Across industries, organisations are racing to adopt new technologies, particularly AI. But as adoption accelerates, a gap is becoming increasingly hard to ignore.

According to PwC’s 2025 Digital Trust Insights, 66 per cent of technology leaders now say cyber risk is their top concern. Yet only two per cent of organisations have achieved true, enterprise-wide cyber resilience.

This disconnect reveals a deeper issue. Cybersecurity is still treated as IT hygiene or operational insurance, rather than what it has become: economic infrastructure. Trust is the invisible layer that determines whether AI, digital commerce, and platforms can scale sustainably or stall under their own risk.

When AI adoption moves faster than governance

AI has unlocked enormous value, but it has also expanded attack surfaces faster than most organisations can respond.

The same PwC survey found that 67 per cent of organisations believe generative AI has increased their cyber attack surface. Inside companies, this shows up in familiar ways: employees experimenting with AI tools outside approved systems, browser-based agents automating tasks, and informal workflows built on powerful but poorly governed technology.

Innovation rarely waits for governance. But when guardrails lag too far behind, trust erodes quietly.

A clear example can be seen in the growing risks around AI prompt injection. OpenAI has acknowledged that prompt injection is a long-term security challenge that may never be fully solved. These attacks can manipulate AI systems into unintended actions, misinterpret user intent, or expose sensitive information — often without users ever seeing what went wrong.

The consequence is subtle but significant. Users may not understand the technical failure, but they experience the fallout. Confidence weakens. Adoption slows. Trust becomes fragile.

Platform-level trust requires structural security decisions

At scale, trust cannot be sustained through messaging alone. It requires architecture, governance, and oversight.

As digital platforms grow larger and more influential, cybersecurity is increasingly treated as a public trust issue rather than a private technical concern. Few examples illustrate this shift more clearly than TikTok’s US restructuring.

Also Read: Why protecting data today means proving you can restore trust

In January 2026, TikTok signed an agreement to divest 45 per cent of its US operations to a consortium of American investors, including Oracle, Silver Lake, and MGX. Under the new structure, Oracle will serve as TikTok’s trusted security partner, responsible for securing and managing US user data, auditing national security compliance, and replicating a US-specific version of the platform’s algorithm under new jurisdiction.

This move is not just about regulation. It reflects a broader reality: data residency, infrastructure control, and third-party oversight are now prerequisites for trust, not optional safeguards. For platforms handling massive volumes of personal data, cybersecurity decisions increasingly shape whether users, regulators, and partners remain willing to engage.

Security is becoming a user-facing trust signal

Cybersecurity is no longer invisible to users, whether platforms want it to be or not.

Recent Cybernews research, as cited in The Guardian, uncovered around 16 billion exposed login credentials circulating through infostealer malware datasets, prompting widespread warnings to reset passwords and strengthen authentication practices. At the same time, credential theft surged by 160 per cent in 2025, now accounting for one in five data breaches, driven by AI-powered phishing and Malware-as-a-Service tools.

These numbers matter because they translate into everyday experience. Compromised accounts lead to forced password resets, suspicious login alerts, and locked services. When trust breaks, users rarely make noise. They disengage quietly and permanently.

This is why security measures increasingly double as reputation management.

Meta’s global anti-scam campaign offers a clear illustration. In 2023, consumers reported losing more than US$10 billion to fraud, a 14 per cent increase year-on-year. 40 per cent of reported social media scams involved online shopping, often leaving victims without the products they paid for.

In response, Meta dismantled over two million scam-related accounts globally. These actions are not just enforcement measures. They are visible trust signals, designed to show users that protection is happening in real time, not buried in policy documents.

Trust drives commerce, especially in emerging digital markets

In digital commerce, trust is not a compliance cost. It is a growth multiplier.

Nowhere is this clearer than in Southeast Asia. According to Lazada and Cube’s research, nearly 90 per cent of online shoppers in the region are active in curated, high-trust Mall environments, and 90 per cent are willing to pay more when buying from these spaces. Notably, eight per cent of respondents are willing to pay over 30 per cent extra for what they perceive as a trust premium.

Also Read: Trust remains travel’s defining currency: Inside travel’s next operating model at MarketHub Asia 2026

These findings reinforce a critical point. Payments, identity verification, live commerce, and cross-border transactions all rely on cybersecurity as a foundation. When platforms feel safe, commerce flows. When they do not, growth stalls.

Cybersecurity is economic infrastructure, not insurance

Taken together, the pattern is clear.

AI is increasing exposure. Platforms are restructuring around security. Consumers are withdrawing trust when risks feel unmanaged. Commerce is rewarding safer ecosystems.

Over the past year, I have personally received multiple notifications informing me that my passwords were exposed in data breaches. Some platforms forced immediate resets. Others quietly suggested updates “as a precaution”. None of these moments felt dramatic on their own. But collectively, they changed how I interact with digital services.

I hesitate before connecting to new apps. I am more selective about where I store payment details. I think twice before adopting new tools, even when they promise speed or convenience.

This is what cybersecurity looks like when it becomes economic infrastructure. It not only prevents worst-case scenarios. It determines who gets to participate confidently in the digital economy and who opts out.

Security, in this context, is no longer insurance against rare disasters. It is the foundation that allows digital systems to function at scale.

Trust is what allows innovation to scale

Innovation moves fast. Trust determines how far it goes.

Security is often framed as the opposite of speed. In reality, it is what makes speed sustainable. When users trust platforms, they experiment more. When businesses trust infrastructure, they invest deeper. When ecosystems trust their safeguards, innovation compounds instead of stalling.

The next phase of the digital economy will not be won by those who ship the fastest features or adopt the most advanced AI. It will be shaped by those who treat cybersecurity as a trust layer rather than a technical afterthought.

For founders, this means building security into product decisions early.

For platforms, it means making protection visible and meaningful.

For policymakers, it means recognising cybersecurity as critical economic infrastructure.

Because in a digital economy built on speed, trust is what allows progress to last.

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From Wall Street to Asian bourses, from oil futures to digital currencies, the message is clear: risk appetite has evaporated, and a defensive crouch has become the default stance. This is not merely a localised correction or sector-specific adjustment. This is a full-scale recalibration of market sentiment, driven by artificial intelligence anxieties, robust economic data that complicates the rate-cut narrative, and a commodity complex under siege from supply gluts.

In my view, what we are witnessing represents a significant stress test for the interconnected global financial system, and the results so far paint a sobering picture.

The epicentre of this week’s turmoil lies squarely on Wall Street, where fresh concerns about the long-term implications of artificial intelligence on commercial real estate and software sectors triggered a violent selloff on Thursday. The Nasdaq Composite plummeted 2.03 per cent, erasing weeks of gains in a single trading session. The S&P 500 fared only marginally better, dropping 1.57 per cent as investors scrambled to reduce exposure to growth-oriented names.

These are not trivial declines. They reflect a fundamental reassessment of valuations in sectors that have carried the market to record highs over the past year. The AI revolution, once celebrated as a catalyst for unprecedented productivity gains, has now become a source of anxiety as market participants question whether the technology will disrupt more businesses than it creates.

This flight from risk assets has produced a predictable but nonetheless significant rotation into safe havens. United States Treasuries rallied sharply, pushing the 10-year yield down to approximately 4.09 per cent, its lowest level since early December. This move tells us something important about investor psychology right now.

When capital flows aggressively into government bonds amid strong economic data, it signals that fear has overtaken greed as the dominant market emotion. The traditional playbook would suggest that robust employment figures and resilient consumer spending should push yields higher. Instead, the opposite has occurred, revealing the depth of concern about potential dislocations in equity markets.

The commodity complex has not escaped the carnage. Oil prices fell more than 2 per cent after a devastating report from the International Energy Agency projected a record global crude surplus of 3.7 million barrels per day in 2026. This figure represents a supply glut of historic proportions, one that threatens to keep energy prices depressed for the foreseeable future.

For oil-producing nations and energy companies, this outlook presents serious challenges to fiscal planning and capital expenditure decisions. For consumers and central bankers, lower energy costs could provide some relief on the inflation front, though the broader economic implications of a weakening commodity complex remain concerning.

Gold, traditionally the ultimate safe haven during periods of market stress, has also stumbled. The precious metal tumbled below the US$5,000 per ounce mark as strong jobs data dampened hopes for immediate interest rate cuts from the Federal Reserve. This development highlights a fascinating tension in current market dynamics.

Also Read: Stablecoins are becoming ‘dollars as a service’ for emerging markets

Investors want protection from equity volatility, but they also recognise that a strong labour market gives the Fed little incentive to ease monetary policy. Higher-for-longer interest rates diminish the appeal of non-yielding assets like gold, creating downward pressure even during periods of elevated uncertainty.

Perhaps the most instructive lesson from this week’s market action comes from the cryptocurrency sector, which has declined 1.55 per cent over the past 24 hours, bringing its total market capitalisation to US$2.28 trillion. What makes this move particularly significant is not its magnitude but its correlation structure.

The crypto market now exhibits a 93 per cent correlation with the S&P 500 and an 89 per cent correlation with gold over the same period. These figures demolish any remaining arguments that digital assets function as uncorrelated portfolio diversifiers during stress events. When correlations approach unity across asset classes, it tells us that macro forces, specifically interest rate expectations and dollar dynamics, are driving all boats in the same direction.

The institutional dimension of the crypto selloff deserves careful attention. Bitcoin exchange-traded fund assets under management fell to US$$93.32billionfrom\; US\$$97.31 billion the previous day, indicating sustained selling pressure from professional investors. This was compounded by US$$98.45million\; inBitcoinliquidations\; over24hours,with\; US\$$80.21 million representing long positions that were forcibly closed.

The combination of spot selling and leveraged position unwinding created a negative feedback loop that amplified the downward move. In my assessment, this dynamic represents one of the most vulnerable aspects of the current crypto market structure, where institutional flows and derivative markets can interact in ways that accelerate price moves beyond what fundamentals would justify.

Also Read: Markets on edge: AI rally fizzles as crypto plunges below US$2.42 trillion

Looking ahead, the technical picture for Bitcoin centres on the US$$65,000to\; US\$$66,000 support zone. A decisive break below this level could open the door to a swift decline toward US$50,000, a scenario that Standard Chartered has publicly identified as possible.

The key near-term catalyst will be the FOMC meeting minutes scheduled for release on February 19, which could provide crucial guidance on the Federal Reserve’s interest rate trajectory. Until then, markets will likely remain in a holding pattern, with participants reluctant to commit capital until they have greater clarity on the direction of monetary policy.

My view on the current situation is that we are experiencing a necessary and ultimately healthy correction in asset prices that had become stretched by optimism about technological transformation and monetary easing. The AI narrative, while powerful, had pushed valuations in certain sectors to levels that assumed perfection in execution and adoption.

Reality rarely cooperates with such assumptions. Similarly, the expectation that central banks would rush to cut rates despite solid economic data always seemed premature. Markets are now adjusting to a more realistic assessment of both opportunities and risks.

The path forward will depend heavily on whether institutional investors interpret current price levels as buying opportunities or as warnings to further reduce exposure. Daily ETF flow data will provide the most immediate signal of sentiment. A return to consistent net inflows would suggest that professional capital views the selloff as a dip worth buying. Continued outflows would indicate that de-risking has further to run.

For now, the burden of proof rests with the bulls, who must demonstrate that support levels will hold up against persistent macroeconomic headwinds and technical pressure. The markets have spoken clearly this week, and their message is one of caution, recalibration, and respect for the powerful forces that shape global capital flows.

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In the high-stakes wealth hubs of Singapore and Bangkok, the definition of a “premium service” is being rewritten. For the region’s wealthy and rapidly expanding mass-affluent segments, traditional wealth management—characterised by scheduled quarterly reviews and static PDF reports—is losing its sheen.

In an era of instant gratification, convenience has become the new currency.

A recent executive insights report, “From Pilots to Production: How Banks Turn AI into Revenue” by Dyna.AI, GXS Partners, and Smartkarma, argues that the promise of AI in wealth management is not only about efficiency. More significantly, it is the ability to bring a higher level of personalisation to customer segments that were previously uneconomic to serve. That capability matters enormously in Southeast Asia, where roughly half of adults have historically remained unbanked or underbanked.

Also Read: Southeast Asia’s banks have entered the AI revenue era

At the same time, a new tier of wealth is emerging across the region—digital entrepreneurs in Jakarta, family-owned conglomerate heirs in Manila, tech founders in Ho Chi Minh City, and high-earning professionals in Kuala Lumpur—who now demand more sophisticated advisory services.

RM co-pilots: from chatbots to strategic partners

At the centre of this shift is what the report dubs the “Relationship Manager (RM) Co-pilot.” These are not simple chatbots. They are sophisticated generative AI systems that synthesise large volumes of data (portfolios, market trends, transaction histories, public filings, social sentiment, and client preferences) to surface relevant investment ideas in near real-time. With these tools, relationship managers can reduce their research time by a reported 95 per cent, freeing them to focus on client strategy, behavioural coaching and bespoke planning rather than data mining.

That speed matters in markets where time-sensitive information can mean the difference between capturing an investment window or missing it entirely. For instance, RMs advising clients exposed to Indonesian commodities or Philippine remittance flows can quickly pull together macro signals, regulatory developments and company-level disclosures to form a coherent client narrative.

Commercial wins and measurable uplift

The commercial impact is already measurable. The report cites a leading multinational bank that saw advisor sales rise by 20 per cent year-on-year after deploying an AI coaching tool. For Asia’s largest private banks, the revenue uplift from scalable personalisation is being counted in hundreds of millions of US dollars annually.

Put bluntly: AI is transforming wealth management from a series of scheduled meetings into an ongoing, data-driven engagement model that keeps the bank present in the client’s financial life.

In practice, banks in Singapore and the UAE are piloting AI-powered concierges that provide seamless portfolio briefings and personalised investment insights during client sessions. In Hong Kong, private banks have used AI to produce rapid scenario analyses for clients considering exposure to opportunities in the Greater Bay Area.

Across Southeast Asia, similar deployments are enabling RMs to bring high-quality, timely investment ideas into conversations–making each interaction materially more valuable.

Mass-affluent: the strategic prize

The mass-affluent opportunity is the real strategic prize. Historically, high-touch advisory was too costly to extend below a threshold of millions in investable assets.

AI changes the unit economics. By automating routine prep and using predictive analytics to recommend a “next best action,” banks can offer a private-banking experience at scale—delivered digitally, affordably and with enough personalisation to resonate. That means middle-aged professionals in Manila with modest but growing portfolios, young tech founders in Jakarta, or dual-income households in Ho Chi Minh City can enjoy richer advice without a four-figure advisory fee.

Also Read: From invisible to investable: How AI is unlocking ASEAN’s MSME goldmine

Local fintechs are already experimenting with scaled advice models. Robo-advisers in Singapore and Malaysia that began as low-cost portfolio managers are increasingly layering human-in-the-loop advice powered by AI insights, creating hybrid offerings that appeal to aspirational clients who want a touch of bespoke guidance without the traditional price tag.

Adoption challenges: trust, governance and change management

Yet deployment is not the same as adoption. The whitepaper cautions that a model can be technically “live” for months before frontline staff actually trust and use it. “Getting a model ‘live’ is fast; getting people to use it takes longer,” the report notes. Cultural and operational factors matter.

In the Philippines, uptake only accelerated once a retail bank began reporting weekly on the tool’s revenue impact rather than solely its algorithmic accuracy.

In Malaysia, banks that paired AI tools with change management—such as training sessions, show-and-tell meetings, and champion programmes—saw far higher and more durable adoption rates.

Regulation and data governance are additional considerations in Southeast Asia’s diverse regulatory landscape. Singapore’s precise data and fintech framework make it a natural testbed for advanced RM co-pilots. Elsewhere, banks must navigate varying data-localisation rules and privacy norms while ensuring models are explainable to clients and regulators.

That reality has encouraged hybrid approaches: keeping sensitive data onshore and using federated learning or encrypted compute to benefit from cross-border models without transferring raw client data.

Speed to context—the ability to deliver relevant context in minutes, not hours—is the intangible competitive edge. One UAE-based wealth manager quoted in the report said, “AI gives me the context I need in minutes, not hours. My conversations are now about the client’s goals, not about me searching for information.”

The same dynamic is playing out across Southeast Asia, where RMs are discovering that AI-driven preparation increases client satisfaction and, crucially, client retention.

Also Read: Why traditional wealth strategies are failing India’s new-age investors

For banks in the region, the message is straightforward. The “new luxury standard” is digital. Those that successfully embed AI co-pilots into RM workflows will deepen share of wallet with existing high-net-worth individuals and capture the vast, underserved mass-affluent market—arguably the region’s most dynamic growth segment.

Implementation requires more than technology: it needs governance, frontline training and metrics that link AI usage to commercial outcomes.

Southeast Asia is approaching a tipping point. As wealth proliferates across cities from Singapore to Surabaya, clients will begin to expect the immediacy and relevance that AI enables. Firms that treat AI as an augmentation of human advisors rather than a replacement will find themselves offering a genuinely new category of service: accessible, personalised and continuously engaged wealth management that, for the first time, feels like true private banking for many more people across the region.

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The image was generated using AI.

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Every day, SME owners and operators face the same reality: hours lost to repetitive tasks, workflows that break down when volume increases, and the nagging sense that there must be a better way to run things. You’re not alone—and now, there’s a solution designed specifically for businesses like yours.

The AI Workflow Competition at Echelon Singapore 2026 is calling on SMEs across Southeast Asia to bring their most pressing operational challenges to the table. This isn’t about vague promises or theoretical benefits. It’s about connecting real business problems with skilled builders who will create practical, deployable AI workflow solutions that actually work.

The challenge every SME knows too well

Your team is talented. Your product or service is solid. But behind the scenes, inefficiency is quietly eating away at growth potential.

Maybe it’s the customer service inquiries that pile up faster than your team can respond. Perhaps it’s the invoice processing that requires three people to touch the same document before payment goes through. It could be the inventory tracking that still relies on spreadsheets and manual counts, or the onboarding process that takes weeks when it should take days.

These aren’t small inconveniences—they’re growth bottlenecks. Every hour your team spends on repetitive manual tasks is an hour they’re not spending on strategic work, customer relationships, or innovation. Every workflow that breaks under pressure is a signal that your current systems won’t scale with your ambitions.

The cost isn’t just measured in time. It’s measured in missed opportunities, team burnout, customer frustration, and the competitive advantage you’re handing to businesses that have already automated.

Also read: How SMEs are using stablecoins to beat currency swings

Why AI workflow automation matters for SMEs

Artificial Intelligence has moved beyond the domain of tech giants and enterprise corporations. Today’s AI tools are accessible, practical, and—most importantly—designed to solve the exact challenges that SMEs face every day.

AI workflow automation means creating intelligent systems that handle repetitive processes end-to-end, from trigger to completion, with minimal human intervention. Think of it as having a tireless digital assistant that can process documents, route information, respond to common queries, update databases, send notifications, and coordinate complex multi-step processes—all while you focus on what humans do best: strategic thinking, relationship building, and creative problem-solving.

The difference between traditional automation and AI-powered workflows is adaptability. Where old-school automation breaks when it encounters something unexpected, AI workflows can handle variations, make contextual decisions, and improve over time. They don’t just follow rigid rules—they understand intent, extract meaning from unstructured data, and adapt to the nuances of real business operations.

For SMEs, this means automation that actually fits how your business works, not systems that force you to conform to rigid templates.

What makes this competition different

The AI Workflow Competition isn’t a hackathon where teams build theoretical solutions that never see the light of day. It’s not an idea competition where winners receive trophies and nothing changes. This is a practical, execution-focused programme designed to produce real, deployable workflow solutions for real business challenges.

Here’s how it works: SMEs submit genuine operational challenges—the specific workflow problems that are actively slowing growth or consuming disproportionate resources. Qualified builders then work directly on these challenges, designing and building AI-powered workflow automations that address the core issues.

Throughout the build phase, teams receive structured mentorship from industry experts and access to platform credits to support development and testing. This isn’t builders working in isolation—it’s a collaborative process where SMEs provide context and feedback, ensuring the solutions align with actual business needs.

The programme culminates at Echelon Singapore 2026, where finalist teams present working demonstrations of their AI workflows to an audience of approximately 10,000 tech professionals, investors, and industry decision-makers. For SMEs, this means visibility, validation, and the opportunity to explore pilot implementation with teams who have already proven they can deliver.

What SMEs gain from participation

Access to Expertise Without the Price Tag

Hiring an AI consultant or automation specialist typically costs thousands of dollars, and there’s no guarantee the solution will match your needs. Through this competition, you get access to skilled builders and mentors working directly on your challenge—at no cost.

Solutions Built for Your Actual Workflow

Generic software rarely fits perfectly. The workflows developed through this programme are designed around your specific operational challenge, using your actual processes as the foundation. The result is automation that integrates naturally into how your business already operates.

No Technical Background Required

You don’t need to understand prompt engineering, API integrations, or machine learning models. You need to understand your business problem. Builders handle the technical execution—you provide the business context and requirements.

Pilot-Ready Concepts

By the end of the programme, you’re not looking at wireframes or slidedeck concepts. You’re seeing working prototypes that demonstrate exactly how the automation would function in your environment. Selected teams may continue post-programme discussions to explore implementation and deployment.

Showcase Opportunity at Echelon Singapore

Your business challenge and its AI-powered solution will be showcased at one of Southeast Asia’s premier tech conferences. This visibility can lead to additional partnership opportunities, investor interest, and ecosystem connections that extend well beyond the competition itself.

What kinds of challenges should SMEs submit?

The best submissions are specific, measurable, and tied to clear business outcomes. Consider challenges where:

• Repetitive processes consume significant staff time: Data entry, document processing, routine customer inquiries, report generation, or administrative coordination that follows predictable patterns.
• Workflow bottlenecks create delays: Approval chains, information handoffs, status tracking, or multi-department coordination where things frequently get stuck or lost.
• Manual work introduces errors: Processes involving multiple data sources, calculations, format conversions, or compliance requirements where human error creates costly mistakes.
• Scaling creates operational strain: Customer onboarding, order processing, inventory management, or service delivery that works fine at low volume but breaks under growth pressure.
• Information silos slow decision-making: Data trapped in separate systems, reports that require manual compilation, or insights buried in unstructured sources like emails and documents.

Think about the workflow challenge that, if solved, would meaningfully accelerate your business or free your team to focus on higher-value work. That’s the challenge worth submitting.

Also read: AI-powered EPOS360 turns small shops into smart businesses

How to get involved

Participation is straightforward, and spaces are limited to ensure quality engagement throughout the programme.

Step 1: Submit Your Challenge

Describe the specific workflow problem your business faces. Be concrete about what currently happens, why it’s problematic, and what success would look like if the workflow were automated effectively. Click here to get started!

Step 2: Qualification Review

The programme team reviews submissions to ensure challenges are suitable for AI workflow automation and align with the competition’s practical execution focus.

Step 3: Collaboration and Build

Once accepted, you’ll be matched with qualified builders who will work on designing and developing an AI-powered solution for your challenge. You’ll provide feedback and context throughout the build phase to ensure the solution addresses your actual needs.

Step 4: Showcase and Next Steps

Finalist teams present their working workflows at Echelon Singapore 2026. You’ll see your challenge solved in real-time demonstration, and explore opportunities for pilot implementation and further development.

SMEs, the time to act is now

Digital transformation isn’t a future consideration—it’s a present competitive reality. The businesses that thrive in the next decade will be those that leverage AI to eliminate operational friction, free their teams from repetitive work, and build scalable processes that grow with demand.

The AI Workflow Competition offers SMEs a rare opportunity: access to technical talent, mentorship, and resources typically available only to well-funded enterprises, all focused on solving your specific operational challenges.

Spaces are limited. The window to submit challenges closes 13 March 2026.

If your business has a workflow challenge that’s holding back growth, draining resources, or frustrating your team—this is your chance to solve it.

Submit your challenge now and take the first step toward operational transformation.

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About the AI Workflow Competition

The AI Workflow Competition is an e27-led programme showcased at Echelon Singapore 2026, designed to explore how AI workflow automation can solve real operational challenges faced by small and medium enterprises (SMEs). Unlike traditional hackathons or idea-based challenges, this programme focuses on execution—bringing together SMEs, builders, mentors, and ecosystem partners to create practical, deployable automation solutions. For more information, visit the website.

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Tower Capital Asia has taken a majority stake in Singaporean security firm V‑Key, marking one of the more consequential private equity moves in Asia’s fintech security space this year.

The deal — framed by both parties as a long‑term partnership to accelerate product innovation and regional growth — positions V‑Key to scale its software-first approach to mobile security at a time when banks and platforms are wrestling with tougher compliance, rising fraud, and the commercial imperative to move everything to mobile.

V‑OS: how software tries to act like silicon

At the heart of V‑Key’s pitch is V‑OS, the company’s patented Virtual Secure Element and App Identity framework. Unlike traditional hardware secure elements (tiny chips or embedded modules that store keys and perform cryptographic operations), V‑OS aims to emulate that protection layer in software across smartphones and tablets.

Also Read: Inside Singapore’s biggest telecom cyber defence operation

V‑Key, which in 2014 secured US$12 million in Series B round from Alipay and IPV Capital, says V‑OS is already deployed across more than 500 million devices globally and underpins its MAPS (Mobile Application Protection and Security) suite.

How V‑OS redefines mobile security and authentication:

• It delivers a secure execution and key storage environment without requiring specialised hardware, lowering deployment friction for banks and platforms that cannot mandate specific device models.
• It ties app identity and cryptographic keys directly to the device and app instance, making it harder for cloned or tampered apps to impersonate legitimate software.
• The software model supports rapid roll‑out and iterative updates, which is valuable where regulatory or threat landscapes change quickly.
• Because it’s designed for scale, the architecture focuses on efficient provisioning, remote lifecycle management, and interoperability with existing identity and transaction flows.

The trade‑off is subtle: software cannot match the absolute tamper resistance of a dedicated secure chip. However, by combining layered protections, continuous attestation, and server‑side controls, V‑OS aims to reach a practical security level acceptable to regulated institutions while offering the flexibility hardware cannot. That’s the gamble Tower Capital is backing.

Why Tower Capital Asia invested in V‑Key

Tower Capital Asia’s stated rationale centres on V‑Key’s technology leadership and product depth. Its investment thesis is more tactical and regionally focused:

• Accelerate product innovation: TCA plans to bankroll R&D, especially around unified digital identity and advanced app‑level protections, helping V‑Key stay ahead of evolving attack vectors.
• Expand regional footprint: With a foothold across 15 countries and over 300 protected applications, TCA wants to deepen relationships with major financial institutions across Asia Pacific and push into adjacent digital sectors.
• Support founder‑led scale: TCA emphasises long‑term partnership and execution support for founder teams — giving V‑Key runway to pursue larger enterprise contracts and more complex, cross‑border deployments.
• Create value through compliance and go‑to‑market: The fund brings regional distribution and operational experience, aiming to convert technical leadership into recurring enterprise revenue.

Put simply, Tower Capital sees V‑Key as an infrastructure bet: security that becomes a necessary utility for mobile banking, payments, and regulated digital services across the region. The fund’s broader portfolio and Asia‑centric network are intended to accelerate commercial traction rather than merely provide short‑term financial engineering.

How V‑Key supports banks and large financial institutions’ digital expansion

Large financial institutions face three simultaneous pressures: regulatory scrutiny, customer demand for seamless mobile experiences, and proliferating fraud. V‑Key addresses these through a layered product approach:

Also Read: In Southeast Asia, cybersecurity is booming but funding is not

• Secure onboarding: V‑Key’s identity and authentication modules enable digital customer onboarding with strong device binding and biometric or multi‑factor flows that meet regulatory KYC and anti‑fraud requirements.
• Authentication and transaction protection: The platform protects session integrity and transaction signing, reducing the need for clunky hardware tokens or SMS one‑time passwords.
• Mobile application protection: Its MAPS toolkit hardens apps against reverse engineering, tampering, and runtime attacks — critical for institutions that must prove application integrity to regulators.
• Scalability and operationalisation: Built for distributed roll‑outs, V‑Key focuses on lifecycle management, remote updates, and monitoring, allowing banks to launch services across markets without bespoke engineering for each jurisdiction.

For a bank moving aggressively into digital services (cardless channels, embedded finance, instant payments, digital wallets), V‑Key promises to reduce friction while maintaining auditable, regulator‑friendly controls. That combination is attractive for institutions that cannot afford either security lapses or degraded user experience.

Risks and realism

Sceptics will point out the inherent limitations: software can be sophisticated, but it remains fundamentally exposed on general‑purpose devices. Attackers continuously innovate; determined adversaries can bypass emulation and control‑flow protections.

V‑Key’s value, therefore, depends not just on V‑OS alone, but on integrating device attestation, server‑side policy, monitoring, and rapid response.

There’s also a commercial test: moving from dozens to hundreds of large bank contracts requires not only technology but enterprise sales muscles, professional services, and local regulatory relationships. Tower Capital’s involvement appears designed to fill those gaps.

The wider implications

This deal underscores a trend: institutional buyers increasingly prefer software solutions that enable quick regional roll‑outs and user‑friendly experiences, even if they trade some theoretical security margin against pure hardware. For Southeast Asia, a region with diverse device ecosystems and a massive mobile‑first population, that trade is often pragmatic.

Also Read: Why Flexxon thinks software-only cybersecurity is no longer enough

V‑Key now has cash and institutional backing to press that advantage. Whether V‑OS becomes a de facto software secure element in Asia will depend on technical resilience, regulatory acceptance, and the company’s ability to convert pilot deployments into enterprise scale. The next 12-24 months will be telling.

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Image was generated using AI.

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Grab’s US$425M acquisition of Stash may look like an American detour, but strategically, it is a Southeast Asian play.

On the surface, buying a US wealthtech platform appears to stretch beyond Grab’s repeated pledge to stay operationally focused on its home region. In reality, this is a capability grab. Stash offers something Grab has long lacked: a subscription-led, mass-market investing engine built under one of the world’s toughest regulatory regimes.

That matters. Grab’s fintech revenues have leaned heavily on payments and lending — businesses that can be cyclical and margin-sensitive. Stash brings recurring, high-margin subscription income and is already EBITDA-positive. For a company that only recently turned fully profitable, that stability is not trivial. It signals discipline, not empire-building.

More importantly, Stash’s AI-powered Money Coach hints at where Grab sees the next frontier: personalised financial guidance at scale. If adapted thoughtfully for Southeast Asia, AI-led investing nudges could deepen engagement, improve financial literacy, and increase retention across Grab’s ecosystem.

The deal structure — majority control now, full acquisition over three years — underscores a risk-managed approach. Grab is not exporting its superapp model to the US. It is importing a tested fintech operating system.

In that sense, this is less about America and more about upgrading Grab’s long-term competitive edge at home.

REGIONAL

Grab’s US$425M Stash acquisition is about AI coaching, not America: Grab will acquire a 50.1% stake in US investing platform Stash for US$425 million, adding profitable subscription revenue and AI-driven wealth capabilities to strengthen its Southeast Asian fintech strategy.

Singapore announces US$790M top-up for Startup SG Equity: Under the Startup SG Equity scheme, the government provides initial capital to drive private funding for promising startups. While startups now find it easier to access early-stage capital compared to a decade ago, many still face challenges at the growth stage.

Malaysian automotive after-sales platform Servauto raises US$4.7M: Investors include Vynn Capital, Jelawang Capital, Openspace Capital, and Gobi Partners. Servauto provides services in the automotive aftermarket sector, focusing on parts and maintenance solutions.

Oneteam secures M&A facility to scale employee-owned SME succession: The facility from GB Helios’ Polaris to fund SME acquisitions, targeting succession-driven deals and scaling its employee-ownership model across Singapore’s essential services sector.

Valiance Health raises pre-seed to fix data fragmentation: The firm aggregates raw clinical, operational, and financial data from hospital systems and run it through AI-driven pipelines that “clean, map and standardise” the information into an “internationally recognised model”, producing a unified repository for analytics and reporting.

Thailand leads ASEAN in student AI adoption: study: In the kingdom, over 90% of students and 81% of teachers using generative AI tools, according to a recent study supported by Google.org. The ASEAN Foundation’s reports, released during a regional policy summit, highlight the rapid integration of AI in Thai education.

FEATURES & INTERVIEWS

From invisible to investable: How AI is unlocking ASEAN’s MSME goldmine: AI-driven alternative data lending is transforming Southeast Asia’s MSME landscape, turning informal digital footprints into credit signals and unlocking profitable financial inclusion across Indonesia, the Philippines, and the wider ASEAN region.

Southeast Asia’s banks have entered the AI revenue era: Regional banks are shifting from using AI for cost-cutting to driving revenue growth, focusing on personalised offers, smarter risk decisions, and faster product iteration, with success hinging on governance, data integration, and production-scale deployment.

Can AI romance fix language learning? Hyperbond believes so: Hyperbond’s Call Me Sensei reimagines language learning through emotionally immersive AI characters, persistent memory, and relationship-driven engagement, prioritising retention and intrinsic motivation over rigid curricula and traditional performance metrics.

Rachel Lee: The talent connector building Asia’s deep tech dreams: e27’s Contributor Spotlight features Rachel Lee, a Singapore-based Talent Acquisition Partner supporting deeptech startups through senior hiring, diversity-focused team building, and weekly HR insights for founders.

INTERNATIONAL

Anthropic hits US$380B valuation after new funding: The AI company raised US$30B in Series G, led by GIC and Coatue. Since launching less than three years ago, Anthropic’s revenue has reached US$14B, with significant growth in enterprise customer spending.

SoftBank’s Vision Fund gains US$2.4B on AI investments: The gain in its December quarter was driven by a rise in the value of its investment in OpenAI. The Japanese conglomerate’s Vision Fund has invested heavily in AI companies, including about US$40B in OpenAI. The fund also holds stakes in chip designer Arm.

Korean banks review crypto partnerships after Bithumb bitcoin error: This follows a payment error at Bithumb involving US$42.78B worth of bitcoin. KakaoBank and Kbank, which have agreements with exchanges like Coinone and Upbit, are assessing whether to renew their contracts amid concerns over reputational risk.

Coupang denies blackmail claims over customer data breach: It said there’s no evidence of any payment demand linked to the breach, which reportedly involved about 3,000 customers purchasing adult products. The allegations were made during a parliamentary session by Rep. Kim Seung-won, who raised concerns about the exploitation of personal data.

HK-based AI trading startup Inference Research bags US$20M seed round: The company develops AI-native quantitative trading systems that integrate digital assets and traditional finance. The funding will support infrastructure expansion and talent recruitment, including quants, engineers, and researchers.

CYBERSECURITY

Tower Capital Asia’s V-Key investment signals mobile security shift: Tower Capital’s majority stake in V-Key underscores growing demand for software-defined mobile security, as banks prioritise scalable authentication, app integrity, and compliance-ready infrastructure across Asia-Pacific’s rapidly expanding digital finance landscape.

The trust problem behind AI adoption and platform growth: AI adoption is accelerating across industries, but cybersecurity maturity is lagging. PwC finds cyber risk is a top concern, yet few achieve resilience. As attack surfaces grow, trust becomes economic infrastructure — shaping platform legitimacy and consumer behaviour.

Building trust in a fast-moving ecosystem: The imperative for Southeast Asia’s tech startups: The region’s startup boom is entering a stricter era where trust matters most. With tougher investors and regulators, startups must prioritise competence, fairness, transparency, and governance to survive and scale.

SEMICONDUCTOR

Lenovo CEO warns rising memory costs after Q4 profit drop: Yang Yuanqing warned that rising memory costs, which doubled in the quarter, could continue to impact the PC industry throughout 2026. The chip shortage is affecting device makers worldwide, as supply is diverted to AI data centres and large-capacity products.

US lawmakers push to limit China’s access to chip tools: The move comes amid reports that China has made progress in developing prototype extreme ultraviolet (EUV) lithography machines, which are critical for manufacturing advanced chips used in AI, smartphones, and military applications.

GlobalFoundries sees strong Q1 on data centre demand: The chip maker expects Q1 revenue of ~US$1.6B, driven by demand for chips used in data centres. It also announced a US$500M share repurchase programme, sending its shares up more than 7% in premarket trading.

AI

AI is making wealth management feel like concierge service: Relationship managers are deploying AI co-pilots to surface investment ideas faster, personalise conversations, and reduce prep work dramatically—creating a concierge-like advisory model that boosts client satisfaction and measurable revenue growth.

Singapore to establish National AI Council, AI missions: The four key areas the AI missions will focus on are: advanced manufacturing, connectivity, finance, and healthcare. This initiative will “push the boundaries of what is possible,” said Minister for Finance Lawrence Wong.

PR for LLM search: How to earn citations without gaming algorithms: AI search is reshaping visibility: brands cited in LLM answers gain trust, traffic, and conversions. Winning requires diversified, evidence-based PR, structured assets, cross-engine measurement, and ethical practices—not shortcuts that risk reputational damage.

AI infra: The unsung hero of technological innovation: GreaterHeat’s CEO argues AI’s transformative promise depends on robust, sustainable infrastructure, urging urgent investment in high-performance, decentralised systems and strategic partnerships to secure competitiveness, innovation, and long-term technological leadership.

THOUGHT LEADERSHIP

Why Asian startups should focus on Southeast Asia in 2026: A physician-founder argues 2026 is the moment for startups to prioritise Southeast Asia, citing its youthful population, digital readiness, unmet needs, improving infrastructure, strong talent pool, and vast opportunities across healthcare and beyond.

Dow hits record high, Nasdaq tumbles 0.6%, Bitcoin miners flee: Signals deeper stress than price alone: Soft retail data and falling yields exposed fragility across equities and crypto, where miner capitulation and ETF outflows deepened stress.

The accidental founder story: How Greytt began without a master plan: After decades in marketing, Preethi chose entrepreneurship at 45, launching Greytt to pursue challenge, purpose, and build empathetic D2C solutions for overlooked midlife consumers through lived experience.

Southeast Asia doesn’t have a startup problem, it has a skills pipeline problem: The region’s digital ambitions are constrained by a shortage of production-ready technical talent. Gaming exposes this execution gap clearly, but similar shortages affect AI, fintech, and platform sectors region-wide.

Crypto market cap drops to US$2.3T as Fed rate cut hopes fade after hot jobs report: Stronger US jobs data delayed rate-cut expectations, triggering a liquidity-driven crypto selloff. Leveraged liquidations amplified losses, highlighting digital assets’ sensitivity to monetary tightening despite continued long-term institutional adoption and structural growth.

Human performance is the next healthtech frontier: Healthtech is shifting from reactive treatment to preventive human performance, combining sport science, coaching intelligence, and community. Beyond tracking data, the next wave will help people sustain physical, mental, and emotional resilience long-term.

Before you can give feedback: Creating the culture where it can be heard: Psychological safety—not feedback frameworks—is the real driver of high-performing teams. Without it, even well-delivered criticism breeds silence, fear, and attrition. This piece explains what safety means, how to spot its absence, and why it matters in Asian startups.

When nation-states shape startup outcomes: The US withdrawal from global climate institutions reshapes rule-setting power, fragmenting standards and increasing geopolitical risk, forcing startups and investors to embed policy literacy, regulatory geography, and interoperability into strategy.

If you’re building for everyone, you’re building for no one: Founders who say they want to sell to “everyone” often lack positioning clarity. The strongest startups win by narrowing focus, sharpening messaging, and building for a defined audience—because conviction, not dilution, drives scalable growth.

From idea to reality: Why an MVP is essential before full-scale development: Building a mobile app without testing demand is risky. An MVP lets startups validate ideas early, cut development costs, launch faster, gather feedback, and reduce failure risk before scaling.

Building a better future: How sustainable architecture is leading the way for the built environment: The built environment sector is expected to focus increasingly on sustainable architecture as environmental concerns continue to grow.

Tech’s new face: Why Southeast Asia is the next UX lab of the world: Southeast Asia is emerging as a global UX innovation hotspot, driven by mobile-first behaviour, superapps, and hyperlocalisation. But its data-driven, addictive design loops raise concerns over ethics, privacy, and user manipulation.

Founders, stop listening to mentors who tell you to build an MVP: The MVP concept is often misunderstood, with many mentors focusing on “minimum” over “viable.” The author argues startups must define MVP strategically, differentiate early, and move faster by onboarding partners before the product is complete.

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For years, healthtech has focused on treatment tracking symptoms, managing illness, and optimising recovery after something goes wrong. While this has moved healthcare forward, it overlooks a far more powerful opportunity: preventive human performance.

Human performance sits at the intersection of physical capacity, mental resilience, recovery, and lifestyle behaviour. It is not about athletes alone. It applies to founders, professionals, operators, and anyone navigating high cognitive and physical demands in modern life.

From fitness to performance systems

Traditional fitness models focus on aesthetics or short-term goals. Human performance takes a systems-based approach:

• How well does the body move under stress?
• How quickly does it recover from workload, sleep debt, or mental fatigue?
• How sustainable is daily output over years, not months?

This shift is already visible in elite sport and corporate leadership circles, but it has yet to be fully translated into scalable, accessible healthtech solutions.

Why healthtech must look beyond data

Wearables, apps, and tracking platforms now provide unprecedented access to data heart rate variability, sleep cycles, step counts, and more. However, data without interpretation creates noise, not progress.

The missing layer is coaching intelligence:

• Translating metrics into action
• Aligning physical training with lifestyle and mental load
• Teaching users how to self-regulate instead of over-optimising

Technology should not replace human understanding; it should enhance it.

Also Read: The future of fintech, healthtech, and edutech industries in the context of the new economy

The role of sport science in everyday life

Sport science has long understood principles such as load management, recovery windows, and nervous system regulation. These principles are now more relevant than ever for non-athletes facing constant cognitive stress and sedentary work patterns.

Applying sport science to daily life means:

• Training for longevity, not burnout
• Building strength as injury prevention
• Treating recovery as a skill, not a luxury

This is where sports and healthtech naturally converge.

Community as a performance multiplier

One overlooked factor in performance is community. Sustainable change rarely happens in isolation. Whether in sport, business, or health, environments shape behaviour.

Digital platforms that combine:

• Education
• Accountability
• Shared standards of discipline

will outperform those that rely solely on individual motivation.

Performance is not just personal,  it is social.

What comes next

The future of healthtech is not another app or tracker. It is an integrated human performance ecosystem blending technology, coaching, and community to help individuals perform better, longer, and with purpose.

The question is no longer How fit are you?

It is How well can you sustain your life’s demands physically, mentally, and emotionally?

That is the real performance metric.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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Across the sprawling archipelagos of Indonesia and the Philippines, a massive economic engine remains stalled — not because entrepreneurs lack hustle, but because they lack legibility in the eyes of banks.

Millions of micro, small, and medium enterprises (MSMEs) are effectively invisible to traditional lending systems. Without formal credit histories, audited statements, or pristine collateral, these businesses are routinely excluded from the capital they need to scale. This is not only a social issue but a commercial blind spot at regional scale.

Also Read: Southeast Asia’s banks have entered the AI revenue era

The executive insights report, titled “From Pilots to Production: How Banks Turn AI into Revenue” by Dyna.AI, GXS Partners, and Smartkarma, estimates the ASEAN MSME finance gap at a staggering US$300 billion.

For years, the bottleneck has been credit scoring itself: rigid models that privilege historical bureau data and formal documentation — precisely what many warung owners, sari-sari store operators, market traders, and home-based sellers do not have.

AI is now changing that equation by shifting the centre of gravity from risk exclusion to risk pricing and ultimately towards financial inclusion that is profitable rather than philanthropic.

Alternative data turns “thin-file” into under-writeable

The region’s digital behaviour footprint has quietly become one of ASEAN’s most valuable assets. By leveraging alternative data (including telco records, e-commerce transaction histories, mobile wallet usage, point-of-sale flows, logistics and delivery patterns, and even psychometric indicators), AI models can underwrite “thin-file” borrowers with far more precision than traditional scorecards.

In Southeast Asia, this is especially powerful because MSMEs increasingly operate digitally even when they are informal. A seller may not have an audited P&L, but they may have:

• A year of daily transactions on Shopee, Lazada, Tokopedia, or TikTok Shop
• Repeat-customer behaviour visible through e-wallet and QR payments
• Inventory turnover patterns in POS systems used by small retailers
• Repayment signals from buy-now-pay-later (BNPL) or supplier credit
• Telco top-up regularity and geolocation stability patterns (where permitted).

The report notes that effective AI-driven personalisation in lending can lift revenues by 10 to 25 per cent. For a mid-sized regional bank, capturing even a slice of the underbanked MSME segment can translate into hundreds of millions of dollars in additional income — not just from interest, but from deposits, payments, insurance, and merchant services.

Philippines: From static lending to dynamic assessment

In the Philippines, where a significant portion of the population remains underbanked, lenders have been leaning into partnerships with AI-as-a-service providers and fintech infrastructure players to modernise decisioning while keeping portfolio quality steady. The direction of travel is clear: lenders want models that update risk views continuously instead of freezing them at the moment a form is signed.

That “dynamic” approach matters in an economy where income can be seasonal, informal, or platform-linked. For example, a micro-merchant’s risk profile can improve sharply as their digital sales stabilise, their returns drop, and their fulfilment performance improves– signals that static models often miss.

This is also where the Philippines’s strong remittance and mobile-money culture becomes relevant. Regular inflows, bill payment behaviour, and wallet velocity can form a proxy for stability when traditional documents are absent.

These models do not just guess; they use predictive analytics to forecast behaviour and risk based on real-time data signals.

Indonesia: QRIS data makes MSMEs visible at scale

Indonesia provides another compelling case study for revenue-generating inclusion, and it comes with a national data exhaust pipe: payments.

Also Read: Bridging the credit gap: CBI’s new bureau targets MSME financing bottlenecks

The rapid adoption of the QRIS national payments network has created a treasure trove of behavioural data. With 39.3 million merchants (93 per cent of them are MSMEs) connected to the system, transaction volumes have surged by 175 per cent year-on-year. Banks and lenders are now deploying AI to identify high-potential merchants within this ecosystem, automating onboarding and offering credit lines based on digital cash flow rather than collateral.

The implications are huge. Once a merchant’s daily QRIS sales can be tracked, lenders can structure products that match reality:

• Revenue-based repayment tied to daily receipts
• Short-tenure working capital for inventory cycles
• Pre-approved limits that expand as payment consistency improves
• Faster renewals with fewer manual checks

This is how lending becomes a scaled growth engine: distribution via embedded channels, underwriting via data, and servicing via automation.

Beyond Indonesia and the Philippines: ASEAN’s rails are converging

While Indonesia and the Philippines are headline examples, similar dynamics are playing out across the region:

• Thailand’s PromptPay and QR adoption have normalised low-friction digital payments for small merchants, improving cash-flow visibility.
• Malaysia’s DuitNow and the broader push for digital payments give lenders more structured signals for MSME activity.
• Singapore’s PayNow and the city-state’s dense SME ecosystem create a testing ground for model governance, though scale often lies north and east, where informality is higher.

Even smaller markets are building rails that generate the data needed for AI underwriting. Cambodia’s Bakong system, for instance, has helped accelerate digital payments adoption, which can support more data-led credit products over time.

The commercial transformation and the caution

As the white-paper notes, “AI shifts lending from exclusion to inclusion”. That shift is commercially transformative because it converts untapped customer segments into profitable borrowers — customers traditional models could not touch.

Global comparisons reinforce the point. In Latin America, similar AI-based credit scoring has been shown to outperform conventional models by up to 85 per cent in accuracy. ASEAN’s digital platforms are just as data-rich; the constraint is less “data existence” and more “data usability”.

The hardcore challenge lies in infrastructure and governance:

• Regulatory fragmentation: An AI model validated in Singapore or Malaysia often needs significant tailoring before it can be deployed in Indonesia, Vietnam, or the Philippines, given differences in data privacy, model risk management expectations, and permissible data sources.
• Consent and trust: Alternative data can expand inclusion, but only if customers understand what is being used and why — and if regulators are comfortable that models are fair, explainable, and auditable.
• Talent scarcity: The region still lacks enough professionals who understand both AI and the nuances of local financial regulation, credit risk, and consumer protection.

Why the momentum is still undeniable

Despite those hurdles, the flywheel is turning. With more than US$30 billion recently committed to AI-ready data centre infrastructure in Singapore, Thailand, and Malaysia, the foundation for scalable AI deployment is being laid — not only for consumer use cases, but for the heavy lifting required in lending: feature stores, real-time decisioning, monitoring, and compliance tooling.

Also Read: How Ant International bridges MSME finance gap with intelligent credit services in the AI era

The winners in this next phase will be the institutions that move fastest from “interesting” pilots to production-grade lending models: turning ASEAN’s “invisible” entrepreneurs into a compounding source of revenue, while expanding access to capital in the places that need it most.

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The image was generated using AI.

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As AI-native language platforms race beyond flashcards and drills, Hyperbond Studio’s Call Me Sensei is betting that engagement, not curriculum, is the true unlock. Blending character-driven AI, memory systems, and relationship mechanics, the Singaporean startup is reimagining language learning as an emotionally immersive experience rather than a structured syllabus.

The AI startup — founded by Shawn Tan (who is also General Partner at TRIVE Ventures) and Jack Vinijtrongjit — recently raised US$500,000 from investors, including NLS Ventures, Loyal VC, and Attribute Global Ventures, for its innovative platform.

In this interview, Tan breaks down the thesis, technology, safeguards, localisation strategy, and business model behind their unconventional approach.

Edited excerpts:

What core problem is Call Me Sensei solving, and what evidence convinced you engagement is the main bottleneck?

Language learning itself is not a new problem. However, most language products start from the assumption that better outcomes come from better curricula (e.g. more structured lessons, more innovative drills, better sequencing). This leads to experiences that feel like work, resulting in high attrition and extremely low long-term retention across the category.

Also Read: Edutech war: How NativeX is taking on the likes of ELSA, Duolingo in Vietnam

Platforms like Duolingo illustrates the limitation of this approach. While it has succeeded as a product, much of its engagement is driven by external mechanics, such as streaks. Many users return daily to protect a streak rather than because they enjoy learning or can communicate fluently. It’s not uncommon to see users with 500-day/1,000-day streaks who still struggle to hold a basic conversation. The system optimises for habit formation, not sustained, intrinsic motivation to communicate.

We take the reverse approach. Instead of starting with “what should someone learn today?”, we begin with “what would someone enjoy doing, voluntarily, for 20 to 30 minutes?” We design an emotionally engaging experience first, and let language learning happen as a byproduct. This makes users want to return and spend more time practising the language.

How do you define and measure “learning” in Call Me Sensei, and will you run studies against Duolingo, Babbel, or human tutors?

Given our approach, we deliberately do not define learning through a fixed curriculum or prescribed outcomes. There is no roadmap, syllabus, or linear progression. Learners decide what and when they want to learn — buying groceries one day, ordering coffee the next — based on immediate interest and context.

As such, we do not “prove” learning through test scores or completion rates. Instead, we focus on retention, session frequency, and time spent as leading indicators. We hypothesise that a learner who voluntarily spends more time speaking and listening will ultimately learn more than one who follows an optimal curriculum and then abandons it.

What makes Call Me Sensei meaningfully different from a generic LLM chat with prompts, characters, and memory?

Call Me Sensei is built on a proprietary AI architecture designed for character-driven, relationship-based interaction, not generic assistance.

Generic LLMs are optimised to be helpful and agreeable. Our system is designed to produce human-like personalities – characters with consistent traits, emotional reactions, memory, and boundaries. Responses are not just linguistically correct; they are situationally and emotionally grounded.

In addition, the experience is conversation-first and embedded within structured scenarios and relationship states. Conversations evolve over time, shaped by past interactions, rather than resetting each session. This creates continuity, emotional stakes, and a sense of progression that cannot be replicated by prompting a general-purpose chatbot.

How does your memory system work, what does it store, and how do users control or delete it without reinforcing unhealthy dynamics?

Memory helps make interactions feel coherent and personalised, but it’s designed with clear limits. Each sensei has a defined personality that influences what they tend to remember—for example, learning preferences or recurring topics while avoiding unnecessary or overly personal data.

Memories are stored in a controlled and privacy-conscious manner and are meant to support learning continuity, not permanence. Users can reset interactions or delete their account at any time, and we intentionally design memory systems to allow for change over time so users aren’t locked into past behaviour, mistakes, or emotional states.

This ensures the experience remains flexible, age-appropriate, and supportive rather than prescriptive or restrictive.

What safety guardrails are in place for romance mechanics, sexual content, manipulation, minors, and self-harm scenarios?

The app is designed for users aged 13 and up, with all romantic mechanics strictly non-sexual and framed around age-appropriate, consent-based interactions. Safety is a core requirement at every level of the experience. We apply strict age-appropriate guardrails around sexual content, harassment, manipulation, and power-imbalanced dynamics.

Interactions are evaluated on a per-message basis using automated systems designed to prevent inappropriate content and to discourage emotional dependency, exclusivity, or coercive behaviour. The system is also designed to respond to signs of distress or self-harm by redirecting conversations and encouraging users to seek trusted external support.

Also Read: Training Gen Z: Why gamification is their language of learning

While conversations are end-to-end encrypted to protect user privacy, we use privacy-preserving safety signals and extensive internal testing to ensure policies are consistently enforced as the product evolves.

What are the key cost drivers of running relationship-based AI at scale, and how will you protect gross margins?

We are not able to share unit economics at this stage. What we can say is that emotionally rich, voice-first AI experiences are computationally expensive, and cost discipline is a core part of our technical design and roadmap as we scale usage.

How do you localise scenarios culturally beyond translation, and who validates tone, taboos, and context across languages?

We do not treat localisation as simple translation. For each language, we work with native speakers and cultural reviewers to ensure interactions feel natural rather than generic. The system is built to support culturally distinct narratives, not one global template reskinned across markets.

Is this a tutoring product, entertainment subscription, or hybrid — and what monetisation model and metrics will guide scaling revenue

Call Me Sensei is intentionally a hybrid. Education defines the outcome; entertainment drives engagement.

The product follows a freemium model for consumers, with premium subscriptions and in-app purchases over time. In the long term, we also see opportunities in B2B partnerships. Monetisation will scale in step with engagement; our priority is first to build something users genuinely want to return to.

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Cryptocurrency assets bore the brunt of a liquidity reassessment triggered by robust American employment data. While Japan’s Nikkei 225 surged past the historic 58,000 threshold amid domestic political momentum and the broader Asia Pacific index touched a record high, digital asset markets retreated two per cent to a US$2.3 trillion valuation.

This divergence underscores a fundamental reality I have observed throughout market cycles. When the Federal Reserve’s policy trajectory shifts, risk assets with the highest duration sensitivity are affected first and most severely. Cryptocurrencies continue to trade as premium risk instruments tethered to global liquidity conditions despite persistent narratives of independence.

The catalyst came from January’s US nonfarm payrolls report, which reported 130,000 new jobs, nearly double economists’ median forecast. This figure alone recalibrated market pricing for Federal Reserve action, pushing anticipated rate cuts from June into July 2026. Traditional equity markets reacted with restraint, with the S&P 500 and Nasdaq Composite closing nearly flat. Crypto markets exhibited a 68 per cent correlation with the Nasdaq 100 index and absorbed the shock with characteristic volatility. This statistical linkage confirms what seasoned observers recognise.

Digital assets function less as an inflation hedge and more as a leveraged bet on expansive monetary policy. When the prospect of cheaper capital recedes, speculative positioning unwinds rapidly. The two per cent decline in market cap represents not a fundamental rejection of blockchain technology but a mechanical repricing of future cash flows under tighter financial conditions.

Compounding this macro-driven pressure, derivatives markets amplified the downturn through forced liquidations. Bitcoin alone saw US$188 million in long-position liquidations in 24 hours, a 130 per cent surge that transformed a measured pullback into a sharp correction. These cascading liquidations reveal the fragility embedded in leveraged crypto trading ecosystems.

When price momentum reverses, algorithmic liquidation engines accelerate selling pressure beyond organic market depth, creating self-reinforcing downward spirals. This dynamic operates independently of underlying project fundamentals, punishing even robust protocols alongside speculative ventures. The phenomenon reflects a structural vulnerability in digital asset markets that persists despite a decade of maturation. Excessive leverage remains the accelerant that turns policy shifts into panic.

Also Read: Markets on edge: AI rally fizzles as crypto plunges below US$2.42 trillion

Sentiment metrics further illustrate the psychological dimension of this retreat. The market-wide fear and greed index plunged to eight, registering extreme fear across participant cohorts. Such readings typically emerge during capitulation phases when retail investors abandon positions after sustained losses. Historically, these moments often coincide with short-term bottoms and also signal prolonged recovery periods ahead. Extreme fear does not reverse instantaneously. It requires sustained positive catalysts to rebuild confidence.

Currently, no such catalyst exists on the immediate horizon. Investors face a rising probability of a US government shutdown to 84 per cent ahead of the February 14 deadline, introducing fiscal uncertainty that compounds concerns about monetary tightening. This dual pressure on both fiscal and monetary fronts creates an unusually constrained environment for risk assets.

Technical structure now determines the near-term trajectory. The US$2.17 trillion market capitalisation represents this year’s low and serves as critical psychological and algorithmic support. A decisive break below this threshold could trigger additional liquidations targeting the 78.6 per cent Fibonacci retracement near US$2.4 trillion.

Current positioning suggests markets may stabilise above the yearly low if macro conditions do not deteriorate further. Any sustained recovery requires reclaiming momentum toward the 38.2 per cent Fibonacci resistance at US$2.86 trillion. This level demands either a dovish pivot from central banks or significant organic capital inflows. Neither scenario appears imminent, given the Fed’s data-dependent stance and persistent institutional caution toward digital assets.

I view this correction as a necessary recalibration rather than a structural breakdown. Crypto markets have expanded dramatically since the previous cycle, attracting capital that entered during periods of abundant liquidity. As monetary conditions normalise, weaker hands exit, concentrating ownership among long-term holders with higher conviction.

This consolidation phase, though painful in the short term, often precedes more sustainable growth trajectories. The current market cap of US$2.3 trillion still reflects substantial institutional adoption compared to prior cycles, suggesting foundational demand remains intact despite tactical withdrawals.

Also Read: Risk assets retreat under macro pressure: Gold, crypto, and tech lead the decline

Tomorrow’s US Consumer Price Index report looms as the next pivotal data point. Should inflation show unexpected moderation, markets might reprice rate cut expectations forward, providing temporary relief. I remain sceptical that one data release will override the Fed’s commitment to ensuring inflation remains anchored.

The central bank has consistently prioritised credibility over market comfort, and recent communications suggest officials welcome some financial tightening to reinforce their anti-inflation resolve. Crypto markets must therefore navigate an extended period of constrained liquidity rather than anticipating imminent policy relief.

The path forward demands discernment between cyclical pressure and secular decline. Digital assets face genuine headwinds from tighter monetary policy, but their underlying utility continues expanding across payments, identity, and programmable finance. The current two per cent drawdown represents a liquidity-driven adjustment within a maturing asset class, not a verdict on blockchain’s long-term viability. Investors who recognise this distinction will view periods of extreme fear not as exit signals but as opportunities to accumulate quality assets at discounted valuations.

Markets ultimately reward patience during liquidity droughts, though the duration of such periods remains unpredictable. For now, preservation of capital and selective positioning offer wiser strategies than either panic selling or aggressive leverage. The US$2.3 trillion market cap reflects a market in transition, shedding speculative excess while retaining its core value proposition for those willing to endure the volatility inherent in technological transformation.

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