Netbank’s Series B, led by Singapore’s Altara Ventures, signals a subtle but important shift in Southeast Asian fintech: investors are betting less on flashy consumer apps and more on the regulated banking plumbing that makes those apps possible.

In a market where compliance, bank integrations and product rollouts remain slow and finicky, Netbank’s rural banking licence and steady B2B traction convinced Altara to double down.

Also Read: What stands in the way of fintech growth in Asia?

We asked Dave Ng, General Partner at Altara, why his firm chose Netbank now, what separates genuine fintech infrastructure from mere “API” buzz, and how investors underwrite the messy trade-off between regulatory defensibility and execution risk. His answers reveal why patient, execution-focused founders — not growth-for-growth ‘s-sake product teams — may hold the keys to the next phase of Philippine fintech. Below is our full Q&A.

Fintech infrastructure is not exactly a fashionable category in tougher funding markets. Why lead this round now, and why Netbank specifically?

It boils down to a specific company or business, because each is unique, and a big part of that is due to the people behind it. As a category, it may take businesses longer to show results because, when you are building a platform or infrastructure, your go-to market is often B2B. Hence, it takes time to get customers: to convince them to try, onboard them for proof of concepts (POCs) and eventually convert to real paying customers. And very often, it is determined by how well you can execute.

We are seeing this in Netbank: their ability to turn ideas into real products and services and to gain good customer traction. They are now looking to scale further, and we believe it is a good time for us to join and value-add along the journey.

Netbank is building on a full banking licence, which creates both advantages and regulatory complexity. As an investor, how do you underwrite that balance between defensibility and execution risk?

To be clear, they already have a rural banking license. Hence, less so of building on a full license. As with most businesses, the differentiating success factor lies heavily with execution capability. The risk doesn’t go away, but we are encouraged by how the Netbank team has been thinking about their business strategy, future opportunities and how they have consistently navigated ups & downs and grown the business. I think the ability to be creative, to be resilient in handling challenges, and to be focused on delivering successful customer stories are very important to any startup. We see these qualities in them.

What are the biggest risks to this thesis from here: regulatory change, credit exposure from embedded lending, slower partner adoption, margin pressure, or competition from incumbents waking up?

Execution risk and continuing to be a good and responsible ecosystem player.

Many venture firms say they back infrastructure, but many still prefer consumer-facing growth stories because they scale faster and are easier to market. Why do you believe the real long-term value in Southeast Asian fintech may sit deeper in the stack?

There are always winners across the stack. I don’t favour one over the other. Rather, in every team and company, I look for certain core principles that I believe are essential as a starting point, and putting that against the track record will tell me how likely (or not) they could succeed.

Also Read: SEA’s fintech boom: Market demand is real, but the numbers need context

Being consumer-facing is typically associated with speed to scaling. But entrepreneurs will need to get the economics right, which is often a struggle in the region. Going deeper into the stack often puts you in the B2B or B2B2C territory. That means you need the grit, stamina, and efficiency to run an enterprise GTM motion. But if you do that successfully, your customers are sticky, and every new logo you onboard successfully builds on a stronger and stronger base.

The post “The risk doesn’t go away; execution decides everything”: Altara’s Dave Ng appeared first on e27.

When Dr. Siti Rahman founded AgriNext in Indonesia, she was not chasing headlines or valuations. She wanted to solve a stubborn problem that farmers in Central Java faced every planting season: unpredictable yields and volatile incomes. Her solution was a cloud-based platform that uses satellite data and AI-driven analytics to help smallholder farmers plan crops, access microloans, and connect directly to buyers. Within three years, AgriNext was profitable and had increased average farmer income by 38 per cent. Investors who backed her vision now hold stakes in a company with both strong earnings and undeniable social impact.

A comparable story is unfolding in India with DeHaat, an agritech platform that connects millions of farmers to seeds, fertilisers, crop advisory, and buyers through a mobile app and a network of local entrepreneurs. By streamlining access to inputs and markets, DeHaat has boosted incomes and reduced post-harvest losses. Its transparent impact measurement has helped it secure funding from global investors such as Sequoia Capital and Temasek, proving that socially impactful agritech can scale profitably.

In Sri Lanka, Aahayani Agri is bringing next-generation agricultural services. The company specialises in drone-based precision farming, automated spraying, mapping, and Data-driven crop advisory to enhance productivity and sustainability. Its service-led model focuses on paddy and other high-yield crops, combining proprietary data analytics with on-ground mechanisation to deliver measurable yield improvements. Partnering with financial institutions, Aahayani Agri allows farmers to pay for services and fertilisers at harvest time, reducing the upfront burden and enabling wider adoption of advanced farming technologies.

Agriculture across Asia employs millions, yet farmers often struggle with outdated practices, poor market access, and lack of financing. Platforms such as AgriNext, DeHaat, and Aahayani Agri address these barriers by pairing technology with practical solutions. Their success shows how combining advanced tools with a deep understanding of local challenges creates businesses that are both profitable and socially relevant.

Also Read: Indonesia’s agritech landscape: Keys to building a scalable agriculture startup

Beyond farming: Impact across sectors

Other sectors reflect the same trend. In the Philippines, MedLink is transforming rural healthcare through telemedicine. By enabling nurses in remote clinics to consult specialists in Manila via a mobile app, it has reduced referral delays by 60 percent.

In Vietnam, EduBridge uses adaptive learning platforms to tailor lessons to individual needs, improving pass rates in underserved communities by 25 percent. In Pakistan, Sehat Kahani connects rural patients to female doctors through telemedicine, expanding healthcare access while creating professional opportunities for women doctors unable to work in hospitals.

Why impact measurement matters

These ventures succeed not only because of their technology but also because of their commitment to measuring impact. Investors no longer accept vague claims of doing good. They want clear metrics that link adoption to outcomes.

AgriNext reports farmer income gains and carbon reductions. DeHaat tracks yield improvements and supply chain efficiencies. Aahayani Agri demonstrates crop productivity increases from drone-based services. MedLink shows reductions in wait times and better treatment adherence. Sehat Kahani tracks patient reach and improved health outcomes.

Also Read: Homegrown solutions for a hungry future: Why Southeast Asia must localise agritech by 2050

This level of transparency builds investor trust. Demonstrating both social and financial returns enables these startups to attract mission-aligned capital from ESG-focused private equity funds, development finance institutions, and impact investors. Clear reporting is becoming a competitive advantage in raising capital.

The future of profit with purpose

Across South and Southeast Asia, ESG is moving from optional to essential in investment decisions. Institutional investors are setting higher sustainability standards. Governments are encouraging entrepreneurs to integrate social outcomes into business strategies. Singapore is positioning itself as a hub for sustainable finance, while India has strengthened ESG reporting requirements. Development banks such as the Asian Development Bank and IFC are co-funding projects that combine commercial viability with measurable impact. This is expanding the pool of capital available for startups that align profit with purpose.

Startups that address deep, systemic challenges build resilience by serving enduring needs. Farmers will always seek better yields. Rural communities will always need healthcare. Students will always pursue education. These are not passing trends but constant demands.

Solving real problems also creates diversified revenue streams. AgriNext earns from subscriptions, transactions, and agribusiness partnerships. DeHaat monetizes through input sales and produce aggregation. Aahayani Agri generates income through precision farming services and financial partnerships. MedLink earns from clinic subscriptions and insurance contracts, while Sehat Kahani combines patient fees with corporate wellness services. This diversity buffers companies against economic shocks and strengthens long-term sustainability.

The stories of AgriNext, DeHaat, Aahayani Agri, MedLink, and Sehat Kahani reveal a broader truth. The future of investing in Asia lies in ventures that blend technological innovation with social impact. These businesses prove that profit and purpose are not opposites. They reinforce each other when thoughtfully combined.

Also Read: From inspiration to impact: My journey in tech for good and ESG innovation

For investors, the choice is becoming clearer. Funding startups with measurable social impact offers both strong financial returns and the satisfaction of contributing to positive change. In markets as diverse as South and Southeast Asia, this approach also provides a strategic edge. Consumers and regulators are watching closely how companies affect communities, the environment, and governance standards. Those that align with these expectations will grow faster and more sustainably.

The question is not whether funding for good can succeed. The evidence is clear that it already is. The real question for investors is whether they are ready to make it the norm. Those who act now will not only capture market share but also help shape a regional economy that thrives on both prosperity and purpose.

—

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.

Enjoyed this read? Don’t miss out on the next insight. Join our WhatsApp channel for real-time drops.

Image courtesy: DALL-E

The post Why investors are betting big on Asia’s social impact startups appeared first on e27.

Meta’s planned acquisition of Manus, the Singapore-based agentic AI startup founded by Chinese engineers, has been derailed by an intervention from China’s National Development and Reform Commission (NDRC).

The commission has ordered the unwinding of Meta’s proposed acquisition, reportedly valued at between US$2 billion and US$3 billion, without publicly explaining its reasoning. That silence is telling. In the current AI race, cross-border deals are no longer judged on commercial logic alone. National interest, control over strategic technology, talent migration and data governance are all part of the same calculation.

Also Read: Meta × Manus: The misread AI deal

For Meta, the fallout is immediate. For Manus, it is existential. And for Singapore, which has spent years positioning itself as a neutral and trusted base for global tech firms, the blocked deal is a sharp reminder that geography can change faster than political memory.

A deal that moved too fast

Meta’s interest in Manus was clearly strategic. Agentic AI, the new industry obsession, promises systems that do not merely respond to prompts but can perform tasks, make decisions across workflows and act more like autonomous digital workers. Every large platform company wants in.

Manus had emerged as an attractive target in that race. Though formally headquartered in Singapore after relocating from China around mid-2025, the startup’s engineering DNA remained closely linked to Beijing. Its founders had earlier built Butterfly Effect in Beijing in 2022 before shifting the company’s centre of gravity to Singapore. Meta moved quickly, announcing the acquisition in December 2025 with plans to plug Manus’s agent technology directly into Meta AI.

The speed of integration suggests Meta believed the political path had already been cleared (or at least contained). Reports say nearly 100 Manus employees had already moved into Meta’s Singapore offices and taken on executive roles. That detail turns this from a simple blocked transaction into a live operational mess. This is no longer about a failed acquisition on paper. It is about teams already embedded, reporting lines already adjusted and strategic plans already drafted.

The NDRC’s order to unwind the arrangement completely now threatens to leave both sides disentangling systems, talent and responsibilities that may already have been partially merged.

More than a US-China story

It would be easy to read this as another chapter in the long-running US-China technology rivalry. That would also be too neat.

What makes the Manus case more significant is that it sits in the grey zone many startups hoped would remain workable: a Chinese-founded company relocated to Singapore, acquired by a US tech giant, and integrated through a Southeast Asian office. On paper, this is the transnational corporate architecture that modern tech companies use to manage regulatory friction.

Beijing’s intervention suggests that structure may not be enough when AI is involved.

If the reported requirement for Manus to exit Chinese ownership and operations formed part of the acquisition framework, Beijing may have viewed the deal less as a normal M&A event and more as a transfer of strategic capability. Agentic AI is still a developing category, but governments are increasingly treating frontier AI talent and technology as assets that should not move freely once they become strategically valuable.

Also Read: Agentic AI in action: How Southeast Asia’s startups are turning constraints into strengths

That changes the rules for every founder who thinks moving the holding company to Singapore solves the geopolitical problem. It may solve a legal one. It does not necessarily solve a sovereignty one.

Why this matters for Singapore’s AI industry

For the island nation, the Manus episode lands awkwardly. The city-state has worked hard to market itself as a trusted hub for AI development: politically stable, regulation-friendly, well-connected to both East and West, and credible enough to host regional headquarters for American, Chinese and European firms alike. In theory, it offers exactly what globally mobile AI founders need: capital access, talent pathways and a rules-based business environment.

But the blocked Meta-Manus deal exposes the limits of that positioning.
Singapore can host the company. It cannot erase the strategic concerns attached to where the founders, engineers and core intellectual lineage came from. In AI, origin stories now matter almost as much as incorporation documents.

That does not mean Singapore loses. In some respects, the case strengthens its relevance. More Chinese-origin startups may still choose Singapore as a base because it remains one of the few jurisdictions with the legal sophistication and international legitimacy to support global expansion. But those startups, and their investors, will need to stop pretending that relocation creates a clean political reset.

The implications for Singapore’s AI industry are threefold.

1. Due diligence will get harder: Investors and acquirers will place greater weight on founder nationality, prior operating history, research origins, cap table exposure and residual links to China. The old startup checklist of product, market, growth and burn rate now comes with a geopolitical appendix.
2. Singapore’s “neutral hub” pitch faces a stress test: Singapore remains one of the best places in Asia to build and scale an AI company, but the Manus case shows it cannot fully insulate firms from strategic interventions by larger powers. Neutrality is useful. It is not magic.
3. Talent and IP governance will come under sharper scrutiny: When nearly 100 employees are reportedly moved into a buyer’s Singapore office before a deal fully settles, regulators elsewhere will notice. So will boards. Expect more caution around pre-close integration, IP transfer, data controls and executive appointments in future AI transactions.

Also Read: AI agents work, until they don’t: Here’s what we learned

That may slow some deals, but it could also push Singapore’s ecosystem towards greater maturity. Less hype, more structure. Fewer narrative-driven exits, more attention to governance. For a serious AI hub, that is not necessarily bad news.

A heavy blow to Meta’s agent plans

The move hurts Meta hard. The company has been moving aggressively to strengthen its position in generative AI, and agentic systems are increasingly seen as the next competitive layer. If Manus’s technology was meant to accelerate Meta AI’s agent capabilities, then the unwinding is not just a legal inconvenience but a strategic delay.

There is also reputational damage. For a company of Meta’s size to get caught mid-integration before a transaction was fully secure suggests either overconfidence or a misreading of the political risk.

The company can, of course, build, hire or buy elsewhere. Large tech groups always have alternatives. But frontier AI deals are not interchangeable. Strong teams are scarce, speed matters, and losing momentum in a category as hot as agents can create openings for rivals.

What next for Manus

For Manus, the way forward is narrower, but not closed.

First, it has to stabilise. That means clarifying who is employed by whom, who controls the product roadmap and whether its Singapore headquarters is genuinely the company’s centre of command or merely a legal wrapper around a more fragmented organisation. A startup cannot build trust with enterprise customers or regulators while its ownership structure looks like a half-erased diagram on a whiteboard.

Second, it needs a cleaner governance story. If Manus wants to remain globally investable, it must reduce ambiguity around control, data flows, board oversight and any continuing China links. In the AI market, opacity is no longer a quirky startup trait. It is a commercial liability.

Third, Manus may need to rethink its endgame. A blockbuster sale to a US tech giant now looks much less straightforward. That does not mean the company is finished. It may instead need to pursue a more gradual path: independent growth, minority strategic investors, enterprise partnerships, and a product strategy focused on revenue before headlines.

Singapore could still be central to that path. The city offers access to multinational clients, a strong legal infrastructure and a credible platform for building in Southeast Asia. If Manus can prove it is more than a politically complicated asset shuffle, it may yet find traction as a serious enterprise AI company.

Southeast Asia’s lesson from the wreckage

The broader lesson for Southeast Asia is blunt. The region wants to benefit from the AI boom not merely as a market, but as a place where important companies are built, financed and exited. That ambition remains realistic. But Manus shows that in AI, the map is crowded with invisible borders.

Capital crosses borders. Engineers cross borders. Headquarters cross borders. Strategic suspicion does too.

Also Read: In the age of AI, people matter more than ever

For Singapore’s startup ecosystem, this is not a reason for pessimism. It is
a reason for realism. The next generation of AI companies in the city will need not only strong products and elite talent, but corporate structures designed for a world in which regulators care deeply about provenance, control and technological sovereignty.

As for Manus, it now has the unenviable task of proving it is still a company rather than the remains of a deal that never fully belonged to itself. In the AI industry, that is a brutal place to be. It is also where the real business occasionally begins.

The post China blocks Meta’s AI bet on Manus: What it means next appeared first on e27.

The digital advertising ecosystem and the broader software-as-a-service landscape are undergoing a foundational architectural shift. The transition from generative conversational AI to autonomous agentic execution represents a migration from systems that merely answer queries to systems that independently complete complex, multi-step workflows. At the epicentre of this technological inflection point is Meta Platforms’ acquisition of the Singapore-based autonomous AI agent startup, Manus, for an estimated US$2 billion in late December 2025.

This monumental acquisition is a highly aggressive strategic manoeuvre designed to connect massive infrastructure investments directly to tangible enterprise and advertising performance. However, the immediate market impact is characterised by a deliberate, phased internal rollout. Meta is actively navigating legacy API constraints, intense geopolitical hurdles, and severe unit economic challenges inherent in agentic computing.

Concurrently, the capabilities demonstrated by Manus pose an existential threat to established dashboard-based SaaS platforms like Cape and Smartly.io. As these agents mature, their integration with the Model Context Protocol (MCP) allows them to bypass manual operations and analytics done by humans based on the dashboards in favour of deterministic enterprise data access, fundamentally altering marketing execution.

The macroeconomics and geopolitics of the AI race

Meta’s decision to acquire an eight-month-old startup for US$2 billion – its third-largest acquisition after WhatsApp and Instagram – was driven by an acute need to close the operational gap in the AI race. Throughout 2024 and 2025, rival technology conglomerates dominated the agentic narrative: OpenAI launched Operator, Google introduced Agent2Agent, and Anthropic deployed its Computer Use capabilities. Despite allocating between US$115 billion and US$135 billion toward AI capital expenditures for 2026, Meta lacked a production-grade execution layer capable of autonomous action.

Manus provided this exact layer. The startup achieved US$100 million in annual recurring revenue within eight months, rapidly scaling to process over 147 trillion tokens and create 80 million virtual computing environments. Through this acquisition, Meta purchased a highly scaled orchestration engine that translates reasoning into end-to-end task execution.

Infrastructure clashes and the economics of agentic consumption

Despite the rapid acquisition, Meta is NOT aggressively pushing Manus to its 4 million-plus front-line advertising customers immediately. The delay is fundamentally rooted in a clash between machine speed and legacy application programming interface architectures.

Also Read: The one-person company was always possible. AI agents make it probable

Contemporary advertising platforms are built upon rate limits designed decades ago for human operators. While a machine-speed agent can formulate and launch hundreds of multivariate tests per second, Meta’s legacy systems cap automated financial adjustments to a maximum of 4 budget changes per hour per ad set. Until Meta finishes building “Andromeda” – a unified ad modelling architecture designed to handle machine volume – the autonomous potential of Manus remains artificially locked.

Furthermore, the economic model of autonomous execution differs vastly from traditional SaaS. Under the hood, Manus utilises 29 specialised tools and is powered by Anthropic’s Claude 3.7 Sonnet model. Because agents operate in continuous, recursive loops, they consume tokens at an exponential rate. Real-world deployments demonstrate that a single complex workflow can burn between 500 and 900 credits per run.

Users have reported exhausting their entire monthly credit allocations within minutes. While advanced prompt caching can drop the cost of Claude 3.7 inference by up to 90 per cent, baseline infrastructure costs remain a substantial hurdle for democratising the technology for small-to-medium businesses.

The extinction event for dashboard SaaS

For the past decade, the industry has relied on custom, dashboard-based SaaS platforms to scale digital campaigns. These platforms operate on an “Empowerment” paradigm, providing human media buyers with advanced steering wheels. The integration of agentic systems into Meta represents a violent shift to a “Replacement” paradigm. When the human is removed from the execution layer entirely, the dashboard interface itself becomes structurally obsolete.

The comparative workflow disruption:

• Research and strategy: A human manually reviews data to formulate hypotheses. The agent continuously monitors signals and identifies audience gaps autonomously.
• Creative assembly: A human designs variations and uploads them. The agent generates copy, iterates variations, and adapts messaging per segment dynamically.
• Budget optimisation: A dashboard executes rigid human-designed rules. The agent calculates real-time economic arbitrage based on fluid performance signals.
• Reporting: A human exports charts for stakeholders. The agent autonomously queries data and translates raw metrics into tailored insights.

MCP: Eradicating vanilla scraping for deterministic data

An autonomous agent authorised to reallocate advertising budgets cannot rely on probabilistic guesses or outdated training data. Historically, AI models relied on “vanilla scraping” to gather external data, which is inherently brittle; any minor website adjustment instantly breaks the extraction logic.

Also Read: When AI agents take the lead in decision-making, who answers when they mess up?

The solution is the Model Context Protocol (MCP). Introduced by Anthropic in 2024, MCP is an open-source standard dubbed the “USB-C for AI”. It eradicates the N x M integration problem by introducing a universally standardised client-server architecture over JSON-RPC 2.0 messages. Instead of visually parsing a webpage, the agent describes the required outcome, and the system selects the appropriate MCP-compliant tool to fetch structured data directly.

When connected to an organisation’s semantic layer, MCP guarantees:

• Safe AI querying: Eliminates the risk of the model hallucinating financial metrics.
• Consistent business logic: Forces the AI to utilise explicit organisational definitions.
• Role-based security: Strictly enforces row-level permissions.

Applied contextual intelligence: The constructor proctor case study

The power of data justification for high-stakes marketing is exemplified by the campaign designed for Constructor Proctor, a specialised division targeting the educational sector in Singapore under the global Constructor Group.

Singapore houses five autonomous polytechnics and 300 universities, administering millions of critical assessments annually. Post-pandemic, the demand for scalable online proctoring is projected to reach US$4.8 billion globally by 2030. Using MCP-integrated Campaign Strategy Agentic AI, an analysis of 246 competitor posts revealed the market was saturated with broad “AI-for-student-success” messaging. None owned the operational narrative of strict exam-level integrity.

This deep insight defined two distinct buyer personas:

• The knowledge seeker (institutional decision-maker): Anxious that AI is enabling cheating. The campaign positioned Proctor as a security guardian, highlighting over 100 dedicated AI parameters (gaze tracking, device detection).
• The transformative educator (key influencer): Frustrated by exam logistics. The campaign highlighted operational simplicity, offering features like 1-click reports to return lost time to educators.

Also Read: Delivery intelligence: The missing link between AI agents and strategic alignment

This deterministic data foundation informed a highly successful omnichannel execution, including precision-targeted LinkedIn advertisements, an experiential testing booth at edutech Asia simulating 10,000 simultaneous exams, and a national thought-leadership feature on Channel NewsAsia.

Conclusion

The convergence of Meta’s monumental acquisition of Manus and the rapid proliferation of the Model Context Protocol signifies the definitive end of the manual operational era in digital advertising. For enterprise marketers, the immediate imperative is restructuring human capital around orchestration, economic modelling, and rigorous data governance.

For the SaaS ecosystem, the threat is undeniably existential. Custom dashboard providers must immediately pivot away from interface-driven value propositions. The future of marketing software lies deep within backend data structures, providing robust, MCP-compliant servers that feed high-fidelity, real-time market intelligence directly into autonomous execution engines.

As API architectures are rewritten for machine-speed interaction, the organisations that will thrive are those that fully embrace AI as the primary engine of autonomous execution, fuelled entirely by the deterministic certainty of structured enterprise data.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post The autonomous agent paradigm: Meta’s Manus acquisition, MCP integration, and the disruption of SaaS appeared first on e27.

In the digital world, “scaling” is synonymous with abstraction: building a single, software-driven platform capable of serving 100,000 customers instantaneously, with near-zero marginal cost. This is the unicorn playbook where there is massive leverage and massive risk.

For the vast majority of SMEs dealing with physical assets, localised services, and specialised operations, this abstract model is not just irrelevant; it’s a dangerous liability. The only sustainable path to growth is replication, standardising a process to build 10 small, profitable, localised asset bases rather than one giant, fragile digital one.

The Good of replication is that it delivers predictable, compounding profitability and superior risk mitigation. The Bad of abstraction is that when the central platform fails, the entire business collapses.

The fragility of abstracted scale

The tech model’s dependence on abstraction means the entire business is concentrated into one logical point of failure. If the central algorithm governing logistics, the database supporting millions of users, or the core payment system breaks, 100 per cent of the company’s revenue stops. Furthermore, the knowledge of how that complex abstraction works is often held by a handful of expensive, hard-to-replace developers.

In contrast, the Replication model embraces physical, structural division:

Imagine an SME that specialises in high-compliance commercial cleaning for data centres. Their growth strategy is to replicate their operation across 10 different major metro areas, with each branch having its own local team, management, and Profit & Loss.

• Risk mitigation: If the branch in a metro area is hit by a local disaster or regulatory issue, the remaining nine branches continue to generate cash flow. The failure is isolated and non-systemic.
• Knowledge diffusion: The expertise (the “secret sauce” of the business) is codified into a standardised, easy-to-teach SME Playbook, not into an opaque algorithm. This knowledge is diffused across 10 local managers, making the company resilient to the loss of a single key person.

Replication trades the massive, overnight revenue spike of a platform launch for a slow, steady, compounding growth curve that is far more resilient.

Also Read: What scaling in Asia teaches you that Silicon Valley doesn’t

Contrasting the scaling models

The strategic difference between these two paths is rooted in their core assets, their technological roles, and their failure tolerances.

• Core asset and technology’s role

In the abstraction model favoured by tech founders, the core asset is the proprietary algorithm or platform. The technology’s job is to handle 100 per cent of the transactions, making the success of the business entirely dependent on the continuous functioning of that central code.

For the Replication model, the core asset is the standardised physical location and the local team. The technology’s role is fundamentally different: it is used only to standardise the setup and management of the physical asset. Technology becomes the blueprint and the management dashboard, not the final product.

• Growth goal and failure mode

The fundamental objective of the two models diverges significantly. The tech-first approach aims to maximise the number of transactions per server. Its ultimate failure mode is catastrophic systemic failure, where one critical bug or outage can wipe out the entire user base and revenue stream simultaneously.

The SME strategy, however, aims to maximise profitability per location. Its greatest strength lies in its failure mode: localised, isolated failure. If one unit fails due to local conditions, the other units are unaffected, allowing the founder time to diagnose and fix the issue without risking the entire enterprise.

Also Read: AI is scaling fast – is your cybersecurity keeping up?

The velocity vs control trade-off

Ultimately, this is a trade-off between velocity and control.

• Velocity (abstraction): You scale immediately, but you have minimal control over the individual customer experience or local operational failure, and your business is always one algorithm change away from obsolescence.
• Control (replication): You scale slowly, but you have absolute control over the quality, localised service, and profitability of every single unit. Your growth is limited by the time it takes to build or acquire the next asset, which is a strategic, manageable limitation.

For the SME that values long-term stability and is not beholden to the VC mandate of the 10x return, the replicable asset base is the only reliable path. It ensures that the company’s success is rooted in the tangible, high-friction world where competence, not code, is the most valuable asset.

If your business model requires you to spend three months of focused work to launch your next revenue-generating unit, is that a failure of speed or a strategic success that proves your business is too high-friction to be copied overnight? Are you chasing the velocity of a tech giant or the durability of a well-run franchise?

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post Scaling through replication: Why 10 small factories beat one abstracted platform appeared first on e27.

At the end of 2025, a group of cassava farmers gathered not to celebrate a harvest but to protest. The price of cassava had collapsed again, bought by formal markets at below US$0.06 per kilogram, despite a recognised standard of US$0.08 for cassava with 24 per cent tapioca content. Chilli farmers across the country know the same story: middlemen manipulate the prices, and nobody in the system has the structural leverage to push back. Even until now, this unstable price structure remains.

The root of this problem is not productivity. The problem is governance. Products from other districts flow freely into local markets without coordination, underselling prices and leaving farmers with no pricing power and no identity in the supply chain. Efforts to fix this have come and gone, such as credit programs, modernisation schemes, and marketing training. Each program mostly only addresses one layer.

One Village One Product: A framework Indonesia already has, but underuses

One Village One Product, or OVOP, was first launched in Japan in 1979 to revitalise rural economies by anchoring each community to its most distinctive product. Instead of competing on volume, compete on identity. Thailand understood this early. Its OTOP program produced Thung Kula Rong-Hai Thai Hom Mali Rice for the European market and Hauymon Pineapple exports across Asia and the United States. These are not niche results. They came from a national commitment to treating agricultural origin as an economic asset.

Indonesia has that asset, too. Lampung is known for coffee and cassava. South Sumatera for citrus. West Java for tea. Gorontalo for corn. The Great Giant Pineapple in Lampung already shows what is possible when a local product competes globally against exports from Thailand and the Philippines. The potential exists, but the architecture to replicate it at the smallholder level does not yet.

Also Read: Turning crisis into capital: Indonesia’s climate x health pivot gains global attention

Indonesia has implemented OVOP in a limited form, but almost entirely for handicraft and textile products. The agricultural sector has largely been left outside its scope, and that needs to change.

Why OVOP is also a GEDSI intervention

Expanding OVOP into agriculture matters beyond economics. When it is applied thoughtfully, it becomes one of the most effective delivery mechanisms for Gender Equality, Disability and Social Inclusion (GEDSI).

• On gender equality, OVOP works because it demands collective participation across the full production and decision-making chain. Deciding which product best represents a village, how it should be processed, and how it should reach buyers: these cannot be carried out by one group in isolation. Female farmers who engage directly with market dynamics develop a sharper understanding of what buyers want and why. When women are embedded in the design of a village’s product identity rather than added as an afterthought, they become indispensable to its success.
• On disability and social inclusion, OVOP’s cooperative model is significant. When a village organises a single product, work is distributed across many roles such as cultivation, quality control, processing, packaging, documentation, and market liaison. A cooperative designed for broad participation can accommodate members across a wide range of physical capacities. A person with a mobility limitation may not harvest in the field, but can lead quality grading at the processing stage. This is not charity. It is a design choice that makes the cooperative more resilient by drawing on a wider talent base.
• Social inclusion follows naturally. The market access will be easier to enter when it is mediated through collective identity rather than individual bargaining power.

Also Read: Underfunded and under fire: Indonesia’s cyber startups face 2025 reality

The farmers who gathered in Lampung to protest a collapsed price were not asking for charity. They were asking for a system that works. OVOP would give Indonesia’s smallholder farmers something that credit programs and modernisation schemes have never provided, that is, a structural position in the market that is difficult to undercut and a collective identity for a sustainable agribusiness system. 

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post Empowering GEDSI: How OVOP can bring better inclusivity for Indonesia’s farmers appeared first on e27.

For decades, the dominant belief in entrepreneurship has been straightforward: To scale a business, you scale a team.

Hiring has traditionally been the default solution to growth: More engineers to build, more marketers to sell, more operators to manage complexity. This model has shaped everything from venture funding to organisational design.

However, a structural shift is underway.

A growing number of founders are no longer scaling through headcount systems, giving rise to a new category of businesses: One-person companies built and operated with AI.

At the centre of this shift sits an increasingly relevant model: micro-SaaS.

From team scaling to system scaling

The emergence of AI introduces a different form of leverage-one that is not dependent on people, but on systems.

Historically, increasing output required proportional increases in resources:

• More hires.
• More coordination.
• More operational overhead.

Today, that relationship is weakening.

With the right AI workflows in place, founders can:

• Automate research and analysis.
• Systemise decision-making.
• Generate and distribute content at scale.
• Manage customer flows with minimal manual intervention.

This marks a transition from team scaling → system scaling.

A founder’s turning point: From burnout to leverage

This shift is not purely theoretical.

In my own experience, the move toward AI systems emerged from necessity rather than optimisation.

Running multiple ventures required constant:

• Decision-making
• Coordination
• Context-switching

The natural instinct was to hire.

But hiring introduced a different set of constraints-misalignment, communication overhead, and increased operational stress.

The inflexion point came with a simple reframing:

The issue was not capacity. It was leverage.

Also Read: The human touch advantage: Why AI alone won’t win Singapore’s customer economy in 2026

From AI tools to AI systems

Most founders begin using AI at the tool level-generating content, automating small tasks, or experimenting with prompts.

While useful, these applications rarely create structural change.

The real shift occurs when AI becomes a system layer rather than a tool.

Instead of asking: “How can AI help me do this task?”

The question becomes: “How can this entire process run without me?”

This is where concepts like AI “digital twins” begin to emerge-systems designed to replicate aspects of a founder’s thinking, workflows, and decision patterns.

Tools assist – systems compound.

Why micro-SaaS is emerging as the dominant model

As AI lowers the technical and operational barriers to building software, micro-SaaS is becoming a natural outcome.

Micro-SaaS businesses are typically:

• Niche and focused.
• Built by individuals or small teams.
• Subscription-based.
• Designed to solve specific, recurring problems.

Previously, building SaaS required:

• Engineering teams.
• Funding.
• Extended development timelines.

Today, AI enables founders to:

• Prototype quickly.
• Launch with minimal infrastructure.
• Iterate continuously based on user behaviour.

This creates a new class of founder, one who builds systems first, companies second.

Case study: From personal system to product

One example of this shift is the development of Seraphina AI.

Originally built as an internal system to manage workflows, decision support, and content execution, the platform evolved into a standalone product.

• Developed by a single founder using AI-assisted workflows.
• Scaled to thousands of paid users within a short period.
• Continues to operate as both an internal system and a commercial product.

The key insight was not technological, but structural:

A system built to solve personal bottlenecks can often be productised for others facing the same constraints.

This pattern is increasingly common across micro-SaaS businesses emerging today.

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

From knowledge to income: A new conversion layer

One of the more significant implications of this shift is the monetisation of knowledge.

Traditionally, expertise was monetised through:

• Consulting
• Services
• Content

These models are often limited by time and scalability.

AI introduces a new pathway: Knowledge → System → Product → Revenue

A founder’s expertise can now be:

• Captured.
• Structured into repeatable workflows.
• Embedded into an AI-driven system.
• Delivered as a scalable product.

This is the foundation upon which many micro-SaaS businesses are being built.

A practical framework: MINT

To operationalise this, a simple framework can be applied:

Make (Idea)

Identify knowledge, expertise, or problems worth solving.

Implement (Offer)

Structure that into a usable format:

• Product
• Workflow
• Service

Nurture (Funnel)

Build systems for engagement and conversion.

Traffic

Drive visibility and distribution.

Rather than focusing on building a business from scratch, the emphasis shifts toward building systems that generate business outcomes.

Rethinking scale

One of the key advantages of micro-SaaS is its economic profile.

Large user bases are no longer required to build meaningful revenue.

For instance:

• 100 users at US$20 per month generate US$2,000 per month.
• 1,000 users generate $20,000 per month.

This reduces:

• Dependency on external funding.
• Pressure to scale prematurely.
• Operational complexity.

It also enables founders to build sustainably and independently.

The misconception around AI adoption

Despite rapid adoption, many founders are still underutilising AI.

A common pattern is treating AI as:

• A content tool.
• A productivity enhancer.
• A tactical add-on.

These applications, while useful, do not unlock the full value of AI.

The real advantage lies not in doing tasks faster, but in redesigning how work is done entirely.

Also Read: GenAI adoption is rising in Asia, but ROI remains elusive: Adobe

From tools to systems

AI is evolving across three stages:

• Tools
• Assistants
• Systems (agentic workflows)

We are currently transitioning between assistants and systems.

The founders who benefit most will be those who:

• Design workflows early
• Structure knowledge into systems
• Build products on top of those systems

The emerging opportunity

While AI capabilities are advancing rapidly, usability remains a key barrier.

Most solutions today are:

• Fragmented
• Technical
• Difficult to integrate

This creates a clear opportunity.

The next wave of founders will not necessarily be the most technical, but the ones who can translate AI into usable, scalable systems.

A different way to build

The rise of one-person AI companies represents more than a productivity shift.

It reflects a fundamental change in how businesses are designed.

Instead of:

• Scaling teams
• Increasing overhead
• Managing complexity

Founders can now:

• Build systems
• Leverage AI
• Create scalable products independently

Micro-SaaS sits at the centre of this transformation, offering a practical pathway for founders to participate in this new model.

The question is no longer whether AI will change how we work. It is whether founders are ready to change how they build.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post The rise of one-person AI companies and why micro-SaaS is at the centre of it appeared first on e27.

Global digital cooperation has moved from aspiration to necessity. The shift to data‑driven economies, AI‑mediated services, and interconnected infrastructures has outpaced the capacity of national institutions to govern them alone. Nowhere is this tension more visible than in Asia, where some of the fastest‑growing digital markets coexist with some of the deepest connectivity and capacity gaps.​

This is precisely where the next phase of global digital cooperation will be won or lost — in whether we can turn overlapping forums and initiatives into a coherent architecture that serves real people, real institutions, and real communities.

The implementation decade for digital cooperation

The World Summit on the Information Society (WSIS) Forum+20 outcome, the Pact for the Future, and the Global Digital Compact have collectively pushed digital cooperation into an implementation phase. The direction of travel is clear:​​

• Digital inclusion is no longer just about “access”; it now spans affordability, skills, language, disability, safety, and the ability to exercise rights online.​
• Digital public infrastructure and digital public goods are recognised as core enablers of inclusive development, not just technical upgrades.​
• AI and other emerging technologies must be governed through human‑centric, rights‑based, risk‑proportionate frameworks, with particular attention to Global South needs.​

Asia is already responding at scale. ASEAN’s new digital masterplan to 2030, anchored in the 2026 Hanoi Digital Declaration, places AI cooperation, resilient digital infrastructure, a future‑ready workforce, and trusted data flows at the centre of regional integration. New work plans with partners like India, the World Bank, the Republic of Korea, and others cover cross‑border data flows, AI safety, submarine cables, and digital ID interoperability.

But regional ambition alone is not enough. The challenge is to align these efforts with global frameworks so that investments in Asia reinforce — rather than fragment — the emerging global digital order.

Asia as a testbed for “cooperation that delivers”

Asia’s digital landscape is defined by paradoxes. The region hosts world‑class cloud and AI hubs, yet hundreds of millions still lack affordable, meaningful connectivity. Sophisticated data‑governance schemes coexist with fragile online safety systems and shallow AI skills pipelines.​

Also Read: Vietnam’s stablecoin shift: From workaround to regulated tool

This duality creates a powerful testbed for global digital cooperation:

• Connectivity and infrastructure. ASEAN is deepening cooperation on 5G/6G, cloud, data centres, and submarine cables, including new guidelines to speed cable repair and strengthen resilience. These initiatives can feed directly into WSIS Action Line C2 on infrastructure and C5 on security, and into the Global Digital Compact’s connectivity targets.​
• Trusted data flows. Regional mechanisms like ASEAN Model Contractual Clauses, new frameworks on cross‑border cloud, and engagement with the Global CBPR system are gradually building interoperable trust frameworks. This experimentation offers valuable templates for other regions struggling with fragmented data regimes.
• AI and emerging tech. ASEAN is building an AI Safety Network and work plans with partners to support AI skills, infrastructure, and regulatory capacity. At the same time, countries such as Viet Nam are starting to work with the UN to deepen cooperation on global technology governance, including GDC implementation.

What Asia is doing, often under intense resource and time pressure, is “full‑stack cooperation”: linking infrastructure, skills, governance, and cross‑border frameworks into actionable regional compacts. For global digital cooperation to succeed, forums like WSIS, the AI for Good Global Summit, and the Global Dialogue on AI Governance need to treat these Asian experiences not as case studies on the margins, but as central design inputs for global norms and investment priorities.

Science as a common good: Bringing AI and quantum into the cooperation agenda

The International Decade of Sciences for Sustainable Development (2024–2033) reframes science — including digital, data‑intensive science — as a global public good that must be shared more equitably. UNESCO‑endorsed initiatives like the Digital Sustainable Development Goals Programme (DSP) show how big data, AI, and open science infrastructure can be oriented explicitly towards SDG challenges, not just commercial efficiency.

For Asia and the wider Global South, this matters for two reasons:

• AI has already exposed how gaps in infrastructure, skills, financing, and governance can leave Global South countries as rule‑takers rather than rule‑makers.​
• Quantum technologies are beginning to follow a similar pattern, with investments and expertise clustered in a few hubs, while many countries lack basic “quantum literacy” in policy and academic communities.

If global digital cooperation continues to treat AI and quantum as niche or purely technical questions, today’s divides will harden into tomorrow’s structural exclusions.

Also Read: Vietnam wants more than factories; it wants the future of tech

This is where initiatives like the Quantum Nexus Initiative (QNI) and the GXS AI Governance Lab: Ethical Quantum–AI Governance and Capacity for Sustainable Development can play a catalytic role.

QNI and GXS AI Governance Lab: building ethical quantum–AI capacity from the ground up

The GXS AI Governance Lab, led by Green Transformation and Sustainability Network (GXS) in Vietnam, is designed as a science‑for‑sustainability initiative that strengthens ethical, inclusive, and policy‑relevant applications of AI and quantum science in the Global South. It speaks directly to the Science Decade’s call to treat science as a common good and to build a stronger science–policy–society interface.

Together, QNI and GXS AI Governance Lab offer four building blocks that are highly relevant for global digital cooperation with an Asian anchor:

• Capacity building and scientific literacy

QNI and the Lab provide open, modular learning pathways on quantum science, AI, and sustainability, delivered via browser‑based simulations and blended pedagogy designed for low‑resource environments. This directly supports WSIS Action Line C4 on capacity‑building and the GDC’s emphasis on strengthening digital and scientific literacy, particularly in developing countries.

• Ethical and governance innovation

Integrated with the Lab, QNI co‑develops governance toolkits, ethics‑by‑design frameworks, and policy labs that apply UNESCO’s AI ethics principles to concrete quantum–AI use cases in areas like climate resilience, agriculture, health, and urban planning. This adds practical, Global‑South‑driven content to WSIS’s C5 and C10 Action Lines on trust and ethics, and to global AI governance discussions that often lack grounded implementation tools.

• Open science infrastructure

QNI and the Lab operate as open platforms hosting shared datasets, simulation environments, and curated case studies linking quantum and AI applications to SDG challenges. This aligns with WSIS Action Lines C3 and C7 (e‑science, e‑environment, e‑agriculture) and complements initiatives like DSP by widening participation from Southeast Asia and other Southern regions.

• International cooperation and science diplomacy

By connecting universities, regulators, and innovators across Southeast Asia and beyond through joint research sprints and policy dialogues, QNI and the Lab embody C11’s call for strengthened international and regional cooperation — but in a way that is lean, distributed, and tailored to local realities rather than centralised in a few labs.

In practice, these initiatives can plug into global digital cooperation processes in three concrete ways:

• As implementation partners in WSIS Action Line roadmaps and AI governance workstreams, especially for capacity‑building and ethics.
• As open infrastructures that make AI and quantum more accessible to policymakers, educators, and practitioners in the Global South.
• As science‑diplomacy platforms that help Asia shape, not just follow, global rules for emerging technologies.

Also Read: Vietnam wants more than factories; it wants the future of tech

A cooperation agenda that works for Asia — and the world

What would it mean to take Asia’s realities and initiatives like QNI and GXS AI Governance Lab seriously in the next decade of global digital cooperation? Three priorities stand out.

• Co‑design norms around real use‑cases

Global frameworks often emerge abstracted from practice. A more effective approach is to build AI and digital governance norms around concrete use‑cases: AI in school systems, quantum‑secure communications for public services, AI‑driven early‑warning systems for climate risks, and digital IDs for social protection.

Asia is rich in such pilots — from smart‑city programmes and digital‑ID systems, to AI in agriculture and health — but governance and ethics components are often under‑resourced. Platforms like QNI and GXS Lab can help turn these scattered efforts into structured “learning systems” that feed evidence and governance patterns back into WSIS roadmaps, AI for Good, and the Global Dialogue on AI Governance.​

• Make capacity and infrastructure non‑optional pillars of governance

The Global South’s “quantum journey” already shows that without deliberate investment in knowledge infrastructure — researchers, open testbeds, long‑term funding — even well‑written strategies will falter. The same is true for AI and digital governance.​

Global digital cooperation must therefore treat capacity and open infrastructure as non‑optional pillars of any governance compact. That means:

• Funding shared AI and quantum learning platforms, especially in Asia and Africa.
• Supporting open science programmes like DSP and emerging initiatives under the Science Decade umbrella.
• Embedding capacity‑building commitments and metrics into WSIS Action Line roadmaps and GDC follow‑up processes, not just into side programmes.​​

• Build a “network of networks” rather than a new mega‑institution

Digital cooperation already has many nodes: WSIS, IGF, AI for Good, Global Dialogue on AI Governance, regional digital fora, and Science Decade programmes. The risk now is duplication and fatigue.

Instead of another mega‑institution, what Asia — and the world — needs is a “network of networks”:

• WSIS provides the Action Line backbone and implementation reporting.
• The Global Digital Compact offers a political umbrella and shared principles.
• AI for Good and the Global Dialogue focus on frontier‑tech opportunities and risks.
• Science Decade programmes (DSP, QNI, GXS Lab and others) anchor data‑intensive science and capacity‑building in real SDG challenges.

If these networks are intentionally connected — through shared roadmaps, common indicators, and interoperable open platforms — digital cooperation can move from beautifully worded resolutions to measurable change in classrooms, clinics, farms, and communities across Asia and beyond.​

In this sense, global digital cooperation is no longer just about “keeping up” with technology. It is about redesigning our institutions, infrastructures, and scientific ecosystems so that AI and quantum advances work for people, not the other way around. Asia, with its mix of velocity, vulnerability, and ingenuity, is uniquely placed to lead that redesign — especially if initiatives from emerging economies are brought into the centre of the conversation, rather than left at the periphery.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post From fragmentation to shared futures: Re-wiring global digital cooperation from an Asian frontline appeared first on e27.

Over the last couple of years or so, the startup landscape around the world has shifted dramatically. Economic turbulence, rising interest rates, and increasing competition have created ripple effects, creating what was then dubbed the ‘funding winter’. 

As investors struggled to raise funds for deployment, they had to become more intentional about where they put their money. They started concentrating capital into fewer, stronger startups.

The result? Investors stopped chasing unicorns and went after the ‘camels’. 

Fundraising became an activity in proof, not narratives. Founders had to change their ‘growth at all costs’ approach and instead emphasise business fundamentals, market traction, real-world problem solving, and proof of concept. 

Public relations for startups is no longer about visibility and attention. It is now about de-risking your startup in the eyes of investors.

The new investor lens in Southeast Asia

VCs and investors, in Southeast Asia especially, now value proof points a lot more than fluffy stories. They want to invest in proven models, scalable solutions, and knowledgeable founders. 

This is evident in the trends and data. Fundraising cycles are longer and scrutiny is higher as capital efficiency and business fundamentals take centre stage. Mid-stage funding numbers have taken a hit, and ‘zombie startups’ are emerging. 

With all this, there is a strong emphasis on PR. Why? Because you need to answer:

• Does the market actually need this solution?
• Is the startup going to survive and thrive?
• Is the founder capable of growing this business?
• Is the startup seeing traction?

Put simply, you need to be able to justify your business. And this is done through market signalling.

Signal one: Proof of business, not just vision

Investors want to see revenue traction or a clear path to it. In a world where risk-takers are few and far between, nobody will place their bets on a vision which has not yet turned into reality.

What does that mean for your startup PR strategy? Continuous and consistent communication. 

Also Read: The hidden risk in AI adoption: Unchecked agent privileges

When engaging in public relations for startups, you need to be consistent with your messaging and show positive movement on a regular basis.

Partnered with an industry leader? Announce it. Made a leadership hire? Announce it. Expanded into a new market? You guessed it. 

Amidst all these announcements, it is important to keep messaging consistent. The company’s mission, vision, and proof points need to tie back to the overall narrative. 

A great example of this is Philippine proptech Lhoopa. With every major update, it is clear that their end goal is to make housing more affordable and accessible. Investors like this consistency and clarity, especially today.

Signal two: Market position and strategic Relevance

Investors also want to know about your market. Is there a strong need and demand for what you offer? And if there is, where do you stand in the market?

Here, your PR strategy is delicate. You need to position yourself at the top of the market, differentiate yourself from the competition, and ensure that your PR strategy shows strong market demand.

Syfe is a great example. They announced their US$80 million Series C round in June 2025, and their strategic positioning supported this fundraise. They did this by communicating strong business performance and differentiating themselves in a cluttered fintech space. Syfe’s positioning is not ‘investment app’, but ‘a platform built in the region, for the region’. This helps them carve out a niche for themselves and stand on top of that mountain.

Similarly, your startup’s PR must plug into a macro narrative investors can believe in. Use thought leadership and strategic messaging to talk about the market, your solution, your USP, and tie it all together.

Signal three: Credibility and reputation

Reputation engineering is an intricate process, but when done right can bring exponential benefits. For investors, third-party validation, founder credibility, and narrative consistency matter. They signal market confidence and show pathways to future success.

This is where PR becomes decisive. 

Also Read: Beyond the US$70K level: Why Bitcoin’s real test isn’t price yet

Reputation is not just about Share of Voice, but about using your media coverage to establish yourself and engineer a reputation in the ecosystem.

How? The first step is to put yourself out there. Be visible. Speak at conferences, share opinions in credible media, create advocacy, talk about your journey on your socials, and ensure AI search shows your business growth. 

One of Southeast Asia’s newest unicorns is Bolttech, and they exemplify reputation. They do not talk too much. But when they do, the industry listens.

PR supports fundraising

The best PR strategy for early-stage startups does not make you look bigger. It makes you look safer, smarter, and surer. 

Do this by communicating proof, traction, and market position. The simplest way is the three C’s framework. Every good PR strategy for startups needs to talk about your three C’s:

• Concept: Market relevance, category ownership
• Community: Traction, revenue, customers 
• Corporate: Founder reputation, business performance

As a Southeast Asian startup in 2026, if you are not engaging in PR, you are not giving yourself a fair shot.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post The new PR playbook: Why proof, not narratives, wins investors appeared first on e27.

In a deeper push into stablecoin infrastructure, Singaporean fintech firm Nium has announced a tie-up with Coinbase to enable its clients to send, receive, and convert USDC across its cross-border payments network.

The integration, now live, gives banks, fintech firms and multinational businesses a way to move money across both traditional fiat rails and blockchain-based systems from a single platform. Coinbase is providing the stablecoin payment infrastructure, wallet layer, liquidity support and custody, while Nium is turning that into a business-facing product for cross-border treasury and payout operations.

Also Read: How is Nium different from a bank?

For years, stablecoins have been discussed as the future of global transfers, supplier payments and treasury management. The problem has been less about the token itself and more about everything around it: compliance, wallet infrastructure, on- and off-ramps, liquidity, settlement, and the task of making digital dollars work in a world still ruled by local banking systems. Nium’s latest move is an attempt to close that gap.

From crypto promise to operational rails

The pitch is clear. Businesses using Nium can now fund cross-border flows in USDC, receive stablecoins and convert them into fiat currencies when it is time to pay out. That means a company no longer has to manage separate crypto providers, wallet tools and liquidity arrangements while also maintaining its existing payments infrastructure.

Instead, Nium is trying to package the messy middle into one interface.

Prajit Nanu, Nium’s CEO and founder, framed the shift bluntly: “The future of money movement is multi-rail. Fiat and onchain infrastructure will increasingly work together, not in isolation.”

That is the central thesis behind the partnership. Stablecoins are not replacing banks or card networks tomorrow. They are being inserted into specific parts of the money movement chain where speed, settlement timing and capital efficiency matter most. In that sense, Nium is not making a maximalist crypto bet. It is making a practical infrastructure bet.

That is important, especially for a company whose customers are not retail traders but institutions looking to move money across borders without tying up too much capital.

Why this matters for cross-border payments

Cross-border payments remain one of the most inefficient corners of finance. Businesses often have to pre-fund accounts in multiple markets to ensure they can settle local payouts on time. That keeps capital parked in different jurisdictions, creating friction for treasury teams and making liquidity management more expensive than it should be.

Stablecoins offer a possible workaround.

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

If a business can hold value in USDC and convert only at the point of payout, it can move closer to a just-in-time settlement model rather than locking funds in multiple accounts ahead of demand. That is what Nium is now selling: a way to reduce dependence on prefunding while still connecting to regulated fiat payout systems in real markets.

In plain English, it is about freeing up idle cash.

That could be particularly relevant for fintechs and enterprises operating across Southeast Asia, where fragmented payment systems, uneven banking infrastructure and multiple currencies often turn regional expansion into an exercise in operational compromise. The more corridors a company manages, the more painful the capital allocation problem becomes.

Nium claims the integration extends stablecoin payout capabilities across its network of more than 40 licences and more than 190 countries. The geographic breadth is important, but the bigger question is whether clients will trust stablecoins not just as an experimental settlement tool, but as part of day-to-day financial operations.

The industry has spent years saying that the moment is near. Nium and Coinbase are now arguing that it has already arrived.

Southeast Asia is fertile ground for hybrid rails

Nium is closely associated with Singapore’s fintech ecosystem and has long positioned itself as an infrastructure company for businesses that need to move money globally rather than through consumer-facing apps. That is a useful vantage point in a region where cross-border commerce is growing faster than the rails underneath it.

Also Read: How stablecoins are disrupting traditional financial systems

Southeast Asia is full of businesses that sell internationally, hire across markets, collect in one currency and pay out in another. Marketplaces, creator platforms, SaaS firms, travel players, gig economy platforms and remittance providers all run into the same problem sooner or later: local payment systems may be digitising, but international settlement is still too slow, too expensive or too dependent on prefunded accounts.

Stablecoins, particularly dollar-backed ones such as USDC, are becoming harder for infrastructure providers to ignore because they offer a parallel route around some of those frictions. It is a workable alternative in places where treasury efficiency really matters.

For Southeast Asian fintechs, the appeal is not ideological. It is operational. If stablecoins can shorten settlement cycles, improve liquidity management and lower the cost of maintaining multiple currency pools, they become useful even for firms that have no broader interest in crypto.

That is the wedge Nium is pursuing.

A bigger play than payments alone

The Coinbase partnership is not limited to payouts. Nium said businesses holding stablecoin balances can also use them to launch USDC-backed card programmes, allowing those balances to be spent anywhere cards are accepted.

That broadens the ambition significantly.

Instead of treating stablecoins as a narrow treasury tool, Nium is positioning them as a base layer across payments, liquidity and card issuance. The announcement follows the company’s recent launch of a stablecoin card issuance platform, suggesting this is part of a larger strategy rather than a one-off feature release.

If that strategy works, Nium could become one of the more important connective layers between the crypto economy and mainstream financial infrastructure, not by asking businesses to become crypto-native, but by making stablecoins feel like another treasury option inside the systems they already use.

Coinbase, for its part, gets a distribution channel into institutional payment flows. Alec Lovett, the company’s head of infrastructure products, said stablecoins are “transforming how money moves globally” and argued that the partnership extends their utility into “real-world payment flows”.

The larger signal

The Nium-Coinbase deal is less about one new feature and more about where financial infrastructure is heading.

Also Read: Stablecoins surge in Southeast Asia 2026: A real shift or just a bridge to CBDCs?

The old model forced businesses to choose between the regulated comfort of fiat systems and the speed of onchain transfers. The emerging model is hybrid: use blockchain rails where they improve liquidity and settlement, then connect back into local banking and card networks where money still needs to land and be spent.

That hybrid future has been talked about for years. What is changing now is that infrastructure providers are starting to package it into products that enterprise customers can actually deploy.

For Nium, this is a chance to stay ahead of a market where cross-border payments are being reshaped by both regulation and technology. For Coinbase, it is a shot at making USDC more than a token parked on exchanges. And for fintechs in Southeast Asia, it is another sign that the next phase of payments innovation may not come from replacing the system, but from stitching two systems together until the distinction matters less.

The post Nium bets on a future where stablecoins swipe like credit cards appeared first on e27.