The National Bank of Bahrain (NBB) has signed a memorandum of understanding with blockchain settlement network Partior to study whether the Bahraini dinar (BHD) can be settled on the Singaporean firm’s rails, with NBB acting as the currency’s settlement bank.

Read the fine print and the ambition shrinks to its proper size. The MoU commits the two parties to evaluate an operating model, implementation requirements and technical feasibility, and to work out what Bahrain’s regulators would require. There is no launch date, no live service and no disclosed financial terms. It is, for now, a feasibility study with a signing ceremony.

Also Read: Tokenised assets have moved on-chain. The liquidity has not followed

Still, the deal says a good deal about where Partior wants to go, and why a piece of Singapore-born financial plumbing keeps turning up in the Gulf.

The plumbing problem

Cross-border bank payments still run largely on correspondent banking: chains of banks holding accounts with one another (the “nostro” and “vostro” accounts of banking jargon), each link adding time, fees and opacity. A transfer can take days, and billions sit idle in pre-funded accounts around the world, waiting for business hours to overlap.

Partior’s answer is a shared ledger on which member banks settle with each other directly and around the clock, using tokenised commercial bank money rather than cryptocurrencies or stablecoins. Each currency is anchored by a settlement bank that holds the real money and issues its digital twin on the network.

The network currently supports three currencies, the US dollar, the euro and the Singapore dollar, through its founding settlement banks, as e27 has explained before.

Adding the dinar would therefore be a first on two counts: a new currency and a new settlement jurisdiction. NBB would do for the dinar what JP Morgan, DBS and Deutsche Bank do for the dollar, the Singapore dollar and the euro.

A Singapore idea, exported

Partior was set up in 2021 by DBS, JP Morgan and Temasek, building on the Monetary Authority of Singapore’s Project Ubin experiments with blockchain-based interbank settlement.

Standard Chartered led its Series A in 2022, and Deutsche Bank later joined as a strategic investor in a Series B that reached US$80 million.

Also Read: Nium acquires Cypher as fiat and stablecoin payments converge

The Gulf has since become its busiest expansion front. Emirates NBD went live with real-time cross-border dollar settlement on the network, with JP Morgan acting as settlement bank in the first phase. When Partior announced its Series B, it named the UAE dirham, the Qatari riyal and the Saudi riyal among currencies it planned to add, alongside the Malaysian ringgit. The dinar was not on that list, which makes NBB a new opportunity rather than a pipeline delivery.

Partior chief executive Humphrey Valenbreder framed the deal as building on the company’s “growing presence across the GCC”. NBB’s Vineet Munot was more guarded, saying the bank would weigh the technical, operational and regulatory considerations before deciding on its value. That caution is the most honest line in the announcement.

Why a small currency matters

The dinar is pegged to the US dollar at roughly US$2.66 per dinar, making it one of the world’s highest-valued currencies but a modest one by volume. For Partior, the prize is less the dinar flow itself than proof that a mid-sized jurisdiction can bring its local currency onto the network, with a local bank as anchor and a local regulator comfortable with the arrangement.

That proof matters for Southeast Asia. Every currency Partior adds beyond its founding trio is a dry run for the ringgit, the baht, the rupiah or the peso. Asia’s cross-border payment flows are enormous, pegged at a US$23.8 trillion opportunity in one contributor’s analysis, yet they remain split across currencies with thin direct liquidity between them.

The Gulf also hosts large communities of Filipino and Indonesian workers, so Gulf-ASEAN corridors are hardly abstract. But a caveat applies: Partior is wholesale, bank-to-bank infrastructure. Faster interbank settlement only reaches a migrant worker’s family if banks and remittance firms pass the savings on, and that has rarely been automatic.

A crowded field

Partior is far from alone. Its own investor JP Morgan runs Kinexys, a blockchain payments network for corporate clients. Swift is building a blockchain-based ledger with Consensys.

Project mBridge, a multi-central-bank platform whose members include the Bank of Thailand and the central banks of the UAE and Saudi Arabia, settles in central bank digital currencies.

Project Nexus, incubated by the Bank for International Settlements, aims to link instant payment systems, including those of Malaysia, the Philippines and Singapore.

Then there are stablecoins, which are chasing the same corridors from the other direction. Circle has agreed to buy Singapore’s Tazapay for US$400 million, while SBI has backed dtcpay’s Japan-SEA stablecoin corridors. Partior’s quiet selling point is that it keeps money inside the regulated banking system, sidestepping the dollarisation worries that stablecoins raise for emerging-market central banks.

MoUs are cheap

Bank-led blockchain consortia have a mixed record. Trade finance networks such as we.trade and Contour were wound down after banks failed to route enough business through them. Partior has done better than most: it is live, it has several settlement banks and it is building round-the-clock liquidity with LSEG’s Digital Settlement House, with production targeted from early 2027.

Also Read: AI agents could help Southeast Asian firms untangle cross-border payment costs

The NBB agreement will be worth more than a press release only when two things happen: the dinar actually goes live, and banks outside the founding circle start sending real volume through it. Until then, Bahrain is a promising line on Partior’s map, not a corridor.

The post Why a small Gulf currency matters for Partior’s Southeast Asian ambitions appeared first on e27.

Every humanoid robot looks brilliant in a launch video. The trouble begins on an ordinary weekday in a warehouse, when a parcel arrives in a shape nobody trained the machine for, a forklift reverses where it shouldn’t, and a worker strolls into its path while scrolling a phone.

That messy gap between the lab and the loading bay is what Hong Kong is now trying to turn into infrastructure. The Hong Kong Productivity Council (HKPC) has launched the Embodied AI International Applications Hub, with the Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR) as its technical lead.

Also Read: Ropedia raises US$22M to build the data layer for robots that understand the real world

HKPC bills it as the city’s first platform built for training, validating and deploying embodied AI. That is the industry’s term for AI housed in a physical body, such as a humanoid, a four-legged robot or an industrial arm, which has to act on the world rather than describe it.

The first phase brings in seven technology partners and eight international “scenario partners”. Scenario partners are the organisations that own the factories, warehouses, shops and public facilities where robots would actually be deployed. The hub targets five areas: manufacturing, smart logistics, retail services, inspection and maintenance, and public services.

A test track, not a showroom

The distinction HKPC is keen to draw is that this is not another glossy demo centre. The pitch is a proving ground. Developers get access to real operating settings, scenario data gets collected, and performance gets measured and fed back into the product.

FLAIR describes a closed loop. It starts with defining the scenario and moves through model adaptation and training, testing, evaluation, data feedback and iteration. The system is then validated again, and the cycle repeats. Systems will be scored on operational accuracy, execution efficiency, reliability, autonomous task completion, safety and how well they adapt to their surroundings.

That list reads like a diagnosis of where robotics keeps stumbling. A robot that behaves perfectly in a controlled lab can come apart when the floor layout changes, the objects differ, the workflow shifts or humans behave like humans. As e27 has reported before, the leap from pilot to production is where many robotics ventures quietly die. Repeated validation across varied conditions is supposed to expose those failure modes before a customer pays to discover them.

The use cases in the launch materials are specific. Logistics robots must handle parcels of all sizes and different warehouse processes. Manufacturing robots must cope with different production lines, equipment standards and safety rules. Inspection and public-facility work may require humanoid or quadruped robots to stay in control in cluttered, unpredictable environments. FLAIR is already showing a flexible embodied-AI production line and industrial robots across manufacturing, logistics, retail and public-service settings.

The quiet prize is data

The deeper value lies less in the hardware than in the data the hub generates. Investors and founders increasingly argue that the real battle in humanoid robotics is about data, not hardware. Real-world scenario data, gathered across many sites and many failures, is scarce and expensive to collect. A hub that systematically harvests it across industries is, in effect, building a dataset as much as a testing facility.

That raises questions the launch materials do not answer. Who owns the scenario data? Can a developer take what it learns in a partner’s warehouse and use it to sell to that partner’s competitor? How are costs split between the government-backed centre, developers and scenario owners? These details will decide whether serious companies bring their best technology or only their safest demos.

Also Read: Why Asia’s Physical AI boom will be decided at the camera, not the model

For scenario owners, the model offers a lower-risk route. A factory or logistics operator can test robots on its own floor without committing to a full rollout. It can also see how a supplier copes with legacy equipment and existing safety procedures before signing a procurement contract. In an industry where purchase decisions are often based on slide decks, evidence collected on site is a useful corrective.

Hong Kong’s bridge play, again

Strategically, the hub fits a familiar Hong Kong script: the city as a bridge between mainland China and the rest of the world. HKPC argues that Hong Kong’s access to the mainland market, combined with its alignment with international standards, makes it a natural cross-regional testing base.

That pitch has a logic. Mainland China is building robot armies at a pace the West is struggling to match. Chinese makers looking to sell abroad, however, face buyers who want proof of safety and compliance on terms they recognise. A Hong Kong validation stamp could serve as that passport. It also extends a broader push in which the city has positioned itself as the place that solves tech’s final mile to market, and has openly courted Southeast Asia as a “super partner”.

At the launch, Secretary for Innovation, Technology and Industry Sun Dong said the government would keep backing AI through research investment, funding and infrastructure, with an emphasis on safety and compliance. HKPC chairman Sunny Tan framed the hub as taking FLAIR’s work in perception, autonomous decision-making, motion control and industrial applications out of the lab and into real operating environments.

What it means for Southeast Asia

Southeast Asia is not short of robotics ambition. Singapore leads the region in robot density, though e27 has argued that its robotics dominance is a warning sign dressed up as good news. Vietnam’s VinRobotics has taken its humanoids to the global stage, while Thai and Malaysian startups are raising rounds to move physical AI into malls and factories. What the region largely lacks is a shared, neutral venue where a startup can prove its machine works somewhere other than its own test bench.

If the Hong Kong hub opens up to regional players on fair terms, it could become a useful credential for SEA robotics firms that want to sell into China or use Chinese supply chains. If it turns mainly into a finishing school for mainland companies heading overseas, it becomes a competitor’s advantage instead.

Also Read: Hivebotics nets US$6M to take restroom-cleaning robot Abluo into volume production

Some scepticism is warranted. Fifteen partners is a modest start. “Alignment with international standards” is a phrase that sounds reassuring and commits to very little. The China Daily Hong Kong coverage echoed the official line that the centre will speed up the move from research to industry, but that is a stated goal, not an outcome.

The real test is simpler and harder. Will the trials produce benchmarks other people trust and can repeat, and deployments that outlive the ribbon-cutting? Robots are good at repeating the same task. The hub now has to show it can repeatedly turn pilots into real deployments.

The post Hong Kong builds a test track for robots that keep failing outside the lab appeared first on e27.

The post The US$82,000 Illusion: Why Bitcoin still trades like a traditional tech stock appeared first on e27.

Singapore’s office workers answer a 10.47pm message within 90 seconds to prove their commitment. Their bosses barely notice.

The Jobstreet by SEEK Singapore Workplace Culture Study 2026, which surveyed 600 employees and jobseekers and 200 hiring decision makers, finds that 63% of staff signal dedication by replying fast, a behaviour only 39% of employers value. Some 52% stay reachable after hours; just 18% of employers rate it. What employers actually want is initiative and outcomes (49%).

The anxiety behind this is imported. AI and automation, followed by inflation and the cost of living, top both sides’ lists of workplace pressures. Employees fear for pay and job security; employers worry about AI’s impact on teams and restructuring towards leaner headcounts. That loop turns permanent availability into cheap insurance against the next round of cuts.

The payout is close to nil. Some 62% say always-on working has done nothing for their career, and 63% of those under pressure report lower energy. Yet 48% of employers who expect it have no plans to add support, and budgets favour AI training (34%) over wellbeing (14%) and manager development (12%). Until employers decide when staff may switch off, the late-night reply keeps costing workers dearly.

REGIONAL

SEA fintech funding triples to US$2.3B as deal count nearly halves: Four US$100M-plus rounds took 86% of capital, led by Ant International’s US$1.2B Series A. Seed funding fell 46% to US$53.3M, Singapore firms raised US$1.9B, and the region logged no fintech IPOs, per Tracxn.

Salary Hero raises US$7.6M Series A to take on Malaysia’s loan sharks: Cento Ventures led the round for the Bangkok-based financial health platform, joined by Kairous Capital and M Venture Partners. Salary Hero has also expanded its credit facility to US$10M with regional banks and lenders.

Parallel Loop nets US$6.6M to feed enterprise AI the context it lacks: Founders Yujun Chean and Prateek Jogani previously built Vouch Insurtech, parent of Thai insurtech FairDee, which raised a US$755,000 seed round in 2018. Eight years on, they return with a bigger cheque in a far more crowded field.

Antares Ventures passes US$50M for debut deep-tech fund, eyes US$100M: Family offices and corporate investors from Southeast Asia, Japan and Korea backed the second close. Fund I has already deployed about US$27M across nine companies, including Singapore’s VFlowTech, spanning energy, compute, industry and urban infrastructure.

TalentHero, DeeMoney form Thai JV to link hiring, payroll and payments: The Singapore-headquartered firm, co-founded by Oliver Lewis and James Miles, targets the post-hire grind of local entities, compliance and payroll, plus the cross-border money transfers that slow companies expanding into Thailand.

85% of Singapore employers expect staff to be always on: Yet half of employees say they receive no organisational support, against 22% of employers who admit offering none. Always-on working rarely pays off: 62% of staff see no difference to their career progression.

Malaysia’s VentureTECH backs biosimilar maker and Sabah agritech firm: Hanan Medicare is moving into biosimilars, including insulin, while AgriData Portal runs a seed-to-market model for smallholders. The AgriData deal is the government-backed investor’s first Sabah bet, aligned with PuTERA35.

Cashku adds Muamalat Invest Shariah funds, including private credit: The Malaysian platform offers sukuk, equities, gold and money market funds with no sales charges. Malaysia’s Islamic capital market grew 4.31% to US$660B in 2025, about 63% of the domestic capital market.

Partior and Bahrain’s NBB study putting the dinar on its rails: The Singapore blockchain settlement network could gain NBB as a dinar settlement bank, but the MoU commits only to feasibility work. Neither side disclosed a live service, a launch date or financial terms.

Beenext, Lotus lead US$2M pre-seed for AI search startup Miracly: Miracly, from Swedish home decor operator The Wild Ventures, tracks brand visibility on AI platforms. Acclime and Pixi Beauty are early customers, and it faces rivals such as Profound and AthenaHQ in AI search optimisation.

INTERVIEWS & FEATURES

Manus’s US$500M round puts the AI startup back in Chinese hands: Boyu Capital and IDG Capital led the raise, months after Beijing ordered Meta’s US$2B acquisition unwound. The lesson for SEA investors: regulatory exposure follows founders and code, not the registered address.

JFrog’s Sunny Rao: tokens are the wrong yardstick for AI agents: Rao once sold the IVR and chatbot automation wave that left customers trapped in looping phone trees. He warns that enterprises measuring AI agents by token use risk repeating that mistake.

Six wallets are replacing cash-on-delivery across Southeast Asia: Cash-on-delivery, long the default where card ownership was thin, once made up about half of e-commerce. Shoppers are finally abandoning it, but each market is choosing differently, fragmenting checkout for merchants.

Employers buy AI tools while their managers are left behind: Jobstreet by SEEK finds 37% of Singapore employees quietly draw their own boundaries to cope with pressure. Employers, meanwhile, rank manager development near the bottom of their investment list, at 12%.

Auto finance loses buyers in gaps that AI agents could close: Up to 80% of SEA cars are bought on credit, yet only about 10% of prospects complete financing, X Star’s Shawn Yang told AIMCONNECT 2026. Delegates ranked connectivity across institutions first as the fix.

INTERNATIONAL

Netflix cuts about 1,000 jobs as YouTube and TikTok eat into viewing: The roughly 6% cut falls mostly on product, tech, legal and HR teams. It follows Netflix losing the Warner Bros. Discovery bidding war to Paramount Skydance earlier this year.

US EV sales grow for a third straight quarter after tax credits end: EVs took 6.1% of the US market, per Cox Automotive, led by Toyota, Rivian and Subaru. Tesla’s sales fell 20% year-on-year, though it still sold over half of all EVs.

Amazon’s Panos Panay tells founders to fail, then harvest the lessons: Panay cited Microsoft’s Surface RT, which triggered a nearly US$1B write-down, as proof that failures stem from decisions made years earlier. His advice: adjust course by 30 degrees, not 180.

CYBERSECURITY

Singapore wants Google’s AI to stop scams inside the phone: Google unveiled two initiatives with the Singapore Government, shifting part of the anti-scam fight onto users’ devices, away from police operations centres, bank back offices and platform blocking rules.

Spyware makers turned online ads into a zero-click hacking pipeline: Citizen Lab says Israeli firm Discover-Tech let Candiru and Bindecy target victims through ad networks. One campaign hit at least 45 computers at Al Jazeera; exposed bidding records show ads reached military devices.

FBI arrests OpenAI staffer over hidden device in executive boardroom: Prosecutors charged Saudi national Abdulrahman Alfozan with intercepting oral communications after a device turned up in a TV’s USB port. OpenAI says it has no sign of stolen weights or customer data.

Ping An Digital Bank reports 96% deepfake detection in HKMA sandbox: Built with OneConnect, the platform checks customer selfies for forged faces during verification. The bank published no methodology or false-positive rates, and gave no deployment date beyond the trial.

SEMICONDUCTOR

SK Hynix’s Solidigm ties US fab plans to China equipment approvals: Reuters reports Solidigm wants more US tools for its Dalian plant while weighing a NAND factory in upstate New York. The unit is reportedly eyeing a US$10B IPO in 2027.

Applied Materials opens AI chip R&D hub with TSMC, Intel, Samsung: The Sunnyvale centre houses nearly four acres of cleanroom, and Applied expects capital spending of about US$5B over time. Micron, SK hynix and five US universities also joined, but the company gave no timeline or spending breakdown.

Cerebras CEO says AI doomsayers are drowning out the benefits: Andrew Feldman told TechCrunch Disrupt the industry also failed to engage local communities over US data centres, fuelling NIMBY pushback, while countries such as Brazil, Finland and Mexico welcome the buildouts.

Japan to invest up to US$21B in two GlobalFoundries fabs in New York: GlobalFoundries will operate the plants, jointly owned by Washington and Japanese interests. The deal forms part of Japan’s US$550B US investment pledge, made in return for US tariffs on Japanese imports falling to 15% from at least 25%.

AI

Anthropic’s Singapore MOU puts AI safety testing ahead of reach: Opening its fifth APAC office, Anthropic counts GovTech, DBS, GIC, Grab and Sea as users. The MOU carries no investment figure or headcount, and Washington still holds the off switch on US frontier models.

Sea Labs gets Google’s frontier models before February pilots report: Google AI Futures Fund calls Sea Labs its first external lab partner, but no money, equity or milestones were disclosed. Sea cut fourth-quarter 2025 R&D spending 7.8% as revenue rose 38.4%.

Singapore bets AI agent assurance can become its next industry: IMDA unveiled new assurance measures for AI agents, reflecting regulators’ shift in focus from software that chats to software that acts, where a wrong refund or misdirected email becomes a liability.

Singtel, IMDA offer 10,000 SMEs free AI-ready document management: SMEs get six months of free access to Singtel’s SPACE plus workshops, aimed at organising messy business data first. Neither party disclosed sign-ups or measured productivity gains.

Disney offers US$20M for tech Google licensed for US$2.7B: Both deals cover a licence to Character.ai‘s technology, marking a fall of over 99% in just over two years. The chatbot startup has since filed for Chapter 11 bankruptcy.

Hong Kong opens embodied AI hub to test robots in real settings: HKPC and FLAIR have signed seven technology partners and eight international scenario partners across manufacturing, logistics and retail. Hong Kong wants the hub to bridge mainland robotics and global markets.

THOUGHT LEADERSHIP

SEA’s funding winter is real, but its builders have not stopped: Faheem Aizat Kamsan notes that zero regional PE funds closed in the first half of 2026. Yet he argues capital fleeing the region is not the same as founders downing tools.

Indonesian fintechs paid more for ASEAN expansion than they planned: Bobby Yulandika Putra revisits the 2018–2022 cohort, from Investree to Modalku, that pushed beyond Indonesia and hit a structural cost few had priced in.

DEFA lowers the cost of entry, but architecture decides cost of scale: Fathhi Mohamed warns that platforms in four ASEAN markets often run four data stores, four KYC flows and four sets of legal entities, so every new market multiplies complexity.

Singapore’s growth upgrade helps some sectors far more than others: MTI lifted its 2026 forecast from 2–4% to 4.5–5.5% in August. Boris Dzhingarov urges founders to read the sector breakdown, not the headline, before planning.

Founders now judge investors on what comes beyond the cheque: Chris Chen opens with a founder weighing two term sheets in one week, including a reasonable US$1.5M offer from a well-known regional VC, to show what founders actually buy.

Decide which decision predictive AI should change before buying tools: Daniel Tan argues startups begin too early with models, data stacks and dashboards. The first question is what decision a prediction changes; without that answer, he suggests, the rest is premature.

AI saves hours, but teams simply fill them with more work: Rubyni Karun warns that as organisations accelerate AI spending, time saved by automation simply gets refilled with tasks, leaving human capacity as stretched as before.

Audiences warm to ads as marketers lose faith in their own metrics: Prof. Aleks Farseev draws on Kantar’s Media Reactions study, built on interviews with 800-plus senior marketers, to ask why green dashboards cannot show teams what worked ahead of 2027 planning.

Human rights are getting lost in the ‘S’ of ESG: Adam Goulston notes the 2011 UN Guiding Principles have long required companies to find, prevent and repair human rights harms, yet the social pillar still dilutes or ignores them.

The West buys longevity scans while SEA still pays cash for basics: Americans pay up to US$2,499 for full-body MRIs, Maria Gaitanidou notes. In the Philippines, many families delay check-ups until symptoms turn serious, and public hospital beds are often full.

The post Ecosystem Roundup: Singapore’s always-on workers are performing to an empty room appeared first on e27.

For two decades, marketers obsessed over a single question: where do we rank on Google? A growing number now ask a stranger one: what does ChatGPT say about us when we are not in the room?

The Wild Ventures, the Swedish-founded company behind home decor brand Wild Palace, is betting that question will turn into a budget line. On October 14, it launched Miracly, an AI marketing platform, after raising a US$2 million pre-seed round.

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

The round was led by Singapore-based BEENEXT and Lotus, with participation from a Swedish family office. Existing backers include Stefan Lindeberg, founder of Nordic VC firm Creandum, and Alex Bouaziz, co-founder and CEO of payroll and HR unicorn Deel. The company did not disclose a valuation.

From wallpaper to software

The launch marks a notable turn for a company that, until recently, described itself as a house of brands. When The Wild Ventures announced itself in April 2025, it closed a US$750,000 angel round at a post-money valuation of around US$7 million. The plan then was to use AI and sharp branding to shake up “stale but big” categories such as wallpaper, pet accessories and paper packaging, through brands including Wild Palace, Poositive Pets and Future Paper.

Miracly is a different animal. It is business software, sold to marketing teams rather than to shoppers.

The founding team helps explain the pivot, or at least the extension. Co-founders Arvid Lithander and Shannon Cullum come from advertising agencies including Saatchi & Saatchi, Wieden+Kennedy and MullenLowe, and are joined by Bjorn Bergstrom and Malin Renström. People who have spent careers fighting for share of voice tend to notice early when the arena moves.

The obvious sceptic’s question is whether a startup can build consumer brands and an enterprise software product at the same time on a pre-seed budget. The generous reading is that Wild Palace doubles as Miracly’s test lab: a live brand on which to try out AI visibility tactics before selling them to others. The company will need to show which reading is closer to the truth.

What Miracly actually does

Miracly said its software helps marketing teams track how brands appear on AI platforms and manage work across channels. In plain terms, it watches how chatbots and AI assistants describe and recommend a brand, then helps the team act on what it finds.

That second half matters. Monitoring alone is quickly becoming a commodity. The harder problem is turning “the chatbot thinks our rival is cheaper” into a coordinated response across a website, PR, social media and paid campaigns, without adding another tool to an already bloated.

Also Read: How earned media drives AI search visibility in ASEAN

Early customers include Acclime, an Asia-focused corporate services firm, and Pixi Beauty. The pairing is telling: one sells complex professional services, where buyers increasingly ask AI tools to compare providers; the other sells skincare, where an AI-generated product recommendation can make or break a purchase.

A crowded new channel

AI search is changing how brands are discovered online, handing marketers yet another channel to monitor and influence. When a user asks an assistant for the best accounting firm in Singapore or a moisturiser for humid weather, the answer usually arrives as a single synthesised reply rather than a page of links. A brand that is absent from that reply barely exists for that customer. It is why marketers have started asking what ChatGPT is saying behind their backs.

For Southeast Asia, the stakes are arguably higher. Discovery in the region is fragmented across languages, super apps and social commerce, and most brands already juggle multiple channels on thin teams. Adding AI answer engines to that mix is not optional, but it is not cheap either.

Miracly is far from alone. New York-based Profound is the category’s heavyweight: it raised a US$96 million Series C at a US$1 billion valuation in February, led by Lightspeed Venture Partners with Sequoia Capital and Kleiner Perkins participating, and serves more than 700 enterprise customers. Industry reports say it followed that with a US$180 million Series D at a US$1.8 billion valuation in September. Y Combinator-backed AthenaHQ, founded by former Google Search engineer Andrew Yan and ex-ServiceNow engineer Alan Yao, came out of stealth in 2025 with US$2.2 million in seed funding and pitches a cheaper, self-serve alternative.

Against that backdrop, US$2 million is a modest war chest. Profound has already moved from tracking into agents that execute marketing tasks, which is precisely the “manage work across channels” territory Miracly is claiming. Competing on features with a company that had raised more than 75 times as much even before its latest round is a losing game. Miracly’s better bet may be geography and price: mid-market brands and Asian firms that Silicon Valley’s enterprise-first players are not prioritising.

That may also be what BEENEXT, which has a long record of backing early-stage startups across Asia, sees in the deal.

The question nobody has answered

There is a deeper uncertainty hanging over the whole category. Search engine optimisation worked because Google published rules, however vague, and the results could be measured. AI models are less predictable: answers shift with model updates, phrasing and even the user’s location. Selling the ability to “influence” those answers requires proof that customers can repeat, not just a dashboard of anecdotes.

Also Read: AI didn’t replace Southeast Asia’s marketing agencies, it repriced them

And as AI platforms experiment with paid placements, the organic window that tools such as Miracly promise to optimise could narrow. If AI search becomes another pay-to-play channel, the winners may be ad buyers, not optimisers.

For now, marketers are paying to find out what the machines think of them. As AI agents rewrite the growth marketing playbook, Miracly’s challenge is to prove that knowing the answer is only half the job, and that it can deliver the other half.

The post What does ChatGPT say about your brand? Miracly raises US$2M to find out appeared first on e27.

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.

The post AI and inflation are driving Singapore’s workplace anxiety. Employers are spending on the wrong fix appeared first on e27.

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?

—

The post AI can increase productivity. It can’t increase human capacity appeared first on e27.

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.

The post Character.ai was worth US$2.7B to Google. Disney just offered US$20M appeared first on e27.

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.

The post Sea and Google open chapter two of their AI pact before chapter one ships appeared first on e27.

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.

—

The post Why Indonesian fintech’s ASEAN expansion cost more than anyone priced in appeared first on e27.