Southeast Asia’s artificial intelligence startup market has entered a new phase. As of July 2026, the region’s Native AI ecosystem raised approximately US$9.3 billion across 261 disclosed equity rounds. Singapore has become Southeast Asia’s main AI fundraising hub, supported by the presence of strong digital infrastructure, numerous global technology companies and government investments. However, the location where a company raises money does not always reflect where its technology is developed, or where its customers are located. Many companies use Singapore as a regional headquarters while operating across several Southeast Asian markets.
This raises a more interesting question: which markets are developing advantages of their own, rather than which country will replace Singapore in the AI startup ecosystem? Indonesia offers scale, Vietnam has a growing engineering base, Malaysia is investing heavily in digital infrastructure, while Thailand and the Philippines present strong opportunities in specific industries.
We explore why Singapore is moving AI governance from the IT department to the boardroom
Singapore has built a lead that goes beyond funding
Singapore’s dominance did not appear overnight. Research has found that Singapore accounted for more than 70% of Southeast Asia’s AI venture investment between 2020 and 2024. Furthermore, Singaporean companies were behind 17 of the region’s 25 largest AI deals over the period.
Several advantages reinforce one another, resulting in this dominance. Startups have access to regional venture capital firms, international investors, high-quality research institutions and potential enterprise customers within a relatively small market. Singapore also attracts multinational technology companies and international talent while providing a business-friendly regulatory environment for businesses developing and deploying AI. Its early focus on AI governance has helped position the country not simply as a place to develop technology, but as somewhere businesses can test how it will operate commercially.
Government policy provides another clear pull-factor. National AI strategies, investment in computing capacity, talent programmes and support for research have helped create infrastructure that is difficult for neighbouring markets to replicate quickly. The result is a powerful cycle as capital attracts startups, which in turn attract talent and consumers. This ultimately results in successful companies, which will then draw in more investors.
Singapore is therefore poised to remain Southeast Asia’s principal AI fundraising centre for the near future. Nonetheless, more immediate competition is taking place around it as neighbouring nations are finding their own routes into the market.
Indonesia’s advantage is the size of the problem
While Indonesia cannot match Singapore’s concentration of venture capital, it possesses something Singapore does not: enormous domestic scale. The country offers AI startups access to one of Southeast Asia’s largest consumer markets alongside major industries including banking, telecommunications, logistics, retail and agriculture. That provides Indonesian companies with an opportunity to build and test technology against problems that exist at significant scale before expanding internationally.
Indonesia has emerged as a sizeable market for AI adoption, buttressed by its large startup ecosystem of more than 2,400 companies, including AI and deep tech businesses. Indonesian knowledge workers have also already reported a particularly high adoption of generative AI tools in their jobs.
For example, in the telecommunications industry, Indonesian companies have been developing AI across network planning, customer service and internal operations while also working on a large language model for Bahasa Indonesia. All these point towards Indonesia’s wider opportunity. Its strongest AI companies may not need to build global foundation models from the ground up. Instead, they can apply existing technology to payments, lending, supply chains, agriculture and customer service in a market where local knowledge matters. However, the challenge for Indonesia remains capital, as Native AI funding outside Singapore remains comparatively small. For Indonesia to produce more regional AI leaders, domestic adoption will eventually need to translate into larger and more consistent growth funding.
Vietnam is building from its engineering base
If Indonesia’s advantage is market scale, Vietnam’s may be technical talent. The country is home to more than 500,000 technical workers and IT professionals as AI startups in Vietnam rose from around 60 in 2021 to 278 in 2024, according to the Boston Consulting Group report. This creates the foundations for a development-led AI ecosystem, particularly in software engineering, enterprise applications and locally adapted AI products.
Vietnam thus appeals to both domestic startups and international companies seeking to build technical teams in the region. Its young and highly skilled workforce offers an attractive and affordable cost base compared with more mature technology centres. The question is, however, whether Vietnam can convert engineering strength into companies capable of raising larger rounds and selling across international markets. After all, while Vietnam ranked as the second-largest Native AI funding market in Southeast Asia, the US$19 million raised paled in comparison with that of Singapore.
The opportunity is therefore clear, but so is the missing piece. Vietnam has increasingly visible technical capability. It now needs deeper venture networks and more companies capable of turning that talent into scalable regional businesses.
Malaysia is betting on infrastructure
Compared with its neighbours, Malaysia is developing a different position in Southeast Asia’s AI landscape. Rather than leading through startup funding, the country is becoming increasingly important for the physical infrastructure needed to run AI.
Johor, in particular, has become increasingly important as data-centre operators seek locations close to Singapore with greater availability of land and power. Malaysia has attracted billions of dollars in announced cloud and data-centre investment from global technology groups including Microsoft, Google and Oracle, with Johor emerging as one of Southeast Asia’s fastest-growing data-centre markets.
While this infrastructure boom does not automatically create a strong startup ecosystem, it can improve the conditions around one. More data centres bring cloud providers, enterprise technology customers, technical employment and supporting services. Malaysia also has large manufacturing, financial services and healthcare industries where AI can be applied to existing operations.
The opportunity for Malaysia may therefore lie less in competing directly with Singapore for headquarters and more in building industrial AI businesses close to real customers. Areas such as manufacturing quality control, semiconductor production, logistics and energy management present opportunities for local companies to combine AI with Malaysia’s existing industrial strengths.
Thailand and the Philippines can win through specialisation
Not every market needs to become another Singapore. Thailand is beginning to demonstrate how AI startups can build around specific industries rather than competing across the entire technology market. Local startups in Thailand are already applying AI to architecture, food manufacturing, agriculture, hotel energy management and Thai-language software. The common thread is practicality. These startups are building solutions around problems already faced by Thai businesses rather than attempting to reproduce the products of global AI leaders.
On the other hand, the Philippines offers another potential advantage through its business process outsourcing (BPO) and services industry. In particular, AI-enabled BPO is one of the country’s potential strengths. The sector already has a large workforce, international customers and years of experience providing outsourced business services. AI could allow local companies to move from lower-value repetitive work towards more advanced customer service, analytics and specialised support.
Despite this, both markets remain critically underfunded compared to Singapore, as both Thailand and the Philippines received far fewer large AI deals between 2020 and 2024. Their opportunity may therefore come from depth rather than ecosystem size.
Southeast Asia does not need its own OpenAI
Trying to predict which Southeast Asian country will produce a direct competitor to OpenAI, Anthropic or China’s largest foundation-model companies may miss the region’s more realistic opportunity.
Training these frontier models requires enormous amounts of capital, computing infrastructure and data. Instead, most Southeast Asian startups will find stronger opportunities by applying AI to problems where global technology companies lack local knowledge.
Language is a prime example of this. Research behind the SeaLLMs project notes that major language models remain biased towards high-resource languages such as English, leaving many Southeast Asian and regional languages less well represented. A model that works well in English may struggle with local vocabulary, mixed-language conversations, cultural context or specialist terminology. That creates opportunities in multilingual customer service, local search, financial services and government applications.
The same logic applies in other sectors. Agricultural AI can be built around local crops and farming conditions. Financial technology can address underbanked populations, while logistics tools can respond to Southeast Asia’s fragmented geography. These companies do not need to build the largest AI model. They need to understand a problem better than an international competitor does.
The next AI race will look different
Singapore’s lead is unlikely to disappear soon. Its combination of capital, infrastructure, research, talent and enterprise customers gives it an advantage that neighbouring markets cannot simply reproduce by launching more startup programmes.
But Southeast Asia’s AI ecosystem does not need a second Singapore to become more competitive. Indonesia can use its domestic scale to build AI around large consumer and enterprise problems. Vietnam can convert its expanding engineering base into exportable technology. Malaysia can connect AI startups with its data centre and industrial economy. Thailand can specialise around sectors where it already has expertise while the Philippines can build on its position in global business services. Funding, however, remains the clearest weakness as Singapore continually dominates the largest transactions.
The region’s next phase will therefore be less about producing another general-purpose AI giant and more about building companies that make AI work for Southeast Asia itself. If neighbouring ecosystems can turn local languages, industries and customer behaviour into genuine competitive advantages, Singapore may remain the region’s financial centre for AI without being the only place where its most important companies are built.