The venture capital exit landscape is undergoing a fundamental reset, and nowhere is this more evident than in today’s M&A environment.

The latest State of Exits 2025 report makes one thing brutally clear: the age of outsized, effortless mega-exits is over. With 96 per cent of transactions valued below US$500 million — and 70 per cent under US$100 million — the market has shifted decisively towards smaller, tightly scrutinised acquisitions.

Yet even as deal volumes fall, values have risen 15 per cent, a contradiction that reveals the new truth of this cycle: buyers are willing to pay, but only for the very best. For Southeast Asian founders, this means the bar for being considered “acquirable” has never been higher. Flaky forecasts, patchy revenue quality, or volatile performance now kill deals instantly.

Compounding this shift is the dominance of structured deals. With 73 per cent of M&A transactions involving earnouts — and 42 per cent of consideration tied to future performance — the risk has moved squarely onto founders’ shoulders. The average earnout now stretches 3.2 years, binding leadership teams long after the cheque is signed.

Add collapsing SaaS valuations, stabilising at a sober 7x revenue, and a surge in early-stage acquisitions (60 per cent occurring before Series A) and the message is unmistakable: founders must plan exits earlier, execute flawlessly, and build relationships long before the data room opens. The new exit reality is unforgiving, but navigable for those who prepare with discipline rather than hope.

REGIONAL

NTT Group to launch its first startup investment vehicle in SEA: Synexia Ventures will target startups in Singapore, Indonesia, Malaysia, and the Philippines, with an emphasis on AI, IoT, smart cities, robotics, and drones. NTT Docomo Ventures and NTT Finance will jointly manage the Singapore-based vehicle.

SG VC firm GFTN, Japan’s SBI launch US$200M fintech fund: The fund will invest in growth-stage fintech companies globally, focusing on AI, digital assets, cybersecurity, and tokenisation. GFTN connects innovators, entrepreneurs, investors, and policymakers across more than 130 countries.

Singapore leads ASEAN fintech with over US$725M funding: This represents 87% of the region’s total fintech investment, according to a report by UOB, PwC Singapore, and the Singapore FinTech Association. Across ASEAN’s six largest economies, fintech funding dropped 36% YoY to about US$835M, with the number of deals down 60%.

Canadia Group enters venture investing with new impact fund, backs Jalat Logistics: The group formalises its CVC arm, targeting high-growth Cambodian startups in logistics, education, healthtech, and renewable energy sectors.

Indonesia’s Superbank reportedly targets US$322M IPO by 2025-end: The IPO on the Indonesia Stock Exchange is expected to take place between mid-November and the end of this year. Superbank is a digital bank based in Indonesia backed by Grab, Singtel, and Kakao Bank.

Singapore regulator to trial tokenised bills in 2026: MAS is also developing a regulatory framework for stablecoins, focusing on reserve backing and redemption reliability. The bank is also supporting trials under the Bloom initiative, which tests tokenised bank liabilities and regulated stablecoins for settlements.

UK, Singapore, Thailand to test cross-border currency settlement: The project will use simulated real-time gross settlement systems and distributed ledger technology environments to examine how different central bank infrastructures can work together.

Anomaly Bio powers the future of ingredient manufacturing with US$2.6M in pre-seed funding: Backed by Pebblebed Ventures, Anomaly Bio aims to reprogram biology to transform global ingredient production and supply resilience.

The Librarian secures US$2M to redefine what an executive assistant can be: Investors include Golden Gate Ventures, Jeremy Stoppelman, and Twenty Five Ventures. Unlike Siri or Alexa, The Librarian mirrors real EA workflows, choosing the right tool and completing tasks autonomously.

Singapore greenlights expanded AV testing as WeRide and Grab prepare for public rollout in 2026: WeRide-Grab secure regulatory approval to quadruple AV test runs, accelerating Singapore’s first residential autonomous shuttle rollout.

REPORTS, FEATURES & INTERVIEWS

Founders face a brutal new reality: Tiny exits, tougher buyers, endless earnouts: M&A exits shrink as buyers grow selective, with 96 per cent of deals under US$500M and structured earnouts dominating founder outcomes.

Sea Limited roars back to profit, yet credit loss provisions flash warning signs: Garena and Shopee drive Sea’s profitability rebound, while Monee’s lending surge triggers a sharp spike in credit loss provisions.

The new exit reality: How secondary deals became the lifeblood of venture capital: Secondaries hit US$152B in 2024, accounting for 71 per cent of exit dollars and redefining how venture-backed companies achieve liquidity.

Poni’s Cassandra Wee on why the most meaningful insurtech innovation will not come from operating in silo: Under her leadership, Poni has emerged a leading insurtech platform that aims to redefine how tech and advisory services converge to deliver smarter, more accessible solutions.

Spectral is breaking Nvidia’s monopoly — one line of CUDA code at a time: How a London startup is reprogramming the rules of accelerated computing, and giving developers freedom from the world’s most powerful moat.

Vincent Tan: The elevation architect designing intentional growth for leaders and teams: Tan, the Elevation Architect, blends architecture and coaching to help leaders design growth through curiosity and adaptability.

INTERNATIONAL

Pine Labs smashes expectations with surging market debut after US$439M IPO: Shares surge up to 28 per cent after Pine Labs’s listing, reflecting renewed investor confidence in its payments platform and Southeast Asia expansion plans.

Apple, OpenAI lose bid to dismiss Musk’s xAI lawsuit: xAI accuses the tech giants of working together to limit competition in the AI sector. The case claims Apple’s integration of OpenAI into the iPhone’s OS restricts consumer choice and stifles innovation.

Tencent Q3 revenue rises 15% on AI, cloud expansion: The Chinese tech giant reported US$27.1B in revenue in Q3 2025. Operating profit was US$9B. The company said AI improved its ad targeting and gaming performance during the quarter.

Blackstone, SoftBank said to eye investment in Indian AI startup: Neysa Networks provides cloud infrastructure for AI. Blackstone may take a majority stake, while SoftBank could take a minority position, but no decisions are finalised and other investors might join.

SoftBank shares plunge 10% after Nvidia stake sale: The Japanese tech investor made the sale to raise funds for further AI-related investments. The Nvidia sale comes as investors debate whether large-scale spending on AI by major tech firms will generate the expected returns.

ECHELON

Scaling with the state: Partnering with governments for growth: This panel underscored the value of understanding local contexts, leveraging government initiatives, and fostering regional collaboration.

The influence advantage: How creators and platforms are sharing the future of business visibility: The discussion also covered the growing influence of AI on content production, the need to balance virality with authenticity.

SEMICONDUCTOR

AMD shares jump 9% on CEO’s forecast for AI, data centre growth: CEO Lisa Su said that hyperscaler clients have increased spending as AI technology reaches a turning point, adding that companies are starting to see the benefits of these investments.

IBM unveils new quantum chips, targets 2029 fault tolerance: The company introduced the Quantum Nighthawk processor with 120 qubits and higher connectivity, set to be available by the end of 2025, and the Quantum Loon processor, which demonstrates components needed for fault-tolerant quantum computing.

ASML opens new chip equipment hub in South Korea: The Dutch firm is the largest producer of extreme ultraviolet lithography systems, which are essential for advanced chip production. The 16,000 sqm facility will function as a manufacturing and repair hub for equipment like EUV and deep ultraviolet systems.

Nvidia secures land for new campus in northern Israel: The chipmaker plans to build a campus in Kiryat Tivon that could eventually host 8,000 employees. The site is part of a larger employment zone called Campus Tivon, which currently allows for 120,000 square meters of construction.

AI

AI takes centre stage in Singapore’s push for Zero-SIF construction sites: Modern construction sites are complex ecosystems. From heavy machinery moving through congested zones to fall-from-height risks, dangers can emerge suddenly and silently. AI is transforming how EHS teams detect and prevent these risks.

When AI talks nonsense: Why it’s not the end of the story: Next time you feel frustrated, remember: you’re not just using AI, you’re teaching it. And like any good student, it learns fastest when the teacher is clear, patient, and willing to try again.

How CCTV-based vision AI is transforming manufacturing: Traditionally, human inspectors check product quality one by one, which is slow and prone to errors. On the other hand, CCTV-based Vision AI watches the production line 24/7 and instantly spots tiny defects before they move forward.

THOUGHT LEADERSHIP

Beyond resilience: A call to action for a climate-proof Philippines to the tech ecosystem: After Super Typhoon Fung-wong, the Philippines must move beyond resilience by investing in startups and innovation for sustainable growth.

No CPI, no confidence: How data paralysis is fueling crypto’s November slide: The path forward is clouded by the absence of the CPI data, but its eventual release or its continued absence will be a critical test.

In finance, intelligence is human before it is artificial: Finance’s slower adoption of AI stems not from conservatism but from accountability. Every output, whether a risk score or a research summary, must be explainable, auditable, and defensible.

Homegrown AI: Mongolia’s blueprint for developing nations: Mongolia’s AI journey shows how developing nations can achieve digital sovereignty through local problem-solving and sustainable growth.

The future of startups: Where AI handles work and humans handle meaning: Automation in 2025 is redefining work by freeing time for creativity and connection, showing that true efficiency amplifies human presence.

Weathering the tariff turbulence: How AI and collaboration can lift SEA SMEs: Tariffs are reshaping trade in Southeast Asia, challenging SMEs while opening space for strategy, regional collaboration, and resilience.

Pakistan’s carbon market: A new opportunity for startups and SMEs: The country’s involvement in the world carbon market offers an opportunity for economic growth as well as an environmental one.

Building Indonesia’s green momentum: What comes after 2025’s lessons: The ecosystem gap between innovation and capital readiness held back Indonesia’s clean energy startups in 2025. If these gaps persist, Indonesia risks losing its competitive edge as the region’s emerging clean-energy hub.

What Southeast Asia can learn from Europe’s insurtech revolution: European insurtech startups have identified a critical gap in traditional insurance models: the one-size-fits-all approach often leaves consumers either over-insured for their actual needs or inadequately covered for specific risks.

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This panel at Echelon Singapore 2025 examined how creators and digital platforms are reshaping business visibility across markets.

Speakers stressed the value of TikTok and Instagram for both B2B and B2C outreach, noting that engagement, click rates and conversion rates remain essential metrics. Influencer marketing stood out for its ability to build trust through authentic content, with micro-influencers seen as more cost-effective than celebrity endorsements.

The discussion also covered the growing influence of AI on content production, the need to balance virality with authenticity and the importance of adapting to shifting algorithms. Panellists highlighted regional differences in platform behaviour and the potential for AI to disrupt traditional marketing strategies.

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The macro landscape this week sits in a state of suspended animation, defined less by new developments than by their absence. At the heart of this inertia is the ongoing US government shutdown, which began on October 1 and has now stretched into its sixth week, becoming the longest in the nation’s history. This institutional paralysis has created a critical data void, most notably delaying the release of the October Consumer Price Index report that was originally scheduled for Thursday, November 13.

The White House has even conceded that this key inflation gauge for October may never be officially released, leaving a permanent blind spot in the economic record. This vacuum of information forces markets to anchor their expectations on whatever data trickles out, elevating the importance of tonight’s release of weekly initial jobless claims, which are expected to show a figure of 218,000 for the week ending November 8.

In this context of uncertainty, risk sentiment has turned cautious. US equities closed mixed on Wednesday, with the Dow showing modest strength while the tech-heavy Nasdaq declined, a divergence that speaks to a subtle but important rotation within the market. This caution was also evident in the Treasury market, where yields edged lower as investors welcomed tentative signs of progress in Congress toward a resolution that would reopen the government. The 10-year yield’s retreat to 4.06 per cent reflects this flight to safety and a renewed hope for a political compromise. The US Dollar Index, for its part, remained largely flat, closing at 99.47, signaling that traders are in a holding pattern, unwilling to make significant directional bets until the political fog lifts and the next concrete piece of economic data arrives.

Also Read: Crypto crashes 3.7 per cent despite US shutdown deal: US$260M liquidations and whale exodus trigger sell-off

The crypto market, however, has been unable to insulate itself from this broader macro malaise. It has fallen a further 0.56 per cent over the last 24 hours, a move that extends a more painful 11.7 per cent monthly decline. This persistent weakness is not a single-factor event but rather a perfect storm of three distinct, reinforcing pressures: a clear pattern of institutional profit-taking, a sharp contagion event in the derivatives market, and an uncomfortably tight correlation with the performance of US tech stocks.

The first of these bearish forces is institutional retrenchment. While spot Bitcoin ETFs have been a major structural support for the market since their launch, their influence has waned in recent weeks. Data from trackers shows a clear trend of capital flight, with the total assets under management for these funds dropping from a recent high of around US$140.7 billion to US$138.9 billion over a single week, a decline of 8.7 per cent. This outflow is more than a simple portfolio rebalance; it signals a deeper shift in sentiment among large, sophisticated players. As the 10x Research CEO warned, a sense of fatigue has set in, driven by Bitcoin’s notable underperformance in 2025 relative to both the soaring price of gold and the resilient gains in the tech-heavy Nasdaq. For institutions that bought the post-ETF approval rally, the current environment offers a compelling reason to trim their exposure and lock in what gains remain.

The second pressure point is a stark reminder of the fragility embedded in the crypto ecosystem’s leverage. The US$63 million liquidation cascade on the Popcat memecoin, centered on the Hyperliquid exchange, was not an isolated incident but a canary in the coal mine. This single event triggered a broader wave of deleveraging across the entire crypto market, evidenced by a 14.7 per cent drop in total open interest. This is the process of overextended, speculative positions, particularly in the volatile altcoin sector, being forcibly closed out, creating a self-reinforcing cycle of selling that spills over into the entire asset class. The subsequent cooling of perpetual funding rates, which fell by 41 per cent in just 24 hours, confirms a sharp and sudden reduction in speculative appetite. The market is in a defensive crouch.

The third and perhaps most inescapable headwind is crypto’s persistent and powerful link to traditional equities, specifically the Nasdaq-100. The market’s 24-hour price action has shown a correlation of 0.88 with the Nasdaq-100, its strongest link to the index since March 2025. This statistic is a powerful testament to the fact that, for all its claims of being a separate, uncorrelated asset, crypto remains a risk asset first and foremost. Its fate is now inextricably tied to the same macro forces that move the markets for Apple, Microsoft, and Nvidia. Therefore, any pre-market weakness in the Nasdaq, such as the 1.2 per cent drop seen on Thursday, driven by fears over sticky inflation and a more hawkish Federal Reserve, will inevitably be mirrored in a retreat across the crypto board.

Also Read: Crypto rebounds as labour data calms markets but is the rally sustainable?

In conclusion, the market’s current malaise is a confluence of its own internal dynamics and the external macroeconomic environment. The derivatives market is in a state of recovery from its recent squeeze, with perpetual funding rates having turned slightly positive again at plus 0.0014 per cent. However, this technical stabilisation is overshadowed by a collapse in market confidence, as evidenced by the Fear and Greed Index plunging into the Extreme Fear territory at a reading of 25.

The path forward is clouded by the absence of the CPI data, but its eventual release or its continued absence will be a critical test. The key question on every trader’s mind is whether Bitcoin can hold the critical psychological and technical support level of US$100,000 if the October inflation data, when it finally emerges, shows a year-over-year increase that exceeds the 3.4 per cent threshold, which would likely cement a risk-off posture across all markets.

Until then, all assets remain chained to this unprecedented political and data-driven uncertainty.

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Image Credit: Traxer on Unsplash

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Ant International, a leading global digital payment, digitisation, and financial technology provider, has added iris authentication features to its AR glasses-embedded payment solution, through partnerships with leading smart glasses producers.

Alipay+ GlassPay currently integrates multi-modal biometric verification measures including the AI-powered voice interface with intent recognition and voiceprint authentication technology. The feature has been successfully tested on AlipayHK, and enables merchants and service providers to create an even smoother, more secure, and more immersive consumer experience via augmented reality. The new functionalities rely on the latest innovations in AI and AR (augmented reality) technologies, developed in conjunction with leading smart glasses manufacturers Xiaomi and Meizu.

Pushing the future of seamless and secure smart-glasses commerce

According to Ant International’s Chief Executive Officer Peng Yang, the company remains “laser-focused on pushing the frontier of payment from all angles: hardware-embedded consumer services, card+QR interoperability, bank-to-wallet connectivity, AI merchant payment orchestration for agentic commerce”. He highlighted that seamless, real-time, around-the-clock secure global payment will continue to be a main engine for global resilience and growth.

Ant International also noted that smart glasses are emerging as a new gateway for interactive commerce by bridging physical and digital consumer experiences, thanks to advances in AI. The devices integrate instant try-ons, interactive shopping and simplified checkout. Iris authentication is considered an especially secure form of biometric verification, given its resistance to spoofing. The method relies on a larger number of distinguishing feature points compared with facial or fingerprint analysis.

Also read: AI-powered EPOS360 turns small shops into smart businesses

Advancing biometric security for next-generation smart-glasses payments

Alipay+ GlassPay’s iris authentication feature compares over 260 biometric feature points to verify and protect the identity of the user. It uses AI and advanced liveness detection technology to counter fraud attempts using photos, videos, or 3D masks. Using advanced imaging algorithms, the solution accurately verifies user identity in various lighting conditions, offering reliable, zero-contact security with a simple glance throughout the day.

The solution integrates an end-to-end security suite for e-wallets and apps, including a unique personal encryption key scheme to safeguard user data. In accordance with laws and regulations, device manufacturers, digital service providers and technology providers will work together to ensure compliance with security requirements in different markets.

The multi-modal security framework of Alipay+ GlassPay is powered by Ant’s gPass, the world’s first trusted connection technology framework for smart glasses, which enables glasses manufacturers and developers to build a secure AI digital services system, innovate new application scenarios for the device, and expand on its utility for consumers. As AI ​​and AR use cases continue to expand, gPass is committed to providing global users with a safer and more convenient experience with smart devices.

Building a more enriched consumer experience

Building on AR-embedded payment, Alipay+ GlassPay will support merchants and digital platforms to develop a more enriched and efficient consumer experience. For example, smart glasses may help consumers to hail a ride and move seamlessly from a satisfactory offline try-on to an instant online purchase, saving merchant warehousing and logistics costs and improving omni-channel management.

Ant International will introduce the enhanced Alipay+ GlassPay solution to manufacturers, service providers and developers in the Asia Pacific.

“Xiaomi smart glasses are a key component of Xiaomi’s AI terminal strategy. Leveraging Xiaomi’s leading advantages in smart personal devices and an ecosystem of diverse use scenarios, we will expand cooperation with partners worldwide to enrich AI-driven lifestyle experience for consumers worldwide,” said Zhang Lei, Vice President of Mobile Phone Department and General Manager of Wearable Devices, Xiaomi.

Also read: Ant International releases Falcon TST to boost global AI forecasting

“The ultimate goal of smart glasses is to seamlessly integrate technology into our lives,” said Guo Peng, Head of XR Business Unit of Xingji Meizu. “Iris payment solution is a critical step toward this vision — it makes the act of paying feel natural again. However, the more invisible the technology becomes, the more visible the safeguards need to be. In our collaboration with Ant, our focus is not only on achieving faster and more seamless recognition but also on building a comprehensive security framework — from encrypted storage to liveness detection — ensuring the complete protection of users’ biometric data. As for smart glasses payment solution, security is not just a feature; it is the very foundation.”

Expanding interoperability and AI-driven innovation across the Alipay+ ecosystem

Today, Alipay+ connects over 1.8 billion user accounts on 40 mobile payment providers to 100 million merchants across 100+ core markets. With one integration, mobile payment partners can access Alipay+’s expanding toolkits for customer engagement and business growth. Among these, Alipay+ now integrates QR-based and card payments via a global NFC solution. It also enables a full range of agentic AI features including MCP-based AI payments built on Alipay+ GenAI Cockpit, an AI-as-a-Service platform for fintechs.

“We are excited to offer our advanced embedded payment solutions to smart hardware innovators and digital service providers to expand the exciting horizon of augmented-reality commerce. Ant International will continue to push payment innovations across the frontiers of interoperability, agentic AI, and new hardware solutions,” said Jiang-Ming Yang, Chief Innovation Officer, Ant International. 

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Autonomous vehicle (AV) deployment in Singapore is poised for a dramatic acceleration following key regulatory approval from the Land Transport Authority (LTA) by global autonomous driving technology leader WeRide and Southeast Asia’s leading superapp, Grab.

The LTA has granted the partners approval to conduct expanded AV testing with their entire Ai.R (Autonomously Intelligent Ride) fleet within the Punggol district.

Also Read: Grab makes strategic bet on WeRide to drive autonomous mobility in SEA

This approval marks a significant step forward, enabling WeRide and Grab to scale up their AV testing program substantially. Having commenced Punggol’s first AV testing in mid-October 2025, the companies now plan to increase the total number of AV test runs on its shuttle service routes by up to four times by the end of the year.

The extensive testing is necessary preparation for the launch of the Ai.R public autonomous ride service. Ai.R is expected to begin carrying its first batch of public passengers by early 2026, establishing Punggol as Singapore’s first residential neighbourhood with an autonomous shuttle service.

Fleet and operational rigour

The Ai.R service, operated by Grab in partnership with WeRide, comprises an 11-vehicle fleet: ten GXRs (equipped with five passenger seats) and one Robobus (eight passenger seats).

The shuttle service will operate on two dedicated routes, connecting Punggol residents to key local amenities, including the Punggol Coast MRT station, the Punggol Coast Mall bus interchange, regional malls, and clinics.

During the AV test runs, the vehicles are engineered to gather and meticulously analyse real-world data to localise their AI driving models. This data accumulation focuses heavily on understanding road infrastructure, traffic flows, and the nuanced behaviours of local road users and pedestrians. To ensure reliability in Singapore’s tropical environment, the AVs are specifically trained to handle diverse weather conditions, including intense sun glare and sudden rain.

The technical calibration efforts are rigorous. The AVs continuously refine their precision driving capabilities, which include honing smooth acceleration and deceleration, executing precise turn timing, and maintaining optimal distance from obstacles. They are also trained to master complex urban scenarios, such as safely navigating tight turns on narrow residential roads and in carparks while maintaining required safety clearance from surrounding objects and pedestrians.

Equipped with an advanced sensor suite featuring LiDARs and cameras, the GXRs and Robobus possess 360-degree vision. This allows the vehicles to “see” up to 200 metres in every direction and detect objects even when conditions are challenging, such as during rainfall, ensuring prompt and dynamic responses to hazards.

Building local talent

Crucially, the partners are simultaneously building the necessary human support for the service. GrabAcademy, Grab’s training institution, and WeRide are training an initial cohort of more than 10 experienced Grab driver-partners to transition into specialised Safety Operator roles.

Also Read: Grab posts rare profit, but cash burn and incentive dependence tell a deeper story

Following theory lessons and closed-circuit practical training, this first cohort has now progressed to on-the-road training. Safety Operators will remain onboard the AVs at all times throughout the testing and initial phase of public rides, providing crucial real-time supervision.

WeRide, a leader in the autonomous driving industry, holds autonomous driving permits in seven markets. Grab, founded in 2012, operates across eight Southeast Asian countries, serving over 800 cities in the mobility, deliveries, and digital financial services sectors.

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Let’s be honest. Many people try AI once and walk away saying, “It talks nonsense. It’s not accurate. I can’t work with this.”

I’ve heard this especially from midlife friends. They imagine AI should give perfect answers every time. When it doesn’t, they feel frustrated, even tricked.

But here’s the thing: bad experiences don’t mean AI is broken. They usually mean we need to learn how to talk to it.

Why do we get stressed before we even start

Here’s something interesting. I’ve noticed that many friends type to AI as if they’re writing a letter to the Prime Minister. Long, stiff, over-polished sentences. Some even practise what they’re going to say before hitting the mic button! By the time they actually speak, they’re already stressed.

And what happens? The AI gives back an equally stiff answer. No wonder they feel it “talks nonsense.”

But AI isn’t your boss. It’s not judging you. You don’t need to impress it. In fact, the more natural and relaxed you are, the better the responses.

Start with clarity and humility

If you’re not clear, AI won’t be either. That’s why the first rule is simple: be humble and just ask. Treat it like a conversation, not a command.

And if the answer doesn’t feel right? Don’t give up. Ask again. Rephrase the question. Add details. Think of it like giving directions. If you tell someone “go straight” but forget to say “then turn left at the big tree,” of course they’ll get lost. AI works the same way.

Also Read: Are large Vietnamese tech enterprises ‘indifferent’ when competing with ChatGPT?

AI can even evaluate itself

Here’s a surprise. You can actually ask AI to check its own work. For example, you might say: “Evaluate this article and tell me what’s missing.” Often, it will critique itself more honestly than a human editor would.

That “nonsense” answer you didn’t like? Ask it why it gave that response, or how it can improve. You’ll be surprised at how quickly it learns to adjust.

Prompt engineering, demystified

When I first heard the term prompt engineering, I imagined scientists in white coats conducting experiments. It sounded intimidating. But really, it just means asking better questions.

Try it the old-school way. Instead of “write about art,” say, “write about art in a warm, emotional way for Facebook.” Instead of “explain AI,” say, “explain AI in simple terms, as if you’re talking to my 70-year-old aunt.”

Prompt engineering is not rocket science. It’s practice, patience, and curiosity. And unlike people, AI never gets tired of your questions.

Closing thought

Yes, sometimes AI talks nonsense. But that doesn’t mean it has nothing to offer. It means we need to guide it better.

So the next time you feel frustrated, remember: you’re not just using AI, you’re teaching it. And like any good student, it learns fastest when the teacher is clear, patient, and willing to try again.

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As global tariffs reshape supply chains, Southeast Asia’s SMEs face an uneven burden and a unique opportunity.

This article explores how Vietnam, Thailand, and Indonesia are navigating the storm, why regional collaboration matters, and what founders must know to thrive in a volatile landscape. It is a call for strategy, resilience, and shared purpose in a time of global uncertainty.

Tariffs and the uneven burden on SMEs

In today’s global climate, tariffs are doing more than reshaping trade routes. They are creating ripple effects that hit small and medium enterprises across Southeast Asia the hardest.

While large corporations can retool supply chains or lobby governments, SMEs- the backbone of Southeast Asia’s economies- face disruptions with thinner margins, fewer resources, and limited negotiating power.

As a founder based in Singapore and deeply connected to the region, I believe this moment demands more than a reaction. It calls for collaboration and smart, forward-looking solutions that help SMEs not only survive but emerge stronger.

China’s factory flight: A blessing or bypass?

The “China +1” strategy has triggered a wave of manufacturing relocations to Vietnam, Thailand, and Indonesia. While this creates enormous opportunity, it also brings risk. Without genuine local partnerships, these moves may become little more than tariff workarounds, attracting international scrutiny and undermining the promise of long-term value creation.

For Southeast Asia, this is the time to show the world that the region is not just a low-cost backup plan. It is a centre of talent, innovation, and accountability. China, too, has a responsibility. Wherever its factories go, it must engage as a true partner, investing in local ecosystems and working alongside communities to build high-quality, future-ready industries.

Singapore, with its technological leadership, can play a pivotal role by setting transparency standards and raising the bar for best practices. Meanwhile, Indonesia should leverage its demographic strength, investing in education to empower its workforce and unlock opportunity across society.

Across the region, supply chains must move upward, driven by ethical use of technology and artificial intelligence that respects privacy while boosting productivity.

Navigating the storm: Vietnam, Thailand, and Indonesia

Vietnam, often hailed as the poster child of China +1, is now confronting its vulnerabilities. Nearly 30 per cent of its exports go to the United States, leaving it highly exposed. A six per cent stock market drop in a single day revealed just how deeply global volatility can cut.

Also Read: Are Asian economies in a position to benefit from the age of Trump’s tariffs?

Thailand’s outlook is more nuanced. Although it was initially forecast to grow around 2.9 per cent in 2025, new tariff pressures risk pulling growth closer to two per cent, exposing underlying structural challenges. Indonesia, supported by a large domestic market, remains relatively insulated but still faces the potential erosion of its trade surplus if global uncertainty persists.

What founders must know: Turning volatility into advantage

For founders navigating this volatile environment, success hinges on a mix of vigilance, adaptability, and relationship-building. Staying informed on tariff changes, trade deals, and regulatory shifts is no longer optional- it’s a survival skill. Compliance can no longer be treated as an afterthought; it needs to be embedded into sourcing strategies, logistics planning, and even product design.

Diversifying export markets is also essential. Relying too heavily on a single destination market leaves companies exposed to sudden shocks, while regional and emerging markets can offer critical buffers. Equally important are relationships. Strong ties with local trade bodies, chambers of commerce, and regulators provide founders with early insights and smoother navigation through potential disruptions.

Beyond these fundamentals, the true competitive edge increasingly lies in using data-driven decision support. This means not just tracking numbers, but harnessing technologies like artificial intelligence in meaningful, practical ways to anticipate shifts across policy, supply chains, and customer needs.

A regional call for collaboration, not competition

The intensifying trade tensions between the US and China are accelerating the shift of manufacturing out of China, and Southeast Asia stands at a pivotal moment. Yet Vietnam, Thailand, and Indonesia cannot afford to become mere way stations or passive hosts.

Also Read: Navigating tariffs and uncertainty: Why software, data, and AI startups are Asia’s path forward

Without genuine collaboration between incoming manufacturers and local production ecosystems, the region risks missing the deeper benefits of this shift-not to mention inviting scrutiny over whether these moves are simply attempts to bypass tariffs.

Singapore can help lead the region by advancing transparency, accountability, and innovation, ensuring Southeast Asia emerges as a trusted, resilient manufacturing hub. The role of AI is no longer just a future idea. It is becoming a practical tool for collaboration, from improving supply chain efficiency to supporting smarter policymaking and strengthening regional networks.

Indonesia, with its vast and youthful population, has the chance to strengthen its workforce through education and training, ensuring all communities can participate in this transformation.

For SMEs, this is not the time to retreat or work in silos. This is the time to engage, share insights, and build resilience together. For those curious about how data-driven insights and ethical AI can quietly fit into this picture, thoughtful exchanges often unlock unexpected opportunities. When founders and SMEs come together, they move not just their companies forward but entire industries and communities as well

We’ve weathered complexity before. Now, the next chapter calls for bold strategy and unshakable resilience. If you’re an SME or founder driven by data and purpose, we’d love to hear from you. Let’s connect-because some of the best breakthroughs begin with a single conversation.

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When we first stepped into the world of alternative lending, it felt like we were entering a space brimming with purpose and potential. Lending was in high demand. The returns looked strong.

And more importantly, the mission felt clear: we would help SMEs grow by giving them access to the capital they needed. If the banks said no, we’d step in — faster, more flexible, more human.

We believed we could build a business that helped other businesses thrive. But the deeper we went, the more uncomfortable truths began to surface. Truths that challenged our assumptions and ultimately forced us to confront a hard question: were we really helping?

And once we saw the cracks, we couldn’t unsee them.

Loans aren’t fuelling growth anymore

One of the first truths that hit us was this: most SMEs aren’t borrowing to grow. They’re borrowing to survive.

More than 90 per cent of the businesses we met weren’t looking for capital to expand their team, enter a new market, or invest in product development. They were borrowing to pay rent. To meet payroll. To cover overdue invoices. In some cases, they were borrowing to repay other loans.

The intent wasn’t growth. It was delay. It was survival.

But business loans were never meant to be life support. They were meant to unlock opportunity. When used correctly, they should catalyse momentum — not postpone collapse.

What we were seeing, instead, was debt being used as a crutch. Not because founders didn’t care, but because they didn’t know what else to do. And the system — lenders, brokers, sometimes even advisors — kept offering another loan as the answer.

Revenue is not rescue

If we had a dollar for every time we heard the word “scale,” we might not need to lend at all. Growth, especially revenue growth, has become the universal North Star for businesses. But scaling without sustainability is dangerous.

We’ve seen companies making US$5 million a year with negative margins. We’ve seen founders push for aggressive growth while their cost structures were leaking cash at every level. Fixed costs outpacing revenue. Staff headcount increasing without productivity gains. And the solution? Borrow more to fuel the next stage.

Also Read: Can Bitcoin rescue US debt? Senator Lummis says yes

But if your core business model is broken — if your margins are thin, your pricing is weak, and your operations are bloated — borrowing doesn’t fix that. It just accelerates the fallout.

Scaling a problem doesn’t solve it. It multiplies it.

How debt became too casual

Another major shift we noticed was how normalised debt had become.

We encountered founders who treated loans like lines of credit — not as strategic capital, but as a rolling buffer. Borrowing from one lender to pay off another. Stacking loans like building blocks, with little consideration for the long-term implications.

What used to be a financial decision had become a cash flow habit.

The conversation had changed. It was no longer about “Should we borrow?” It was “Who else can lend us more this month?”

But this approach doesn’t build resilience. It builds fragility. Businesses may appear to function — until the repayments pile up, interest costs mount, and options run dry.

That’s when everything falls apart. And by then, it’s often too late to course-correct.

The collapse has begun

This isn’t just theory or fear-mongering. We’re already seeing the consequences in the numbers.

According to The Business Times, compulsory wind-ups in Singapore surged over 50 per cent in early 2024 compared to the same period the year before. That’s not a small spike — that’s a trend.

These aren’t companies making graceful exits. These are businesses that ran out of money, out of credit, and out of time. These are the final chapters of the “borrow-to-survive” playbook.

And what’s most painful is that many of these businesses didn’t fail because of a lack of effort or even demand. They failed because they didn’t have the tools — or the knowledge — to manage their finances properly.

Why we had to pivot

We didn’t get into lending to hurt businesses. But slowly, we began to realise that we were participating in a system that, knowingly or unknowingly, rewarded short-term thinking. We were becoming part of the problem.

And that sat uncomfortably with us even though the money was great, I’m not going to lie.

When you realise that SMEs account for over 70 per cent of jobs in Singapore, it’s clear that this is not just a business issue — it’s a societal one. If SMEs don’t survive, neither do the jobs, nor the families that depend on them.

Also Read: Adapting to automation: Embracing no-code platforms for job security

So we made the hardest decision of all: to step back from lending and redirect our entire business toward something we believed was even more impactful — business financial literacy.

Not as a charity. Not as a nice-to-have. But as the core of everything we do. Because what became painfully clear was this: no amount of capital can save a business that doesn’t know how to manage it.

What SME owners need to understand

After reviewing hundreds of financial statements, and sitting down with countless founders, we discovered a pattern. Most business owners are not lazy or reckless — they’re simply overwhelmed. And what they often lack is not drive, but clarity.

If we could teach just 3 principles to every SME owner, they would be:

• Know your numbers

Your income statement isn’t just for the accountant. It’s your business’s health report. Learn to read it. Know your gross margin. Understand where your profit (or loss) is coming from. It’s not enough to see revenue growing — you need to know if it’s actually making you money.

• Plan your cash flow

Profit is not cash. You can have strong sales and still run out of money if your expenses hit before your payments come in. A simple 12-week cash flow forecast can prevent countless sleepless nights.

• Watch for red flags early

Late payments to suppliers. Increased borrowing frequency. Growing interest costs. These aren’t minor glitches — they’re flashing warning lights. Don’t ignore them.

A better way forward

Let us be clear — we’re not against lending. Lending can be an incredible enabler. But only when it’s used strategically, and supported by a solid financial foundation.

We need a new culture. One that treats debt with respect, not recklessness. We need founders to shift their obsession from top-line growth to bottom-line sustainability. We need lenders to start investing in education, not just extending credit.

And we need to build a community that celebrates responsible growth — not just rapid one. Because if the only plan is to borrow “just one more time” — the next time may be the end.

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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.

Join us on Instagram, Facebook, X, and LinkedIn to stay connected.

We’re building the most useful WA community for founders and enablers. Join here and be part of it.

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Everyone is talking AI. Everyone is building AI.

The fact is, we are now living in an AI-ready and AI-fuelled world. Some businesses are just starting to dip their toes into AI, while others are already using it to drive innovation and shape strategy.

But here’s a shocking truth: Over 85 per cent of AI projects fail.

And as business leaders, what could you do to avoid the 85 per cent?

AI projects crash and burn

AI isn’t the magic bullet it’s sometimes made out to be. A report from Gartner highlights that a staggering 85 per cent of AI projects fail to meet expectations, leaving companies with little to show for their efforts – and sometimes, a lot less in the bank.

New research from data management platform Qlik reveals that 11 per cent of UK businesses have more than 50 AI projects stuck in the planning stage. Meanwhile, 20 per cent have had up to 50 projects progress beyond the planning phase, only to be halted or cancelled due to various setbacks.

“AI has the potential to impact nearly every industry and department, but it’s not universally applicable,” says James Fisher, Chief Strategy Officer at Qlik.

The key factors? 

Poor data quality, inadequate data availability, and a lack of understanding about AI’s requirements are at the core of many failures. AI models, without access to clean, relevant, and accurate data, simply cannot perform. According to a 2023 McKinsey report, 70 per cent of AI projects fail because of issues with data quality and integration.

Data, however, is not the only case.

Define your mistakes and own up to it

The data kryptonite

The recent public debacle involving law firm Levidow, Levidow & Oberman serves as a cautionary tale. The firm used ChatGPT to generate legal opinions, which contained fake quotes and citations. The results were disastrous: the firm faced legal fines and a PR nightmare. The firm and its lawyers “abandoned their responsibilities when they submitted non-existent judicial opinions, then continued to stand by the fake opinions after judicial orders called their existence into question,” a judge said in a June ruling, which also levied a US$5,000 fine.

If there’s one thing I learned from my time integrating generative AI chatbots like ChatGPT, Gemini, or Claude, even the free and the upgraded, premium, newest version, one problem always stuck with me – their data is never up to date. AI models can only work with the information they are trained on, and if that data isn’t accurate or current, the results will be unreliable.

“AI applications are only as good as the data they are trained on,” says Troy Demmer, co-founder of Gecko Robotics. “Trustworthy AI requires trustworthy data inputs.”

Also Read: How AI helped me build a seven-figure side hustle while healing

Worldwide data creation is projected to grow to more than 180 zettabytes by the end of 2025. With so much data available, possessing quality data starts with having a complete picture of the information generated by your organisation. These issues also highlight the need for meticulous data management and strategic planning, including the integration of cloud-based models and large language models (LLMs).

Rising cost, insufficient funding

Generative AI tools might appear cheap and accessible at first. But when companies move from pilot projects to full-scale deployments, the costs quickly spiral out of control. Gartner estimates that a retrieval-augmented generation (RAG) AI document search project can cost up to US$1 million to deploy, with recurring costs of up to US$11,000 per user annually. In more specialised fields like medical or financial AI models, costs can exceed US$20 million.

Not to mention, 42 per cent of companies face setbacks due to inadequate funding or resource allocation.

AI isn’t cheap, and pilot projects that produce no value can be money pits. You want to integrate feature A in your AI bots, while still maintaining feature B, and oh, let’s add in feature C. And that will be, say, another US$30,000 to US$300,000 more.

And we don’t even know if it will work that well.

Chasing shiny and unrealistic expectations

Thanks to the crazy hype, every business leaders are now seeing AI as a magic bullet. If it’s not “AI-powered” or ‘using AI”, they don’t want it.

Expectations often exceed what AI can deliver, leading to frustration when the technology fails to meet the hype. As Ajgaonkar, CTO of product innovation at Insight, points out, some leaders expect AI to magically predict things like pricing without considering the complex data preparation and training required.

The key to machine learning success is constant tuning. “In AI engineering, teams often expect too much too soon,” explains Shreya Shankar, a machine learning engineer at Viaduct. “They don’t build the infrastructure needed to continually test and improve the system.”

Business leaders often expect AI to effortlessly analyse historical data, pull relevant insights, and apply them to new customer requests, such as predicting purchasing behaviour based on past transactions. Instead of doing the necessary groundwork – cleaning data, testing, and retraining models to ensure accurate results – they’re eager to jump straight to the end goal, bypassing the critical steps that drive success.

This, in turn, leads to unrealistic expectations.

The real key to machine learning success is something that is mostly missing from generative AI: the constant tuning of the model. It’s all the work that happens before and after the prompt, in other words, that delivers success.

Siloed teams, failed collaboration: The blind leading the blind

No one really noticed this,

But the common cause of AI failure isn’t really about the technology, sometimes, it’s about the people.

This starts with the people at the top – and their view on AI.

Also Read: How AI helped me build a seven-figure side hustle while healing

Business leaders frequently misinterpret the problems AI is supposed to solve. Many executives also have inflated expectations, fuelled by the hype around AI from sales pitches and flashy demos. They underestimate the time, resources, and careful planning needed for AI to succeed. Often, models are delivered at only 50 per cent of their potential due to shifting priorities and unrealistic timelines.”

Deloitte found that 40 per cent of companies struggle because their technical and business teams aren’t aligned. Even if the AI model works technically, if these teams don’t work together, the project often fails to deliver tangible value to the business. Additionally, many engineers and data scientists are drawn to the latest technological trends, even when simpler solutions would suffice.

Teams may spend time on cutting-edge technologies that don’t necessarily address the core issue.

Check the boxes: The five phases of AI readiness

No matter the size of your business, don’t panic.

If you’re feeling uncertain about your AI product (still), there’s a simple way to check your progress.

Just run through the five phases of AI readiness. If you’ve ticked all the boxes, you’re on the right path.

Awareness: The knowledge bases

At this stage, your goal is to build awareness of AI and how it can be applied to your industry. Educate leadership through workshops and seminars, research AI use cases for your organisation, and identify where AI can solve real business problems. Research shows that 60 per cent of organisations are still in this phase, with no formal AI initiatives in place.

• A manufacturing company exploring AI might find that predictive maintenance could reduce downtime by 20-30 per cent, saving millions annually. But first, they need to understand the basics of how AI works.

Exploration: Start small

In this phase, businesses experiment with small-scale, low-risk AI projects to demonstrate its potential. These pilot projects are often low-cost and involve small teams (e.g., one data scientist and one engineer). Gartner reports that 25 per cent of companies in this phase see measurable returns within six months of starting AI pilots.

A focused, straightforward pilot helps secure stakeholder buy-in, provides early insights to refine your strategy, and sets the stage for more complex AI applications in the future.

Operationalisation: Building scalable infrastructure

Once you’ve moved beyond pilots, it’s time to build the infrastructure needed for scalable AI adoption. This includes setting up governance structures, ensuring data privacy, and deploying AI in real-world use cases.

Establish an AI Center of Excellence (CoE), create scalable data platforms like data lakes, and develop policies for compliance. McKinsey reports that companies in this phase see a 20 per cent improvement in operational efficiency.

•  Use AI-powered routing to escalate critical cases, such as VIP churn risks or sensitive issues, while allowing AI to handle routine queries. By setting clear business rules, AI can make accurate distinctions between scenarios and smoothly hand off more complex cases to human agents, ensuring the right support at the right time. Liberty London uses AI to direct customer service inquiries based on agent skillset and customer intent, streamlining the process. This approach resulted in a 73 per cent reduction in first reply time and a nine per cent boost in customer satisfaction.

Also Read: 5 reasons why impact investing is becoming mainstream investing

Proficient: Making AI a part of everything

AI becomes part of everyday operations. Businesses establish systems to monitor the performance and fairness of AI models while training employees to use AI tools effectively. AI solutions are scaled across departments, and employees are trained to integrate AI into their daily roles.

The crucial element of AI readiness here is human involvement. By analysing both AI-resolved and human-assisted issues, you can gain a comprehensive view of performance. Track key metrics, such as automated resolution rates, human escalation frequency, and customer satisfaction, to refine and improve the process.

Leader: An “AI-first” culture

The final phase is where businesses fully integrate AI into their core strategy, operations, and innovation. Companies at this level use cutting-edge techniques like generative AI and autonomous systems to drive competitive advantage. They foster an AI-first culture through continuous employee up-skilling.

Only 10 per cent of organisations are at this stage, but they account for 70 per cent of all economic gains from AI.

Be part of the 15 per cent, not the 85 per cent

This isn’t a one-size-fits-all solution for every business leader, nor is it the ultimate guide to creating a perfect AI model or product for your company.

But there is one thing you, as a business leader, can learn from.

Success doesn’t hinge on avoiding failure—it’s about learning from it and adapting.

If your business is struggling with AI, the problem may not lie with the technology itself, but with how it’s integrated into your organisation. Take a step back, and check the boxes. The key to AI success starts with a solid foundation: ensuring alignment between your teams, setting realistic expectations, and creating the right infrastructure to support growth.

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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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Small and mid-sized enterprises (SMEs) in Singapore and across the globe are quickly adopting artificial intelligence (AI) into their operations, yet a significant capability gap persists, with 95 per cent of SME decision-makers admitting they need more training to effectively leverage the technology.

This paradox comes despite 72 per cent of these leaders considering themselves AI experts.

Also Read: SMEs struggle to turn data into decisions, says OpenMinds CEO Jan Wong

The findings stem from a global TeamViewer survey, which polled 1,400 business leaders, including 427 from SMEs. The research, conducted in October 2024, highlights a critical challenge for the rapidly evolving digital economies, particularly in Southeast Asia, where SME agility is a key growth driver.

The AI paradox: High adoption, low maturity

While AI is firmly established on the SME agenda, its integration often lacks depth. A notable 86 per cent of SME leaders are comfortable with employees outside of IT using AI tools.

However, this widespread comfort does not always translate into frequent usage. Only every third SME respondent uses AI daily, and just 16 per cent report weekly use.

Despite less frequent use, SMEs surprisingly perceive themselves as more AI-mature than larger enterprises. Thirty-five per cent of SME decision-makers describe their AI usage as “very mature,” compared to only 22 per cent of larger organisations. This disparity between perception and actual proficiency underscores the urgent need for targeted training and support.

The stakes: Automation gaps and business optimism

The consequences of inaction weigh heavily on SMEs. For 28 per cent of SME decision-makers, the biggest fear is increased operational costs due to missed opportunities for automation. This concern diverges from the broader business community, where falling behind competitors (cited by 26 per cent) was the primary worry.

Despite these challenges, optimism about AI’s potential remains high. Seventy-two per cent of SME leaders expect AI to drive the most significant productivity surge of the century, with 76 per cent seeing it as essential for overall business performance. Furthermore, 70 per cent believe AI can help expand job opportunities for parents and caregivers.

Overcoming hurdles: Skills, security, and infrastructure

The report identifies several persistent barriers slowing down AI maturity for SMEs:

Insufficient AI training: More than a third of leaders (38 per cent) cite this as the main obstacle.

Security concerns: A significant 74 per cent are worried about data management risks. Moreover, 65 per cent state they only use AI tools within tightly controlled security frameworks.

Lack of confidence in risk management: A telling 77 per cent admit they would not bet a week’s salary on their organisation’s ability to effectively manage risks like unauthorised AI tool usage.

Infrastructure readiness: Nearly half of SME decision-makers (47 per cent) report not having the necessary systems in place to scale AI as quickly as they would like.

Southeast Asia’s unique challenge and investment outlook

For Southeast Asian SMEs, the diverse digital maturity across countries and sectors presents a unique challenge, necessitating tailored AI strategies. The region’s economic growth is heavily propelled by the agility and innovation of its SME sector, making timely and effective AI adoption crucial for national digital economies.

Also Read: A new insights attitude for SMEs in the era of the ‘insights engine’

Despite the current hurdles, momentum for AI investment is building. Three in four (75 per cent) SME leaders plan to increase their AI investment in the next 12 months, with the same proportion expecting this rise within the next six to twelve months. This signals a clear intent to move from experimental adoption towards more advanced implementation.

Bridging the gap: TeamViewer’s solution

Companies like TeamViewer are stepping in to help SMEs bridge this capability gap. TeamViewer CoPilot, a digital assistant integrated into remote support sessions, helps IT agents stay focused, move faster, and make better decisions. This practical solution aims to improve IT efficiency, reduce downtime, and raise service quality without adding complexity or requiring additional resources.

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