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.

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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.

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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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