A year ago, some of the most nervous entrepreneurs in the world were in Hong Kong, Taipei and Shanghai. Trade tensions, a property slump in mainland China and a stalled listings market had put founders there among the most cautious respondents in HSBC’s annual survey of business owners. Twelve months on, they are the most cheerful people in the room.

The reason has little to do with apps and almost everything to do with the machines the apps run on.

Also Read: Gen Z is bringing its own AI to work. Singapore’s SMEs need rules, not bans

HSBC’s Global Entrepreneurial Wealth Report 2026, which polled more than 3,000 founders across 17 markets, finds that 54 per cent of Hong Kong entrepreneurs now feel “very positive” about their business prospects, up 20 percentage points year-on-year. Optimism about personal wealth rose 22 points to 53 per cent. In Taiwan, the share of “very positive” founders climbed 15 points to 43 per cent.

Follow the shipments and the mood swing makes sense. Mainland China (18 per cent), Taiwan (14 per cent) and Hong Kong (12 per cent) together accounted for nearly half of all global AI-related exports by value in the first quarter of 2026. The report says Taiwan’s AI hardware exports are now equivalent to almost half the island’s GDP, a figure that says as much about concentration risk as it does about success.

Hong Kong, meanwhile, logged its busiest first half for IPOs in five years, with AI listings leading the queue. It is the same report that told us AI will create jobs, provided you read the fine print.

For Southeast Asia, the uncomfortable takeaway is that the region barely features in this story. Its startup ecosystem spent a decade perfecting super-apps, ride-hailing and digital marketplaces: software businesses that monetise users rather than ship silicon. The money now flowing through the AI supply chain is largely going elsewhere.

Also Read: Gen Z doesn’t need more AI courses, it needs the skills AI can’t replicate

There are efforts to change that. Thailand has approved a semiconductor strategy that targets roughly US$80 billion in cumulative investment by 2050, while Singapore has given its chip sector a national identity in SG Semiconductor. But national roadmaps run on decades, and founders run on 18-month runways.

The generation that treats AI as plumbing

The second half of the report is about who is building companies, not where. Gen Z founders lead every age group on AI adoption: 65 per cent say they have fully embedded AI into their operations, compared with 57 per cent of Gen X and older founders. In mainland China, the Gen Z figure reaches 91 per cent.

Desmond Kuang, Chief Investment Officer for Asia at HSBC Private Bank, describes the gap as a matter of temperament. Young founders, he says, are “more comfortable experimenting, iterating and tolerating model risk”, while older peers “often require clear ROI, stronger governance”.

In practice, that means AI agents handling scheduling, research, customer support triage and code refactoring from day one. Some young founders in financial services say they have redesigned entry-level hiring altogether, handing routine junior analyst work to software and hiring humans only for senior roles.

That looks efficient on a spreadsheet. It also raises an awkward question for an ecosystem that already struggles to grow mid-level talent: if nobody hires juniors, where do tomorrow’s seniors come from? Singapore’s job market is already treating agentic AI as the hottest skill on the CV, and the people being priced out are rarely the ones writing the job ads.

Worried, but only a little

The report is keen to show that Gen Z founders are not reckless. They are twice as likely as older founders to worry that AI will erode critical thinking in their teams. Female founders show a similar gap over male peers, at 8 per cent versus 4 per cent.

The framing hides the base, though. “Twice as concerned” means 10 per cent versus 5 per cent. Roughly nine in ten of the most AI-fluent founders surveyed did not flag cognitive decline as a concern. That is less a generation guarding against automation bias than a thoughtful minority within one.

Also Read: The AI agent boom is exposing Southeast Asia’s startup codebase problem

Data privacy is a different matter. It is Gen Z’s top adoption concern, cited by 36 per cent, while every other age group puts implementation cost first. That matches what engineering leaders across the region describe: teams are happy to let AI agents draft and test, but not touch production. The threat surface is widening too, with cheap AI-driven attacks now within reach of the region’s small online merchants.

The barbell, and the gold

The most revealing data may sit outside the business. Thirty-eight per cent of Gen Z founders name inflation as their biggest personal wealth worry. Their response is what the report calls a barbell strategy: high-risk bets on their companies paired with defensive personal holdings. Sixty-seven per cent of Gen Z business owners plan to buy more physical gold over the next year, and 60 per cent intend to increase direct investments in private companies, compared with 44 per cent of Gen X.

That last number deserves attention from Southeast Asian VCs and family offices. A cohort of young, AI-native founders who want to write their own cheques into private companies is a potential co-investor, not just deal flow.

What the region should actually take from this

The report’s implicit prescription, plugging into North Asia’s hardware engine, is easier written than done. Partnering with Taiwanese foundries or Shenzhen robotics makers takes purchasing power, technical credibility and, increasingly, geopolitical nerve, as US-China export controls redraw who can sell what to whom.

A more useful reading is narrower. Southeast Asian software startups that want to matter in this cycle should build for the hardware that is arriving, such as edge devices, robotics, EVs and factory automation, rather than another chatbot wrapper. Taiwan, for its part, is trying to move in the opposite direction, betting on Gen Z founders to outgrow its chip-supplier image. The two ecosystems have more to trade than either tends to admit.

Also Read: Banks want AI agents to move money, as long as someone is accountable

The Gen Z lesson, meanwhile, is not simply “adopt more AI”. It is to adopt it with the privacy discipline young founders say they want, and with the human judgement that only a minority of them are actively protecting.

North Asia’s founders are optimistic because they sell the picks and shovels. Southeast Asia’s will need a sharper plan than renting them.

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HSBC’s latest survey of wealthy entrepreneurs contains an awkward finding for the men who still write most of the cheques. Their own confidence is slipping, while the confidence of the women they rarely fund is rising.

The Global Entrepreneurial Wealth Report 2026 polled more than 3,000 business owners across 17 markets. Over the past year, the share of male entrepreneurs who call themselves “very positive” about their business prospects fell nine percentage points to 47 per cent. Among women, it rose three points to 54 per cent.

The gap widens on money. Forty-nine per cent of female business owners expect their personal wealth to grow substantially, up a point. Male expectations dropped 10 points to 42 per cent.

Also Read: HSBC says AI will create jobs. Southeast Asia should read the fine print first

Before anyone puts that on a pitch-deck slide, there is a caveat. Every respondent holds at least US$2 million in investable assets. These are established and often large businesses, not a pre-seed founder in Manila juggling payroll.

The report also offers no Southeast Asia-specific gender split, so any regional reading is an extrapolation. What the numbers can show is how women who have already made it think about the next wave of technology. That is where it gets interesting.

Self-belief, with receipts

Asked why they feel good about their prospects, women cite self-belief more often than men do (31 per cent versus 26 per cent). Thirty-nine per cent say they have what it takes to ride out economic uncertainty, against 33 per cent of men. The report points to long-run economic research suggesting women earn disproportionately more running their own firms than in salaried jobs, which may explain why the conviction holds.

That conviction contrasts sharply with the capital that reaches them. Female-only founding teams secured just 2.3 per cent of global VC funding in 2024. In Southeast Asia, only 16.5 per cent of venture investment went to female entrepreneurs as recently as 2020. Confidence, evidently, is not the bottleneck.

Trust in AI, pointed at the front office

AI is the main engine of this optimism. Fifty-eight per cent of female entrepreneurs name AI and broader technology advances as their top reason for feeling positive. Fifty-one per cent place “a great deal” of trust in AI to support their business, seven points ahead of men (44 per cent). The pattern carries into personal finance: 86 per cent of women trust AI tools for portfolio analysis and wealth planning, against 83 per cent of men.

That trust stands out in a region where tech leaders are learning to trust AI agents, but not with production. More telling is what the women plan to do with it.

Both sexes rank employee productivity as AI’s main job. Men, however, lean harder on AI as a cost lever. Forty per cent want fatter profit margins (37 per cent of women), and 27 per cent want a leaner workforce (24 per cent of women). Women tilt towards revenue. Thirteen per cent use AI to improve customer service and experience, versus 8 per cent of men. Ten per cent use it to mine consumer insights, versus 7 per cent.

Also Read: Gender gap in GenAI skills is narrowing, but progress remains uneven, Coursera finds

The usual health warning applies. These are single-digit shares, and a five-point gap on a base of 13 per cent is a nudge, not a revolution. Still, the direction matters in markets where winning customers is expensive and loyalty is fickle. An AI deployment that keeps a customer is worth more than one that trims a salary line, provided it actually works. That proviso is why SEA’s retailers are being urged to protect AI-powered customer experience with proper monitoring.

The scepticism that cuts both ways

Higher trust does not mean blind faith. Women are twice as likely as men to worry that AI will erode critical thinking and decision-making, although the figures are 8 per cent versus 4 per cent. They are also more anxious about falling behind rivals on AI adoption (13 per cent versus 10 per cent).

Both worries are rational. The first echoes a debate already running through the region’s talent pipeline. The junior developer dilemma has employers asking who will audit machine output once entry-level grunt work disappears. The second is a reminder that ambition alone does not ship products. Singapore SMEs are discovering that messy data keeps AI pilots stuck.

What the survey cannot tell us is why male sentiment fell so sharply. The cause may lie in the sectors men dominate, the size of their firms, or their exposure to a turbulent geopolitical year. The report presents the divergence without explaining it, which leaves a gap worth closing in next year’s edition.

What investors should take from it

Strip away the cheerleading, and three practical points survive.

First, women founders appear more inclined to deploy AI where it touches customers. Investors weighing AI-era startups should therefore look harder at retention and lifetime value, not just headcount savings. That lens happens to favour the founders the industry has historically overlooked.

Second, guardrails are not a sign of timidity. Founders who worry about AI degrading judgement are more likely to set policies before a customer finds the problem. The same logic explains why Singapore’s SMEs need rules, not bans, as staff bring their own AI tools to work.

Also Read: AI could redefine women in the workplace, and companies must act now

Third, the confidence data undercuts one of the industry’s laziest explanations for the funding gap: that women simply don’t pitch boldly enough. On this evidence, at least among owners who have already built wealth, women are the more optimistic half of the room.

Whether that optimism turns into term sheets is a question for the people writing them.

The post Male founders are losing confidence. Women are doubling down on AI appeared first on e27.

In Singapore, a car is less a vehicle than a ten-year licence wrapped in steel. Carro now wants institutional money to treat that licence as collateral.

The Singapore-headquartered automotive marketplace has set up a S$450 million (about US$350 million) asset-backed securitisation facility backed by Singapore car loan receivables. In a statement on Thursday, Carro called it a key step in diversifying its long-term funding sources and said it is the first securitisation of Singapore automotive loan receivables.

Also Read: Carro acquires CarPlace: what it means for SEA, Australia markets

Securitisation sounds exotic, but the mechanics are simple. A lender pools loans it has already made and sells them to a separate entity, which raises money from investors against the borrowers’ future repayments. The lender gets cash today to write more loans, and investors get a stream of instalments.

In Carro’s case, that entity is a bankruptcy-remote special purpose vehicle (SPV), a legally separate company built so the assets stay protected even if the parent runs into trouble. It buys and securitises auto loan receivables originated by Genie Financial Services, Carro’s wholly owned lending arm. The structure is approved under the Monetary Authority of Singapore’s (MAS) SPV regime.

Why the COE matters

Carro’s pitch rests on a quirk of Singapore’s car market. The company says the deal creates a new asset class for hire-purchase securitisation because the loans are secured by vehicles, and the Certificate of Entitlement (COE) system, which licenses vehicle ownership, can give that collateral a floor value and regulatory certainty.

The logic holds up to a point. A car in Singapore cannot be driven without a valid COE, and the certificate retains residual value over its lifespan, which makes repossessed cars easier to price than in markets where used-car valuations are a guessing game.

But a floor is not a fixed number. COE premiums swing sharply with quota supply and demand, and a certificate’s value erodes as its ten-year life runs down. Investors will be underwriting a cushion, not a guarantee. A more reliable buffer may come from MAS’s own motor-financing caps, which limit how much of a car’s price buyers can borrow and force them to put real equity into every purchase.

The plumbing behind the pitch

The facility can run as a multi-funder structure, letting lenders participate through capital markets notes or direct loans on equal footing within the same vehicle. That flexibility matters: banks wary of a new asset class can lend directly, while bond investors can buy notes.

Carro also says the transaction includes fraud-risk safeguards and asset-protection measures. Independent third-party calculation agents will prepare reports, while automated data controls and a planned API integration will allow real-time performance reporting and monitoring of transaction triggers, the pre-set thresholds, such as rising arrears, that can change how cash is distributed or halt new loan purchases.

Also Read: Carsome posts tenth profitable quarter as SEA’s used-car race matures

Those safeguards are not decoration. The 2025 collapse of US subprime auto lender Tricolor, amid allegations that collateral had been pledged to more than one lender, reminded credit markets how badly things go when nobody verifies what sits inside the pool.

Aaron Tan, Carro’s co-founder and CEO, said the facility shows the strength of the company’s AI-enabled platform, data infrastructure and underwriting, and gives it the financial flexibility to expand across the region.

What the announcement does not say is equally telling. Carro has not named its funders, disclosed pricing, said whether the notes carry a credit rating, or shared delinquency data on the Genie book. S$450 million is a ceiling, not cash in the bank, and the market will judge the deal by how much is drawn and at what cost.

A lender wearing a marketplace’s clothes

The deal underscores what Southeast Asia’s used-car platforms have quietly become: lenders with showrooms attached. Ernest Chew, Carro’s CFO, said the company plans to diversify funding through bank loans, block discounting (selling hire-purchase receivables to a financier at a discount for upfront cash) and securitisation.

Its closest rival, Malaysia’s Carsome, has made the same shift, attributing its record quarterly EBITDA to a larger share of retail and financing services. In Indonesia, Moladin has targeted the used-car and auto-financing chain, while in the Philippines, Salmon recently bet US$140 million on secured lending that includes motorbikes.

Globally, the playbook is well worn. In the US, Carvana and CarMax regularly package auto loans into asset-backed securities, and both have shown how exposed the model becomes when credit cycles turn or used-car prices fall.

For Carro, the appeal is cheaper and more stable money. Lending businesses live or die on funding costs, and a platform reliant on a handful of bank lines is vulnerable when those banks tighten. The move also fits a wider regional appetite for structured debt, from private credit funds raising fresh capital to government-backed debt pools for tech firms. For readers new to the asset class, private credit has been steadily filling gaps banks leave behind.

The IPO subtext

There is also the listing question. Carro has said it is IPO-ready but would list only when conditions are favourable. A securitisation programme that survives independent scrutiny gives public-market investors something they rarely get from platform companies: third-party evidence of loan-book quality.

Also Read: From cash loans to motorbikes: Salmon bets US$140M on secured lending in the Philippines

Carro transacted more than 120,000 new and used vehicles in its 2026 financial year and operates in eight markets, including Australia following its acquisition of CarPlace.

The harder test lies beyond Singapore. The COE gives Carro an unusually clean collateral story at home. In Indonesia, Thailand or Malaysia, there is no such licence propping up residual values, and repossession is messier. If Carro wants this facility to fund regional expansion, it will eventually have to prove its underwriting works where the cars are cheaper, the borrowers riskier, and the floor much lower.

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Before I used Mailchimp, my monthly newsletter was one of those tasks I kept postponing. Not because it was complicated. Because it was annoying.

I was running a small business, which meant the newsletter was always competing with more urgent things. Customer messages, fulfilment, invoices, product updates, random admin. By the time I got to the email, I was usually rushing.

The old process was clumsy. I would copy email addresses into BCC, write the message in a normal email editor, hope the formatting looked decent enough, and send it without really knowing what would happen after that. I did not know who opened it. I did not know which links were clicked. I did not have a proper unsubscribe flow. And every month, I had to repeat the same messy setup from scratch.

At the time, I thought the problem was just that I needed a better email tool. That was only part of it. What actually changed things was moving from a one-off sending habit to a more structured email workflow.

Mailchimp happened to be the tool I started with because the free plan was enough for my list size. But the real shift was not “I switched software.” The shift was that the newsletter stopped being a manual blast and started becoming a repeatable process.

Also Read: Hong Kong builds a test track for robots that keep failing outside the lab

The first obvious improvement was formatting. My emails stopped looking like something thrown together at the last minute. Templates helped. Headers helped. Buttons helped. Even basic layout made the newsletter feel more deliberate.

The second improvement was list management. I no longer had to keep a messy list somewhere and wonder whether it was current. Subscriptions and unsubscribes were handled properly. New sign-ups could be kept in one place. That alone removed a surprising amount of friction.

The third improvement was visibility. For the first time, I could see whether anyone was actually engaging with what I sent. Not in a deep analytics sense. Just enough to stop guessing. Which subject line got opened more? Which link got clicked? Which section of the newsletter seemed to get attention?

That was the point where the workflow changed. Before, sending the newsletter felt like completing a chore. After, it started feeling like a small loop I could improve.

I could look at what got opened, what got ignored, and what people clicked. Then I could decide what to keep, what to shorten, and what to change in the next issue. It was still simple, but it was no longer blind.

That was also where ChatGPT became useful, though not in the way people usually pitch it. The useful part was not asking it to write the entire newsletter from nothing. That usually gave me something too polished, too generic, or too eager. What worked better was using it inside the workflow.

I would gather the rough pieces first. A product update, one customer question that kept coming up, a useful article, maybe one thing I wanted to promote. Then I would use ChatGPT to help turn that pile into a cleaner draft by reading that read for context before giving it all the necessary files via the Dropbox connection. It could suggest a tighter subject line, rewrite a clumsy paragraph, shorten a section that dragged, or give me two versions of the intro depending on whether I wanted the tone to feel more direct or more relaxed.

That made the task lighter, but it still stayed mine. I was not handing over the newsletter voice to a machine. I was using it more like an extra pair of hands inside a workflow that already made sense. More like a sounding board or someone who can arrange some things before I take over.

Also Read: Employers are buying AI tools. Their managers are being left behind

The lesson for me was simple. The hardest part of a small-business newsletter is often not writing one email. It is building a repeatable way to gather updates, draft them, send them properly, and learn something from the result.

Once that workflow existed, ChatGPT became more useful too. It had something to work with. It could help refine a draft, not pretend to replace the whole process. Unfortunately, ChatGPT does not have a Mailchimp app yet. Else I could do this end to end and have more of the workflow handled by AI. Automaid could use my browser but seeing it trial and error from clicking on the wrong buttons while my token usage climbed was too much for my liking. (Perhaps next what I would do is create and submit a community card that is much more descriptive so that the agent knows exactly which button goes where!)

I would not really describe this as a Mailchimp review, even though Mailchimp was the trigger for the change. The real improvement came from structure.

Before, the job was: remember to send something. After, the job became: collect the updates, draft the email, send it through a proper system, check what happened, and improve the next one.

That is a small operational change, but for a small business, those are often the ones that take up time and are easy to neglect.

I still do not treat newsletters like a grand strategy exercise. But I no longer treat them like an annoying monthly scramble either.

That changed more than the template did.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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A few months ago, I was trying to understand why one high-protein formulation puffed well during low-moisture extrusion while another did not.

I gave ChatGPT the formulation details, expansion results, rheological measurements and observations from the extrusion trials. Then I started asking what might explain the difference.

One line of reasoning concerned critical strain — broadly, how much deformation a material could tolerate before its structure began to break down. A plausible hypothesis was that greater deformation tolerance might help a material expand.

There was a problem. One of the materials with the highest critical strain had performed poorly in expansion.

So I pushed back: if that explanation was right, why did the experiment show the opposite?

We reconsidered the interpretation, brought in other observations and explored alternative explanations. Eventually, the important lesson was not that one rheological measurement could predict puffing. It was that several properties and processing events had to be considered together.

That exchange made me realise that I was no longer simply using AI to retrieve information.

I was using it as a sparring partner.

Not just returning the ball

I used to play table tennis, and a good sparring partner does not simply return the ball comfortably.

One shot goes to your forehand, another to your backhand. Sometimes the ball goes to a corner you would rather not cover. That is how you discover the weaker parts of your game.

That increasingly describes how I work with AI.

I can put forward an explanation. ChatGPT offers another possibility. I question it. It challenges an assumption. I add another experimental observation. Sometimes the original explanation survives; sometimes it does not.

And sometimes the correct conclusion is simply that we do not yet have enough evidence to decide.

The value is not that AI gives me the answer.

It is that the exchange helps me reach better questions.

There is an understandable concern that easy access to AI-generated answers may make us think less. My experience has often been the opposite. Used this way, AI makes me interrogate my own thinking more.

Also Read: Hong Kong builds a test track for robots that keep failing outside the lab

Getting up to speed faster

This has become particularly useful in my advisory work.

I sometimes need to evaluate research and innovation projects spanning disciplines outside my own technical specialisation. Before I can assess the scientific basis of a proposal, what may be genuinely novel, or what questions should be put to the researchers, I first need to understand enough of the subject.

Previously, entering an unfamiliar technical area could be slow. I would search for background material, begin reading, encounter terminology or mechanisms I did not understand, search again and gradually assemble a mental map of the field.

AI has shortened that first stage considerably.

I can begin with elementary questions. If an explanation introduces another unfamiliar concept, I can ask immediately. I can compare mechanisms, ask why one interpretation is preferred over another, and keep probing until I know what I should be reading and questioning.

It does not evaluate the project for me. I still need to examine the original material and evidence and make the judgement myself.

But it gets me to the point where I can ask useful questions much faster.

For me, AI has reduced the cost of being curious.

When collaboration becomes trust

AI also saves time in more obvious ways. I use it to organise long documents, compare versions, restructure technical material and work through scientific manuscripts.

But efficiency creates another issue: trust.

Recently, I worked through many versions of a long document with ChatGPT. This was not a case of handing over the task and accepting whatever came back. I questioned arguments, requested specific changes and repeatedly refined the direction.

After each round, ChatGPT summarised what it had changed.

Gradually, I realised that I had begun trusting those summaries. If it told me that a particular point had been corrected, I did not always reopen the entire document and verify every amendment before proceeding to the next issue.

The collaboration had become useful enough that I trusted the account of what had been done.

Also Read: Before you use predictive AI, decide what decision it should change

I became conscious of the risk when I later reread part of a scientific manuscript. The writing was coherent, but an important part of my scientific intention was not sufficiently prominent.

Nothing was obviously wrong. But it was not quite telling the scientific story I intended.

That is a boundary I now pay much more attention to.

AI can help me construct an argument. It can challenge it, revise it and tell me what it has changed.

But if my name is attached to the work, I still have to ask: Is this accurate? Is this what I mean? Am I prepared to stand behind it?

AI cannot make that decision for me.

Covering more of the table

I therefore find myself asking a different question from when I first started using AI.

Not simply: What work can AI do for me?

But: What kind of thinking does working with AI allow me to do better?

Sometimes it helps me enter an unfamiliar field more quickly. Sometimes it helps me manage an overwhelming amount of information. And sometimes it sends a question back across the table that exposes an assumption I had not realised I was making.

That is why I keep returning to the table-tennis analogy.

A good sparring partner does not play the match for you. It does not remove the need for technique, practice or judgement.

It makes you cover more of the table.

The best use I have found for AI is not outsourcing my judgement. It is giving my judgement a better sparring partner.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

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I’ve been running The Wellness Insider since 2016, and over the years, there have been countless occasions when I’ve looked at my website and thought, “I wish I could change that easily.”

Sometimes, it was something relatively minor, such as the spacing between menu items or the way an image appeared on a page. Other times, I wanted to change an entire section of the website, improve how readers navigated between articles or introduce new features.

The problem wasn’t that I didn’t know what I wanted. It was that I didn’t know how to make it happen.

I’m a marketer and content creator, not a web developer. I understand website structure, user experience and what makes a digital platform effective. I can navigate WordPress, publish articles and make basic changes with my limited html knowledge. But when something requires editing code or troubleshooting a technical issue, that’s usually where my abilities end.

For years, that meant either living with the limitations of my website or paying someone to make the changes.

Then I started using AI to help me do something that was beyond my technical capabilities: revamp my own websites.

Knowing what you want is different from knowing how to build it

The Wellness Insider is a digital wellness publication, so the website isn’t merely a blog or an online magazine. It’s where readers discover articles, browse events and engage with our content. It’s also part of how we work with advertisers and partners.

As the publication evolved, I became increasingly dissatisfied with certain aspects of the website.

I wanted a more polished homepage that would give greater prominence to our latest stories. I wanted readers to be able to discover more articles without navigating through unnecessarily complicated menus. I also wanted to improve our events section, making it easier for people to find upcoming activities as we have seen how some of our event listings are very popular with our readers.

These weren’t particularly revolutionary ideas. They were improvements that many established websites already had.

But knowing that something should be improved and knowing which WordPress settings, CSS rules or template files to modify are two very different things.

I could explain exactly what I wanted a page to look like. I just couldn’t translate those ideas into code.

This is where AI changed things.

From describing a problem to actually fixing it

I began using ChatGPT to first analyse my website and give me the blunt truth about what works and what could be improved. After which, I looked through its suggestions and picked what I liked about them while also describing what I disliked about my site and what I wanted instead.

Rather than searching through countless tutorials, trying to figure out which instructions applied to my particular WordPress theme, I could explain the issue in ordinary language.

Also Read: What does ChatGPT say about your brand? Miracly raises US$2M to find out

For example, I wanted The Wellness Insider’s homepage to look more like a premium digital publication. We worked on giving the logo greater prominence, featuring a selection of articles more effectively and improving the layout of the newsletter section.

I also wanted to improve our events calendar. What started as a request to make events easier to discover developed into a more substantial redesign, combining a calendar view with event listings and a submission form.

There were smaller improvements too: correcting menu spacing, fixing sidebar behaviour, adjusting article layouts and dealing with elements that weren’t displaying as intended in the original theme template.

Individually, some of these changes might seem insignificant. But when you operate a content-heavy website, small frustrations accumulate. A poorly positioned element, a confusing navigation menu or a sidebar that behaves strangely can affect how people experience your content.

What I appreciated most was that I could approach these issues from the perspective of a website owner rather than a developer.

I didn’t need to know the technical solution before asking for help. I needed to understand the problem well enough to describe it.

Then I tried it on another business

Encouraged by the improvements to The Wellness Insider, I began working on the Proving Grounds website.

Proving Grounds operates in the gaming and esports space, so its website serves a different purpose. It needs to communicate with the gaming community while also presenting the business professionally to potential corporate clients and partners.

Our previous website wasn’t doing both particularly well (sorry hubs!).

I wanted a design that reflected the brand’s personality, with a darker visual identity, stronger typography and clearer navigation. I also wanted the site to distinguish between community activities and the services we offer organisations.

This project involved a bigger change because we were moving away from a website-builder setup towards a more flexible WordPress site.

AI helped me work through the structure, page layouts, design requirements and technical implementation.

I could specify the brand colours and fonts, explain how I wanted different sections organised and review the results. When something wasn’t right, I could ask for it to be changed quickly.

We also worked on functionality, including navigation, enquiry forms and a separate mascot-naming campaign with a voting form.

It was particularly satisfying to see ideas that had previously existed only in my head become functioning website features.

I still wasn’t writing the code myself. But I was no longer entirely dependent on someone else to interpret and implement my ideas.

Also Read: Employers are buying AI tools. Their managers are being left behind

Of course, AI didn’t get everything right

I don’t want to give the impression that I typed a few instructions, went for coffee and returned to two perfectly redesigned websites.

There were plenty of moments when something didn’t work as expected.

A layout might look fine in one area but create problems elsewhere. A change could require additional adjustments. Sometimes, an issue that appeared to have been resolved needed another round of troubleshooting.

And there were occasions when I had to repeat instructions or remind AI of decisions we’d already made.

This was frustrating, particularly when I thought we had finished working on a particular section.

It also taught me something important: using AI doesn’t remove the need to understand what you’re trying to achieve.

I might not know how to write CSS or PHP from scratch, but I still need to recognise when a website looks wrong, when a feature isn’t behaving properly or when a proposed change doesn’t make sense for the business.

AI can help with implementation. It cannot take responsibility for the final result.

I also learnt to be more specific with my instructions. Saying “make this look better” leaves considerable room for interpretation. Explaining what feels wrong, what should change and what needs to remain untouched generally produces more useful results.

In that sense, working with AI isn’t entirely different from briefing a human developer or designer.

The clearer the brief, the better the chances of getting something usable.

The biggest benefit wasn’t just saving money

As a solopreneur, I’m constantly balancing what needs to be done against the time and resources available.

Hiring a developer for every small website adjustment isn’t always practical. Even when the cost is manageable, there’s the additional process of explaining the problem, requesting quotations, coordinating changes and checking the completed work.

AI has helped reduce some of that friction.

I can experiment with an idea without immediately committing to a development budget. I can make incremental improvements instead of waiting until I’ve accumulated enough changes to justify hiring someone.

Also Read: Tokens are the wrong yardstick for AI agents, says JFrog’s Sunny Rao

But the benefit goes beyond cost savings.

Previously, I sometimes left things unchanged because I didn’t have the technical knowledge to tackle them. Now, I’m more inclined to ask whether something can be improved and explore how to do it.

That’s a significant shift in mindset.

It doesn’t mean I suddenly possess the skills of an experienced developer. Nor does it mean I would use AI alone for every technical project, particularly one involving complex security, payment systems or sensitive customer information.

What it means is that the threshold for attempting improvements has become much lower.

For a small business owner, that matters.

AI hasn’t made me a developer. It has made me less constrained by what I don’t know

One of the most interesting things about this experience is how it has changed my relationship with technology.

I’ve always been comfortable with digital marketing tools, content management systems and website analytics. But coding felt like a separate discipline, one that required knowledge I didn’t possess.

AI hasn’t eliminated that knowledge gap. I still don’t fully understand every line of code involved in the changes we’ve made.

What it has done is give me a way to work across that gap.

I can bring my understanding of branding, content, audience behaviour and user experience to a technical project, while AI helps translate those requirements into something that can be implemented.

That doesn’t make human expertise irrelevant. Experienced developers bring architectural judgement, security knowledge and technical understanding that an AI-generated solution still needs to be assessed against.

But not every website improvement requires rebuilding an entire system or engaging a development agency.

Sometimes, a business owner simply wants to fix an awkward layout, improve navigation or introduce a feature that makes the website more useful.

For me, AI has made those improvements more accessible.

And perhaps that’s what I appreciate most about using it.

It’s not that AI knows everything, or that it always gets things right. It certainly doesn’t.

It’s that I can finally do something about the parts of my websites that have been bothering me for years, instead of looking at them and thinking, “I wish I knew how to fix that.”

—

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Southeast Asia does not have a shortage of AI pilots. It has a shortage of AI pilots that ever leave the lab. A recent regional survey found that 57 per cent of SEA firms can’t scale their AI pilots, a stretch of corporate limbo now known in industry jargon as the “frozen middle”.

Red Hat, the IBM-owned open-source software company, thinks it can help thaw it. The firm on Thursday launched its APAC AI Centre of Excellence (CoE) in Singapore, backed by the Singapore Economic Development Board (EDB). Its first named collaborator is the Defence Science and Technology Agency (DSTA), the body that develops technology for the Singapore Armed Forces.

Also Read: Southeast Asian tech leaders are learning to trust AI agents, but not with production

The pitch is simple: help organisations move AI from experimentation to production using Red Hat’s open-source stack and engineering staff. The details are harder to pin down.

What the centre will actually do

Red Hat says the CoE’s engineers will work along two tracks. The first is building reusable software architectures, essentially templates for problems that keep recurring across industries, so that each client is not reinventing the plumbing. The second is contributing code “upstream”, meaning into the shared open-source projects that Red Hat’s commercial products are built on, rather than keeping fixes locked inside one customer’s deployment. The company names system deployment, runtime optimisation and platform management as priority areas.

This is familiar Red Hat territory. Over the past two years, the company has pushed hard on open-source inference tooling, betting that enterprises will want to run AI models across their own servers and multiple clouds rather than rent everything from a single hyperscaler.

“AI adoption is moving rapidly from experimentation towards execution, and organisations need open platforms and hands-on engineering expertise to make that transition responsibly,” said Daniel Aw, Vice President and General Manager of Red Hat Asia Pacific.

What Red Hat did not say is just as telling. The announcement carries no investment figure, no headcount, no timeline and no disclosure of what EDB’s “support” amounts to in grants or incentives. For a centre pitched as a regional hub, that leaves readers to take its scale on trust.

The defence angle

The DSTA tie-up is the most concrete part of the launch, and the most sensitive. The two organisations plan to jointly develop solutions in AI, cloud computing, application modernisation and edge computing, which refers to processing data close to where it is generated rather than in a distant data centre. They will also set up practices for DevSecOps, which builds security checks into software development from the start, and “Day 2 operations”, the unglamorous work of keeping systems running, patched and stable after launch.

The work will span on-premise and off-premise environments. In defence, that distinction matters: sensitive systems often cannot touch the public cloud at all, which is precisely where an open-source, run-it-anywhere vendor has an edge over hyperscalers.

“As threats become more complex, it is important that we continue to push the boundaries of technology and innovation to stay ahead of the curve,” said Gayle Chan, Deputy Chief Executive (Information) at DSTA.

Also Read: Singapore’s AI dividend will depend on what happens after the pilot phase

The language is still exploratory. DSTA is collaborating to “explore and co-develop” solutions it “can then apply” to defence uses. That is a long way from a deployed system. It also raises questions the announcement does not touch: what kinds of defence applications are in scope, and what oversight governs AI built for military use. A bank’s chatbot gets public scrutiny when it misbehaves. Defence AI rarely does, which is why clarity on guardrails matters more here, not less. The governance gap is not unique to defence; enterprises across the region still aren’t sure who referees their AI agents.

Selling talent as much as tech

Red Hat is also framing the CoE as a skills play, a theme that lands well with Singapore’s policymakers. The centre includes an innovation team of engineers, architects, project managers and forward-deployed engineers. The last category, popularised by Palantir, describes engineers who embed directly within client organisations rather than advising from afar, an admission that most AI projects fail on messy, company-specific problems rather than model quality.

The centre will also run training in software engineering, automation, cloud-native development and DevSecOps for customers and partners. That addresses a real bottleneck. Singapore firms are scrambling for staff who can build and manage autonomous systems, and agentic AI is now the city-state’s hottest job skill. Meanwhile, messy data keeps Singapore SMEs’ pilots stuck, a problem no platform alone can fix.

“The Centre bolsters Singapore’s position as a key AI hub creating opportunities for local talent to deepen their engineering capabilities,” said Yueh Chinn Seah, Vice President of Digital Industry at EDB.

A crowded rescue mission

Red Hat is far from alone in pitching itself as the cure for pilot fatigue. Earlier this month, Singtel’s RE:AI partnered the SIT-NVIDIA centre to tackle the enterprise AI deployment gap. Singapore-based OneByZero raised US$20 million in Series A funding for the same mission, while Thinking Machines joined the Temus group in a regional push for production-grade AI. Above them all sit AWS, Microsoft Azure and Google Cloud, each with deep pockets, regional data centres and its own enterprise AI programmes. Open-source rivals such as SUSE and Canonical compete for the same hybrid-cloud budgets.

Also Read: Everyone is talking about AI adoption. But are enterprises actually ready for it?

Red Hat’s differentiator is portability. Its argument is that enterprises and governments should not have to hand their AI future to a single cloud provider. That message resonates with the region’s growing anxiety that Southeast Asia cannot build sovereign AI on borrowed choices. It is worth noting, though, that “open” and “independent” are not the same thing: Red Hat is ultimately a US company owned by IBM.

The real test

Centres of excellence are easy to open and hard to judge. The proof will not be the ribbon-cutting but the case studies: how many regional clients move workloads into production, how much code the Singapore team ships upstream, and whether the DSTA work produces anything beyond press releases.

Until Red Hat puts numbers on the table, the CoE is a promising signal rather than a solved problem. For a region stuck in the frozen middle, even a signal counts, but enterprises would be wise to ask for receipts.

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Taiwan’s Fair Trade Commission has blocked Delivery Hero’s sale of foodpanda Taiwan to Grab, scuttling a US$600 million all-cash deal agreed in March. The regulator said the remedies on offer could not offset the harm to competition.

The sticking point is Uber. The US company holds about 13.1% of Grab, a legacy of their 2018 regional deal, and the commission argued the stake would blunt rivalry between a Grab-owned foodpanda and Uber Eats, even though Grab’s dual-class structure caps Uber’s votes at 3.9%. It also cited the 2018 merger itself, for which Singapore’s competition watchdog fined both companies about US$9.5 million. There is a further twist: Uber agreed in July to buy Delivery Hero, leaving it on every side of the table.

This is the second time in under two years that Taipei has stopped the sale, after it rejected Uber’s US$950 million bid in December 2024.

Foodpanda Taiwan generated US$1.8 billion in GMV last year, and the deal would have handed Grab just over half the market. Delivery Hero can appeal.

For Southeast Asia, the ruling is a preview: if Uber’s Delivery Hero deal closes, regulators across the region will face the same cap-table questions.

Editor.

REGIONAL

Singapore security robot maker Kabam Robotics to shut down: The startup gave customers no reason for the closure in its email. SingHealth has deployed its robots at hospital facilities, and it remains unclear what happens to existing deployments or staff; LinkedIn lists 54 people linked to the firm.

VinFast’s US$500M Philippine plant bets on exports, not local demand: Built for 200,000 vehicles a year, the plant could produce six times local EV demand forecast for 2026. Construction starts in Q1 2027, while VinFast’s Q2 loss from continuing operations widened 128% to US$1.2B.

Carro launches Singapore’s first car-loan securitisation at US$350M: The MAS-approved vehicle buys loans from subsidiary Genie Financial Services and lets multiple lenders join through notes or direct loans. Carro, which transacted over 120,000 vehicles across eight markets in FY2026, wants to diversify funding for regional expansion.

Malaysia’s US$110M startup push runs into a deal-matching problem: Budget 2027 tasks Khazanah’s Jelawang Capital and KWAP’s Dana Perintis with deploying US$110.4M into startups. Investors say money is not the bottleneck; finding everything meant to accompany it is the harder part.

Ex-PropertyGuru CEO’s Genie Health raises US$3M to unify health data: The Singapore startup’s seed round, led by the Nadathur family office, funds an app that pulls screening reports and wearables into one view, targeting health data now scattered across PDFs, smartwatches and clinic records.

Homage turns in-house care software into a product as Singapore ages: The home care company is commercialising the rostering and visit-note tools built for its own care managers, betting Singapore’s super-aged shift will lift demand for back-office automation across providers.

Thunes opens a real-time payout corridor into Venezuela: The Singapore payments firm now routes instant transfers through Venezuela’s interbank platform, targeting a market where remittances hit about US$6B in 2025, roughly 7% of GDP, half of it sent from the US.

UK and Singapore widen cooperation on tokenisation, AI and quantum: The 11th UK-Singapore Financial Dialogue covered Project Guardian, stablecoins and MAS’s SAFR framework for AI agents handling financial tasks, alongside post-quantum migration and transition finance.

Circle says stablecoins fit Vietnam’s cross-border leg, not checkouts: With 17-21 million Vietnamese holding digital assets, Circle argues USDC could cut pre-funding delays in remittance settlement while recipients still receive dong, as Vietnamese law bars digital assets as payment.

Sunway lines up WeRide, Neolix and AutoFlight trials in Malaysia: The transport ministry has conditionally approved a pilot of two autonomous buses on BRT-Sunway Line. The driverless delivery and eVTOL plans still await technical testing and regulatory clearance.

Taiwan brings 33 startups to SWITCH 2026 in Singapore: Led by the National Development Council-backed Startup Island TAIWAN, the delegation exhibits at Sands Expo from 27 to 29 October as part of Taiwan Tech Week in Singapore.

INTERVIEWS & FEATURES

SEA’s US$1T digital economy target slips to 2035 as AI props up hope: This year’s e-Conomy SEA report projects US$368B in GMV for 2026, up 17%, but pushes the trillion-dollar milestone back five years, with AI now carrying the region’s optimism.

Global founders turn inward while SEA entrepreneurs look abroad: HSBC’s Global Entrepreneurial Wealth Report 2026 finds the share eyeing international expansion fell six percentage points globally, while Southeast Asian business owners buck the inward turn.

HK and Taiwan founders ride the AI boom; Southeast Asia misses out: A year after ranking among HSBC’s most cautious respondents, founders in Hong Kong, Taipei and Shanghai are now the most upbeat in HSBC’s survey, a turnaround Southeast Asian peers have yet to share.

SEA merchants must now optimise their listings for AI shopping agents: Shopee went live inside ChatGPT across seven markets in June and Lazada runs its Qwen-powered agent Lazzie, so sellers who optimised for human shoppers must now win over machine buyers too.

Techable’s Amulya Vishwanath: physical AI is still in its LEGO era: The ex-Nvidia marketer turned VC says robotics’ ChatGPT moment awaits foundational hardware and software building blocks such as actuators and hands, and calls talent access her firm’s moat.

INTERNATIONAL

Bain Capital commits up to US$250M to Hong Kong’s FundPark: The package starts with US$100M upfront, with US$150M more subject to conditions. FundPark has lent US$10B cumulatively to over 40,000 online sellers across Asia, underwriting with real-time sales data.

Walmart welcomes AI shopping agents as Amazon shuts them out: Its Agent Gateway MCP server lets approved agents search and buy, while Agent Profiles let shoppers cap each agent’s spending limit and approve transactions. Non-compliant agents get blocked.

Meta’s Muse passes 10M users, with shopping a top use: About 25% of users open the AI agent daily, and over a quarter of engaged shoppers average more than US$200 per purchase. Meta expects Muse to eventually out-earn its social apps per user.

Tesla to sell driverless Cybercabs by year-end despite NHTSA probe: Fleet buyers could run the steering wheel-free two-seater before unsupervised FSD reaches owners in Q2 2027, but regulators are still examining whether Tesla’s safety self-certification holds.

CYBERSECURITY

AI agents hacked 100 firms at US$50 each; SEA’s shops fit the profile: One operator using the Cairn agent platform targeted mid-sized online shops for payment-system access. With only 68.5% of ASEAN SMEs running specialised security software, the region’s D2C brands fit the profile.

New Mexico court orders Meta to pay US$25B over privacy deception: A jury found 43 million consumer-protection violations, each carrying a US$5,000 penalty, partly tied to the Cambridge Analytica-linked app. Meta calls the ruling a tax on speech and will appeal.

Scouter brings a listen-first pitch to Singapore’s busy cyber market: Scouter pitches diagnosing security teams’ real problems before selling products, a rebuke of the slide-deck-and-quarterly-target routine that security leaders at large Singapore organisations know too well.

ChatGPT adds PIN-locked chats, but no end-to-end encryption: Locked chats sit in a separate section with their own memory, secured by PIN or biometrics. OpenAI says the feature does not change data retention, and resetting the PIN deletes everything locked.

SEMICONDUCTOR

BMI: AI capex will keep driving Asia Pacific growth into 2027: Taiwan, South Korea and Singapore gain most from chip and electronics demand, while Malaysia draws data centre investment. BMI flags a hyperscaler slowdown and trade restrictions as key risks.

Chinese chipmaker CXMT’s shares slide 7.8% despite swing to profit: The DRAM maker forecasts January–September net profit of up to US$19.8 billion, reversing a US$790 million loss, as AI drives shortages. Its global DRAM share doubled to 10%, but investors fear a supply glut.

Qualcomm to cut jobs worldwide in performance-focused layoffs: CEO Cristiano Amon’s memo says notices begin in November but gives no headcount; Qualcomm had about 52,000 staff in 2025. Weak handset demand and shrinking Apple revenue weigh on the chip designer, which bets on data centres.

AI

DeepSeek reportedly raises US$15B days after US federal ban: Bloomberg says CATL and Tencent are among potential investors in one of China’s largest rounds, following a US$7B raise in June at a US$50B valuation. A Chinese IPO could follow next year.

GoTo’s 27B model speaks Batak, but who can afford to run it?: The model targets regional languages such as Batak Toba that global chatbots handle poorly, but the cost of running it may decide whether smaller Indonesian firms can actually use it.

Enterprise Singapore commits US$35.2M to push AI into SME factories: The three-year Catalytic AI Solutions scheme pairs SMEs with vendors and trade bodies to deploy proven tools for design, quoting, inspection, energy monitoring and predictive maintenance.

Singapore launches embodied AI challenge built on a Micron use case: Teams worldwide will program robots to service a mock semiconductor chamber, competing for a US$69,000 prize pool. Simulation runs to 31 December, with finals at RoboSG! 2027 in March.

Agentic AI tops Singapore’s job-skill demand, Jobstreet data shows: Jobstreet by SEEK’s AI Gauge shows recruiters rewriting job ads around agentic AI workflows, which raises hard questions for workers whose routine tasks those agents are built to absorb.

Enterprises can’t say who settles disputes between their AI agents: An OutSystems report finds executives confident about keeping individual agents in line, but unsure what happens when agents disagree, a gap that widens as firms chain agents together.

OpenAI buys Orchestra Bio to bolster its Rosalind life sciences AI: The four-year-old startup, which raised about US$20M from a16z and 8VC, builds agents for drug-discovery operations. Its team joins OpenAI’s Rosalind effort, already used by Moderna and Novo Nordisk.

THOUGHT LEADERSHIP

AI won’t fix messy processes; it just speeds up the mess: A Singapore AI marketing founder, drawing on a talk by Kaliber’s Robert Lai, argues most AI automates broken workflows instead of fixing them, so the disorder simply moves faster.

An AI trainer on turning a 40-hour week into 120 hours of output: The author says AI strips out the grind only when humans stay in the loop, and shares lessons on what to delegate, many of them learnt from AI itself.

A PR firm used Gemini to find which pitches actually pay off: After logging 300 hours on proposals in four months, the author compared five years of pitch proposals against client invoicing records to see which business was worth chasing.

AI took flawless workshop notes, then produced an unusable deck: A strategy workshop went well and an AI tool captured every point, but the resulting deck showed why synthesis still needs judgement, the author argues.

How one creator decides which part of an AI video workflow to fix: When LLM storyboards for a TikTok commerce creator produced loose shots, the author chose to diagnose the pipeline before rewriting scenes by hand.

A Vietnamese software firm audits its backlinks: only two count: The Ho Chi Minh City company assumed its directory listings helped search rankings, but an audit on 15 August found four domains linked, two counted for anything.

Google may already be showing its route to AGI: The author traces Demis Hassabis’s long-held view that AGI needs the right components, arguing Google’s recent moves reveal which pieces it now believes are in place.

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For a Singapore or Jakarta SaaS founder who has already sweated through GDPR paperwork to land a European client, Japan can look like a box already ticked. Strong privacy law, a mature regulator, a long history with data protection. Surely the European playbook will do?

It won’t. Japan’s Act on the Protection of Personal Information (APPI) shares a family resemblance with the EU’s General Data Protection Regulation (GDPR), but it handles consent, cross-border transfers and cloud hosting in ways that catch overseas startups out.

Also Read: Why Japan’s booming AI market is harder to crack than it looks

The stakes are rising. Japan is one of the world’s largest consumer markets, its corporates are hungry for partners, and its legal system is famously stable. That combination is drawing a steady stream of foreign founders, at the same time as Japanese companies are moving into Southeast Asia faster than many Western rivals. The traffic runs both ways, and personal data travels with it.

Who and what APPI covers

The law applies to “Personal Information Handling Business Operators”, a mouthful that covers almost any private company using a personal information database for business. Public-sector bodies sit under a separate regime.

APPI then sorts data into three tiers. Personal information is anything about a living person that can identify them, alone or when combined with other data. Personal data is that information once it sits in a structured database. Retained personal data is the subset a company has authority to act on when an individual makes a request. Data a company handles purely as a processor for someone else generally falls outside that last tier, which matters a great deal for SaaS vendors.

The obligations follow the data’s lifecycle. Companies cannot collect information through improper means, must state the purpose of use when collecting it, and cannot stray beyond that purpose. They must secure the data, supervise employees who touch it, and supervise any processors they hand it to. Sharing with third parties needs consent, with exceptions for outsourcing, business succession and joint use.

Individuals can ask a company to disclose, correct, add to or delete their retained data, to stop using it, or to stop sharing it with third parties.

Breaches carry a hard trigger. The company must notify both the Personal Information Protection Commission and the affected individuals when a leak involves sensitive information, could cause financial harm, looks malicious, or touches more than 1,000 people. For a SaaS platform with enterprise clients, crossing that last threshold is a bad afternoon, not a rare catastrophe.

Where GDPR muscle memory fails

The first trap is consent. Founders raised on GDPR expect to document a lawful basis for everything. APPI is in some ways lighter: ordinary personal information needs only a notice of purpose, not consent. Sensitive information is the exception, requiring prior consent before collection.

The second trap is cross-border transfers, where APPI leans much harder on consent than Europe does. Under GDPR, explicit consent is a narrow fallback behind adequacy decisions and standard contractual clauses. Under APPI, informed consent is a frontline option, but “informed” carries weight: the company must tell individuals the destination country, summarise that country’s privacy laws, and describe the recipient’s safeguards. The alternatives are sending data to a country Japan recognises as adequate, or a contract that binds the recipient to APPI-equivalent obligations.

Direct email marketing is the third. APPI expects prior consent, and a clause buried in the terms of service is discouraged, though an existing business relationship may help. Europe offers no single answer either, since the ePrivacy rules vary by member state.

Then there is infrastructure. Under APPI, moving data to a server abroad inside the same legal entity is not treated as a cross-border transfer, though the company must still review the foreign environment as part of its security duties. GDPR, by contrast, still demands safeguards for transfers within a corporate group.

Also Read: The compliance mosaic: Navigating SEA’s cybersecurity chaos

The biggest divergence is the “cloud exception”. If a cloud provider contractually agrees not to handle the personal data and enforces proper access controls, the cross-border and processor-supervision rules may not apply at all. GDPR has no such carve-out; every cloud provider needs an Article 28 processing agreement. For a lean startup, that exception could save months of legal back-and-forth, provided the contracts and access logs genuinely support it.

The SaaS reality check

Picture a foreign SaaS company selling to Japanese enterprises. The legal burden technically falls on the Japanese client, which must justify sending data abroad and must supervise its vendor by selecting it carefully, signing proper agreements and monitoring how data is processed.

In practice, that burden lands on the vendor’s desk. Japanese procurement teams will want terms of service that bind the provider to APPI-level obligations, so the client can rely on the contractual route rather than chasing consent from every employee or customer. A vendor that arrives with those terms ready shortens the sales cycle; one that arrives with a lightly edited GDPR template invites a long negotiation. Startups such as fileAI, which have expanded in Japan with corporate backers, offer a reminder that enterprise doors open through trust as much as product.

For Southeast Asian founders, this is familiar terrain. The region already juggles a patchwork of national data laws, and regulators such as Singapore’s are sharpening their focus on personal data in AI products. Japan simply adds another tile, with its own grain.

Trust is the real currency

APPI’s text is only half the story. Japanese companies and consumers have unusually high expectations around compliance, and those expectations have risen sharply. A regulatory slip can trigger reputational damage that outlasts any fine and quietly closes doors to the corporate partnerships that drew founders to Japan.

Also Read: Beyond market entry: Japan and Southeast Asia in a fracturing world

That is the uncomfortable takeaway for overseas startups: there is no shortcut by copy-paste. The founders who do well will treat APPI as a market-entry cost to budget for from day one, building a framework designed for Japan rather than retrofitting a European one. As privacy and sovereignty demands keep slowing enterprise AI deals across the region, getting this right early may be the most underrated go-to-market move a SaaS founder can make.

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

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