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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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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The e27 team produced this article sponsored by CynLing Software.

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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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