Nine in ten Singapore internet users now reach for an AI tool every month. Barely a third say they are comfortable doing so. That gap between habit and confidence is the most telling finding in Digital 2027: Singapore, the annual report released today by creative agency We Are Social and media intelligence firm Meltwater.

According to the report, 89.9 per cent of Singapore internet users aged 16 and above used an AI tool in the past month, ahead of the global figure of 85.2 per cent. Yet only 34.6 per cent say they feel comfortable using such tools.

Put plainly, more than half of the city-state’s online population is leaning on a technology it does not fully trust.

Also Read: AI saves me hours, but I don’t trust it blindly

That is less contradictory than it sounds. Singaporeans have spent years living with search engines that serve ads and social feeds that serve outrage. Using a tool while keeping it at arm’s length is a familiar digital posture. Still, a gap of roughly 55 percentage points is wide enough to matter for the AI firms, brands and startups that treat Singapore as Southeast Asia’s test market.

ChatGPT leads, but search refuses to die

ChatGPT is the clear front-runner. Nearly half (48.7 per cent) of Singapore internet users used it in the past month, well above the 34.7 per cent worldwide. Google Gemini trails at 29.8 per cent.

The obituaries for search, however, look premature. Some 88 per cent of users still turn to a traditional search engine monthly, within two percentage points of AI tool usage. Even regulators are struggling with where one ends and the other begins; the EU has called ChatGPT a search engine, a label with knock-on effects for AI startups in the region.

Among active AI users in Singapore, finding information is the top reason for use (63.4 per cent), followed by getting advice on problems (39.6 per cent) and saving time or boosting efficiency (36.1 per cent). In other words, most people are using chatbots as a faster search box, the very job search engines have done for two decades.

Heavy users, light spenders

The monetisation picture is less flattering. Just 8.1 per cent of Singapore internet users pay for an AI subscription, below the global average of 9.5 per cent. For one of Asia’s wealthiest markets, and one that outpaces the world on usage, that is a notable shortfall.

The release does not explain why, nor does it break the figure down by age or income. But the pattern fits a wider story: free tiers are generous, employers increasingly supply the tools, and Gen Z workers are bringing their own AI to the office regardless of company policy. Why pay at home for something you use at work and only half trust?

For model makers pouring money into Asia Pacific expansion, that is an awkward data point. High engagement with little willingness to pay is the same trap that caught a generation of Southeast Asian consumer internet startups.

Real or fake? Singapore isn’t sure

The trust deficit stretches well beyond AI. Nearly three in four (73.4 per cent) online news consumers in Singapore worry about what is real and what is fake on the internet, the eighth-highest level in the report.

Also Read: The consumer is still human. The gatekeeper isn’t

That anxiety is not paranoia. Deepfake fraud losses have climbed into the billions globally, and Singapore has leaned hard on platforms, including enlisting Google’s AI to flag scams before victims hit send. The chatbots carry their own credibility baggage, with recent research showing AI models can cave on facts under pressure, and Southeast Asian languages may fare worst.

Singapore ranks ninth globally for AI comfort. That sounds respectable until you remember what ninth place is worth here: barely a third of users. If that is a top-ten score, public confidence in AI is thin almost everywhere.

Brands still get found the old-fashioned way

For marketers, the report punctures some of the hype around AI-led discovery. Only 23 per cent of Singapore internet users use AI tools as a main source when researching brands, and just 12.6 per cent discover new brands through them. Search engines (51 per cent), product and brand websites (37.4 per cent) and social networks (34.9 per cent) still dominate.

None of this makes optimising for AI answers a fad. Southeast Asia’s sellers now have to learn to sell to AI, and those discovery numbers will climb. But today, a brand that neglects its search ranking to chase chatbot mentions is optimising for the minority.

Social media remains the main stage. 94 per cent of Singapore’s population has an active social media user identity, and internet users aged 16 and above juggle an average of 7.3 platforms a month. WhatsApp reaches 84.8 per cent, while Instagram commands the most time among the major apps at one hour 45 minutes a day. Singapore also ranks fourth for LinkedIn use and joint sixth for Reddit, and more than a third of social users say reading news is a main reason they log on.

Read the sales pitch between the numbers

The report’s authors have drawn their own lessons. Mimrah Mahmood, APAC Vice President of Enterprise Sales at Meltwater, said brands must understand how they show up “from search and social to AI answers”. Arthur Altounian, Managing Director of We Are Social Singapore, put it more pointedly: “Singaporeans have embraced AI, but they haven’t handed it their trust.” The brands that grow in 2027, he argued, “will be the most believed”, not the most visible.

Also Read: Inside Singapore’s AI push: Lighthouses, rice importers and robot labs

Both conclusions line up neatly with what each firm sells: cross-channel monitoring in Meltwater’s case, creator-led campaigns in We Are Social’s. That does not make them wrong, since reach and credibility are not the same thing in the creator economy. But readers should note who is drawing the conclusions, and that the release discloses neither sample sizes nor survey methodology.

The bigger question is whether trust ever catches up with usage, or whether Singaporeans settle into a permanent state of wary dependence. For AI companies, the answer will decide whether 89.9 per cent usage turns into revenue. For everyone else, the 73.4 per cent who worry about telling real from fake may be the number that matters most.

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The last few weeks have had a pattern. Every week, another company announces an AI agent, an AI assistant or an AI workflow. Every week, another headline tells us something has been breached, leaked or drained. Those two streams of news are connected.

On 30 October, Binance suffered the biggest theft in crypto history, with US$6.4 billion gone, and Chainalysis’s reconstruction says the outcome was decided in 54 minutes. Binance is a well-funded exchange with a large security team, so this was not a case of a small shop forgetting to lock the door. The speed is the lesson. Attackers no longer move at human pace, and many of them now use agents to do it.

That matters well beyond crypto. A recent study found that AI agents broke into 100 firms for pocket change, and that Southeast Asia’s small online shops fit the target profile. For years, obscurity protected smaller businesses, because breaking in took skill and patience. Agents have made it cheap, and cheap attacks get aimed at everyone.

Also Read: The hidden risk in AI adoption: Unchecked agent privileges

The background rate is climbing as well. Global organisations faced an average of 2,336 cyber attacks per week in July, up 16 per cent from a year earlier, with education, energy and travel among the sectors under growing pressure. Supply chains add to the exposure: in Southeast Asia, supply chain attacks are becoming routine, in a region short of security talent and heavily dependent on third-party technology providers. Add agents that can probe thousands of targets cheaply, and the economics tilt further toward the attacker.

We are building faster than we are securing

The picture inside companies is not reassuring. Gravitee’s research found that most organisations have deployed AI agents into production, yet fewer than one in five have full security approval in place. Adobe’s Southeast Asia findings point the other way, with 57 per cent of firms stuck in AI’s “frozen middle”, unable to get projects past their own IT and security teams. Some firms are rushing ahead unsecured while others are stuck waiting on security. Neither is a healthy way to adopt a technology.

The risk grows once agents start acting on their own. An OutSystems report found that enterprise executives are confident they can keep their AI agents in line, but the room goes quiet when they are asked what happens if two agents disagree. Elsewhere, technology leaders in the region are learning to trust AI agents, but not with production. That caution is sensible, and it is also an admission: the controls most firms rely on were designed for people, not for software that makes decisions.

The other half of the problem is us. Many incidents begin with something ordinary: a reused password, a convincing email, or an employee pasting a supplier contract into a free chatbot to save time. That is what shadow AI looks like in practice, and banning AI does not fix it. The phishing playbook is also changing. Check Point Research’s latest data shows phishers now want you to type the malware in yourself, and AI-written lures are eroding trust in the old cues staff were trained to spot.

Security spend should match AI spend

My view is simple. If we are investing seriously in AI, we should invest at least as seriously in securing it, and I would argue for more. Every agent we deploy is one more actor with access to data and systems. As AmanTrue’s Evin McMullen puts it, AI agents need someone standing behind them. That means identity, accountability and limits on what each agent can touch.

Over the past four weeks we have met companies approaching this from different directions. Three stood out: Dropbox, Yubico and Aleph. Each answers a different part of the problem, and I would encourage you to look closely at all three.

Some are building guardrails around the data itself. Dropbox, which has been careful to stay neutral in the ChatGPT, Claude and Gemini contest, has launched Dropbox Protect, and it deserves attention from anyone putting AI on top of company files. It is less about classic cyber defence and more about the question every AI rollout should answer first: what happens to our data when people run large language models over it? Protect, formerly Nira, shows who can access company files across Dropbox, Google Drive and Microsoft 365, so overshared content can be found and fixed before AI tools surface it. If your documents already live in Dropbox, have that conversation now, before shadow AI forces it on you.

Others are fixing access, and this is where I would start. Phishing, reused passwords and stolen credentials all end the same way: someone logs in who should not. A hardware security key such as Yubico’s YubiKey makes that far harder, because it is phishing-resistant and a stolen password alone is no longer enough. Yubico told us that some customers place orders of around 100,000 keys at a time, partly because of staff turnover and keys that are never handed back. That is the scale at which organisations are already treating access as a hardware problem. I believe a large share of today’s incidents could be prevented by securing access this way, and the same discipline should extend to the agents acting on our behalf.

Also Read: Singapore leads on security governance but struggles to enforce it, report finds

And a new generation of firms is going after segments the big vendors have underserved. Scouter’s entry into Singapore’s crowded cyber market is a recent example.

Aleph, launched on 13 October by Red Alpha Singapore and nuKudo, goes after the people side of the problem. It is a technology workforce provider whose portfolio spans AI, cybersecurity, cloud and DevOps, and it hires on aptitude rather than CVs. With security talent in short supply, that matters: you cannot buy protection you do not have the people to run. If your team is stretched, Aleph is a partner worth calling.

What a security twin looks like in practice

A security twin is a simple rule: every AI budget line gets a matching security line, approved at the same time and tracked by the same owner. It is not a fixed ratio, and I would not pretend one number fits every company. It is a discipline that stops security from being the step that blocks a launch or the afterthought that follows a breach.

In practice, I would start with four questions. Which AI tools and agents are running in the company today, including the ones nobody approved? Who or what is each one acting as, and what can it reach? Where does our data go when it is used with a model? And who is accountable when something goes wrong?

The last question matters most. UOB’s research finds that banks want AI agents to move money, as long as someone is accountable, and Penta’s report makes the wider point that cyber incidents are now about trust and leadership as much as firewalls. Boards will not accept “the agent did it” as an answer.

Each partner above maps to one of those questions. Dropbox Protect covers where data goes. Hardware keys from Yubico cover who or what gets in. Aleph covers the people to run all of it. The rest is governance, which costs less than a breach and, as security reports keep showing, often fails at the point of follow-through rather than intent.

The three partners at a glance

Dropbox Protect is a data access governance product. It gives admins one view of who can access files across Dropbox, Google Drive and Microsoft 365, shows which files are shared internally, externally or publicly, and flags overshared files with their owners. Teams can remove public or company-wide links in bulk, revoke access for external users and former employees, and transfer ownership. Policies can alert on or automatically fix risky sharing, such as confidential files shared with personal accounts, and an exportable log supports audits and access reviews. For AI, the point is to restrict overshared content before AI tools and large language models can surface it.

Yubico makes hardware security keys in which the private key is generated on the device and never leaves it, which is what makes them phishing-resistant. The range includes the YubiKey 5 Series, a FIPS-validated series for regulated sectors, the YubiKey Bio Series, the Security Key Series and the YubiHSM hardware security module. The keys support FIDO2, passkeys and PKI, work without a phone or network connection, and integrate with identity providers including Microsoft Entra ID, Okta, Ping and Google Workspace. YubiKey as a Service covers pre-registered keys, backups and global fulfilment for large rollouts. In May, OpenAI added an opt-in setting for ChatGPT and Codex that removes password sign-in and lets users choose a security key.

Also Read: Singapore’s AI adoption surges, but data complexity raises security risks: Report

Aleph is a technology workforce solutions provider formed by Red Alpha Singapore and nuKudo, launched on 13 October. Where a traditional recruiter places candidates, Aleph combines talent assessment, training, deployment, professional services and technology solutions in one model. Its offerings include Select, Train, Deploy, Assessment as a Service, an In-House Talent Engine, Staffing Plus, Custom Workforce Solutions and AI workforce transformation. The roles it covers span AI, cybersecurity, cloud and DevOps, software and data engineering and network infrastructure, including Forward Deployed Engineers. Candidates are assessed on aptitude and problem-solving rather than CVs.

What we are doing, and what we ask of you

At e27, we are running a Startup Clinic for a curated group of startups from the Cybersecurity Research Programme Office (CRPO) technology translation programmes. These are teams turning research into products, and the market needs more of them. We would like the wider ecosystem to meet them at the Demo Day: investors looking for early exposure to security, and enterprises willing to pilot technology built close to home.

If you build, buy or back technology, here is the ask. Treat security as part of your AI plan from day one. Put guardrails around your data before AI touches it, which is the case for Dropbox Protect. Secure access with hardware keys such as Yubico’s, so a stolen password is no longer enough. Close the talent gap with partners such as Aleph, rather than waiting for the market to produce more security engineers. And watch this space. In an AI world that changes every quarter, it is becoming one of the most important ones.

The post Equal spend: why AI budgets need a security twin appeared first on e27.

Google will spend US$15 billion on Malaysian digital infrastructure across 2027 and 2028, a sum it unveiled on Wednesday with Prime Minister Anwar Ibrahim in the room. The package bundles new data centre campuses with skilling, water and clean energy commitments.

The scale is the story. In 2024, Google pledged US$2 billion for its first Malaysian data centre and cloud region. The new figure is seven-and-a-half times larger, and it is compressed into just two years.

Also Read: Server sanctuaries or net-zero derailers? Southeast Asia’s data centre dilemma

It also carries something rarer in hyperscaler press releases: an explicit promise that Google will pay for 100 per cent of the power its campuses draw, plus the direct cost of connecting them to the grid. In a region where the data centre boom has begun colliding with household electricity bills and water anxiety, that line may matter more to Malaysians than the headline number.

Elmina is live, Negeri Sembilan is next

Google’s first Malaysian campus, at Elmina Business Park in Selangor, is already running. More campuses are planned in Selangor and in neighbouring Negeri Sembilan, and the Selangor capacity will also anchor a forthcoming Google Cloud region for enterprise customers.

Conspicuously absent from the map is Johor, the southern state that has soaked up much of the region’s data centre spillover from Singapore, along with much of the scrutiny over what Johor gets from hyperscale capital. By staying in and around the Klang Valley, Google sits closer to Malaysia’s enterprise and government buyers, and to the talent pool it says it is recruiting from.

Alphabet President and Chief Investment Officer Ruth Porat framed the spend as aligned with Malaysia’s development priorities, pitching computing capacity as the base layer for homegrown AI applications. That dovetails with Putrajaya’s AI Nation 2030 agenda, which assumes local compute will be available on tap.

Malaysia’s own pitch is hard to dispute: Google cites 99 per cent internet penetration and fast AI uptake across government, business and consumers. Digital Minister Gobind Singh Deo said the blueprint sets a high bar for sustainable, high-impact infrastructure.

37,000 jobs a year, with an asterisk

Google says construction in 2027 and 2028 will add an average of US$1.1 billion a year to Malaysia’s GDP and support more than 37,000 local jobs annually, generating US$378 million in yearly labour income.

The fine print tempers that. Only about 6,000 of those roles are in construction itself; the rest are the indirect and induced jobs that economic impact models tend to conjure. Divide the labour income by the headcount and it works out to roughly US$10,200 per job a year, hardly a windfall.

Also Read: Malaysia’s Budget 2027 hands startups US$110.4M but the bigger cheques go elsewhere

Once operational, the campuses are projected to sustain more than 9,000 jobs a year, paying on average 45 per cent above the national median. Data centres are famously lean to run, so most of those jobs will again sit outside the fence. Google says it already has a full on-site team at Elmina and is hiring technical and facilities staff for the rest, while more than 80 Malaysian firms, from civil engineers to caterers and waste managers, are in its supply chain.

The power bill pledge is the real headline

Energy is where data centre economics and politics meet. Southeast Asia’s AI server boom is already straining power supply, and governments across the region are wrestling with who pays when hyperscaler load lands on the grid.

Google’s answer has three parts. It will pay for all the electricity its campuses use and the cost of connecting them. It will bring more than 1 GW of new clean energy capacity to the National Grid, anchored by a collaboration with Tenaga Nasional Berhad under the Corporate Renewable Energy Supply Scheme, which lets companies buy green power through the national grid. And it will explore round-the-clock clean energy to plug solar’s gaps, on top of existing tie-ups with consortiums led by Shizen Energy-Solarvest and TotalEnergies.

The most telling detail sits near the end of the statement. From 2027, Google plans to fund community programmes that strengthen grid resilience and bring down electricity bills for households in the states hosting its campuses. Companies rarely pay to soften a problem they don’t believe exists.

Water gets similar treatment. Google announced two replenishment projects, a river basin stewardship programme for Sungai Buloh and Sungai Selangor and water resilience centres in schools, with volumes measured under the Volumetric Water Benefit Accounting method and audited by independent hydrologists.

What is missing is the denominator. Google did not say how many megawatts its Malaysian campuses will draw or how much water they will consume, which makes “1 GW added” and “water replenished” hard to weigh. Replenishment is an offset, not a reduction, a distinction water-tech startups chasing data centre clients understand well.

A crowded queue of hyperscaler cheques

Google is hardly alone in courting Malaysia. Microsoft committed US$2.2 billion to Malaysian cloud and AI infrastructure in 2024, AWS has pledged about US$6.2 billion through 2037, and Oracle announced US$6.5 billion for a public cloud region the same year. Across the region, AI infrastructure dealmaking hit a record this year as Singapore, Indonesia and Thailand chase the same capacity.

US$15 billion in two years puts Google well ahead of those announced figures, though hyperscaler pledges are notoriously hard to compare: timelines, definitions and what counts as “investment” vary. For Malaysia, the competition is the point. Each new campus strengthens its claim to be the region’s compute hub, even as its push to climb the semiconductor value chain shows how hard it is to turn volume into market share.

800,000 trainees, and the harder number

Google also unveiled four programmes to upskill 800,000 Malaysians between 2027 and 2031, aimed at educators and youth, micro-entrepreneurs, civil servants and vocational jobseekers, starting in the states that host its campuses.

Also Read: Malaysia’s digital economy’s second wave looks nothing like the first

Headcount targets are easy to announce and hard to audit. Putrajaya wants 300,000 AI jobs by 2030, and the gap between a training certificate and a paying role is where such programmes usually lose momentum.

For founders, the nearer-term upside is more tangible: a local Google Cloud region promises lower latency and data residency for AI startups selling to Malaysian enterprises and government. Whether the power and water promises hold will only be clear once the campuses start drawing load, and once Google tells Malaysians exactly how much.

The post Google’s US$15B Malaysia bet comes with a promise to pay its own power bill appeared first on e27.

For two years, the rules of AI fundraising were simple: whoever asked for the biggest number got it. Last week, that stopped being true at the very top of the market.

OpenAI cut its fundraising target by US$10 billion to US$20 billion as falling tech stocks and weaker investor demand complicated its plans ahead of an anticipated 2027 listing. The valuation pitch slid to around US$1.25 trillion from an earlier US$1.4 trillion, and the US$11 billion secured so far is less than half the original goal. One description of investor demand that did the rounds was a single word: “thud.”

Also Read: Thailand’s AI startup push gets OpenAI backing through new public-private accelerator

In the same week, Anthropic’s amended IPO paperwork showed the other side of the ledger. The company disclosed roughly US$895.4 billion in aggregate spending commitments under purchase agreements and leases, almost all tied to compute and infrastructure.

Read those two stories together and the message for Southeast Asia’s AI founders is uncomfortable: the cost of building frontier AI is still climbing, while the patience of the people funding it is not.

The froth was already thinner than it looked

Before anyone in Singapore panics about a cooling that started in San Francisco, it’s worth admitting that the region’s AI funding boom was narrower than the headlines suggested.

Southeast Asia’s native AI startups raised US$4.1 billion across 23 disclosed rounds as of July, more than double 2025’s US$2 billion across 41 rounds, according to Tracxn. Impressive, until you look inside. Kling AI’s US$2.8 billion Series D accounted for about 68 per cent of that total. Strip it out and 2026 falls to roughly US$1.3 billion, below last year’s full total, spread across 18 fewer rounds.

Geography is just as lopsided. Singapore attracted US$9.3 billion across 227 rounds, while Vietnam, Malaysia, Indonesia and Thailand combined raised less than US$40 million.

So this is a region where AI capital was already concentrating into fewer, bigger cheques in one city. When the investors behind the world’s largest AI rounds start negotiating harder, that pressure doesn’t skip Southeast Asia. It lands first on the mid-tier startup in Jakarta or Ho Chi Minh City that was hoping the halo would reach it.

Your gross margin is someone else’s debt service

Here’s the bit that matters more than valuations. Most Southeast Asian AI startups don’t train frontier models; they build on them. That makes the frontier labs’ balance sheets part of every regional founder’s cost structure, whether they’ve read the filing or not.

Also Read: Anthropic, DeepSeek and drones: 10 takeaways from the 2026 unicorn boom

Anthropic’s disclosure is instructive because it’s so explicit. The total includes US$321 billion in leases that haven’t started and US$265.6 billion in purchase agreements. Broadcom accounts for US$161 billion, Google for US$114.5 billion, Amazon for US$110 billion and Microsoft for US$45 billion, and the filing flags that the company relies heavily on a small number of providers. The company itself calls its approach “bold but sane.” Critics are less generous: New Constructs CEO David Trainer labelled such obligations “hidden debt.”

Whichever label you prefer, the logic is the same for OpenAI and every other frontier lab carrying similar commitments. Obligations of that size get paid out of revenue, and a growing share of that revenue comes from the API customers building on top. If capital gets more expensive or more cautious, the pressure to protect margins moves down the stack, through pricing, rate limits, or the quiet retirement of cheaper model tiers.

For a Singapore legal-tech startup or a Philippine customer-service AI company whose biggest cost line is model inference, that’s not an abstract risk. It’s a supplier with enormous fixed obligations and every reason to stop subsidising your growth.

Singapore’s savings are in this trade too

There’s a second Southeast Asian exposure that rarely gets discussed in startup circles: the region’s sovereign money is sitting near the top of the frontier AI cap table.

GIC led Anthropic’s US$30 billion Series G, and in May it co-led the next round while Temasek joined as a significant investor. That Series H valued Anthropic at US$965 billion. On the OpenAI side, a UAE investor group including MGX could lead the current financing.

That isn’t a scandal; sovereign funds are supposed to take long-term bets. But it does mean that how the frontier AI IPO window plays out over the next 12 months will shape the risk appetite of the same institutions that anchor much of Southeast Asia’s venture ecosystem. A bruising listing season in New York could make Singapore’s limited partners more cautious about AI risk everywhere, including in their backyard.

What a picky market rewards

None of this means the AI story is over. The demand is real. Anthropic generated US$33.2 billion in revenue in the first nine months of 2026, against US$2.5 billion a year earlier. OpenAI is still raising one of the largest private rounds in history. A cooler top of the market is not a collapse; it’s investors rediscovering that price matters.

One reading of the OpenAI reset is that bargaining power is shifting toward investors. Southeast Asian founders should assume that shift will travel, and act accordingly.

That means building on more than one model provider so a single price change can’t wreck a P&L. It means pricing on outcomes rather than passing through token costs that someone else controls. It means taking the fair round now rather than holding out for the 2025 multiple that a fading comparable once justified. And it means treating inference costs the way e-commerce founders once treated logistics: as the line item that decides whether the business works at all.

Also Read: Anthropic’s Singapore MOU bets on safety testing, not consumer reach

The founders who come through this well will be the ones whose gross margins can survive their suppliers getting greedier.

The case against worrying

To be fair to the optimists, there are good reasons to read last week differently. A trimmed US$20 billion round is still enormous. Compute commitments stretching into the 2040s are not due tomorrow, and revenue growing at that pace can cover a lot of obligations. Competition between labs could just as easily push API prices down as up, which would be a gift to every startup building on top. And some of the cooling is plainly about public tech stocks falling, not about AI itself.

Those are serious arguments. But they all describe a market where Southeast Asia’s AI startups are price-takers, not price-setters. When the people at the top of the stack start counting their money more carefully, the sensible response at the bottom is to count yours.

The post The frontier AI money is getting picky. Southeast Asia’s AI founders should price that in now appeared first on e27.

SINGAPORE: The aisles around the Taiwan Pavilion at SWITCH 2026 were busy. At Booth Q01 of Sands Expo and Convention Centre, 33 Taiwanese startups spent three days, from 27 to 29 October, meeting visitors, investors and potential partners from across the region.

Among the visitors was Dr Tung Chen-yuan, Representative of the Taipei Representative Office in Singapore, who walked the pavilion and later spoke to e27 about what he saw.

His outlook was an optimistic one. Provided the founders find the right partners, he said, “I believe in the near future they will become very strong companies in the world.”

Also Read: Startup Island TAIWAN leads 33 deep-tech startups to SWITCH 2026, advancing their expansion into Singapore

The pavilion was led by Startup Island TAIWAN, the national startup brand backed by the National Development Council. e27 has covered the full line-up. This story is about what Dr Tung and seven of the founders told e27, and what happened at the booths.

Walking the floor

Asked on camera what he sees in these founders that tells him they will go far, Dr Tung started with the size of the problems they have chosen. “All of them are trying to tackle many difficult issues, including AI, energy, climate change and healthcare,” he said. “All of them are quite innovative and quite efficient.” He expects them to find partners quickly: “I believe they can find a lot of partners in Singapore.”

What happened at the conference

The pavilion was built for meetings, and the three days produced some. Several startups held investment talks with Openspace Capital, Qualgro Ventures and Anatres Ventures. Two more are discussing testing partnerships with a global semiconductor company and a Singapore engineering group. Two signed MOUs, one with a Singapore company on software licensing and hardware distribution and one with a South Korean company on glasses-free 3D content. Others plan to set up in Singapore, using StageOne, Singapore’s official startup platform, to find sites to test their products.

Some of the work was simpler: finding partners, one at a time. MantaGO is an omni-channel AI chatbot that connects social media and e-commerce marketplaces, and its maker reports 2,280 clients worldwide and 25 distributors. Oriana Lau, who heads up the Singapore market, said customers worry that AI can feel too robotic, so the team fine-tunes the bot to sound more human. For Southeast Asia, the plan is to treat Singapore as a kickstart and grow mainly through partners, with a target of 52 partners across the region by 2027, about one a week. “Our key focus is to acquire more partners and accelerate the usage of our solution with small and medium enterprises in the region,” she said.

Innovative, efficient and already international

On what he hopes for them five and ten years from now, Dr Tung talked about experience rather than potential. “All of them have a lot of international experience,” he said. “They already have a lot of clients in this region and in some international markets. So I believe they are quite capable. They are quite outstanding.”

His condition is partnership. As long as the teams find the right partners, he said, he sees them becoming “global players” in the near future, with Singapore and Southeast Asia as the first step in building those relationships, with Singapore being an important hub for the startups’ global ambitions.

LongGood is one example of that reach. The digital rehabilitation company already has customers across Taiwan, Japan, Singapore, Thailand and Hong Kong. In Singapore it wants to connect with hospitals and daycare centres. Ray Chen gave the long-term ambition in one line: “We aim to become the go-to brand for rehabilitation.”

Taiwan’s strengths, in his words

On what Taiwan can offer Southeast Asia that nobody else can, Dr Tung started with the relationship and then moved to capability. “Taiwan and Singapore have a very strong foundation of partnership and friendship in the long term,” he said. “Taiwan currently is very strong at advanced manufacturing capability, including semiconductors and AI-related products. All these companies are building their capability based upon Taiwan’s strengths, so they can bring a lot of integrated ICT capability to cooperate with partners in Singapore and Southeast Asia.”

Also Read: Taiwan Pavilion at SWITCH 2026: Meet the 33 Taiwanese startups heading to Singapore

He gave two examples. In healthcare, he said, companies are using AI and semiconductors to work out how to address people’s health concerns, and “Taiwan has a lot of advantages in this regard.” He sees a match with Singapore’s healthcare sector, which he described as full of innovation. In energy, he said, the same tools are being used to manage supply and demand.

EpiSante Biomedical, a liver cancer screening company, fits the healthcare case Dr Tung described. Its founder, Lance Lu, said Asia accounts for 70 per cent of liver cancer cases globally, and that the company’s patented test detects early-stage cancer with 91 per cent accuracy, against 10 to 20 per cent for conventional methods. The company plans to set up a headquarters in Singapore and then expand to Malaysia, Thailand and Vietnam through hospitals and clinics. “We’re looking to build an office here, a headquarters here,” Lance said.

Apollo Power covers the energy case. The company sells AI-driven energy management, and its founder, Sun Hsieh, said the system lets customers track their carbon goals, and that AI data centres are being built quickly across Southeast Asia and need stable power. The plan is to start in Singapore, move into Malaysia, Vietnam and Thailand, and be working with data centres or factories within six to 12 months. “Show real savings, then we scale,” Sun said.

On the AI side, futurenest builds software to govern enterprise AI. Its Xerno product controls what the AI knows and its Xparse product controls how it acts, which matters to regulated industries. The company reports 12 signed enterprise customers in active deployment across telecom, national defence and national transport. Founder Bob Hsu said the company’s edge is strong technology, and that its clients in Taiwan already include government agencies, financial institutions, healthcare providers and manufacturers.Hsu also said the product was picked this month as one of the world’s top 40 in a global NVIDIA competition. “We hope everyone can use AI safely and with peace of mind,” Hsu said, because AI today comes with too many governance problems and budgets that overrun, which leave users constrained. 

Why Singapore comes first

Dr Tung gave his own reason. “Singapore will be a very important first step for them to build their international partnership,” he said, “because Singapore is a hub of logistics, finance and talent. So this would be a very important step for them to go abroad.”

Hsu gave the most detailed answer from the founders. The company serves many large clients in Taiwan, and sees many top-tier enterprises in Singapore that can afford a high-level product. The plan is to set up a company and local team in Singapore, build reference cases there, and then add distributors and resellers across Southeast Asia. Hsu is looking for two kinds of partners: those who sell to AI data centres, whether in hardware, software or integration, and those who sell software or do system integration for medium and large enterprises.

Speculum AI builds a runtime layer for stable, controlled AI in production and targets regulated industries such as finance, banking, insurance and government. Within six months of entering the market, it secured an enterprise proof of concept with a major Japanese system integrator, according to the company. Dominique Tu, co-founder and CEO, said Singapore is “our most important stepping stone for Southeast Asia”, because it treats governance and AI stability as the core of deploying AI. The company aims to incorporate in Singapore within 2026.

Also Read: Taiwan bets on Gen Z founders to move beyond its chip-supplier image

eCloudEdge works where Taiwan’s manufacturing strength meets software. Rick Peng, CEO and founder, called Singapore the hub of Southeast Asia and the place to find a partner. He said the company governs operational technology (OT) and IT data, and wants a Singapore partner with both OT knowledge and software skills. “If you are purely on the OT side, you may focus on the OT application,” Rick said. “But we need someone with a software background.” Customers can deploy on premises or in the cloud, combined with ISO 27001 for security.

Startup Island TAIWAN describes the division of roles as Taiwan developing and validating the technology and Singapore serving as the base for commercialisation and expansion across Southeast Asia, with room to deepen cooperation in trustworthy AI governance and physical AI, drug validation standards and a clean energy ecosystem. The pavilion has closed. Dr Tung’s test for what comes next is whether the partnerships started in Singapore carry these companies abroad.

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