI standards and reap broad economic and military benefits,” underscoring an intent to secure “unquestioned and unchallenged global technological dominance.” This vision leans heavily on rapid private-sector innovation, an expansive digital infrastructure buildout, and exportable AI standards designed to consolidate influence among allies. In contrast, China has chosen to position AI as “an international public good that benefits mankind,” advocating for a governance model rooted in inclusivity, multilateral cooperation, and alignment with United Nations frameworks. Its action plan seeks to pair technological progress with societal safeguards, urging collective development of high-quality datasets, sustainable energy use, and interoperable standards. Between these two approaches lies a spectrum of national strategies, as documented in comparative governance analyses that map diverse priorities, from Europe’s rights-based frameworks to Japan’s industrial optimization and Canada’s ethical leadership. Pakistan, meanwhile, has entered the conversation with a policy that envisions
“A robust AI ecosystem where artificial intelligence is used responsibly and ethically to protect individuals, strengthen local innovation and industries, address local challenges, and drive inclusive growth.”
This roadmap is notable for its dual commitment to domestic capacity building, through initiatives like the National Artificial Intelligence Fund and a nationwide network of Centres of Excellence, and to fostering global partnerships that can amplify its reach. The challenge for Pakistan, and for other mid-tier players in the AI race, is to navigate between the hyper-competitive innovation arms race exemplified by Washington and the cooperative, governance-first architecture promoted by Beijing. The stakes are high: the direction chosen will shape not just technological capability but also a country’s role in the evolving international order, the resilience of its economy, and the freedoms of its citizens. By examining these contrasting strategies side by side, and situating Pakistan within this matrix, it becomes possible to see not only where the major powers are steering the future of AI, but also how emerging actors might chart their own course in an increasingly complex and interconnected digital era.
United States: Innovation, Infrastructure, and Influence
The United States approaches artificial intelligence not as a peripheral technology, but as a central pillar of its economic vitality, national defense, and diplomatic leverage. Its AI Action Plan begins with a stark framing: “Whoever has the largest AI ecosystem will set global AI standards and reap broad economic and military benefits.” This is not presented as a distant aspiration but as an imperative rooted in competition with geopolitical rivals. The administration’s stated objective is “to achieve and maintain unquestioned and unchallenged global technological dominance,” positioning AI at the forefront of a national strategy that blends technological advancement with security doctrine.
At the core of this plan is a belief that innovation flourishes when barriers are dismantled. The policy commits to “removing barriers to American leadership in artificial intelligence” by stripping away “onerous regulation” that, in the administration’s view, could smother progress “at this early stage.” It calls for a review of “all FTC final orders, consent decrees, and injunctions” that might “unduly burden AI innovation,” and instructs agencies to avoid directing federal AI funding to states with rules that “waste these funds” through overregulation. This emphasis on deregulation reflects a confidence in the private sector’s ability to lead the next wave of breakthroughs without being slowed by excessive oversight. Yet the plan is not only about freeing industry from constraints. It also outlines targeted measures to shape the values embedded in AI systems. The document states that AI must be “free from ideological bias and be designed to pursue objective truth rather than social engineering agendas when users seek factual information or analysis.” In federal procurement, this translates into selecting “frontier large language model (LLM) developers who ensure that their systems are objective and free from top-down ideological bias.” This requirement signals a clear expectation that technology procured for government use should adhere to a standard of factual neutrality as defined by the administration.
Beyond these value statements, the United States envisions a technical and physical foundation capable of sustaining leadership at scale. Pillar II of the plan calls for streamlined approval processes for “data centers, semiconductor manufacturing facilities, and energy infrastructure,” alongside efforts to “restore American semiconductor manufacturing” and “build high-security data centers for military and intelligence community usage.” These actions are intended to ensure that the computational and physical capacity for AI remains under domestic control, reducing vulnerability to external supply chain disruptions and geopolitical pressure. The plan’s military dimension is especially prominent. The administration’s approach to defense adoption is encapsulated in the instruction to “drive adoption of AI within the Department of Defense” as a way to “maintain global military preeminence.” It recognizes AI’s potential to reshape “both the warfighting and back-office operations” of the armed forces, and emphasizes coordination between the Department of Defense and the intelligence community to track AI adoption rates among competitors and adversaries.
Workforce strategy also features prominently in the American blueprint. The administration commits to a “worker-first AI agenda,” pledging to “help workers navigate that transition” as automation alters job requirements. This includes “AI skill development as a core objective of relevant education and workforce funding streams” and guidance from the Treasury Department to classify such training as “eligible educational assistance” for tax purposes. Furthermore, the creation of an “AI Workforce Research Hub” will allow ongoing assessment of AI’s “impact on the labor market” and inform targeted retraining programs, ensuring that the workforce can adapt to emerging demands. In the realm of research and standards, the plan supports “open-source and open-weight AI” as a driver for innovation and as a way to strengthen the “geostrategic value” of American-led open models. This vision is backed by proposals to expand access to large-scale computing for startups and academics, support the National AI Research Resource pilot, and publish an updated National AI Research and Development Strategic Plan to guide investment priorities. Security considerations are woven throughout the plan. It highlights the need to “prevent our advanced technologies from being misused or stolen by malicious actors” and pledges to “monitor for emerging and unforeseen risks from AI.” This extends to a commitment to “combat synthetic media in the legal system,” address “national security risks in frontier models,” and invest in biosecurity measures where AI applications could intersect with sensitive domains.
Taken together, these components reflect a distinctly American blend of market-led innovation, values-driven procurement, defense integration, and infrastructure control. The ambition is not simply to lead in AI development but to create an environment where “American AI, from our advanced semiconductors to our models to our applications” becomes “the gold standard for AI worldwide.” By pairing deregulation with targeted investments and international outreach, the United States seeks to secure both the technological and geopolitical high ground in a rapidly evolving digital arena.
China: Cooperative Governance and Inclusive Growth
China’s approach to artificial intelligence rests on a markedly different philosophical foundation than that of the United States. Where Washington frames AI leadership as a race to secure “unquestioned and unchallenged global technological dominance,” Beijing presents AI as “an international public good that benefits mankind.” This starting point shapes not only its technical priorities but also the manner in which it seeks to engage with the world in shaping AI’s future.
The Action Plan for Global Governance of Artificial Intelligence released at the 2025 World Artificial Intelligence Conference in Shanghai positions cooperation, sustainability, and inclusivity as the primary drivers of its strategy. It calls for the creation of “an inclusive, open, sustainable, fair, secure and reliable digital and intelligent future for all,” linking AI progress to the broader aims of the United Nations’ Global Digital Compact and Compact for the Future. Rather than emphasizing national dominance, China’s policy urges “all parties to take effective actions to jointly promote global AI development and governance” under shared principles. A central element of Beijing’s blueprint is the belief that AI should “empower thousands of industries,” from “industrial manufacturing, consumption, business circulation, medical care, education, agriculture, poverty alleviation and other fields,” while also being embedded in “autonomous driving, smart cities and other scenarios.” This framing treats AI not as a single-sector innovation but as a force multiplier across the real economy, where deployment in diverse domains strengthens productivity, public welfare, and technological adoption.
To make such integration possible, the plan calls for accelerated investment in digital foundations. It pledges to “accelerate the construction of global clean power, next-generation networks, intelligent computing power, data centers and other infrastructure,” with particular attention to “help the Global South truly contact and apply artificial intelligence.” This focus on extending capabilities beyond China’s borders is designed to establish Beijing as a champion for equitable AI access, reinforcing its alignment with development-oriented diplomacy. Open collaboration features prominently in the Chinese vision. The policy encourages building “a transnational open source community and a safe and reliable open source platform” to “lower the threshold for technological innovation and application” while avoiding duplication of effort. It also highlights the importance of “open sharing of basic resources” and “construction of open source ecosystems such as upstream and downstream product compatibility and interconnection,” which serve both as technical enablers and as instruments of soft power.
Data governance receives sustained attention. Recognizing that “high-quality data” is essential to AI performance, the action plan seeks to “promote the orderly and free flow of data in accordance with the law” and “cooperate to create high-quality data sets to inject more nutrients into the development of artificial intelligence.” This data strategy is paired with safeguards: “actively maintain personal privacy and data security,” ensure diversity in datasets, and “eliminate discrimination and prejudice” to protect “the diversity of the AI ecosystem and human civilization.” Sustainability is another defining thread. China’s plan advocates for “sustainable artificial intelligence,” including “artificial intelligence energy efficiency and water efficiency standards” and promotion of “green computing technologies such as low-power chips and high-efficiency algorithms.” Here, environmental considerations are woven into AI development, positioning China as a leader in integrating ecological responsibility into digital transformation. Standards-setting is treated as a vital arena of influence. The document supports “dialogue among national standard-setting institutions” and collaboration with “international standards organizations such as the International Telecommunication Union, the International Organization for Standardization, and the International Electrotechnical Commission.” The aim is to “establish a scientific, transparent, and inclusive normative framework in the field of artificial intelligence” while countering risks like algorithmic bias and ensuring interoperability. Security governance is not overlooked. The plan proposes building “a security governance framework with broad consensus” that includes “artificial intelligence risk testing and assessment” and “sharing of threat information” internationally. It promotes measures to “improve the interpretability, transparency, and safety of artificial intelligence” and to “prevent the misuse and abuse of artificial intelligence technology,” underscoring the need for accountability even within a cooperative model.
Finally, Beijing envisions institutional mechanisms to sustain this governance architecture. It calls for establishing “the International Artificial Intelligence Scientific Group and the Global Artificial Intelligence Governance Dialogue” under the UN framework to hold “meaningful discussions on global AI governance” and “promote the safe, equitable and inclusive development of artificial intelligence.” By embedding these initiatives within multilateral structures, China seeks to position itself as both a standard-bearer and a convener for worldwide AI policy. In essence, China’s AI policy is constructed around the premise that technological progress and international trust are mutually reinforcing. By aligning infrastructure development, open collaboration, data stewardship, environmental responsibility, and governance under a shared global vision, Beijing offers an alternative to unilateral, competition-driven AI strategies. This cooperative framing is not without strategic benefits for China, it strengthens its diplomatic influence, expands its technical ecosystems through shared platforms, and deepens its role in shaping the norms and standards that will define AI’s role in the global order.
Other Players in the Matrix
While the United States and China dominate the narrative of AI geopolitics, a growing set of actors are shaping the governance landscape in ways that both complement and challenge the approaches of these two giants. The AI Governance in Comparison study makes clear that these players, ranging from the European Union to OECD members, from the United Kingdom to Singapore, bring their own philosophies, legal frameworks, and policy instruments to the table. The European Union has positioned itself as “a rule-setter for trustworthy AI,” with the landmark Artificial Intelligence Act serving as its central instrument. The document emphasizes that the Act “lays down a uniform legal framework for the development, placement on the market, and use of AI in conformity with Union values.” It introduces a risk-based classification of AI systems, ensuring that those deemed “high-risk” face stringent requirements on safety, transparency, and accountability. By mandating human oversight and traceability, the EU aims to ensure that “AI systems are safe and respect existing law on fundamental rights and Union values.” This approach differs sharply from the deregulatory tendencies in the U.S. plan and the cooperative development framing in China’s blueprint, favoring a tightly structured regulatory environment that prioritizes rights protection.
The United Kingdom’s policy, while inspired by similar principles, diverges in execution. The AI Governance in Comparison matrix notes that the UK “adopts a pro-innovation regulatory framework” based on “five cross-sector principles” applied through existing regulators rather than a single AI law. This allows for sector-specific adaptation while maintaining consistency in core governance priorities such as “safety, security and robustness,” “appropriate transparency and explainability,” and “accountability and governance.” The flexibility of this model is designed to attract AI investment while preventing the creation of regulatory silos. Singapore, often cited as an agile policy innovator, integrates AI into its Model AI Governance Framework to focus on practical guidance for industry. The text highlights that it “provides detailed and readily implementable guidance to private sector organizations to address key ethical and governance issues.” Unlike the binding legal force of the EU’s approach, Singapore’s model is voluntary but deeply embedded in its economic development strategy. The framework’s guidance on “internal governance structures and measures,” “operations management,” and “stakeholder interaction and communication” reflects a focus on actionable, business-friendly standards that still safeguard public trust.
OECD members collectively provide another layer of influence. The matrix recalls that the OECD AI Principles, adopted in 2019 were “the first intergovernmental standard on AI,” emphasizing inclusive growth, sustainable development, human-centered values, transparency, robustness, and accountability. These principles are voluntary but have informed national strategies across multiple jurisdictions, creating a common reference point for democratic states. The OECD’s monitoring mechanisms also help track member implementation, which in turn shapes global conversations about responsible AI. Other emerging actors include Canada, which has developed the Directive on Automated Decision-Making, a policy instrument requiring government departments to assess the impact of automated decision systems before deployment. As the document states, it mandates that “higher impact systems must undergo algorithmic impact assessments and be subject to more stringent requirements.” This reflects a public sector accountability lens that is narrower than the EU’s comprehensive approach but highly relevant to governance within state institutions.
Japan offers a hybrid model, blending industrial competitiveness goals with a governance structure informed by its Social Principles of Human-Centric AI. These principles “ensure that AI will be designed to be fair, accountable, transparent, and explainable.” They are implemented alongside economic measures to strengthen Japan’s role in AI-related trade and research collaborations, especially within Asia. When viewed as a whole, the matrix reveals a spectrum of governance philosophies. At one end is the EU’s prescriptive, law-centered model, prioritizing the codification of rights and duties in binding form. Another is Singapore’s business-oriented, voluntary framework, emphasizing practical ethics and innovation enablement. Between these poles are the UK’s regulator-led flexibility, Japan’s principle-driven hybrid, and Canada’s targeted focus on automated decision-making in the public sector. These varied approaches create both opportunities and frictions in the global system. On one hand, they allow countries to tailor AI governance to their political cultures, economic priorities, and institutional capacities. On the other, the lack of harmonization poses challenges for cross-border interoperability, especially in areas like risk classification, data governance, and algorithmic transparency.
For states navigating their own AI policy choices, particularly those in the Global South, this mosaic of models provides both inspiration and caution. The AI Governance in Comparison report underlines that “there is no one-size-fits-all governance model for AI,” but that participation in multilateral discussions and alignment with widely recognized principles can increase both domestic effectiveness and international credibility. For Pakistan, as we will explore in the next section, the decision will be whether to align with a rights-first regulatory model, a flexible innovation-led system, or a cooperative globalist approach or, more ambitiously, to craft a distinctive blend that draws on the strengths of all three.
Pakistan: Positioning in the Global AI Policy Arena
Pakistan’s entry into the global AI policy conversation is marked by a deliberate effort to balance technological ambition with ethical responsibility, situating itself between the high-speed innovation models of advanced economies and the cooperative governance frameworks advocated by multilateral actors. The National Artificial Intelligence Policy – 2025 begins with a clear vision: “a robust AI ecosystem where artificial intelligence is used responsibly and ethically to protect individuals, strengthen local innovation and industries, address local challenges, and drive inclusive growth for national prosperity while preserving human rights and the rule of law.” This vision aligns with international commitments such as the United Nations Sustainable Development Goals and UNESCO’s Recommendations on the Ethics of AI, anchoring Pakistan’s approach in globally recognized social and ethical benchmarks.
From the outset, the policy identifies both development objectives and ethical objectives. On the development side, it pledges “to boost economic and technological growth by promoting an innovation-driven AI ecosystem that strengthens industry, enhances public service delivery and addresses socio-economic challenges.” This ambition is supported by commitments “to integrate AI education into national curricula,” “to build domestic AI capabilities, AI infrastructure, including computational resources, local talent, and innovation ecosystems,” and “to promote research, development, and commercialization of indigenous AI solutions to reduce dependency on imported technologies.” The ethical objectives place equal emphasis on ensuring “fairness, transparency, and accountability” and protecting “personal data, privacy and security” while preserving cultural identity “by leveraging AI in context-sensitive ways that empower communities and promote local narratives.” One of the most significant structural features in Pakistan’s plan is the creation of the National Artificial Intelligence Fund (NAIF), which will “support research, development, and commercialization of AI and allied technologies” by allocating “at least 30% of Ignite’s R&D Fund on a perpetual basis” for AI-focused initiatives. This financial commitment is coupled with the establishment of a nationwide network of Centres of Excellence in Artificial Intelligence (CoE-AI), designed to “facilitate demand-driven research and development in AI and allied technologies that align with national priorities and are relevant and beneficial to citizens.” These centers will provide “access to state-of-the-art computing infrastructure, AI labs, and test-beds,” while also “nurturing the growth of local startups by providing incubation and acceleration programs.”
Human capital development occupies a central place in the policy. Through the National AI Skill Development Program, Pakistan aims “to train 200,000 individuals annually in AI including AI ethics and allied technologies through hybrid learning mechanisms (online and onsite).” To sustain this pipeline of talent, the policy outlines a “Train the Trainer” initiative to prepare “10,000 trainers by 2027,” ensuring long-term capability in AI instruction. Higher education is also targeted, with a plan to “offer 3,000 scholarships annually for postgraduate and doctoral programs in AI including AI ethics and allied technologies” and an “interest-free education financing scheme… to support 15,000 students annually pursuing high-tech certifications, training, and degrees in AI.” Inclusion is treated not as an afterthought but as an explicit policy pillar. The document commits to designing “a specialized offshoot of the National AI Skill Development Program… for marginalized women and PWDs through special coursework and online means of imparting education to ensure inclusivity and access.” The policy also seeks to “encourage female entrepreneurship, participation and engagement in all stages of an AI system life cycle” and to implement measures that avoid “exacerbating the gender digital divide and gender wage gap.”
Security and governance are addressed through a “Secure AI Ecosystem” framework, which will “develop AI-integrated security guidelines for end-to-end protection during the development and deployment of AI systems,” deploy “AI-driven threat detection systems to monitor and respond to security breaches in real-time,” and enforce “human oversight mechanisms for critical AI operations, particularly in high-risk scenarios.” A “National Data Security” strategy will outline “the security standards, including auditing and monitoring strategies” needed to safeguard sensitive datasets. International collaboration is embedded in Pakistan’s strategy, with commitments to “strengthen international collaborations with global AI leaders to exchange knowledge, conduct joint research, and ensure global competitiveness.” This includes “establishing and supporting bilateral and multilateral partnerships with global AI leaders, such as international organizations, AI investors, etc., to share knowledge, conduct joint research, and develop innovative AI solutions.” By doing so, Pakistan positions itself not only as a consumer of AI technologies but also as a contributor to global innovation networks.
In the broader geopolitical context, Pakistan’s policy offers a middle path. It does not fully emulate the deregulated, market-first orientation of the United States, nor does it wholly adopt the governance-heavy model promoted by China. Instead, it seeks to blend domestic capability-building with ethical safeguards and active participation in multilateral cooperation. This approach enables Pakistan to align with international standards, tap into global partnerships, and adapt AI solutions to its specific socio-economic realities. Whether this balance can be maintained in the face of rapid technological change and shifting geopolitical currents will determine how effectively Pakistan can translate its policy commitments into tangible leadership within the global AI landscape.
Comparative Analysis: Mapping the Three Approaches
When examined side by side, the AI strategies of the United States, China, and Pakistan reveal three distinct, yet occasionally overlapping, visions for how artificial intelligence should be developed, deployed, and governed. These differences reflect broader political cultures, economic priorities, and international ambitions, while also intersecting with the governance philosophies of other actors in the AI Governance in Comparison matrix. The American plan is unambiguous in its competitive framing. The White House asserts that “whoever has the largest AI ecosystem will set global AI standards and reap broad economic and military benefits” and positions this as a “national security imperative… to achieve and maintain unquestioned and unchallenged global technological dominance.” The United States pursues this goal through three pillars: “innovation, infrastructure, and international diplomacy and security.” Deregulation is a central feature, with directives to “identify, revise, or repeal regulations… that unnecessarily hinder AI development or deployment” and to ensure that AI systems are “free from ideological bias and… pursue objective truth rather than social engineering agendas.” The inclusion of measures to “drive adoption of AI within the Department of Defense” underscores a readiness to integrate AI into both strategic and operational aspects of national defense.
China’s vision, in contrast, frames AI as “an international public good that benefits mankind” and seeks to create “an inclusive, open, sustainable, fair, secure and reliable digital and intelligent future for all.” Rather than emphasizing national preeminence, Beijing’s strategy calls on “all parties to take effective actions to jointly promote global AI development and governance.” Its plan is structured around empowering “thousands of industries,” accelerating “the construction of global clean power, next-generation networks, intelligent computing power, data centers,” and promoting “the open sharing of basic resources” through transnational open-source communities. Governance mechanisms are integral, including proposals to “establish a scientific, transparent, and inclusive normative framework in the field of artificial intelligence” and to form “the International Artificial Intelligence Scientific Group and the Global Artificial Intelligence Governance Dialogue” under UN auspices.
Pakistan’s policy blends elements from both models while anchoring them in its own socio-economic context. It articulates a vision for “a robust AI ecosystem where artificial intelligence is used responsibly and ethically to protect individuals, strengthen local innovation and industries, address local challenges, and drive inclusive growth for national prosperity while preserving human rights and the rule of law.” The policy aims “to boost economic and technological growth by promoting an innovation-driven AI ecosystem,” to “integrate AI education into national curricula,” and to “strengthen international collaborations with global AI leaders to exchange knowledge, conduct joint research, and ensure global competitiveness.” Key mechanisms include the National Artificial Intelligence Fund, allocating “at least 30% of Ignite’s R&D Fund” to AI development, and the National AI Skill Development Program to “train 200,000 individuals annually in AI including AI ethics and allied technologies.” When placed in the context of the broader matrix, the distinctions become sharper. The European Union is described as “a rule-setter for trustworthy AI,” with its Artificial Intelligence Act introducing a “uniform legal framework… in conformity with Union values.” The United Kingdom adopts a “pro-innovation regulatory framework” implemented through “five cross-sector principles” applied by existing regulators. Singapore’s Model AI Governance Framework offers “detailed and readily implementable guidance” to industry, while OECD members continue to advance the OECD AI Principles, which stress on:
“inclusive growth, sustainable development, human-centered values, transparency, robustness, and accountability”
Across these strategies, several thematic contrasts emerge:
- Innovation vs. Governance as the Primary Driver
The U.S. favors rapid, private-sector-led innovation, with government intervention largely to remove constraints and ensure value alignment in procurement. China embeds innovation within a governance-led, cooperative framework aimed at reducing disparities between nations. Pakistan positions itself between the two, using governance to guide domestic capacity building while pursuing global integration. - Infrastructure and Capacity Building
The U.S. focuses on “streamlined permitting for data centers, semiconductor manufacturing facilities, and energy infrastructure” and restoring domestic manufacturing capability. China emphasizes infrastructure as a shared global resource, aiming to “help the Global South truly contact and apply artificial intelligence.” Pakistan combines national infrastructure goals, through high-performance computing networks and Centres of Excellence, with policies to engage international partners. - Workforce and Skills Development
Washington’s worker-first framing includes an “AI Workforce Research Hub” and tax incentives for training. Beijing calls for capacity building “to protect and strengthen the digital and intelligent rights and interests of women and children, and bridge the intelligence gap.” Islamabad commits to large-scale skills programs, scholarships, and a “Train the Trainer” initiative to sustain its talent base. - Diplomatic and Normative Influence
The United States seeks to “export American AI to allies and partners” and “counter Chinese influence in international governance bodies.” China’s approach is to embed its governance model in multilateral systems and promote consensus-based norms. Pakistan’s plan focuses on entering these global dialogues as a contributor rather than a dominant force, leveraging partnerships to amplify its presence.
In synthesis, the American model is built for speed, competition, and market leverage; the Chinese model for coordinated growth, governance, and multilateral influence; and the Pakistani model for balanced development, ethical safeguards, and international collaboration. The strategies of the EU, UK, Singapore, and OECD members demonstrate that a spectrum exists between these poles, with multiple governance and innovation configurations available. For Pakistan, the challenge will be to sustain the middle path, drawing on the agility of market-led innovation without losing the protective structures of governance, and to ensure that its approach remains adaptive as the global AI environment evolves.
The Road Ahead for Pakistan
As artificial intelligence becomes a defining force in economic development, governance, and international influence, Pakistan stands at an inflection point. It has crafted a policy that does not simply emulate the paths taken by the United States or China, but instead seeks to blend domestic innovation, ethical safeguards, and international engagement. This hybrid approach reflects a clear recognition that Pakistan’s future in AI will depend on both building robust internal capacity and embedding itself in global networks where knowledge, standards, and resources are increasingly shared. The National Artificial Intelligence Policy – 2025 sets this ambition in precise terms, envisioning “a robust AI ecosystem where artificial intelligence is used responsibly and ethically to protect individuals, strengthen local innovation and industries, address local challenges, and drive inclusive growth for national prosperity while preserving human rights and the rule of law.” This vision is not limited to technological adoption; it links AI directly to “boost[ing] economic and technological growth” and “address[ing] socio-economic challenges” through targeted measures in education, industry, and public services.
One of the most decisive features of Pakistan’s plan is its structural investment in research and talent. The commitment to “allocate at least 30% of Ignite’s R&D Fund… to the NAIF to support research, development and commercialization of AI-focused initiatives” signals an intent to sustain innovation over the long term. Equally important is the scale of its human capital ambitions, including the National AI Skill Development Program to “train 200,000 individuals annually in AI including AI ethics and allied technologies” and the provision of “3,000 scholarships annually for postgraduate and doctoral programs” in the field. By placing ethical governance alongside economic development, Pakistan aligns itself with the growing international consensus that AI must be transparent, accountable, and equitable. Its pledge to “strengthen international collaborations with global AI leaders to exchange knowledge, conduct joint research, and ensure global competitiveness” provides an avenue for influencing, and being influenced by, global norms, without being locked into a singular geopolitical bloc.
The challenge will be execution. Converting these policy statements into functioning programs, sustainable infrastructure, and measurable societal benefits will require consistent political commitment, agile adaptation to technological shifts, and the capacity to engage productively in multilateral forums. Pakistan’s success will hinge on whether it can preserve the balance between rapid innovation and careful governance, ensuring that its AI ecosystem evolves in a way that is not only competitive, but also inclusive and trusted. In this moment, Pakistan’s choice is not whether to lead or follow, but how to chart a path that draws on its strengths, mitigates its constraints, and positions it as a constructive actor in shaping the global AI future.
