In its 2025 Annual Retail Report, global fintech platform Adyen reveals a sharp rise in AI adoption among Asia Pacific (APAC) consumers and retailers, highlighting a significant shift in shopping behaviors and business strategies across the region.

The report, based on a survey of 41,000 consumers across 28 markets including Singapore, Australia, Hong Kong, India, Japan, and Malaysia, underscores how the tech is reshaping the retail experience and signaling broader trends for the region’s digital economy.

According to Adyen, over a third (38 per cent) of APAC consumers now use AI to assist with shopping—a 39 per cent increase from 2024. Notably, more than one in ten APAC consumers (11 per cent) tried AI-powered shopping for the first time in the past year.

The appeal of AI lies largely in its ability to offer fresh inspiration and personalised recommendations. Nearly two-thirds (63 per cent) of AI-using consumers said it helps them discover new choices for everything from outfits to meals faster than any human assistant could.

Additionally, 62 per cent expressed interest in using AI to find unique brands and shopping experiences, opening doors for retailers to drive sales through partnerships and cross-selling strategies.

Cross-generational adoption

While younger generations remain at the forefront of AI shopping, older cohorts are quickly catching up. In Malaysia and Hong Kong, Gen Z adoption stands at 74 per cent and 64 per cent, respectively.

Also Read: Ecosystem Roundup: AI’s capital frenzy, bolttech’s US$147M funding, and Southeast Asia’s VC crunch

Meanwhile, Singapore’s Generation X and Millennials have shown substantial growth, with AI shopping adoption increasing by 45 per cent and 28 per cent respectively over the past year. Even among consumers aged 60 and above, nearly a third (30 per cent) reported using AI to assist with purchases.

“The introduction of AI in shopping has created new shopper journeys that are more exciting than ever,” said Warren Hayashi, President of Asia Pacific at Adyen. “For retailers, embracing AI isn’t just about staying current; it’s about meeting evolving consumer expectations and staying competitive in a fast-changing retail landscape”.

On the business side, more than a third (34 per cent) of APAC retailers plan to increase their AI investments in the coming year to enhance sales, marketing, product innovation, and security. Payments data — a largely untapped resource — presents significant potential for AI-driven optimisation.

While AI garners attention, the report also points to gaps in omnichannel capabilities. Less than half (46 per cent) of APAC retailers currently support seamless cross-channel shopping, though another 19 per cent plan to enable this within the next year.

Consumer expectations are evolving quickly: 46 per cent want businesses to offer integrated experiences across online platforms, social media, and physical stores. Despite the rise of digital commerce, 42 per cent of consumers still value in-store shopping equally alongside online channels.

As the region’s retail landscape continues to digitise, AI is emerging not only as a tool for personalisation and convenience but also as a strategic differentiator for retailers navigating an increasingly competitive market.

Also Read: The art of artificial intelligence: How Hagia Labs is reimagining creativity

Balancing innovation with security concerns

Despite enthusiasm for AI, concerns around fraud persist. About 26 per cent of consumers expressed heightened worries about scams, and 20 per cent avoid storing payment details on devices for security reasons. Currently, 40 per cent of APAC retailers are leveraging AI to combat fraud by detecting anomalies and predicting fraudulent activities using their transaction data.

“Besides optimising revenue, AI could aid in the fraud-fighting efforts of retailers,” Hayashi noted. “It can spot anomalies, identify patterns, and predict fraud attempts – ultimately ensuring consumer trust and protecting retailers’ hard-earned revenue”.

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Image Credit: Mike Petrucci on Unsplash

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In 2024, venture capital across Asia-Pacific sank to its lowest level since the 2021 peak. In Southeast Asia, startup funding dropped by 42 per cent, with investors either pulling back entirely or doubling down only on high-traction, near-profitable, or already profitable startups. At Spacely AI, we had no choice but to rethink everything.

Early on, we made a decision: build a product people would pay for, and structure our growth around revenue—not runway. We raised a modest pre-seed round, stretched every dollar, and aimed for profitability from day one. We didn’t call it seed-strapping at the time—but that’s exactly what it was.

We haven’t reached profitability yet. But this approach extended our runway far beyond projections. It allowed us to keep our team small—under 10 full-time employees. We avoided layoffs. And unexpectedly, it gave us something most founders struggle to find during turbulent times: leverage, clarity, and freedom.

The VC model is breaking in Southeast Asia

The region’s venture landscape is facing serious headwinds. Billion-dollar exits are few and far between. And without reliable exits, LPs are more cautious, which makes it harder for VC funds to raise capital. Many are quietly failing to raise their third or fourth fund. It’s not because they can’t find good startups, but because the math no longer adds up.

The silver lining? There’s still capital out there—but it’s more selective than ever. It’s reserved for companies showing real traction and a clear path to profitability. The bar has shifted. The days when “potential” alone could raise millions are over. That’s the new reality: many VCs simply can’t invest, not because they don’t believe in you—but because they’re trying to survive, too.

Also Read: Building future sustainable business: The role of rural commerce platforms

The rise of seed-strapping

This is why the smartest founders I know are shifting to the “seed-strapping” model. Seed-strapping is quickly becoming the new startup playbook—raise just enough capital to reach cash-flow positive, then let revenue take you the rest of the way. You don’t need a massive seed round. You need just enough to reach profitability.

We stayed lean with fewer than 10 FTEs, automated as much as possible with AI, and focused entirely on finding product-market fit. We didn’t grow through expensive paid campaigns. Our customer base and revenue were built through organic acquisition. That forced us to stay disciplined. No distractions. Just sell, build, test, repeat.

Let me be clear: this path is not easy. At one point, we reduced salaries across the entire company by 50 per cent. It was painful, but necessary. It wasn’t about bravado. It was about survival. And in the midst of this, we found focus. That constraint gave us perspective. And it opened our eyes to the real power of AI—not just as a product, but as a company-building force.

AI is the deflationary force changing everything

One of the biggest tailwinds behind seed-strapping is AI. Not just because we’re an AI company—but because it changed how we work, scale, and think about cost.

Every founder faces the same three levers: raise money, cut costs, or grow revenue. And AI can supercharge all three. We’ve trained ourselves to ask: “Can we AI this before hiring for it?” For example, at Spacely AI, we run all our growth channels (social media, blog, and SEO) with one growth analyst. That analyst is empowered with the right AI tools, templates, and workflows to do the job of an entire team. The result? Lower cost, more output, and better quality.

AI didn’t just help us survive. It helped us operate better. Founders who understand this dynamic—who treat AI as a margin engine, not just a product feature—are going to win.

Revenue is the best funding you can get

Cutting costs and increasing productivity only get you so far. The other side of survival is revenue. That’s where real leverage lives.

VC money is useful. But customer money is better. Revenue is non-dilutive. It’s fast. It’s proof that you’re solving a real problem. And every US$10,000 in MRR buys you more than just another month of runway—it gives you proof.

Most startup advice focuses on perfecting your pitch. But what if you pitched less and sold more? What if you built your business around the customers you’re trying to serve—not the investors you’re trying to impress?

There’s a quote I read recently that feels especially true in this climate: “Profits solve all problems.” Reflecting on our journey, I couldn’t agree more.

Also Read: Turning intimidation into innovation: Embracing sustainability’s new opportunities

The new playbook: PMF, margin, and discipline

If you’re building a startup in Southeast Asia right now, I’d challenge you to adopt this lens. The old “growth at all costs” mentality doesn’t fit the current market. The new playbook looks like this:

• PMF first: Lock in one clear use case. Prove it. Then scale.
• Profitable unit economics: 70 per cent+ gross margin, 12-month payback period or better.
• Lean teams, AI-enabled: Ten high-performers with AI > 50 without.

We didn’t invent this strategy. We adopted it out of necessity. But it’s made us sharper and more resilient.

Profitability is the ultimate leverage

There’s a saying: “The best time to raise money is when you don’t need it.” Every founder loves that phrase—and for good reason. Once you approach or hit profitability, the entire game changes.

You get clarity—on what to build, who to build for, and what it takes to scale. You gain options. Not just the option to raise or not raise, but the power to choose who to raise from—and who to walk away from.

Let’s be honest: fundraising takes time. Some say six months. Lately, I’ve heard twelve. Profitability—or even a credible path to it—gives you the endurance to survive those cycles. More importantly, it keeps you in control.

The founder’s checklist for surviving the funding winter

If you’re navigating this market, here’s what I recommend:

• Solve a painful problem customers will pay for
• Use AI to increase productivity and stay lean
• Focus on leading indicators of PMF—activation, engagement, referrals (for SaaS)
• Track your burn rate, CAC, and LTV
• Rally your team around cash-flow positive as a company-wide goal
• Treat VC funding as optional fuel, not oxygen

Final thoughts

We’re in a funding winter. But winters don’t last forever—and they often produce the most resilient companies. If you can build around customers, automate smartly, and seed-strap your way forward, you’ll emerge stronger, faster, and freer.

At Spacely AI, we chose to seed-strap because we didn’t want to depend on a volatile capital market. AI helped us stay lean. Our focus on customer value gave us breathing room. And our users—real people paying real money—turned out to be the best VCs we could ever ask for.

If you’re building in 2025, don’t wait for a term sheet to start acting like a real company. The new playbook is clear: sell first, build something people want, and spend your customers’ money wisely.

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At a recent AI vs Human keynote showdown, someone in the audience threw me a question many founders quietly ask: “But AI hallucinates. Isn’t that dangerous?”

My reply was simple, but it caught a few off guard: “Yes. But humans hallucinate, too. And often, it’s far more dangerous.”

The debate isn’t whether AI makes mistakes — we know it does. The real problem is who we choose to trust when confidence meets uncertainty. As founders, that’s where the true risk lives.

What is hallucination, really?

Let’s start by demystifying the term.

AI hallucination happens when large language models (LLMs) like GPT generate responses that are factually incorrect but sound completely plausible. They aren’t lying. They’re simply predicting text based on probability patterns.

Public examples prove this risk. Sky News’ Sam Coates confronted ChatGPT live for generating false podcast transcripts. OpenAI’s own testing data shows significant hallucination rates:

• 33 per cent false information rate for its o3 model.
• 48 per cent for its o4-mini model.

AI can sound extremely confident even while being wrong, and that’s precisely what triggers automation bias, when humans trust machine outputs simply because they “sound right.”

But here’s the uncomfortable truth: Humans hallucinate too, and we rarely catch ourselves doing it.

The human hallucination problem: Narratives we build

AI hallucinates through prediction. Humans hallucinate through narrative.

We build impressions. Those impressions become judgments. Judgments turn into stories. And those stories drive our business decisions.

• We overestimate market size based on a handful of customer interviews.
• We assume product-market fit based on early interest.
• We hire poorly because of a great interview.
• We raise funding on projections fuelled more by hope than data.

These aren’t rare. They are startup norms.

In many cases, founders hallucinate entire business models with full conviction. The difference? There’s rarely a system that alerts us when we’re slipping into narrative-driven delusion.

Also Read: AI adoption in SEA e-commerce: The clock is ticking for sellers

The confidence trap: Why founders trust the wrong things

Both AI and humans share one dangerous similarity: They deliver outputs with confidence, whether right or wrong.

That confidence triggers trust. And trust, unchecked, leads to bad decisions.

• AI: “The answer is definitely X.”
• Founder brain: “We’ll definitely 10x next year.”

The issue isn’t hallucination itself, it’s how quickly we surrender our skepticism when something sounds certain.

The true founder risk isn’t just AI hallucination. It’s our reflex to accept confidence as truth.

My operator view: How I design around hallucination

Across my ventures, I’ve built AI into daily workflows. But I never outsource my thinking.

Here’s my personal system design:

• Separate generation from verification: AI helps structure thoughts, draft options, and synthesise. But facts get independently verified.
• Build multi-step logic chains: I don’t ask for one-shot answers. I design prompts that generate reasoning, assumptions, counterpoints, and validations.
• Cross-check everything: Whether it’s market data, analysis, or competitor signals, I verify across multiple sources.
• Use AI as augmentation, not authority: Seraphina AI, my personal assistant, mirrors my thought process because it was trained to follow how I already operate. It amplifies my logic but doesn’t replace it.

The meta-moment: While writing this article

Even while drafting this article with AI assistance, I actively ask: “Is the AI hallucinating here?”

The answer? No, because I’m not asking it to invent facts. I’m using it to structure my thinking, arrange arguments, and explore narrative flows. The core reasoning remains mine, AI simply amplifies and organises.

AI is my logic partner, not my fact source. That distinction is where most founders struggle: they surrender too much authority too quickly.

The founder’s three guardrails against hallucination

Here’s the framework I live by and recommend to every founder:

• Separate generation from verification: Never let AI verify its own outputs. Always layer external data and checks.
• Build multi-step prompts: Don’t chase immediate answers. Build prompt chains that explore reasoning, objections, and edge cases.
• Treat AI like a team member: You wouldn’t trust a junior hire’s first draft without review. Apply the same discipline to your AI assistant.

Also Read: Startups, is your email strategy driving growth, or just gathering dust?

The harder truth: Human hallucination is more dangerous

The brutal reality? We can engineer systems to reduce AI hallucinations. But human hallucination, especially founder hallucination, is far more difficult to catch.

• Ego pushes us to double down on flawed assumptions.
• Investor pressure accelerates premature scaling.
• Team echo chambers reinforce dangerous narratives.
• Emotional attachment clouds product decisions.

Human hallucination isn’t probabilistic — it’s emotional. And emotions rarely fit into predictable guardrails. That’s why many startups fail — not from AI errors, but from founders’ unchecked certainty.

AI hallucination is mechanical. Human hallucination is narrative.

The founder advantage today isn’t about trusting AI more or less. It’s about developing the cognitive discipline to manage both AI and human fallibility simultaneously.

The hybrid founder edge

The founders who thrive in this AI-powered era won’t be those who fear hallucination.

They’ll be the ones who:

• Build operating systems that minimise blind spots.
• Maintain cognitive sovereignty over both algorithms and their own internal narratives.
• Use AI to amplify clear thinking, not replace it.

AI doesn’t replace thinking. It exposes who never learned how to think systematically in the first place. And in this new landscape, that, not hallucination itself, will define who scales and who fails.

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As we reach the midpoint of 2024, it’s an ideal time to reflect on emerging trends that have shaped our perspectives. For me, it’s Multimodal Large Language Models (MLLMs).

2023 was a game-changer for AI, no thanks to ChatGPT. We saw a surge in large language models (LLMs) and generative AI, which made everything from chatting with bots to getting content way faster and better.

I won’t lie — I wasn’t very fond of the AI hype. Seeing everyone generate low-quality stock images for their posts and slides and being wowed by trivial advancements was honestly quite frustrating.

While the generative AI hype still prevails, I do have to admit it is maturing. Slowly.

These advancements and the growing consumer adoption of AI technology have paved the way for what we’re seeing in 2024: the emergence of multimodal AI models (MLLMs).

‘Multimodality’ is a somewhat new term for an old concept, i.e. the way humans have always learned about things. People have always gathered information through various senses like sight, sound, and touch. Then, our brains merge these different types of ‘data’ to create our understanding of reality.

So basically, multimodal language models are advanced AI systems that can process and understand multiple types of data, like text, images, audio, and video, all at once. I know, shocking, right?

Ironically, many people have interacted with aspects of multimodal AI without even realising it.

I didn’t even realise I was building my startup, LFG, around this technology and its concept. Learning about multimodal AI has completely shifted my views on its implications and potential.

The rise of visual commerce in retail

This is an emerging trend that has stood out for me this year

So far, the exciting trend I’ve seen in 2024 is the rise of visual commerce, especially in the fashion and beauty sector. Multimodal AI is making waves here by enabling consumers to use natural language, images, and videos to search and buy, transforming how we shop for clothes, accessories, and beauty products.

Also Read: From mining engineer to travel tech visionary: Darryl Han transforms trip discovery

In the US, startups focusing on multimodal search have received significant funding and support, like Daydream (US$50M seed funding) and Lumona (YCW24), underscoring the growing importance of this technology.

With ViSenze (a Singapore tech company at the forefront of multi-search), for example, you can upload a photo of a dress you love (even from a social media post), and their AI-powered search will find similar styles available for purchase. This makes shopping more engaging and personalised, and it’s clear that visual content is becoming a major player in retail decisions.

The shift towards personalised travel experiences

Insights gained about the direction of the travel industry

While this technology is being experimented with and refined in the fashion and beauty sectors, I believe its potential impact on the travel industry is even more profound. Travel encompasses a whole range of services and experiences, from flights and hotels to tours and local attractions. You can already see how big this sector is on its own.

There is a shift in consumer mindsets that personalisation is no longer optional — it’s essential. Multimodal AI can simplify and personalise these offerings by analysing a combination of text, images, and videos, making it easier for travellers to discover exactly what they’re looking for and enhancing their overall experience.

For instance, a traveller searching for “hidden, speakeasy late bars in Kuala Lumpur” can benefit from an AI that not only processes the textual description but also analyses images and videos to find the perfect match. This leads to more precise and personalised recommendations, enhancing user satisfaction.

Also Read: Into the metaverse: How to extract real business value from the hype?

Some further implications of multimodal AI for travel that I’m eager to build and see developed include the following:

• Dynamic pricing: Adjusting prices and offers in real-time based on market trends and user behaviour, maximising revenue and satisfaction.
• Streamlined bookings: Understanding natural language queries and providing instant booking assistance and results, improving user experience.
• Smart assistants: Offering real-time support with voice commands, travel document and location analysis, and instant translations, making travel easier and more enjoyable.

Embracing multimodal AI for future growth

Lesson learned that will shape my approach for the rest of 2024

As multimodal AI continues to advance, it will undoubtedly shape the future of any form of commerce, driving growth and enhancing the overall travel and shopping experience for consumers worldwide.

For travel startups like ours, we’ll definitely be exploring and leveraging these multimodal applications to redefine how travellers search, discover and experience the world — bringing a more intuitive and enjoyable journey before the trip begins.

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Seedefy, a Singapore-registered company, began with a proposition that resonated with the region’s startup ecosystem. It positioned itself as a bridge between early-stage founders and capital, promising structure, access, and a clearer path through the fragmented world of seed funding. In a market where many first-time founders struggle to navigate investors, accelerators, and legal complexity, the narrative proved compelling.

For a period, that positioning gained traction. Seedefy attracted attention, partnerships, and a growing community of founders who viewed it as an entry point into the funding landscape. Its pitch aligned with Southeast Asia’s appetite for platforms that simplify complexity and lower barriers to entry. Momentum followed, as is often the case in early-stage ventures.

That trajectory later shifted, and the shift was comparatively rapid.

When momentum masks fragility

As with many young startups, Seedefy’s perceived growth appeared to advance more quickly than its underlying foundations. Expansion of scope progressed faster than the development of internal controls. Governance arrangements, incentive structures, and execution capacity became increasingly difficult for external stakeholders to evaluate, even as visibility increased.

This sequence is familiar. Early traction tends to foster confidence, which in turn reduces scrutiny. Investors and partners extrapolate short-term signals into assumptions of durability. In Seedefy’s case, the narrative retained credibility even as signs of structural strain became more apparent.

When doubts began to surface more broadly, they translated into erosion rather than prolonged decline. Confidence weakened. Relationships became strained. The business model showed limited resilience under closer examination. What followed was not a dramatic collapse, but a relatively swift loss of relevance. The platform receded from the centre of the ecosystem it had sought to organise.

Also Read: Why due diligence matters, especially when investing in early-stage startups

Due diligence rarely fails loudly

Seedefy’s trajectory appears less rooted in overt misconduct than in layered assumptions. Investors anticipated governance would mature over time. Founders expected scale to address early gaps. Partners inferred alignment where documentation, incentives, and regulatory positioning were not always clearly articulated.

One element proved particularly consequential. Certain aspects of the model operated close to regulated financial and crypto-adjacent activities, in a context where licensing requirements and regulatory boundaries were not always clearly delineated. While this point rarely dominated discussion, it increased exposure once scrutiny intensified.

Due diligence often falters in this understated manner. The issue is not missing documentation, but deferred questions. Who ultimately holds decision-making authority? How conflicts are resolved. How regulatory considerations are managed as models evolve. How downside scenarios are addressed in practice.

In early-stage investing, such questions are frequently postponed. Speed, access, and fear of missing out tend to prevail. The cost typically emerges later.

The Founder remains the central risk

What Seedefy illustrates is a reality many investors acknowledge privately but underweight in practice. The founder remains the most influential variable in any early-stage company.

Markets evolve. Products pivot. Strategies adjust. Behavioural patterns, decision-making styles, and approaches to accountability tend to display greater continuity.

Effective due diligence, therefore, extends beyond pitch decks and data rooms. It involves examining a founder’s operating history with care, including discussions with former colleagues, employees, contractors, and prior investors. The objective is not to seek consensus, but to identify consistency in how pressure was handled, conflicts addressed, and responsibility assumed when outcomes disappointed.

Such conversations rarely yield definitive judgments. They do, however, add depth. They transform narrative into context. For investors, that additional dimension is often decisive.

Integrity, responsibility, and how founders exit matters

The contrast becomes clearer when viewed alongside how other founders have handled comparable outcomes. In recent years, a number of early-stage founders have chosen a more explicit approach when ventures failed to meet expectations. They communicated directly with investors and partners, acknowledged misjudgements, and remained accessible after operations ceased.

A recent example shared publicly by a founder illustrates this approach clearly, documenting the decision to wind down a startup with transparency, personal accountability, and continued engagement with stakeholders even after commercial prospects had ended.

Also Read: ‘Due diligence is like dating before the long-term marriage’: Accion Venture Lab’s Paolo Limcaoco

In some cases, founders publish post-mortems. In others, they remain reachable long after operations have stopped. These actions rarely alter financial outcomes, but they materially shape trust. Investors are left informed rather than uncertain. Employees and partners receive closure rather than silence. The failure of the venture does not extend into a failure of responsibility.

This distinction matters. In early-stage companies, failure itself is rarely disqualifying. How founders behave once momentum breaks often proves more revealing than how they behave during periods of growth. Transparency under pressure, willingness to engage when prospects dim, and respect for stakeholder relationships tend to persist into future ventures.

From a due diligence perspective, this underscores the value of examining not only how founders build, but how they unwind. Past shutdowns, difficult chapters, and public accountability often provide more signal than success stories alone.

The asymmetry of startup risk

Early-stage investing remains defined by asymmetry. Upside attracts attention. Downside determines outcomes.

Seedefy demonstrates how non-financial risks can shape results. Governance risk. Execution risk. Regulatory exposure. Alignment risk. These factors resist simple modelling, yet they frequently influence survival.

For angel investors and early backers, narrative proximity can feel reassuring. Distance and scepticism tend to offer greater protection.

A broader warning for the ecosystem

Southeast Asia’s startup ecosystem continues to mature. Capital is more accessible. Structures appear more sophisticated. The fundamentals of risk remain unchanged.

Platforms such as Seedefy emerge because they address genuine pain points. Their failure does not negate those problems. It reinforces the importance of discipline on the investment side of the table.

Notably, Seedefy did not conclude with a clearly communicated endpoint. Activity diminished. Public communication subsided. The company appeared to wind down without a formal announcement or resolution. For investors, that absence of closure carried its own implications.

Due diligence is not an administrative exercise. It sits at the core of early-stage investing. When treated as secondary, outcomes tend to converge toward disappointment.

What investors should take away

The rise and fall of Seedefy offers a restrained but instructive reminder. Early momentum does not ensure durability. Visibility does not guarantee governance. Access does not confer protection.

Investors who remain active over time tend to develop a preference for structure, context, and downside analysis. They ask fewer aspirational questions and more operational ones. In early-stage investing, those questions often separate informed risk from avoidable loss.

The cost of overlooking them is rarely immediate. When it materialises, it is usually conclusive.

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Artificial intelligence (AI) is no longer a futuristic concept reserved for tech giants and multinationals. For small and medium-sized enterprises (SMEs), particularly in Southeast Asia (SEA), AI is increasingly becoming a practical tool that offers immediate business value.

William Smith, Head of Mid-Market Sales, Asia at Zoom, offers an insider’s perspective on how AI is already transforming the region’s SMEs and how these companies can strategically leverage it to accelerate growth while controlling costs.

According to Smith, most SMEs he engages with share two core business priorities: driving explosive growth and managing tight budgets. “Resources are finite, whether that is manpower or capital … SMEs are looking to AI as a force multiplier that enables them to scale rapidly without proportionally increasing headcount.”

AI allows SMEs to reallocate human resources to more strategic, value-added tasks while automating time-consuming operational work. This dual benefit is crucial for businesses trying to penetrate new markets or expand beyond their home countries.

“Every country in SEA has its own culture and language, making regional expansion particularly complex,” Smith notes. “AI can help bridge these gaps by simplifying cross-border communication and improving operational efficiency.”

Also Read: AI, seed-strapping, and the new playbook: Why customers are the best VCs

Barriers to AI adoption: Misconceptions and trust issues

Despite AI’s immense potential, many SMEs hesitate to adopt it fully. Smith identifies three main concerns: perceived cost, data reliability, and workforce integration.

Firstly, many SMEs believe AI solutions are prohibitively expensive. Smith argues this is often a misconception driven by legacy pricing models. “Some companies charge US$20 to US$30 per AI licence, which adds up quickly for SMEs. In contrast, platforms like Zoom bundle AI features into existing subscriptions at no extra cost, making AI accessible even for smaller businesses.”

Secondly, trust in AI-generated data remains a significant hurdle. SMEs worry whether the outputs are reliable and actionable. “It’s not just about security, but about the trustworthiness of the information they receive,” says Smith.

He emphasises the importance of SMEs selecting AI platforms that adopt robust data models, such as Zoom’s federated approach, which aggregates information from multiple verified sources to ensure accuracy.

Finally, the complexity of integration often intimidates SMEs. Many believe that adopting AI requires extensive feasibility studies, proof-of-concept trials, and technical expertise that they may not possess. Smith counters that AI adoption doesn’t have to be complex if businesses choose platforms that seamlessly integrate AI into their existing workflows.

“We weave AI directly into our platform, allowing users to see immediate ROI without complicated implementation.”

Smith also shares several compelling examples of how SMEs harness AI to achieve tangible business outcomes.

Also Read: Your AI product may fall, and how you can save it

One such company is an education provider in Hong Kong. Before adopting AI, they employed five staff members solely to attend virtual classes and take notes manually. “With Zoom’s AI Companion generating automatic meeting summaries in over 30 languages, those employees are now focused on curriculum development and student engagement, significantly increasing their productivity and business impact,” Smith recounts.

Another case involves Nexusguard, a Singapore-based company with a global footprint. Facing challenges around multilingual communication across various regions, Nexusguard leveraged Zoom’s translated captions to enable smooth internal and external communications without the need for costly language specialists.

“They saved on staffing costs while enhancing their ability to expand into new markets quickly and professionally,” Smith says.

A strategic framework for AI adoption

For SMEs contemplating their AI journey, Smith recommends a focused, outcome-driven approach. “Start by identifying your business goals and then work backwards to pinpoint gaps AI can help address,” he advises.

Internally, SMEs should examine whether their employees spend excessive time on low-value tasks that AI can automate. Externally, they should evaluate whether customer experience and service levels could be improved with AI-powered tools such as chatbots or virtual agents.

“AI should not be seen as a one-size-fits-all solution,” Smith stresses. “It’s about identifying specific use cases where AI can relieve pressure on limited resources while enhancing customer satisfaction.”

While the benefits are clear, Smith cautions SMEs against rushing into AI adoption without a clear purpose. “AI means different things to different industries. A retail business facing customer service bottlenecks will require different AI solutions than a manufacturing firm focused on supply chain optimisation.”

Also Read: SEA mobile gaming surges: 1.93B installs and growing global influence

SMEs should take time to understand what AI means within the context of their own operations. “Do your due diligence,” Smith advises. “Make sure any solution you adopt aligns with your industry needs, excites your employees, and delivers measurable returns.”

Ultimately, Smith believes that AI’s true role is to enhance human potential rather than replace it. “The human remains at the centre of everything … AI should empower SMEs by freeing up their people to focus on strategic initiatives that drive sustainable growth.”

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Lyra Reyes also contributed to this article.

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At this year’s Asia Tech x Singapore, the region’s premier flagship technology event, a standout moment came from a fringe speaking panel titled, “Unlocking Southeast Asia’s AI Potential: From Multilingual Models to Market Impact”. As I listened to Temus’ AI Leader, Matt Johnson and the other panellists, three personal reflections took shape, each pointing to what it will take for ASEAN to secure its place in the global AI value chain.

ASEAN needs an interoperable AI production base

Today, all ten ASEAN states have published national AI strategies. Singapore’s NAIS 2.0 pushes practical, sector-level AI. Malaysia’s MADANI AI Framework emphasises ethical and inclusive development. Indonesia’s BRAIN project links AI to economic modernisation.

But taken together, these efforts reveal a structural problem: Fragmentation. As of 2025, ASEAN lacks aligned data protection regimes, shared compute infrastructure, or interoperable standards for AI. These gaps limit scalability and regional trust.

Against this backdrop, Europe’s aspiration for a Silicon Schengen offers a useful analogy. It envisions seamless cross-border collaboration in the semiconductor industry, mirroring how the Schengen Area enables the free movement of people. While there are no formal members, the idea draws from real initiatives like the Silicon Europe Alliance — a robust network of 12 leading technology clusters across the continent.

These clusters, focused on microelectronics, photonics, and software, bring together over 2,500 companies and research institutions—from nimble SMEs to global giants. Key hubs include Silicon Saxony in Germany, Minalogic in France, and DSP Valley in Belgium, each playing a vital role in Europe’s drive for a more integrated semiconductor ecosystem.

Southeast Asia, with its rich diversity and geopolitical complexity, doesn’t need to replicate Europe’s model, but the spirit behind Silicon Schengen is worth emulating. If you’ll allow me a little creative license, imagine an ASEAN AI Archipelago: a connected chain of digital ecosystems stretching from Jakarta to Hanoi, Singapore to Manila, giving member states the chance to shape a model uniquely their own. One not defined by uniformity, but grounded in interoperability, inclusivity, and regional relevance.

The recently-concluded inaugural ASEAN-GCC-China Summit underscored the centrality of regional cooperation and ASEAN’s growing potential as a production hub, linking Gulf capital with China’s technological strengths. It signalled the beginning of a new trilateral effort to build more resilient and diversified supply chains. Amid a fracturing global trade system, could the idea of an ASEAN AI Archipelago serve as a foundational layer in a new architecture of trade, innovation, and production for regional blocs?

Localisation must be a design principle

Before ASEAN can build a unified, AI-enabled production base, it must first secure two essentials: local relevance and workforce readiness. AI in this region doesn’t thrive on scale or technical sophistication alone—it succeeds when it is trusted, usable, and adapted to the rhythms of local businesses and communities. 

This is what makes SEA-LION, Southeast Asia’s first multilingual large language model, so important. Trained on Bahasa Indonesia, Vietnamese, Thai, Tagalog, and English, it was built not only to understand language, but to operate efficiently on local infrastructure, even for smaller businesses. That matters deeply in ASEAN, where SMEs make up 97 per cent of all enterprises and employ over 85 per cent of the region’s workforce. If AI cannot work for SMEs, it cannot work for ASEAN.

Also Read: US tariffs on semiconductors and autos put Malaysia’s trade at risk

The stakes are rising with the emergence of Agentic AI, systems capable of acting autonomously, not just making predictions. These tools promise to transform how work gets done, but their success is far from guaranteed. In Southeast Asia, 75 per cent of failed AI deployments can be traced to poor cultural and operational alignment.

And while 85 per cent of businesses in the region now recognise that localisation, beyond language, into workflows and business norms, is essential, adoption remains uneven. Without addressing these gaps, AI will remain concentrated in large enterprises, bypassing the SME backbone that powers the regional economy. 

Singapore offers a preview of what’s possible when policy, partnership, and capability-building align. In 2023, thousands of local SMEs adopted AI tools, and the national target is to raise this number to 15,000 by 2026. Over 8,000 mid-career professionals have already completed structured AI training through public programs.

At Temus, we’ve contributed to this effort through a strategic partnership with AI Singapore, working directly with local organisations to prepare datasets, implement practical AI tools, and sustain them in real-world environments. Our collaborative programs, including AI for Leaders and the AI Apprenticeship Programme, are designed not just to educate, but to embed lasting capability. By 2025, over 30 AI apprentices had been hosted at Temus, gaining firsthand experience while helping organisations build capacity from within.

AI adoption requires a resilient semiconductor backbone

None of this is possible without chips. ASEAN Semiconductor Market is estimated to reach SG$67 billion by 2032. Singapore contributes 11 per cent of global semiconductor assembly, testing, and packaging (ATP) capacity, while Malaysia adds another 7 per cent. Vietnam and the Philippines are rapidly scaling their capabilities, bolstered by foreign investment and industrial park expansion.

But the real promise of an ASEAN AI Archipelago lies in integration. 

The Johor–Singapore Special Economic Zone (SEZ) exemplifies this complementarity in action. Singapore leads with strengths in IC design, R&D, and advanced manufacturing. Johor, directly across the Causeway, offers scalable land, cost-competitive labour, and logistics infrastructure. Just north of the SEZ, Penang, home to more than 350 electronics firms, including Intel and AMD, adds vital capacity in wafer fabrication, assembly, and back-end testing. 

These nodes are tied together not just by geography, but by policy coherence. Mutual recognition of professional qualifications, streamlined customs procedures, and flexible labour mobility enable cross-border teams and supply chains to function with agility. 

Also Read: Building smart: A tech founder’s guide to the semiconductor supply chain revolution

In a world of rising geopolitical tension and tightening export regimes, ASEAN’s integrated production corridors offer something rare: Optionality. For global chip firms like Nvidia, that could be existential.

Nvidia’s greatest vulnerability is not technological, it’s geopolitical. Its AI chips depend on a complex global supply chain: design and IP from the US; wafers from Taiwan and Japan; EUV lithography from ASML in the Netherlands; fabrication at TSMC; packaging and testing in South Korea, Malaysia, and Vietnam.

Finally, Southeast Asia’s logistics hubs move these high-value chips into data centres and markets around the world. As export controls tighten and chokepoints multiply, Nvidia, and others, must redesign for anti-fragility. That means investing across ASEAN for strategic depth. Partnerships in Singapore, Malaysia, Vietnam, and Thailand offer distributed resilience.

At the same time, encouraging ecosystem partners like TSMC and Samsung to expand their advanced packaging operations across Southeast Asia, investing in local talent and collectively, establishing a semiconductor backbone, or a corridor, if you like. 

Insights from Echelon Singapore 2025

Whether through an ASEAN AI Archipelago or another model of regional integration, Southeast Asia’s role in the global AI value chain must evolve, from serving as a low-cost transit point to becoming a critical node for innovation, production, and resilience.

These themes took centre stage at Echelon Singapore 2025, where I had the privilege of moderating a panel titled “Building in the Semiconductor Age: What Tech Founders Need to Know About Supply Chains, Partnerships, and Strategic Positioning”.

Joined by Teong Wei Tan (Infineon), Chan Yip Pang (Vertex Ventures), and Jinsong Xu (Innowave Tech), we explored how founders across Southeast Asia can ground their innovation efforts in strong semiconductor foundations, strategic partnerships, and a skilled regional workforce.

The discussion reinforced one key takeaway: ASEAN’s future in AI will be defined not just by what we build, but by how we build together.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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In an era where artificial intelligence, automation, and advanced analytics are rapidly transforming the world of work, the conversation in Southeast Asia must shift from “what technology can do” to “how humans can use technology to solve real problems.”

The rise of AI has created both excitement and anxiety. But at its core, AI is a tool—an incredibly powerful one—but still a tool. It is not a new master to bow to.

Our region must not fall into the trap of glorifying technology for its own sake. Instead, we must focus on cultivating human skills that harness and direct technology meaningfully, especially in ways that serve the real challenges of our communities, businesses, and governments.

Across Southeast Asia, countries are investing in digital transformation, smart cities, fintech, and e-government platforms. Yet, many organisations are still hiring for technical know-how without emphasising critical thinking, creativity, empathy, collaboration, and ethical judgment.

These are the very human skills that cannot be easily replicated by machines—and they are essential for ensuring technology serves society, not the other way around.

AI without purpose creates friction

AI is often seen as the latest shiny object. But without a clear use case, it becomes a solution looking for a problem. For example, many Southeast Asian SMEs adopt AI chatbots, only to frustrate customers with rigid, robotic interactions. Why? Because they focus on the technology, not the user experience.

Contrast this with a successful example from Indonesia, where AI-powered mobile apps are helping rural farmers forecast crop yields and access micro-loans. The difference lies in the application: tech that solves a real-world problem, guided by human insight.

To truly leverage AI and other emerging technologies, we need to train our workforce differently. The traditional education system in much of Southeast Asia emphasises rote learning and technical proficiency. While these are important, they must be complemented with project-based learning, interdisciplinary problem-solving, and industry immersion.

Programs that connect students with real business challenges—such as digital marketing for SMEs, or logistics optimisation for rural supply chains—help young people see tech as a means, not an end.

Also Read: The resume is dead: Why 80 per cent of companies fail to hire based on real skills

Singapore, for instance, is beginning to model this shift. Initiatives like SkillsFuture and AI Singapore promote continuous learning and applied AI research that involves industry partnerships. But there is still a long way to go in ensuring these skills reach beyond the tech elite. In Malaysia and the Philippines, where talent is abundant but access to high-quality training is uneven, public-private partnerships can help democratise AI literacy while reinforcing problem-solving skills as the core of any tech deployment.

AI needs human direction

Human-centric hiring means looking beyond the resume. Southeast Asian employers must begin to value traits like adaptability, curiosity, empathy, and storytelling—especially when paired with basic tech fluency.

A developer who can explain the societal impact of their algorithm is more valuable than one who can only write clean code. A healthcare worker who uses digital tools to track patient outcomes while listening compassionately can bridge the human-tech divide in meaningful ways.

So, should we fear AI? Not if we remember that it works best when directed by people who understand the problem, care about the outcome, and ask the right questions. AI can scale our ideas, but it cannot generate purpose. It can detect patterns, but not set values. It can optimise, but not empathise.

The real promise of technology

In conclusion, Southeast Asia’s future does not depend on how many coders we produce, but how many problem-solvers we empower.

Let us shift our focus from hiring for tech, to hiring for human potential in a tech-heavy world. Let us build a workforce that sees AI not as an authority, but as a collaborator in crafting better services, stronger economies, and more inclusive societies.

That is the real promise of technology—and the leadership challenge of our time.

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There has been a growing debate about putting AI data centres into space.

AI needs enormous amounts of power. Space has constant sunlight for solar power. And if launch costs keep falling, maybe it will finally make sense to move the data centres into orbit.

Until recently, this could be dismissed as science fiction. Today, it deserves to be taken seriously — but only if we follow the economics all the way through.

When you do, the answer turns out to be very clear, and very different to what is being discussed.

Why is this question being asked now

This conversation is not driven by AI. It is driven by launch costs.

For most of the space age, lifting large amounts of mass into orbit was prohibitively expensive. That constraint has changed dramatically in the last decade.

Launch costs to low Earth orbit have followed a steep and dramatic decline: from more than US$40,000/kg historically to US$2500–3000/kg today and targeting US$100–300/kg in SpaceX’s new Starship

To stay conservative, this analysis assumes US$150/kg to LEO — not a promise, but no longer a fantasy.

That single shift turns space from an exotic environment into something closer to infrastructure.

The data centre energy stack

To ground this discussion in reality, consider a facility like xAI’s Colossus, operating at roughly 300 MW of continuous power.

The current “best possible” energy stack is a mix of onsite gas turbines, grid connections, a small amount of solar and a few batteries for smoothing

Some of that power is delivered via the grid, some via on-site generation. For a true cost comparison, we can treat the energy stack as if it were fully dedicated to the site.

The cost of building the energy stack is around US$550–1050M

Plus annual maintenance and fuel costs of US$100–180M a year

Gas is not a backup in this model. It is structural.

Also Read: Breaking into the data centre sector: Beyond technical expertise

Why look to space at all?

Because AI needs power at scale, and it needs it to be stable, and we need a route there that doesn’t depend on extracting and burning ever more fossil fuels.

Solar is an obvious solution; however, on Earth, even excellent solar installations deliver only 25–30 per cent of their theoretical output over a year. Solar in orbit benefits from constant sunlight 40 per cent stronger than on the surface of the earth and is effectively firm by default. There is no night, no weather, and no seasonal variation. Once built, it can be 100per cent solar without fuel or large storage.

That single difference is what makes space interesting.

What does a 300 MW space-based solar energy stack weigh?

The cost to get a solar plant in space is the cost per kg we discussed before times the number of kgs it weighs. Modern space-solar designs use ultralight photovoltaic membranes rather than glass-and-steel terrestrial panels. With no wind or gravity, structures can be far lighter.

Consensus estimates a conservative near-term figure of 0.8 kW per kilogram of photovoltaic material is plausible.

At that density, 300 MW requires ~375 tons of panels.

Even in space, you still need structural support, wiring, power electronics, and control systems. These add mass, though far less than on Earth.

Using optimistic but defensible assumptions, non-panel components add roughly one to two times the panel mass.

That puts the total mass required to generate 300 MW in orbit at approximately 750–1,100 tonnes.

At US$150/kg to LEO, and another 15–20 per cent to raise to GEO, it is expensive, but single-time — and crucially, it buys something Earth-based solar cannot: firm power without fuel.

These figures reflect a post-industrialised SBSP cost regime; today, a kind of 300 MW GEO system would cost over a billion dollars, but with repeat builds and learning-curve effects, these ranges are plausibly achievable within ~10–25 years.

Annual operating costs are minimal:

Annual cost of operations and maintenance: US$3–6M / year

No fuel. No price volatility.

Also Read: The AI age is changing the data centre industry – Here’s how Singapore can pivot

What about the data centre?

At this point, now that we know that moving the energy stack to space is feasible, we can look at moving the data centre itself.

This is when the numbers break.

A data centre is not just chips. It also comprises power electronics, cooling systems, structural containment, cabling, and radiation shielding. Even reducing the weight of the structure for zero gravity, we’re looking at a 300 MW AI data centre of 13,000–15,000 tons.

Plugging in our conservative near-term launch costs of ~US$150/kg to LEO, that implies:

• US$1.95–2.25 billion to launch the data centre, before orbital transfer.

And a second factor has to be added: unlike the solar infrastructure, this cost is not one-time.

Chips are replaced every three to five years. That means most of the compute mass would need to be relaunched on that cadence.

No plausible launch-cost trajectory fixes this asymmetry.

That is why putting compute in orbit fails economically — even in a world where space-based energy begins to make sense.

The pivot the numbers force

Once it becomes clear that data centres are too heavy and too short-lived to move economically, the problem reframes itself.

The thing that should move to space is the energy stack.

The thing that should stay on Earth is the computer.

Beaming power is real technology: The basic architecture is straightforward: collect sunlight in space, convert it to microwaves, beam it at low intensity, below that of radar, to a large “rectenna” on Earth, which is a simple large mesh and convert it back to electricity. No exotic physics or speculative materials are required; power beaming has been demonstrated terrestrially and at a small scale in space.

End-to-end efficiency is not 100 per cent. A reasonable near-term assumption is that only about two-thirds of generated power reaches the data centre, which means the orbital solar array must be oversized by roughly 1.5×.

For a 300 MW continuous data-centre load on Earth, that implies ~450 MW of space solar generation, plus transmission hardware.

Also Read: Is Southeast Asia’s data centre boom headed for a PR crisis?

Adding transmission capabilities and increasing the capacity to 450MW changes our costs: (again, after price optimisation, not first of a kind today, which would be two to three times the cost for a prototype)

The logical conclusion

Although the upfront cost of space-based solar is higher, the difference in annual fuel and operating cost is large enough to repay that in a handful of years.

And with no future fuel cost risk.

Why isn’t everyone doing this? Because launch costs only crossed the threshold very recently. Even a 2024 NASA study still assumed Falcon 9–era economics.

And politically, it’s easier to talk about “AI in space” than “power beamed from orbit.”

But narratives follow incentives.

As launch becomes cheap and power demand explodes, the industry will pivot.

Not to data centres in space.

But to something far more powerful: Data centres on Earth, powered by cheap, stable, solar energy from space.

That’s the real solution hiding in plain sight.

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In late November 2025, Soulpower Acquisition Corporation and SWB LLC, the company behind SOUL WORLD BANK, announced a merger agreement that would bring the business public on the NYSE at an estimated $8.1 billion valuation, backed by $5 billion in committed funding, pending shareholder and regulatory approval.  

Soul World Bank was formed in response to a financial system that remains fragmented, border-dependent, and difficult to access for many individuals and businesses operating across markets. The company intends to offer a range of international financial services, built around newer technologies such as artificial intelligence, stablecoins, and tokenization. To make this happen, the company lists technology partners that are designed to make financial services more accessible and efficient for everyday users, rather than only large institutions.

Through this collaboration — alongside partners such as Animoca Brands — NewCampus brings its community and systems building experience into finance, extending the same mission into a larger and more complex arena.

Justin Lafazan, CEO of Soulpower and founder and managing member of SWB, underscored the importance of that partnership, saying, “We are all in on Will Fan and the NewCampus team and could not imagine building SOUL WORLD BANK without them. Our partnership with NewCampus means we are starting with a proven team and in-place network across Southeast Asia.”

NewCampus, an innovation firm based in Singapore, focuses on community building and operational infrastructure for emerging institutions. Its involvement in Soul World Bank reflects a broader recognition that technology alone is no longer sufficient to build complex financial systems. This article examines how ecosystem-building principles developed in one sector can translate effectively across others.

A decade of building communities, now building for finance

NewCampus’ core mission focused on rethinking how leaders in Asia learn, grow, and work together. NewCampus has built over a decade of experience as one of the top “challenger universities in Asia” –  an alternative model to traditional universities that focuses less on formal degrees and more on practical learning, community, and real-world outcomes. Working with over 500 companies worldwide and reaching more than 130,000 learners, NewCampus continues their mission that real progress begins when people are seen fully, not just as account holders, but as workers, parents, builders, and dreamers. 

That experience is now being applied in a new context in an unusual pairing between education and banking. NewCampus has repeatedly built institution-like systems from scratch where participation, trust, and empathy support leadership pipelines and more efficient operational structures, including its partnership with Open Campus to invest in more than 140 edtech companies, impacting over 20 million learners globally.

Their multi-country, stakeholder approach has built a systems-first legacy that applies to old industries that need disruption. While core expertise lies in learning infrastructure and leadership development, NewCampus also boasts an impressive track record building ecosystems that scale.

Soul World Bank is built on the premise that the next chapter of global banking must be built with the communities it serves. These same capabilities – building engaged communities, creating operational infrastructure, and enabling peer networks – are exactly what new financial institutions need.

As CEO Will Fan has shared publicly, “The past decade, I’ve been building a challenger university to reimagine how leaders in Asia learn, grow, and build. Today, I’m bringing those lessons into a new arena: launching a challenger world bank.” He is excited about joining forces with the Lafazan Brothers and Animoca Brands to help build SOUL WORLD BANK calling it the “same mission, bigger playground (and) reshaping access to opportunity.”

More is expected to come as NewCampus helps shape this next chapter of global finance, specifically, one rooted in technology, humanity, and the courage to reimagine what’s possible through new economy banking for frontier markets.

Also read: Bring your most authentic self to the table whether at home or work: Will Fan of NewCampus

An $8.1 billion bet on new economy banking

An $8.1 billion valuation is notable for an Asia-linked institution listing in the United States. Historically, most companies from the region debut on the New York Stock Exchange at significantly smaller sizes, often well below the $1–3 billion range (compared to Singapore Carro’s predicted $3bn valuation last August). In this context, the scale of Soul World Bank’s proposed listing stands out, particularly as it enters the public markets with substantial capital commitments already in place.

The transaction follows a SPAC merger designed to form a new economy financial services group with a global footprint. Based on publicly disclosed information, the venture combines an $8.1 billion valuation with a $5 billion committed equity facility, an uncommon pairing at the point of listing. The structure also includes a partnership with Animoca Brands to support stablecoin development, as well as plans to acquire a British Virgin Islands banking license, subject to regulatory approval. Its portfolio spans a range of real-world assets, including land and mineral resources, and is built around an AI-native banking model with stablecoin denomination.

Within this structure, NewCampus is engaged as an independent contractor providing what it describes as operational infrastructure. Rather than functioning as a traditional vendor, NewCampus is positioned as a longer-term partner, contributing to how the organization is set up to operate as it develops. The collaboration reflects a broader approach to building new financial institutions—one that pairs capital and technology with operational systems designed to support scale from the outset.

Beyond vendor relationships: The ecosystem builder approach

Most financial institutions operate through a network of vendors such as technology providers, and service firms, with each being responsible for a defined function. In that model, tools are delivered, frameworks are recommended, and implementation is often left to the organization itself. 

NewCampus’ role differs in scope to traditional models. Rather than delivering short term advisory and execution, NewCampus partners with organizations at the operational level, focusing on how people, systems, and processes are designed to function as institutions scale.

One area of focus is community infrastructure. For financial institutions—particularly challenger banks serving underserved or cross-border markets—growth depends on trust and sustained participation, not just customer acquisition. NewCampus designs systems that support long-term engagement, peer networks, and leadership pathways, 

For newer or challenger banks, trust and participation cannot be assumed. It needs to be deliberately designed. NewCampus brings experience in building community infrastructure that encourages long-term engagement, supports peer networks, and moves relationships beyond purely transactional interactions. This approach treats users as participants in a broader ecosystem—an important distinction for financial institutions operating across markets where trust, inclusion, and sustained engagement are critical.

A second focus is operational systems for innovation. As institutions combine traditional financial services with newer technologies such as blockchain and tokenization, NewCampus helps design internal processes that allow legacy systems and new infrastructure to coexist. The emphasis is on enabling teams to move quickly while remaining within regulatory constraints.

With experience operating across Asia and multiple regulatory environments, the company brings a cross-border perspective to expansion, shaped by building distributed communities and organizations. The underlying insight is straightforward: the skills required to build and scale a learning ecosystem—coordination, trust and adaptability—translate directly to building a financial ecosystem.

Also read: Southeast Asia’s marketing renaissance: How up-and-coming marketers are leading the charge

Building for what comes next: what this partnership signals for fintech and enterprise partners

The NewCampus–Soul World Bank collaboration reflects a broader convergence between sectors that were once treated as separate. 

For fintech partners, it points to collaboration models that extend beyond pure SaaS offerings, where technology is paired with partners focused on operations, governance, and community design. 

For enterprise solution providers, it highlights an opportunity to move upstream—from selling tools to helping shape how institutions are structured and operated. Soul World Bank’s partnership with Chainstarters, an AI and real-world asset firm based in Connecticut, reinforces this trend toward complex ventures being built through multiple specialized partners working in concert.

Looking ahead, the Soul World Bank transaction is expected to close in the first quarter of 2026, with all milestones subject to regulatory approval. Beyond the timeline, the partnership raises a larger question about the types of partners new economy institutions will require as they take shape.

NewCampus’ positioning reflects a track record of building operational infrastructure for complex ventures, first in education and now applied to finance, offering a collaboration model that may become increasingly relevant at the intersection of finance, technology, and community. Overall, NewCampus is expected to navigate cross border complexity with the same defensible formula: community creates the system that scales. 

The transferable skill: Building ecosystems that last

The same skills that enabled NewCampus to build a challenger university are now being applied in a new context: the formation of a challenger bank. At its core, ecosystem-building is a transferable competency—one rooted in designing systems where participation is intentional, trust can form, and operations are able to scale without breaking.

As finance continues to evolve, the partnerships that shape the next generation of institutions may be defined less by technology alone and more by the ability to build durable communities and operational infrastructure. In that sense, the NewCampus–Soul World Bank collaboration offers a glimpse into how complex, multi-stakeholder ventures may increasingly be built—through collaboration, specialization, and shared institutional design.

Technology partners interested in exploring operational infrastructure collaborations for new economy ventures can reach out to NewCampus to discuss potential partnerships.

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