Some Japanese words resist translation.

Take rōrō kaigo (老老介護). The character rō means “elderly”, and repeating it describes an elderly person caring for another elderly person, such as an 80-year-old husband looking after his equally frail wife on his own. That is the reality the word captures.

Then there is the darker ninnin kaigo (認認介護). Here, the repeated character refers to dementia (ninchi-shō in Japanese). The phrase describes a person with dementia caring for a spouse who also has dementia.

That these situations are common enough to have earned vernacular shorthand says a great deal about what Japan is up against. The elderly population keeps growing, but the supply of caregivers does not.

Also Read: iWOW’s US$11M placement tests investor appetite for Singapore’s ageing economy

Japanese media call it the “2025 Problem”: the year the postwar baby-boomer generation crossed the age of 75. Since then, the labour shortage has stopped being a headache for individual care homes and has become a looming crisis for the country’s social infrastructure.

For decades, caregiving has run on human hands, experience and compassion. The assumptions behind that model are now cracking. Technology is moving in to fill the gap, with physical AI and next-generation communications at the centre. As AI moves off the screen and starts interacting with real bodies in real rooms, the nature of care itself is set to change.

Why physical AI, and why Japan?

At first glance, “Japan” and “AI leader” don’t sit comfortably in the same sentence. The country is regularly ranked among the slowest AI adopters in the developed world. In generative AI use and software-led development, it is usually described as having missed the digital transformation wave.

So how could it lead in physical AI? Part of the answer is robotics. Japanese companies account for roughly 70 per cent of the global industrial robotics market. Giants such as FANUC, Yaskawa Electric and Kawasaki Heavy Industries have spent decades building the physical backbone of manufacturing worldwide.

Japan may have lagged in software, but it remains one of the world’s deepest pools of expertise in machines that work in the physical world. Physical AI plays directly to that strength.

The other part of the answer is urgency. The labour shortage is nearing the point where recruitment drives and pay rises alone can’t fix it. Across government, industry and the startup ecosystem, there is growing acceptance that caregiving as a social service may become unsustainable without technological help.

Also Read: Singapore’s robotics dominance is a warning sign dressed up as good news

That changes the usual debate. The argument here is not that physical AI will replace workers. It is that physical AI can fill roles that there simply aren’t enough people willing or able to do. Caregiving could become the first arena where Japan stages a meaningful comeback, through physical rather than purely digital innovation.

Why caregiving became so hard

The shortage is not simply a matter of the profession being unpopular. It is structural and long-running. Heavy physical demands, relatively low wages and high turnover feed a vicious cycle. The caregiving workforce is itself ageing, and too few young people are joining it.

Demand, meanwhile, keeps climbing. Both home-based and institutional care are expanding, and the need for dementia support, night-time monitoring and specialised assistance is becoming more complex.

Japan’s Ministry of Health, Labour and Welfare estimates that the country could be short of approximately 690,000 caregivers by the 2040s.

Physical AI moves into care facilities

Until recently, AI in caregiving was mostly about handling information, such as documentation support and analytics on monitoring-camera footage. Now robotic arms, autonomous mobility and remote operation are converging to push AI into the physical side of the job.

Patient transfers, mobility assistance, night patrols, dish collection, laundry and restocking supplies are all becoming tasks that AI-powered robots can perform.

What care robots actually need to do

Care robots need capabilities fundamentally different from those of their factory-floor cousins.

Every care recipient differs in physical condition, cognitive state and daily habits. Some use canes, while others rely on wheelchairs. Even a task as simple as delivering a meal tray changes with the circumstances.

So caregiving-focused physical AI has to do three things:

• adapt to constantly changing environments
• interact safely with the human body, using precise force control
• run reliably on its own for long periods, including overnight

The challenge is less about automation and more about building intelligence that can work alongside people.

The network behind the robot

Physical AI does not work in isolation. Care robots, monitoring sensors, staff smartphones and remote management systems all have to stay connected in real time for the system to function. Low-latency networks, private 5G, edge AI and IoT sensors form the foundation that keeps care robots running in real-world environments.

The same is true in logistics and supply chains. The robot gets the attention, but the communications and data infrastructure behind the scenes decides whether the system succeeds.

The Japanese companies to watch

Enactic

Tokyo-based startup Enactic builds physical AI solutions for caregiving environments.

Its humanoid care assistant robot, Ena, does not perform direct physical caregiving. Instead, it handles the peripheral chores that caregivers usually juggle on top of their main duties, including laundry, dish collection and restocking supplies. The aim is to free staff for human-centred work: personal care, emotional support and meaningful time with residents.

As of April 2026, Enactic had signed MOUs with more than 80 caregiving organisations across Japan. It planned to begin pilot testing in care facilities in the summer of 2026. The company has also been recognised through its participation in Amazon’s advanced AI development programmes.

CYBERDYNE

CYBERDYNE was founded in Tsukuba, Ibaraki Prefecture, and has offices in Tokyo. It is best known for HAL (Hybrid Assistive Limb), a wearable robotic exoskeleton.

HAL reads bioelectrical signals from the user’s nervous system and provides muscular assistance in response. It is used for gait rehabilitation and patient-transfer support.

What sets HAL apart is that it responds to the user’s intention to move, rather than simply applying mechanical force. That makes it useful as a rehabilitation tool as well as an assistive device.

In care homes and hospitals, transfer-assistance technology is also expected to reduce the physical strain on caregivers, particularly lower-back injuries. Some rehabilitation hospitals and care facilities have already adopted HAL, making it one of the earliest examples of physical AI in healthcare and eldercare.

ugo

Tokyo-based ugo develops and deploys a series of robots, also called ugo, that combine autonomous navigation with remote operation.

The company’s core idea is a hybrid approach. Instead of pursuing full automation, ugo designs its systems around flexible collaboration between humans and machines. Its robots patrol predefined routes on their own, and human operators step in remotely when a detailed task or an unusual situation needs attention.

In care facilities, that means night patrols and monitoring. The platform also connects to sensors and cameras for data collection and analysis.

Through its Robot-as-a-Service (RaaS) platform, ugo lets operators manage multiple robots across multiple sites from one place, which makes adoption easier. The company has also worked with major organisations such as Tokyo Gas and leading security firms, extending its technology beyond caregiving into broader social infrastructure.

Also Read: Why patient intake is becoming healthcare’s most important AI use case

Care is becoming an infrastructure business

Caregiving has long been a labour-intensive industry. The amount of care available was set by the number of people available to provide it.

In the future, it may look more like an infrastructure industry, powered by robotics, AI, communications networks and edge computing. Connectivity systems, robot-management platforms and shared data networks could become the invisible backbone of care delivery.

The real value won’t lie in the robots themselves. It will lie in the data generated on the ground and the intelligence built from it. Can the knowledge of experienced caregivers, including their instincts, their individual approaches and their hard-won best practices, be captured, learned from and passed on? That question may decide who leads this industry.

Two questions sit at the centre of it all. Can we protect the dignity of older adults even when there aren’t enough people to care for them? And can caregivers do meaningful work without burning out?

Physical AI is beginning to offer answers to both. Autonomous mobility, remote operation, edge AI and private 5G, combined and deployed in real care environments, are laying the foundation for a new kind of caregiving infrastructure.

—

This article was originally published by Black Box, a global media outlet that reports on the Japanese startup scene.

The post When the carer has dementia too: Japan turns to physical AI to rescue eldercare appeared first on e27.

AI coding agents have moved quickly from novelty to daily tool inside Southeast Asian engineering teams. Across Singapore, Bangkok, Jakarta and Ho Chi Minh City, developers are no longer just asking AI to complete a line of code or explain an error message. They are using agents to plan tasks, refactor repositories, generate tests and execute multi-step workflows that once sat firmly with human engineers.

But as these tools become more capable, a harder question is emerging for founders and CTOs: how much autonomy is too much?

Also Read: From Samsung to startups: Kevin Choi’s bet on AI-powered software creation

The Agoda AI Developer Report 2026, which surveyed more than 800 software developers and engineering leaders across Southeast Asia and India, offers a clear answer. The region is not blindly handing software development to machines. Instead, engineering teams are adopting what might be called risk-calibrated autonomy: letting AI move fast where mistakes are low-cost and reversible, while putting human approval gates around decisions that could break systems, expose security gaps or affect customers.

According to the report, 53 per cent of developers say AI agents are already deployed in production or broad organisational use. That is a significant jump from the earlier phase of AI adoption, when most usage centred on autocomplete-style copilots. Yet the data also shows that developers remain cautious at the point where AI touches live environments.

As Harley Young, Head of High-Tech at Microsoft, puts it in the report: “The winners won’t be whoever adopts agentic AI first, but whoever builds trust and accountability around it. The opportunity we all share is to treat AI adoption as an operating-model change, not just a productivity tool.”

The new autonomy gradient

The most striking finding is not that developers trust AI, but that they trust it selectively.

For low-risk tasks, Southeast Asian engineering teams are willing to give agents considerable freedom. Documentation is the clearest example. The report found that 43 per cent of developers allow AI full autonomy to generate and update documentation, while 36 per cent permit limited autonomy and only 21 per cent require human approval.

Routine code generation and refactoring also sit relatively low on the risk ladder. For code generation, 28 per cent of developers grant AI full autonomy, 42 per cent allow limited autonomy and 30 per cent require human sign-off. For refactoring, 30 per cent allow full autonomy, 42 per cent allow limited autonomy and 28 per cent require human approval.

Also Read: Securing Agentic AI for Singapore enterprises: A reference architecture

The logic is simple. If an AI agent writes documentation poorly, generates a test scaffold incorrectly or suggests a messy refactor, the cost of catching and reversing the error is usually manageable. These tasks can speed up development without necessarily endangering the business.

The picture changes sharply as AI approaches security, code review and deployment.

For security reviews, 43 per cent of developers require explicit human approval, while only 15 per cent permit full autonomy. For pull request reviews, 41 per cent demand human approval and just 16 per cent allow full autonomy. When it comes to deploying to staging, 52 per cent require human sign-off.

Production is where the line becomes almost absolute. Some 79 per cent of developers say human approval is required before AI-generated work can be pushed live. Only 5 per cent allow full autonomy for production deployments.

This is a pragmatic engineering culture rather than an anti-AI one. Developers are happy to delegate execution volume. They are far less willing to delegate authority over irreversible or high-impact decisions.

Shawn Wong, CTO of CrossPath AI, captures that divide neatly in the report: “Software development will be fully owned by an AI agent. Business objectives will always remain human-led.”

Hallucinations have become an operating risk

The caution is not theoretical. AI systems still produce hallucinations, incorrect outputs and overconfident answers. In 2025, 79 per cent of surveyed developers selected inconsistent output as a primary concern. In 2026, the same share still cites hallucinations or incorrect outputs as a central operational concern.

What has changed is how teams respond. Hallucination is no longer treated as a reason to reject AI outright. Instead, it is being absorbed into software governance, much like security risk, infrastructure failure or human error.

SCB 10X, the venture innovation arm of Thailand’s Siam Commercial Bank, offers a useful example. The organisation has used AI agents to build production microservices, automate venture deal sourcing and curate research presentations. The productivity gains are real. But so are the gaps.

“Speed has genuinely surprised us. Trustworthiness in production hasn’t,” says Oravee Smithiphol, Tech Intelligence and Insights Manager at SCB 10X.

In shadow-testing pipelines, SCB 10X found cases where agents confidently reported that tasks were complete even though underlying functional requirements had failed. In one instance, an agent declared work finished despite generated code failing to meet core specification criteria.

That experience pushed SCB 10X to place agent deployments behind operator-controlled gateways. In practice, this means AI-generated work can move through parts of the development pipeline, but live release is held until human engineers verify system integrity.

Also Read: Singapore firms embrace agentic AI, but audit trails remain thin

Smithiphol describes this as an authority gap rather than a capability gap. Agents may be able to build, but humans still need to define what “done” and “reliable” mean. For startup leaders, her warning is blunt: “Do not scale agents faster than the organisation’s ability to evaluate and govern them.”

Accountability still sits with people

For Southeast Asian founders, the accountability question may be the most important one. If an AI agent introduces a critical bug or causes a production outage, who is responsible?

The report suggests the region’s developers have reached a firm consensus: humans remain accountable.

Some 86 per cent of developers say they always or mostly review and validate AI-generated output before deploying it. When asked who should be responsible if an AI agent causes a production outage or defect, 42 per cent point to the individual developer who approved the code. Another 27 per cent favour shared responsibility, while 21 per cent assign responsibility to the engineering team or lead architect. Only 3 per cent place primary responsibility on the AI vendor or model provider.

That has practical consequences for how companies should train engineers. AI use cannot become a loophole for weaker ownership.

Sylvain Dormieu, Director of Engineering at regional payments platform Omise, says: “AI is pushing the frontier of automatable tasks, but accountability remains with humans.”

At Omise, where agents assist with platform upgrades, code impact analysis and customer support workflows, engineers are taught that approving AI-generated work means owning it. If a developer merges an AI-written pull request, they are responsible for it as if they had written every line themselves.

Governance becomes a competitive advantage

This shift is turning AI risk management into a leadership discipline. When engineering leaders were asked to name their top priority for the next 12 months, 31 per cent cited managing AI risks. That ranked ahead of integrating AI into workflows, at 21 per cent, and upskilling existing talent, at 18 per cent.

The report also shows a link between governance and adoption. Companies with formal AI guidelines report higher production agent adoption, at 43 per cent compared with 30 per cent among those without such policies. They also report stronger codebase readiness, at 56 per cent compared with 40 per cent.

For startups, that finding matters. In a region where engineering teams often need to do more with less, AI agents can help stretch talent and accelerate delivery. But moving too quickly without controls can create hidden technical debt, security exposure and operational fragility.

The better approach is to make autonomy explicit. Documentation, test scaffolding and routine refactoring can be given more room. Database migrations, security decisions, pull request approvals and production deployments should sit behind mandatory review. CI/CD gates, staging checks and shadow testing should be designed for AI-generated code from the start.

Also Read: The hidden economics of autonomous AI agents

The deeper leadership shift is from coding to orchestration. CTOs and engineering managers now need to understand system architecture, risk management and agent supervision as much as tool adoption. The job is not merely to buy AI tools, but to redesign how work moves through the organisation.

For Southeast Asia’s startup ecosystem, the lesson is clear. The companies that benefit most from AI agents will not be the ones that remove humans from the loop entirely. They will be the ones that know exactly where humans must remain.

The post Southeast Asian tech leaders are learning to trust AI agents, but not with production appeared first on e27.

Southeast Asia’s foodtech story was supposed to be about disruption: lab-grown shrimp on every plate, groceries at your door in 15 minutes, ghost kitchens replacing the corner restaurant. The reality has been humbler, and arguably more interesting. The 2022 correction thinned the herd, the eFishery scandal and TaniHub’s collapse left scars, and many of the moonshots quietly became suppliers to the very incumbents they once vowed to topple.

Yet the money has not disappeared; it has simply grown pickier. Amazon is backing quick commerce in Jakarta, Novonesis is betting on Singapore-born precision fermentation, and app-first coffee chains have raised like tech startups.

Also Read: Why Southeast Asian agritech must build for acquisitions, not IPOs

This list profiles 27 foodtech startups worth watching across four clusters: alternative proteins, agri-food supply chains, online grocery, and restaurant tech and F&B brands. Some are scaling, some are surviving, and a few come with caveats. All of them tell us where the region’s appetite is heading.

Alternative proteins and food science

Singapore’s early regulatory openness to novel foods made it the region’s alt-protein capital. The sector’s mood has shifted from moonshot to margin: the survivors are increasingly selling ingredients and tools to big food companies rather than building consumer brands of their own.

1. Umami Bioworks (Singapore)

Umami Bioworks builds the cell lines and production platform behind cultivated seafood, and in 2024 it absorbed Shiok Meats, Singapore’s first cultivated-shrimp startup.

That deal mattered less for its size than for what it signalled: in cultivated meat, consolidation has replaced mega-rounds as the default exit. Umami’s bet is that it can be the picks-and-shovels supplier to an industry that has yet to prove consumers will pay a premium for lab-grown fish.

Founded: 2020

Founders: Mihir Pershad

Key backers: Maruha Nichiro, Better Bite Ventures, Hatch Blue, Aqua-Spark, CULT Food Science, Plug and Play

2. Allozymes (Singapore)

Allozymes uses microfluidics to screen and engineer enzymes far faster than conventional lab methods, which matters to anyone trying to make flavours, sweeteners or natural ingredients by fermentation at a sensible cost. Its US$15 million Series A in 2024, led by Seventure Partners and Temasek-backed Xora Innovation, was one of the larger food-science rounds in the region that year. It is a deep-tech business that happens to serve food, not the other way round.

Founded: 2019

Founders: Peyman Salehian, Akbar Vahidi

Key backers: Seventure Partners, Xora Innovation (Temasek), SOSV, Entrepreneur First, NUS Technology Holdings

3. Prefer (Singapore)

Prefer ferments surplus bread, soy pulp and spent grain into a coffee substitute that early reviewers have found surprisingly close to the real thing. The pitch is timely: climate stress and commodity spikes have made arabica and cocoa painfully expensive, and Prefer sells into cafés and food manufacturers looking for a cheaper hedge. Whether ‘bean-free’ becomes a category or remains a curiosity depends on how long bean prices stay high.

Founded: 2022

Founders: Jake Berber, Ding Jie Tan

Key backers: At One Ventures, Forge Ventures, 500 Global, Better Bite Ventures, SEEDS Capital, Entrepreneur First

4. ProfilePrint (Singapore)

ProfilePrint scans a sample of coffee, cocoa or another commodity and uses AI to predict its quality grade in seconds, replacing a slow, subjective process run by human cuppers and graders. Its cap table reads like a who’s who of agri-commodity trading, which is both its strongest validation and its biggest dependency. Few startups in this list have customers this large; fewer still have customers who are also shareholders.

Founded: 2018

Founders: Alan Lai, Rehan Amarasuriya

Key backers: Louis Dreyfus Company, ofi (Olam), Sucafina, Cargill, Greenwillow Capital, SEEDS Capital

5. Green Rebel (Indonesia)

Green Rebel makes whole-cut plant-based meat designed for rendang, satay and other Asian dishes rather than for burgers, and it has leaned heavily on food-service partnerships with chains to get onto plates. The backing of South Korean food conglomerate CJ Group gives it a strategic ally few regional peers have. Its challenge is the one facing every plant-based brand: getting price-sensitive Indonesian diners to pay more for less meat.

Also Read: TaniHub, prison and grace: Cynthia Wihardja’s post gives a human face to VC risk

Founded: 2020

Founders: Helga Angelina Tjahjadi, Max Mandias

Key backers: Unovis, AgFunder, Teja Ventures, Better Bite Ventures, CJ Group

6. ImpacFat (Singapore)

ImpacFat grows fish fat cells rich in omega-3s, a narrower and arguably smarter target than cultivated fillets: fat is what gives food much of its flavour and mouthfeel, and it can be blended into plant-based products or sold to cosmetics makers. Strategic money from Japanese packaging giant Toyo Seikan suggests industrial buyers see a use for it. It remains early-stage, and regulatory approval timelines will dictate its pace.

Founded: 2019

Founders: Mandy Hon, Shigeki Sugii

Key backers: Toyo Seikan Group, 144 Ventures, Lin Xiangliang (Esco Aster CEO)

Agri-food supply chain and B2B procurement

The unglamorous middle of the food chain, where produce, fish and chicken move from farms to kitchens, has drawn some of the region’s largest foodtech cheques and produced its sharpest reversals. The survivors tend to be the ones that stayed close to paying business customers and away from consumer subsidies.

7. EdenFarm (Indonesia)

EdenFarm supplies fresh produce to restaurants, hotels and wet-market traders, sourcing directly from farmers and cutting out layers of middlemen. It is a margin game played in a perishable category, and the startup has raised about US$19 million to play it. TaniHub’s collapse showed how badly this model goes when it overreaches into consumer delivery and lending; EdenFarm’s discipline in staying B2B is the reason it is on this list.

Founded: 2017

Founders: David Setyadi Gunawan, Ramavito Mountaino, Febrianto Gamal

Key backers: Telkomsel Mitra Inovasi, AC Ventures, AppWorks, Global Founders Capital, Y Combinator, OCBC Ventures

8. Kamereo (Vietnam)

Kamereo is Vietnam’s answer to restaurant procurement: an online ordering platform that supplies F&B outlets and retailers in Ho Chi Minh City and Hanoi with produce, meat and dry goods. Its Japanese founders have assembled a cap table heavy with Japanese corporates and megabanks, plus Thai agri-food giant CPF. In a country where food-service supply remains overwhelmingly informal, Kamereo is one of the few venture-backed players trying to formalise it.

Founded: 2018

Founders: Taku Tanaka, Hiroshi Tokaku

Key backers: Sumitomo Corporation, SMBC Venture Capital, Mitsubishi UFJ Capital, CPF Group, Quest Ventures, Genesia Ventures

9. Freshket (Thailand)

Freshket supplies Thai restaurants with ingredients through an online ordering platform, and it has been unusually successful at attracting strategic Thai money: energy conglomerate PTT Oil and Retail led its Series B, and Thai President Foods, the maker of Mama noodles, is also a backer. For a B2B food platform, corporate shareholders who also run thousands of outlets are worth more than a marquee VC logo.

Also Read: Why Indonesia’s agritech winners will be phygital, not purely digital

Founded: 2017

Founders: Ponglada Paniangwet, Tuangploi Chiwalaksanangkoon

Key backers: PTT Oil and Retail, Thai President Foods, Kliff Capital, Openspace Ventures, 500 TukTuks

10. Aruna (Indonesia)

Aruna connects small-scale fishers across the archipelago with domestic and export buyers, handling collection, quality checks and cold-chain logistics in between. Its Series A, extended to US$65 million, was the largest of its kind in Indonesian agri-maritime tech. The eFishery scandal in late 2024 cast a long shadow over Indonesian aquatech, and Aruna has been more muted since its 2022 highs, but it remains operational and one of the sector’s few scaled players.

Founded: 2016

Founders: Farid Naufal Aslam, Indraka Fadhlillah, Utari Octavianty

Key backers: Vertex Ventures, Prosus Ventures, East Ventures, AC Ventures, MDI Ventures, SIG

11. TreeDots (Singapore)

TreeDots sells surplus, ‘ugly’ and near-expiry food that would otherwise be dumped, mostly to F&B businesses, and runs the cold-chain logistics to move it. It is that rare food-waste startup with a business model rather than a mission statement: buy cheap, sell at a discount, keep the spread. It raised an US$11 million Series A in 2021, and its survival through the downturn says more than the round did.

Founded: 2017

Founders: Tylor Jong, Lau Jia Cai, Nicholas Lim

Key backers: Amasia, East Ventures, ACTIVE Fund (Ayala), SEEDS Capital

12. Chickin (Indonesia)

Chickin gives Indonesian broiler farmers IoT sensors to monitor temperature, humidity and feed in their coops, then buys and distributes the chickens they raise. Chicken is Indonesia’s most-consumed animal protein, and the gap between well-run and badly run farms is measured in dead birds and wasted feed. Chickin’s pitch is that data plus a guaranteed buyer can close that gap.

Founded: 2020

Founders: Tubagus Syailendra, Ashab Alkahfi, Ahmad Syaifullah

Key backers: East Ventures, 500 Global

13. JALA (Indonesia)

JALA started with a water-quality monitoring device for shrimp ponds and has grown into a full-stack aquaculture platform offering farm-management software, inputs and help selling harvests. Indonesia is one of the world’s biggest shrimp exporters, yet disease and poor water quality routinely wipe out ponds. JALA’s impact-heavy investor base, including Mirova and the Meloy Fund, reflects its smallholder focus.

Founded: 2017

Founders: Aryo Wiryawan, Liris Maduningtyas

Key backers: Intudo Ventures, Sinar Mas Digital Ventures, Mirova, Meloy Fund, Real Tech

14. Food Market Hub (Malaysia)

Food Market Hub sells procurement and inventory software to restaurants, helping them track what they order, what they waste and what their suppliers charge. It is a SaaS business in a sector where most kitchens still run on WhatsApp and paper invoices, which is both the opportunity and the problem. It has raised money to expand from Malaysia into Indonesia, Thailand and Vietnam.

Founded: 2017

Founders: Anthony See, Shayna Teh

Key backers: Go-Ventures, SIG, 500 Startups

Online grocery and fresh-food commerce

This is the cluster that took the 2022 correction hardest, and it shows: of the seven grocery names on our long list, four make the cut, and one of those with a caveat. Indonesia dominates, and the business models split cleanly into farm-direct e-grocers, dark-store quick commerce and agent-led social commerce.

15. Sayurbox (Indonesia)

Sayurbox sources fruit, vegetables and fresh food directly from farmers and delivers it to households and businesses in Java and Bali. Its Series C, worth Rp1.7 trillion (about US$120 million), was among the region’s biggest e-grocery rounds. Like every player in the category it has had to cut costs since, but it has outlasted rivals that raised less and spent faster.

Also Read: Why quick commerce is really about frequency, not speed

Founded: 2017

Founders: Amanda Susanti, Rama Notowidigdo, Metha Trisnawati

Key backers: Northstar, Alpha JWC Ventures, IFC, Astra Digital, Syngenta Group Ventures, Global Brain

16. Segari (Indonesia)

Segari runs a farm-to-doorstep e-grocery service across Greater Jakarta, built around next-day delivery and pre-ordered demand rather than expensive instant delivery. Having Alfamart, one of Indonesia’s largest convenience-store chains, on its cap table hints at where its long-term value may lie. Trackers put its total funding at roughly US$39.5 million across three rounds.

Founded: 2020

Founders: Yosua Setiawan, Farand Anugerah, Farandy Ramadhana

Key backers: Go-Ventures, Beenext, AC Ventures, Alfamart, Gunung Sewu Group, Saison Capital

17. Astro (Indonesia)

Astro is the last quick-commerce player standing in Jakarta, delivering groceries from dark stores in as little as 15 minutes. Quick commerce was supposed to be dead in Southeast Asia; then Amazon led a US$52 million round in Astro in 2025. Whether that is a vote of confidence in the model or a cheap option on the Indonesian market is the question worth asking.

Founded: 2021

Founders: Vincent Tjendra

Key backers: Amazon, Accel, Tiger Global, AC Ventures, Global Founders Capital, Lightspeed, Peak XV

18. Super (Indonesia)

Super uses community agents to aggregate grocery orders in towns and villages outside Jakarta, starting in East Java, where goods often cost more than in the capital. It has raised more than US$100 million, including a US$70 million Series C led by NEA in 2022. Super has kept a low public profile since, so treat its momentum with some caution, but the underserved-consumer thesis it pioneered remains sound.

Founded: 2018

Founders: Steven Wongsoredjo, Debeasinta Budiman, Garret Koeswandi

Key backers: NEA, SoftBank Ventures Asia, DST Global Partners, Y Combinator Continuity, B Capital

Cloud kitchens, restaurant tech and F&B brands

The app-first coffee chain is Southeast Asia’s most successful foodtech export, and VCs have treated it as a tech play. Restaurant software, meanwhile, has quietly become a better business than the restaurants themselves.

19. Hangry (Indonesia)

Hangry began as a delivery-only, multi-brand cloud kitchen and, unlike most of its peers, read the post-pandemic market correctly: it pivoted into dine-in outlets for brands such as Moon Chicken. That move from ghost kitchen to real restaurant is the main reason it is here while many cloud-kitchen operators are not. It is now as much a restaurant group as a tech company.

Founded: 2019

Founders: Abraham Viktor, Andreas Resha, Robin Tan

Key backers: Alpha JWC Ventures, Surge (Sequoia), Atlas Pacific Capital, SALT Ventures, Heyokha Brothers

20. ESB (Indonesia)

ESB sells restaurants an all-in-one stack: point of sale, self-ordering, kitchen display and back-office ERP. Its Rp420 billion (roughly US$29 million at the time) Series B was large for vertical SaaS in Indonesia. When restaurants struggle, they cut marketing before they cut the system that runs their tills, which makes ESB one of the more resilient bets in this cluster.

Founded: 2018

Founders: Gunawan Woen, Eka Prasetya, Setiadi Prawiryo Moeljadi, Dwi Prawira

Key backers: Northstar Group, Alpha JWC Ventures, BEENEXT, AC Ventures, Vulcan Capital

21. Kopi Kenangan (Indonesia)

Kopi Kenangan turned Indonesia’s grab-and-go coffee habit into a unicorn, hitting a US$1 billion valuation with a US$96 million Series C in late 2021. It is well past ’emerging’, but no list of Southeast Asian foodtech is complete without the company that proved an app-led beverage chain could raise like a tech startup. Its expansion beyond Indonesia will test whether the model travels.

Also Read: Cata raises US$5.3M to bring enterprise app tech to F&B and retail operators

Founded: 2017

Founders: Edward Tirtanata, James Prananto, Cynthia Chaerunnisa

Key backers: Tybourne Capital, Horizons Ventures, Kunlun, B Capital, Falcon Edge, Sequoia India, Verlinvest, Sofina

22. ZUS Coffee (Malaysia)

ZUS Coffee has become Malaysia’s largest coffee chain by outlet count in a few short years, and has pushed into the Philippines, where it even sponsors a volleyball team. It has reportedly weighed a listing on Bursa Malaysia. Like Kopi Kenangan, it stretches the definition of a startup, but its app-first loyalty model is the template the rest of the sector copies.

Founded: 2019

Founders: Ian Chua, Venon Tian, Terence Ho

Key backers: KV Asia Capital, KWAP, Kapal Api Group

23. Pickup Coffee (Philippines)

Pickup Coffee sells sub-P100 cups through compact grab-and-go kiosks, and has scaled across Metro Manila at speed. It attracted about US$40 million in investment within a few years of launch. Its price point is its moat and its risk: in a market where ZUS, Starbucks and local chains are all fighting for the same commuter, there is little room to raise prices.

Founded: 2022

Founders: Diego Lorenzo, Jaime González Fernández

Key backers: Go-Ventures, Venturi Partners, DSG Consumer Partners, Openspace, Kickstart Ventures, Antler

24. Tomoro Coffee (Indonesia)

Tomoro Coffee brings a Luckin-style playbook of automation, small stores and aggressive pricing to Indonesia, and has expanded to Singapore and beyond. Its founders’ China background shows in its operational intensity. It is the clearest sign that Chinese coffee-chain economics are now being exported to Southeast Asia, and that local incumbents will have to compete on cost.

Founded: 2022

Founders: Xing Wei ‘Star’ Yuan, Fish Sun

Key backers: ATM Capital

25. Flash Coffee (Indonesia)

Flash Coffee is a survivor story with an asterisk. The Rocket Internet-incubated chain extended its Series B to US$50 million, expanded across the region, then retreated, and is now headquartered in Jakarta and focused on Indonesia. It is still operating and still led by founder David Brunier. Its arc is a useful cautionary tale about how quickly regional ambition can outrun unit economics.

Founded: 2020

Founders: David Brunier, Sebastian Hannecker

Key backers: White Star Capital, Delivery Hero, Rocket Internet, Global Founders Capital

26. JIWA Group (Kopi Janji Jiwa) (Indonesia)

JIWA Group runs Kopi Janji Jiwa, one of Indonesia’s largest grab-and-go coffee brands, along with food brands such as Jiwa Toast. It grew rapidly through a franchise-heavy model and has drawn backing from Openspace. It is less of a technology company than Kopi Kenangan or Tomoro, but its scale in the mass market makes it impossible to ignore.

Founded: 2018

Founders: Billy Kurniawan

Key backers: Openspace, Capsquare Asia Partners

27. Lemonilo (Indonesia)

Lemonilo makes ‘healthier’ instant noodles and snacks without MSG or synthetic colouring, and took on Indomie on its home turf, starting online and moving into modern retail. Its US$36 million Series C made it one of the best-funded consumer food brands in Indonesia. Health-positioned FMCG is a crowded shelf, and its next test is whether it can defend share once incumbents copy the formula.

Founded: 2016

Founders: Shinta Nurfauzia, Ronald Wijaya, Johannes Ardiant

Key backers: Sofina, Sequoia India, East Ventures, Alpha JWC Ventures, Unifam Capital

The key lesson

If one lesson runs through this list, it is that Southeast Asia’s foodtech survivors have learned to follow the money rather than the hype. The cultivated-meat pioneers now sell cell lines and ingredients to food giants instead of chasing supermarket shelves. The supply-chain players still standing are the ones that stayed close to restaurants and traders who pay their invoices, not consumers who expect free delivery. Even the coffee chains, the sector’s flashiest success, are winning on loyalty apps and unit economics rather than buzz.

Also Read: Nadiem Makarim, eFishery, and the end of blind faith in startups

Look at who is writing the cheques, too. Commodity traders, convenience-store chains, Japanese megabanks, Thai conglomerates and an American e-commerce titan now sit alongside traditional VCs on these cap tables. That is validation, but it is also dependency, and the line between strategic partner and eventual acquirer is thin.

The coming years will test whether these companies can build durable businesses in a region of price-sensitive diners and wafer-thin margins. The appetite is clearly there. The real question is who can afford to feed it.

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Winning new customers is usually treated as one of the clearest signs that a company is growing. More contracts mean more revenue, more logos and more evidence that the market wants what the business is selling. For an early-stage company in particular, saying no to a paying customer can feel almost irrational.

Yet revenue can hide very different economics. One customer may buy the standard product, renew regularly and require relatively little support. Another may generate the same annual revenue while demanding permanent discounts, custom development, unusual legal terms, frequent executive attention and a level of service that cannot be repeated across the rest of the customer base. Both appear as growth on the top line, but they are not necessarily building the same company.

Customer acquisition does more than add revenue. It also shapes the organisation required to serve that revenue. A business that keeps saying yes to every customer can eventually discover that it has not built a scalable model, but a collection of exceptions.

Revenue can grow while the business becomes harder to scale

The problem often begins with customisation. A major prospect asks for one additional feature, a different workflow or a special integration. The request looks reasonable, particularly when the contract is large enough to justify the effort. Another customer then asks for something slightly different, while a third requires its own reporting process.

Each deal may still look attractive when viewed in isolation, but the complexity accumulates across the organisation. Engineering maintains more variants, customer success learns more exceptions, sales becomes dependent on bespoke promises, finance manages non-standard pricing and the product roadmap increasingly reflects the demands of whichever large customer signed most recently.

McKinsey has documented how product and service complexity can create costs that are difficult to see when a company looks only at the immediate economics of a sale, from maintenance and rework to additional support and operational overhead. Its research into subscription businesses has also found that greater quote-to-cash complexity is associated with slower sales processes, poorer customer experience and a reduced ability to grow.

Customisation is not inherently a problem. Some customers justify it because they help a product mature or open a market that will matter later. The mistake is assuming that every contract is strategically valuable simply because it adds revenue.

Also Read: “AI amnesia” is quietly costing Southeast Asian brands their customers

The real cost of a customer is larger than the invoice

Most growing companies know what it costs to acquire a customer. Far fewer understand the full cost of serving one.

That cost includes onboarding, implementation and support, but also product meetings, additional testing, legal negotiations, manual reporting, billing exceptions, management time and the opportunity cost of delaying work that would benefit a much larger share of the customer base. A large customer can therefore produce more revenue and still create less value if servicing the account consumes a disproportionate amount of organisational capacity.

This is particularly dangerous in startups and scale-ups, where engineering time, product attention and leadership bandwidth can be more constrained than cash itself. McKinsey has repeatedly argued that understanding total cost to serve is essential because apparently attractive revenue can conceal significant costs elsewhere in the business.

Customer quality therefore cannot be assessed through annual contract value alone. The more useful question is what kind of company the business must become in order to keep earning that revenue.

The best customers often make the product more repeatable

Good customers do not necessarily ask for nothing. Demanding customers can be extremely valuable because they expose weaknesses, identify missing capabilities and force a company to improve. The distinction lies in whether those demands reveal a problem that is likely to matter to a broader market.

If several strong customers require the same capability, that may be a useful product signal. If one account needs a feature only because of an unusual internal process, building it may create little value beyond that relationship. The revenue arrives immediately, while the complexity remains long after the contract has been signed.

Stripe’s guidance on product-market fit makes a similar distinction. It describes the strongest customer segments not simply as those willing to pay, but as those combining strong conversion, low churn and attractive contract value. Bessemer Venture Partners has also warned about revenue-centric startups that continue closing deals through broad use cases, extensive customisation and heavy post-sale service, while drifting away from a repeatable product.

A strong customer fit can reinforce the product, the sales process and the operating model at the same time. A poor fit may pull all three in different directions.

Also Read: Asia’s research-tech companies: Millions of users and nearly invisible to funders and customers

Strategic value can justify imperfect short-term economics

None of this means every account should be judged through a rigid profitability formula. Some customers are valuable precisely because their short-term economics are imperfect.

A respected company can become a reference that reduces friction in future sales. A first customer in a new country can help a business understand a market that later becomes significant. A demanding enterprise account may force the product to meet security, compliance or integration requirements that subsequently unlock an entire category of buyers.

These exceptions become useful when they are intentional. The company knows why it is accepting lower margins or greater complexity and what it expects to gain in return. That is very different from carrying an expensive customer indefinitely because nobody has ever questioned whether the relationship still makes strategic sense.

Customer value can therefore include recurring revenue potential, cost to serve, fit with the core offer, reference value and the ability to open a new market. The strongest accounts are often those where several of these characteristics reinforce one another.

Saying no can be a growth decision

For founders and sales teams, rejecting revenue remains emotionally difficult. Early-stage companies are encouraged to listen intensely to customers, move quickly and do things that do not scale while they are still learning what the market wants. That approach can be essential in the beginning, but it becomes dangerous when learning quietly turns into dependency.

If every large customer can redirect the roadmap, negotiate a new pricing model and create its own version of the product, revenue may continue to rise while the company becomes progressively less scalable. Y Combinator has long argued that good customer service does not mean serving every potential customer, particularly when doing so pulls a company away from the problem it has chosen to solve.

The issue becomes more important as the business grows because scale depends heavily on repetition. Sales becomes more efficient when the offer is clear, onboarding improves when implementation is predictable, margins become stronger when support can be standardised and product development accelerates when teams are not constantly maintaining exceptions.

The quality of growth is therefore partly determined before a contract is signed. One customer can add revenue while making the next hundred easier to serve; another can add the same revenue while making the entire organisation more complicated.

A growing company still needs customers, but it also needs to understand what kind of growth those customers are creating. Sometimes the most strategic response to a prospect is not another discount, another custom feature or another exception. It is knowing that the revenue is not worth becoming the wrong company to earn it.

—

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East Asia’s crypto market is no longer moving as one bloc. New regional data shows a fragmented landscape where South Korea is trading artificial intelligence-linked tokens at scale, Hong Kong is pulling in institutional capital, Japan’s retail users are quietly moving on-chain, and China’s stablecoin activity is expanding despite official restrictions.

The region’s overall crypto economy contracted modestly between July 2025 and June 2026, in line with the global bear market. But the headline decline masks sharp differences between markets. South Korea remained the region’s largest crypto economy at US$449.1 billion, followed by Japan at US$228.3 billion, Hong Kong at US$192.2 billion, China at US$176.3 billion, and Taiwan at US$140.4 billion.

Also Read: Taiwan’s stablecoin moment: Why the NTD could outshine the dollar

For Southeast Asian founders, exchanges and regulators, the message is clear: crypto adoption is increasingly shaped by local market structure rather than broad regional sentiment. Tax policy, licensing, capital controls, retail culture and institutional access are now producing very different outcomes across Asia.

As Daniel Kim, CEO of Tiger Research, put it, “Asia is that rare region with both grassroots retail depth and institutional firepower.”

South Korea turns the AI trade into a crypto trade

South Korea’s crypto economy grew 12.3 per cent during the period, helped by an additional US$51.1 billion in exchange-related flows. Unlike Hong Kong, where institutions drive much of the activity, Korea’s market remains heavily retail-led.

That matters because Korean retail investors have a long record of chasing high-conviction, high-volatility themes. In equities, the country’s AI trade has centred partly on SK Hynix, whose memory chips are critical to data centres. In crypto, the same appetite has spilled into tokens linked to AI projects and infrastructure.

By June 2026, AI-related cryptocurrencies were the most popular thematic category in won-denominated trading volume, surpassing payment tokens such as XRP. This made South Korea a global outlier. AI-crypto trading reached only 0.91 per cent of yen-denominated activity in June 2026, while the equivalent share in won was 19.5 times higher.

The names changed quickly. Worldcoin led with US$7.41 billion in volume, followed by SAHARA at US$3.2 billion, VIRTUAL at US$2.7 billion, BIO at US$2 billion and NEAR at US$1.7 billion.

The turnover suggests Korean traders are not simply buying a sector and holding it. They are rotating rapidly through whichever token best captures the current AI narrative.

Also Read: Japan shows how non-USD stablecoins complement USDC and USDT

Korea’s regulatory environment has helped sustain that retail dominance. Crypto profits remained untaxed during the study period, although a delayed 22 per cent tax is scheduled to begin in 2027. At the same time, corporate participation is only gradually opening up after long-standing restrictions.

Francis Kang, Executive Director of Korea Blockchain Week, described institutions as being in “preparatory mode”, with major banks and securities firms running pilots around stablecoins, tokenisation and custody. If the tax is implemented while corporate access expands, Korea’s market could begin shifting from retail momentum to a more balanced structure.

Japan’s retail market is more active than it looks

Japan is often viewed as an institution-first crypto market because of its strict licensing regime and the growing role of banks and financial firms. Yet the data points to a more active consumer base than that reputation suggests.

The country recorded a US$228.3 billion crypto economy during the period. Decentralised exchanges, or DEXs, accounted for nearly 35 per cent of Japan’s service activity, the highest share among mature centralised-exchange markets in East Asia. DEX activity has risen more than 200 per cent since 2022, while centralised-exchange activity has been broadly flat.

Much of this is happening in smaller, retail-sized transactions. About 65.7 per cent of Japanese retail DEX swaps were in the US$10 to US$1,000 range. Roughly one in four users who withdrew funds from exchanges operating in Japan later deposited into decentralised finance protocols.

Taishi Sato, CEO of DeFimans, a subsidiary of SBI, said the most visible activity is in perpetual futures, a type of derivative contract with no expiry date. He linked this to Japan’s large base of foreign-exchange traders, who are already used to hedging macro exposure.

Still, tax has been a major brake. Japanese crypto traders faced a maximum marginal rate of 55 per cent during the study period. Reforms advanced in July 2026 could shift eligible gains towards separate taxation of about 20 per cent. If implemented, that could bring more retail volume onshore and into regulated venues.

For Southeast Asia, where retail crypto participation is high but tax treatment remains uneven, Japan’s case is worth watching. A clearer, lighter regime could show whether tax reform moves users back into compliant platforms rather than offshore or peer-to-peer channels.

Hong Kong builds the institutional layer

Hong Kong’s crypto economy, at US$192.2 billion, is defined less by retail speculation and more by its role as an institutional settlement hub.

Also Read: Nearly half of Asia Pacific consumers open to stablecoins within five years, Visa says

Institutional platforms, including over-the-counter desks, custodians and market makers, captured 16 per cent of Hong Kong’s service inflows during the period, up from around nine per cent two years earlier. No other East Asian market exceeded six per cent.

The city drew nearly US$24 billion in inbound service-to-service transfers, around six times Japan’s figure and 44 times South Korea’s. Outbound flows reached about US$11 billion, suggesting Hong Kong is functioning as a two-way corridor for institutional capital.

This is the result of policy design. Hong Kong has made stablecoins, tokenised finance and central bank digital currencies part of its financial strategy. It issued its first stablecoin licences in 2026 and has moved into market testing with institutional players.

For Singapore and other Southeast Asian financial centres, Hong Kong’s trajectory is a direct benchmark. Both cities want to attract serious digital-asset businesses without reopening the excesses of the last crypto cycle. The lesson so far is that institutional money appears willing to move into markets where licensing is strict but workable.

China shows prohibition does not erase demand

China remains the hardest market to measure because crypto services are officially banned. Even so, the report estimates its crypto economy at no less than US$176.3 billion.

Unlike its neighbours, China’s activity is dominated by peer-to-peer flows rather than exchanges. Domestic peer-to-peer activity accounted for 59.1 per cent of the country’s all-in crypto economy, a 3.5-fold increase in share over the prior period.

Stablecoins appear to be central to this shift. The number of unique wallets sending peer-to-peer stablecoin transactions grew 43-fold between the first quarter of 2024 and the second quarter of 2026. China’s self-custodied stablecoin holdings also turned over 33.2 times a year, more than three times the global average of 9.3 times.

That high velocity suggests stablecoins are being used less as a store of value and more as working capital or payment rails. The report notes a possible link with the expansion of China’s social credit system into finance and the internet in March 2025, though the relationship remains a hypothesis rather than proven causation.

The broader takeaway is familiar across emerging markets, including parts of Southeast Asia: when users face friction in formal financial channels, crypto activity may not disappear. It may simply move into less visible peer-to-peer networks.

Also Read: SEA’s stablecoin boom has a dollarisation problem nobody’s pricing in

East Asia now offers four different crypto futures at once. Korea shows the power and risk of retail narratives. Japan shows how tax can suppress an otherwise sophisticated user base. Hong Kong shows institutional capital following regulatory clarity. China shows the limits of prohibition.

For Southeast Asia, the question is no longer whether crypto adoption will continue, but what kind of adoption its rules will encourage.

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Across Southeast Asia’s technology hubs, engineering leaders are getting used to a new kind of productivity curve. In Singapore, Jakarta, Manila and Ho Chi Minh City, software teams are shipping faster, clearing backlogs more quickly and leaning on AI tools for work that once consumed hours of developer time.

The latest shift is not just about coding copilots suggesting the next line of code. The region is moving into the age of autonomous AI agents: tools that can generate tests, review pull requests, draft documentation, refactor code and execute multi-step engineering tasks with limited human input.

Also Read: I stopped hiring. I’m not sure it’s a strategy yet

On the surface, this looks like an unambiguous win for startups. According to the Agoda AI Developer Report 2026, which surveyed more than 800 developers and engineering executives across Southeast Asia and India, 55 per cent of developers now save at least seven hours a week using AI, up from 18 per cent a year earlier.

For founders managing tight runways, that kind of efficiency is hard to ignore. But beneath the productivity gains is a more difficult question: if AI absorbs the routine work that once trained junior developers, how will the region produce its next generation of senior engineers?

The rise of the super-senior engineer

Software teams used to scale in a fairly predictable way. As product demands grew, companies added headcount. Junior developers handled simpler tickets, built UI components, wrote tests, fixed bugs and learned under the supervision of more experienced engineers. Mid-level engineers took on larger features. Senior engineers reviewed architecture, guided technical decisions and caught mistakes before they reached production.

AI agents are weakening that link between output and team size.

“One highly experienced engineer can now achieve the output that previously required a team of five to ten engineers,” said Tajrij Kawakibi, CTO at Indonesian software firm PT. Quadra Konten Persada, in the Agoda report.

Jerome Asuncion, Head of Data and Engineering at LiVeritas Philippines, made a similar point, saying agentic AI has enabled his lean team to tackle initiatives that would once have required far more engineering capacity.

That is changing how Southeast Asian startups think about team design. Only 14 per cent of developers surveyed expect software engineering team structures to remain unchanged over the next 12 months. Twenty-nine per cent expect fewer engineers to manage a growing matrix of AI workflows. Another 25 per cent expect AI agents to become autonomous team members, and 23 per cent expect smaller, hyper-productive squads.

Also Read: AI startups are hiring around answers they haven’t earned yet

For early-stage startups, the calculation can seem brutal. Why hire several junior developers who need onboarding, mentoring and code review when one senior engineer with the right AI workflow can produce similar output more quickly?

Junior anxiety, executive optimism

The tension shows up clearly in how different levels of the engineering workforce view their own future.

Across the survey, 37 per cent of developers believe AI agents will make their roles less secure, while 32 per cent expect no change and 31 per cent believe their roles will become more secure. But the anxiety is not evenly distributed.

Among junior developers, 49 per cent feel less secure about their future. That falls to 30 per cent among mid-level developers and just 17 per cent among CTOs and VPs of Engineering.

The gap reflects what each group sees in day-to-day work. Senior leaders experience AI as leverage. It removes repetitive implementation work and gives them more time for architecture, risk management and product trade-offs. The report found that 83 per cent of CTOs and VPs are already using production-grade AI agents, compared with only 33 per cent of junior developers.

For juniors, the picture is less reassuring. The very tasks they were once given to build confidence (writing basic documentation, generating unit tests, refactoring small functions, fixing minor bugs) are among the first to be automated. Junior developers are also nearly three times as likely as senior engineers to cite lack of skills as their main operational barrier.

This is not simply a labour market issue. It is a training issue.

Skills are moving up the stack

As AI takes on more code production, the definition of engineering value is changing. Syntax still matters, but it is no longer the main differentiator. The higher-value work now sits in system design, judgement, orchestration and review.

Developers surveyed by Agoda ranked system architecture and design as the most important future skill, cited by 66 per cent of respondents. AI literacy came next at 61 per cent, covering the ability to understand model behaviour, context limits, prompting, failure modes and when not to trust outputs. Agent orchestration and management followed at 51 per cent.

Traditional coding fundamentals, by contrast, were selected by only 18 per cent.

Also Read: Vietnam’s returning engineers are high-quality talent. Keeping them is the real problem

That does not mean coding basics are irrelevant. A developer who cannot read code, reason through execution paths or understand data flows will struggle to evaluate AI-generated work. But the premium is shifting from producing code manually to knowing what should be built, how systems might fail and whether an automated output is safe to deploy.

Developers appear to understand this. Forty-three per cent are actively building new technical skills, while 37 per cent are trying to shift their daily work towards higher-value tasks beyond raw code production.

For Southeast Asia, where many startups have historically relied on young engineering talent to scale affordably, this shift could be disruptive. The region does not just need more developers; it needs developers who can grow into architects, security-minded reviewers and technical leaders.

The mentorship gap

The hard part is that engineering judgement has traditionally been built through repetition. Senior developers did not become senior by skipping the mundane work. They wrote imperfect code, broke things, handled edge cases, received blunt pull request feedback and slowly developed intuition.

If junior developers use AI agents to generate most of their code from day one, they may move faster but learn less deeply. They may not develop the instinct to spot a subtle security vulnerability, a flawed database design, a hallucinated dependency or an architectural shortcut that will become expensive later.

M. Ridwan Agustiawan of dataxet described this as a “judgment gap” in the report: less experienced developers may either accept AI outputs too readily or fail to recognise when human validation is essential.

That risk is already visible in the data. Eighty-six per cent of developers agree that human review of AI outputs remains essential, and 42 per cent say the individual developer should carry primary responsibility for outages caused by AI agents.

In other words, AI can write the code, but humans still own the consequences.

What startups should do now

Southeast Asian startups cannot afford to stop hiring juniors. Doing so may improve short-term productivity but create a leadership shortage later. Instead, CTOs and engineering managers need to redesign how junior talent is selected and trained.

First, hiring should move beyond syntax-heavy tests. Candidates still need fundamentals, but interviews should also assess whether they can debug AI-generated code, explain trade-offs and question flawed assumptions.

Second, mentorship should become more deliberate. Junior developers should be paired with senior engineers during AI-assisted workflows, not only after code is written. The key lesson is no longer just “how do you write this function?” but “why is this the right design, what could go wrong, and how do we verify the agent’s output?”

Also Read: AI has answers, experience has judgment

Third, companies should create safe ownership zones. Juniors can manage low-risk AI workflows such as test generation, internal tooling or documentation automation, giving them room to make mistakes without threatening production systems.

AI tools will soon be widely available to every serious engineering team. The real advantage will belong to companies that know how to turn those tools into learning systems.

For Southeast Asia’s startup ecosystem, the junior developer dilemma is not a reason to retreat from AI. It is a warning that productivity without mentorship can hollow out the talent pipeline. The startups that solve this early will not just ship faster; they will build the next generation of engineering leaders.

The post The junior developer dilemma: How AI is reshaping tech talent in Southeast Asia appeared first on e27.

Selecting a scalable enterprise solution is no longer just an operational choice; it is a core business strategy. For small and medium businesses navigating a highly digitalized economy, legacy architectures fail to meet modern efficiency standards. Integrating advanced automation into financial workflows is crucial for maintaining agility and staying competitive in a rapidly evolving market.

Modern Tech Frameworks: MCP and Agentic AI

The integration of Model Context Protocol (MCP) and Agentic AI represents a massive leap forward for enterprise software. Static systems require rigid, manual configurations to pass data between an AI model and internal database structures. Dynamic MCP, however, maps entire corporate memory frameworks automatically. This allows autonomous agents to execute complex, contextual financial workflows with zero human intervention.

For small and medium enterprises, this technical shift drastically minimizes processing bottlenecks. Instead of human operators spending hours reconciling accounts or chasing cross-border invoice discrepancies, certified AI agents securely interact with real-time operational data. This ensures absolute workflow compliance and continuous strategic optimization.

 

The Steep Financial Cost of Rigid, Legacy Platforms

Sticking with low-cost, off-the-shelf software with no room for no-code customization or modern MCP adoption carries major hidden expenses. While the initial subscription price might look attractive, these rigid systems quickly create operational barriers.

• Exponential Integration Overheads:Connecting third-party productivity tools or proprietary operational modules requires custom software development, which adds significant recurring IT costs.
• Massive API Consumption Fees:Legacy architectures rely on heavy, unoptimized API calls. Without natively integrated infrastructure, routing data out to LLMs drains tokens and leads to massive cloud computing invoices.
• Severe Operational Stagnation: When business models evolve, a rigid platform traps the enterprise in slow, manual workflows. This completely wipes out the initial savings of choosing a cheaper option.

Also Read: Top 3 popular GEO monitoring tool for SEO optimisation targeting service industry in Singapore

Evaluating the Top 5 AI Accounting ERP for Modern SMBs

Navigating the enterprise application market requires balancing immediate software functionality against long-term operational costs and architectural flexibility. Below is an analytical review of the leading solutions tailored for small and medium operations.

Multiable

• Pros:

• • Features over 500 agent-ready APIs that cut agentic AI token costs by over 90% compared to systems without built-in API frameworks.
  • Incorporates an advanced dynamic MCP covering standard features as well as custom-made functions configured or developed directly by the user.
  • Operates on a highly optimized, lightweight Linux-based cloud infrastructure to minimize underlying hardware overheads.
  • Delivers comprehensive multi-currency tracking along with native, real-time financial consolidated reporting.
  • Users can access the platform at Multiable to seamlessly integrate their entire front-end and back-end operational workflows.
• Cons:
  • Support services requested during weekends or public holidays will incur extra charges.
  • The pricing model may be out of touch for small mom-and-pop businesses with fewer than 10 staff members.
  • Customizations made outside the standard no-code platform require specialized internal training.

Evaluation Summary:
By delivering a vast array of pre-built APIs along with an adaptive database architecture, Multiable ERP slashes the hidden costs of AI integration. It stands out as the single best AI Accounting ERP for SMB in Singapore, particularly for scaling organizations that require complete custom flexibility without paying massive premium fees.

Chillaccount

• Pros:

• • Built from the ground up as a cloud-native platform tailored for agile, rapid deployments.
  • Features a highly intuitive financial ledger configuration designed to simplify everyday corporate book-keeping.
  • The digital interface at Chillaccountoffers straightforward automated invoicing right out of the box.
• Cons:
  • Completely lacks deep supply chain or advanced manufacturing module integrations.
  • Offers limited multi-company consolidation capabilities for complex, scaling corporate groups.
  • The platform lacks a robust, built-in dynamic MCP architecture to handle complex external AI orchestration.

Evaluation Summary:
A clean, cloud-native design ensures that early-stage businesses can automate their core accounting books quickly. It serves as a dependable starting option for straightforward operations that do not yet face intricate supply chain or multi-tiered corporate tracking challenges.

Microsoft Dynamics 365

• Pros:

• • Native, seamless data connectivity across the entire Microsoft 365 product ecosystem.
  • Highly scalable framework capable of managing huge multi-national operations.
  • Offers extensive global compliance reports that cover a massive variety of regulatory jurisdictions.
• Cons:
  • The resource-hungry Windows Server operating system requirement means hardware and infrastructure costs can be up to 10x higher than Linux-based solutions.
  • Real-time performance issues and query latency within AzureSQL remain a noted concern during heavy data loads.
  • Implementation paths are exceptionally complex and require highly paid external consultants.
  • The subscription matrix features convoluted, multi-tiered licensing structures that escalate quickly.

Evaluation Summary:
Large businesses benefit significantly from deep ecosystem ties and extensive global compliance reporting. However, smaller growing firms must carefully balance these features against the high infrastructure costs and complex setup demands of this heavy environment.

Oracle NetSuite

• Pros:

• • Provides a highly comprehensive cloud-based single database architecture for unified corporate tracking.
  • Features powerful, deeply detailed business intelligence dashboards.
  • Strong global tax management engines built directly into the core platform.
• Cons:
  • Customers frequently face steep increments in SaaS fees upon renewal, which can hit up to 50% of the first SaaS contract price.
  • Suffers from a complete lack of built-in MES support, forcing firms to rely on clumsy third-party integrations for manufacturing floors.
  • General service availability is a notable concern, with three serious outages and system malfunctions occurring in 2025 alone.
  • Custom scripts rely on specialized proprietary languages, making in-house maintenance difficult.

Evaluation Summary:
This platform offers powerful global business consolidation and an all-in-one corporate record database. Even so, companies must be ready for sharp pricing shifts at renewal and evaluate alternative integrations if they run complex manufacturing or shop-floor operations.

Odoo Enterprise

• Pros:

• • Modular system architecture allows businesses to install only the apps they currently need.
  • Modern user interface ensures a fast learning curve for internal teams.
  • Large international open-source community provides a massive library of community apps.
• Cons:
  • Heavy customization paths often break or encounter validation errors during major version upgrades.
  • The official documentation lacks depth regarding complex multi-currency accounting rules.
  • Connecting independent modules frequently leads to data validation mismatches.
  • Relies heavily on third-party community extensions to handle region-specific tax reporting.

Evaluation Summary:
A flexible, app-based ecosystem lets organizations build out their digital platform step-by-step. It provides a solid baseline for companies with standard workflows, provided they carefully manage app dependencies to avoid issues during system upgrades.

Also Read: Top 5 best HRMS software for large enterprise with multiple workplaces in Singapore

Regional Operational Needs

Running an enterprise within this specific financial hub brings unique operational challenges compared to Western markets. Software cannot just handle basic book-keeping; it must natively adapt to the high-velocity trade environments of the region.

• Advanced Cross-Border Trade Support:Platforms must handle complex multi-currency transactions and real-time exchange adjustments automatically, keeping regional supply chains moving without manual recalculations.
• Strict Digital Compliance:Systems must align smoothly with strict local tax structures and electronic filing mandates, ensuring clean audit trails for regional authorities.
• Agile No-Code Workflows:Fast-moving business environments require teams to modify operational modules instantly. Solutions like Multiable ERP let users adjust layouts and workflows on the fly without waiting for lengthy IT development cycles.

Static vs. Dynamic Model Context Protocol

Understanding the difference between static and dynamic Model Context Protocol (MCP) is vital when planning an AI-driven enterprise strategy:

• Static MCP:This configuration only supports the built-in, out-of-the-box standard functions of an enterprise system. If a business creates a custom fields array or sets up a unique operational workflow, the background AI agent remains blind to those areas. It cannot access or read the customized data points without manually writing new integration code.
• Dynamic MCP:This advanced protocol automatically maps and exposes the entire active system database to AI models. It covers standard modules as well as custom functions configured or developed by the end-user. As a result, autonomous AI agents can instantly understand, query, and run automated workflows across the company’s entire unique operational setup.

System Selection Risks to Watch in 2026

Meanwhile, according to a study by Everfair Management, when evaluating modern enterprise software architectures, business owners need to focus on technical challenges that have emerged alongside recent AI developments:

1. Ecosystem Infrastructure Boundaries:Avoid purchasing platforms that are completely bound to the Windows Server ecosystem. Because the vast majority of popular LLMs and cutting-edge agentic AI tools run natively on Linux, enterprise platforms that cannot run on Linux risk becoming obsolete in the near future.
2. The Shift Toward Regional Innovation:While AI tools in Western markets dominated early conversations, Asian software providers haSve rapidly closed the gap. Regional platforms now frequently deliver a significantly higher ROI than traditional enterprise software names from the US or EU by offering native, cost-effective automation tailored to local workflows.
3. The Stability of Direct Vendor Procurement:Purchase directly from the core software developer instead of relying on a consultation partner or third-party reseller. The long-term service quality and business sustainability of a reseller are inherently weaker and more volatile than dealing directly with the company that builds and maintains the platform.
4. Built-In MCP Requirements:Do not purchase a system that lacks native, built-in MCP support. Without this layer, attempting to securely route corporate operational data out to modern AI tools results in massive integration costs and fragile data connections.
5. Native Custom API Generation:Ensure the platform includes an automated API generator for user-configured modules. Without this feature, adding a custom workflow requires a slow, expensive development loop: writing the custom function, manually building a corresponding API, and hard-coding that data into a rigid interface.

Why We Write this Article?

This piece is authored by Sam Cheong, the Principal Consultant at Synchro RKK Sdn Bhd and one of Malaysia’s most respected business software authorities. Driven by a passion for complex problem-solving, Sam fell in love with Enterprise Resource Planning (ERP) architecture early in his career. Following a proven track record of high-impact deployments, he successfully acquired the ERP business unit from SRKK to found Synchro ERP. Today, he leverages his deep technical expertise and strategic vision to help organizations streamline operations, scale infrastructure, and navigate digital transformation. Witnessing the rising wave of compromised implementations in the region, Sam shares these insights to guide enterprises away from costly deployment pitfalls.

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Ask anyone to name Southeast Asia’s most successful startups and you will hear the same names. Grab. Sea. GoTo. Each is a consumer company. Each won by reaching enormous numbers of users, burning enormous amounts of capital, and surviving long enough to consolidate a market.

This is the story the region tells about itself. It is also the reason Southeast Asia is structurally underbuilt in the categories that, almost everywhere else in the world, produce the most durable companies.

The boring layer is where durable value compounds

Globally, the companies that compound value over decades are rarely the exciting consumer names. They are the unglamorous infrastructure businesses that sit underneath the economy: supply chain software, vertical applications for traditional industries, payments rails, logistics tooling, compliance systems. These companies are boring to talk about. They are also extraordinarily hard to displace once they are embedded, which is exactly why they compound.

Southeast Asia has underweighted this layer for a decade. The capital chased consumer scale because consumer scale produced headlines, and the founder pipeline followed the capital. The result is a region with world-class consumer platforms sitting on top of a thin and underbuilt layer of business infrastructure.

The data shows both the gap and the turn.

Southeast Asia’s B2B digital commerce market passed US$90 billion in 2024 and is heading toward US$130 billion by 2026. Average SaaS spending per employee in the region rose from US$3.79 in 2020 to US$13.47 in 2025, a 2.5-fold increase in five years. The share of software and services in emerging industry investment jumped from 28 per cent in the second half of 2023 to 50 per cent in the first half of 2024. On the physical side, McKinsey estimates a roughly US$60 billion gap between existing or announced supply chain infrastructure investment and what the region’s future trade flows will require.

These are not the numbers of a mature market. They are the numbers of a market that has barely started building its business infrastructure, growing quickly from a very low base.

Also Read: What Southeast Asia’s edutech startups can learn from AI’s limits in education

Why the underbuild persists

Three forces keep the boring layer underfunded.

The first is capital habit. A decade of consumer outcomes trained the region’s investors to look for winner-take-all scale stories. Business infrastructure does not look like that early on. It grows through slow, defensible adoption inside individual industries, not through viral user curves. To an investor pattern-matching on the last cycle, a vertical software company solving a specific operational problem for mid-sized manufacturers looks small. It is not small. It is early.

The second is founder training. Founders in the region have been taught, implicitly, that the prize is consumer scale. The most ambitious technical talent gravitates toward consumer problems because that is where the celebrated outcomes have been. The boring problems, the ones embedded deep in logistics, procurement, financial operations, and regulatory compliance, attract less talent than their economic importance warrants.

The third is visibility. A consumer app is legible to everyone. A supply chain traceability platform serving Vietnamese manufacturers under pressure to meet global compliance standards is invisible to most observers, even though it may be solving a more durable and defensible problem than most consumer apps ever will.

The categories that are most underbuilt

For founders and investors willing to look at the boring layer, several categories in Southeast Asia are conspicuously underbuilt relative to the size of the problem they address.

Supply chain and logistics tooling, where the region’s emergence as a manufacturing alternative to China is creating demand far faster than software is being built to serve it. SME financial software, where tens of millions of small and mid-sized businesses still run on spreadsheets and manual processes. Vertical SaaS for traditional industries, where deep workflow integration creates the network effects and switching costs that make companies durable. Compliance and regulatory technology, where fragmented rules across ten markets create a problem that is painful, recurring, and exactly the kind of thing businesses pay for indefinitely. B2B payments and financing infrastructure, where cross-border trade is growing but the rails underneath it remain thin.

None of these will produce the next viral consumer story. All of them can produce companies that are still compounding in twenty years.

Also Read: The capital drought: Over 7,500 SEA startups extinguished since 2020

What this means for capital allocation

The investors who recognise this early have an advantage that the consumer cycle no longer offers. Vertical software companies in the region are already attracting stronger conviction precisely because they show what consumer companies often could not: sustainable unit economics and high retention. Sector-focused funds are increasingly treating these as defensible plays that can dominate a niche before global incumbents arrive.

The thesis is straightforward. The next generation of durable Southeast Asian companies will disproportionately be built in the boring categories, because that is where the combination of real demand, defensibility, and underbuilt supply is strongest. Capital that continues to crowd into the exciting categories will compete for diminishing returns. Capital that moves into the boring categories will be early to the layer where durable value is actually created.

A region’s most valuable companies are not always its most visible ones. Southeast Asia spent a decade building the visible layer. The durable companies of the next decade are being built underneath it, in the categories most people find too boring to watch.

That is exactly why they are worth watching.

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The post Why Southeast Asia is underbuilt in the categories that produce its most durable companies appeared first on e27.

MDI Ventures, the corporate venture capital arm of Indonesia’s TelkomGroup, is narrowing its Southeast Asia investment focus around enterprise AI, AI infrastructure, governance software, real-world asset tokenisation, institutional custody, and regulated digital asset rails.

The Jakarta-headquartered firm said its updated thesis reflects a shift in the region’s technology cycle. After more than a decade in which consumer internet, e-commerce, ride-hailing, and digital payments defined Southeast Asia’s startup story, MDI believes the next phase will be shaped by the infrastructure and software needed to make AI and digital assets usable at enterprise scale.

That means less focus on broad digitisation as a theme in itself and more on the systems behind it: compute, data governance, cybersecurity, compliance, workflow automation, payment settlement, custody, and tokenisation.

Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning

For MDI, the strategy is also tied closely to TelkomGroup’s domestic reach. The firm plans to source more companies from Southeast Asia, beginning with Singapore, and help them enter Indonesia through TelkomGroup’s enterprise relationships, digital infrastructure, market channels, and strategic partners.

“Southeast Asia’s next wave of technology growth will be led by companies that solve real infrastructure and enterprise problems,” said Shannon Lee Chaluangco, Investment Director at MDI Ventures Singapore. “Our role is to provide the strongest regional founders with Indonesia’s market scale, TelkomGroup’s network, and the strategic resources needed to build durable companies across AI, blockchain, and digital assets.”

Why Singapore-to-Indonesia matters

The Singapore-Indonesia corridor has long been one of Southeast Asia’s most important startup routes. Singapore offers access to capital, regional headquarters, regulators, multinational customers, and technical talent. Indonesia offers market scale: more than 270 million people, a large enterprise base, and a fast-growing digital economy still unevenly served by modern software.

MDI’s updated thesis sits directly on that bridge. Singapore remains Southeast Asia’s leading AI funding hub, capturing around 57 per cent of the region’s AI funding over the 12 months to June 2025. During that period, 495 AI startups in the city-state raised US$1.31 billion.

For many of these companies, however, regional expansion is difficult. Indonesia is not a plug-and-play market. Enterprise sales cycles can be relationship-driven, regulatory interpretation varies by sector, and localisation often goes beyond language. Founders must understand local data rules, procurement behaviour, payment preferences, and how large incumbents actually buy technology.

Also Read: Indonesia’s AI hiring gap is real, just not 28×

This is where corporate venture capital can be more useful than capital alone. MDI is betting that its link to TelkomGroup can give founders a faster route into live commercial deployments, especially in sectors where trust, distribution, and regulatory familiarity matter.

AI moves from pilots to production

The firm’s timing reflects a wider reallocation of venture capital. In 2025, AI companies accounted for 61 per cent of global venture capital investment, representing US$258.7 billion. In Southeast Asia, more than US$2.3 billion flowed into roughly 680 to 700 active AI startups over the 12 months to June 2025, equal to about 32 per cent of all private funding in the region.

That resilience stands out because the broader funding environment has been much weaker. Southeast Asian private funding fell to a six-year low of US$1.85 billion across 229 deals in the first half of 2025. AI, in other words, has become one of the few categories still able to attract strategic capital at scale.

Indonesia also presents a large deployment opportunity. MDI’s thesis work points to roughly 2,400 AI and deep tech startups in the country, while 92 per cent of knowledge workers already use AI, compared with a global average of 75 per cent. Yet among 336 Indonesian AI companies tracked by the firm, only 66 have raised institutional funding.

That gap suggests a market where usage is running ahead of venture backing. MDI sees opportunities in “Bahasa-first” AI applications, workflow automation, customer engagement tools, data governance, compliance software, and sector-specific AI systems for industries such as telecommunications, finance, logistics, and public services.

The infrastructure layer is just as important. Indonesia’s data centre pipeline is projected to reach around 1,519MW by 2028, while cloud adoption across Southeast Asia is growing at about 20 per cent annually. As companies move from AI experiments to production systems, they will need more than chat interfaces. They will need secure deployment, model monitoring, integration with legacy systems, and clear governance over how data is used.

Digital assets beyond speculation

MDI’s second major focus is blockchain and digital assets, but not in the retail trading sense that dominated the previous crypto cycle. The firm is looking at institutional infrastructure: custody, tokenisation, payments, settlement, compliance, and regulated digital asset services.

The market case is large, though still early. The potential market for tokenised real-world assets is projected to reach US$30.1 trillion within the next decade. More than 70 per cent of Southeast Asia’s internet users now use digital financial services, and digital payment gross transaction value in the region is expected to reach US$359 billion by 2025.

Adoption signals are also strong. Asia accounts for six of the world’s top ten countries in crypto adoption. Indonesia recorded about US$3.1 billion in crypto transaction volume in May 2024, up 506 per cent year-on-year. Vietnam’s annual digital asset transaction volumes exceed US$220 billion, while Singapore has built an ecosystem of more than 300 blockchain companies.

Also Read: SBI buys majority stake in Coinhako to deepen Singapore digital asset push

Still, the next phase will likely depend less on retail enthusiasm and more on whether regulated institutions can safely use blockchain-based rails. Tokenised bonds, fund units, carbon credits, invoices, and other real-world assets require custody, identity checks, settlement processes, and legal clarity. That makes compliance a core product feature, not an afterthought.

“Web3 in Southeast Asia is moving from speculation to infrastructure,” said Alvin Evander, VP of Strategy at MDI Ventures. “As tokenisation, institutional custody and regulated digital asset rails move from pilots into real-world use, the winners will be founders who build for trust and compliance from day one.”

MDI has been raising its profile in this space. It participated as an institutional partner at Indonesia Blockchain Week 2026 in Jakarta and co-hosted “Tokenize Indonesia – Brunch by the Beach” at Coinfest Asia 2026 in Bali, bringing together regulators, financial institutions, and industry players. The firm also plans to meet founders and ecosystem partners at TOKEN2049 Singapore.

The competitive field

MDI is not alone in chasing the region’s next infrastructure cycle. Corporate and strategic investors such as Singtel Innov8, SCB 10X, Krungsri Finnovate, and Vertex Ventures have also backed startups across enterprise software, fintech, AI, and blockchain-linked infrastructure. In Indonesia, firms such as East Ventures, AC Ventures, Alpha JWC Ventures, and BRI Ventures remain active across technology sectors. MDI’s differentiator is its connection to TelkomGroup, but that advantage will matter only if it translates into real contracts, integrations, and distribution for portfolio companies.

Also Read: Four VC executives. Zero personal gain. Three years in prison

For founders, the updated thesis signals where strategic capital may be heading next. Southeast Asia’s startup market no longer rewards growth stories as generously as it did during the zero-interest-rate boom. Investors are asking harder questions about revenue quality, defensibility, regulation, and enterprise adoption.

MDI’s bet is that the next durable companies in the region will not simply digitise existing behaviour. They will build the rails that make AI and digital assets safe, compliant, and commercially useful in large markets such as Indonesia.

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