I’ve spent 15+ years building across multiple tech ventures and cultures—starting in Vietnam, sharpening my craft in Japan and Singapore, then expanding to the US, Australia, and Europe. Each stop taught me how different ecosystems turn constraints into capability: how to ship product under pressure, build companies from zero, grow talent pipelines, and lead teams through the hardest execution challenges. Along the way, I co-founded ventures across domains—from cloud content security and AI-driven fraud detection in finance to AI-powered talent vetting and AI-powered graphic design and marketing.

That journey left me with a simple conviction: AI is fundamentally changing how we build software, how we build companies, and how we build the skills to operate at a new level of business innovation. The shift is so deep that founders and SME owners must rethink how they imagine products, platforms, and transformation—or risk shipping the right features on the wrong foundations. This is why I’m sharing what I’ve learned about building AI-first products and AI-first companies now.

The way we built the software product in history, now, and next

Before 2000: The PC/OS era — “software in a box.”

• What it looked like: You bought a CD, installed a program on your Windows or Linux computer, and used it on that one machine.
• Where the work happened (“runtime”): On your personal computer.
• How updates worked: Rare and manual—new CD, new installer.
• Everyday example: Installing Microsoft Office from a disc.
• What this meant for builders: Ship a product once, hope it works on many different PCs, and plan big, infrequent upgrades.
  

2000s–2020s: The Cloud/SaaS era — “software in the browser.”

• What it looked like: You visited a website, logged in, and your app just worked—anywhere, on any device.
• Where the work happened: In big, remote data centres (“the cloud”).
• How updates worked: Continuous and invisible—features improved without you doing anything.
• Everyday examples: Gmail, Salesforce, Shopify.
• What this meant for builders: Design for millions of users, run on elastic servers, charge subscriptions, and ship improvements weekly.
  

Now: The AI-first era — “the model is the new runtime.”

• What it looks like: You tell the system what you want in natural language (“Close last month’s books and flag anything unusual”), and it figures out the steps—pulling data, calling tools, checking rules—then asks for help only when needed.
• Where the work happens: In an AI model that plans and coordinates actions across your systems. Think of the model as the place where decisions get made before tools are used.
• How updates work: Not just new features—better reasoning, safer behaviour, and lower cost per task as models, prompts, and policies improve.
• Everyday examples:
  • A support “assistant” that reads past tickets + policy, drafts the best reply, and only escalates tricky cases.
  • A finance “copilot” that reconciles invoices, highlights anomalies, and prepares a month-end summary with sources.
  • A logistics “agent” that spots late shipments, calculates SLA risk, drafts messages to customers, and logs everything.
• What this means for builders: Interfaces become language, services act like agents (software teammates) with tools and memory, and operations becomes LLMOps—you manage AI quality and safety the way you manage uptime and security.

Also Read: How e27 and Findy brought engineering leaders together in Singapore

What actually changes under the hood

• From clicks to conversation: Yesterday, we clicked buttons. Today, we describe goals in plain language. Software translates those goals into steps.
• From apps to agents: Yesterday, apps waited for you to click. Today, agents can plan tasks, call your CRM/ERP/payment systems, and report back with an audit trail.
• From “it works” to “it works, is safe, and proves it”: We add guardrails (safety checks), evals (quality tests), and rollbacks (easy undo) so the AI stays helpful, polite, and compliant.
• From bigger servers to smarter placement: Some AI runs in the cloud; some runs on the device/at the edge for privacy and instant response (stores, warehouses, field teams).

A quick cheat sheet

• Model (LLM): The AI brain that understands your request and decides the next steps.
• Runtime: Where the real work happens. It used to be your PC, then the cloud; now, the model’s planning/execution is part of that “where.”
• Agent: Software that can act—plan steps, call tools, remember context, and ask for help when unsure.
• Tools: Your existing systems are exposed as safe actions (e.g., “CreateInvoice,” “GetShipmentETA,” “CheckKYC”).
• Memory: Short-term and long-term context, so the agent doesn’t forget what just happened or what’s true for your business.
• RAG (retrieval) => Agentic RAG: Letting the AI “look up” your documents/policies so answers come with sources, not guesses.
• LLMOps: The day-to-day discipline of running AI in production—tests, monitoring, safety checks, and quick rollback when quality dips.
• SLA (service level agreement): Your quality promises, now expanded beyond “uptime” to include “accuracy,” “latency,” and “cost per task.”

Founder/SMEs takeaway

Moving from OS → Cloud → Model-as-Runtime isn’t another feature cycle—it’s a mindset change. If you keep thinking in old categories (screens, clicks, tickets), you’ll bolt AI on top of yesterday’s product. If you think in goals, agents, tools, guardrails, and proof, you’ll design AI-first products and AI-first companies that actually move the P&L.

That’s the shift—and why it matters now.

Also Read: Deeptech’s secret: Ignore the market, master the engineering, and let opportunity find you

Why this moment belongs to Asia’s founders and SMEs

Southeast Asia used to pay a “complexity tax”: many languages, uneven infra, fast-shifting rules. Agentic AI flips that from handicap to advantage. If you already know the domain—freight, clinics, F&B, construction, retail finance—you can translate that know-how into AI-first products and operations faster (and cheaper) than at any time in the last 20 years.

Large enterprises are retooling too, but they move with more friction; that’s your window. (Even management consultancies are telling their clients: agentic AI requires a reset of the transformation approach.) . You’re closer to an AI-First business than you think. Agentic AI lets you describe outcomes in plain language, wire those outcomes to your existing tools, and keep humans only where judgment truly matters.

What shifts in your favour

Go global from day-one

• Language-first products: Ship onboarding, support, and docs in Vietnamese, Bahasa Indonesia, Thai, Tagalog, and English on the same release. Build Digital Sales Agent support client 24/7 with any languages
• Policy packs by market: Agents apply country/province-specific rules (KYC, tax, data) and keep an audit trail—so cross-border isn’t a cliff, it’s a checklist.

10× Productivity—with smaller AI driven tech team

• Agents as operators: They plan steps, call your CRM/ERP/accounting tools, and escalate only on edge cases.
• Where it bites (and pays): KYC throughput, catalog enrichment, late-shipment comms, AR collections, month-end close—measured in hours saved and error rates dropped.

Strategy-grade insight at a fraction of big-four consulting cost

• Boardroom analysis, on tap: Market maps, comps, unit-economics scenarios, pricing simulations—drafted from your data so you spend real consultants on judgment and deals, not spreadsheets.

New business models you can actually run

• Outcome-as-a-Service: Sell verified outcomes (e.g., “cleared invoices,” “verified onboardings,” “recovered carts”) with SLAs, not just software seats.
• Vertical agents: Package your domain playbooks (“clinic intake,” “factory maintenance,” “freight exceptions”) and license them usage-based.
• AI-enabled franchises: Combine your process IP with agents, brand, and training; replicate city-by-city without head-office bottlenecks.

CapEx → OpEx, and cost per task becomes your lever

• You mix hosted AI APIs, open-weight models (when your data differentiates), and small on-device models for privacy/latency. You measure cost per completed task like COGS—and tune it down month by month.

Also Read: With AI comes huge reputational risks: How businesses can navigate the ChatGPT era

The mindset that unlocks it

• Domain first, tooling second. Your industry know-how is the moat; AI is the amplifier.
• Outcomes over features. Ask, “What result am I selling?” not “What screen should I build?”
• Proof beats promise. If it doesn’t show sources, acceptance criteria, and an audit trail, it isn’t ready for customers.
• Iterate in public (with customers). Month-over-month improvements in cost per task and first-pass yield are your real marketing.

What we’ve learned building with customers (and what I’d keep)

At DigiEx Group, we built the company as a tech talent hub + startup studio because that’s what our region needs: deep AI-Powered engineering paired with product thinking, LLMOps discipline, and localisation. We’ve shipped cross-border onboarding that explains its decisions, catalog ops that self-clean, and logistics agents that detect SLA risk and draft multilingual comms—always with human escalation and an audit trail.

Across wins and misses, a few lessons keep paying rent:

• Mindset over tools: The hardest part wasn’t the tech, or teaching employee how to use tools — it was helping every team member think differently. Change management, open communication, and change old habits to reimagine what was possible.
• Focus on high-impact first: Instead of applying AI everywhere, we prioritised areas where it could deliver the greatest impact — whether in speed, decision-making, or innovation. Then learn, make standardise and scale from there
• Automate with intention: Not every workflow needs AI. We asked: Does it enhance quality? Speed things up? Enable better decisions? If not, we left it out.
• Safety as muscle memory: Mask PII before prompts, keep sensitive data in-region, design reversible actions, and run SRE-style incident reviews: root cause → guardrail update → new test. (Yes, agents can fail; design so failures teach.)
• Ship a lighthouse workflow in 30–60 days: Pick the ugliest, most measurable pain. Baseline it; ship an agent with guardrails; publish the delta. Momentum beats theory.

So — Why now, why Asia

If the last two decades were Cloud-first, the next decade is AI-first—and that doesn’t just mean new features. It means a new way to build: the model as runtime, language as interface, agents as services, and LLMOps as the production discipline. Companies that internalise this shift won’t just ship faster; they’ll operate differently—measuring quality, cost per task, and trust with the same rigour we once reserved for uptime.

Asia—especially Southeast Asia—is built for this moment. We’re multilingual by default, comfortable with constraints, and close to real customers and real operations. That combination turns agentic AI from a buzzword into Tuesday-afternoon wins: onboarding that explains itself, catalogs that self-clean, logistics comms that happen before the complaint.

And for non-technical founders and SME owners with deep domain knowledge, the door is finally open. You can go global from day one, get 10× productivity where it hurts, and access strategy-grade insight at a fraction of old consulting costs.

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For many in Southeast Asia, AI isn’t just a futuristic concept; it’s already an invisible part of daily life. It’s in the ride-hailing app that finds the fastest route through Bangkok’s traffic, the e-commerce platform that predicts what you’ll buy next, and the social media feed that knows your preferences.

Yet, beyond these everyday conveniences, a fundamental question lingers in the minds of founders, investors, and tech professionals across the region: Is AI a threat that will displace jobs, or is it a powerful tool that can finally solve our most persistent, regional challenges?

This duality is at the core of AI’s narrative in emerging markets. While concerns over job automation are valid, the greater opportunity lies in its potential to bridge critical gaps. In a region where access to quality education, healthcare, and financial services remains uneven, AI isn’t just about efficiency—it’s about democratising access. It’s the catalyst that can lower costs, personalise services, and create new economic opportunities where they were once out of reach.

Part one: AI now – The everyday fabric

While discussions about the future of AI often conjure images of autonomous robots or complex algorithms, the reality is that its most profound impact is already woven into our daily lives. Much like electricity became an invisible utility, AI has transitioned from a niche technology to an indispensable part of our digital infrastructure.

Think about a typical day in a Southeast Asian city. Your morning commute is likely guided by a mapping application that uses predictive AI to analyse traffic patterns and suggest the fastest route. Your online shopping experience is curated by recommendation engines that analyse your past purchases and browsing behaviour.

When you engage with a chatbot on a telco or banking website, you’re interacting with a language model designed to understand and respond to your queries. Even the spam filters in your email and the facial recognition on your smartphone are quiet, efficient forms of AI at work.

Beyond personal use, AI has become a silent partner in business operations. E-commerce platforms use it to manage inventory and optimise logistics, while financial institutions deploy it to detect fraudulent transactions in real-time. Media companies use AI to personalise content delivery and manage user-generated content, ensuring a more tailored and safer experience. In essence, AI is no longer an emerging technology; it is the underlying intelligence that makes our digital world function with greater efficiency, personalisation, and security.

This current state of widespread, if often unnoticed, AI integration serves as the foundation for what comes next. It proves that the technology is ready for prime time—ready to be applied to larger, more complex challenges that truly matter to the region. With this established baseline, we can now turn our attention to the frontiers where AI is poised to make its most significant impact yet.

Also Read: Circular capital: Inside the closed-loop ecosystem propelling (and distorting) the AI boom

Part two: AI next – Emerging frontiers

As AI moves beyond its role as an invisible utility, it is poised to redefine entire industries. This next wave of innovation is less about incremental improvements and more about fundamental disruption, driven by applications tailored to specific needs. Here’s how this is playing out across key sectors from the perspective of the ecosystem’s main players.

Healthcare: Redefining diagnosis and access

• Founder’s view: For founders, the frontier is about building intelligent systems that can augment, not just automate, clinical work. This includes developing AI-powered diagnostic tools that can analyse medical scans with greater speed and accuracy than the human eye, or creating platforms for personalised medicine by analysing a patient’s genomic data. The challenge is not just the technology but also navigating complex regulatory approvals and gaining the trust of a risk-averse medical community.
• Investor’s view: Investors are placing their bets on startups that can tackle scalable problems. They are funding companies that can bridge the healthcare gap in underserved communities through telemedicine platforms, or those that can optimise hospital operations and supply chains using predictive analytics. The appeal is a market with a clear, urgent need for more efficient and accessible services.
• End-user’s view: For the patient, AI translates to tangible benefits. It means a faster, more accurate diagnosis for a critical illness, or access to specialist medical advice via a mobile app in a rural area. It’s the convenience of a public healthcare portal chatbot that explains medical jargon in simple terms, or an AI-powered wellness app that provides personalised health recommendations. The end-user perspective is one of hope for better outcomes and democratised access.

Finance: From exclusion to inclusion

• Founder’s view: The FinTech frontier is about leveraging AI to serve the massive unbanked and underbanked populations of Southeast Asia. Founders are building innovative credit scoring models that use alternative data—such as mobile phone usage and e-commerce transactions—to assess creditworthiness. They are creating AI agents that can automate everything from loan applications to fraud detection, making financial services faster and more secure.
• Investor’s view: Investors see a massive opportunity in AI-driven financial inclusion. They are backing FinTech companies like Indonesia’s Amartha, which uses AI to provide microloans to grassroots entrepreneurs, a segment long ignored by traditional banks. The investment thesis is simple: use AI to unlock a market of hundreds of millions of people, creating a virtuous cycle of economic empowerment and high returns.
• End-user’s view: For the consumer or small business owner, AI in finance is about gaining access. It’s the ability to get a loan to start a business without collateral, or to securely send money via a super-app. It’s a personalised investment plan managed by a robo-advisor or an immediate alert that prevents a fraudulent transaction. This is a shift from being a spectator to becoming a participant in the formal economy.

Also Read: Safe spaces, not just smart tools: How edutech can build confidence in learners

Education: The personal tutor for everyone

• Founder’s view: Founders are focused on creating AI-powered edutech platforms that can scale personalised learning. This means building adaptive learning models that adjust the curriculum to each student’s pace and proficiency. The goal is to move beyond one-size-fits-all education and develop AI tutors and chatbots that can provide 24/7 assistance, liberating teachers to focus on mentorship and higher-level engagement.
• Investor’s view: Investors are scouting for edutech startups that are not just digitising content, but fundamentally changing how people learn. They are drawn to companies that use AI to localise content and make it culturally relevant. The a-ha moment for investors is a model that uses AI to overcome traditional barriers like teacher shortages or a lack of physical infrastructure, offering a path to quality education for millions.
• End-user’s view: For students, AI is a tool for empowerment. It’s a personalised learning app that helps them prepare for exams, a language assistant that corrects their pronunciation, or a platform that connects them to high-quality, international courses they couldn’t otherwise afford. For working professionals, it’s an efficient way to upskill for a rapidly changing job market, ensuring they stay relevant.

Agriculture: Sowing the seeds of a smarter future

• Founder’s view: In a region with millions of smallholder farmers, founders are building AI solutions for precision agriculture. This includes developing drone-based imagery analysis for early disease detection, or AI-powered advisory systems that recommend optimal planting schedules and fertiliser use based on real-time weather data. The key is creating solutions that are low-cost, easy to use, and can run on basic smartphones.
• Investor’s view: Investors see AgriTech as a way to address food security and climate change while generating strong returns. They are funding startups that use AI to optimise supply chains, connect farmers directly to urban markets, and reduce food waste. The investment is driven by the potential for AI to increase agricultural yields and create more sustainable farming practices across the region.
• End-user’s view: For the farmer, AI is a lifeline. It means getting a text message alert about a potential pest outbreak, or using a simple app to understand soil moisture levels. It helps them make data-driven decisions that increase crop yield and income, and reduce reliance on expensive and wasteful inputs. Ultimately, AI offers a path to greater resilience and a more stable livelihood.

Also Read: Singapore tops global AI hiring charts: One in six jobs now reference AI

Conclusion: Navigating the AI horizon with purpose

The journey from AI as a silent helper in our daily lives to a transformative force on the horizon is both rapid and profound. We’ve seen that its current role is one of invisible efficiency—powering our searches, curating our content, and streamlining our businesses without fanfare. However, the true promise of AI lies not just in improving what exists, but in creating what’s next.

Ultimately, the future of AI is not a foregone conclusion written in code, but a narrative being authored every day by its key players. For the founder, it’s a call to build solutions that address a tangible need. For the investor, it’s an opportunity to back ventures that can unlock new markets and generate both returns and social impact. And for the end-user, it’s the possibility of a better, more accessible life.

The pragmatic view suggests that AI is neither a panacea nor a harbinger of doom. It is a powerful tool with immense potential, and its trajectory will be determined by how mindfully and purposefully we choose to wield it. For Southeast Asia, this means focusing on the problems only AI can solve and ensuring that the coming wave of innovation creates an inclusive and prosperous future for all.

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The Federal Reserve has officially initiated its rate cut cycle, a move long anticipated by markets that have already priced in approximately 3.8 cuts over the next 12 months. I forecast two additional reductions on October 29 and December 12.

Historically, the period immediately following the first rate cut has been marked by heightened volatility as markets recalibrate their expectations, reassess risk premiums, and digest the implications of a new monetary regime.

This transitional phase is particularly delicate because it often coincides with divergent signals from economic data, political uncertainty, and evolving investor positioning. All of these factors are present in today’s environment.

Equities and the magnificent seven effect

Equity markets, led by the S&P 500’s impressive 32 per cent rally from its recent lows, reflect a strong recovery narrative driven disproportionately by the so-called Magnificent Seven (Mag7) stocks. These technology and growth-oriented giants continue to post earnings growth roughly four times that of the remaining 493 companies in the index, underscoring their outsized influence on overall market performance.

However, this concentration introduces significant risk. Mag7 valuations now exceed 30 times forward earnings, not yet at their historical peaks but certainly in elevated territory. While historical backtesting suggests that equities generally perform well in the 12 to 24 months following the start of a Fed easing cycle, the current context differs in important ways.

The market’s narrow leadership, combined with stretched valuations, makes indiscriminate exposure to these names increasingly perilous. Chasing performance at this juncture could expose investors to sharp corrections if earnings disappoint or if macro risks materialise. Diversification, not just across sectors but across geographies and asset classes, emerges as a critical defensive and offensive strategy.

Investor positioning and market signals

Investor positioning data reveals that asset managers hold high equity allocations, though not at extreme levels that typically precede major drawdowns. Meanwhile, equity volatility remains subdued, a condition that historically correlates with lower forward returns over the subsequent six to twelve months.

This low-volatility complacency can lull investors into underestimating tail risks, especially when other asset classes also appear expensive. Gold, for instance, has seen renewed inflows into bullion-backed ETFs and now trades near US$3,760 per ounce, supported by geopolitical tensions and a modestly weaker US dollar, which closed at 98.152.

Yet gold positioning is once again crowded, suggesting limited room for further upside without a significant catalyst such as a sharp escalation in global instability or a deeper-than-expected economic slowdown. Similarly, credit markets show tight bond spreads, indicating that investors are not demanding much compensation for credit risk. In such an environment, security selection becomes paramount.

Broad exposure to high-yield or investment-grade debt may not suffice. Instead, bottom-up analysis of issuer fundamentals is essential.

Also Read: Markets on edge: Fed ambiguity fuels risk-off mood as Aster surges amid crypto bloodbath

Mixed economic signals and political risks

The macroeconomic backdrop offers mixed signals. August’s Personal Consumption Expenditures (PCE) Price Index, the Fed’s preferred inflation gauge, rose 0.3 per cent month-over-month, resulting in a 2.7 per cent annual headline rate. Core PCE, which excludes food and energy, stood at 2.9 per cent year-over-year after a 0.2 per cent monthly increase.

This remains above the Fed’s two per cent target but shows signs of gradual moderation. The data came in largely in line with expectations and did not provoke a strong market reaction, suggesting that investors have already internalised a path of gradual disinflation. However, political risks loom large.

The September 30 deadline for US government funding is fast approaching, and with congressional negotiations stalled, the probability of a partial shutdown is rising. While past shutdowns have had limited economic impact, they inject uncertainty into market psychology and could delay fiscal policy decisions or data releases, further complicating the Fed’s communication strategy.

Wall Street’s reaction and treasury yields

Market reactions to recent events have been muted but telling. Wall Street closed higher last Friday, ending a three-day losing streak, with the Dow Jones up 0.7 per cent, the S&P 500 gaining 0.6 per cent, and the Nasdaq rising 0.4 per cent.

Notably, markets barely flinched at the announcement of new sector-specific tariffs by the Trump administration, signalling either desensitisation to trade rhetoric or confidence that such measures will not significantly disrupt the broader economic trajectory. Treasury yields reflected this calm.

The 10-year yield edged up just one basis point to 4.183 per cent, while the two-year yield dipped two basis points to 3.645 per cent, flattening the yield curve slightly. This dynamic suggests that while near-term rate expectations are stable, longer-term growth and inflation concerns persist.

Asia’s cautious mood and key data ahead

In Asia, equities dipped on Friday as resilient US data prompted a modest reassessment of rate cut expectations. US equity index futures point to a higher open, indicating that global investors remain cautiously optimistic. The coming week will be pivotal, with the September nonfarm payrolls report on October 3 serving as a key barometer of labor market health.

Additional insights will come from the JOLTS job openings data on Tuesday and the ADP private payroll report on Wednesday. Labour market strength remains the Fed’s primary concern. If employment data remains robust, it could delay further rate cuts or reduce their magnitude, directly impacting risk asset valuations.

Also Read: Soft landing or FOMO return? Markets rally on Fed cut amidst inflation caution

Bitcoin’s technical recovery

Meanwhile, Bitcoin has staged a “reasonable” technical recovery, rising 2.24 per cent in the past 24 hours to US$111,966 after a 7-day decline of 2.23 per cent. This rebound appears driven by three converging factors. First, price action held firm at the US$108,680 support level, breaking a bearish trend line and reclaiming the US$111,000 mark.

Hourly indicators, including a bullish MACD crossover and an RSI stabilising around 47-48, suggest that short-term momentum has shifted in favour of buyers. The 200-day exponential moving average at US$106,200 continues to act as a structural support, reinforcing the asset’s resilience.

However, the critical test lies ahead. A sustained close above US$112,500, the 50 per cent Fibonacci retracement of the recent decline, could pave the way for US$113,700 to US$115,000. Failure to break this resistance may invite profit-taking and a retest of the lower support level.

On-chain metrics and corporate adoption

Second, on-chain metrics show improving demand dynamics. The 60-day Buy/Sell Pressure Delta has entered what analysts describe as an opportunity zone, indicating reduced selling pressure.

The decline in sending addresses and stable miner reserves, holding steady at 1.8 million BTC, suggests that long-term holders are not capitulating. That said, the 90-day delta remains cautious, reflecting lingering uncertainty among larger participants. The Coinbase Premium Index, currently at +0.041, will be a key gauge of sustained US institutional interest.

Third, corporate adoption continues to provide narrative support. The rebranding of 164-year-old Japanese textile firm Marusho Hotta to Bitcoin Japan and its announcement of a BTC treasury business, while small in absolute scale, aligns with a growing trend among Asian corporations seeking alternative stores of value amid declining traditional revenues.

Firms like Metaplanet and Kitabo have similarly adopted Bitcoin, reinforcing its digital gold thesis in a region that is increasingly skeptical of fiat stability.

Final thoughts: Selectivity over momentum

In sum, the current market environment demands a nuanced approach. While the Fed’s pivot to easing should, in theory, support risk assets over the medium term, the combination of expensive valuations, narrow market leadership, and external risks ranging from a potential government shutdown to geopolitical flare-ups calls for disciplined selectivity.

Investors should avoid chasing momentum in already crowded trades and instead focus on quality earnings, global diversification, and tactical entry points during pullbacks.

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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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Singapore-based smart city IoT solutions provider iSense Global has received an undisclosed strategic investment from DNAKE (Xiamen) Intelligent Technology, a publicly listed Chinese corporation specialising in smart intercom and building automation.

Under this agreement, the two companies have allied to strengthen iSense’s regional expansion strategy and underpin its ambitious long-term objective of achieving a Nasdaq listing within the next five years.

Also Read: Beyond smart cities: The IoT startups engineering Asia’s tomorrow

iSense, which has active initiatives progressing in the Philippines and Vietnam, will further expand into Indonesia, Australia, Europe, and the US. The Southeast Asian smart city market is projected to triple in valuation from US$49.1 billion in 2024 to US$145.8 billion by 2033.

iSense seeks to transform urban infrastructure through cutting-edge IoT solutions. Its platforms deliver actionable insights that enhance energy efficiency, public safety, and city operations. iSense is the primary technology provider for Singapore’s Housing & Development Board (HDB) Smart Lighting Programme.

The firm said it has over 80 per cent market share in the HDB smart lighting network. It claims to have delivered up to 70 per cent energy savings in parks and achieved over 50 per cent savings in public housing estates. Individual IoT deployments managed by iSense can reach over one million connected devices, with valuations reaching hundreds of millions of US dollars.

It will leverage its domestic success to expand its international footprint rapidly. Flagship projects are already underway across Asia, including the Bangkok Metropolitan Administration (BMA) Smart City Lighting Project in Thailand. The company has also secured citywide smart lighting contracts in Koriyama City and Mito City, Japan, and manages nationwide highway networks in Malaysia.

The alliance with DNAKE extends significantly beyond mere capital investment, focusing on fundamental operational integration. iSense will transition its manufacturing processes away from third-party suppliers and utilise DNAKE’s facilities. This move will yield key strategic benefits, including enhanced quality control, greater cost efficiency, and faster scaling.

Furthermore, the two entities are set to co-develop advanced IoT solutions. These initiatives will combine DNAKE’s established expertise in hardware and automation with iSense’s proven track record in massive IoT deployments and AI-driven analytics. The combined development pipeline targets high-growth sectors such as healthcare, city-scale monitoring, security, and access control.

Also Read: Unleashing Singapore’s smart city potential: A gateway to limitless opportunities

Christopher Lee, CEO of iSense Global, said, “Partnering with DNAKE is a game-changer for iSense. Their manufacturing expertise and public market experience empower us to scale faster, expand internationally, and take on larger, more complex projects. Together, we will accelerate smart city innovation at a global scale”.

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What if the difference between thriving and merely surviving in Southeast Asia’s digital economy came down to a single decision made in a cramped office in Jakarta or a bustling shophouse in Singapore?

For Yihao Zhang, CEO of Indonesia’s Lita Global, that moment arrived when his gaming platform faced a choice: embrace artificial intelligence or watch competitors leave them behind. “Either you grow and adopt, or you die,” warns Professor Jochen Wirtz from the National University of Singapore Business School—a stark reality that captures the urgency facing Southeast Asia’s 70 million small and medium enterprises today.

The numbers tell a compelling story of both promise and paradox. While AI and generative AI are projected to contribute US$120 billion to Southeast Asia’s GDP by 2027, only 23 per cent of organisations in the region are truly transformative in their AI adoption. Even more striking, among SMEs specifically, adoption plunges to a mere 4.2 per cent. Yet those who do make the leap are reaping remarkable rewards: 75 per cent of ASEAN small businesses investing in AI are 1.8 times more likely to experience growth, with 90 per cent reporting positive results.

The pioneers: Real stories from the digital frontlines

• Lita Global: From translation bottlenecks to revenue multiplication

In the linguistically diverse landscape of Southeast Asia, Lita Global discovered that AI could solve one of their most persistent challenges. The Indonesia-based social media platform for gamers was struggling to expand across the region due to language barriers. Marketing events that could boost weekly revenues by 20 per cent were limited by the time-consuming process of translating announcements from English to Vietnamese, Thai, and other Southeast Asian languages.

Since integrating OpenAI’s models in the second half of 2024, the transformation has been dramatic. The company now hosts almost twice as many online gaming events monthly, directly translating to significant revenue growth. But the AI implementation went beyond translation. The platform’s chat function, where users hire gamers for online sessions, now uses AI-recommended responses during peak demand periods. Gamers using these AI suggestions have seen a 10 per cent to 20 per cent uptick in orders.

“So we’re using AI to really help them to improve their efficiency, to help them to be more available to the users,” Zhang explains. The case illustrates how AI can address multiple business challenges simultaneously—language barriers, operational efficiency, and customer service—creating compound benefits that justify the investment.

• The livestreaming revolution: TopviewAI’s US$1-per-minute solution

For many SMEs across Southeast Asia, the rise of live shopping presents both an opportunity and a barrier. Traditional livestreaming requires studio rental, merchandise samples, and human hosts—costs that can quickly overwhelm small business budgets. TopviewAI has emerged as a game-changer in this space, offering AI livestreaming services at approximately US$1 per minute.

Jensen Wu, CEO of TopviewAI, explains how this technology democratises access to live commerce: instead of investing in full studio setups, companies can have one person monitor the AI-powered livestream while achieving what Wu describes as a “pretty good” return on investment. This innovation is particularly significant in a region where Google’s e-Conomy SEA report notes that live shopping has become increasingly popular, yet many SMEs lack the resources to participate effectively.

Also Read: Anthropic data shows businesses use AI to automate, not collaborate

The adoption landscape: Understanding the divide

The contrast between early adopters and the majority reveals important patterns about AI readiness in Southeast Asia. Research shows that high-growth ASEAN SMEs are focusing on three key areas: using AI-driven insights to better understand customers, automating processes with agentic AI to reduce manual workloads, and integrating systems to eliminate operational inefficiencies.

Vernon Cheo, Regional Vice President for SMB at Salesforce ASEAN, captures the transformative potential: “AI is rapidly transforming the way ASEAN’s small and medium businesses operate, helping them do more with less while staying competitive in an increasingly digital economy. By automating routine tasks, delivering data-driven insights, and enhancing customer engagement, AI empowers SMBs to scale efficiently and focus on what truly matters—building relationships and driving growth.”

The data supports this optimism. SMBs across ASEAN are doubling down on technology, with 76 per cent increasing their investment in digital tools and only five per cent cutting back on tech spending. Singapore-based SMBs are leading the charge, quickly adopting cloud solutions and automation to work smarter, not harder.

The barriers: Why 77 per cent still haven’t made the leap

Despite the success stories, significant obstacles prevent widespread adoption. The primary challenges include resource constraints, with many SMEs viewing AI implementation as prohibitively expensive; technical expertise gaps, highlighted by Singapore’s 5,000-person shortage of AI specialists; and data quality issues, with 72 per cent of Singapore SMEs citing data problems as their primary obstacle.

The talent challenge is particularly acute. Average tenure for AI specialists in small businesses is just 16 months, as they’re often lured away by larger organisations offering better compensation packages. This creates a vicious cycle where SMEs struggle to build internal AI capabilities, making them more dependent on external solutions that may not fully address their specific needs.

Integration challenges compound these difficulties. A Singapore Business Federation study found that 58 per cent of SMEs experienced operational disruptions during technology implementation projects, often due to conflicts between new AI tools and legacy systems. The average SMB uses seven different business applications, and over half report frequent data inconsistencies across their various tools.

Also Read: AI lead generation for B2B sales: A practical guide

The path forward: Lessons from success stories

The experiences of successful adopters reveal several key strategies for overcoming these barriers. First, starting small with high-impact, low-complexity use cases—such as customer service automation or basic predictive maintenance—allows SMEs to build confidence and demonstrate value before scaling up.

Second, leveraging government support can significantly reduce financial barriers. Singapore’s Productivity Solutions Grant funds up to 70 per cent of pre-approved AI solutions, while the Enterprise Development Grant supports broader transformation projects. The government has also allocated SG$150 million (US$116.4 million) for the Enterprise Compute Initiative, enabling eligible businesses to access AI tools and compute power through cloud service providers.

Third, focusing on solutions that address multiple business challenges simultaneously—as Lita Global demonstrated with translation and customer service—maximises return on investment and justifies the initial expenditure.

The regional advantage: Youth, connectivity, and ambition

Southeast Asia possesses unique advantages for AI adoption. Countries like Vietnam, Malaysia, and the Philippines have the highest percentage of business owners and leaders under 40 years of age, bringing digital nativity and openness to new technologies. As Soumik Parida from RMIT University Vietnam notes about Vietnam’s prospects: “the future is bright because it’s a very young population, is a very internet-savvy population. They are starting to have a global voice and they’re very easy to adapt any new technology.”

The region’s high internet penetration and growing digital economy create fertile ground for AI adoption. Singapore, the Philippines, and Malaysia rank among the top 10 globally for AI-related searches and demand, indicating both awareness and appetite for these technologies.

Also Read: Empowering retailers: The transformative potential of  digital shelf in e-commerce 

The imperative: From everyday use to transformative impact

The evidence is clear: AI has moved beyond experimental technology to become a competitive necessity for Southeast Asian SMEs. The companies that embrace AI today are not just improving their operations—they’re positioning themselves to capture disproportionate value as the technology becomes more sophisticated and accessible.

The success stories from Lita Global, TopviewAI, and others demonstrate that with the right approach, even small businesses can harness AI to achieve remarkable growth. The key lies in starting with clear business problems, leveraging available support systems, and building capabilities incrementally.

For Southeast Asia’s 70 million SMEs, the question is no longer whether to adopt AI, but how quickly they can begin their transformation journey. Those who act now will shape the region’s economic future; those who wait risk being shaped by it instead.

The divide between AI adopters and non-adopters will only widen. The time for Southeast Asian SMEs to bridge that gap is now.

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The Singapore Fashion Council (SFC) has invested US$300,000 in Loom Carbon, a local climate tech startup, as part of its inaugural accelerator programme, The Bridge Fashion Innovator (TBFI) Scale Up.

Loom Carbon will receive tailored mentorship, enhanced investment readiness support, and access to a curated network of investors, corporate partners, and government stakeholders through the TBFI programme.

The startup will also benefit from pilot opportunities across Singapore and Southeast Asia.

Also Read: Climate tech’s shift from doing good to doing well

About 92 million tonnes of textile waste are landfilled or incinerated annually. Loom Carbon aims to solve this massive textile waste challenge using technology.

The company utilises a proprietary, science-led approach that adapts proven pyrolysis technology. Its modular pyrolysis system converts mixed discarded textiles into high-value circular materials. These resultant materials include bio black and renewable oil, which can then be utilised as low-carbon fuels or precursors for new textile production.

During conversion, Loom Carbon removes per- and polyfluoroalkyl substances (PFAS) and microplastics. The modular systems are deployable at the source and designed to scale rapidly.

Benjamin Tan, Senior Director of Innovation & Technology at SFC, said: “Textile waste is one of the fashion industry’s most persistent and overlooked challenges. Innovations like Loom Carbon are crucial for advancing real, science-based solutions that move us closer to a truly circular economy. Their impact-driven approach aligns strongly with SFC’s vision of building Asia into a vibrant hub for responsible fashion.”

To achieve its scale-up ambitions, Loom Carbon is collaborating with world-leading research and academic institutions in Singapore, the US, and South Africa. The company is targeting commissioning its first commercial plant in Singapore, which is expected to have the capacity to process more than 20,000 tonnes of textile waste annually.

Ryan Wiener, co-founder at Loom Carbon, added. “SFC is playing an important role in promoting sustainability and innovation across Southeast Asia… we align perfectly with Singapore’s Green Plan 2030, and now with the SFC on board, we are well positioned to deliver impact at home and across the region.”

Also Read: Korean brothers’ startup Nibertex develops chemical-free fabric for sustainable textiles

The TBFI Scale Up, launched in 2025, is SFC’s dedicated accelerator for growth-stage ventures in fashion, beauty, and fashion-tech, offering personalised, one-on-one mentorship rather than a conventional cohort-based programme.

Filipino startup Phinix is another player in this segment. This startup runs a recycling centre that collects textile wastes and transforms them into higher valued products such as footwear, fashion accessories and lifestyle pieces.

Recently, Nibertex, a Singapore- and Philippines-based deeptech startup specialising in waterproof breathable membranes, closed a US$7 million Series A funding round led by TNB Aura. The startup develops PFAS-free membrane solutions for the textile industry.

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Autonomous delivery vehicles are quickly becoming the future of logistics, with companies racing to harness the potential of self-driving tech to speed up deliveries, cut costs, and improve efficiency.  But these vehicles will face challenges far tougher than the smooth, controlled test environments they’ve been trained in—especially when it comes to unpredictable roads and extreme weather.

Take suburban roads. Narrow, full of potholes, sometimes not even paved. No lights, no clear markings. A human driver wouldn’t blink at rolling over a few fallen branches or debris. But for an autonomous vehicle? That may be a problem. These delivery vehicles are learning to “see” and “assess” their surroundings. Traditional object detection systems tell the vehicle what an obstacle is—but not whether it’s safe to drive over.

Leading industry players are increasingly adopting occupancy networks instead of just identifying objects. These systems collectively assess whether a vehicle can safely pass, which is critical for handling unpredictable roads, especially when visibility is low. At Cainiao, for instance, we apply these networks to manage mixed road conditions in our pilot programmes, ensuring that obstacles like fallen debris or uneven surfaces don’t derail an entire delivery schedule.

Then there’s the challenge of navigating real-world traffic laws. Following rules is easy when every sign is clear, and every traffic light works perfectly. But what happens when a truck blocks a signal? When road markings are so faded they’re barely visible? A self-driving vehicle can’t afford to hesitate.

To tackle this, many logistics innovators are turning to graph neural networks to help vehicles read not only traffic signals but also how other cars behave, analysing patterns to make an educated guess about what’s happening.

Also Read: Our voyage of innovation: Reshaping global maritime logistics

And they don’t just react in the moment; these evolving systems can use past data to stay consistent, even when the situation gets messy. In our experience, layering in additional contextual information–such as congested traffic conditions or regional driving norms–helps our vehicles maintain stable navigation under unpredictable scenarios.

And then we have the wildcard: other drivers, cyclists, pedestrians—unpredictable, constantly moving objects. A self-driving delivery vehicle needs to do more than just recognise them; it needs to understand their size, speed, and trajectory in real-time. Across the industry, companies are deploying multi-frame, multi-task, multi-modal sensor fusion approaches — combining data from various sensors to build a detailed, continuously updated model of everything moving around them.

They process large numbers of moving objects in fractions of a second, balancing near-range precision with long-range awareness to ensure safe, stable navigation in crowded environments. This level of real-time perception is what makes safe autonomous navigation possible.

The same principle applies to weather. Rain, snow, fog—bad enough for human drivers, but a serious challenge for autonomous systems. LIDAR can get blinded by fog, cameras blur in the rain, and radar struggles with fine details. No single sensor can handle everything. Accordingly, top providers integrate multi-sensor fusion to let different sensors cross-check each other, enabling fallback options if one sensor becomes compromised by adverse weather.

One emerging industry best practice: built-in LIDAR cleaning systems. If rain or snow starts blocking the sensor, the vehicle slows itself down to below 25 km/h, ensuring it stays stable and safe. These small details make all the difference in making autonomous delivery actually viable.

And what’s more important these vehicles aren’t just running on static programming. Reinforcement learning means they improve with every mile they drive. The more real-world data they collect, the better they get at making smart, split-second decisions. In many pilot programmes worldwide, companies have tested such vehicles on semi-closed roads to sharpen decision-making under real-world conditions. Over time, they can master the chaos of real roads.

Also Read: Electrifying Southeast Asia: Unleashing the radical potential of electric vehicles

As one of the developers and producers of autonomous delivery vehicles, Cainiao applies these same industry-wide concepts to real-world deployments. Recent industry deployments on open roads and sold to real-world clients, mainly courier stations and parcel pickup stations, playing a key role in major sales promotions—saving labor costs during peak seasons. This experience underscores Cainiao’s belief that no technology is perfect, and autonomous delivery vehicles still have a long way to go.

That said, with every new challenge–whether it’s unmarked suburban roads or a sudden downpour–these vehicles are getting better at handling the chaos of the real world. Ultimately, the entire logistics sector is progressing toward a future where autonomous delivery is not only feasible but optimised for various road and weather conditions.

So, the real question isn’t if autonomous delivery vehicles can be ready for all conditions — it’s when. Through collective innovation by multiple players that “when” will come sooner rather than later.

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The global trade landscape is undergoing a seismic shift. The imposition of substantial tariffs, such as the US’s 145 per cent levy on Chinese imports, has led to a significant decline in US-China trade volumes, with container shipments plunging by up to 40 per cent in April 2025.

This disruption has accelerated the diversification of supply chains, positioning Southeast Asia (SEA) as a pivotal player in global logistics.

Trade: From complexity to clarity

The acronym TRADE today feels heavy. It could easily stand for:

• Tariffs
• Retaliation
• America-first policies
• Deficits
• Export controls

This version of Trade reflects a zero-sum mindset. It’s transactional, reactive, and often exclusionary. But what if we could reframe Trade to represent a more hopeful, collaborative, and future-ready paradigm?

Let’s consider a new framing:

• Trust: Confidence in secure and fair trade
• Resilience/RCEP: Shock-proof supply chains and regional unity
• Agility: Rapid response to change and disruption
• Digitisation: Seamless, data-driven, paperless trade
• Empowerment: Using trade as a lever for economic inclusion and growth

This new interpretation underscores how the ASEAN region can lead global efforts in rebuilding the fractured scaffolding of trade—from fragmentation to federation, from defensiveness to design.

Also Read: How is the UK-US trade deal shaping cryptocurrency and stock market trends?

The six flows of supply chains, a new operating system

To effectively rethink trade, we must move beyond viewing it solely through the lens of physical goods. Today’s global supply chains are governed by six interrelated flows:

• Product/cargo flow: the traditional movement of goods.
• Data flow: critical for real-time visibility, regulatory compliance, and automation.
• Money flow: financing, payments, and risk mitigation.
• Value flow: tracking where value is created and captured across the chain.
• Risk flow: mapping geopolitical, cyber, and climate-related disruptions.
• Carbon flow: understanding the emissions footprint of each logistical leg.

Any attempt to strengthen ASEAN’s trade posture must acknowledge these six flows. Digitalisation, especially, underpins them all—without interoperable data systems, secure cross-border documentation, and smart contracts, trade slows and trust erodes.

This is where Southeast Asia must distinguish itself—not just with competitive pricing and infrastructure, but with digitally harmonised, data-driven, and climate-conscious logistics ecosystems.

Technology: The enabler of agility and resilience

In navigating this complex trade environment, technology has become the single most important enabler of supply chain agility and resilience.

Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and cloud-based platforms are empowering companies to rewire their operations, increasing visibility and responsiveness across global networks. Predictive analytics allow businesses to anticipate disruptions, optimise routing, and manage inventory dynamically.

IoT enables real-time tracking of goods, enhancing control and quality assurance. Meanwhile, blockchain provides a secure and immutable digital ledger, which helps build transparency and trust across suppliers, logistics providers, and regulators.

Importantly, digital tools are not just about optimisation—they’re about resilience. They allow Southeast Asian economies to manage complex interdependencies, improve compliance, and reduce manual friction in customs, trade documentation, and multi-party coordination.

The China+1 strategy and Southeast Asia’s critical role

The China+1 strategy is no longer just a buzzword—it’s a survival tactic. In a world defined by trade unpredictability and geopolitical rivalry, multinational companies have accelerated efforts to diversify their supply chains beyond China, creating multiple production and sourcing bases to hedge against concentration risks.

Southeast Asia has risen to the top of the list. Vietnam, Thailand, Indonesia, and Malaysia offer competitive labor costs, improving infrastructure, and a growing base of skilled workers. In addition, many of these countries are part of trade-friendly frameworks like the Regional Comprehensive Economic Partnership (RCEP), offering broader market access and policy stability.

Also Read: Navigating market volatility: Bitcoin Hits US$99K, US stocks rally amid trade talks and fed decisions

Yet diversification comes with its own complexities. Take Vietnam, for example. While it has been a top beneficiary of diverted investment flows, the country is also vulnerable to tariff fallout as its own exports come under scrutiny for Chinese-origin components.

Recent studies have warned of potential GDP impacts of up to six per cent for some ASEAN economies, underscoring that supply chain relocation is not a zero-sum game—it requires deeper structural readiness.

The opportunity for Southeast Asia lies not just in being a substitute manufacturing base, but in positioning itself as a regional value-added hub, deeply integrated and digitally enabled.

ASEAN’s path forward: From buffer zone to builder zone

The solution doesn’t lie in isolation or duplication, but in regional coordination. ASEAN economies must strengthen their collective response—not just at the diplomatic level but through aligned investment in trade tech infrastructure, logistics corridors, and digital trust ecosystems.

Public-private partnerships should be encouraged, particularly in building digital ports, trusted e-commerce zones, bonded warehouses, and intelligent trade corridors. The ASEAN Smart Logistics Network, already in motion, could benefit from integrating a digital trust overlay—making goods traceable not just physically but across ownership and compliance checkpoints.

Multilateral Development Banks (MDBs) and Development Finance Institutions (DFIs) can play a catalytic role here, funding not just infrastructure but the digital public goods needed for smart, secure, and inclusive trade ecosystems.

Singapore: A trusted trade and supply chain hub

Singapore stands as a case study of how small economies can punch above their weight by combining physical logistics excellence with digital trust infrastructure. Ranked first in the World Bank’s 2023 Logistics Performance Index, Singapore leads in infrastructure, customs efficiency, and international shipments.

But it’s the integration of digital trade enablers—like AI-driven port systems, just-in-time customs clearances, and partnerships with industry on traceability platforms—that has turned Singapore from a transshipment giant into a trusted trade & supply chain hub.

Rather than merely promoting pilot solutions, Singapore scales innovation through collaborations. The Advanced Remanufacturing and Technology Centre (ARTC), for instance, accelerates industry R&D and commercialisation in supply chain solutions. At the same time, national initiatives like SBF’s COFTI (Center of Future Trade & Investment) exemplify Singapore’s push to shape the future of digitally enabled, rules-based trade.

Singapore is increasingly functioning as a “control tower”—orchestrating flows of data, money, value, risks, and carbon remotely—while physical goods may bypass Singapore entirely. This positions the country as a leading offshore and wholesale trade node, anchoring trust and governance in global trade networks.

Increasingly, regional players like Indonesia, Malaysia, and Vietnam are also looking to such digital models, adapting them to local contexts while striving to create more trustworthy and connected ecosystems.

Also Read: Book Excerpt: Why successful fundraising begins with understanding your company’s needs

TTTT: Trade, transport, technology, and trust – A new framework for the region

To thrive in today’s volatile trade environment, Southeast Asia must adopt a new paradigm—TTTT: Trade, transport, technology, and trust.

• Trade: Southeast Asia must go beyond traditional FTAs and focus on digital trade enablement. Simplifying origin rules, integrating regulatory tech (RegTech), and enabling real-time compliance checks will be critical to stay competitive.

• Transport: Multimodal logistics corridors—from dry ports in Vietnam to high-speed cross-border rail between Laos and China—need better integration. Investment in inland connectivity, last-mile digitisation, and cross-border logistics data exchange will ensure that physical goods keep flowing seamlessly.

• Technology: Smart warehousing, AI-based forecasting, and end-to-end supply chain digitisation must become the norm. Governments should facilitate SME onboarding onto digital platforms and fund sandboxing and cross-border pilots for logistics tech and supply chain visibility tools.

• Trust: This is the newest and arguably most important dimension. Whether it’s trust in origin, compliance, data integrity, or contractual execution, digital trust is the currency of modern trade.

Platforms that ensure tamper-proof documentation, trade authentication, and traceability—such as digital certificate exchanges or secure interoperability frameworks—will be essential.

While early-stage efforts like the Infocomm Media Development Authority (IMDA)’s TradeTrust have laid a foundation, the region now needs interoperable frameworks that allow for shared, verifiable, and cross-certified trade data between countries and across private sector systems.

Conclusion: From shock to strategy

The US tariffs are not just a policy shock—they are a wake-up call. Southeast Asia must now take strategic ownership of its position in the global supply chain map.

By embracing the TTTT framework—Trade, transport, technology, and trust, recognising the six flows of supply chains, and reframing Trade as a tool for inclusion and empowerment, the region can shift from a reactive to a resilient trade powerhouse.

More than just a manufacturing alternative to China, Southeast Asia can emerge as a trusted, technologically empowered, and interconnected supply chain hub—a cornerstone of tomorrow’s global trade system.

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The world’s bracing for a slowdown.

US tariffs on Chinese imports have surged to historic highs — the average rate now sits at 22.5 per cent, the highest in over a century. New levies of up to 145 per cent are hitting everything from electronics to everyday consumer goods.

The result? Panic.

US$6.6 trillion in global market value was wiped out in just two days. Bloomberg called it a “market meltdown.” The Straits Times put it bluntly: “a tariff nightmare.”

Economists now warn of a possible U.S. recession, with up to 2 million jobs at risk. Former Treasury Secretary Larry Summers estimates average household income could fall by as much as US$5,000 if escalation continues.

And yet, in the thick of all this chaos, a Singapore startup is quietly thriving.

Not because it’s chasing hype. But because it’s doing the exact opposite.

The startup that chose the road less scalable

While the rest of the tech ecosystem is busy spinning up AI agents, launching crypto loyalty tokens, or building the next vertical SaaS for pets — NNIO decided to make… fans.

Not smart fans. Not WiFi-enabled fans. Just really good ones.

Fans. Vacuum cleaners. Shower heaters. Air fryers. That’s the NNIO playbook.

It’s not the kind of business you’d expect to see in a pitch deck. But that’s precisely what makes it work.

Also Read: Bitcoin, S&P 500, Nasdaq surge amid strong manufacturing data and trade hopes

Designing for real life, not demo day

NNIO was co-founded by Beng Kwee (BK) Tan, an industry veteran with over three decades in the appliance sector. He’s seen all the waves: smart homes, IoT, app-integrated everything.

But here’s what stuck:

“Most people don’t want to download an app to turn on their fan,” BK says. “They just want something reliable, affordable, and decent looking in their home.”

That insight is at the heart of NNIO’s product philosophy: strip away the fluff, double down on what matters. No bloated feature sets. No app-for-the-sake-of-an-app integrations. Just minimal, essential functionality – built to work, and built to last.

It’s product line reflects that approach. Branded under four categories:

• Air circulators and fans (A-Cool+)
• Cordless vacuum cleaners ( V-Clean+)
• Cooking appliances (C-Tasty+)
• Shower heaters (S-Refresh+)

Also Read: Advanced safety solutions: Supporting, not replacing, human oversight in manufacturing

Designed for performance and priced for accessibility, these products have seen strong traction across Singapore, particularly among value-conscious consumers seeking practical innovation over flash.

Betting on what doesn’t break

Unlike the blitzscale-at-all-costs playbook, NNIO took a different route.

With a strong anchor, a focus on operational discipline, and smart distribution through both modern retail and e-commerce, the company stayed lean. There’s no sky-high valuation to defend, no PR hype cycle to chase.

Just real customers. Buying real products. Month after month.

Here’s the thing about home appliances: they don’t go viral. But they do get bought, especially when budgets tighten.

The global household appliance market is massive, projected to hit US$1.11 trillion by 2032. But beyond size, its resilience is what stands out. People still need to cook, clean, and stay cool — regardless of funding slowdowns or inflation.

In downturns, essentials outperform. And that’s where NNIO quietly wins.

“Boring sells — especially when the economy doesn’t.”

The moat you didn’t see coming

While hype-driven startups are burning cash to justify sky-high growth, NNIO is building something harder to replicate: a defensible niche grounded in habit and necessity.

It’s not trying to “disrupt” your home. It’s just trying to make it more liveable: affordably, reliably, and without friction.

In an era where tech is increasingly defined by noise, simplicity might just be the sharpest edge.

And in a time of economic uncertainty, that kind of boring might just be brilliant.

—

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Artificial Intelligence has quickly become part of our daily routines. Whether it’s asking ChatGPT for travel recommendations, using AI to polish an email, or letting GitHub Copilot suggest lines of code, we’ve reached the point where AI feels almost invisible; it’s just there, helping us get things done faster.

But here’s the catch: in all the excitement, many people are overlooking a serious issue. Is the AI we rely on actually secure?

When convenience meets risk

Let’s take a common example. A developer runs into a tricky bug and pastes part of the company’s source code into ChatGPT or Copilot for help. Within seconds, the AI proposes a neat fix. Problem solved, right?

Not so fast.

• What happens to that code once it’s pasted into an AI tool?
• Is it stored somewhere outside the company’s control?
• Could it resurface in another response for a completely different user?
• And most importantly: how do we know the “fixed” code doesn’t contain hidden security flaws?

That single copy-paste could become a doorway for data leaks or application vulnerabilities, risks that are often invisible until it’s too late.

Companies are already waking up

This isn’t just theory. Some organisations have already moved to block risky AI use. For example, Skyhigh introduced policies to stop employees from pasting sensitive information into ChatGPT. Why? Because they recognised that what feels like an innocent productivity hack could lead to intellectual property leaks, compliance violations, or even open the door to cyberattacks.

The message is clear: AI tools are powerful, but they’re not risk-free.

Also Read: Cybersecurity in the AI age: How startups can stay ahead

The security blind spot

AI is incredibly good at giving quick answers. But it doesn’t guarantee those answers are safe. In fact, AI-generated code might:

• Introduce insecure patterns that developers don’t notice.
• Reuse snippets that contain outdated or vulnerable logic.
• Skip context-specific security checks that your team would normally apply.

This is the “blind spot”: people trust AI’s speed and convenience but rarely question its security implications.

Security for AI, security with AI

So, what’s the way forward? It’s not about avoiding AI altogether. That’s unrealistic, AI is here to stay. The real answer is building guardrails:

• Set clear policies: Define what data employees can and cannot share with AI tools.
• Educate teams: Make sure developers understand the risks of pasting code into public platforms.
• Double-check AI output: Treat AI suggestions as drafts, not production-ready fixes.
• Use AI securely: When possible, adopt enterprise AI solutions that offer stronger data privacy and security controls.

Think of it like this: AI can be your co-pilot, but you still need a seatbelt and traffic rules.

Final thoughts

AI tools like ChatGPT and Copilot are transforming how we work, but they also introduce a new category of risks that organisations can’t afford to ignore. The next time you’re about to paste something into an AI tool, pause and ask:

Would I be comfortable if this information appeared outside my company?
Do I trust this code is not just functional but secure?

AI is smart, but staying secure requires us to be smarter.

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