Last month, I sat across from Kenny, a software engineer in his early thirties, based in Singapore, someone who reads financial news, has a brokerage account, and thinks carefully about his money. I asked him about his portfolio. He listed: Apple, Microsoft, Google, Nvidia, and Amazon.

I asked about Singapore stocks. He paused. “I don’t really look at those,” he said. “I just feel like I understand tech companies better.”

He uses an iPhone. He uses Google Maps. He watched the Netflix documentary about Enron. He follows Elon Musk on X.

He does not follow Singtel’s earnings calls.

He was not investing in what he understood. He was investing in what felt familiar. Those are not the same thing.

The data behind the anecdote

We work on an AI-native portfolio intelligence platform. Over a recent two-month period, we analysed 82 anonymised retail portfolios submitted by investors across Singapore and Vietnam. What we found was not what conventional financial theory would predict.

The three most frequently held securities across our sample were Apple (AAPL), Microsoft (MSFT), and JPMorgan Chase (JPM), appearing in 32.9 per cent, 32.9 per cent, and 30.5 per cent of portfolios respectively. Gold (GLD) and long-duration US Treasuries (TLT) each appeared in 29.3 per cent of portfolios.

Not a single SGX-listed security appeared in the top 30 most commonly held positions.

Read that again. In a sample where the majority of users are Singapore-domiciled retail investors, no Singapore-listed stock was commonly enough held to crack the top 30.

Behavioural finance has a well-established concept called home bias, the tendency of investors to overweight domestic stocks relative to the theoretically optimal global portfolio. French and Poterba documented it in 1991. It has been replicated in virtually every market studied since. The academic consensus is that investors buy what is local, familiar, and proximate.

Our data suggests something has changed, or at least, something is changing at the leading edge of digital investor behaviour in Southeast Asia. These investors are not exhibiting home bias toward Singapore. They are exhibiting a different bias entirely: anchoring to the US mega-cap companies whose products they use every single day.

We call it reverse home bias. And it carries risks that standard suitability frameworks were not designed to catch.

Also Read: Tried-and-tested marketing strategies for startups across all stages in Singapore

The mechanism: You invest in your ecosystem, not your address

This is not simply the observation that technology has lowered the barriers to international investing, though that is true. The question is not whether you can buy Apple from a Singapore brokerage; the question is why one in three retail investors in our sample have chosen to.

The answer, I suspect, is cognitive availability. Apple is not a foreign stock to someone in Singapore. It is the company that made the phone in their pocket, the laptop on their desk, and the watch on their wrist. It appears in their social media feeds, in the financial content they consume on YouTube and TikTok, and in the investment discussions on Reddit and Seedly. JPMorgan appears daily in financial news. Microsoft is their workplace operating system.

This is the availability heuristic, a concept from Tversky and Kahneman’s foundational work on cognitive bias, operating across national borders. What you can easily imagine tends to feel safer. What saturates your attention feels like information, even when it is not.

The Singapore investor who holds Apple is not making an informed bet on AAPL’s earnings trajectory relative to its valuation. They are making a bet that feels safe because they cannot imagine a world without iPhones.

Why this is a problem worth naming

A portfolio concentrated in US mega-cap technology and financial stocks is not a balanced, internationally diversified portfolio. It is concentrated exposure to: US equity market risk, Nasdaq sector concentration, US Federal Reserve interest rate sensitivity, and USD/SGD currency risk.

None of these risk factors appears on a standard retail investor suitability questionnaire. Brokers ask whether you are growth-oriented or conservative. They do not ask: Does your portfolio move in lockstep with Nasdaq? Are you exposed to a single country’s monetary policy? Do you hold any asset that is genuinely uncorrelated with US equities?

Also Read: Singapore lands OpenAI’s first lab outside the US with US$225M commitment

The scoring system of DNA Score, a composite behavioural risk metric computed from portfolio position data, flagged meaningful risk differentiation in the sample. Portfolios in the Speculative Investor archetype, which held high-momentum narrative stocks like MicroStrategy (MSTR) and Palantir (PLTR), scored a mean of 53 out of 100. The more diversified archetypes scored in the mid-80s.

The investors with the low scores were not taking conscious speculative positions. They were following communities, chasing stories they had absorbed on financial social media, and concentrating on names that felt exciting and familiar in equal measure.

The most dangerous portfolio is the one that feels safe and is not.

What the AI era changes, and does not

There is reason to think reverse home bias will intensify, not diminish, as AI-assisted investing goes mainstream. When a retail investor in Singapore asks ChatGPT which stocks to consider, the names most represented in the AI’s training data are overwhelmingly US large-caps. When TikTok’s finance creators in the region discuss their portfolios, they discuss Apple, Nvidia, and Tesla, not Keppel or ComfortDelGro.

The infrastructure of financial information has globalised faster than the infrastructure of financial advice has localised. The result is that millions of first-generation retail investors in Southeast Asia are being guided by content optimised for engagement rather than advice optimised for their specific risk profile, currency exposure, and financial goals.

This is not an argument against holding US equities. It is an argument for holding them consciously, knowing why you own them, what risks they carry, and whether your overall portfolio is as balanced as it feels.

Also Read: RIE2030’s hidden flaw: The one capability Singapore’s startups are missing

What you can do right now

Run your portfolio through a behavioural diagnostic. Not the risk tolerance questionnaire your broker sent you when you signed up; those are designed to satisfy regulatory minimums, not to give you genuine insight. A real diagnostic looks at what you actually hold, computes your concentration, identifies your factor tilts, and tells you which behavioural biases are embedded in your current positions.

A true behavioural finance system processes your portfolio and returns a DNA Score, a breakdown of seven behavioural bias flags, and a regime-aligned action plan. It requires no broker credentials, no passwords, no transaction data, just your positions.

And the question is worth asking: when you look at your portfolio, are you seeing a strategy, or are you seeing a reflection of your screen time?

The post Why Singapore investors hold more Apple than Singtel, and why it should worry you appeared first on e27.

When we run citation checks for clients at ESBO Ltd, my agency, the sources assistants quote for buying questions are rarely brand websites or big media. They are creators: a YouTube comparison from someone who tested four products on camera, or a forum thread with real numbers in it. The people being quoted are, almost without exception, not being paid for it.

That is a pricing failure, and I doubt it lasts long.

The machines quote creators now

The scale surprised me when the data landed. In January, Adweek pulled together citation research from four independent firms, 6.1 million AI citations in all, and found that YouTube had overtaken Reddit as the most quoted social platform in AI answers. YouTube now appears in roughly 16 per cent of assistant responses, and its share of social citations more than doubled between August and December 2025 while Reddit’s halved.

The why matters more than the ranking. Models reach for creator content on buying questions because it looks like evidence. Brand pages make claims. A creator video shows the thing working, with a transcript, specific numbers and an audience arguing in the comments. When an assistant has to answer which accounting tool suits a small Philippine agency, somebody’s tested comparison beats a feature page.

The region built its house on the unquoted platform

Here is the uncomfortable part for Southeast Asia. This region’s creator economy runs on TikTok to a degree no other market matches, with over 150 million active users regionally, and Momentum Works found content commerce drove 32 per cent of Southeast Asian e-commerce GMV in 2025. As a selling engine, it works. For the answer layer, it barely exists: the same Adweek data set has AI systems citing YouTube about 50 times more often than TikTok and 18 times more often than Instagram. Short vertical video with thin transcripts is close to invisible to the machines assembling recommendations.

Also Read: The creator economy is distribution, not marketing. Most Asian businesses are still scaling it like a campaign

The reach economics stay brutal at the same time. TikTok’s Creator Rewards Programme was still not live in a single Southeast Asian market as of mid-2026, and creators with mainly domestic audiences earn effective payout rates measured in cents per thousand views. A creator in Manila or Jakarta needs a multiple of the audience a London creator needs to earn the same platform payout. So the region’s creators are optimised for a currency that pays them worst, on the platform the answer layer reads least.

Pricing the quote

Influence stopped being a follower count a while ago; citation data just makes the replacement measurable. A reviewer with 30,000 subscribers whose comparison video gets quoted whenever assistants answer a category question carries more commercial weight than a lifestyle account with three million followers and zero citations. The tracking tools to see this per brand and per market already exist, and what brands can measure, they eventually price.

In practice that means paying creators for durable presence in the content machines quote, rather than for a burst of reach. A two-year-old tested review that assistants keep citing is media that never stops running. One warning belongs here: sponsorship has to be disclosed, and undisclosed astroturf is the fastest way to lose the audience and the citations together, since models lean on community validation. The value comes from genuine testing with a sponsor attached, never from a script.

Also Read: Why reach doesn’t equal credibility in the creator economy

For founders reading this from the brand side, I argued in June that startups need public proof before they scale. Creator citations are becoming part of that proof layer, and in this region they are still remarkably cheap to earn honestly.

Building citability

Four habits separate quoted creators from merely famous ones.

Own a narrow set of questions. “Best payroll tools for Indonesian SMEs” gets answered by machines thousands of times a month, and somebody’s testing will be the source. Pick the questions and become that somebody.

Go where transcripts live. Long-form video with clean captions, plus a blog or newsletter carrying the same findings in text. Machines read words. Give them words.

Publish numbers. “It felt fast” does not survive summarisation. “It processed 500 invoices in 40 minutes” does, and gets attributed.

Keep your name consistent across platforms, so authority accumulates to one entity instead of scattering across handles the models cannot connect.

For a decade this region’s creators have been paid for attention. The machines now pay attention to something else, and the first creators and brands to price it will look early for about a year, then look obvious.

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Bitcoin rose 0.82 per cent in 24 hours to US$76,318.25. Ethereum gained 0.80 per cent to US$2,418.94. The total crypto market cap increased one per cent, and the broader crypto market rose 0.99 per cent. These numbers point to a single conclusion. A relief bounce tied to the Federal Reserve lifted the entire asset class.

No coin-specific catalyst appeared in the data. The primary force came from the central bank. Bitcoin slightly underperformed that broad rise even as it gained. Ethereum tracked its larger peer almost exactly. This synchronised move indicates that the market is currently driven by macro headlines rather than project-level news.

The Fed raised rates by 25 basis points on September 16 to a target range of 3.75 per cent to 4.00 per cent. Market participants had widely anticipated this unanimous decision. The confirmation removed near-term uncertainty. Risk assets responded with a modest rally. The two largest digital assets moved in lockstep with that broader tide.

Bitcoin’s 90-day correlation with gold recently hit a multi-year high. That detail matters. It shows the leading cryptocurrency now trades more like a macro asset than a speculative tech bet. Ethereum remains highly sensitive to central bank cues and Bitcoin’s direction in the short term. The move has less to do with each network’s fundamentals and more with a market-wide sigh of relief.

This is a beta trade, not a fundamental repricing. A priced-in event often produces this kind of reaction. Traders sell the rumour and buy the fact. The fact here was a rate hike that no longer surprised anyone. The market had already absorbed the news before the Fed spoke, so the actual announcement simply cleared the air.

Also Read: The CLARITY Act vote could send crypto to US$2.73T or crash it to US$2.6T

Supporting data in derivatives markets adds nuance. Bitcoin open interest fell 3.1 per cent. Liquidations dropped 65.79 per cent. That decline in forced selling suggests a calmer backdrop. Bitcoin dominance stayed elevated near 58.85 per cent. Capital has not rotated aggressively into riskier altcoins.

Instead, it remains defensive. Ethereum told a slightly different story. Average perpetual funding rates rose 40.74 per cent over 24 hours to +0.0053 per cent. Some derivatives traders leaned cautiously bullish. The absolute rate stayed far from extreme levels. Ethereum also benefited from its place in the Layer 1 narrative, which posted a 0.99 per cent sector gain.

Risk capital is rotating toward large-cap blockchain platforms, but it is doing so selectively. Bitcoin still leads. Ethereum follows. That relationship defines the current market structure. The lack of a leverage washout and the sustained dominance of the largest asset create a stable floor, but they also limit upside momentum. When capital stays defensive, rallies tend to be measured and shallow rather than explosive.

Institutional flows provide the most important test. U.S. spot Bitcoin ETFs recorded US$450 million in outflows on September 15. That figure shows hesitation among institutional investors. A return to net inflows would confirm renewed demand. Until then, price stability rests more on reduced selling pressure than on a fresh wave of buying.

Ethereum faces a similar question. The daily ETF flow report will show whether spot Ethereum ETF flows turn positive in the next 24 to 48 hours. Positive flows would confirm a return of institutional interest. Sustained outflows could pressure the support zone. The bounce then looks technical rather than durable.

This flow data matters more than any single derivative metric because it reflects real capital allocation from large investors. Without that capital, the rally depends on short-term traders and macro sentiment. That foundation is thin and can crack quickly if the next data release or policy comment shifts the mood.

Also Read: The Fed is the real crypto story, Bitcoin and Ethereum are just following

Technical levels define the near-term battlefield. Bitcoin trades just above the US$75,000 support level, which has held for weeks. If the largest asset holds above US$75,000, a retest of US$78,189 resistance becomes possible. A break below US$75,000 would shift focus to the next support near US$74,000.

Ethereum consolidates between support at US$2,350-US$2,400 and resistance at US$2,500-US$2,600. Its 4-hour RSI sits at 53.17, a neutral reading. A daily close above US$2,500 would signal a breakout attempt. A break below US$2,350 would risk a deeper correction toward US$2,200.

The market is in a wait-and-see mode. It balances relief from the Fed against lingering regulatory uncertainty from the failed CLARITY Act. That legislative setback removed a potential positive catalyst and left the market without a clear regulatory path forward. Without that path, institutional investors may continue to hesitate, and that hesitation shows up in ETF flows.

In my view, the synchronised price action tells a story of a market where macro forces set the tone but internal dynamism remains weak. The Fed-induced relief rally is welcome. It is also fragile. It is a pause, not a pivot.

The path forward depends on two developments. One is that ETF flows must reverse from negative to positive. That shift would provide fresh institutional demand. The other is that both assets need convincing technical breaks above resistance. Bitcoin must reclaim and hold above US$78,189. Ethereum must close above US$2,500. Without those confirmations, the crypto complex remains vulnerable to the next macro shock or regulatory headline.

The high correlation with gold and Bitcoin’s persistent dominance show that capital seeks the safest harbours within the asset class during uncertainty. Until capital rotates more clearly into Ethereum and beyond, the recovery remains a beta-chasing exercise rather than a genuine broad-based bull market.

The next 24 to 48 hours of ETF flow data will offer the primary real test of whether this relief rally has legs. I would watch the US$75,000 level for Bitcoin and the US$2,350 level for Ethereum as the lines that separate consolidation from correction. I would also watch funding rates for signs of overheating. A sharp reversal there could trigger a squeeze and undermine the calm that currently supports prices. For now, the market has bought itself time, but it has not earned a new trend.

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Picture the scene. The lights dim. A drone shot. A cello. Employees smiling at the camera. Then the line: We are changing the world.

The film was beautifully made. The investor picked up his phone ten seconds in.

I didn’t set out to start a company over that. But a founder I was coaching kept running into the same wall — and eventually asked me a question I couldn’t ignore.

His startup was heading to CES. He needed a film. He’d already worked with media agencies, spent real money, and wasn’t satisfied. The results were professional. Polished. Something was missing underneath. He asked me: How should we actually do this?

The problem wasn’t the marketing firm or the production company. It was that they showed up too early.

Before anyone wrote the copy or picked up a camera, someone needed to ask the questions an investor would ask:

Why would an investor care? Where would they attack this business? What changes the investment case — and what can wait?

Answer those first, and the agency has something real to work with. Skip them, and even a beautiful film is built on the wrong foundation.

That gap — between investment logic and production — sat between professions. Nobody owned it.

So I jumped in. Not with a business plan. The founder had a problem; I thought I could solve it. That decision was closer to just do it than anything I’d call strategy.

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

I began with the investor’s questions. What is this business worth paying attention to? Where’s the evidence? Why now? Then I approached it as a journalist — strip away the company’s own language, find what makes an outsider stop.

Only then did I think about the film.

The first real test came before CES. I was working with a startup entering the Korea Ministry of SMEs and Startups’ Global IR competition — 92 companies in the field, every one of them with a deck, a pitch, a story they believed in.

What I focused on wasn’t the slides. It was the sequence of recognition: what does an investor see first, and does it make them want to see the next thing?

The startup won. Grand Prize, out of 92.

I didn’t think much of it at the time. One competition. Maybe the company was simply strong.

Then came CES 2025.

This time the scope was wider: pitch deck, investor film, and a piece examining the company’s technology with the rigour of business journalism rather than the language of a brochure. Three formats working in sequence — 90 seconds earns attention, the article builds conviction, the deck closes the argument.

The startup went on to win a CES Innovation Award — and raise funding.

That’s when I stopped thinking of this as pitch coaching.

Maybe this wasn’t a better way to make a pitch. Maybe there was an entire category missing between investment logic and production.

Founders know their companies better than anyone. That becomes a liability when they have to explain them to someone who doesn’t.

When investors don’t respond, the instinct is to add more. Another slide. More market data. A longer technical explanation. A 20-page deck becomes 30, then 40. The assumption: if the investor has enough information, eventually they’ll understand.

Also Read: Why Beyond Border thinks visas are now part of the founder playbook

But nobody gives you time to explain.

Investors spend an average of 2 minutes 14 seconds on a first-pass deck review, according to DocSend analytics, 2024–2025.

The investor doesn’t owe a founder 40 minutes of attention. The founder has to earn the next minute.

Recognition happens before a paragraph is finished. Analysis comes after — but only if recognition happened first. Every slide you add before earning that moment is a petition to a decision that hasn’t started yet.

The question stopped being: How do I explain everything?

It became: What does an investor need to recognise first?

The purpose of 90 seconds isn’t to replace the next 60 minutes. It’s to earn them.

I became fairly ruthless about this. If I can’t make the investment case in 90 seconds or on one page, I don’t make the pitch longer. I go back to the business. Because sometimes the problem isn’t the story. You may not have a fundable business idea yet.

That’s why I came to see compression not as an editing technique, but as a stress test. And it’s what separates what AN Lab does from video production — or storytelling.

What I hadn’t expected was how cleanly three decades of apparently disconnected work converged on this one problem.

Thirty years in investment banking and finance taught me to look past the product and find the investment logic underneath it. Writing as a guest columnist for international business media taught me to cut through complexity and find the story that matters to an outsider. I discovered video as an extraordinary compression tool — data, numbers and moving images can communicate in seconds what takes pages to explain. Working with a documentary filmmaker whose work includes BBC and CNN commissions showed me something else: how powerfully film can reveal the human conviction behind a business. And AI became a creative partner — a way to show what a camera can’t capture, what doesn’t exist yet, what would otherwise be impossible to film.

For years, these looked like separate chapters. Only when I was sitting with that founder’s problem did they resolve into one toolkit.

AN Lab is that bet: that investment logic, journalistic compression, and film grammar belong in the same room — applied to the same 90 seconds.

Most founders preparing to fundraise ask: What should we put in the deck?

After two experiments and one pattern I couldn’t unsee, I think there’s a more useful question.

What does an investor need to recognise — before they owe you another minute?

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Every cycle, people say that crypto is dead. By retail, and by funding. Attention moves on to the next bubble, this time it is AI. However, when Y Combinator published its list of Biggest Startup Opportunities of 2026, crypto kept its place alongside AI, healthcare, defence, enterprise software, and climate technology.

What surprised me was not that crypto is on the list. Rather, it was the particular formulation of crypto opportunities that caught my eye: YC did not ask the startups to design the next Layer 1, to build another memecoin or NFT marketplace, or to develop a yield farming protocol. Instead, they pointed to the need for stablecoin financial services, crypto infrastructure, institutional crypto products, tokenised assets, and agentic commerce.

This is the first sign that crypto is slowly evolving from an innovation layer to an enabler of other innovations, and, therefore, moving from an industry to an infrastructure. This is not the first sign, either, if you have been paying attention to the broader ecosystem.

Recently, Stripe announced Stablecoin Financial Accounts, a product that allows businesses in more than 100 countries to hold and transfer digital dollars around the world, bypassing the traditional banking system to a large extent. Meanwhile, Visa continues to develop stablecoin settlement and tokenised asset initiatives, and PayPal has expanded the use of its PYUSD stablecoin beyond Ethereum and blockchain-based payments.

The most notable trends in crypto adoption as financial infrastructure are also visible in Southeast Asia. GCash, the largest digital wallet in the Philippines with over 94 million registered users, has partnered with Ava Labs to tokenise EURC, USDC, and USDT on Avalanche in GCrypto. This makes it possible for everyday users to make payments in digital dollars via the most popular local app, instead of buying crypto on centralised exchanges.

Also Read: How AI and blockchain could make commerce decisions more accountable

By contrast, the competition between different Layer 1s may well be turning into a race to enable the adoption of digital assets as financial instruments by banks, payment processors, asset managers, governments, and enterprises. BlackRock’s BUIDL, the world’s largest tokenised money market fund, has also joined the ecosystem.

The same can be said for Stellar, which has embarked on a long-term journey to enable cross-border value transfers and displace traditional financial infrastructure in emerging markets and remittance corridors. The recent partnership with MoneyGram and UNHCR, as well as the introduction of Paxos Global Dollar (USDG), are all examples of this.

The broader financial services industry is also undergoing a similar transition. OKX and Standard Chartered Bank recently announced the launch of a collateral mirroring programme, which allows institutional customers to use tokenised money market funds and crypto assets as collateral in OKX’s custody under a regulated framework. The growing consensus within traditional finance is that digital assets will become an unavoidable part of the financial ecosystem.

However, the integration of crypto into traditional finance is much more nuanced and complex than many in the crypto community have cared to admit. DeFi is undergoing a similar transformation at the protocol level, as evidenced by Aave’s recent decision to sunset several smaller Layer 2 markets in favour of a more focused approach.

Also Read: The real status of blockchain gaming in Southeast Asia: Not hype, not dead — just growing up

In other words, Aave has opted for quality over quantity by shifting its resources to more liquid and relevant chains and products. Once again, we see the signs of a maturing ecosystem that is beginning to move away from the narrative of limitless possibilities and multiple ecosystems to the pursuit of efficiency and pragmatism.

In many ways, the evolution of crypto as an infrastructure layer has already begun. And, ironically, it may well be the payment apps, enterprise software, payroll processors, and AI agents that enable the greatest number of daily crypto transactions around the world.

The end-user will hardly distinguish between a transaction settled on the XRP Ledger, Ethereum, as long as it is cheap, seamless, quick, and available 24/7. Ripple continues working with banks and central banks on CBDC pilots through its CBDC Platform. Hedera is being used by organisations exploring tokenisation and digital identity. The industry’s competitive edge is gradually shifting away from speculation toward financial infrastructure.

It is no wonder that YC keeps believing that crypto is one of the major startup opportunities for the next decade. The next major leap for crypto may be driven not by crypto natives but by traditional payment companies that are looking to disrupt the financial system with better UX and more attractive yield opportunities. Perhaps one day, we will look back on this period of programmable money experiments as a brief episode of adolescence, when people talked a lot about crypto but used it even more.

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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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Part of my work as an international market expansion specialist, supporting government and companies in the process of promoting and attracting opportunities, is reading a market’s readiness not just through headlines, but through the infrastructure underneath: banking systems, regulatory friction, and the everyday experience of the people actually trying to operate there. That’s what pulled my attention to the Maybank announcement, earlier this year, and to a pattern I keep seeing repeatedly across the expat communities I’m active in.

Maybank plans to deploy MYR 10 billion (US$2.5 billion) over the next five years, with one core target tied to its CASA ratio, Current Accounts and Savings Accounts as a share of total deposits. In plain terms: the more everyday accounts a bank opens, the less it needs to rely on expensive funding sources like fixed deposits.

When global mobility is the hidden gem

One growth lever behind this is Malaysia’s still-sizable unbanked population. The other, less discussed, is global mobility. Malaysia currently ranks third globally for expat-friendliness in several credible global ranks. On paper, that’s a strong signal for banks: more people relocating should mean more accounts opened.

But the on-the-ground reality tells a different story. Across the expat groups I follow closely, one complaint comes up consistently: opening a bank account as a foreigner in Malaysia remains genuinely difficult, regardless of employment status or intent to stay. There’s a visible gap between government ambition to attract global talent and the private banking sector’s operational readiness to onboard them. That gap is exactly the kind of friction I look for when assessing underlying opportunities: the typical market inefficiencies that hide strong potential.

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

This is where Brazil enters the picture, not as a random comparison, but as a useful counter-case from my own expansion work. Brazil is a market I know from the inside, and it’s a useful stress test for Maybank’s targets: not because the two markets are comparable in maturity, but because they sit at opposite ends of the same infrastructure question, which both markets could learn from each other.

Maybank’s long-term targets (figure 1), ROE of 13 to 14 per cent, cost-to-income at 47 per cent or lower, net interest margin above 2.05 per cent (already achieved), are healthy, competitive numbers within Malaysia’s banking environment. Though, once applied to Brazil’s benchmarks to that same structure, it would collapse.

Take for instance one of Brazil’s largest banks, Itaú Unibanco, which posts an ROE of 24.3 to 25.7 per cent, a cost-to-income ratio of 35.5 to 37.3 per cent, and a NIM of 6.2 to 6.7 per cent. A margin that looks solid in Kuala Lumpur wouldn’t keep a Brazilian bank alive for one cycle.

Also Read: Malaysia fines, Singapore funds: How two governments are forcing SEA’s second digital wave

Is banking infrastructure telling us a different story about market readiness?

The difference is less about performance and more about infrastructure maturity. Brazil’s Pix, Open Finance, and heavy automation have compressed operational costs to a degree Malaysia’s banking sector is pursuing it as we speak although it hasn’t reached yet. That’s precisely the kind of variable I flag in market readiness: two very different playbooks telling different stories for the same objective: governments fulfilling their needs, companies growing abroad in a healthy structure.

Zoom out to market capitalisation (figure 2), and the layering continues: Nubank (~US$77B) and Itaú Unibanco (~US$73B) each dwarf Maybank’s ~US$34.5B, while Maybank still edges out Banco do Brasil and Bradesco. None of these figures are directly transferable between markets, and that’s the point.

For any business, or institution, eyeing expansion into global markets, the lesson is the same: rankings signal potential, infrastructure determines execution. Closing that gap isn’t a footnote, it’s the actual opportunity.

—

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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Waymo is preparing to bring its driverless ride-hailing service to Singapore in 2028, marking one of the most closely watched tests yet of whether fully autonomous mobility can work in a dense, highly regulated Southeast Asian city.

The Alphabet-owned autonomous vehicle company said it is working with Singapore’s Ministry of Transport and Land Transport Authority to lay the groundwork for a public commercial launch through the Waymo app. Its initial fleet of all-electric Jaguar I-PACE vehicles will arrive in the city-state in the coming months, before a readiness phase begins in 2027.

Also Read: Singapore greenlights expanded AV testing as WeRide and Grab prepare for public rollout in 2026

During that phase, trained autonomous specialists will manually drive the vehicles around Singapore to help adapt Waymo’s technology to local road layouts, traffic behaviour, rain conditions and operating rules. If regulatory approvals and testing milestones are met, the service is expected to open to riders in 2028.

The move adds Singapore to a growing list of overseas markets in Waymo’s expansion plan, alongside Tokyo, London and Munich. It also places the city-state in the middle of a global race to commercialise autonomous vehicles beyond carefully controlled pilots.

For Singapore, the partnership fits into a broader transport strategy: fewer privately owned cars, better first- and last-mile links to public transport, lower emissions, and more efficient use of limited urban space. For Waymo, it is a chance to prove that a service built and scaled first in US cities can be translated into one of Asia’s most demanding road environments.

A cautious route to driverless deployment

Waymo is not promising an overnight rollout. Its Singapore roadmap is deliberately phased, reflecting how sensitive autonomous mobility remains for regulators and the public.

The company said its vehicles will first be used to establish local operations. In 2027, autonomous specialists will begin manual driving to map and understand Singapore’s roads, including local geometry, traffic patterns and monsoon weather. Only after that process will Waymo seek to open a fully autonomous commercial ride-hailing service in 2028.

That sequencing matters. Singapore’s roads are orderly by regional standards, but they are also complex. The country has high traffic density, frequent construction diversions, heavy rain, multi-storey road networks, cyclists, pedestrians, buses, private-hire cars, taxis and delivery riders sharing tight urban corridors. A robotaxi that works on wide roads in parts of the US still has to demonstrate it can handle the more compressed, mixed-use nature of Asian city driving.

Also Read: Grab makes strategic bet on WeRide to drive autonomous mobility in SEA

Waymo enters with a substantial operating record. The company says it has served more than 20 million fully autonomous rides and driven more than 300 million fully autonomous kilometres. It also cites a 94 per cent reduction in injury-causing crashes compared with human drivers in the US cities where it operates fully autonomously.

Those numbers will help its case with regulators, but Singapore will still need local evidence. The city-state has long been open to autonomous vehicle testing, including earlier trials involving companies such as nuTonomy, Aptiv and Motional. Yet it has also been careful not to let the technology run ahead of safety frameworks, insurance models and public acceptance.

Why Singapore matters

Singapore is a small market by population, but it is strategically useful for mobility companies. It has strong public transport, clear regulation, high digital adoption and a government willing to test new urban technologies when they align with national priorities.

That makes it a natural Southeast Asian entry point for Waymo, even if the company’s longer-term regional opportunity may lie elsewhere. Cities such as Jakarta, Bangkok, Manila and Ho Chi Minh City face more acute congestion and transport informality, but their road environments are also less predictable and more difficult to regulate. Singapore offers a controlled but meaningful first step: dense enough to be challenging, structured enough to be feasible.

The company is also positioning its service as a complement to public transport rather than a replacement for it. Waymo said many riders in its current commercial service areas use its vehicles to connect to mass transit. In San Francisco, more than a third of riders are picked up or dropped off near transit stations, according to the company.

That argument is especially relevant in Singapore, where the MRT and bus network already covers much of the island. The question is not whether robotaxis can replace trains, but whether they can fill gaps: late-night trips, short connections from estates to stations, rides for people with mobility constraints, and routes where private car ownership is inefficient.

Waymo’s all-electric fleet also gives the launch an environmental angle. The company said its vehicles produce zero direct emissions and support the Singapore Green Plan 2030. At its current scale, Waymo estimates its fleet prevents about 480 metric tonnes of carbon dioxide from road travel every week.

Still, the green case will depend on usage. Electric robotaxis can reduce emissions if they replace private car trips, improve vehicle utilisation and link people to public transport. They are less helpful if they pull riders away from buses and trains or add empty vehicle kilometres while waiting for passengers.

Rivals and the wider robotaxi race

Waymo arrives in Singapore as the global autonomous vehicle sector enters a more selective phase. The early hype around self-driving cars has faded, and investors now care less about futuristic demos than about unit economics, safety records and regulatory durability.

Also Read: Southeast Asia isn’t losing the robotaxi race. It’s running a different one

Globally, Waymo’s most visible rivals include Amazon-owned Zoox, which is developing purpose-built autonomous vehicles; Tesla, which is pursuing a camera-led autonomy strategy; and Chinese players such as Baidu’s Apollo Go, WeRide and Pony.ai, all of which have pushed robotaxi services in parts of China and, in some cases, overseas. General Motors-backed Cruise was once Waymo’s closest US competitor, but its robotaxi ambitions were sharply curtailed after regulatory and safety setbacks.

In Southeast Asia, the competitive landscape is less mature. Singapore has hosted autonomous vehicle trials for years, but no company has yet turned driverless ride-hailing into a mass-market commercial service. Local transport operators, private-hire platforms and public agencies will be watching Waymo’s entry closely, not only as a mobility launch but as a signal of what role foreign autonomous vehicle companies may play in the region.

Jobs, trust and the public test ahead

Waymo and Singapore officials are framing the partnership around capability-building as well as transport. The company said it intends to create high-skilled local operational jobs, while the government sees the entry of a major autonomous vehicle operator as a way to strengthen the domestic ecosystem.

“Singapore welcomes Waymo’s entry as our newest autonomous vehicle operator,” said Jeffrey Siow, Minister for Transport and Second Minister for Finance. “Waymo brings world-class technology and operational expertise to Singapore, and will move us towards our vision of creating new transport options for Singaporeans.”

Waymo co-CEO Tekedra Mawakana said the company would work with national leaders to complement Singapore’s public transport network while bringing its commercial safety record to the city.

The harder task begins after the announcement. Autonomous mobility depends on trust built slowly: uneventful rides, transparent safety reporting, clear accountability when things go wrong, and a service that solves real transport problems rather than simply showcasing technology.

Singapore gives Waymo one of the best possible urban laboratories in Asia. It also gives the company little room for error. In a city where transport is expected to be safe, reliable and tightly managed, the robotaxi promise will be judged less by novelty than by whether it can quietly become useful.

The post Waymo’s Singapore entry raises the stakes for autonomous mobility in Asia appeared first on e27.

On September 18, 2026, global markets rose together. Asian stocks and bonds gained as oil extended its decline. European equities climbed. The STOXX 600 rose 0.9 per cent to 642.6 points. Mining and automotive sectors led those gains. The FTSE index gained 1.2 per cent. That marked its best one-day performance in over two months.

The Bank of England halted sales of long-dated gilts. That decision supported UK assets. The crypto market joined the rally. It rose 0.59 per cent to US$2.62T in 24 hours. The Federal Reserve had raised rates by 25 basis points on September 17, 2026. That was the first hike since 2023. Markets had fully priced in the move. The reaction became a relief rally. Investors focused on the end of the tightening cycle rather than the hike itself. That shift in sentiment lifted nearly every risk asset.

The economic backdrop aided risk assets. West Texas Intermediate crude fell 0.7 per cent to US$101.20 a barrel. Cheaper oil reduces inflationary pressure. US 10-year Treasury yields retreated from recent multi-year highs. Lower yields ease pressure on global equities. They also reduce the opportunity cost of holding non-yielding assets such as digital assets and gold. Spot gold remained steady following earlier weekly fluctuations. This combination of cheaper oil and steady bond yields created a helpful climate for digital assets. This macro mix gave traders a reason to add exposure.

The asset class traded as a rates-sensitive instrument on that day. Its correlation with the S&P 500 was 0.43. That is a moderate positive reading. It is lower than the 71 per cent figure that appeared in May of this year. The Bitcoin-gold tie was above 50 per cent at the start of this month. Some short-term gauges reached 0.8. That still shows a meaningful tie, but it is not 79 per cent. These figures indicate a looser connection than some earlier reports suggested. That matters for how investors interpret the advance. It does not mean digital assets ignore macro. It means the link varies with the news cycle. On this occasion, the Fed decision and the oil move mattered more than the usual internal drivers.

Also Read: The CLARITY Act vote could send crypto to US$2.73T or crash it to US$2.6T

Group rotation amplified the market-wide move. The AI Applications category gained 5.83 per cent. The Privacy group rose 3.98 per cent. Independent verification did not directly confirm that exact figure. The broader privacy space has surged 213 per cent since October 2025.

Zcash drove almost all of that rise. Zcash posted a 13 per cent daily advance on September 16. It jumped another 15 per cent on September 17 following the Fed decision. Protocol upgrades and institutional interest fuelled that move. These movements indicate that market appetite extends beyond Bitcoin. Funds are seeking alpha in specialised narratives with strong fundamentals. That broadening of strength across asset classes is a healthy sign. It suggests the advance has a base value greater than one coin.

The near-term path for digital assets hinges on key technical marks. The current market cap sits just above the 50 per cent Fibonacci retracement level at US$2.6T. That mark now acts as support. The immediate trigger for the advance is past. The focus shifts to whether the advance can sustain. A close above the 23.6 per cent Fib threshold at US$2.67T could pave the way for a retest of the yearly high at US$2.73T. Failure to hold US$2.6T risks a pullback toward the US$2.57T to US$2.53T base zone. The 61.8 per cent Fib sits at US$2.57T. A break below that mark could signal a return to range trading.

My point of view is cautiously bullish. The combination of a digested rate increase and strong group rotation points to underlying strength. The market passed its immediate test. It absorbed a rate hike without collapsing. That is a significant signal. I still want to see confirmation.

Bitcoin needs to stabilise. The breadth of smaller coins needs to continue. The advance cannot rely on one group or a single economic event. The Privacy and AI Applications groups show leadership. That is encouraging. They remain relatively small parts of the overall capitalisation. For the advance to challenge US$2.73T, funds need to flow more broadly. I would like to see a weekly close above that pivot before turning more constructive.

Also Read: The Fed is the real crypto story, Bitcoin and Ethereum are just following

I also watch the Ethereum Foundation AMA on September 16 for further sentiment cues. That event could provide insight into developer activity and network upgrades. It may not move prices on its own, but it adds to the narrative mosaic. The digital asset space is increasingly responsive to fundamental developments. That is a maturation story.

The worldwide economic backdrop remains the dominant driver. Cheaper oil and steady bond yields create a supportive climate for speculative assets. The central bank’s increase became a bullish catalyst because markets had already priced it in. The UK central bank’s decision on long-term government bonds added to the calm. Asian equities confirmed the trend. This is a coordinated advance. It is not a digital asset-specific event. That makes it more durable, but also more dependent on economic conditions remaining stable.

If oil continues to decline and yields stay contained, digital assets can test US$2.73T. If oil reverses or yields spike, the US$2.57T floor will come under pressure. The US$2.6T pivot is the line in the sand. Holding above it keeps the positive case alive. Breaking below it shifts the story back to choppy conditions.

In conclusion, the outlook is cautiously bullish momentum. Investors have digested the rate increase. Group rotation is strong. Chart marks are clear. The question now is whether Bitcoin can stabilise and the breadth of smaller coins can continue. Can investors capitalise on this economic clarity to challenge the US$2.73T resistance? I believe they can, but only if the speculative climate remains supportive. The next few sessions will tell us whether this upward move has true staying power or whether it fades into another range phase. I lean toward the former, but I remain watchful.

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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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The post Did the Fed just accidentally kick off the next crypto bull run? Or just a dead cat bounce? appeared first on e27.

Crunchbase’s numbers for the first half of 2026 are the kind that are supposed to make an entire industry feel good. Global venture funding hit US$510 billion, beating the whole of 2025 in six months and smashing the previous half-year record of US$375 billion set in the second half of 2021. Startup capital, by the headline, is back.

Read past the headline and the story curdles. OpenAI and Anthropic alone accounted for US$217 billion of that total — 43 per cent of every venture dollar deployed on the planet in six months. Anthropic’s US$65-billion second-quarter raise by itself was close to a third of all global venture funding for the quarter.

Also Read: Anatomy of a shakeout: what 7K+ deadpooled startups reveal about Southeast Asia’s new tech reality

AI’s share of capital jumped from under 50 per cent a year earlier to more than 70 per cent by Q2. Deal count, meanwhile, barely moved. This was not a broader boom lifting more founders. It was two companies, and a handful of AI-infrastructure bets around them, absorbing a record-breaking pool of capital that the rest of the world’s startups mostly watched pass by.

What Southeast Asia actually got

Set that number against what Southeast Asia raised over the same stretch and the gap stops being abstract. Startups in the region pulled in US$2.81 billion across 98 equity deals in the first quarter of 2026, the lowest quarterly deal count in at least eight years, according to DealStreetAsia. More than 70 per cent of that quarter’s value came from a single transaction: Singapore-based data centre operator DayOne’s US$2-billion Series C. Strip that one round out and the region raised roughly US$800 million in three months.

For the whole of 2025, Southeast Asia’s tally was US$5.37 billion across 461 deals. OpenAI and Anthropic’s combined first-half haul is more than 40 times that entire annual figure, extracted from global markets in half the time.

We wrote e27‘s own retrospective on this drought a few days ago: 7,538 Southeast Asian tech startups have deadpooled since January 2020, with peak closures in 2021 and 2022 and attrition still running through 2025. The instinct in the region has been to read that as a home-grown correction, a hangover from pandemic-era excess, high interest rates, and investors demanding a path to profitability that many grocery-delivery and social-commerce plays never had.

All of that is true, but none of it is the whole story. The bigger truth is that the capital pool available to everyone who is not building a frontier model has been quietly shrinking as a share of the total, even as the total itself hits records.

Concentration is not a US problem you can watch from a distance

It is tempting for Southeast Asian founders and investors to treat this as someone else’s bubble — a Bay Area story about two labs, a handful of hyperscalers, and a debt-financed data centre build-out that has already drawn warnings from the IMF and the Bank of England about opaque leverage. But the region is not a bystander. GIC and Temasek-linked vehicles are direct participants in the AI mega-rounds reshaping the market — Temasek-backed Xora led a US$53-million seed round in Hang Ten earlier this week, and sovereign capital from the region sits inside several of the infrastructure deals now competing for the same limited pool of late-stage dollars that used to flow more evenly across sectors.

When four transactions can account for roughly two-thirds of a quarter’s global venture dollars, as insights4vc’s analysis of Q2 2026 found, every LP with exposure to venture as an asset class is making a portfolio decision about how much of that concentration it wants, whether it says so explicitly or not.

Also Read: Analysis: SEA’s June funding spike masks a narrow recovery in VC funding

Southeast Asia’s own funding data is starting to rhyme with the global pattern, just at a smaller scale. Five megadeals accounted for 93 per cent of June 2026’s US$4.22-billion regional total, a four-year high built almost entirely on DayOne, Supabase, AI startup Acrab, Airwallex, and Vietnam’s Vinpearl.

The region’s headline funding numbers are increasingly a story about a handful of outsized rounds, mostly in data centres, payments infrastructure, and AI, rather than broad-based conviction in the next generation of SEA founders. Concentration, in other words, is not just something happening to the region from outside. It is becoming how the region’s own capital behaves.

The uncomfortable trade-off nobody in SEA wants to name

None of this means Southeast Asia is being deliberately starved. Foundation-model economics are genuinely different: training frontier systems requires sustained, enormous capital in a way that a fintech Series B never did, and some of the money flowing into regional data centres is itself SEA’s cut of the AI infrastructure build-out, not capital diverted away from it.

Malaysia, Indonesia, and Thailand now have 31 planned data centres above 100 megawatts, versus just two a few years ago — real, if capital-intensive, participation in the cycle.

But participation in infrastructure is not the same as participation in venture. A data centre lease is not a Series A term sheet, and the jobs and equity created by hosting compute for someone else’s model are structurally different from the jobs and equity created by building the model, or the application layer, yourself.

If the next decade of technology value accrues overwhelmingly to two or three frontier labs and the infrastructure landlords around them, Southeast Asia’s founders need a funding strategy that does not depend on a rising tide that has, for the first time in venture-capital history, stopped lifting most boats.

What actually needs to change

The honest response is not another op-ed lamenting a “funding winter” as if it were weather. It is building permanent, regionally-controlled capital — sovereign funds, corporate venture arms, and family offices willing to underwrite unfashionable sectors precisely because global LPs have stopped bothering to look at them.

Also Read: The end of Southeast Asia’s unified startup funding story?

It is also being candid with founders that the pitch of “just build something AI-adjacent and the capital will find you” is a bet on scraps from a table two companies are eating alone.

Southeast Asia’s startups do not need to out-raise OpenAI and Anthropic. They need capital that was never going to chase them in the first place, and right now, that capital is nowhere near enough of it.

The post When 43 per cent of the world’s funding goes to two firms, where does that leave SEA? appeared first on e27.

For most of my career, I believed professional value followed a relatively stable equation.

The more capable you became, the more valuable you became.

That assumption shaped how many of us approached work, leadership, and identity itself. We invested years building expertise because expertise carried weight. We learned to execute reliably because reliability created trust. Capability created leverage. Experience accumulated into authority. And for a long time, that model worked.

Organisations rewarded people who could think clearly, solve difficult problems, communicate effectively, and execute consistently under pressure. Intelligence differentiated people. Knowledge created advantage.

I think AI is beginning to destabilise that equation in ways many organisations still do not fully understand.

Most conversations around AI remain focused on productivity. The dominant language revolves around automation, efficiency, operational acceleration, and workforce transformation. Major consulting firms continue to frame AI primarily through the lens of productivity and economic value creation rather than deeper organisational consequences.

I think the deeper disruption is something else entirely.

AI is exposing what organisations actually value about humans.

That distinction matters, because for years, many companies claimed to value creativity, leadership, strategic thinking, and human insight. But operationally, many organisations still rewarded people primarily for speed, responsiveness, optimisation, information processing, and scalable execution.

In other words, many firms were already optimising for machine-compatible behaviour long before machines became capable enough to compete. AI changes the economics of that arrangement.

Over the past year, I have noticed something psychologically significant happening across industries. Work that once signalled expertise is becoming increasingly compressible. Strategic summaries, first-draft ideation, communication scaffolding, structured analysis, and presentation logic can now be generated almost instantly. Even high-skill knowledge work is increasingly being reframed through the lens of AI-assisted productivity.

Most people interpret this as a productivity breakthrough. I think it is actually a value disruption. Because once intelligence becomes abundant, intelligence itself stops being the differentiator. And that forces organisations into a question many are still avoiding:

What exactly remains valuable about human contribution once execution is no longer scarce?

I increasingly believe the answer is judgment. Not intelligence. Judgment.

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

The ability to interpret reality correctly before decisions get made. The ability to navigate ambiguity without collapsing into noise. The ability to preserve trust under pressure. The ability to frame problems clearly enough for coordinated action to happen.

These are not soft skills. They are system-stabilising capabilities. And I think many organisations are dangerously underestimating how important they are becoming.

Most firms are still asking:

“How do we implement AI?”

“How do we increase productivity?”

“How do we automate workflows?”

Far fewer are asking:

“What kind of human judgment becomes more important once intelligence becomes infrastructural?”

That is the more important strategic question. Because the organisations that survive the next decade may not necessarily be the ones with the most AI. They may be the ones that remain capable of coherent judgment while operating inside machine-amplified environments.

That is much harder than automation. Automation is primarily technical. Judgment is cultural. And this is where I think the real leadership challenge begins.

Many organisations are still structurally designed to reward execution more than discernment. They reward responsiveness more than reflection. Optimisation more than interpretation. Speed more than coherence.

AI amplifies all of those tendencies. Which means many companies are unintentionally accelerating toward environments filled with more outputs, more information, more generated intelligence, but weaker human judgment. That is not organisational evolution. That is organisational fragility at scale.

Researchers are already beginning to describe this shift as the rise of a “verification economy,” where human value increasingly moves away from producing information and toward validating, interpreting, and judging machine-generated outputs.

The deeper issue underneath all this is not technological. It is psychological.

For decades, many professionals unconsciously built identity around being knowledgeable, capable, and difficult to replace. Competence became more than economic value. It became legitimacy. Meaning. Self-worth.

AI compresses those signals simultaneously. That is why I think the anxiety emerging across industries is not merely about job displacement. It is about significance.

People are quietly asking:

“If intelligence is no longer rare, what exactly makes me valuable now?”

And organisations are beginning to face the same question at a systems level. What kind of human contribution do they actually want to preserve? Because if firms continue optimising humans primarily for machine-compatible execution, machines will eventually outperform humans under those exact conditions.

Also Read: Nobody gives you time to explain. That’s the real fundraising problem

That leaves leaders with a choice. Either continue building organisations around scalable execution and gradually reduce humans into supervisory infrastructure surrounding increasingly intelligent systems.

Or redesign organisations around the things machines still struggle to do well: judgment, interpretation, trust-building, contextual reasoning, ethical navigation, and coherent decision-making under uncertainty.

Interestingly, many of these same capabilities are now emerging as priority future skills in global workforce research. The World Economic Forum increasingly identifies analytical thinking, resilience, adaptability, leadership, and creative thinking as critical capabilities in AI-shaped economies.

I think this is the real strategic fork emerging beneath the AI economy. Not AI versus humans. But whether organisations continue optimising for execution alone, or begin redesigning themselves around higher-quality human judgment.

The companies that figure this out early may gain something far more valuable than productivity. They may become environments where human intelligence still retains meaning. And I suspect that will become one of the most important competitive advantages of the next decade. Because once intelligence becomes abundant, the real scarcity is no longer intelligence itself. It is the ability to use it wisely.

And I think the leaders who understand that shift early will begin asking a very different kind of question:

Not “How do we use more AI?”

But: “What kind of organisation do humans still meaningfully belong inside after AI?”

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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.

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

The post The AI economy is quietly exposing what organisations truly value about humans appeared first on e27.