The rapid change in the future of work has been accelerated by the proliferation of AI agents.

The future of work isn’t arriving gradually anymore—it’s shifting in sharp, compressed waves. Over the past three years alone, we’ve seen entire job categories emerge, peak, and become obsolete, all within a single product cycle. What used to take decades now takes quarters.

At the centre of this transformation is artificial intelligence. But the real story isn’t just about better models or smarter tools—it’s about how AI is fundamentally reshaping who creates value, how work is done, and what a “company” even looks like.

Three forces define this moment:

• The rapid evolution of AI-related roles
• The shift from technical depth to business-process fluency
• The rise of the solopreneur and the one-person company (OPC)

Together, they point to a radically different future of work—one that is already here.

From AI scientists to agentic deployment experts

If you zoom out, the evolution of AI-related jobs over the past three years tells a powerful story.

Phase 1: The AI Computer Scientist (2023)

In the early days of generative AI, value was concentrated among the deeply technical.

Large language models existed—but they were unreliable, prone to hallucination, and difficult to operationalise. Extracting value required:

• Knowledge of APIs
• Model fine-tuning
• Prompt structuring at a low level
• Engineering intuition

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

In short, AI was a tool for specialists. If you weren’t a machine learning engineer or a highly technical developer, you were largely a spectator.

Phase 2: The Prompt Engineer (2024–early 2025)

Then came the “prompt engineering” era.

As tools like ChatGPT and Claude improved, a new skill emerged: crafting highly specific prompts to coax useful outputs from AI systems. This gave rise to one of the fastest-growing job titles in tech history, but it came with limitations:

• Prompts were often brittle and non-transferable
• Outputs depended heavily on wording tricks
• Workflows were difficult to scale across teams

For a brief moment, prompt engineers sat at the centre of AI value creation. And then—almost as quickly—the role began to fade.

Phase 3: The Agentic Deployment Expert (2025–present)

Today, we are in a new phase entirely.

AI systems have matured. Interfaces are cleaner. Capabilities are more reliable. And most importantly, AI is now deployable by generalists.  The highest-value role is no longer the person who builds AI models—or even the one who writes clever prompts. It is the person who can:

• Identify where AI creates real business value
• Select the right AI-Agents as tools
• Integrate them into workflows
• Train the AI agents to operate effectively
• Measure ROI and iterate

This is what some are now calling the “agentic deployment expert”—someone who doesn’t build AI, but deploys it to drive outcomes. And crucially, this role is less about technical depth and more about understanding business processes.

The great skill shift: From code to context

What makes this transition so important is not just the new job title—it’s the type of skill that is now valuable.  Previously, the advantage came from:

• Writing code
• Understanding model architecture
• Navigating technical complexity

Now, the advantage comes from:

• Understanding workflows
• Mapping AI to business problems
• Designing systems that integrate humans and machines
• Driving adoption within organisations

In other words, the bottleneck has shifted from technology to application. One no longer needs to understand how a model works internally. But you do need to understand:

• How a sales pipeline operates
• How customer support flows
• How marketing campaigns convert
• Where inefficiencies exist

Also Read: Inside the next phase of AI-driven banking in Southeast Asia

AI has lowered the barrier to entry—but raised the bar for contextual intelligence. This is why many non-technical operators are suddenly outperforming traditional engineers in AI adoption. They don’t build the tools—but they know exactly where to apply them.

AI as a force multiplier, not just an efficiency tool

One of the biggest misconceptions about AI is that it’s primarily about automation and cost-cutting. In reality, AI is doing something more profound: it is compressing the scale required to create value. Tasks that were once required:

• Teams of analysts
• Entire marketing departments
• Dedicated design resources

…can now be executed by one person with the right stack of AI agents. This compression is what enables the next major shift in the future of work.

The rise of the solopreneur and the one-person company

Across markets, we are seeing the emergence of a new kind of economic actor: the AI-powered solopreneur.

In China, this trend is accelerating rapidly. Local governments are actively supporting “one-person companies” (OPCs), recognising their potential to drive innovation and employment. Several forces are converging:

• Affordable and powerful AI tools
• High youth unemployment is pushing alternative career paths
• Low startup costs enabled by digital infrastructure

The result? Individuals building viable businesses without teams. Examples include:

• Designers using AI for image, video, and music generation
• Content creators scaling output exponentially
• Solo founders running marketing, sales, and operations with AI assistance

Some are even matching—or exceeding—the income they previously earned in traditional corporate roles. As one solopreneur put it, AI is “an extension of my brain”—expanding what a single person can do.

From teams to systems

This shift challenges one of the core assumptions of modern business: that growth requires headcount. Historically, scaling meant:

• Hiring more people
• Building larger teams
• Increasing organisational complexity

But AI introduces a different model: Scale through systems, not people.

Also Read: It’s not the chatbot but the access: Why AI agents are the real threat

A well-designed AI-enabled workflow can:

• Replace repetitive human tasks
• Augment decision-making
• Enable faster iteration

This doesn’t eliminate the need for people, but it dramatically changes how many are needed, and what they do. In this new model, the most valuable individuals are not those who execute tasks, but those who:

• Design systems
• Orchestrate tools
• Continuously optimise workflows

The new competitive divide

This transformation is creating a growing gap between the two types of organisations and individuals.

• The deployers

• Actively integrating AI into workflows
• Experimenting with tools monthly
• Measuring real business impact
• Building internal capability

These organisations feel fast, adaptive, and energised.

• The observers

• Talking about AI in abstract terms
• Running isolated pilots or demos
• Waiting for “maturity”
• Treating AI as a future initiative

These organisations risk falling behind—not because AI is inaccessible, but because they are not using it. The same divide exists at the individual level.

The defining question is no longer: “Do you use AI tools?”

It is: “What have you deployed that creates real value?”

The double-edged nature of solopreneurship

While the rise of one-person companies is exciting, it also comes with caveats.

Not all solopreneurs succeed. In emerging ecosystems:

• Only a minority achieves a sustainable income
• Many are still experimenting or struggling
• Some risk of becoming part of a broader gig economy with limited stability

AI lowers barriers—but it does not eliminate the need for:

• Market demand
• Business acumen
• Execution discipline

In fact, as tools become more accessible, competition increases. The differentiator is no longer access to technology, but how effectively it is applied.

Also Read: Why inclusive AI is the next frontier of product strategy

What this means for Southeast Asia

For ecosystems like Southeast Asia, this shift presents both an opportunity and a challenge.

Opportunity

• Lower barriers to income-generation
• Increased productivity for SMEs
• Ability to compete globally with smaller teams
• New pathways for talent beyond traditional employment

Challenge

• Workforce displacement in certain roles
• Need for rapid reskilling
• Risk of widening gaps between AI adopters and laggards

The region’s strength—its large base of adaptable, business-savvy operators—may actually position it well for this transition. But only if adoption happens quickly.

The future of work is already here

The future of work is no longer a distant concept—it is unfolding in real time. We are moving toward a world where:

• Technical skill is no longer the primary bottleneck
• Business context understanding becomes the key differentiator
• Individuals can operate at the scale of small teams
• Companies are defined more by systems than by headcount

The progression from AI scientist → prompt engineer → agentic deployment expert is not just a shift in job titles. It is a signal of something deeper: The centre of gravity in work is moving—from building technology to applying it intelligently. And for the first time in modern history, the tools to do that are accessible to almost everyone.

Final thought: The new question

In this new era, the most important question you can ask—whether you are a founder, an operator, or a policymaker—is simple:

What have you deployed?

Not what you’ve explored. Not what you’ve read about. Not what you’re planning.

But what you’ve actually put into the real world—and made work. Because in the age of AI, the winners won’t be those who understand the technology best. They will be those who use it best.

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

The post The future of work you don’t expect appeared first on e27.

The most dangerous sentence in modern analytics is not that the business has too much data. It is that everyone needs access.

That idea sounds collaborative, even progressive. In practice, it is often how organisations turn a useful analytics platform into a quiet governance failure. The least privilege is defined as restricting users and processes to the minimum authorisations and resources needed to perform their function. Its zero-trust guidance makes the same point in broader form, arguing that access decisions should be accurate, least privilege, and made as though the network is already compromised. Proper identity and access management is critical to securing cloud resources, and access control policies should be carefully configured so users receive only the least privilege necessary.

That matters because a data lake is not risky simply because it holds a great deal of information. It becomes risky when access design lags behind platform ambition. The aggregation of critical data makes cloud services attractive targets for adversaries. In other words, the lake itself is not the story. The trust model around it is.

The real failure is rarely storage

This distinction is important because many analytics estates are still run with a mindset inherited from file shares and shared drives. Teams create broad access groups because it is operationally convenient. Engineers grant wide permissions because deadlines are real. Business users are told to work inside a common zone because it speeds up adoption. For a while, this feels efficient. Then the platform expands. Finance wants customer level granularity. Operations wants plant data. Trading wants market and position signals. Sustainability teams want emissions views. External partners want extracts. Suddenly the lake is serving half the enterprise, but the permissions model still behaves as though it is a team folder with a better user interface.

That is where the trouble begins. The technology scales faster than the trust design.

Why shared folders and hope break at scale

Shared access works only while the business is small enough for trust to be social rather than architectural. Once the platform becomes important, informal trust stops being sufficient.

Also Read: Data minimisation vs AI context maximisation: The battle defining the future of smart systems

The leading cloud data platforms have already moved beyond this. AWS Lake Formation is built around central governance, with fine-grained access controls that can restrict access at the database, table, column, row, and even cell level, with audit history across services. Databricks makes a similar shift in Unity Catalogue, where access is layered through workspace restrictions, explicit privileges and ownership, attribute-based policies, row filters, and column masking. The significance of this is not vendor marketing. It is the market admitting that broad shared access does not survive real scale. Modern analytics platforms increasingly need access to be designed as a first-class product capability.

What product grade access design actually looks like

Product grade access design starts with the idea that access is part of the user experience, not an afterthought for the security team. If a data product is meant for operations managers, field engineers, finance partners, and external contractors, then each of those audiences should encounter a deliberately shaped version of the product. They should not all land on the same raw surface and rely on restraint.

The first requirement is explicit ownership. Every securable object should have an owner, and access is allowed only when the relevant privileges have been granted. That sounds basic, but it changes behaviour. A platform with named owners forces someone to be accountable for who gets access and why. A platform without clear ownership drifts into inherited permissions and quiet overexposure.

The second requirement is policy at the data level, not only at the folder or environment level. AWS Lake Formation’s model of row, column, and cell-level control, and Databricks’ use of tag-based policies, row filters, and masks, point in the same direction. The future of lake governance is not coarse access to broad zones. It is context-aware access that follows the sensitivity and purpose of the data itself. That is especially important in sectors like energy and industrials, where commercial, operational, maintenance, and customer information increasingly sit in the same analytical estate.

The third requirement is environment separation, which actually means something. Databricks documents workspace restrictions that can isolate production data to production workspaces, even where a user may hold wider privileges elsewhere. This is an important lesson. In too many organisations, development, experimentation, and production are separated in slides but blurred in practice. Product grade access design makes the boundary enforceable.

The fourth requirement is auditability that supports management, not just forensics. AWS provides comprehensive audit logs through CloudTrail for data access attempts across services. This is not just about catching intruders. It is about allowing a platform owner to answer a basic leadership question with confidence: who accessed what, when, through which service, and under which policy.

Why this matters more in the age of AI and self-service

The old permissions model was already weak. AI and self-service analytics make it weaker.

Also Read: Server sanctuaries or net-zero derailers? Southeast Asia’s data centre dilemma

Every new agent, notebook, model training job, dashboard layer, and external share increases the number of identities acting on data. NIST’s definition of least privilege explicitly applies not only to users but also to processes acting on their behalf. That is a useful reminder, because many organisations are still good at reviewing human access and poor at governing service accounts, pipelines, automated jobs, and data science workflows with the same discipline.

This is where the phrase product grade becomes especially useful. Product teams know that scale does not come from more manual approvals. It comes from designing good defaults, clear roles, bounded entitlements, observable behaviour, and predictable escalation paths. Analytics platforms need the same thinking. A mature platform should make the secure path the easy path. If getting the right access is slower than getting broad access, the broad access will win every time.

The mistake leaders keep making

Too many executives still treat access control as a technical hygiene issue. It is not. It is one of the main determinants of whether a data platform can become a trusted enterprise product.

If permissions are too loose, the organisation eventually suffers a data exposure, a partner trust issue, an internal credibility problem, or a regulator’s question it cannot answer cleanly. If permissions are too rigid and badly designed, the platform becomes a bottleneck and the business routes around it. The winning position sits in the middle. Tight enough to be credible, usable enough to support real work.

That is why this is not mainly a storage conversation. It is a product and operating model conversation. The leading platforms have already evolved toward central governance, fine-grained controls, attribute-based access, audit trails, and explicit ownership because the old approach does not survive enterprise scale. The organisations that still rely on shared folders with better branding are not simplifying access. They are postponing a more serious design decision.

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

The post Data lakes do not leak, permissions do appeared first on e27.

Globally, small and medium-sized enterprises (SMEs) are experiencing unprecedented opportunities. Digital tools, cloud platforms and the rapid rollout of 5G connectivity are enabling businesses to scale beyond local markets and tap into global demand.

In fact, 5G-driven digital growth alone is projected to add nearly US$130 billion to the Asia-Pacific economy by 2030. For many SMEs, the digital economy has levelled the playing field, allowing smaller companies to compete in ways that were once only possible for large enterprises. 

But this digital transformation has also introduced a growing vulnerability. The same technologies that enable growth are expanding the cyberattack surface. At the same time, advances in artificial intelligence are making cyberattacks more sophisticated and accessible. What once required deep technical expertise can now be automated, enabling highly personalised phishing and large-scale attack campaigns that are harder to detect and more likely to succeed.

SMEs are no longer just participants in the digital economy. Increasingly, they are finding themselves on the front lines of cybercrime.

The Department of Statistics Singapore shared that Singapore SMEs make up more than 99 per cent of businesses and employ around 70 per cent of the workforce. According to the Cyber Security Agency of Singapore (CSA), SMEs accounted for a stunning 84 per cent of cybersecurity victims in 2023. At the same time, two in three companies have yet to implement basic cybersecurity measures, with many citing limited expertise or manpower as key barriers.

This combination of high exposure and limited resources has made SMEs particularly attractive targets for cybercriminals.

Why SMEs are increasingly targeted

SMEs often lack a dedicated cybersecurity team, operate with tighter budgets and may not have fully implemented cyber hygiene practices. Yet these businesses are deeply interconnected with the broader economy. SMEs sit within supply chains, provide services to larger corporations and increasingly rely on digital platforms to run their operations. This presents an efficient opportunity for cyber attackers: compromising a smaller company can open the door to a much wider network of targets.

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

The consequences can be severe. A global survey by Mastercard found that 47 per cent of SMEs have experienced a cyberattack. More concerningly, nearly one in five businesses that suffered an attack eventually filed for bankruptcy or closed their operations.

The impact goes far beyond technology. Cyber incidents can halt operations, disrupt supply chains and erode trust with customers and partners. In many cases, businesses must also contend with regulatory obligations, reputational damage and the costly process of restoring systems. For smaller companies operating on tight margins, a cyberattack is not simply a technical problem – it can quickly become a financial crisis.

The hidden protection gap facing SMEs

Despite these risks, many SMEs remain underprotected. Traditional cyber insurance models were designed primarily with larger enterprises in mind. The underwriting process can be lengthy and paperwork-heavy, often requiring detailed technical information that smaller companies may struggle to provide.

Many policies include deductibles that require businesses to pay a significant portion of the incident response costs upfront even when coverage is obtained. These out-of-pocket costs can delay recovery at the exact moment when speed matters most, particularly for SMEs already grappling with the operational shock of a cyberattack.

This creates a paradox: the businesses most vulnerable to cyber threats are often the least able to activate the financial protection available to them.

Building cyber resilience as a national priority

Recognising the growing threat landscape, Singapore has taken significant steps to strengthen the cyber resilience of its business ecosystem.

Also Read: Digital Growth, fragile defences: Inside Philippines’s cybersecurity gap

Initiatives led by the Cyber Security Agency of Singapore aim to equip SMEs with practical tools and frameworks to improve their cybersecurity posture. Programmes such as Cyber Essentials and Cyber Trust provide structured guidance on implementing baseline security practices. At the same time, new support structures are emerging to help businesses respond more effectively when incidents occur.

The upcoming Cyber Resilience Centre, established by the Singapore Business Federation in partnership with organisations such as SGTech and the Singapore Chinese Chamber of Commerce and Industry, is one such example. The centre will offer cyber diagnostics, incident response guidance and access to cybersecurity expertise for businesses that may otherwise lack internal capabilities.

These initiatives reflect an important shift: cybersecurity is no longer solely an IT issue, but a broader economic and operational challenge.

From passive protection to active cyber resilience

Businesses must rethink how they approach cyber protection as the threat landscape evolves. Historically, cybersecurity and cyber insurance have often been treated as separate layers of defence. Companies invest in technical tools to prevent attacks, while insurance acts as a financial safety net if those defences fail.

However, modern cyber threats – increasingly powered by automation and artificial intelligence – are evolving too quickly for static defences alone. What is needed instead is a more active approach to cyber resilience. This means combining continuous risk assessment, proactive defence measures and rapid incident response capabilities. It also means ensuring that financial protection mechanisms are structured in a way that enables businesses to recover quickly when an incident occurs.

Also Read: Navigating hybrid cloud strategies: Enhancing cybersecurity for businesses in the APAC region

Encouragingly, new models are beginning to emerge that align cyber insurance more closely with proactive cybersecurity practices. Businesses that invest in stronger security frameworks can be rewarded with improved coverage terms or more favourable premiums.

In this way, insurance can become not just a financial safeguard, but also an incentive for better cybersecurity behaviour.

Preparing SMEs for a more volatile digital future

Cyber threats are unlikely to diminish in the years ahead. As businesses continue to digitise their operations and connect with global markets, the attack surface will only expand. For SMEs, the question is no longer whether cyber incidents will occur, but how prepared they are to respond and recover.

Building cyber resilience, therefore, requires a collective effort – from government agencies and industry bodies to technology providers and insurers. Together, these stakeholders can help ensure that smaller businesses have access to the tools, expertise and financial protection needed to operate confidently in the digital economy. Because ultimately, the resilience of SMEs is closely tied to the resilience of the wider economy itself.

In a world where cyber threats are becoming an everyday reality, the businesses that will thrive are not those that avoid attacks entirely, but those that are prepared to withstand them – and recover quickly when they occur.

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

The post Why cyber resilience is the new standard for SME survival appeared first on e27.

The Philippine white-collar job market may look stable on the surface, but the data suggests something more fragile beneath. What appears to be low turnover is, in many cases, not a sign of employee satisfaction but of hesitation. With 54% of professionals considering a move within the next year, and 66% willing to leave even after a counteroffer, the foundations of this “stability” look increasingly temporary.

This creates a dangerous illusion for employers. Companies that interpret low attrition as loyalty risk being blindsided when delayed decisions suddenly convert into exits. The reality is that many employees have already disengaged; they are simply waiting for the right opportunity, timing, or market conditions to act.

What has changed is not just compensation expectations, but awareness. Filipino professionals are benchmarking themselves regionally, exposed to global opportunities through remote work and digital networks. As a result, traditional levers like reactive salary increases or counteroffers are losing effectiveness.

For founders and executives, the implication is clear: retention is no longer a reactive function. It requires proactive engagement: transparent career pathways, flexible work structures, and management quality that builds trust before resignation letters appear.

The risk is not a gradual rise in turnover, but a sudden correction. And when that wave comes, companies unprepared for it may find themselves scrambling for talent in an already constrained market.

Regional

SEA tech funding surges to US$2.8B in Q1 2026, more than doubling YoY: Late-stage deals and mega-rounds in enterprise tech drove the acceleration, with Singapore-based firms accounting for 93% of all funding. DayOne’s US$2B Series C was the quarter’s largest single round.

Bybit invests US$8M in Hata to crack Malaysia’s regulated crypto market: The dual-licensed Kuala Lumpur exchange now has US$12.2M in disclosed fundraising, as Bybit bets on compliance-first growth in a tightly supervised market where licensing is the real competitive moat.

Nium bets on a future where stablecoins swipe like credit cards: Singapore’s Nium has partnered with Coinbase to let businesses send, receive, and convert USDC across its cross-border payments network spanning 40-plus licences and more than 190 countries.

Netbank lands fresh Series B to power the invisible rails of Philippine fintech: Led by Altara Ventures, the round backs Netbank’s pitch to be the licensed banking layer underneath other fintechs, after the company reported 88% revenue growth and profitability in FY2025.

Airwallex to launch in Indonesia and Vietnam this year: The payments giant acquired licensed entities in both markets and recently received full approval in Malaysia, where it grew its team 66% in 2025 and plans to double headcount by year-end.

SEA’s fintech boom: market demand is real, but the numbers need context: UnaFinancial’s study crowns SEA as Asia’s most fintech-dense subregion at 14 companies per million people, but Singapore’s outsized 619-per-million density masks a far more modest picture across the rest of the region.

The real opportunity in ASEAN’s EV market lies in regional coordination: Dongfeng’s experience entering Malaysia and managing ASEAN operations from Singapore shows that winning the EV race will depend on centralised strategy and localised execution, not technology alone.

SEA’s next-gen leaders earn global spotlight in WEF 2026 cohort: Eighteen innovators from Indonesia, Singapore, Vietnam, Thailand, Cambodia, and the Philippines were named to the World Economic Forum’s Young Global Leaders class, spanning healthtech, fintech, climate action, and digital inclusion.

Vietnam startup visa gap: why founders are renting, not residing: Despite 8.02% GDP growth and a 17.9% rise in its startup ecosystem, Vietnam lacks a purpose-built startup visa, leaving foreign founders cycling through e-visas while Thailand and Malaysia actively court them with accessible programmes.

Korea-Vietnam to sign more than 70 MOUs in AI, infrastructure, energy: During President Lee Jae Myung’s state visit to Hanoi, Samsung, SK, LG, and Hyundai joined more than 500 executives at a business forum covering AI ecosystems, batteries, and Korean railway exports to Ho Chi Minh City.

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Interviews & Features

Flexible work is no longer a perk in the Philippines, but the price of talent: With 78% of candidates preferring hybrid or remote arrangements, rigid office mandates are shrinking the already-scarce talent pool, particularly for digital, leadership, and highly specialised roles.

Tsuklio brings US$155-a-week dinners to Singapore’s convenience economy: Japan’s Tsuklio, which has served over 30M meals across 46 prefectures, is targeting dual-income households and working professionals with a dietitian-supervised, central-kitchen subscription model in its first overseas market.

The new PR playbook: why proof, not narratives, wins investors: Southeast Asian VCs now demand traction, scalable models, and founder credibility, making consistent market signalling across concept, community, and corporate dimensions the most effective fundraising tool for startups in 2026.

The Vietnam startup visa gap: why founders are renting, not residing: Foreign founders drawn by Vietnam’s booming digital economy find existing investor visa thresholds too high for pre-revenue startups, putting Vietnam at a structural disadvantage compared with Thailand’s and Malaysia’s founder-friendly programmes.

The human touch advantage: why AI alone won’t win Singapore’s customer economy: Braze’s 2026 research reveals that while 93% of marketing leaders trust AI for customer insight, only 53% of consumers feel accurately understood, pointing to a widening trust gap that real-time context, orchestration, and transparency must close.

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International

Bitcoin surges 2.75% as US-Iran ceasefire extension lifts risk appetite: A 95% correlation with the S&P 500 over 30 days confirmed that Bitcoin is acting as a high-beta macro proxy, with a US$187.33M short squeeze amplifying the move toward the critical US$78K-US$8K resistance zone.

Why institutional money is buying crypto while geopolitical risks mount: Bitcoin ETFs drew US$272.59M in net flows while whale accumulation, including a single US$80M Ethereum purchase, and the SEC’s new five-bucket token taxonomy are together laying a more structural floor under crypto valuations.

Anthropic hits ~US$1T secondary valuation, surpassing OpenAI: Driven by limited share supply and strong institutional demand on Forge Global, Anthropic’s secondary price now exceeds OpenAI’s roughly US$880B, following its January 2026 funding round backed by Singapore’s GIC and Coatue.

SoftBank seeks US$10B margin loan backed by OpenAI shares: The two-year facility follows a US$40B bridge loan secured in March and Vision Fund 2’s commitment of US$30B to OpenAI, as SoftBank deepens its debt-fuelled bet on the AI arms race.

Tencent and Alibaba in talks to invest in DeepSeek at US$20B-plus valuation: The Chinese AI startup, owned by hedge fund High-Flyer Capital Management, is raising at least US$300M in its first-ever external funding round, with deal terms still subject to change.

OpenAI in talks to invest up to US$1.5B in private equity joint venture: The venture, internally called DeployCo, would see OpenAI contribute an initial US$500M in equity, with a targeted US$10B valuation at a funding close expected in early May.

South Korea’s economy grows 1.7% in Q1, fastest pace in five and a half years: Strong chip exports rising 5.1% and a rebound in both construction and facility investment drove the outperformance, beating the central bank’s 0.9% forecast by a wide margin.

Vingroup scraps 4.8GW LNG plant in favour of wind, solar, and storage: Chairman Pham Nhat Vuong cited Middle East war-related supply risks as the trigger for the pivot, while VinFast targets breakeven in 2027 and 300,000 vehicle deliveries in 2026.

Elon Musk bought US$1.4B of SpaceX shares from employees in 2025: The purchase added to a March board-approved plan granting Musk 60M more shares, tied to growing SpaceX’s valuation from US$1.1T to US$6.6T and building AI data centres in space.

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Cybersecurity

SEA’s digital paradox: US$300B in growth, US$3.2M per breach: With over 135,000 ransomware attacks recorded in 2024 alone, cybersecurity has become the foundational trust layer of the region’s digital economy, a competitive moat and investor signal, not merely a cost centre.

Cyber risk is a business risk: why communication defines corporate resilience: Penta’s analysis of 4.8M global cybersecurity mentions found that response quality matters more than breach severity, companies that communicate transparently and act quickly recover faster than those that stay silent.

The trust layer: how cybersecurity became hospitality’s most valuable asset: RedDoorz’s repeat booking rate of approximately 70% is built on a security-by-design architecture that keeps AI workloads within its own data warehouse, masks all PII, and treats every customer-facing automation as a potential attack surface.

Why trust is the only currency that matters in the AI era: PwC’s 2026 Global Digital Trust Insights survey found 60% of organisations rank cyber risk among their top three strategic priorities, yet only 6% say they are fully prepared, making trust-by-design a competitive differentiator rather than a baseline.

Architecting cyber defence: transforming the talent deficit into strategic advantage: The global cybersecurity talent gap is a strategic vulnerability, with systemic misalignments including outdated hiring, brain drain, and lack of diversity limiting organisations’ ability to innovate, manage risk, and operate securely across Asia-Pacific.

Australia working with Anthropic over Mythos AI cybersecurity vulnerabilities: Early tests of the model found thousands of major vulnerabilities, prompting the Australian government and central banks of both Australia and New Zealand to monitor the release, with experts warning autonomous AI tools could accelerate sophisticated attacks on banking systems.

Why endpoint security is so important for small businesses: Remote work and BYOD policies have elevated endpoint devices to the frontline of cybersecurity, with ransomware, phishing, and IoT vulnerabilities making endpoint protection a must-have rather than a nice-to-have for businesses of any size.

Data privacy for startups: simple steps to protect sensitive documents: Phishing, poor access management, and lack of encryption are the most common vulnerabilities facing fast-moving startups, but basic controls — encryption by default, role-based access, MFA, and regular training — can build a strong compliance foundation without large budgets.

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Semiconductor

TSMC shows smaller, faster chips without pricey new ASML tool: The foundry’s A13 process enters production in 2029, while its 2028 packaging target of 10 large chips with 20 memory stacks far exceeds Nvidia’s current Vera Rubin design, though heat, material expansion, and cracking remain unresolved engineering hurdles.

ASMPT sees Q2 revenue beat driven by AI semiconductor demand: The Singapore-based assembly and packaging equipment maker guided for Q2 revenue of US$540M-US$600M, above consensus, after Q1 revenue of US$507.9M beat estimates and profit from continuing operations reached HK$326.4M.

Samsung workers rally at Pyeongtaek chip campus ahead of planned strike: About 40,000 employees gathered after wage talks collapsed, with three unions threatening an 18-day strike from May 21 to June 7 demanding that bonuses be funded by 15% of annual operating profit, over 80% of the largest union’s members are in the semiconductor division.

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AI

Singapore’s AI adoption surges, but data complexity raises security risks: Hitachi Vantara’s research shows 66% of Singapore respondents have already succeeded with AI, yet only 23% believe they have industry-leading readiness for long-term ROI, as fragmented data environments and expanding attack surfaces become the defining constraints.

The rise of one-person AI companies and why micro-SaaS is at the centre of it: AI is enabling founders to move from team scaling to system scaling, with micro-SaaS — niche, subscription-based, AI-operated — emerging as the dominant model for lean founders who build systems first and companies second.

Why generative AI is raising the ceiling of custom software ROI: Generative AI has not simplified software development, it has amplified both good and bad decisions, lowering the floor by making more projects viable while raising the ceiling by compressing iteration cycles, with human product judgment remaining the decisive variable.

Why AI projects fail without strong data governance: A 2024 Deloitte benchmark found fewer than one in ten organisations have a governance framework robust enough to track data lineage, bias, and model oversight, a gap that compounds sharply as systems move from pilots to agentic, autonomous production deployments.

How are the companies you invest in leveraging AI?: With 90% of AI startups failing, investors must distinguish between AI-enabled incumbents bolting on AI to existing stacks and AI-native startups built from the ground up, continuous iteration, clear use cases, and defensible market position separating survivors from casualties.

The foundation of Southeast Asia’s tech future: Southeast Asia’s complexity — across languages, cultures, and regulations — is actually a forcing function that produces globally-ready AI startups, while the Singapore-Johor data centre corridor illustrates how physical infrastructure is now shaping where and how AI workloads run.

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Thought Leadership

From fragmentation to shared futures: re-wiring global digital cooperation from an Asian frontline:ASEAN’s 2030 digital masterplan, anchored in the Hanoi Digital Declaration, positions Asia not as a case study on the margins but as a design input for global norms on AI safety, data flows, submarine cables, and digital ID interoperability.

Empowering GEDSI: how OVOP can bring better inclusivity for Indonesia’s farmers: Cassava prices collapsing to below US$0.06 per kilogram expose a governance failure in Indonesia’s agricultural supply chain, one that the One Village One Product framework could fix by giving smallholder farmers a collective market identity that middlemen cannot easily undercut.

AI as a question of national security and independence: Governments building critical services on a handful of dominant AI platforms risk the same fragility seen in WTO paralysis, TPP withdrawal, and financial sanctions, making domestic chip production, data centre investment, and sovereign AI governance a matter of national resilience, not just innovation policy.

Why integrated communications drive stronger business outcomes: In a region expected to generate over US$1T in digital value over the next decade, fragmented PR, content, social, and digital marketing erodes momentum, integration compounds impact by ensuring every channel reinforces a single narrative and generates real-time learning.

On-chain data and Web3 security: insights from industry experts: Panellists at SMU’s security forum agreed that on-chain analytics — combining graph analysis, game theory, and machine learning — gives blockchain security a structural advantage in detecting fraud, validating smart contracts, and transitioning from reactive to proactive defence.

Earth Day: the surprising connection of cybersecurity and sustainability: Strong cybersecurity practices reduce energy consumption through efficient data transmission, extend device lifespans by preventing breach-driven replacements, and protect the critical infrastructure that underpins climate resilience, making cyber hygiene an environmental act as much as a security one.

Asia’s fintech hubs are not just shaping finance; they are redefining economic paradigms: Singapore, China, and India lead, but Vietnam, the Philippines, and Indonesia are rapidly emerging — driven by mobile-first consumers, regulatory sandboxes, and cross-border payment connectivity frameworks that are turning the region into the world’s fintech proving ground.

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The rapid proliferation of AI agents across enterprise environments is reshaping how organisations build and operate software, according to Datadog’s State of AI Engineering 2026 report. Based on telemetry data drawn from thousands of organisations running AI in production, the findings paint a picture of an industry accelerating into complexity—and beginning to encounter the operational limits that come with it.

Two findings stand out. First, the shift toward multi-model strategies is no longer a niche approach; it has become standard practice. Second, AI agents running in production are hitting a hard capacity ceiling, with rate limit errors emerging as the single most common cause of failure.

A multi-model world takes shape

A year ago, OpenAI commanded a 75 per cent share of enterprise LLM usage among Datadog customers. That figure has since fallen to 63 per cent: not because OpenAI lost ground in absolute terms, but because the broader market expanded rapidly around it. The number of Datadog customers using OpenAI more than doubled over the same period, even as Google Gemini and Anthropic Claude gained 20 and 23 percentage points of market share, respectively.

The more telling shift is happening inside organisations themselves. More than 70 per cent now deploy three or more models, and the proportion using more than six models nearly doubled year-on-year. Rather than selecting a single default provider, engineering teams are assembling model portfolios. They are matching lightweight models to extraction and tagging tasks and reserving frontier models for synthesis and reasoning.

This approach offers genuine advantages. Teams can optimise for cost, latency, and output quality at each stage of a workflow. But it introduces significant operational overhead. Coordinating API calls across disparate providers makes it harder to enforce safety and compliance standards consistently and leaves systems more vulnerable when any single provider throttles requests or degrades in performance. The report recommends that teams adopt modular routing mechanisms—such as a gateway service—rather than rely on direct provider API calls scattered across their environments.

Also Read: From fragmentation to shared futures: Re-wiring global digital cooperation from an Asian frontline

The compounding nature of this challenge is also reflected in how organisations manage model versions. Teams are quick to test new releases but slow to retire older models already running in production. Each additional model in the fleet increases evaluation burden and operational risk, a form of AI-specific technical debt that accumulates quietly until it becomes difficult to unwind.

AI agents stall at the capacity ceiling

The second major finding concerns how reliably AI agents perform once deployed. Datadog’s analysis of LLM call failures in customer traces reveals that in February 2026, five per cent of all LLM call spans reported an error with 60 per cent of those errors were caused by exceeded rate limits. The following month, the overall error rate fell to two per cent, but rate limit errors still accounted for nearly a third of failures, totalling approximately 8.4 million incidents in March alone.

The implication is significant. As AI agents take on more complex, multi-step workflows such as orchestrating tool calls, chaining model requests and operating with greater autonomy are running up against the throughput limits of model providers. Reliability, at scale, is becoming a function not just of code quality or prompt engineering, but of infrastructure capacity.

Datadog’s report recommends a combination of operational patterns, including request budgeting and backpressure systems, alongside prompt-level optimisations to reduce unnecessary token consumption.

“AI is starting to look a lot like the early days of cloud,” said Yanbing Li, Chief Product Officer at Datadog.

The parallel is instructive. Cloud computing unlocked enormous capability but demanded an entirely new discipline of operational management. AI agents appear to be following the same trajectory and organisations that invest in observability and reliability infrastructure now may find themselves considerably better positioned as the technology continues to mature.

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Image Credit: Igor Omilaev on Unsplash

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Southeast Asia (SEA) has emerged as Asia’s most fintech-dense subregion, according to a new study by UnaFinancial, an international fintech group headquartered in Singapore. The research maps fintech concentration across 19 economies using a per capita metric, arriving at a weighted average of 14 companies per million people for the subregion.

On the surface, it is an impressive figure. Look closer, however, and the story becomes more nuanced.

The density figures are not simply an artefact of investor enthusiasm or regulatory permissiveness. They reflect something more fundamental: a large and underserved population that traditional banking has consistently failed to reach. Across markets including Indonesia, the Philippines, and Vietnam, significant portions of the adult population remain unbanked or underbanked, relying on informal financial systems for payments, credit, and savings.

Fintech companies operating on mobile-first platforms and alternative credit-scoring models have moved into that gap at considerable speed. The proliferation of digital wallets, buy-now-pay-later (BNPL) services, and peer-to-peer (P2P) lending platforms across the region speaks to genuine consumer demand rather than supply chasing a non-existent market.

Where traditional banks required branch infrastructure, credit histories, and formal employment records, fintech operators have found ways to serve customers who lack them.

Also Read: The US$80K Bitcoin wall: What happens next could define the next quarter

This dynamic matters because it distinguishes SEA from fintech markets, where density is primarily a function of regulatory arbitrage or institutional capital seeking returns. The underlying demand in this region is structural, tied to demographic scale, rising smartphone penetration, and decades of underinvestment in conventional financial infrastructure. That foundation gives the ecosystem a degree of durability that pure capital-driven booms typically lack.

One city is doing a lot of heavy lifting

It is important to note that a substantial portion of the statistics is driven by a single market: Singapore, which registers a density of 619 companies per million, by far the highest of any economy in the study.

Singapore’s position is the product of specific and largely unreplicable conditions. As a city-state with a sophisticated regulatory environment, deep capital markets, and a long-standing policy of attracting international financial services firms, it functions more as a regional headquarters hub than as a representative SEA market. Many of the fintech companies counted in its figures are operationally focused elsewhere in the region or globally, using Singapore primarily as a base for licensing, fundraising, and corporate structuring.

Strip Singapore out of the subregional calculation, and the weighted average would fall considerably. The remaining markets—each contending with fragmented digital infrastructure, varying regulatory maturity, and populations spread across thousands of islands and rural provinces—present a more modest picture.

Also Read: Nium bets on a future where stablecoins swipe like credit cards

Treating Singapore’s density as indicative of broader regional progress risks overstating how far the ecosystem has actually developed in the markets where most SEA residents live.

Apart from that, a high company count per capita says nothing about whether these companies are financially sustainable, adequately regulated, or genuinely serving their stated customer base. Fintech markets that expanded rapidly during the low-interest-rate environment of the early 2020s are now under pressure, with funding harder to secure and profitability timelines under greater scrutiny.

The consumer demand underpinning SEA’s fintech growth matters. But demand alone does not guarantee that the companies formed to meet it will survive long enough to deliver on their promise. As the sector matures, the more meaningful measure of progress will not be how many fintech firms exist per million people. It will be how many of them are still serving those people a decade from now.

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We have spent the last three years in a storm of hype. Every week, a new model that promised to change the world; every month, companies scrambled to integrate whatever appeared to be the “next big thing.” But as we look toward 2026, the wind is changing. We are moving from the era of building the basics of AI to the era of living with it.

The conversation has moved away from how impressive the technology looks in a demo. What matters now is whether it delivers consistent, measurable value to a real human being. Here is my view on the seven major trends that will define our lives in 2026.

Software is no longer the “moat”, data is

For decades, building complex software was like building a castle. If you had the best code, you had the highest walls, and no one could touch you. That era is essentially over. In 2026, writing software will be trivial. AI can write production-ready code instantly. The “Moat” (your defensive business advantage) is no longer the app itself—it is the data inside it.

Imagine two companies launch a tennis coaching app. One has slightly better software; the other has 10 years of proprietary data on how professional athletes serve. In 2026, the second company wins instantly. Data, not software, is the new foundation of advantage.

AI moves off the screen, and into the world

AI is breaking free from the confines of the screen. We are entering an era of ‘presence-based’ hardware – devices are designed not just to respond, but to exist alongside us in specific environments. We are starting to see specialised AI hardware. Think of a small desk device that acts specifically as a “Doctor’s Assistant,” listening to patient symptoms and drafting notes securely.

By 2026, we will see them begin to converge into a new category of consumer hardware- something that might eventually challenge the smartphone itself. The new generation of devices will not simple compute on demand, they will be ambient, contextual and present.

Also Read: Bridging the last mile: How AI can transform agriculture, health, and education in SEA

Small is the new big (SLMs)

For a long time, the race was to build the biggest “Brain” possible (Large Language Models). This is giving way to a more pragmatic approach.

Giant, general-purpose systems are powerful, but they are also expensive, slow and difficult to control. The future belongs to smaller, specialised models trained to do one job exceptionally well. For instance, a bakery does not need AI that understands geopolitics. It needs someone who understands inventory, suppliers, and recipes. Small Language Models make AI systems easier to debug, easier to trust, and easier to compose. This allows multiple focused intelligences to work together.

The “agentic” factory

The way we build products is being redesigned from the ground up. The traditional development cycle of humans designing, coding and testing has already begun to erode. By 2026, teams will increasingly operate through fully agentic workflows.

Humans will define objectives and constraints. AI agents will design interfaces, write code, and attempt to break the system through automated testing. The human becomes the Architect, not the bricklayer. This will make software development faster and cheaper than we ever imagined.

Video becomes precise and controllable

Until now, AI-generated video has been impressive but unreliable. Small changes often produced unintended distortions, limiting serious adoption. In 2026, that changes. Advances in model precision are enabling object-level control within moving video. Creators will be able to modify a single element—such as the colour of a car—without affecting the rest of the scene. Video generation moves from novelty to utility, becoming a precise, surgical tool rather than an unpredictable experiment.

Also Read: The agritech challenge in Indonesia: Can AI and mobile apps enhance productivity?

Fighting the “slop”

The internet is flooding with AI-generated “slop”—low-quality, spammy content that feels like junk food for your brain. Social platforms are finally taking the gloves off. Expect aggressive new measures to filter out this low-effort noise. We will see a premium placed on human-verified reality. “Verified Human” might become the most valuable badge on the internet this year.

Protecting our minds

Perhaps the most sensitive frontier is psychological rather than technical. As AI companies become more conversational, empathetic and available, they can also become more addictive. Imagine an AI friend that knows exactly what you want to hear, 24/7. It is incredibly validating, but can be potentially manipulative.

2026 will be the year of regulation and ethical design. We will see features that prevent AI companions from becoming “digital sugar”—addictive and unhealthy. Just as we have warnings on physical products, we might start seeing “dependency warnings” on 9hyper-realistic AI chat apps. The goal will not be to eliminate companionship, but to ensure it remains healthy.

The verdict

2026 isn’t about AI becoming “smarter”. It is about AI becoming reliable, specific, and safe. It means we stop obsessing over the technology itself and start focusing on what really matters: human potential.

For business leaders, the takeaway is simple. Stop asking “How can we use AI?” Instead, start asking “what unique data do we own that no AI can replicate?” In a stabilised AI world, data, not the technology itself, will be the castle that will matter for the next decade.

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In the global technology landscape, the conversation around artificial intelligence is often dominated by the race for ever-larger models and the dazzling capabilities of generative applications. For many, AI is a feature—a new button to press, a smarter chatbot, an enhanced recommendation engine.

However, for the dynamic and rapidly digitising economies of Southeast Asia, this perspective is not just limiting; it is a fundamental miscalculation. To unlock the projected US$1 trillion in regional GDP uplift by 2030, the region’s startups, enterprises, and policymakers must embrace a more profound paradigm: AI as core infrastructure.

This is not merely a semantic distinction. Treating AI as a feature means bolting it onto existing systems, a superficial enhancement to legacy processes. Treating it as infrastructure means building the entire enterprise on a new foundation, reimagining workflows, business models, and value creation from the ground up.

For Southeast Asia, a region defined by its vibrant complexity, this infrastructural approach is not just an opportunity—it is a necessity.

The complexity advantage: A launchpad for global-ready AI

What makes Southeast Asia the ideal launchpad for the application layer of AI is the very fragmentation often cited as a business challenge. The region’s diversity across languages, cultures, and regulatory frameworks acts as a powerful forcing function, compelling founders to design for scale and adaptability from day one. This environment makes it nearly impossible to succeed with narrow, single-market solutions, inadvertently creating a generation of startups building inherently global-ready AI.

Several real-world problems unique to the region are proving to be fertile ground for this new breed of AI infrastructure companies:

“Being based in Asia is for us a very good starting point because most of the world’s business processes are actually outsourced to Asia in general. So we’re using that base as a foundation for building a global company.” — Christian Schneider, CEO, fileAI

This proximity to complex, real-world workflows provides an unparalleled advantage. While Western counterparts may theorise about enterprise automation, Southeast Asian startups are building it at the source, creating horizontal platforms capable of navigating the intricate realities of global business process outsourcing (BPO), cross-border compliance, and hyper-localised customer engagement.

Also Read: How are the companies you invest in leveraging AI? 

From AI-first to AI-native: A foundational shift

The most forward-thinking companies in the region are already moving beyond simply being “AI-first.” A recent study found that 29% of businesses across ASEAN have now adopted AI, a significant increase from 21% the previous year, marking a 38% year-over-year growth. More importantly, a strategic shift is underway from merely experimenting with AI to fundamentally re-architecting operations to be “AI-native.”

This transition requires what Carro’s COO, Zi Yong Chua, warns against avoiding: building “AI for AI’s sake.” Instead, it demands a focus on tangible business value and an enterprise-ready foundation built on precision, preparation, and people. It means focusing on narrow, high-value use cases that deliver immediate ROI, doing the hard groundwork of data preparation, and investing in talent. This shift is evident in the rise of indigenous and sovereign Large Language Models (LLMs), such as Thailand’s open-source Typhoon model, which are being developed to support local languages and reduce reliance on foreign tech stacks.

The physical infrastructure paradox

The concept of AI as infrastructure is not just a metaphor; it is a physical reality. The exponential growth in AI adoption is colliding with the hard constraints of energy and data centre capacity. A single rack of AI servers can consume 40–60 kW of power, a tenfold increase over traditional cloud computing racks. This has created an infrastructure paradox in the region.

Singapore, long the undisputed data hub of Asia, is running out of power. With data centres already consuming nearly seven per cent of the nation’s electricity, a moratorium was placed on new construction, only recently lifted for operators meeting the strictest sustainability standards. This has pushed demand across the border to Johor, Malaysia, which has rapidly become the region’s new hyperscale frontier, with abundant land and power to support the massive, liquid-cooled data centres required for AI workloads.

This Singapore-Johor corridor is a prime example of how physical infrastructure is shaping the future of AI, creating a cross-border digital ecosystem where data-intensive training and latency-sensitive inference are run in different sovereign territories.

Also Read: AI, seed-strapping, and the new playbook: Why customers are the best VCs

The future is horizontal

As the region’s AI maturity grows, the strategic imperative is shifting from siloed, vertical solutions to powerful horizontal platforms. The most valuable AI companies will not be those that solve one problem well, but those that provide the foundational building blocks for others to innovate upon. This approach, championed by companies like fileAI, focuses on creating proprietary AI components that allow users to construct and automate a multitude of complex workflows.

This platform-based model is the essence of AI as infrastructure. It democratises access to powerful capabilities, enabling a broader ecosystem of businesses to become AI-native without each having to build its own core models from scratch. It is a strategy that recognises that the true value of AI lies not in a single application, but in its ability to become a pervasive, foundational layer of the new digital economy.

For Southeast Asia, the path forward is clear. The startups, corporations, and governments that recognise and invest in AI as fundamental infrastructure—both digital and physical—will be the architects of the region’s future. The trillion-dollar opportunity is not in building more features, but in laying the rails for a new era of innovation.

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