We have entered a cautious stretch. Bitcoin has slipped 0.72 per cent over the past 24 hours to US$76,697.14. Ethereum has fallen harder, down 1.95 per cent to US$2,474.93. This pullback reflects a broader market decline of 0.99 per cent. The dominant force behind this move is macro uncertainty ahead of the Federal Reserve’s September 16 interest rate decision.

Traders are pricing in a high probability of a rate hike. That expectation has triggered risk-off sentiment across financial markets. A cascade of leveraged long liquidations has added pressure. My own view is simple. This is not a crypto story right now. This is a Fed story.

Bitcoin and Ethereum are trading as risk assets. Their next major move will come from the central bank, not from their own networks or adoption trends. The market-wide nature of this decline matters. Bitcoin does not show a unique weakness. It follows the same liquidity and policy expectations that shape other risk assets. That is why I focus on the Fed rather than on crypto-specific headlines.

Bitcoin’s decline looks modest on its own. Its alignment with the broader market matters more. The total crypto market cap has fallen 0.99 per cent. Bitcoin’s 0.72 per cent drop closely mirrors that move. This correlation tells me Bitcoin is following market beta rather than reacting to a coin-specific catalyst. The derivatives data shows a sharp spike in liquidations, up 1,331.93 per cent in 24 hours. That number sounds dramatic. This is a symptom of the sell-off and a leverage flush, not the primary cause. Forced selling from over-leveraged longs can accelerate a decline. It does not create the original spark. The provided data did not show a clear secondary driver for Bitcoin. I found no specific news event, exploit, or technical failure that explains the move independently of the macro backdrop.

The near-term path for Bitcoin depends on one level. The US$76,000 support zone is critical. If Bitcoin holds above US$76,000, a rebound toward US$78,500 is possible. A break below that support would risk a drop to US$74,000. This makes the Fed’s decision and its commentary on September 16 the key watch point. The market is in a holding pattern. Bitcoin sits at the centre of that wait. There is no need to overinterpret the small percentage decline. The larger signal is that traders have reduced risk ahead of a major policy event. Liquidity expectations and rate projections now matter more than short-term chart patterns for the largest cryptocurrency.

Also Read: Bitcoin drops to US$76,796.54 as 91% S&P 500 correlation exposes crypto’s macro trap

Ethereum faces a more difficult setup. Its 1.95 per cent decline to US$2,474.93 means it has underperformed a slightly weaker Bitcoin. The primary driver is a technical rejection at the US$2,530 to US$2,550 resistance zone. That area has drawn attention from multiple analysts as a critical ceiling. Ethereum tested it and failed to break through. This rejection occurred alongside rising Treasury yields and tightening macro expectations for a Fed rate hike. Those forces dampen appetite for risk assets like crypto. The provided data showed no clear coin-specific catalyst.

The move aligns with broader macro-driven caution. In my view, Ethereum’s underperformance makes sense. It faced a technical barrier and macro headwinds at the same time. Ethereum’s failure at resistance carries more weight because it happened during a macro-sensitive window. Traders already faced rising Treasury yields. A high probability of a Fed rate hike made them less willing to chase a breakout. The rejection at US$2,530 to US$2,550 gave them a reason to sell.

Forced selling from derivative liquidations amplified Ethereum’s decline. Liquidations wiped out over US$8.9M in ETH positions recently. One post highlighted US$8.9M in ETH liquidations at the US$2,523 level. The majority came from longs. That kind of forced selling creates short-term downward pressure. It does not necessarily reflect a fundamental shift in sentiment. A flush of over-leveraged traders exacerbated the drop. This is a common feature in volatile markets. This as a leverage cleanout rather than a verdict on Ethereum’s long-term value. The technical rejection gave the initial push. The liquidation cascade turned that push into a faster slide.

The near-term outlook for Ethereum is neutral to bearish while it remains below US$2,550. If ETH holds above the US$2,450 support, it could regroup for another attempt at the US$2,550 resistance. A decisive break below US$2,450 would target the next significant support zone around US$2,350 to US$2,400. Short-term moving averages converge in that zone. The critical event remains the Federal Open Market Committee meeting concluding September 16.

Market-implied probability for a hike is high. That creates uncertainty. The Fed’s policy statement and any changes in rate projections will likely drive the next significant move across crypto markets. My bias here is cautious. Ethereum needs to defend US$2,450 through the Fed announcement. A hawkish surprise could trigger a deeper correction toward US$2,350. I would treat the US$2,450 support as the line that separates a pause from a deeper move. A hold there keeps the current range intact. A break there shifts the focus to US$2,350 to US$2,400.

Also Read: Will Bitcoin hold US$77,000 or drag the market to US$2.51T? The September 10 answer

My point of view on this entire setup is that the crypto market is trading on macro beta, not on its own fundamentals. Bitcoin’s slight dip is a function of macro-driven, market-wide risk aversion ahead of a key Fed meeting. A flush of leveraged long positions amplified that move. The move lacks a distinct, coin-specific catalyst.

Ethereum’s pullback combines a failed technical breakout with pre-Fed risk reduction. Derivative liquidations added fuel. I would watch Bitcoin at US$76,000 and Ethereum at US$2,450. Those levels define the near-term battle lines. If support holds, both assets can attempt rebounds. Bitcoin could target US$78,500. Ethereum could retest US$2,550. If support breaks, Bitcoin risks US$74,000. Ethereum risks US$2,350 to US$2,400.

The broader market outlook is neutral to cautious for Bitcoin and cautiously bearish for Ethereum. The Fed’s interest rate decision and forward guidance on September 16 will set the tone for Bitcoin and other risk assets. Until that event passes, I expect choppy, headline-driven price action. The modest Bitcoin decline does not alarm me on its own. The Ethereum underperformance deserves more attention because it combines technical rejection, macro pressure, and a leverage flush. Both assets are waiting on the same catalyst. That catalyst is the Fed. The market has already moved into a defensive stance. Now it waits to see whether the central bank confirms or challenges that caution.

—

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 The Fed is the real crypto story, Bitcoin and Ethereum are just following appeared first on e27.

Tesla has formally established a Vietnamese subsidiary, marking a small but closely watched step into one of Southeast Asia’s most dynamic electric vehicle markets.

According to a filing on Vietnam’s National Business Registration Portal dated September 12, Tesla Motors Vietnam LLC has been incorporated in Ho Chi Minh City with charter capital of VND77.7 billion (about US$3 million).

Also Read: Electric vehicles at the crossroads: Trust vs innovation

The company is registered across seven business sectors, including wholesale and retail sales of vehicles, sales of vehicle parts, sales of machinery, and other wholesale and retail activities.

The filing does not confirm when Tesla vehicles will be sold in Vietnam, whether the company plans to open showrooms or service centres, or if it will begin with imports before considering deeper operations. But for a company that has so far had only limited direct exposure to much of Southeast Asia, the creation of a local entity is a meaningful signal.

Tesla Motors Vietnam has three legal representatives: David Jon Feinstein, listed as president; Isabel Ching Fan, listed as general director; and Nguyen Manh Hung, listed as assistant to the general director. Feinstein is a senior Tesla executive, and the filing lists his address as 1 Tesla Road, Austin, Texas, the US headquarters of Tesla.

The move comes at a time when Vietnam’s automotive market is being reshaped by electrification, local industrial policy, and the rapid rise of homegrown EV maker VinFast.

Why Vietnam matters

Vietnam is not Southeast Asia’s largest car market. Indonesia and Thailand remain far bigger in terms of vehicle sales and manufacturing capacity. Yet Vietnam has become one of the region’s most interesting EV markets because electric cars are already visible on the road, helped by VinFast’s aggressive rollout of vehicles, charging infrastructure, and taxi fleets.

Automobile sales in Vietnam reached 48,484 units in August 2026, down 18 per cent from July, according to the data cited in the filing-related source material. VinFast accounted for 20,161 units, or 42 per cent of the total. That figure excludes imported cars, but it still underlines how unusual Vietnam has become: a Southeast Asian market where an EV-focused domestic brand is already a major force in overall vehicle sales.

For Tesla, Vietnam presents both an opportunity and a complication. On one hand, the country has a young, increasingly urban consumer base, rising incomes, and a government that has shown interest in cleaner transport and industrial upgrading. On the other, its car market remains price-sensitive, import duties and taxes can affect affordability, and public charging access outside major cities is still developing.

Tesla’s global playbook has typically relied on direct sales, strong brand recognition, over-the-air software updates, and an expanding charging ecosystem. In Vietnam, however, it will be entering a market where the most important EV infrastructure advantage currently belongs to VinFast, not to foreign entrants.

A local filing, not yet a full launch

The incorporation of Tesla Motors Vietnam should not be read as an immediate product launch. Multinationals often establish local companies before making decisions on distribution, hiring, compliance, supply chain arrangements, or after-sales service. In the automotive sector, those steps matter especially because buyers need confidence that vehicles can be serviced, repaired, and supported over many years.

Still, the scope of Tesla’s registered business activities is notable. The company is not only registered for vehicle sales, but also for vehicle parts and machinery-related activities. That gives it room to operate beyond simple brand representation if it chooses to do so.

Also Read: Electrifying Southeast Asia: Unleashing the radical potential of electric vehicles

For Vietnamese consumers, Tesla is already a familiar name, even if official access has been limited. Imported Tesla cars have appeared in the country through private channels, usually at prices that reflect the cost of importing a premium foreign EV into a regulated market. A direct presence could, over time, improve pricing transparency, servicing, software support, and warranty coverage.

The bigger question is whether Tesla sees Vietnam as a standalone sales market, a node in a broader Southeast Asian strategy, or both.

Tesla has already made moves in parts of the region, including Singapore, Malaysia, and Thailand. Southeast Asia is becoming more important to global automakers as EV adoption rises from a low base and governments compete to attract investment in batteries, assembly, and charging networks. Thailand has positioned itself as a regional EV production hub, while Indonesia has used its nickel reserves to court battery and EV manufacturers. Vietnam’s edge is different: it has a domestic EV champion that has created local market momentum.

The competitive field

If Tesla begins selling directly in Vietnam, it will face a very different competitive environment from its early days in the US or Europe. VinFast is the obvious local rival, with a wide domestic footprint, strong brand visibility, and an expanding EV line-up. It also benefits from local familiarity and infrastructure, particularly charging.

Chinese automakers are another major factor. BYD, which has become one of the world’s largest EV makers, is expanding across Southeast Asia and has been increasingly active in markets such as Thailand, Indonesia, Malaysia, and Singapore. Other Chinese brands, including SAIC-backed MG and Wuling, have shown that more affordable EVs can gain traction among Southeast Asian buyers who may be curious about electrification but unwilling to pay premium prices.

Traditional automakers cannot be ignored either. Hyundai, Kia, Toyota, Mercedes-Benz, and BMW all have varying degrees of EV or hybrid presence in the region. In Vietnam, as elsewhere in Southeast Asia, hybrids may remain attractive for consumers who want lower fuel consumption without depending fully on charging infrastructure.

Tesla’s advantage is brand power. Its challenge is localisation. Vietnamese buyers are not only comparing acceleration, software, or range; they are also comparing price, service access, financing options, charging convenience, and resale value.

Southeast Asia’s EV race gets more crowded

Tesla’s Vietnam filing also reflects a wider shift in Southeast Asia’s automotive sector. For years, the region was seen mainly as a market for petrol cars, motorcycles, and later ride-hailing. EV adoption was slowed by cost, limited charging, and uncertainty over battery performance in tropical climates.

That picture is changing. Governments are offering incentives, charging networks are growing, and fleet operators are experimenting with electric taxis, vans, and two-wheelers. Consumers are also becoming more familiar with EV ownership as more models enter the market at different price points.

Vietnam sits at the centre of this transition because it is not waiting for foreign brands alone to create demand. VinFast’s domestic push has effectively educated the market, normalised EVs, and forced competitors to think more seriously about the country. In that sense, Tesla may benefit from groundwork laid by a rival.

Also Read: Thinking out loud: Are electric vehicles as sustainable as we believe?

But entering a market after EV awareness has already formed also means Tesla cannot define the category on its own. In Vietnam, electric mobility already has a local face. Tesla will need to show not just that it is a global EV leader, but that it can meet the everyday needs of Vietnamese drivers.

For now, the filing is a beginning rather than a launch. Yet it is the clearest sign so far that Tesla is preparing for a more formal role in Vietnam’s auto market. If that turns into direct sales, Vietnam’s EV race could move from a domestic-led story to a more open contest between local ambition, Chinese scale, and American brand power.

The post Tesla establishes Vietnam subsidiary as EV rivalry with VinFast looms appeared first on e27.

MoneyHero’s latest earnings tell two stories. The first is the one the NASDAQ-listed fintech aggregator wants investors to focus on: artificial intelligence automation, better approval rates, and a more efficient operating model. The second sits deeper in the numbers: falling revenue, weaker user traffic, wider losses, and a sharp rise in cash incentives used to keep transaction activity from slipping.

The Singapore-based company, which operates financial comparison and application platforms across markets including Singapore, Hong Kong and the Philippines, reported revenue of US$15.8 million for the second quarter of 2026, down 13 per cent from US$18.0 million a year earlier. For the first half, revenue was almost flat at US$32.3 million.

Also Read: MoneyHero swings to profit, but only on cost cuts and FX gains

For a consumer fintech platform in Southeast Asia, where customer acquisition has long been expensive and loyalty is thin, that would be notable on its own. But MoneyHero’s disclosures show the pressure is not just on headline revenue. It is also on the mechanics of how the company is sustaining activity on its platform.

Incentives rise as reported revenue falls

Management attributed part of the second-quarter revenue decline to higher cash rewards offered to users in Singapore and Hong Kong. Under IFRS 15 accounting rules, such rewards are deducted from gross revenue rather than booked as marketing expenses. In simple terms, if MoneyHero pays users cash to complete financial product applications, those payouts reduce the revenue it reports.

To provide what it says is a fuller picture of platform activity, the company introduced “Total Transaction Value”, or TTV, a non-standard metric that adds cash rewards back to revenue. On that basis, MoneyHero said platform volume was broadly flat year on year at US$20.9 million.

The problem is the cost of holding that line. Cash rewards reached US$5.1 million in the quarter, up 77 per cent from US$2.9 million a year earlier. In Singapore alone, cash handouts totalled US$4.2 million, while reported revenue in the market fell 20 per cent year on year.

That matters because aggregators such as MoneyHero sit between consumers and financial institutions, earning fees when users apply for or take up products such as credit cards, loans and insurance. The model works best when platforms can attract users cheaply and convert them efficiently. Heavy incentives can boost applications, but they also raise the question of whether demand is organic or being rented with cash.

Core operations swing into the red

MoneyHero’s executive commentary pointed to foreign exchange fluctuations as a key reason for the company’s US$1.2 million net loss in the quarter. Currency movements can be meaningful for a company operating across several Asian markets and reporting in US dollars.

Still, the operating line shows a more direct deterioration. MoneyHero swung to an operating loss in the second quarter of 2025, moving from operating income of US$366,000 to an operating loss of US$2.52 million in the latest quarter. For the first half of 2026, its net loss widened to US$7.95 million, compared with US$2.23 million a year earlier, an increase of 256 per cent. Cash reserves declined by US$3.0 million to US$28.2 million.

The company’s Credit Cards segment, historically a major revenue engine for comparison platforms in Asia, also weakened. Revenue from the segment fell 18 per cent year on year to US$8.9 million. The Philippines, where MoneyHero has built a large registered user base, saw revenue fall 43 per cent to US$969,000.

This mix is important. Credit cards have often been among the most lucrative products for financial comparison sites because banks are willing to pay for qualified leads and approved customers. But the category is sensitive to bank appetite, consumer credit conditions and competition from direct bank channels, digital banks and superapps.

Traffic decline comes with a methodology change

MoneyHero highlighted an improvement in application approval rates, which rose by nine percentage points to 48 per cent. That suggests the company is sending higher-quality users to financial partners, a useful metric in a market where banks do not want low-intent traffic clogging their funnels.

But the top of the funnel shrank sharply. Monthly unique users fell 30 per cent year on year to 3.7 million. Total traffic dropped 29 per cent to 11.8 million sessions. Platform clicks fell 35 per cent to 1.31 million, while total applications declined 30 per cent to 310,000.

Also Read: Decoding MoneyHero’s Q1: The profit push amid shrinking revenues

Management framed the decline as part of a deliberate shift away from low-intent paid traffic towards users more likely to convert. That strategy is plausible: in a tighter funding environment, many Southeast Asian fintechs have shifted from growth-at-all-costs to profitability and better unit economics.

However, a footnote complicates the comparison. Effective April 1, 2026, MoneyHero updated its analytics filters to exclude non-human automated bot traffic. The company did not recast prior periods. That means previous traffic figures may have included automated activity that is now filtered out, making year-on-year traffic comparisons less clean.

For investors and partners, the distinction matters. If traffic is down because MoneyHero cut wasteful acquisition spend, that may be a healthy reset. If prior traffic included bot activity, earlier scale claims were less meaningful than they appeared. If both are true, the company is now being measured against a clearer but smaller audience base.

A large member base, but uneven monetisation

MoneyHero said it reached 10.1 million registered members, up 17 per cent year on year. On paper, that gives the company one of the larger consumer finance audiences in the region.

The distribution, however, is uneven. Around 7.1 million members, or roughly 70 per cent of the total, are in the Philippines. Yet the market generated less than 6.2 per cent of total revenue in the quarter. Hong Kong, by contrast, contributed about half of platform revenue while accounting for only 1.1 million members, or 10.6 per cent of the member base.

This is a familiar Southeast Asian internet problem. User numbers in emerging markets can look impressive, but monetisation varies sharply by income levels, financial product penetration, bank commission structures and consumer purchasing power. The Philippines offers long-term promise, given its young population and rising digital finance adoption, but turning registered users into high-value financial product customers is a different challenge.

Rivals are fighting for the same high-intent users

MoneyHero is not alone in chasing this opportunity. In Singapore, it competes with MoneySmart and other financial comparison platforms for credit card, insurance and loan customers. Across the wider region, players such as RinggitPlus in Malaysia and global comparison brands including Finder operate in overlapping segments, while banks, digital banks and brokerages increasingly acquire customers directly through their own apps.

The competitive pressure is not just about web traffic. It is about who owns high-intent financial decisions at the moment a consumer is ready to apply. That is why cash rewards have become common in markets such as Singapore, where affluent consumers compare sign-up gifts as closely as interest rates or card benefits. The risk is that incentives become an arms race, squeezing margins for platforms that lack differentiated products or proprietary distribution.

Also Read: Nasdaq-listed MoneyHero slashes 80 jobs to ‘streamline operations’

MoneyHero is betting that automation can help offset those pressures. The company pointed to AI-driven engineering savings, including a voucher management system built by a single engineer in under three months. Such gains may help lower internal costs and speed up product delivery.

But software efficiency alone does not solve the central question raised by the quarter: can MoneyHero grow revenue sustainably without paying ever-larger rewards to bring users through the door? Its approval-rate improvement suggests the company may be attracting better users. Its falling traffic, shrinking credit card revenue and wider losses show the transition is far from complete.

For Southeast Asia’s fintech ecosystem, the results are a reminder that aggregators remain useful but difficult businesses. They can simplify financial choice for consumers and provide banks with digital distribution. Yet when acquisition costs rise and users chase the best giveaway, the economics can turn quickly. MoneyHero’s second quarter shows that in this market, scale is only valuable if it can be converted profitably.

The post MoneyHero’s Q2 exposes the rising cost of fintech growth in Southeast Asia appeared first on e27.

For many Southeast Asian merchants, selling overseas has never been a question of demand alone. A small brand in Vietnam, Indonesia or Thailand may find buyers on Amazon, eBay, Shopify or social platforms, but the harder problem begins after checkout: paperwork, duties, customs clearance, tracking gaps and freight rates designed for companies shipping far larger volumes.

Amilo is trying to turn that messy middle into a single operating layer. The Singapore-based third-party logistics provider has completed and integrated its fourth acquisition in four years, folding cross-border specialist SG Link into its regional network. SG Link now operates as ShipX and connects merchants in Southeast Asia to more than 220 destinations globally.

Also Read: J&T Express leans on Southeast Asia as China parcel growth cools

The financial terms of the deal were not disclosed. But the acquisition is less notable as a standalone transaction than as another marker of how Amilo is building: buying operators with specific capabilities, then moving them onto its own logistics and technology stack rather than running them as separate businesses.

A cross-border layer for regional sellers

Founded to serve Southeast Asian commerce, Amilo provides warehousing, fulfilment and delivery services through a proprietary platform covering marketplaces, order management, warehouse operations, transport, customs and delivery management. The company says it integrates with major marketplaces including Amazon, eBay and Shopify.

SG Link, now ShipX, adds a piece that many domestic fulfilment providers struggle to offer at depth: end-to-end international shipping for smaller exporters. The business has operated in Vietnam since 2020 and focuses on merchants that lack the shipment volumes needed to negotiate strong freight terms on their own.

By pooling demand, ShipX can help smaller sellers access better shipping economics, while handling customs documentation, duties and tracking through a more unified process. That matters in a region where many small and mid-sized businesses can find overseas customers online but still face old-fashioned logistics bottlenecks once goods leave the warehouse.

Cross-border commerce is also becoming more important as Southeast Asian sellers look beyond crowded domestic marketplaces. Platforms such as Shopee, Lazada and TikTok Shop have widened access to consumers, but exporting remains uneven. A merchant may be able to list products globally in minutes, yet still spend days dealing with tax codes, tariff changes, broker hand-offs and customer complaints when parcels disappear from view.

Amilo’s bet is that solving those problems requires more than a booking portal. It requires control over the operational rails behind it.

The acquisition playbook

The company describes its approach as a repeatable acquisition and integration model. Rather than buying logistics firms and leaving their systems intact, Amilo looks for businesses with something difficult to build from scratch: a customer base, a licence, a trade lane, a local network or a specialist capability. It then migrates them onto a single technology stack.

Also Read: Thai logistics unicorn Flash Express launches full services in the Philippines

Some acquired businesses needed a turnaround, while others needed a broader network to scale. The common thread, according to Amilo, is that they eventually operate on the same infrastructure instead of sitting beside one another on a group organisation chart.

“We have now proven our acquisition, integration and turnaround story four times in four years,” said Chris Revord, Head of Finance at Amilo. “With heavy AI usage across all functions, the platform keeps getting more robust — and we are extending it across the commerce and distribution space.”

That model reflects a wider shift in Southeast Asian logistics. The sector remains highly fragmented, with thousands of local providers strong in individual cities, corridors or services but weak across borders. For startups and mid-sized logistics players, building everything organically can be slow and capital-intensive. Buying capability is faster, but only if integration does not create more complexity.

This is where many logistics roll-ups stumble. Warehouses may run on different systems, fleets may follow different routing logic, and customs processes may depend on local expertise that is hard to standardise. Amilo is arguing that repeated integration makes each acquisition cheaper and faster because the target operating system does not change.

Whether that continues to hold as the company expands will be the test. Cross-border logistics is a low-error, low-margin business, and mistakes are visible quickly: delayed parcels, inaccurate duty estimates and poor customer updates can damage both merchant trust and marketplace ratings.

Why ShipX matters

For Amilo, ShipX brings expertise in one of the harder segments of logistics. Domestic fulfilment is challenging, but cross-border shipping adds more variables: changing tariff regimes, fuel-price pressure, destination-specific documentation and fragmented last-mile partners in receiving markets.

“SG Link, now ShipX, is an amazing team,” said Arun Mambully, founder and CEO of Amilo. “Their knowledge of complex cross-border processes is a core pillar of our strategy of helping millions of ASEAN SMEs expand globally. Despite fuel-price and tariff-war pressure, our teams have come together very well and delivered positive growth in the first half of this year.”

The reference to tariffs and fuel costs is important. Cross-border shipping has become more volatile in recent years, affected by geopolitical tensions, supply chain disruptions and fluctuating air and sea freight prices. For smaller merchants, those changes can wipe out margins quickly if delivery costs or landed duties are misquoted.

A more integrated cross-border service could help sellers show clearer shipping costs to consumers, reduce failed deliveries and avoid surprise charges. It could also make it easier for merchants to test new markets without setting up overseas warehousing from day one.

Rivals in a crowded logistics field

Amilo is not alone in trying to own more of the commerce logistics chain. In Southeast Asia, Ninja Van has built one of the region’s largest delivery networks, alongside J&T Express and Flash Express, while Janio has long focused on cross-border logistics for e-commerce merchants. Global incumbents such as DHL eCommerce, FedEx, UPS and Aramex also serve exporters with international shipping, customs and fulfilment products. The difference Amilo is trying to claim lies in combining acquisitions, warehousing, marketplace integrations and cross-border shipping on one proprietary platform. That integrated pitch may appeal to growing sellers, though larger rivals still have advantages in scale, brand trust and global infrastructure.

AI on top of physical infrastructure

Amilo is also positioning the deal as a foundation for heavier use of artificial intelligence across logistics decisions. The company says the hard infrastructure — warehouses, delivery capacity, licences and border processes — has already been built or acquired, and that AI can now sit on top of those rails.

In practical terms, that could mean smarter routing, automated customs checks, better demand forecasting, exception handling and customer support agents that help merchants resolve delivery issues faster. But logistics is a sector where AI claims can easily run ahead of reality. Automation is useful only when the underlying process is stable; otherwise, it can simply accelerate errors.

Also Read: The great divide: How Southeast Asian SMEs are bridging the AI gap between survival and success

Amilo appears aware of that risk. Its argument is that it has first standardised the operating base, and only then layered intelligence onto it. Future acquisitions, it says, should integrate more quickly as more migration work becomes automated.

For Southeast Asian SMEs, the promise is straightforward: fewer systems, fewer intermediaries and fewer blind spots between a warehouse shelf at home and a customer abroad. For Amilo, the ShipX deal is another step towards becoming not just a logistics provider, but the connective tissue for regional merchants trying to sell to the world.

The post Amilo acquires SG Link to widen cross-border shipping for SEA sellers appeared first on e27.

AI is automating tasks that used to take hours for humans to do. It’s pushing some costs down and it’s making some teams more productive.

Don’t make the mistake, however, of thinking that using AI to do things more productively is enough to ensure your business has a competitive edge and succeeds.

If you strip away all the hype, you can look at AI as just the latest in a long line of technologies that help companies do things faster. What’s unique, though, is how quickly it has been adopted. It only took two years for about 40 per cent of US adults to begin using AI, which is twice as fast as the Internet, according to Harvard.

Rapid adoption is good news for the AI companies, but for you it means that your competitors are also benefitting from any productivity boosts you can obtain by using off-the-shelf AI to improve basic business tasks.

When all competitors improve their productivity in much the same way and at the same rate, productivity alone doesn’t give anyone a great advantage. Yes, AI is very good at helping you operate more efficiently. You can improve your logistics. You can create content. You can turn your data into insights.

The limitation is that your competitors can access the same AI tools and make the same improvements. Everyone gets more productive, but no one gets ahead. Business consultants call this the ‘Red Queen Effect’. Just like the Red Queen in the book, Through the Looking-Glass, you have to run as fast as you can just to stay in place.

To add further pressure, your customers may demand more results or lower costs because they also know that AI is making it cheaper for you to deliver your products and services. And because you’re using AI in the same ways as your competitors, you’re starting to look more and more like them. You stand apart even less.

Also Read: I built an AI that keeps receipts. The mistakes became the useful part

Thus, the picture is of a company that is working more productively. Yet, it is failing in its most important job, which is beating the competition.

How do we, then, use AI to help us achieve a real advantage over our competitors? If the answer is not in the realm of productivity, where is it?

Use AI to extend your strategic advantage

As valuable as efficiency is, it takes strategy to win. By strategy, I mean the way you differentiate yourself. It determines where you focus your energy and make your investments.

Also Read: The end of the universal a-player: Dynamic talent matching in the AI-driven supply chain

Let’s look at how this plays out in practice with some well-known companies. For example, Apple’s strategy is to make premium products combining both hardware and software. That provides a seamless user experience and keeps customers coming back.

Apple’s AI strategy thus should involve integrating the new technology directly into its hardware and software products. The result is to give consumers new tools within the Apple ecosystem, like writing tools and a better Siri, that reinforce Apple’s premium reputation and customer loyalty.

Here’s another example. Netflix competes on the depth of its library of bingeable content and its ability to suggest something you actually want to watch whenever you log in. Reinforcing those strategic advantages could mean using AI to improve the content suggestion engine and make it possible to create new hits more cheaply and quickly. That builds on Netflix’s two existing competitive advantages.

Competing companies that also produce phones and computers, or stream content, were already falling behind Apple and Netflix in these areas. By making their AI investments here, the two companies make it even harder for competitors to catch up.

Apple and Netflix reveal that the strategy for making effective use of artificial intelligence is to combine it with your business’s existing unique assets. These can include your domain expertise, proprietary data, and customer relationships. Off-the-shelf AI is available to everyone, but your unique assets constitute your moat. By using AI to extend them, you widen and deepen your moat.

One simple way to test your AI strategy is to ask yourself, if my competitors bought the same AI tools now, would I still have an advantage? If not, you probably don’t have a competitive AI strategy.

Strategy really isn’t that difficult to understand, but few companies so far have figured out how AI can help them implement their strategy and win in their markets. But this is exactly how you must invest in AI, to augment your business’s strategic advantages.

Don’t use AI just to run faster in place. Use it to extend the moat around your business by improving on the things your business already does better than anyone else.

—

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 How to use AI to win (Hint: It has nothing to do with being more productive) appeared first on e27.

The conversation around AI agents has moved quickly from demos to organisational design. Microsoft’s 2025 Work Trend Index for Singapore reported that 56 per cent of Singapore leaders were already using agents to fully automate workstreams or business processes, while 46 per cent expected their teams to build multi-agent systems.

I understand the appeal. I built an internal AI office with 21 defined roles across strategy, finance, marketing, sales, customer success, delivery, engineering, design, quality assurance, security, research and data.

I expected the hard part to be technical. It was not. The harder questions were managerial: Who owns the work? Who reviews it? What can an agent decide on its own? When does a human step in?

Most of my 21 roles are real and used in live work today, but most orchestration is still interactive. That distinction matters because the biggest lesson was not how to remove humans. It was how to design responsibility around machines.

The first mistake: Capability is not ownership

My earliest agents were too broad. Help with marketing or help with development sounded reasonable because modern models can do many things.

The problem was not output quality. It was ownership.

If one agent can research, write, publish and evaluate the result, who is actually responsible for the job? If something is wrong, where does the failure sit?

I eventually stopped defining roles by what the model could do and started defining them by what the role should own.

A writing role writes. A publishing role prepares distribution. A research role scouts for information but does not install what it discovers. A builder builds, but does not declare its own work production-ready.

That sounds obvious in a human company. With AI, it is easy to forget because the same underlying model may be capable of doing all of those jobs.

Also Read: When a slot opens, let the AI agent act – within limits

The second mistake: Letting one agent close the loop

I also learned that an agent should not be allowed to create, review and approve the same important output.

When a builder tests its own work and then declares it ready, the process can look efficient while removing the independent challenge that catches weak assumptions.

So I separated three responsibilities for higher-impact work: builder, reviewer and acceptor.

The builder produces the work. A separate role tests or reviews it. Final acceptance of important client-facing, production or irreversible actions still comes back to a human.

Not every task needs three layers. Drafting an internal note does not need the same controls as deploying software or sending something to a client.

The point is proportionality. The higher the impact of being wrong, the more important independent review becomes.

The third mistake: Treating autonomy as a maturity score

At first, I saw autonomy as progress. If an agent could do more without asking me, the system felt more advanced.

I no longer think that way.

The better question is: what happens if this action is wrong?

Singapore’s updated Model AI Governance Framework for Agentic AI uses a similar risk-based approach. One case study in the framework tiers actions by severity, reversibility and the feasibility of human oversight. Low-risk and reversible actions can be automated. Moderate-risk actions require human approval. High-risk actions with limited reversibility may be blocked entirely.

That logic changed how I design workflows.

An agent drafting an internal summary can have wide freedom. An agent changing permissions, deploying code, committing money or sending an external communication should face a much higher bar.

More autonomy is not always better. Appropriate autonomy is better.

The fourth mistake: Managing the agent but not its access

An agent is not just a prompt.

It may have access to tools, databases, files, connectors, scheduled jobs and external services. As my system grew, I realised the real management problem was not simply how many agents do I have. It was what can each of them touch.

That pushed me to maintain an inventory of the agents, tools and capabilities around them.

This aligns with Singapore’s agentic AI framework, which recommends bounding agents’ powers upfront, including their autonomy and access to tools and data.

For founders, this is easy to overlook because experimentation moves quickly. A useful prototype can become part of an operating process before anyone has paused to document what permissions accumulated around it.

I now treat that inventory as basic operating hygiene, not administrative paperwork.

Also Read: From KYC to KYA: how AI agents are reshaping payment risk

The most important lesson: Humans did not disappear

The fully unattended version of my AI office is still a work in progress.

Some workflows run automatically. Many still begin with a human instruction. Important outward actions still need approval.

I once saw that as a sign the system was unfinished. Now I think the better goal is not zero humans. It is humans only where judgement, accountability or relationships genuinely matter.

The AI roles can absorb repeatable work, prepare options, check outputs, monitor systems and move tasks forward. The human role becomes narrower but more important: direction, trade-offs, relationships and final accountability.

That is also why I am cautious with the term AI employee. An employee is not valuable because they can perform many tasks. They are valuable because there is clarity around what they own, what authority they have and how their work fits with everyone else.

What I would tell another founder

Before adding another agent, I would ask four questions:

• What job does this agent actually own?
• What systems and data can it access?
• Who independently checks important work?
• Which actions can it take without a human, and why?

If those answers are unclear, adding more agents does not create an AI workforce.

It creates more capability without more management.

That was the biggest change in my thinking. I started by trying to build better agents. I ended up redesigning the organisation around them.

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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 I built a 21-role AI workforce. The hardest part was management appeared first on e27.

Quantinuum, one of the world’s leading quantum computing companies, has already put a number on it: a trillion-dollar market waiting to be unlocked once fault-tolerant quantum computing arrives. That’s not a typo, and it’s not a crypto-style promise built on speculation — it’s an estimate built on real, quantifiable industries: drug discovery, materials science, chemicals, energy and finance, where a single better answer can be worth billions on its own. The uncomfortable question for founders, investors and policymakers in Asia is the same one many asked too late about AI and too late (or too early, and badly) about crypto: will you be building the picks and shovels for this boom, or reading about it after the fact?

Unlike the crypto cycle, this isn’t hype chasing a use case. And unlike the early days of AI, the economic bar for quantum computing is brutally explicit: DARPA and industry alike are converging on one test — a quantum calculation only counts commercially once it demonstrably saves more money than it costs to run. A pharmaceutical company that spends billions developing a single drug, for instance, could justify an extraordinarily expensive quantum calculation if it meaningfully cuts the odds of a failed candidate. That is the bar quantum computing has to clear before it can have its own “ChatGPT moment” — and it’s a far harsher bar than the one artificial intelligence had to clear.

Quantum computing is no longer purely theoretical. The harder question is whether it can become economically useful. For Asia, the opportunity may be less about owning quantum computers and more about building the industries, infrastructure and applications around them.

Why quantum computing’s commercial breakthrough won’t look like AI’s

Artificial intelligence had been around for decades before ChatGPT made it commercially legible. Neural networks existed. GPUs existed. Large language models existed. What changed was that several prerequisites converged at roughly the same time: sufficient computing power, enormous datasets, scalable cloud infrastructure, better algorithms, usable interfaces, and a business model that let millions of people access the technology without understanding the underlying mathematics.

Quantum computing may be approaching its own equivalent transition. But there’s an important distinction. AI became useful while remaining imperfect. Quantum computing must overcome a much harsher economic constraint: its output must be valuable enough to justify an extraordinarily expensive physical machine.

DARPA has put the question unusually clearly. Its Quantum Benchmarking Initiative is trying to determine whether a quantum computer can achieve “utility-scale” operation by 2033 — meaning its computational value exceeds its cost. That may ultimately be the only quantum benchmark that matters.

Quantum today looks a little like AI before the commercial explosion

There’s a useful, if imperfect, analogy. AI progressed roughly like this:

Academic research → specialist systems → cloud APIs → foundation models → consumer applications → enterprise infrastructure

Quantum computing could follow a parallel but distinct path:

Physics experiments → error-corrected logical qubits → specialist scientific applications → cloud-accessible accelerators → industry workflows → national computational infrastructure

The difference is that AI could run on increasingly commoditised silicon. Quantum computing requires extremely specialised infrastructure: superconducting systems may need temperatures close to absolute zero; trapped-ion machines need lasers and ultra-high vacuum systems; neutral-atom machines require sophisticated optical control. The result is that the quantum revolution is unlikely to put a quantum computer on everyone’s desk.

Also Read: AI, quantum computing and the future of cyber threats

Instead, picture a future cloud architecture where a business application sits above an AI orchestration layer, which in turn routes work across CPUs, GPUs and QPUs — CPUs for business logic, GPUs for AI models, and QPUs for quantum chemistry or optimisation — before producing a single business output. Most users may eventually consume quantum computing without ever knowing a quantum processor was involved. That’s probably the real commercial end state.

The quantum roadmap: From laboratory to invisible infrastructure

Any timeline remains speculative, but current company roadmaps provide useful boundaries. IBM’s current hardware roadmap targets its Starling fault-tolerant system for 2029, with 200 logical qubits and 100 million quantum gates. Quantinuum’s published roadmap targets universal, fully fault-tolerant quantum computing by the end of the decade. These are company targets, not guarantees.

DARPA provides a useful external counterweight to vendor roadmaps: its 2033 utility-scale test is explicitly designed to separate industrial usefulness from hype. On the hardware side, Google’s Willow chip demonstrated “below-threshold” error correction in late 2024 — a long-sought milestone where adding more physical qubits makes a logical qubit more reliable rather than less, which is itself a prerequisite for any of these roadmaps to hold.

Five prerequisites have to converge before quantum computing pays off

• Fault tolerance must become economically practical. Physical qubits are noisy. Useful machines need logical qubits created using error correction. The commercial question isn’t whether error correction works in principle, but how much physical infrastructure, energy, control hardware and runtime are required to produce one reliable logical computation.
• Useful algorithms need to appear. Having a quantum processor without useful algorithms is analogous to having GPUs without deep learning. The most commercially important breakthrough may come not from hardware but from discovering algorithms that transform high-value industries.
• Classical computing has to lose somewhere. Quantum doesn’t compete against computers from 2020. It competes against whatever GPUs, supercomputers, AI models and optimisation software exist when fault-tolerant quantum systems arrive. The baseline is a moving target.
• Developers need abstraction. Most software engineers will never design quantum circuits. Commercial adoption requires APIs, orchestration layers and hybrid workflows that route only the structurally suitable part of a problem to the QPU.
• The economics must close. The final equation is simple: the economic value of the quantum result must exceed the cost of the QPU, HPC, energy, talent, integration, sampling and error correction. Quantum can be scientifically revolutionary while still being commercially irrational.

In practice, a hybrid workflow is emerging: AI decomposes a problem, classical HPC narrows the candidates, a QPU tackles the hard quantum subproblem, AI interprets the output, and a human validates the result before it’s acted on.

Also Read: The AI-quantum collision: Navigating the 2026 infrastructure inflection point

Where the money may actually be in quantum computing

The strongest early applications for quantum computing use cases share one characteristic: a single computational answer can be worth enormous amounts of money.

For the Philippines example, see DOST-PCIEERD’s ongoing STArQE quantum algorithms project for EV and renewable-energy planning. DOST-PCIEERD’s 2026 programme update also frames quantum technology as a national capability-building priority for future applications in energy, health, defence and information technology.

Who is leading the global quantum computing race?

There’s no universally accepted national ranking, because countries lead in different layers of the stack. The broad competitive map looks like this:

Singapore’s commitment is unusually concrete for Southeast Asia. The Singapore National Quantum Strategy has close to SG$300 million (US$233 million) set aside under RIE2025 for quantum research, talent and coordinated capability building.

What does a quantum-enabled Singapore actually look like?

Singapore’s geography makes it an unusually good candidate for early application. It’s small, urbanised, capital-rich, highly networked and concentrated around sophisticated sectors.

A bank doesn’t need to own a quantum computer. Its risk platform could send an unusually hard computational subproblem to a regional quantum service, sitting on top of a national HPC and quantum cloud. A pharmaceutical laboratory could use AI to generate molecules, conventional computing to eliminate obvious failures, and quantum processing for the subset where classical modelling becomes prohibitive. A port system could combine AI forecasting, classical optimisation and quantum algorithms only for hard search spaces where there’s a demonstrated advantage.

Also Read: Quantum computing’s double-edged sword could threaten cybersecurity: Report

Singapore’s advantage would be density: finance, biotech, government, universities, data centres and advanced infrastructure exist within one compact geography. Its national play may therefore be to become the application, finance, governance and integration layer for quantum computing in Southeast Asia, rather than to manufacture every layer of the stack itself.

The Philippines needs a very different quantum computing strategy

The Philippines should probably not copy Singapore. Its competitive advantage — and problem set — is almost the opposite: a geographically fragmented archipelago of more than 7,600 islands, uneven infrastructure, and large national-scale coordination problems across energy, logistics, and climate and disaster response.

Consider energy. The Philippines must coordinate island grids, renewable generation, storage, transmission limits, typhoon exposure, fuel imports, electricity demand and future EV infrastructure. AI can forecast demand, weather and failures. Classical optimisation can solve most operational decisions. A quantum service should be invoked only when a specific combinatorial or physical problem demonstrably exceeds the economic performance of classical computing.

The country is already building capability rather than waiting for mature hardware. DOST-PCIEERD has identified quantum technology as a national R&D priority and is currently funding projects including quantum-based forecasting and optimisation for the electric power grid.

The AI lesson quantum computing cannot afford to ignore

AI created enormous value. It also gave us benchmark inflation, hallucination, opaque models, exaggerated capabilities, concentration of compute and deployment ahead of governance. Quantum has an opportunity to avoid some of this by being more precise about what “advantage” actually means.

Only the fourth consistently creates a sustainable commercial market. A machine that performs a specialised calculation one million times faster but costs one billion times more has produced an impressive experiment, not an economic revolution.

Also Read: Quantum’s inflection point: Why the smart money is watching now

What to actually watch over the next decade

The most important quantum headline will probably not be “Company X reaches one million qubits.” A more consequential headline would be: “A pharmaceutical company identifies a commercially successful molecule using a quantum calculation that could not economically have been performed classically.”

At that point, investment follows. Software ecosystems emerge. Cloud providers commoditise access. Consultancies build implementation practices. Universities produce specialists. Startups stop selling “quantum” and start selling better batteries, new medicines, cheaper chemicals and lower financial risk.

The commercial endgame for quantum computing

The likely end state is a division of labour between human, AI, CPU, GPU and QPU — each solving the class of problem for which it has a structural advantage. AI determines what to investigate. Classical computing handles the overwhelming majority of computation. Quantum processors tackle narrow problems where quantum mechanics provides an economically meaningful advantage. Humans determine whether the answer should be acted upon.

For Singapore, that could create a new regional infrastructure and services industry. For the Philippines, it could offer access to computational capabilities that would be prohibitively expensive to build domestically.

The commercial quantum era begins not when the machines become powerful. It begins when someone can prove that not using one costs more.

Author’s note: Company roadmaps are forward-looking statements and are presented as targets, not independently guaranteed outcomes. The commercial timeline in this article is an analytical scenario based on current roadmaps and public evidence, not a prediction of certainty.

—

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 Quantum’s ChatGPT moment is coming — and it’s worth trillions appeared first on e27.

Singapore has placed 25 companies on the Best Workplaces in Asia 2026 list, the highest count in Southeast Asia, at a time when employers across the region are fighting harder to hold on to skilled workers.

The list, published by Great Place To Work, ranks 200 companies across Asia based on employee survey responses. Of the Singapore-linked companies recognised this year, 20 are in the large-employer category, covering firms with at least 500 staff in Asia, while five sit in the small and medium category, for companies with 50 to 499 employees.

Also Read: Why building a people-first work culture in HR tech matters more than ever in Southeast Asia

That makes Singapore the fifth-largest contributor to the Asia list by company count, behind the UAE with 42, India (40), Greater China (39), and Japan (34). Great Place To Work’s Asia region includes the Gulf, which explains the UAE’s position at the top.

Among Southeast Asian markets, Singapore narrowly led Vietnam, which had 24 companies on the list, followed by the Philippines with 20, Indonesia with 19, Thailand with 18, and Malaysia with 15. Together, the six Southeast Asian markets accounted for 121 of the 200 recognised workplaces.

The ranking arrives in a year when workplace culture is no longer a soft metric for companies in the region. Aon’s 2025 Salary Increase and Turnover Study, which surveyed more than 700 businesses across six Southeast Asian markets between July and September last year, projects that 19.3 per cent of skilled Singapore workers will change jobs during 2026. That is the second-highest expected skilled-worker turnover rate in the region, behind only the Philippines at 20 per cent.

The pressure is made sharper by salary expectations. Singapore employers are budgeting pay rises of 4.3 per cent, the lowest among the six Southeast Asian markets covered by Aon and below the regional average of 5.3 per cent. For founders, operators, and HR leaders, that raises a familiar problem: when companies cannot compete on pay alone, they have to compete on trust, fairness, flexibility, and the quality of day-to-day management.

Real estate makes a rare appearance

One of the more striking features of Singapore’s showing is the presence of two real estate companies, a sector that barely appears in Southeast Asia’s workplace rankings.

Co-working operator JustCo ranked fifth among small and medium workplaces in Asia, while Pontiac Land Group placed 97th in the same category. They were the only real estate entrants from Southeast Asia. Indonesia, Malaysia, the Philippines, Thailand, and Vietnam had none.

Across the whole Asia list, real estate accounted for just 11 places, six of them held by UAE companies. Singapore was the only market outside the Gulf and Japan to have more than one real estate company recognised.

That matters because property has not typically been viewed as a talent magnet in the same way as technology, financial services, or consumer brands. In Singapore, however, the sector sits close to several major shifts: hybrid work, premium office demand, hospitality-linked real estate, and the changing use of commercial space after the pandemic. JustCo’s appearance also points to how flexible workspace operators are trying to position themselves not simply as landlords, but as workplace experience companies.

Biotechnology and pharmaceuticals formed another visible Singapore cluster. AbbVie ranked fourth in Asia, Merz Aesthetics ranked ninth among small and medium workplaces, and Amgen came in 50th. Thailand matched Singapore’s count in this sector, with the same three companies appearing on its national list.

Also Read: Growth meets purpose: Rethinking impact in startup culture

IT remained the largest single group in Singapore’s tally, with six companies. These included Cisco, which ranked third in Asia, Visa at 39th, and Mastercard at 65th. Manufacturing and production followed with four Singapore entrants, while hospitality and biotechnology had three each.

Overall, Singapore’s 25 companies covered 10 industries, slightly less broad than Vietnam’s 14 industries from a similar total.

The companies that climbed

Several Singapore entrants improved their regional positions from last year.

Visa posted the largest gain among Singapore companies, rising 19 places from 58th in 2025 to 39th in 2026. Micron Technology climbed 12 places, from 19th to seventh, entering Asia’s top 10. Amgen also gained 12 spots, moving from 62nd to 50th.

Other climbers included AbbVie, which rose from seventh to fourth; Cisco, which moved from fifth to third; Capella Hotels and Resorts, which went from 12th to 10th; and Marriott International, which edged up from third to second.

Hilton held first place in Asia for the second consecutive year and also appears on Singapore’s list. It was followed regionally by Marriott International, Cisco, AbbVie, and DHL. All five have Singapore operations recognised in the 2026 ranking, reflecting the Republic’s role as a regional base for many multinational groups.

At the same time, Singapore’s list was not only a story of established global names. Ten of its 25 companies were new to the Asia ranking: Allianz, Experian, Zim Integrated Shipping Services, Mastercard, Jebsen & Jessen, Heineken, JustCo, TC Acoustic, Home Nursing Foundation, and Pontiac Land Group.

Four of the five small and medium entrants were new, with Merz Aesthetics the only carry-over in that category. JustCo, TC Acoustic, Home Nursing Foundation, and Pontiac Land Group appeared on the Asia list through Singapore alone, rather than through multiple national markets. That makes their inclusion more locally specific than the listings of large multinationals that qualify through operations across several countries.

Why the ranking matters

Workplace lists can be easy to dismiss as employer branding exercises, especially in a region where companies often use awards to signal stability to customers, partners, and prospective hires. The Great Place To Work methodology, however, is built around employee responses rather than a judging panel.

Employees answer 60 statements on a five-point scale, along with two open-ended questions. The survey covers issues such as whether leaders are accessible and honest, whether pay and promotion are seen as fair, and whether employees feel their work has meaning.

A key part of the ranking is consistency. Great Place To Work measures how much responses vary within the same organisation. A company where one department is highly engaged but another is disengaged will be marked down compared with one that produces more even scores across teams and job functions.

This is particularly relevant in Southeast Asia, where companies often manage large differences between headquarters staff, frontline workers, technical teams, regional offices, and outsourced functions. A polished culture at the management level does not necessarily translate into trust across the organisation.

Also Read: The work culture paradigm in a hybrid-first world

This year’s Asia list drew on surveys of companies employing more than 8.9 million people, with more than 3.8 million individual employee responses across 36 countries and territories.

For Singapore, the result reinforces its position as a regional hub for talent-intensive industries, from enterprise technology and payments to biotech, hospitality, real estate, and manufacturing. But the wider labour market signals are less comfortable. With skilled-worker turnover projected to remain high and pay increases trailing the regional average, employers will need more than brand recognition to keep people.

In a market where employees have options, the companies that stand out may be those that can make fairness and trust feel consistent, not occasional.

The post Singapore tops Southeast Asia on Best Workplaces in Asia 2026 list appeared first on e27.

Beyond Border, the Singapore-founded, tech-enabled US immigration platform for founders and highly skilled professionals, has added former Airbnb China COO Kum Hong Siew to its advisory bench as Business Advisor.

The appointment comes as the company, which says it has stayed net profitable for two years while hitting a multimillion-dollar annual revenue run rate, pushes further into the US and prepares to expand into Latin America.

Also Read: Late-stage deals revive in Southeast Asia, but early-stage founders remain under pressure

Siew’s résumé reads like a masterclass in scaling across regulatory complexity: one of Airbnb’s earliest Asia Pacific hires, he went on to run the company’s regional business before becoming COO of Airbnb China, a market notorious for grinding down foreign operators. Before that, he cut his teeth at law firm Rajah & Tann and at Yahoo!.

e27 sat down with Siew (and on two questions specifically about Beyond Border’s market strategy, with CEO and co-founder Fred Ng) to unpack what “operational scaling” actually means for an immigration platform, why the O-1 visa’s “Einstein” reputation is mostly myth, and what a law-and-tech background taught him about vetting founders before agreeing to advise them.

You went from Airbnb to immigration tech. What drew you to Beyond Border specifically, and what parallels do you see between scaling Airbnb’s regional operations and helping founders navigate US immigration?

Kum Hong Siew: What drew me in was the founders first. Fred Ng and Arnold Ip are strong founders who are building something special. Silicon Valley is the indisputable epicentre of the AI revolution, and ambitious founders and builders around the world increasingly understand that they need to move there to have truly global impact.

I also see the mission in broader terms than a typical visa-processing pitch. When someone moves to the US and succeeds, that success flows back home too, especially true for folks hailing from emerging markets like Latin America.

The Airbnb connection, though, is about something more specific than mission alignment for me. When it involves cross-border movement of people, local nuance and understanding is critical. And it can be challenging to maintain this local mindset as you scale a business. I learnt this from my time at Airbnb, and I look forward to helping Fred and Arnold do this as well.

You went from one of Airbnb’s earliest APAC hires to COO of Airbnb China, a market known for regulatory complexity. What lessons from operating there apply to Beyond Border’s move into Latin America?

Kum Hong Siew: I’m careful not to overclaim a one-size-fits-all playbook here. Every country really is different. But there are some fundamental truths in running and scaling a business. For example, succeeding in China requires a relentless focus on the customer, and that translates everywhere else.

Beyond Border frames immigration as sitting “at the intersection of legal, operational and business strategy.” Can you unpack that with a concrete example? How does a founder’s visa status actually shape decisions like where to hire or raise capital?

Kum Hong Siew: Most of Beyond Border’s customers have already made the harder decision before they ever speak to us. They’ve already decided that they want to be in the US, usually because that’s where funding, talent and customer demand are frequently the deepest and densest in the world. And then Beyond Border helps them get the right visa to be there.

Physical presence isn’t optional if a founder actually wants to build a team or raise capital in the US. It’s very hard to do those things successfully if you are constantly flying in for two weeks before leaving, and coming back only in a few months’ time.

Also Read: Why the smartest founders are interviewing investors before investors interview them

I’ll offer a data point from my own angel investing. I have a portfolio founder from Hong Kong who now spends more than half their time in San Francisco. Since they started doing this late last year, they’ve more than 4x-ed their annualised revenue.

As Business Advisor, you’ll work on legal operations and operational scaling. What does “operational scaling” mean for an immigration platform — internal processes, attorney networks, client experience, or all three?

Kum Hong Siew: I’ll be blunt about the industry’s starting point. Immigration service providers have historically been heavy on manual processes and operations. With AI, there is now an opportunity to reinvent how such services can be delivered, to enable faster, cheaper, and higher-quality outcomes.

But I push back on treating AI as a bolt-on. It’s not about taking an existing workflow and automating select steps with AI, while essentially doing the same steps in fundamentally the same way. Instead, it’s about designing and building a completely new workflow from first principles using the technology that is now possible, while maintaining or even improving the quality of the deliverables.

My bet is on combining Beyond Border’s talented workforce and attorney networks with technology to serve far more clients without diluting the experience.

You’ve worked across law, tech, and now immigration. How has that cross-disciplinary background shaped how you evaluate a company like Beyond Border before agreeing to advise it?

Kum Hong Siew: My background allowed me to quickly understand the problem they were solving, and the different ways in which they could attack it as they scale. What sealed the decision for me was less the numbers than the founders themselves. They have already built a good business, but want to make it even bigger and better. They had a really good understanding of their business and the levers for further growth, and were also open and hungry to learn. At this stage, everything starts with and flows from the founders. So what I saw made the decision easy.

Beyond Border has grown to a multimillion-dollar revenue run rate while staying net profitable for two years. What told you this was a fundamentally sound business rather than just a fast-growing one?

Kum Hong Siew: Those metrics, together with my understanding of how such businesses operate, show that Beyond Border could grow to be a great business at scale.

Singapore-founded startups often face the question of when and how to internationalise. What do you make of Beyond Border’s decision to headquarter in Singapore while its core product serves the US market?

Fred Ng: I’d correct the framing a little there. Beyond Border was founded in Singapore, but today we are headquartered across the US and Hong Kong. Singapore remains an important part of our story, and Asia continues to be a core market for us.

I see our geographic spread as a philosophy rather than an accident. One of our core beliefs is that where you come from should not define where you can go. Singapore gave us a strong launchpad, with its concentration of talent and its position as a regional business hub, but the company has grown organically into an increasingly international business.

Also Read: Connecting founders across SEA used to be the easy part of the job, and now it’s becoming the whole job

The numbers back that up: more than 60 per cent of enquiries now come from the US, with the rest split across Asia, Europe and Latin America. For us, internationalisation has therefore been less about choosing between Singapore, Asia or the US, and more about building the company in the markets where our customers are today and where they want to go next. I think it also shows that a consumer company founded in Singapore can build from Asia and find meaningful traction with a global audience.

A press release cites that immigrants co-founded 59 per cent of America’s billion-dollar startups. From your vantage point, what’s the biggest misconception people have about who actually needs O-1 or EB-1A pathways?

Fred Ng: I’ll go straight for the myth that keeps eligible applicants from even trying. The biggest misconception is that you have to be “Einstein-smart” to qualify for an O-1 or EB-1A. These pathways have developed a reputation as the “Einstein visas,” which leads many founders and professionals to assume they are out of reach.

I’d point to approval data as the counter-argument. O-1 approval rates, for instance, have remained above 90 per cent even across both Trump administrations. What matters is whether you can demonstrate a strong track record of professional achievement and recognition within your field. As a rule of thumb, if you have achieved meaningful professional milestones that are recognised by your industry, it is worth assessing your eligibility rather than ruling yourself out because of the “extraordinary ability” label.

Beyond Border is expanding into Latin America alongside its US and Asia footprint. What operational or cultural challenges do you anticipate in building trust with founders and professionals in a market quite different from Southeast Asia?

Kum Hong Siew: I’ll return to a theme I raised earlier: there’s no universal playbook, only a universal starting point. I always advise founders to start with the customer. When you do that, you can use first principles to figure out the best way to build trust with and to serve the customer. The way to do that will vary across countries and cultures, hence local nuance and understanding is critical. That is why Beyond Border’s obsession with being customer-centric has served and will continue to serve them well.

The post Why Beyond Border thinks visas are now part of the founder playbook appeared first on e27.

The AI community keeps asking: “Where’s crypto’s killer app?” They may be asking the wrong species. Blockchain never found product-market fit with humans. It may find it with machines. AI creates intelligence. Blockchain creates verification.

For years, one of the strongest arguments from the AI community has been that blockchain never found a meaningful product-market fit. While artificial intelligence is transforming software development, healthcare, education and enterprise productivity, much of the crypto industry continues to be associated with speculative trading, meme coins and short-lived narratives. From this perspective, blockchain appears to solve few problems that traditional software cannot already address. Ironically, however, the rapid rise of AI agents may become the strongest argument yet for why blockchain infrastructure matters.

The key distinction is that humans and AI agents operate under completely different economic constraints. Most people already have access to bank accounts, credit cards, digital wallets and payment processors such as Visa, Stripe or PayPal. AI agents have none of these privileges. They cannot open bank accounts, complete Know Your Customer (KYC) verification, own payment cards or initiate cross-border bank transfers as legal entities. Yet they are increasingly expected to purchase APIs, rent GPU resources, subscribe to SaaS products, hire freelancers, negotiate cloud computing prices and interact autonomously with other software agents. Intelligence alone is insufficient; autonomous systems also require programmable identity, ownership and payments.

Also Read: EVs gain ground in the Philippines as fuel costs squeeze drivers

Blockchain provides precisely these missing primitives. A wallet serves as a native financial account for software, stablecoins enable instant global settlement without relying on banking hours, smart contracts automate payment conditions, decentralised identity establishes verifiable credentials, and tokenised assets create programmable ownership. Instead of replacing AI, blockchain increasingly functions as the financial infrastructure layer that allows autonomous agents to participate in the digital economy.

“AI makes everything fake. Crypto makes things real again,” said Balaji Srinivasan.

This transition is already visible across the industry. Coinbase recently introduced x402, an open protocol that enables AI agents to pay for APIs automatically using stablecoins by reviving HTTP status code 402 (“Payment Required”). Visa launched Intelligent Commerce, allowing AI assistants to make purchases within predefined spending limits, while Mastercard introduced Agent Pay to support secure AI-driven transactions. Stripe has expanded programmable stablecoin payment infrastructure, and startups such as Skyfire and Payman AI are building dedicated financial rails for autonomous software agents. None of these initiatives focus on retail crypto speculation; instead, they address a practical question: how can software become an economic participant?

This shift also changes the way blockchain should be evaluated. During the previous cycle, most discussions revolved around transaction throughput, token prices and decentralised finance yields. In the AI era, the more relevant question may be whether blockchain can become the operating system for machine-to-machine commerce. Autonomous vehicles paying charging stations, AI researchers purchasing proprietary datasets, robots ordering replacement components, and software agents negotiating cloud infrastructure all require trusted payments, programmable ownership and verifiable identity. These are infrastructure problems rather than consumer applications.

“Crypto startups shouldn’t pivot to AI. Crypto is foundational infrastructure for AI,” said Brian Armstrong, Coinbase.

The debate therefore may have been framed incorrectly from the beginning. Artificial intelligence and blockchain solve fundamentally different problems. AI produces intelligence by enabling machines to reason, generate content and make decisions. Blockchain produces trust by enabling ownership, payments and coordination without centralised intermediaries. Rather than competing for the same market, the two technologies increasingly appear to be complementary layers of the same digital economy. The next decade is unlikely to be defined by AI versus blockchain. It is far more likely to be defined by AI powered by blockchain, where intelligence and trust evolve together to support an economy increasingly driven not only by humans, but also by autonomous machines.

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