We talk a lot about artificial intelligence (AI) shaping the next frontier of global innovation, yet Southeast Asia continues to struggle with a widening funding gap that risks pushing the region back from the AI revolution altogether.
Well, let’s look at the data — while global investments in AI surged to new highs in 2024, with the United States and China attracting USD 68.5 billion and USD 11 billion respectively, Southeast Asia, on the other hand, only raised about USD 3 billion across 122 deals. These data have definitely shown a huge gap and point to deeper issues that must be unpacked — why is this happening, and how exactly can change be made?
Why Southeast Asia healthtech funding is facing its worst year in 7 years
The region is not without its strengths. A high-quality and fast mobile Internet, a youth population who are digitally fluent, as well as a rising class of tech and digital entrepreneurs that make Southeast Asia a solid ground for any AI-led creation. Although the region has the potential to be great, the funding landscape still remains cautious of this. Often not, investors somewhat perceive Southeast Asia as a complex and very fragmented market, formed by different legal systems, languages, data regulations, and even the levels of economic development.
Take one factor from this, which is levels of economic development — Singapore’s economy is different from Malaysia’s, and what works in Thailand doesn’t necessarily work in Indonesia. This actually makes it difficult for the investors to deploy capital with confidence in AI startups, and eventually, they will fail to scale regionally.
What ties these factors together and really makes this issue even harder to solve is the lack of foundational infrastructure to support serious AI initiatives. Of course, Southeast Asia is no Silicon Valley or Shenzhen, because these two regions have both the funding and solid ground to be great. Meanwhile, startups in Ho Chi Minh or Jakarta are struggling with access to high-performance computing power and specialised AI tools. Even if the cloud services become more widely available, the cost of training these large models or running inference-intensive applications still would restrict many early-stage ventures.
Pockets of promise: What’s working in Southeast Asia
Despite the challenges, the story is not purely one of shortfalls. Looking at the region specifically, Southeast Asia has produced standout AI players that offer a blueprint for what “success” can look like. Take PatSnap Pte, for example, a Singapore-based company that uses AI to deliver data and insights into intellectual property and R&D trends. Their ability to secure global investment with the support of SoftBank really comes down to a blend of technical depth, global market orientation, as well as strong support from Singapore’s public sector ecosystem.
The last factor is really important to these startups’ growth, and not many understand the government’s role in facilitating access to data, funding schemes, and AI talent through dedicated agencies. Similar to that, companies like ADVANCE.AI and WISE AI have made significant moves by focusing on applied AI use cases in identifying verification and financial services, demonstrating that localisation and domain expertise can build investor confidence to invest in their businesses.
Still, these are the exceptions, not the norm for Southeast Asia. For most startups, the road to capital is still a long way to go. One of the significant contributors to this is the smaller number of venture capital firms in the region with deep AI expertise. This can break or make investors’ decisions because many of them are much more comfortable with proven verticals such as e-commerce or logistics, where exit strategies and business models are familiar and not unconventional. AI, particularly deep tech or research-heavy applications, require longer time spans and more patient capital, something in short supply across many local VC networks.
Closing in the gap between talent and policy
Getting started to build the foundation for these startups’ growth requires extensive planning, and one aspect of it is governance and policy frameworks. For countries where AI guidelines are still unclear and non-existent, there is greater uncertainty about long-term risks, particularly when it comes to data privacy, cybersecurity, and ethical AI deployment. These have been the main concerns with AI in general. While Singapore has taken the lead in developing responsible AI governance and national AI strategies, a lot of these regions are still playing catch-up.
Of course, some governments have introduced AI roadmaps and digital economy blueprints, but these often do not include implementation details and funding support for these startups. So, the result is, having an ecosystem where entrepreneurs are eager and ready to go but lack the structural backing to move fast and scale with confidence.
Another big aspect is the talent. Although. The region produces a number of good engineers and data scientists, but many have moved abroad and taken more attractive job opportunities with higher-paying salaries in North America or Europe. And those who remain in Southeast Asia often lack access to cutting-edge research environments or even experienced mentors, who eventually will help them with the connection and network to future investors. Without solid plans to develop and retain top-tier talent in the Southeast Asia region, the issue will continue, and eventually the region will have to face a major shortage of skilled workers, which will make Southeast Asia unattractive and lose its competitive edge.
Even more so, the region has the potential to showcase a unique identity on the global AI stage — an identity that is rooted in practical applications and real-world impact. It should go hand in hand as, as Southeast Asia’s diverse cultural backgrounds — it is a strength to take advantage of and not one to shy away from, as that’s what makes this region stand out from the rest. In this case, having AI solutions tailored for local languages or fragmented supply chains rather than one-size-fits-all models.
Making a big break for inclusive AI innovation
To truly unlock the region’s potential, a coordinated approach is needed. Governments, firstly, must take bolder and active steps — that include investing in shared infrastructure, incentivising AI research and development, and harmonising data policies to enable cross-border collaboration. On the other hand, investors need to develop a deeper understanding of the AI landscape of each region and be willing to actually take the risks on early-stage innovation.
Let’s be real, a lot of startups have issues in the beginning, it’s just a matter of the investors willing to take a chance on them to get that first “go ahead” moment. Apart from that, universities and training institutions should also play a bigger role in this by equipping the next generation digital fluency, whilst at the same time having both technical skills and an entrepreneurial mindset.
Southeast Asia may be behind the curve today, but it is not without options. It takes a while to grow and if the right policies, partnerships, as well as investments are put in place — the region will be a success story, one that leaps from traditional development pathways into a thriving AI ecosystem that is uniquely its own with a rich cultural background that no region can copy from.