Most Singaporeans would have first encountered artificial intelligence (AI) through the likes of ChatGPT — OpenAI’s famous (or infamous) chatbot. Its ability to generate human-like responses with impeccable fluency has captured public imagination, sparking conversations and debates on how AI might transform the way we live and work.

Across Singapore and the broader Asia Pacific region, AI development has moved beyond novelty and into mainstream adoption. It is now an essential enabler of digital transformation, helping businesses scale efficiencies, address labor constraints, and unlock new avenues of value creation.

In Singapore, where digital readiness is among the highest in the region, AI is being embedded into national strategies — from precision medicine and port operations to smart city services. At the regional level, other markets are increasingly leveraging AI to optimise supply chains, improve the customer experience, and empower digital ecosystems across financial services, manufacturing, and retail.

Far from being an emerging technology, AI has its roots in decades of research. But its real-world impact has exploded in recent years, thanks to massive improvements in compute power and data availability.

In Singapore, this has led to expanded AI applications across government and industry. Initiatives like AI Singapore have spurred local talent development, while targeted collaborations with institutes of higher learning continue to advance responsible AI innovation.

Also Read: The fine art of building presentations in the AI era

AI is now deeply embedded in our everyday lives and the fabric of enterprise. It automates routine tasks, augments decision-making with real-time data insights, and enhances operational resilience. In Singapore, where many sectors are already embracing AI to overcome productivity plateaus, the technology’s ability to improve returns while reducing complexity is proving indispensable.

Generative AI (GenAI) is attracting significant interest. According to Lenovo’s CIO Playbook 2025, titled It’s Time for AI-nomics, Asia Pacific organisations expect a 3.6x ROI on average from AI initiatives, with many focusing on ITOps, software development, and cybersecurity as key areas for implementation.

Contrary to concerns about job displacement, AI is also creating new employment pathways and enriching professional development. By eliminating repetitive tasks, it empowers employees to focus on innovation and strategic problem-solving. In Singapore, workforce reskilling is a national imperative — AI adoption is aligned with this by enabling continuous upskilling and higher-value opportunities for professionals.

With these benefits, it’s no surprise that investment is pouring into AI infrastructure. In Asia Pacific, 65 per cent of enterprises now rely on on-prem or hybrid cloud infrastructure for AI workloads, especially in countries like Singapore where data sovereignty, latency, and compliance are critical concerns. To accelerate this momentum, Lenovo has invested US$100 million in its AI Innovators program, delivering over 165+ AI solutions and more than 80+ AI-optimised platforms.

But with great adoption comes greater responsibility. As GenAI use grows, so do concerns about its ethical implications. Governments and businesses alike must ensure that AI systems are transparent, fair, and explainable — particularly as they are applied in sensitive contexts such as healthcare, law enforcement, or public services.

Also Read: The ageing economy: Why investors should bet on longevity over AI

Singapore’s own approach to AI governance is widely recognised as a benchmark. Its Model AI Governance Framework, developed by the Infocomm Media Development Authority and Personal Data Protection Commission, exemplifies how regulation can foster innovation while managing risk.

Still, the responsibility doesn’t fall on regulators alone. Businesses must actively participate in shaping responsible AI practices. In fact, across APJ, only 25 per cent of organisations have fully enforced AI GRC (governance, risk, compliance) policies — a gap that must be closed if trust and transparency are to keep pace with progress.

Bias, in particular, is a persistent challenge. Algorithms reflect the data and assumptions used to build them. If unchecked, they can reinforce historical inequities, with potentially harmful outcomes. Testing, retraining, and human oversight are crucial to mitigate such risks. As industry watchers and advocates for AI safety have noted, AI does not have the capability to govern itself, GRC must be embedded into the organisational fabric from day one.

To that end, regulations must evolve in step with the technology. That means ongoing collaboration between policymakers, academia, and industry — not only to refine rules, but also to anticipate emerging risks. In fast-paced digital economies like Singapore’s, agility in governance will be as important as agility in innovation.

Ultimately, building a trusted and resilient AI ecosystem will require a whole-of-society effort. From regulators to developers, enterprises to end-users — every stakeholder has a role to play in shaping an AI future that is inclusive, secure, and beneficial for all.

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The 2020s have been defined by an explosion in Generative AI (GenAI), with models like GPT-4, Claude, and DALL·E not just making headlines but profoundly transforming industries across Southeast Asia. This technology is proving to be a game-changer, driving significant value through improved work efficiency, content accuracy, and audience engagement across the board.

Downstream applications: Where GenAI delivers concrete ROI

As per an East Ventures White Paper, titled “AI-first: Decoding Southeast Asia trends”, the true power of GenAI lies in its downstream applications, offering a fertile ground for innovation and investment due to “low technical barriers of entry”.

Also Read: AI adoption in SEA e-commerce: The clock is ticking for sellers

Southeast Asian startups are seizing this opportunity to develop solutions that tackle localised problems across a myriad of sectors. Here’s how GenAI is fundamentally altering business operations and customer interactions:

E-commerce & digital marketplaces: GenAI-powered automated content generation and targeted marketing are leading to a 25 per cent increase in user engagement and conversion.

Customer support: Automated, personalised GenAI customer support solutions are achieving a remarkable 40 per cent reduction in response times and enhancing resolution efficiency.

Edutech: AI-driven content creation and adaptive learning modules are delivering up to a 90 per cent improvement in learning outcomes.

Financial management & SaaS: GenAI-powered SaaS solutions are cutting manual reconciliation time by 35 per cent through advanced financial insights and analysis.

Digital media & content creation: Dynamic, AI-generated visual and graphic content is driving a 30 per cent increase in audience engagement, revolutionising digital storytelling.

Workplace automation: GenAI note-taking and summarising tools are cutting post-meeting follow-up time by 50 per cent, significantly boosting productivity and accuracy.

Data insights & personalisation: Advanced GenAI-powered data analysis and hyper-personalised recommendations are leading to a 25 per cent uplift in conversion rates.

Real-world impact: East Ventures portfolio leading the charge

Prominent venture capital firms like East Ventures are already seeing their portfolio companies leverage GenAI to achieve significant breakthroughs:

Customer support: Multiple startups have implemented GenAI for first-layer customer support, freeing human teams for more complex cases.

Content creation: Novelship is using GenAI to automate product descriptions at scale, enhancing quality and SEO for hundreds of thousands of listings.

Healthcare: Mesh Bio is building a “human digital twin” using multi-dimensional patient data for personalised insights and enhanced care recommendations.

Analysis & productivity: A fintech startup has developed an internal GenAI solution to generate and debug code, analyse data, and generate insights, significantly increasing staff productivity.

Education: Ruangguru and Prep are using AI to deliver personalised and automated learning content at scale.

Logistics: Waresix leverages AI to analyse historical data, optimise pricing, and enhance delivery routes.

Also Read: Burning billions: AI’s capital frenzy and its global implications

HR & operations: Another fintech startup is automating partner onboarding and training, cutting time by more than 70 per cent, while Meeting.ai and Nexmedis are automating transcription and summarisation for meetings and medical consultations, respectively.

These examples underscore GenAI’s profound capacity to drive fundamental changes in how businesses operate, create, and engage, cementing its status as a core driver of value in Southeast Asia’s digital economy.

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The image was created by ChatGPT.

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Building a successful company takes vision, grit, and relentless ambition, but it shouldn’t come at the cost of personal well-being. For founders, the pressure to grow fast and do it all can easily lead to burnout.

This article explores how founders at the scaling stage can strike a healthier balance, pursuing bold goals while protecting their energy, focus, and the well-being of their teams. Through real-world insights and practical strategies, we will look at what sustainable company building truly looks like.

Emotional and mental challenges of scaling

“Scaling a company is a marathon, not a sprint; sustainable growth requires zeal moderated with restraint,” as stated by the founder of StartUp Growth Guide.

The burn from the high growth of an organisation can be unbearable. According to Business Entrepreneur, more than 53 per cent of founders reported burnout in 2024, and research attributes nearly five per cent of startups failing to burnout. These numbers reveal that mental fatigue is an obstacle branded as the “scaling problem” recognised around the world.

Everyone knows starting a company can be exhausting yet rewarding all at once. Paul Graham puts it nicely by describing it as “at least a roller coaster… ups after the downs.” This forethought can assist entrepreneurs in coping during downtimes: persistent lulls aren’t unusual setbacks, but rather expectant turns, enabling grit often leads to progress down the line.

Redefining ambition and setting boundaries

Bold ambition drives growth, but unchecked ambition can destroy well-being. Justin Welsh warns, “Business boundaries matter. They’re in charge of your freedom”. In practice, this means defining clear limits: for example, no meetings after a certain time or no weekend work. One founder describes how he now has a “hard stop at five pm” and goes offline on personal days – reclaiming those hours as non-negotiable personal time.

Successful scaling requires working smarter, not just harder. Arianna Huffington puts it plainly: after her burnout, she changed her life to focus on efficiency over hours, saying “It’s not about working longer or harder. It’s about working smarter”. Redefining ambition means learning to say no to low-value work and yes to strategic focus – a shift many scaling founders find liberating in the long run.

Cultivating a resilient company culture

A company’s growth is sustainable only when the employees are thriving. Founders should strive to create an environment wherein team members appreciate and feel empowered even during stressful periods. “Motivation comes from working on things we care about. It also comes from working with people we care about,” noted Sheryl Sandberg.

This can be operationalised through providing recognition that celebrates small wins, nurturing collaboration, guiding alignment around shared purpose, and ensuring that challenging phases are met with team support as opposed to isolation.

Also Read: My mission: Creating space for diverse voices in leadership

Employees view leadership as role models, considering their example Governance sets the bar for morale. Team member motivation shifts more easily than business management as Altman commented ‘the hardest part of managing is looking after your own and your team’s motivation’”.

In resilient cultures, founders tend to model stress openly which encourages discussion on challenges unabashedly.” When leaders value breaks and time away from work, it encourages self-care norms and burnout stigma sags.

Designing systems to prevent burnout

Establishing preventive measures works best. Founders tend to experience burnout due to increased workloads that stem from lack of systems in place. This can be alleviated by automating certain processes and task complex delegation, establishing proactive decision-making systems at team levels that will reduce the need for problem escalation to the founder’s level.

In a company setting, not all concerns are pressing or demanding immediate attention. Jeff Bezos delineates decisions into two divisions which he refers to as “two-way doors” and “one-way doors.”

He encourages resolving most dilemmas at lower organisational levels rather than bringing them up to higher tiers because only lower-level decisions should pose minimal repercussions. Embracing this approach conserves mental resources for founders since there is no need for deep contemplation on minor challenges less than half the time they arise.

Assisting such types of thinking are calendars and broken down tasks called guardrails. Having specific times when work ceases completely provides structure within which overworking is impossible. Gradually, these tendencies become default rules that enhance well-being across the board while safeguarding a work-life balance.Financial Planning for Sustainability.

Recovery cycles: Pacing growth sustainably

Incorporating time off and recovery is as essential as moving forward. Recovery periods can serve innovation-boosting purposes. Everyone needs some winding-down time throughout a day; otherwise, the precious equilibrium between stillness and dynamic existence shrinks to nonexistence. This balance can manifest as small breaks, weekly digital detachment rituals, and even major milestone-triggered sabbaticals.

Graham has reassured many startup executives that low momentum periods are expected and highly transitory. As long as they float through life without actively venturing out to “fix” anything during these frames, they will come up with solutions after problem identification on their own: “If you know it’s going to feel terrible sometimes, then when it feels terrible you won’t think ‘I give up…’ Just hang on; things will probably get better.”

Founders tend to be brave if they realise such awful feelings empower relaxation, not chaos. In fact, aided by temporary disconnection from rigid responsibilities, gaining fresh perspectives becomes easier due to step back strategies.

Financial planning for sustainability

Drastic scaling is not a substitution for sound finance; it entirely overlooks financial strategy. Founders should consider runway as a precious resource. As an example, Entrepreneur reported that nine per cent of founders took no salary in 2024 while the average paid founder was US$150K.

Operating without sufficient founder compensation is risky: underpaying yourself “financially shackles you and increases the likelihood of burnout.” Wise founders set proper compensation with robust buffers and establish fair salary structures from the start.

Also Read: Navigating Asia’s business boom: The quantum leadership advantage

Sustaining long-term viability means balancing growth-expenditure with returns- one must pay attention to net profitability. Businesses whose focus is on sustainable growth outperform competitors who accelerate user acquisition, often spend to capture every dollar whenever possible.

There are increasingly preferred businesses where they succeed by spending within their limits. Financial plans should reward longevity, as in strategically multi-year planning, diverse funding sources, cautious debt control – all ensure acceleration without crashing.

Personal routines and support networks

Founders maintain daily habits that preserve well-being. Across the tech world, leaders preach self-care: Tumblr’s David Karp insists “no laptops in the bedroom” to protect sleep, and IBM’s Ginni Rometty (echoed by Mark Zuckerberg) stresses, “I make time to exercise… it’s got a lot to do with your ability to manage properly and stay focused.”

In practice, this may mean morning workouts, family dinner time (Sandberg leaves the office by 5:30 pm for dinner with her kids), or a fixed journaling routine. Such anchors ensure founders recharge physically and mentally each day.

Support networks are equally crucial. Close friends, mentors, and family provide perspective when business pressures mount. Ev Williams, founder of Medium, bluntly notes: “Failure of your company is not failure in life; failure in your relationship is.”

This mindset reminds founders that relationships and personal health outlast any business outcome. Peer groups or founder communities can also serve as sanity checks – sharing struggles with fellow entrepreneurs helps you realise you’re not alone and may surface practical coping strategies.

Transparent leadership through highs and lows

Trust is built through communication. The journey toward scaling a business comes with its challenges such as turbulent hiring, revenue stream fluctuations, or partnership delays. Founders that openly tackle these topics are way ahead of any rumour mill. Coach Manuel Saez comments, “a lack of enthusiasm for tasks I once enjoyed” and “feeling a constant sense of overwhelm” often signal burnout. Leaders can do better by out openly accepting the reality of low morale; it allows collaboration on solutions instead of anxiety.

Teams notice when their leaders have a human side – it underscores vulnerability as a strength to be celebrated rather than masked. When founders share their wins right next to losses, other employees feel comfortable doing the same because sharing is encouraged.

In practice, this could mean regularly scheduled all-hands meetings for status updates (good and bad), welcoming feedback mechanisms on pain points, or modelling stress reduction rite publicly. Employee experience research shows when company leaders model work-life balance during recovery periods, staff will feel empowered encourage healthy long-term habits across the organisation which bolsters resilience.

Moving beyond hustle myths to long-term thinking

The “always-on” hustle is increasingly seen as counterproductive. Alexis Ohanian warns that “unless you are suffering, grinding… you’re not working hard enough” is “one of the most toxic, dangerous things in tech right now”.

Sustainable founders reject this myth, recognising that relentless overwork damages creativity and decision-making. Instead, many embrace marathon-like pacing: One Silicon Valley veteran quips that the real founder superpower is pacing yourself through the journey.

Also Read: Community in thought leadership: Highlights from the e27 Contributor Programme Roundtable at Echelon Singapore 2025

The true advantage lies in patience. Altman notes that “the biggest competitive advantage in business… is long-term thinking”. Startups that prioritise lasting impact over quick wins tend to build deeper customer relationships and more durable products.

For a scaling founder, this means making decisions with 5–10 year outcomes in mind – for example, choosing customer loyalty over short-term revenue boosts or investing in team development rather than only rapid hiring. This long-horizon approach naturally tempers the impulse to sacrifice well-being for immediate results.

Conclusion

Exponential scaling may be a common goal, but success also reflects a business built to withstand personnel burnout. With balance at the centre of their leadership strategy, founders have the ability to set boundaries through cultivation of healthy organisational culture and long-term vision. A sustainably growing business starts with sustainable systems in place throughout all levels.

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It started with a simple suggestion: “Let’s integrate AI into our customer service workflow.” On the surface, it seemed like a no-brainer. We were handling hundreds of inquiries a day, and AI-powered chatbots promised faster response times, 24/7 support, and reduced pressure on our human agents.

But the moment we proposed the change, tension surfaced—subtle but unmistakable. Team members asked: “Will we lose our jobs?” “What happens to the personal touch?” “How do we know we can trust this tool?” That tension revealed something deeper than a technical shift—it was a trust issue. Not just in the tools, but in the future we were collectively walking into.

This moment reflects a larger reality facing organisations across Southeast Asia and the globe. Adopting AI is no longer optional; it’s an imperative. Yet the real transformation isn’t just about software deployment or process automation—it’s about people.

If we want to succeed with AI, we need to change our perspective: from using tools to replace human roles, to using tools that create more trust, amplify human strengths, and foster a culture where technology is a partner, not a threat.

From hesitation to co-creation: Building trust with the team

The first step in navigating the transition was acknowledging the fear. We didn’t gloss over it. We held a team town hall, not to present the solution, but to invite everyone into the process.

We asked, “What do you hope to gain from these tools? What are you afraid of losing?” The answers were honest, sometimes raw—fear of redundancy, fear of being judged by machine-generated data, and fear that “efficiency” would override empathy.

Also Read: Can AI make clean energy pay off? CynLing Software thinks so.

Rather than imposing the tool, we co-created its role with the team. For instance, we designed the chatbot to handle only repetitive Tier-1 inquiries, while routing complex or emotional concerns directly to human agents. This division of labour allowed AI to support the team, not sideline them.

We also made transparency a pillar: every interaction with the AI tool could be reviewed, corrected, and learned from by a human. It wasn’t about trusting the tool blindly; it was about giving the team the power to direct and improve it.

Keeping the culture intact amid change

Tools, no matter how advanced, can fracture culture if introduced without care. We were clear: the heart of our team wasn’t speed—it was empathy, creativity, and clarity of communication.

So, while automation handled volume, we doubled down on human training. We introduced regular AI-literacy sessions—not technical bootcamps, but scenario-based learning where we asked: “If the AI misunderstands a customer’s concern, how would you intervene? What would you want it to learn from that moment?”

We also celebrated human-AI collaboration. When an agent improved the bot’s script based on a customer feedback loop, we highlighted it in team meetings. This wasn’t about glorifying the tool; it was about showing how our human values shaped the tool’s growth. Slowly, the team began to see AI not as a threat to their identity but as a way to express it even more clearly.

Language mattered too. We avoided phrases like “the AI will take over this task” and instead used “the AI will assist you here.” This subtle shift respected the team’s autonomy and reinforced that tools serve people, not the other way around.

What we’ve learned and what we’re still figuring out

Three things became clear through this journey.

First, trust is not built by tech—it’s built by transparency. Trusting the tool came only after the team trusted that their input mattered, that they wouldn’t be blindsided by change, and that leadership was accountable for the outcomes.

Second, AI adoption works best when framed as augmentation, not automation. By showing that AI handled the routine, while humans handled the relationship, we protected the team’s sense of purpose. It’s this clarity of role that sustained morale even as workflows changed.

Third, learning must be ongoing and human-centred. We didn’t just train the team to use tools—we empowered them to shape how the tools evolve. This mindset shift—from passive user to active director—is what truly bridges the skills and trust gap.

Also Read: Generative AI: The unstoppable force reshaping work and engagement across SEA

But we’re still learning. Not every concern disappears with one conversation. As AI grows more sophisticated—generating content, analysing sentiment, even mimicking tone—the ethical questions deepen.

Who’s accountable when the AI makes a mistake? How do we balance efficiency with empathy when AI nudges us to respond faster than we can think? These are not tech problems; they’re human ones. And they require an ongoing, honest conversation.

The tools must serve the trust

In Southeast Asia, where cultural nuance, interpersonal relationships, and community orientation are vital, AI must be embraced with wisdom and humility. We can’t afford to blindly import tech-driven models that prioritise speed over substance. Instead, we must lead with values—using tools to build trust, not erode it.

Let AI be the assistant, not the master. Let teams direct the tools, not be directed by them. And above all, let our cultures remain human at the core—even as they become increasingly digital.

Because in the age of AI, it is not the tools we deploy that define us—but how we use them to protect what makes us human.

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There’s a silent shift happening in India’s education system, and it’s not another edutech startup promising to “disrupt learning.” It’s quieter, older, and far better funded.

Private equity (PE) firms, KKR, Kedaara, Gaja, Apollo, and the like, are now deeply invested in schools. Not apps. Not coaching centres. Not gamified math. Actual schools.

This trend could redefine how capital flows into education, raising questions not just for educators, but also for startups, founders, and investors across Asia.

And not in a one-off “let’s uplift society” kind of way. We’re talking ₹1000 crore+ (US$1.2 billion+) deals, multiple rounds, aggressive M&A, and full-blown portfolio strategies.

Here’s what’s already public:

•  Kedaara: ₹1,200 (US$1.45 billion) crore in Orchids International
•  KKR: ₹1,475 crore (US$1.78 billion) into EuroKids, EuroSchools, Lighthouse Learning
• Gaja Capital: 130 crore in Kangaroo Kids
• BanyanTree, Apollo, Navis, and others circling similar assets

This isn’t just capital inflow. This is the McKinsey-fication of the Indian classroom.

But wait… aren’t schools supposed to be non-profit?

They are. In fact, the law is very clear.

Under Indian law, private unaided schools must be registered as:

• A trust (under the Indian Trusts Act)
• A society (under the Societies Registration Act)
• Or a Section eight company (under the Companies Act, 2013)

All three are non-profit legal structures, which means:

• No dividends
• No equity stakes
• No profit distribution to shareholders
• No “sale” or “buyout” of the school entity itself

So how does PE get in? Through the backdoor — fully legal, and frankly, very smart.

The real play: Parallel companies

You see, while the school remains a non-profit, there’s nothing stopping the same promoters from setting up for-profit companies around the school. These might:

• Own or lease the school real estate
• Sell uniforms and books
• Provide canteen, transport, or IT services
• License curriculum IP
• Supply teachers or staff through manpower companies
• Build edutech platforms used by the school

The school trust pays these vendors. The vendors are profit-making. And the profit accumulates outside the school, in structures where PE can invest, hold equity, and exit.

Also Read: The future of edutech: Personalising learning for all

If this sounds like a related party playground, that’s because… it kind of is. But if structured right, and it usually is, it’s perfectly legal.

Profit vs purpose: Are we crossing a line?

Not necessarily. Because frankly, India’s school sector needed capital. It also needed structure, scale, and systems. And PE is bringing all of that.

The older model, a single-school trust run by a family, resistant to any change has its charm, but also limitations:

• No clear succession planning
• Low investment in infra
• No capacity for M&A or regional expansion
• No incentive for teacher training or tech upgrades

So now, PE brings:

• Operational discipline
• Standardised curriculum
• Hiring practices
• Brand equity
• And… yes, return expectations

The fear is whether these “returns” will come at the cost of quality or accessibility. But let’s be honest, the idea that private schools were ever truly non-profit is a fairy tale we told ourselves. All this does is move it from under the table to on the cap table.

Why PE is now obsessed with education

Greg Parry, an education investor who’s seen both the big wins and the belly flops calls education the next big frontier for private equity. His thesis?

Education is:

• US$7 trillion globally, and growing
• Recession-resistant
• Emotionally inelastic (parents don’t cut tuition spend easily)
• And full of predictable revenue from tuition fees, often paid upfront

In other words, this isn’t just a good sector. It’s future-proof capital deployment.

But here’s the tradeoff, profit isn’t pedagogy

If you zoom out of the models and multiples, what you often see is: PE’s 5–7 year horizon clashing with education’s 15–20 year gestation cycle. It’s a cultural mismatch.

Some real, tangible risks:

• Curriculum narrowing (what’s measurable > what’s meaningful)
• Decreased investment in teachers or “non-core” programs
• Arts and humanities fade out as “non-revenue-generating”
• Students become monthly metrics

As one educationist put it: the child, the very heart of education is missing from most investment decks.

Education as a balance sheet category?

Some fund managers are openly calling schools “predictable assets with EBITDA potential and regulatory moats.”

That sounds more like a toll highway than a classroom.

And sure, in India’s context, with 320 million children in K–12, rising incomes, and a growing aspiration for private schooling the economics are sound.

But just because a thing makes sense on Excel, doesn’t mean it sits right in real life.

Legally speaking: Where’s the line?

This model operates in a grey-ish zone that lawmakers have… well, quietly tolerated.

Also Read: In this age of digitalisation, is edutech a bane or boon for educators?

What’s legal:

• Having related companies provide services to schools
• Paying market-rate (or even slightly higher) fees to these vendors
• Licensing curriculum content
• Leasing school land from a promoter-owned entity

What’s not:

• Diverting school fees into private accounts
• Inflating vendor charges disproportionately
• Transferring school assets to for-profit entities
• Misusing charitable status to evade taxes or regulation

The trick is to keep the “non-profit” entity clean and audit-friendly, while letting the economics play out in the surrounding shell. It’s a game of optics, governance, and tax structuring, something PE excels at.

But regulators are slowly catching on.

The bigger picture

Why are global investors so bullish on Indian schools?

Because India offers the holy grail of investing:

•  Massive demand (320M kids in K-12)
•  Urbanisation + nuclear families = school selection pressure
•  Rising disposable income
•  Parents who treat education as non-negotiable
•  Low churn: students stay 12-15 years in the system

Also, education in India isn’t cyclical. It’s recession-proof, emotion-driven, and billable.

Some open questions worth asking:

• Is this funding improving quality or just valuation?
  Are we getting better schools, or just better investor decks?
• Are teachers benefiting?
  Or are they just another cost centre being “optimised”?
• Is there enough transparency for parents?
  Most don’t even know their fees flow through 3-4 vendor layers.
• Are regulators keeping up?
  Because if this goes unchecked, schools might become the new NBFCs — too big, too complex, and too slippery to monitor.

My two paise

I’m not anti-capital in education. Money can do good, if governed right.

But what we can’t afford is:

• commodified classrooms,
• academic assembly lines,
• or “value-added services” that are just fee bloat in disguise.

The school, for all its evolution must remain a place of learning, not just earning.

There’s room for both heart and hustle. But if we start optimising education the way we optimise quick commerce, we’ll lose the very thing we’re trying to build: a smarter, more equitable future.

Final note

India’s schools are becoming more than just places of learning, they’re turning into strategic assets in the portfolios of global investors. For the startup ecosystem, this raises a key question: how will the influx of private equity reshape opportunities in edutech, education services, and the wider learning economy?

If we optimise education the way we optimise quick commerce, we risk losing sight of its true purpose. The challenge, and opportunity, is to strike the balance between heart and hustle.

Next time you see a school franchise opening 30 branches overnight with a glossy brochure, 3 logos, and a parent app, check who’s funding it. Because somewhere between the SmartBoards and STEM labs, there’s a term sheet.

And behind every term sheet… is a thesis.

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

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The old guard of leadership, fixated on sheer headcount as a badge of honour, is already a relic. In a world increasingly shaped by powerful AI agents, the very definition of economic value, power, and prestige is undergoing a seismic shift. We’re not just looking at minor tweaks to our current system; we’re staring down the barrel of a multi-layered economic reality.

I see a future, one closer than you might think, where our global economy splits into three distinct, yet interconnected, ecosystems. Forget your traditional supply chains and corporate structures.

Get ready for the tri-economy.

Ecosystem one: Human-to-human – The enduring heartbeat

Even as AI scales new heights, the uniquely human elements of our economy will not just survive but thrive. Think of this as the craft and connection economy. It’s where value is derived directly from human interaction, bespoke skill, and the irreplaceable nuances of our nature.

This is the realm of:

• High-touch services: The empathetic therapist, the inspiring teacher, the skilled surgeon, the dedicated caregiver. These are roles where genuine human connection, intuition, and complex emotional intelligence are paramount.
• Art, culture and experience: Live music, original paintings, artisanal crafts, gourmet dining, immersive travel experiences designed by human experts. The authenticity and shared experience here are priceless.
• Deep problem solving and innovation: While AI assists, the grand leaps in scientific discovery, philosophical thought, and solving humanity’s most complex challenges will still be driven by human creativity, interdisciplinary collaboration, and that spark of genius only we possess.

In this ecosystem, the “power” won’t be about scale or efficiency, but about authenticity, mastery, and the ability to forge genuine human bonds. It’s a reminder that even in a hyper-automated world, we are, at our core, social creatures.

Ecosystem two: Human-to-agent – Amplified ambition

This is the bridge we’re already rapidly crossing, where human capabilities are supercharged by intelligent AI agents. Call it the augmented productivity economy. Here, AI isn’t replacing us entirely but acting as an indispensable co-pilot, an infinitely patient assistant, and a powerful analytical engine.

Also Read: Why AI won’t replace developers — but CEOs must lead the transformation

Think about it:

• Personalised everything: Your personal AI agent could manage your finances, optimise your health plan, curate your learning journey, and even design your next vacation, all tailored precisely to your preferences, far beyond what any human assistant could achieve alone.
• Supercharged professionals: Doctors using AI to diagnose rare diseases faster, lawyers leveraging agents to scour legal precedents in seconds, architects designing complex structures with AI-powered simulations. Humans remain in charge, but their reach and impact are multiplied exponentially.
• New job roles emerge: This isn’t just about efficiency; it’s about creation. We’ll see roles like “Agent Orchestrators” designing and managing AI teams, “AI Ethicists” ensuring responsible deployment, and “Human-AI Collaboration Specialists” bridging the gap between human intent and agent execution.

In this ecosystem, human leadership shifts from command-and-control to vision- setting, ethical oversight, and strategic direction. The power lies in effectively leveraging AI to unlock unprecedented productivity and achieve goals previously deemed impossible for individual humans.

Ecosystem three: Agent-to-agent – The autonomous frontier

This is where things get truly mind-bending, and where the “sand pile collapse” moment described by researchers feels most imminent. This will be the autonomous exchange economy, driven by human intent but executed by agents transacting directly with each other, with minimal human intervention.

Imagine this:

• Self-operating businesses: A customer places an order on your website. Your sales agent confirms it, triggering a production agent to initiate manufacturing. A procurement agent automatically sources raw materials from a supplier agent, negotiating prices and delivery times. A logistics agent then coordinates shipping, all transactions settled autonomously via smart contracts and digital currencies. You, the human business owner, simply set the initial parameters and monitor the dashboards.
• Dynamic resource allocation: In a smart city, traffic management agents could negotiate with energy grid agents to optimise power for public transport based on real-time demand, or with autonomous vehicle agents to reroute traffic during emergencies – all without human middle-men.
• Hyper-efficient markets: AI trading agents already exist, but in this future, they would evolve into highly specialised micro-agents engaging in hyper-frequency transactions across vast, interconnected markets, optimising resource distribution with unprecedented speed.

Also Read: Inclusive AI isn’t optional – it’s Asia’s tech advantage

This ecosystem will redefine efficiency and scale. The “power” here will reside in the design and robustness of the agent protocols, the underlying data infrastructure, and the initial human intent that sets these autonomous systems in motion.

Not science fiction, but imminent reality

This tri-economy isn’t a distant, fantastical vision. The building blocks are already here: advanced large language models, sophisticated APIs for inter-system communication, blockchain for trustless transactions, and the rapid advancements in robotics and multimodal AI.

The “sand pile collapse” is the critical warning: as individual agent capabilities and coordination mechanisms improve incrementally, there will be a sudden, non-linear jump in their collective performance. This means our capabilities could rapidly outstrip our current infrastructure, regulations, and even our understanding of what it means to work and live.

The implications are profound. We need to start asking:

• How do we educate and re-skill our workforce for these new realities?
• What new ethical frameworks are required for truly autonomous economic actors?
• Who is accountable when an agent swarm makes a mistake in a complex, multi-agent workflow?
• How will wealth be distributed in an economy driven by hyper-efficient AI-to-AI transactions?

The shift from “I have 1,000 people under me” to “my agents manage 1,000 autonomous tasks” is more than just a change in jargon. It’s a fundamental reordering of our economic landscape. The future isn’t just coming; it’s already here, taking shape across these three powerful, emerging economies. Are you ready to navigate them?

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

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Across Southeast Asia and the broader Asia Pacific, a quiet revolution is underway—not in boardrooms or data centres, but on the frontlines of retail stores, factories, and warehouses.

As artificial intelligence (AI) becomes more embedded in everyday operations, its impact is no longer confined to knowledge workers. Frontline teams—often overlooked in digital strategies—are emerging as key drivers of operational efficiency, engagement, and customer experience.

AI’s potential on the frontline is now tangible. When applied thoughtfully, it delivers not only business value but also more inclusive, human-centric workplaces.

From fragmented to frictionless: AI as a communication engine

One of AI’s most immediate impacts is streamlining communication. For large, distributed frontline teams, tasks and updates can be inconsistent, delayed, or lost in translation.

Now, head offices can issue instructions that are clear, multilingual, and tailored to each employee’s role and location. Frontline staff can access policies or procedures through AI-powered agents in real time—using their native language, at any hour. Whether clarifying a return policy or checking a shift protocol, employees get direct, reliable answers.

These micro-interactions add up. They reduce delays, bolster confidence, and improve productivity—transforming AI into a quiet but powerful co-pilot across geographies and workflows.

Context matters: Localising AI for Asia’s frontline

Asia’s diversity makes one-size-fits-all solutions ineffective. A delivery driver in Jakarta faces different challenges from a retail associate in Singapore or a warehouse clerk in Manila. AI tools must adapt to sector-specific workflows and local realities.

Also Read: The hidden barrier to AI sustainability: Why clean data matters

In retail, AI can flag low inventory and automatically assign restocking tasks. In logistics, it can surface updated SOPs or training based on real-time operational needs. Whether it’s prompting compliance checklists, delivering bite-sized learning, or surfacing urgent communications, AI agents like WorkJam’s aren’t just analysing data—they’re orchestrating action. The common thread? The best tools are designed for the frontline—not simply deployed at them.

Language as a productivity lever

Language diversity is both a hallmark and a hurdle across Southeast Asia. A single store might include speakers of Malay, Mandarin, Tamil, and English. Historically, this created gaps in communication and onboarding.

AI-powered translation eliminates these friction points. Employees ask questions and receive guidance in the language they’re most comfortable with. This inclusivity translates into better understanding, quicker responses, and improved morale. It’s not just a matter of convenience—it’s foundational to performance.

Responsible AI: Embedding governance and trust

Regulatory frameworks vary widely across Asia. As AI tools proliferate, governance must be part of their design—not an afterthought. From Singapore’s AI ethics guidelines to Australia’s employee protections, compliance is essential.

AI platforms are increasingly incorporating governance by default—restricting after-hours communications, tailoring information access by role, and ensuring employee privacy. These measures aren’t just about following rules; they build credibility and demonstrate that the technology serves the worker, not the other way around.

Designing for digital inclusion

As AI tools spread, digital literacy remains a concern. How do we ensure that technology is an enabler, not a barrier?

Design plays a central role. Interfaces that mirror familiar messaging apps, natural language tools, and voice-based interactions reduce the need for technical fluency. Paired with peer support—where tech-savvy employees help teammates onboard—organisations can foster adoption organically.

Also Read: Blockchain to the rescue: How tech can combat food waste and secure our food supply

When AI “just works,” it accelerates productivity without alienating the very users it’s meant to help.

Trust is the ultimate adoption metric

Adoption isn’t driven by flashy features—it’s driven by trust. Employees must see AI not as a burden but as a benefit. Trust is built through consistent, helpful experiences that save time, reduce complexity, and support their goals.

Transparency helps. When companies collect feedback, share updates, and iterate based on frontline input, employees feel heard. Some organisations go further, establishing internal AI councils to ensure ethical deployment, cross-functional alignment, and shared accountability.

ROI is in the details

To gain momentum, AI initiatives must show measurable returns. Improved customer service, reduced turnover, streamlined onboarding, and time savings all contribute to ROI.

Even small efficiencies can yield outsized gains. One global retailer reduced task time by five minutes across 250,000 employees—a minor tweak that translated to millions in annual savings. With the right metrics, the value of AI is not just theoretical—it’s tangible and scalable.

From the ground up: Asia’s competitive edge

AI isn’t replacing the frontline—it’s elevating it. Asia’s frontline workers are becoming more agile, more informed, and more central to digital transformation.

For business leaders, the message is clear: the next wave of innovation won’t originate from the cloud or the corner office. It will come from empowering those closest to customers and operations. Investing in frontline AI isn’t just good strategy—it’s the next competitive advantage.

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

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Energy systems are becoming more complex and volatile as the world accelerates towards decarbonisation. Behind-the-meter (BTM) energy storage is emerging as a key solution—helping stabilise power, lower costs, and support industrial sustainability. Yet few companies have made BTM projects both technically sound and financially viable.

CynLing Software is among the companies working to change that. As BTM storage gains global momentum, driven by unpredictable energy prices, stressed grids, and the growing need to decarbonise without sacrificing profitability, the demand for intelligent, financially sound solutions is more urgent than ever. From factories to data centres, operators are looking for systems that don’t just manage energy but prove their return.

How CynLing uses AI to turn strategy into precision

CynLing Software, a Singapore-based spinoff from Taiwan’s CynLing Renewables Inc., is tackling this head-on. The company focuses on AI-powered digital energy management, especially behind-the-meter solutions. “We split the energy storage project into two parts: planning and operations,” says EVP Nathan Lei. “AI plays a critical role in both.” 

The software uses artificial intelligence to simulate capacity needs, optimise control strategies, and model financial return. In operations, it helps ensure that the assets perform exactly as predicted, hour by hour.

Forward AI: Forecasting every hour, every scenario

At the heart of CynLing Software’s solution is Forward AI, an advanced digital energy management system. Unlike traditional energy software that relies on static assumptions, Forward AI is built on dynamic forecasting and reinforcement learning.

“We predict solar generation and factory load in 15-minute intervals using in-house models,” Lei explains. “For example, we integrate with a factory’s MES system to get production schedules. From there, we simulate the entire microgrid, from solar, to battery, to load, over 8,760 hours per year.”

These simulations aren’t just academic. In one instance, a competitor claimed a battery system would last 20 years. CynLing’s models, however, showed that due to high temperatures and intensive cycles, the actual lifespan would be closer to 16 years. “That insight saved our client millions in miscalculated investment,” Lei notes.

Also read: Empowering the future of Singapore: The need for SMEs to embrace renewable energy solutions

Generalization is the game-changer

One of CynLing Software’s most significant innovations lies in its ability to generalize AI models across geographies. This is something most energy management systems struggle to achieve.

“Legacy EMS solutions are often hardcoded for a single use case,” says Lei. “They don’t adapt well when conditions change.” In contrast, CynLing’s platform is trained using reinforcement learning in simulated environments, enabling it to handle diverse energy profiles, regulatory frameworks, and usage patterns across markets like Taiwan, Australia, and Southeast Asia.

This scalability, from model to deployment, is what powers CynLing’s broader digitalisation vision. “It’s what makes our software portable, cost-effective, and future-ready,” Lei adds.

Sustainability begins with bankability

CynLing Software doesn’t just optimise energy use, it proves financial viability, making clean energy projects more attractive to investors, banks, and insurers.

“Let’s face it,” Lei says, “renewables are unstable by nature. Sunlight fluctuates. Demand shifts. Batteries are expensive. The only way to scale this infrastructure is to prove it pays back.”

That’s why CynLing’s core service is focused on simulating real-world revenue and degradation models. It shows not just how energy is stored, but how much it earns, when, and for how long.

This matters especially in Southeast Asia, where clean energy demand is rising, but market trust is still fragile. “We’re working with private equities and developers in Thailand and Malaysia,” Lei adds. “Our models help them validate investments before deployment.”

Also read: 5 AI trends to watch in the next 12 months: Intelligent agents, cost reductions and compute power

From Singapore to the world: What’s next

With operations in Taiwan, Japan, Australia, and the U.S., CynLing Software is using Singapore as a launchpad for its regional ambitions. And while the company remains selective about new markets, it’s already eyeing broader Southeast Asian opportunities. It is particularly interested in data centers and industrial zones.

But growth isn’t the only goal. “We’re not here just to sell batteries,” says Lei. “We want our clients to optimize their assets. If the market crashes tomorrow, we’ve already simulated that for you. You’ll know how to pivot.”

When asked what drives him to keep pushing forward, Nathan Lei pauses. “At the end of the day, proving bankability is what allows sustainability to scale. That’s our mission: not just technology, but trust.

Join Smart Storage Taiwan in Nangang Exhibition Center Hall 1, Taipei, Taiwan on 29-31 October to connect with CynLing Software.

For more information, visit their website at https://cynling.com/en/.

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Filipino early growth investment firm Foxmont Capital Partners has hit the first close of its third fund at US$30 million.

This milestone more than doubles the VC firm’s assets under management (AUM) and surpasses the combined size of its initial two funds.

The first close of Fund III sees the addition of two key institutional investors. The Dutch Good Growth Fund (DGGF), a development finance institution, serves as an anchor investor. Furthermore, Grab Holdings has also joined the fund.

Also Read: Philippine startups break records in 2024: What’s driving the boom?

The Philippines is rapidly solidifying its position within the Southeast Asian funding landscape, which has witnessed remarkable growth, with funding surging from US$440 million in 2019 to US$1.12 billion in 2024.  The nation now commands 19 per cent of the regional funding, a substantial increase from just 2 per cent in 2021, according to Foxmont’s 2025 Philippine Venture Capital Report.

“We have moved from proving Philippine startups can succeed to showing that they can dominate,” noted Managing Partner Franco Varona. “Foxmont’s early access lets us ride this curve from first check to exit. With Fund 1 and Fund 2, we seeded the ecosystem, and with Fund 3 we are now prepared to grow the ecosystem.”

In addition, the nation’s consumption-driven economy continues to outperform its regional counterparts, boasting a 5.7 per cent GDP growth rate compared to the 4.9 per cent regional average in 2024.

Despite its significant potential, the market remains underserved relative to its regional standing. “The Philippines accounts for 20 per cent of the region’s population but has captured just 13 per cent of funding over the last three years,” said Kenneth Albolote, who has been promoted as General Partner. “This asymmetry creates the most compelling capital allocation thesis in ASEAN today. Foxmont’s early-growth dominance positions it to capture this delta as startups mature.”

According to Ronald Roda, Grab Philippines Managing Director, Grab’s participation underscores the growing confidence in the Philippine tech ecosystem. “As a company deeply rooted in Southeast Asia, we believe the Philippines is poised to become one of the region’s most exciting tech frontiers. Our participation in Foxmont’s Fund III is a vote of confidence in the ingenuity of Filipino founders and the strength of the Philippine startup ecosystem.”

Also Read: Pavilion Capital, AppWorks invest in US$21.3M Fund II of Philippine VC Foxmont Capital

Founded in 2018, Foxmont Capital has invested across various sectors in the Philippine market. The firm boasts a strong track record of attracting follow-on capital and scaling companies, maintaining top-quartile returns through its local presence and first-mover advantage. Its co-investors include prominent names such as General Atlantic, the Susquehanna Group, Singapore’s Pavilion Capital, the Asian Development Bank, and the Philippines’ Startup Venture Fund.

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Solid earnings from megacap technology firms have failed to buoy broader market confidence, while movements in currencies, stock indices, Treasury yields, commodities, and even cryptocurrencies like Bitcoin reflect a pervasive sense of caution.

I will walk you through what’s driving this retreat, weaving in my perspective on its implications for investors and the global economy.

Trump’s tariffs: The spark of uncertainty

At the forefront of this market unease is President Trump’s tariff policy update. The White House has confirmed that a minimum global tariff of 10 per cent will persist, with countries enjoying trade surpluses with the United States facing steeper duties of 15 per cent or more. Specific nations have been hit harder: Canada now faces a 35 per cent levy, and Switzerland a hefty 39 per cent.

What amplifies the market’s anxiety is the lack of clarity on when these new rates will take effect. This ambiguity leaves businesses and investors grappling with unanswered questions about how these tariffs will reshape global trade flows, corporate profitability, and economic growth.

This tariff strategy reflects Trump’s ongoing commitment to addressing perceived trade imbalances, but it risks igniting a broader trade conflict. Tariffs of this magnitude could disrupt supply chains, particularly for countries like Canada, a key US trading partner, and Switzerland, known for its precision exports. The absence of a timeline only deepens the uncertainty, forcing companies to delay investment decisions and prompting markets to price in potential downside risks.

I see this as a double-edged sword: while it may bolster certain domestic industries, it could also inflate costs for consumers and businesses reliant on imported goods, potentially stoking inflation at a time when central banks are already on edge.

The immediate market response underscores this concern. US stock markets closed lower, with the S&P 500 slipping 0.4 per cent, the NASDAQ holding flat, and the Dow Jones dropping 0.7 per cent. These declines suggest that investors are prioritising the macroeconomic fallout of tariffs over other positive signals, a theme that recurs across asset classes.

Tech earnings: A bright spot overshadowed

Amid this tariff-induced turbulence, megacap tech firms have delivered robust earnings reports. Companies like Apple, Microsoft, and Amazon have showcased strong quarterly results, buoyed by resilient demand for technology products and services. Under normal circumstances, such performances might spark a rally in equity markets. They have failed to lift broader sentiment, a telling sign of the market’s preoccupation with larger forces.

In my view, this disconnect highlights a critical shift in investor psychology. While these tech giants demonstrate operational strength, their success cannot offset the uncertainty surrounding trade policies. Investors appear more focused on how tariffs might erode profit margins for multinational corporations, many of which rely on global supply chains.

Also Read: The Fed, tariffs, and Bitcoin: Unpacking the market dynamics

For instance, higher duties on imported components could squeeze profitability, even for firms reporting solid earnings today. This suggests to me that the market is in a risk-off mode, where macroeconomic narratives trump individual company fundamentals.

Currency markets: Diverging reactions

Currency markets offer a mixed picture, reflecting the varied impacts of Trump’s policies. The US Dollar Index climbed 0.2 per cent, signaling a modest strengthening of the dollar. This uptick likely stems from its safe-haven status amid uncertainty, as well as expectations that tariffs might bolster US economic activity in the short term by favouring domestic production.

However, other currencies tell a different story. The Swiss franc edged lower, likely pressured by the 39 per cent tariff on Swiss exports, which could dent its export-driven economy. Meanwhile, the Canadian dollar held steady despite a 35 per cent levy, perhaps buoyed by its linkage to commodity prices, particularly oil.

The dollar’s modest gain suggests cautious optimism about US resilience, but the stability of the Canadian dollar surprises me given the tariff burden. It may indicate that traders see Canada’s energy exports as a buffer, though I suspect prolonged trade tensions could eventually weigh on the loonie. The franc’s decline, conversely, aligns with expectations, as Switzerland’s smaller, trade-dependent economy has less room to absorb such shocks.

Treasury yields and commodities: Inflation fears and demand worries

In the bond market, US Treasury yields rose, with the 10-year yield increasing 0.4 basis points to 4.374 per cent and the two-year yield climbing 1.7 basis points to 3.957 per cent. This upward movement stands out against the risk-off backdrop, where yields typically fall as investors seek safety in bonds.

To me, this suggests that markets are anticipating higher inflation, possibly driven by tariffs raising the cost of imported goods. It could also reflect concerns about the fiscal implications of trade policies, as reduced trade volumes might not offset the revenue gains Trump envisions.

Commodities present a contrasting narrative. Gold rose 0.5 per cent to US$3,290 per ounce, reinforcing its role as a safe-haven asset during uncertain times. I view this as a classic flight to safety, with investors hedging against both geopolitical risks and potential economic slowdowns.

Also Read: ESG frameworks and standards: Cutting through the complexity for private markets

Brent crude, however, fell 1.0 per cent to US$72.5 per barrel, driven by expectations of increased OPEC+ output following their upcoming meeting to set September quotas. This decline puzzles me somewhat: while higher supply makes sense, softening global demand due to trade tensions could also be at play, signalling broader growth concerns.

Jobs report: A looming test

The market’s gaze now shifts to the upcoming July jobs report, due Friday, which economists predict will show a more deliberate pace of hiring and an unemployment rate rising to 4.2 per cent. This data point carries significant weight.

A softening labor market could amplify fears of an economic slowdown, especially if paired with tariff-related headwinds. Conversely, a stronger-than-expected report might offer temporary relief, though I doubt it would fully dispel the tariff overhang.

In my opinion, this report will serve as a litmus test for US economic resilience. A tick up in unemployment could prompt the Federal Reserve to reconsider its rate stance, particularly if inflation pressures from tariffs persist. For investors, it’s a moment to watch closely, as it could either reinforce or challenge the current risk-off sentiment.

Bitcoin’s plunge: A crypto microcosm

The cryptocurrency market, particularly Bitcoin, mirrors this broader retreat. Bitcoin’s price dropped 2.18 per cent to US$115,621 over 24 hours, a decline fuelled by leveraged liquidations, technical breakdowns, and waning institutional enthusiasm. Between July 31 and August 1, over US$560 million in crypto positions were liquidated, with US$153 million tied to Bitcoin alone.

This cascade of forced selling intensified as Bitcoin breached the US$118,859 support level (the 23.6 per cent Fibonacci retracement of its 2024-2025 rally), turning it into resistance and accelerating technical selling.

Technical indicators reinforce this bearish turn. The Relative Strength Index (RSI) is at 49.44, and a MACD histogram at -630 signals weakening momentum, with the next support at US$114,500 (38.2 per cent Fibonacci) in sight. If breached, an additional US$149 million in liquidations could follow, per technical analysis data.

Also Read: Balancing ambition and well-being: A founder’s take on sustainable company building

Beyond technicals, institutional demand has cooled, with spot Bitcoin ETF assets under management stagnating at US$151.48 billion despite US$47 billion in corporate purchases. Meanwhile, a shift toward altcoins has seen Bitcoin’s dominance dip 0.51 per cent, as capital flows to riskier crypto assets.

Coinglass data paints a stark picture: in one hour on August 1, US$284 million in liquidations hit the crypto market, with US$276 million from long positions, including US$91.6493 million for Ethereum and US$76.0871 million for Bitcoin. Over four hours, liquidations exceeded US$409 million. The Fear & Greed Index slid to Neutral (57) from Greed (62), capturing this sentiment shift.

To me, Bitcoin’s woes encapsulate the broader market’s struggles. The liquidation wave reflects overleveraged optimism meeting harsh reality, while the technical breakdown and institutional pullback suggest a maturing market reacting to global cues. I see this as a warning sign: if even speculative assets like Bitcoin falter, the risk-off mood may be deeper than it appears.

For me, the key takeaway is adaptability. Investors must brace for volatility, balancing safe havens like gold with selective exposure to resilient sectors. The interplay of inflation risks, trade disruptions, and labor market signals will shape the near-term outlook.

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