Singapore’s AI national strategy has received its most significant tune-up since the launch of NAIS 2.0 in December 2023. Describing the update as a “double-click rather than a system reboot,” Minister for Digital Development and Information Josephine Teo announced 10 refreshed priorities at the ATxSummit on May 20, underscoring the government’s intent to accelerate rather than reinvent its AI agenda.

The refreshed priorities follow the establishment of the National AI Council in February 2026, chaired by Prime Minister Lawrence Wong, which was set up to provide strategic direction for Singapore’s AI agenda. The update is organised around three focus areas: deepening sectoral and public sector transformation, mainstreaming AI adoption across the broader economy, and cementing Singapore’s position as a regional AI hub.

A central pillar of the plan is the launch of National AI Missions in advanced manufacturing, financial services, connectivity, and healthcare — four industries that are critical to the country’s economic backbone. Together, those four sectors contributed roughly 40 per cent of Singapore’s GDP in 2025. The strategy envisages AI being embedded more deeply across government agencies as well, with the aim of accelerating public sector transformation and improving citizen services.

Workforce development features prominently. The National AI Impact Programme targets 10,000 SMEs for meaningful AI adoption, while the Champions of AI programme offers more targeted support for enterprises seeking to go further. Broad-based capability-building and what the strategy terms “AI bilingual talent” — professionals fluent in both domain expertise and AI application — are positioned as foundational requirements for the transformation ahead.

Also Read: Singapore lands OpenAI’s first lab outside the US with US$225M commitment

On the infrastructure front, Singapore will expand local research compute capacity from 2026 through the National Supercomputing Centre’s ASPIRE 2B supercomputer, as part of a planned national advanced compute, AI, and scientific computing platform. A Digital Infrastructure Act is also expected to be tabled in Parliament to set baseline sustainability standards for data centres.

Internationally, Singapore is leaning into its role as a convener. Minister Teo was candid about the country’s constraints, noting that Singapore’s domestic market alone may not warrant the level of attention it receives, but that its value lies in the global networks it is connected to and its track record in trusted technology adoption.

Business analysis: Opportunity tempered by execution risk

From a business perspective, the updated strategy presents a compelling and largely coherent value proposition. The targeting of four high-GDP sectors with dedicated AI Missions gives multinationals and local enterprises alike a clearer signal of where government support, regulatory clarity, and talent pipelines will converge.

The commitment of more than S$1 billion to public AI research and talent development from 2025 to 2030 — announced earlier this year — provides meaningful financial scaffolding for the private sector to build upon. NVIDIA’s new Research Lab in Singapore and the Punggol Digital District are early markers that global tech players are already responding to Singapore’s hub ambitions.

Also Read: Why you should be hiring humans when others are hiring AI agents

Yet the strategy is not without its risks. The sheer breadth of the 10 refreshed priorities — spanning compute, data governance, workforce capabilities, international partnerships, and sectoral transformation — raises legitimate questions about execution capacity and coordination across agencies.

Smaller enterprises, in particular, may find the SME-focused programmes insufficient to keep pace with the pace of AI disruption, especially as global competitors ramp up investment at scale. Singapore’s acknowledged constraint of a small domestic market also means that the hub ambitions are ultimately contingent on sustained foreign investment and geopolitical stability — factors that lie well beyond any national strategy document. For businesses watching closely, the direction is right; the proof will lie in delivery.

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Quantum themes are now everywhere—from multiverse plots to Schrödinger’s cat, the famous paradox of a cat that is both alive and dead until observed. This surge in storytelling isn’t just cultural curiosity. Experts see it as a sign that imagination is beginning to meet scientific feasibility.

When Sci-Fi becomes signal

The trajectory echoes that of artificial intelligence. AI appeared in fiction for years, but only reshaped industries after deep learning breakthroughs in 2012. Quantum computing may be approaching a similar tipping point.

Until recently, quantum ideas lived mostly in theory. But researchers are now building real systems—stabilising qubits and testing early quantum algorithms.

As science catches up to storylines, analysts note that public imagination often signals where real progress is emerging. For those watching closely, quantum’s rise in fiction could be more than a coincidence—it could be the earliest sign of real-world transformation.

A new kind of computation

Quantum computers differ fundamentally from classical machines. While traditional systems handle tasks step by step, quantum computers explore many possibilities at once—thanks to quantum phenomena like superposition and entanglement.

One expert likens it to a scene from Avengers, where Doctor Strange scans millions of futures simultaneously to find the best path forward. That’s essentially how quantum systems approach complex problems—by evaluating countless outcomes in parallel.

This makes them especially suited for challenges that overwhelm classical systems, such as:

• Cracking next-generation encryption
• Optimising vast logistics networks
• Simulating molecular interactions in drug or material discovery

Quantum isn’t here to replace everyday computing. But for specific high-complexity problems, it represents a fundamentally new—and more powerful—computational model.

Where quantum will hit first

Quantum computing is expected to make its earliest impact in industries where computational complexity is high and financial upside is significant.

Key early applications include:

• Pharmaceuticals: simulating molecular interactions to accelerate drug discovery
• Advanced materials: designing new compounds or batteries at the atomic level
• Finance: optimising asset portfolios, particularly in ETFs and derivatives

Take ETF construction, for example. Selecting the ideal combination of dozens of assets involves combinatorial optimisation—a task that becomes exponentially harder as the number of variables increases. While AI tools help, classical systems struggle beyond a point. Quantum computers, by evaluating multiple combinations simultaneously, offer a clear advantage.

Also Read: Navigating Asia’s business boom: The quantum leadership advantage

In the short term, industries that combine high complexity with high value potential are best positioned to adopt quantum solutions—because the benefits justify the infrastructure investment.

Early wins in the quantum race

While today’s quantum computers remain in the early stages—most with only a few dozen usable qubits—they are already beginning to show practical value in select domains. Many available qubits are still dedicated to error correction, reflecting the sensitivity of current hardware.

Yet despite these limits, meaningful use cases are emerging.

Notable early applications include:

• Drug discovery: simulating molecular behaviour at quantum levels
• Advanced materials: modelling atomic interactions for next-gen compounds
• Finance: improving asset rebalancing strategies in complex portfolios
• Logistics: optimising large-scale routing problems that scale exponentially

These are all areas where classical systems struggle as complexity increases.
Even with today’s constraints, quantum systems are starting to outperform traditional methods in narrow but high-impact scenarios.

The technology may still be maturing, but its real-world value is no longer theoretical—it’s beginning to take shape.

Why quantum-AI hybrids matter

Quantum–AI hybrid computing is drawing growing attention as a practical way to extract early value from quantum systems. Rather than replacing classical computing, the hybrid model assigns different parts of a task to the most suitable processor.

• Classical computers or AI handle large-scale, repetitive calculations
• Quantum systems tackle tasks involving simulation, optimisation, or quantum-specific modelling
• Cloud platforms or machine learning layers integrate and interpret the combined outputs

This division of labour leverages the strengths of each architecture, delivering faster, more efficient results than either could alone. Experts see hybrid models as the most viable short-term strategy—not only technically, but also commercially and operationally—to scale quantum’s impact without waiting for perfect hardware. 

Rethinking the internet for a quantum era

If quantum computers reach commercial viability, today’s internet security architecture will require a complete overhaul. Most current encryption methods—used in banking, e-commerce, communications, and authentication—are vulnerable to quantum algorithms capable of breaking them. This wouldn’t just call for software updates; it would demand a structural redesign of global digital infrastructure.

Experts describe it as a foundational shift, not a technical patch. That said, the transition is expected to unfold gradually over the next decade, giving rise to quantum-resistant cryptographic standards and long-term planning by governments and enterprises.

For infrastructure providers and investors, the key is timing: anticipating when and how to adapt before disruption becomes inevitable.

Korea’s quantum edge: Beyond hardware

While Korea has produced notable quantum researchers, including one of IonQ’s co-founders, full-scale hardware development remains concentrated in global hubs like the US, where companies such as IBM, Google, and IonQ lead in capital and infrastructure.

Instead, Korea is gaining ground in quantum-resilient infrastructure, particularly in quantum-safe cybersecurity. A standout example is SK Telecom, which acquired Swiss-based ID Quantique—a global leader in quantum key distribution (QKD)—and later entered a strategic partnership with IonQ.

Also Read: Horizon Quantum CEO on the Singapore advantage in starting a quantum computing company

This positions Korea to lead in quantum-proof security systems, a field likely to reach commercial scale well before universal quantum computing becomes mainstream.

Experts draw parallels to the early AI wave (circa 2014–2015), when Korea didn’t build foundational frameworks but found success through application-level innovation. Similarly, Korea’s future in quantum may lie in industry-specific algorithms, secure infrastructure, and applied software—not hardware.

How big tech is positioning for quantum leadership

Major tech companies are taking two main paths toward quantum computing: in-house development and strategic partnerships or acquisitions.

• IBM and Google have pursued full-stack integration from the outset—developing quantum hardware, software tools, and embedding quantum capabilities directly into their cloud platforms. They remain the most vertically integrated players in the field.
• Microsoft and Amazon initially focused on enabling quantum access through their cloud ecosystems, partnering with startups to provide tools via Azure and AWS. But as the commercial potential grows, both are moving toward greater internal control.

Recent shifts include:

• Microsoft’s launch of proprietary tools through initiatives like MyOrionow
• Reports of Amazon collaborating with or acquiring startups such as OxenT to build its own quantum stack

This signals a broader trend: big tech is transitioning from collaboration to ownership, aiming to secure key positions as quantum computing moves from theory to viable markets.

Quantum investing: Echoes of the deep learning era

Some early-stage investors see clear parallels between today’s quantum computing landscape and the deep learning boom of the mid-2010s.

In 2012, deep learning began to show real promise. By 2013–2014, major tech firms were investing heavily. During that wave, investors backed AI startups that later went public after a 7-year growth cycle.

Quantum computing now appears to be following a similar arc:

• Foundational research is maturing
• Big tech is entering aggressively
• Use cases are emerging in finance, pharma, and cybersecurity

Many investors haven’t placed direct bets yet, but they’re watching closely.
Angel investors may need a 10-year horizon, while VCs could see returns within three to five years years.

The consensus among early movers? This is the beginning of a new wave—and the time to position is now.

The quantum era won’t arrive overnight—but once it’s here, it will move fast. Those who act early won’t just adapt to the future—they’ll help shape it.

Special thanks to Dr. Jihoon Jeong, General Partner of Asia2G Capital, for his valuable contributions to this article.

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AI adoption across Southeast Asia is accelerating, and with the edge AI market expected to reach US$66.47 billion by 2030 (21.7 per cent CAGR), organisations are moving quickly from pilots to embedding automation in core operations. Agentic AI is expanding what can be automated, prompting enterprises to reassess their technology foundations amid growing regional expansion and rising demands on infrastructure design, visibility and control.

For manufacturers, diversification often replaces a mature ecosystem with a fragmented, cross-border supply chain. Navigating differing regulations and dispersed suppliers deepens reliance on public cloud and hyperscaler AI services, turning infrastructure choices into strategic decisions that determine performance, reliability and resilience.

At the same time, enterprises are shifting from traditional data centres to hybrid control planes. Intelligent edge devices now span factories, clinics and shop floors, unlocking new automation gains but also multiplying attack surfaces. This underscores the need to secure AI workloads consistently across distributed environments and build scalable, resilient architectures.

The AI-cybersecurity arms race

The barrier to entry for sophisticated cyberattacks has collapsed. Agentic AI allows attackers to automate reconnaissance, identify vulnerabilities and scale targeted attacks without deep expertise. Threats that once required manual skill can now be executed by prompting an AI agent to map weaknesses and exploit them efficiently.

Meanwhile, supply chain diversification has fractured defensive perimeters. Thousands of devices and sensors across dispersed facilities now form a sprawling attack surface. As business speed increases, attacks move at the same pace. Manipulative social engineering, deepfake voice impersonation and automated phishing campaigns overwhelm human analysts and exploit the weakest links.

Also Read: How an AI cybersecurity company harnesses the power of AI for optimal business performance

This raises a critical question: how can organisations detect and neutralise AI-enabled threats quickly enough to prevent meaningful damage?

The quantum computing effect

Quantum computing introduces another layer of urgency. As organisations expand their digital borders into fragmented environments, attackers gain conditions to automate and accelerate intrusions. One threat has become especially concerning: “harvest now, decrypt later”. Attackers can steal encrypted data today, store it and wait until quantum systems can break the underlying cryptography. Health records, intellectual property and long-term customer data could become liabilities once decrypted.

This makes the migration window critical. Organisations have limited time to upgrade cryptographic systems before quantum technologies render them vulnerable. Upgrading at scale – discovering dependencies, securing keys and deploying post-quantum algorithms across many systems takes years. If migration takes five years and quantum capability arrives in ten, the clock is already ticking.

Visibility complicates matters. Many enterprises lack a full inventory of keys, certificates or hard-coded encryption calls. Manual audits are slow and incomplete. AI-powered code scanners can accelerate discovery, map quantum-susceptible components and guide modernisation. AI can also detect subtle data exfiltration patterns and deploy countermeasures such as injecting fake data to neutralise stolen datasets.

Compliance will tighten across Asia

Regulators are tightening supply chain mandates and raising expectations for cybersecurity maturity. Japan’s Ministry of Economy, Trade and Industry (METI), will introduce its Cybersecurity Measures Evaluation System for Strengthening Supply Chains in 2026. Similarly, South Korea is strengthening cybersecurity oversight and Hong Kong’s Protection for Critical Infrastructures (Computer Systems) Bill, effective 2026, imposes stronger obligations on organisations to modernise defences.

Compliance is no longer a checkbox exercise — it is a strategic imperative tied to operational resilience and competitive readiness.

Data-heavy industries, look out

Healthcare illustrates the stakes. When sensitive data flows across cloud systems, hospitals and connected devices, even a minor breach can trigger cascading disruption. Similar vulnerabilities appear in manufacturing, logistics, finance and retail, where interconnected digital ecosystems amplify the impact of AI-driven threats.

Also Read: Unchecked shadow AI poses a major cybersecurity risk for 2026: Exabeam

A realistic scenario: an attacker scrapes public data to profile a medical professional, generating a cloned voice and calling the IT help desk to reset authentication credentials. Once inside, the attacker can move laterally and quietly. AI-powered defences are essential because they detect behavioural anomalies — unfamiliar browser fingerprints, impossible travel events or unusual directory access — rather than relying on malware signatures.

How enterprises can stay ahead

• Correlate telemetries at scale: Organisations can improve detection accuracy by correlating telemetry across networks, devices and applications. This uncovers hidden anomalies designed to evade traditional tools. Proactive red-teaming of AI models uncovers vulnerabilities such as data poisoning or manipulation. Explainable AI techniques support forensic analysis by showing why alerts were generated.
• Enforce data provenance and sanitisation: Security begins at the data layer. Organisations should validate data at every ingestion point and prevent modified or corrupted inputs from entering critical systems. Immutable ledgers or blockchain mechanisms ensure trusted provenance and integrity for high-assurance pipelines.
• Address the human element and “shadow AI”: Cybersecurity awareness must extend to all staff. Shadow AI – unvetted tools in daily workflows – poses a growing risk. Core hygiene practices such as least privilege access, multi-factor authentication and granular role-based controls remain essential. Training helps staff recognise modern risks, including seemingly harmless third-party AI tools that could execute tasks autonomously on corporate networks.

The winners in this new era will be those who treat AI security as a strategic advantage, not an afterthought. Building resilience at machine speed requires more than technology—it demands a mindset shift towards dynamic, multi-layered defence. In Southeast Asia’s AI-driven economy, confidence will belong to enterprises that synchronise innovation with security, turning risk into a competitive edge.

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Secai Marche, a Tokyo-headquartered startup operating a farm-to-table fresh food distribution platform across Southeast Asia, has closed an additional financing round led by NTT Docomo Ventures and Synexia Ventures.

As per the agreement, the startup has entered into a strategic partnership with NTT Data to digitise invoicing and payments for Malaysia’s HORECA (hotel, restaurant, and catering) sector.

Also Read: Japanese logistics giants bet on Secai Marche’s cold-chain network vision

The deal marks a shift in Secai Marche’s product roadmap: from pure logistics and marketplace services to embedding payments, invoicing, and financial products, such as buy now, pay later (BNPL), supply chain finance, and microloans for farmers and small producers. The move targets long-standing inefficiencies in Malaysia’s food procurement workflows, where manual invoicing and payment processes create back-office burdens and working capital headaches across the supply chain.

Why payments matter for fresh food distribution

Secai Marche’s platform connects producers directly with restaurants, retailers and other buyers, handling ordering, logistics and market development. The company argues that while physical distribution has seen incremental improvement, the transactional layer (invoicing, payment reconciliation, and financing) remains stubbornly analogue in many Southeast Asian HORECA markets. That gap not only slows operators but also leaves smallholder farmers exposed to irregular cash flows and delayed payments.

“Procurement, invoicing and payment workflows remain highly analogue, leaving substantial inefficiencies that can constrain business growth,” Secai Marche’s representative director said in a statement. The company plans to integrate NTT DATA’s payment and invoicing solutions into its marketplace to provide a unified service that covers procurement through to settlement.

For restaurants, automated invoicing and online payments can reduce human error, speed reconciliation and improve visibility into cash positions. For producers, digitised accounts payable (AP) flows can shorten receivable cycles and open the door to underwriting based on transaction histories rather than traditional collateral, a key point if Secai Marche’s BNPL and supply chain finance ambitions materialise.

NTT DATA’s role: tech, payments and ecosystem

NTT Data will supply payment and invoicing technology and collaborate on integrating those capabilities into Secai’s marketplace. Shinichiro Nishikawa, head of NTT Data’s Global Payments & Services Division, framed the collaboration as more than operational efficiency: “We believe that enhancing financial services through the utilisation of payment and invoice data can help improve companies’ capital efficiency and create new access to finance.”

That line signals a common fintech playbook: digitise flows, capture transaction data, then overlay credit and liquidity products. For NTT Data, the partnership aligns with broader ambitions to leverage enterprise payment data for financial services across markets. For Secai Marche, it could mean moving from being a logistics layer to becoming a finance-enabled supply chain platform.

Venture backers see regional scale

NTT Docomo Ventures, one of the new investors, said it backed Secai because the startup addresses fundamental pain points for farmers and HORECA operators and because the NTT Group’s assets can accelerate scale. “We have high expectations for SECAI MARCHE’s growth into a platform that connects Southeast Asia and, ultimately, the world,” Yuma Kotake, Director at NTT Docomo Ventures, said.

Also Read: Secai Marche cultivates US$6M to build a fresher, smarter food ecosystem in SEA

Synexia Ventures is also making Secai Marche its inaugural portfolio investment. “Secai Marche has built a unique position in Southeast Asia’s farm-to-table fresh produce supply chain,” its MD Kuan Hsu said, signalling confidence that the combined JV with NTT DATA could unlock additional value.

Putting finance on top of perishables is not a trivial pursuit. Fresh produce supply chains operate on thin margins and tight timing constraints; payment products must be reliable, low-friction and closely integrated with logistics and invoicing data to succeed.

A practical case: how integration could change day-to-day operations

Today, many restaurant operators in Malaysia still receive paper invoices or spreadsheets, then manually approve payments and reconcile bank transfers. For popular eateries operating on slim margins, delayed supplier payments and opaque receivable cycles create unpredictability, forcing either precautionary cash buffers or frequent short-term borrowing.

Integrated invoicing and payments would let restaurants approve digital invoices, initiate payments from a single dashboard, and view real-time payment status. From the supplier side, producers could see when payments will arrive and, eventually, choose to monetise upcoming invoices through supply chain finance or BNPL arrangements underwritten by the platform using its transaction data.

By centralising procurement-to-payment flows, Secai Marche can also reduce reconciliation costs, a non-trivial overhead for SMEs that currently spend significant time on bookkeeping. This could be an attractive sell to restaurant chains and medium-sized caterers, which value predictable cash conversion cycles as much as timely deliveries.

Risks and execution challenges

The plan is ambitious and faces several hurdles. First, regulatory frameworks governing payments, lending, and data use vary across Southeast Asia; navigating Malaysian rules alone will require careful compliance and possibly local financial services licences. Second, underwriting credit to farmers and small vendors is inherently risky; default management and fraud prevention will be crucial. Third, customer adoption requires trust — operators and producers must be convinced that the platform’s financial services are reliable, affordable, and tailored to their cash flow patterns.

Moreover, embedded finance models often need scale to make economics work. Transaction volumes must be sufficient to justify credit exposure and to provide the data richness required for risk models. Secai Marche will need to expand its transaction footprint in Malaysia quickly while ensuring unit economics remain viable.

Strategic rationale and regional ambitions

For Secai Marche, integrating payments is a logical extension of its core marketplace. Data from orders, deliveries and invoices provides a relatively rich signal for credit assessment compared with alternative data sources. If executed well, the company could capture more of the value chain — from order placement to payment collection and financing — rather than just taking a cut on logistics or marketplace fees.

Investors’ emphasis on regional scaling suggests Secai Marche may replicate the model beyond Malaysia. However, the company will be tested first on the ground: convincing HORECA operators to switch from manual, familiar processes to an integrated, digital service and proving that financial products can actually reduce friction rather than add complexity.

Looking ahead: BNPL, supply chain finance and microloans

Secai Marche explicitly mentioned plans to explore BNPL for procurement, supply chain finance and microfinance for farmers, using accumulated transaction and invoice data to underwrite loans. Microfinance to upstream producers is a particularly appealing public-good narrative: it could stabilise upstream supply, improve quality and create longer-term relationships between producers and buyers.

Also Read: Secai Marche wins US$4M grant from Japan govt. to transform farm-direct e-commerce in SEA

But turning transaction history into credit access requires robust risk models and often access to capital or third-party underwriters. The partnership with NTT Data and backing from NTT Docomo Ventures may help here by opening institutional channels and technology resources, but execution will require both engineering and prudent financial risk management.

Bottom line

Secai Marche’s fresh capital and strategic partnership push the company into the crowded but potentially lucrative territory of embedded finance for supply chains. Its success will hinge on execution: building seamless integrations that materially reduce back-office friction, proving credit products that demonstrably help small producers and buyers, and navigating regulatory and operational risks in Malaysia before scaling across Southeast Asia.

If the farm-to-table startup can convert transaction data into reliable finance at scale, it could become a critical infrastructure layer for the region’s perishable food supply chains, but the path from marketplace to finance provider is littered with complexity. The new funding and ties to NTT Group assets give it a better shot than many, but much still depends on adoption, unit economics and risk control in the months ahead.

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As a founder, one of the hardest lessons I’ve had to learn is this: You can outsource tasks, but you cannot outsource ownership.

Not to AI. Not to agencies. Not to communities. Not even to people genuinely trying to help you succeed.

And strangely enough, I didn’t learn this lesson from a failed product launch or a difficult investor meeting.

I learned it from people with potential.

Over the years of building businesses, communities, and founder ecosystems, I’ve met many individuals who were creative, intelligent, and full of ideas. Some of them were vocal, charismatic, and clearly capable of building something meaningful.

The potential was obvious.

But potential and ownership are not the same thing.

That distinction matters far more than most people realise.

Ideas are common, ownership is rare

One pattern I kept noticing was how easy it was for people to get excited about possibilities.

A new business idea. A personal brand. A community initiative. An AI tool. A collaboration opportunity.

In the early stages, energy is everywhere. Conversations are exciting. Ideas flow endlessly. Everyone feels inspired.

But the moment friction appears, things change.

Some people pause. Some people wait. Some people retreat. Some people start looking externally for reassurance, validation, or permission to continue.

Founders do not have that luxury for very long.

Because building anything meaningful requires the ability to continue moving even when things become uncomfortable, uncertain, or inconvenient.

Also Read: The shadow ledger: Why AI governance is the new architecture of brand trust and enterprise revenue

That is where ownership begins.

And over time, I realised something uncomfortable myself: Many people want the outcome of entrepreneurship without fully accepting the responsibility that comes with it.

They want growth without consistency. Visibility without vulnerability. Momentum without initiative.

Most importantly, they want transformation without ownership.

The founder trap: Caring more than the other person

I naturally enjoy helping people build.

It’s one of the reasons I created founder communities, educational programmes, and AI-powered systems in the first place. I genuinely love seeing people gain confidence, clarity, and momentum.

I enjoy helping people move faster.

But somewhere along the way, I realised I had fallen into a trap many founders quietly experience.

I was trying to push people toward opportunities they weren’t even asking for.

I would see someone’s strengths clearly before they saw it themselves. I could often identify their strongest positioning, the direction with the highest potential leverage, or the opportunities sitting right in front of them.

Sometimes I helped structure their branding. Sometimes I opened doors. Sometimes I provided platforms, systems, tools, introductions, or guidance.

And yet, despite all of that support, very little happened.

Not because the opportunities weren’t real. Not because the systems were broken. But because ownership never fully materialised.

That was difficult for me to accept at first.

As founders, especially those who enjoy building communities or mentoring others, we often believe that if we provide enough support, enough tools, or enough encouragement, people will eventually move.

But eventually I realised something important: You cannot want success more than the other person does.

Also Read: Everyone wants AI in their product, but few know why (and when it actually works)

AI amplified this lesson for me

Ironically, AI made this reality even clearer.

Today, we live in a world where access has become incredibly democratised. People now have access to tools that previously required entire teams.

AI can help generate content, automate workflows, brainstorm ideas, accelerate execution, organise operations, and dramatically reduce friction.

In my own businesses, AI has significantly accelerated the speed at which I can move.

When I built earlier ventures years ago, reaching the first meaningful revenue milestones took months of experimentation, uncertainty, and manual effort.

Today, execution happens much faster.

Part of that comes from experience. Part of it comes from pattern recognition. And part of it comes from AI systems like Seraphina, my AI-powered digital twin, which helps me structure ideas, streamline workflows, and move from concept to execution far more efficiently.

But AI only accelerated the movement that already existed.

It did not create the movement itself.

That distinction is critical.

A trained AI is similar to a trained team. It amplifies direction, speed, and execution. But it still requires initiative, clarity, and decision-making from the person using it.

AI can reduce friction. It cannot manufacture discipline.

AI can accelerate execution. It cannot replace ownership.

And I think that is where many people misunderstand both entrepreneurship and AI today.

Buying tools is not the same as building. Joining communities is not the same as executing. Consuming information is not the same as moving.

The people who benefit the most from AI are usually those who were already willing to take action in the first place.

Builders move before they feel ready

One thing entrepreneurship taught me very early is that progress compounds.

Nobody starts at 10,000 users. Nobody starts fully prepared. Nobody starts with complete certainty.

You start with zero.

Then you learn. Then you adjust. Then you improve. Then you repeat.

Over time, the speed compounds because experience compounds.

That is why founders who have built before often move faster the next time around. The execution muscle becomes stronger. Pattern recognition improves. Decision-making sharpens.

But none of that happens without movement.

And that is why I eventually stopped trying to carry people who were unwilling to carry themselves.

Not because I stopped believing in people. Not because I stopped caring.

Also Read: The agentic shift: Why AI agents are rewriting the rules of ERP software in Singapore and Malaysia

But I finally understood the limits of what founders, mentors, AI systems, and communities can realistically do for someone else.

We can open doors. We can provide tools. We can shorten the learning curve.

But we cannot walk the path for them.

Leadership without self-sacrifice

I still believe deeply in helping people.

I still believe technology and AI can empower everyday individuals to build businesses, create freedom, and accelerate opportunities that previously felt inaccessible.

But I no longer believe it is my responsibility to save people from themselves.

That realisation changed how I lead, how I build communities, and how I approach growth.

Today, I focus less on convincing people to move and more on supporting the people already moving.

Because ownership changes everything.

And in an era where AI can help almost anyone execute faster than ever before, ownership may become the most valuable skill of all.

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