Kazakhstan is not a country most Southeast Asian founders think about when mapping expansion routes. But Magzhan Madiyev, CEO of Astana Hub, wants to change that fast. In seven years, the organisation has helped transform a country with virtually no venture culture into one where 2,200 companies generate US$2 billion annually, tech exports have grown twentyfold to US$1.14 billion, and the nation’s first AI unicorn, Higgsfield, is now valued at over US$3 billion.

The engine behind this shift is not government largesse; it is a calculated infrastructure of talent pipelines, global accelerators, and regulatory architecture built to attract serious founders and investors.

Also Read: How Big Sky Capital and Astana Hub are helping startups scale across Southeast Asia’s technology ecosystem

With Astana Hub opening a presence in Kuala Lumpur, partnerships deepening with Temasek and Quest Ventures, and interest growing from Singaporean investors, Central Eurasia’s most ambitious tech bet is increasingly looking in Southeast Asia’s direction. Here is what Madiyev had to say.

Below are edited excerpts:

Building a startup ecosystem from scratch in an emerging market is notoriously difficult. What was the single biggest structural barrier Kazakhstan had to overcome?

When Astana Hub was conceived, Kazakhstan’s venture market was virtually non-existent. There was no venture culture, no capital, no critical mass of entrepreneurs, and no founder community. The ecosystem was dominated by companies chasing government contracts or running small e-commerce operations.

We had to fight on multiple fronts simultaneously: changing the mindset of specialists, creating innovation-friendly legislation, and overcoming government bureaucracy. None of it was quick.

The results speak for themselves: Almaty has entered the global TOP10 Rising Star ecosystems according to Dealroom’s 2026 index. Astana Hub now hosts over 2,200 companies generating roughly US$2 billion annually, and Kazakhstan’s tech exports surpassed US$1.14 billion in 2025.

Tax incentives can create artificial ecosystems that collapse once the support dries up. How are you ensuring startups are genuinely market-ready?

Every successful tech ecosystem, be it Silicon Valley, Israel, or Singapore, has had government backing at some point. The question is how you deploy it.

We did not ask the state to pour billions into the industry. We asked only for tax incentives and introduced a 1 per cent revenue contribution from participants to reinvest back into the ecosystem. That, combined with full infrastructure, such as education, acceleration, regional access points, and  pathways to Silicon Valley, meant all parties were satisfied without creating dangerous dependency.

The incentives will continue, but they will increasingly push participants to build globally competitive products. If we do not develop our own champions, our economy simply becomes a market for international vendors who are often not even fully taxed here.

How many startups have completed your programmes, and what does the success rate actually look like?

Astana Hub is not a single accelerator; it is an ecosystem of more than 50 programmes serving audiences from school students to institutional investors. Metrics vary accordingly.

Also Read: Big Tech’s efficiency paradox: Record profits, record layoffs

For startup-specific programmes, around 40 per cent of graduates continue building and scaling after completion. Across the broader ecosystem, participant companies have collectively attracted more than US$945 million in investment and generated US$4.9 billion in cumulative revenue over seven years, with a growing footprint in Central Asia, the Middle East, Europe, and North America.

What does the alem.ai Center offer that a startup could not get from Singapore, Dubai, or London?

The alem.ai centre is a vertically integrated AI ecosystem under one roof, something we believe is unique globally. It runs a continuous talent pipeline from children aged 12, through adult re-skilling, to dedicated founder tracks and Big Tech lab placements, all in one building.

That building is also an iconic structure in Central Asia, a deliberate signal from the country’s leadership about where Kazakhstan is placing its long-term bet. In August, we will host part of the International Olympiad in Artificial Intelligence, welcoming national teams from over 60 countries.

Are graduates staying in Kazakhstan or leaving for higher-paying markets?

We do not frame this as brain drain; the world is becoming borderless, and what matters is building a strong community. We actively help founders go abroad to attract investment and launch products in the US, the UAE, and China. Most of them still build their teams and R&D centres back in Kazakhstan.

The most powerful argument for staying is Higgsfield, Kazakhstan’s first AI unicorn, now valued at over US$3 billion, built in Almaty with a team of 300 engineers whose average age is 25. You do not need to leave to build something world-class.

Which international partners have delivered real outcomes, not just signed MOUs?

Several partnerships have moved well beyond paper.

With Google, we launched the Silkway Accelerator — seven batches in, with graduates now carrying a combined valuation exceeding US$500 million.

With OpenAI and Stanford, we ran the AI Leaders programme across nine countries, reaching 800-plus executives.

With Telegram, we are launching the AI Olympiad and ICPC Bootcamp, and the company is opening an AI Lab at alem.ai.

With Draper University and Alchemist Accelerator, Tim Draper has personally invested US$2 million into Kazakhstani startups.

With Apple, a training centre is operating within the ecosystem.

Is there a genuine pipeline of Southeast Asian investors and startups looking at Kazakhstan?

Yes, though the potential has not been fully realised yet. We are opening a presence in Kuala Lumpur, working with Temasek and Quest Ventures, and seeing growing interest from Singaporean investors.

The roadblock is not a lack of opportunity, but a lack of awareness. Southeast Asia is very familiar with its own corridor; Central Eurasia remains undiscovered despite its talent base and fast-growing digital economy. That is precisely what we are working to fix.

Kazakhstan borders both Russia and China. How do you navigate that geopolitically when attracting Western partners?

Kazakhstan is a neutral country by design and we have turned that into a strategic advantage. After 2022, more than 500 global tech companies relocated here from Russia, bringing significant developer talent. From China, we adopt hardware and industrial technology. From the US, we attract capital and infrastructure, including NVIDIA chips. Our most active international hub is in Silicon Valley, where resident companies have attracted over US$300 million in investment.

Also Read: Is the AI industry profitable? Yes, just not where you’re looking

It is a puzzle only a country with Kazakhstan’s geography, neutrality, and foreign policy could assemble.

Venture capital in Central Asia is thin. Are there any homegrown VC funds of meaningful scale?

Kazakhstan leads the venture market across Central Asia and the CIS. The recently established fund of funds, Alem Capital Management, has a first fund of US$100 million, 70 per cent from private capital, with a target of attracting US$1 billion in venture investment over five years. Active local investors include Freedom Holding, MOST Ventures, Big Sky, Astana Hub Ventures, MA7 Ventures, and the Silkroad Angels Club.

What sectors are producing Kazakhstan’s most promising startups, and is it market demand or government money driving them?

AI, edutech, and fintech, and they are driven by talent, not government priorities. That distinction matters. Startups chasing government contracts stay domestic. Kazakhstan’s market is simply not large enough to produce valuations above US$100 million on its own. Unicorns are built for global markets. Higgsfield proves that.

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Thailand-founded enterprise AI company Amity has opened a Singapore office and AI Research & Application Center (ARAC), placing the city-state at the centre of its regional expansion, product development, and planned path to a public listing in 2027.

The move follows Amity’s US$100 million Series D round, led by EDBI, the investment arm operating under SG Growth Capital, alongside Asia Partners and SMDV. Existing and new backers, including CMLIM Capital, also participated.

Also Read: Amity’s US$100M raise signals Southeast Asia’s AI coming of age

Amity said the Singapore hub will serve as its Southeast Asia regional headquarters and the global base for its AI research capabilities. The company is targeting US$200 million in annualised revenue by the end of 2026, after surpassing US$100 million in annualised revenue in 2025. It claims to have grown more than tenfold since 2022.

For Amity, Singapore offers more than a prestige address. It gives the company proximity to regional enterprise buyers, access to AI talent, a recognised regulatory environment, and a launchpad for acquisitions across Southeast Asia and Europe. The company is deploying its latest capital across three areas: accelerating agentic AI development, pursuing strategic M&A, and hiring engineering and commercial talent in Singapore.

Why Singapore matters for Amity

Amity’s bet comes as Southeast Asian enterprises move from AI experimentation to deployment. Banks, telcos, retailers, healthcare groups, logistics players, and large consumer companies are under pressure to automate customer engagement, extract more value from data, and reduce operational complexity across multilingual markets.

This is where Amity wants to position itself. The company’s ARAC will focus on vertical AI models trained on industry-specific data rather than generic datasets. Its current focus areas include agentic AI — systems that can execute end-to-end business processes with limited human intervention — and models tailored for sectors such as retail, telecommunications, and services.

The company argues that Asian enterprises need AI products built for fragmented channels, language diversity, and local business workflows. That is a valid pain point in Southeast Asia, where a single enterprise may operate across markets with different languages, messaging platforms, payment habits, regulations, and consumer behaviours.

“When we started Amity, we believed that Asian enterprises deserved AI that is built for their reality, not tools designed for other markets and retrofitted for ours,” said Korawad Chearavanont, Executive Chairman and Founder of Amity. “Singapore is where that belief comes to life.”

That statement also reveals the company’s broader ambition: to become a regional enterprise AI platform rather than just another SaaS vendor.

The market opportunity

Amity is entering a fast-growing but crowded market. Enterprise AI, customer experience automation, communications analytics, and vertical AI software are attracting capital globally, as companies search for tools that can move beyond chatbots and dashboards into workflow execution.

Global estimates vary, but the enterprise AI market is widely projected to become a multi-hundred-billion-dollar opportunity by the end of the decade. For Southeast Asia specifically, the prize is broader than software revenue alone. A widely cited Kearney estimate has suggested that AI could contribute up to US$1 trillion to Southeast Asia’s economy by 2030.

Also Read: AI user roles surge as Singapore pivots from specialist to mainstream hires

That economic upside is drawing both global players and regional challengers. Microsoft, Google, AWS, Salesforce, OpenAI-linked partners, and a wave of specialised AI startups are all targeting enterprise budgets. Amity’s differentiation will depend on whether it can turn local market knowledge, vertical datasets, and acquisitions into products that large companies are willing to buy at scale.

The company already has some distribution. It claims its platforms serve over 10 million monthly active users across more than 20,000 organisations in over 20 countries. Its portfolio includes Amity Solutions, Tollring, EGG Digital, Amity Accentix, and Amity-Nordstar, covering customer experience, analytics, communications, and voice capabilities.

More recently, Tollring entered into a definitive agreement to acquire UK-based Code Software. If completed, the transaction would expand Amity’s presence across the UK, US, EU, and ANZ markets, especially in communications analytics and recording.

What Southeast Asia could gain

The Singapore hub could bring several benefits to the region if Amity delivers on its plans. First, it could create higher-value AI roles locally. Amity plans to hire across AI research, engineering, and go-to-market functions in Singapore, with up to 60 roles expected over the next three years.

Second, the move could deepen Southeast Asia’s enterprise AI capabilities. Much of the region still depends on imported software stacks designed primarily for Western enterprises. A regional AI player building for Asian languages, regulations, and operating environments could help narrow that gap.

Third, Amity’s Singapore base could strengthen the region’s startup exit and scaling narrative. Southeast Asia has produced strong consumer internet and fintech companies, but fewer enterprise software companies with global ambitions. If Amity reaches its revenue target and lists publicly in 2027, it could become a reference point for the region’s AI and SaaS ecosystem.

Fourth, Amity’s acquisition strategy may create liquidity and growth options for smaller software companies in Southeast Asia. The company has said it will pursue M&A targeting high-potential software businesses in Europe and Southeast Asia. For regional founders building niche B2B software, this could open another route beyond venture funding or slow organic growth.

A tougher phase begins

The challenge is execution. Raising the largest AI/ML round in Southeast Asia for Q1 2026 gives Amity a strong balance sheet, but enterprise AI is not an easy market. Sales cycles are long, integration is complex, and buyers are increasingly sceptical of AI tools that do not produce measurable outcomes.

Agentic AI also carries operational and regulatory risks. Enterprises will need systems that are secure, auditable, and reliable across markets. Singapore’s policy environment may help Amity build credibility here, especially as the country pushes its refreshed National AI Strategy and National AI Council.

EDBI’s backing is also strategically significant. For Singapore, attracting Amity reinforces its ambition to be a regional AI hub not only for multinational labs but also for Southeast Asian technology companies scaling outward.

“Singapore provides a strong foundation for companies looking to develop and scale enterprise AI, with access to deep talent, a trusted and collaborative innovation ecosystem, and strong regional connectivity,” said Yeung Chia Li, Senior Partner at EDBI.

Also Read: Southeast Asia’s AI blind spot is getting bigger

For Amity, Singapore is now the test bed for a bigger question: can a Southeast Asian enterprise AI company build products that compete not only regionally, but globally? If it can, the company’s 2027 IPO plan may be more than a fundraising milestone. It could mark one of the region’s first major enterprise AI scale-up stories.

The post Thailand-founded Amity sets up Singapore AI hub, eyes 2027 IPO appeared first on e27.

There’s a moment in the film Who Am I? where Jackie Chan’s character wakes up with amnesia—no name, no rank, and no recallable past. He’s still capable. Still acting. Still surviving. But the story that once made his actions feel like they belonged to him is gone.

The film keeps circling a single question: Who am I?

But what it stages is something more unsettling. Identity is not something waiting intact beneath the surface. It is something that must be reconstructed when the context that once confirmed it falls away.

Amnesia, in that sense, is not just loss of memory. It is forced exposure to the question of the self.

And increasingly, this is no longer just a cinematic idea.

It is becoming a condition of the age.

Not amnesia, but displacement

The arrival of AI does not erase identity. Memory remains intact. You still know your history, your role, your credentials, and your past achievements. Nothing is removed.

And yet something subtle breaks.

The systems that once stabilised identity—output, expertise, measurable competence—no longer function as reliable mirrors of value. AI can draft cleaner, analyse faster, synthesise broadly, and execute tasks once considered uniquely human markers of capability.

This creates a strange condition: we haven’t forgotten who we are, but we’re losing certainty about what our old markers actually mean. Identity becomes visible, but less anchored. Present, but less confirmed by the world.

It’s not amnesia.

It is a displacement of meaning, an alienation of self.

The identity scaffolding was never just personal

To understand why this feels destabilising, we have to recognise a hard truth: modern identity was never purely innate.

For much of modern history, identity has been socially manufactured through work, a social construct. Thinkers from Marx to Goffman all pointed to the same thread: we don’t just do work. Work tells society who we are—and over time, we inhabit that reflection.

Profession becomes identity. Output becomes a signal. Competence becomes selfhood.

This is the scaffolding AI is now quietly dismantling.

Also Read: What I tell my kids to be able to thrive in the age of AI

When the mirror cracks: Two paths forward

As AI absorbs cognitive labour—coding, writing, analysis, even strategic drafting—the exclusivity of these skills flattens. What once differentiated us becomes abundant.

The loss is double: it’s not just about shifting job descriptions, but about losing a primary anchor of self-worth.

When intelligence is no longer a reliable differentiator, the question changes: What exactly am I expressing when I say “this is what I do”?

When work stops functioning as a stable mirror of selfhood, identity does not disappear. It loses external confirmation.

In moments of systemic disruption, human adaptation rarely moves in one direction. It splits in multiple ways, but generally falls into two dominant forms of responses:

• Compression into optimisation: If intelligence and labour become more machine-readable, humans adapt by becoming highly efficient nodes in a larger wheel. It’s rational. It’s adaptive. But it’s also narrowing.
• Expansion into interpretation: If machines take over execution, what remains human is judgment, framing, and meaning-making. Identity shifts away from output and toward intention: what problems are worth solving, how they’re defined, what gets ignored, and what actually matters.

This isn’t a binary choice. It’s a tension—closer to a Yin–Yang dynamic than a linear progression.

Both emerge at the same time. Neither disappears. The question is not which exists, but which becomes dominant in different contexts and individuals.

AI as a mirror: The self becomes visible

There is a third layer that muddles the id.

AI is not only a displacer of identity. It is also a mirror.

What you get back from AI is shaped by how you think into it—how you frame prompts, what assumptions you carry, what you refine, and what you repeatedly return to. AI doesn’t just extend capability; it reflects cognition.

It reveals the structure of you.

Not who you are in a fixed sense, but how your thinking is organised in real time.

As external validation weakens and internal reflection becomes more visible, identity shifts from something assigned by roles to something observable in patterns of attention.

You begin to see yourself not as a position, but as a way of engaging with the world.

Also Read: Bite-sized innovation: A practical path for SMEs to sustain growth

Liberation through expansion

There is a quieter implication here—one that is easy to miss.

AI not only displace identity structures but also reflects cognitive patterns. It also collapses the barriers between intellectual domains. Plato, poetry, physics, politics, programming—fields that once required years of initiation, institutional access, or rigid disciplinary boundaries—now become fluid, conversational spaces.

• Philosophy: You can question your own self-attachment in dialogue with Zhuangzi: “Now I do not know whether I was then a man dreaming I was a butterfly, or whether I am now a butterfly dreaming I am a man.”
• Physics: You can probe the limits of reality by engaging Niels Bohr on quantum superposition: “Everything we call real is made of things that cannot be regarded as real.”
• Politics: You can confront the fragmentation of social identity through Friedrich Nietzsche—recognising that when AI hyper-personalises your worldview, it constructs a digital tribe of one, isolating identity from the native cultural fabric.
• Programming: You can visualise recursive self-awareness through a simple Python loop—an architecture that mirrors how identity continuously reflects and redefines itself:

def identity(input_self):
    # AI mirrors human thought, which mirrors AI output
    reflection = f"AI reflects: {input_self}"
    print(reflection)
    return identity(reflection)  # The endless loop of self-definition

• Poetry: You can interrogate your own performativity by stepping onto Shakespeare’s stage: “All the world’s a stage, / And all the men and women merely players.” As AI automates the script of daily labour, we are no longer confined to being “merely players” in predefined roles—forcing a more difficult question: who are you when the performance falls away? If these disciplines ignite curiosity, the exploration does not stop there.
• Psychology: You can turn inward with Carl Jung: “Until you make the unconscious conscious, it will direct your life, and you will call it fate.” Because the cost of entry has collapsed, the outcome is not simply frictionless access to information. It is access to entirely new modes of thinking. In this sense, the world does not just expand. It becomes exactly as large as your willingness and ability to move across it.

Deconstructing the shell, eeconstructing the self

This creates a quieter awakening.

When identity is no longer defined primarily by output, it becomes harder to outsource selfhood to systems of performance. What remains is not absence, but exposure.

Exposure to how attention moves. How curiosity unfolds. How judgment forms. How questions are shaped—and reshaped.

Identity begins to shift—from what you produce to how you engage.

This is not comfortable.

But it is clarifying.

Because it reveals something long obscured by the apparent stability of roles: identity was never something we simply possessed. It was something continuously negotiated through systems that reflected us back to ourselves.

This is where the self begins to awaken—and that is larger than AI.

In other words, identity does not emerge from static labels, but from dynamic interaction—from the ongoing “ping” between self and world.

Not a title. Not a role. But a pattern of engagement with the universe itself.

Also Read: Workers sprint ahead of bosses in AI adoption in Singapore, exposing a transformation gap

The one question AI cannot answer

There is a paradox at the centre of all this.

AI can simulate reasoning. It can generate language. It can approximate styles, arguments, and even forms of creativity.

But there is one question it cannot answer for you.

Who are you?

Not as a profile. Not as a dataset. Not as an aggregation of outputs.

If work no longer defines identity, and intelligence is no longer uniquely human, then “Who am I?” stops being a philosophical abstraction.

Cogito, ergo sum?

The only role you cannot outsource

In the end, identity becomes something like a film that no Generative AI can recreate.

A narrative without a pre-trained model. A story without a dataset.

You are not the prompt. You are not the output. You are the one who must live the sequence.

You are the main actor. You are the scriptwriter. You are the only director across every scene.

Ultimately, “Who am I?” means synthesising your own humanness together, frame by frame.

—

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When I was 12, I walked around my uncle’s warehouse in the Netherlands. He’d started a jewellery import business and grown it into a big business. I stood there thinking: he started with nothing and built all of this, that’s what I want to do!

I also noticed the Porsche. And the house. I wanted those too. (I gave up on the Porsche a while ago.)

That wasn’t a career decision. I didn’t weigh the pros and cons of entrepreneurship versus getting a job. I didn’t have the vocabulary for any of that. Something clicked, standing in that warehouse, and it never unclicked.

I meet people every week who want to start a company. They’ve done the research. They’ve read the books. Some of them have compared the risk profile of founding versus staying in their corporate job. A few have actual spreadsheets.

If you need a spreadsheet to decide whether to start a company, you probably shouldn’t start one.

I know how that sounds. But I’ve watched people try this for 25 years, and the pattern holds. The founders who survive the first two years (the ones still standing when the money runs out, the co-founder leaves, the product doesn’t work and needs to be rebuilt from nothing) almost never chose this the way you choose an MBA program. They chose it the way you choose breathing. They couldn’t not do it.

Also Read: The founder’s labyrinth: Why the US$2T climate finance industry is failing ‘atoms’ in SEA

Right now, founding looks like a career option. AI tools let one person build a product in a weekend. Capital is around. The stories are everywhere. So people jump. They leave a corporate job, register a company, start showing up at startup events. For a while, it feels like being a founder.

Then the other stuff starts. The financial spreadsheets you hate making. The contracts you can’t afford to get wrong. The client who’s unhappy and threatens to sue you. The 19th pitch that doesn’t land. The employee who wants more salary and 20 days off.

Corporate life trains you to be a specialist. You get good at one thing, inside a structure someone else built. Founding is the opposite. You do everything nobody else wants to do, for as long as it takes, with no certainty any of it will work. Most people from corporates come with the exact opposite preparation for this.

That’s where the itch matters. When all of it hits you at once (and it will), the only thing keeping you in the chair is that you can’t imagine sitting anywhere else.

If you’re weighing whether to start a company the same way you’d weigh a job offer, you’re in the wrong frame. The people who build things that last didn’t weigh it. They just started. Usually before they were ready. Usually before anyone around them thought it was a good idea. And they kept going, no matter what.

The post If you need a spreadsheet to decide whether to start a company, you probably shouldn’t start one appeared first on e27.

Modern life has become deeply optimised.

There are apps to improve our focus, watches to track our sleep, systems to organise our mornings and endless advice on how to squeeze more output into the same 24 hours. Social media feeds are filled with productivity routines, side hustle culture and carefully engineered lifestyles designed to maximise performance.

And yet, many people feel more emotionally exhausted than ever.

Not necessarily because they lack ambition, but because optimisation itself has quietly become a full-time mindset.

For years, modern work culture has operated on the assumption that the solution to overwhelm is better efficiency. Better systems. Better routines. Better time management. More automation. More hacks.

But increasingly, many people are no longer looking for ways to do more.

They are looking for ways to live that actually feel sustainable.

Productivity culture expanded beyond work

What began as workplace optimisation slowly spread into every corner of life.

Careers are no longer enough on their own. People are encouraged to build personal brands, monetise hobbies, maintain online visibility and continuously improve themselves professionally and personally. Even activities that were once considered leisure now come with subtle pressure to become productive.

Exercise becomes performance tracking. Reading becomes self-improvement. Vacations become content opportunities. Social media becomes networking. Hobbies become side hustles.

Life itself starts to feel operationalised.

The result is that many people no longer feel fully “off”, even during their downtime. Notifications continue. Messages continue. The mental tabs remain open.

The rise of always-on work culture has also blurred the boundaries between productivity and recovery, contributing to rising levels of what many professionals now describe as digital burnout.

At some point, the issue stops being about workload alone. It becomes about the inability to psychologically disengage.

Also Read: The AI productivity gurus are bluffing too

The problem is not ambition, it is an unsustainable ambition

This distinction matters.

Most people still want meaningful careers, financial stability (or freedom, which explains the popularity of creating passive income streams) and opportunities to grow. Founders still want to build successful businesses. Professionals still want purpose and progress.

The issue is not that people suddenly want less from life.

The issue is that many modern systems reward constant optimisation without acknowledging human limits.

In many industries today, being busy has become intertwined with being valuable. People are expected to move quickly, stay visible, adapt constantly and remain mentally available at all times. Even rest is often framed as recovery for more productivity later.

But human beings are not designed to remain perpetually “on”.

Even high performers eventually experience the effects of fragmented attention, continuous responsiveness and prolonged mental stimulation. And unlike traditional burnout, modern exhaustion is often quieter and more difficult to identify because it accumulates gradually.

For many professionals, the issue is no longer just long hours, but prolonged exposure to fragmented attention, constant responsiveness and elevated stress hormones throughout the day.

The irony is that many modern workers are not necessarily lacking productivity tools. They are lacking meaningful opportunities for psychological recovery.

People are not just seeking rest, they are seeking permission to be “off”

One of the most overlooked aspects of modern productivity culture is the guilt people increasingly feel around doing nothing.

There is now subtle pressure to optimise almost every waking hour. If someone is resting, they should be resting productively. If they are scrolling social media, it should somehow lead to inspiration, learning or monetisation. Even hobbies increasingly come with pressure to become content, side income or personal branding opportunities.

But increasingly, what many people actually want is far simpler.

They want time to:

• Spend time with family without multitasking
• Enjoy hobbies whenever they want
• Rest without guilt
• Be mentally unreachable for a while
• Experience moments that are not constantly interrupted by notifications, deadlines or content demands

In other words, they want enough emotional and mental space left to actually enjoy the lives they are working so hard to build.

And that desire is not laziness. It is a response to years of overstimulation and perpetual optimisation.

Sustainable living is not about doing less, it is about designing better priorities

The answer is probably not another productivity framework.

Nor is it abandoning ambition altogether.

If anything, sustainable ambition may become one of the most important skills modern professionals and founders need to develop.

Also Read: The productivity pivot the Philippines can’t delay

That means recognising:

• Not every opportunity deserves a yes
• Not every platform deserves constant attention
• Not every hobby needs monetising
• Not every hour needs to be maximised
• Not every moment of rest needs justification

It also means organisations may need to rethink what sustainable performance actually looks like.

Many companies still reward responsiveness over deep thinking, visibility over focus and busyness over meaningful outcomes. But constant interruption and overstimulation eventually reduce creativity, emotional resilience and long-term decision-making quality.

The businesses that adapt best in the future may not simply be the ones moving fastest.

They may be the ones capable of building cultures where people can sustain high-quality thinking and meaningful work over time without permanently operating in survival mode.

Because people are not searching for another productivity hack but are searching for lives that still feel emotionally livable.

Perhaps the real luxury now is spaciousness

For years, success has often been associated with acceleration: faster growth, faster scaling, faster output, faster responses.

But perhaps the next real luxury is not speed.

Perhaps it is spaciousness.

The ability to think slowly sometimes. To be unreachable occasionally. To spend time with people you love without simultaneously checking notifications or replying to messages. To enjoy moments that are not being turned into content, strategy or productivity metrics.

Technology will continue evolving. Work will continue changing. Economic pressures are unlikely to disappear anytime soon.

But eventually, both individuals and companies may need to ask a more uncomfortable question:

What is the point of building successful lives if we are too mentally exhausted to actually experience them?

Because perhaps the future advantage will not belong to the people who can optimise themselves endlessly.

It may belong to those who can build ways of working and living that remain psychologically sustainable over the years, not just being productive for quarters.

—

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Southeast Asia has never followed a single digital playbook. A customer in Thailand may expect to interact with a brand through LINE. A shopper in Indonesia or Malaysia may prefer WhatsApp. In Vietnam, Zalo remains deeply embedded in daily communication. In the Philippines, Messenger continues to shape how people connect, discover, and transact.

This makes the region different from many Western markets, where customer journeys are often designed around websites, email, apps, and scheduled support hours. In Southeast Asia, the customer journey is increasingly conversational, mobile-first, and always on.

That is why AI agents will not simply become another customer service tool. They will reshape how brands design the entire customer journey, from discovery and onboarding to service, retention, and reactivation.

Southeast Asia is already a messaging-first region

The case for AI agents starts with user behaviour.

Southeast Asia has high levels of internet and social media adoption. We Are Social’s Digital 2025 Singapore report notes that internet adoption across Southeast Asia reached 78.2 percent, while social media use stood at 61.5 percent of the total population. In Singapore, 92.4 percent of internet users are active on social media.

Country-level data shows how deeply digital behavior is embedded across the region. In Thailand DataReportal found that there were 65.4 million internet users at the start of 2025, with internet penetration at 91.2 percent. The country also had 51 million social media user identities, equal to 71.1 percent of the population. LINE reported 56 million monthly active users in Thailand, equivalent to 78.2 percent of the total population and 85.7 percent of internet users.

In Vietnam, DataReportal recorded 79.8 million internet users and 76.2 million social media user identities in January 2025. In the Philippines, there were 97.5 million internet users and 90.8 million social media user identities at the start of 2025. Singapore, meanwhile, had 5.61 million internet users and 5.16 million social media user identities, equal to 95.8 percent and 88.2 percent of the population respectively.

These numbers point to a simple reality: brands in Southeast Asia are not trying to bring customers online. Customers are already online. The harder challenge is meeting them in the channels where they already spend time, in the language they prefer, and at the moment they need help.

Also Read: The new cybersecurity threat: Why AI agents are the wild card in enterprise security

Customers now expect always-on engagement

The traditional customer journey assumes a certain rhythm. A customer sees an ad, visits a website, submits a form, receives an email, waits for a reply, and eventually speaks to a salesperson or support agent.

That journey is becoming too slow for Southeast Asia’s mobile-first consumers.

In messaging-first markets, customers often expect brands to behave more like people in their contact list. They want to ask a question, get a response, clarify a detail, change a booking, check delivery status, or complete a transaction without switching channels. If a brand takes hours to respond, the customer can easily move to another seller, another platform, or another app.

This is where AI agents change the equation.

Unlike traditional chatbots, which are usually limited to fixed menus and scripted answers, AI agents can understand intent, retrieve context, take action, and escalate when needed. They can support customers outside office hours, handle repetitive questions, personalise recommendations, and help human teams focus on more complex or sensitive interactions.

Globally, companies are already moving in this direction. Zendesk’s 2025 CX Trends report found that consumers increasingly expect AI interactions to feel more human, personalised, and engaging. The report also describes a widening gap between companies that embrace AI in customer experience and those that remain tied to traditional support models.

For Southeast Asia, the opportunity is even more urgent because customer journeys are fragmented across countries, languages, channels, and behaviours.

Local behaviour matters more than global templates

One mistake brands often make in Southeast Asia is assuming that a customer engagement strategy built for the US or Europe can simply be localised with translation.

But localisation is not only about language. It is also about behaviour.

A customer in Bangkok may be comfortable using LINE for brand updates, payments, service reminders, and support. A customer in Jakarta may discover a product through social content, ask questions through WhatsApp, and expect the conversation to continue with a human seller. A customer in Ho Chi Minh City may use local platforms as part of their daily routine in ways that do not map neatly to Western customer journey models.

This means brands need AI agents that understand context, not just words. They need to know when to be proactive, when to wait, when to escalate, and when a conversation requires local nuance.

For example, an AI agent for a bank in Southeast Asia should not only answer questions about loan eligibility. It should be able to guide a customer through documentation, remind them of missing steps, hand off to a human agent when trust is needed, and operate across local languages and channels.

For e-commerce, an AI agent should not only track orders. It should help customers compare products, ask preference-based questions, recover abandoned carts, handle delivery issues, and continue the conversation after the purchase.

The winning brands will be those that design AI agents around local journeys rather than forcing customers into imported workflows.

Also Read: Why you should be hiring humans when others are hiring AI agents

AI agents can connect fragmented customer journeys

Southeast Asia’s digital economy is full of fragmented touchpoints. Customers move between ads, marketplaces, super apps, social platforms, messaging apps, call centres, and offline interactions. For businesses, this creates a major challenge: the customer journey is often distributed across systems that do not talk to one another.

AI agents can become the connective layer.

When integrated properly, an AI agent can recognise a returning customer, understand past interactions, continue a conversation across channels, and recommend the next best action. This moves customer engagement from reactive support to proactive journey orchestration.

This is especially important in Southeast Asia, where businesses often operate across multiple countries with different languages, channels, and service expectations. Agora’s 2025 partnership with WIZ.AI, for example, focused on enterprise-ready AI agent solutions with multilingual support and contextual understanding for call centres.

The broader shift is also being recognised by global consulting firms. BCG argues that AI-powered agents will enable brands to deliver more personal customer interactions at lower cost-to-serve, making customer experience less tedious for consumers and more efficient for businesses.

Human agents will still matter, but their role will change

The rise of AI agents does not mean human teams will disappear. In Southeast Asia, where trust, empathy, and relationship-building remain important, human support will continue to matter.

What will change is the role of human agents.

Instead of spending most of their time answering repetitive questions, human teams can focus on high-value conversations: complex complaints, sensitive financial decisions, healthcare concerns, enterprise sales, VIP customers, or moments where emotional intelligence is needed.

AI agents can handle the first layer of engagement, collect information, summarise context, and route the customer to the right human expert. This makes the handoff faster and more informed.

For customers, the experience becomes smoother. They no longer need to repeat the same issue multiple times. For businesses, teams can scale support without sacrificing quality.

The next customer journey will be conversational

In Southeast Asia, AI agents will reshape customer journeys not because the technology is new, but because it fits how consumers already behave.

The region’s customers are mobile-first, messaging-first, and increasingly unwilling to wait for support that follows office hours or rigid workflows. For brands, this creates a clear opportunity: use AI agents not as a chatbot upgrade, but as the connective layer between discovery, service, sales, and retention.

The companies that win will be those that build around local behaviour. In Southeast Asia, a better customer experience will come from conversations that are instant, contextual, multilingual, and easy to continue.

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The product labelled MVP is usually not a minimum. It is a compromise between anxiety, ambition, internal politics, and the hope that one release can settle more questions than it ever will. By the time it reaches execution, the so-called minimum version has absorbed edge cases, stakeholder comfort features, reporting needs, admin controls, future-proofing logic, and enough polish to make the team feel less exposed when customers finally see it.

What gets shipped is not a minimum. It is a fear-managed version of the idea.

Why teams keep getting this wrong

Most teams do not overscope because they are careless. They overscope because their definition of minimum is quietly corrupted.

Product thinks minimum should still feel strategically credible. Engineering thinks that the minimum should not create avoidable technical debt. Design thinks minimum should not feel incomplete to users. Sales thinks the minimum should not be embarrassing in front of prospects. Leadership thinks minimum should still look meaningful enough to justify the bet.

Each of those instincts is understandable. Together, they are how small ideas become large commitments.

The core problem is that minimum gets interpreted through internal discomfort rather than market need. Teams are not asking, “What is the smallest thing that can teach us whether this matters?” They are asking, “What is the smallest thing we can ship without feeling exposed?”

Those are very different questions.

The first creates learning. The second creates bulk.

Minimum is not about feature count

One reason the MVP discussion gets so muddled is that people talk about it as a scope exercise. They reduce the challenge to cutting screens, dropping workflows, or trimming integrations. That is part of the work, but not the heart of it.

A real minimum is not defined by how little you build. It is defined by what must be true for the test to mean something.

Also Read: How a cross-border tech team built a fintech MVP in 3 months

That means the minimum should be tied to evidence, not volume. If your product idea depends on customers trusting the output, then credibility is part of the minimum. If your concept relies on repeated use, then enough continuity for a second use matters more than broad functionality. If the whole point is to prove willingness to adopt, then the minimum may sit less in the interface and more in whether the user can actually get to value without excessive explanation.

This is where many startup teams lose discipline. They cut obvious features while keeping hidden complexity. They remove visible scope but preserve all the machinery underneath it. They tell themselves the product is lean because the roadmap looks shorter, even though the build still assumes full workflow coherence, broad edge case support, and an operational model fit for a much more mature product.

The result is a product that looks smaller on paper but behaves like a much bigger bet.

What real minimums actually look like

A useful way to think about minimum is to stop treating it as one thing. In practice, there are several minimums that matter, and confusing them is how teams get into trouble.

The first is the minimum value. What is the smallest meaningful improvement in the user’s world that they would actually notice and care about? Not admired in a demo. Not politely praised in feedback. Actually care about enough to change behaviour.

The second is the minimum proof. What is the least you need to observe to know whether the problem is real, the proposition is resonating, or the workflow has legs? This is often much smaller than the team wants to believe. Most early products do not fail because they lacked feature breadth. They fail because nobody got honest about what evidence would count as real progress.

The third is the minimum credibility. This is where product belief often becomes unhelpful. Some ideas can survive with a rough edge. Others cannot. If you are asking a user to trust a recommendation, a financial action, a workflow decision, or something that touches their customers, quality and coherence may be part of the minimum from day one. Not because you are polishing for vanity, but because, without credibility, the test itself becomes false.

The fourth is the minimum operability. Can the thing actually be supported, explained, monitored, and recovered when it breaks? Startups often ignore this because it feels too early. Then they wonder why the product produces noisy feedback that is impossible to interpret. If usage fails because onboarding is confusing, support is absent, or obvious issues cannot be diagnosed, you are not testing the product cleanly. You are testing a muddled experience.

Real minimums sit at the intersection of those four questions. Anything beyond them deserves much more suspicion than most teams apply.

The hidden reason MVPs grow

There is another force at work here, and product leaders need to name it more honestly. Large MVPs are often a way of buying emotional reassurance.

A bigger first release lets more people feel covered. It reduces the number of awkward questions before launch. It creates the impression of momentum. It allows teams to believe that if adoption is weak, the issue must be go-to-market execution rather than the shape of the product itself.

In other words, size becomes a defence against ambiguity.

Also Read: Founders, stop listening to mentors who tell you to build an MVP

When an MVP is too big, you are not managing risk; you are relocating it

The usual argument for a broader MVP is risk reduction. Teams say they need more before launch because they want to avoid customer disappointment, reduce rework, or make the proposition more complete.

Sometimes that is valid. Often it is a sleight of hand.

What they are really doing is shifting risk from the market to the build. Instead of risking that customers might not engage with a thinner offer, they risk extra months of effort, deeper architectural commitment, noisier prioritisation, and greater internal attachment to the solution. The commercial uncertainty has not disappeared. It has simply been wrapped inside a larger delivery motion.

That is a dangerous trade because it creates the illusion of progress while increasing the cost of being wrong.

The better question is not “what can we cut?”

Most teams approach MVP scoping in the wrong direction. They start with the full imagined product, then ask what can be removed. That approach almost always leaves too much intact because the emotional centre of gravity remains with the larger vision.

A better way is to start with the evidence you need and work forward from there.

• What are we trying to learn?
• What user behaviour would count as real traction?
• What has to exist for that behaviour to happen credibly?
• What can fail quietly without invalidating the test?
• What is the narrowest path to value we can support properly?

These questions change the conversation. They force the team to design for proof rather than aspiration. They also make it easier to identify fake necessities, which are features that sound important only because the team has already become attached to the more complete story.

This is not just a scoping technique. It is a discipline of strategic honesty.

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The question “Is the AI industry profitable?” has two correct answers, and they point in opposite directions. At the chip-design and leading-edge-fabrication layers, AI is already one of the most profitable industries in commercial history. At the layers the market calls “AI”, frontier model labs, GPU-rental builders, and most applications built on someone else’s model, it is among the most loss-making activities ever financed.

Both statements are true. The investment question sits in the distance between them.

The reason is mechanical. Across the AI stack, the cost of intelligence is falling rapidly. But a falling cost only becomes profit somewhere. Whether that decline lands as margin, as a lower price to the customer, or as a transfer to a supplier depends on one question: who owns the bottleneck between the falling cost and the price the customer will pay?

Where a firm owns that bottleneck, it keeps the cost decline as margin. Where it owns none, competition forces the decline through. Walk the AI value chain from chips to applications, and the pattern is already visible. Profit sits where a pass-through is blocked. It evaporates where competition lets pass-through run free.

Start with the two companies that keep the money. NVIDIA reported fiscal-2026 revenue of US$215.9 billion, GAAP operating income of US$130.4 billion, and GAAP net income of US$120.1 billion, a net margin of nearly 56 per cent. TSMC earned 2025 net income of US$55.2 billion on revenue of US$122.4 billion, a 45.1 per cent net margin. Together, Nvidia and TSMC booked roughly US$175 billion of net profit in their latest fiscal years.

This is not a forecast. It is where AI profitability already exists.

Both companies sit behind gates that the rest of the stack must pass through. NVIDIA’s moat rests on CUDA, networking, scale, and the difficulty of coordinating around an alternative. TSMC’s moat is harder still: leading-edge fabrication is gated by physics, capital, yield learning, and process knowledge that takes years to reproduce. These are not normal suppliers. They are toll collectors.

The cloud layer is more ambiguous. AWS, Microsoft Azure, and Google Cloud are large, profitable businesses. AWS earned a 37.7 per cent operating margin in the first quarter of 2026, and Microsoft’s Intelligent Cloud has run margins in the low 40s. But hyperscaler free cash flow is being consumed by the AI build-out, and the cloud owners are trying to escape Nvidia’s toll through custom silicon. Amazon’s Trainium, Google’s TPUs, and Microsoft’s Maia are attempts to become bottleneck owners rather than resellers of someone else’s bottleneck.

Also Read: The AI economy is moving faster than our institutions

Where a cloud owner runs its own silicon, it can keep more margin. Where it buys Nvidia capacity, finances data centres, and rents compute to model labs, its economics compress. The cloud business is profitable, but AI infrastructure may not be unless demand arrives fast enough and custom silicon works well enough.

The neoclouds show what happens when revenue grows without a bottleneck. CoreWeave more than doubled first-quarter 2026 revenue to US$2.08 billion and reported a 56 per cent adjusted EBITDA margin. But adjusted operating margin was only 1.0 per cent, and GAAP net loss was US$740 million, with quarterly interest expense of US$536 million. Depreciation on GPUs and debt service consume the economics. CoreWeave buys Nvidia hardware at market prices, finances it with borrowing, and rents capacity into a competitive market. It owns no gate.

The frontier labs invert the popular intuition. OpenAI’s annualised revenue run-rate was roughly US$20 billion at the end of 2025. Anthropic reportedly reached around US$30 billion in April 2026 and about US$47 billion by late May. The growth is real, even if the figures are reported rather than audited. But revenue is not profit. OpenAI’s gross margin has been reported to be around one-third, constrained by inference costs, and internal projections reported publicly pointed to a multibillion-dollar loss in 2026.

Through the Abundance Economics lens, the labs are not toll collectors. They are in demand. A large share of their revenue flows upstream to chips and cloud and lands there as margin. The model itself is becoming less defensible because two forces push price down at once: open-weight models keep closing the capability gap, and the cost of fixed model performance keeps falling. In a layer with low switching costs and credible substitutes, falling input costs cannot be retained. Competition forces it through. That is why a lab can scale revenue explosively and still lose money.

The application layer needs more care. “AI apps” are not one category. Thin wrappers over frontier APIs own no gate and are likely to be crushed. Embedded workflow systems can be different because they control customer data, procurement position, operating processes, or a regulated context. Distribution-owned applications can also hold margin where they already own the user relationship.

Also Read: Give physical AI a soul: Why your voice AI still feels like a bot

Palantir is the clearest example. It is not just “an AI app.” It is an embedded data-and-workflow layer inside government and enterprise operations, and that position can behave like a bottleneck. By contrast, implementation consultants capture demand but earn consulting economics. Accenture may book billions in generative-AI work, but its overall operating margin remains around 15 per cent.

• The first capital-allocation implication is simple: do not price the AI stack as one trade. NVIDIA, TSMC, hyperscalers, neoclouds, model labs, workflow software, and thin apps have different economics because they sit in different places in the pass-through chain.
• The second implication is that revenue is the wrong metric at the model layer. A lab’s run-rate measures how much compute it is buying as well as how much value it is keeping. Treating model revenue like Nvidia revenue is a category error.
• The third implication is that the most durable profit pool may be less glamorous than the market assumes. NVIDIA’s moat is powerful but contestable: the cloud owners’ custom silicon is an attack from above. TSMC’s moat is harder to clone because it rests on physics, capital intensity, yield learning, and years of manufacturing execution. That does not make TSMC risk-free. It has Taiwan exposure, customer concentration, and cyclicality. But the moat itself is structurally harder to erode.
• The fourth implication concerns the capital now being committed. The major hyperscalers are reportedly tracking toward a combined 2026 AI capital spending approaching US$700 billion. That spending is ahead of demand. At the enterprise buyer level, an MIT study found that 95 per cent of organisations deploying generative AI had seen no measurable profit-and-loss impact. If the five per cent of successful deployments scale into the majority, the capital may be repaid. If not, much of the non-bottleneck stack remains a transfer mechanism feeding the gates.

The thesis can break in several ways. NVIDIA’s gate could erode faster than expected if custom silicon scales. A frontier lab could become a true toll collector if one model achieves a durable capability lead, or if regulation entrenches a small number of approved model owners. Enterprise demand could arrive faster than current evidence suggests. Or the binding constraint could migrate from chips to power, shifting the profit pool toward whoever controls dispatchable energy near data centres.

There is also a circularity risk. Some AI demand is financed by the same companies that benefit from it, through equity stakes, cloud commitments, reseller structures, and compute deals. That does not make Nvidia’s or TSMC’s profits fake. Their margins are real. But it does mean some of the revenue feeding those margins may be more fragile than organic demand would be.

The investor question is not whether AI is profitable. It plainly is, at the gates. The question is whether the demand behind those gates is durable enough, and arrives quickly enough, to repay everyone standing in line behind them.

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MoneyHero Group wants you to focus on the bright spots. Revenue climbed 15 per cent. Its shiny Wealth vertical surged 53 per cent. Its AI transformation story is compelling. And its Adjusted EBITDA loss? Down a whopping 68 per cent year-over-year to US$1.1 million.

On the surface, the Singapore- and Hong Kong-based personal finance comparison platform appears to be a company turning a corner.

Also Read: MoneyHero’s ‘turnaround’ built on a shrinking user base and retreat from SEA

Dig past the press release language, and the picture is considerably more complicated.

The net loss nobody wants to talk about

For the three months ended 31 March 2026, MoneyHero posted a net loss of US$6.7 million, nearly three times the US$2.4 million loss it recorded in the same period last year. That is not a rounding error. That is a 175 per cent deterioration at the bottom line.

The company’s explanation? Non-cash accounting items: a US$1.1 million swing in the fair value of warrant liabilities and US$2.4 million in unrealised foreign exchange losses from regional currency weakness against the US dollar. Fair enough, these are real accounting adjustments. But even if you strip them out entirely, the residual loss still lands around US$3.2 million, worse than the prior year’s US$2.4 million net loss. The narrative that operational performance is “robust” requires significant suspension of disbelief.

The press release buries this detail in a single paragraph, quickly pivoting to the Adjusted EBITDA figure, a non-IFRS metric that requires stripping out no fewer than six categories of charges to reach that headline-friendly US$(1.1) million number.

The mystery of US$1.6M in legal fees

Perhaps the most glaring anomaly in the report is a line item that receives precisely zero words of explanation in the management commentary: US$1.596 million in “non-recurring legal and professional fees and other expenses” incurred during the quarter.

In the same period last year, this figure was US$0.

The company categorises this as a non-recurring item and strips it out of Adjusted EBITDA. But US$1.6 million in legal costs is not a footnote; it is 9.7 per cent of the quarter’s total revenue. What litigation, regulatory matter, or professional engagement generated this bill? The report does not say.

We have reached out to the company for details and we will update this piece with the details as and when we hear from them.

This is likely a significant contributor to the 60 per cent spike in general, administrative and other operating expenses, which ballooned from US$2.19 million to US$3.51 million year-over-year, another figure conspicuously absent from the management commentary.

Revenue grew, but gross margins compressed

MoneyHero’s revenue of US$16.5 million is real and commendable. Double-digit growth across its core verticals — Credit Cards up 10 per cent, Personal Loans and Mortgages up 13 per cent, Wealth up 53 per cent, Insurance up 12 per cent — tells a story of genuine commercial momentum, particularly in Hong Kong, which surged 33 per cent to US$8.5 million and now accounts for 51.3 per cent of total revenue.

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

But here is what the report does not highlight: the cost of revenue grew at 23.6 per cent, significantly faster than the 15 per cent revenue increase, rising from US$6.4 million to US$7.9 million. Gross margins are quietly compressing.

The company instead draws attention to the combined decline in technology costs, employee benefits, and advertising and marketing expenses, which came down 13 per cent year-over-year to US$8.5 million. This is a legitimate operational achievement, but the framing deliberately excludes the cost of revenue, which is by far the largest single cost line. It is a selective presentation designed to emphasise efficiency while obscuring margin erosion.

The “strategic retreat” in Southeast Asia

MoneyHero’s two smaller markets (the Philippines and Taiwan) posted revenue declines of 17 per cent and 12 per cent, respectively. The company frames these as deliberate strategic decisions: optimising margins, cutting low-quality volume, and building structural leverage. Perhaps. But consider this: the Philippines is home to 6.9 million of MoneyHero’s 9.8 million registered members (70 per cent of its entire user base), yet it contributed just US$1.47 million, or 8.9 per cent, of total revenue in the quarter. A market representing seven-in-ten of the group’s members is generating less than a tenth of its revenue. That is not a margin quality story. That is a monetisation failure, and calling it “strategic” is cold comfort for investors watching Southeast Asia’s largest member base sit largely idle.

Meanwhile, the platform’s overall traffic footprint shrank dramatically: monthly unique users fell 31 per cent year-over-year from 5.7 million to 3.9 million, and total sessions dropped 29 per cent from 17.5 million to 12.4 million. Clicks fell 33 per cent. The company is converting a smaller, higher-intent audience more efficiently (that part is true and defensible), but the scale of audience attrition is a genuine long-term risk that the report effectively sidelines.

Cash burn and the runway question

MoneyHero ended the quarter with US$27.984 million in cash, down from US$31.185 million at the end of 2025. That is a US$3.2 million cash burn in a single quarter. The company describes its balance sheet as “healthy” and highlights its debt-free status, which is accurate. But net current assets also declined, from US$37.5 million to US$32.8 million over the same period.

At the current burn rate, the company has roughly eight to nine quarters of runway, about two years. That is not a crisis, but it is not the picture of financial comfort the press release implies. The clock is ticking toward the “sustainable Adjusted EBITDA profitability” the company keeps promising.

The Adjusted EBITDA problem

“Our Adjusted EBITDA loss narrowed sharply by 68 per cent year-over-year,” said Danny Leung, Interim Chief Executive Officer and Chief Financial Officer, in the company’s earnings statement.

That figure is technically accurate. But to get from a US$6.7 million net loss to a US$1.1 million Adjusted EBITDA loss, the company removes US$5.4 million worth of charges — unrealised FX losses, warrant fair value changes, share-based payments, legal fees, depreciation, and interest. The adjustments are five times larger than the resulting metric. When a non-IFRS measure requires stripping out more than 80 per cent of the underlying loss to produce a headline number, investors should treat it as a directional indicator at best, not a proxy for cash profitability.

What is genuinely promising

None of this is to say MoneyHero is without real momentum. Its approval rate expansion, from 36 per cent to 48 per cent, is a meaningful operational achievement, particularly as total approved applications held flat at 156,000 despite a significant reduction in total applications. The Wealth vertical’s 53 per cent growth and the broader shift toward higher-margin products are structurally sound strategies. Hong Kong’s dominance is real. The AI-driven cost reduction story, though early, has tangible evidence in the declining technology and headcount costs.

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

The question is whether the company can translate these genuine operational improvements into actual IFRS profitability and do so before its cash reserves force a capital raise or a more dramatic restructuring.

For now, MoneyHero is a company with a strong narrative, a compelling direction, and some numbers it would rather you did not look at too closely.

The post MoneyHero’s winning quarter has a US$6.7M problem appeared first on e27.

Southeast Asia’s semiconductor story is no longer limited to assembly, testing and outsourced manufacturing. This list points to a region, led largely by Singapore and Malaysia, that is building more of the stack itself: custom ASICs, silicon IP, chiplet packaging, photonics, test equipment and fab services.

Some of these startups are tackling narrow but essential problems, such as radiation-hardened chips, RF test components and FPGA design software. Others are pushing local industry further upstream into design and advanced packaging.

Also Read: The quiet layer keeping the chip boom alive

Taken together, they suggest a semiconductor ecosystem that is becoming more specialised, more technical and less reliant on being the backend of someone else’s supply chain.

GreatAsic Technology

FusionAP

Silicon Box

Zero-Error Systems (ZES)

SkyeChip

Global TechSolutions (GTS)

Swift Bridge Technologies

Infinecs Systems

MaiStorage

Oppstar

LightSpeed Photonics

Core Semiconductor

Cloptech

Plunify

Divergent Technologies

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