Recently, I was contacted by a local business owner who had spent over US$50,000 on a consulting project. The consultant had promised to help them set up their regional outreach, with the added incentive of helping them secure government grants to fund the expansion.

It sounded like a win-win. Until it wasn’t.

After nearly a year of waiting, the final delivery was a hastily assembled report — the kind of document an intern could have compiled in a week. It included five half-hearted meetings with local business owners, no concrete leads, and no actionable strategy.

Fifty thousand dollars. One year lost. Zero results.

And this isn’t an isolated case. It’s a pattern — a structural market failure powered by information asymmetry.

When trust becomes a liability

For a small business, that isn’t just disappointing — it’s devastating. That US$50,000 could have gone into hiring a sales manager, a business development lead, and expanding marketing outreach with localised content.

Let’s be honest; if you had hired a local sales manager and a BD executive instead of a consultant, would you have accepted the same results? Five casual meetings in a year? A recycled report with no measurable outcome?

Of course not. As a boss, you’d have set clear KPIs — leads generated, partnerships closed, conversion targets met. You’d track progress weekly, demand accountability, and release pay based on performance, even firing underperforming staff to find someone that meet your expectations.

Yet when it comes to consultants, agencies, and external vendors — businesses suspend these same expectations: They pay upfront. They wait for results. They accept excuses.

It’s not because they’re careless — it’s because the system is built on trust without verification.

Also Reda: The architecture of bad deals: Moral hazard in modern business

How businesses can protect themselves

The truth is, you don’t need to be cynical to stay safe — just systematic.

Trust doesn’t have to disappear from business. It just needs structure.

Here’s how to protect your company from the broken outsourcing ecosystem, and make sure every partnership you pay for produces results.

Define clear KPIs before you sign anything

Before you hire any consultant, agency, or vendor, ask: “What does success look like, and how will we measure it?”

If they can’t answer in numbers or milestones, walk away.

A real professional defines outcomes, not adjectives.

Examples:

• “Generate 1000 qualified leads in 3 months” — not “support business growth.”
• “Secure a minimum of 10 verified partner meetings” — not “explore opportunities.”
• “Launch campaign with three deliverables and two iterations” — not “increase brand awareness.”

Why it matters: Vague scope = no accountability. Clarity creates leverage.

Demand transparency in process and people

Ask who is actually doing the work, and how it’s being managed. Don’t settle for brand names or titles; request profiles, portfolios, and project structures.

Questions to ask:

• Who will be the point of contact executing the project?
• Will any part of the work be subcontracted or outsourced?
• What reporting tools or dashboards will we use to track progress?

Why it matters: When you pay an agency, you’re often paying for coordination — not expertise. Transparency helps you see where your money truly goes.

Also Read: Starting a business in 2026: What Founders should consider before chasing capital

Use milestone-based payments

Never pay 100 per cent upfront. Structure payments around delivery checkpoints.

For example:

• 20 per cent deposit upon signing (to begin work)
• 30p er cent after the first milestone (e.g., draft, mock-up, or report)
• 30 per cent after the second milestone (e.g., review and revisions)
• 20 per cent after final approval and delivery

This aligns incentives. If they disappear, you lose 20 per cent — not everything. If they deliver, everyone wins.

Why it matters: Payment schedules turn trust into measurable progress.

Keep a paper trail

Every conversation, deliverable, and update should be documented — in writing. WhatsApp messages and phone calls don’t protect you; written agreements do.

What to keep:

• Contract with clear deliverables and deadlines
• Email summaries after every key meeting
• Shared folders for deliverables and reports
• Written confirmation on change requests

Why it matters: Paper trails turn “he said, she said” into verifiable truth. They’re your best defense if accountability breaks down.

Verify before you trust

Due diligence is not optional, it’s survival.

Before hiring, check:

• References and client testimonials (and call them)
• Portfolio authenticity (ask for proof of ownership)
• Business registration and legal standing
• Presence across verified channels (LinkedIn, website, directory listings)

If something feels off, it probably is. You’re not being paranoid — you’re being professional.

Insist on performance reviews

Treat external vendors like internal staff. Schedule review checkpoints to evaluate output, timing, and quality. Don’t wait until the end to discover failure.

Example: Weekly or bi-weekly status calls, written updates, and deliverable progress reports.

Why it matters: Early detection prevents total collapse.

Use platforms that engineer trust

The easiest way to ensure all of this happens? Use systems built for it.

That’s where Globaloca Asia comes in: we are building a AI powered platform designed to provide transparent vendor sourcing and accountability in project management.

We make transparency, verification, and milestone tracking part of the workflow:

• Every vendor is verified.
• Every project runs through milestone-based escrow.
• Every deliverable is tracked and timestamped on your dashboard.

It’s not about distrust — it’s about design. We don’t replace human relationships. We protect them.

Final thought

Every business owner deserves confidence — but confidence should come from clarity, not charisma.

Define your metrics. Document your expectations. Design accountability into every deal.

Because in the Information Age, trust without verification isn’t partnership — it’s risk you should avoid.

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In this interview, e27 speaks with Kai Yong Kang, Partner at GenAI Fund, Southeast Asia’s first AI-focused fund dedicated to helping large organisations adopt AI responsibly and at scale. Founded by former senior executives from Amazon Web Services, the fund brings a rare inside-out perspective on enterprise transformation, shaped by years of working with governments, multinationals, and high-growth technology companies across the region.

Rather than treating AI as a standalone technology bet, GenAI Fund operates at the intersection of enterprise decision-making, execution, and long-term value creation.

This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and governing AI in real-world settings.

Advancing responsible enterprise AI adoption

e27: Briefly describe what your organisation does, and where AI plays a meaningful role in your work or offering.

Kai Yong: GenAI Fund is Southeast Asia’s first AI-focused fund dedicated to helping large organizations adopt AI responsibly and at scale. The fund was founded by former senior executives from Amazon Web Services, who spent many years building and scaling the AWS startup and enterprise ecosystem across Southeast Asia and Pakistan, growing the regional business by more than 10×. In their previous roles, they worked closely with governments, multinational corporations, and high-growth technology companies, supporting the adoption of new technologies including AI that directly shaped how people work, live, and make decisions.

At GenAI Fund, we operate at the intersection of enterprise decision-making, emerging technology, and long-term value creation. Every day, we see how AI moves from an abstract idea into real systems that power banks, hospitals, manufacturers, and national infrastructure and just as importantly, where AI should not be applied.

Our work spans three closely connected areas. The first is digital transformation. We help large enterprises and governments understand where AI truly creates value and where it does not. Often described as the McKinsey for AI, our role is to guide organizations from initial awareness, through pilot programs, and into scaled, production-level deployment. To date, we have supported more than 100 large enterprises across Asia, including global companies such as Coca-Cola and KFC, as well as regional institutions like UOB and Prudential. Our work involves shaping AI strategy, identifying the right use cases, and connecting organizations with the right partners from a curated network of more than 2,600 AI startups across the region. Alongside this, we have trained over 20,000 government officials and enterprise executives on how to think about AI clearly, responsibly, and pragmatically.

The second area is investment. We invest in AI startups that are ready to work with real enterprises and real constraints, rather than theoretical use cases. Through our flagship FastTrack AI Accelerator program, run in collaboration with NVIDIA, selected startups receive direct investment from GenAI Fund, are matched with guaranteed enterprise engagement opportunities, gain access to up to US$1,000,000 in compute resources, and are supported through live enterprise pilot projects. This approach ensures that innovation is tested in real business environments, where outcomes matter and assumptions are challenged.

The third area is ecosystem development. Because we see the same patterns repeated across industries and countries, we believe it is important to share what actually works. Since 2023, we have hosted 30 AI events and programs across Asia, including Japan, together with partners such as AWS, Google Cloud, NVIDIA, Databricks, and FPT. These initiatives have reached more than 4,000 participants, including C-level leaders, senior executives, and technology founders. This experience culminates in our upcoming regional AI adoption conference, which will bring together 5,000 participants, showcase more than 100 real enterprise AI case studies, and facilitate 500 curated sessions between enterprises and startups, with a clear goal of launching 100 real AI pilot projects.

Also read: AI Pulse Exclusive: How CoBALT is designing AI that teams can actually trust

Accelerating enterprise AI sourcing through matchmaking

e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?

Kai Yong: One concrete way our AI platform creates value for enterprises is by significantly reducing the time required to source, evaluate, and engage qualified AI solution providers for real operational use cases. Traditionally, enterprise AI sourcing is a slow and fragmented process, often taking weeks or months of manual research, referrals, and vendor screening before meaningful discussions begin. Our AI matchmaking platform compresses this cycle into minutes by translating enterprise use cases into structured requirements and automatically shortlisting and ranking AI startups based on technical fit, industry relevance, and deployment readiness.

This was demonstrated at Tasco Innovation Day, where Tasco JSC opened more than 30 live use cases across mobility, automotive, insurance, and infrastructure. Using our platform, Tasco was able to review over 300 global AI startup proposals and move directly into 71 closed-door business meetings with decision-makers within six weeks—something that would typically take several months through traditional sourcing channels.

Beyond a single event, this capability is scaled through our GenAI Open Innovation initiatives, where the platform supports over 100 enterprises and a curated database of 2,600+ AI startups across the region. To date, more than 500 AI startup–enterprise matches have been facilitated, with over 100 progressing into active or launched Proofs of Concept (PoCs), including one FastTrack startup that recently secured a multi-million-dollar enterprise deployment following this AI-enabled sourcing process.

Evolving from investment fund to transformation platform

e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?

Kai Yong: A key decision we made was to evolve from a traditional investment-led model into an end-to-end enterprise AI transformation platform. Early on, we realized that capital alone does not drive real-world AI adoption—especially in Southeast Asia, where enterprises face fragmented data, limited internal AI readiness, and complex procurement processes. To generate meaningful outcomes for both startups and enterprises, we chose to move beyond being passive investors and become an active execution partner across the entire adoption journey—from leadership alignment and use-case definition to pilot delivery and production scaling. This meant investing in our AI matchmaking platform and transformation frameworks, and running “Working Backwards” workshops to help enterprise leaders align on high-impact use cases before any technical work begins.

Working this closely with enterprises requires dedicated time and new capabilities across strategy, technology, and change management, which also led us to build a strong regional network of domain experts, technical advisors, and operators who now support deployments alongside our team. That investment has paid off. We have supported over 100 enterprise AI initiatives into Proof of Concept, created structured pathways for startups to engage real buyers, and helped multiple projects progress toward production deployment and commercial contracts—turning AI from isolated experiments into revenue-generating collaborations.

Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region

Momentum in enterprise collaboration and scaling challenges

e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?

Kai Yong: First, looking back at 2025, what exceeded our expectations most was the level of openness from large enterprises to collaborate deeply with AI startups. We initially anticipated a slow, conservative adoption curve. Instead, over 100 enterprises across banking, mobility, retail, manufacturing, and infrastructure actively engaged our ecosystem with real operational problems. Many moved quickly from exploration to pilots—and in several cases beyond—showing strong urgency to deploy AI for immediate business impact. Most surprisingly, some enterprises were willing to go beyond being customers and explore co-investment opportunities with startups following successful Proofs of Concept. This created a high-velocity environment where startups could secure multi-million-dollar enterprise deals and regional contracts far faster than traditional B2B cycles typically allow. It reinforced our belief that when enterprise innovation is anchored in real use cases and supported by the right execution framework, momentum accelerates rapidly.

Second, the biggest challenge has been moving from Proof of Concept to production at scale. While building a working prototype is often fast, enterprise-wide deployment introduces human and structural complexities that go far beyond the technology itself. Through our work with over 100 enterprises, three recurring friction points emerged:

1. Organizational readiness: AI cannot simply be “plugged in” to existing workflows. Successful deployment requires rethinking processes, ownership, and decision-making. Without this, even strong solutions struggle to take root.
2. Stakeholder alignment: Many projects stall due to gaps between executive intent, technical teams, and frontline operators. Without buy-in from middle management and clear operational ownership, momentum fades after the pilot phase
3. Measuring production impact: While pilots demonstrate technical feasibility, translating results into clear cost savings or revenue impact—aligned to existing business OKRs—is often harder, making it difficult to secure long-term investment for scaling.

Ultimately, we learned that the real work begins after the PoC. Moving from pilot to production is less a technical challenge and more an organizational one. Enterprises that succeed are those that treat AI as an operating model shift—not just a software deployment—and invest as much in change management and execution readiness as they do in the technology itself.

AI adoption as an organisational challenge

e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate

Kai Yong: One lesson leaders and founders consistently underestimate is that deploying AI is primarily a people and operating-model challenge—not a technical one. Many organizations assume that once the tools are in place, adoption will follow. In reality, most AI initiatives stall because teams are not aligned on ownership, workflows, or decision-making. Without clear executive sponsorship, cross-functional accountability, and practical integration into daily operations, even strong AI solutions end up sitting unused. Another overlooked factor is employee perception. When AI is introduced without clear communication, it is often viewed as a threat rather than an enabler. This slows adoption, degrades data quality, and limits feedback—ultimately reducing the effectiveness of the system itself.

At GenAI Fund, we address this by starting with leadership alignment through Working Backwards workshops, building team readiness via AI Readiness bootcamps, and driving behavior change through hands-on Proofs of Concept in our GenAI Open Innovation programs. Practical exposure to real use cases helps demystify AI and builds internal confidence far more effectively than theoretical training. The leaders who succeed with AI in real-world settings are those who invest as much in change management, ownership, and execution readiness as they do in models and infrastructure.

Practical guidance for early AI adoption

e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?

Kai Yong: Based on our experience supporting enterprise AI adoption across Southeast Asia, one practical recommendation for organizations starting or scaling AI is to focus early on two things: organizational readiness and fast, outcome-driven execution tied to business KPIs.

1. Start with people and operating readiness—not technology. Most AI initiatives fail not because of model performance, but because teams are not aligned on ownership, data access, or decision-making. Enterprises should establish clear executive sponsorship, cross-functional ownership (business + IT), and transparent communication with employees. When teams understand that AI is meant to augment their work rather than replace them, data quality improves and adoption accelerates.
2. Drive quick wins linked directly to cost savings or revenue growth—and map them to existing OKRs. Rather than launching broad transformation programs, enterprises should start with narrowly scoped use cases that can demonstrate measurable impact within 60–90 days—such as reducing manual processing costs, improving conversion rates, or accelerating sales cycles. Anchoring pilots to existing organizational OKRs ensures accountability, unlocks budget, and prevents teams from getting stuck in “pilot purgatory.” In practice, organizations that combine readiness with fast, metric-driven execution move significantly faster from experimentation to production—turning AI from isolated projects into a scalable business capability.

Also read: AI Pulse Exclusive: How Explico is building AI teachers can actually rely on

Accelerating enterprise AI deployment timelines

e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?

Kai Yong: Over the next 12 months, we expect a fundamental acceleration in how enterprises move from AI exploration to real deployment. Historically, large organizations take 3–5 years to progress from initial awareness to production-scale AI adoption. At GenAI Fund, our goal is to compress this cycle into a single year by using AI itself to orchestrate the transformation journey.

Our 2026 strategy focuses on accelerating three critical stages:

Awareness: Moving enterprises beyond surface-level AI curiosity through leadership alignment initiatives, masterclasses, and “Working Backwards” workshops—helping executive teams translate operational challenges into prioritized AI use cases tied directly to business outcomes.

Pilot: Leveraging our GenAI Open Innovation model coupled with our AI matchmaking platform to reduce solution sourcing and validation from weeks to minutes, enabling enterprises to rapidly identify qualified AI providers and launch structured pilots within weeks rather than months.

Scale: Transitioning successful Proofs of Concept into production through our FastTrack AI Accelerator, supported by hyperscalers and hands-on execution sprints—providing technical guidance, deployment support, and commercialization pathways to drive enterprise-wide adoption. By integrating these stages into a single operating model, we expect enterprises to move faster from intent to impact.

Rather than simply helping companies “adopt” AI, our platform and programs are designed to help them operationalize AI at speed—turning fragmented experimentation into measurable business outcomes and building AI-native capabilities within one fiscal year.

Building toward GenAI Open Innovation Summit 2026 (GOI Summit 2026)

e27: Anything else you want to share with the audience?

Kai Yong: One final thing we’d love to share is what we’re building toward in 2026. Later this year, we’ll be launching GOI Summit 2026 as our flagship enterprise AI conference—bringing together enterprises, AI startups, hyperscalers, governments, and investors from across the region. Our ambition is to create a regional “big bang” moment that positions Southeast Asia as the fastest AI adoption market globally. GOI Summit is not a standalone event—it’s the culmination of a year-long program designed to drive real adoption.

Leading up to the summit, we are running a series of initiatives including monthly GenAI Builders Meetups, GenAI Open Innovation programs with enterprises, and our scaling accelerator with hyperscaler support to help startups move from pilots to production.

Together, these form our “Road to GOI Summit,” continuously matching enterprises with AI builders, validating use cases, and pushing real deployments throughout the year. At the summit itself, we expect over 5,000 attendees, including more than 100 CIOs from large enterprises, thousands of enterprise executives, and 1,000 AI startups globally. Our goal is to facilitate 500 curated enterprise–startup matchmaking sessions and catalyze at least 100 new AI Proofs of Concept directly from the event, alongside showcasing 100+ real enterprise AI case studies.

More broadly, we see Southeast Asia at a unique inflection point. With rapidly digitizing enterprises, growing AI talent, and increasing urgency to stay competitive, the region has the opportunity to leapfrog into global leadership in applied AI. GOI Summit 2026—and everything leading up to it—is our way of accelerating that future by turning AI ambition into measurable execution.

Enterprise AI adoption at scale

This conversation highlights the accelerating shift from AI experimentation to real enterprise deployment across Southeast Asia. As organisations move beyond pilots toward operational integration, the focus increasingly turns to execution readiness, ecosystem collaboration, and measurable business outcomes. Initiatives that combine investment, transformation expertise, and ecosystem building may play a key role in shaping how AI adoption scales across the region.

For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, subscribe to our newsletter. You can also explore more AI stories here.

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The second quarter of 2026 marks a defining moment for digital assets, as regulatory milestones and macroeconomic shifts converge to reshape the crypto landscape. As someone who has navigated this industry for over fifteen years and advised governments on blockchain policy, I see these upcoming events not as isolated developments but as interconnected forces that will determine whether crypto matures into a legitimate pillar of global finance or remains trapped in regulatory limbo.

The period between late March and early July presents five catalysts that demand close attention, each carrying the potential to unlock capital, clarify rules, or alter the monetary conditions that underpin risk asset performance. Understanding how these events interact requires looking beyond headlines to the structural changes they introduce for investors, builders, and policymakers alike.

The CLARITY Act (April 3, 2026)

Industry leaders anticipate President Trump could sign the CLARITY Act by April 3, 2026, a move that would finally delineate regulatory responsibilities between the SEC and CFTC. This legislation matters because legal ambiguity has long stifled innovation in the world’s largest capital market.

When projects face uncertain enforcement actions rather than clear compliance pathways, talent and capital migrate elsewhere. The passage would reduce legal risks for US-based crypto initiatives and signal to traditional finance that digital assets operate under a predictable framework.

I have long argued that regulation should enable rather than constrain technological progress, and this bill represents a step toward that balance. Reduced uncertainty often precedes capital deployment, so we could see accelerated institutional participation once the rules of engagement become transparent. Projects that previously hesitated to launch in the United States may now proceed, knowing which agency oversees their token structure and what disclosures they must provide.

SEC Crypto ETF Decisions (March 27, 2026)

Just one week earlier, on March 27, 2026, the SEC must issue final decisions on 91 pending crypto ETF applications spanning 24 tokens. Analysts expect verdicts to arrive sooner, given the perceived friendlier regulatory stance, but the deadline itself creates a hard boundary for market expectations.

Approval of altcoin ETFs, such as those tracking Solana or XRP, would replicate the institutional access wave that Bitcoin and Ethereum ETFs initiated. These products serve as regulated conduits for pension funds, endowments, and registered investment advisors who cannot directly hold digital assets.

Also Read: While S&P 500 struggles, crypto’s low correlation to gold and stocks attracts institutional attention

The scale of potential inflows remains substantial, and I view this as a critical test of whether US regulators will allow market demand to shape product availability. Institutional capital moves deliberately, but once allocated, it tends to remain invested, providing a stabilising influence on volatile markets. The applications represent diverse strategies and underlying assets, meaning approvals could broaden exposure beyond the largest cryptocurrencies and introduce investors to protocols with different risk and return profiles.

Tax-Advantaged Crypto ETNs (April 6, 2026)

The United Kingdom takes a different approach, allowing crypto exchange-traded notes to be held in tax-advantaged accounts starting April 6, 2026. This policy change qualifies these instruments for Individual Savings Accounts and self-invested personal pensions, granting millions of retail investors and pension funds a familiar wrapper for crypto exposure.

The significance lies in the stickiness of this capital. Retirement savings and tax-efficient accounts typically exhibit lower turnover than speculative trading capital, potentially reducing volatility over time. From my perspective, this move demonstrates how progressive regulation can expand access without compromising investor protections.

The UK framework may attract global crypto firms seeking a clear European base, especially as other jurisdictions grapple with more fragmented rules. Millions of UK residents now have a straightforward way to allocate a portion of their long-term savings to digital assets, and pension fund managers have a compliant vehicle to explore this emerging asset class within their fiduciary mandates.

Federal Reserve Leadership Transition (May 15, 2026)

Monetary policy leadership also shifts in May 2026 when Federal Reserve Chair Jerome Powell’s term ends on May 15. The nomination process that follows could usher in a more dovish approach to interest rates and balance sheet management.

History shows that easier monetary conditions boost liquidity for risk assets, and crypto has consistently correlated with periods of expanding money supply. A new chair selected by President Trump might prioritise growth-oriented policies, which would indirectly support digital asset valuations. I monitor these macro signals closely because crypto does not exist in a vacuum.

Also Read: Ethereum leads fragile crypto rebound as markets navigate holiday thin liquidity

Global liquidity conditions often outweigh project-specific developments in driving price action, making the Fed chair transition a pivotal variable for the second half of 2026. A shift toward lower rates or faster balance sheet expansion would increase the pool of capital seeking yield, and digital assets often benefit when investors search for returns beyond traditional fixed income.

MiCA Implementation Deadline (July 1, 2026)

Finally, the European Union’s Markets in Crypto Assets regulation comes into full effect on July 1, 2026, requiring all crypto firms operating in the bloc to meet comprehensive compliance standards. MiCA creates a regulatory passport that allows approved entities to serve customers across all member states, but it also raises operational costs and may force smaller projects to exit the market. This consolidation could strengthen the remaining players while enhancing consumer trust through standardised disclosures and reserve requirements.

Having studied regulatory frameworks globally, I recognise that MiCA’s rigour may initially slow innovation but ultimately lend credibility to the sector. Firms that adapt early will gain competitive advantages in the world’s largest single market, while those that resist may find their access limited. The July 1 deadline creates a clear timeline for compliance investments, and companies that treat this as a strategic priority rather than a bureaucratic hurdle will position themselves for long-term growth.

Among these catalysts, the Federal Reserve leadership transition stands out as the most immediate market-moving factor, as it directly influences global liquidity that underpins all risk assets. The interplay between these events will define crypto’s trajectory through 2026 and beyond, rewarding those who understand both its technical and macroeconomic dimensions. Investors who track regulatory deadlines alongside central bank communications will gain an edge in anticipating capital flows and positioning portfolios for the next phase of digital asset adoption.

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Southeast Asia’s AI narrative is usually told as a startup story. The reality is more steel, concrete, power contracts, and undersea cables than pitch decks.

A study titled “AI in Southeast Asia: An era of opportunity” by McKinsey and the Singapore Economic Development Board argues the region is becoming “the world’s AI arena”. The evidence it puts forward is blunt: over US$50 billion has already been poured into AI-ready data centres and cloud infrastructure by hyperscalers such as AWS, Google, and Microsoft — before you even count the second-order spending on connectivity, construction, and energy.

Also Read: Embracing AI in Southeast Asia: The strategy for avoiding cost overruns

This is the new great game in Southeast Asia: East and West stacks competing side by side, often within the same conglomerates. It is less ideology than latency.

The region is being rewired for compute

Singapore is still the anchor point. The report notes the city-state hosts more than 60 AI centres of excellence (CoEs), including those of Alibaba Cloud, IBM, NVIDIA, and Oracle. That density matters: it pulls in talent, vendors, and enterprise workloads, and it turns “AI adoption” into something companies can buy rather than build from scratch.

But the infrastructure story has shifted south. Malaysia is no longer just the “cheaper neighbour”; it is being positioned as a compute destination. The report highlights:

• AWS committing an additional US$9 billion investment in Singapore by 2028 and US$6 billion in Malaysia until 2038
• Google announcing a US$2 billion data centre and Google Cloud region in Malaysia (2024)
• Microsoft investing US$2.2 billion in cloud and AI services in Malaysia
• Alibaba Cloud opening its third data centre in Malaysia (July 2025)
• Tencent Cloud operating a data centre in Jakarta since 2021

That list is not just bragging rights. It’s a signal that enterprises in Jakarta, Bangkok, Manila, and Ho Chi Minh City can now choose between Chinese and US platforms without shipping data halfway across the planet.

Connectivity is being upgraded to match. The report flags the Southeast Asia-Japan Cable 2 (SJC2)going live in mid-2025: a 10,500-kilometre subsea cable designed to boost redundancy and low-latency links for AI and cloud traffic. The point is simple: compute without connectivity is just expensive heat.

“East meets West” is not a slogan — it’s procurement

The report describes a pragmatic regional approach: companies mix providers, sometimes within the same corporate group, to find the best fit for each workload. It cites a telling example from Indonesia: Tokopedia using Google Cloud for live video and analytics at scale, while GoTo Financial migrated Tokopedia’s core infrastructure to Alibaba Cloud data centres in Jakarta.

That kind of split is not indecision. It’s what happens when the region becomes a battlefield where providers must compete on price, services, and sovereignty—and where enterprises want leverage.

In consumer commerce, the competition is even more visible. The report points to TikTok’s re-entry into Indonesia (via a Tokopedia partnership), YouTube and Shopee rolling out YouTube Shopping in Indonesia, and Temu expanding across markets. AI infrastructure is not being built for fun; it is being built to win commerce, payments, and advertising.

The dirty secret: data centres are a risk business

For all the investment headlines, the report is unusually candid about the downside. Data centres come with volatile returns, and the volatility is structural: AI demand may not ramp at the pace the market is pricing in; hardware cycles are accelerating; and GPU prices can fall, turning today’s premium infrastructure into tomorrow’s stranded asset.

Also Read: AI is eating the world and startups are riding the infrastructure wave

Then there is the stuff nobody loves to talk about at launch events:

• Energy demand: AI compute is power-hungry, and grid constraints can become the actual bottleneck to “AI transformation”.
• Water and cooling: many modern data centres require significant cooling capacity.
• Carbon footprint and materials: the report notes rare earth dependencies and emissions pressures.

It also flags a particularly sharp figure: in Malaysia, data centres are expected to account for around 30 per cent of power demand by 2030. That is not a marginal planning issue. That is a national infrastructure question—one that can drag regulators, utilities, and hyperscalers into the same room, whether they like it or not.

Southeast Asia’s startups aren’t the main beneficiaries yet

The infrastructure wave does not automatically translate into a thriving local AI startup ecosystem. The report argues venture funding remains uneven. In 2024, of roughly US$20 billion in venture investment across the entire Asia–Pacific region, Southeast Asia’s young AI firms received as little as US$1.7 billion. The deal count gap is even starker: 122 AI funding deals in Southeast Asia versus 1,845 across APAC.

So yes, the region is becoming an AI arena. But the early winners are not necessarily local builders; they are often the platforms selling compute and the enterprises with the budgets to consume it.

The talent push is becoming part of the cloud pitch

Even hyperscalers know that infrastructure without skills is dead capital. The report quotes AWS’s Vikram Rao: “AI is the biggest opportunity since cloud computing and possibly even since the internet. . . . Our customer base has grown by five times over 2024 to 2025 alone, and with use cases across every industry.”

Rao also says: “We’ve trained over 1.8 million people in the region since 2017. We have initiatives such as AWS Skill Builder, which offers 600 free digital courses available in local languages…”

Also Read: The real risk in ASEAN’s AI race is not falling behind. It is falling apart

Training is not charity. It’s customer acquisition.

What to watch next: power, policy, and pricing wars

Southeast Asia’s AI infrastructure build-out is entering its more challenging phase. The first phase was announcements and land grabs. The next phase is operational reality: power availability, regulatory compliance, and pricing competition across providers.

If the region wants to be more than a consumption market, it will need to pair the hyperscaler build-out with mechanisms that help local firms capture value: funding, procurement access, and cross-border scale. Otherwise, Southeast Asia risks becoming what the supply chain already knows it can be: a world-class production zone—this time for compute.

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The image was created using AI.

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The AI Workflow Competition at Echelon Singapore 2026 is calling builders who can prove their skills through execution, not just ideas. This is your chance to work on real business challenges from Singapore SMEs, build production-ready AI workflow automations, and showcase your solution live at one of Southeast Asia’s premier tech conferences.

If you can design, build, and demonstrate working AI workflows that solve actual operational problems, this competition is for you. Only 150 builder spots are available.

Why this competition is different

Most developer competitions end with pitch decks and prototypes that never see production. The AI Workflow Competition operates on a different principle: execution over ideas, working solutions over concepts, live demos over slideshows.

This is not a pitch competition, idea jam, or innovation theatre. It is a qualification-driven programme focused on execution. If you can’t demonstrate a working workflow, you won’t progress.

Real SME problems, not hypotheticals

Singapore SMEs submit real operational bottlenecks that become the competition’s official challenge statements. These aren’t made-up scenarios designed to test specific technologies—they’re genuine workflow problems costing businesses time, money, and growth potential.

Challenges fall into three categories aligned with SME operational priorities:

• Save-a-Hire (Time Savings): Reduce manual labor and free up team members for higher-value work. Target metric: Hours Saved Per Week. Ideal for admin and support teams.

• Revenue Rocket (Revenue Increase): Enable new revenue streams or increase capacity to process more orders. Target metric: Additional Revenue/Orders. Ideal for sales and marketing teams.

• Cash Flow Guardian (Cost Reduction): Reduce operational costs, minimize waste, and optimize spending. Target metric: Cost Savings Per Month. Ideal for finance and ops teams.

The qualification filter: Only builders progress

Before you work on the main challenge, you must pass a technical mini-challenge proving you can execute. This isn’t a knowledge test—it’s a practical demonstration that you can design and implement working AI workflows within a defined timeframe. Only qualified participants move forward to the build phase.

Workflows are built, deployed, and demonstrated

During the 5-day build sprint (4-8 May 2026), you’ll develop working AI automations with real logic, error handling, and functional outputs. These aren’t wireframes or mockups—they’re deployed workflows that process actual inputs and produce verifiable results.

Live execution on the Echelon stage

Finalists don’t just present on 3-4 June 2026—they demonstrate their workflows running live at Suntec Singapore. You’ll show how your automation handles standard cases, edge cases, and how it self-corrects when things go wrong. The audience sees your solution in action, not just hears about it.

Also read: Is your business stuck in manual mode? It’s time to automate with AI

What builders gain

Work on real business challenges

The SME challenges represent genuine operational problems affecting revenue, efficiency, and growth. Solving these means creating automation that delivers measurable business impact—the kind of work that translates directly to professional credibility and portfolio strength.

Infrastructure credits

Selected participants receive cloud and GPU support during the build phase through competition partners. This includes access to Alibaba Cloud’s Qwen AI for production-ready LLMs, allocated cloud credits for qualified teams, and technical support from solution architects. Bitdeer provides high-performance A100/H100 GPU instances, acceleration capabilities to scale models with enterprise compute, and expert advisory on optimization.

Technical guidance

Architect-level mentorship and technical enablement throughout the build phase. Mentors provide both technical implementation support and business context guidance to help align your solution with SME operational realities.

Career acceleration

Engage directly with sponsors, enterprises, and ecosystem leaders. The competition creates direct pipelines to internships and jobs with partner organizations, while giving you access to Singapore’s tech ecosystem.

Global recognition

Showcase your work live at Echelon Singapore 2026 in front of approximately 10,000 tech professionals, investors, industry leaders, and SME decision-makers. This visibility extends beyond a competition trophy—it’s a platform that opens doors to partnerships, opportunities, and professional connections.

Portfolio credibility that matters

Documented proof that you can deliver production-ready automation against real business requirements. You won’t just list technologies—you’ll show a live workflow solving an actual problem, complete with video documentation of it running at a major industry event.

Who should join

The competition welcomes:

• AI Engineers: Builders with experience in LLMs, RAG, and agentic workflows who can implement intelligent automation that adapts to business context.
• Full-stack developers: Developers capable of building end-to-end integrations and APIs, connecting disparate systems into cohesive automated workflows.
• No-code experts: Masters of n8n, Zapier, Make, and automation tools who can rapidly build and deploy functional solutions without traditional coding.
• Student innovators:University talents ready for real-world challenges who want practical experience beyond classroom projects.

How the competition works

Call for participants (12 February – 17 April 2026)

Open application period for AI builders. Complete the builder application form and provide details about your technical background and relevant experience. Individual and team applications are accepted. You may apply individually or as a team—individual applicants may be matched with other builders if needed.

Virtual workshops (27-29 April 2026)

Hands-on sessions to prepare participants for the build phase. Technical workshops and orientation sessions conducted virtually provide practical preparation and technical enablement.

Build phase (4-8 May 2026)

A 5-day virtual sprint where builders design and develop production-ready AI workflows. You’ll work on one of the provided SME challenge statements with structured mentorship, platform credits (for selected participants), and direct SME collaboration.

Demo day ( 3-4 June 2026)

Finalist teams present their completed AI workflows live at Echelon Singapore 2026, Suntec Singapore. Demonstrations show working solutions processing real inputs, handling errors, and delivering business outcomes.

Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region

Key questions answered

Do I need a team to apply?

No. You may apply individually or as a team. Individual applicants may be matched with other builders if needed.

What tools can I use?

The competition is platform-agnostic. Builders may use any tools or frameworks, including LLM APIs, workflow automation tools, or custom code. The focus is on the solution’s impact and reliability, not the technology stack. However, your workflow must be deployable and maintainable by the SME partner.

Will infrastructure support be provided?

Yes. Selected participants receive partner credits to support development during the build phase, including cloud infrastructure, GPU compute, and technical guidance.

Do I need to be based in Singapore?

No. The competition focuses on Singapore SME challenges, but builders may participate remotely. Finalists will be required to present during the live finals at Echelon Singapore 2026, with presentation arrangements to be confirmed.

What is the time commitment?

Selected builders must attend the virtual workshops (27-29 April), commit to the 5-day build sprint (4-8 May), and participate in the live demo if shortlisted as a finalist (3-4 June).

Is it free to apply?

Yes. There is no application or participation fee.

What do finalists receive?

Finalists demo live at Echelon Singapore 2026 and receive certificates, partner credits, and ecosystem exposure including direct access to sponsors, SME partners, investors, and industry leaders.

Who will judge, and what are the criteria?

Judging focuses on practicality (can this be implemented?), business impact (does it solve a real pain point with measurable ROI?), and deployability (can the SME maintain this workflow?). Judges include industry experts, SME representatives, and technology leaders.

Are the workshops virtual?

Yes. Technical workshops and orientation sessions are conducted virtually prior to the build phase.

Why now matters

AI workflow automation is moving from experimental to essential for Singapore SMEs. Businesses need practical solutions that improve efficiency, reduce operational friction, and enable growth without proportional headcount increases.

The builders who can deliver reliable, deployable solutions with clear business impact will define the next wave of enterprise automation. This competition gives you the platform to prove you’re one of those builders.

Registration closes 17 April 2026. Only 150 builder spots available.

Register as a builder and showcase your solution to the world.

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About the AI Workflow Competition

The AI Workflow Competition is an e27-led programme showcased at Echelon Singapore 2026, designed to explore how AI workflow automation can solve real operational challenges faced by small and medium enterprises (SMEs). Unlike traditional hackathons or idea-based challenges, this programme focuses on execution—bringing together SMEs, builders, mentors, and ecosystem partners to create practical, deployable automation solutions. For more information, visit echelon.e27.co/ai-workflow-competition.

 

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The Singapore Budget 2026 has placed AI at the centre of the nation’s economic transformation, signalling, according to industry leaders, a decisive shift from experimentation to execution.

From the creation of a National AI Council, chaired by Prime Minister Lawrence Wong, to the launch of sector-specific AI “missions”, the Budget outlines a coordinated push spanning governance, infrastructure, enterprise support, and workforce development. For technology executives across the region, Singapore Budget 2026 represents more than incremental policy refinement. It is an attempt to hardwire AI into the Republic’s long-term competitiveness.

At the heart of Singapore Budget 2026 is the establishment of a National AI Council to steer policy, coordinate research, and align regulation with investment promotion. The council will oversee new AI missions across advanced manufacturing, connectivity, finance and healthcare, ensuring AI development remains safe, responsible and aligned with national priorities.

Niko Walraven, Area VP – APAC at Neat, said the move confirms that “Singapore is no longer just AI-curious. It is AI-first”.

“The establishment of the National AI Council … signals a clear commitment to securing a strategic advantage in a fractured global economy,” he said, pointing also to the S$1 billion injection into Startup SG Equity as reinforcing that ambition.

For Megan Hughes, Managing Director and Vice President, JAPAC at HubSpot, central coordination is critical. She noted that aligning technological innovation, industry expertise, and public sector regulation will ensure Singapore’s AI transformation is implemented cohesively and responsibly.

Also Read: Southeast Asia’s AI boom is built on steel, not startups

Sector missions move beyond chatbots

A defining feature of Singapore Budget 2026 is its sectoral focus. The AI Missions targeting advanced manufacturing, connectivity, finance, and healthcare aim to accelerate development, testing and scaling of solutions from best-in-class factories to automated airport and seaport operations.

Haresh Khoobchandani, Vice President, APAC & Japan at Autodesk, welcomed the shift towards deeper industrial transformation.

“Design & Make industries like construction and manufacturing don’t just need general chatbots,” he said. “They need high-level coordination, strategic direction, and support that these new initiatives promise.”

The emphasis on sector missions suggests the government is targeting productivity and resilience in industries that underpin Singapore’s hub status. Rather than broad AI evangelism, Singapore Budget 2026 signals intent to embed AI where it delivers measurable economic value.

Beyond governance, tech leaders highlighted enhanced support schemes as a key enabler of adoption.

The new “Champions of AI” programme will offer tailored support for firms seeking comprehensive AI transformation, including organisational change and workforce training. Meanwhile, the Enterprise Innovation Scheme’s 400 per cent tax deduction will be expanded to cover qualifying AI expenditures, capped at S$50,000 annually for 2027 and 2028. The Productivity Solutions Grant will also be broadened to include more AI-enabled solutions.

Andrew McCarthy, GM of ANZ, Southeast Asia and India at Notion, described the measures as proof that “we’re no longer asking if AI works — we’re asking how to make it work systematically across every sector.”

However, he cautioned that technology alone is insufficient. Notion’s research shows 70 per cent of Singaporean workers find AI tools lack company context, while 72 per cent spend time editing generic outputs.

Also Read: Top 5 best ERP software for building material business in Singapore | 2026 guide

“The issue isn’t AI capability — it’s increasing busywork due to fragmented systems,” McCarthy said. “Budget 2026 provides the incentives. Now businesses must use them strategically.”

Hughes echoed this concern, arguing that AI adoption is often slowed not by lack of ambition but by weak data foundations. Citing HubSpot’s 2025 Singapore State of Business Growth Report, she said organisations with fully integrated systems are ten times more likely to outperform peers.

“A unified data foundation provides the context that AI needs to deliver outcomes that leaders can stand behind,” she said.

Infrastructure, testbeds, and human-AI collaboration

Singapore Budget 2026 also includes plans for a new AI park at one-north, developed by JTC near existing research clusters. Designed to host startups, researchers and companies, the park will support test-bedding and scaling of AI solutions.

This initiative forms part of a broader S$37 billion economic transformation package spanning connectivity, sustainability and AI. Jornt Moerland, Senior Vice President APAC at Siemens Data & AI, described the investment as a “critical inflexion point”.

“Singapore is no longer observing the AI revolution but is institutionalising it,” he said. “This commitment cements Singapore’s status as a global testbed.”

Workforce transformation is another cornerstone of the Singapore Budget 2026. The revamped SkillsFuture platform will offer clearer AI learning pathways, while Singaporeans who take selected AI courses will receive six months of free access to premium AI tools.

Khoobchandani called the complimentary access a “practical answer” to the risks posed by AI if talent development does not keep pace.

Also Read: DBS doubles down on private markets with US$110M AI IPO fund

Moerland added that empowering non-technical leaders and frontline staff with AI literacy is essential to scaling adoption. “AI is a strategic imperative, requiring broad adoption to unlock its potential,” he said.

Walraven also welcomed the closer integration of SkillsFuture and Workforce Singapore, arguing that future-ready skills in practical AI capabilities will help create a more inclusive, human-centric hybrid workplace.

Taken together, the measures in Singapore Budget 2026 signal a coordinated effort to move AI from pilot projects into everyday workflows.

For Hughes, the goal is clear: “When coordination and capability come together, AI can move beyond experimentation and into everyday workflows.”

Tech leaders broadly agree that the Budget has solved the “why”. The next phase — embedding AI into core operations with proper data context, unified systems and trained talent — will determine whether Singapore Budget 2026 delivers on its AI-first promise.

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The lead image in this article was generated by AI.

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We are currently suffering from a severe case of data paralysis. Every company, from the massive multinational corporation (MNC) to the smallest ambitious startup, is collecting data at a furious, often pointless, pace. Analysts are busy building mountains of spreadsheets and dashboards, yet most of these digital monuments are utterly inert. They sit there, accurate, detailed, and completely unmoved.

The problem isn’t the quality of the data; it’s the quality of the delivery. The human mind is hardwired for narrative, not for parsing endless rows of numbers. When data is presented in a vacuum, a bar chart here, a KPI there, it fails to cross the crucial bridge from information to action.

The most valuable skill in the modern economy is no longer the ability to collect the data, but the ability to translate those cold, hard facts into a compelling story. This combination of data, visuals, and imagination is the secret weapon for gaining a decisive edge, and it’s one that too many businesses foolishly neglect.

The fatal flaw of the facts

Data, nakedly presented, is just noise. It lacks context, consequence, and character. Why should the board fund this new project? Why should a customer switch allegiance? Simply pointing to an upward-trending line is rarely enough to compel a significant, risky decision.

A compelling story provides the context. It transforms a “20 per cent increase in user retention” into the story of Sarah, the customer whose life was made demonstrably easier by your product. It transforms a “drop in regional sales” into a cautionary tale of a specific operational failure in that territory, complete with a villain (the outdated process) and a hero (the proposed solution).

Also Read: Are social sellers missing an important piece of the data puzzle?

This isn’t just fluffy window dressing. It’s the mechanism by which complex data is stripped of its intellectual friction and allowed to penetrate the decision-making centres of the brain. When you tell a story, you leverage emotion and memory, ensuring that the data point is not just acknowledged, but retained and acted upon.

An advantage for all sizes

The mistake many make is assuming that sophisticated data storytelling is only necessary for the vast, labyrinthine structures of MNCs. They imagine a high-priced consulting firm producing slick animations for a global strategy meeting.

The truth is that this skill is more critical for a small business or startup.

• For the startup: Your entire existence is a gamble. You don’t have a decades-long track record or massive cash reserves to build trust. Your data story (how you use the numbers to visually prove product-market fit, articulate your unique traction, and forecast your blitzscaling) is the single most important tool for securing investment and validating your idea. Your pitch deck is useless if it’s just charts; it must be a visually driven narrative that makes the investor feel the urgency of the opportunity.
• For the small business: Your competitive advantage against the monolithic chains is often superior service and customer understanding. Data storytelling allows you to show your community how you serve them specifically. By visually communicating the impact of your local buying habits or the efficiency of your bespoke service, you create a powerful, localised narrative that the distant MNC cannot touch.

In a market saturated with similar products, the ability to visually articulate why your data leads to a better future is the defining competitive edge.

Also Read: The hidden barrier to AI sustainability: Why clean data matters

The necessity of the expert interrogator

This capability rarely resides naturally within a standard data science team. Data scientists are experts in accuracy and cleaning. They are not necessarily experts in persuasion and imagination.

To gain an edge, organisations must stop treating data visualisation as a final step done by a junior analyst. They must invest in experts, whether internal or external, who specialise in data narrative. These are the people who understand psychology, design, and statistics equally. They know how to take the complex output of your data team and craft a presentation that is not only accurate but also utterly unforgettable.

These experts are the interrogators who know which three numbers truly matter among the three million you collected, and who can design the visual framework that guides the viewer’s eye, ensuring they absorb the intended conclusion without effort. The money you spend on experts to create a compelling, visually integrated story will yield a higher return on investment than the money spent acquiring yet another generic data tool.

Stop settling for reports that are technically correct but practically ignored. The future belongs to those who understand that in the human sphere of influence, logic informs, but stories compel.

If your business is defined by its data, why are you trusting its most critical interpretation — the story — to chance?

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Tourism industries worldwide face the same question: can automation coexist with warmth?

As destinations rush to deploy AI, self-service systems, and digital platforms, many are discovering an unintended consequence – efficiency gains at the cost of emotional connection. Singapore is taking a different approach: using technology not to replace people, but to create capacity for more meaningful human interactions.

The global challenge

Industry research suggests that while travellers appreciate efficiency, many remain dissatisfied with impersonal digital interactions. A 2025 Booking.com study found that a majority of travellers recognise AI’s role in making journeys easier. At the same time, research by Simon–Kucher indicates growing frustration with automated recommendations that fail to understand context, emotion, or intent.

These findings echo a warning from Wang Peng, an associate research fellow at the Beijing Academy of Social Sciences, who cautioned against blindly pursuing AI interaction at the expense of human warmth when discussing shifts in China’s tourism landscape. The message is clear: automation delivers speed and scale, but on its own, it struggles to replicate empathy, nuance, and trust.

As destinations worldwide automate, the main challenge would be to deploy technology without losing warmth.

Singapore’s approach

Singapore faces this challenge head-on. With 6,700 tourism vacancies in Q2 2025 and intense competition for talent, automation has become essential for maintaining personalised service at scale.

The Singapore Tourism Board’s (STB) Tourism 2040 strategy prioritises sustainable development while addressing evolving traveller preferences. The strategy targets between US$47 billion and US$50 billion in tourism receipts by 2040 while maintaining Singapore as a destination that residents proudly advocate for. To achieve this, the tourism industry is embracing automation strategically by using it to create capacity for human connection. 

Also Read: Singapore’s AI edge depends on slack

Freeing staff for what matters

Singapore’s indoor skydiving attraction iFly has self-service ticketing kiosks with facial recognition features that automate check-ins and payment processing for photos and videos taken during flights. Data analytics track visitor demographic profiles, allowing staff to personalise interactions and tailor experiences. What once required 10-15 minutes of staff time explaining waiver forms and processes now happens digitally, freeing staff to focus on other tasks like safety briefings and creating the reassuring presence that makes the experiences memorable.

At Mandai Wildlife Reserve, the mobile app enhances guests’ visits by providing digital wayfinding where visitors are presented with multiple routes to choose from, presentation reminders, and curated park itineraries. By handling these informational needs digitally, the app eliminates routine queries, allowing staff to focus more on deeper engagement and delivering more personalised interactions with visitors. This creates spontaneous educational moments that visitors remember long after leaving the park.

Behind the scenes, Gardens by the Bay’s Smart Garden project uses a consolidated IoT dashboard for real-time monitoring of plant health and environmental conditions. Over 250 wireless sensors track parameters like temperature, humidity, and soil moisture, while 200+ smart lamps are equipped with an intelligent lighting system that sends instant malfunction alerts.

In addition, wireless tree tilt sensors on mature specimens monitor structural stability, enabling early intervention. Instead of manual inspections, the horticulture team can access precise data on dashboards and prevent operational failures that may affect visitor experiences and cause disappointment. The time saved goes towards hands-on plant care and exhibit curation, ultimately enhancing the quality of each visit.

Immersive tech that creates a connection

Singapore is also exploring how Augmented Reality (AR) and Virtual Reality (VR) can create richer contexts for meaningful interactions.  

The ArtScience Museum exemplifies this approach by using VR and AR to deepen visitor engagement with complex themes. Its VR Gallery offers immersive experiences like The Drone Shepherd by Liam Young, a VR graphic novel set in Planet City, where Earth’s entire population lives in one hyper-dense city. Built from hand-painted illustrations, the work allows visitors to viscerally experience climate collapse.

While VR immerses visitors in complete worlds, AR invites them to participate in creating one. Installations like Deep Field by Tin&Ed allow visitors to design plants that bloom into augmented reality structures while hearing soundscapes of extinct species. This connects them emotionally to environmental loss.

Also Read: How I built Singapore’s 8th fastest-growing company without investors

These immersive experiences create space for conversations and questions beyond technology itself. Museum staff can assist with technical setup and support interactive workshops, creating touchpoints that transform solitary digital experiences into shared human moments.

What’s next

Singapore’s progress shows promise, but like destinations worldwide, the work continues.

Current priorities include predictive infrastructure monitoring, immersive experiences that do not require heavy physical installations, integrated data platforms across attractions, and safer methods for maintenance work in sensitive environments, including underwater operations that protect both staff and marine life.

Each of these challenges reflects a broader opportunity: to embed warmth more deeply into tourism systems by ensuring technology works quietly and reliably in the background.

At its best, automation does not replace human connections; it enables them. Every routine task automated is a human interaction made possible. Every operational failure prevented is an experience preserved. Every immersive tool thoughtfully deployed becomes a bridge, not a barrier, between people.

As Singapore continues to open real tourism environments for pilot deployments, initiatives such as the Singapore Tourism Accelerator play a role in connecting technology providers with operators to test deployable solutions that balance efficiency with empathy. The future of tourism innovation may be built on advanced systems — but it will be felt most strongly in the human moments they protect.

Applications for the Singapore Tourism Accelerator (STA) Cohort 8 are now open, addressing several priority challenges across the tourism sector. The application window closes in February 2026.

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The modern startup ecosystem has a paradox: we have more tools than ever, yet founders feel more disconnected than ever. The problem is not a lack of solutions. The problem is too many of them.

As someone who spent two years building a startup that ultimately failed, I learned this lesson the hard way. The ecosystem is not broken because we lack resources. It is broken because those resources are scattered across dozens of platforms that do not talk to each other.

The fragmentation problem facing Founders today

Consider the typical founder journey. You need to find a co-founder, so you post on LinkedIn, join Slack communities, and browse dedicated matching platforms. You need to hire, so you use AngelList, Wellfound, and traditional job boards. You want to raise funding, so you chase investors through warm intros, Twitter DMs, and pitch events. Each need requires a different platform, a different profile, and a different approach.

This fragmentation creates three major problems for the startup ecosystem.

• Time lost to platform hopping

Founders already wear too many hats. Adding platform management to the list steals hours that could go toward building product, talking to customers, or iterating on strategy. Every new tool requires onboarding, profile creation, and ongoing maintenance. The cognitive load adds up quickly.

• Signal buried in noise

When opportunities are spread across multiple platforms, finding the right match becomes exponentially harder. Investors miss promising startups because they are not on the right platform. Talented developers never see job posts because they are in the wrong Slack group. Co-founders who would be perfect together never connect because they use different tools.

• Vanity metrics over real performance

Most startup platforms rank companies by follower counts, funding announcements, or self-reported metrics. This creates perverse incentives where marketing prowess matters more than actual business performance. Founders optimise for visibility instead of value creation.

Also Read: When nation-states shape startup outcomes

What the startup ecosystem actually needs

The solution is not another niche platform. The solution is consolidation around verified value.

Imagine a single hub where founders can post any type of opportunity, whether they are hiring, seeking co-founders, raising capital, or selling their company. Where investors can discover startups ranked by actual revenue, verified through integrations with payment providers like Stripe. Where talent can browse opportunities without creating five different accounts.

This is not a radical idea. Other industries have already made this transition. E-commerce consolidated around a few major marketplaces. Professional networking consolidated around LinkedIn. The startup ecosystem is overdue for similar consolidation.

Principles for a unified startup platform

Any platform attempting to unify the startup ecosystem should follow several key principles.

• Verified metrics over self-reported data

Credibility should be earned through demonstrated performance, not claimed through marketing. Integration with revenue providers allows startups to verify their traction. This creates trust and helps investors, talent, and partners make better decisions.

• No gatekeeping on discovery

Connection requests, paywalls on viewing profiles, and algorithmic filtering all create artificial barriers. A truly open ecosystem lets anyone browse opportunities, explore profiles, and reach out directly. The startup world has enough barriers already.

• Multi-purpose profiles

Instead of maintaining separate identities across platforms, founders should have one profile that serves multiple purposes. The same profile can attract co-founders, investors, employees, and acquirers. Context determines how the profile is discovered, not which platform it lives on.

Also Read: Why startups need mobile apps to thrive in today’s competitive market

Lessons from two years of startup failure

My perspective comes from experience, including failure. I spent two years with co-founders building a mobile game. We travelled across Europe for events and flew to Australia to expand our network. We did everything the startup playbook said to do.

What I learned is that the ecosystem rewards activity over results. Posting updates, attending events, and growing followings felt productive, but moved us no closer to product-market fit. The fragmented ecosystem made it easy to stay busy without making progress.

This failure shaped a different philosophy: build fast, test immediately, be transparent about what works and what does not. Fail for others so they do not have to make the same mistakes.

The path forward for startup infrastructure

The startup ecosystem in Southeast Asia and globally is maturing. As it matures, the infrastructure supporting it should mature as well. This means moving from fragmentation toward consolidation, from vanity metrics toward verified performance, and from gatekeeping toward open access.

Founders deserve better than jumping between ten platforms to accomplish basic tasks. Investors deserve better than sorting through unverified claims. Talent deserves better than scattered job posts across incompatible systems.

The startup ecosystem does not need another tool. It needs fewer tools that do more. The question is not whether this consolidation will happen, but when and who will lead it.

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Nasdaq-listed Society Pass (SPI), which provides a data-driven loyalty platform,  and its former CMO Thomas O’Connor emerged from a bruising, multi‑year New York trial on February 5 with a mixed verdict that recalibrates liability, equity and control over contested stock issuances.

Justice Joel M. Cohen’s decision upholds earlier valuations of pre‑IPO warrants but applies New York’s “faithless servant” doctrine to strip O’Connor of pay and future vesting beyond July 2019.

Also Read: Dennis Nguyen steps down as Society Pass CEO amidst court cases, SEC probe

At the same time, the court rescinded key contracts executed by O’Connor’s Singapore vehicle, CVO Advisors, for fraudulent inducement. Several headline counterclaims by Society Pass were dismissed for lack of provable damages.

Background and the valuation fight

The dispute traces to a September 2023 ruling that O’Connor had validly exercised a Common Stock Purchase Warrant for 1,148 shares. The valuation of those warrant shares became the case’s financial core. After a 10‑day valuation hearing in late 2024, a Special Referee placed the per‑share value at US$5,763, a figure Justice Cohen confirmed in July 2025. That valuation implied roughly US$6.62 million for the block; with court‑sanctioned penalty interest set at 9 per cent per annum, accrual has pushed the total owed to about US$11.5 million, according to O’Connor’s counsel.

The judgment landed at a precarious juncture for Society Pass. Its SEC Form 10‑Q showed only US$10.9 million in cash; the company had been dropped from the Russell 2000 and its stock had plunged from a US$77 peak to roughly US$0.40. Court filings also disclosed an SEC investigation and a separate suit from ex‑CTO Rahul Narain seeking about US$1.3 million. Society Pass appealed the earlier partial judgment; that appeal remained pending when the trial verdict issued.

Key holdings from the trial

Faithless servant doctrine eliminates pay claims: Justice Cohen found that beginning in June 2019 O’Connor effectively abandoned his CMO responsibilities, including an unauthorised five‑week trip to Japan that produced no meaningful business outcomes. More seriously, in August 2019 he met a lead prospective investor, Lester Chan of Fund Singapore (an equity & lending-based crowdfunding platform), where the court found he disparaged Society Pass CEO Dennis Nguyen, expressed doubt about the company’s viability, and pitched an alternative investment. Under New York law, an employee who becomes disloyal forfeits compensation. The court therefore dismissed O’Connor’s claims for unpaid salary and severance.

Warrant rights preserved, but truncated: The judge reaffirmed that the Common Stock Purchase Warrant was a standalone agreement not tied to employment KPIs. O’Connor had already been awarded US$6,615,934 for shares that vested before June 2019. The trial award adds US$824,109 for shares vesting in June-July 2019. Crucially, any shares vesting from August 2019 onward were forfeited because the court pegged the start of his disloyal conduct to that month. The ruling effectively preserves equity that vested before the loyalty breach and cuts off later vesting.

CVO contracts rescinded for fraudulent inducement: The court found O’Connor misrepresented CVO’s ownership status when he executed a Subscription Agreement and a Software Development Agreement in November 2018, claiming to be CVO’s sole owner despite owning zero shares until May 2019. Because those misstatements materially induced Society Pass to contract, both agreements were rescinded and CVO must return all SPI shares issued under them.

Also Read: Ex-CTO drags Society Pass into court for “breaching employment contract”, seeks over US$1.3M in damages

SPI counterclaims fail for lack of proof: Society Pass argued O’Connor’s conduct reduced Fund Singapore’s expected investment from an anticipated US$10-15 million to just US$1 million. The court rejected that causation claim, pointing instead to SPI’s failures to secure a digital wallet license and a strategic partner. Claims for breach of fiduciary duty and unjust enrichment were dismissed where the company failed to present concrete damages or financial records proving improper personal spending.

Financial and corporate fallout

The judgment leaves SPI exposed to a material monetary liability tied to the warrant valuations — US$6.62 million already recognised plus US$0.82 million now, each carrying interest. O’Connor’s legal team places the aggregate with interest at roughly US$11.5 million. That sum is significant against SPI’s limited cash reserves and battered market capitalisation.

Rescinding the CVO agreements reduces dilution by clawing back shares issued under those contracts, but it does not erase SPI’s monetary exposure for warrants that vested through July 2019. The faithless‑servant finding curtails salary and severance outflows and narrows future vesting, providing the company partial financial relief.

Operationally, Society Pass must undertake a cap‑table cleanup to unwind share issuances, update registers and manage potential secondary disputes. Collections and enforcement of the monetary awards will hinge on appeals, interest computations, and any negotiated settlement. With several of SPI’s counterclaims dismissed, the company gains narrative relief but still faces the harder task of stabilising liquidity and restoring investor confidence.

Legal and governance lessons

The case is a stark illustration of how New York courts apply the faithless‑servant doctrine to senior executives, the separability of certain equity instruments from employment terms, and the perils of inaccurate or misleading representations in cross‑border deals. For founders and boards, the ruling underscores the need for meticulous documentation of ownership, clear linkage (or separation) of warrants from employment KPIs, and close oversight of executives’ conduct with investors.

Also Read: US court orders Society Pass to pay pre-IPO shares to co-founder and ex-CMO; company under SEC probe

Conclusion

Justice Cohen’s decision is a split result: O’Connor retains significant pre‑August 2019 equity value but loses salary and future vesting for disloyal conduct, while CVO’s contracts are voided for fraud. Society Pass limits several major counterclaims and claws back shares, yet remains on the hook for warrant‑linked awards and mounting interest — leaving both parties with partial victories and substantial follow‑on risks.

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