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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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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Southeast Asia’s superapp giant has agreed to acquire US-based investing platform Stash at an enterprise value of US$425 million at closing, a deal that will hand Grab a 50.1 per cent stake upfront.

The remaining shares will be acquired over the next three years at fair market value.

The transaction is subject to regulatory approvals and is expected to close in the third quarter of 2026. Payment at closing will be made in cash and stock, with subsequent payments made in cash and/or stock at Grab’s discretion.

Also Read: Grab-Gojek merger talks resurface amid market optimism and regulatory challenges

On paper, it’s an unusual geographic leap for a company that has repeatedly stressed its operational focus on Southeast Asia. In strategy terms, it’s a very Grab-like move: expand capability first, then decide how and where to deploy it.

Why the US and why Stash?

Grab’s entry into the US via Stash is less about planting a flag in New York and more about buying a proven Operating System for mass-market wealth products — one that has already been stress-tested under some of the world’s strictest financial regulations.

Stash sits squarely in a segment Grab has long wanted to deepen in Southeast Asia: consumer fintech that goes beyond payments and credit into wealth-building. The platform serves over one million subscribers and manages more than US$5 billion in assets.

Importantly for Grab’s post-profitability era, Stash runs on a subscription model, recurring revenue that is typically less volatile than transaction-driven income.

Grab also said Stash is adjusted EBITDA and cash flow-positive and has been profitable on that basis since its Series H fundraising round in 2025. Based on current performance, Grab expects Stash to generate more than US$60 million in adjusted EBITDA in the 2028 calendar year. Those numbers matter because they signal something Grab’s investors have been demanding for years: growth that doesn’t set cash on fire.

Anthony Tan, Group CEO and co-founder of Grab, framed the acquisition as both a revenue and capability play: “This acquisition brings more than just recurring, high-margin subscription revenue; we will strengthen Grab’s fintech know-how with Stash’s AI-powered investing app, designed with existing US regulatory requirements at its core.

While we remain operationally focused on Southeast Asia and scaling our regional loanbook, this move reinforces our mission of democratising financial services for everyone.”

Also Read: Wealthtech, insurtech, SaaS fintech are the new hot verticals in Indonesia: AC Ventures report

In plain English, the US is where you learn to build fintech with the safety rails bolted on, and then you bring the playbook home.

The long game: capability transfer, not just country expansion

In the long run, this deal helps Grab in four compounding ways.

1. It diversifies Grab’s fintech earnings. Grab’s financial services push has leaned heavily on lending and payments. Stash adds a different revenue profile: subscription-led, high-margin, and less sensitive to day-to-day consumer spend patterns.
2. It upgrades Grab’s product stack. Instead of building a mass-market investing platform from scratch (and learning the hard way about user education, compliance workflows, and suitability), Grab is acquiring an established machine with an existing customer base and behaviour data.
3. It creates an option for Southeast Asia’s wealth products. Grab said it will support Stash’s US growth while exploring whether to introduce its investing capabilities in Southeast Asia over time. That “over time” is doing work: it implies sequencing and regulatory pragmatism, not a rushed cross-border rollout.
4. It brings in AI-led personal finance engagement, which could become a moat in a region where customer acquisition is expensive and retention is fickle.

What the acquisition reveals about Grab’s global expansion strategy

The structure of the deal is a tell. Grab takes majority control now and then buys the rest over three years. That’s a risk-managed approach to global expansion: secure strategic control, keep founders incentivised, and stage capital deployment while performance and regulatory approvals play out.

It also suggests the superapp’s international growth strategy is shifting from “new geography, same playbook” to “new capability, multiple geographies”. Rather than exporting the superapp model into the US — a market crowded with entrenched consumer platforms — Grab is importing a fintech capability that can strengthen its core Southeast Asian ecosystem.

In other words, Grab is going global selectively: buying assets that can deepen the company’s competitive edge at home, while still capturing upside abroad.

How Stash’s AI could reshape financial services in Southeast Asia

The headline capability here is Stash’s AI Money Coach, designed to provide personalised financial guidance. Stash said interactions are auditable and governed by defined policies and controls, a critical point for any AI tool touching consumer finance.

Since launching in late 2024, Stash says about one in two users have taken a financial action on the same day, with that figure up nearly 40 per cent in 2025. That kind of conversion is not just a nice product metric; it’s a blueprint for changing financial behaviour at scale.

If Grab ultimately adapts similar AI-driven coaching for Southeast Asia, the impact could be significant:

• Lower-cost, always-on guidance for first-time investors who don’t have access to traditional advisors.
• Better financial literacy embedded in the product, rather than as separate, easily ignored content.
• Personalised nudges tied to real behaviour, which can drive saving, investing, and responsible borrowing.
• Regulator-friendly controls via auditable interactions — crucial in markets where AI governance in finance is tightening.

For Grab, which already sits on rich signals from mobility, deliveries, payments, and lending, layering AI financial coaching could turn its ecosystem data into something consumers actually feel day to day: clearer decisions, fewer missteps, and more confidence. That’s how fintech becomes sticky.

How the deal strengthens Grab’s financial performance

Beyond the narrative of “entering the US”, this acquisition is fundamentally a financial architecture upgrade.

• Recurring, high-margin subscription revenue can stabilise Grab’s fintech earnings and improve predictability.
• EBITDA-positive operations reduce the integration burden: Grab is not buying a turnaround story; it’s buying a running engine.
• Cross-ecosystem monetisation potential: if Grab eventually brings investing to Southeast Asia, it can increase ARPU and retention across its user base, while creating more reasons to keep money within the Grab ecosystem.
• A stronger fintech mix: pairing lending (which can be cyclical and risk-sensitive) with wealth and subscription services can smooth performance over time.

The timing is also notable. Grab reported its first full year of net profit in 2025, after years of losses, alongside continued growth in revenue and user engagement. Profitability changes the playbook: it gives Grab more credibility to pursue acquisitions that are strategic, not desperate.

Also Read: Super apps, fintech wallets and mobile payments: Southeast Asia’s next big cyber risk

After closing, Stash will continue operating as an independent brand in the United States under its existing leadership, including co-founders and co-CEOs Brandon Krieg and Ed Robinson, who said: “Grab has a track record of ecosystem-building through harnessing user data and a culture of entrepreneurship that will serve our growth ambitions.

This acquisition gives us the best of both worlds: the capabilities to double down on growth in the US, and the resources of a technology powerhouse to accelerate our vision of personalised, AI-driven financial guidance for millions of people across all parts of their financial lives.”

In Southeast Asia, the subtext is clear: Grab is not abandoning its home turf but it’s importing sharper tools to win it.

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