If you have read enough Southeast Asian startup pitch decks, you have already read all of them. Customer-centric, tech-driven, regionally focused and purpose-led. The language is interchangeable because the thinking behind it is. Not because founders are lazy, but because they are solving for the wrong problem at the wrong time.

Differentiation in this region is not primarily a branding problem. It is a sequencing one. And the sequence most founders follow, which is build, raise, brand, is part of what creates the trap.

Branding is a multiplier, not a rescue

There is a durable idea in business strategy: that any product has layers beyond its core function — the trust it carries, the experience it delivers, the meaning it accumulates over time. These augmented layers are where lasting differentiation lives. The problem is that this idea gets applied prematurely.

Branding amplifies what already exists. Applied to genuine market fit and real operational strength, it accelerates the right things. Applied before those foundations are solid, it accelerates the wrong things faster. A lot of Southeast Asian startups are discovering this the expensive way.

The examples most commonly cited, Grab, Gojek or Carsome, are instructive but easily misread. Grab and Gojek achieved regional scale through capital deployment and network effects that most founders will never access. Carsome is the more honest model: a genuinely opaque market, a real trust problem, a communications approach built around resolving both. The differentiation was grounded in operational reality and not layered on top of it.

That distinction matters more than most pitch narratives acknowledge.

Also Read: Why money won’t save Bangladesh’s startups: The ecosystem readiness crisis

The region is not a localisation exercise

Southeast Asia’s diversity is not a localisation challenge. It is a strategic one, and most regional strategies treat it as the former.

Indonesia’s scale and price sensitivity, Malaysia’s multicultural and regulatory complexity, Vietnam’s younger and faster-moving consumer base, Thailand and the Philippines with their own cultural and platform dynamics — these are not variations on the same market. They are completely different markets that require different thinking, not the same message translated.

High mobile penetration and platform dominance across Shopee, Lazada, and TikTok mean that feature-based advantages compress quickly. What creates differentiation in this environment is not product innovation alone. It is trust, accumulated over time, through consistent and credible communication.

In WhatsApp-driven, review-heavy, socially networked markets, reputation travels faster than most founders plan for. That cuts both ways. A brand that builds credibility through earned media, founder visibility, and consistent stakeholder communication reaches conversion with less friction than one relying on performance spend alone. A brand that overpromises and underdelivers finds out what its market actually thinks before the next funding round.

The adaptation that resonates is not translated, but reconsidered.

Most positioning problems are actually timing problems

The temptation is to reach for positioning before the product has earned it. Founders feel the pressure, be it from investors, from competitors, or from the general acceleration of everything, and respond by building the brand narrative ahead of the business reality.

The result is communication that is technically correct and operationally empty. It sounds like every other startup in the deck because it is describing an aspiration rather than a reality. Audiences in this region are not sentimental about that gap because they detect it, usually through the texture of what is missing rather than through what is said.

The more credible regional brands built their communications progressively. Product and performance clarity in the early stages. Deliberate brand development once the business had something real to say. Pre-purchase trust is treated not as a marketing function but as an operational one built through consistency, earned coverage, and founder credibility rather than manufactured through spend.

That sequencing is less glamorous than a brand campaign, but it also tends to last longer.

Also Read: Why investors and customers are betting on ESG-aligned startups

What the stronger founders do differently

They resist the pressure to sound bigger than they are. In markets where purchase decisions travel through WhatsApp groups and peer networks before they reach any formal channel, the authenticity of the claim matters more than the sophistication of execution. A specific, substantiated story about why this product in this market reaches this customer more effectively than a polished regional narrative that could belong to anyone.

They build credibility specifically rather than broadly. A founder who is visibly present in a vertical, through media, through events, through a consistent and substantiated point of view, builds a different kind of authority than one running awareness campaigns. In a region where trust is relational before it is institutional, that presence compounds.

And they are honest about localisation. Not as a principle to acknowledge in a strategy deck, but as a genuine operational question: does our positioning actually make sense in this market, for this consumer, given what they already believe and what they need to be convinced of? Most regional strategies answer that question once, at the beginning, and then proceed uniformly. The ones that revisit it tend to fare better.

The underlying point

The startups that build lasting presence in Southeast Asia are not always the ones with the best product at launch. They are the ones who communicated clearly, built trust consistently, and understood their market with enough depth to remain relevant as it shifted.

The commodity trap is not a branding failure. It is what happens when founders try to skip the work that makes branding meaningful: the operational credibility, the specific positioning, the willingness to say something particular rather than something safe.

Sounding different is not the goal. Being different, and then communicating it with enough precision that the right audience recognises it — now that is the work.

—

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For the last two decades, software has been the foundation of how we build businesses.

• Need accounting? Buy accounting software.
• Need marketing? Buy a CRM.
• Need project management? Buy another tool.

Every business became a collection of software subscriptions stitched together with APIs and automation.

For years, that worked.

But I believe we’re quietly entering the next phase. The future won’t be defined by the software we buy. It will be defined by the AI organisations we build.

Software gave us tools, AI gives us teammates

The conversation around AI has largely focused on replacing individual tasks.

• Can AI write?
• Can AI code?
• Can AI design?

Those are the wrong questions.

The more interesting shift isn’t that AI can perform work. It’s that AI can now coordinate work.

We’re moving beyond single chatbots and isolated assistants into coordinated AI systems made up of specialised agents, each responsible for a different function, working together toward a shared outcome.

In other words, we’re moving from software stacks to AI crews.

My AI isn’t my assistant anymore

When I first started building Seraphina, my vision was simple. I wanted a highly personalised executive assistant who understood how I think, remembered context, and helped me make better decisions.

Over time, something unexpected happened. As my workload grew, Seraphina stopped behaving like an assistant. She became my chief of staff.

Instead of doing every task herself, she began coordinating specialised AI agents.

• A writing agent drafts content.
• A research agent gathers information.
• A design agent creates visuals.
• A development agent works with platforms like Lovable to build products.

Support, finance, sales and operations each have their own specialised workflows. Seraphina decides which agent is best suited for each task, reviews their output, sends work back for revisions when necessary, and only brings it to me once it meets the standard I’m looking for.

Also Read: Southeast Asia’s AI buildout is racing toward a power wall

That’s no longer an assistant. That’s management.

AI is beginning to mirror organisational structures

What’s fascinating is that AI systems are starting to resemble how companies have always operated. Human organisations have juniors, seniors, team leads, managers and executives. AI organisations are evolving in a surprisingly similar way.

Specialised agents perform focused work. Other agents review and audit that work. Higher-level agents coordinate multiple specialists. At the top sits an orchestrator responsible for ensuring everything aligns with the overall objective.

This isn’t very different from how modern companies function today. The difference is that these management layers are increasingly becoming digital.

The biggest shift isn’t automation, it’s delegation

One of the biggest changes in how I work is that I no longer think about which AI should complete a task. I care about the outcome.

Just as a CEO doesn’t personally assign every task to every employee, I don’t need to decide whether a research agent, a writing agent or a design agent should handle a request. My chief of staff does.

That layer of coordination is becoming increasingly autonomous. In many cases, Seraphina has the authority to make operational decisions without waiting for my approval. For higher-impact decisions, I remain in the loop.

It’s a hybrid model where AI manages execution while humans continue setting direction.

AI managing AI

This is the shift I think many people are underestimating. Today’s conversation is largely about humans using AI. Tomorrow’s conversation will be about AI managing other AI.

We’re already seeing early signs of this through agentic workflows, where one AI delegates work to specialised sub-agents before combining the results. I believe this is only the beginning.

Also Read: Delaware C Corp, Cayman exempted company or Singapore Pte Ltd: A tax advisor’s view on the fundraising vehicle

As AI systems mature, we’ll see digital organisations with increasingly sophisticated structures.

• Specialist agents.
• Senior agents.
• Quality assurance agents.
• Department-level orchestrators.

Eventually, entire AI departments will work alongside human teams.

The challenge won’t be building a single powerful AI. It will be designing how these AI systems collaborate.

Humans still own the vision

Does this mean founders become obsolete? Not at all. Today, Seraphina can prioritise my work, recommend strategies, audit outputs and even make operational decisions. But she doesn’t define the vision. I do.

That’s an important distinction. Strategy isn’t just about analysing data. It’s about understanding culture, values, long-term direction and the kind of company you want to build.

Data can tell you what’s optimal. Only humans can decide what matters.

I still believe the strongest organisations will combine AI’s consistency and speed with human judgement and intuition. Neither is enough on its own.

The companies that win won’t simply adopt AI

People often ask what businesses will look like five years from now.

I don’t think success will come from having the largest teams. Nor do I think it’ll come from using the latest AI model. The companies that win will be the ones that design the best operating systems.

Just as high-performing sports teams don’t win because they have five-star players, businesses won’t succeed simply because they have access to powerful AI.

They’ll succeed because every human, every AI, every specialised agent and every workflow operate as a cohesive system.

For the past twenty years, we’ve been building software. The next twenty years will be about building AI organisations. And I believe that’s a far more profound shift than most people realise.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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Choco Up, the Singapore- and Hong Kong-based alternative financing platform, has launched an accounts payable (AP) financing product aimed at small and medium-sized enterprises (SMEs) facing widening cash flow gaps between supplier payments and customer collections.

The product allows businesses to access up to approximately SGD2 million (~US$1.56 million) in credit for supplier payments. It sits alongside Choco Up’s accounts receivable (AR) financing product, which can advance up to 90 per cent of unpaid invoices, with funding limits of up to approximately US$3.9 million per business.

Also Read: Choco Up taps US$30M to tackle Asia’s SME funding squeeze

The company is positioning the combined offer as a supply-chain financing suite for SMEs that need to pay suppliers before they receive payment from customers. That is a familiar pressure point across Southeast Asia, where SMEs often operate with limited collateral, thin cash buffers and payment cycles that can stretch well beyond 60 days.

The cash-flow problem behind SME growth

For many SMEs, the challenge is not simply winning contracts. It is financing the execution of those contracts.

Businesses in manufacturing, logistics, marine and offshore, engineering, healthcare supplies, wholesale, B2B technology and professional services often need to buy inventory, pay subcontractors or mobilise teams before revenue is collected. Suppliers may demand payment within 30 days, while customers can take 60, 90 or even 120 days to settle invoices.

That mismatch can turn growth into a working-capital problem. A company may have signed orders and a credible revenue pipeline but still struggle to fund procurement, payroll or project delivery. Traditional bank financing does not always move quickly enough for these situations, especially for SMEs without substantial fixed assets or long credit histories.

Choco Up said delayed settlements have become more pronounced, citing slow payments rising year-on-year to 44.39 per cent in the fourth quarter of 2025. The figure underlines a broader reality: SMEs are increasingly being asked to absorb financing pressure across the supply chain.

“These businesses often have to commit significant upfront resources to procure materials, fulfil orders, or deliver projects, while receiving customer payments only months later,” said Percy Hung, CEO and founder of Choco Up. “They also frequently require access to sizeable amounts of working capital at short notice, which traditional financing channels may not always be able to provide quickly or predictably.”

Why this matters in Southeast Asia

The product launch comes as SME financing remains one of the largest unresolved gaps in the region’s financial system.

MSMEs account for about 97 per cent of enterprises in ASEAN and contribute a major share of employment across the region, according to ASEAN policy research. Yet access to credit remains uneven, particularly for smaller firms that lack collateral, audited financials or established banking relationships.

Also Read: Choco Up to invest up to US$5M in social startups developed by Dream Impact of Hong Kong

The Asian Development Bank has estimated the global trade finance gap at around US$2.5 trillion, with SMEs disproportionately affected. While that is a global figure, the implications are acute in Southeast Asia, where cross-border trade, fragmented supplier networks and extended payment terms are common features of business.

Singapore has a more developed financial infrastructure than many neighbouring markets, but SMEs still face pressure from rising costs, cautious lenders and slower customer payments. In markets such as Indonesia, Vietnam, the Philippines and Malaysia, the issue can be more severe because of fragmented credit data and less standardised invoicing practices.

This is where alternative lenders, embedded finance players and supply-chain finance platforms have tried to build a wedge. Instead of underwriting only against historical financial statements or hard collateral, they increasingly use transaction data, invoices, payment history, platform integrations and bank account flows to assess creditworthiness.

A crowded financing market

Choco Up is not entering an empty category. Across Southeast Asia, SME financing has attracted a wide range of fintech players, including Funding Societies, Validus, Capital C, Aspire and regional invoice-financing providers. Globally, supply-chain finance and receivables platforms such as C2FO, Taulia, Stenn and PrimeRevenue have built models around improving cash conversion for suppliers and buyers.

The competitive question for Choco Up is whether it can deliver speed and risk control at the same time. SME lending is attractive because the financing gap is large, but it is also difficult because default risk can rise quickly when economic conditions soften or when businesses use short-term financing to cover structural cash-flow weakness.

Choco Up said the new AP and enhanced AR financing products will use AI tools to streamline applications and underwriting. The company said its systems automate client document checks and flag potentially fraudulent submissions for human review. In theory, that should reduce manual processing time and improve credit assessment.

But AI does not remove credit risk. In SME finance, the quality of underlying data matters more than the sophistication of the model. Fraud detection, invoice verification, counterparty checks and repayment monitoring are likely to determine whether the product scales safely.

From growth capital to working capital

Choco Up has historically positioned itself around alternative financing for growth companies, offering non-dilutive capital to SMEs and digital businesses. The AP financing product shifts the emphasis more clearly towards working capital and supply-chain liquidity.

That is a pragmatic move. Equity funding has become harder to secure across Asia since the funding correction, and many SMEs do not fit venture capital’s return profile in any case. Debt and revenue-based financing providers have therefore sought to serve businesses that are growing but not necessarily venture-scale.

For SMEs, the appeal is straightforward: preserve cash, pay suppliers on time and continue fulfilling orders while waiting for customers to settle. For Choco Up, the opportunity lies in becoming part of a company’s operating finance stack rather than a one-off capital provider.

Also Read: Choco Up, Wonder Capital join forces to launch US$50M private credit funds for APAC SMEs

The next test will be execution. If Choco Up can underwrite quickly without loosening credit standards, its combined payables and receivables product could find demand among procurement-heavy SMEs in Singapore and beyond. If payment delays worsen, the market need will only grow.

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Momentum Works has released a new report on East Asia’s food delivery sector, arguing that the region is entering its biggest leadership shift in more than a decade as Delivery Hero’s acquisition-led expansion model comes under pressure from Asian operators with deeper operating playbooks.

The Singapore-headquartered venture outfit said in its “Food Delivery Platforms in East Asia 2026” report that Hong Kong, Taiwan, South Korea and Japan generated an estimated US$38.6 billion in food delivery platform gross merchandise value in 2025. South Korea accounted for US$28.3 billion, or about 73 per cent of the total. Japan, Taiwan and Hong Kong generated US$4.1 billion, US$3.6 billion and US$2.6 billion, respectively.

Also Read: How mobile marketing is powering the next phase of food delivery growth in Southeast Asia

The headline finding is not simply market size. Momentum Works argues that East Asia shows how food delivery penetration is shaped less by income, urban density or restaurant culture alone, and more by how aggressively operators build supply, manage subsidies, improve logistics density and integrate delivery into broader consumer ecosystems.

That matters for Southeast Asia because the region’s dominant delivery platforms — particularly Grab, GoTo’s Gojek, ShopeeFood and LINE MAN Wongnai — face similar questions around profitability, competitive intensity and regulatory scrutiny. Google, Temasek and Bain estimated Southeast Asia’s online transport and food segment at US$28 billion in gross merchandise value in 2023, making it one of the region’s largest internet economy verticals. But growth has increasingly shifted from land-grab spending to unit economics, cross-selling and ecosystem retention.

Delivery Hero’s Asia model hits limits

For years, Delivery Hero built one of the broadest delivery portfolios in Asia by acquiring local leaders and consolidating fragmented markets. That approach gave the German company meaningful positions in Hong Kong, Taiwan and South Korea, while it also operated in Japan before exiting the market.

Momentum Works argues that this model is now reaching an inflexion point. Foodpanda Taiwan is being sold to Grab, Baemin in South Korea is on the market, foodpanda has lost leadership in Hong Kong, and Delivery Hero has already pulled out of Japan.

The issue is not that acquisitions failed to create scale. In several markets, they did. The problem is that consolidation alone has proved insufficient against rivals that continue to invest in operational depth. These competitors are not merely buying share; they are shaping demand through pricing architecture, merchant density, rider efficiency, subscription programmes and adjacent services.

“People often assume food delivery success is determined by how developed a market is. East Asia shows that isn’t true,” said Jianggan Li, CEO of Momentum Works. “These four markets look remarkably similar on paper, yet their outcomes are completely different. Market readiness is only the precondition. But markets don’t grow by themselves; operators’ relentless push grows markets.”

Also Read: SEA’s food delivery wars heat up: Market hits US$19.3B as TikTok enters arena

That point is visible in the stark difference between Japan and South Korea. Both are wealthy, urbanised and have sophisticated foodservice sectors. Yet Momentum Works estimates food delivery penetration at around 3 per cent in Japan, compared with more than 20 per cent in South Korea.

Keeta’s Hong Kong lesson

Hong Kong offers the clearest example of how an aggressive entrant can change a market that once appeared settled.

Meituan’s Keeta entered Hong Kong in 2023 and focused on subsidised one-person meals, rapid merchant onboarding and network density. Within 29 months, according to Momentum Works, it became profitable and overtook foodpanda. The report says Keeta shifted the battleground from blanket subsidy spending to operational efficiency, a familiar pattern for Meituan, which endured years of intense competition in mainland China before expanding overseas.

The Hong Kong case is relevant to Southeast Asia because it shows that incumbent delivery positions can be vulnerable even in dense, high-income cities. Singapore, Bangkok, Jakarta and Ho Chi Minh City all have entrenched players, but the economics remain sensitive to fee structures, rider supply and restaurant participation. A well-capitalised entrant with a sharper single-market playbook can still unsettle the hierarchy.

Keeta’s expansion is also being watched because Meituan has become one of Asia’s most sophisticated local services platforms. Globally, its closest reference points are not only food delivery peers such as Uber Eats, DoorDash and Deliveroo, but also superapp ecosystems that use delivery to reinforce broader consumer frequency.

Taiwan gives Grab a test outside Southeast Asia

Taiwan may be the most important market in the report for Southeast Asian readers because of Grab’s planned acquisition of foodpanda Taiwan. Momentum Works describes Taiwan as a profitable but comfortable duopoly where food delivery penetration has been stuck around 10 per cent for years and growth slowed to 5.5 per cent as competitive pressure faded.

Taiwanese regulators blocked Delivery Hero’s earlier attempt to sell foodpanda Taiwan to Uber Eats, reflecting the antitrust concerns that now surround food delivery consolidation across Asia. Grab’s entry therefore raises a different question: whether a Southeast Asian operator can reignite growth in a mature North Asian market rather than simply inherit an existing platform.

Grab’s experience is relevant. In Southeast Asia, it has fought Gojek, ShopeeFood, Foodpanda and local challengers across markets with different labour rules, payment habits and restaurant structures. It has also pushed delivery towards profitability by bundling services with mobility, financial products, subscriptions and advertising.

Still, Taiwan will not be a simple replication of Singapore or Malaysia. Consumer expectations, merchant relationships and regulatory treatment of platform labour differ. Grab will need to prove that its regional operating muscle travels beyond its home geography.

Korea and Japan show two extremes

South Korea remains East Asia’s heavyweight. Its US$28.3 billion food delivery market was built on long-standing consumer habits rather than platform invention alone. Baemin, owned by Delivery Hero, remains the leader, but Coupang Eats has gained share by leveraging Coupang’s broader commerce, logistics and membership ecosystem.

That creates a strategic dilemma for any future owner of Baemin. The asset is large, but it competes against a company that can use grocery, e-commerce, payments and membership to subsidise frequency and deepen loyalty. The same ecosystem logic is increasingly visible in Southeast Asia, where Grab, GoTo and Sea Group all treat food delivery as part of a wider consumer stack.

Japan sits at the other end of the spectrum. Despite its density and wealth, food delivery penetration remains low. Convenience stores, affordable prepared meals and a deeply embedded solo-dining culture reduce the frictions that food delivery solves elsewhere. Coupang’s Rocket Now is testing whether affordable solo delivery can unlock demand, but Japan has repeatedly frustrated global and regional platforms.

Also Read: How mobile marketing is powering the next phase of food delivery growth in Southeast Asia

Momentum Works’s broader argument is that Asia’s next phase of food delivery competition will be led by operators shaped by difficult home markets, not by financial consolidators alone.

“Ownership changes the balance sheet. It doesn’t change the competitive dynamics,” Li said. “Whoever owns these assets will still have to compete against operators that have spent years learning how to win in highly competitive markets.”

For Southeast Asia, the message is direct. The food delivery market is no longer about who can buy the most assets or spend the most on discounts. The winners will be those that can build density, defend margins, manage regulators and turn delivery into part of a larger consumer ecosystem. East Asia is becoming the testing ground for that transition.

The post Food delivery’s old consolidation model is cracking in East Asia appeared first on e27.

There is a fond joke in Singapore’s startup circles: give us a bold idea, and we will hand it back with a business plan, a risk register, and a steering committee, all before anyone has shipped version one. It is told with affection, and like the best jokes, it carries a grain of truth. We are world-class at getting ready.

That is only half the story; the better half is what we choose to do next. Preparation is a genuine strength, not a flaw. The next leap is to pair it with the courage to begin. What our system has unfortunately not yet produced is enough world-class founders.

By every structural measure, Singapore should be minting breakout companies at speed. It ranks among the world’s top startup ecosystems. It is home to some of Asia’s finest universities. It has deep technical talent and generous public funding. The foundations are not the problem. The opportunity now is to build the culture that turns those foundations into bold, breakout companies, and that is exactly the work we have set out to do at NUS Enterprise.

The ingredients are already here

Let me be clear: Singapore is no startup backwater. It is one of the world’s wealthiest economies by GDP per capita, and Asia’s richest. It has a well-capitalised venture market with more than 500 active VC firms, one of the highest densities in Asia, alongside a fast-growing set of deep tech programmes. Our leading universities, including the National University of Singapore (NUS), Nanyang Technological University, and Singapore Management University, all run dedicated innovation and entrepreneurship platforms.

Singapore now ranks fourth globally in StartupBlink’s 2026 Global Startup Ecosystem Index, up from tenth in 2021, the fastest five-year climb of any top-ten ecosystem.

What Silicon Valley gets right

Silicon Valley has sat at the top of the global startup map for decades. Its rise was not an accident. It was built on students who think beyond the brief, a willingness to explore unproven ground, and faculty who mentor rather than merely grade.

Walk into a Stanford classroom, and you are not just absorbing theory. You are defining problems, building prototypes, defending decisions to real stakeholders, and being pushed by professors who back potential over credentials. Failure is not a red mark. It is part of the curriculum.

I experienced this firsthand. When I was first rejected from Stanford’s master’s programme, a senior professor advocated for my admission because he had seen my work and believed in me, not in what appeared on paper. That is the culture in a single decision: bet on the person, not the paperwork.

Also Read: Singapore, AI, and the rise of emotional outsourcing

In Silicon Valley, investors back founders through repeated rejection, and students ship before they feel ready, because the ecosystem rewards the attempt, not only the outcome. The results compound. Companies founded by Stanford alumni now number close to 40,000 and generate some US$2.7 trillion (SG$3.5 trillion) in annual revenue. That is not a programme. That is a culture compounding over generations.

Compare that with the reflex that still greets many unconventional ventures here: “We need to study this further.” This response delays momentum, dampens ambition, and quietly shelves the long-horizon, research-intensive ideas that tend to change the world.

The Munich model and why it matters

Silicon Valley is not the only reference point worth studying.

The Technical University of Munich, through UnternehmerTUM, has been ranked Europe’s leading startup hub by the Financial Times for three years running. Since 2002, it has supported more than 1,000 startups and currently helps spin out over 100 high-growth technology companies a year. In 2024 alone, its ventures raised more than €2 billion (SG$3 billion). Its alumni include Celonis, Germany’s first decacorn, alongside companies such as Personio, FlixMobility, and Isar Aerospace.

It built all of this not by imitating Silicon Valley, but by making entrepreneurship the third pillar of the university, alongside research and teaching: embedded in degrees, credit-bearing, and wired into a dense network of corporates, investors, and operators. Not a module, not an elective, not an optional enrichment activity.

The lesson is simple: you do not need Sand Hill Road to build great companies. You need a university that treats entrepreneurship as core to its mission and means it.

A different strategy at NUS Enterprise

This is the gap we have set out to close, and we are doing it through a deliberately different approach.

We start with immersion. The NUS Overseas Colleges programme places students inside high-growth startups around the world for up to a year. The results make the point we keep returning to: Our cohort based in Sweden has produced founders at close to Silicon Valley’s rate, clear evidence that entrepreneurial outcomes are not geography-dependent. They are culture-dependent.

We have paired that with capital built for deep tech. NUS Enterprise has launched a S$150 million Venture Capital Programme, the first of its kind by a university in Asia, alongside a co-investment framework of up to S$20 million. The partners we brought on, Granite Asia, 4BIO Capital, Playground Global, and Matter Venture Partners, were chosen for how they build and scale research-based companies, not simply for how they write cheques.

Also Read: Founders think they win on nerve. In Singapore, they win on foresight

And we have planted a flag abroad. NUS Enterprise has opened its first global outpost in Silicon Valley, at The Studio, Playground Global’s incubation facility. Our team there will connect Singapore’s innovation ecosystem to one of the world’s most demanding markets bi-directionally.

We are also moving into the frontier where deep tech now matters most. Building on a decade-long partnership with Munich, we are collaborating with TUM Venture Labs, with a focus on defence and dual-use technology. For the first time, Singapore will host the Singapore Defence Tech Hackathon, co-organised with the European Defence Tech Hub and TUM Venture Labs, extending a platform that builds defence startups in Europe to Singapore. Our reference point here is Israel: a nation of comparable size that turned deep technical talent and hard necessity into one of the world’s most productive venture engines. The lesson we take from it is not about any single sector. It is that a small country with serious talent and serious resolve can build globally significant companies, provided it backs its founders early, decisively and without flinching.

None of this sits at the edge of the system. It is a deliberate redesign of the core: embed entrepreneurship into the institution, back founders through uncertainty, and give Singapore’s best ideas a global runway from day one.

The real shift is what we measure

Singapore has a world-renowned education system, and that is precisely where the next opportunity lies. It has been optimised for certainty. Assessments reward correct answers over interesting questions. Students learn to reduce risk rather than manage it, and many enter the workforce trained to wait for complete information before they act.

Entrepreneurship sits at the other end of that spectrum. It is forged in uncertainty: talking to users before you are ready, shipping imperfect prototypes, and iterating fast rather than waiting for every answer. When institutions optimise only for certainty, they do not remove risk. They postpone the learning. And in a global race, postponed learning is the most expensive choice of all.

The evidence is clear. The Global Entrepreneurship Monitor’s 2023/24 report found that in 31 of 49 economies surveyed, experts rated entrepreneurial education at school as the weakest of 13 framework conditions. Singapore scores strongly on overall startup conditions, but the global pattern is unmistakable: the thinnest layer everywhere is experiential, mindset-building education. That is a gap we can lead in closing.

Also Read: Singapore’s AI opportunity is no longer about adoption, it’s about discipline

From capability to courage

The pattern is familiar. In its early days, Google was turned away by the major internet portals and passed over by several prominent venture investors. The search market looked crowded, the founders looked unproven, and the commercial case looked unclear. A number of those who passed later admitted they had underestimated both the technology and the team.

That story repeats across every generation of breakthrough companies. Early-stage innovation rarely fits a conventional evaluation framework. It looks uncertain because it is uncertain, and the ecosystems that back it anyway are the ones that win.

Singapore has built a well-oiled system for entrepreneurs to survive and thrive. What it needs next is the institutional courage to treat curiosity, resilience, and bold attempts as real measures of success, not footnotes to flawless execution.

The goal is not to clone Silicon Valley. It is to build a Singaporean model of entrepreneurship where discipline and daring coexist, where ideas move from classroom to market with confidence, and where we stop asking “what if it doesn’t work?” long enough to find out.

Entrepreneurial ecosystems are not built on perfect plans. They are built on imperfect experiments, repeated at speed.

Singapore has every ingredient. At NUS Enterprise, we have stopped studying the recipe and started cooking.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

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The Southeast Asia Health Innovation Hub (SEA HI Hub) has joined the SPARK GLOBAL network to launch SPARK Southeast Asia, a translational health innovation programme aimed at helping academic medical research move from laboratories into clinical and commercial use.

The announcement was made at the SEA Health Summit 2026 in Bangkok. The programme will work with researchers, clinicians, hospitals, pharmaceutical companies, investors, and government health agencies across the region.

Also Read: The most-funded healthtech startups in Southeast Asia: A decade in review

The launch gives SPARK GLOBAL its first affiliated translational health innovation programme in the region. SPARK GLOBAL grew out of the SPARK programme founded at Stanford University in 2006 by Dr Daria Mochly-Rosen, with the goal of helping academic discoveries cross the difficult gap between early research and patient-ready medical products.

SEA HI Hub, a non-profit platform, currently claims to reach more than 25 million patients through its partner network. It has set a target of reaching 100 million patients by 2028.

Filling Southeast Asia’s translation gap

The new programme is not a healthtech accelerator in the usual sense. Southeast Asia already has a long list of digital health startups tackling telemedicine, hospital software, insurance access, pharmacy delivery, and chronic disease management. Companies such as Halodoc in Indonesia, Doctor Anywhere in Singapore, Alodokter in Indonesia, and MyDoc in Singapore have focused largely on service delivery and access.

SPARK Southeast Asia is addressing a different problem: how to turn university and hospital research into drugs, diagnostics, devices, and clinical interventions that can survive regulatory, clinical, and commercial scrutiny.

That gap remains significant across the region. Southeast Asia has strong clinical demand, rising healthcare expenditure, large patient populations, and increasingly capable research institutions. But translational infrastructure remains uneven. Many academic projects fail before they reach validation, not necessarily because the science is weak, but because researchers lack access to development expertise, regulatory advice, intellectual property strategy, clinical trial design, and early commercial guidance.

“For years, a lot of promising research has stayed within academia, not because the science was not good, but because there was no clear path to turn it into solutions for patients,” said Dr Kid Parchariyanon, founder of SEA HI Hub and Co-Director of SPARK Southeast Asia. “Joining SPARK GLOBAL gives us that path.”

Under the partnership, SPARK Southeast Asia will operate under the SPARK GLOBAL framework. Researchers and clinicians will be able to access mentorship in drug development, diagnostics, and commercialisation, as well as global industry experts and volunteers connected to the SPARK network.

The programme also plans to support the region’s investigator-initiated trial community by linking clinical researchers with academic networks, regulatory guidance, and trial development support.

Why Southeast Asia matters

The timing is notable. Southeast Asia has a population of more than 680 million, with rapidly ageing societies in Thailand, Singapore, and Vietnam, and a growing burden of non-communicable diseases across the region. Diabetes, cardiovascular disease, cancer, and chronic respiratory illness are placing pressure on public health systems that were not designed for such demand.

Also Read: Profit with purpose: Bridging the digital divide in healthcare

World Bank data show that out-of-pocket healthcare spending remains high in several markets in this region, particularly in countries such as the Philippines, Cambodia, and Myanmar. Thailand, by contrast, has one of the region’s more developed universal health coverage systems, making it a logical base for a programme seeking to connect clinical demand, hospital networks, and public sector engagement.

The region also remains underrepresented in global clinical research compared with its population and disease burden. Singapore has built a stronger biomedical research base through institutions such as A*STAR, Duke-NUS Medical School, National University Health System, and SGInnovate-backed initiatives. Thailand has deep clinical capacity and strong medical tourism infrastructure. Indonesia and Vietnam offer scale but face regulatory and infrastructure constraints. Malaysia has tried to position itself as a clinical research hub through Clinical Research Malaysia.

The challenge is that these strengths are still fragmented. Unlike the US, where translational ecosystems benefit from dense clusters of universities, hospitals, venture investors, specialist lawyers, contract research organisations, and experienced biotech executives, Southeast Asia’s biomedical innovation landscape is spread across markets with different rules, reimbursement systems, languages, and institutional capacities.

That makes a regional network potentially useful, but also difficult to execute.

From mentorship to measurable outcomes

SPARK GLOBAL says its model combines education, mentorship, and financial support for selected translational research projects. Its network includes more than 40 academic institutions worldwide.

Mochly-Rosen said Southeast Asia has “strong clinical expertise, clear unmet medical needs, and a growing innovation ecosystem”, adding that the partnership fits SPARK GLOBAL’s original purpose of supporting translational scientists across borders.

The value of such a programme will depend on more than brand association with Stanford. Translational medicine is expensive, slow, and failure-prone. Drug development timelines can stretch beyond a decade. Diagnostics and medical devices may move faster, but still require clinical validation, regulatory approval, reimbursement strategy, and adoption by hospitals or physicians.

For SPARK Southeast Asia, early credibility will likely depend on the quality of projects it selects, the seniority of mentors it can attract, and whether it can help researchers make hard decisions about which ideas are commercially and clinically viable.

There is also a funding question. Southeast Asia’s venture capital market has cooled since the peak of 2021, with investors becoming more cautious about long development cycles and uncertain exit routes. Healthtech funding has continued, but much of it has gone into care delivery, insurance enablement, and enterprise health software rather than deep biotech or translational therapeutics.

That creates both a constraint and an opportunity. If SPARK Southeast Asia can de-risk academic projects before they reach investors, it may help expand the pool of investable healthcare science in the region. If it cannot connect research projects to capital, regulatory pathways, and industry partners, it risks becoming another well-intentioned platform with limited downstream impact.

A regional test case for health innovation

SEA HI Hub’s broader ambition is to build a connected health innovation ecosystem across Southeast Asia. The SPARK partnership adds a research translation layer to that agenda.

The immediate focus appears to be Thailand, where SEA HI Hub has been building its network. But the stated ambition is regional. That will require engagement beyond Bangkok, particularly with institutions in Singapore, Malaysia, Indonesia, Vietnam, and the Philippines.

Also Read: Solving multiple medtech problems with a single device powered by AI

The competitive context is also changing. Global pharmaceutical companies are looking for more diverse clinical trial populations. Regional hospitals are digitising. Governments are exploring healthcare sovereignty after the COVID-19 pandemic exposed supply chain vulnerabilities.

At the same time, AI-enabled drug discovery, decentralised trials, and precision diagnostics are creating new possibilities for countries that historically lacked large biotech clusters.

SPARK Southeast Asia sits at the intersection of these trends. Its task is practical rather than rhetorical: identify promising science, impose translational discipline, and help projects reach patients.

For Southeast Asia, that would be a meaningful shift. The region does not lack unmet medical needs or entrepreneurial energy. What it has lacked is a consistent bridge between academic discovery and clinical deployment. SPARK Southeast Asia is now attempting to build one.

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Singapore’s enterprise AI adoption is moving faster than the data infrastructure required to support it, according to a new Confluent report that points to a widening gap between experimentation and production readiness.

The company’s “2026 Data Streaming Report” found that 78 per cent of the city-state’s IT leaders say a lack of real-time data infrastructure is stalling their ability to scale AI.

Also Read: AI is eating the world and startups are riding the infrastructure wave

The finding is notable because Singapore is among Southeast Asia’s most aggressive adopters of AI policy, enterprise digitisation, and data governance frameworks. Yet the survey suggests that the next phase of AI adoption may depend less on model access or boardroom appetite, and more on whether companies can modernise their underlying data systems.

Confluent, an IBM company, surveyed 4,625 IT leaders across 14 markets, including Singapore, Indonesia, Thailand, India, Japan, Australia, the US, Canada, the UK, Germany, France, Spain, Saudi Arabia, and the UAE. Respondents worked in companies with at least 500 employees and held roles ranging from C-suite executives to senior contributors and consultants.

The report was conducted with Freeform Dynamics and Radma Research.

The survey comes as companies across Southeast Asia are moving beyond generative AI pilots into more operational use cases, including customer support automation, fraud detection, logistics optimisation, financial risk analysis, and software development. Singapore, in particular, has positioned itself as a regional AI hub through initiatives such as the National AI Strategy 2.0 and its Model AI Governance Framework. But enterprise adoption remains uneven, especially among companies operating on legacy infrastructure or fragmented data estates.

From model hype to data constraints

According to Confluent, 75 per cent of Singapore organisations are already deploying or piloting agentic AI solutions. Agentic AI refers to systems that can take actions or complete multi-step tasks with limited human intervention, rather than simply generate text or images in response to prompts.

That shift raises the stakes for data reliability. Unlike standalone chatbots, agentic systems need access to timely, accurate, and contextual business data. If the data is stale, incomplete, poorly governed, or locked in silos, the risks move beyond inaccurate answers to faulty actions.

The report found that 78 per cent of Singapore IT leaders have encountered at least three challenges when scaling AI. The most common barriers include insufficient infrastructure for real-time data processing, cited by 78 per cent of respondents; fragmented data ownership, cited by 73 per cent; and insufficient skills in managing AI, also cited by 73 per cent.

These figures broadly reflect what many technology leaders in Southeast Asia are encountering as AI pilots collide with production realities. Large banks, telcos, retailers, and logistics operators in Singapore, Indonesia, Malaysia, Thailand, and Vietnam have accumulated years of customer, transaction, and operational data. But much of it sits across separate systems, cloud environments, on-premise databases, and departmental platforms.

That makes it difficult to feed AI applications with consistent and governed data streams. It also complicates compliance in a region where data protection rules vary significantly, from Singapore’s Personal Data Protection Act to Indonesia’s Personal Data Protection Law and Thailand’s PDPA.

Greg Taylor, Senior Vice President for APAC at Confluent, said Singapore’s AI momentum needs to be matched by stronger data foundations.

Also Read: How to capture AI’s gains without wrecking your company

“Businesses across Singapore are rapidly embracing AI, strengthening the country’s position as a global leader in AI governance. But as AI systems become more embedded in business processes, trust cannot come from regulation alone, especially given the different regulatory approaches across APAC,” he said.

Agentic AI exposes legacy weaknesses

The report suggests that agentic AI is where infrastructure weaknesses become most visible. About 95 per cent of Singapore IT leaders said they experience or expect struggles with data infrastructure and quality, while the same proportion pointed to legacy system integration. Another 93 per cent cited large language model reliability as a concern.

These constraints are already affecting projects. More than 73 per cent of Singapore respondents said agentic AI initiatives had stalled, with half saying projects had been completely abandoned. Across APAC, the figures were similar: 74 per cent reported stalled projects and 53 per cent said work had been abandoned.

The findings should be read with some caution. Confluent is a data streaming company, and the report naturally frames the problem through the lens of streaming infrastructure. Still, the broader diagnosis is consistent with enterprise technology trends in the region. AI adoption is increasingly constrained by the quality, latency, and governance of the data layer.

This is also why infrastructure vendors have been repositioning around AI. Confluent competes in a market that includes open-source Apache Kafka deployments, Redpanda, StreamNative, Aiven, and cloud-native services such as Amazon Kinesis, Google Cloud Pub/Sub, and Azure Event Hubs. Broader data infrastructure players, including Databricks and Snowflake, are also pushing AI-oriented data platforms as enterprises look to unify analytics, governance, and machine learning workloads.

In Southeast Asia, the competitive context is shaped by both cloud adoption and regulatory caution. Banks and insurers in Singapore and Malaysia, for example, face stricter requirements around data lineage, explainability, and outsourcing risk. Digital banks, e-commerce platforms, and ride-hailing companies need low-latency data flows to support fraud monitoring, personalisation, and real-time pricing. These use cases make batch processing increasingly inadequate.

Governance becomes part of AI infrastructure

Confluent’s report found that 86 per cent of Singapore IT leaders rate continuous and up-to-date business visibility as a top priority. The same proportion said effective data sovereignty management is important, while 82 per cent valued data provenance and tracking capabilities. Across APAC, those figures stood at 91 per cent, 90 per cent, and 86 per cent respectively.

That emphasis reflects a shift in how enterprises think about AI governance. Earlier debates focused heavily on model behaviour, bias, and regulatory compliance. Those issues remain important, but companies are increasingly recognising that governance must start upstream, at the point where data is created, moved, transformed, and accessed.

In the report, 90 per cent of Singapore respondents said data streaming platforms can help address governance, risk, and compliance issues in agentic AI by enforcing data access and usage policies upstream. Another 91 per cent said these platforms can improve large language model (LLM) reliability by ensuring data is more complete and current, while 92 per cent said they make data more trustworthy, contextualised, and discoverable.

Shaun Clowes, Chief Product Officer at Confluent, framed the issue as a data problem rather than an AI spending problem. “Most organisations do not have an AI investment problem, they have a data problem. AI systems depend on fresh, accurate and contextual information, but too many are still being built on fragmented data, batch processes, and infrastructure that was not designed for continuous intelligence,” he said.

Investment follows the infrastructure layer

The report found that 86 per cent of Singapore leaders rank data streaming as an investment priority, close to AI and machine learning solutions at 85 per cent and data management and governance at 90 per cent.

That pattern matters because technology budgets are beginning to move from experimentation into implementation. Enterprises that spent 2023 and 2024 testing generative AI tools are now asking whether those tools can be embedded into core operations. In Singapore and the wider region, the answer will depend on whether companies can connect AI systems to live operational data without compromising security, compliance, or reliability.

Also Read: Can your AI actually read your data?

For Confluent, the commercial implication is clear: AI adoption creates demand for the infrastructure that moves and governs data in real time. For enterprises, the message is more sobering. Access to advanced models is becoming commoditised. The harder work lies in cleaning up data ownership, modernising legacy systems, and building governance into the flow of information.

Singapore may remain ahead of much of Southeast Asia in AI policy and enterprise readiness. But the report suggests that even in the region’s most mature digital economy, AI scale is now running into the same unglamorous constraint that has slowed many technology transformations before it: the plumbing.

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Qapita is an ESOP platform for startups through to listed companies, helping founders unlock the Power of Ownership for their stakeholders. Qapita’s focus aligns with a growing global trend: as startups stay private for longer, the complexity of managing cap tables, liquidity events, and investor reporting has created a surge in demand for various ESOP management tools. Powering over 2,400 clients globally, Qapita offers cap table, ESOP advisory, liquidity programmes, as well as valuation and financial reporting services tailored to meet the needs of both shareholders and employees.

Managing an ESOP within a Singapore-incorporated private company comes with a structural limitation that direct share issuance and traditional trusts don’t fully solve. Singapore limits private companies (Pte Ltd) to 50 shareholders, presenting a unique challenge for founders to manage this statutory restriction.

For a firm in Singapore, allowing employees (ex-employees and advisors) to exercise their options early may lead to additional admin, including but not limited to potentially crossing this 50-shareholder private company threshold sooner than expected. With many founders considering incorporating an entity or a holding company in Singapore, this signals a need for alternatives in share delivery solutions across the Southeast Asia region.

To address this, Qapita has recently launched ESOP SPV, a first-of-its-kind share delivery solution built specifically for Singapore-incorporated entities. This could be particularly useful for founders who have yet to set up their ESOP plan and want to incorporate an SPV from the start to ensure a clean cap table before future team expansion and fundraises.

Also Read: From perk to power: Rethinking ESOPs in the modern talent economy

A Special Purpose Vehicle (SPV) acts as an alternative share delivery method that consolidates shareholder names in a single entity. When employees exercise, they become shareholders of the SPV instead of the company directly, encouraging tangible employee ownership in a flexible yet compliant manner.

Here’s how Qapita’s ESOP SPV works

A Singapore Private Company (Pte Ltd) is set up to hold shares. Employees hold shares in the SPV proportionate to their allocation. Only the SPV appears on the cap table. Here are some of the key features of Qapita’s new and improved product:

• Clean cap table from day one: A single SPV entry is cleaner for investors than a list of employee names. Employees stay consolidated in a single SPV entity, which simplifies due diligence, cap table documentation, and future fundraising rounds.
• Flexibility on employee share exercises: Employees can exercise more regularly without the company crossing the 50-shareholder limit. This lets them act when it’s most tax-efficient — rather than waiting for a liquidity event. As employees become shareholders of the SPV instead of the company directly, encouraging share exercises can allow them to feel a sense of ownership.
• Perfect middle ground between direct share issuance and trusts: ESOP trusts require a licensed trustee, ongoing fees, and greater regulatory overhead. For a startup, an SPV delivers the same structural benefit at a fraction of the cost.

To sum up, ESOP SPVs are best suited for early-to-growth-stage Singapore-incorporated startups with up to 50 ESOP participants. As the SPV allows employees to exercise their options and participate as shareholders through a structured vehicle, this reduces administrative hassle, maintains a clean, investor-ready cap table, and results in potential tax-saving opportunities for employees.

Ultimately, the right ESOP structure depends on your goals, your team size, and how you want employees to engage with their equity. Qapita can help you figure out what works, including implementation, structural, and taxation considerations for your employees.

Discover how an ESOP SPV compounds future benefits, giving employees real ownership while keeping your cap table investor-ready, at a fraction of the cost of a trust. Whether you’re setting up a new ESOP plan or already have an existing programme, Qapita’s advisory team can help you evaluate whether an SPV is the right structure for your startup and set it up end-to-end.

Learn more here: https://www.qapita.com/sg/companies/equity-compensation-advisory/spv

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The e27 team produced this article in partnership with Qapita.

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Featured Image Credit: Qapita

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The venture capital world is awash with AI hype. Every fund claims to back the next frontier. Few can point to companies that have crossed from demo to dollars in under a year. Antler, the global early-stage VC with a growing Asia footprint, is making that claim and backing it with numbers.

Jussi Salovaara, co-founder and Managing Partner for Asia, sat down to defend the firm’s thesis on agentic AI, its “One Asia” platform spanning Korea, Japan, and Southeast Asia, and its controversial bet on China-outbound founders. He also confronts the hard questions: enterprise trust, deeptech timelines, talent wars with Samsung and Hyundai, and what happens to these startups if the AI spending bubble pops.

Also Read: Why Antler is going all-in on Japan’s earliest-stage founders

The answers are sharper and more candid than most VCs offer.

Edited excerpts:

You’re describing a shift from AI copilots to autonomous systems. But most enterprise buyers are still struggling to trust AI with basic decisions. Aren’t you getting ahead of reality?

The question assumes AI autonomy is binary. It isn’t. Think of your best manager training a new employee. With the right guidance, that employee can make basic decisions and handle well-defined responsibilities. AI is at a similar stage. Most modern models already have the logical reasoning needed for many business tasks. The real challenge is designing the right context, guardrails, and scope.

That’s exactly what we look for at Antler. We’re not backing companies claiming artificial general intelligence. We’re backing founders who identify a narrowly defined problem, codify domain expertise into AI systems, and enable reliable decisions within a carefully crafted scope.

The results speak for themselves. IndustrialMind.ai, founded by three ex-Tesla Gigafactory executives, built AI that replaces up to 80 per cent of repetitive engineering work. AppSecAI automatically writes, validates, and delivers security patches in 30 minutes at one-hundredth of the cost of manual processes. CONPA secured six-digit contracted revenue within three months of launch. These are commercial outcomes, not experiments.

What exactly counts as “meaningful commercial traction”? Is that a paying customer, a signed pilot, or something else?

Meaningful traction means contracted revenue, live ARR, or a very large qualified pipeline with documented ROI. We do not count free pilots or letters of intent.

To give specific examples: ChainShift secured six-figure contracted revenue within 10 months. i10x reached seven-digit annualised revenue in eight months. This pace is significantly faster than historical benchmarks for early-stage software, which often took 18 to 24 months to reach similar milestones.

Korea, Japan, and Southeast Asia have very different startup cultures and enterprise buyer behaviours. How does Antler actually operate as a unified “One Asia” platform in practice?

The starting points are genuinely different. Japan and Korea offer unmatched industrial depth, robotics expertise, and corporate R&D budgets. Southeast Asia offers a massive, mobile-first digital economy and an agile scale-up environment. Chinese founders bring frontier AI research talent and an execution intensity forged in the world’s most competitive technology market. These are not interchangeable, and we do not pretend they are.

What the Antler platform provides is a common outcome opportunity: building a global company. The friction appears in localisation, regulatory compliance, and enterprise sales cycles. We mitigate that with experienced, on-the-ground partners across our 27 global locations.

Global VCs like a16z, Sequoia, and Lightspeed are all doubling down on agentic AI. What does Antler genuinely offer an AI founder in Asia that they can’t get from a brand-name fund?

Several of those funds have backed companies we first invested in at inception; they operate at a different stage and serve a different need. What we bring beyond capital is a network of local partners with boots on the ground across 27 locations, embedded in the ecosystems where founders are expanding.

The most concrete expression of this is our Embark programme, a four-week immersion that bridges our strongest Asian portfolio companies into Silicon Valley, connecting them with US enterprise customers, investors, and operators. Twelve startups across Asia have gone through three Embark cohorts. Every single one has secured US traction. We build the infrastructure and systematic support to get founders to the stage where global funds are ready to write the next cheque.

In a press release, you mentioned backing “China-outbound entrepreneurship.” Given geopolitical tensions and scrutiny in Western markets, how do you assess those risks?

China has spent two decades producing some of the world’s most technically rigorous engineers and AI researchers. A growing number of those founders are choosing to build for global markets from day one. That combination of frontier technical training and genuine global ambition is rare, and it is concentrated in this cohort right now.

Also Read: Analysis: SEA’s June funding spike masks a narrow recovery in VC funding

The question we assess at the investment stage is simple: where is your customer, where is your data, and where is your team? If the answers point towards global ambition from inception, the geopolitical risk profile is fundamentally different from a company that started in China and is now trying to expand outward.

Several portfolio companies are in sectors with notoriously long commercialisation timelines. How does Antler’s inception-stage model align with deeptech?

Deeptech companies with long timelines are precisely where early conviction creates the most asymmetric returns. We help founders compress the timeline from lab to first enterprise deployment, then hand them off to the right capital partners to carry the journey forward.

At inception, we look for technical validation, strong IP protection, and the first commercial signal — a paid pilot, a joint development agreement, or a signed letter of intent. Korea’s conglomerates and Japan’s industrial corporates are among the most sophisticated early adopters of deeptech in Asia, and we work closely with those networks to connect our founders with the right enterprise partners.

What happens to these companies if the enterprise AI spending correction some analysts are warning about actually materialises?

A spending correction would actually accelerate the path for the companies we back. A correction is, by definition, a correction in spending on broad horizontal platforms, experimental tooling, and marginal productivity gains. When budgets tighten, enterprise buyers do not cut tools that are reducing their costs or generating their revenue.

Our founders create business value through genuine domain expertise, not generalist AI. Verixus Labs CEO Joel Kosmin holds an Oxford PhD in Molecular Genetics, has over a decade of research experience, and worked at AstraZeneca before building an AI-powered operating system for biomanufacturing. His platform delivers 61 per cent higher mammalian stem cell yields and 66 per cent fewer experiments compared to standard approaches. That is not a product that gets cut when AI budgets tighten. A correction would validate it.

AI talent in Asia is fiercely competed for by Samsung, Hyundai, and SoftBank-backed companies. How are early-stage founders competing for engineers without matching corporate salaries?

Early-stage founders compete on ownership, autonomy, and the chance to build category-defining technology from scratch. The best engineers are often frustrated by bureaucracy and slow deployment cycles inside large conglomerates.

Also Read: Antler invests US$5.6M across 14 AI startups with early commercial traction

The founders in our portfolio are the very talent those conglomerates want to hire. IndustrialMind.ai was founded by executives who led Tesla’s manufacturing AI transformation. Infron was founded by ex-Alibaba AI researchers who left one of the most well-resourced AI environments in the world. They didn’t leave because they couldn’t get corporate salaries. They left for equity, creative control, and the chance to define a category. That’s the story they tell every engineer they recruit, and it’s credible precisely because they made the same choice themselves.

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