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Singapore’s mass affluent and high-net-worth investors are using artificial intelligence (AI) for finance and investment at a higher rate than their global peers, but most still want a human adviser involved before they act.

A new HSBC study conducted by Ipsos found that 76 per cent of Singapore investors surveyed use AI for finance and investment, compared with a global average of 72 per cent. The findings are based on responses from 609 Singapore investors collected in January and February 2026, as part of a broader survey of 9,993 mass affluent and high-net-worth individuals across ten markets.

Also Read: The AI stack trap: Why more AI tools aren’t translating into more growth

The headline number is less interesting than the behaviour behind it. Singapore investors are not simply outsourcing decisions to chatbots or portfolio tools. They are using AI to research markets, compare ideas and stress-test assumptions, then taking those conclusions to advisers for validation.

Only 8 per cent of Singapore respondents said AI was the single most influential source in their most recent major investment decision, below the global figure of 12 per cent. That suggests a market that has adopted AI quickly but remains cautious about treating it as a final authority.

A hybrid model takes shape

HSBC’s research points to a hybrid advisory model becoming more entrenched in Singapore’s wealth market. Some 69 per cent of Singapore respondents use AI to research and analyse investments, 44 per cent use it for strategy support, and 34 per cent use it to test their own ideas.

Yet 79 per cent still look to professional advisers for reassurance, while 71 per cent value advisers for strategic expertise. More than half of Singapore respondents, or 57 per cent, said they preferred AI and advisers working together, ahead of the global average of 50 per cent.

This is notable because Singapore is one of Asia’s most mature wealth management centres. The city-state had SGD5.4 trillion in assets under management in 2023, approximately US$4 trillion, according to the Monetary Authority of Singapore. A large share of that money is managed on behalf of regional and international clients, making Singapore a test bed for how private banks, wealth platforms and relationship managers adapt to AI-assisted investing.

The generational spread is also significant. AI use in finance among Singapore’s Gen X investors stood at 72 per cent, compared with 65 per cent globally. Among Baby Boomers, the gap was wider: 72 per cent in Singapore versus 59 per cent globally.

That challenges the assumption that AI-led wealth tools are mainly a younger investor phenomenon. In Singapore, older and wealthier clients appear comfortable using AI as part of the discovery process, provided the final judgement remains anchored in professional advice.

Banks are arming advisers, not replacing them

The survey lands as HSBC Singapore accelerates its own adviser-facing AI rollout. The bank launched Wealth Intelligence in Singapore and Hong Kong in September 2025. The platform gives relationship managers access to insights and research drawn from more than 10,000 sources, including HSBC Chief Investment Office material and external data.

In May 2026, HSBC introduced AI Prepare, a tool designed to generate client engagement packs by pulling together a client’s financial overview, investment insights and tailored talking points before meetings. The bank says the aim is to reduce manual preparation time for relationship managers and allow them to focus more on advice.

Also Read: A step-by-step framework to build your AI adoption roadmap for B2C service businesses

HSBC has also widened its AI ambitions through a multi-year partnership with Google Cloud announced on 17 June 2026. Hyper-personalised wealth management support is one of the first three focus areas. The bank expects the partnership to support more than 200 AI use cases across its global operations within two years.

Ashmita Acharya, Head of International Wealth and Premier Banking at HSBC Singapore, framed the shift as a change in expectations rather than a threat to advisers.

“Singapore’s investors are using AI in their financial decision-making with discipline. They are doing more of their own analysis, arriving at conversations better prepared, and expecting more of the professional advisers who help them as a result,” she said.

That is the central tension for banks. AI makes clients more informed, but it also raises the bar for relationship managers. Generic market commentary and templated portfolio reviews become harder to defend when clients can generate their own summaries and comparisons in minutes.

High-net-worth clients are moving faster

Among Singapore high-net-worth investors, defined by HSBC as those with at least US$2 million in investable assets, AI adoption rises to 90 per cent. That compares with 82 per cent globally.

This group also appears more willing to quantify AI’s role in investment outcomes. Singapore’s high-net-worth respondents attributed an average of 40 per cent of their investment returns over the past 12 months to AI influence, above the 31 per cent average across all Singapore respondents. Two-thirds, or 65 per cent, said AI made them feel more in control.

Banks will treat that as both opportunity and warning. Wealthy clients are not waiting for financial institutions to introduce them to AI. Many are already using external tools, research platforms and model-driven analysis. The bank’s challenge is to make its advisory relationship relevant in a world where clients can arrive with their own data-backed conclusions.

Competitive pressure in Southeast Asia

HSBC is not alone. Singapore’s large domestic banks, including DBS, OCBC and UOB, have been investing heavily in data analytics, personalisation and AI-enabled wealth tools. Global private banks such as UBS, Citi, Standard Chartered and Julius Baer are also trying to make relationship managers more productive through AI-assisted research, client segmentation and portfolio monitoring.

At the same time, digital wealth platforms such as Endowus, Syfe and StashAway have normalised lower-cost, technology-led investing for affluent and mass affluent clients in Singapore and parts of Southeast Asia. While these platforms do not compete directly with private banks across all client segments, they have changed expectations around transparency, access and digital experience.

Also Read: The next phase of business: We are moving to AI crews

For Southeast Asia, the implications extend beyond Singapore. The region has a growing affluent class, but wealth advisory remains uneven across markets. Singapore and Hong Kong dominate private banking, while countries such as Indonesia, Thailand, Malaysia and Vietnam continue to deepen their wealth ecosystems. AI could help advisers serve more clients more efficiently, but it also raises regulatory and suitability questions, particularly around explainability, bias and accountability.

Singapore’s regulatory environment gives it an advantage here. MAS has spent years pushing financial institutions to adopt responsible AI practices, including fairness, ethics, accountability and transparency principles. That matters in wealth management, where unsuitable recommendations can carry significant financial consequences.

The HSBC study ultimately shows that AI adoption does not automatically mean adviser displacement. In Singapore, the wealthiest clients are embracing AI, but not surrendering judgement to it. They want faster research, sharper conversations and more personalised advice.

For banks, that means the real competition is not simply between humans and machines. It is between advisers who can use AI to improve the quality of advice, and those who cannot.

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RedDoorz’z planned 2027 SGX mainboard listing marks a notable milestone for Southeast Asia’s budget hospitality sector, and its strategy deserves attention for its unconventional approach: using IPO proceeds primarily to fund acquisitions rather than organic growth.

This M&A-first playbook, targeting profitable but tech-lagging hospitality businesses across Australia, India, and Southeast Asia, reflects a pragmatic bet that buying market position is less risky than building it from scratch, especially in fragmented, capital-intensive hospitality markets.

The timing also signals confidence in Singapore’s equity market revival, as SGX has struggled with thin listing pipelines in recent years. RedDoorz choosing SGX as its primary venue, despite generating most revenue outside Singapore, reinforces the exchange’s appeal to tech-enabled firms headquartered there, even as Saberwal keeps a Nasdaq dual-listing option open for deeper tech-investor liquidity.

Equally significant is RedDoorz’s profitability turnaround in 2024, driven by AI-enabled automation across software development and operations, a template increasingly common among tech firms seeking to scale without proportional headcount growth.

With Indonesia’s 24% growth anchoring performance amid regional softness, RedDoorz’s resilience stems from its focus on value-conscious domestic travelers, a segment less exposed to geopolitical shocks than international tourism. Still, execution risk around integrating diverse acquisitions across multiple markets and regulatory regimes remains the key variable determining whether this ambitious roll-up strategy delivers sustainable returns for public investors.

REGIONAL

RedDoorz eyes 2027 IPO in Singapore: Budget hospitality platform RedDoorz is targeting a Singapore IPO in 2027, as the company looks to capitalise on a recovery in regional travel and position itself for public markets after years of restructuring.

Nium acquires Cypher as fiat and stablecoin payments converge: Singapore-based Nium has acquired Cypher, a stablecoin infrastructure firm, to build a unified payment rail spanning fiat and stablecoin transactions, a signal that cross-border payment firms in SEA are repositioning ahead of the stablecoin regulatory wave.

Atome’s US$88M AUB facility tests Philippine BNPL’s next phase: Atome has secured an US$88M credit facility from Asia United Bank, its largest debt raise in the Philippines, to scale buy-now-pay-later lending as the market matures beyond early adopters into mainstream credit access.

Shopee expands fast grocery delivery across Indonesia: Shopee is scaling its rapid grocery delivery service across more Indonesian cities, intensifying competition with Grab and GoTo in the country’s quick-commerce segment, which remains one of the region’s most contested battlegrounds.

B Capital closes oversubscribed Ascent Fund III at US$500M: B Capital, co-founded by Facebook’s Eduardo Saverin, has closed its Southeast Asia-focused Ascent Fund III at its US$500M hard cap, oversubscribed, signalling sustained LP appetite for SEA venture despite a cautious global funding environment.

Temasek targets 10-15% AI allocation in portfolio by 2031: Singapore’s Temasek is planning to raise its AI-related investments to 10–15% of its total portfolio within five years, one of the most concrete AI allocation targets announced by a major sovereign investor in the region.

QAI Ventures backs four startups in Singapore quantum accelerator: QAI Ventures has selected four startups for Singapore’s first quantum-focused accelerator cohort, backing early-stage companies at the intersection of quantum computing and AI as the city-state moves to anchor the sector.

Choco Up moves deeper into supply chain finance for SMEs: Hong Kong-based Choco Up is expanding into supply chain finance to help SEA SMEs manage delayed payments, a structural pain point that has grown more acute as global trade uncertainty squeezes working capital cycles.

LINE MAN Ride targets 3,000 EV drivers in Thailand: LINE MAN Wongnai’s ride-hailing arm is recruiting 3,000 EV drivers in Thailand, a move that tests whether EV unit economics can make ride-hailing margins viable as fuel costs continue to pressure the sector.

Thailand to invest US$1.99B in AI, clean energy, and aviation: The Thai government has announced a US$1.99B investment plan spanning AI, electronics, aviation, and clean energy as it tries to attract supply chain investment shifting out of China.

Stanford-born Spark enters SEA via health innovation hub: Spark, a health innovation programme with Stanford roots, has entered Southeast Asia through a health innovation hub partnership, adding institutional weight to the region’s health-tech and medical innovation pipeline.

Sprouts AI raises US$9M for enterprise sales agents: Singapore-based Sprouts AI has raised US$9M to develop AI revenue agents that automate enterprise sales workflows, targeting B2B companies across Southeast Asia and beyond.

TurtleTree raises new capital to scale lactoferrin output: Singapore precision fermentation startup TurtleTree has raised a new funding round to scale production of lactoferrin, a high-value milk protein, as it moves from lab to commercial scale amid growing demand from infant nutrition markets.

Maybank: SEA e-commerce growth stays strong; ride-hailing under pressure: A Maybank research note finds SEA e-commerce continuing to grow robustly while ride-hailing platforms face mounting fuel-cost pressure, with Singapore operators particularly exposed.

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INTERVIEWS & FEATURES

Carousell’s recommerce pivot: the quiet death of classifieds: As recommerce hits 50% of Carousell’s revenue mix, this deep-dive examines how the platform has gradually deprioritised its classifieds roots in favour of a commerce model with stronger monetisation potential.

MiracleFeet: a US$500 fix closing Asia’s clubfoot gap: MiracleFeet is using a low-cost brace to treat clubfoot across Asia, a condition that can be fully corrected for under US$500 but remains largely untreated due to access and awareness gaps.

Food delivery’s consolidation model is cracking in East Asia: A structural analysis of how East Asia’s food delivery market is fracturing as super-app economics weaken, regulatory pressure grows, and niche players challenge dominant platforms on unit economics.

AI is reshaping Singapore wealth management, not replacing advisers: Wealth managers in Singapore are deploying AI for data analysis and client profiling, but human advisers remain centralto relationship management, a nuanced finding that complicates both the AI-doom and AI-hype narratives.

Singapore’s Gen Z handles money differently; here’s what it means: Gen Z in Singapore are saving earlier, investing via apps, and avoiding debt more than prior generations, with significant implications for fintech product design and financial services marketing strategies.

Corporate travel in SEA was never built and that’s the opportunity: A feature arguing that Southeast Asia’s corporate travel infrastructure was never properly developed, making the sector ripe for tech-native solutions rather than fixes to legacy systems.

Fundamentum launches US$231M Fund III for Indian startups: Indian VC firm Fundamentum has launched its third fund at US$231M, targeting growth-stage Indian startups, a signal that India’s VC market is staging a strong recovery from the 2023-24 funding winter.

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INTERNATIONAL

Tencent in talks to become Manus AI’s largest shareholder: Tencent is in advanced talks to take a major stake in Manus, the viral autonomous AI agent startup, in a deal that would mark one of China’s most significant AI investments and has direct implications for how Chinese AI platforms compete globally.

OpenAI launches GPT-5.6 in new model family: OpenAI has released GPT-5.6 as part of a new model family, continuing its rapid release cadence. The launch raises the competitive bar for AI developers across SEA building on foundation model APIs.

Fidji Simo steps down from OpenAI’s No. 2 role: Fidji Simo has resigned as OpenAI’s CEO of Applications, the second-highest role at the company, in a leadership shake-up that signals internal restructuring as the firm navigates its commercial expansion.

NYT says OpenAI hid evidence in ChatGPT copyright trial: The New York Times has alleged in court filings that OpenAI concealed evidence during the ongoing copyright lawsuit, a development that could reshape how AI training data practices are scrutinised globally, including in SEA markets.

Nandan Nilekani exits GP role as his VC firm launches US$200M Fund III: India’s Nandan Nilekani, architect of Aadhaar, has stepped down as a general partner at Fundamentum as the firm launches its US$200M third fund, a transition worth watching given his influence over India’s digital infrastructure thinking.

US tech rebound and what it means for SEA’s AI ecosystem: An analysis of how the recovery in US tech valuations is filtering into SEA’s AI and venture landscape, with implications for fundraising sentiment, LP allocations, and founder confidence across the region.

AI boom drives Taiwan exports up 40.3% in June: Taiwan’s exports surged 40.3% year-on-year in June, driven almost entirely by AI-related semiconductor demand, a data point with direct implications for SEA’s own AI infrastructure buildout costs and supply chain dependencies.

Vivo JV marks new phase in India’s smartphone manufacturing boom: Following Apple’s shift to India, Vivo has entered a joint venture to manufacture smartphones locally, further consolidating India as the world’s next major electronics production hub and a competitive alternative to SEA manufacturing bases.

Hong Kong AI startup GIM raises US$20M Series A: GIM, a Hong Kong-based AI startup, has raised a US$20M Series A, expanding the Greater China AI funding scene at a time when regional investors are closely watching how Hong Kong positions itself as an AI hub relative to Singapore.

Truecaller clashes with India’s telecom regulator over anti-spam rules: Truecaller is in a public dispute with India’s TRAI over new anti-spam regulations that could undermine its core caller-ID product, a regulatory conflict with lessons for SEA telecom and identity-tech firms.

Malaysia PM to debut an AI double: Malaysia’s Prime Minister is set to launch an AI-generated digital double for public communications, a move that makes Malaysia one of the first governments in the region to deploy AI avatars at the head-of-state level.

Bitcoin’s move to US$63K linked to Iran tensions, not crypto fundamentals: An analysis arguing that Bitcoin’s recent price surge was driven by geopolitical risk hedging around Iran rather than on-chain or crypto-native demand signals, relevant context for SEA’s growing retail crypto investor base.

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CYBERSECURITY

Massive breach exposes millions of drivers’ licence numbers: A major data breach has leaked millions of drivers’ licence records, adding to a growing global pattern of identity-document exposures that SEA regulators and digital ID advocates are closely monitoring.

Fraud officer in Yogyakarta won’t catch the AI wave — and ASEAN banks know it: A sharp examination of how ASEAN’s financial institutions are dangerously under-prepared for AI-enabled fraud, with frontline compliance staff lacking the tools, training, and mandates to respond effectively.

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SEMICONDUCTOR

Rebellion’s IPO puts South Korea’s AI chip ambitions on trial: South Korean AI chip firm Rebellion is preparing for an IPO that will test whether Korea’s homegrown semiconductor sector can credibly challenge Nvidia with implications for how SEA governments assess domestic chip development strategies.

Apple tests CXMT chips for China-sold devices: Apple has begun testing memory chips from China’s CXMT for devices sold in the Chinese market, a significant supply chain shift that signals deepening chip bifurcation between US and China ecosystems.

Meta’s new AI chips begin production in September: Meta’s custom AI inference chips are entering mass production in September, a move that will reduce the company’s dependence on Nvidia and reshape the competitive dynamics of AI infrastructure globally.

Nvidia is a victim of the compute marketplace it created: An analysis arguing that Nvidia’s dominant position is being undermined by the very ecosystem of cloud and marketplace compute it enabled with direct read-across for how SEA’s AI infrastructure buyers evaluate GPU procurement.

Korea’s Rebellions raises stakes ahead of chip IPO: South Korea’s Rebellion chip firm is making final preparations for its stock market listing, with the IPO expected to be a bellwether for investor appetite in non-US AI semiconductor plays.

SEA’s AI buildout races toward a power wall: Southeast Asia’s rapid AI infrastructure expansion is running into hard energy capacity constraints, with power availability emerging as the binding constraint on data centre growth across the region.

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AI

Agentic AI ambitions in Singapore run into legacy systems: Singapore enterprises are struggling to deploy agentic AI at scale due to legacy system fragmentation and data quality gaps, revealing a structural readiness problem beneath the city-state’s AI ambitions.

Singapore’s AI adoption problem is weak execution, not worker resistance: A new assessment finds that Singapore’s AI rollout is stalling not because employees resist the technology but because organisations lack the implementation discipline to embed it effectively.

Why most enterprise AI in APAC is stuck in the proof-of-concept room: An industry analysis finds that the majority of APAC enterprise AI projects never advance beyond pilot stage, with procurement inertia, integration costs, and unclear ROI metrics cited as the primary blockers.

Patient intake is becoming healthcare’s most important AI use case: A detailed examination of why AI-driven patient intake systems are gaining traction in healthcare, reducing admin burden, cutting wait times, and improving data quality at the point of care.

Vision-based AI is transforming construction site safety: AI-powered vision systems are being deployed across construction sites to detect safety violations in real time, a fast-growing use case in SEA where construction remains a high-fatality industry.

AI-quantum collision creates 2026 infrastructure inflection point: An analysis of how converging AI and quantum computing developments are forcing enterprise infrastructure teams to make bet-the-firm decisions on technology architecture in 2026.

Why Asia already knows how the AI economy ends: A pointed argument that Asia’s historical experience with technology-led economic disruption gives the region a clearer-eyed view of AI’s endgame than Western commentators, who tend to oscillate between utopia and dystopia.

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THOUGHT LEADERSHIP

Why traditional hiring won’t work for APAC’s AI roles: Conventional recruitment processes are ill-suited for sourcing AI talent in APAC, where the skills are non-traditional, the candidate pool is thin, and speed-to-hire is a competitive disadvantage most companies haven’t addressed.

The AI stack trap: more tools, less growth: Enterprise teams across SEA are adding AI tools at pace but seeing diminishing returns, as tool sprawl without integration strategy creates complexity rather than productivity.

‘It works, don’t touch it’ is tech’s most dangerous sentence: A sharp argument that legacy system complacency, the instinct to leave functioning but outdated infrastructure alone, is now a critical enterprise risk in an era of fast-moving AI and security threats.

From ESG dashboards to delivery: where SEA sustainability startups should build: An essay arguing that SEA’s sustainability startups have over-invested in reporting tools and must shift to building operational infrastructure that delivers measurable environmental outcomes.

Most SEA startups sound the same, and that’s not an accident: A critique of how founder communication in Southeast Asia has converged on a set of generic narratives — mission-led, impact-driven, category-defining — that obscure differentiation and weaken investor conviction.

When startups fail, should VCs go to jail?: A provocative examination of VC accountability and legal liability when portfolio companies collapse, particularly in markets where founder-investor power dynamics are opaque and governance frameworks are weak.

Founding a company is not a career move: A candid essay challenging the notion that entrepreneurship is a rational career-optimisation decision, arguing that treating it as one is precisely why so many ventures fail to achieve the ambition they claim.

The future of marketing isn’t AI; it’s judgement: A counterintuitive argument that as AI automates execution, the scarcest marketing skill is not technical fluency but editorial judgement, knowing what to say, to whom, and why.

Singapore has the ingredients for world-class founders — just not the culture: An essay arguing that Singapore’s infrastructure, capital, and talent base are sufficient to produce globally competitive founders, but that a risk-averse, consensus-driven culture remains the decisive constraint.

Delaware C-Corp, Cayman, or Singapore Pte Ltd: a tax adviser’s view: A practical breakdown of the tax and fundraising implications of the three most common incorporation structures used by SEA startups raising venture capital, told from a tax adviser’s perspective.

We are moving to AI crews, the next phase of business: A forward-looking argument that the shift from individual AI tools to coordinated AI crews, multi-agent systems working in concert, represents the next step-change in how businesses will operate.

Every job in your GBS needs an upgrade, so does every person in it: An essay on why Global Business Services functions in APAC must reskill their entire workforce for an AI-augmented operating model, not just retrain isolated teams.

How creativity, commerce, and AI collide in mid-2026’s marketing mix: A mid-year assessment of how AI is reshaping the marketing stack — compressing creative cycles, enabling hyper-personalisation, and forcing brands to rethink the role of human creativity in campaigns.

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Startup ecosystems teach founders what progress looks like. The question is whether they teach founders to become better at navigating the ecosystem, or better at building companies that can survive outside it.

Few countries illustrate this more clearly than South Korea.

Over the past two decades, Korea has built an extensive, publicly backed startup support system. For 2026, the Ministry of SMEs and Startups received approval for a record 16.5 trillion won (US$11.3 billion) budget, spanning startup support, SME development, venture investment, R&D, and programs for small businesses.

Also Read: South Korea’s top 30 VC deals of 2024: A year of shifts and surprises

The country also runs initiatives such as TIPS and K-Startup Grand Challenge, which show two sides of Korea’s startup strategy: supporting domestic founders through public-private acceleration and inviting global startups to use Korea as a platform for growth. TIPS uses selected private accelerators to identify and support promising startups, while K-Startup Grand Challenge is designed to help international startups enter, establish, and scale in Korea.

I have watched that development closely since first arriving in Korea in 2006. During that time, I have seen the ecosystem from several positions: as a founder of a Korean startup, as an executive at a Korean unicorn, and through running accelerator and innovation programs for Korean founders. That perspective has made me appreciate how much Korea has done right. It has also made one point increasingly clear: startup ecosystems do not only support founders. They train them.

Support systems create incentives

This is not an argument against government support. Korea’s investment has lowered the barriers to entrepreneurship, expanded access to resources, and made startups a more visible and credible career path. Many founders have benefited from programs, grants, mentors, networks, and overseas opportunities that would have been much harder to access two decades ago.

But every support system creates incentives.

If grant applications are repeatedly rewarded, founders learn how to write better grant applications. If pitch competitions are rewarded, founders allocate more time to presentations. If overseas participation is treated as progress, founders attend more international events. If awards and media exposure are treated as evidence of success, founders will rationally devote more attention to visibility.

Also Read: 5 Seoul startups made their Southeast Asia debut at Echelon Singapore 2026 under the SBA pavilion

None of these activities is inherently wrong. Grants can extend runway. Pitch competitions can improve communication. Awards can create credibility. Overseas exhibitions can open doors. Public-private programs can give founders access to networks, corporate partners, investors, and global markets that would otherwise be difficult to reach.

The risk begins when these activities start to substitute for company-building.

Activity is not the same as progress

A startup can look active from the outside while still being far from the market. It may have attended conferences, met investors, and won awards, while still not knowing whether enough customers have the problem it claims to solve, whether those customers will pay, or whether the team can repeatedly sell beyond its home market.

In accelerator programs, I have encountered founders who could explain their government support history, competition results, and international exhibition schedule in detail, but struggled to identify the purchasing decision-maker inside their target customer, estimate the sales cycle, or explain how a pilot would become a recurring contract. The issue was not a lack of effort. In many cases, participation milestones were more visible and measurable within the ecosystem than customer learning or commercial progress.

This is why Korea is such a useful example for other startup ecosystems. Korea has gone further than many markets in answering the first question: how do we create more startup activity? Its infrastructure, funding, and institutional support have helped make entrepreneurship more visible and accessible. The next question is more difficult: how do we make sure that all this activity produces commercially stronger companies?

That requires looking beyond easy-to-count metrics. It is natural for programs to track the number of startups supported, mentoring hours delivered, investor meetings arranged, demo days held, countries visited, or MOUs signed. These figures are useful because they measure activity. They do not necessarily measure progress.

The better questions are more demanding. Did the startup understand its customers better after the program? Did a pilot convert into a paid contract? Did an investor meeting produce serious follow-up or a sharper fundraising strategy? Did an overseas visit produce qualified leads, local partners, regulatory insight, or a clear decision not to enter that market? Did the founder become better at selling, hiring, adapting, and making hard decisions?

The next stage of ecosystem development

This distinction matters because government-backed ecosystems influence founder habits at scale. When public money funds startup support, it is not only buying workshops, mentoring sessions, booths, or demo days. It is shaping what thousands of founders believe progress should look like.

For Korea, this should be seen as an opportunity. The country has already built much of the infrastructure of a serious startup ecosystem. The next stage is refinement: designing programs, incentives, and evaluation metrics that push founders toward customer validation, commercial capability, and global readiness.

For other ecosystems watching Korea, the lesson is equally important. Across Southeast Asia and beyond, governments are increasingly using grants, accelerators, corporate partnerships, international missions, and startup hubs to strengthen entrepreneurship. These interventions can expand access and accelerate ecosystem development, but only if they are designed around the right outcomes. Startup support should not simply make founders better at participating in the ecosystem. It should make them better at succeeding beyond it.

Also Read: From Korea to ASEAN: 10 startups building bridges at Echelon Singapore 2026

That may be Korea’s most valuable lesson for global innovation hubs. Ecosystems should not be judged only by the activity they generate, but by the capabilities they develop in founders. Systems that primarily reward participation will tend to develop founders who become skilled at navigating programs. Systems that demand customer evidence and commercial execution are more likely to help founders build companies capable of succeeding beyond them.

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For many startups across Asia, international expansion begins with excitement. An overseas exhibition, government-backed delegation, foreign investor meeting, or promising distributor conversation can make global growth feel suddenly within reach.

These opportunities matter. They can open doors that would otherwise take years to access. They help founders meet potential partners, understand unfamiliar markets, and see how their product compares beyond their home country.

Also Read: Why operational readiness is key to successful international expansion for SMEs

But global exposure is not the same as global readiness.

A startup is not ready for a new market simply because it attended an event there, pitched to international investors, or received positive feedback from potential partners. These are useful signals, but they are only the beginning. Real market entry requires evidence that the company can repeatedly sell, deliver, support, and grow in that market.

Having worked with founders preparing to expand beyond their home markets, especially Korean startups entering overseas markets, I have found that the strongest teams do not treat international expansion as a milestone. They treat it as a new validation process.

Before committing significant resources, startups should test their readiness across five areas.

1. A clearly defined buyer

Market entry begins before the founder boards a plane. It starts with a clear understanding of who will buy the product and why.

Founders should be able to identify the actual buyer, the people who influence the decision, and the problem urgent enough for the customer to pay to solve. They should also understand how customers currently address that problem and what evidence buyers require before approving a purchase.

Many startups know the estimated size of a market but not the procurement process within their target customer organizations. They may have several partner meetings scheduled but no clear definition of the partner they need. They may describe demand broadly without having spoken to enough customers who experience the problem directly.

This does not mean the company is weak. It means the company is still learning. The risk comes when founders mistake international activity for customer evidence.

2. A viable route to market

Interest is not the same as a sales channel.

Before entering a market, startups need a credible view of how customers will discover, evaluate, purchase, and adopt the product. In some markets, direct sales may be practical. In others, a distributor, system integrator, enterprise partner, or local representative may be necessary.

A potential partner should not be judged only by enthusiasm or reputation. Founders should assess whether the partner reaches the right customers, has incentives to prioritize the product, understands the buying process, and can support the relationship after the initial introduction.

Also Read: How to get hired as an International Expansion Executive

A full meeting calendar can create the appearance of momentum. What matters is whether those meetings reveal a repeatable route to revenue.

3. A localized commercial model

Localization is often interpreted too narrowly. Translating a website, pitch deck, or product interface may be necessary, but it is not enough.

Localization can include pricing, packaging, product expectations, sales processes, regulation, payment behavior, customer support, partner incentives, and proof requirements. The core product may solve the same broad problem across markets, but customers may buy it for different reasons.

A feature valued in Korea may matter less elsewhere. A pricing model that works in Singapore may not work in Indonesia. A sales message that feels credible in one market may be unconvincing in another.

This is why founders should avoid treating Southeast Asia as a single market. A company does not enter Southeast Asia in the abstract. It enters a specific country, industry, and customer segment under specific regulatory and commercial conditions.

4. The ability to deliver and support

Winning the first customer is only one part of market entry. The company must also be able to onboard, serve, and retain that customer.

Founders should ask whether the product can be deployed locally, whether support can be provided across languages and time zones, whether regulatory requirements can be met, and whether the company can maintain service quality as demand grows.

Trust is part of this operating capability. Customers may like the product but hesitate because the company has no local references. Partners may express interest but wait to see whether the startup is committed to the market or only visiting for a program.

For an early-stage company, trust is built through fast follow-up, credible commitments, local relationships, and consistent support. A startup does not always need a full local team, but it needs access to people who understand the market from the inside.

5. Evidence of demand

Every overseas program, exhibition, delegation, or market visit should be tied to a specific learning objective.

Is the startup testing customer demand, pricing, partner quality, regulatory feasibility, sales-cycle length, or local competition? Each activity should produce evidence.

A productive market visit should lead to more than photographs, meetings, and social media posts. It should produce qualified leads, customer insights, partner assessments, agreed next steps, or a clear conclusion that the market is not currently suitable.

Sometimes the most valuable outcome is learning where not to expand.

The goal of international expansion is not to be present in as many countries as possible. It is to build repeatable traction in the right markets.

More Asian startups now have access to overseas programs, accelerators, investor networks, and cross-border partnerships. That access is valuable, but access alone does not create global companies.

Also Read: Ready for expansion? Here’s how to decide where to take your business

Global companies are built when founders convert exposure into evidence: a defined buyer, a viable route to market, a localized commercial model, the ability to deliver, and credible proof of demand.

Market entry should not be treated as a badge of progress. It should be treated as a test. The startups that understand this will be better prepared not only to enter new markets, but to remain, compete, and grow in them.

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Across Southeast Asia, healthcare systems are facing mounting pressure.

In Singapore, an ageing population is driving a sharp rise in chronic disease management and long-term care demand. In Malaysia, the ongoing migration of healthcare professionals overseas continues to strain both public and private hospitals. Across the region, clinics are handling higher patient volumes with limited manpower, while medical teams struggle with fragmented systems and increasing administrative workloads.

For years, digital transformation was seen as the answer. Hospitals invested heavily in electronic medical records, patient management systems, and workflow software. But for many frontline doctors, digitisation did not necessarily simplify their work. In many cases, it simply replaced stacks of paperwork with multiple disconnected screens and time-consuming data entry.

The issue was never just about going digital. The real challenge is whether technology can actually reduce operational friction inside clinical workflows.

That is why a growing number of healthcare providers are now shifting their focus toward a different category of AI: autonomous AI agents designed to manage operational tasks quietly in the background.

And one of the clearest opportunities may lie in a surprisingly overlooked moment: a patient’s very first visit.

The hidden bottleneck inside specialist clinics

The first consultation between a patient and a specialist is often the most important stage of the care journey. It is also one of the most operationally inefficient.

When a patient arrives at a specialist clinic for the first time, their medical history is frequently scattered across multiple systems, facilities, and formats. Lab reports may sit in separate databases. Imaging records may come from external clinics. Previous treatment histories are often incomplete or difficult to interpret quickly.

As a result, highly trained specialists end up spending a significant portion of the consultation piecing together information manually before meaningful clinical discussion can even begin.

In practice, this means doctors often spend the first ten to fifteen minutes of an appointment acting more like administrators than clinicians — searching for records, reviewing fragmented histories, summarising previous treatments, and manually preparing follow-up requests.

Also Read: Healthcare finance has a missing middle, someone has to own it

The operational impact extends beyond the consultation room. Administrative delays slow down appointment flow, increase patient waiting times, and place additional pressure on already overburdened medical staff.

For healthcare systems already struggling with workforce shortages and rising outpatient demand, these inefficiencies compound quickly.

Why AI agents are gaining traction

This is where AI agents are beginning to reshape healthcare operations.

Unlike traditional chatbots that rely heavily on prompts and manual interaction, AI agents are designed to work autonomously within workflows. Their role is not to replace doctors, but to reduce the administrative burden surrounding clinical care.

Several hospitals and healthcare providers across Asia are now experimenting with AI-powered intake workflows that automate much of the information-gathering process before consultations begin. One example comes from NeuroBrain Dynamics, whose Argon platform was introduced within specialist clinic workflows to streamline first-visit preparation processes.

Instead of relying on doctors to manually consolidate patient histories, the system automatically gathers available records, extracts relevant information from unstructured clinical data, and generates concise summaries ahead of the consultation.

The platform can also suggest follow-up investigations based on intake information and generate preparation instructions for patients before additional testing.

By the time the consultation starts, doctors are presented with a more organised overview of the patient’s history, allowing them to focus more directly on diagnosis, treatment planning, and patient interaction.

Giving time back to clinicians

The most meaningful outcome of healthcare AI may not be automation itself, but the recovery of time.

Administrative overload has become one of the largest contributors to clinician fatigue globally. According to multiple healthcare workforce studies, doctors increasingly spend large portions of their day interacting with systems rather than patients.

Reducing repetitive administrative tasks creates a ripple effect across the entire patient journey.

Also Read: The rise of AI agents in healthcare: Designing man-machine systems

When consultations move more efficiently:

• waiting times decrease,
• follow-up processes become clearer,
• operational bottlenecks are reduced,
• and clinicians can spend more attention on patient care rather than documentation.

Equally important, patients experience less confusion during what is often an already stressful process. Clearer instructions, faster coordination, and more structured communication can significantly improve the overall healthcare experience.

This is particularly relevant in Southeast Asia, where many healthcare systems are attempting to balance rising demand with limited specialist availability.

The future of AI in healthcare may look operational, not futuristic

Much of the public conversation around AI in healthcare tends to focus on futuristic possibilities such as AI diagnostics, robotic surgery, or fully automated hospitals.

But the more immediate transformation may happen in smaller, operational workflows that quietly improve efficiency behind the scenes.

Patient intake is one example. Scheduling coordination, discharge planning, clinical documentation, and administrative routing may be next.

The success of AI in healthcare will likely depend less on replacing medical expertise and more on supporting it. Hospitals do not necessarily need more dashboards, interfaces, or software layers. They need systems that reduce complexity instead of adding to it.

That is why AI agents are attracting growing attention across the healthcare industry. Their value lies not in making hospitals appear more technologically advanced, but in helping overloaded systems function more sustainably.

In the end, the biggest opportunity for AI in healthcare may not be creating smarter hospitals.

It may simply be giving doctors more time to be doctors again.

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QAI Ventures has launched the first cohort of its Singapore Quantum Accelerator, a five-month programme backed by Enterprise Singapore that aims to help quantum and advanced computing startups enter the Asia-Pacific market.

The venture firm, which focuses on quantum technologies and advanced computing, selected four startups from 63 applications across 12 countries. Each company will receive a SGD300,000 (~US$234,000) investment package along with coaching, masterclasses, workspace access in Singapore, and introductions to investors, corporate partners and public-sector stakeholders.

Also Read: The AI-quantum collision: Navigating the 2026 infrastructure inflection point

The inaugural cohort comprises Quantum Logic from the Netherlands, Qualia Therapeutics from Armenia, QPICs from the United States, and Regenesis Materials from Indonesia. The companies work across cryogenic quantum hardware, adaptive neurostimulation, photonic-chip manufacturing and sustainable advanced materials.

The accelerator will run from July to October 2026, with four in-person masterclass weeks in Singapore and weekly one-on-one coaching between sessions. It will end with an Investor Day and Demo Day for investors and ecosystem partners.

Singapore as a quantum launchpad

The launch comes as Singapore attempts to convert nearly two decades of quantum research into commercial companies. The country established the Centre for Quantum Technologies at the National University of Singapore in 2007, long before quantum computing became a mainstream venture theme.

That early investment has given Singapore scientific credibility, but commercialisation remains a harder test. Quantum startups typically face long development cycles, expensive hardware requirements, specialised talent constraints and uncertain timelines to revenue. For investors, the sector sits somewhere between deep-tech conviction and patient capital.

QAI Ventures is betting that Singapore can serve as a bridge between research, capital and Asia-Pacific customers. The firm established its Asia-Pacific headquarters in Singapore in September 2025 and is now using the accelerator to build a regional pipeline of quantum and advanced computing ventures.

“Singapore made an early and patient bet on quantum and that foundation is now translating into a commercial opportunity that is maturing,” said Alexandra Beckstein, CEO of QAI Ventures. “We bridge the lab and the market. We know the players, we understand what the industry needs and we know how to turn that into real commercial traction for our startups.”

Also Read: Quantum computing’s double-edged sword could threaten cybersecurity: Report

The claim is directionally credible, but the commercial quantum market remains early. Many quantum computing companies globally are still selling access, tools, components, software layers or research partnerships rather than at-scale production systems. For Singapore, the immediate opportunity may lie less in building a dominant quantum computer company and more in anchoring regional commercial activity around components, applications, talent and enterprise adoption.

A regional race for quantum advantage

Quantum technology has become a strategic priority across Asia Pacific. China remains the region’s largest public investor, with government quantum spending widely estimated at around US$15 billion. Japan has committed roughly US$1.4 billion to its national quantum plan through 2030, while South Korea has pledged about US$2.3 billion for quantum research and development through 2035.

India’s National Quantum Mission is backed by about US$730 million, and Australia has allocated more than US$660 million under its National Quantum Strategy. Singapore, meanwhile, has committed S$37 billion (~US$28.9 billion) under its broader Research, Innovation and Enterprise 2030 plan, within which deeptech fields such as quantum sit alongside areas including artificial intelligence, semiconductors and advanced manufacturing.

The scale of regional public funding reflects both scientific ambition and geopolitical anxiety. Quantum computing could eventually affect drug discovery, materials science, optimisation and cryptography. Quantum communications and sensing have potential defence, financial services and infrastructure applications. Governments want domestic capability before the technology becomes commercially and strategically decisive.

For startups, however, public funding alone does not create customers. The more immediate question is whether Asia Pacific can produce enough enterprise demand, specialised suppliers and patient investors to sustain quantum companies before the market matures.

McKinsey’s Quantum Technology Monitor has estimated that quantum technologies could create up to US$2 trillion in economic value by 2035, although that figure depends heavily on technical progress and adoption. In Southeast Asia, quantum demand is still nascent. Banks, telcos, logistics players and government-linked research institutions are experimenting, but adoption remains mostly exploratory.

Why the cohort matters

The mix of startups in QAI Ventures’s first Singapore cohort suggests the accelerator is not narrowly focused on universal quantum computing. Quantum Logic is working on cryogenic quantum hardware, an area tied to the infrastructure needed to run certain quantum systems. QPICs focuses on photonic-chip manufacturing, a field relevant to quantum communications, computing and broader semiconductor supply chains.

Qualia Therapeutics, which works on adaptive neurostimulation, sits closer to advanced computing and medical technology than pure quantum. Regenesis Materials, from Indonesia, brings a Southeast Asian company into the cohort through sustainable advanced materials.

That Indonesian presence is important. Singapore’s deeptech ecosystem often functions as a regional headquarters and fundraising hub, but the wider Southeast Asian startup market has historically been more associated with fintech, e-commerce, logistics and consumer platforms. Deeptech founders from Indonesia, Vietnam, Thailand, Malaysia,and the Philippines frequently face limited domestic risk capital, weaker university-to-startup pathways and fewer specialised commercial partners.

If Singapore can provide the infrastructure while neighbouring markets supply founders, use cases and industrial demand, the accelerator could become more than a relocation vehicle for foreign startups. But that will depend on whether programmes like this create durable Asia-Pacific businesses rather than short-term demo-day visibility.

Sophia Ng, Executive Director for Startup Ecosystem at Enterprise Singapore, framed the programme as part of the country’s next phase of deep-tech development.

Also Read: Quantum’s inflection point: Why the smart money is watching now

“Singapore has built a strong foundation in quantum science and deep-tech innovation. The next phase is to build globally competitive, best-in-class quantum companies,” she said. She added that the accelerator would support international quantum startups entering the region while giving local founders access to networks, capital and commercial support.

Competitive field is widening

QAI Ventures is entering a market where several global and regional players are already building around quantum commercialisation. In Singapore, companies such as Horizon Quantum Computing and SpeQtral have emerged from the country’s research base. Globally, firms including PsiQuantum, IonQ, Rigetti, Quantinuum, and Pasqal have raised significant capital to pursue different quantum hardware and software approaches.

The accelerator also offers access to quantum hardware, cloud computing resources and simulation testbeds through partnerships that include IonQ, QuEra and Fujitsu. That may help startups avoid some infrastructure bottlenecks, although access to hardware does not remove the larger challenge of proving commercial value.

QAI Ventures says its portfolio companies have collectively raised more than US$250 million in follow-on capital from investors and strategic backers including IBM, GitHub, Toshiba and the European Investment Bank. That track record will matter if the Singapore programme is to move beyond ecosystem signalling and help companies raise institutional capital.

For Southeast Asia, the accelerator’s relevance lies in whether it can connect frontier technologies with actual regional demand. Singapore has the policy support, research base and investor networks. The harder task is turning those advantages into companies that can sell into Asia-Pacific markets where quantum readiness varies widely.

The first cohort is therefore less a verdict than an experiment. It tests whether Singapore can play a serious role in the global quantum startup pipeline — not merely as a host for research, but as a market-entry base for companies trying to commercialise one of deep tech’s most difficult sectors.

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Product teams like to talk about features as if each one begins with a fresh question. A need appears, a team investigates it, a decision gets made, and a new capability is brought into the world through careful judgement.

That is the official version.

The real version is messier and more revealing. Most features are not clean inventions. They are descendants. They carry traces of the product that produced them. They inherit its assumptions, its operating model, its commercial instincts, its biases about control and flexibility, and its view of what users should have to learn in order to get value.

Every product passes something down

The phrase product DNA gets used casually in companies, usually to mean culture, taste, or a vague sense of identity. I think it is more useful than that if used properly.

A product’s DNA is the set of deep traits that quietly reproduce themselves across decisions. It is not the headline positioning. It is not the current release theme. It is the underlying logic that keeps showing up whether teams intend it or not.

Some products carry a strong bias towards user freedom. Even when they add governance, they do it reluctantly. New features in these products tend to arrive open-ended, configurable, and slightly under-opinionated. The product keeps trusting the user to assemble the experience.

Other products are born from operational control. They prefer explicit structure, clear permissions, auditable actions, and prescribed flows. New features in these products tend to mutate in that image. Even when the team believes it is building flexibility, the result still carries a visible frame. The product wants the system to stay legible to the organisation, not just useful to the individual.

Some products inherit a service instinct. They are built by organisations that learned the market through customer pain and manual intervention. Features born from these products often contain hidden accommodations. They are trying not only to solve a problem but to absorb complexity on the customer’s behalf.

Others inherit a platform instinct. They assume extensibility matters more than convenience. Their features often arrive as primitives, hooks, and frameworks rather than fully finished experiences. The product expects others to build the final meaning around what it provides.

Feature mutation is rarely random

This is why successful products spawn predictable feature mutations. They do not create anything in any shape. They create new capabilities that still obey the grammar of the parent product.

A workflow product that became successful through process discipline will usually add collaboration in a structured way. It will not suddenly become socially fluid in the way a communication tool would. A trust-based financial product will add automation carefully, because its DNA says credibility matters more than speed. A self-serve consumer product may try to add enterprise controls, but unless the product’s underlying logic changes, those controls often feel bolted on rather than native.

Also Read: The problem with ‘PM as CEO of the Product’: A myth that hurts more than helps

The most important inheritance is not visual

Teams often notice inherited traits first in the interface. Similar patterns, repeated interaction models, recognisable information structures. That is the visible layer, but it is not the most important one.

The deeper inheritance is philosophical.

Every product carries a point of view on where effort should live. Should the product do more thinking for the user, or should the user stay in control? Should default behaviour be strong, or should choice be broad? Should ambiguity be hidden, surfaced, or pushed into configuration? Should the product optimise for speed of action, safety of action, explainability, flexibility, or recoverability?

Good product strategy is partly a genetic management

Once you accept that features inherit traits, product strategy starts looking less like a pure prioritisation problem and more like genetic management.

The job is not only to decide what to build. It is to understand what your product naturally reproduces well, what it consistently distorts, and which feature ideas are likely to emerge strong or weak inside your system.

This is where mature product leaders separate themselves from feature collectors.

A weak product leader sees a successful pattern elsewhere and asks how to copy it. A stronger one asks a harder question. If we import that idea into our product, what will our product’s DNA do to it? Will it become more rigid, more configurable, more enterprise-shaped, more self-serve, more admin-heavy, more workflow-driven, or more opaque? Will it still solve the problem in a way the market values, or will it become a local mutation that satisfies internal logic while missing the original reason the feature worked elsewhere?

Parent products pass down strengths and weaknesses together

This is the part product teams often prefer not to name. A product’s greatest strengths frequently carry the seeds of its future awkwardness.

A product known for flexibility usually produces powerful features, but it can also pass down sprawl. Over time, too many descendants inherit the same tolerance for optionality, and the system becomes harder to navigate. A product known for strong structure produces coherence and trust, but its features often inherit friction.

Over time, every new capability asks the user to respect the system before the system fully earns that respect. A product known for elegant simplicity may produce beautifully restrained features, yet struggle to spawn serious administrative depth when its market matures.

Also Read: The systemic minimum effective dose: Redesigning productivity through precision

This is where many scale stage products begin to look confused

You can often spot a product at an awkward stage of growth by looking for inherited traits that no longer match the market it is trying to serve.

A product that won through ease of use starts adding enterprise controls, but they feel shallow because the system still assumes informal adoption. A product that won through operational rigour starts chasing broader adoption, but its new features still arrive with too much ceremony. A product built around expert users tries to move into mainstream teams, yet its descendants keep inheriting too much assumed knowledge.

From the outside, this looks like uneven execution. From the inside, it is often a lineage conflict.

Predictable mutations are a competitive clue

There is also a broader market implication here. Once you learn to read product DNA, you can often predict where competitors will struggle next.

If you understand what traits their product keeps passing down, you can anticipate what their adjacent moves will probably look like. You can see where their future features will likely feel natural and where they will probably become strained. That gives you a better view of competitive openings than simple feature comparison ever can.

Most companies benchmark at the surface level. They ask what another product has launched and whether they need an equivalent response. Better product strategy goes deeper. It asks what that launch reveals about the competitor’s inherited logic, and whether the same logic will help them or trap them as they move further.

This is one reason thoughtful product leaders often look more prescient than others. They are not simply reacting faster. They are reading the family tree.

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Thirteen years after launching as an upstart classifieds app, Carousell Group has finally hit positive EBITDA. FY25 revenue climbed 18 per cent to US$141 million, nearly tripling since FY21. At the same time, recommerce, the company’s resale-and-trade-in engine, grew 40 per cent to become 45 per cent of total revenue.

The group now runs 29 physical stores across Singapore, Hong Kong, Malaysia, and Indonesia, claims that AI can write a listing in three seconds, and says over 99 per cent of transactions are completed scam-free.

Also Read: Carousell acquires luxury bag reseller LuxLexicon to strengthen recommerce play

It’s an impressive scorecard on paper. But underneath the milestone lies a company quietly rewiring its entire business model — trading a capital-light classifieds engine for a capital-intensive recommerce and physical-retail bet, all while leaning on AI to fend off deep-pocketed rivals like Shopee, Lazada, and Facebook Marketplace.

So is this profitability real, or IPO-ready packaging? Is classifieds being sidelined? And can a 13-year-old marketplace still call itself a “transformation story”?

e27 put these questions directly to Carousell Group Chief Strategy Officer Shing Tai Leung.

Below is the edited version:

You’ve reached EBITDA profitability in FY25, but your press release stops short of sharing a net profit figure or your path to it. Is EBITDA profitability a genuine inflexion point, or is this milestone being used to manage investor expectations ahead of a potential IPO or fundraise?

Positive EBITDA is a meaningful milestone for us because it reflects the effectiveness of the structural transformation we’ve driven over several years. We’ve focused on growing revenue streams, expanding recommerce and improving operational efficiency and discipline, all while continuing to invest for long-term growth.

This is by no means the goal, though. We’re confident in our growth strategy and operating leverage, and we continue to make progress on our profitability journey. We have a strong balance sheet and significant opportunities ahead as recommerce continues to grow across the region.

Revenue grew 18 per cent to US$141 million, which is impressive, but recommerce now accounts for 45 per cent of total revenue. That means your original classifieds business is essentially a shrinking share of the pie. Is the classifieds model dying and are you quietly pivoting away from it?

Both businesses continue to play important roles and serve different user needs. The overall pie is growing — classifieds continues to grow, while recommerce grows at a faster pace as a second engine of growth.

Our classifieds marketplace remains a foundational, healthy, high-margin business that continues to generate meaningful engagement and revenue. At the same time, recommerce’s growth is validation that users want trusted, seamless transaction experiences, particularly in higher-value categories like luxury, mobile phones and fashion.

Also Read: Carousell acquires Ox Street to double down on its re-commerce efforts in Greater SEA

Recommerce expands the ways people can buy and sell on Carousell, letting us serve a broader range of users and use cases. This is a multi-year transformation to add a second monetisation engine on top of core classifieds and bring more resilience to the business overall.

You operate 29 physical stores across Singapore, Hong Kong, Malaysia, and Indonesia. Offline retail is capital-intensive and notoriously difficult to scale. What’s the unit economics story here? Are these stores profitable, and how many do you plan to open in FY26?

Physical stores are an augmentation of our omnichannel recommerce strategy. These stores give our users greater trust and convenience as they buy, sell and trade in items. We’ve seen that stores meaningfully improve customer experience and contribute to higher transaction volumes in categories like luxury, fashion and mobile phones.

We remain disciplined in our expansion and evaluate opportunities market by market. Rather than targeting a specific number of stores, we’re focused on expanding where customer demand and economics support sustainable long-term growth.

Recommerce grew 40 per cent YoY, but from what base? The “Sell to Carousell” model means you’re now taking on inventory risk that a pure classifieds platform never would. How are you managing that risk, and what happens if secondhand demand softens?

Recommerce today represents 45 per cent of Group revenue, reflecting sustained growth. Inventory is only one part of our broader recommerce ecosystem, which includes marketplace transactions, integrated payments, shipping and services. Where we do operate inventory-based models, we rely heavily on pricing data, demand signals and operational discipline to manage inventory efficiently. Sell to Carousell also gives a great seller experience to those who are time-starved.

More broadly, I remain confident in the long-term growth of recommerce. Consumers are increasingly looking for trusted, value-driven and sustainable ways to shop, and I believe those structural trends will continue over time.

You claim AI helps ensure over 99 per cent of transactions are completed without a scam incident, a remarkable stat. But scam complaints on platforms like Carousell remain a recurring headline in Singapore media. How do you reconcile that 99 per cent figure with persistent user trust issues on the ground?

The 99 per cent figure refers to the proportion of transactions completed scam-free. Achieving this requires a multi-layered approach that combines AI- and machine learning-based detection with human moderation, proactive sweeps and community reporting to identify and remove scams, prohibited listings and bad actors.

That said, online platform safety is an industry-wide challenge. Scam prevention is a constant race against increasingly sophisticated bad actors, which is why we continue to strengthen our technology, policies and user education. This includes tightening our defences against phishing attempts that try to move conversations off-platform, as well as preventing repeat account creation by bad actors.

Beyond proactive detection, we also make it safer for users to transact through integrated payments and shipping, alongside our certified programme for higher-value transactions that require additional trust and assurance.

Also Read: Carousell enters unicorn club after a new US$100M round led by Korea’s STIC Investments

We also work closely with law enforcement and government agencies across our markets, because tackling scams requires industry-wide collaboration. This is a long-term commitment, and we’ll continue investing in trust and safety to make Carousell one of the safest places to buy and sell secondhand.

“List with AI” generates a listing in three seconds. That’s a feature, not a moat. Lazada, Shopee, and Facebook Marketplace can replicate this quickly. Where exactly is AI creating a defensible competitive advantage for Carousell that larger, better-funded rivals cannot easily copy?

The way I see it, we start by understanding what our users want and then work backwards to find the best solution. Using AI is one of the ways we solve those problems faster and more effectively, and I see it as a foundational capability across Carousell Group. AI is an ongoing journey, and we’re continuously improving it to make it more accurate, efficient and useful for our community.

What’s unique about Carousell is that the real advantage doesn’t come from AI alone. It comes from combining AI with more than a decade of proprietary transaction data and a deep understanding of secondhand commerce. That enables us to build AI capabilities purpose-built for our classifieds marketplace and recommerce, delivering more relevant experiences for buyers and sellers.

Carousell has been around for over a decade, yet you’re still describing your business as a “transformation.” At what point does a 13-year-old company stop transforming and start defending? What does your competitive moat actually look like in 2026?

If you compare the Carousell app when we first started with what it is today, the transformation is clear. Building a platform is never a one-time effort; it’s a continuous journey of evolving alongside our users and improving the experience over time.

Over the years, we’ve transformed from primarily a listings marketplace, where buyers and sellers connected and arranged transactions on their own, into a broader recommerce ecosystem with integrated payments, logistics, authentication, physical stores and AI-powered experiences. Achieving positive EBITDA reflects the success of that multi-year transformation.

Today, our competitive advantage comes from combining a large and engaged community, trusted transaction services, omnichannel capabilities and AI that enhances both the customer experience and operational efficiency.

Like any tech company, we’ll continue to evolve and reinvent ourselves. Just as mobile transformed marketplaces a decade ago, I believe AI presents the next opportunity to reimagine what Carousell can be.

You operate across seven markets under eight different brands. That’s a complex, fragmented portfolio. Wouldn’t Carousell be a stronger, more focused business if it rationalised some of these brands rather than continuing to spread resources thin?

The nature of our region is that it’s diverse, so it’s important for us to have brands that serve different customer segments and market needs. While consumers may interact with different brands, many of the underlying technology, AI capabilities and operational infrastructure are shared across the Group. This lets us leverage common platforms and economies of scale while continuing to serve local market preferences effectively.

The recommerce and circular economy narrative is compelling for investors and the press, but Southeast Asia is still a predominantly “new goods” consumer culture. What’s your honest assessment of how long it will take for secondhand to become genuinely mainstream in markets like Indonesia or Vietnam? And what’s Carousell’s role in accelerating that shift?

I believe recommerce will continue to grow across Southeast Asia, which is precisely why we’re excited about the opportunity ahead.

We’re already seeing strong consumer adoption driven by affordability, sustainability and increasing trust in buying secondhand. While adoption will vary across markets, I believe the long-term direction is clear.

Also Read: Move over social commerce: The conversational commerce renaissance is here

Our role is to make secondhand buying and selling trusted, convenient and seamless. By investing in authentication, payments, logistics, physical touchpoints and AI, we’re helping remove the friction that’s traditionally prevented more consumers from participating in recommerce.

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The fraud officer I sat with in Yogyakarta last month had spent eleven years catching counterfeit invoices, suspicious wire transfers, and informal collusion patterns at a regional bank’s branch network. She is exceptionally good at her job. She has also spent the last three weeks trying to learn how to detect a deepfake — and she does not know whether her bank will pay for the training, whether the training even exists in her language at the level she needs, or whether the job will still be hers by the time she finishes.

That conversation, repeated across enough branches and back offices, is the part of the AI upskilling story we are not reading honestly.

We talk about AI talent and AI upskilling as if they are the same wave hitting the same shore. They are not. The corporate upskilling programmes I see across ASEAN — generative AI literacy modules, prompt engineering courses, model evaluation training — are reaching the same demographic in every market: urban, English-speaking, mid-career, mostly male, mostly already inside one of the region’s twenty largest institutions. They are not reaching the eleven-year veteran in Yogyakarta. They are not reaching the back-office compliance officer at a regional multifinance company who has just been told to monitor AI-driven credit decisions she has no training to read.

This is the second governance debt — and it is compounding alongside the first.

The shape of the divide

The actual divide cuts across at least four dimensions, and they reinforce one another.

Geographic. Most AI training in Indonesia happens in Jakarta and a handful of secondary cities. The branch officer in Surabaya might catch the wave. The same role in Manado will not.

Linguistic. The strongest AI literacy materials are still written in English, with second-best versions in Bahasa Indonesia for general audiences — and almost nothing in Bahasa at the technical depth that risk and compliance work actually requires.

Gender. The pipelines into AI roles across ASEAN financial services skew male more sharply than the underlying workforce does. The mid-career women who staff much of the back office — fraud detection, customer due diligence, claims, member services — are simultaneously the most exposed to displacement and the least likely to be inside a corporate upskilling cohort.

Role. The credit officers, branch managers, and customer service staff are being treated as if their jobs will be unchanged by AI. The credible forecast is the opposite: their jobs change first, fastest, and most. They are also the layer least visible to the head-office programmes designed to upskill people who already look like the people designing the programmes.

Also Read: The future of marketing isn’t about AI, it’s about judgment

Why traditional upskilling is missing them

The corporate AI upskilling model assumes three things that do not hold for most of the workforce.

It assumes the learner already has digital fluency at the level a generative AI tutorial requires. For a large slice of the regional financial workforce, that baseline is uneven.

It assumes the learner has discretionary time. The branch officer running a six-day workweek with overtime cannot complete a six-hour learning module — and her manager is not measured on whether she does.

It assumes the learner will apply the new skill in their current job. For the workforce most exposed to displacement, that is not the right framing. They need either a new role inside the institution or a transition plan out of it. The programme that does not address that question reads as condescension.

What is starting to work

A few quieter responses are visible if you look for them.

Peer-led learning channels. The most active AI literacy communities I see in Indonesian financial services right now are running inside WhatsApp and Telegram groups organised by mid-career practitioners themselves — sharing tutorials, screenshots, and case discussions in Bahasa, often at a pace that no formal corporate programme can match. The credential is informal, but the practical literacy is real.

Vernacular content. A small but growing number of practitioners are publishing tutorials and case discussions in Bahasa Indonesia on YouTube and TikTok, often in fifteen-minute formats that match how working professionals actually learn. The audience is large. The production cost is low.

Internal apprenticeship over external certification. The institutions making the most progress are the ones that have paired senior practitioners with frontline staff inside cross-functional projects. The certificate is a side effect of the work, not the work itself.

Also Read: When startups fail, should VCs go to jail?

What the stakeholders should be doing

Institutions should stop measuring upskilling by completion rates of vendor-delivered courses and start measuring it by retention and internal mobility of frontline staff. The metric drives the programme. Change the metric.

Regulators should require disclosure of who is being upskilled, not just how many. If a bank reports that ninety per cent of its head office has completed AI training while sixteen per cent of its branches have, that asymmetry should be visible to the supervisor.

Government and civil society should invest in vernacular AI literacy at scale, particularly for the back-office workforce that will be most affected. The cost of this investment, relative to the cost of the dislocation it would prevent, is small.

The macro stakes

In every wave of automation, the people who adapt first compound advantages, and the people who adapt last absorb the dislocation. AI will not be different. What is different about this wave is the speed and the visibility.

ASEAN has roughly thirty-six months before the second-order effects of the current upskilling pattern become irreversible — before the branch closures, the role re-bundlings, and the displacement decisions are made on the basis of who has and has not learned the new tools. We are not running out of time to teach. We are running out of time to teach the right people.

The first governance debt was about who governs the AI. The second governance debt — quieter, slower, more politically charged — is about who gets to work alongside it. The institutions that ignore the second one will pay for both.

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