Most Singapore office workers know the ritual. A message lands at 10.47pm. It is not urgent. You answer it within 90 seconds anyway, not because the work demands it, but because you want someone to notice that you answered it within 90 seconds.

It turns out that performance is playing to a near-empty theatre.

The Jobstreet by SEEK Singapore Workplace Culture Study 2026, conducted by Milieu Insight among 600 employees and jobseekers and 200 hiring decision-makers, finds that bosses are mostly not watching the clock. They say they want initiative and outcomes. Workers, meanwhile, are busy proving something else entirely.

Also Read: When global uncertainty becomes workplace anxiety

The numbers make the mismatch stark. Some 49 per cent of employers say taking initiative and delivering outcomes is what they value most; only 32 per cent of employees say that is how they demonstrate commitment. Instead, 63 per cent of employees lean on responding quickly, a behaviour just 39 per cent of employers prize. And 52 per cent of workers signal dedication through availability outside working hours, which only 18 per cent of employers actually rate.

In plain terms, Singapore’s workforce is optimising hard for a KPI its managers barely track.

The anxiety is imported

Why would rational people exhaust themselves chasing the wrong metric? The report’s answer is that workplace stress has stopped being homegrown. It is less about the difficult manager or the overloaded sprint, and more about forces arriving from outside the office: the inflation print and the AI roadmap.

Both sides name the same culprits. AI and automation are cited as drivers of workplace change by 56 per cent of employees and 53 per cent of employers. Economic uncertainty, inflation and cost of living follow at 53 per cent and 46 per cent respectively.

Where they part ways is in what that fear means. Employees’ top concern is salary growth that keeps pace with living costs (60 per cent), followed by job security (48 per cent), work-life balance (37 per cent) and rising competition for roles (33 per cent). Employers, by contrast, list the impact of AI on workflows and teams (52 per cent), managing salary expectations amid rising costs (49 per cent) and restructuring towards leaner teams (24 per cent).

Read those two lists side by side and a feedback loop appears. When leadership answers volatility by tightening budgets and accelerating automation, it feeds the job-security fears that push staff into hyper-visibility. Being permanently reachable becomes a cheap insurance policy against becoming the next line on a restructuring spreadsheet.

This is not a Singapore-only phenomenon. Across Southeast Asia, the post-funding-winter instinct to run lean, AI-first teams instead of hiring has made “do more with fewer people” the default operating mantra, while AI is already set to redesign how companies work, not merely which jobs survive.

An expensive habit with no payout

The always-on norm is now mainstream. Some 63 per cent of employees feel the need to be always-on almost every day (32 per cent) or a few times a week (31 per cent). Employers are not innocent bystanders: 85 per cent say their organisation expects staff to be available somewhat often (62 per cent) or often (23 per cent). Tellingly, 55 per cent of employees and 45 per cent of employers call it a shared responsibility.

Also Read: Burnout isn’t just personal, it’s becoming an operations problem

The cost is real. Among those under always-on pressure, 63 per cent report lower energy, 47 per cent report reduced job satisfaction and 22 per cent call the culture unsustainable because of burnout. Some 43 per cent rely on taking leave simply to cope, and 9 per cent say they lack the time or energy to recover at all. Annual leave, in other words, has become a repair shop rather than a holiday.

The return is close to nil. Some 62 per cent say always-on working has made no difference to their career progression, and 59 per cent see no difference in recognition. On work quality, 52 per cent see no change and 18 per cent report a decline; only 30 per cent report an improvement. Just 6 per cent believe being always-on is the main route to advancement.

If a fund manager pitched that risk-return profile, no LP in Singapore would sign the cheque.

The bill eventually lands on the balance sheet. Across the wider workforce, 36 per cent of employees are actively exploring new opportunities. Among those feeling always-on pressure, 10 per cent are actively looking for an exit, 11 per cent want out but feel unable to leave, and 14 per cent have disengaged to doing only what their job description requires. Founders who still treat this as a soft HR issue should look at the case for building burnout-resistant teams as an operating discipline.

Employers know. They are spending elsewhere

Here is the uncomfortable part. Some 81 per cent of employers who expect always-on behaviour acknowledge its long-term downside, chiefly burnout (66 per cent) and disengagement or turnover (15 per cent). Yet 48 per cent have no plans to introduce related support policies.

The budget tells the same story. AI and digital skills training (34 per cent), technology tools (25 per cent), flexible work arrangements (23 per cent) and upskilling (22 per cent) top the investment list. Wellbeing and mental health (14 per cent), workload management (13 per cent), manager development (12 per cent) and extra hiring to ease pressure (12 per cent) trail behind.

Even the support that exists is largely invisible. Half of employees say they receive no organisational support at all, against just 22 per cent of employers who admit to offering none. Workload conversations are offered by 40 per cent of employers but recognised by only 22 per cent of employees; boundary protections, 36 per cent versus 19 per cent; confidential feedback channels, 33 per cent versus 13 per cent. Support that lives only on the HR intranet is, functionally, not support.

The irony is hard to miss in a city that tops Southeast Asia on the Best Workplaces in Asia 2026 list. Neighbours are improvising their own coping mechanisms: in the Philippines, workers are already using AI as a burnout shield.

A caveat worth stating: this is a Singapore-only sample of 800 respondents, employer answers are self-reported, and a jobs platform has an obvious interest in workers being restless. Still, the direction of the gaps is too consistent to wave away.

What would actually change behaviour

The report proposes three shifts.

• First, move from availability to outcomes: define performance around milestones and business impact, and say explicitly that a fast reply at midnight is not a commitment metric.
• Second, move from flexibility to sustainable work design, with regular workload reviews, clear asynchronous norms and a shared definition of what is genuinely urgent.
• Third, move from support on paper to support in practice, by training line managers to hold honest conversations about workload, pay and restructuring.

Also Read: The AI layoff trap points straight at Southeast Asia

The sharpest point concerns AI. Upskilling will not calm anyone if staff suspect it is a prelude to headcount cuts. It has to be tied visibly to internal mobility and career pathways, especially as AI begins to reshape entry-level tech roles.

Singapore’s Tripartite Guidelines on Flexible Work Arrangement Requests, in force since December 2024, cover how staff ask for flexibility, not when they may switch off. Until employers draw that line themselves, the 10.47pm reply will keep doing what the data says it does: costing workers dearly and earning them almost nothing.

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We may be using AI to save hours of work, only to fill those hours with more work.

This is something I have been thinking about as organisations accelerate their investment in AI.

The productivity opportunity is undeniable. Generative AI can draft in seconds, analyse large volumes of information, automate administrative work and give leaders access to capabilities that previously required significantly more time and resources.

A 2025 field experiment involving more than 7,000 knowledge workers across 66 companies found that workers who actively used generative AI spent roughly two fewer hours each week on email and reduced work outside normal hours.

Yet look at what is happening alongside these gains. In Singapore, 58 per cent of leaders said productivity needed to increase, while 81 per cent of the workforce reported lacking sufficient time or energy to do their work. Globally, Microsoft workplace data found that during core working hours, employees can be interrupted by meetings, emails or messages as frequently as every two minutes.

Something does not quite add up.

We are giving people tools that allow them to work faster, while many of those same people are telling us they are running out of time and energy.

This is why I believe we need to separate two conversations that are often treated as one.

Productivity is not the same as human capacity.

We are increasing output, but are we increasing capacity?

Productivity asks how much we can produce.

Human capacity asks how much complexity, pressure, change and decision making a person can sustainably hold while continuing to think, lead and perform well.

AI can significantly expand the first. Leadership needs to take responsibility for the second.

Consider what happens when an executive saves five hours each week through AI. If those five hours are immediately replaced by additional meetings, decisions, targets and deliverables, have we improved the way that executive works?

Or have we simply made it possible for them to carry more?

This distinction matters because human beings have cognitive, emotional and attentional limits. Under sustained pressure, our judgement changes. We can become more reactive. We listen differently. Strategic thinking becomes harder because our attention is consumed by what is immediately in front of us.

The concern I have is that organisations could become significantly more productive through AI while the people inside them become increasingly depleted.

That is not sustainable transformation.

Human performance needs to become part of AI strategy

Human performance beyond productivity deserves its own attention.

Can a leader access their best judgement when the stakes are high? Can they maintain clarity when information is coming from every direction? Can they regulate their own response to pressure while creating confidence and stability for the people around them?

These questions become more important as AI accelerates the speed at which leaders receive information and are expected to act on it.

Also Read: AI and inflation are driving Singapore’s workplace anxiety. Employers are spending on the wrong fix

Senior leaders are already navigating transformation, geopolitical uncertainty, changing workforce expectations, stakeholder pressure and difficult questions about where human judgement should sit alongside machine intelligence.

Giving those leaders more information does not automatically help them lead better.

A tired executive can still answer another email and attend another meeting. The more important question is whether that executive still has the capacity to notice what others have missed, challenge an assumption, listen properly to resistance from the organisation or make a difficult decision with clarity.

This is why I believe human performance belongs inside the AI investment conversation, alongside technology, productivity and return on investment.

Reinvest the capacity AI gives back to people

We need to stop measuring the value of AI only by how much additional work people can produce.

The 2025 field experiment gives us an interesting example. AI users reduced the time they spent on email, but there was no significant reduction in meetings.

Technology changed part of the individual’s workload, but it did not automatically change the organisation around them.

That requires leadership.

If AI gives an executive team back 10 or 20 per cent of its time, organisations have a choice about what happens to that capacity. Some of it can certainly support greater productivity, but some should be reinvested into strategic thinking, customer conversations, developing people, experimentation and simply having enough space to think before another important decision is made.

For me, this is a much more interesting measure of AI return on investment.

It is not only about getting more output from people. It is about making better use of their human capability.

Also Read: Singapore’s next AI industry may be the people who check the agents

Build human capacity alongside AI capability

The second opportunity is to deliberately develop the human skills required to perform in an AI enabled environment.

We need leaders who know how to manage their attention, not simply their calendars. Leaders who can remain grounded under pressure, separate urgency from importance and question an AI recommendation rather than accepting it because the analysis looks convincing.

We also need leaders who can create enough psychological safety for their teams to experiment, make mistakes, challenge assumptions and admit when they do not know the answer.

Most importantly, we need to move away from the idea that peak performance means operating at maximum intensity indefinitely.

Sustainable peak performance is the ability to access greater capacity when the situation requires it and to know how to recover that capacity afterwards.

The strongest leader in an AI enabled organisation may therefore not be the person capable of carrying the greatest workload. It may be the person who can retain clarity, judgement, trust, energy and connection as the speed and complexity around them increases.

What will we do with the capacity AI gives us?

AI gives organisations extraordinary access to intelligence and efficiency. I am optimistic about what that can create.

But as intelligence becomes more accessible, our deeply human capabilities become even more important: judgement, discernment, creativity, courage, trust, presence and connection.

The organisations that thrive will understand that AI capability and human performance need to grow together.

As the founder of XfinitySG, a leadership and human performance practice, I believe this is where the next phase of the AI conversation needs to go. We have spent considerable time asking how quickly organisations can adopt AI and how much productivity it can unlock.

Now we need to ask what happens to the humans working inside those faster organisations.

Technological capability can scale rapidly. Human capacity needs to be intentionally developed, protected and expanded.

So, as AI gives your organisation the ability to do more, perhaps the more important question for leaders is:

What will you do with the human capacity AI gives back to you?

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In August 2024, Google wrote Character.ai a cheque for roughly US$2.7 billion. On Monday, Disney offered US$20 million. Both were for a licence to the same company’s technology. Allowing for the fine print, that is a fall of more than 99 per cent in a little over two years.

Character.ai filed for Chapter 11 bankruptcy protection in the US on 12 October, naming Disney as the stalking-horse bidder for some of its intellectual property. The company blamed legal, regulatory and operational headwinds. The court filing puts estimated assets and liabilities at US$50 million to US$100 million each. If no buyer wants to keep the platform running, it will be wound down.

Also Read: AI governance is moving from promises to proof

It is tempting to read this as an American story about an American lawsuit culture. It is not. It is a preview of what happens when a business sells intimacy to the people least equipped to handle it. Southeast Asia (young, mobile-first and lightly regulated on this front) should be reading the filing very carefully.

A licence, a rehire, then a shell

Start with how Character.ai got here. The 2024 Google deal was officially a technology licence. Its real prize, as The Wall Street Journal reported, was bringing co-founder Noam Shazeer back to Google, where he went on to help lead Gemini. Shazeer reportedly made hundreds of millions of dollars from his stake, an unusually large payout for a founder who never sold his company or took it public.

The talent left. The company stayed behind, and so did its users, its moderation problems and its lawsuits.

Those lawsuits came from families who alleged the platform contributed to their teenagers’ self-harm and deaths, among them the Florida mother of a 14-year-old boy. Character.ai and Google agreed to settle five cases, including hers, earlier this year. Kentucky’s attorney general has separately sued the company, alleging that the platform exposed children to dangerous responses.

The irony writes itself. A year ago, Disney sent Character.ai a cease-and-desist letter over bots impersonating Elsa, Moana and Darth Vader. Disney then hired former Character.ai chief executive Karandeep Anand as its first chief technology officer. Now it is bidding for the leftovers.

Nobody in this chain did badly except the company itself and, far more painfully, the families. That is the structure worth naming: the reverse acqui-hire, where the buyer licenses the technology and hires the people, and the liabilities stay with a shell that has to keep the lights on.

Engagement was the product. That was the problem

The deeper failure is the business model. A companion app makes money from attachment. The more hours a user spends, the better the metrics look. The users most willing to give those hours are often young, lonely or both.

That creates a trap no product manager can design their way out of. Every safety feature cuts into the engagement the business runs on. Character.ai added pop-ups pointing users to mental-health resources. In April, it widened age restrictions to more regions and capped some features. Each change was right. Each one also made the numbers worse, while compute bills and legal costs kept arriving and ChatGPT and Gemini offered the same conversations for free inside products people already used.

Also Read: The EU called ChatGPT a search engine. SEA’s AI startups should worry about what comes next

So the lesson is not that Character.ai moderated too little, or too late, though critics will argue both. It is that in companion AI, duty of care is not a feature. It is the biggest line on the cost sheet, and a business that cannot afford it does not have a business.

Southeast Asia’s rulebook has a chatbot-shaped hole

To its credit, Southeast Asia has moved faster than most on children online. Indonesia’s PP Tunas regulation took effect on 28 March, forcing high-risk platforms to deactivate accounts belonging to under-16s. The named platforms include YouTube, TikTok, Facebook, Instagram, Roblox and Bigo Live. TikTok alone has deactivated nine million underage accounts. Malaysia’s Online Safety Act came into force in January, and since June, social media platforms must verify the ages of new users with a MyKad, passport or digital ID.

Notice what those lists have in common. They are social and gaming platforms. A 15-year-old in Surabaya locked out of Instagram can still spend her evenings confiding in a chatbot that remembers everything she says and is engineered never to get bored of her. The bot is arguably the more intimate product. It is also the one the rules barely name.

Other governments have spotted the gap. Canada’s proposed Safe Social Media Act brings AI chatbots under the same child-safety regulator as social platforms, with a legal duty to act responsibly. The UK has pledged to bar under-18s from romantic and sexual AI companions. Southeast Asian regulators have already shown they can act fast on AI harms: Indonesia, Malaysia and the Philippines all blocked Grok this year over non-consensual sexualised deepfakes. Extending child-protection regimes explicitly to companion AI, rather than waiting for a test case, is not a stretch.

A note to founders building the ‘AI best friend’

The region has no shortage of founders pitching companion and wellness bots that speak Tagalog, Bahasa or Vietnamese, understand homesick migrant workers and fill gaps left by thin mental-health services. Some of that need is real. But the pitch deck’s best line, that users are lonely and the app is always there, is also its biggest liability.

Three things follow from Character.ai’s filing. First, budget for duty of care from day one: age assurance, crisis escalation and human review cost money, and they are the product, not an add-on. Second, stop treating time-in-app as the north star. A companion that keeps a vulnerable teenager talking until 3am is not succeeding.

Third, do not assume an exit will carry the risk away. Character.ai’s investors got their liquidity through Google; the liabilities stayed exactly where they were.

Investors have homework too. Due diligence on a companion startup should ask about harm, not just retention curves. If a fund cannot get a straight answer on how the product handles a user in crisis, that is the answer.

Don’t wait for the bankruptcy court

Character.ai’s founders were paid. Google got its talent. Disney may get a bargain. The families got settlements, and nothing that settles what they lost.

Also Read: Why Southeast Asia’s next healthtech winners will be built around healthcare workflows, not just AI

Southeast Asia does not have to learn this lesson the American way, one lawsuit at a time. Its regulators have already accepted that platforms owe children a duty of care. Its founders still have time to build companion products that earn trust rather than harvest attention. The question is whether anyone acts before the region produces its own case file.

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Google has been busy making friends in Singapore. On 7 October, its AI Futures Fund teamed up with Granite Asia to write cheques of up to US$2 million into Asian AI startups. Eight days later, the same fund has walked into Sea Limited’s office carrying something different: not a term sheet, but a key to Google’s model cupboard.

Sea, the Singapore-founded parent of Shopee, Garena and Monee, announced on Thursday that Sea Labs, its technology innovation and development arm, will work directly with Google AI Futures Fund to explore and build new applied AI products.

Teams across Sea will get access to selected frontier models from Google, along with Google experts who can help develop and test Sea’s new product ideas.

Also Read: Granite Asia, Google AI Futures Fund team up to back Asia’s AI founders

Jonathan Silber, co-founder and Director of Google AI Futures Fund, called Sea Labs the fund’s “first partnership with an external AI lab”. It is the most interesting line in the announcement. It is also the one that raises the most questions.

What is actually being traded

The AI Futures Fund sits inside Google Labs and usually backs startups with a bundle of early access to Google DeepMind models, capital, distribution and mentorship. Sea is not a startup. It is a NYSE-listed company that booked US$22.9 billion in revenue in 2025. It does not need Google’s cheque, and the announcement does not mention one.

No investment amount, equity stake, compute commitment, exclusivity terms, timeline or product milestones were disclosed. The currency here is access, and it flows both ways.

Google gets what every model maker craves: a live testbed of hundreds of millions of daily users across Southeast Asia and Latin America, shopping, gaming and borrowing in dozens of languages, on budget phones and patchy networks. Sea gets hands-on time with Google’s most advanced models, plus engineers who can help it decide what is worth building on top of them.

The logic holds. At the API level, frontier models are steadily becoming interchangeable. What separates winners is how fast a model turns into a product people use every day, and few companies in the region ship consumer products at Sea’s scale.

Chapter two, with chapter one still unread

The tie-up builds on the expanded strategic partnership the two signed in February 2026, which covered gaming, agentic commerce and agentic payments. Back then, Shopee and Google said they would explore an AI shopping agent spanning both platforms, while Monee agreed to give feedback on Google’s Agent Payments Protocol (AP2).

Also Read: Sea’s blockbuster 2025 masks a tightening profitability squeeze

Thursday’s announcement offers no update on how those February pilots have fared. Instead, it adds a new layer on top. For a company that wants investors to see it as an AI story, that is a little like announcing a sequel before anyone has watched the first film.

Forrest Li, Sea’s Chairman and CEO, framed the move as part of a bigger shift. “Our ambition to become an AI-native company goes beyond improving what we do today,” he said, adding that some of the most meaningful applications of AI “are still to be discovered”.

The R&D question

That ambition sits a little awkwardly next to Sea’s own numbers. Its research and development expenses fell 7.8 per cent in the fourth quarter of 2025 to US$277.6 million, even as group revenue rose 38.4 per cent. Shopee’s operating margin in that quarter was roughly 2.5 per cent.

Read that way, borrowing a frontier lab’s brainpower is a sensible move for a thin-margin commerce business. It buys frontier capability without frontier capex. The announcement does point to Sea’s own in-house AI stack, including proprietary models and accelerated compute infrastructure, so this is not a company starting from scratch.

The risk is dependency. Every prototype Sea’s teams build on Google’s models makes switching later more expensive. Founders in the Granite Asia programme were explicitly promised full technical choice across their AI stack. Sea’s announcement makes no such statement.

Everyone has a model patron now

Sea is hardly alone in pairing up with a big AI player. Grab, its closest regional rival, has worked with OpenAI since 2024 and last week signed a framework agreement with SoftBank and PETROS to explore an AI infrastructure platform in Sarawak. In Indonesia, GoTo co-developed the Sahabat-AI language model with Indosat Ooredoo Hutchison.

In the region’s e-commerce fight, Lazada draws on parent Alibaba’s Qwen models, and TikTok Shop sits on ByteDance’s in-house AI. Every major Southeast Asian platform now has a foundation-model patron. Sea’s choice of Google fits Google’s own regional playbook of scaling Gemini through schools, telcos and local languages.

The difference is depth. Most of these tie-ups are supplier relationships. Google calling Sea Labs a fellow “lab” suggests it wants something closer to co-development, with Sea’s markets as the proving ground.

What to watch

The announcement promises products tailored to local contexts and needs. In practice, that means multilingual customer service, tools for small sellers, and fraud detection for first-time digital borrowers. These are areas where Southeast Asia’s messiness is a genuine advantage for whoever learns from it first.

Also Read: Vietnam’s shoppers are asking AI first, but Google still gets the last word

But the question raised in February remains unanswered. When an AI agent becomes the storefront, whose interest does it optimise: the buyer’s, the seller’s, or the platform’s take rate? A lab partnership with no disclosed guardrails does not settle it.

For now, “two labs” building together at the frontier makes for a good soundbite. The real test is whether, by the next 12.12 sale, a seller in Surabaya or a gamer in São Paulo notices anything different.

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Indonesian fintech expansion into ASEAN looks different in 2026 than it did when Investree opened its Thailand operations in 2018 and its Philippines unit shortly after. The cohort of Indonesian platforms that pushed beyond the domestic market between 2018 and 2022 (Investree, Modalku, several payment players, a handful of insurtechs) encountered a structural cost that the original expansion theses tended to underestimate.

Some platforms have scaled regionally. Several have pulled back. Investree, whose Indonesian licence was revoked by OJK in October 2024, is one of the more prominent examples of a platform whose domestic situation eventually reshaped its regional footprint.

After 15 years inside Indonesian risk functions, including a period reporting to a foreign parent inside an Indonesian banking subsidiary, I have watched the cross-border fintech ambition with more interest than most. The structural pattern underneath the regional expansion outcomes is more useful than the company-by-company narrative.

What Indonesian fintech expansion aimed to do

The thesis driving Indonesian fintech expansion into ASEAN between 2018 and 2022 was straightforward. Indonesia had built fintech infrastructure (credit underwriting models, alternative data pipelines, distribution channels, regulator-engaged operating practices) that was, in principle, transferable to other ASEAN markets with similar structural characteristics. The implicit assumption was that the cost of expansion would be additive but the underlying playbook would translate.

What turned out to be harder

Three structural factors made the actual cost of cross-border expansion higher than original models priced in.

Regulatory archipelago. ASEAN’s financial regulators are coordinating more than they used to, but they are not converging. Indonesia’s OJK, Thailand’s Bank of Thailand and SEC, Bangko Sentral ng Pilipinas, the Monetary Authority of Singapore: each operates a distinct licensing regime, capital requirement, reporting framework, and supervisory culture. A platform expanding into three ASEAN markets is effectively running three different regulatory institutions inside one company. The compliance cost is multiplicative, not additive.

Also Read: Fintech, DeFi and applied AI define Southeast Asia’s new venture discipline

Local credit physics. The credit risk profile an Indonesian platform underwrote successfully at home did not always translate cleanly. Thai SME borrowers behave differently from Indonesian SME borrowers. Filipino consumer credit operates inside a different demographic and macro context. Models calibrated on Indonesian data systematically misfit other markets, and the recalibration took longer and cost more than expansion budgets typically provided for.

Operational gravity. Senior Indonesian fintech talent did not relocate easily to regional offices. Local hires required a different management cadence than the founder-led culture that had worked in Jakarta. Customer acquisition costs were higher because brand recognition was lower. Each operational decision required either expensive local presence or expensive remote management.

Lessons learned

Six principles emerge from the cross-border ambition cycle.

Expansion is not a discount. The unit economics of running an Indonesian fintech regionally are worse than running the same operation domestically until scale is reached. Platforms that priced expansion as a growth multiplier without pricing the compliance and operational drag underestimated how much capital the journey would consume before it returned.

Regulatory engagement compounds locally, not regionally. A platform’s reputation with OJK does not transfer to Bank of Thailand or Bangko Sentral ng Pilipinas. Each regulator relationship has to be built locally, over years, with local presence and local engagement.

Credit models are local. Alternative data underwriting models trained on Indonesian borrowers misfit other ASEAN borrowers in ways not always visible at deployment. Cross-border platforms that treat their models as transferable without explicit recalibration accumulate hidden default risk.

Also Read: The Philippines doesn’t need more fintech apps; needs rails

Local partnership matters more than ownership share. The cross-border platforms that worked best entered through serious local partnerships with banks, distributors, or operators who already had regulatory standing and customer trust. Going alone with a Jakarta-trained team and an Indonesian playbook produced consistent underperformance.

Domestic stability is a precondition. Regional expansion accelerates when the home market is operating cleanly. When the home market faces stress (regulatory action, credit cycle pressure, governance challenges), the regional footprint becomes a liability that consumes management attention and capital exactly when domestic operations need both. Investree’s experience makes this clearest: regional positions cannot insulate against domestic regulatory consequences.

The ASEAN fintech franchise is not yet a thing. Despite a decade of expansion attempts, no Indonesian fintech has built a genuinely scaled regional franchise in the way Grab built one in ride-hailing or Sea Group built one in gaming. Whether the model is wrong or the time has been too short, the data so far suggests the regional fintech franchise is harder than the regional consumer-tech franchise.

The macro stakes

ASEAN’s fintech landscape will eventually have regional champions. The structural opportunity (fragmented incumbent banking, mobile-first populations, integrating regulators, growing SME credit demand) is real and not going away. But the path will be slower, more capital-intensive, and more dependent on local partnership than the first wave attempted to walk.

For Indonesian fintechs still considering regional expansion in 2026 and beyond, the question is no longer whether the opportunity exists. It is whether the platform has built the domestic foundation, the partnership infrastructure, and the capital depth required to pay the cross-border tax that the first generation revealed.

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