In 2022, Shein was valued by private investors at around US$100 billion, making it one of the world’s most valuable fashion companies.

This month, it went public in Hong Kong at a valuation of around US$27 billion — roughly 70 per cent below that 2022 peak. Its growth has also slowed: from 19 per cent in 2023 and 21 per cent in 2024, to eight per cent in 2025 and further again in 2026. Euronews questioned if it “signalled the death of ultra-fast fashion.”

There are plenty of reasons for that, from regulation and tariffs to investor scrutiny.

But there is also a bigger question worth asking: has the category that Shein helped build, evolved into something different?

How fast became ultra-fast

Fast fashion wasn’t new when Shein hit the market.

Zara and H&M had already changed the way people bought clothes, making trends available faster and at more accessible prices. The term fast fashion itself dates back to the early 1990s, when Zara’s model could take a garment from design to store in around 15 days.

Shein pushed that model much further.

Between July and December 2021, it was reportedly adding between 2,000 and 10,000 new products to its app every day. Small initial production runs allowed it to see what was selling, then quickly produce more of the winners.

The result was a clear category proposition: more choice, more often, at even lower prices. Shein came to define what became known as ultra-fast fashion.

But that category wasn’t created in isolation. It was enabled by a particular set of market conditions: global supply chains optimised for cost, access to low-cost production, inexpensive cross-border logistics and trade rules that made sending huge volumes of low-value parcels directly to consumers economically viable.

For years, those conditions supported the category, but recent conditions have changed.

The fast fashion fatigue

The US has removed the de minimis exemption that allowed low-value imports to enter without duties. Europe has introduced its own charge on low-value parcels.

France has gone one step further, with new legislation specifically targeting ultra-fast fashion, based partly on the volume of clothing a company puts onto the market and the cost of repairing a garment relative to its purchase price. The levy begins at different levels depending on the garment and can eventually reach €19.50 per item by 2030.

Also Read: Skate to where the puck will be: How category design gives you a breakaway

The characteristics that helped define the category are now becoming characteristics regulators use to identify and regulate it.

Environmental pressure adds a further layer.

Fast fashion’s model depends on high production volumes and short product cycles, with significant consequences for water, emissions and textile waste. Earth.Org estimates that 85 per cent of textiles end up in landfill each year, while synthetic textiles are also a major source of ocean microplastics.

These aren’t new problems — what’s changing is how difficult they are becoming for companies to treat as external to the business model. Regulation, trade policy and supply-chain scrutiny are increasingly turning them into questions of cost, compliance and competitiveness.

Then there is the customer.

People haven’t stopped wanting affordable clothes, but the consumer mindset that built the category is shifting.

Fast fashion solved a clear problem: more choice, more trends, at prices that made it possible to keep buying. Now, there are signs of fatigue with that cycle. Consumers are becoming more selective, with greater emphasis on quality, durability and value-per-wear over constant newness. The New York Times noted this shift in mindset as: ‘Buy Better, Buy less, Feel Smug About It.’

If consumers start valuing better over more, the advantage fast fashion was built around becomes less valuable. The opportunity is no longer to win harder at the existing category — but to re-think, and re-design the new category.

Categories aren’t fixed

We often talk about Category Design in the context of creating something new: identifying a problem the market hasn’t properly articulated, defining a different solution to it, and building a category around that new way of thinking.

But categories don’t stay still once they’re created.The customer changes. Technology changes. Regulation changes. New alternatives appear. What people value changes.

And eventually, the problem a category was designed to solve can start to look different too.

The mistake is assuming the category that made you successful will remain the right category simply because you lead it.

Netflix began with DVDs delivered by post, but the underlying customer need wasn’t DVDs. It was easier access to entertainment. As technology and behaviour changed, the category around that need changed too. Netflix was brilliant in recognising early that entertainment would one day be delivered digitally — something even its name anticipated. They saw where the category was heading and positioned themselves for it.

Also Read: Seizing opportunity when the competition blinks: Look for category and ecosystem openings

Category Design and ecosystem intelligence go hand in hand.

Categories are shaped by more than the companies competing within them. Customers, regulators, governments, analysts, investors and other stakeholders all influence what the market values, permits and expects. Tracking how those forces are moving in real-time is how you spot a category shift before it becomes obvious — and position yourself ahead of it.

What comes after ultra-fast fashion?

There are signs that Shein itself is already looking beyond the model that made it successful.

Following its IPO, it’s preparing for an acquisition-driven phase, targeting brands across different price points. Its first announced deal is Everlane, a brand associated with higher-quality basics, sustainability and supply-chain transparency — almost the opposite end of the fashion spectrum from Shein.

At the same time, Shein is expanding its marketplace and Xcelerator programme, giving other brands access to its manufacturing network, logistics and global customer base. It is using the infrastructure advantage it built in ultra-fast fashion to evolve beyond the current category it helped build.

Whether that represents an evolution of ultra-fast fashion or the beginning of something different is harder to say. If the category is changing, the answer isn’t simply to make fast fashion a little faster, cheaper or more efficient. It is to understand and design the new category to drive what consumers will value next.

Maybe value becomes less about the lowest possible purchase price and more about cost-per-wear.

Maybe resale becomes part of the original purchasing decision rather than something that happens afterwards.

Maybe consumers still want novelty, but expect brands to provide it with less waste.

Maybe supply-chain transparency itself becomes part of the value proposition, rather than something sitting behind the product.

Or maybe something else entirely becomes the basis on which the next generation of fashion companies competes.

The category isn’t clear yet.

And that’s exactly when Category Design matters.

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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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From project management and customer service to human resources (HR) and finance, businesses have access to more prebuilt software options than ever before.

However, buying prebuilt software that is designed to serve businesses across a range of industries is unlikely to accommodate your specific needs. That’s why many businesses are building custom tools, rather than adapting to generic software.

With three in four organisations embracing low-code development, building custom software has become just as accessible as buying generic tools.

Low-code and no-code platforms allow businesses to build custom tools whilst avoiding expensive software development projects and the need for coding knowledge. This supports faster prototyping and iteration, allowing businesses to test and refine tools based on user feedback.

This article explores four examples where building a custom tool makes more sense than buying prebuilt software.

• Your processes are unique or complex

Most prebuilt software is designed to serve many businesses at once, so its processes and workflows are built around common use cases. Buying this software may make sense for businesses with common, well-defined processes.

However, if your business has unique or complex processes, it is often a sign that building may be a better option than buying.

Low-code platforms allow businesses to create a solution that better reflects how they actually operate, rather than changing the way employees work to fit the limitations of prebuilt software.

• You want to integrate multiple systems

Businesses rarely rely on a single software, with teams using separate tools for project management, customer relationship management, finance, HR, reporting, and inventory.

Prebuilt software may offer some helpful integrations, but these are often limited. When your software doesn’t integrate properly, teams may have to manually transfer information between tools and spreadsheets, which can increase the risk of manual errors, disrupt productivity, and reduce employee satisfaction.

Custom software can be built specifically around your existing technology environment and how your business actually operates. For example, the warehouse team could upload inventory data that is automatically sent to the HR team responsible for orders.

Also Read: Why so many startups are cutting down on the number of tools they use

• You need more control over data 

Prebuilt software typically comes with security features determined by the provider. While these may be sufficient for many businesses, they may not provide the level of data and security control your business requires. This is particularly true if you are often handling sensitive information.

Custom software gives businesses greater control over how their data is collected, stored, accessed, and shared. You can incorporate data and security requirements directly into the software and create user permissions based on specific roles.

For example, a business could determine exactly which of their employees can access particular information, rather than relying on standard permissions provided by prebuilt software.

• Your business is adapting

If every business change requires purchasing another software, your technology stack can quickly become expensive and difficult to manage.

If you are expanding into new markets or changing the way your teams operate, consider building a custom tool that can adapt alongside your business.

Businesses can use low-code or no-code platforms to quickly add new fields, adjust workflows and processes, or update permissions to support additional users. This makes custom software a better long-term fit than prebuilt tools.

Also Read: The AI stack trap: Why more AI tools aren’t translating into more growth

Should you build instead of buy in 2026?

The goal is not to replace every prebuilt application with a custom-built alternative. Instead, it’s to have one custom-built software that accommodates your needs, processes, workflows, and business model.

Businesses should start by identifying where prebuilt tools are creating friction and consider whether a custom option would be better. This helps businesses avoid adding unnecessary tools to their technology stack and build solutions that better support how their teams actually work.

In most cases, businesses should consider building instead of buying when they have unique or complex processes, require seamless integration, need greater control over data and security, or are adapting and growing.

Low-code and no-code platforms ensure businesses no longer have to choose between adapting their processes to fit prebuilt software and commissioning a lengthy, expensive development project.

—

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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Artificial intelligence has become one of the most exciting areas of healthcare innovation. From clinical documentation and diagnostics to patient engagement and remote monitoring, founders are finding new ways to bring AI into almost every part of the healthcare journey.

But for healthtech startups in Southeast Asia, having an impressive AI model may not be enough to build a successful company.

The harder and ultimately more valuable challenge is making technology work within real healthcare environments.

Healthcare is not simply a collection of datasets waiting for better algorithms. It is a complex network of patients, clinicians, hospitals, laboratories, pharmacies, insurers, medical devices, regulations, and existing software systems.

The startups that understand these connections may have a much better chance of moving from an interesting pilot to a product that healthcare organisations actually use.

The opportunity is real, but so is the complexity

Southeast Asia provides significant opportunities for digital health innovation.

Across the region, healthcare systems face growing demand, ageing populations, chronic disease burdens, uneven access to specialists, and differences between urban and rural healthcare.

Digital health can help address some of these challenges.

Telehealth can extend access beyond major cities. Remote monitoring can help clinicians follow patients outside hospitals. AI can support increasingly data-intensive clinical and operational work. Digital platforms can also make parts of the patient journey easier to navigate.

Yet Southeast Asia should not be treated as one homogeneous healthcare market.

Singapore, Indonesia, Malaysia, Vietnam, Thailand, and other regional markets differ in healthcare infrastructure, regulations, digital maturity, reimbursement models, and patient behaviour.

This means a healthtech product that succeeds in one market cannot always be introduced into another with only minor changes.

For founders, localisation needs to go much deeper than translating an interface.

The real problem may be workflow, not technology

Imagine an AI system that can identify a clinically relevant pattern in patient information.

Its accuracy may be impressive.

But what happens next?

Can the system access the right information from the hospital’s existing software? Does the result appear where a clinician already works? Can the clinician review or override the recommendation? Is the decision recorded properly? Can that information move to another system without being entered manually again?

Also Read: Vietnam’s healthtech boom has a talent problem nobody is talking about

If the answers are no, a sophisticated AI model can still become another disconnected tool.

This is why workflow integration deserves much more attention from healthtech founders.

Clinicians already work under considerable time pressure. Technology that adds another login, dashboard, or manual process may technically solve one problem while creating another operational burden.

Effective healthcare technology should fit naturally into how care is delivered.

That requires founders to understand the environment surrounding their product, not only the feature they are building.

Interoperability is becoming a product issue

For years, interoperability could largely be treated as an enterprise IT concern.

That distinction is becoming harder to maintain.

A digital health product may need to exchange information with electronic health records, laboratory systems, pharmacy platforms, medical devices, payer systems, or other healthcare applications.

When these systems cannot communicate effectively, the consequences become visible at the product level.

Users re-enter information. Clinicians switch between applications. Patient records become fragmented. Automation stops halfway through a workflow.

For a startup, interoperability therefore affects usability, adoption, and scalability.

Standards-based approaches can help, but supporting a technical standard is only part of the answer. Founders also need to understand how information moves through real healthcare processes and where their product belongs within those processes.

The question should shift from “Can our platform connect to another system?” to “Can information move through this care journey without unnecessary friction?”

AI needs healthcare context

The rapid improvement of generative AI has lowered the barrier to creating healthcare prototypes.

Building a demonstration is increasingly easy.

Building a dependable healthcare product remains difficult.

Healthcare AI operates in a field where incomplete context, inconsistent data, and inaccurate outputs can have consequences far beyond a poor user experience.

That makes human oversight especially important.

Rather than trying to remove healthcare professionals from the process, startups can design AI around them.

Also Read: Healthtech in South and Southeast Asia – Seeing beyond the “obvious”

A clinical documentation tool, for example, can prepare information for professional review instead of automatically treating generated content as final. A decision-support system can surface relevant information while leaving clinical judgement with the professional responsible for the patient.

This approach may appear less dramatic than the idea of autonomous healthcare, but it can create something much more valuable: trust.

Trust is also one of the hardest things for a young healthcare company to earn.

Infrastructure will separate pilots from scalable products

Many healthtech companies begin with a narrow use case, and that is often sensible.

Problems emerge when the underlying product is built only for that first use case.

A startup might initially serve one clinic, one hospital department, or one type of patient. Growth can later require supporting multiple organisations, new integrations, larger datasets, and different regulatory environments.

Architecture decisions made early can suddenly become business constraints.

This does not mean every startup needs enterprise-scale infrastructure from day one. Overengineering can be as damaging as underengineering.

Instead, founders should understand which technical decisions will be difficult to reverse.

Data architecture, security, interoperability, auditability, and the separation of core product capabilities from market-specific requirements deserve early consideration.

Scalability is not simply about handling more users. In healthcare, it also means handling greater organisational, technical, and clinical complexity.

The strongest healthtech founders will think beyond the feature

Southeast Asia does not lack healthcare problems worth solving, and it certainly does not lack entrepreneurial ambition.

The next phase of healthtech innovation, however, may reward companies that look beyond individual features.

Instead of asking only whether AI can perform a task, founders can ask whether that capability improves an actual healthcare workflow.

Instead of viewing integration as something to address after gaining customers, they can consider how the product will coexist with the systems healthcare organisations already depend on.

Instead of treating compliance, security, and governance as barriers to innovation, they can use them as foundations for building trust.

Instead of designing a product for an abstract Southeast Asian market, they can recognise that healthcare remains deeply local.

AI will undoubtedly influence the region’s healthcare future.

But the most successful companies may not be the ones with the most impressive AI demonstrations.

They may be the ones that solve the harder problem: making technology genuinely work within healthcare.

—

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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The next portfolio concentration problem may not come from investors staying too close to home, but from millions of investors becoming familiar with the same handful of global companies.

For decades, one of the most persistent puzzles in investing has been home bias.

Investors have traditionally tended to allocate disproportionately to companies and assets from their own countries, even when international diversification could give them access to a broader opportunity set. But something interesting may be happening among a new generation of digitally native investors in Southeast Asia. They are not necessarily staying close to home. They may be travelling too far in exactly the same direction.

In an early analysis of self-directed investor portfolios conducted while building Neufin, we encountered a pattern that challenged the conventional home-bias assumption. Across a sample of self-selected digital-investor portfolios, US-listed global companies repeatedly dominated the holdings.

Apple, Microsoft and JPMorgan each appeared in more than 30 per cent of the sampled portfolios. Even more strikingly, no SGX-listed security appeared among the 30 most frequently observed holdings in the sample. The dataset is small and self-selected. It should not be interpreted as representative of investors across Singapore or Southeast Asia. But the pattern raises a much bigger question. What if the digital era is not eliminating home bias, but reversing it?

When familiarity is no longer geographical

Traditional home bias is intuitive. People understand the companies they encounter around them. They recognise their local banks, telecom operators, property groups and retailers. Geography creates familiarity, and familiarity influences investment decisions. Digital investing changes what familiar means.

A Singaporean, Malaysian, Indonesian or Vietnamese investor may interact with Apple products every day, search through Google, watch Netflix, use Microsoft software, read about Nvidia and Amazon, and encounter commentary on these companies continuously across financial media and social platforms. These businesses may be headquartered thousands of kilometres away, but psychologically they can feel closer than a locally listed industrial or property company.

The investor’s geographical home has not moved. Their information home has. That creates what I would describe as reverse home bias: a tendency for digitally connected investors outside the US to disproportionately gravitate towards a relatively small universe of globally familiar securities. This is not simply international diversification. It is familiarity bias operating across borders.

The diversification illusion

There is an important distinction between owning international companies and being genuinely diversified. Consider a hypothetical portfolio containing Apple, Microsoft, Nvidia, Amazon, Alphabet, Meta and Tesla. At first glance, it looks sophisticated and global. These are enormous businesses serving customers across many countries and industries. Yet at the portfolio level, the investor may still have significant common exposures. Several holdings can respond simultaneously to US interest-rate expectations, technology valuations, movements in the US dollar, American economic policy, changes in risk appetite or a broad sell-off in growth equities.

Also Read: India’s IPO boom is rewriting the exit playbook for global investors

The logos are different. The underlying risk drivers may be much less different than they appear. That matters because conventional portfolio interfaces tend to emphasise what an investor owns: ticker symbols, sectors, countries, gains and losses. They are less effective at showing why those positions may move together. A portfolio can therefore appear diversified at the security level while remaining concentrated at the behavioural, factor or narrative level.

The algorithmic layer could make this stronger

There is another reason reverse home bias deserves attention now. Investment discovery is becoming increasingly algorithmic. A decade ago, an investor might have discovered companies through a broker, newspaper, research report or financial adviser. Today, discovery can begin with a YouTube video, TikTok clip, Reddit discussion, financial app notification, search engine or increasingly an AI assistant.

This changes the mechanics of familiarity. Algorithms naturally amplify information that already has high visibility, engagement and availability. Large global companies have enormous digital footprints: thousands of articles, earnings transcripts, analyst reports, videos, discussions and historical references. Generative AI introduces another layer.

Ask an AI system for examples of leading technology companies, innovative businesses or investment themes and globally documented companies have an obvious informational advantage. They exist abundantly within the digital knowledge environment from which answers are constructed.

This does not mean AI will automatically recommend American equities, nor that investors will blindly follow AI-generated information. But it creates a question worth examining:

Could AI-mediated financial discovery make the world’s most information-rich companies even more cognitively dominant? If so, the next generation of investor bias may be shaped as much by information architecture as by geography.

Why this matters for Southeast Asia’s fintech ecosystem

For wealthtech platforms, advisers and financial institutions, this is more than an interesting behavioural-finance observation. Most suitability and portfolio-review processes are designed around relatively visible risks.

How much equity exposure does the client have? How concentrated is the portfolio? What is the client’s risk tolerance? What sectors and geographies are represented? Has the asset allocation drifted? Those remain important questions.

But digital investing may require additional ones. How many holdings are ultimately driven by similar macroeconomic factors? How much of the portfolio reflects the same investment narrative? Does apparent geographical diversification conceal currency or factor concentration? Are holdings becoming more concentrated because of repeated digital exposure? Has the client’s portfolio gradually moved away from their stated risk profile even though each individual trade seemed reasonable at the time? And eventually, one particularly difficult question:

Is an investor choosing an asset because it fits the portfolio, or because the asset has become exceptionally visible to them? These are difficult questions for a traditional portfolio dashboard to answer. They are increasingly important questions for an AI-enabled financial system.

Also Read: How to pitch Southeast Asia’s investors: A founder’s guide

Reverse home bias is not an argument for buying local

There is an important distinction here.

Reverse home bias should not become an argument that Southeast Asian investors ought to own more domestic securities simply because they are domestic. That would replace one familiarity bias with another. US markets offer extraordinary businesses, deep liquidity and access to industries that may be difficult to obtain through local exchanges. International exposure can be an important part of portfolio construction.

The issue is not whether Apple is preferable to a Singapore-listed company, or whether an investor should allocate a particular percentage to one market. The issue is whether familiarity is being mistaken for diversification. A rational global portfolio and a globally familiar portfolio are not necessarily the same thing.

We may need a new definition of portfolio intelligence

The investment industry has become exceptionally good at describing portfolios. The next challenge is understanding the context behind them. A future portfolio-risk system may need to evaluate more than securities, sectors and historical volatility. It may also need to understand behavioural concentration, correlated narratives, suitability drift, attention patterns and the sequence of decisions that created the portfolio.

That becomes particularly important as AI begins participating more deeply in financial research, recommendations and eventually financial actions. The critical question will no longer be simply: “What does this investor own?” It will increasingly become: “Why does this investor own these assets, what common risks sit underneath them, and is the portfolio still consistent with what the investor is trying to achieve?”

Our initial sample of 82 portfolios is nowhere near sufficient to declare reverse home bias a regional phenomenon.

But it is enough to make the hypothesis worth testing. If larger datasets reveal the same pattern, Southeast Asia may offer an early view of a significant change in investor behaviour: a world in which capital becomes geographically global while investor attention becomes increasingly concentrated.

The old home bias was created by proximity. The new one may be created by visibility. And in an AI-mediated financial world, visibility could become one of the most important investment biases we learn to measure.

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For decades, the semiconductor industry has relied on one basic bargain: make silicon transistors smaller, and chips become faster, cheaper and more power-efficient. That bargain is getting harder to keep.

As AI models grow larger and data centres consume more electricity, chipmakers are running into a materials problem. Silicon, the foundation of modern computing, can only be shrunk so far before electrons begin to leak through transistor channels, wasting power and limiting performance gains.

Singapore-based Nexstrom believes the next step will not come from squeezing more out of silicon, but from replacing parts of the transistor with atomically thin semiconductor materials.

Also Read: NASA-linked, MIT-trained founders’ nSWX raises US$2M for AI chip packaging

The company has secured US$12 million in seed funding led by Xora Innovation, with participation from Foothill Ventures and SEEDS, an arm of SG Growth Capital. The round brings Nexstrom’s total capital raised to US$15 million, including US$3 million in non-dilutive funding.

Nexstrom said the capital will be used to commercialise its wafer-scale platform for two-dimensional (2D) semiconductor materials. Its near-term target is ambitious: to develop what it describes as the industry’s first 12-inch single-crystal 2D semiconductor wafer growth platform using production-ready manufacturing tools.

That matters because 12-inch, or 300 mm, wafers are the standard used by advanced chip foundries. Many promising semiconductor materials have performed well in laboratories but failed to make the jump to large, uniform wafers that can survive commercial manufacturing.

Why 2D materials matter

2D semiconductors are materials only a few atoms thick. One of the best-known examples is molybdenum disulphide, or MoS₂, a compound that has attracted research attention because it can help control electron flow at extremely small dimensions.

In simple terms, thinner channels give chipmakers better control over the movement of electrons. That could reduce leakage, lower power consumption and support further transistor scaling at a time when silicon is becoming harder to push.

This is especially relevant for AI and high-performance computing, where the economics of performance are increasingly tied to energy efficiency. Training and running large AI models already require vast amounts of computing power, and every incremental gain in chip efficiency can translate into meaningful savings for hyperscale data centres.

But the obstacle has never been scientific promise alone. The bigger question is whether 2D materials can be produced at the scale, consistency and cost that advanced foundries demand.

Also Read: Synopsys, A*STAR team up to tackle AI chip packaging challenges

“Silicon has fuelled decades of computing innovation, but the industry now needs a new materials platform to continue scaling performance,” said Dr Lance Li, Nexstrom’s co-founder and Chief Scientist. “For years, the challenge has not been demonstrating the promise of 2D materials, but manufacturing them at the scale and quality advanced foundries require.”

From research to foundry floor

Nexstrom was founded in 2024 and incubated through Xora Innovation’s venture-building model, which focuses on turning deeptech research into companies that can address industrial markets.

Its platform combines proprietary chemical vapour deposition hardware, process technology and wafer-scale 2D material growth. Chemical vapour deposition, or CVD, is a manufacturing process used to deposit thin films of material onto wafers.

Nexstrom’s approach is designed to fit into existing foundry workflows rather than requiring chipmakers to rebuild their manufacturing lines from scratch.

That compatibility will be important. In semiconductors, even promising materials face long adoption cycles because foundries are highly conservative environments. Any new process must deliver uniformity, repeatability and yield, while fitting into an industry already built around expensive equipment and tightly controlled production steps.

Nexstrom says its technology is aimed at continuous, single-crystal 2D material growth across 12-inch wafers. “Single-crystal” refers to material with a uniform atomic structure, which is important because defects and grain boundaries can affect electronic performance. In commercial chipmaking, uniformity across the wafer is just as important as performance in a single device.

The company is working with industry partners to validate its technology within existing chip manufacturing workflows. It did not name those partners.

A Singapore bet on upstream semiconductor technology

For Southeast Asia, Nexstrom’s financing lands at an interesting moment. The region has long been part of the global semiconductor supply chain, particularly in assembly, testing, packaging and equipment services. Singapore, Malaysia, Vietnam and the Philippines all play important roles in chip production, though most cutting-edge logic manufacturing remains concentrated in Taiwan, South Korea and the US.

Singapore has been trying to move further upstream, building on its base of wafer fabrication, precision engineering and research talent. The city-state already hosts operations from major semiconductor companies and has pushed deep tech as a strategic priority through public funding, university research and state-linked investment vehicles.

Nexstrom fits into that broader shift. Rather than building another application-layer AI company, it is attempting to address a bottleneck much closer to the physical foundations of computing. If successful, such technology would be relevant not only to AI chips, but also to data centres, advanced processors and future low-power electronics.

Still, the road from seed-stage materials company to semiconductor supplier is unusually long. Deep-tech hardware startups face lengthy validation cycles, high capital requirements and demanding customers. Unlike software companies, they cannot iterate through code alone; they must prove performance in physical systems, often over many years.

The competitive field

Nexstrom is entering a race that includes some of the world’s most sophisticated chipmakers, equipment companies and research institutes. Major players such as TSMC, Samsung, Intel and imec have explored 2D materials as possible candidates for future transistor channels, while universities and specialist materials companies are also working on graphene, MoS₂ and other post-silicon approaches.

Its challenge, therefore, is not merely to show a better material, but to build a platform that foundries can realistically adopt. That may be where a focused startup has room to compete: by solving one narrow but critical manufacturing bottleneck rather than trying to build an entire chip ecosystem around the technology.

Also Read: Southeast Asia’s chip-hub ambition is colliding with its chip-smuggling problem

Nexstrom’s technical foundation is led by Li, a recognised researcher in 2D materials and a Clarivate Highly Cited Researcher since 2018, a distinction covering roughly the top 0.1 per cent of researchers globally by citation influence. He has worked on single-crystal MoS₂ growth since 2012 and later led corporate research at TSMC on post-silicon electronics.

The company is also supported by advisors including Dr Sundar Ramamurthy, Dr Philip Wong, Dr Aaron Thean and Dr John Langan.

“The question is no longer whether 2D semiconductors matter. They are already on the technology roadmap of major semiconductor companies. The challenge is making them manufacturable,” said Wong, Board Advisor at Nexstrom and Inez Kerr Bell Professor at Stanford University.

That line captures both the opportunity and the risk. The semiconductor industry knows it needs new materials to keep scaling performance. But knowing what comes next is different from manufacturing it at commercial scale.

With its new funding, Nexstrom will expand platform development, deepen collaborations with foundries and grow its engineering and leadership teams. For Singapore’s deep-tech ecosystem, the company will be one to watch: not because 2D semiconductors are guaranteed to win, but because the next era of computing may depend on companies willing to work at the atomic edge of what silicon can no longer do.

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For many consumers in Southeast Asia, the smartphone has become both a work tool and a status symbol. It is also getting harder to afford.

That tension sits at the centre of 3cat’s next phase of growth. The Singapore-incorporated, Malaysia-born retailer of pre-owned electronics has raised US$4 million in Series A funding to expand into the Philippines, betting that a more formal, warrantied second-hand device market can win over consumers being squeezed by rising handset prices.

The round was led by Foxmont Capital Partners, with participation from the Asian Development Bank (ADB) and Golden Gate Ventures. ADB has also authorised a further US$2.8 million for future funding rounds in the company.

Also Read: Cinch Impact Report 2026: What it actually takes to fix device access and e-waste in SEA

Founded in 2023, 3cat started as an online business focused on pre-owned Apple products in Kuala Lumpur. It has since grown into more than 20 retail stores across Malaysia, combining physical outlets, online customer acquisition, financing, warranty coverage and an internal technology platform.

Its first market outside Malaysia will be the Philippines, where the company sees a larger and more price-sensitive opportunity. The country has more than 110 million people, a young population and one of the region’s most active mobile-first consumer markets. According to Euromonitor data cited by the company, 18.4 million smartphones were sold in the Philippines in 2025, almost twice the size of Malaysia’s market.

The timing is not accidental. Smartphone makers have been dealing with higher component costs, supply chain volatility and a shift towards more expensive premium devices. In markets where wages have not risen at the same pace, the result is a widening gap between what consumers want and what they can reasonably pay.

3cat says a new mobile phone in the Philippines can now cost up to double a Filipino’s median monthly income. That makes the pitch for a cheaper, tested, warranty-backed device more compelling, if consumers trust the seller.

Turning second-hand into mainstream retail

The used-device market is not new in Southeast Asia. For years, consumers have bought phones through informal shops, classifieds, social media groups and peer-to-peer platforms. These channels are often cheaper, but they also carry familiar risks: unclear device history, battery issues, counterfeit parts, limited recourse after purchase and little consistency in pricing.

3cat’s wager is that the category can move from informal trade to mainstream retail if those concerns are addressed in a structured way.

Its model includes device checks, a 12-month warranty, return policies, mall-based stores where customers can inspect products, and online sales touchpoints. The company says most store transactions involve an online interaction somewhere in the buying journey, including engagement with its proprietary AI sales agents. In practice, customers may discover, compare or ask questions about a device online before completing the purchase in-store.

“Trust is what makes this category scalable. We have spent the last three years building it deliberately, through the quality of the devices we sell, the warranties we stand behind, the stores we invest in and the market-leading experience we deliver at every customer touchpoint,” said Karl Loo, CEO and co-founder of 3cat.

The company claims its devices can be up to 60 per cent cheaper than new equivalents. That price gap matters in Southeast Asia, where demand for smartphones continues to grow but consumers remain highly value-conscious. It is also where the circular economy angle comes in: extending the life of electronics can reduce e-waste, one of the fastest-growing waste streams globally.

Also Read: Singapore’s e-waste crisis: 2.9M idle phones highlight urgent need for circular tech solutions

For ADB, that environmental and inclusion argument appears central to the investment.

“3cat is helping build greater confidence in the second-hand device market by bringing clearer standards around quality, reliability and after-sales assurance,” said Charles Navarro, Investment Specialist at ADB Ventures. “As the company expands in Malaysia and the Philippines, its model has the potential to help raise standards across the wider market while extending the useful life of devices.”

Why the Philippines matters

The Philippines is a logical but demanding next step. It is one of Southeast Asia’s largest consumer markets, with high social media usage, strong mobile commerce behaviour and a population that often accesses the internet primarily through smartphones. At the same time, household purchasing power remains uneven, especially outside major urban centres.

That creates space for a retailer that can offer relatively aspirational devices, especially iPhones and higher-end Android models, at lower prices. But expansion will require more than opening shops. The company will need to build supply, refurbishment standards, customer service operations, financing partnerships and brand trust in a market where second-hand buying is already common but fragmented.

Foxmont’s participation is notable because of its local knowledge. The Philippine venture capital firm has backed consumer and commerce businesses in the country, giving 3cat a potential advantage in understanding retail behaviour, site selection and local partnerships.

“Across Southeast Asia, smartphones are both essential and aspirational, but premium devices are increasingly out of reach for many consumers,” said Jelmer Ikink, Managing Partner at Foxmont Capital Partners. “3cat makes refurbished electronics trustworthy, warrantied, and accessible.”

3cat’s founding team brings a mix of regional retail and technology experience. Loo is joined by Chris Ng, a former founding senior executive at Oppo Malaysia, and Heinrich Wendel, who has worked across product, technology and digital businesses.

A crowded race for trusted resale

3cat will not have the regional used-electronics space to itself. Southeast Asia already has several players trying to professionalise refurbished and second-hand devices.

CompAsia, founded in Malaysia, operates across multiple Asian markets and works with brands, telcos and enterprises on device trade-ins and resale. Reebelo, which has roots in Singapore and Australia, runs an online marketplace for refurbished electronics and has expanded across several markets.

Carousell, while broader and more peer-to-peer in nature, remains a major channel for second-hand phones in the region. In the Philippines, informal mobile shops and Facebook Marketplace also remain powerful competitors, especially on price.

The difference 3cat is trying to build lies in its mix of physical retail, warranty, financing and digital sales support. That omnichannel approach may help convert buyers who are interested in cheaper devices but uncomfortable with purely online second-hand transactions.

The challenge is whether that model can scale profitably. Physical stores build trust, but they also add rent, staffing and inventory costs. Device quality control is operationally complex. Used-phone prices can move quickly as new models launch and older ones depreciate. And in emerging markets, financing can expand affordability but also introduces credit and collections risk.

Also Read: Circular raises US$7.6M funding for electronic gadgets subscription service

Still, the broader direction of travel is clear. As smartphones become more expensive and replacement cycles lengthen, Southeast Asia’s used-device market is likely to become more organised. Investors are increasingly looking at circular economy models that combine commercial viability with resource efficiency.

For 3cat, the Philippines will be an important test of whether its Malaysia playbook can travel. If it works, the company could position itself not merely as a reseller of old phones, but as part of a new layer of consumer infrastructure: one that makes quality devices more accessible without pushing every buyer towards the latest new handset.

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I recently presented at LEAP in Saudi Arabia, where I spent several days talking with founders, startup teams and people building businesses around emerging technology. Unsurprisingly, AI was everywhere.

Much of the conversation centred on what AI could help people do faster: research that once took hours could be summarised in minutes, first drafts could appear almost instantly, routine analysis could be automated and teams could produce more with fewer delays. The enthusiasm was understandable, because the efficiency gains are real.

What I kept wondering, though, was what happens to workload once individual tasks become faster.

If a report that once took an hour now takes 20 minutes, does that create 40 minutes of genuine capacity, or does it simply create room for two more reports?

That distinction sounds small, but psychologically it is not. If every efficiency gain is immediately absorbed by additional output, the working day does not become easier. It becomes denser.

For startups, this deserves more attention because speed is already embedded in the culture. Teams tend to work across broad roles, priorities shift quickly and people are often expected to absorb new responsibilities as the business grows. AI can make that environment more efficient, while also making it surprisingly easy for expectations to expand without anyone explicitly deciding that the job itself has changed.

A person may retain the same title while significant parts of their role are generated, summarised or analysed by AI, with expectations about turnaround and volume changing almost overnight. The technology may be adopted quickly, but human adjustment rarely works quite that cleanly.

Recent research reflects some of this complexity. A 2026 study involving 541 employees in Chinese technology firms found that greater use of generative AI was associated with both increased confidence about taking on broader responsibilities and increased role ambiguity. Employees could feel more capable while simultaneously becoming less clear about where their role began and ended.

That combination is worth paying attention to. When AI allows someone to produce more, take on more and move faster, it can be tempting to read that as straightforward progress. Yet people generally function better when expectations, autonomy and responsibility remain reasonably clear. When those boundaries become blurred, additional cognitive effort is spent simply trying to work out what the job now requires.

This is particularly relevant in startups because ambiguity is often already part of the environment. Roles are broad, people move between functions and formal job descriptions rarely capture everything someone actually does. AI can increase that flexibility, although it can also make it harder to notice when a role has quietly expanded beyond what was originally expected.

Also Read: SEA’s venture capital shifts from mega-rounds to AI and SaaS

There is also the question of control.

Research published in 2025 examined workers using AI decision support and found that partial AI assistance could support autonomy, competence and meaningfulness, while more complete automation reduced those experiences over time. The implication reaches beyond the specific tasks used in the study because how AI is introduced appears to affect whether people continue to feel that they are exercising judgement or simply supervising output.

That shift can be subtle. A task may become easier while also giving the person less say over how it is done, and over time this can change how much ownership they feel over their work even when the technology itself remains useful.

This is why I would be cautious about treating productivity as the only measure of successful AI adoption. If a team is producing more, but the workday has become more compressed, responsibility less clear and meaningful judgement thinner, then efficiency is only telling part of the story.

Research into employee adoption of generative AI is already showing that people actively reshape their roles around the technology, particularly when AI affects their sense of control and whether their work feels meaningful. That makes AI adoption as much a work-design question as a technology question.

Also Read: The AI productivity paradox: Why finance must  move beyond automation 

For founders, the practical issue is whether the organisation is consciously redesigning work or simply allowing expectations to expand around the technology.

If AI makes a task faster, what happens to the time that has been saved? Does it create genuine capacity, better thinking, more recovery between cognitively demanding tasks or more space for work that requires human judgement? Or does the organisation simply increase the volume expected from the same person?

There is also a longer-term question about capability. If AI increasingly performs the early thinking involved in a role, organisations need to consider how people will develop the judgement required for more senior work later. Expertise usually develops through repeated exposure to problems, mistakes, uncertainty and decisions, so removing too much of that developmental work may produce efficiencies now while creating different problems further down the track.

None of this requires startups to slow down their adoption of AI. It does require them to pay attention to what happens after the efficiency gain appears.

One of the things I came away from LEAP thinking about was how much energy we are putting into imagining what AI will be capable of doing next. That conversation is moving extraordinarily quickly, but the more immediate question for founders may be much simpler: when AI makes work faster, what are you choosing to do with the time it saves?

—

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A scam rarely begins with a dramatic breach. More often, it starts with something ordinary: a Facebook page selling discounted skincare, an Instagram post promising easy investment gains, or a message from a group that claims to have found a way to beat the market.

By the time victims realise what has happened, the people behind these schemes may have already shifted accounts, changed names, moved chats to another app and started again elsewhere. That is what makes online scams so difficult to police. They are not isolated posts or rogue pages, but networks built to move quickly across platforms and borders.

Also Read: Singapore tightens scam rules for messaging, social media and e-commerce platforms

Meta now says a two-year information-sharing partnership with the Singapore Police Force (SPF) has helped it take action against more than 3.7 million scam-linked accounts, pages and pieces of content across Facebook and Instagram in 2026. The bulk of that figure came from dormant “shell pages” that were taken down before they could be activated.

The figures underline how Singapore has become both a target and a testbed for anti-scam enforcement in Southeast Asia. The city-state is highly connected, financially sophisticated and heavily reliant on digital payments, social platforms and messaging apps. Those same strengths have made it attractive to criminal syndicates looking for victims who are online, mobile-first and accustomed to transacting digitally.

From takedowns to network disruption

According to Meta, the partnership with SPF is designed to go beyond removing individual scam posts after users report them. Instead, Meta’s investigators use information from the police to map wider clusters of activity and identify related accounts, pages and assets.

Between January and June 2026, Meta took action against more than 113,000 entities and pages on Facebook and Instagram connected to fraud and scams, using information shared by SPF. Much of the content involved investment scams, in which victims are lured by promises of guaranteed or unusually high returns.

SPF referred more than 20,000 accounts, pages and pieces of content to Meta during that period. Meta said those signals led it to act against more than five times as many assets, suggesting that one suspicious account can lead investigators to many more linked to the same operation. That matters because scam groups build redundancy into their operations. If one page disappears, another is ready to replace it.

In June, ahead of the school holidays, Meta and SPF also ran an enhanced disruption exercise focused on e-commerce scams. These schemes relied on familiar tactics: misleading prices, fake promotions for well-known brands, exaggerated product claims and countdown-style pressure to make users act quickly. That operation led Meta to take action against more than 33,600 entities.

The largest number came in July, when SPF information helped identify a newer scam pattern: shell pages. These pages may appear empty and harmless, with no obvious scam content or ads. But they function as pre-built infrastructure, waiting to be repurposed for fraudulent campaigns. Meta said it acted against more than 3.6 million such pages before they could be used.

Also Read: Singapore disrupts 30,000 iMessage accounts as scam losses hit US$1.7M

Daryl Poon, Meta’s Director of Law Enforcement Outreach for APAC, said scammers rely on fragmented visibility across institutions. “Scammers count on the fact that no single organisation sees the full picture,” he said, adding that SPF’s information allows Meta to identify threats it might not see on its own.

Why Singapore is pushing public-private enforcement

Singapore’s urgency is not hard to understand. Scam losses in the country have risen sharply in recent years, with police figures showing victims lost more than US$800 million in 2024 alone. The problem is no longer confined to crude phishing links or impersonation calls. Scammers now use social engineering, fake investment communities, impersonated brands, mule accounts and encrypted messaging channels to build trust and extract money.

For authorities, that creates a structural problem. Police can investigate complaints and arrest suspects, but much of the early scam activity happens on private digital platforms. Platforms, meanwhile, can remove content and accounts, but may lack the external intelligence needed to connect what looks like scattered activity into a criminal network.

Senior Assistant Commissioner Justin Wong, Commander of SPF’s Cyber Command, framed the Meta partnership as part of a broader shift in enforcement. He said tackling sophisticated scam networks requires collaboration with international and private stakeholders, with shared scam signals helping platforms disrupt criminal infrastructure before victims are defrauded.

The Singapore model also reflects a wider regional challenge. Southeast Asia has seen the rise of industrial-scale scam operations, some linked to compounds operating across borders. Victims may be in Singapore, the platform may be American, the payment trail may pass through multiple jurisdictions, and the operators may be based elsewhere in the region. Traditional enforcement struggles when the crime scene is spread across apps, countries and financial rails.

Meta’s wider anti-scam push

The SPF partnership sits within Meta’s broader anti-scam efforts. The company said that, so far this year, it has removed 65 million scam ads from Facebook and Instagram, with 94 per cent taken down before users reported them.

Meta has also taken part in larger cross-border operations. Over two weeks in May and June 2026, the US Department of Justice’s Scam Center Strike Force brought together companies including Meta, Microsoft, Coinbase and Starlink, alongside law enforcement agencies from the US, UK, Australia, Canada, New Zealand and Thailand. The operation disrupted more than 1.4 million accounts, pages and groups across Facebook and Instagram, along with 20,000 Microsoft accounts and thousands of Starlink kits. Thai police also arrested 63 people linked to scam operations.

Also Read: Deepfake fraud losses hit US$3.7B as scams spread beyond social media

In Singapore, Meta, SPF and the National Crime Prevention Council have also worked on public education through the “One Step Ahead” campaign. It encourages people to turn on security features such as two-factor authentication and passkeys for Facebook and Instagram, use WhatsApp linked-device notifications, and rely on local tools such as ScamShield. Meta said the campaign has reached 1.6 million people in Singapore.

Such education efforts are necessary, but they also expose the limits of relying on users to protect themselves. Many scams are designed to exploit moments of haste, trust or financial stress. Security prompts help, but organised scam networks require organised countermeasures.

Rivals face the same trust problem

Meta is not alone in facing this pressure. TikTok, Google-owned YouTube, X, Telegram, WhatsApp, online marketplaces and messaging platforms all sit somewhere along the scam economy’s path, whether as discovery channels, impersonation surfaces, payment touchpoints or migration routes. In Southeast Asia, where social commerce and chat-based selling are deeply embedded, the lines between content, commerce and messaging are often blurred.

That makes anti-scam work as much a competitive trust issue as a compliance obligation. Platforms that cannot control fraudulent activity risk losing user confidence, advertiser trust and regulatory goodwill. Governments in the region are also becoming more assertive, pushing platforms, banks and telcos to share signals faster and shoulder more responsibility for online harms.

Also Read: ASEAN Foundation, Google.org launch US$5M drive to combat scams across Southeast Asia

For Meta, the Singapore partnership offers a glimpse of what that future may look like: less reliance on one-off user reports, more intelligence-sharing, and faster action against infrastructure that has not yet gone live.

The challenge is scale. Scammers adapt quickly, and every successful disruption teaches them what to avoid next. But the latest figures make one thing clear: in the fight against online scams, waiting for victims to report the damage is no longer enough.

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Wall Street finished near record highs on 23 September 2026. Asian equities extended gains. A technology rebound and falling oil prices supported the positive tone. The S&P 500 closed flat and sat roughly 0.4 per cent below its record high. The Nasdaq Composite and Nasdaq 100 advanced 0.5 per cent. The Nasdaq 100 touched new record territory as chipmakers showed strong momentum.

The Dow Jones Industrial Average slipped 185 points, or 0.4 per cent. In Australia, the ASX 200 climbed higher. A drop in crude prices triggered rallies in technology and consumer counters. This split performance showed investors favouring growth names over industrial and financial heavyweights. The mixed close also revealed a market digesting recent gains rather than rushing into new positions.

Crude oil tumbled below US$98 to US$100 a barrel. Productive diplomatic talks between the United States and Iran and reports of potential supply routes reopening pushed prices lower. Investors also focused on comments from Federal Reserve officials. Those officials signaled caution on rapid rate cuts.

Geopolitical developments remained a primary focus for participants across asset classes. The energy retreat offered relief to sectors sensitive to fuel costs. It also weighed on oil producers and reminded investors that supply headlines can shift sentiment quickly. Lower crude prices can ease inflation pressure, but the Federal Reserve’s cautious stance kept expectations for rapid rate cuts in check. That combination left equity investors with a supportive but not euphoric backdrop.

Ethereum declined 0.56 per cent over 24 hours to US$2,768.05. The second-largest digital asset underperformed a nearly flat broader market. A technical rejection at the US$2,800 resistance level drove the pullback. That ceiling has capped rallies since 2024. Trading volume fell 43.69 per cent. Weakening volume confirmed the pullback after a powerful 80 per cent three-month rally.

Ethereum shows a strong 68 per cent correlation with the S&P 500. This correlation indicates a shared macro-driven cooling of risk appetite. The drop did not signal panic. It reflected a market digesting a large advance and waiting for a fresh reason to push higher. The US$2,800 zone matters because sellers have defended it for more than a year. A failure there forces buyers to prove they can absorb profit-taking.

Also Read: Can Ethereum clear US$2,672 this week and unlock a run to US$3,000?

Institutional demand through spot ETFs also cooled. United States spot Ethereum ETFs recorded a net inflow of US$269.98 million on Monday, 21 September. That figure marked a record. Demand then appeared to ease. Analysts noted that the Coinbase Premium Gap has narrowed. This narrowing suggests United States spot buying pressure has eased.

The initial surge of institutional capital that fuelled the rally has paused. That pause removed a major source of short-term support. Without steady ETF inflows, the spot market must rely more on existing holders and broader risk sentiment. ETF flow data now acts as a real-time gauge of institutional conviction. A return of positive flows would give buyers a stronger hand.

The near-term trigger for Ethereum is whether spot ETF flows reaccelerate. The important level to hold is the 38.2 per cent Fibonacci retracement at US$2,634. If the token stabilises above US$2,650, it could gather strength for another attempt at US$2,800. A break below that support opens the path toward the 50 per cent retracement near US$2,581. A deeper pullback could reach US$2,500.

A daily close above US$2,800 would signal a breakout. The next major resistance sits at the 161.8 per cent Fibonacci extension near US$3,083. The structure remains bullish but overextended. The asset needs consolidation or renewed demand to continue higher. Ethereum is taking a healthy breather in my opinion. Record exchange outflows signal the underlying accumulation trend remains intact. Investors should watch whether United States spot Ethereum ETF flows turn positive again in the next 24 to 48 hours. That flow would provide the fuel for a decisive break above US$2,800.

Hyperliquid moved independently. Its HYPE token rose 3.84 per cent over 24 hours to US$97.75. Bitcoin dipped slightly during the same period. Over the last seven days, HYPE gained 26 per cent. That gain ranks highest among the top 10 coins. The primary driver is strong on-chain utility. The protocol generated nearly US$4 million in revenue in 24 hours. This revenue funded the buyback and burn of 39,840 HYPE tokens. Those tokens were worth about US$3.77 million.

The burn permanently removed 4.88 per cent of the maximum supply. This mechanism directly converts platform activity into token demand. It also reduces supply. The result creates buy pressure tied to real product usage rather than speculation alone. That link between revenue and token destruction gives HYPE a different demand profile from assets that rely mainly on market sentiment.

Also Read: Why did Bitcoin and Ethereum move in near-perfect lockstep after the Fed rate hike?

Sector rotation added a secondary tailwind. The CMC Altcoin Season Index rose 53 per cent over the past week. This rise signals that capital may be rotating into altcoins. HYPE trades near its all-time high with over US$1.2 billion in daily volume. The token benefits from broader risk-on sentiment and its own strong price trend. Hyperliquid offers a clear example of deflationary tokenomics working with active ecosystem usage. That combination provides a fundamental floor. Altcoin season tailwinds offer upward potential.

The near-term outlook for HYPE depends on continued platform activity and burn execution. If buying pressure from burns persists and the token holds above the recent swing low of US$92.25, the path toward the US$100 psychological level is clear. A failure to hold this support could see a retracement toward the US$88 area. The bias is cautiously bullish, contingent on ecosystem metrics remaining strong. A decisive break and close above US$100 would confirm continued uptrend. Traders should track whether daily token burn value remains above US$3 million. A sustained drop could signal waning buy pressure from core utility.

Across assets, the 23 September 2026 session showed selective risk appetite. Technology stocks led. Oil’s decline helped consumer and technology counters in Australia. The Dow’s 185-point drop showed that not every sector participated. Federal Reserve caution on rapid rate cuts kept investors measured. In crypto, Ethereum and Hyperliquid displayed two different paths.

Ethereum consolidated after an 80 per cent rally and faced a known resistance level. Hyperliquid advanced on token burns and altcoin rotation. This divergence suggests capital is discriminating rather than simply chasing all risk assets. My point of view is that the next 24 to 48 hours will matter for both. Ethereum needs positive ETF flows to challenge US$2,800. Hyperliquid needs daily burn value above US$3 million to sustain its push toward US$100. The broader market remains near record highs. The path forward depends on whether demand broadens or remains concentrated in leading sectors and tokens.

—

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.

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Seedstars has named the 15 ventures joining the third cohort of SEED Inclusivity, its accelerator for startups building products and services for people with disabilities across Asia Pacific.

Supported by Visa Foundation, the six-month programme brings together companies linked to India, Indonesia, Pakistan, the Philippines, Singapore and Vietnam. The cohort covers a wide range of needs: sign-language communication, speech therapy, developmental support for children, mobility, rehabilitation, learning tools and access to care.

The third cohort also marks a visible Southeast Asian tilt. India remains strongly represented, but ventures from Indonesia, the Philippines, Singapore and Vietnam point to a wider regional push in a market where disability inclusion is often discussed in policy terms yet remains underserved by scalable, affordable technology.

Also Read: These 8 Southeast Asian startups work with people with disabilities to build a more inclusive society

Over the next six months, founders will receive coaching, take part in expert-led sessions and attend two in-person bootcamps in Vietnam. The structure follows Seedstars’ broader emerging-market playbook: help early-stage companies sharpen their business models, connect them with mentors and investors, and prove that impact-led ventures can also be commercially viable.

Disability inclusion moves from charity to market-building

Asia Pacific’s disability community is large, diverse and poorly served. Access to diagnosis, therapy, assistive devices and inclusive education remains uneven, particularly outside major cities. Families often face long waiting lists, high private-care costs and a shortage of trained specialists. For people with hearing loss, mobility impairments or developmental needs, the gap is not just medical. It affects schooling, work, communication and independence.

That is where this cohort sits. The selected ventures are not tackling disability inclusion through a single lens. Some are building assistive hardware, others are using artificial intelligence for screening and communication, and several focus on therapy delivery, rehabilitation and home-based care.

In Southeast Asia, these gaps are especially pressing. Public health systems in Indonesia, the Philippines and Vietnam are expanding, but access to specialist disability services remains inconsistent. Families often rely on a patchwork of hospitals, schools, therapists, non-profits and informal support networks. Startups that can lower the cost of therapy, extend care beyond clinics or make communication easier could play a meaningful role, provided they can prove quality, trust and affordability.

The Southeast Asian cohort

Among the Southeast Asian startups selected is Indonesia’s Hear Me, which is developing technology for Indonesian Sign Language translation and interpretation. The need is clear: sign-language access remains limited in many public and private settings, from classrooms and workplaces to healthcare facilities. Technology can help bridge that gap, but local language and cultural context matter. A generic sign-language solution does not automatically work across countries, let alone across Southeast Asia’s linguistic diversity.

Also from Indonesia, Birru focuses on speech therapy and language-learning support at home and in school. This reflects a broader trend in paediatric care: parents and teachers need tools that extend support beyond occasional clinical sessions. Where trained therapists are scarce or concentrated in urban centres, blended models that combine professional input with at-home reinforcement may become increasingly important.

The Philippines is represented by Mylo Speech Buddy, which supports speech therapy and at-home practice for children with speech delays. Its inclusion underlines a pain point familiar to many families in the region: early intervention can make a major difference, but regular therapy is often out of reach because of cost, distance and availability.

Vietnam’s Ba Bánh Nam Hà brings a different angle, building adapted three-wheel vehicles and wheelchair attachments. Mobility remains one of the most practical barriers to participation in education, work and community life. In dense cities and rural provinces alike, transport systems are rarely designed around wheelchair users. Locally adapted vehicles can be more relevant than imported devices if they are built for the roads, budgets and daily routines of their users.

Also Read: These startups are using AI to help improve the lives of people with disabilities

Singapore features through GenElek Technologies, an India- and Singapore-linked venture developing robotic exoskeletons for rehabilitation and personal mobility. It sits at the advanced hardware end of the sector, where engineering, clinical validation and cost control must come together. For Southeast Asia, the question is whether such technologies can move beyond specialist institutions and reach wider rehabilitation settings over time.

A broad Asia Pacific mix

The rest of the cohort includes ConnectHear from Pakistan, which provides AI-powered sign-language communication and accessibility services; FyndHealth from India, which offers developmental assessments and multidisciplinary therapy for children; and Gabify, also from India, which builds AI screening and practice-management tools for developmental care.

Several Indian ventures focus on children’s developmental and learning needs. HireForCare provides assessment and therapy for children with developmental needs, Kidaura Innovations builds digital tools for therapy delivery and home reinforcement, and Giftolexia uses AI for early screening and learning support to help identify learning difficulties sooner.

Others target mobility, rehabilitation and neurological care. Lifespark Technologies develops wearable devices and digital care for neurological conditions. BeAble Health builds game-based rehabilitation devices and software for movement recovery. Ksham Innovation is developing bone-conduction smart glasses for people with hearing loss, while Thinklude offers AI-powered live captioning and Indian Sign Language interpretation.

Taken together, the cohort shows how disability-focused innovation is spreading across product categories. It is no longer confined to assistive devices. Increasingly, ventures are combining hardware, software, AI, telecare and data tools to make services more accessible and continuous.

Building on earlier cohorts

According to programme reporting, the 15 ventures in Cohort 1 have reached 2.97 million people and raised US$12.8 million since completing the programme. Cohort 2 brought together 17 ventures from India, Indonesia, Pakistan and Singapore, and concluded in April 2026 with a Demo Day in Jakarta.

Those figures matter because disability inclusion startups often face a difficult funding environment. Investors may see the market as fragmented, heavily regulated or too dependent on public-sector and non-profit buyers. Founders must prove both impact and commercial viability, while navigating sensitive issues such as clinical outcomes, accessibility standards and user trust.

The jury for the third cohort included Bernard Chiira, founder and CEO of Assistive Technologies for Disability Trust and General Partner at Momentous Fund; Katharine Lindquist, Programme Officer at Visa Foundation; Brianna Losoya-Evora, Head of Impact Measurement and Management at Sweef Capital and Director of The Sweef Institute; Pierre-Alain Masson, CEO and co-founder of Seedstars; and Charlotte McClain-Nhlapo, Global Disability Advisor at the World Bank Group.

Also Read: What this digital shift means for people with disabilities in SEA

For Southeast Asia, the more interesting question is what happens after the accelerator. The region does not lack pilots, hackathons or inclusion pledges. What it needs are solutions that can survive procurement cycles, win the confidence of families and clinicians, and reach people outside elite urban settings.

That is the test facing this cohort. If even a handful can scale across borders while staying affordable and locally relevant, SEED Inclusivity could become more than an accelerator. It could help define a new generation of disability-focused businesses in Asia-Pacific, built not around charity but around access, dignity and everyday use.

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