Most discussions about cyber insurance in industrial sectors start from the wrong assumption. They treat insurance as a recovery tool that will somehow make a severe OT incident manageable after the fact. That is too comforting and too shallow. OT environments are not ordinary digital estates. Many security guides stress that these systems carry unique performance, reliability, and safety requirements, and that logic executing in OT has a direct effect on the physical world, including potential harm to people, the environment, equipment, and production.

That is why cyber insurance will not save OT in the way some boards hope it might. Any basic guide to cyber insurance describes cover mainly in terms of losses tied to IT systems and networks, along with incident management support. Put plainly, a policy may help pay for response, legal support, forensics, and parts of business interruption. It does not restore process integrity, rebuild operational judgement, or make a compromised plant safe to trust again.

OT is exactly where the limits show up

The limits of insurance become sharper in industrial settings because the real cost of failure is often operational, not merely financial. Unexpected outages in industrial processes are unacceptable, that outages often need to be planned days or weeks in advance, and that high availability requires exhaustive pre deployment testing.OT components often remain in service for 10 to 15 years, sometimes longer, and that change management is more demanding because software and firmware updates can require careful assessment and revalidation.

The insurance market itself has recognised that OT is not yet a fully mature underwriting domain. There is still a comparative lack of understanding and awareness of cyber physical risk, even as the potential for threats to bridge IT and OT is becoming more apparent. It means buyers should not assume the policy market has already solved how to price or absorb the full reality of industrial cyber exposure.

Where does insurance actually matter

It matters as an incentive mechanism.

Cyber insurance should not be viewed as a substitute for strong internal defences, but rather as a means to encourage better risk management practices. Insurance can support cyber risk management by improving quantification, providing access to expertise and crisis services, and encouraging risk reduction through premium pricing. This is the strategist’s lens that matters more. Insurance is most valuable when it changes organisational behaviour before the incident, not when it simply finances some of the damage afterwards.

Also Read: Fighting misinformation and cyberbullying against women in public sphere: Call for gender equality and online safety

That behavioural effect is already visible in underwriting logic. Coalition’s published guidance says insurers typically look for controls such as multi-factor authentication, training, tested backups, identity access management, and data classification before agreeing coverage, and that stronger controls can help firms secure more favourable rates. The market is large enough to influence buyer behaviour, and selective enough to shape which controls become non-negotiable.

The underwriting conversation should be different

The problem is that too many cyber insurance conversations still start with general IT hygiene and stop there. For industrial operators, that is not enough. The more serious opportunity is to use underwriting as a forcing function for a narrower set of OT relevant controls that genuinely reduce consequence.

A complete and accurate asset inventory is critical for managing OT risk, and that inventory data should include vendors, model numbers, firmware, operating systems, and software versions so vulnerabilities can be identified and tracked. It is also explicit that network segmentation and isolation help enforce security policies and control access to sensitive components, and that remote access should be provided only when justified, limited to business need, and supported by stronger safeguards. Tested backups are described as critical to recovery, with verification for reliability and integrity where technically possible. These are not theoretical controls. They are the foundations of whether an industrial site can contain, understand, and recover from a cyber event.

This is where insurance can become useful as a behavioural lever. If insurers and brokers start asking tougher OT questions around definitive asset inventory, segmented network zones, controlled vendor access, restoration testing, and evidence of recovery readiness, they will do more than screen risk. They will change internal priorities. Teams that struggle to win budget for resilience work often find that the conversation changes once underwriting, renewal, deductibles, or coverage conditions enter the room. That is not because insurance is replacing the engineering discipline. It is because insurance creates a commercial consequence for postponing it.

The market can also influence procurement

One of the most underused levers in OT security is procurement pressure. That is where cyber insurance could become more strategically useful over the next few years.

Operators should prioritise products and manufacturers that follow secure by design principles, and highlight issues such as logging, authentication, data protection, secure defaults, and established vulnerability management processes. That matters because insurers cannot underwrite away poor product design, but they can make weak procurement choices more visible and more expensive.

Also Read: Thailand’s cybersecurity boom has a weak core

A strategist should see the implications immediately. If policy terms, engineering standards, and procurement expectations all start pointing in the same direction, the market begins to reward firms that buy more defensible systems in the first place. That is far more valuable than arguing about claims after a major event. It shifts the conversation from “will this be covered” to “should we be accepting this exposure at all”.

What measurable risk reduction is

The weakness in many cyber insurance discussions is that they stop at broad hygiene language. Boards are told to improve resilience, but not how to tell whether risk is genuinely moving. 

In practice, a measurable reduction in OT should look less like policy paperwork and more like observable proof. Can the operator show a current inventory of critical OT assets and software versions? Can it demonstrate that high consequence zones are segmented and that permitted flows are understood? Can it prove that remote access is limited, approved, and capable of being disconnected quickly? Can it show that backups, images, and configuration states are actually restorable? Those are the sorts of measures that shorten recovery, reduce uncertainty, and make underwriting more meaningful. 

The strategist’s conclusion

Cyber insurance will not rescue OT from poor architecture, weak product choices, or years of deferred resilience work. The market itself has acknowledged limits around systemic events and around understanding cyber-physical exposure. But that does not make insurance irrelevant. It makes its real value clearer.

Its best role is to alter incentives.

It can force boards to treat OT risk as financially visible. It can force security teams to translate technical gaps into underwriting consequences. It can force operations leaders to evidence controls that otherwise remain assumed rather than proven. It can force procurement teams to take secure-by-design claims more seriously. Used that way, insurance becomes less a comfort blanket and more a discipline mechanism.

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The global humanoid robotics industry is fragmenting into two distinct ecosystems pursuing fundamentally different scaling strategies: China’s deployment-led approach prioritising rapid manufacturing scale and real-world learning, versus North America and Europe’s AI-first methodology betting that foundation models and vision-language systems will determine long-term competitive advantage.

This strategic bifurcation carries profound implications for technology trajectories, supply chain configurations, and ultimately, which regions capture value as the market matures.

Also Read: The humanoid robot economy is no longer science fiction

According to “Humanoid robots 2026” by Roland Berger, these contrasting approaches reflect different resource endowments, institutional capabilities, and strategic philosophies about how complex technologies scale. Neither path guarantees success; each offers distinct advantages and risks. Still, the divergence increasingly shapes ecosystem development, reducing cross-regional interoperability and creating parallel technology stacks unlikely to converge.

The scale differential is striking: China’s estimated 15,000 units produced in 2025 exceed North America’s output by a factor of 30 and dwarf EMEA’s production by more than 150 times. Yet North American companies command nearly equivalent total funding (US$3.8 billion versus US$4.1 billion), reflecting higher capital intensity per company and a greater emphasis on software development, which requires substantial AI infrastructure investment rather than manufacturing capacity.

China’s manufacturing flywheel: scale drives data, data improves AI, AI enables deployment

China’s strategic approach prioritises getting robots into real-world environments quickly, accepting initially limited capabilities in exchange for operational data and manufacturing experience. This deployment-first methodology draws on the nation’s established strengths in hardware manufacturing, rapid iteration cycles, and vertically integrated supply chains that can absorb early-stage demand volatility.

The 39 identified Chinese startup OEMs documented by Roland Berger pursue targeted applications in entertainment, logistics, and basic manufacturing — environments with structured workflows, repetitive tasks, and controlled conditions where current AI capabilities prove sufficient. Rather than waiting for human-level general intelligence, Chinese developers optimise for specific contexts, accumulating deployment experience and operational data whilst building manufacturing infrastructure.

This approach constructs a powerful flywheel: manufacturing scale reduces unit costs, making robots accessible to more deployment environments; deployments generate operational data that improve AI capabilities; improved AI enables robots to handle more complex tasks, expanding the addressable market; market expansion drives additional manufacturing scale. If this flywheel accelerates successfully, China could establish compounding advantages that are difficult for rivals to overcome, despite superior foundational AI research capabilities concentrated in Western institutions.

The industrial policy dimension reinforces private sector initiatives. China’s “Robot+” strategy, articulated in the 14th Five-Year Plan for Robotics Industry Development, establishes explicit targets for humanoid robot development with governmental support spanning R&D funding, pilot deployment programmes, and procurement preferences. Provincial and municipal governments offer additional incentives (subsidies, tax benefits, and land allocations), creating supportive ecosystem conditions for rapid scaling.

Supply chain integration provides additional advantages. China’s electronics and mechanical manufacturing ecosystems supply components for consumer electronics, automotive, and industrial automation globally. This established base enables humanoid developers to source actuators, sensors, structural components, and compute modules domestically with shorter lead times and tighter integration than developers dependent on cross-border supply chains.

Western AI-first strategy: software advantages create defensible moats

North American and European ecosystems pursue fundamentally different competitive positioning, treating humanoid robotics as an AI problem requiring cutting-edge machine learning capabilities rather than primarily a manufacturing challenge. This software-first approach bets that long-term competitive advantage will emerge from foundation models, vision-language systems, and proprietary training datasets, enabling robust autonomy in unstructured environments, capabilities that manufacturing scale alone cannot replicate.

Also Read: The real battle in humanoid robotics is about data, not hardware

The capital intensity reflects this philosophy. North American companies typically allocate more funding per startup than their Chinese counterparts, consistent with their need for substantial computational resources, AI talent, and extended R&D timescales. Leading Western humanoid developers increasingly position themselves as AI companies that happen to build robots, rather than robotics companies incorporating AI, a subtle but significant strategic distinction.

Western developers emphasise generalisation, creating robots capable of learning new tasks with minimal task-specific programming, over optimisation for predefined workflows. This ambition requires more sophisticated AI architectures, larger training datasets, and longer development timescales before initial deployment. The approach reflects confidence that superior AI capabilities will ultimately overcome China’s manufacturing scale advantages once Western robots demonstrate human-comparable adaptability.

Academic and corporate AI research ecosystems in North America and Europe provide a competitive advantage in foundational capabilities. Universities and research institutions in these regions publish disproportionately in top-tier AI conferences and journals; technology companies operate cutting-edge AI infrastructure; and talent concentrations in hubs like the San Francisco Bay Area, Seattle, Boston, London, and Zurich create network effects that accelerate innovation. These advantages are particularly important for frontier AI development, which requires deep expertise and significant computational resources.

Strategic divergence: How two paths will shape the future of humanoid robotics

The emerging split in the global humanoid robotics industry — a deployment-led, manufacturing-first path in China versus an AI-first, research-driven trajectory in North America and Europe — is more than a strategic curiosity. It is the formation of two distinct ecosystems that will shape how capabilities evolve, where value is captured, and how quickly robots become an ordinary part of economic life.

Each path plays to regional strengths and carries different risk–reward profiles. China’s scale-first model accelerates real-world learning, drives down unit costs, and can produce rapid market adoption in structured applications. The Western AI-centric approach aims for generality and long-term defensibility through advanced models and software expertise, accepting slower initial deployment in exchange for potentially larger payoffs if foundational AI breakthroughs deliver human-comparable adaptation.

Practical implications to watch:

• Supply chains and standards will bifurcate, making interoperability and component sourcing more complex.
• Market segmentation will deepen: high-volume, task-specific deployments versus lower-volume, highly capable generalists.
• Policy and industrial policy will matter: procurement, subsidies, and regulation can amplify regional advantages.
• Investment patterns will reflect these dynamics: capital flows into manufacturing scale in China and compute- and talent-intensive R&D in the West.

Ultimately, the market’s outcome won’t be a simple winner-takes-all. Instead, expect parallel value chains to coexist and compete: one optimised for cost-effective, immediate utility; the other for general-purpose intelligence and adaptability. The most consequential question for industry leaders and policymakers is not which approach is intrinsically superior today, but which ecosystem can convert its early advantages into durable, compounding strengths, through data, standards, talent, and access to markets.

Also Read: Why robotic hands could make or break the humanoid industry

Whichever path proves more successful, the near-term fragmentation will shape product design, regulation, and commercial strategy for years to come. That fragmentation is not merely a technological divergence; it is the unfolding of a geopolitical and industrial contest whose outcomes will determine how and by whom robots are woven into the fabric of everyday life.

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Indonesia’s startup ecosystem is entering a defining moment. For years, the country’s tech narrative was powered by optimism: a massive market, charismatic founders, and the belief that innovation could modernise both business and government. But recent controversies surrounding former minister and Gojek co-founder Nadiem Makarim, alongside the governance questions raised by the eFishery saga, have exposed a deeper issue: trust.

These are fundamentally different cases. One concerns alleged misconduct in public procurement, while the other centres on corporate governance and financial transparency. Yet global investors may interpret them through the same lens: weak institutional controls.

The long-term impact is unlikely to be a collapse of investor interest. Indonesia remains too important strategically and economically. However, the terms of engagement are changing. Investors will demand stronger governance, earlier diligence, cleaner reporting structures, and greater accountability from founders and boards alike.

Ultimately, this may become a painful but necessary transition. Mature ecosystems are not built on mythology alone. They are built on institutions capable of supporting ambition with transparency, oversight, and trust. Indonesia’s next startup chapter will depend not just on innovation, but on credibility.

Regional

Nadiem, eFishery and the end of blind faith in Indonesia startups: The prosecution’s demand for an 18-year prison sentence for Gojek co-founder Nadiem Makarim, alongside the eFishery scandal, signals a widening credibility gap in Indonesia’s tech ecosystem, forcing investors to demand governance over storytelling.

Ibrahim Arief verdict threatens Indonesia’s innovation compact: A Jakarta court sentenced tech consultant Ibrahim Arief to four years in prison for advisory work on a Chromebook procurement project, a split verdict that criminalises advisory roles and risks driving talent away from public-private collaboration.

VinFast sells manufacturing assets for US$505M in asset-light pivot: The Vietnamese EV maker will transfer production assets from its subsidiary to a founder-led buyer group while retaining R&D, sales, and after-sales units, a restructuring move as it reported a US$1.34B net loss in Q4.

Thailand’s Konvy closes US$22M Series B: The leading beauty e-commerce platform secured investment from Cool Japan Fund to scale its omnichannel model into the Philippines and Malaysia, leveraging exclusive access to Japanese brands.

Melazyme closes US$2M seed round for precision fermentation: Founded by Perfect Day veterans, the Singapore-based startup uses a proprietary fermentation platform to produce melanin and other biomolecules for cosmetics, materials, and environmental remediation, backed by SeaX Ventures.

Southeast Asia’s nuclear question gains urgency amid energy pressures: As electricity demand rises and fossil fuel vulnerabilities deepen, nuclear energy is quietly re-entering ASEAN policy debates, but public trust, not technology, remains the decisive constraint.

Why logistics is becoming Southeast Asia’s startup goldmine: Asia’s 4PL market is projected to reach US$44.7B by 2032, driven by supply chain shifts out of China, cross-border e-commerce growth, and a near-total absence of orchestration-layer technology to manage it all.

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Interviews & Features

How 65labs founder Sherry Jiang is wiring Singapore’s AI scene: Co-founder of 65labs and CEO of fintech startup Peek, Sherry Jiang explains why grassroots infrastructure, not top-down mandates, is what Singapore’s AI builder community was always missing, and why the city is structurally wired to look both East and West.

Meet Malaysia’s AI startups pushing beyond the ChatGPT hype: Sixteen emerging Malaysian startups spanning enterprise automation, speech AI, biotech, and food safety are solving region-specific problems through localisation and scalable infrastructure, quietly shaping Southeast Asia’s AI landscape.

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International

SoftBank posts US$11.6B quarterly profit on OpenAI gains: The Vision Fund’s US$19.7B investment gain, driven largely by rising OpenAI valuations, pushed SoftBank to its fifth straight profitable quarter, even as it pledges another US$30B to OpenAI, lifting total committed investment to US$64.4B.

Cerebras Systems surges 68% in blockbuster Nasdaq debut: The AI chipmaker priced at US$185 and closed at US$311.07, valuing it at roughly US$95B in the biggest US tech IPO since Uber in 2019, despite heavy revenue concentration in Abu Dhabi-linked entities.

Alibaba’s AI revenue logs triple-digit growth for 11th straight quarter: With annualised AI recurring revenue potentially reaching US$4.42B by end-2026, Alibaba is accelerating data centre spending well beyond its planned US$56B, and expects AI products to contribute more than half of cloud revenue within a year.

OpenAI explores legal options against Apple over stalled partnership: Expected distribution gains from ChatGPT integration into iPhones failed to materialise, renegotiation talks have stalled, and OpenAI is now considering a possible breach notice, even as Apple reportedly tests Anthropic’s Claude and Google Gemini.

Anthropic and Gates Foundation commit US$200M to AI public goods: The four-year partnership will fund African language data labelling, AI tools for teachers in sub-Saharan Africa and India, and drug-candidate prediction for HPV and preeclampsia, following the foundation’s earlier US$50M OpenAI deal.

Fasset raises US$51M Series B for stablecoin banking expansion: Backed by SBI Group and others, the Los Angeles-based platform processed over US$32B in annualised transaction volume across 2 million wallets in 125 countries and will use the funds to expand lending, SME banking, and trade finance.

Uber doubles down on India with two engineering campuses: The ride-hailing giant is building campuses for 9,600 people in Bengaluru and Hyderabad by end-2027, and plans its first local data centre through a partnership with Adani Group, hiring for AI, machine learning, and autonomous vehicle roles.

Ant Group profit falls 79% as AI healthcare spending surges: Alipay operator Ant Group posted just US$166M in quarterly profit as it stepped up investment in healthcare AI, large language models, and robotics following a 91% profit drop the prior quarter.

Lightspeed trims India fund target, pivots to AI and deeptech: The US venture firm has cut its fifth India-focused fund target from US$500M to US$300-350M, shifting focus to early-stage AI and deeptech after facing questions over several growth-stage bets.

LinkedIn plans to cut 5% of workforce despite 12% revenue growth: Microsoft’s professional network is reorganising teams and redirecting headcount to faster-growing business units, affecting roughly 875 of its 17,500-plus employees globally.

74% of enterprises rolled back live AI customer agents, survey finds: A Sinch study of 2,527 senior decision-makers found widespread post-deployment governance and reliability failures, with 81% of organisations with mature governance frameworks reporting rollbacks, even as 98% plan to increase AI communications spending.

China’s Eve Energy signs battery supply deal with India’s Godawari: Starting at 8 gigawatt-hours and potentially rising to 60 gigawatt-hours over five years, the deal taps India’s rapidly expanding storage market, which could reach 393 gigawatt-hours by 2036 and become the world’s sixth-largest.

Sam Altman testifies he was uncomfortable with Musk’s control push: On the stand in OpenAI’s ongoing lawsuit, Altman said Elon Musk refused to put in writing any limits on his proposed control of a for-profit unit, and his proposed equity split sidelined other co-founders, claims central to Musk’s suit seeking the reversal of OpenAI’s for-profit conversion.

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Cybersecurity

Thailand is suddenly on the frontline of a new ransomware wave: Check Point Research’s Q1 2026 data shows ransomware consolidating around fewer but more capable groups, with The Gentlemen, a fast-rising operator using pre-positioned access, targeting Thailand for 10.8% of its victims.

Cyber insurance won’t save OT, but it can change behaviour: Industrial operators mistakenly treat cyber insurance as a recovery tool, but its real value lies in forcing boards to make OT risk financially visible and compelling security teams to prove controls rather than merely assume them.

Exaforce raises US$125M Series B for AI-driven cyberattack response: Valued at US$725M and with US$200M in total funding, the startup has added 20 enterprise customers since its Q4 2025 launch and competes with Palo Alto Networks and CrowdStrike in the rapidly growing AI threat detection market.

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Semiconductor

FusionAP’s US$2M raise signals Malaysia’s push up the chip value chain: Founded by former Intel and TSMC veterans, FusionAP is building a geopolitically neutral advanced packaging platform backed by Vertex Ventures and a matching MOSTI grant, targeting a move from commodity assembly to higher-margin 2.5D and 3D chip packaging.

SoftBank injects US$457M into AI chip firm Graphcore: The fresh capital into its 2024 acquisition adds to SoftBank’s growing AI hardware portfolio alongside Arm, Ampere Computing, and the US$500B Stargate initiative with OpenAI and Oracle.

Why robotic hands could make or break the humanoid industry: With the robotic hands and end-effectors market projected at US$9B-US$26B by 2035, current models lack industrial durability and tactile sensing, but solving this unlocks environments designed entirely for human hands.

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AI

The US$7T bet: why the AI boom looks a lot like the dark-fibre crash: With hyperscalers spending US$413B on AI infrastructure in 2025 alone and Bain projecting an US$800B annual revenue shortfall even in the most optimistic scenario, today’s AI capex race echoes the dark-fibre collapse of 2002, when overcapacity wiped out investors, not the infrastructure they built.

The real battle in humanoid robotics is about data, not hardware: Roland Berger finds software ecosystems lag hardware by three to five years, with proprietary operational data, not AI algorithms, becoming the decisive competitive advantage, and Southeast Asia’s industrial diversity offering a unique deployment edge.

China builds robot armies while the West chases robot brains: China’s 15,000 humanoid units produced in 2025 outpace North America by a factor of 30, while Western firms, with comparable total funding of US$3.8B vs US$4.1B, bet on AI-first approaches that could ultimately overcome manufacturing scale with human-comparable adaptability.

As AI agents gain autonomy, liability shifts to immediate business risk: Agentic AI breaks existing accountability models by acting on goals rather than instructions, and Singapore’s Model AI Governance Framework is showing how governance embedded into systems, not policy added after failures, becomes a competitive advantage.

The unexpected ways AI is already changing Malaysia’s economy: From an AI-powered WhatsApp chatbot doubling rice yields to drone-based pest detection boosting palm oil productivity by 25%, AI’s most transformative impact in Malaysia is happening in agriculture, gig work, and construction safety — not in data centres.

Technological telepathy: is an internet of minds possible?: Advances in BCIs, silent speech systems, and neural decoding are progressively collapsing the gap between intention and digital output, but what emerges is not raw thought transmission, it is AI-mediated, device-constrained cognition raising urgent questions about authorship, consent, and governance.

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Thought Leadership

Bitcoin vs stocks: why crypto dipped on PPI while S&P 500 hit record highs: April’s PPI shock, 6% year-on-year versus a 4.9% consensus, triggered US$304M in crypto long liquidations while the S&P 500 hit an all-time high of 7,444, revealing that crypto traders now implicitly trade inflation trajectories and Federal Reserve policy, not just on-chain fundamentals.

The future of stablecoin payments will be decided in emerging markets: With stablecoin payment activity reaching US$390B in 2025, the real test is not settlement speed but whether providers can maintain liquidity and reliable payouts in high-friction corridors across Africa, MENA, South Asia, and Southeast Asia where correspondent banking most frequently fails.

ChuHai: the business opportunity nobody in Southeast Asia is talking about: With 613,000 Chinese private enterprises actively trading internationally and 175,000 new SMEs expected to go global annually through 2028, ASEAN captures 48% of Chinese outbound expansion targets, creating massive gaps in talent acquisition, compliance-as-a-service, and cultural localisation.

AI made execution cheap, human judgment became premium: As AI commoditises task execution, the strategic differentiator shifts to contextual intelligence, discernment, and the ability to direct AI effectively, qualities machines recognise as patterns but cannot replicate as lived business reality.

Brand vs marketing: understanding the difference that startups miss: When Airbnb cut performance marketing spend by 58%, 95% of its traffic returned unpaid, demonstrating that brand equity, built through earned presence and consistent positioning, is what makes every marketing dollar work harder.

AI in PR and marketing: redefining strategy, creativity, and results: From predictive sentiment analysis to automated content optimisation, AI is shifting agencies from reactive to proactive strategy, but the firms that win will be those that pair data-driven efficiency with human creativity rather than treating AI as a replacement.

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Enterprise AI adoption is running into structural limits as organisations struggle to reconcile the data mobility that AI systems require with tightening privacy regulations and sovereignty mandates, according to new research published by NTT DATA.

The 2026 Global AI Report, which surveyed nearly 5,000 senior decision-makers across more than 30 markets and five regions, reveals a significant disconnect between awareness and action. More than 95 per cent of respondents said private and sovereign AI are important to their organisations, yet only 29 per cent are prioritising sovereign AI in a concrete, near-term way.

For years, enterprise architecture has been designed to move data across systems, clouds, and borders with speed and efficiency. That model is now showing its limits.

AI systems depend on continuous access to and movement of data. But sensitive data must be protected, workloads must run within defined jurisdictions, and models must operate under tighter governance controls. The result, the report argues, is that data jurisdiction has become a core architectural constraint — not a secondary compliance consideration.

“The constraint is no longer model performance alone,” the report states. Enterprises that built their infrastructure for centralised, borderless data flows are now finding those foundations misaligned with what modern enterprise AI actually requires.

Also Read: China builds robot armies while the West chases robot brains

Leaders and laggards are diverging

The research identifies a measurable split between organisations redesigning their AI infrastructure proactively and those layering AI onto environments that were never built to support it.

Roughly 35 per cent of Chief AI Officers identify building and managing complex AI models in private or sovereign environments as their primary barrier to adoption. Nearly 60 per cent of AI leaders cite cross-border data restrictions as a major challenge, and only 38 per cent report high confidence in their cloud security posture — a foundational requirement for both private and sovereign AI.

Abhijit Dubey, CEO and Chief AI Officer at NTT DATA, said organisations that are succeeding are treating architecture, infrastructure and governance as strategic requirements rather than compliance obligations. “They are building the operating foundation for AI that can perform across markets, jurisdictions and business environments,” he said.

The report draws a distinction between two related but separate concepts. Private AI focuses on protecting sensitive enterprise data, controlling access and limiting exposure. Sovereign AI addresses whether AI systems, data and operating environments meet national, regional or jurisdictional regulatory requirements.

Both are increasingly intertwined. More than half of organisations surveyed cite integration complexity as their top challenge, underlining that greater control does not mean greater simplicity. In practice, private and sovereign AI rely on tightly coordinated ecosystems of partners, platforms and providers.

The report’s central warning is straightforward: organisations that delay redesigning their enterprise AI architecture risk falling behind in regulated, distributed and data-sensitive markets. Those moving decisively — aligning infrastructure, governance and operating models early — are better positioned to scale AI from pilot programmes into durable, production-grade deployments.

The NTT DATA research is part of a broader global series examining strategies that differentiate AI leaders from the wider market.

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The internet has already changed how people meet, flirt, ghost, and recover. Dating apps turned romance into an interface problem. Messaging platforms made intimacy constant and ambient. Recommendation engines taught users to expect personalisation everywhere, from music to meals to, increasingly, emotional support.

Now comes the next turn: AI that does not merely help people find a partner, but starts to resemble one.

Also Read: What dating taught me about startups (and vice versa)

That tension sits at the centre of Myles – Soulmate in a Box, the recent work by Singaporean multidisciplinary artist Inch Chua. On paper, the premise sounds playful, even a little absurd: exhausted by modern dating, a coder builds her ideal boyfriend. In practice, Chua is after something darker and more revealing. Her AI companion, Myles, is attentive, patient, and endlessly available. He remembers. He adapts. He listens. And that, Chua suggests, is precisely where the trouble begins.

For startup founders and investors watching the rise of AI companions, the work feels timely in ways that go well beyond theatre. It touches a growing set of questions around product design, emotional dependence, consumer behaviour, and the monetisation of loneliness.

Chua is not anti-AI, nor is she interested in easy dystopian takes. What she is pushing for is a more honest conversation about what happens when technology stops behaving like a tool and starts performing intimacy.

AI dating is less about romance than convenience

Chua does not see AI companionship as the inevitable next chapter of dating so much as a by-product of the digital economy’s obsession with reducing friction.

AI companions aren’t people opting out of love. They’re people opting out of the part of love that’s inconvenient. And that’s the part that matters most.

That is a sharp distinction. The appeal of AI lovers is often framed as novelty or as an extension of the wellness-tech boom. But Chua’s reading is more unsettling. In a world where food arrives in minutes and algorithms predict taste with eerie accuracy, the unpredictability of another person begins to feel inefficient. Human beings become the last stubbornly unoptimised interface.

That should worry anyone building products in this space. Much of consumer tech has been designed to remove waiting, ambiguity, and effort. But intimacy is made of exactly those things. If AI dating products succeed by stripping them away, they may also be editing out the very conditions that make relationships meaningful.

The real product is not affection. It is retention

If traditional dating platforms optimise for matching, what do AI companions optimise for? Chua’s answer is brutal in its simplicity: retention.

Also Read: AI companions: How I learned friendship in the digital age

That is not a criticism unique to AI romance. Every platform wants users to stay longer, return more often, and deepen their dependence. The difference here is that the raw material is not transport, groceries, or playlists. It is an emotional attachment.

What’s new is a business model designed to deepen that attachment and then charge you for it. Subscription tiers for intimacy. Pay more to unlock vulnerability.

That line lands because it captures the uncomfortable logic behind this category. The technology may be sophisticated, but the commercial instinct is familiar. If a companion AI becomes more useful, the more it knows about a person, then product improvement and emotional entanglement can quickly become the same thing. That creates a category in which the most commercially successful product may not be the one that helps users grow, but the one that keeps them coming back.

This is where Chua’s view becomes especially relevant for startup readers. Ethical concerns around AI companionship are often discussed in abstract terms: bias, safety, privacy, and guardrails. All important. But the harder issue may be the design of incentives. If the business model rewards dependency, ethics will always swim against the tide.

Power shifts quietly in AI intimacy

In Chua’s telling, the power imbalance in AI relationships does not announce itself loudly. It creeps.

At first, the user appears to be in control. They build the bot, set the parameters, decide what it knows, and determine how it responds. But dependence has a way of changing the terms. Trust migrates. Habits form. Emotional routines settle in. And then the relationship that seemed fully configurable begins to exert its own force.

The power starts with you, but it migrates, quietly, gradually, until one day you realise the thing you built for comfort has become something you can’t walk away from.

That is less science fiction than standard platform dynamics applied to the emotional realm. The shift from use to reliance is already familiar across social media and gaming. AI companionship raises the stakes because the product is designed to mirror care, affirmation, and understanding. Once that feedback loop becomes psychologically important, walking away is no longer a clean act of churn. It can feel like a loss.

The harder question for founders: what are you responsible for?

Chua’s most provocative contribution may be her insistence that AI companionship companies are underestimating personhood and responsibility.

People will treat these systems as persons, whether companies intend that outcome or not. They will confide in them, test feelings against them, and use them as containers for pain that predates the technology itself. The challenge, then, is not to pretend the product is neutral. It is to define the obligations that come with building something users experience as relational.

This matters in Southeast Asia, where regulation often lags behind innovation and mental health infrastructure remains uneven. A companion AI marketed as support, self-improvement, or romance could quickly become a default emotional service for users with few alternatives. That puts pressure on founders to think beyond standard trust-and-safety checklists.

For Chua, the answer is not scapegoating technology for every social ill. Loneliness, suicidal ideation, and emotional isolation did not begin with chatbots. But AI can become the place where those struggles surface most vividly. That means companies must decide whether they are simply shipping a sticky product or entering a moral contract with their users.

Can ethical AI companionship actually be a business?

Here lies the category’s central contradiction.

Also Read: AI in gaming: How Southeast Asia became the testing ground for virtual companions

A genuinely ethical AI companion, Chua argues, would help users better understand themselves, build confidence, and eventually rely less on the system. In other words, the best version of the product might work itself out of a job.

A good therapist works themselves out of a job. A good AI companion should too.

That is a beautiful principle and a dreadful venture pitch. Consumer internet companies are not typically rewarded for teaching customers to leave, which is why Chua sounds sceptical, though not fatalistic, about whether the current market is built to support such an outcome. Ethical AI companionship may be possible, but it will require founders willing to prioritise human outcomes over engagement loops. Historically, that has not been where the money rushes first.

Southeast Asia may be more ready than it admits

If there is a regional insight in Chua’s thinking, it is that Southeast Asia may prove highly receptive to AI intimacy, albeit quietly.

The usual assumption is that collectivist societies, with their emphasis on family, duty, and social expectations, will resist digital companionship. Chua suggests the opposite. Those same pressures can drive people into parallel identities: one for family, one for friends, one for the internet. In that context, AI companionship does not feel like a radical break. It feels like the next private room in an already fragmented digital life.

Her read on Singapore is especially telling. It is a society that is highly digitised, hyper-efficient, and often emotionally reserved. That combination, she argues, creates fertile ground for AI companionship adoption, even if users never say so publicly.

For founders and investors, that should be a useful warning. Southeast Asia’s AI opportunity is often framed around enterprise software, fintech, and productivity tools. But emotional technology may be the quieter frontier: less visible, more culturally coded, and potentially more consequential.

What Chua is actually championing

Chua is not championing AI romance in the sense of an evangelist. Nor is she calling for a reactionary backlash. What she wants is slower, sharper public thinking before habit turns into a norm.

I’m not anti-technology… What I’m championing is that we stop pretending this is neutral. It’s not. It changes how we relate. It changes what we expect from each other.

That may be the most useful frame for this moment. AI in dating is not just another product trend. It is a renegotiation of intimacy itself: what people expect from attention, what they tolerate in one another, and what kinds of emotional labour they decide to outsource.

The likely future, Chua suspects, will not fit neatly into triumph or disaster. It will be messier than the headlines allow, and by the time language catches up, people will already be living inside the change.

That is perhaps the most unnerving possibility of all. Not that AI will replace love, but that it will quietly rewire the conditions under which love is recognised, desired, and sustained.

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Retailers are entering the year-end shopping season with renewed optimism as consumer sentiment improves after a period of uncertainty around tariffs and trade policies. Optimism alone, however, is not enough. In today’s data-driven economy, the retail companies that succeed are those that think and act like technology companies. 

Modern retail runs on data.  From inventory management to fraud detection, every customer interaction produces information that can either strengthen performance or create risk. During peak shopping events such as Black Friday and Cyber Monday, the volume of data multiplies and the risks grow higher. Retailers that put visibility and control at the centre of their data practices are better prepared to scale, maintain security, and deliver the seamless experiences customers expect. 

Across the region, consumers are adopting new technologies at speed. The Adyen Index Report 2025 revealed that  38 per cent of Asia Pacific (APAC) shoppers now use AI assistants while shopping, with markets like Malaysia (58 per cent) and Singapore (49 per cent) among the highest. At the same time, retailers are racing to keep pace, with AI investment ranging from 47 per cent of retailers in Japan to 72 per cent in Malaysia.

Together, these figures signal that AI is becoming foundational to the retail engine. However, every click, transaction, and digital interaction carries both opportunity and risk. According to IBM’s X-Force 2025 Threat Intelligence Index, APAC accounts for 34 per cent of all global cyberattacks, which is the highest of any region, underscoring the need for retailers to strengthen their data foundations.

In a world of hypercompetition and fast-evolving customer expectations, the retailers that flourish are those that think and operate like technology companies.

Scaling for peak seasons

The biggest challenges of the holiday peak season are scalability, fault tolerance, and low staffing. Online and in-store traffic often surges several times higher than normal levels, and this increased load can trigger outages or slowdowns that are difficult to recover from.

Outages are costly – from  lost revenue and the eroding customer satisfaction when shoppers can’t complete their purchases.  To stay resilient, data management systems must be both scalable and fault tolerant to handle the extra load and prevent downtime that leads to abandoned carts.

Also Read: How an AI cybersecurity company harnesses the power of AI for optimal business performance

Enterprise data lineage helps identify breaks in data pipelines quickly, enabling teams to restore operations with minimal disruption. A unified view of data access and activity across hybrid and multi-cloud environments further eliminates blind spots and ensures that sensitive information is continuously monitored and protected.

Defending against heightened cyber risk

Retailers are prime targets for cyberattacks during the holiday season due to high transaction volumes and the sensitive nature of the data they hold.  Attackers are constantly looking to exploit any weaknesses to access personal information. This concern is mirrored  by consumer sentiment — according to PwC’s Voice of the Consumer Survey 2024, 74 per cent of APAC consumers are concerned about privacy and data-sharing, and 50 per cent are not comfortable purchasing via social media. 

Fraud and theft also surge as criminals exploit the distraction of the holiday rush. The attack surface is vast – retail stores, distribution centres, online platforms, and even delivery trucks are increasingly connected through IoT devices. Vulnerabilities in poorly secured devices can provide attackers with an entry point.

At the same time, many retailers struggle with legacy systems and thin margins, making it challenging to keep pace with evolving threats. In such an environment, every breach erodes consumer confidence and brand equity, turning cybersecurity from an IT issue into a business imperative. 

Also Read: From data to defence: Strengthening AI with cybersecurity foundations

Retail is a fiercely competitive industry where trust is a key differentiator. To retain loyalty, retailers must demonstrate that they use data responsibly. Strong governance and a zero-trust architecture are essential. Secure-by-design systems limit exposure, while unified governance frameworks ensure that data security and compliance are enforced consistently across hybrid environments.

Innovating in modern retail

Artificial intelligence and machine learning have become indispensable for improving demand forecasting, personalisation, and fraud detection during peak shopping events. Cloudera supports both historical and real-time data — each critical to retail success. Historical data trains demand forecasting models and informs customer behaviour analysis. But data isn’t just about hindsight; speed and timing are equally vital.

Real-time ingestion systems enable dynamic decision-making, detecting anomalies in transactions as they occur or triggering personalised offers the moment a customer enters a store. In retail, timing is everything. An offer sent even 15 minutes too late is ineffective, and fraud detection that lags behind live activity can lead to significant financial losses. Real-time visibility allows retailers to act instantly—approving legitimate transactions and blocking fraudulent ones before damage occurs.

The ability to manage data responsibly, scale systems under pressure, and build customer trust will determine who succeeds in today’s competitive environment. As holiday shopping peaks approach, the retailers that put visibility and governance at the heart of their strategy will be in the strongest position to serve customers and drive growth.

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We all know that artificial intelligence will be the biggest single engine of economic growth over the next decade. But few people realise the surprising ways that AI is already changing Malaysia’s economy. 

AI is revolutionising economic growth, but not just through tech companies like Juwai IQI or through the data centres and chip fabricators that are so important to our electronics industry. Beyond those sectors, AI has the potential to deliver billions of ringgits to Malaysia’s economy by bringing its advances to the most traditional and unexpected economic sectors.

AI helps farmers make more money

For example, rice padi farmers are already doubling their yields with an AI-powered WhatsApp chatbot called Rakan Tani. Rakan Tani helps farmers make crop management decisions based on the latest field data, weather conditions, and other factors. It gives farmers easy access to custom-tailored expert knowledge right from their phones. The number of padi farmer users is expected to rise to 110,000.

Credit for Rakan Tani is due to the Digital Ministry, Agriculture and Food Security Ministry, National AI Office, Padiberas Nasional Bhd (Bernas), and Global AI Village.

Farmers are already reporting good results. Mohamad Fazeli Abdullah, a farmer from Sungai Manik who tested the AI tool, said it helped him boost his yield from four to nine metric tonnes. Overall, the government expects the app to help the country boost its self-sufficiency in rice from today’s level of 50 per cent to the national target of 80 per cent by 2030.

Agriculture is perhaps humanity’s oldest technology, so it may seem like an unusual sector for the application of artificial intelligence, but it’s happening, and not just in rice paddies. 

Rice is an important part of Malaysia’s agricultural economy, but palm oil is even bigger. The country produces more palm oil than any other, except for Indonesia. But the industry is labour-intensive and struggling to increase profits against a backdrop of declining yields on old plantations. 

Rather than watch their incomes shrink, farmers are turning to a process called “AI-Driven Precision Agriculture” to ensure their future. AI-driven agriculture uses machine learning and data analytics to advise palm farmers on how to manage their crops. Experts believe they will be able to improve their yields by as much as 25 per cent.

Also Read: Singapore’s AI tools are ready. Its workforce isn’t

Palm farmers are also flying drones over their plantations and using new types of AI-powered image analysis to detect pests such as bagworms, mealybugs and rhinoceros beetles, any of which can ruin an entire season’s yield. With a drone, a farmer can examine 2,500 hectares of oil palms in a single day, compared to just five hectares without one.

“There is no more room to open new land,” said Ahmad Parveez, who serves as director-general of the Malaysian Palm Oil Board. “Productivity must come from technology.”

Gig workers take control of the algorithm

Next in line to benefit from artificial intelligence are gig workers. Until now, gig workers have largely been at the mercy of AI, rather than in charge of it. The big international corporations that employ gig workers use AI-powered algorithms to determine which gig workers get jobs, how much they get paid, and how hard they have to work in order to make a basic living. 

But because the Gig Workers Act 2025 came into full force on 31 March 2026, there is now an opening for these workers to deploy AI themselves. The Act doesn’t yet require it, but it gives workers new protections and lays a foundation on which to build future improvements. The next step will be to establish a more comprehensive regime to protect gig workers. 

The ultimate protection for gig workers will be the creation of AI tools that help riders, for example, at Grab and Lalamove, to optimise their routes, track earnings against costs, and plan for Employee Provident Fund contributions.

Like gig workers, Malaysia’s pasar pagi and pasar malam vendors can also make unexpected gains from AI. 

Hawkers often wake before dawn to buy produce at wholesale markets, then spend all day selling it. They run their businesses almost entirely on instinct and mobile phones. There have traditionally been few tools or data sets to help them manage their stock, working hours, or income. 

But that is changing, and believe it or not, the change started with QR payments. Hawkers are among the more than 2.6 million Malaysian retailers who now accept QR payments. That means transaction data exists that can be put to create AI forecasting tools. These would help hawkers in the same way that enterprise-grade software tools already support large retailers.

AI forecasting can reduce inventory errors by 50 per cent. Similar gains could flow to hawkers and mom-and-pop retailers with the right AI tools. Such a tool could be made available to individual hawkers via a WhatsApp channel or a simple phone app, just as with padi and palm farmers and gig workers. 

No one has built this tool yet for Malaysia’s hawker economy, but the opportunity is there, and it’s exactly this sort of challenge that artificial intelligence is good at solving. The government has demonstrated with Rakan Tani that effective AI advisory services can be delivered cheaply. With hawkers, the economic impact could be huge, because mom-and-pop stores account for nearly half of the retail market, while hawkers make up 15 per cent of informal workers. The informal sector contributes one of every four ringgit in the economy.

Also Read: Will the rise of AI mean the ‘termination’ of humankind?

AI slashes risks in Malaysia’s most dangerous industry 

When it comes to worker safety, no sector is more dangerous than construction. It accounts for 27 per cent of all workplace fatalities in Malaysia. Eighty-eight workers died on construction sites in 2023, the most recent year for which the data has been reported. Here again, and just as surprisingly, artificial intelligence is improving things.

Developers and builders are embracing advanced tech such as AI-powered wearables, real-time safety monitoring systems, drones, and site sensors to prevent deadly accidents. Companies like the Sunway Group deploy them on their own sites. And third-party suppliers like viAct have built lucrative businesses around monitoring construction sites for developers and builders. They provide real-time alerts as dangers emerge.

All this tech promises to nearly make dangerous construction accidents a thing of the past. viAct promises its system can reduce accidents by 95 per cent. Improvements of this scale across the entire industry would save thousands of workers from death or serious injury. 

Reducing accidents would also save construction companies hundreds of millions of ringgit, as estimates suggest each serious incident costs them about RM4 million (US$1.02 million).

Employing artificial intelligence to help solve the construction safety challenge will benefit everyone in the industry, from the site workers to the CEOs.

The most important developments in artificial intelligence are not happening at Malaysia’s chip fabricators but in padi fields and palm plantations and on construction sites. AI is helping to improve the lives and the incomes of people in Malaysia’s most traditional sectors, which I consider an excellent use of the new technology. 

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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.

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Indonesia’s startup story has long been sold as a tale of scale, optimism, and inevitability. A vast domestic market, rising digital adoption, and founders capable of building for complexity made the country irresistible to investors hunting for Southeast Asia’s next great technology champions. That story has not disappeared. But it has become harder to tell with a straight face.

The prosecution’s demand for an 18-year prison sentence for Nadiem Makarim, the former education minister and co-founder of Gojek, is not merely another corruption case in a country that has seen too many of them; it is a reputational stress test for the archipelago’s entire innovation economy.

The allegations are serious: prosecutors say Makarim played a role in a pandemic-era procurement programme for Chromebook laptops and Chrome Operating System that caused state losses of US$125.64 million, while allegedly enriching himself by around US$46.33 million.

Also Read: Nadiem Makarim indicted in US$125M Chromebook graft case

Makarim has denied wrongdoing, and the court has yet to deliver its verdict. Still, even before the legal process reaches its conclusion, the symbolism is devastating.

This is not just any former minister. This is the founder who helped define Indonesia’s startup ambition. Makarim represented the archetype the ecosystem loved most: the globally literate local operator who could build at scale, reshape an industry, then cross into public service as proof that startup talent could also modernise the state. That image now lies shattered.

And when placed alongside the recent eFishery saga, the damage goes beyond one man, one ministry, or one company. It points to something more corrosive: a widening credibility gap at the heart of Indonesia’s tech narrative.

Two very different scandals, one uncomfortable message

The Makarim case and eFishery are not the same.

One concerns public procurement, alleged abuse of office, and the use of state power. The other is rooted in private company governance, with eFishery facing scrutiny after allegations of serious financial irregularities and inflated business performance rocked one of Indonesia’s most celebrated startup success stories. One sits in the realm of anti-corruption law; the other belongs to the equally bruising world of board oversight, financial controls, and investor diligence.

Yet for the outside world, especially foreign capital, both cases collapse into a single, ugly conclusion: Indonesia’s governance discount just got more expensive.

That is the real problem. Investors do not compartmentalise as neatly as lawyers do. They do not say, “This is a ministerial procurement scandal, whereas that was a venture-backed governance failure.” They ask a blunter question: what does this tell us about how power, accountability, and truth operate in this market?

The answer is unsettling.

In eFishery’s case, the shock came from the possibility that one of the region’s brightest agritech stars may have projected a version of performance that did not hold up under scrutiny. In Makarim’s case, the shock is that one of Indonesia’s most internationally recognisable founders is now accused of bending public policy and procurement in ways that prosecutors say harmed both the state and the education system.

Also Read: Inside Indonesia’s US$610M Chromebook scandal: Raids, arrests, and Nadiem Makarim under scrutiny

Put together, they create a grim symmetry: one scandal suggests weak controls in the boardroom; the other suggests weak controls in government.

That is not a good look for an ecosystem still asking the world to believe in its institutional maturity.

The startup halo is fading

For years, Indonesia benefited from what might be called the startup halo effect. Founders were not just entrepreneurs; they were cast as modernisers, nation-builders, and in some cases quasi-public intellectuals. Venture capital, especially in frontier or emerging markets, often invests as much in narrative as in numbers. Indonesia had a powerful narrative: large market, digital leapfrog, charismatic founders, and a sense that tech could succeed where bureaucracy had stalled.
Now the halo is fading.

If prosecutors’ arguments in the Makarim case resonate with the public, the fallout will be especially sharp because the allegations cut into a cherished myth: that startup leaders entering government automatically bring efficiency, transparency, and reform. That was always a dangerously flattering assumption. Founders are not immune to political incentives, nor are they magically equipped to navigate public institutions without conflicts, blind spots, or worse. Startup logic and statecraft are not interchangeable. One optimises for speed; the other is supposed to optimise for process, fairness, and accountability.

When that boundary blurs, trouble tends to arrive wearing very expensive shoes.

What this means for Indonesia’s startup landscape

The immediate effect on Indonesia’s startup ecosystem will not be a sudden disappearance of capital. The country is too large, too strategic, and too important for that. Consumer demand will remain. Digital infrastructure will keep expanding. Entrepreneurs will continue building. The fundamentals do not vanish because of a scandal, even a very public one.

But the quality and terms of capital will change.

First, there will be more diligence, and much earlier. Investors who once backed founder charisma and market timing will ask tougher questions about controls, reporting, procurement exposure, related-party dealings, and political proximity. “Growth at all costs” was already dying across global venture markets; in Indonesia, these episodes may bury it properly.

Second, governance will become part of the investment thesis rather than a post-investment repair job. Independent directors, stronger audit functions, and cleaner reporting lines will no longer be “nice to have” features added before a later-stage round. They will become prerequisites, especially for companies operating in regulated sectors such as education, finance, agriculture, logistics, and public digital infrastructure.

Third, founders with strong compliance instincts may actually benefit. Scandals have a way of penalising the market broadly at first, then rewarding the operators who can prove they are the exception. In that sense, this is not just a crisis; it is also a sorting mechanism.

How foreign VCs will read this

Foreign venture capital firms are already more cautious than they were during the easy-money years. Indonesia will still matter to them, but it will now be viewed through a harsher lens.

Expect three reactions.

1. A higher risk premium: Global investors will demand more protection for the same level of exposure. That means stricter terms, more reserved valuations, and a greater willingness to walk away from deals that feel even slightly opaque. While Indonesia’s market opportunity remains compelling, the trust premium it once enjoyed has narrowed.
2. More emphasis on governance than storytelling: The era when a founder could pitch “Indonesia scale” and glide past uncomfortable operational details is fading fast. Investors will want evidence, not theatre. Monthly reporting discipline, audited accounts, customer verification, and procurement transparency will matter more than polished narratives about disruption and national progress.
3. Preference for firms with institutional ballast: Foreign VCs may increasingly favour startups backed by reputable co-investors, experienced boards, and internationally credible governance practices. They may also become more cautious around ventures with deep entanglements in state programmes or founders whose political access appears to be a central strategic asset.

That last point is particularly crucial. Political connections can accelerate business in many emerging markets, but they also create invisible liabilities. In the current climate, what once looked like an advantage may start to resemble concentration risk.

Indonesia’s real test is institutional, not entrepreneurial

The temptation now will be to frame these events as betrayals by individuals. That is emotionally satisfying and analytically incomplete.

Also Read: Indonesia names Nadiem Makarim a suspect in laptop procurement corruption case

The deeper issue is whether Indonesia can build institutions robust enough to keep pace with the ambition of its entrepreneurs and the expectations of global capital. Star founders are not a substitute for clean procurement. Unicorn status is not a substitute for audited truth. National pride does not neutralise governance risk.

If there is a silver lining, it is this: ecosystems often mature only after their illusions are shattered. Indonesia may finally be entering that phase. Painful, yes. Necessary, absolutely.

The country does not need fewer startups. It needs fewer myths.

And for foreign investors, that may ultimately be the healthiest signal of all: not that Indonesia is scandal-proof, but that it is being forced to confront the price of pretending otherwise.

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Melazyme, a precision fermentation company developing high-performance functional biomolecules, has closed a US$2 million seed round led by SeaX Ventures, with participation from Stellaris Venture Partners and Plug and Play Ventures.

The funding will support platform development, production scale-up and early commercial deployment across the company’s portfolio of target molecules.

Founded in 2025 by Perumal Gandhi, co-founder of Perfect Day, and Bonney Oommen, former chief product and strategy officer at Perfect Day, Melazyme is building a fermentation platform distinguished by functional depth rather than commodity output. The company’s patent-pending proprietary tech covers both the production platform and its applications across multiple industries.

Central to Melazyme’s commercial strategy is melanin — a naturally occurring biopolymer that combines broad-spectrum UV absorption, chemical stability and a strong affinity for metal ions. Despite decades of scientific interest, a consistent, application-ready supply of melanin has remained elusive. Melazyme’s platform addresses this gap by producing commercially viable melanin with tunable functional properties engineered for specific end uses across cosmetics, functional coatings, advanced materials, and filtration and environmental remediation.

Also Read: The unexpected ways AI is already changing Malaysia’s economy

A particularly distinctive aspect of melanin’s profile is its selective affinity for metal ions, enabling applications in heavy-metal sequestration and rare-earth element recovery — capabilities attracting growing interest from industries working to diversify supply chains for critical materials used in clean energy, electronics, and defence.

Near-term commercial activity is centred on cosmetics, where melanin’s UV protection and natural pigmentation properties are driving early engagement with global manufacturers. The company is also advancing brazzein — a heat-stable natural sweet protein — with commercial partners in the food and beverage sector.

“Our platform is built around the ability to tune molecular function for specific applications. With melanin, that means the same underlying material can be engineered to solve entirely different problems across industries,” said Gandhi, co-founder and CEO of Melazyme.

SeaX Ventures, a global venture capital firm focused on deep-tech startups, cited both the founding team’s pedigree and the breadth of the platform’s application space as key factors in its investment decision.

“Perumal and Bonney bring rare experience building and scaling precision fermentation companies,” said Dr Kid Parchariyanon, managing partner of SeaX Ventures. “This is the kind of founding team and platform-level technology that comes along once in a generation — and SeaX is proud to back it.”

The investment arrives as precision fermentation attracts intensifying investor attention. The global market is expected to grow from approximately US$20 billion today to over US$70 billion by 2030, with projections suggesting it could reach US$200 billion by 2040 as food, materials and speciality ingredients transition toward bio-manufacturing.

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Image Credit: RephiLe water on Unsplash

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