AI Economy for Developing APAC

Xiuli Chen

— 49 min read

The rise of artificial intelligence (AI) is transforming global economic paradigms, and the 21 member economies of APEC (Asia-Pacific Economic Cooperation) are poised to harness this technology for growth. As APEC looks toward 2026, leaders are focused on leveraging AI for resilient, inclusive development across diverse national contexts. This article provides a structured analysis of how APAC can develop its economies through AI. By distinguishing the emerging AI-driven economy from the traditional knowledge economy, we categorize APEC economies into logical groups based on their national strengths, from advanced digital hubs to resource-driven and industrializing economies, and recommend AI development strategies tailored to each group’s industrial base. We then present deep-dive case studies on the United States, South Korea, and China’s Greater Bay Area, examining how these innovation powerhouses should lead or pivot in the AI economy given their ecosystems, industries, geopolitical roles, and human capital. We also offer guidance for all APEC members on fostering inclusive AI-driven growth by 2026, including initiatives in digital upskilling, policy frameworks, regional collaboration, and infrastructure investment.

From Knowledge Economy to AI Economy

The knowledge economy is an economic system in which growth is primarily driven by intellectual capital, information, and human knowledge rather than physical goods or manual labor. In a knowledge-based economy, products and services are created through the application of specialized skills, research, and technological know-how. Knowledge (e.g. scientific discoveries, technical expertise) becomes a key asset, and intangible factors like innovation capacity, education, and intellectual property are central to value creation. This concept gained prominence during the late 20th-century Information Age (the “third industrial revolution”), when advancements in ICT and globalization allowed knowledge-intensive industries to flourish. Core drivers of the knowledge economy include a highly skilled workforce, strong R&D institutions, robust information networks, and supportive institutions (e.g., intellectual property rights and innovation policies). Its economic implications have been profound: developed APEC economies today derive a large share of their output from knowledge-intensive sectors (such as finance, education, and high-tech services), yielding high productivity and growth. However, the knowledge economy also elevated the importance of human capital – skilled labor became essential, often commanding wage premiums, and nations with better education and innovation systems pulled ahead. At the same time, disparities emerged between those with access to education/technology and those without, challenging policymakers to ensure inclusive participation in the knowledge-driven era.

The AI economy is an emerging paradigm in which AI technologies, including machine learning algorithms, intelligent automation, robotics, and data-driven analytics, become core to economic activity and a primary engine of growth. If the knowledge economy was defined by humans leveraging knowledge and ICT, the AI economy is characterized by intelligent machines augmenting or automating cognitive tasks, generating value in new ways. In the historical context, AI is often seen as a general-purpose technology underpinning the fourth industrial revolution, analogous to steam in the 1st or electricity in the 2nd industrial revolution. In previous eras, technology-enabled new economic paradigms were named after their key productive factor – for example, the “industrial economy” (not simply the “steam economy”) and later the “information/knowledge economy” (rather than just the “digital economy”). By extension, the AI economy signifies that AI itself is becoming a fundamental productive factor across industries, driving efficiencies and spawning new sectors.

Key Drivers

The AI economy is fueled by a combination of technological and human factors distinct from (though building upon) the knowledge economy. Data is often touted as the “new oil” – vast datasets (from business processes, sensors, consumer behavior, etc.) provide the raw material for AI systems to learn and make decisions. Computing infrastructure is another driver: high-performance computing, cloud services, and specialized AI chips enable the training and deployment of complex AI models. Algorithms and innovation ecosystems are critical as well – continuous R&D in machine learning, supported by open-source communities and tech industry investment, propels AI capabilities forward. Additionally, human capital remains important, but with a shift in the skillset: the AI economy demands new skills and competencies focused on analytical, creative, and interpersonal abilities that complement automated systems. Unlike the knowledge economy where humans exclusively managed knowledge work, in an AI-driven economy human roles evolve toward oversight, design, and ethical guidance of AI, while routine decision-making or pattern-recognition tasks can be handled by algorithms. Indeed, “the AI economy requires a workforce with a completely new set of skills and competencies” emphasizing creativity, emotional intelligence, and adaptability. Supportive policy and regulatory frameworks (for data governance, AI ethics, etc.) and public trust in AI are enabling factors that determine how broadly AI can be adopted across society.

Economic Implications

The transition from a pure knowledge economy to an AI economy carries significant implications. In terms of productivity and growth, AI has the potential to unlock new frontiers of efficiency by automating complex processes and uncovering insights from big data beyond human cognitive limits. Early evidence shows AI and automation can increase productivity and overall GDP growth, though widespread gains may be realized after complementary innovations (business process redesign, upskilling, organizational change) catch up. Entirely new industries and services are emerging – from AI-driven healthcare diagnostics to autonomous transportation – much as the knowledge economy spawned the rise of IT and software industries. However, the AI economy also poses challenges. Labor markets will be disrupted as AI automates not only manual labor but also white-collar knowledge work. Many routine cognitive jobs (data entry, basic analysis, customer support, etc.) can be performed by AI, potentially displacing workers, while simultaneously new jobs (AI system engineers, data scientists, AI ethicists, etc.) are created. This raises the urgency of reskilling programs and lifelong learning in all economies. Over time, economists expect that short-term job losses will be offset by new opportunities and productivity-driven job creation, as happened in past technological shifts. The distribution of benefits is another concern – countries or companies that lead in AI could capture outsized gains, potentially widening global or domestic inequalities if others fall behind. Finally, the AI economy brings to the fore issues of ethical governance and data privacy: unlike the knowledge economy which largely amplified human decision-making, AI systems may act autonomously in high-stakes domains, requiring robust frameworks to ensure fairness, transparency, and accountability in their economic and social impacts. In summary, the AI economy builds on the foundations of the knowledge economy but represents a distinct shift: intelligence itself becomes a key economic resource. By leveraging AI to convert information into actionable insight at scale, it promises major gains in innovation and prosperity, provided that societies adapt through skill development and wise policies to mitigate transition costs.

National Advantages and AI Strategies

APEC’s member economies vary widely in their development stages and competitive advantages – from high-income technology exporters to developing nations reliant on agriculture or natural resources. To craft effective AI-driven growth strategies, it is useful to group these economies based on structural similarities and strengths. Below, we identify three broad groups of APEC economies (with some overlap between groups) and recommend AI development strategies aligned to each group’s industry base and resources. Within each group, we highlight key sectors (e.g. manufacturing, agriculture, finance, logistics, healthcare, education) where AI adoption can be most impactful.

1. Advanced Digital Economies and Innovation Leaders

This group comprises APEC’s highly developed, knowledge-intensive economies with advanced digital infrastructure and strong innovation ecosystems. It includes the United States, Japan, South Korea, Singapore, Hong Kong (China), Taiwan(China), Canada, Australia, and New Zealand – all economies characterized by widespread broadband connectivity, high R&D investment, and a skilled workforce. These economies have largely completed the shift to a knowledge economy (where intangible assets and services dominate) and are at the forefront of the AI revolution. For example, knowledge-intensive output forms a significant share of GDP in most of these economies, underpinned by educated labor and robust innovation networks. They also host leading tech companies and research institutions that drive AI research and commercialization. The core advantages of this group include: near-universal internet access (often 5G or fiber networks), large pools of tech talent and top universities, deep capital markets to fund innovation, and governments that actively support high-tech industries.

Strategic AI Focus

Advanced digital economies should position themselves as global leaders in AI innovation and governance. Having already built knowledge economies, their strategy is to push the productivity frontier with frontier AI technologies while ensuring inclusive benefits. Key recommendations for this group include:

Illustrative Sectors and Initiatives

  • Smart Manufacturing​. Japan and South Korea are expanding their use of AI-driven robotics and predictive maintenance in factories. South Korean conglomerates in electronics and autos now invest in AI systems that predict equipment failures, enabling autonomous production lines that minimize downtime.
  • Healthcare AI. The United States and Canada are at the forefront of AI in healthcare. Hospitals are piloting AI diagnostic tools (for radiology and pathology), while biotech firms use AI to accelerate drug discovery. For example, AI-enabled diagnostics have grown rapidly in the U.S., with hundreds of systems approved for clinical use. These technologies improve accuracy and can alleviate doctor shortages, especially as populations age.
  • Financial Technology. Singapore and Hong Kong, global financial centers, employ AI in algorithmic trading, fraud detection, and credit scoring. Regulators in these economies are concurrently establishing regulatory sandboxes to safely test AI in finance and issuing guidance on responsible AI in banking to manage bias and cybersecurity risks.
  • Public Sector and Smart Cities.Several advanced APEC cities (e.g. Seoul, Tokyo, Sydney) are integrating AI into urban management – optimizing traffic flow via AI analytics, deploying AI chatbots for citizen services, and using predictive models for infrastructure maintenance. These smart city initiatives not only improve efficiency but also create lead markets for AI solutions that can be exported regionally.

2. Resource-Driven and Commodity-Based Economies

It includes Australia and Canada (developed economies with large mining and energy sectors), Russia (a major oil & gas and minerals exporter), Chile and Peru (copper and mineral exporters), Brunei Darussalam (oil-rich), Indonesia (fuels, palm oil, minerals), Malaysia (palm oil, oil & gas, plus electronics), and Papua New Guinea (minerals and LNG). These economies share the characteristic of abundant natural resources which contribute substantially to GDP, exports, and government revenues. Their national advantage lies in these endowments, but they often face challenges such as commodity price volatility, a need to move up the value chain, and in some cases, underdeveloped innovation sectors outside resource extraction. Digital infrastructure and human capital levels in this group vary widely: Australia and Canada have advanced research and tech capabilities (indeed Canada is a pioneer in AI research), whereas PNG or Brunei have more limited tech workforces. The unifying theme is that AI offers a chance for resource-driven economies to both optimize their core industries and diversify into new sectors for sustainable long-term growth.

Strategic AI Focus

For resource-based economies, the AI strategy should be twofold: enhance productivity and sustainability in commodity industries (mining, oil & gas, agriculture) through automation and analytics, and invest resource wealth into building digital capabilities (infrastructure, education, startups) that pave the way for a diversified knowledge/AI economy. Key strategic elements include:

  • AI for Efficient and Sustainable Resource Extraction. Applying AI and machine learning in mining, energy, and agriculture can yield significant gains. For example, autonomous mining equipment and AI-driven optimization systems can improve safety and output in mines. Australia has been a trailblazer here – it was an early adopter of self-driving haulage trucks in its iron ore mines, and today operates over 1,000 autonomous or autonomous-ready mining trucks (the most in the world after China). These AI-guided vehicles and drills operate 24/7 in remote sites, reducing accidents and boosting productivity. Similarly, in oil and gas, AI can improve exploration (through seismic data analysis), monitor pipelines with predictive analytics to prevent spills, and optimize drilling processes. Predictive maintenance algorithms can be deployed on heavy equipment (rigs, trucks, refinery machinery) to foresee failures and schedule repairs, minimizing costly downtime. These technologies help resource economies extract more value efficiently from their resources while lowering environmental and safety risks.
  • Smart Agriculture and Fisheries. Many resource-reliant APEC economies also have large agricultural sectors (e.g. Indonesia, PNG, Peru) or fisheries. AI offers tools to increase agricultural productivity and resilience – often crucial for rural employment and food security. Precision farming systems use AI to analyze data from sensors, drones, and satellites to guide farmers on optimal irrigation, fertilizer use, and pest control. For instance, AI-based crop monitoring (like NEC’s “CropScope” system in Asia) can advise even less-experienced farmers, helping them compete by improving yields and resource efficiency. These innovations can narrow the knowledge gap between large agribusinesses and smallholders, promoting greater equity if made accessible. Governments should invest in such agricultural AI tools and train farmers in digital literacy, as well as improve rural connectivity so that even remote communities benefit. Over time, raising farm productivity through AI contributes to inclusive growth and can free up labor for higher-value industries.
  • Value-Addition and Diversification. Resource-driven economies can leverage AI to diversify into downstream value-added industries. Rather than exporting raw commodities, countries can develop processing and manufacturing that use those inputs, enhanced by automation. For example, Chile could use AI in mineral processing plants to improve copper yields and then venture into manufacturing of tech components using copper; similarly, Indonesia and Malaysia can expand agro-processing industries with AI-run factories (e.g. smart palm oil mills or food processing with robotic quality control). These moves up the value chain create more skilled jobs domestically. Furthermore, resource revenues should be strategically invested into building a domestic tech sector – funding AI research centers, incubating startups (perhaps focusing on mining technology, clean energy tech, or agri-tech where they have domain expertise). Canada provides a useful case: though rich in resources, it invested in AI research early (Montreal, Toronto, etc. became global AI hubs) and now has a thriving AI startup scene and expertise in fields like AI for healthcare and finance. This shows that resource economies can become knowledge economies by reinvesting in human capital and innovation.
  • Infrastructure and Human Capital Investments. Many commodity-focused economies need improvements in digital infrastructure to fully exploit AI. This means expanding high-speed internet (especially beyond urban centers), deploying cloud computing and data center capacity, and ensuring reliable energy grids to power advanced tech. Energy-exporting economies in particular might channel funds to build “AI infrastructure as vital as power grids”, recognizing that data centers and connectivity are foundational for the AI age. Concurrently, developing human capital is essential: training programs in data science, engineering, and computer science should target youth in these countries, possibly funded by public-private partnerships using resource royalties. The long-term vision is to reduce dependence on commodity cycles by equipping the workforce with skills for the digital economy. Initiatives could include scholarships for STEM, partnerships with foreign universities or companies to transfer knowledge, and bootcamps to retrain workers from mining or oil fields into tech and support roles.

Illustrative Sectors and Initiatives:

  • Mining 4.0. Australia is integrating AI extensively in mining operations. Major companies like Rio Tinto use an Autonomous Haulage System with AI-coordinated driverless trucks and drills in the Pilbara iron ore mines. The results are higher output and a safer work environment with fewer humans in hazardous sites. Australia’s mining sector also employs AI for mineral exploration (predicting ore deposits from geological data) and for environmental monitoring (using AI-equipped drones to survey tailings dams). These efforts not only cut costs but also help meet stricter sustainability and safety standards.
  • Energy and Utilities. Russia and Brunei can apply AI in their oil & gas industries for predictive analytics (anticipating equipment maintenance in refineries and offshore platforms) and for optimizing supply logistics. In Russia’s vast pipeline network, AI systems are being developed to detect anomalies or leaks in real-time via sensor data, crucial for preventing accidents in remote Siberian stretches. Resource exporters are also exploring AI in energy management – e.g., using AI to balance power grids and integrate renewable energy, which is especially relevant for economies like Canada or Australia aiming to transition to cleaner energy while leveraging their natural assets.
  • Precision Agriculture.Indonesia is experimenting with AI for palm oil and rice cultivation. Startups and research institutes have introduced smartphone apps that use machine learning to identify crop diseases from farmer-taken photos and recommend treatments. Drones with AI vision are used on large plantations to monitor crop health and optimize fertilizer application, increasing yields sustainably. Likewise, Peru has pilot programs where AI models combined with climate data help farmers in the Andes adapt planting decisions to weather variability, improving resilience for subsistence farming communities.
  • Resource-Financed Tech Hubs. Saudi Arabia’s neighbor (though not in APEC) offers a parallel example with its investment in tech cities, but within APEC, Australia and Canada have used public research funding (partly bolstered by resource sector taxes) to build AI clusters. Western Australia, a mining-heavy state, is now funding a Mining Innovation Hub focusing on AI and robotics for mineral extraction. Chile, as the world’s largest copper producer, has established a public-private initiative “Chile Artificial Intelligence Fund” aiming to sponsor local AI startups that can also serve the mining industry (e.g. AI for ore grade estimation, or robotics for mining safety). These strategies illustrate how resource economies can convert commodity wealth into digital-era strengths, aligning with APEC’s vision of inclusive, innovation-driven growth.

3. Emerging Industrializing Economies

This category includes APEC’s middle-income economies that have significant manufacturing or service industries and are striving to move up the value chain. Key examples are China (as a whole, although parts of China are already advanced, the country has transitioned from a low-cost manufacturing base to a tech powerhouse), Malaysia, Thailand, Vietnam, Mexico, Indonesia, Philippines, and Peru (which, besides resources, has light manufacturing). These economies typically have an emerging industrial base – e.g. assembly of electronics, automotive parts, textiles – or large service outsourcing sectors (such as call centers or business process outsourcing in the Philippines). Their advantages often include a young labor force, growing domestic markets, and in many cases, improving digital infrastructure. However, they face pressure from rising wages and competition, making productivity gains imperative. They may not lead in cutting-edge R&D yet, but they are avid adopters of technology developed elsewhere. The goal for this group is to leverage AI to accelerate industrial upgrading and innovation, ensuring they don’t get stuck in the “middle-income trap.”

Strategic AI Focus

Emerging industrial economies should focus on adopting and adapting AI technologies to their key industries (to boost productivity and quality), and developing human capital and startups to gradually build an indigenous innovation capacity. Their strategies include:

  • Industry 4.0 and Smart Manufacturing. Embracing AI in manufacturing is critical as these economies often rely on export-oriented factories. By introducing AI-driven automation, intelligent quality control, and supply chain optimization, they can produce goods more efficiently and remain competitive as labor costs rise. There is a marked disparity currently: advanced and large emerging economies are rapidly adopting such practices, while some lower-income manufacturing hubs risk lagging. To bridge this gap, governments and firms in economies like Thailand, Vietnam, and Mexico should invest in modernizing factories with AI. Predictive maintenance is a low-hanging fruit – for example, Thai electronics firms have begun using sensors and machine learning models that predict machine failures before they happen, reducing downtime in production. In Malaysia’s automotive parts plants, computer-vision AI systems now automatically detect product defects on the assembly line, far faster and more consistently than manual inspection. These examples illustrate how mid-tier industries can deploy off-the-shelf AI solutions (often in partnership with multinational technology providers) to achieve leaps in productivity and quality. Additionally, robotics can be introduced in a phased manner – for repetitive tasks in electronics or appliance assembly – working alongside human workers (collaborative robots). Governments should support SMEs in accessing these technologies, for instance via grants or by setting up demonstration “smart factories” that showcase the ROI of AI investments.
  • AI in Logistics and Trade.Many emerging APEC economies are deeply integrated in global supply chains, so logistics is a strategic sector. AI can optimize port operations, shipping routes, and warehouse management. Ports in economies like Vietnam and Malaysia are beginning to use AI algorithms for container stacking and routing to shorten ship turnaround times. Mexico, as a manufacturing hub connected to the U.S., can use AI to streamline cross-border logistics – e.g. predictive analytics to manage inventory and transportation in its automotive supply chain, reducing delays and costs. Embracing AI in customs and trade facilitation (such as automated document processing and anomaly detection for security) would also speed up trade flows. These improvements strengthen these countries’ attractiveness as manufacturing locations and trade partners.
  • Skilling the Workforce for AI. A potential risk of automation is displacing workers, particularly in labor-intensive sectors. Thus, emerging economies must couple automation with aggressive upskilling and reskilling programs to move their workforce into higher-value roles. This could include technical training for workers to manage and maintain AI-driven machines, programming skills for those in IT, and more generally, improving digital literacy across the labor force. Given the youthful population in many of these economies, integrating AI and data science modules into vocational training and university curricula is essential. Governments can partner with industry and educational institutions to create short-term certificate courses on topics like basic coding, machine learning fundamentals, and using AI tools in business. APEC’s new initiative explicitly encourages members to share effective upskilling programs and prepare workforces to take advantage of AI opportunities. By 2026, these economies should aim to have a sizable pool of technicians and engineers capable of implementing AI solutions locally, which will also attract investment.
  • Fostering Local AI Innovation and Startups. While initial AI adoption might involve importing technology, emerging economies should also cultivate domestic innovation ecosystems. This can start with nurturing tech startups that tailor AI solutions to local market needs (for example, Indonesian startups developing AI for Bahasa Indonesia language processing, or Vietnamese startups creating AI tools for rice farmers). Policy support such as startup incubators, sandboxes, and innovation grants can encourage entrepreneurship in AI. Over time, a homegrown tech sector can drive job creation and reduce reliance on foreign technology. Regional collaboration can help here: economies can jointly establish innovation hubs or share resources (for instance, Vietnam has proposed an APEC Innovation Center for digital and AI collaboration). Such centers could connect talent from various emerging members to work on common challenges (like AI for food security or disaster management in Southeast Asia).

Illustrative Sectors and Initiatives:

  • Electronics and Automotive Manufacturing.Vietnam has rapidly become an electronics manufacturing base (for smartphones, etc.), and to stay competitive, firms in Vietnam are exploring AI for production planning and quality control. For example, a Vietnamese factory assembling mobile phones uses AI to schedule assembly tasks in real-time based on sensor data, improving throughput. In Thailand’s automotive industry, some plants use AI-based vision systems to inspect paint quality on cars, reducing rework. The Thai government’s “Industry 4.0” policy supports such upgrades with incentives, acknowledging that AI-enhanced manufacturing can help Thailand escape the middle-income trap.
  • Textiles and Apparel. Mexico and Malaysia still have significant textile/apparel sectors. Trials in these countries use AI for fabric defect detection and demand forecasting. In one pilot, a Malaysian textile mill installed an AI-driven camera system (trained to recognize weaving flaws) on its looms; it catches defects in real-time, preventing waste and ensuring higher-grade output. Similarly, Mexican apparel exporters use AI forecasting tools that analyze retail data from the U.S. to adjust their production mix quickly, aligning supply with market trends. These applications show AI aiding both manufacturing efficiency and supply-chain responsiveness.
  • IT-BPO and Services. Philippines, known for its call centers and BPO (business process outsourcing), is at a crossroads as AI chatbots and automation begin handling basic customer service and back-office tasks. The strategy here is to upskill BPO workers to work alongside AI – for instance, training call center agents to manage AI chatbot outputs and handle only complex inquiries, thereby increasing productivity per agent. Philippine BPO firms are investing in AI platforms that can augment their employees, and the government is pushing digital skills initiatives for the services sector. Moreover, new opportunities arise in AI data services – e.g. Filipino firms providing data labeling and AI model training services for global clients, effectively riding the AI wave rather than being displaced by it.
  • Public-Private Collaboration. In Malaysia, the government launched an “AI in Manufacturing” grant program that co-funds SMEs to adopt AI solutions, such as warehouse robots or machine learning software, with the condition that local tech companies are involved as vendors or partners. This not only helps modernize mid-sized factories but also stimulates the local tech industry. Indonesia, aiming to boost its Industry 4.0, has created a network of “Future Factory” demo centers where companies can see AI and IoT systems in action (for example, a smart manufacturing testbed in Bandung showcases an AI-run production line) – a way to evangelize and diffuse technology to industry. These kinds of initiatives align with APEC’s emphasis on capacity building and knowledge sharing so that all member economies can benefit from AI transformation.

By tailoring AI strategies to their unique economic structures – whether capital-rich digital hubs, resource-endowed nations, or emerging manufacturing bases – APEC economies can each find a pathway to leverage AI for growth. Next, we will examine in depth how three major APEC regions – the USA, South Korea, and China’s Greater Bay Area – should lead or pivot in the AI economy, given their existing strengths and roles.

USA, South Korea, and China

In the APEC region, certain economies are especially influential in driving AI advancement and setting trends for others. Here we provide a deep analysis of three such leaders – the United States, South Korea, and China – focusing on how each should lead or adjust its strategy in the AI economy. We consider their existing innovation ecosystems and industrial bases, their geopolitical roles, and their human capital and talent pools. Each of these has distinct strengths: the U.S. as a global AI innovation hub and norm-setter, South Korea as a high-tech manufacturing powerhouse with a coordinated national strategy, and the Greater Bay Area as an emerging super-region blending China’s manufacturing might with cutting-edge research and finance. Their trajectories in AI will significantly influence the broader APEC outcome.

United States

Sustaining AI Leadership through Innovation and Inclusion

The United States entered the AI era with formidable advantages – a dynamic innovation ecosystem anchored by tech giants (Google, Microsoft, Apple, Amazon, etc.), top research universities, and a culture of entrepreneurship. Its industrial base, while diverse, has seen a long-term shift toward high-tech services and advanced manufacturing (e.g. aerospace, semiconductors, pharmaceuticals). The U.S. is currently the world leader in AI research and development, which produces the majority of top-tier AI research (though China has surpassed in sheer number of papers and patents) and dominates key AI resources like high-performance computing capacity. In 2024, U.S. institutions outpaced others by producing 40 prominent AI models versus China’s 15, and U.S. private investment in AI reached $109 billion – nearly 12 times the level in China. This reflects unparalleled venture capital availability and industry willingness to invest in AI at scale. The U.S. also benefits from being home to the leading AI talent pools: it attracts experts globally, with over 70% of AI graduate students and a majority of PhD-level AI researchers in the U.S. being foreign-born (a testament to its magnetic pull for talent). However, the U.S. faces challenges in maintaining its edge – competition from other countries (especially China’s rapid progress), potential domestic talent shortfalls, and socio-economic divides in AI adoption.

Geopolitically, America sees AI as a critical domain of strategic competition and a pillar of economic security. U.S. policy has increasingly focused on “AI leadership” as vital to both economic prosperity and national security. The U.S. aims to set global standards for AI aligned with democratic values – emphasizing openness, transparency, and human rights – in contrast to more state-controlled models. It has led the formation of initiatives like the Global Partnership on AI (GPAI) and works with allies on emerging tech governance. The U.S. is also using export controls on advanced chips and AI technologies to maintain a lead over strategic rivals, reflecting AI’s importance in the U.S.-China rivalry. As an APEC member, the U.S. plays a convening role in promoting digital economy principles and was instrumental in APEC’s recent AI cooperation framework. Domestically, the U.S. government is ramping up support: for example, the CHIPS and Science Act (2022) earmarked substantial funding for semiconductor and AI research to ensure supply chain resilience and tech leadership.

The U.S. has a large, highly educated workforce but also a pressing need to retrain workers who may be displaced by AI-driven automation. While its universities produce top-tier talent, there is concern about STEM education quality at earlier stages and uneven access to digital skills across different communities. Moreover, the reliance on international talent is double-edged: it fuels innovation, but immigration barriers or global competition for talent could create bottlenecks. A key priority is broadening the domestic talent pipeline – improving K-12 science/math education, expanding AI-related programs in colleges, and encouraging diversity in tech fields to tap all segments of the population. Public-private initiatives like new AI research institutes across the country and AI apprenticeships are steps being taken.

Recommended Strategy – “Innovate, Regulate, and Educate”

The United States should lead the AI economy by doubling down on innovation while ensuring inclusive growth and shaping global governance. Concretely:

  • Frontier Innovation Leadership. Investing heavily in AI R&D, including fundamental research, the U.S. government and private sector should collaborate on moonshot projects (e.g. AI for climate change mitigation, or AI-enabled biotech) to maintain a technology edge. One example path is fostering foundation models and generative AI – U.S. firms currently lead in developing large AI models (like GPT series); keeping that lead will require support for computing infrastructure (HPC and cloud) and favorable regulatory environments that promote innovation and competition. The U.S. also controls about 74% of global high-end AI computing capacity, a strength to maintain via continuous infrastructure upgrades. It should leverage this to attract global researchers and companies to build on U.S. platforms. Importantly, the U.S. must also invest in AI chips and hardware to secure the supply chain (reducing dependency on foreign chipmakers), as recognized by recent policy efforts.
  • Ethical AI Governance.The U.S. should craft balanced regulations that address AI risks without stifling innovation. In 2024, U.S. federal agencies introduced 59 AI-related regulations – more than double the previous year – indicating a rising focus on AI oversight. This includes guidelines on AI in hiring (to prevent bias), discussions on AI liability in autonomous vehicles, and efforts to protect privacy in AI-driven services. The U.S. can set an example by implementing the AI Bill of Rights principles proposed by the White House (which cover user rights such as privacy, explanation, and freedom from algorithmic discrimination). Internationally, American leadership in forums like APEC can promote frameworks for responsible AI – encouraging all APEC members to adopt principles of transparency, security, and fairness in AI applications. By coordinating with allies (e.g. through the OECD AI principles or the Quad’s tech collaboration with Japan, India, Australia), the U.S. can help establish a normative environment where AI is used to reinforce open societies. This not only addresses ethical imperatives but also differentiates U.S. AI products in terms of trustworthiness, which can be an advantage in global markets.
  • Inclusive Workforce Development. The U.S. should massively scale up programs to retrain workers for the AI economy. Federal and state governments, perhaps in partnership with community colleges and companies, need to implement workforce transition programs for industries heavily affected by AI (such as trucking, retail, customer service). The goal is to avoid a scenario where AI exacerbates inequality by concentrating benefits in certain coastal tech hubs while displacing middle-class jobs elsewhere. Instead, AI should be harnessed to augment workers’ abilities across the country. For example, manufacturing workers in the Midwest could be trained to operate collaborative robots (cobots) rather than perform repetitive tasks themselves. The U.S. should also focus on STEM education reform, introducing computer science in all high schools, funding AI labs in universities, and ensuring underrepresented groups get access to these fields. According to one analysis, by following a coordinated strategy of investment in education and AI skills, governments can prepare their workforces for the AI era and boost competitiveness. The U.S. must heed this by not just importing talent but cultivating it domestically – particularly as other countries attempt to lure back their AI experts, the U.S. should provide clear pathways for skilled immigrants to stay and contribute (visa reforms etc.).
  • Geopolitical Tech Alliances. Strategically, the U.S. should leverage alliances to strengthen its position in the AI economy. This includes continuing joint research initiatives and talent exchange with allies (like Canada, the UK, EU, Japan, South Korea – many of which are APEC or close partners). It can also involve aligning export control regimes so that allied nations coordinate on responsible AI tech sharing while limiting access by malicious actors. Within APEC, the U.S. can lead capacity-building efforts for developing members – for example, funding workshops on AI governance or sponsoring pilot projects in Southeast Asia that demonstrate AI for social good (health diagnostics, disaster response), thereby building goodwill and setting standards. The U.S. hosting of APEC 2023 already saw commitments to digital innovation; this momentum can continue through to APEC 2026 by the U.S. championing an agenda of “inclusive innovation,” where all economies are helped to partake in the AI opportunity.

In summary, the United States is well-positioned to remain the prime mover in the AI economy if it reinvests in innovation, guides the technology’s ethical trajectory, and ensures its own population is prepared for the transformations ahead. Its large, open innovation ecosystem and thought leadership can greatly shape how AI is applied across APEC – whether AI becomes a force that widens divides or one that raises prosperity for all will depend in part on U.S. actions in the coming years.

South Korea

Harnessing Digital Infrastructure and Industrial Strength for AI

South Korea (ROK) is a leading APEC economy in terms of digital infrastructure and industrial sophistication. Renowned for its advanced broadband and 5G networks (covering the vast majority of its population) and extremely high R&D intensity (over 4% of GDP on R&D in recent years), Korea has built a strong base for the AI era. Its industrial ecosystem is dominated by large conglomerates (chaebols) such as Samsung, Hyundai, LG, and SK, which excel in electronics, semiconductors, automotive, and chemicals – all sectors now integrating AI and automation. Korea’s innovation ecosystem traditionally relied on these big firms and government-led initiatives rather than a large pool of startups, but this is gradually changing as more AI startups and SMEs emerge in fields like AI software, robotics, and biotech. South Korea is frequently ranked among the top innovative nations globally, and specifically aims to be a “Top 3 AI powerhouse” in the world within the coming decade. It has a well-educated workforce with strong STEM orientation, though some observers note a need for more AI-specialized researchers and a more internationally diverse talent pool.

Korea’s government has been proactive in AI policy: it released a National AI Strategy in 2019 with a vision to integrate AI across the economy and society, and more recently enacted an AI Framework Act (2023) to guide AI development and ethics. These efforts underscore a national commitment to AI-driven growth. Moreover, as host of APEC 2025, South Korea spearheaded the APEC AI Initiative 2026-2030, signaling regional leadership in capacity building and governance for AI.

South Korea occupies a unique geopolitical position – a U.S.-allied democracy sharing a region with China. It aspires to lead in high-tech industries (like AI, semiconductors, batteries) and often plays the role of a bridge or testbed in the Asia-Pacific tech landscape. For instance, Korea is part of U.S.-led discussions (such as the “Chip 4” alliance) on semiconductor supply chains, while also deeply economically intertwined with China (China is a major market for Korean chips and electronics). In AI, Korea wants to be seen as a global leader, but also as a collaborator that helps set Asian perspectives in AI ethics and governance. By establishing an Asia-Pacific AI Center in Seoul, as announced at APEC 2025, South Korea is stepping up to facilitate regional cooperation and knowledge exchange in AI. This positions Korea as a regional hub for AI capacity-building – a role that can enhance its soft power and influence. Additionally, South Korea’s strength in manufacturing means it will heavily influence AI deployment in supply chains across Asia (e.g., if Korean automakers adopt AI globally, they will bring suppliers in other APEC countries along).

South Korea’s population is highly educated and tech-savvy (with one of the highest literacy and tertiary education rates). The country produces many engineers and scientists, yet there is intense domestic competition for talent from the big chaebols and increasing difficulty attracting foreign experts (due to language and cultural barriers, though efforts are being made to create English-friendly research environments). Nonetheless, Korea’s human capital in AI is bolstered by a generation of researchers educated abroad who have returned, and government programs to train AI specialists (like AI graduate schools initiative). An area for growth is encouraging more fundamental AI research and creative, interdisciplinary thinking, as Korea’s education system is sometimes critiqued for its exam-focused approach that may not cultivate the kind of open innovation culture seen in Silicon Valley. The government is aware of this and is promoting more creative education and startup incubation in AI.

Recommended Strategy – “Scale, Specialize, and Share”

South Korea should leverage its superb digital infrastructure and industrial champions to scale up AI adoption, specialize in key sectors, and share its expertise regionally. Key components of this strategy:

  • Massive AI Infrastructure Build-Out. Korea is making a bold push to expand its AI computing infrastructure, treating it as critical national infrastructure. In late 2025, at the APEC Summit in Gyeongju, the Korean government announced plans to deploy an enormous number of AI processors domestically – including 50,000 high-end NVIDIA GPUs across a new National AI Computing Center and cloud services. In total, Korea’s public and private sectors are adding over 250,000 GPUs to power AI development, essentially building “AI factories” for research and industry. This reflects a mindset that “accelerated computing infrastructure becomes as vital as power grids and broadband” in the AI age. By aggressively scaling computing capacity, South Korea ensures that its researchers and companies have the resources to develop advanced AI models domestically (reducing reliance on foreign cloud providers). The strategy is to offer sovereign AI cloud capacity that any Korean startup or agency can use, lowering the barrier to entry for AI innovation. South Korea should continue this path, possibly expanding into quantum computing and edge AI infrastructure (5G/6G combined with AI at the edge), given its strength in telecommunications. This immense infrastructure also serves as a platform for Asia-Pacific collaboration – e.g., the Asia-Pacific AI Center could allow researchers from other APEC countries to access some of Korea’s computing resources for joint projects, boosting regional innovation.
  • AI in Manufacturing and Robotics (Korean Specialization). Playing to its strengths, South Korea should aim to be the world leader in AI for advanced manufacturing and robotics. Its industrial base in electronics and automobiles is already integrating AI: Samsung is incorporating AI to optimize semiconductor fabrication (using AI-driven digital twins to improve chip yields), and Hyundai is using AI in autonomous driving and smart factories for car production. Korea has the highest robot density in factories globally, and by adding AI, these robots become even more effective via machine vision and autonomous decision-making. The government’s support via pilot projects (26 AI-powered manufacturing pilots were launched in 2024, with plans for 200 by 2027) should continue, aiming to diffuse AI solutions to thousands of SMEs in manufacturing supply chains. South Korea can also focus on service robots and AI for its future growth (given an aging population) – for example, AI-powered eldercare robots, cleaning robots, and logistics robots. Being a pioneer user of such technologies domestically gives Korean firms a springboard to export these solutions as other countries also face aging demographics. In addition, South Korea has expertise in specific AI domains like natural language processing for Korean (important for cultural and administrative adoption of AI) and could become a leader in multilingual AI technologies in Asia.
  • Innovation Ecosystem & Human Capital Boost. To support these ambitions, South Korea needs a vibrant innovation ecosystem. This means continuing to foster startups and SMEs in AI – the government has been funding AI incubators and sandboxes, and big companies are also investing in or acquiring startups (for instance, telecom operators like KT and SK Telecom have AI research arms and accelerator programs). Ensuring that the regulatory environment encourages innovation is key: Korea recently passed an AI Framework Act which likely balances innovation promotion with basic ethical safeguards. Implementation of this law should involve consultation with industry and academia to keep it flexible. On talent, Korea might consider more openness to foreign AI experts – perhaps creating special visas or research professorships to bring in global talent to Korean universities and companies. Enhancing AI education at home is also a priority: expanding the AI-specialized graduate schools, encouraging more interdisciplinary programs (combining AI with biomedical, finance, design, etc.), and improving English and research skills to increase international collaboration. The government can incentivize the chaebols to support academia (through funded chairs, joint labs) to cultivate next-gen talent.
  • Regional and Global Collaboration. South Korea should amplify its role as a regional AI collaborator. Through APEC and bilateral ties, Korea can share best practices with developing economies (for example, how to roll out 5G and leverage it for AI applications – something Korea did first). The Asia-Pacific AI Center planned by Korea will serve to “promote AI-related capacity building and innovation and facilitate information-sharing in the region”, which APEC leaders welcomed. By 2026, this center could be hosting training workshops for officials and engineers from other APEC countries, running joint research on AI standards or multilingual AI, and serving as a hub connecting Silicon Valley, Seoul, and other innovation centers. Additionally, Korea’s geopolitical stance allows it to mediate in tech dialogues: it can work with the U.S. on aligning AI governance (as an ally) while also cooperating with Chinese researchers in non-sensitive areas, maintaining an open channel in the region’s two tech spheres. For example, Korean and Chinese companies could jointly develop certain AI solutions (like smart city tech) for the GBA, leveraging Korea’s hardware and China’s scale. Korea should also contribute actively to global AI ethics discussions, perhaps highlighting values like human-centric AI which resonate with its own experience of rapid tech adoption in a Confucian but democratic society.

Signs of Progress

Already, South Korea’s efforts show concrete outcomes. By late 2025, Korean industry titans are building huge AI “factories”: Samsung, SK Group, Hyundai and others each announced AI data center projects with ~50,000 GPUs to advance their domain-specific AI (from semiconductors to autonomous vehicles). NVIDIA’s CEO Jensen Huang lauded Korea’s position “at the heart of the AI industrial revolution”, noting its strength in both technology and manufacturing and envisioning Korea exporting “intelligence” as a new product. This captures Korea’s potential to lead in merging AI with industrial might. The country is also extending AI into public services: pilot projects in Seoul use AI for traffic control and crime prediction, and the national military is incorporating AI for surveillance and logistics. By staying the course – investing in infrastructure, focusing on its comparative advantages in manufacturing/robotics, and leading regional cooperation – South Korea is poised to be one of the prime drivers of the AI economy in APEC, second perhaps only to the U.S. and China, and in some niches the global leader.

China’s Greater Bay Area

Integrating Innovation and Manufacturing for AI Leadership

China’s Greater Bay Area (GBA) refers to the integrated economic region encompassing Hong Kong, Macau, and nine major cities in Guangdong Province (including Shenzhen, Guangzhou, Zhuhai, etc.). This region, often likened to Silicon Valley or the Tokyo Bay area, is a linchpin of China’s technology and innovation ambitions. The GBA combines the strengths of Hong Kong’s international finance and world-class universities with Shenzhen’s high-tech manufacturing and entrepreneurial dynamism, and Guangzhou’s industrial depth and talent pool. Covering less than 1% of China’s land, the GBA contributes about 11% of China’s GDP – a testament to its productivity and concentration of high-value industries. It is one of the most open and economically dynamic regions in China. In terms of innovation, the Shenzhen–Hong Kong–Guangzhou cluster recently ranked first in the world in the 2025 WIPO Global Innovation Index’s list of top science & technology clusters. This reflects rapid progress in collaborative R&D and a rich startup ecosystem in the GBA.

The GBA is home to Chinese tech giants like Tencent, Huawei, DJI, SenseTime, and many others at the forefront of AI, telecommunications, and hardware. Shenzhen is known as “China’s Silicon Valley,” especially for hardware and electronics – it produces drones, smartphones, IoT devices, and increasingly EVs, and has a burgeoning AI sector (e.g., computer vision and facial recognition firms). Hong Kong contributes with strong fundamental research (its universities rank high in AI and computer science research output) and as a financial hub that can fund tech ventures. Guangdong’s factories provide the scale to prototype and mass-produce new AI-driven products quickly. The region has a complete industrial system, from components to final products, now being augmented with AI. The Chinese government designated the GBA as an international innovation center; policies encourage free flow of resources between Hong Kong and Mainland (e.g., cross-border data sharing pilot, easing movement of tech talent). Already cross-border R&D projects between Guangdong, HK, and Macau have surged (a 40% increase in joint projects recently), aided by initiatives like the Shenzhen-Hong Kong Innovation Cooperation Zone where labs and companies from both sides co-locate.

When it comes to AI, the GBA is one of China’s leading regions. The “core AI industry” in the GBA has exceeded ¥220 billion (about US$31 billion) in value, encompassing AI firms and related services. Shenzhen alone has thousands of AI enterprises focusing on everything from smart drones to fintech AI. Additionally, the GBA hosts several of China’s top AI laboratories (for example, Tencent’s AI Lab in Shenzhen, Huawei’s Ascend AI chip R&D, SenseTime’s R&D center in Hong Kong, and the Hong Kong Centre for AI Research (CAiRE) which is a collaboration with mainland institutions). The region’s prowess in mechatronics – the integration of mechanical and electronic engineering – is particularly noted; NVIDIA’s CEO Jensen Huang pointed out that the GBA is “so good at mechatronics” and that this intersection is key to excelling in robotics and hardware AI systems.

The GBA is central to China’s goal of technological self-reliance and global tech leadership. Geopolitically, it sits at the frontline of U.S.-China tech competition: Shenzhen’s firms like Huawei and DJI have faced export controls, and Hong Kong’s status has evolved under new security laws – yet the region remains outward-looking and vital for China’s international tech collaborations (Hong Kong still hosts many multinational firms and venture capital funds active in tech). The GBA is also meant to exemplify the “one country, two systems” synergy – using Hong Kong’s legal and financial system to complement Mainland’s manufacturing and R&D. As such, success of AI in the GBA is not just economic but political for Beijing: it demonstrates the value of integration and openness within Chinese governance. The GBA can serve as a bridge between China and the global tech community – e.g., attracting foreign researchers to Hong Kong’s universities or international capital to Guangzhou’s biotech startups – at a time when direct China-West ties are under strain. In APEC terms, Hong Kong and China (Mainland) are separate members; the GBA thus embodies a unique intra-APEC collaboration zone.

The GBA’s talent pool is considerable. Shenzhen and Guangzhou produce tens of thousands of STEM graduates annually from local universities and technical institutes. Hong Kong contributes with its internationally acclaimed universities that draw students from across Asia and beyond (though retaining that talent after graduation is a challenge if opportunities are better abroad). The Guangdong cities have aggressively recruited high-end talent with various incentive programs, attracting Chinese diaspora scientists to set up labs. A challenge has been ensuring a smooth flow of talent across the border – recent measures allow easier work permits for Hong Kong residents in Shenzhen and vice versa, which should help. The region’s mix of cultures (Cantonese, international in HK, Mainland Chinese) can be a strength if leveraged for creativity. However, competition for top AI experts is stiff; GBA firms sometimes struggle to match the appeal of Beijing’s Zhongguancun or Shanghai’s cosmopolitan environment for talent. Strengthening GBA’s appeal – perhaps through quality of life improvements and global branding – could help draw more AI researchers to settle in Shenzhen or HK.

Recommended Strategy – “Integrate, Innovate, and Internationalize”

The Greater Bay Area should capitalize on its integrated economy to drive end-to-end AI innovation – from foundational research to product deployment – and present itself as China’s window for AI collaboration with the world. Key strategic directions:

  • Deepen GBA Integration for Innovation. The power of the GBA comes from collaboration between its constituent cities. Policies should further eliminate barriers between Hong Kong, Macau, and Guangdong in terms of data sharing, capital flow, and talent movement, specifically for innovation projects. For example, a researcher in Hong Kong should easily be able to train an AI model on a Guangdong high-performance computer using Mainland datasets (with appropriate privacy safeguards), or a Shenzhen AI startup should be able to secure funding on the Hong Kong Stock Exchange’s tech board. By 2025, the GBA made strides in connectivity – high-speed rail and road links cut travel times (researchers can commute from HK to Shenzhen in 15-30 minutes). This physical connectivity must be matched by institutional connectivity. Joint innovation funds, cross-border incubators, and shared research infrastructure (like labs accessible to all GBA members) will create a holistic ecosystem. The “innovation corridor” linking Hong Kong, Shenzhen, and Guangzhou should be further developed with dedicated science parks that host mixed teams from different cities. The Chinese government’s recent policy support reiterates making GBA an international innovation center – execution of this vision will involve local governments coordinating on AI development plans so they complement rather than duplicate each other’s efforts (e.g., maybe Hong Kong focuses on basic AI research and fintech, Shenzhen on hardware and application development, Guangzhou on industrial application and commercialization).
  • Sectors to Lead. The GBA should focus on sectors where its combined strengths give it a world-leading edge. Advanced Manufacturing with AI is a clear choice: the region already has nine industrial clusters each exceeding one trillion yuan in output, in areas like electronics, machinery, home appliances, and automobiles. Upgrading these with AI (smart production lines, AI for supply chain management) could vastly improve efficiency and output quality, reinforcing GBA’s manufacturing dominance. The region is already looking ahead to emerging sectors such as the low-altitude economy (e.g., drones, urban air mobility) and bio-manufacturing, aiming to form new trillion-yuan industries in those fields. Shenzhen’s global leadership in drones (DJI commands a huge share of the world market) can be extended – integrating AI for autonomous drone flights and services (for delivery, mapping, agriculture) could birth a new economy of AI-driven aerial services across Asia. Similarly, the GBA’s strength in biotechnology (Hong Kong and Shenzhen both have growing biotech sectors) can merge with AI – using AI for drug discovery, personalized medicine, and biotech manufacturing. The Greater Bay Area should also lead in smart city and urban AI solutions: with extremely dense cities like Hong Kong, Guangzhou, and Shenzhen, the region can pioneer AI for traffic management, smart energy grids, and e-government. These solutions can then be exported to other megacities.
  • AI Chip Design and Digital Infrastructure. Given tech export restrictions, China is pushing for self-reliance in semiconductors. The GBA, especially Shenzhen, should spearhead AI chip design and production at home. Companies like Huawei (with its Ascend AI chips) and Tencent (exploring AI chips for its data centers) are in GBA; leveraging local talent in hardware engineering and the presence of fabs in Guangdong (albeit not the most cutting-edge yet) will be crucial. The GBA could specialize in certain AI chips (e.g. efficient chips for edge AI in IoT devices, leveraging its electronics supply chain). Additionally, expanding cloud and data center capacity in GBA is needed to support the booming AI industry – possibly with green data center initiatives given power constraints. A recent development is the use of AI in energy optimization: GBA cities can implement AI to manage their energy consumption (important for a sustainable growth since GBA’s energy demand is huge). In fact, APEC’s AI roadmap encourages leveraging AI for energy efficiency and grid management, something the GBA could showcase by deploying city-wide AI energy systems.
  • Global Collaboration and Standards Leadership. The GBA should carve out a role in international AI collaboration despite geopolitical headwinds. Hong Kong, in particular, can be the gateway: its universities and companies can partner with Western institutions on neutral topics like medical AI or climate change modeling. The GBA can host international AI conferences and competitions, to signal openness. For example, Hong Kong could host an annual “APEC AI Summit” bringing researchers and policymakers, building on its east-meets-west position. Moreover, firms in the GBA should engage in global standards bodies for AI (ISO, ITU) to contribute Chinese perspectives and understand global norms. Jensen Huang’s comment that the GBA provides an “extraordinary opportunity” for China underscores that if the region capitalizes on its strengths, it can outpace other AI hubs. Indeed, the GBA rose from sixth to fourth in Nature’s global ranking of innovation hubs recently, indicating momentum. To continue this, GBA stakeholders (corporate and governmental) might adopt a more international mindset – for instance, publishing more research in international journals (not just Chinese journals), using English alongside Chinese in key forums, and ensuring that regulatory measures (like China’s new AI algorithm regulations) are implemented in a way that doesn’t stifle the GBA’s global partnerships.

Outlook

The Greater Bay Area is on track to be one of the foremost AI centers in the world. With supportive policy, by APEC 2026 the GBA could see: autonomous vehicles and robotaxis commonplace on Shenzhen and Guangzhou streets (Baidu’s Apollo Go already runs robotaxi services in Chinese cities), AI-driven smart ports in Hong Kong moving goods ultra-efficiently, and a slew of AI startups scaling up to unicorn status thanks to the region’s vibrant funding scene. The cluster’s topping of the Global Innovation Index shows its trajectory. If it continues integrating innovation with its manufacturing powerhouse, while navigating geopolitical realities carefully, the GBA will not only lead China’s AI development but also heavily influence technology adoption patterns across the Asia-Pacific. For APEC, a successful GBA means a potent source of AI technologies and possibly a model of regional integration for innovation that others might study.

Guidance for All APEC Economies Leading up to 2026—Inclusive Growth and AI

As APEC economies pursue AI-driven development, a critical priority is ensuring that growth is inclusive – benefiting broad segments of society and not leaving behind those in less-developed areas or in vulnerable groups. To that end, APEC’s collective vision (articulated in the Putrajaya Vision 2040 and recent Gyeongju Declaration) emphasizes empowering people through technology and collaboration. By 2026, APEC members aim to have laid the groundwork for an inclusive regional AI economy. Below, we offer tailored guidance for economies at various stages, centered on four key pillars: Digital Upskilling, Enabling Policy Frameworks, Regional Collaboration, and Infrastructure Investment. These measures will help ensure AI serves as a tool for shared prosperity and capacity-building across the Asia-Pacific.

Digital Upskilling and Workforce Development

Building human capital is the linchpin of inclusive growth in the AI era. All APEC economies – whether developed or developing – should undertake large-scale efforts to equip their citizens with the skills needed to thrive alongside AI.

  • Lifelong Learning for All. Governments must promote a culture of continuous learning, offering opportunities for workers of all ages to gain new digital and AI-related skills. This includes integrating STEM and AI curriculum in schools (to prepare the next generation) and providing mid-career training programs for current workers. For instance, Singapore’s SkillsFuture program could be a model, funding individuals to take courses in data analytics or AI. APEC leaders have agreed to “encourage lifelong learning for all” and share knowledge on effective upskilling programs across economies. By exchanging best practices, economies can replicate successful initiatives quickly. For example, one economy’s experience in training manufacturing workers to use AI-powered equipment can inform another’s similar effort.
  • Targeted Reskilling for Affected Workers.Special attention should go to workers in industries likely to be disrupted by AI (e.g., routine manufacturing, clerical jobs, call centers). These workers should be identified and given access to reskilling pathways before displacement happens. Public Employment Services in each economy can partner with tech firms to deliver “crash courses” in relevant new skills. For example, training a call center operator to become a supervisor of AI chatbot systems, or a truck driver to transition into a logistics data analyst managing fleets of autonomous vehicles. In many cases, the human role will shift rather than disappear – thus, training should focus on complementary skills (like complex problem-solving, customer relations, oversight of automated systems). Governments can incentivize companies to retain and retrain workers instead of laying them off when introducing AI. Wage insurance or stipends during training can encourage worker participation.
  • Boosting AI and Digital Literacy for MSMEs and the General Public. Inclusivity means not only large firms benefit from AI, but also small businesses and ordinary people. APEC economies should implement programs to strengthen digital and AI literacy at the community level. This could mean setting up local tech hubs or using public libraries as venues for basic coding or data science workshops. For micro, small, and medium enterprises (MSMEs), tailored capacity-building is needed: many MSMEs lack awareness or expertise to adopt AI. APEC has called for sharing best practices to promote sector-specific AI adoption for MSMEs. Governments can create toolkits or free advisory services that help small entrepreneurs understand how AI might improve their business (e.g., an AI inventory management tool for a small retail shop, or an AI-driven marketing platform for a family-run tourist lodge). By 2026, each APEC economy could establish an “AI extension service” (analogous to agricultural extension services) that proactively reaches out to MSMEs and provides mentorship on simple AI solutions.
  • Public Sector Upskilling. Inclusivity also involves strengthening government capacity to use AI for public good. Training public sector personnel in basic AI concepts can accelerate e-government improvements. APEC’s initiative suggests economies “upskill public sector personnel to accelerate AI adoption in government, improving administrative efficiency”. This is crucial in developing economies where bureaucratic capacity is sometimes limited – AI could help leapfrog to better service delivery (e.g., using AI to triage citizen service requests or detect fraud in social programs), but civil servants must understand and manage these tools. Therefore, each government should run AI training modules for civil service, perhaps via their civil service colleges or through partnerships with universities.

In summary, no person or business should be left behind due to lack of skills. The APEC region could consider a collective goal (for example, train X million workers and Y% of SMEs in basic AI/digital tools by 2026), with economies contributing according to their size. Through forums like APEC’s Human Resources Development Working Group, members can track progress and cooperate on curriculum development and certification standards. Ultimately, human talent is the most important resource in an AI economy – widespread upskilling ensures that talent is not confined to elite circles but is broadly distributed, enabling inclusive innovation.

Policy Frameworks and Governance

Sound policy frameworks are essential to foster innovation while safeguarding society in the AI era. APEC economies should develop or refine their national AI strategies and regulatory approaches with an eye toward inclusivity, ethics, and flexibility. Key actions include:

  • National AI Strategies with Inclusive Vision.By 2026, every APEC member should ideally have a clear AI strategy or action plan. These strategies should articulate how AI will benefit various sectors and communities, and set targets for inclusive development (such as improving rural healthcare access via AI, or increasing women’s participation in AI jobs). For developing members that haven’t yet drafted such strategies, APEC can facilitate expertise-sharing from those that have. For example, Japan’s Society 5.0 concept or Australia’s AI Ethics Framework could provide templates. The strategies should also identify priority areas for policy support: e.g., education reform, data infrastructure, innovation incentives, and ethical guidelines.
  • Ethical and Responsible AI Guidelines. Inclusive growth requires maintaining public trust in AI systems. Each economy should establish or adopt a set of AI ethical principles – addressing issues like transparency, accountability, fairness/non-discrimination, and privacy. These can be based on international norms (OECD AI Principles, etc.) but tailored to local context. Concrete measures could involve setting up an AI ethics committee, requiring algorithmic impact assessments for high-risk AI applications, and implementing privacy laws to protect personal data used in AI. APEC’s collective stance is to encourage “secure, accessible and reliable AI ecosystems for all”. That means policies ensuring AI systems are safe and biases are mitigated, so that marginalized groups are not negatively impacted by algorithmic decisions (for instance, ensuring a lending AI doesn’t unfairly deny loans to certain communities). Regulators might also mandate explainability in certain sectors – e.g., if an AI system rejects a job applicant or a benefit claim, the person should be able to get an explanation. These governance measures will help prevent a public backlash against AI and ensure its benefits can be realized broadly.
  • Regulatory Sandboxes and Innovation-Friendly Laws. To encourage inclusive innovation, governments should implement flexible regulatory approaches such as sandboxes where companies can pilot new AI solutions under supervision. This is especially useful in sectors like fintech, healthtech, or autonomous vehicles where outdated regulations might hinder useful AI deployments. For example, an APEC economy could allow a trial of autonomous delivery robots in a district even if formal traffic laws don’t yet account for them, learning from the trial to inform future laws. By 2026, members could report sandbox learnings to each other, accelerating region-wide modernization of regulations. At the same time, structural reform (a topic in APEC discussions) is needed to remove bureaucratic barriers to tech adoption – simplifying business licensing for AI startups, updating procurement rules so governments can buy from innovative small firms, etc. These reforms help small players, not just big corporations, to innovate and compete, which in turn spreads AI benefits more widely.
  • Data Policy and Sharing Frameworks.Data is a cornerstone of AI, and inclusive AI development requires making data available while respecting rights. Policies should promote open data initiatives, where governments share non-sensitive datasets (e.g., transport, weather, agricultural data) for public use, enabling startups and researchers to develop useful AI solutions. Some APEC economies like Taiwan and Australia have good open data portals that others can emulate. Additionally, regionally, APEC can work towards frameworks for cross-border data flows with trust – to allow data-driven innovation without compromising privacy or security. This might include interoperable privacy standards or certifications that facilitate data sharing for agreed purposes (like disaster management or epidemiology) among economies. The APEC Internet and Digital Economy Roadmap (AIDER) already highlights the importance of cooperation on data and digital rules. Implementing its principles will be key by 2026, such as reducing data localization barriers in exchange for strong data protection commitments, thus helping especially smaller economies access larger datasets for AI training (e.g., pooling medical data across countries to improve disease diagnosis AI for all).

In essence, policy frameworks must walk a fine line, encouraging rapid adoption of AI to drive growth and competition, but also put guardrails that protect citizens and ensure the gains do not concentrate only among a few. Through collaboration, APEC members can learn from each other’s policy experiments and converge towards best practices that suit the fast-evolving AI landscape.

Regional Collaboration and Knowledge Sharing

No economy can master the AI revolution alone; regional cooperation in APEC can vastly amplify individual efforts and ensure that less-developed members also progress. APEC’s strength is as a platform for sharing knowledge and resources across very diverse economies. To leverage that:

  • APEC-Wide AI Cooperation Initiatives. Building on the newly adopted APEC AI Initiative (2026–2030), members should launch cooperative programs to implement its strategic goals. For example, APEC could establish an “AI Skills Development Fund” where advanced economies contribute to finance training workshops or scholarships for developing member economies’ students in AI fields. This would operationalize the capacity-building objective of increasing meaningful participation for all. Another idea is an APEC AI Innovation Network linking tech incubators and innovation labs across member economies – allowing startups from, say, Vietnam or Peru to connect with mentors and markets in the U.S. or Korea. Such networks could host an annual APEC AI Challenge where teams solve social problems (like disaster response AI, or translation tools for rare languages), fostering cross-country collaboration.
  • Focused Knowledge Exchange. APEC can convene policy dialogues and technical workshops on specific AI topics of interest. For instance, one workshop could be on “AI in Agriculture for Food Security,” sharing success stories like how IoT and AI improved farming in economies like Australia or China, and how those could be applied in economies like Papua New Guinea or the Philippines. Another dialogue might focus on “AI and SMEs,” where economies share how they support small businesses in adopting AI. According to an NCAPEC study, targeted fora on AI help economies share success stories and best practices tailored to their context. By 2026, APEC could produce a repository of case studies and guidelines drawn from these exchanges – a toolkit any member can reference when developing their own AI programs.
  • Cross-Economy Projects. Collaboration can go beyond talk to joint projects. For example, neighboring APEC members could partner on AI infrastructure that serves a region – imagine a shared research cloud for ASEAN members funded cooperatively, or a joint AI research center by economies with complementary strengths (perhaps a center on AI for natural disaster prediction jointly run by Japan, Chile, and Indonesia given their common seismic risks). Likewise, economies can team up to create datasets that benefit all: e.g., an APEC-wide dataset of marine health indicators (useful for Pacific Rim economies) which researchers across the region can use to build AI models for sustainable fisheries. “Explore possibilities of cross-economy public-private partnerships,” the APEC leaders urged – by acting on this, members can achieve together what one might not do alone. Collaborative projects also build trust and understanding, which is valuable given differing political systems in APEC.
  • Inclusivity in Collaboration. It’s important that collaboration includes the less-developed members, not just the big ones. For instance, economies like Papua New Guinea or Peru might have small AI communities; APEC collaboration should deliberately involve them through exchange programs or twinning arrangements (e.g., pairing a university in PNG with one in Canada on AI curriculum development). When advanced members like the U.S., China, or Japan host AI events, they could allocate slots or sponsorship for participants from developing member economies. APEC’s consensus-driven nature gives each member a voice – this should be used to ensure that even the smallest economies can articulate their needs (be it training, infrastructure, or regulatory assistance) and find partners within APEC to help.

Ultimately, regional collaboration ensures that AI progress in one economy can spill over positively to others, rather than widening gaps. By fostering a sense of community – where AI advances are shared and adapted across borders – APEC can make the sum greater than the parts, driving inclusive growth at a regional scale.

Infrastructure Investment and Bridging the Digital Divide

Physical and digital infrastructure forms the backbone on which the AI economy runs. Without adequate connectivity, power, and compute capacity, many benefits of AI cannot reach all communities. APEC economies must invest in resilient, high-quality infrastructure with a focus on bridging gaps between the “haves” and “have-nots” in the digital age.

  • Digital Connectivity for All Regions. Expanding internet access (especially broadband and mobile internet) is paramount. Rural and remote areas in several APEC economies still lack reliable connectivity, effectively excluding them from participating in digital opportunities. Economies should continue programs to extend broadband backbone and last-mile connectivity – whether through fiber, 5G wireless, or even satellite internet for remote islands and highlands. The goal would be to drastically raise the percentage of population with internet access by 2026. Innovative financing, like public-private partnerships or universal service funds, can bankroll these expansions. In the interim, community centers or satellite-based kiosks can provide shared access in the remotest areas. Affordable access is as important as physical access: policies to enhance competition among ISPs or subsidies for low-income households ensure that connectivity isn’t just an urban luxury. APEC’s stance is that growth of AI adoption “relies on deployment of high-quality, affordable, secure and resilient communications infrastructure”, and members should upgrade infrastructure to meet data demand in an energy-efficient way. Implementing this means heavy investment now in fiber optic networks, 5G networks, and possibly preparing for 6G, so that bandwidth isn’t a bottleneck for data-intensive AI applications.
  • AI Infrastructure (Cloud, Compute) and Open Access. Beyond connectivity, countries need computing power and platforms that local innovators can use. Not every country can build giant data centers like the U.S. or Korea, but through cloud services or regional facilities, they can access compute resources. A possible approach is for multilateral development banks or APEC’s own projects to co-fund regional cloud hubs that smaller economies can leverage (perhaps a subsidized cloud service for startups or universities in those economies). On a national level, governments might invest in one or two high-performance computing centers dedicated to AI research (as Thailand, Malaysia, and others have begun doing). Ensuring energy reliability for these is key – AI data centers need consistent power, which implies strengthening electricity grids and incorporating clean energy so that powering AI doesn’t conflict with climate goals. APEC encourages creating an enabling environment for resilient AI infrastructure investment. Members should thus streamline policies for data center development (clear regulations, incentives for building in underserved regions, perhaps repurposing old facilities into data centers). Additionally, “AI-as-a-service” models could be promoted where small businesses or government departments can use AI tools via cloud without needing their own advanced hardware.
  • Energy and Transportation Infrastructure Adapted for AI. Physical infrastructure like roads, public transport, and power grids can all be enhanced by AI. When upgrading infrastructure, economies should integrate sensors and IoT devices from the start (building “smart infrastructure”). For example, new highways can be built with IoT capability to communicate with autonomous vehicles, and power grids can be modernized with AI for demand management and integrating renewables. Inclusive growth here means making sure improvements aren’t city-centric only – smart city projects should have analogous “smart village” or regional programs. For instance, using AI for microgrids can bring stable electricity to off-grid villages (with solar plus battery systems managed by AI predictions of usage). Likewise, AI-enhanced telehealth infrastructure (like remote clinics with AI diagnostic tools) can be set up in far-flung areas if broadband and electricity are provided – drastically improving access to healthcare.
  • Public-Private Partnerships and Investment Mobilization. Given the scale of infrastructure needs, governments should actively engage private investors, including through vehicles like infrastructure bonds or APEC investment forums. The APEC Business Advisory Council (ABAC) is working towards promoting private sector investment in secure, resilient AI infrastructure. Members could consider APEC-wide initiatives such as an “AI Infrastructure Fund” that blends public and private capital to invest in projects with regional impact (e.g., a cross-border fiber optic cable system, or a series of data centers in under-served markets). Public-private dialogues can also help surface regulatory or financial barriers that, if addressed, can unlock more investment. For example, easing restrictions on foreign cloud service providers might attract more infrastructure build-out in a country, or clarifying cybersecurity laws might encourage telecom firms to extend networks.

By 2026, tangible progress on infrastructure could look like: significant increases in internet penetration in developing APEC members; more equitable access to AI tools via cloud platforms; and pilot smart infrastructure operating in several locales (like an AI-managed public transit system in a secondary city, or AI-driven precision irrigation networks in an agricultural region). The combined effect would be that the benefits of AI – be it tele-education, e-commerce, telemedicine, or efficient logistics – are not confined to metropolitan elites, but reach small businesses, rural communities, and less-developed economies across APEC.

In conclusion, the APEC region’s embrace of artificial intelligence offers a transformative opportunity to accelerate economic development, provided it is guided wisely. We have distinguished the AI economy from the previous knowledge economy paradigm – highlighting that AI’s rise introduces new drivers (data, algorithms, automation) and necessitates new skills and policies. By grouping APEC economies based on their advantages, we can see that each has a tailored path: advanced economies pushing innovation frontiers and ethical governance, resource-rich economies modernizing and diversifying with AI, and emerging economies boosting industrial productivity and skills. The United States, South Korea, and China’s Greater Bay Area are positioned to lead by example – through sustained R&D, infrastructure investment, and regional outreach – thereby setting benchmarks for the region. Finally, with collaborative effort in upskilling, policy coordination, infrastructure development, and shared innovation, APEC members can ensure that the dividends of AI are broadly shared. As we approach APEC 2026, the vision is clear: a region where AI is a catalyst for inclusive growth, empowering diverse industries (from agriculture to finance) and communities (from Silicon Valley to small Pacific islands) alike. By adhering to this roadmap of development and cooperation, APEC economies can collectively navigate the AI revolution and build a prosperous, equitable future in the digital age.

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