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.

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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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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.

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

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

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

The post Why Southeast Asia’s next healthtech winners will be built around healthcare workflows, not just AI appeared first on e27.

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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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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