Artificial intelligence is reshaping Southeast Asia at an unprecedented pace, presenting a paradox: immense economic opportunities alongside growing threats to regional cohesion and worker stability. While the technology promises to add nearly $1 trillion to regional GDP by 2030, the benefits are concentrating in advanced economies like Singapore, potentially widening the gap between rich and poor nations within ASEAN. This transformation reveals a critical fault line: without coordinated regional policies and proactive workforce development, AI adoption could deepen economic inequality and threaten the region’s collective growth trajectory.
The Promise: AI’s Economic Potential
Regional Growth Projections
AI adoption is widely expected to unlock substantial economic gains across Southeast Asia. Consulting firm Kearney estimated in 2020 that AI might add nearly $1 trillion to regional gross domestic product (GDP) by 2030, representing a transformative boost to productivity. The Boston Consulting Group (BCG) projected more specifically that by 2027, AI could boost the combined output of ASEAN’s six largest economies—Singapore, Indonesia, Malaysia, Thailand, the Philippines, and Vietnam—by as much as 3 per cent each. Singapore is expected to gain the most relative to its size, while Indonesia would see the biggest overall increase in absolute terms.
These figures underscore AI’s potential to accelerate development across one of the world’s most dynamic regions. For context, a 3 per cent productivity boost could translate to tens of billions of dollars in additional economic activity, particularly transformative for emerging economies seeking to climb the value chain.
Investment Momentum
The financial commitment to AI infrastructure is already substantial. Singapore pledged over $1 billion to develop its AI ecosystem over the next five years, and announced another $150 million in 2025 to help firms adopt the technology. Microsoft pledged $2.2 billion in Malaysia in 2024 to boost cloud and AI capabilities. In Indonesia, sovereign wealth fund INA teamed up with Granite Asia to channel up to $1.2 billion into tech and AI. The 2024 e-Conomy SEA report, released by Google, Temasek, and Bain, notes that more than $30 billion was committed in the first half of 2024 alone to build AI-ready data centres across Singapore, Thailand, and Malaysia.
Enterprise Adoption Rates
Adoption momentum is accelerating. A survey published in August by IDC and UiPath revealed that 86 per cent of South-east Asian organisations expect to adopt AI “agents”—programmes that can handle multiple tasks—within the next 12 months, up from 42 per cent already doing so. This rapid scaling suggests that AI integration will soon become industry standard rather than competitive advantage.
The Divide: Unequal AI Distribution
The Concentration Problem
Despite the region-wide potential, AI benefits are concentrating dangerously in a few advanced economies, primarily Singapore. Data from the Organisation for Economic Cooperation and Development shows that Singapore has attracted $8.4 billion in AI venture capital—three-quarters of ASEAN’s total. By comparison, Indonesia has drawn just under $2 billion, while Vietnam, Thailand, and Malaysia have secured only a fraction of that. This concentration reflects not just investor preferences but deeper structural disparities in digital infrastructure, human capital, and regulatory frameworks.
The Talent Gap
The most glaring disparity lies in AI talent distribution. Singapore has approximately 3.5 AI professionals per 1,000 workers, far ahead of Malaysia at 0.5, and the Philippines, Thailand, Indonesia, and Vietnam, which hover around 0.2 each. This gap means that advanced economies can rapidly scale AI implementation while developing nations struggle to find qualified personnel. The talent constraint becomes self-reinforcing: without AI professionals, countries cannot attract investment; without investment, they cannot develop talent.
Adoption Disparities
The 2024 Lazada–Kantar study tracking actual business use of AI in ASEAN reveals the uneven adoption landscape. Indonesia and Vietnam lead with 42 per cent of online sellers already using AI tools, while Singapore and Thailand sit in the middle at 39 per cent. The Philippines and Malaysia trail significantly at 32 per cent and 26 per cent respectively. These figures suggest that even regional leaders like Singapore are not dramatically ahead in business adoption, though this reflects online seller adoption rather than enterprise-wide patterns where Singapore likely leads.
Infrastructure and Preparedness
The structural reasons for these disparities run deep. Joanne Lin, a senior fellow at the ISEAS–Yusof Ishak Institute, notes that different “starting levels” among nations stem from varying access to basics such as reliable power, affordable connectivity, access to cloud computing, digital skills, and data governance frameworks. Digitally advanced economies will feel the productivity lift sooner, while less advanced members will see slower, patchier gains because adoption is harder outside major cities and without support from large multinational corporations or established firms.
Dr Jayant Menon, also at ISEAS, explains that nations late to AI adoption will face compounding disadvantages. Less well-off countries have limited digital infrastructure, relatively low skill levels among workers, and limited quality and versatility in educational institutions—all factors that will constrain their ability to prepare for AI integration. He warns that “the AI revolution is likely to increase the disparities that currently exist within and between countries in South-east Asia. This is mainly due to the fact that the more developed countries are better prepared to take advantage of the opportunities presented by AI.”
The Labor Market Crisis
Scale of Disruption
The human cost of AI adoption is potentially immense. A study by Access Partnership in January estimated that 57 per cent of Southeast Asia’s workforce—approximately 164 million people—could see their jobs reshaped or disrupted. This represents nearly three out of every five workers, suggesting that labor market disruption will be the most visible and consequential impact of AI adoption in the region.
Sector-Specific Vulnerabilities
Service-heavy industries face the sharpest disruption. Professor Jochen Wirtz of NUS Business School predicts that “over the next three to five years, AI will automate many low-level service roles across South-east Asia, especially call centres, business process outsourcing, and shared services such as payroll and finance.” This assessment directly threatens one of Southeast Asia’s largest employment sectors.
The Philippines provides a stark case study. The BPO sector is worth $35 billion annually and employs nearly 2 million Filipinos, representing 8 per cent of the nation’s GDP. This sector has been built on the competitive advantages of labor cost and English proficiency, but AI threatens both. As automation capabilities improve, the labor cost advantage diminishes while language models trained primarily on English data may eventually reduce the need for human agents. Retrenchments have remained modest so far, with about 8 per cent of BPO firms reporting headcount reductions in 2024, though 13 per cent reported increased hiring—suggesting cautious expansion despite uncertainty. However, this modest disruption may represent only the beginning of a longer transition.
Demographic Vulnerability
The burden of disruption falls disproportionately on vulnerable populations. LinkedIn data from January showed women and younger workers dominate the jobs most likely to be vastly reshaped by generative AI. In Indonesia and Singapore, more than 70 per cent of women are in jobs involving repetitive and structured tasks compared with 62 to 64 per cent of men. In Malaysia, analyst Amir Fareed Rahim of KRA Group notes that women, workers in their 20s and 30s, and those in mid-skilled clerical or administrative roles are most exposed to job loss “because these jobs involve structured, predictable tasks that generative AI can easily replicate.”
Jobs relying heavily on routine cognitive tasks, such as data processing or translation, face the most pressure. However, jobs requiring creativity or human interaction may be enhanced rather than replaced. Work involving manual dexterity has lower displacement risk because robots have a longer way to catch up with human physical capabilities than AI has with human cognitive tasks in many areas.
The Case of Call Centre Workers
The lived experience of workers like Mylene Cabalona, a 46-year-old call centre worker in Manila and head of the labour group BPO Industry Employees Network, illustrates the anxiety gripping service workers. She reports seeing colleagues abruptly reassigned, accounts shut down without warning, and entire teams displaced. “Of course I feel threatened because I know eventually our jobs could become redundant or there would be layoffs because of AI,” she said. “AI can easily be improved (upon). It’s scary!”
More troubling than potential job losses is the transformation of work itself. Cabalona describes how call centre agents are now monitored by AI tools that track tone and sentiment in real time, penalizing staff who do not sound upbeat enough, even when speaking to irate customers. “We’re expected to use positive or cheerful words no matter what,” she explained. “Even if the customer is shouting or clearly frustrated, we can’t mirror that tone or even just sound neutral. The system marks us down for that.” Such requirements create significant emotional strain by forcing workers to suppress their own reactions simply to satisfy algorithmic definitions of “good customer service.” This represents not just job displacement but the degradation of work quality and worker autonomy.
Singapore’s Dominant Position
Infrastructure and Investment Leadership
Singapore has emerged as Southeast Asia’s clear AI leader, a position built on strategic investments, regulatory advantages, and historical advantages in digital infrastructure. The city-state’s dominant position is evident in multiple metrics. Singapore has attracted $8.4 billion in AI venture capital—three-quarters of ASEAN’s total—establishing it as the region’s primary hub for AI-related investment. The government has committed substantial resources to this sector, pledging over $1 billion to develop its AI ecosystem over the next five years and announcing another $150 million in 2025 specifically to help firms adopt AI technology.
Policy and Talent Development
Singapore’s policy framework supports AI integration at an institutional level. Prime Minister Lawrence Wong mentioned AI approximately 40 times during Singapore’s National Day Rally on August 17, outlining the Republic’s vision to drive productivity through widespread adoption of the technology. This high-level political commitment signals serious long-term investment and coordination across government agencies.
The city-state’s advantages extend to education and talent development. Singapore possesses the region’s densest AI talent pool, with approximately 3.5 AI professionals per 1,000 workers—seven times Malaysia’s density and 17 times that of the Philippines, Thailand, Indonesia, or Vietnam. This talent concentration enables rapid implementation and allows Singapore-based firms to export AI expertise throughout the region, deepening Singapore’s role as the region’s technology hub.
Sectoral Impact and Employment Transformation
Rather than experiencing the outright job losses threatening other countries, Singapore faces employment transformation. AI will likely reduce clerical and administrative roles, as well as those in customer service, copywriting, and software programming. However, new AI-related roles are emerging simultaneously. DBS Bank announced in February plans to cut 4,000 contract and temporary jobs over the next three years while creating 1,000 new AI-related positions. This three-to-one job loss ratio is unfavorable in absolute terms but suggests potential for retraining and upskilling within Singapore’s stronger institutional framework.
Entry-level positions face the sharpest disruption, as AI has the potential to replace many junior roles. This threatens the traditional career progression pathway where workers begin in entry-level positions and advance through experience. However, sectors most transformed by AI—such as information and communications technology and media—stand to gain the most in productivity. Singapore’s strength in these sectors positions it to capture productivity gains even as it experiences disruption.
Cushioning Effects
Singapore’s ability to cushion AI disruption stems from its institutional advantages. The city-state has stronger training systems, a higher-skilled workforce, and greater institutional capacity for managing transitions than other regional economies. The government can afford more robust social safety nets and retraining programs. The private sector comprises relatively large multinational corporations and advanced tech firms that can invest in workforce development. These factors combine to make Singapore’s AI transition more manageable than that facing less developed regional peers.
Regional Leadership and Technology Exports
Singapore is leveraging its AI advantage to establish itself as the region’s technology leader and standard-setter. The city-state launched Sea-Lion in December 2023, a $70 million large language model that is open-source and tailored for Southeast Asia. Sea-Lion recognizes 13 languages, from Javanese and Sundanese to Malay, Thai, and Vietnamese, with more than 235,000 downloads already achieved. The model is being adopted by firms such as Indonesia’s GoTo Group, establishing Singapore as a technology provider within the region.
This approach serves Singapore’s strategic interests by establishing technological standards and frameworks that Singapore’s firms and institutions can lead in implementing. By exporting technology and expertise, Singapore deepens its role as the region’s tech hub while building dependencies that enhance its regional influence. However, this also creates opportunities for smaller countries to access advanced AI tools without developing their own capabilities, potentially exacerbating long-term dependency rather than building regional capacity.
The Growing Enthusiasm-Anxiety Paradox
Regional Optimism
Interestingly, Southeast Asians are among the world’s most enthusiastic about AI. An Ipsos survey from June shows that eight in 10 respondents in Indonesia said products and services using the technology excite them, followed by 79 per cent in Thailand, 77 per cent in Malaysia, and 67 per cent in Singapore—all substantially above the global average of 52 per cent. This enthusiasm extends into daily life: about three in four people in Indonesia, Thailand, and Malaysia, and seven in 10 in Singapore, say AI has already changed how they live over the past three to five years, compared with about half of respondents worldwide.
This enthusiasm likely reflects both genuine excitement about technology’s potential and relative unfamiliarity with AI’s disruptive capacity. Southeast Asia’s large young populations and historical openness to technological innovation create receptiveness to AI adoption.
Growing Worker Anxiety
Yet this optimism coexists with substantial unease. Nearly two-thirds of workers in Thailand, and more than half in Malaysia and Indonesia, fear AI could replace their jobs within five years. In Singapore, the figure reaches 50 per cent—notably lower than other countries but still indicating that half the workforce worries about AI-driven job loss. This anxiety likely reflects growing awareness of AI’s labor market impact and the precarious position of workers in vulnerable sectors.
The gap between general enthusiasm and worker anxiety reveals an important dynamic: broad societal optimism about AI’s potential coexists with acute anxiety among those whose jobs are most at risk. This divergence suggests that AI gains may benefit affluent consumers and educated professionals while threatening less-skilled workers, further widening inequality.
Reimvention Possibilities: The Upskilling Path
Individual Success Stories
The article highlights contrasting trajectories that illustrate both AI’s opportunities and the barriers to accessing them. Singaporean Mr Lim Yi Ping walked away from a job as a healthcare marketing strategist in 2024 and bet on AI, spending up to eight hours a day taking courses and experimenting with various tools. He taught himself to stage live demos on automating workflows and generating content in a corporate voice. When job hunting earlier this year, three out of four interviews for roles in marketing ended in offers. His success, now working as a digital marketing strategist at FOZL Group, demonstrates that AI skills command premium compensation in the job market.
Similarly, Associate Professor Hasyiya Karimah Adli, a 38-year-old Malaysian trained as a chemist, pivoted into data science in 2020 after realizing machine learning could optimize renewable energy systems. This sparked her passion for bridging engineering science, internet of things, and AI. Her trajectory led her to become founding dean of the Faculty of Data Science and Computing at Universiti Malaysia Kelantan, demonstrating that AI expertise can create leadership opportunities and entire career reinventions.
In Thailand, 54-year-old farmer Jamras Inpuek has experimented with AI-powered apps that generate a digital layout of his farm and forecast rainfall months in advance. Though the predictions are only 60 to 70 per cent accurate, when they prove right, his yields rise by as much as 20 per cent—a significant margin in farming where climate determines income. This example shows AI applications extending beyond knowledge work into agriculture and primary industries, creating opportunities for less-educated workers to boost productivity and income.
The Upskilling Imperative
These success stories share a common element: individuals took proactive steps to acquire AI skills through intensive study, practical experimentation, and strategic career positioning. Miss Elena Chow, founder of talent consultancy ConnectOne, emphasizes that understanding AI will eventually become as basic as knowing how to use applications like Microsoft Word or Excel. However, she notes that employers increasingly want more than theoretical knowledge—they want to see concrete, innovative applications.
“Go for practical courses, not theory, like learning how to code with AI or optimising agentic use cases or open-source models—and then build something for your own use like an expense tracker, a calorie tracker,” Chow recommends. This emphasis on practical application over theoretical knowledge suggests that workers cannot simply take courses and expect to improve their prospects; they must demonstrate competency through real projects.
Mr Lim acknowledged that not every industry affords workers the luxury of taking time off to retrain, highlighting the privilege underlying his successful transition. His warning is sobering: “If you don’t use AI, you might lose out in job seeking.” For workers in precarious sectors with limited savings, the opportunity to take time for intensive upskilling may be simply unavailable.
The Structural Inequality of Upskilling
The emphasis on individual upskilling masks structural inequalities that will determine who can actually access retraining. Workers in stable, well-paying jobs like Mr Lim’s initial position can afford to take time off to retrain. Workers in unstable, poorly-paying jobs cannot. Workers in countries with strong educational institutions, broadband access, and digital literacy programs have better chances of acquiring AI skills than those in countries with weak infrastructure. Workers in cities with AI training resources have advantages over rural workers. Young workers with more years of career ahead of them have more incentive to retrain than older workers facing potential age discrimination.
These structural factors suggest that upskilling, while valuable, will not be sufficient to manage AI disruption fairly across Southeast Asia. Individual initiative matters, but so do systemic factors that determine who has access to opportunities.
Regional Cohesion at Risk
The Existential Threat to ASEAN Unity
Beyond labor market disruption and inequality within countries, AI threatens Southeast Asia’s regional cohesion. The uneven pace of AI adoption threatens to harden divides between advanced and less-prepared economies, potentially undermining ASEAN’s ability to coordinate policies, share opportunities across borders, and present itself as a credible destination for global investors.
Dr Mustafa Izzuddin, a senior international affairs analyst at Solaris Strategies Singapore, warns of “an existential risk in AI adoption driving a wedge between members of Asean, if not harnessed properly and governed effectively through sustained regional cooperation.” He also points to the potential for AI-driven cyber attacks, deepfakes, and overdependence on opaque systems to sow discord and strain trust among member states.
Supply Chain Recentralization
If AI becomes a fault line dividing advanced from developing ASEAN members, the economic consequences will ripple through regional supply chains. Joanne Lin warns that supply chains could re-centralize in a few digital hubs while firms in lagging economies struggle to plug in. This recentralization would undermine ASEAN’s goal of creating integrated regional production networks and could redirect investment flows away from less developed members.
This risk is particularly acute for countries like the Philippines, Thailand, Vietnam, and Indonesia that have built economic strategies around regional production networks and exported manufacturing. If AI-driven automation and recentralization concentrate production in advanced hubs with strong digital infrastructure, these countries could find themselves unable to compete even in low-cost manufacturing—their traditional advantage.
Regional Initiatives: Bridging the Divide
The Digital Economy Framework Agreement
Recognizing these risks, ASEAN has begun developing coordinated policy responses. In 2023, the grouping launched the Digital Economy Framework Agreement (DEFA) to boost cross-border digital trade and services. Studies suggest DEFA could double ASEAN’s projected digital economy value from $1 trillion to $2 trillion by 2030. This framework aims to harmonize regulations, reduce barriers to digital commerce, and facilitate the movement of digital services across borders.
DEFA represents a constructive step toward creating a more level playing field within ASEAN by reducing fragmentation and enabling less-developed members to participate in regional digital commerce. However, the framework works best for countries that already have reasonably developed digital infrastructure and e-commerce capabilities. For the least-developed members, DEFA’s benefits may remain limited without complementary investments in basic infrastructure and digital skills.
The Responsible AI Roadmap
In 2025, ASEAN advanced its policy response with the Responsible AI Roadmap (2025-2030), setting out actionable steps for policymakers and stakeholders to promote responsible use of the technology across member states. The roadmap addresses concerns about AI’s social impact, data privacy, and ethical deployment, recognizing that unmanaged AI adoption could exacerbate social problems alongside economic benefits.
The roadmap’s success will depend on member states’ willingness to implement its recommendations despite potential short-term costs. Advanced economies like Singapore might view strict AI governance as slowing innovation, while developing economies might struggle to implement enforcement mechanisms. Balancing innovation incentives with social protection remains a persistent challenge.
Building Regional AI Capabilities
ASEAN’s developers have sought to level the AI playing field by building language models that better represent the region’s languages, worldviews, and values. Data collated by AI Singapore from Hugging Face, a collaborative platform for the machine-learning community, indicates that 73 per cent of existing large language models come from the US and China, with 95 per cent of these models trained primarily on data in English or with a mix of Arabic, Chinese, or Japanese. This linguistic dominance means that AI systems reflect Western and East Asian perspectives, potentially marginalizing Southeast Asian contexts and languages.
Singapore’s Sea-Lion represents the most developed Southeast Asian response, recognizing 13 languages including Javanese, Sundanese, Malay, Thai, and Vietnamese. Other initiatives include the multilingual models SEA-LLM and Sailor, plus monolingual models such as Indonesia’s IndoBERT, Malaysia’s ILMU and MaLLaM, Thailand’s OpenThaiGPT, and Vietnam’s PhoGPT. These models allow Southeast Asian developers to create AI applications that better serve regional needs and preserve indigenous knowledge stored in local languages.
However, creating regional AI extends beyond technology. As Elina Noor, a senior fellow in the Asia Program at the Carnegie Endowment for International Peace, noted in a Project Syndicate commentary published in June, this effort means filtering out old biases, questioning assumptions about identity, and drawing on indigenous knowledge stored in local languages. “We cannot project our cultures faithfully through technology if we barely understand them in the first place,” she emphasized. This observation highlights that technology development cannot be divorced from cultural and social reflection.
The Role of National Policies
Despite these regional initiatives, progress ultimately depends on individual member states’ commitment and capacity. As Joanne Lin notes, “AI infrastructure like broadband access and national IT literacy programmes will be determined by individual (members) according to their national development plan. That said, Asean can close parts of the gap through initiatives like the DEFA, which is undergoing negotiations.”
This assessment suggests that regional frameworks are necessary but insufficient. Without strong national-level investment in digital infrastructure, education, and workforce development, regional initiatives alone cannot close the AI divide. Singapore’s trajectory illustrates this point: the city-state’s regional leadership stems not from ASEAN initiatives but from sustained national investments in infrastructure, education, and research.
The Path Forward: Critical Challenges
The Skills Development Dilemma
Southeast Asia faces a profound skills development challenge. Dr Maria Monica Wihardja of ISEAS warns that without stronger skills policies, middle-income countries could see their mid-skill workers squeezed out of stable employment. Current evidence in Indonesia and other countries like Vietnam shows that mid-skill service jobs such as sales and customer service are already being hollowed out. Without better skill development, workers risk moving into less-skilled or labor-intensive jobs like those in agriculture or front-line services rather than moving up the value chain.
This represents a troubling reversal of development expectations. Countries like Indonesia and the Philippines have built growth strategies around service sector employment and business process outsourcing. If these sectors collapse under automation pressure while workers lack skills to transition into higher-value activities, these countries could experience a development stall—a period where growth stagnates as workers cannot move up the skill ladder and employers face incentives to relocate operations to countries with stronger AI infrastructure.
The Digital Divide Within Countries
Inequality within countries may mirror regional inequality. Urban areas with strong broadband infrastructure and access to training can participate in AI transformation; rural areas cannot. This threatens to accelerate rural-to-urban migration and potentially concentrate benefits in a few metropolitan centers while marginalizing vast rural populations. This dynamic could undermine social cohesion and create political tensions within countries as disparities become visible and threatening to rural communities.
The Dependency Risk
As Singapore and other advanced Southeast Asian economies develop and export AI technologies, less-developed countries risk becoming dependent on external technology providers rather than developing indigenous AI capabilities. Singapore’s Sea-Lion represents positive knowledge sharing, but the broader pattern of Southeast Asian reliance on US and Chinese AI platforms suggests that the region could become locked into technological dependency where key decisions about AI development, deployment, and governance occur outside the region.
Conclusion: Opportunity and Peril
Artificial intelligence presents Southeast Asia with a genuine opportunity to accelerate development, boost productivity, and improve living standards across a population of over 650 million people. The projected addition of nearly $1 trillion to regional GDP by 2030 could be transformative, enabling investments in education, healthcare, and infrastructure that raise development levels across the board.
However, this optimistic scenario is not inevitable. Instead, the current trajectory points toward a more troubling outcome where AI benefits concentrate in a few advanced economies—particularly Singapore—while less-developed countries fall further behind. This divergence will widen inequality both within and between countries, threaten employment for millions of workers, and potentially undermine ASEAN’s unity and collective bargaining power in global affairs.
Singapore’s dominant position reflects genuine advantages in infrastructure, education, talent, and institutional capacity. The city-state’s regional leadership in AI investment and capability development is well-earned. However, this leadership also carries responsibility for the region’s collective development. Singapore benefits from ASEAN membership through market access, supply chain integration, and political influence; these benefits depend on other member states’ stability and prosperity.
The path to inclusive AI development requires sustained regional cooperation combined with strong national commitment from each member state. ASEAN’s Digital Economy Framework Agreement and Responsible AI Roadmap represent necessary steps. However, their success depends on member states’ willingness to invest heavily in digital infrastructure, education, and workforce development—investments that typically compete with other development priorities in constrained budgets.
Individual workers like Mr Lim, Ms Adli, and Mr Jamras demonstrate that AI adaptation is possible. However, their success stories highlight both the possibilities and the barriers: they possessed existing education, resources, or motivation to pursue intensive upskilling in an economically secure context. Most Southeast Asian workers lack these advantages.
The critical question facing Southeast Asia is whether the region’s leadership will treat AI as an opportunity for shared development or allow it to become another axis of inequality. The answer will determine whether AI elevates Southeast Asia’s development or deepens the divisions that threaten the region’s future.
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