VinFast is taking a familiar electric-vehicle (EV) problem in Southeast Asia, namely high upfront cost, and attacking it with a less familiar answer for the region: long-term rentals aimed at ride-hailing and transport drivers.

The Vietnamese automaker said it is rolling out a green vehicle rental programme in Indonesia and the Philippines, starting in Greater Jakarta and Metro Manila. The idea is that instead of asking drivers to buy EVs outright, VinFast will let them rent models from its Green line through authorised dealers, with daily rates starting at IDR 312,500 (~US$18.6) in Indonesia and PHP 1,000 (~US$17.5) in the Philippines.

Also Read: Inside Thailand’s EV and battery push: Balancing growth with sustainability

While it may sound like a small product tweak, it is indeed a strategic attempt to open two markets where interest in EVs is rising, but affordability, financing, and access to charging are still hindering adoption. It is also another sign that VinFast is not merely selling cars in Southeast Asia, but is trying to build an operating model around fleets, charging, incentives, and urban transport economics.

Why rentals matter more than another EV launch

VinFast’s target is not the aspirational private buyer; instead, it is going after drivers who care less about brand theatre and more about whether the vehicle helps them earn.

That matters in Indonesia and the Philippines, where ride-hailing, shuttle services, and informal transport networks form a large part of urban mobility. In both countries, thousands of drivers work on thin margins, making the total cost of ownership more important than horsepower or touchscreen size. For this group, buying an EV outright can still feel risky. Battery concerns, patchy charging networks, and financing costs have kept many on the petrol treadmill.

A rental model changes this scenario. It lowers the upfront hurdle, shortens the time needed to start earning, and shifts EV adoption from a capital expenditure decision to an operating expense. In other words, VinFast is trying to turn electric mobility into a cash-flow product.

The company framed the move as part of the “green transition of the commercial transport sector”. That is fair enough. But the harder-edged reading is this: if consumers are still hesitant to buy EVs, get drivers to use them for work first.

Indonesia is the stronger bet

Of the two markets, Indonesia gives VinFast the clearer runway.

The country has the largest automotive market in Southeast Asia, strong government support for EV manufacturing, and a growing ecosystem that includes local assembly ambitions, tax breaks, charging investments, and aggressive competition from Chinese brands, such as BYD and Wuling, as well as Hyundai. Indonesia also has something else the EV industry loves to talk about: nickel. Whether that turns into a lasting competitive advantage is another debate, but it has unquestionably helped put EVs near the centre of industrial policy.

In market terms, Indonesia is already several steps ahead of the Philippines. Battery-electric passenger car sales have climbed sharply in recent years, crossing roughly the 40,000-unit mark in 2024 based on industry data, with penetration still modest but no longer trivial. Electric two-wheelers and commercial fleets add further volume. The overall market remains small relative to total vehicle sales, but it is now large enough for automakers to test more tailored distribution models.

Also Read: 5 ways Indian EV makers can achieve world-class manufacturing efficiency

The adoption drivers are clear:

• Fuel-price sensitivity among drivers and fleet operators
• Government incentives for EV production and purchases
• Urban congestion, which makes lower running costs attractive for high-mileage users
• Ride-hailing and delivery demand, which suits vehicles with predictable daily routes
• Growing charging infrastructure, especially in major cities

That does not mean VinFast’s strategy is guaranteed to work. Indonesia is already crowded, and price competition is becoming vicious. But if a rental-led EV model is going to gain traction anywhere in Southeast Asia outside Vietnam, Indonesia is one of the most plausible places.

The Philippines is promising, but harder

The Philippines is a different story: promising, but operationally tougher.
EV adoption is growing from a much smaller base. The Electric Vehicle Industry Development Act gave the sector a policy push, import duties on some EVs were eased, and higher fuel costs have made electric mobility more attractive on paper. But the country still faces stubborn constraints: a less mature charging network, a fragmented geography, and transport economics often dictated by daily cash flow rather than long-term cost calculations.

That said, Metro Manila is the place where an EV rental proposition can make sense. Traffic is brutal, daily driving distances are high, and many drivers need a vehicle they can monetise immediately. If VinFast can ensure dependable after-sales support and convenient charging, the rental model could remove some of the hesitation that has slowed EV uptake.

The Philippine EV market remains small by regional standards, with electric car sales still in the low thousands annually and overall penetration in the low single digits. Yet growth rates are strong, partly because the base is so small. For VinFast, that can be an advantage. It is easier to shape a young market than steal share in a fully formed one.

The risk is execution. A rental programme only works if vehicle uptime is high. Drivers will not tolerate a future-of-mobility pitch if it leaves them waiting for chargers, parts or repairs.

This is bigger than vehicle access

VinFast’s move is also about ecosystem control.

The company is not just offering vehicles; it is pairing them with financing, dealership access and support from V-Green charging stations, including free charging in Southeast Asia through March 2029. Its parent, Vingroup, has also been pushing related incentive campaigns, including trade-in offers and discounted Green SM electric ride fares in Indonesia.

Also Read: How electric luxury cars are reshaping the industry

This layered approach matters. EV adoption in emerging markets rarely hinges on the vehicle alone. It depends on whether the manufacturer can reduce friction across the entire ownership or usage cycle: financing, charging, servicing and resale. VinFast appears to have concluded that the region’s next wave of growth will not come from waiting for the middle class to buy in en masse. It will come from making EVs useful to workers first.

That is where the rental model differentiates itself. It shifts the sales pitch from environmental virtue to unit economics.

Is the EV market in these countries big enough?

Big enough to matter, yes. Big enough to be easy, no.

Indonesia is already one of Southeast Asia’s most important EV battlegrounds. In value terms, it is a multi-billion-dollar opportunity over the coming decade, supported by domestic manufacturing ambitions and steadily rising consumer awareness. Passenger EV volumes are climbing, commercial adoption is growing, and competition is intensifying.

The Philippines is smaller, but it has a credible long-term case. Urban transport demand is huge, fuel costs remain a political and economic issue, and policy support is gradually improving. The market is still embryonic compared with Indonesia, but that also means there is room for unconventional models such as rentals, especially in commercial fleets.

In both countries, the near-term winners are likely to be companies that can solve affordability and operating costs rather than simply import more models.

Is the Middle East war affecting EV sales in Southeast Asia?

Indirectly, yes, but not in the tidy way automakers might hope.

Conflict in the Middle East tends to feed volatility in oil prices and shipping costs, which can strengthen the case for EVs by making petrol and diesel vehicles more expensive to run. For commercial drivers in Jakarta or Manila, higher pump prices can sharpen the appeal of fixed-cost electric usage models.

But war-driven uncertainty cuts both ways. When households and small businesses feel economically squeezed, they often delay big-ticket purchases, including vehicles. That can dampen consumer EV sales even as the logic of electrification improves.

In Southeast Asia, the impact is therefore mixed. Higher fuel prices help the EV narrative, but they are not the main driver of adoption. Infrastructure, financing, policy support, battery confidence and after-sales service still matter far more. For VinFast’s rental strategy, however, Middle East-linked fuel volatility may provide exactly the nudge it needs. If drivers can avoid petrol price shocks by paying a fixed daily rental and charging at low or no cost, the maths becomes easier to explain.

The real question: can VinFast make the economics stick?

That is ultimately what this announcement comes down to.

VinFast is trying to crack two difficult markets not by waiting for EV demand to mature naturally, but by engineering a usage model that lowers risk for working drivers. It is a smart reading of Southeast Asia, where many consumers do not lack interest in electric vehicles so much as trust in the economics.

Also Read: SLEEK EV’s US$8.5M Series A funding signals a more mature EV playbook

Indonesia gives the company scale, policy momentum and stronger EV tailwinds. The Philippines offers a tougher but potentially high-upside urban transport play. In both markets, the strategy has a decent chance, provided VinFast can deliver the unglamorous things that matter most: charger availability, reliable servicing, low downtime, and predictable driver earnings.

If it cannot, this will look like another ambitious EV rollout dressed up as accessibility. If it can, VinFast may have found a more effective way to push electrification in Southeast Asia than simply opening another showroom and hoping sentiment catches up.

The post VinFast bets on EV rentals to crack SEA’s affordability problem appeared first on e27.

In May, PLAUD introduced its wearable AI voice recorder, PLAUD NotePin, to the Singapore market, following recognition at the 2025 Red Dot Design Awards.

The device is designed to help users record, transcribe, and summarise spoken content using a minimalist, clip-on form factor. Intended for professionals, students, and creatives, it features tools such as one-tap activation, AI-generated summaries, and integration with services such as Zapier.

Available in Singapore from May 14, the device will be available through physical and online retail channels.

Nathan Hsu, CEO and Co-founder of Plaud.ai, said in an email interview with e27 that winning the Red Dot Design Award was an “incredibly meaningful” experience.

“The design philosophy was simple: make it so light and versatile that users forget they are wearing it … We wanted it to adapt to you, not the other way around. But good design is not just about looks – it is about the experience. That is why we focused on making it dead simple: one press to start recording with haptic feedback so you know it’s working, even without looking.”

Also Read: AI to add US$950B to SEA’s GDP—Here’s where the growth will come from

In this interview, Hsu shares the ideas behind developing this product, including why the company chose to release it as a hardware device. The following is an edited excerpt of the interview:

What inspired the creation of the PLAUD NotePin, and how does it address the evolving needs of professionals, students, and creatives in today’s fast-paced world?

The PLAUD NotePin was inspired by the need to free professionals from the mundane task of manual note-taking, allowing them to focus on creative, high-value work. It is more than just an AI device; it is designed to function as a “memory capsule” that helps users improve productivity and efficiency in their careers.

The device addresses evolving needs by eliminating friction, supporting diverse professionals, saving significant time, and enabling full engagement.

Given the dominance of mobile and cloud-based tools, why did PLAUD.AI choose to release its solution as a hardware device? What benefits does hardware bring to the user experience?

It is a great question: Why hardware when everyone’s phone can record? The truth is, we have all been in that moment where a brilliant idea strikes or an important conversation starts, and by the time you unlock your phone, find the app, and hit record, the moment’s gone. With NotePin, it is just one press and you are recording. No friction, no fuss.

Also Read: AI search is quietly eating Google — Here’s what startup founders need to know

The dedicated hardware also means you are not draining your phone battery or interrupting other tasks. Plus, there’s something about the audio quality – our high-fidelity microphones are specifically designed for voice capture, and you can position the device optimally rather than awkwardly holding your phone.

In professional settings, it is also more discreet and respectful than pointing a phone at someone. We have built in 30-hour continuous recording capability and 64GB of storage, which goes way beyond what most phones can handle. And here’s the thing – it works even without network connectivity, so you never miss a moment. Sometimes the best technology is the one that gets out of your way and just works, and that is exactly what dedicated hardware delivers.

Singapore is the first market in Southeast Asia to have the NotePin. What made it the ideal launchpad, and what have you learned from early adopters here?

We believe Singapore’s young, digitally native population will readily embrace AI solutions. We also value the professional demographics here, highly concentrated in knowledge workers who value productivity tools. A multilingual business environment aligns with NotePin’s 112-language support, a culture of early adoption for productivity-enhancing technology, and professional templates suited for Singapore’s business-first culture.

Can you share more about PLAUD.AI’s user acquisition strategy—how are you building awareness and encouraging adoption across different user segments?

Our user acquisition strategy is about meeting people where they are and showing them immediate value. We have positioned ourselves clearly as the “World’s No.1 AI Voice Recorder Brand” and created targeted messaging for different professionals – whether it is helping salespeople capture every client detail or enabling healthcare workers to focus on patient care rather than documentation.

Also Read: Why AI needs context and curiosity, not toxic positivity

The pricing strategy is key here: We offer a Free Starter Plan with 300 minutes per month so people can experience the value before committing. We are building credibility through recognition such as the Red Dot Design Award, integrating with tools people already use through Zapier, and expanding strategically—we recently opened our Japan office and launched “PLAUD for Business” for enterprise clients.

Most importantly, we let the ROI speak for itself: when users realise they can save 260 hours a year, that is US$8,845 in potential earnings. That message resonates across all segments.

Looking ahead, how does PLAUD.AI plan to evolve its product ecosystem?

We are excited about what is coming next. In the immediate future, we are launching a Template Community where users can share and discover templates for every professional scenario, expanding our integrations from Zapier to 50+ apps, and releasing a desktop application that can automatically record online meetings.

We’re constantly upgrading our AI capabilities – we are already working with the latest models such as GPT-4.1, Claude 3.7, and Gemini 2.5.

But beyond the technical roadmap, our vision is to create an ecosystem where PLAUD becomes essential. We are deepening our enterprise offerings, exploring new features based on user feedback, and expanding across Southeast Asia and beyond.

The goal is not just to be a recording device. It is to be the intelligent layer that captures, understands, and organises the information that matters in your professional life. We are building for a future where no important idea or conversation is ever lost.

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Image Credit: PLAUD.AI

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Artificial Intelligence (AI) is no longer a futuristic concept in Singapore; it has definitively arrived, reshaping the professional landscape and demanding a proactive response from employers and workers alike.

Since the mainstream arrival of generative AI in 2022, its influence has become deeply rooted across Singapore’s workforce, fundamentally altering how decisions are made and work is executed. This shift is not just beginning; it is accelerating.

AI’s mainstream arrival and workforce sentiment

The findings of a national study, “Work Ahead,” commissioned by Indeed, a leading job site and global hiring platform, reveal that Singaporean professionals are far from strangers to AI. Over one in three professionals (36 per cent) are already actively utilising tools like ChatGPT, Perplexity, and Gemini in their daily workflows.

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

This pervasive adoption indicates that discussions around AI’s ability to boost productivity have moved beyond theoretical questions; the pressing concern now is how AI will redefine careers, create new jobs, render others obsolete, and fundamentally change the relationship with work.

Despite the rapid integration, the workforce exhibits mixed emotions regarding new technologies. While a significant portion remains optimistic (36 per cent), excited (34 per cent), and confident (34 per cent), a notable 11 per cent feel overwhelmed by the immense digital change impacting job opportunities. This highlights a critical insight: AI adoption is present, but the true opportunity lies in deepening its impact through more structured, consistent, and official training initiatives.

The critical role of employer-led training

For Singaporean jobseekers, the real draw of an employer is their tangible commitment to AI learning, fostering confidence to thrive in a rapidly changing workplace. This preference is evident in the fact that over two-thirds (67 per cent) of workers who currently utilise technology at work report receiving structured training or certification from their companies.
The demand for learning is robust, with nearly four in five (77 per cent) of these workers indicating a desire for more training in the next two to five years.

Intentional upskilling is not merely a beneficial practice; it acts as a career accelerator. Workers who build AI skills are better positioned for higher pay, promotions, and future roles. However, a significant barrier remains: 42 per cent of workers state they do not receive time off or compensation for training, while a third prefer hands-on learning over poor-quality instruction, and one in five are actively avoiding new technologies altogether. This suggests a gap between worker aspirations and employer provisions.

Bridging the digital divide and generational shifts

The report also sheds light on a crucial digital divide within the workforce. Blue-collar workers are more than five times more likely to avoid using new technologies at work (20 per cent) compared to their white-collar counterparts (4 per cent). This disparity exists despite both groups demonstrating similar levels of confidence in their current technology usage (26 per cent vs 30 per cent respectively).

Furthermore, business leaders are setting the pace for tech adoption, with 45 per cent describing themselves as more tech-confident than their teams, compared to only 26 per cent of non-leaders.

While younger generations show higher current usage of generative AI tools at work—42 per cent for 18-24 year olds and 40 per cent for 25-34 year olds—compared to older demographics, including 26 per cent for workers aged 55 and above, the core message remains clear: confidence in workplace technology is not inherently tied to age or job type. Instead, it hinges on the provision of support and opportunities. As tech access expands, stagnant confidence could push workers towards employers who invest in digital readiness, making training a key driver of retention.

Boosting confidence and retaining talent

For employers, attracting and retaining growth-minded talent in an AI-shaped economy hinges on providing practical, inclusive upskilling opportunities. The top five confidence boosters for tech adoption include:

Also Read: AI to add US$950B to SEA’s GDP—Here’s where the growth will come from

• Easy-to-use, well-documented technology.
• Structured training (e.g., workshops, courses).
• Using the technology in a safe, low-pressure environment.
• Access to self-paced online learning tools.
• Clear communication about upcoming tech changes.

Ultimately, the report underscores a fundamental truth for Singapore: “Talent thrives where growth is backed. In Singapore, inclusive upskilling is the edge in the talent game”.

As AI continues to accelerate, confidence among the workforce is lagging. Employers who prioritise practical, inclusive upskilling will not only retain their current talent but also actively attract the new wave of growth-minded workers.

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The image was generated using ChatGPT.

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Hasan Venture Capital has integrated AI across its investment processes to boost efficiency while maintaining its ethical investment principles. At the helm of this vision is Umar Munshi, Managing Partner at Hasan VC, who believes that AI is a technological advantage and a moral imperative in the evolving venture capital space.

The firm’s approach to due diligence exemplifies how AI can refine venture operations without compromising human insight. “AI is now embedded in our analyst processes to augment and empower our team to carry out faster and better evaluation of startups,” Munshi shares in an email interview with e27.

The firm has significantly enhanced its decision-making efficiency by integrating AI tools to collate and interpret data from various formats, including video interviews and documents. Yet, this acceleration in analysis is balanced by human judgment.

“We directly benefit from AI at low cost now, while simultaneously recognising the value of human input, perspective and intuition,” says Munshi. This hybrid model fosters an investment process that is “more intelligent, ethical, and geared toward long-term value.”

AI for portfolio empowerment

Hasan Venture Capital’s application of AI doesn’t stop at internal operations. As a venture capital firm with a strong focus on halal innovation, the firm actively helps its investee companies integrate AI in ways that align with their values.

“We promote a knowledge-sharing culture within our founders’ community,” explains Munshi. “We assist investees in strategic grants, global networks, and ecosystem bridges—specifically those aimed at scaling values-based AI-powered businesses.”

Also Read: Wan Wei Soh: Driving AI inclusivity and growth for innovators

One striking example is Qara’a, an AI-powered Quran learning app within the Hasan VC portfolio. The platform personalises learning for over two million users worldwide using machine learning, while adhering to strict ethical guidelines.

“All content is reviewed by qualified scholars, ensuring integrity and trust,” Munshi notes. “This reflects our broader vision: technology should serve humanity, not exploit it.”

With AI now saturating startup narratives, distinguishing substance from spin has become crucial. “We have observed that some companies engage in AI-washing, marking exaggerations of their use of AI,” Munshi cautions. Hasan Venture Capital counters this by examining the tangible impact of AI implementations.

Their evaluation framework is rooted in the AAOIFI Shariah principles, guided by Islamic finance ethics. With Adl Advisory as their Shariah advisor, every potential investment undergoes rigorous screening of commercial, legal, and financial practices to ensure justice and participatory investment terms.

Beyond compliance, the firm prioritises startups with authentic market fit and a community-first ethos. “Our focus lies on businesses that operate within expanding markets and cater to underserved populations, including Muslim communities,” says Munshi. “Founders must show deep passion, strong values, and a commitment to solving real-world problems.”

A future of purposeful, AI-driven investing

Looking ahead, Hasan Venture Capital views AI as a catalyst for ethical transformation in venture capital. Munshi envisions a future where “AI offers new ways to measure impact, improve transparency, and scale values-driven innovation.” This aligns with a model that Munshi refers to as the “camel startup model”—emphasising resilience, capital efficiency, and sustainable growth over rapid, risky expansion.

Also Read: Your supply chain isn’t just boxes, it’s personal data too

“We’re not interested in hype,” Munshi asserts. “We are actively supporting AI ventures that align with Shariah principles and embody the camel ethos: companies built to endure, deliver consistent value, and grow responsibly.”

This long-term outlook, coupled with a principled investment framework, sets the firm apart in a crowded, sometimes ethically ambiguous, venture landscape. “At Hasan VC, we prioritise long-term value over short-term trends, challenging the conventional VC mindset that often favours quick gains and fast exits over real, enduring potential,” says Munshi.

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Image Credit: HASAN Venture Capital

The post How Hasan Venture Capital uses AI to build an ethically grounded investment future appeared first on e27.

If you want to better understand where the future of AI is being built, stop watching the biggest stages and start looking at the edges.

While big tech dominates AI headlines, I believe the real progress is being made elsewhere, in the world of AI startups. Small, focused teams are quietly driving the true potential of AI and unlocking tangible AI products that are not only working, they are transforming how business gets done, not just in the future, but today.

And it’s time CFOs, boards, and executive leaders recognised their tremendous value.

Startups don’t just drive innovation; they are the innovation engine

In every major tech wave, it’s rarely the established incumbents who create the breakthrough products, services and apps; it’s often the outsiders.

Apple didn’t invent ride sharing, Uber did.

Google didn’t invent the leading online marketplace for accommodation, Airbnb did.

Amazon didn’t invent one of the first streaming platforms that provides us access to millions of songs for free, Spotify did.

Our smartphones and their app stores just enabled them. That’s the playbook. Big tech and their platforms scale the infrastructure, but startups often bring the ingenuity, urgency, and risk appetite to build the new ideas that change the world.

Also Read: 3 game-changing GenAI insights every digital-native business needs to know

The same is playing out in this new race to unlock AI’s potential.

In my work at Meliora, I’ve seen firsthand how generative AI founders are focusing less on hype and more on building tools that solve real problems for businesses today. From automating compliance to streamlining procurement, these founders aren’t imagining the future of AI. They’re distributing it.

Urgency beats infrastructure

While big rech fine-tunes large models and negotiates internal processes, startups are sprinting. With smaller teams and sharper focus, they’re closer to the problem and faster to the solution.

Take Quickfind AI, which simplifies purchasing decisions for SMBs with intelligent, conversational workflows. Or Fluency AI, which turns fragmented SOPs into usable, generative playbooks for large teams. These companies are delivering practical, scalable AI, not someday, but right now.

That speed, focus, and user obsession is what big organisations often lose. But it’s exactly what they need to recapture if they want to stay relevant in an AI-first world.

Real AI is already here, and it doesn’t look like AI

Forget the keynote hype reels. The best AI today doesn’t try to look futuristic. It just makes work better, enabling existing systems and the people behind them to work smarter.

Relevance AI is empowering teams to build sophisticated productivity tools without writing lines of code. Blunge AI is helping marketing teams generate brand-safe visuals in seconds. None of this is a “future vision.” It’s happening already and at scale.

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

And that’s the shift we need to recognise. The future of AI won’t be one big leap. It will be thousands of small, usable innovations that spread quickly because they work.

Business need startups more than ever

Startups are no longer just disruptive, they are an essential part of the innovation ecosystem. In fact, many of the most powerful tools business uses today were born in dorm rooms, not boardrooms.

From Slack to Stripe to Canva, the pattern is clear. Startups build, platforms enable, and enterprises adopt.

It’s not a matter of big versus small, it’s an ecosystem. But if companies want to keep pace with AI’s next evolution, they can’t build everything in-house. They need to plug into the creativity, focus, and urgency that startup teams deliver best.

It’s this clarity and urgency that has shaped our approach at Meliora as we continue to back founders driving meaningful AI innovation.

The bottom-up future of AI

If you want to truly understand what’s truly next in AI, don’t default to the biggest names in the room. The most meaningful breakthroughs are coming from focused, fast-moving startups solving real problems with clarity, speed, and purpose.

Because this is where the creative intelligence of AI lives, and the future belongs to those who know where to look.

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

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Image courtesy of the author.

The post What big tech won’t show you about the future of AI appeared first on e27.

Southeast Asia’s March 2026 funding surge is less a sudden spike than a signal of structural maturity in the region’s tech ecosystem. The sharp rebound from February’s dip reflects how capital cycles, rather than sentiment alone, increasingly shape investment flows.

What stands out is not just the US$378M raised, but the composition of that capital. Repeat funding rounds for companies like Carsome and growing interest in B2B SaaS players such as Amity Solutions suggest investors are prioritising scalability and proven business models over speculative bets. This marks a shift from earlier growth-at-all-costs strategies toward more disciplined deployment.

At the same time, the diversity of active investors (from state-backed entities like EDBI to private capital and regional VCs) highlights a deepening capital pool. This reduces dependence on any single funding source and strengthens ecosystem resilience.

However, the optimism should be tempered. Rapid funding increases often precede recalibrations, especially in markets grappling with regulatory complexity and talent shortages. The real test lies in whether startups can convert capital into sustainable growth.

Ultimately, March’s numbers reinforce Southeast Asia’s position as an emerging innovation hub, but one transitioning from exuberance to execution.

REGIONAL

SEA tech funding surges 322% to US$378M in March 2026: After a bruising funding winter, investor confidence roared back across 23 rounds driven by Carsome, Amity Solutions, and active VCs including Vertex Ventures, EDBI, Asia Partners, and Kairous Capital.

Indonesia FMCG e-commerce hits record IDR40T in Q1 2026: Food and Beverage surged 88% year-on-year, fuelled by Ramadan and Eid al-Fitr spending, while ShopTokopedia posted the strongest platform growth across nearly all categories, and Lazada shed between 49% and 66%.

eFishery fraud chills SEA agritech investment pipeline: Investor Aqua-Spark says eFishery’s systematic inflation of performance data has shut off mainstream capital flows into aquaculture, a sector urgently needing institutional funding ahead of a global protein crisis by 2050.

Central Asia opens Malaysia tech hub to enter SEA markets: IT Park Uzbekistan, Kazakhstan’s Astana Hub, and VC firm Big Sky Capital signed a tripartite MOU to give B2B SaaS and AI startups a structured soft landing into Malaysia and Singapore, with commercialisation — not just pilots — as the stated priority.

SEA SMEs are already using AI daily, but gaps remain: Only 4.2% of Singapore SMEs had adopted AI in 2023 versus 44% of large firms, yet over 75% of APAC SMEs are already using AI-enabled digital tools, pointing to a wide gap between passive use and meaningful, scalable adoption.

Indonesia’s AI shopping adoption hits 82%, but trust lags: A YouGov-Visa study found 82% of Indonesian consumers use AI for product searches and price comparisons, yet only 32% are open to completing purchases through AI, citing data security and hidden fee concerns as the main barriers.

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INTERVIEWS & FEATURES

Cooley’s David He: eFishery “poured cold water” on the ecosystem: He tells e27 that the collapse exposed a simple governance failure; investors relied on management-reported accounts rather than audited ones. However, the reputational damage is disproportionate given the many credible founders still operating across the region.

Hatch’s founder: Inclusion is expensive, slow, and worth it: The founder of workforce development firm Hatch argues that Southeast Asia’s skills gap is an infrastructure problem, not a talent problem, and that true inclusion demands time, flexibility, and long-term presence, not just a placement metric.

The fundable founder trap: Why “investor-ready” can kill you: A B2B SaaS founder who met every conventional standard still shut down when investor appetite shifted upstream while he was still preparing his pitch, illustrating why a layered capital stack matters more than a polished data room.

The VC hunger games: How investors fight for unicorns: From Benchmark’s high-stakes bet on Uber to Accel’s relationship-first win at Facebook, the tactics VCs use to secure deals — overbidding, influence-building, and timing plays — reveal that chasing valuations, as WeWork showed, often ends badly.

Sustainability tech founder: Ambition without humanity is a dead end: After an AI lifecycle assessment startup imploded due to broken trust and misaligned egos, the founder rebuilt a green-economy platform by prioritising empathy, psychological safety, and “No-Meeting Wednesdays”over technical firepower.

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INTERNATIONAL

Crypto market hits US$2.36T on regulatory clarity and ETH supply move: The joint SEC-CFTC digital commodities framework classifying BTC, ETH, and SOL fuelled a 2.06% rally, while the Ethereum Foundation’s decision to stake US$93M worth of ETH tightened liquid supply, though elevated leverage and the April 16 CLARITY Act roundtable remain key risk flashpoints.

Bitcoin retreats to US$68,765 as Iran deadline looms over markets: After briefly reclaiming US$70,000 on short liquidations totalling US$145M, Bitcoin pulled back as Strait of Hormuz tensions pushed Brent crude to US$110 per barrel, with the Fear and Greed Index sitting at 26 and US equity markets posting four consecutive sessions of gains despite the volatility.

OpenAI calls for Musk investigation over for-profit restructuring block: OpenAI urged California and Delaware attorneys general to probe Musk’s anticompetitive efforts to block its shift to a for-profit structure, with a trial against Musk, OpenAI, and Microsoft imminent and damages claims reaching US$134B.

OpenAI alumni launch US$100M VC fund targeting early-stage AI: Zero Shot has hit a first close of US$20M and already backed Worktrace AI and Foundry Robotics, with founding partners Evan Morikawa, Andrew Mayne, and Shawn Jain drawing on their engineering and research roots at OpenAI.

OpenAI Korea partners with Shinsegae on AI commerce rollout: The MOU will see AI shopping agents deployed first at E-Mart, allowing users to search, build purchase lists, and complete payment and delivery through conversational AI, with OpenAI also supporting productivity tools across the broader Shinsegae Group.

India’s gig worker drought disrupts quick commerce delivery: Seasonal migration for harvest and elections has left daily active gig workers 10–12% below early-2026 levels in Delhi-NCR, Bengaluru, and Mumbai, forcing platforms to cap instant delivery, raise bonuses, and brace for a potential 25% surge in demand ahead.

South Korea orders five-minute crypto ledger checks after Bithumb error: Following a major reconciliation failure, South Korea’s Financial Services Commission mandated all exchanges verify internal ledgers against actual crypto holdings every five minutes by end-May, with daily public disclosure and monthly accounting firm audits also required.

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CYBERSECURITY

China targets Taiwan’s chip talent through covert recruitment networks: Taiwan’s National Security Bureau warns China is using indirect channels and shell entities to poach semiconductor and AI engineers, while Taiwan’s Government Service Network faced more than 170 million intrusion attempts in Q1 alone, with deepfake election interference also flagged.

Taiwan investigates 11 Chinese firms for illegal chip worker poaching: Authorities raided 49 sites and questioned 90 people after firms hid mainland ties and operated in Taiwan without approval, part of a crackdown handling 100 cases since 2020, even as 77.7% of Taiwanese chip companies now report hiring difficulties.

Ambiguous AI policy is a security risk, not just a governance gap: When AI models act as policy executors, unclear rules create inconsistent enforcement that attackers can probe for weak edges, erode user trust, and blind internal security teams — demanding machine-operational definitions that are decisionable, testable, and auditable.

Corporate mental health strategies are failing; AI can help fix that: Singapore’s employee engagement sits at just 59%, yet only 36% of local employers are comfortable discussing mental health at work; AI-powered platforms can detect early distress signals through anonymised sentiment analysis and personalise support pathways, reducing stigma and scaling clinical care responsibly.

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SEMICONDUCTOR

Samsung forecasts Q1 profit of US$37.8B on AI chip demand surge: Revenue is projected to rise 68% to US$87.8B, with the chip division alone contributing an estimated US$35.6B in operating profit as customers stockpiled inventory ahead of anticipated DRAM price increases of over 50% this quarter, far exceeding LSEG’s SmartEstimate of US$26.8B.

Nvidia’s SchedMD buy raises vendor-neutrality fears for Slurm software: Slurm, which runs approximately 60% of supercomputers worldwide, is now under Nvidia ownership, prompting concern from AI and HPC specialists that the chipmaker could favour its own hardware over rivals like AMD, despite pledges to keep Slurm open source and vendor-neutral.

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AI

The AI wave is real, but it won’t lift everyone equally: With Jensen Huang projecting US$1T in AI infrastructure spending through 2027 and a gigawatt data centre costing US$40B before a single chip is installed, the application layer — not the infrastructure layer — is where SEA founders can still compete, provided the on-ramp gap for SMEs is closed through operator-first tools and local capability-building.

AI didn’t invent bias; it inherited and amplified it: From Google’s gender-skewed hiring tool to IBM Watson’s flawed oncology recommendations, biased training data scales institutional inequality at machine speed, making human critical oversight — not just algorithmic audits — a non-negotiable check on AI deployment.

The hidden dangers of AI bias and what startups are doing about it: A 2025 study found AI-generated summaries influenced 84% of purchase decisions even when containing hallucinated facts in up to 60% of cases; startups like Pymetrics, Truera, Zest AI, and H2O.ai are building fairness frameworks, explainability tools, and bias-audited credit models to counter these risks.

AI moves from workplace safety experiment to mandatory infrastructure: Singapore’s Ministry of Manpower already mandates video surveillance on construction sites valued at SG$5M or more, and with Vietnam passing SEA’s first comprehensive AI law in December 2025, regulators across the region are shifting from voluntary guidelines to enforceable AI oversight frameworks.

Agentic AI is the next frontier for SEA’s small businesses: Beyond automating invoices and social media captions, the emerging shift is toward autonomous systems that connect point-of-sale alerts, supplier orders, loyalty updates, and manager reports into a single continuous workflow — moving AI from a helper to a genuine operational partner for lean SME teams.

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THOUGHT LEADERSHIP

Solar grids in Sierra Leone, innovation hubs in SEA: A shared climate vision: The founder of Green Sphere Power Company argues that Africa and Asia share both the urgency of energy access and the tools to solve it, envisioning solar startups in Sierra Leone learning from Singapore’s green tech ecosystem and Nairobi engineers collaborating with Bangkok’s AI innovators.

AI policy enforcement without clarity is governance at scale, done wrong: Organisations deploying AI as a policy executor, flagging transactions, removing content, throttling accounts, must match automation speed with governance maturity, because ambiguous policy doesn’t stay unclear under automation; it becomes inconsistent enforcement that attackers exploit and users distrust.

The fundable founder trap: Build a capital stack, not just a pitch: Indonesian B2B startup Stoqo had real traction and still shut down in 2020 because it could not bridge to the next round; the lesson is that founders must layer grants, venture debt, revenue-based instruments, and equity rather than betting on a single source of capital arriving on schedule.

Human-centric technology isn’t built with code; it’s built with culture: A product marketer turned sustainability tech founder argues that after watching an AI lifecycle assessment startup implode from broken trust, companies that design around human needs first — using AI to amplify judgment rather than automate it –will outlast those chasing “hi-tech, low-touch” shortcuts.

Inclusion is a long game and most institutions aren’t built for it: Hatch’s seven-year journey placing overlooked workers (youth, people with disabilities, caregivers) reveals that real workforce inclusion costs far more than a placement metric captures: it demands patience, flexible pathways, and the willingness to redesign the route when the first one doesn’t fit.

AI inherited society’s biases and human oversight is the only real fix: Drawing parallels from convict leasing laws to Google’s gender-biased hiring algorithm, an Accelerating Asia Ventures partner argues that bias embedded in training data is not a model quality problem but a systemic one, requiring diverse data curation, algorithmic auditing, and human review at every stage.

The AI wave is reshaping who can build — but the on-ramp is still broken: While Jensen Huang’s US$1T infrastructure forecast and Karpathy’s codeless workflow signal a profound shift in who can create products, only 5% of SEA SMEs that claim AI adoption use it meaningfully, underscoring that access to tools and the ability to deploy them at scale remain two very different things.

Crypto’s 2.06% rally reflects policy maturity, not speculative impulse: The market’s 55% correlation with gold signals growing perception as an inflation hedge, while the CLARITY Act’s progress through Congress and the April 16 SEC roundtable will determine whether regulatory clarity translates into sustained institutional flows or triggers a retreat to the US$2.33T support level.

VC hunger games: Relationships and timing beat the highest bid: Accel’s US$12.7M bet on Facebook, won through mentorship and a hands-off approach rather than the largest cheque, returned billions at IPO, while WeWork’s US$47B valuation collapse showed what FOMO-driven investing without governance scrutiny ultimately costs.

The post Ecosystem Roundup: Boom or turning point? appeared first on e27.

When Singtel’s network went dark for eight hours on March 16, the ripple effects were immediate and far-reaching. Emergency services faltered. Digital payments stalled. Thousands of gig workers lost an entire day’s income. For a city-state that has staked its economic future on digital leadership, the outage was more than an inconvenience: it was a stress test the infrastructure did not entirely pass.

The incident arrives at a pivotal moment. Southeast Asian (SEA) telcos are bracing for mobile data consumption to surge to 40GB per user by 2030, driven in large part by the accelerating deployment of AI across every sector of the economy. At the heart of this transformation is a concept gaining urgent traction in boardrooms and policy circles alike: Sovereign AI — the principle that nations must own, operate, and govern the AI infrastructure that underpins their critical systems, rather than cede that control to foreign platforms or distant cloud providers.

For Singapore, Sovereign AI is not merely a geopolitical aspiration. It is an infrastructural imperative. As AI workloads demand always-on compute, low-latency data processing and ironclad network reliability, the question is whether the telco sector — the backbone of the digital economy — is architected for what comes next.

Mayank Srivastava, chief executive of BDx Data Centres, argues the Singtel outage carries a lesson that extends well beyond fault attribution. “As economies digitise, dependencies concentrate across networks, data centres, and cross-border links,” he said in an email interview with e27.

Also Read: Singtel launches US$250M AI fund to turn its telco empire into an AI deployment platform

The following is an edited excerpt of the conversation.

Can you explain what Sovereign AI means in practice, and why you believe Singapore and SEA risks “data colonisation” if it doesn’t act now?

In practical terms, Sovereign AI is about ensuring that value creation happens locally. In the AI economy, the data centre is the factory housing large‑scale GPU clusters. It is where data is processed, models are trained, and decisions are generated. Sovereign AI means that data, compute, and governance frameworks are aligned within national or regional jurisdictions, under local laws and accountability.

This is not about isolation or exclusion. It is about economic participation. Historically, economies that exported raw materials but imported finished goods captured less long‑term value. In the digital economy, data is the raw material. If it is consistently processed elsewhere, the economic and strategic value associated with it accumulates outside the region.

The implication is not just revenue, it is agency. Critical systems in healthcare, finance, and public services increasingly rely on AI‑driven decision layers. Ensuring that these systems are supported by trusted, locally governed infrastructure strengthens transparency, resilience, and public trust. When we use the term “data colonisation,” we are referring to this economic value‑capture dynamic, not a political concept.

Singapore is well positioned to lead in this area. Recent IMDA initiatives around trusted infrastructure, AI governance, and high‑efficiency data centres reflect a thoughtful, forward‑looking approach. By supporting secure, energy‑efficient AI infrastructure within its regulatory framework, Singapore can anchor value creation locally while remaining globally connected—benefiting enterprises, startups, and the broader digital economy.

With SEA telcos projecting 40GB of data per user by 2030, what does that demand curve actually mean for the physical infrastructure required to support it?

The headline number has indeed shifted. With 5G-Advanced, V2X, and persistent machine-to-machine traffic, SEA’s mobile data usage is now projected at around 38-40GB per user by 2030 per Ericsson and GSMA baselines, though aggressive AI/IoT scenarios could push toward 60GB+ in high-growth markets. But the real infrastructure implication isn’t just volume. It is the shape of the demand curve.

Also Read: Echelon Philippines 2025 – Building at telco-scale: How startups can leverage Globe’s ecosystem for fast-track market entry

What matters is the nature of the data. More ultra‑high‑definition video, far more real‑time AI inference, and continuous IoT traffic fundamentally change infrastructure requirements. These are latency‑sensitive, always‑on workloads that stress power delivery, cooling, and network resilience in ways traditional mobile traffic never did.

Around 40GB per user is not a gentle increase; it is a vertical climb when translated into physics. Five years ago, a typical rack ran at 5kW. Today, NVIDIA DGX GB200-scale AI racks reach 120-200kW in production (scaling to 700-800kW in dense clusters). By 2030, 1-2MW per rack is realistic as power density becomes the limit.

Supporting 2MW racks requires an order‑of‑magnitude shift in cooling, including direct‑to‑chip and liquid‑immersion systems, along with redesigned power trains and grid interfaces. As highlighted in BDx discussions on AI‑first facilities, this represents 10× cooling retrofits compared to conventional designs.

Regionally, this demand cannot be met by any single market alone. It points to the need for coordinated capacity development across Singapore, Indonesia, Malaysia, Thailand, and Vietnam. Given the long lead times involved in power provisioning and construction, infrastructure planning must move well ahead of demand rather than react to it.

Do you think telcos should stop worrying about power density and refocus on services that drive growth? What is the danger of telcos continuing to build and operate their own data infrastructure rather than partnering with specialist providers like BDx?

Telcos face a genuine strategic balancing act. Modern data centres have evolved into highly specialised environments requiring deep expertise in power engineering, thermal management, and increasingly AI‑optimised design. These capabilities sit alongside—but are distinct from—core telecommunications operations.

The question is less about capability and more about focus. As networks evolve and services become more sophisticated, tying up capital and leadership attention in highly specialised infrastructure can limit flexibility elsewhere.

Also Read: A new dawn in the post-2G era: How cloud technology can propel the telco industry to new heights

Partnership models offer an alternative. By working with specialist providers, telcos can access AI‑ready, future‑proof infrastructure while concentrating their investment and innovation efforts on network quality, platforms, and customer‑facing services. This separation of roles also lowers friction for startups and enterprises, who benefit when telcos focus on service innovation while infrastructure specialists focus on scale, efficiency, and resilience.

It is a collaborative approach that allows each participant to operate where they add the most value.

How should we think about the ROI of reliability? When a telco goes down, the costs are obvious, but what is the business case for investing heavily in resilience before a crisis happens, especially when margins are already under pressure?

Reliability investments are challenging to justify because their value is most visible in what does not happen—outages avoided, customers retained, regulatory scrutiny prevented. Traditional ROI models struggle to capture this.

A useful analogy is healthcare. There is an accepted baseline of reliability below which systems simply cannot operate. In a digital economy, communications infrastructure increasingly occupies that same category. As AI supports real‑time finance, healthcare, and public services, reliability becomes a prerequisite rather than a differentiator.

In that context, resilience is not defensive spending. It is a condition for participating in higher‑value use cases. Operators that can demonstrate consistent, measurable reliability operate in a different commercial and regulatory conversation than those competing solely on cost.

As Southeast Asian telcos consolidate to boost valuation, there is a tension between leaning out operationally and building the robust backbone an AI-native economy needs. How do telcos resolve that contradiction?

Consolidation can create scale, but scale alone does not solve architectural complexity. The opportunity lies in being precise about what to optimise internally and what to access through partnerships.

Also Read: Founders’ playbook for resilience in 2026: Building in atoms in a fractured world

AI is going to be a utility, like electricity or the internet. The backbone required for an AI‑native economy is layered. It includes networks, specialist infrastructure, cloud platforms, and regulatory frameworks working together. No single balance sheet needs to own every layer.

By treating infrastructure as something to access strategically rather than own entirely, telcos can redirect capital toward network quality and differentiated services while still supporting the depth and resilience AI workloads require.

If you were advising Singapore’s policymakers today, what are the two or three most urgent infrastructure decisions they need to make in the next 12 to 24 months to ensure the country is genuinely ready for an AI-driven future?

First, aligning power policy with AI timelines.

AI infrastructure investments move faster than traditional approval cycles. While Singapore’s regulatory rigour is a strength, there is scope for clearer, faster pathways for high‑efficiency, AI‑optimised capacity with defined sustainability standards. As AI adoption accelerates, the next 12 to 24 months become disproportionately important for setting these frameworks.

Second, strengthening trusted compute for critical sectors.

As AI becomes integral to finance, healthcare, and public services, ensuring that these workloads are supported by resilient, trusted infrastructure is essential. Periodic stress‑testing of dependencies and encouraging meaningful infrastructure diversity can further strengthen confidence.

Third, keeping regulations practical and enabling.

Singapore has a strong track record of using regulation to unlock innovation rather than constrain it—across sectors such as aviation, fintech, and border security. Changi Airport’s use of facial recognition is a clear example of regulation providing the clarity and confidence needed for large‑scale adoption.

Also Read: A new dawn in the post-2G era: How cloud technology can propel the telco industry to new heights

AI infrastructure requires the same seamless approach: clear rules, aligned incentives, and strong governance, implemented in a way that matches the pace and capital intensity of the technology. When regulatory frameworks are predictable and outcomes‑based, they enable infrastructure providers to take the long‑term investment risks required to keep Singapore—and the region—at the forefront of global AI development.

This balance between oversight and enablement is one of Singapore’s defining strengths, and applying it thoughtfully to AI infrastructure will be key to sustaining leadership as the ecosystem continues to evolve.

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Image Credit: Taylor Vick on Unsplash

The post When the backbone breaks: Can Singapore’s telcos power a Sovereign AI future? appeared first on e27.

As Southeast Asia navigates a new era of global trade marked by shifting tariffs and geopolitical pressures, many businesses are rethinking their supply chains. But while much of the attention focuses on rerouting shipments or finding new trade partners, there’s another, often invisible layer that needs equal scrutiny: data.

For e-commerce businesses across the region, especially small and medium-sized enterprises (SMES), data has become just as critical as the goods themselves. Every customer order involves a trail of personal information: names, emails, addresses, payment details, and browsing behaviours. And just like your physical inventory, this data flows through a complex web of systems and partners.

In an age where data breaches can disrupt operations, damage reputations, and trigger regulatory scrutiny, SMEs in Southeast Asia must rethink their approach. It’s time to treat data as a strategic asset within the supply chain.

How did data become part of the supply chain?

Traditionally, supply chains moved goods. Now, they also move data, especially in e-commerce, where orders are digital, fulfilment is multi-system, and customer touchpoints span multiple platforms.

It starts with a customer creating an account and ends with delivery (and sometimes returns), but in between, that data flows through:

• Your website and checkout forms
• Order management and warehouse systems
• Third-party logistic partners and courier services
• Payment processors and fraud detection tools
• CRM, email marketing, and support platforms

At each handoff, there’s a risk. A weak link in your vendor ecosystem could become the entry point for attackers or trigger compliance penalties.

Also Read: Building resilience against cyber attacks in ASEAN through data

Why Southeast Asia’s SMEs are at higher risk

Southeast Asia is experiencing rapid e-commerce growth, driven by mobile-first consumers, rising digital adoption, and an increasingly tech-savvy population. But SMEs face unique challenges:

• Lean teams with limited cybersecurity expertise
• Dependence on third-party services without full visibility into security practices
• Inconsistent data protection regulations across the region
• Growing exposure to cross-border customers and compliance obligations (e.g., GDPR, PDPA)

In this climate, SMEs become attractive targets: they hold valuable data, yet often lack enterprise-grade defenses.

Where the breaches happen

Here are the common weak spots in e-commerce supply chains:

• Unsecured web forms: Personal data submitted via non-encrypted connections
• Outdated plugins and platforms: Easy entry points for attackers
• Open access in cloud storage: S3 buckets or Google Drives set to public
• Courier handoffs: Emailing spreadsheets with full customer details
• Weak access control: Everyone from interns to vendors having admin rights
• Missing contracts: No Data Processing Agreements (DPAs) with partners

The result? A bigger attack surface and a greater chance of incidents—not only cyberattacks but accidental leaks and regulatory missteps.

Consequences: Beyond compliance

Data breaches hurt more than just your security posture. They come with steep costs:

• Regulatory fines (PDPA, GDPR, etc.)
• Investigation and legal fees
• Lost trust and customer churn

In a region where consumer trust is hard-won and word-of-mouth drives growth, a single breach can knock your brand out of the running.

Also Read: How a data-driven approach can optimise decarbonisation in the built environment

Ten practical steps for SMEs

Fortunately, protecting your data doesn’t require a massive budget. Start here:

• Strong passwords + MFA: Require complex passwords and enable multi-factor authentication.
• Employee training: Teach your team how to avoid phishing and handle data responsibly.
• Encrypt everything: Secure data in transit (HTTPS, TLS) and at rest.
• Limit access: Give employees and partners only what they need—nothing more.
• Map your data: Know what you collect, where it’s stored, and who has access.
• Keep software patched: Don’t leave doors open through outdated systems.
• Vet your vendors: Ask about their security measures and insist on DPA clauses.
• Have a breach plan: Know how to respond if something goes wrong.
• Use open-source tools: Leverage cost-effective solutions for monitoring and scanning.
• Avoid data hoarding: Don’t keep data you don’t need. Delete securely.

Build supply chains with privacy in mind

Just as trade routes are being recalibrated in response to tariffs and trade tensions, your digital supply chain needs strategic redesign too. Start building systems that respect data from the ground up:

• Collect less
• Secure more
• Automate safely
• Limit access by role

This approach aligns with “privacy by design” and positions your business to handle future regulations, audits, and customer scrutiny.

Final word: Data is your trade advantage

As Southeast Asia adapts to new global trade realities, digital resilience becomes part of economic resilience. E-commerce SMEs that protect customer data won’t just avoid fines—they’ll build brands that last.

Trust is currency. In uncertain times, protecting that trust is how you future-proof your business.

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The enterprise AI story has moved well beyond chatbots and novelty pilots. In large companies, AI agents are now being connected to finance systems, customer databases, internal knowledge bases, payment rails, cloud consoles and software development pipelines. That shift is why “The AI Agent Governance Gap” report by US-based API management company Gravitee lands with the force of a fire alarm, not a polite policy memo.

The report cites findings from Cybersecurity Insiders showing that 71 per cent of large enterprises have already deployed AI agents with direct access to core business systems, yet only 16 per cent effectively govern that access. In other words, the corporate world has handed the keys to the machine before installing the locks.

Also Read: AI agents could become the new OTAs: What it means for Agoda and the future of travel

That framing matters enormously in Southeast Asia, where enterprises are modernising fast but often unevenly. The region’s banks, telcos, insurers, logistics giants, government-linked companies and fast-scaling tech firms are running a dense mix of legacy systems, cloud services, outsourced IT operations, and regional data flows. Add AI agents into that patchwork, and the attack surface does not merely expand. It becomes harder to even describe.

The problem is not adoption. It is architecture

Gravitee’s core argument is that the governance gap is architectural, not procedural. AI agents do not behave like human employees, and they do not fit neatly into identity and access models designed for human beings signing in from laptops. Agents operate at machine speed, can chain actions across multiple systems, inherit permissions quietly and create activity logs that are difficult for security teams to interpret in real time.

The numbers in the report are stark. It says 92 per cent of organisations lack full visibility into their AI identities, while 95 per cent doubt they could detect or contain misuse if it occurred. Nearly half of surveyed CISOs (47 per cent) say they have already seen AI agents exhibit unintended or unauthorised behaviour. That is not a theoretical risk. That is production risk wearing a name badge.

For Southeast Asia, the implications are especially sharp because many businesses operate across multiple jurisdictions with different compliance expectations. A Singapore-headquartered company may have engineering in Vietnam, a customer service operation in the Philippines, merchant relationships in Indonesia and cloud workloads spread across several regions. One poorly scoped AI agent plugged into a CRM, data warehouse, and payment workflow can turn into a compliance and security headache across borders in a matter of seconds.

Regional digitisation has created fertile ground for agent sprawl

There is a reason the region is vulnerable to this problem. Southeast Asia’s digital economy has been built on speed, interoperability and relentless integration. Super apps connect payments, food delivery, transport and lending. E-commerce platforms rely on real-time logistics and fraud tools. Banks are exposing more services through APIs.

Manufacturers are digitising procurement, forecasting and maintenance. Every one of those changes creates more structured workflows for an AI agent to enter.

And once agents arrive, they rarely stay in one lane. A sales operations agent may begin by summarising pipeline data, then request permission to update records, trigger marketing actions, and request access to billing information to answer customer queries. Over time, what began as a productivity tool becomes a semi-autonomous operator within the business.

This is where the report’s warning becomes uncomfortable. Most organisations still govern access as if the main risk is a human clicking the wrong button. But the bigger danger increasingly comes from a non-human identity making a thousand correct calls, in the wrong sequence, at the wrong scale, with the wrong level of access.

Also Read: AI agents are outpacing security: The crisis hiding in plain sight

That problem is not abstract in Southeast Asia. Regional companies often rely on managed service providers, third-party integrators and offshore development teams to stitch systems together. Credentials are shared. Service accounts linger. Documentation ages badly. In that environment, AI agents do not arrive in a pristine architecture. They arrive in a house whose wiring is already creative.

Why visibility is collapsing

The report argues that the first casualty of agentic AI is visibility. Traditional dashboards can tell security teams that an API was called or a database was queried. They are far less effective at expressing why an agent took a particular action, what chain of prompts or tool calls produced it, and whether the access was proportionate to the task.

That matters because AI agents do not simply authenticate once and sit still. They discover tools, call APIs, retrieve documents, invoke external models and sometimes delegate subtasks to other services. Each of those steps creates a miniature trust decision. According to the report, most enterprises are not instrumented to observe that flow in any coherent way.

In Southeast Asia, this visibility gap intersects with another reality: many organisations are using AI to compensate for talent shortages. Teams want automation because they are under pressure to do more with fewer specialists. That business case is real. But it also increases the temptation to grant broad permissions quickly, especially when the alternative is slower manual work.

The result is a pattern security teams know all too well: access first, governance later. Except that later, when the workflow is live, the vendor is embedded,, and the business unit is already dependent on the outcome.

The hidden boardroom risk

There is also a strategic issue here that founders and boards should not ignore. Many executives still view AI risk through the lens of model accuracy, bias or data leakage. Those issues matter, but agent governance is different. It is an operational power risk. It is the risk that software can now do things in enterprise systems, not merely analyse or recommend.

That shifts the conversation from ethics decks to control planes. If an agent can touch ERP, procurement, payroll, code repositories or customer records, then the real question is no longer whether the model is clever. The real question is whether the organisation knows what the agent is allowed to do, when, under what policy and with what audit trail.

For Southeast Asian enterprises racing to prove they are AI-ready, this is where the story gets serious. The most immediate threat may not be a headline-grabbing model failure. It may be a quiet overreach: an agent with too much access, too little monitoring and too many connected systems.

The coming divide

The Gravitee report points towards a coming divide in enterprise AI. On one side will be organisations that treat agents as first-class operational actors requiring identity, authorisation, monitoring and lifecycle management. On the other hand, there arehand, there those who continue to treat agents as convenient add-ons to existing software.
The first group will move more slowly at the beginning and much faster later. The second group will look agile until something breaks.

Also Read: Agentic AI is powerful, but power isn’t product-market fit

In Southeast Asia, where growth markets often reward speed and execution, that distinction could become a competitive fault line. The winners will not simply be the companies with the most AI agents. They will be the ones who know exactly what those agents are doing, what they can touch and how quickly their access can be changed or revoked.

The age of AI agents in the enterprise has already begun. The age of controlling them has barely started. That, as the report makes clear, is the real story.

The post AI agents are already inside your systems, but who’s controlling them? appeared first on e27.

A rat at the end of the rat race is still a rat.

It is an intentionally uncomfortable line, but it captures something important about how we often talk about progress in the digital economy. Too often, the goal is framed as helping more women and marginalised communities enter the system, compete harder, and succeed within structures they did not shape. But participation alone is not equity. If the rules, incentives, and power dynamics remain unequal, then bringing more people into the race does not create fairness. It simply expands the pool of people expected to navigate the same system. That is why equity matters. Not because it helps more people run faster, but because it asks whether the race itself should be redesigned.

This matters especially in Southeast Asia, where the digital economy is growing quickly but not evenly. New platforms, AI tools, financial services, and digital business models are creating real opportunities across the region. But access, mobility, and outcomes are still shaped by gender, income, geography, language, education, and social norms. In this context, equity cannot be treated as a side conversation. It has to be built into how innovation is designed, funded, and scaled.

For a long time, conversations about women in tech have focused on visibility. How many women are in the room? How many are founding companies, writing code, raising capital, or taking on leadership roles? These remain important questions, but they are no longer enough. Representation matters, but it does not tell us whether the systems people are entering are fair, inclusive, or empowering by design.

Technology does not emerge in a vacuum. Every platform, funding process, AI model, and workplace culture reflects the assumptions of the people and institutions behind it. If those assumptions go unexamined, inequality does not disappear in a digital environment. It becomes embedded into it.

Also Read: Ethical implications of using AI in hiring

At a systems level, this becomes visible in four areas.

The first is access. Participation in the digital economy is still unevenly distributed. Access is not only about being connected to the internet or owning a device. It is also about whether people have the tools, literacy, trust, safety, and confidence to engage meaningfully. Many individuals may be technically online but still excluded from the real benefits of the digital economy because products are unaffordable, systems are difficult to navigate, or pathways into jobs, markets, and networks remain out of reach.

The second is capital allocation. Capital does more than fund innovation. It determines which ideas are taken seriously, which founders are seen as credible, and which markets are considered worth building for. These decisions are often shaped by pattern recognition and inherited assumptions about what a promising founder or business should look like. As a result, capital can reinforce familiarity rather than recognise overlooked value. This does not just create unequal funding outcomes. It also shapes the direction of innovation itself.

The third is product design. Even when people can access digital systems and businesses can secure funding, exclusion can still be built into the product itself. Design choices reflect whose experiences are considered normal and whose are treated as exceptions. This can be seen in AI systems trained on narrow datasets, financial tools that overlook informal work realities, or digital services that assume levels of language fluency or digital confidence that many users do not share. When products are not designed with a wider range of lived realities in mind, they do not simply fail to serve some users well. They reproduce exclusion at scale.

The fourth is workplace culture. An equitable digital economy cannot be built by organisations that remain unequal on the inside. Workplace culture shapes who gets hired, who gets heard, who is trusted with responsibility, and who is able to progress into leadership. Too often, inclusion is measured by representation at the entry level while deeper questions of sponsorship, decision-making power, and belonging remain unresolved. If people from underrepresented backgrounds are brought into the system but not supported to shape it, the broader structure does not meaningfully change.

Taken together, these are not separate issues. They are different layers of the same system. A more equitable digital economy will not come from visibility alone. It will come from redesigning the structures that determine participation, validation, experience, and power.

Also Read: A new era of automation: Establishing best practices for intelligent automation and generative AI

Even the language we use deserves scrutiny. There is a quiet contradiction in the word inclusion. It sounds generous, but it also reveals power. To include is to decide who was outside, who belongs, and on what terms. That is why inclusion, on its own, can be insufficient. The deeper goal is not to be admitted into systems built by others, but to reshape the system so belonging is not conditional.

There is a similar tension in the way we celebrate the extraordinary. We usually mean the exceptional, the rare, the remarkable. But taken apart, extraordinary also returns us to the ordinary, the everyday person whose life and labour hold society together. Equity matters because a fair system cannot be designed only for the exceptional few who manage to break through. It must also work for the ordinary person, who should not need to be extraordinary just to be seen, supported, and given a fair chance.

That means asking harder questions. Who gets included in pilot opportunities and industry networks? Who is represented in the datasets behind the tools we build? Who gets trusted with strategic roles or technical leadership? Who finds the application process intuitive, and who finds it alienating? Who remains invisible in the innovation ecosystem, not because they lack talent, but because the system was not designed to recognise them clearly?

These are not abstract concerns. They affect the quality of innovation itself. An ecosystem that excludes is not just unfair. It is less capable. It misses markets, overlooks pain points, narrows the range of solutions being built, and concentrates opportunity in ways that weaken resilience.

For those of us working in innovation ecosystems, this creates a shared responsibility. We are not only supporting what gets built. We are also shaping the conditions under which innovation happens. That includes who gets access to capital, platforms, partnerships, distribution, and legitimacy.

The goal, then, is not simply to help more people enter existing systems. It is to build better systems in the first place. Because the real measure of progress is not how many people we let into the race, but whether we are willing to redesign it.

Otherwise, we risk mistaking movement for change. A rat at the end of the rat race is still a rat. Equity matters because the ambition should never have been to help more people survive the same race. It should be to build a digital economy where dignity, opportunity, and leadership are not conditional on fitting into a system that was never designed for everyone to begin with.

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